Showing papers in "Journal of Computer Science and Technology in 2018"

ShadowEth: Private Smart Contract on Public Blockchain

Abstract: Blockchain is becoming popular as a distributed and reliable ledger which allows distrustful parties to transact safely without trusting third parties. Emerging blockchain systems like Ethereum support smart contracts where miners can run arbitrary user-defined programs. However, one of the biggest concerns about the blockchain and the smart contract is privacy, since all the transactions on the chain are exposed to the public. In this paper, we present ShadowEth, a system that leverages hardware enclave to ensure the confidentiality of smart contracts while keeping the integrity and availability based on existing public blockchains like Ethereum. ShadowEth establishes a confidential and secure platform protected by trusted execution environment (TEE) off the public blockchain for the execution and storage of private contracts. It only puts the process of verification on the blockchain. We provide a design of our system including a protocol of the cryptographic communication and verification and show the applicability and feasibility of ShadowEth by various case studies. We implement a prototype using the Intel SGX on the Ethereum network and analyze the security and availability of the system.

Scalable and Privacy-Preserving Data Sharing Based on Blockchain

Abstract: With the development of network technology and cloud computing, data sharing is becoming increasingly popular, and many scholars have conducted in-depth research to promote its flourish. As the scale of data sharing expands, its privacy protection has become a hot issue in research. Moreover, in data sharing, the data is usually maintained in multiple parties, which brings new challenges to protect the privacy of these multi-party data. In this paper, we propose a trusted data sharing scheme using blockchain. We use blockchain to prevent the shared data from being tampered, and use the Paillier cryptosystem to realize the confidentiality of the shared data. In the proposed scheme, the shared data can be traded, and the transaction information is protected by using the (p, t)-threshold Paillier cryptosystem. We conduct experiments in cloud storage scenarios and the experimental results demonstrate the efficiency and effectiveness of the proposed scheme.

CoC: A Unified Distributed Ledger Based Supply Chain Management System

Abstract: Modern supply chain is a complex system and plays an important role for different sectors under the globalization economic integration background. Supply chain management system is proposed to handle the increasing complexity and improve the efficiency of flows of goods. It is also useful to prevent potential frauds and guarantee trade compliance. Currently, most companies maintain their own IT systems for supply chain management. However, it is hard for these isolated systems to work together and provide a global view of the status of the highly distributed supply chain system. Using emerging decentralized ledger/blockchain technology, which is a special type of distributed system in essence, to build supply chain management system is a promising direction to go. Decentralized ledger usually suffers from low performance and lack of capability to protect information stored on the ledger. To overcome these challenges, we propose CoC (supply chain on blockchain), a novel supply chain management system based on a hybrid decentralized ledger with a novel two-step block construction mechanism. We also design an efficient storage scheme and information protection method that satisfy requirements of supply chain management. These techniques can also be applied to other decentralized ledger based applications with requirements similar to supply chain management.

Robust Visual Tracking Based on Convolutional Features with Illumination and Occlusion Handing

Abstract: Visual tracking is an important area in computer vision How to deal with illumination and occlusion problems is a challenging issue This paper presents a novel and efficient tracking algorithm to handle such problems On one hand, a target’s initial appearance always has clear contour, which is light-invariant and robust to illumination change On the other hand, features play an important role in tracking, among which convolutional features have shown favorable performance Therefore, we adopt convolved contour features to represent the target appearance Generally speaking, first-order derivative edge gradient operators are efficient in detecting contours by convolving them with images Especially, the Prewitt operator is more sensitive to horizontal and vertical edges, while the Sobel operator is more sensitive to diagonal edges Inherently, Prewitt and Sobel are complementary with each other Technically speaking, this paper designs two groups of Prewitt and Sobel edge detectors to extract a set of complete convolutional features, which include horizontal, vertical and diagonal edges features In the first frame, contour features are extracted from the target to construct the initial appearance model After the analysis of experimental image with these contour features, it can be found that the bright parts often provide more useful information to describe target characteristics Therefore, we propose a method to compare the similarity between candidate sample and our trained model only using bright pixels, which makes our tracker able to deal with partial occlusion problem After getting the new target, in order to adapt appearance change, we propose a corresponding online strategy to incrementally update our model Experiments show that convolutional features extracted by well-integrated Prewitt and Sobel edge detectors can be efficient enough to learn robust appearance model Numerous experimental results on nine challenging sequences show that our proposed approach is very effective and robust in comparison with the state-of-the-art trackers

A Survey on Expert Recommendation in Community Question Answering

Abstract: Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify the questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.

Hashtag Recommendation Based on Multi-Features of Microblogs

Abstract: Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to the data sparsity problem, it is difficult for typical hashtag recommendation methods to achieve accurate recommendation. In light of this, we propose HRMF, a hashtag recommendation method based on multi-features of microblogs in this article. First, our HRMF expands short text into long text, and then it simultaneously models multi-features (i.e., user, hashtag, text) of microblogs by designing a new topic model. To further alleviate the data sparsity problem, HRMF exploits hashtags of both similar users and similar microblogs as the candidate hashtags. In particular, to find similar users, HRMF combines the designed topic model with typical user-based collaborative filtering method. Finally, we realize hashtag recommendation by calculating the recommended score of each hashtag based on the generated topical representations of multi-features. Experimental results on a real-world dataset crawled from Sina Weibo demonstrate the effectiveness of our HRMF for hashtag recommendation.

LLMP: Exploiting LLDP for Latency Measurement in Software-Defined Data Center Networks

Abstract: The administrators of data center networks have to continually monitor path latency to detect network anomaly quickly and ensure the efficient operation of the networks. In this work, we propose Link Layer Measurement Protocol (LLMP), a prototype latency measuring framework based on the Link Layer Discovery Protocol (LLDP). LLDP is utilized by the controller to discover network topology dynamically. We insert timestamps into the optional LLDPTLV field in LLDP, so that the controller can estimate latency on any single link. The framework utilizes a reactive measurement approach without injecting any probe packets to the network. Our experiments show that the latency of a link can be measured accurately by LLMP. In relatively complex network conditions, LLMP can still maintain a high accuracy. We store the LLMP measurement results into a latency matrix, which can be used to infer the path latency.

Privacy-Preserving Algorithms for Multiple Sensitive Attributes Satisfying t-Closeness

Abstract: Although k-anonymity is a good way of publishing microdata for research purposes, it cannot resist several common attacks, such as attribute disclosure and the similarity attack. To resist these attacks, many refinements of kanonymity have been proposed with t-closeness being one of the strictest privacy models. While most existing t-closeness models address the case in which the original data have only one single sensitive attribute, data with multiple sensitive attributes are more common in practice. In this paper, we cover this gap with two proposed algorithms for multiple sensitive attributes and make the published data satisfy t-closeness. Based on the observation that the values of the sensitive attributes in any equivalence class must be as spread as possible over the entire data to make the published data satisfy t-closeness, both of the algorithms use different methods to partition records into groups in terms of sensitive attributes. One uses a clustering method, while the other leverages the principal component analysis. Then, according to the similarity of quasiidentifier attributes, records are selected from different groups to construct an equivalence class, which will reduce the loss of information as much as possible during anonymization. Our proposed algorithms are evaluated using a real dataset. The results show that the average speed of the first proposed algorithm is slower than that of the second proposed algorithm but the former can preserve more original information. In addition, compared with related approaches, both proposed algorithms can achieve stronger protection of privacy and reduce less.

BENCHIP: Benchmarking Intelligence Processors

Abstract: The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware). However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BenchIP, a benchmark suite and benchmarking methodology for intelligence processors. The benchmark suite in BenchIP consists of two sets of benchmarks: microbenchmarks and macrobenchmarks. The microbenchmarks consist of single-layer networks. They are mainly designed for bottleneck analysis and system optimization. The macrobenchmarks contain state-of-the-art industrial networks, so as to offer a realistic comparison of different platforms. We also propose a standard benchmarking methodology built upon an industrial software stack and evaluation metrics that comprehensively reflect various characteristics of the evaluated intelligence processors. BenchIP is utilized for evaluating various hardware platforms, including CPUs, GPUs, and accelerators. BenchIP will be open-sourced soon.

A Flocking-Based on Demand Routing Protocol for Unmanned Aerial Vehicles

Abstract: The interest shown by some community of researchers to autonomous drones or UAVs (unmanned aerial vehicles) has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate more efficiently in an ad hoc manner in order to achieve specific tasks in specific environments. To do so, each drone navigates autonomously while staying connected with other nodes in its group via radio links. This connectivity can deliberately be maintained for a while constraining the mobility of the drones. This will be suitable for the drones involved in a given path of a given transmission between a source and a destination. This constraint could be removed at the end of the transmission process and the mobility of each concerned drone becomes again independent from the others. In this work, we proposed a flocking-based routing protocol for UAVs called BR-AODV. The protocol takes advantage of a well known ad hoc routing protocol for on-demand route computation, and the Boids of Reynolds mechanism for connectivity and route maintaining while data is being transmitted. Moreover, an automatic ground base stations discovery mechanism has been introduced for a proactive drones and ground networks association needed for the context of real-time applications. The performance of BR-AODV was evaluated and compared with that of classical AODV routing protocol and the results show that BR-AODV outperforms AODV in terms of delay, throughput and packet loss.

Empirical Research in Software Engineering — A Literature Survey

Abstract: Empirical research is playing a significant role in software engineering (SE), and it has been applied to evaluate software artifacts and technologies. There have been a great number of empirical research articles published recently. There is also a large research community in empirical software engineering (ESE). In this paper, we identify both the overall landscape and detailed implementations of ESE, and investigate frequently applied empirical methods, targeted research purposes, used data sources, and applied data processing approaches and tools in ESE. The aim is to identify new trends and obtain interesting observations of empirical software engineering across different sub-fields of software engineering. We conduct a mapping study on 538 selected articles from January 2013 to November 2017, with four research questions. We observe that the trend of applying empirical methods in software engineering is continuously increasing and the most commonly applied methods are experiment, case study and survey. Moreover, open source projects are the most frequently used data sources. We also observe that most of researchers have paid attention to the validity and the possibility to replicate their studies. These observations are carefully analyzed and presented as carefully designed diagrams. We also reveal shortcomings and demanded knowledge/strategies in ESE and propose recommendations for researchers.

Exploiting Pre-Trained Network Embeddings for Recommendations in Social Networks

Abstract: Recommender systems as one of the most efficient information filtering techniques have been widely studied in recent years. However, traditional recommender systems only utilize user-item rating matrix for recommendations, and the social connections and item sequential patterns are ignored. But in our real life, we always turn to our friends for recommendations, and often select the items that have similar sequential patterns. In order to overcome these challenges, many studies have taken social connections and sequential information into account to enhance recommender systems. Although these existing studies have achieved good results, most of them regard social influence and sequential information as regularization terms, and the deep structure hidden in social networks and rating patterns has not been fully explored. On the other hand, neural network based embedding methods have shown their power in many recommendation tasks with their ability to extract high-level representations from raw data. Motivated by the above observations, we take the advantage of network embedding techniques and propose an embedding-based recommendation method, which is composed of the embedding model and the collaborative filtering model. Specifically, to exploit the deep structure hidden in social networks and rating patterns, a neural network based embedding model is first pre-trained, where the external user and item representations are extracted. Then, we incorporate these extracted factors into a collaborative filtering model by fusing them with latent factors linearly, where our method not only can leverage the external information to enhance recommendation, but also can exploit the advantage of collaborative filtering techniques. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed method and the importance of these external extracted factors.

A Survey on Task and Participant Matching in Mobile Crowd Sensing

Abstract: Mobile crowd sensing is an innovative paradigm which leverages the crowd, i.e., a large group of people with their mobile devices, to sense various information in the physical world. With the help of sensed information, many tasks can be fulfilled in an efficient manner, such as environment monitoring, traffic prediction, and indoor localization. Task and participant matching is an important issue in mobile crowd sensing, because it determines the quality and efficiency of a mobile crowd sensing task. Hence, numerous matching strategies have been proposed in recent research work. This survey aims to provide an up-to-date view on this topic. We propose a research framework for the matching problem in this paper, including participant model, task model, and solution design. The participant model is made up of three kinds of participant characters, i.e., attributes, requirements, and supplements. The task models are separated according to application backgrounds and objective functions. Offline and online solutions in recent literatures are both discussed. Some open issues are introduced, including matching strategy for heterogeneous tasks, context-aware matching, online strategy, and leveraging historical data to finish new tasks.

An Intelligent Transportation System Application for Smartphones Based on Vehicle Position Advertising and Route Sharing in Vehicular Ad-Hoc Networks

Abstract: Alerting drivers about incoming emergency vehicles and their routes can greatly improve their travel time in congested cities, while reducing the risk of accidents due to distractions. This paper contributes to this goal by proposing Messiah, an Android application capable of informing regular vehicles about incoming emergency vehicles like ambulances, police cars and fire brigades. This is made possible by creating a network of vehicles capable of directly communicating between them. The user can, therefore, take driving decisions in a timely manner by considering incoming alerts. Using the support of our GRCBox hardware, the application can rely on vehicular ad-hoc network communications in the 5 GHz band, being V2V (vehicle-to-vehicle) communication provided through a combination of Android-based smartphone and our GRCBox device. The application was tested in three different scenarios with different levels of obstruction, showing that it is capable of providing alerts up to 300 meters, and notifying vehicles within less than one second.

BCDC: A High-Performance, Server-Centric Data Center Network

Abstract: The capability of the data center network largely decides the performance of cloud computing. However, the number of servers in the data center network becomes increasingly huge, because of the continuous growth of the application requirements. The performance improvement of cloud computing faces great challenges of how to connect a large number of servers in building a data center network with promising performance. Traditional tree-based data center networks have issues of bandwidth bottleneck, failure of single switch, etc. Recently proposed data center networks such as DCell, FiConn, and BCube, have larger bandwidth and better fault-tolerance with respect to traditional tree-based data center networks. Nonetheless, for DCell and FiConn, the fault-tolerant length of path between servers increases in case of failure of switches; BCube requires higher performance in switches when its scale is enlarged. Based on the above considerations, we propose a new server-centric data center network, called BCDC, based on crossed cube with excellent performance. Then, we study the connectivity of BCDC networks. Furthermore, we propose communication algorithms and fault-tolerant routing algorithm of BCDC networks. Moreover, we analyze the performance and time complexities of the proposed algorithms in BCDC networks. Our research will provide the basis for design and implementation of a new family of data center networks.

Understanding and Generating Ultrasound Image Description

Abstract: To understand the content of ultrasound images more conveniently and more quickly, in this paper, we propose a coarse-to-fine ultrasound image captioning ensemble model, which can automatically generate the annotation text that is composed of relevant n-grams to describe the disease information in the ultrasound images First, the organs in the ultrasound images are detected by the coarse classification model Second, the ultrasound images are encoded by the corresponding fine-grained classification model according to the organ labels Finally, we input the encoding vectors to the language generation model, and the language generation model generates automatically annotation text to describe the disease information in the ultrasound images In our experiments, the encoding model can obtain the high accuracy rate in the ultrasound image recognition And the language generation model can automatically generate high-quality annotation text In practical applications, the coarse-to-fine ultrasound image captioning ensemble model can help patients and doctors obtain the well understanding of the contents of ultrasound images

Lightweight and Manageable Digital Evidence Preservation System on Bitcoin

Abstract: An effective and secure system used for evidence preservation is essential to possess the properties of anti-loss, anti-forgery, anti-tamper and perfect verifiability. Traditional architecture which relies on centralized cloud storage is depressingly beset by the security problems such as incomplete confidence and unreliable regulation. Moreover, an expensive, inefficient and incompatible design impedes the effort of evidence preservation. In contrast, the decentralized blockchain network is qualified as a perfect replacement for its secure anonymity, irrevocable commitment, and transparent traceability. Combining with subliminal channels in blockchain, we have weaved the transaction network with newly designed evidence audit network. In this paper, we have presented and implemented a lightweight digital evidence-preservation architecture which possesses the features of privacy-anonymity, audit-transparency, function-scalability and operation-lightweight. The anonymity is naturally formed from the cryptographic design, since the cipher evidence under encrypted cryptosystem and hash-based functions leakages nothing to the public. Covert channels are efficiently excavated to optimize the cost, connectivity and security of the framework, transforming the great computation power of Bitcoin network to the value of credit. The transparency used for audit, which relates to the proof of existence, comes from instant timestamps and irreversible hash functions in mature blockchain network. The scalability is represented by the evidence chain interacted with the original blockchain, and the extended chains on top of mainchain will cover the most of auditors in different institutions. And the lightweight, which is equal to low-cost, is derived from our fine-grained hierarchical services. At last, analyses of efficiency, security, and availability have shown the complete accomplishment of our system.

Modeling the Correlations of Relations for Knowledge Graph Embedding

Abstract: Knowledge graph embedding, which maps the entities and relations into low-dimensional vector spaces, has demonstrated its effectiveness in many tasks such as link prediction and relation extraction. Typical methods include TransE, TransH, and TransR. All these methods map different relations into the vector space separately and the intrinsic correlations of these relations are ignored. It is obvious that there exist some correlations among relations because different relations may connect to a common entity. For example, the triples (Steve Jobs, PlaceOfBrith, California) and (Apple Inc., Location, California) share the same entity California as their tail entity. We analyze the embedded relation matrices learned by TransE/TransH/TransR, and find that the correlations of relations do exist and they are showed as low-rank structure over the embedded relation matrix. It is natural to ask whether we can leverage these correlations to learn better embeddings for the entities and relations in a knowledge graph. In this paper, we propose to learn the embedded relation matrix by decomposing it as a product of two low-dimensional matrices, for characterizing the low-rank structure. The proposed method, called TransCoRe (Translation-Based Method via Modeling the Correlations of Relations), learns the embeddings of entities and relations with translation-based framework. Experimental results based on the benchmark datasets of WordNet and Freebase demonstrate that our method outperforms the typical baselines on link prediction and triple classification tasks.

An Efficient Two-Phase Model for Computing Influential Nodes in Social Networks Using Social Actions

Abstract: The measurement of influence in social networks has received a lot of attention in the data mining community. Influence maximization refers to the process of finding influential users who make the most of information or product adoption. In real settings, the influence of a user in a social network can be modeled by the set of actions (e.g., “like”, “share”, “retweet”, “comment”) performed by other users of the network on his/her publications. To the best of our knowledge, all proposed models in the literature treat these actions equally. However, it is obvious that a “like” of a publication means less influence than a “share” of the same publication. This suggests that each action has its own level of influence (or importance). In this paper, we propose a model (called Social Action-Based Influence Maximization Model, SAIM) for influence maximization in social networks. In SAIM, actions are not considered equally in measuring the “influence power” of an individual, and it is composed of two major steps. In the first step, we compute the influence power of each individual in the social network. This influence power is computed from user actions using PageRank. At the end of this step, we get a weighted social network in which each node is labeled by its influence power. In the second step of SAIM, we compute an optimal set of influential nodes using a new concept named “influence-BFS tree”. Experiments conducted on large-scale real-world and synthetic social networks reveal the good performance of our model SAIM in computing, in acceptable time scales, a minimal set of influential nodes allowing the maximum spreading of information.

A Generative Model Approach for Geo-Social Group Recommendation

Abstract: With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem: 1) the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2) users have different influences when in different groups. In this paper, we propose a generative geo-social group recommendation model (GSGR) to recommend points of interest (POIs) for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.

Mining Semantic Trajectory Patterns from Geo-Tagged Data

Abstract: User-generated social media data tagged with geographic information present messages of dynamic spatio-temporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.

A Large-Scale Study of Failures on Petascale Supercomputers

Abstract: With the rapid development of supercomputers, the scale and complexity are ever increasing, and the reliability and resilience are faced with larger challenges. There are many important technologies in fault tolerance, such as proactive failure avoidance technologies based on fault prediction, reactive fault tolerance based on checkpoint, and scheduling technologies to improve reliability. Both qualitative and quantitative descriptions on characteristics of system faults are very critical for these technologies. This study analyzes the source of failures on two typical petascale supercomputers called Sunway BlueLight (based on multi-core CPUs) and Sunway TaihuLight (based on heterogeneous manycore CPUs). It uncovers some interesting fault characteristics and finds unknown correlation relationship among main components’ faults. Finally the paper analyzes the failure time of the two supercomputers in various grains of resource and different time spans, and builds a uniform multi-dimensional failure time model for petascale supercomputers.

Multiple Auxiliary Information Based Deep Model for Collaborative Filtering

Abstract: With the ever-growing dynamicity, complexity, and volume of information resources, the recommendation technique is proposed and becomes one of the most effective techniques for solving the so-called problem of information overload Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, ie, ratings According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions In this work, we integrate plot information as auxiliary information into the denoising autoencoder (DAE), called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem

Search-Based Cost-Effective Software Remodularization

Abstract: Software modularization is a technique used to divide a software system into independent modules (packages) that are expected to be cohesive and loosely coupled. However, as software systems evolve over time to meet new requirements, their modularizations become complex and gradually loose their quality. Thus, it is challenging to automatically optimize the classes’ distribution in packages, also known as remodularization. To alleviate this issue, we introduce a new approach to optimize software modularization by moving classes to more suitable packages. In addition to improving design quality and preserving semantic coherence, our approach takes into consideration the refactoring effort as an objective in itself while optimizing software modularization. We adapt the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) of Deb et al. to find the best sequence of refactorings that 1) maximize structural quality, 2) maximize semantic cohesiveness of packages (evaluated by a semantic measure based on WordNet), and 3) minimize the refactoring effort. We report the results of an evaluation of our approach using open-source projects, and we show that our proposal is able to produce a coherent and useful sequence of recommended refactorings both in terms of quality metrics and from the developer’s points of view.

3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising

Abstract: Block matching based 3D filtering methods have achieved great success in image denoising tasks. However, the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network (CNN) for the 3D filtering step to learn a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model (BMCNN) could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio (PSNR) when denoising images with high noise levels (σ > 40), and the best visual quality when denoising images with all the tested noise levels.

How to Wear Beautifully? Clothing Pair Recommendation

Abstract: In this paper, we present a practical system to automatically suggest the most pairing clothing items, given the reference clothing (upper-body or low-body). This has been a challenge, due to clothes having a variety of categories. Clothing is one of the most informative cues for human appearance. In our daily life, people need to wear properly and beautifully to show their confidence, politeness and social status in various occasions. However, it is not easy to decide what to wear and how to coordinate their own clothes. To address this problem, we propose a recommendation approach that includes clothing region detection, clothing pair recommendation and distance fusion. Clothing region detection based on Faster R-CNN is used to detect clothing region. Clothing pair recommendation consists of a quadruple network architecture, where one dual network of the architecture adopts Siamese convolution neural network architecture. Training examples are pairs of upper-body and low-body clothing items that are either compatible or incompatible. The other dual network is used to learn clothing style features of the input image. This framework is designed to learn a feature transformation from the images of clothing items into two latent spaces, which we call them compatible space and style space respectively. After training the two dual networks, we use a distance fusion method to fuse the features extracted from the compatible and style dual networks. To acquire an optimized model and verify our proposed method, we expand an existing large clothing dataset WoG (Weather-to-Garment), and the resulted dataset is called “How to Wear Beautifully” (H2WB). Experiments on the H2WB dataset demonstrate that our approach is effective with clothing region detection and clothing pair recommendation as well as distance fusion.

Image Smoothing Based on Image Decomposition and Sparse High Frequency Gradient

Abstract: Image smoothing is a crucial image processing topic and has wide applications. For images with rich texture, most of the existing image smoothing methods are difficult to obtain significant texture removal performance because texture containing obvious edges and large gradient changes is easy to be preserved as the main edges. In this paper, we propose a novel framework (DSHFG) for image smoothing combined with the constraint of sparse high frequency gradient for texture images. First, we decompose the image into two components: a smooth component (constant component) and a non-smooth (high frequency) component. Second, we remove the non-smooth component containing high frequency gradient and smooth the other component combining with the constraint of sparse high frequency gradient. Experimental results demonstrate the proposed method is more competitive on efficiently texture removing than the state-of-the-art methods. What is more, our approach has a variety of applications including edge detection, detail magnification, image abstraction, and image composition.

Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands

Abstract: The development of robotic hand prosthetic aims to give back people with disabilities, the ability to recover the functionality needed to manipulate the objects of their daily environment. The electrical signals sent by the brain through the nervous system are associated with the type of movement that the limbs must execute. Myoelectric sensors are non-intrusive devices that allow the capture of electrical signals from the peripheral nervous system. The relationship between the signals originated in the brain tending to generate an action and the myoelectric ones as a result of them, are weakly correlated. For this reason, it is necessary to study their interaction in order to develop the algorithms that allow recognizing orders and transform them into commands that activate the corresponding movements of the prosthesis.The present work shows the development of a prosthesis based on the design of an artificial hand Open Bionics to produce the movements, the MyoWare Muscle sensor for the capture of myoelectric signals (EMG) and the algorithm that allows to identify orders associated with three types of movement. Arduino Nano module performs the acquisition and control processes to meet the size and consumption requirements of this application.

Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

Abstract: Deduplication has been commonly used in both enterprise storage systems and cloud storage To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based (ie, SSD-based) read cache can be deployed for speeding up by caching popular restore contents dynamically Unfortunately, frequent data updates induced by classical cache schemes (eg, LRU and LFU) significantly shorten SSDs’ lifetime while slowing down I/O processes in SSDs To address this problem, we propose a new solution — LOP-Cache — to greatly improve the write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular (LOP) data among data written into SSD-based cache LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache We implemented LOP-Cache in a prototype deduplication system to evaluate its performance Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 373% at the cost of a small SSD-based cache with only 556% capacity of the deduplicated data Importantly, LOP-Cache improves SSDs’ lifetime by a factor of 977 The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems

Visual Simulation of Multiple Fluids in Computer Graphics: A State-of-the-Art Report

Abstract: Realistic animation of various interactions between multiple fluids, possibly undergoing phase change, is a challenging task in computer graphics. The visual scope of multi-phase multi-fluid phenomena covers complex tangled surface structures and rich color variations, which can greatly enhance visual effect in graphics applications. Describing such phenomena requires more complex models to handle challenges involving the calculation of interactions, dynamics and spatial distribution of multiple phases, which are often involved and hard to obtain real-time performance. Recently, a diverse set of algorithms have been introduced to implement the complex multi-fluid phenomena based on the governing physical laws and novel discretization methods to accelerate the overall computation while ensuring numerical stability. By sorting through the target phenomena of recent research in the broad subject of multiple fluids, this state-of-the-art report summarizes recent advances on multi-fluid simulation in computer graphics.