Showing papers on "State (computer science) published in 2019"

Quantum resource theories

Abstract: This review introduces a new development in theoretical quantum physics, the ``resource-theoretic'' point of view. The approach aims to be closely linked to experiment, and to state exactly what result you can hope to achieve for what expenditure of effort in the laboratory. This development is an extension of the principles of thermodynamics to quantum problems; but there are resources that would never have been considered previously in thermodynamics, such as shared knowledge of a frame of reference. Many additional examples and new quantifications of resources are provided.

REDQUEEN: Fuzzing with Input-to-State Correspondence.

Abstract: Automated software testing based on fuzzing has experienced a revival in recent years. Especially feedback-driven fuzzing has become well-known for its ability to efficiently perform randomized testing with limited input corpora. Despite a lot of progress, two common problems are magic numbers and (nested) checksums. Computationally expensive methods such as taint tracking and symbolic execution are typically used to overcome such roadblocks. Unfortunately, such methods often require access to source code, a rather precise description of the environment (e.g., behavior of library calls or the underlying OS), or the exact semantics of the platform’s instruction set. In this paper, we introduce a lightweight, yet very effective alternative to taint tracking and symbolic execution to facilitate and optimize state-of-the-art feedback fuzzing that easily scales to large binary applications and unknown environments. We observe that during the execution of a given program, parts of the input often end up directly (i.e., nearly unmodified) in the program state. This input-to-state correspondence can be exploited to create a robust method to overcome common fuzzing roadblocks in a highly effective and efficient manner. Our prototype implementation, called REDQUEEN, is able to solve magic bytes and (nested) checksum tests automatically for a given binary executable. Additionally, we show that our techniques outperform various state-of-the-art tools on a wide variety of targets across different privilege levels (kernel-space and userland) with no platform-specific code. REDQUEEN is the first method to find more than 100% of the bugs planted in LAVA-M across all targets. Furthermore, we were able to discover 65 new bugs and obtained 16 CVEs in multiple programs and OS kernel drivers. Finally, our evaluation demonstrates that REDQUEEN is fast, widely applicable and outperforms concurrent approaches by up to three orders of magnitude.

Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification

Abstract: This paper considers the domain adaptive person re-identification (re-ID) problem: learning a re-ID model from a labeled source domain and an unlabeled target domain. Conventional methods are mainly to reduce feature distribution gap between the source and target domains. However, these studies largely neglect the intra-domain variations in the target domain, which contain critical factors influencing the testing performance on the target domain. In this work, we comprehensively investigate into the intra-domain variations of the target domain and propose to generalize the re-ID model w.r.t three types of the underlying invariance, i.e., exemplar-invariance, camera-invariance and neighborhood-invariance. To achieve this goal, an exemplar memory is introduced to store features of the target domain and accommodate the three invariance properties. The memory allows us to enforce the invariance constraints over global training batch without significantly increasing computation cost. Experiment demonstrates that the three invariance properties and the proposed memory are indispensable towards an effective domain adaptation system. Results on three re-ID domains show that our domain adaptation accuracy outperforms the state of the art by a large margin. Code is available at: this https URL

AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition

Abstract: The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.

Functional Analysis Attacks on Logic Locking

Abstract: This paper proposes Functional Analysis attacks on state of the art Logic Locking algorithms (Fall attacks). Fall attacks use structural and functional analyses of locked circuits to identify the locking key. In contrast to past work, Fall attacks can often (90% of successful attempts in our experiments) fully defeat locking by only analyzing the locked netlist, without oracle access to an activated circuit. Experiments show that Fall attacks succeed against 65 out of 80 (81%) of circuits locked using Secure Function Logic Locking (SFLL), the only combinational logic locking algorithm resilient to all known attacks.

Adaptive NN Control Without Feasibility Conditions for Nonlinear State Constrained Stochastic Systems With Unknown Time Delays

Abstract: In the novel, an adaptive neural network (NN) controller is developed for a category of nonlinear stochastic systems with full state constraints and unknown time delays. The control quality and system stability suffer from the problems of state time delays and constraints which frequently arises in most real plants. The considered systems are transformed into new constrained free systems based on nonlinear mappings, such that full state constraints are never violated and the feasibility conditions on virtual controllers (the values of virtual controllers and its derivative are assumed to be known) are removed. To compensate for unknown time delayed uncertainties, the exponential type Lyapunov–Krasovskii functionals (LKFs) are employed. NNs are utilized to approximate unknown nonlinear functions appearing in the design procedure. In addition, by employing dynamic surface control (DSC) technique and less adjustable parameters, the online computation burden is lightened. The control method presented can achieve the semiglobal uniform ultimate boundedness of all the closed-loop system signals and the satisfactions of full state constraints by rigorous proof. Finally, by presenting simulation examples, the efficiency of the presented approach is revealed.

Towards Large-Scale Quantum Networks

Abstract: The vision of a quantum internet is to fundamentally enhance Internet technology by enabling quantum communication between any two points on Earth. While the first realisations of small scale quantum networks are expected in the near future, scaling such networks presents immense challenges to physics, computer science and engineering. Here, we provide a gentle introduction to quantum networking targeted at computer scientists, and survey the state of the art. We proceed to discuss key challenges for computer science in order to make such networks a reality.

Video Activity Recognition: State-of-the-Art.

Abstract: Video activity recognition, although being an emerging task, has been the subject of important research efforts due to the importance of its everyday applications. Surveillance by video cameras could benefit greatly by advances in this field. In the area of robotics, the tasks of autonomous navigation or social interaction could also take advantage of the knowledge extracted from live video recording. The aim of this paper is to survey the state-of-the-art techniques for video activity recognition while at the same time mentioning other techniques used for the same task that the research community has known for several years. For each of the analyzed methods, its contribution over previous works and the proposed approach performance are discussed.

A Family of Stateful Memristor Gates for Complete Cascading Logic

Abstract: The conditional switching of memristors to execute stateful implication logic is an example of in-memory computation to potentially provide high energy efficiency and improved computation speed by avoiding the movement of data back and forth between a processing chip and memory and/or storage. Since the first demonstration of memristor implication logic, a significant goal has been to improve the logic cascading to make it more practical. Here, we describe and experimentally demonstrate nine symmetry-related Boolean logic operations by controlling conventional Ta/TaOx/Pt memristors integrated in a crossbar array with applied voltage pulses to perform conditional SET or RESET switching involving two or three devices, i.e., a particular device is switched depending on the state of another device. We introduce a family of four stateful two-memristor logic gates along with the copy and negation operations that enable two-input-one-output complete logic. In addition, we reveal five stateful three-memristor gates that eliminate the need for a separate data copy operation, decreasing the number of steps required for a particular task. The diversity of gates made available by simply applying coordinated sequences of voltages to a memristor crossbar memory significantly improves stateful logic computing efficiency compared to similar approaches that have been proposed.

Giving State to the Stateless: Augmenting Trustworthy Computation with Ledgers.

Abstract: In this work we investigate new computational properties that can be achieved by combining stateless trusted devices with public ledgers. We consider a hybrid paradigm in which a client-side device (such as a co-processor or trusted enclave) performs secure computation, while interacting with a public ledger via a possibly malicious host computer. We explore both the constructive and potentially destructive implications of such systems. We first show that this combination allows for the construction of stateful interactive functionalities (including general computation) even when the device has no persistent storage; this allows us to build sophisticated applications using inexpensive trusted hardware or even pure cryptographic obfuscation techniques. We further show how to use this paradigm to achieve censorship-resistant communication with a network, even when network communications are mediated by a potentially malicious host. Finally we describe a number of practical applications that can be achieved today. These include the synchronization of private smart contracts; rate limited mandatory logging; strong encrypted backups from weak passwords; enforcing fairness in multi-party computation; and destructive applications such as autonomous ransomware, which allows for payments without an online party.

Deep Multi-state Object Pose Estimation for Augmented Reality Assembly

Abstract: Neural network machine learning approaches are widely used for object classification or detection problems with significant success. A similar problem with specific constraints and challenges is object state estimation, dealing with objects that consist of several removable or adjustable parts. A system that can detect the current state of such objects from camera images can be of great importance for Augmented Reality (AR) or robotic assembly and maintenance applications. In this work, we present a CNN that is able to detect and regress the pose of an object in multiple states. We then show how the output of this network can be used in an automatically generated AR scenario that provides step-by-step guidance to the user in assembling an object consisting of multiple components.

ILGNet: inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation

Abstract: In this study, the authors address a challenging problem of aesthetic image classification, which is to label an input image as high- or low-aesthetic quality. We take both the local and global features of images into consideration. A novel deep convolutional neural network named ILGNet is proposed, which combines both the inception modules and a connected layer of both local and global features. The ILGnet is based on GoogLeNet. Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune their connected layers on a large-scale database of aesthetic-related images: AVA, i.e. domain adaptation . The experiments reveal that their model achieves the state of the arts in AVA database. Both the training and testing speeds of their model are higher than those of the original GoogLeNet.

Differential Game Logic.

Abstract: Differential game logic (dGL) is a logic for specifying and verifying properties of hybrid games, i.e., games that combine discrete, continuous, and adversarial dynamics. Unlike hybrid systems, hybrid games allow choices in the system dynamics to be resolved adversarially by different players with different objectives. The logic dGL can be used to study the existence of winning strategies for such hybrid games, i.e., ways of resolving the player’s choices in some way so that he wins by achieving his objective for all choices of the opponent. Hybrid games are determined, i.e., from each state, one player has a winning strategy, yet computing their winning regions may take transfinitely many steps. The logic dGL, nevertheless, has a sound and complete axiomatization relative to any expressive logic. Separating axioms are identified that distinguish hybrid games from hybrid systems. Finally, dGL is proved to be strictly more expressive than the corresponding logic of hybrid systems by characterizing the expressiveness of both.

Dynamic Controller that Operates over Homomorphically Encrypted Data for Infinite Time Horizon.

Abstract: In this paper, we present dynamic systems over encrypted data that compute the next state and the output using homomorphic properties of the cryptosystem, which has equivalent performance to the linear dynamic controllers over real-valued signals. Assuming that the input as well as the output of the plant is encrypted and transmitted to the system, the state matrix of the system is designed to consist of integers. This allows the proposed dynamic system to operate for infinite time horizon, without decryption or reset of the state. For implementation in practice, the use of cryptosystems based on Learning With Errors problem is considered, which allows both multiplication and addition over encrypted data. The effect of injecting errors during encryption is in turn controlled under closed-loop stability.

State-of-the-art Speech Recognition using EEG and Towards Decoding of Speech Spectrum From EEG

Abstract: In this paper we first demonstrate continuous noisy speech recognition using electroencephalography (EEG) signals on English vocabulary using different types of state of the art end-to-end automatic speech recognition (ASR) models, we further provide results obtained using EEG data recorded under different experimental conditions. We finally demonstrate decoding of speech spectrum from EEG signals using a long short term memory (LSTM) based regression model and Generative Adversarial Network (GAN) based model. Our results demonstrate the feasibility of using EEG signals for continuous noisy speech recognition under different experimental conditions and we provide preliminary results for synthesis of speech from EEG features.

Feature selection and a method to improve the performance of tool condition monitoring

Abstract: Tool condition monitoring (TCM) is especially important in the modern machining process. In order to distinguish different tool wear states accurately and reduce the computation cost, it is of great significance to extract and select appropriate features that can reflect changes in tool wear states but are insensitive to cutting parameters. In this work, Fisher’s discriminant ratio (FDR) is adopted as the criterion for feature selection by evaluates every feature’s classification ability. However, it is found that the continuous hidden Markov models (CHMM) trained based on the features selected by the conventional method could recognize some tool state well, but have poor ability to classify other wear states. The reasons for this phenomenon have been analyzed, then a simple and effective method that used two feature sets for TCM has been proposed to improve the recognition performance. Four tests with different cutting parameters are carried out, and the new method has been implemented and verified its usefulness and validity.

SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology

Abstract: Automated feature extraction from program source-code such that proper computing resources could be allocated to the program is very difficult given the current state of technology. Therefore, conventional methods call for skilled human intervention in order to achieve the task of feature extraction from programs. This research is the first to propose a novel human-inspired approach to automatically convert program source-codes to visual images. The images could be then utilized for automated classification by visual convolutional neural network (CNN) based algorithm. Experimental results show high prediction accuracy in classifying the types of program in a completely automated manner using this approach.

Markov Decision Process-based Resilience Enhancement for Distribution Systems: An Approximate Dynamic Programming Approach

Abstract: Because failures in distribution systems caused by extreme weather events directly result in consumers' outages, this paper proposes a state-based decision-making model with the objective of mitigating loss of load to improve the distribution system resilience throughout the unfolding events. The sequentially uncertain system states, e.g., feeder line on/off states, driven by the unfolding events are modeled as Markov states, and the probabilities from one Markov state to another Markov state throughout the unfolding events are determined by the component failure caused by the unfolding events. A recursive optimization model based on Markov decision processes (MDP) is developed to make state-based actions, i.e., system reconfiguration, at each decision time. To overcome the curse of dimensionality caused by enormous states and actions, an approximate dynamic programming (ADP) approach based on post-decision states and iteration is used to solve the proposed MDP-based model. IEEE 33-bus system and IEEE 123-bus system are used to validate the proposed model.

Collusion and Cynicism at the Urban Margins

Abstract: This article examines the clandestine connections between participants in the illicit drug trade and members of state security forces to understand how they impact everyday understandings of the law. Drawing on a unique combination of long-term ethnographic fieldwork in a poor, high-crime district in Argentina and wiretapped conversations drawn from a court case involving a drug trafficking group active in the same area, we find that traffickers use illicit relationships to maintain economic control of the territory, and that collusion fosters widespread cynicism about law enforcement among residents. This article expands the literature on the covert relationships between drug trade participants and agents of the state by detailing the inner workings of collusion. Furthermore, it analyzes residents’ perceptions of police complicity as an underexplored source of legal cynicism. Finally, it offers a methodological blueprint of how to access and analyze data that capture state actions usually hidden from public view. Resumen Este articulo examina las conexiones clandestinas entre participantes en el trafico de drogas ilegales y miembros de las fuerzas de seguridad del estado a los efectos de entender como esas relaciones impactan en la manera en que la ley es entendida en la vida cotidiana. Combinando trabajo etnografico en un barrio pobre con altos niveles de criminalidad y escuchas telefonicas registradas en un expediente judicial que involucra a un grupo de traficantes de la misma zona, encontramos que: a) los traficantes utilizan esas relaciones clandestinas para mantener control economico del territorio, y b) la colusion entre agentes del estado y traficantes alimenta un cinismo legal generalizado entre los residentes de la zona. Este articulo hace tres contribuciones. En primer lugar, expande la literatura sobre relaciones encubiertas entre participantes en el mercado de drogas ilicitas y los agentes del estado al detallar el funcionamiento de la colusion. En segundo lugar, analiza las percepciones sobre la complicidad policial como una fuente no estudiada de cinismo legal. Por ultimo, ofrece una estrategia metodologica para acceder y analizar datos sobre acciones del estado que suelen estar ocultas.

Variational circuit compiler for quantum error correction

Abstract: Quantum error correction is vital for implementing universal quantum computing. A key component is the encoding circuit that maps a product state of physical qubits into the encoded multipartite entangled logical state. Known methods are typically not 'optimal' either in terms of the circuit depth (and therefore the error burden) or the specifics of the target platform, i.e. the native gates and topology of a system. This work introduces a variational compiler for efficiently finding the encoding circuit of general quantum error correcting codes with given quantum hardware. Focusing on the noisy intermediate scale quantum regime, we show how to systematically compile the circuit following an optimising process seeking to minimise the number of noisy operations that are allowed by the noisy quantum hardware or to obtain the highest fidelity of the encoded state with noisy gates. We demonstrate our method by deriving novel encoders for logic states of the five qubit code and the seven qubit Steane code. We describe ways to augment the discovered circuits with error detection. Our method is applicable quite generally for compiling the encoding circuits of quantum error correcting codes.

One-Stage Five-Class Polyp Detection and Classification

Abstract: The detection and classification of anatomies from medical images has traditionally been developed in a two-stage process, where the first stage detects the regions of interest (ROIs), while the second stage classifies the detected ROIs. Recent developments from the computer vision community allowed the unification of these two stages into a single detection and classification model that is trained in an end to end fashion. This allows for a simpler and faster training and inference procedures because only one model (instead of the two models needed for the two-stage approach) is required. In this paper, we adapt a recently proposed onestage detection and classification approach for the new 5class polyp classification problem. We show that this onestage approach is not only competitive in terms of detection and classification accuracy with respect to the two-stage approach, but it is also substantially faster for training and testing. We also show that the one-stage approach produces competitive detection results compared to the state of the art results on the MICCAI 2015 polyp detection challenge.

CRF-based Single-stage Acoustic Modeling with CTC Topology

Abstract: In this paper, we develop conditional random field (CRF) based single-stage (SS) acoustic modeling with connectionist temporal classification (CTC) inspired state topology, which is called CTC-CRF for short. CTC-CRF is conceptually simple, which basically implements a CRF layer on top of features generated by the bottom neural network with the special state topology. Like SS-LF-MMI (lattice-free maximum-mutual-information), CTC-CRFs can be trained from scratch (flat-start), eliminating GMM-HMM pre-training and tree-building. Evaluation experiments are conducted on the WSJ, Switchboard and Librispeech datasets. In a head-to-head comparison, the CTC-CRF model using simple Bidirectional LSTMs consistently outperforms the strong SS-LF-MMI, across all the three benchmarking datasets and in both cases of mono-phones and mono-chars. Additionally, CTC-CRFs avoid some ad-hoc operation in SS-LF-MMI.

A Rebalancing Strategy for the Imbalance Problem in Bike-Sharing Systems

Abstract: Shared bikes have become popular traveling tools in our daily life. The successful operation of bike sharing systems (BSS) can greatly promote energy saving in a city. In BSS, stations becoming empty or full is the main cause of customers failing to rent or return bikes. Some truck-based rebalancing strategies are proposed to solve this problem. However, there are still challenges around the relocation of bikes. The truck operating costs also need to be considered. In this paper, we propose a customer-oriented rebalancing strategy to solve this problem. In our strategy, two algorithms are proposed to ensure the whole system is balanced for as long as possible. The first algorithm calculates the optimal state of each station through the one-dimensional Random Walk Process with two absorption walls. Based on the derived optimal state of each station, the second algorithm recommends the station that has the largest difference between its current state and its optimal state to the customer. In addition, a simulation system of shared bikes based on the historical records of Bay Area Bikeshare is built to evaluate the performance of our proposed rebalancing strategy. The simulation results indicate that the proposed strategy is able to effectively decrease the imbalance in the system and increase the system’s performance compared with the truck-based methods.

Stateful functions as a service in action

Abstract: In the serverless model, users upload application code to a cloud platform and the cloud provider undertakes the deployment, execution and scaling of the application, relieving users from all operational aspects. Although very popular, current serverless offerings offer poor support for the management of local application state, the main reason being that managing state and keeping it consistent at large scale is very challenging. As a result, the serverless model is inadequate for executing stateful, latency-sensitive applications. In this paper we present a high-level programming model for developing stateful functions and deploying them in the cloud. Our programming model allows functions to retain state as well as call other functions. In order to deploy stateful functions in a cloud infrastructure, we translate functions and their data exchanges into a stateful dataflow graph. With this paper we aim at demonstrating that using a modified version of an open-source dataflow engine as a runtime for stateful functions, we can deploy scalable and stateful services in the cloud with surprisingly low latency and high throughput.