Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

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Social Media and Fake News in the 2016 Election

Cloud computing is today’s most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such great significance comes with the support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. Their share in power consumption generates between 1.1p and 1.5p of the total electricity use worldwide and is projected to rise even more. Such alarming numbers demand rethinking the energy efficiency of such infrastructures. However, before making any changes to infrastructure, an analysis of the current status is required. In this article, we perform a comprehensive analysis of an infrastructure supporting the cloud computing paradigm with regards to energy efficiency. First, we define a systematic approach for analyzing the energy efficiency of most important data center domains, including server and network equipment, as well as cloud management systems and appliances consisting of a software utilized by end users. Second, we utilize this approach for analyzing available scientific and industrial literature on state-of-the-art practices in data centers and their equipment. Finally, we extract existing challenges and highlight future research directions.

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Cloud Computing: Survey on Energy Efficiency

We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.

/pdf/semeval-2020-task-12-multilingual-offensive-language-59c04s046m.pdf

SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)

Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

/pdf/catboost-for-big-data-an-interdisciplinary-review-utp91h5i0y.pdf

CatBoost for big data: an interdisciplinary review

Software-as-a-Service (SaaS) and Model-Driven Engineering (MDE) are two of the most dominant software engineering paradigms nowadays. Multi-tenancy is the key to successful SaaS. In this paper, we introduce a data middleware to customize the multi- tenant database first. In addition, with the help of model transformation, it is possible to generate SaaS applications from the models. However, most of the current model transformation approaches do not fully support the requirements for model synchronization, and they do not cater for the specific problems faced in the multi-tenancy. Therefore, an effective and simple template-based model transformation and model synchronization approach based on model evolution of MDE paradigms is fully integrated for the development of SaaS multi-tenant applications. The proposed framework uses a novel extensible business component model (xBC) to sufficiently describe both the structural and behavioral properties of SaaS applications. The distribution and uninterrupted running of the generated SaaS applications proves that our approach is feasible and correct in practice.

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A Template-based Model Transformation Approach for Deriving Multi-Tenant SaaS Applications

Nowadays, the cloud computing owners lack management and monitoring tools to ensure the performance, robustness, dependability, and security. To address this limitation, this paper described our experience with a lightweight monitoring framework using some extra development work. This framework performed end-to-end measurements at virtual machine instances and software in the public cloud. It monitors quality of service parameters of the IaaS and SaaS layer without modifying the implementation of the monitored object. In addition, we discussed the manager-agent and module-centralized architecture in details. All the modules make up the entire proposed framework to improve the performance of applications in public clouds.

/pdf/toward-a-lightweight-framework-for-monitoring-public-clouds-37j0no4by8.pdf

Toward a lightweight framework for monitoring public clouds

Today, wireless sensor networks (WSNs) in electronic engineering are used in the monitoring of remote intelligent buildings, and the need for emerging Web 3.0 is becoming more and more in every aspect of electronic engineering. However, the key challenges of monitoring are the monitoring approaches and storage models of huge historical monitoring data. To address these limitations, we attempt to design a WebSocket-based real-time monitoring system for remote intelligent buildings. On one hand, we utilize the latest HTML5 WebSocket, Canvas and Chart technologies to monitor the sensor data collected in WSNs in the Web browser. The proposed monitoring system supports the latest HTML5 browsers and legacy browsers without native WebSocket capability transparently. On the other hand, we propose a storage model with lifecycle to optimize the NoSQL data warehouse. Finally, we have made the monitoring and storage experiments to illustrate the superiority of our approach. The monitoring experimental results show that the average latency time of our WebSocket monitoring is generally lower than polling, FlashSocket, and Socket solution, and the storage experimental results show that our storage model has low redundancy rate, storage space, and latency.

https://journals.sagepub.com/doi/pdf/10.1155/2013/867693

Introducing WebSocket-Based Real-Time Monitoring System for Remote Intelligent Buildings

Large-Scale Schema-Free Data Deduplication Approach with Adaptive Sliding Window Using MapReduce

In this paper, a Topic-based probabilistic model named GISTis proposed to infer group activities, and make group recommendations. Compared with existing individual-based aggregation methods, it not only considers individual members’ interest, but also consider some subgroups’ interest. Intuition might seem that when a group of users want to take part in an activity, not every group member is decisive, instead, more likely the subgroups of members having close relationships lead to the final activity decision. That motivates our study on jointly considering individual members’ choices and subgroups’ choices for group recommendations. Based on this, our model uses two kinds of unshared topics to model individual members’ interest and subgroups’ interest separately, and then make final recommendations according to the choices from the two aspects with a weight-based scheme. Moreover, the link information in the graph topology of the groups can be used to optimize the weights of our model. The experimental results on real-life data show that the recommendation accuracy is significantly improved by GIST comparing with the state-of-the-art methods.

https://opus.lib.uts.edu.au/bitstream/10453/120750/6/ESWA-18-Ji.pdf

Kun Ma

Papers

A Template-based Model Transformation Approach for Deriving Multi-Tenant SaaS Applications

Toward a lightweight framework for monitoring public clouds

Introducing WebSocket-Based Real-Time Monitoring System for Remote Intelligent Buildings

Large-Scale Schema-Free Data Deduplication Approach with Adaptive Sliding Window Using MapReduce

GIST: A generative model with individual and subgroup-based topics for group recommendation