Reviewerly/exHarmony huggingface dataset:
GLAD: A Method of Microgrid Anomaly Detection Based on ESD in Smart Power Grid

A microgrid is defined as a group of interconnected loads and distributed energy resources, that the electrical loads fluctuate with respect to the grid. When focusing on load anomaly in microgrid, different from the traditional centralized grid (macrogrid), a method of microgrid anomaly detection based on the extreme studentized deviate(ESD) test, is proposed as GLAD (grid load anomaly detection). GLAD under the enhanced ESD is adapted to solve this problem properly, using the detect_ts function of PyCuliarity library to carry out anomaly detection simulation experiment in Python software, according to a series of statistical analysis. In the paper, the existing time series and anomaly detection methods are firstly analyzed and summarized, then GLAD are designed to detect the grid load variations. Some conventional anomaly detection methods are also discussed for higher efficiency of GLAD. Moreover, there are still better methods for anomaly detection of microgrid, Finally, GLAD with machine learning modeling is discussed for the future smartgrids of anomaly detection in distributed energy resources.
A General Framework for Stable Roommates Problems using Answer Set Programming

Abstract The Stable Roommates problem (SR) is characterized by the preferences of agents over other agents as roommates: each agent ranks all others in strict order of preference. A solution to SR is then a partition of the agents into pairs so that each pair shares a room, and there is no pair of agents that would block this matching (i.e., who prefers the other to their roommate in the matching). There are interesting variations of SR that are motivated by applications (e.g., the preference lists may be incomplete (SRI) and involve ties (SRTI)), and that try to find a more fair solution (e.g., Egalitarian SR). Unlike the Stable Marriage problem, every SR instance is not guaranteed to have a solution. For that reason, there are also variations of SR that try to find a good-enough solution (e.g., Almost SR). Most of these variations are NP-hard. We introduce a formal framework, called SRTI-ASP, utilizing the logic programming paradigm Answer Set Programming, that is provable and general enough to solve many of such variations of SR. Our empirical analysis shows that SRTI-ASP is also promising for applications.
Improving query expansion strategies with word embeddings

Representation learning has been a fruitful area in recent years, driven by the growing interest in deep learning methods. In particular, word representation learning, a.k.a. word embeddings has triggered progress in different natural language processing (NLP) tasks. Despite the success of word embeddings in tasks such as named entity recognition or textual entailment, their use is still embryonic in query expansion. In this work, we examine the usefulness of word embeddings to represent queries and documents in query-document matching tasks. For this purpose, we use a re-ranking strategy. The re-ranking phase is conducted using representations of queries and documents based on word embeddings. We introduce IDF average word embeddings, a new text representation strategy based on word embeddings, which allows us to create a query vector representation that provides higher relevance to informative terms during the process. Experimental results in TREC benchmark datasets show that our proposal consistently achieves the best results in terms of MAP.
Hybrid Machine Learning System for Solving Fraud Detection Tasks

In parallel with technological development the problem of fraud detection is becoming more and more important. Increasing number of electronic transactions in various business environments, on the one hand, and software and technology development, on the other hand, lead to an active increase in electronic crime. In the paper the hybrid system of machine learning for solving tasks of anomalies detection has been proposed. This hybrid system consists of two subsystems - anomalies detection subsystem (based on unsupervised learning) and the interpretation subsystem of anomaly type (based on supervised system). The advantage of proposed hybrid system is the high-speed data processing when the data are fed in real time. The effectiveness of the proposed approach was confirmed during the solution of the detecting anomalies problem based on real data streams.
Education Innovations through Mobile Learning Technologies for the Industry 4.0 Readiness of Tertiary Students in Malaysia

Abstract The formal education sphere is one that is dynamic and ever-changing, even more so with current developments in technologies of teaching and learning. The process of education needs new innovations to keep pace with these rapid developments, particularly for tertiary students on the cusp of radical Industry 4.0 ‘disruptions’ that will permeate all social and personal domains in human societies around the globe. One area where innovation is happening is in the implementation of mobile learning technologies for tertiary college and higher learning institutions. The study examines why and how tertiary students utilize their mobile devices in the education setting as both a conduit and catalysts for e-learning. Tertiary students find the utilize of mobile devices easy and natural, thus helping them to learn anytime, anywhere. Today, the popularity of mobile practices amongst tertiary education is steadily growing and they are now able to utilize their mobile devices for a myriad of activities from developing websites to creating mobile apps to acquiring knowledge non-stop. Mobile application for learning is expected to be prevalent in forthcoming, as more devices procured by the younger generation. This study examines results from a survey on the perceptions and practices of mobile learning for Malaysian tertiary students. A quantitative research methodology was adopted to analyse data; results demonstrate that these students have positive perceptions towards mobile technology utilize in learning, and they actively and autonomously utilize mobile devices to enhance their experience of learning.
High-frequency flashing of light source for synchronous measurement of temperature and deformation at elevated temperature

Synchronous measurement of full-field temperature and deformation at elevated temperature using non-contact optical methods attracts increasing attention in evaluating the high temperature properties of materials. The current optical methods all face one major challenge, which is the strong light reflection on the surface of the specimen. Such strong light reflection could greatly impair the measurement accuracy of the temperature field measured based on the radiation light information. Here, we develop an effective and simple testing system to overcome this difficulty. The measurement system consists of an image and temperature acquisition device, and a light source with the feature of high-frequency flashing and periodic flicking. High-frequency flashing of the light source is driven by a motor and controlled by a field programmable gate array controller. Our system can realize the synchronous control of light flashing, image capturing, and temperature acquisition. Images with light on are used to calculate the deformation field based on the digital image correlation method, and those with light off (without reflection) are used for the calculation of temperature. Based on the continuous change of temperature over time, the temperature field at the moment when light is on can be obtained by interpolating the temperature fields before and after the moment through the fitted curve. Experimental validation on the thermal heating of C/C fiber composites shows a satisfactory result for temperature and deformation measurement.
Super-resolution of satellite imagery using a wavelet multiscale-based deep convolutional neural network model

Nowadays, satellite images are used in various governmental applications, such as urbanization and monitoring the environment. Spatial resolution is an element of crucial impact on the usage of remote sensing imagery. As such, increasing the spatial resolution of an image is an important pre-processing step that can improve the performance of various image processing tasks, such as segmentation. Once a satellite is launched, the more practical solution to improve the resolution of its captured images is to use Single Image Super Resolution (SISR) techniques. In the recent years, Deep Convolutional Neural Networks (DCNNs) have been recognized as a highly effective tool to reconstruct a High Resolution (HR) image from its Low Resolution (LR) counterpart, which is an open problem due to the inherent difficulty of estimating the missing high frequency components. The aim of this research paper is to design and implement a satellite image SISR algorithm by estimating high frequency details through training Deep Convolutional Neural Network (DCNNs) with respect to wavelet analysis. The goal is to improve the spatial resolution of multispectral remote sensing images captured by DubaiSat-2 satellite. The accuracy of the proposed algorithm is assessed using several metrics such as Peak Signal-to-Noise Ratio (PSNR), Wavelet-based Signal-to-Noise Ratio (WSNR) and Structural Similarity Index Measurement (SSIM).
Correlated data in differential privacy: Definition and analysis

Summary Differential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real‐world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: (1) using parameters to describe data correlation in differential privacy, (2) using models to describe data correlation in differential privacy, and (3) describing data correlation based on the framework of Pufferfish. First, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy.
Total Variation and Mean Curvature PDEs on the Homogeneous Space of Positions and Orientations

Abstract Two key ideas have greatly improved techniques for image enhancement and denoising: the lifting of image data to multi-orientation distributions and the application of nonlinear PDEs such as total variation flow (TVF) and mean curvature flow (MCF). These two ideas were recently combined by Chambolle and Pock (for TVF) and Citti et al. (for MCF) for two-dimensional images. In this work, we extend their approach to enhance and denoise images of arbitrary dimension, creating a unified geometric and algorithmic PDE framework, relying on (sub-)Riemannian geometry. In particular, we follow a different numerical approach, for which we prove convergence in the case of TVF by an application of Brezis–Komura gradient flow theory. Our framework also allows for additional data adaptation through the use of locally adaptive frames and coherence enhancement techniques. We apply TVF and MCF to the enhancement and denoising of elongated structures in 2D images via orientation scores and compare the results to Perona–Malik diffusion and BM3D. We also demonstrate our techniques in 3D in the denoising and enhancement of crossing fiber bundles in DW-MRI. In comparison with data-driven diffusions, we see a better preservation of bundle boundaries and angular sharpness in fiber orientation densities at crossings.
An M-Cache-Based Security Monitoring and Fault Recovery Architecture for Embedded Processor

Recently, extensive research attention has been drawn to the program executing security of embedded processor since increasing code tamper attacks, as well as transient faults severely affect the safety of embedded systems. The security monitoring and fault recovery technique is one of the effective methods to ensure the security and performance of embedded devices. In this article, an architecture for the security monitoring and fault recovery is proposed for run-time program execution, which builds a Monitoring Cache (M-Cache) and then checks the integrity based on reference data. Especially, the proposed architecture will build the checkpoint once the M-Cache is missed and also take the rollback operation after the unsuccessful of integrity check. In addition, three tampered positions (e.g., instruction register within the pipeline, instruction within the cache, and code within memory) have been elaborately focused on to guarantee the normal running of the embedded system. Eventually, by adopting the open RISC processor for algorithm implementation and verification, the proposal has been proven to be promising for the detection of a fault or tampered program, as well as the fast recovery of running environment and code.
Towards classes of architectural dependability assurance for machine-learning-based systems

Advances in Machine Learning (ML) have brought previously hard to handle problems within arm's reach. However, this power comes at the cost of unassured reliability and lacking transparency. Overcoming this drawback is very hard due to the probabilistic nature of ML. Current approaches mainly tackle this problem by developing more robust learning procedures. Such algorithmic approaches, however, are limited to certain types of uncertainties and cannot deal with all of them, e.g., hardware failure. This paper discusses how this problem can be addressed at architectural rather than algorithmic level to assess systems dependability properties in early development stages. Moreover, we argue that Self-Adaptive Systems (SAS) are more suited to safeguard ML w.r.t. various uncertainties. As a step towards this we propose classes of dependability in which ML-based systems may be categorized and discuss which and how assurances can be made for each class.
The Timer Compression of Data and Information

The ability to find short representations, i.e. to compress data, is crucial for many intelligent systems. This paper is devoted to data compression and a transform-based quantitative data compression technique involving quick enumeration in a unary-binary time-based numeral system (NS). The symbols comprising the alphabets of human-computer interaction languages (HCIL), which are used in an informational message (IM), are collected in primary code tables, such as the ASCII table. The statistical-oriented data compression method using unconventional timer encryption and encoding information are proposed by us. It was constructed probability - discrete model of the set of character sequences and characterized some probabilistic algorithms associated with the recovery of text by its public key and its cipher. We find the possibility of parallel implementation of this method by building a block of timer tags. The necessary estimations of complexity are obtained. The method can be used to compress SMS messages. Probabilistic-statistical analysis and evaluation of their effectiveness are obtained.
A machine learning approach for improved shop-floor operator support using a two-level collaborative filtering and gamification features

The increasing gap in shopfloor operators' skillset regarding advanced information and communication technologies along with workforce's diversity require a cognitive system bridging such technical gaps in order to address evolving production demands and satisfy the human need for self-fulfillment and self-actualization at work. This study discusses on a two-level collaborative filtering approach to improve the distribution of information content provided to an operator for completing a manufacturing activity while considering his or her feedback. A prototype implementation is evaluated in a case study related to the operator's job rotation on a shopfloor that involves multiple workstations and tasks.
Software Quality Prediction Using Machine Learning Techniques

Software quality prediction is one of the most challenging tasks in the development and maintenance of the software. Machine learning (ML) is widely being incorporated for the prediction of the quality of final product in the early development stages of SDLC. ML prediction model is set using software metrics and faulty data of previous projects to detect the high-risk modules for future projects so that the testing efforts can be targeted to those specific ‘risky’ modules. Hence, ML-based predictors contribute to the detection of development anomalies early and inexpensively and ensure the timely delivery of a successful, failure-free and supreme quality software product within budget. This paper brings a comparison of 30 software quality prediction models built on five ML techniques such as artificial neural network and support vector machine, decision tree, k-nearest neighbor and Naïve-Bayes classifiers using six datasets. These models exploit the predictive power of static code metrics—McCabe complexity metrics—for the quality prediction. All thirty predictors are compared using ROC, AUC and accuracy as performance evaluation criteria. The results show that ANN technique is the most promising for accurate quality prediction irrespective of the dataset used.
“Who doesn’t like dinosaurs?” Finding and Eliciting Richer Preferences for Recommendation

Real-world recommender systems often allow users to adjust the presented content through a variety of preference elicitation techniques such as "liking" or interest profiles. These elicitation techniques trade-off time and effort to users with the richness of the signal they provide to learning component driving the recommendations. In this paper, we explore this trade-off, seeking new ways for people to express their preferences with the goal of improving communication channels between users and the recommender system. Through a need-finding study, we observe the patterns in how people express their preferences during curation task, propose a taxonomy for organizing them, and point out research opportunities. We present a case study that illustrates how using this taxonomy to design an onboarding experience can lead to more accurate machine-learned recommendations while maintaining user satisfaction under low effort.
Energy Efficient Neural Network Embedding in IoT over Passive Optical Networks

In the near future, IoT based application services are anticipated to collect massive amounts of data on which complex and diverse tasks are expected to be performed. Machine learning algorithms such as Artificial Neural Networks (ANN) are increasingly used in smart environments to predict the output for a given problem based on a set of tuning parameters as the input. To this end, we present an energy efficient neural network (EE-NN) service embedding framework for IoT based smart homes. The developed framework considers the idea of Service Oriented Architecture (SOA) to provide service abstraction for multiple complex modules of a NN which can be used by a higher application layer. We utilize Mixed Integer Linear Programming (MILP) to formulate the embedding problem to minimize the total power consumption of networking and processing simultaneously. The results of the MILP model show that our optimized NN can save up to 86% by embedding processing modules in IoT devices and up to 72% in fog nodes due to the limited capacity of IoT devices.
An examination of susceptibility to spear phishing cyber attacks in non-English speaking communities

Spear phishing is a fraudulent practice that targets specific and well-researched users in an organization to collect their credentials. Previous studies have addressed the underlying drivers that significantly influence susceptibility to spear phishing. However, findings may not be generalized to other cultures and environments such as the developing Non-English-speaking countries. To fill this knowledge gap, this research investigated the drivers that affect susceptibility to spear phishing in the Middle Eastern culture. We proposed and tested a theoretical model that explains users' behavior toward phishing material in the context of Non-English-speaking countries. We created the proposed model relying on the perceived risk theory, the theory of planned behavior, and the OSIR decision making model. The proposed model addressed the impact of information privacy risks, information security risks, and information security knowledge on the susceptibility to spear phishing attacks through the moderating trust construct. The study was conducted in Jordan, a developing and Non-English-speaking country in the Middle East. We designed a lab experiment to evaluate the robustness of the proposed model based on a multistage research, where 83 university students used a phishing website then answered a related survey. Collected data were empirically tested and evaluated using Partial Least Square Analysis and Structural Equation Modeling. The results demonstrated the influence of the identified factors on the susceptibility to spear phishing. The study may provide an assistance in evaluating and selecting tools, methods and features for handling targeted types of phishing. There are several novel aspects in this study. 1) the experimental nature of study, where we used a real-life spear phishing scenario. 2) the nature of the targeted websites. We created spoofed pages of two webpages that provide academic activities, where students’ level of trust in those websites is likely higher than other websites. 3) the investigation of the mediation role of trust construct, particularly within a university environment, is a new direction in susceptibility to spear phishing. Unlike existing models that measure the direct effect of personality characteristics on phishing susceptibility, our model introduces trust attitude as an aggregation of positive and negative security-privacy interpretations. Finally, the study was conducted in a developing country environment where the Arabic Language is used in initiating and executing the attack.