Reviewerly/exHarmony huggingface dataset:
TEACHING ENGLISH FOR YOUNG LEARNERS THROUGH ICTs

Nowadays, most people throughout the world learn English because English has an important role in economics, politics, and other aspects, especially for facing free trade in the ASEAN Economic Community (MEA). However, Teaching English as second language becomes more challenging to teach, particularly for young learners. In this case, Information and communication technologies (ICTs) has a pivotal role in assisting teachers in teaching and learning English and for helping students to learn English easier. Numerous scholars, educators and teachers believe that ICT has positive effects in teaching and learning English. Through ICT by incorporating local cultures and values, gender equality, and the others, it will make the activities in classroom to be more interactive and interesting. Language, cultures and IT should be embedded and incorporated. Teachers can encourage children’s motivation and improve their English abilities through ICTs. This reviews some theories of using ICTs by integrating the diversity of cultures and values either local or national and international cultures and values in the teaching and learning process. It can assist teachers to establish their teaching and learning in classrooms to be more effective, interactive, and communicative.
Characterizing Motor Control of Mastication With Soft Actor-Critic

The human masticatory system is a complex functional unit characterized by a multitude of skeletal components, muscles, soft tissues, and teeth. Muscle activation dynamics cannot be directly measured on live human subjects due to ethical, safety, and accessibility limitations. Therefore, estimation of muscle activations and their resultant forces is a longstanding and active area of research. Reinforcement learning (RL) is an adaptive learning strategy which is inspired by the behavioral psychology and enables an agent to learn the dynamics of an unknown system via policy-driven explorations. The RL framework is a well-formulated closed-loop system where high capacity neural networks are trained with the feedback mechanism of rewards to learn relatively complex actuation patterns. In this work, we are building on a deep RL algorithm, known as the Soft Actor-Critic, to learn the inverse dynamics of a simulated masticatory system, i.e., learn the activation patterns that drive the jaw to its desired location. The outcome of the proposed training procedure is a parametric neural model which acts as the brain of the biomechanical system. We demonstrate the model’s ability to navigate the feasible three-dimensional (3D) envelope of motion with sub-millimeter accuracies. We also introduce a performance analysis platform consisting of a set of quantitative metrics to assess the functionalities of a given simulated masticatory system. This platform assesses the range of motion, metabolic efficiency, the agility of motion, the symmetry of activations, and the accuracy of reaching the desired target positions. We demonstrate how the model learns more metabolically efficient policies by integrating a force regularization term in the RL reward. We also demonstrate the inverse correlation between the metabolic efficiency of the models and their agility and range of motion. The presented masticatory model and the proposed RL training mechanism are valuable tools for the analysis of mastication and other biomechanical systems. We see this framework’s potential in facilitating the functional analyses aspects of surgical treatment planning and predicting the rehabilitation performance in post-operative subjects.
Item Based Collaborative Filtering Approach in Movie Recommendation System Using Different Similarity Measures

Recommender systems use the concept of data analysis and knowledge discovery to essentially provide personalized recommendations on the web. A number of different algorithms has been proposed to effectively capture users interest and provide accurate recommendations. One of such promising technique is collaborative filtering. The predominantly used technique to date in recommendation system is collaborative filtering because of its effectiveness. User-based collaborative filtering approach is popularly and extensively used in practice but yet faces some key challenges in providing enough scalable and quality recommendations due to the daily increase of items and visitors in different websites. In this project we used collaborative item based technique to provide recommendations of items in real time by first analyzing the user-item rating matrix to determine the similarities between items in two different methods of computing items similarity (i.e. item-item correlation and item-item adjusted cosine similarity) and then use this similarity to predict unknown ratings of items, from which the top most rated items are chosen and be recommended to the intended user.
An Accurate Fall Detection System for the Elderly People Using Smartphone Inertial Sensors

Abstract In developed countries, the number of elderly people living alone is continuously increasing. These people are more vulnerable to serious health issues, such as falling down. A sensor-based system, augmented to mobile phones, can provide a much-needed prediction to the falls, where injuries and fracture possibilities can be significantly decreased. The purpose of this study is to develop a fall recognition system based on smartphone inertial sensors, which is a combination of accelerometer and gyroscope. The system can distinguish between falls and other activity daily livings (ADLs). The data output from the inertial sensor have been used by two different classifiers; artificial neural network (ANN) and support vector machine (SVM), where the objective is to find an accurate falling classifier using smartphone inertial sensors. Results show that SVM based classifier offers an accuracy of 99.27%, which outperforms the state of the art results that use smartphone data.
CrashSim: An Efficient Algorithm for Computing SimRank over Static and Temporal Graphs

SimRank is a significant metric to measure the similarity of nodes in graph data analysis. The problem of SimRank computation has been studied extensively, however there is no existing work that can provide one unified algorithm to support the SimRank computation both on static and temporal graphs. In this work, we first propose CrashSim, an index-free algorithm for single-source SimRank computation in static graphs. CrashSim can provide provable approximation guarantees for the computational results in an efficient way. In addition, as the reallife graphs are often represented as temporal graphs, CrashSim enables efficient computation of SimRank in temporal graphs. We formally define two typical SimRank queries in temporal graphs, and then solve them by developing an efficient algorithm based on CrashSim, called CrashSim-T. From the extensive experimental evaluation using five real-life and synthetic datasets, it can be seen that the CrashSim algorithm and CrashSim-T algorithm substantially improve the efficiency of the state-of-the-art SimRank algorithms by about 30%, while achieving the precision of the result set with about 97%.
Effects of Uncertainty Shocks on Household Consumption and Working Hours: A Fuzzy Cognitive Map-Based Approach

This paper aims to model an individual’s decision-making process in relation to macroeconomic dynamics that involve a large number of variables, which might inflict dimensionality issue in empirical analysis. We employ the fuzzy cognitive map (FCM) for this purpose, and present a parsimonious approach in assessing the impacts of uncertainty shocks on individual households by constructing FCM where households adjust their consumption and working hours in response to changes in exogenous economic uncertainty. We employ FCM to analyze how uncertainty shocks affect the households’ consumption, working hours, and income sources. We further conduct simulations to examine roles of expansionary fiscal policy in alleviating the negative impacts of uncertainty shocks. Our simulations yield similar results as compared to the existing literature on the impacts of uncertainty shocks. We suggest a hybrid algorithm of constructing FCM, and hence demonstrate the extensibility of FCM in analyzing complex macroeconomic systems.
Accurate and Efficient Calculation of Three-Dimensional Cost Distance

Cost distance is one of the fundamental functions in geographical information systems (GISs). 3D cost distance function makes the analysis of movement in 3D frictions possible. In this paper, we propose an algorithm and efficient data structures to accurately calculate the cost distance in discrete 3D space. Specifically, Dijkstra’s algorithm is used to calculate the least cost between initial voxels and all the other voxels in 3D space. During the calculation, unnecessary bends along the travel path are constantly corrected to retain the accurate least cost. Our results show that the proposed algorithm can generate true Euclidean distance in homogeneous frictions and can provide more accurate least cost in heterogeneous frictions than that provided by several existing methods. Furthermore, the proposed data structures, i.e., a heap combined with a hash table, significantly improve the algorithm’s efficiency. The algorithm and data structures have been verified via several applications including planning the shortest drone delivery path in an urban environment, generating volumetric viewshed, and calculating the minimum hydraulic resistance.
Privacy Preserving Calculation in Cloud using Fully Homomorphic Encryption with Table Lookup

To protect data in cloud servers, fully homomorphic encryption (FHE) is an effective solution. In addition to encrypting data, FHE allows a third party to evaluate arithmetic circuits (i.e., computations) over encrypted data without decrypting it, guaranteeing protection even during the calculation. However, FHE supports only addition and multiplication. Functions that cannot be directly represented by additions or multiplications cannot be evaluated with FHE. A naïve implementation of such arithmetic operations with FHE is a bit-wise operation that encrypts numerical data as a binary string. This incurs huge computation time and storage costs, however. To overcome this limitation, we propose an efficient protocol to evaluate multi-input functions with FHE using a lookup table. We extend our previous work, which evaluates a single-integer input function, such as f(x). Our extended protocol can handle multi-input functions, such as f(x,y). Thus, we propose a new method of constructing lookup tables that can evaluate multi-input functions to handle general functions. We adopt integer encoding rather than bit-wise encoding to speed up the evaluations. By adopting both permutation operations and a private information retrieval scheme, we guarantee that no information from the underlying plaintext is leaked between two parties: a cloud computation server and a decryptor. Our experimental results show that the runtime of our protocol for a two-input function is approximately 13 minutes, when there are 8,192 input elements in the lookup table. By adopting a multi-threading technique, the runtime can be further reduced to approximately three minutes with eight threads. Our work is more practical than a previously proposed bit-wise implementation, which requires 60 minutes to evaluate a single-input function.
Frameworks for an Archaeology of the Body

One of the richest prehistoric resources available to archaeology are the rock carvings of the county of Goteborgs och Bohuslän, situated on the west coast of Sweden and extending from the city of Göteborg to the Norwegian border. Representations of the human figure make up the second most common representational motif found in the rock carvings, and the county has the highest proportion and absolute number of human figures of all areas with prehistoric art in northern Europe. The two major studies of the rock carvings of Bohuslän - J. Nordbladh and U. Bertilsson - are both preoccupied with the place of the carvings in the landscape and the co-occurrence of the different classes of motif on the panels. Nordbladh has also discussed the occurrence of armed figures in the rock carvings of Kville hundred.
Crime and/or Punishment: Joining the Dots between Crime, Legality and HCI

We aim to bring together a number of designers, researchers and practitioners to share their experience of the influence of crime and legality on their work. Through these discussions, we aspire to highlight the existing knowledge base for discussions of crime within HCI, provide a space for sharing researcher's personal experiences in their work with and against crime, and highlight best practice going forwards. We will do this by using three considerations to inform our critical focus on crime: (1) mapping out the existing ways that HCI has addressed crime; (2) considering what part crime plays in approaches to social justice; (3) questioning who is thus morally responsible for the criminal activity of others, and what does this entail for ensuring fair approaches within technical design.
A Novel Approach for Software Requirement Prioritization

Requirement Engineering is an early phase of Software Development Life Cycle where software requirement prioritization becomes a real challenge when dealing with large number of requirements. A number of requirement prioritization techniques exist including Cumulative Voting, Planning Games, Analytic Hierarchy Process, MOSCOW etc. It is analyzed that for large number of requirements, these techniques are not sufficient due to complexity and ranking problems. In this paper, we have purposed a novel approach named as “MAHP” for Requirement Prioritization by integrating the AHP concepts in MOSCOW technique which will reduce the number of pairwise comparisons thus reducing the complexity. MAHP is validated using case study of Library Management System. Results shows that our proposed approach MAHP is more efficient because it has generated forty five (45) comparisons as compared to AHP which is able to generate more than two hundred (210) pairwise comparisons for just twenty one (21) functional requirements.
An Abnormal Behavior Detection Method using Optical Flow Model and OpenPose

Abnormal behavior detection and recognition of pedestrian in escalator has always been a challenging task in intelligent video surveillance system.To cope this problem, a method combining optical flow vector of passenger with human skeleton extraction is proposed.At first, adaptive dual fractional order optical flow model is used to estimate the optical flow field under scenes with illumination changes, low contrast and uneven illumination.At the same time, the OpenPose deep convolutional neural network is used to extract body skeleton and persons in image can be located.Then, the optical flow field and the human skeleton are combined to obtain the optical flow vector of the passenger head.After that the optical flow field of the passenger head and the step of escalator under the passenger foot are used for abnormal behavior detection and recognition, random forest is employed to behavior classifier.Experimental results show that our proposed method and its improvement strategy can accurately estimate the optical flow field in real time of low contrast outdoor videos with insufficient illumination, uneven brightness and illumination changes, the accuracy of abnormal action detection and recognition can reach to 97.98% and 92.28%.
Automatic Techniques to Systematically Discover New Heap Exploitation Primitives

Heap exploitation techniques to abuse the metadata of allocators have been widely studied since they are application independent and can be used in restricted environments that corrupt only metadata. Although prior work has found several interesting exploitation techniques, they are ad-hoc and manual, which cannot effectively handle changes or a variety of allocators. In this paper, we present a new naming scheme for heap exploitation techniques that systematically organizes them to discover the unexplored space in finding the techniques and ArcHeap, the tool that finds heap exploitation techniques automatically and systematically regardless of their underlying implementations. For that, ArcHeap generates a set of heap actions (e.g. allocation or deallocation) by leveraging fuzzing, which exploits common designs of modern heap allocators. Then, ArcHeap checks whether the actions result in impact of exploitations such as arbitrary write or overlapped chunks that efficiently determine if the actions can be converted into the exploitation technique. Finally, from these actions, ArcHeap generates Proof-of-Concept code automatically for an exploitation technique. We evaluated ArcHeap with real-world allocators --- ptmalloc, jemalloc, and tcmalloc --- and custom allocators from the DARPA Cyber Grand Challenge. ArcHeap successfully found 14 out of 16 known exploitation techniques and found five new exploitation techniques in ptmalloc. Moreover, ArcHeap found several exploitation techniques for jemalloc, tcmalloc, and even for the custom allocators. Further, ArcHeap can automatically show changes in exploitation techniques along with version change in ptmalloc using differential testing.
Area-based non-maximum suppression algorithm for multi-object fault detection

Unmanned aerial vehicle (UAV) photography has become the main power system inspection method; however, automated fault detection remains a major challenge. Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously. The object detection method involving deep learning provides a new method for fault detection. However, the traditional non-maximum suppression (NMS) algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers. In this study, we propose an area-based non-maximum suppression (A-NMS) algorithm to solve the problem of one object having multiple labels. The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects. Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58% and 91.23%, respectively, in case of the aerial image datasets and realize multi-object fault detection in aerial images.
AN EFFECTIVE REVERSIBLE DATA HIDING METHOD BASED ON PIXEL-VALUE-ORDERING

This paper presents a new effective reversible data hiding method based on pixel-value-ordering (iGePVO-K) which is improvement of a recent GePVO-K method that recently is considered as a PVO-used method having highest embedding capacity. In comparison with GePVO-K method, iGePVO-K has the following advantages. First, the embedding capacity of the new method is higher than that of GePVO-K method by using data embedding formulas reasonably and reducing the location map size. Second, for embedding data, in the new method, each pixel value is modified at most by one, while in GePVO-K method, each pixel value may be modified by two. In fact, in the GePVO-K method, the largest pixels are modified by two for embedding bits 1 and by one for bits 0. This is also true for the smallest pixels. Meanwhile, in the proposed method, the largest pixels are modified by one for embedding bits 1 and are unchanged if embedding bits 0. Therefore, the stego-image quality in proposed method is better than that in GePVO-K method. Theoretical analysis and experiment results show that the proposed method has higher embedding capacity and better stego image quality than GePVO-K method.
Refund fraud analytics for an online retail purchases

Online shopping is growing fast across the globe and so are its complexities. Fraud is a complicated phenomenon and its mitigation is critical for running a smooth business. The case considered for the present study is fraud mitigation in return – refund process managed by the customer services of an online retail business. Predictive analytics approach was used to identify early indicators of agent refund fraud – a rare event. The technique used to solve the problem was a Penalised Likelihood based Logistic Regression model. The proposed model allowed the business to select top 5% sample of refund transactions with a higher likelihood of fraud as indicated and queue them for an audit. Implementation of this model resulted in an incremental lift in fraud capture rate.
Managing the social aspects of software development ecosystems: An industrial case study on personality

Abstract The social aspects of software development encompass concerns such as motivating practitioners, building effective teams, and developing personal relations. Not surprisingly, perhaps, many software projects fail due to personality conflicts within team members. This study investigates the personality traits of 132 software practitioners by employing a tailored interactive assessment that was specifically developed for software development organizations. To assess the personality characteristics of the software teams as a whole, the results of 20 project teams were visualized by using personality–team radar charts. The validity part of the study was performed through validation interviews with experts from the field, discussing their experiences using the interactive assessment. The findings of this investigation complement those of earlier studies that suggest that productive team members who were working on social isolation showed higher introversion. In particular, this study strengthens the idea that agreeableness was observed in agile teams. Ultimately, the present data also highlight the existence of conscientiousness personalities in agile software development.
Enhancing Digital Well-being using Opinion Mining and Sentiment Classifiers

Opinion mining on various issues is a very popular trend in the micro-blogging research community. Advanced data mining techniques using sentiment analysis and machine learning algorithms on large datasets like microblogging websites are popular and trending in the data science community. But such analytics are limited to only certain aspects of interest. In this paper, we present a search engine that presents a user’s emotions over a timeline. This would serve as a novel approach to adding search capability over opinion mining procedures and exploring potential optimizations for a wide range of features and methods for training sentiment classifiers. Users should be able to chart down their emotional quotient using the proposed application. This would aid in promoting their digital well-being.
Emotion Classification on Twitter Data Using Word Embedding and Lexicon Based Approach

Twitter is one of the leading social media platforms for its users for tweeting 280 characters in a single tweet. Social media influence the users to share data, promoting advertisement and posting useful information to the followers. The social media data helps the individuals and business personals to take decision based on the analysis with the data. Twitter sentimental analysis is important for identifying the similar text patterns present in the given input text. The analysis further classified with emotional, positive and negative tweets. Existing approaches limited in accuracy, therefore, the word embedding and lexicon based approach has been introduced for increasing the accuracy. The twitter data stream is taken as an input and preprocessed by removing stop words, hash tags and urls. Further, the data is tokenized and applied with the word embedding method to detect the location and lexicon based approach to segregate the sentimental and emotional tweets. The system has been tested with the live data set as well as offline dataset, and the result shows very promising.