Reviewerly/exHarmony huggingface dataset:
Learning Based On Principles Of Cognitivism.

Sometimes, knowledge or information becomes difficult to understand and/or memorize. Cognitive load theory aids a learner to gain information effectively. It provides a scientific roadmap to the design of learning materials. Through a complex set of mental processes, information is acquiring, process, retain, and comprehend information. Applying mental effort for remembering is a very crucial phenomenon. The core of memory process is apprehending and saving of information into long term memory. Because of limited capacity of working memory, delivering or acquiring of information should not be overloaded with irrelevant materials. Based on cognitive load theory dozens of strategies can be intervened at different loci of cognitive process and every piece of information is easily remembered. Not only learners, but teachers can also get benefits from them. In this commentary, John Sweller ideology is further discussed and strategies are presented for better comprehension and memorization of difficult information.
Secure Social Recommendation Based on Secret Sharing

Nowadays, privacy preserving machine learning has been drawing much attention in both industry and academy. Meanwhile, recommender systems have been extensively adopted by many commercial platforms (e.g. Amazon) and they are mainly built based on user-item interactions. Besides, social platforms (e.g. Facebook) have rich resources of user social information. It is well known that social information, which is rich on social platforms such as Facebook, are useful to recommender systems. It is anticipated to combine the social information with the user-item ratings to improve the overall recommendation performance. Most existing recommendation models are built based on the assumptions that the social information are available. However, different platforms are usually reluctant to (or cannot) share their data due to certain concerns. In this paper, we first propose a SEcure SOcial RECommendation (SeSoRec) framework which can (1) collaboratively mine knowledge from social platform to improve the recommendation performance of the rating platform, and (2) securely keep the raw data of both platforms. We then propose a Secret Sharing based Matrix Multiplication (SSMM) protocol to optimize SeSoRec and prove its correctness and security theoretically. By applying minibatch gradient descent, SeSoRec has linear time complexities in terms of both computation and communication. The comprehensive experimental results on three real-world datasets demonstrate the effectiveness of our proposed SeSoRec and SSMM.
Scheduling a Time-Varying Workload of Multiple Types of Jobs on Distributed Resources

Recent technological advances, such as cloud and fog computing, have made prevalent the utilization of distributed computational resources. There is a variety of complex workloads that need to be processed on such platforms. The type of the workload may vary over time. Two of the most important challenges in a distributed environment are efficient resource allocation and the employment of appropriate workload scheduling techniques. In this paper, we examine dynamic scheduling of complex workloads on distributed resources. The workload consists of single-task jobs, bag-of-task jobs and linear workflows. The characteristics of the investigated workload vary over time. Two scheduling techniques are studied. Simulation modeling is used to compare their performance under different workload and system load scenarios.
Clustering Algorithm Consistency in Fixed Dimensional Spaces

Kleinberg introduced an axiomatic system for clustering functions. Out of three axioms, he proposed two (scale invariance and consistency) are concerned with data transformations that should produce the same clustering under the same clustering function. The so-called consistency axiom provides the broadest range of transformations of the data set. Kleinberg claims that one of the most popular clustering algorithms, k-means does not have the property of consistency. We challenge this claim by pointing at invalid assumptions of his proof (infinite dimensionality) and show that in one dimension in Euclidean space the k-means algorithm has the consistency property. We also prove that in higher dimensional space, k-means is in fact inconsistent. This result is of practical importance when choosing testbeds for implementation of clustering algorithms while it tells under which circumstances clustering after consistency transformation shall return the same clusters.
Application of content based image retrieval in digital image search system

Multimedia data is growing rapidly in the current digital era, one of which is digital image data. The increasing need for a large number of digital image datasets makes the constraints faced eventually drain a lot of time and cause the process of image description to be inconsistent. Therefore, a method is needed in processing the data, especially in searching digital image data in large image dataset to find image data that are relevant to the query image. One of the proposed methods for searching information based on image content is content based image retrieval (CBIR). The main advantage of the CBIR method is automatic retrieval process, compared to traditional keyword. This research was conducted on a combination of the HSV color histogram methods and the discrete wavelet transform to extract color features and textures features, while the chi-square distance technique was used to compare the test images with images into a database. The results have showed that the digital image search system with color and texture features have a precision value of 37.5% - 100%, with an average precision value of 80.71%, while the percentage accuracy is 93.7% - 100% with an average accuracy is 98.03%.
Insight into ethical cyber behaviour of undergraduate students at selected African universities

Background: Worldwide, immoral cyberspace users have continued to use the Internet to commit crimes; this has caused unease and has called for quick response to the problem especially within the educational sector. The practical value of this study is in its benefit to other researchers who may be attempting to understand South African or Nigerian cyber technology user‘s behaviour; it may also help relevant educational authorities to get relevant understanding of behaviour in the realm of cyberspace. Objectives: This study examined undergraduate students in relation to cyber technology at the University of Zululand (UNIZULU), South Africa, and the Federal University of Agriculture in Abeokuta (FUNAAB), Nigeria. Method: A survey design, questionnaire as the tool for data collection was adapted and samples for the study were drawn from undergraduate students in two conveniently selected universities in South Africa and Nigeria. Overall, 450 undergraduate students were invited to participate in the survey; 380 respondents completed and returned the questionnaire, resulting in a response rate of 84.4%. Results: Most of the respondents from the sampled universities reported that they were aware of what constitutes unethical cyber behaviour. Furthermore, the participants revealed that they hardly received orientation at the universities on cyber behaviour. The challenges that the students faces were reported. Conclusion: This study recommends that universities should sustain orientation and/or training programmes on cyber-ethics and cyber security awareness at the start of each academic year. The results of this study may spark further discussions and research on cyber technology access and use in contemporary society.
INVITED: Efficient Synthesis of Compact Deep Neural Networks

Deep neural networks (DNNs) have been deployed in myriad machine learning applications. However, advances in their accuracy are often achieved with increasingly complex and deep network architectures. These large, deep models are often unsuitable for real-world applications, due to their massive computational cost, high memory bandwidth, and long latency. For example, autonomous driving requires fast inference based on Internet-of-Things (IoT) edge devices operating under run-time energy and memory storage constraints. In such cases, compact DNNs can facilitate deployment due to their reduced energy consumption, memory requirement, and inference latency. Long short-term memories (LSTMs) are a type of recurrent neural network that have also found widespread use in the context of sequential data modeling. They also face a model size vs. accuracy trade-off. In this paper, we review major approaches for automatically synthesizing compact, yet accurate, DNN/LSTM models suitable for real-world applications. We also outline some challenges and future areas of exploration.
Learning From A Big Brother - Mimicking Neural Networks in Profiled Side-channel Analysis

Recently, deep learning has emerged as a powerful technique for side-channel attacks, capable of even breaking common countermeasures. Still, trained models are generally large, and thus, performing evaluation becomes resource-intensive. The resource requirements increase in realistic settings where traces can be noisy, and countermeasures are active. In this work, we exploit mimicking to compress the learned models. We demonstrate up to 300 times compression of a state-of-the-art CNN. The mimic shallow network can also achieve much better accuracy as compared to when trained on original data and even reach the performance of a deeper network.
A recurrent planning strategy for UAV optimum path identification in a dynamic environment based on bit-coded flight manoeuvres

The applicability of a novel algorithm identifying optimal paths for Unmanned Aerial Vehicles in 2-D dynamic environments has been preliminarily assessed in this paper. Optimality is evaluated taking path length or flight time as objective functions. Flight trajectories, compliant with both air vehicle and environmental constraints, are made up of a finite number of circular arcs and straight lines. Such a geometrical sequence is converted into a finite sequence of two bit-coded basic flight manoeuvres. Identification of optimum path is obtained coupling such a manoeuvering model with a Particle Swarm Optimizer (PSO). To deal with the problem of a dynamic environment a recurrent planning strategy has been developed. Following this approach, final path is obtained by performing both the path planning and the path tracking phase within a series of constant time windows. While the air vehicle is tracking the current sub-optimal trajectory, the algorithm identifies a new sub-optimal trajectory, based on the obstacles estimated position at the end of the current time window, that will be tracked by the air vehicle in the next time window. This way, the computed path turns out to be a piecewise sequence of sub-optimal trajectories reaching the destination point. The identification of obstacles future position is carried out by the algorithm only by monitoring their current position. To test the effectiveness of the proposed flight path planner, we set different 2-D scenarios with obstacles having both constant and variable speed.
Assessment on Consequences and Benefits of the Smart Farming Techniques in Batticaloa District, Sri Lanka

ICT in agriculture (e-Agriculture) is an emerging field focused on improving agricultural production and rural development. The study was aimed to identify the consequences, promotion and benefits of farmer community towards the e-agriculture. Therefore, primary data were collected from the randomly selected 1580 farmers by means of a well-designed questionnaire survey during the period of February to April, 2019. The demographic characteristics of the farming community showed that only 5.1% of respondents were illiterate in this area. According to the study, 36.1% of respondents used telephone as ICT tool for agriculture.0% of respondents used any ICT tools. Consequences index (CI) ranged from 114 to 586, where 114 indicated that the farmers strongly disagreed that there would be some consequences by not using ICT and 586 indicated that the farmers accepted that they would suffer in the future by not using ICT in their agricultural activities. Promotion measures index (PMI) ranged from 508 to 618, where 508 indicated the farmers? response on the provision of a computer, Internet access, and technician to each village was comparatively less whereas 618 indicated that the farmers accept the provision of incentives and finance may promote the use of ICT by a greater extent. Benefits of usage index (BUI) ranged from 86 to 140, where 86 indicated that the response of farmers on the option ?cheaper? was less and 140 indicated that the farmers accepted the use of ICT in Agriculture helps them to acquire timely information related to their particular agricultural activities. Limiting factors index (LFI) ranged from 100 to 454, where 100 indicated that a high number of farmers strongly disagreed on ?no perceived economic benefit? by using ICT and 454 indicated that a high number of farmers accept that the lack of training is the main limiting factor of using ICT in their agricultural activities.
Sentiment Classification on Online Retailer Reviews

Sentiment Classification is a continuing area of research in text mining. Sentiment Analysis the automatic representation of the ideas, emotions and subjectivity of text, whose purpose is to define the polarity of the content of text, and opinion of the expresses in the form of binary ratings such as likes or dislikes, or a more granular set of choices, such as a 1 to 5 rating. This paper focuses primarily on high-level, end-to-end workflow to solve text classification problems using machine learning algorithm such as Naive-Bayes classifier for text classification issues to mining opinions and Amazon User Reviews.
A Systematic Review on Product Recognition for Aiding Visually Impaired People

In recent years, Computer Vision and Machine Learning techniques have been extensively explored in the creation of assistive systems for the visually impaired.One of the most challenging tasks for visually impaired people is object recognition.In this paper, we conducted a systematic review to identify the current state of the art in designing these assistive systems.Due to the huge amount of object categories, we focused on recognizing products, such as those found in grocery stores, pantries and refrigerators.We analyze the techniques used, noting the efficiency and economy of hardware resources such as processing, memory and battery. Thus we verify if they can be used in wearable systems and adapted to existing devices of the Internet of Things (IoT), enabling the proposition of efficient and accessible assistive product recognition systems.
Freedom of Expression and Alternatives for Internet Governance: Prospects and Pitfalls

This article dives into the ongoing debate on how to address concerns of personal safety and respect online, as well as consequences for exposure to polarizing and in various ways harmful information, while at the same time safeguarding the democratic essentials of freedom of expression and participation. It does so by examining the issue from a less common angle, namely who governs the Internet and the platforms where much of the toxic material appears. By applying a model of free speech regulation conceptualized by legal scholar Jack Balkin (2018a, 2018b), the article explores different theoretical future scenarios of Internet governance involving three main players, namely governments, private companies, and speakers. The analysis finds that depending on which player is at the forefront, the outcomes from the standpoint of participation and freedom of speech may be drastically different. While there is potential for transformation that can enable more ownership, transparency, and agency for citizens and news media, some potential paths will place ever-increasing control over the interests of users.
Artificial Intelligence and Early Warning Systems

Early warning systems (EWSs) are designed to effectively and efficiently disseminate appropriate information related to disaster events, in the form of alarms or warnings, to vulnerable communities before or during a disaster so that proactive and preventive measures can be taken to minimize the loss and damage associated with such events. With the advent of the Internet of Things and the advanced technology driven sensor devices, large amounts of data are getting generated at a rapid speed. This data needs to be captured, stored and analyzed by EWS, since it possesses useful indicators and could provide enormous opportunities for monitoring and managing both natural and man-made disasters. The use of artificial intelligence (AI) can enable EWS to mine early warning signals from this data, so that proactive and preventive measures for disaster mitigation, preparedness, response and recovery can be planned leading to timely alerts and warnings being disseminated to the relevant stakeholders. In this paper, an overview of EWS and AI based machine learning techniques capable of being used for designing such systems are discussed. Further, an overview of various real world examples of AI based EWS are also outlined.
Implementation of TF-IDF Method and Support Vector Machine Algorithm for Job Applicants Text Classification

Tens of thousands of people are applying for job in PT. Telkom each year. The goal of the recruitment process is to get new employees which can fit PT. Telkom's working culture. Due to the high number of applicants, the recruitment process takes a lot of time and affecting higher cost to spend. We're proposing a popular combination of Term Frequency-Inverse Document Frequency (TF-IDF) as the extraction method and Support Vector Machine (SVM) as the classifier to filter the applicants' interview text. SVM generally produces better accuracy in text classification compared to Random Forest or K-Nearest Neighbors (KNN) algorithm. However, TF-IDF has several developments to improve its flaws, one of them is Term Frequency-Relevance Frequency (TF-RF). As a comparison, in this study we use three extraction methods: TF only (without IDF), TF-IDF, and TF-RF. We use interview texts from PT. Telkom as the data source. The results of combination SVM with TF-IDF can produce 86.31\% of accuracy, with TF only can produce 85.06\%, and with TF-RF can produce 83.61\% of accuracy. The results show extracting method TF-IDF can still outperform TF-RF in term of accuracy.
An Empirical Study on Deep Neural Network Models for Chinese Dialogue Generation

The task of dialogue generation has attracted increasing attention due to its diverse downstream applications, such as question-answering systems and chatbots. Recently, the deep neural network (DNN)-based dialogue generation models have achieved superior performance against conventional models utilizing statistical machine learning methods. However, despite that an enormous number of state-of-the-art DNN-based models have been proposed, there lacks detailed empirical comparative analysis for them on the open Chinese corpus. As a result, relevant researchers and engineers might find it hard to get an intuitive understanding of the current research progress. To address this challenge, we conducted an empirical study for state-of-the-art DNN-based dialogue generation models in various Chinese corpora. Specifically, extensive experiments were performed on several well-known single-turn and multi-turn dialogue corpora, including KdConv, Weibo, and Douban, to evaluate a wide range of dialogue generation models that are based on the symmetrical architecture of Seq2Seq, RNNSearch, transformer, generative adversarial nets, and reinforcement learning respectively. Moreover, we paid special attention to the prevalent pre-trained model for the quality of dialogue generation. Their performances were evaluated by four widely-used metrics in this area: BLEU, pseudo, distinct, and rouge. Finally, we report a case study to show example responses generated by these models separately.
Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer

Effective lifelong learning across diverse tasks requires diverse knowledge, yet transferring irrelevant knowledge may lead to interference and catastrophic forgetting. In deep networks, transferring the appropriate granularity of knowledge is as important as the transfer mechanism, and must be driven by the relationships among tasks. We first show that the lifelong learning performance of several current deep learning architectures can be significantly improved by transfer at the appropriate layers. We then develop an expectation-maximization (EM) method to automatically select the appropriate transfer configuration and optimize the task network weights. This EM-based selective transfer is highly effective, as demonstrated on three algorithms in several lifelong object classification scenarios.
Unsupervised ranking of clustering algorithms by INFOMAX

Clustering and community detection provide a concise way of extracting meaningful information from large datasets. An ever growing plethora of data clustering and community detection algorithms have been proposed. In this paper, we address the question of ranking the performance of clustering algorithms for a given dataset. We show that, for hard clustering and community detection, Linsker’s Infomax principle can be used to rank clustering algorithms. In brief, the algorithm that yields the highest value of the entropy of the partition, for a given number of clusters, is the best one. We show indeed, on a wide range of datasets of various sizes and topological structures, that the ranking provided by the entropy of the partition over a variety of partitioning algorithms is strongly correlated with the overlap with a ground truth partition The codes related to the project are available in https://github.com/Sandipan99/Ranking_cluster_algorithms.