Reviewerly/exHarmony huggingface dataset:
An Analysis of Human Activities Recognition using Smartwatches Dataset

Today, the era of smart devices evolving the human behavior interaction to a changing environment where the learning of activities is monitored to predict the next step of human behavior.The smart devices have these sensors built-in (accelerometer and gyroscope), which are continuously generating a large amount of data.The data used to identify the novel patterns of human behavior, together with machine learning and data mining techniques.Classification of human motions with motion sensor data is among the current topics of study.The classification is an important part of data mining techniques and used in this work to find the accuracy of instances in the given dataset.Thus, it is possible to follow the activities of a user carrying only a smartwatch.The smartwatches consisting of four different models from two manufacturers are used.Furthermore, the experiment contains nine users and seven activities performed by them.After the classification was determined, the data set to which the principal component analysis has been applied was classified by decision stump, j48, Bayes net, naive Bayes, naive Bayes multinomial text, random forest, and logit boost methods, and their performances were compared.The most successful result was obtained from the random forest method.The accuracy of the Random Forest classification algorithm on nominal datasets is 99.99% on both accelerometer and gyroscope sensors.
CNN-Based Suppression of False Contour and Color Distortion in Bit-Depth Enhancement

It is a challenge to transmit and store the massive visual data generated in the Visual Internet of Things (VIoT), so the compression of the visual data is of great significance to VIoT. Compressing bit-depth of images is very cost-effective to reduce the large volume of visual data. However, compressing the bit-depth will introduce false contour, and color distortion would occur in the reconstructed image. False contour and color distortion suppression become critical issues of the bit-depth enhancement in VIoT. To solve these problems, a Bit-depth Enhancement method with AUTO-encoder-like structure (BE-AUTO) is proposed in this paper. Based on the convolution-combined-with-deconvolution codec and global skip of BE-AUTO, this method can effectively suppress false contour and color distortion, thus achieving the state-of-the-art objective metric and visual quality in the reconstructed images, making it more suitable for bit-depth enhancement in VIoT.
Wayang Image Classification Using MLP Method and GLCM Feature Extraction

Wayang is a form of shadow art that has been known to the Javanese people more than 1500 years ago. For Javanese people, the function of wayang is not only as a spectacle but also as a request, because in the wayang story there are values that are important to Javanese society. Wayang has developed from time to time, there are many types of wayang in Indonesia, with many types of wayang in Indonesia, of course preserving the art of wayang kulit is not an easy thing, especially because this traditional art is not yet very popular among young people, especially in the regionsburban. Today's young people use technology more in finding information, such as using laptops or smartphones. Because to make it easier for people who want to know about puppets and their types, a technology is created that can distinguish the types of puppets based on wayang images. So this research was made using the MLP (The Multi Layer Perceptron) method and its extraction feature GLCM (Gray-Level Co-Occurrence Matrix) with a total system accuracy of recognizing wayang image objects up to 73.4%.
Psychological factors that affect the acceptance and need for ICT services for older adults with chronic diseases

Background: To encourage older adults with chronic disease to use information and communications technology (ICT) services effectively, it is necessary to identify users' needs and factors affecting acceptance.Objective: To identify the psychological factors affecting the acceptance of ICT services, and the service-related preferences/priorities in home-dwelling South Korean older adults.Method: We conducted focus group interviews with 2 groups of older adults (6 males and 6 females, age = 65-75 years) with chronic diseases in June 2019.To identify which services they prioritized, we made 20 service show cards, scored on a 5-point Likert scale.Additionally, we utilized a self-developed semi-structured interview guide based on the unified theory of acceptance and use of technology 3 model (UTAUT3) to comprehensively identify how the factors affect older adults' acceptance.Results: Most participants prioritized their autonomy and worried about being dependent on ICT services.They were also reluctant to learn about and use new technology; feared the social stigma related to "being old"; and were anxious that the use of ICT services could result in deepened isolation.There was ambivalence regarding the impact of ICT services on connection with family/acquaintances.The prioritized services were emergency assistance (e.g., notifying family in case of emergencies), diet and exercise-related information, and medication reminders.Service needs and psychological factors affecting the acceptance differed by gender. Conclusion:The ICT services offered to the elderly should be developed in a way that guarantees autonomy and supports self-reliance.Services must also ensure the enhancement of social networks and maintenance of connectedness with family.Older people have a fear of learning new services; so, personal guidance and tailored education need to be offered.Further, service development must reflect the diverse needs of each user.
Generalized swarm intelligence algorithms with domain-specific heuristics

&lt;span lang="EN-US"&gt;In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.&lt;/span&gt;
THE INFLUENCE OF POWTOON MEDIA IN TEACHING LISTENING

The development of technology and information greatly affect the use of media as a tool in teaching process. The role of the teacher here is not just to convey the material, but is expected to be able to utilize the technology to support teaching and learning activities, the selection of appropriate learning media is very influential on the achievement of educational goals. The purpose of this study was to determine how the influence of the use of powtoon media in the process of learning listening in eighth grade students at one of the junior high schools in Cimahi. The research method used is descriptive qualitative. Subjects in this research were eighth grade students with a total 25 students consisting of 10 male students and 15 female students. The instrument used was a questionnaire with 10 statement items, consisting of 5 positive statements and 5 negative statements. The results of this study indicate that the influence of the use of powtoon as a media in learning listening of eighth grade students at one of junior high schools in Cimahi is categorized very well with an average percentage of 100%. Keywords: Case Study, Listening, Powtoon Media
Safety Guarantees for Iterative Predictions with Gaussian Processes

Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty. However, tracking uncertainty for iterative, multi-step predictions in general leads to an analytically intractable problem. While approximation methods exist, they do not come with guarantees, making it difficult to estimate their reliability and to trust their predictions. In this work, we derive formal probability error bounds for iterative predictions with GPs. Building on GP properties, we bound the probability that random trajectories lie in specific regions around the predicted values. Namely, given a tolerance ε > 0, we compute regions around the predicted trajectory values, such that GP trajectories are guaranteed to lie inside them with probability at least 1 - ε. We verify experimentally that our method tracks the predictive uncertainty correctly, even when current approximation techniques fail. Furthermore, we show how the proposed bounds can incorporate a given control law, and effectively bound the trajectories of the closed-loop system.
Adaptive Gene Level Mutation

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.
SAM: Modeling Scene, Object and Action With Semantics Attention Modules for Video Recognition

Video recognition aims at understanding semantic contents that normally involve the interactions of humans and related objects under certain scenes. A common practice to improve recognition accuracy is to combine object, scene and action features for classification directly, assuming that they are explicitly complementary. In this paper, we break down the fusion of three features into two pairwise feature relation modeling processes, which mitigates the difficulty of correlation learning in high dimensional features. Towards this goal, we introduce a Semantics Attention Module that captures the relations of a pair of features by refining the relatively “weak” feature with the guidance from the “strong” feature using attention mechanisms. The refined representation is further combined with the “strong” feature using a residual design for downstream tasks. Two SAMs are applied in a Semantics Attention Network (SAN) for improving video recognition. Extensive experiments are conducted on two large-scale video benchmarks, FCVID and ActivityNet v1.3—the proposed approach achieves better results while requiring much less computational effort than alternative methods.
E-Learning Readiness and Learning Engagement during the Covid-19 Pandemic

&#x0D; &#x0D; &#x0D; This study aims to determine the effect of e-learning readiness on learning engagement and to find out which aspects have the greatest influence. The design of this research is correlational research, conducted via a questionnaire. The research sample was comprised of 125 students from the Faculties of Psychology, Law, Education, and Teacher Training at the Muhammadiyah University of Surabaya, Indonesia. Measuring e-learning readiness consists of 3 dimensions: computer / internet self-efficacy; online communication self-efficacy; and self-directed learning abilities. The dimensions of measuring learning engagement are cognitive, affective, and behaviour. Data were analysed using regression models. The results indicate that learning readiness contributed to the variance of learning engagement by 26%. The biggest contribution from each dimension of e-learning readiness to engagement is self-directed learning, then internet self-efficacy, and online communication self-efficacy. However, the study also obtained anecdotal evidence that the subjects were not satisfied with e-learning because they did not get data about the material.&#x0D; Keywords: learning enggagement, e-learning readiness, covid19&#x0D; &#x0D; &#x0D;
The effect of business model innovation on organization performance

The aim of the study is to investigate the impact of business model innovation (BMI) on firm performance. The sample of the study consisted of 120 managers from Alban Al-youm Company in Jordan, a leading dairy company. Data were collected using a questionnaire administered to managers. Eighty-seven questionnaires were retrieved valid for the purpose of data analysis. BMI was measured using three components: value creation, value proposition and value capture innovations while company performance was assessed via self-rated questions about operational measures of performance. The results accepted the hypotheses that all dimensions of BMI had significant effects on company performance. That being so, the study contributed to the literature on BMI on company performance in the absence of such studies that use samples for Arab countries, particularly, from Jordan in one of the most vital industries, which is a dairy industry.
Eth2Vec: Learning contract-wide code representations for vulnerability detection on Ethereum smart contracts

Ethereum smart contracts are computer programs that are deployed and executed on the Ethereum blockchain to enforce agreements among untrusting parties. Being the most prominent platform that supports smart contracts, Ethereum has been targeted by many attacks and plagued by security incidents. Consequently, many smart contract vulnerabilities have been discovered in the past decade. To detect and prevent such vulnerabilities, different security analysis tools, including static and dynamic analysis tools, have been created, but their performance decreases drastically when codes to be analyzed are constantly being rewritten. In this paper, we propose Eth2Vec, a machine-learning-based static analysis tool that detects smart contract vulnerabilities. Eth2Vec maintains its robustness against code rewrites; i.e., it can detect vulnerabilities even in rewritten codes. Other machine-learning-based static analysis tools require features, which analysts create manually, as inputs. In contrast, Eth2Vec uses a neural network for language processing to automatically learn the features of vulnerable contracts. In doing so, Eth2Vec can detect vulnerabilities in smart contracts by comparing the similarities between the codes of a target contract and those of the learned contracts. We performed experiments with existing open databases, such as Etherscan, and Eth2Vec was able to outperform a recent model based on support vector machine in terms of well-known metrics, i.e., precision, recall, and F1-score.
Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting

In this paper, we propose Sequence Span Rewriting (SSR), a self-supervised task for sequence-to-sequence (Seq2Seq) pre-training.SSR learns to refine the machine-generated imperfect text spans into ground truth text.SSR provides more fine-grained and informative supervision in addition to the original textinfilling objective.Compared to the prevalent text infilling objectives for Seq2Seq pretraining, SSR is naturally more consistent with many downstream generation tasks that require sentence rewriting (e.g., text summarization, question generation, grammatical error correction, and paraphrase generation).We conduct extensive experiments by using SSR to improve the typical Seq2Seq pre-trained model T5 in a continual pre-training setting and show substantial improvements over T5 on various natural language generation tasks.
Attentive generative adversarial network for removing thin cloud from a single remote sensing image

Land-surface observation is easily affected by the light transmission and scattering of semi-transparent clouds, high or low, resulting in blurring and reduced contrast of ground objects. To improve the visual appearance of remote sensing images, the authors present a deep learning method for thin cloud removal using a new attentive generative adversarial network without prior knowledge or assumptions, which copes with thin clouds that are unevenly distributed on different images and learns the attention map with weighted information about spatial features. Such a spatial attention model can endow each pixel with the global spatial context information. Consequently, the generative network focuses on the thin cloud regions to generate better local image restoration, and the discriminative network can evaluate the local consistency of the repaired regions. The experimental results show that this method is superior to state-of-the-art methods in recovering detailed texture information.
Neural Sequence-to-grid Module for Learning Symbolic Rules

Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning. In particular, even state-of-the-art neural networks fail to achieve \textit{out-of-distribution} (OOD) generalization of symbolic reasoning tasks, whereas humans can easily extend learned symbolic rules. To resolve this difficulty, we propose a neural sequence-to-grid (seq2grid) module, an input preprocessor that automatically segments and aligns an input sequence into a grid. As our module outputs a grid via a novel differentiable mapping, any neural network structure taking a grid input, such as ResNet or TextCNN, can be jointly trained with our module in an end-to-end fashion. Extensive experiments show that neural networks having our module as an input preprocessor achieve OOD generalization on various arithmetic and algorithmic problems including number sequence prediction problems, algebraic word problems, and computer program evaluation problems while other state-of-the-art sequence transduction models cannot. Moreover, we verify that our module enhances TextCNN to solve the bAbI QA tasks without external memory.
Automatic Segmentation of Remote Sensing Images on Water Bodies Based on Image Enhancement

By virtue of high-resolution remote sensing satellites, there is a possibility to analyze remote sensing images on water bodies through digital image processing (DIP). In many remote sensing images, however, the water bodies have similar gray values as other ground objects. To effectively distinguish water bodies from other ground objects in these images, this paper proposes a logarithmic enhancement method for remote sensing images on water bodies based on adaptive morphology. The proposed method can filter the noise of non-target area, and enhance the water body in the original image. On this basis, a morphology-based segmentation method was designed for remote sensing images on water bodies. Experimental results show that our method achieved a high segmentation accuracy, controlling the mean segmentation error at below 1.32%.
Improved semi-supervised learning technique for automatic detection of South African abusive language on Twitter

Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter. However, the existing semi-supervised learning methods have been skewed towards small amounts of labelled data, with small feature space. This paper, therefore, presents a semi-supervised learning technique that improves the distribution of training data by assigning labels to unlabelled data based on the majority voting over different feature sets of labelled and unlabelled data clusters. The technique is applied to South African English corpora consisting of labelled and unlabelled abusive tweets. The proposed technique is compared with state-of-the-art self-learning and active learning techniques based on syntactic and semantic features. The performance of these techniques with Logistic Regression, Support Vector Machine and Neural Networks are evaluated. The proposed technique, with accuracy and F1-score of 0.97 and 0.95, respectively, outperforms existing semi-supervised learning techniques. The learning curves show that the training data was used more efficiently by the proposed technique compared to existing techniques. Overall, n-gram syntactic features with a Logistic Regression classifier records the highest performance. The paper concludes that the proposed semi-supervised learning technique effectively detected implicit and explicit South African abusive language on Twitter.
Eco enzyme and its benefits for organic rice production and disinfectant

Eco enzyme is a fermented liquid from organic waste fruits, vegetables, and other organic waste which is very useful for agriculture, health, and households. One of Deli Serdang villages that are very vulnerable to Covid-19 is Karang Anyar Village, located in Beringin District, only about 5 km from Kuala Namu International Airport. On the other hand, in Karang Anyar Village, there is a Mekar Pasar Kawat farmer group that cultivates organic rice that needs liquid organic fertilizer (LOF). This community service aims to educate the public about using an eco enzyme from household waste as liquid organic fertilizer (LOF) and disinfectant. Implementing community service that is carried out is training and practice of making eco enzyme and its benefits, direct practice of demonstration plot of rice cultivation by utilizing eco enzyme as LOF. The results are training and mentoring in making eco enzyme based on household waste, training and hands-on practice on applying eco enzyme in the environment as disinfectant, training, and direct demonstration plot rice cultivation by utilizing eco enzyme produced as organic fertilizer liquid
Exploring interpersonal meanings on the discourse of the Indonesian national anthem from the CDA perspectives

This research is aimed at describing how the composer of Indonesia Raya (IR) construed and negotiated interpersonal meanings which represent his ideology to the Indonesians. The data in this qualitative study was the text of IR which is ideologically contested. The study analyzed the lexicogrammar properties dealing with words and structures and production processes. The data were collected by the researcher as the research instrument by using a documentation technique. The procedures of data analysis were conducted following the stages of Fairclough’s Critical Discourse Analysis framework (1989), including micro analysis concerning lexical features in the text, meso analysis concerning the process production and interpretation of the text, and macro analysis concerning ideological effects and hegemonic processes in which discourse participates. The attitude system of appraisal was analyzed based on that proposed by Martin and White (2005), and Martin and Rose (2003). The results showed that from the affect viewpoint, the IR composer is seen as an educated young man who witnessed people suffer and did not only express his feeling of joy with the coming independence of Indonesia and the gratefulness, but also the insecurity and anxiety with the possibility of other forms of colonialism. Meanwhile, from the appreciation viewpoint, Indonesia is described as a noble, heredity, sacred, and magical land. Not only is it an expression of his admiration and love, but also it is a warning to maintain his emotional intimacy with the nation. The judgment subcategory illustrated that the Indonesians were mostly emotionally weak. The composer encouraged the Indonesian people to unite for a greater Indonesia.