Reviewerly/exHarmony huggingface dataset:
Use of Sensor Technologies in Online Courses in Post-COVID-19 Era

MOOCs), multiple-criteria decision-making (MCDM) methodologyIn this study, educational social learning theory and a statistical multiple-criteria decisionmaking (MCDM) methodology are creatively cross-employed to comprehensively cross-evaluate online courses and sensor technologies.This was accomplished by means of an in-depth survey of large-scale current online-course users and professional experts with the highest research reliability, validity, accuracy, and representativeness.The three most valuable and contributive conclusions of this study are as follows: (1) The repurposing technology function (RTF) of online-course technology can combine software sensor (SS), motion sensor (MS), and environment sensor (ES) technologies to not only detect moving objects but also achieve cognition in the environment (e.g., by using a face sensor) to extract emotions of course participants in response to words and phrases during lectures to increase online-course learning performance.( 2) The course professionalization technology function (CPTF) of online-course technology can merge SS, MS, and ES technologies to control online-course hardware sensor devices and equipment to control the depth and span of online-course content to strengthen online-course learning performance.(3) The course evaluation technology function (CETF) of online-course technology can consolidate SS, MS, and ES technologies to not only empirically evaluate online-course implementation but also indirectly appraise online-course learning performance.
Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer

We take the first step towards multilingual style transfer by creating and releasing XFOR-MAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian.Results on XFOR-MAL suggest that state-of-the-art style transfer approaches perform close to simple baselines, indicating that style transfer is even more challenging when moving multilingual. 1
Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention

Natural language processing techniques have demonstrated promising results in keyphrase generation.However, one of the major challenges in neural keyphrase generation is processing long documents using deep neural networks.Generally, documents are truncated before given as inputs to neural networks.Consequently, the models may miss essential points conveyed in the target document.To overcome this limitation, we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences.SEG-Net uses Transformer, a selfattentive architecture, as the basic building block with a novel layer-wise coverage attention to summarize most of the points discussed in the document.The experimental results on seven keyphrase generation benchmarks from scientific and web documents demonstrate that SEG-Net outperforms the state-of-the-art neural generative methods by a large margin.
Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms

Quadrotor UAVs are one of the most preferred types of small unmanned aerial vehicles, due to their modest mechanical structure and propulsion precept. However, the complex non-linear dynamic behavior of the Proportional Integral Derivative (PID) controller in these vehicles requires advanced stabilizing control of their movement. Additionally, locating the appropriate gain for a model-based controller is relatively complex and demands a significant amount of time, as it relies on external perturbations and the dynamic modeling of plants. Therefore, developing a method for the tuning of quadcopter PID parameters may save effort and time, and better control performance can be realized. Traditional methods, such as Ziegler–Nichols (ZN), for tuning quadcopter PID do not provide optimal control and might leave the system with potential instability and cause significant damage. One possible approach that alleviates the tough task of nonlinear control design is the use of meta-heuristics that permit appropriate control actions. This study presents PID controller tuning using meta-heuristic algorithms, such as Genetic Algorithms (GAs), the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) to stabilize quadcopter movements. These meta-heuristics were used to control the position and orientation of a PID controller based on a fitness function proposed to reduce overshooting by predicting future paths. The obtained results confirmed the efficacy of the proposed controller in felicitously and reliably controlling the flight of a quadcopter based on GA, CSA and PSO. Finally, the simulation results related to quadcopter movement control using PSO presented impressive control results, compared to GA and CSA.
Hierarchical Reinforcement Learning With Automatic Sub-Goal Identification

In reinforcement learning an agent may explore ineffectively when dealing with sparse reward tasks where finding a reward point is difficult. To solve the problem, we propose an algorithm called hierarchical deep reinforcement learning with automatic sub-goal identification via computer vision (HADS) which takes advantage of hierarchical reinforcement learning to alleviate the sparse reward problem and improve efficiency of exploration by utilizing a sub-goal mechanism. HADS uses a computer vision method to identify sub-goals automatically for hierarchical deep reinforcement learning. Due to the fact that not all sub-goal points are reachable, a mechanism is proposed to remove unreachable sub-goal points so as to further improve the performance of the algorithm. HADS involves contour recognition to identify sub-goals from the state image where some salient states in the state image may be recognized as sub-goals, while those that are not will be removed based on prior knowledge. Our experiments verified the effect of the algorithm.
An Overview of the Use of Chatbots in Medical and Healthcare Education

Chatbots are becoming a trend in many fields such as medical, service industry and more recently in education. Especially in healthcare education, there is a growing interest in integrating chatbots in the learning and teaching processes mostly because of their portability and affordance. In this paper, we seek to explore the primary uses of chatbots in medical education, as well as how they are developed. We elaborate on current chatbot applications and research enacted in the domains of medical and healthcare education, We focus in the areas of virtual patients in medical education, patients’ education related to healthcare matters but also chatbots as course assistance in for enhancing healthcare professionals’ curricula. Additionally, we examine the metrics that have been used to evaluate these chatbots, which include subjective ones like the usability and acceptability by the users, and objectives ones, like their accuracy and users’ skills evaluation. Overall, even though chatbots offer a flexible solution and a vast possibility to improve healthcare education, our literature review suggests that their efficacy has not been thoroughly tested. Also, limited examples of chatbots in European Healthcare curricula have been found. These call of the need for further research towards this direction.
Blind robust image watermarking based on adaptive embedding strength and distribution of quantified coefficients

The image watermarking technique is a reliable method for protecting digital image rights. The most widely used digital imaging format is the JPEG (joint photographic expert’s group). This paper proposes a novel reversible robust watermarking scheme based on the JPEG image. In JPEG image, quantified Discrete Cosine Transform (DCT) coefficients with varying frequency provide varied capacities and embedding deformities. To minimize total distortion for the watermarked image, we adaptively select embedding strengths and coefficients from effective frequency selection that produce less embedding distortions. Then, by weighting the difference between the quantified DCT coefficient and the DCT coefficient on the block, an advanced block selection strategy is used to the corresponding distortion, which are produced from the whole quantified alternating current (AC) shift capacity and half of the insertion capacity. The experimental results show that the proposed method can maintain good visual quality while requiring little bitstream expansion and is robust to geometrical and image processing attacks.
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models

Recent work has shown pre-trained language models capture social biases from the large amounts of text they are trained on.This has attracted attention to developing techniques that mitigate such biases.In this work, we perform an empirical survey of five recently proposed bias mitigation techniques: Counterfactual Data Augmentation (CDA), Dropout, Iterative Nullspace Projection, Self-Debias, and SentenceDebias.We quantify the effectiveness of each technique using three intrinsic bias benchmarks while also measuring the impact of these techniques on a model's language modeling ability, as well as its performance on downstream NLU tasks.We experimentally find that: (1) Self-Debias is the strongest debiasing technique, obtaining improved scores on all bias benchmarks; (2) Current debiasing techniques perform less consistently when mitigating non-gender biases; And (3) improvements on bias benchmarks such as StereoSet and CrowS-Pairs by using debiasing strategies are often accompanied by a decrease in language modeling ability, making it difficult to determine whether the bias mitigation was effective. 1
Videoconference and Embodied VR: Communication Patterns Across Task and Medium

Videoconference has become the dominant technology for remote meetings. Embodied Virtual Reality is a potential alternative that employs motion tracking in order to place people in a shared virtual environment as avatars. This paper describes a 210 participant study focused on behavioral measures that compares multiparty interaction in videoconference and embodied VR across a range of task types: a factual intellective task, a subjective judgment task and two negotiation tasks, one with visual grounding. It uses state-of-the-art body, face and finger tracking to drive the avatars in VR and a carefully matched videoconferencing implementation. Significant behavioral differences are observed. These include increased activity in videoconference related to maintaining the social connection: more person directed gaze and increased verbal and nonverbal backchannel behavior. Videoconference also had reduced conversational overlap, increased self-adaptor gestures and reduced deictic gestures as compared with embodied VR. Potential explanations and implications are discussed.
Studi Eksploratif Dampak Pandemi COVID-19 Terhadap Proses Pembelajaran Online di Sekolah Dasar

Lately, many social media have reported the existence of a contagious epidemic, namely the Covid-19 Pandemic or known as the corona virus. In the world of education, there has also been a polemic due to the corona virus outbreak, so that all students are sent home or learn online. This research was carried out with the aim of knowing the use of information technology for online learning at SD N 1 Dalang during the Covid pandemic mass, by using the exploratory case study method and the research approach using the qualitative case study method used to obtain error information and the consequences of the COVID-19 pandemic on teaching and learning activities in schools The implementation of a physical distance policy which then becomes the basis for online implementation. By utilizing the social technology used in this bold learning, namely Google Classroom and WhatsApp Group. The results of this study are that there are some compliance made by students, teachers and parents. a In online teaching and learning activities, namely mastery of technology is still lacking, additional internet quota costs, additional work for parents in accompanying children to learn, and some students find it difficult to get internet signals and socialization between students, teachers and parents is reduced and hours work is not limited to teachers because they have to communicate and coordinate with parents, other teachers, and school principals. Brave learning is considered less effective because of the lack of knowledge of students in using online media before the Covid-19 outbreak. But the effect of online student learning is not only just studying or seeking knowledge, but can also develop talents and potential.
Continuous Release of Data Streams under both Centralized and Local Differential Privacy

We study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value in the stream can be quite large, leading to enormous DP noise and bad utility. To reduce the maximal value and noise, one way is to estimate a threshold so that values above it can be truncated. The intuition is that, in many scenarios, only a few values are large; thus truncation does not change the original data much. We develop such a method that finds a suitable threshold with DP. Given the threshold, we then propose an online hierarchical method and several post-processing techniques.
Personalizing Medication Recommendation with a Graph-Based Approach

The broad adoption of electronic health records (EHRs) has led to vast amounts of data being accumulated on a patient’s history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information to help doctors personalize the prescribed medications. However, existing medication recommendation systems have yet to make use of all these information sources in a seamless manner, and they do not provide a justification on why a particular medication is recommended. In this work, we design a two-stage personalized medication recommender system called PREMIER that incorporates information from the EHR. We utilize the various weights in the system to compute the contributions from the information sources for the recommended medications. Our system models the drug interaction from an external drug database and the drug co-occurrence from the EHR as graphs. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems while achieving the best tradeoff between accuracy and drug-drug interaction. Case studies demonstrate that the justifications provided by PREMIER are appropriate and aligned to clinical practices.
An In-Depth Study and Improvement of Isolation Forest

Historically, anomalies detection was an important issue for industrial applications such as the detection of a manufacturing failure or defect. It is still a current topic that tries to meet the ever increasing demand in different fields such as intrusion detection, fraud detection, ecosystem change detection or event detection in sensor networks. That’s why anomalies detection remains a research topic of great interest for various research communities. In this paper, we focused on Isolation Forest (IForest), a well known, efficient anomalies detection algorithm. We provided a deep and complete view on IForest. We evaluated the impact of its input parameters (number of trees, sample size and decision threshold) on the efficiency of the detection and on the execution time. We discussed the benefit of including some anomalies into the training phase. To address the limits of IForest, we performed different experiments on commonly used real datasets and also on synthetic datasets with non trivial distributions. We designed multidimensional datasets where anomalies are carried by several dimensions simultaneously. Moreover, we used a varying density and distance between anomalies and normal data, for a variable similarity between these two data classes. We compared the performance of IForest against its improved version called Extended IForest. Finally, we designed and validated a new extension of IForest, based on the different individual trees decisions instead of a global forest decision that we call Majority Voting IForest (MVIForest). The experiments show that MVIForest has a shorter execution time than IForest, with almost the same accuracy.
The effects of Pre-Processing Techniques on Arabic Text Classification

In the last two decades, the amount of available Arabic text data on the World Wide Web is dramatically growing, making it the fourth most used language on the web. Accordingly, the demand for efficient Arabic text classification is increasing, especially for web page content filtering, information retrieval, and e-mail spam detection. Several Machine Learning algorithms have been implemented to classify Arabic documents. However, the results achieved are not comparable with those obtained in other languages such as English, primarily when using preprocessing techniques that do not take into consideration the Arabic language features. This paper investigates the impact of wisely selected preprocessing techniques on the efficiency of different text classification algorithms. The effects of stop words removal, stemming, lemmatization, and all possible combinations are examined. The reported results (+10.75% to +28.73%) prove the effectiveness of using these techniques either individually or in combination.
A statistical comparison for evaluating the effectiveness of linear and nonlinear manifold detection techniques for software defect prediction

Most of the software systems are released without predicting defects and therefore, this paper presents a new effective technique - manifold detection technique (MDT) is essential and different than earlier applied defect prediction methods like regression, feature selection methods, etc. In this paper, performance of classifiers has been compared with or without MDTs to evaluate the effectiveness of different MDTs (linear and nonlinear) by reducing the dimensions of software datasets. In this process, eight classifiers were applied to four PROMISE datasets to determine the best performing classifier with respect to prediction performance measuring factors (accuracy, precision, recall, F-measure, AUC, misclassification error) with or without MDTs. The experimental results statistically tested by paired two-tailed t-test proved that FastMVU is the most accurate result producing technique as compared to all other nonlinear MDTs and Bayesian network (BN) is the most effective technique for software defect prediction using with or without MDTs.
Crypto anchors

Blockchain technology can increase visibility in supply-chain transactions and lead to more accurate tracing of goods as well as provide evidence of whether a product is authentic or not. A shared, distributed ledger or blockchain alone, however, does not guarantee correct and trustworthy supply-chain traceability. We argue that blockchain technology (and any other digital traceability solution) must be enhanced with methods to “anchor” physical objects into information technology, Internet-of-Things and blockchain systems. Only when trust from the digital domain is extended to the physical domain can the movement of goods be accurately traced (e.g., for callbacks and provenance) and product authenticity determined. In this paper, we introduce the concept of crypto anchors, propose a classification and system architecture, and give implementation examples for different use cases and industries.
Estimation of Cell Number by Hemocytometry Counting

The number of mammalian cells in a defined volume of medium can be measured using a hemocytometer. Automated methods using cell-counting devices such as those produced by Coulter are desirable when large numbers of individual samples are to be counted. A hemocytometer contains two chambers, each of which when filled and coverslipped contains a total volume of 9 µL. Each chamber is ruled into nine major squares, and each square is 1 × 1 mm with a depth of 0.1 mm. Thus, when coverslipped, the volume of each square is 0.1 mm3 or 0.1 µL. Additional subdivisions of the major nine squares are not necessary for counting and can be ignored.
On-site safety inspection of tower cranes: A blockchain-enabled conceptual framework

The on-site safety inspection is an essential procedure to ensure tower cranes’ safety in the usage stage by finding equipment defects (e.g., loose bolts) and potential environmental hazards (e.g., poor ground conditions) in advance. However, the following two issues should be tackled to secure its effectiveness: (1) how to guarantee the execution of safety inspections, especially under a tight construction schedule? and (2) how to ensure the inspection information is immutable, transparent, and traceable? This research, guided by the design science approach, aimed to develop a blockchain-enabled framework for assuring the effectiveness of on-site safety inspection of tower cranes (OSITC). First, a literature review was conducted to identify OSITC challenges and blockchain potential. Then, a blockchain-based conceptual framework was provided, with two major components: smart contracts and the consensus process, being discussed. Finally, using the Hyperledger Fabric architecture, a prototype system was developed to instantiate and test the proposed framework. The findings suggest that the blockchain can protect the OSITC’s effectiveness by allowing safety inspections to be automatically executed via smart contracts and providing relevant stakeholders with trustworthy inspection records via consensus algorithms. This study provides a novel solution for tower crane safety management to construction researchers and practitioners, inspiring more discussions about blockchain technology.
Remittances and income inequality in Africa: Financial development thresholds for economic policy

This study employs macrodata on 42 African countries to examine whether remittances and financial development (including its sub-components of access, depth and efficiency) contribute to the equalisation of incomes across the continent. Robust evidence based on the dynamic GMM estimator shows that: (i) remittances heighten income inequality in Africa, (ii) Africa’s financial system is not potent enough for propelling remittances towards the equalisation of incomes, and (iii) vis-à-vis financial access and depth, inefficiencies characterising Africa’s financial institutions is the main reason remittances contribute to the widening of the income disparity gap. Nonetheless, the optimism which we provide by way of threshold analysis shows that channelling resources into the development of Africa’s financial sector could yield shared income distribution dividends. In particular, efforts should be made to achieve a minimum threshold of 23.05 (index) for financial access, and 3.02 (index) for that of financial institutions efficiency if Africa’s financial sector is to repackage remittances towards the equalisation of incomes. A few policy recommendations are provided in the end.