Reviewerly/exHarmony huggingface dataset:
A Semantic Textual Similarity Calculation Model Based on Pre-training Model

As a basic research topic in natural language processing, the calculation of text similarity is widely used in the fields of plagiarism checker and sentence search. The traditional calculation of text similarity constructed text vectors only based on TF-IDF, and used the cosine of the angle between vectors to measure the similarity between two texts. However, this method cannot solve the similar text detection task with different text representation but similar semantic representation. In response to the above-mentioned problems, we proposed the pre-training of text based on the ERNIE semantic model of PaddleHub, and constructed similar text detection into a classification problem; in view of the problem that most of the similar texts in the data set led to the imbalance of categories in the training set, an oversampling method for confusion sampling, OSConfusion, was proposed. The experimental results showed that the method proposed in this paper was able to solve the problem of paper comparison well, and could identify the repetitive paragraphs with different text representations. And the ERNIE-SIM with OSConfusion was better than the ERNIE-SIM without OSConfusion in the prediction process of similar document pairs in terms of precision and recall.
Using Plan Libraries for Improved Plan Execution

The difﬁculty of task planning for robotic agents arises from the stochastic nature of their environment and the high cost of a failure during execution meaning frequent replanning is required. One way to address this problem is to make use of a pre-deﬁned plan library. In this paper, we present work that combines a plan library with task planning. Initial results show that such an approach alleviates the computational burden of synthesising plans, while providing the same level of autonomy as using a planner that starts from scratch.
Sen4Smells: A Tool for Ranking Sensitive Smells for an Architecture Debt Index

Technical debt indexes are metrics for assessing the quality of a software system. Both academic and commercial tools have begun to provide computations of such indexes based on design violations and smells (e.g., cycles among system elements). When computing a debt index for a given project, a common use case is that engineers look at the index values for spotting design issues that negatively affect system evolution and quality. In this context, those smells being critical for the system architecture should be promptly identified so as to evaluate proper remediation actions. However, the interpretation of an index value in terms of problematic smells is usually a manual and labor-intensive task for engineers. To help with this task, we propose a tool called Sen4Smells that performs an automated sensitivity analysis for a given debt index based on the evolution of both the index values and the corresponding smells across (past) system versions. The Sen4Smells output is a ranking of smells that, due to their variations or instability, are major contributors to the debt index, and thus, can impact on architecture quality. Sen4Smells is designed as a pipeline that combines information from existing tools for smell detection, predefined debt index formulas, and the Sobol method for sensitivity analysis. As a demonstration of the tool functionality, we briefly present implementations for the Arcan and Sonargraph tools with their respective debt indexes.
Logistic Regression Analysis on Social Networking Advertisement

In most of the Machine learning models tables and charts need to be plot for result analysis and predictions to be made. The result analysis is a fundamental need for any company reuired to analyse their objective of their product development. In the present work Logistic regression model is developed to illustrate social networking advertisement dataset. With this fundamental Logistic Regression Model implementation one can predict whether a user is willing to purchase their product or not. In the present paper Logistic Regression Model is designed in python to predict by evaluating accuracy.
PEDAGOGICAL CONDITIONS OF FORMATION OF PROFESSIONAL COMPETENCE OF STUDENTS OF A TECHNICAL UNIVERSITY

Fundamental government documents set the priority of the educational system in public policy, declaring the need for successful scientific and professional training and innovative educational activities of future specialists. The article is aimed to propose a solution to the formation of professional competence of future specialists of technical universities at the stage of modernization of education in modern Russian society. The work reveals the essence of the leading methodological approaches to solving the problem under study: competency, creative, personality-oriented, system-activity, technological, task-based approaches. A retrospective analysis made it possible to reveal the pedagogical conditions, simulating the real professional activity of students of a technical college. The ways of successful development of professional competence of future specialists are described (establishing innovative-creative environment; developing the subject position of a personality; strengthening practice-oriented activities; organizing reflection and self-reflection; using computing, information and telecommunication technologies, and systems; effective management of students' academic, scientific, and professional activities). Based on the leading principles, the methodological approaches, the requirements of the Federal State Educational Standard of Higher Education for the preparation of future specialists in the Russian Federation, the article presents the innovative experience of the organization of educational process, which is aimed to form a professional competence among graduate students of Ryazan Institute of Moscow Polytechnic University, as well as Ryazan State Agrotechological University named after P.A. Kostychev. This study made it possible to develop and implement a block-modular structure and content of engineering and construction training of students, which includes design, organizational and managerial, information-applied, creative competencies corresponding to the types of professional activity.
Smoothing Data Association for Linear Multi-target Tracking in Clutter

This paper proposes smoothing data association in the linear multi-target based on integrated probabilistic data association algorithm (sLM-IPDA). Rather than performing joint data association for estimating multi-targets state which is computationally more complex method, the data association probabilities could be derived simultaneously and individually for estimation like a single target tracker. The proposed algorithm utilizes LMIPDA method in two-way directions: forward and backward separately, and fuses their predictions for smoothing estimations. False track discrimination technique is used to verify the proposed algorithm in the simulation study.
Learning science during summer vacations and its effects on attitude and anxiety towards research

The outbreak of COVID-19 had a great impact on education with the sudden shift from traditional to online learning. As a result, the difficulties experienced by students increased, professors had to adapt the teaching method of the contents and experimental activities were all transmitted from a screen, making this a difficult task for everyone. In this context, the resumption of in-person activities is of great importance to surpass the difficulties that this pandemic crisis has brought to students learning. Motivated by this necessity, the Foundation for Science and Technology (FCT), proposed a program called “Summer with Science”. This program aimed to develop presential activities with the participation of students, professors, and researchers in the summer of 2020, that involved innovative solutions related to the COVID-19 pandemic. i9Masks was one of the interesting projects that emerged from this initiative and it consisted of developing transparent facial masks for preventing the virus’ dissemination. This project joined students of different engineering and biological sciences at any level in a Higher Education Institution to learn science. In order to understand the impact of this course on student’s motivation to conduct research, in the present study, a questionnaire was used to collect the data from the students who participated in the i9Masks summer project. The same questionnaire was applied to non-participants as a control group. After the data are gathered, the results were evaluated, and it was observed that effectively this course had a great and positive impact on student’s perspective on research activities. Significant differences were observed regarding the attitudes towards research between participants and non-participants. © 2021 University of Minho. All rights reserved.
Control of Wheelchair by Eye Movement Using Image Processing

According to the latest report prepared by the World Health Organization and the World Bank, 15 percent of the world's population is disabled [2]. The use of power driven wheelchairs with high navigational intelligence is a great step to integrate severely handicapped and mentally ill people. Different systems are being developed, allowing the end-user to perform safe movements and accomplish some daily life important tasks [4]. The notion is to create an Eye Monitored System that allows wheelchair navigation depending on the movement of the eyes. We have built a device where a patient sits in a Wheel Chair looking directly at the camera; can move in a direction just by looking in that direction. Our Robotic wheelchair uses the image processing system to control the wheelchair. The user’s eye movements are turned to a screen position using a camera, without any direct contact. In addition, we can give more independence to the disabled person to communicate with the devices in a room, for example: a light, a fan. This communication is done using a MEMS Accelerometer. Using this, the person can handle various devices easily
Adversarial Training With Stochastic Weight Average

Although adversarial training is the most reliable method to train robust deep neural networks so far, adversarially trained networks still show large gap between their accuracies on clean images and those on adversarial images. In conventional classification problem, one can gain higher accuracy by ensembling multiple networks. However, in adversarial training, there are obstacles to adopt such ensemble method. First, as inner maximization is expensive, training multiple networks adversarially becomes overburden. Moreover, the naive ensemble faces dilemma on choosing target model to generate adversarial examples with. Training adversarial examples of the members causes covariate shift, while training those of ensemble diminishes the benefit of ensembling. With these insights, we adopt stochastic weight average methods and improve it by considering overfitting nature of adversarial training. Our method take the benefit of ensemble while avoiding the described problems. Experiments on CIFAR10 and CIFAR100 shows our method improves the robustness effectively.
Identifying Ransomware Actors in the Bitcoin Network

Due to the pseudo-anonymity of the Bitcoin network, users can hide behind their bitcoin addresses that can be generated in unlimited quantity, on the fly, without any formal links between them. Thus, it is being used for payment transfer by the actors involved in ransomware and other illegal activities. The other activity we consider is related to gambling since gambling is often used for transferring illegal funds. The question addressed here is that given temporally limited graphs of Bitcoin transactions, to what extent can one identify common patterns associated with these fraudulent activities and apply themto find other ransomware actors. The problem is rather complex, given that thousands of addresses can belong to the same actor without any obvious links between them and any common pattern of behavior. The main contribution of this paper is to introduce and apply new algorithms for local clustering and supervised graph machine learning for identifying malicious actors. We show that very local subgraphsof the known such actors are sufficient to differentiate between ransomware, random and gambling actors with 85%prediction accuracy on the test data set.
Developing Methodology and Applications for Virtual Reality in the Humanities

This paper summarizes the research agenda and work in progress in the project “The Humanities in Virtual Reality (HumaniVR)”. It discusses how disciplines such as linguistics, sociology and anthropology can benefit from research on Virtual Reality (VR) as a new space of social interaction, communication, and culture, particularly concerning its role as a new social medium of growing importance (Social VR). It summarizes the first research results of the experimental work in HumaniVR, and future directions.
Progressively Detect Faults in a Large Power System: A Visual Analytics Approach

With rapid expanding in power system equipment, enormous attention is now paid on developing methods to detect and reason faults in large power system data set. Existing techniques mainly focus on data analyzing methods, while little work studies visual analytics approaches. In this paper, we propose a progressively visual analytics procedure (i.e., overview, detecting candidate faulted buses, locating buses and check reasons) to detect fault origins and check possible reasons. In particular, an efficient clustering algorithm associated with a novel visualization for time-varying multivariate as well as multiple and coordinated views are developed to locate faulted buses easily and quickly. Case studies have indicated that our approach is much beneficial to real-life applications.
The evolving nature of the Japanese lexicon in a tradition of lesson study

The Japanese tradition of lesson study has created a teaching community in which observation and discussion of classroom teaching are integral parts of professional practice. Within the community a system of pedagogical terms and phrases has been developed and shared through the practice of studying and improving classroom teaching. This chapter reports on the results of research that explored the constituent elements and structural characteristics of the Japanese Lexicon as a part of The International Classroom Lexicon Project. We created a lexicon of 70 lexicon items with their entries focusing on forms and functions of participants’ action. The process and the result of developing a lexicon are outlined. A questionnaire and electronic survey were conducted, respectively, for a local and national validation to examine how familiar the lexicon items were for the mathematics teachers in Japan. We discuss that the Japanese Lexicon can be characterised by its evolving nature and that some of the lexicon items can be seen as value-laden items derived from the practice of lesson study.
Strategy for Prevention and Quality Improvement of Urban Slumps Based on Community Empowerment

The purpose of this study was to examine the characteristics of slum areas and to analyze strategies for preventing and improving the quality of community empowerment-based slum settlements in Langara City, Konawe Islands Regency. This research method is descriptive qualitative with a case study approach, while the research informants are individuals and groups who are involved in the slum prevention and quality improvement program. The data were collected using in-depth interviews, observation, and document studies, while the data analysis techniques were carried out qualitatively. The results show that the indicators of a slum in Langara City are: the condition and quality of houses are generally less regular, dense, with minimal air and light circulation, environmental sanitation is not supported by wastewater disposal, access to settlements is relatively good, the garbage disposal system is not supported by landfills. Finally, limited distribution of net water, and the unavailability of fire protection. Furthermore, the strategy for preventing and improving the quality of slum based on community empowerment shows 1) slum prevention strategies with indicators: the high rate of community participation in attending counseling on the importance of maintaining environmental health and the high involvement of residents in preventing slums through cooperation, community service, and helping out; 2) slum quality improvement strategy with indicators: high capacity and community involvement in the renovation and restoration of slum shelters; and the low level of public awareness of joining the relocation program in slum pockets.
Visual Navigation Using Inverse Reinforcement Learning and an Extreme Learning Machine

In this paper, we focus on the challenges of training efficiency, the designation of reward functions, and generalization in reinforcement learning for visual navigation and propose a regularized extreme learning machine-based inverse reinforcement learning approach (RELM-IRL) to improve the navigation performance. Our contributions are mainly three-fold: First, a framework combining extreme learning machine with inverse reinforcement learning is presented. This framework can improve the sample efficiency and obtain the reward function directly from the image information observed by the agent and improve the generation for the new target and the new environment. Second, the extreme learning machine is regularized by multi-response sparse regression and the leave-one-out method, which can further improve the generalization ability. Simulation experiments in the AI-THOR environment showed that the proposed approach outperformed previous end-to-end approaches, thus, demonstrating the effectiveness and efficiency of our approach.
Adherence of people with type 1 diabetes to self-care activities / Adesão das pessoas com diabetes tipo 1 às atividades de autocuidado

Objective: to assess the adherence of people with type 1 diabetes to self-care activities. Method: quantitative study with 60 diabetics, from Porto, Portugal. The Self-Care Scale for Diabetes in its version translated and adapted to Portuguese was applied in January to February 2019. Results: respondents are mostly young (36.7%), employed (63.4%), female (73.3%), married (36.7%), high educated (60.0%) and diagnosed with recent diabetes (50.0%). Most have good mean adherence to self-care behaviors related to food (5.0), blood glucose monitoring (6.8), foot care (5.6) and medication (6.4). Regarding physical activity (2.6) and smoking, 73.3% of respondents say they smoked a cigarette in the last seven days lower adherence was identified. Conclusions: there is a need to develop educational programs to ensure a higher adherence to healthy lifestyles.
A Conceptual and Practical Exploration of Electrovibrating Wearables

Electrovibration has been used extensively with touchscreens and other rigid objects. We propose extending electrovibration to wearables such as wristbands, jewelry and clothing, and present an exploration of the resulting design space and preliminary experimentation with haptic prototypes made of flexible materials and conductive textiles.
A Geophysical Multi-Method Approach to Investigate the Archaeological Landscape of Lanakerveld (NL)

– Large scale geophysical prospection of an archaeological landscape in the Netherland. – Integrated and multimethodological approach for archaeological interpretation. – Challenges for the prospecting and interpretation of multiperiod archaeological sites.
Comparative Latency Analysis of Optical and Inertial Motion Capture Systems for Gestural Analysis and Musical Performance

Wireless sensor-based technologies are becoming increasingly accessible and widely explored in interactive musical performance due to their ubiquity and low-cost, which brings the necessity of understanding the capabilities and limitations of these sensors.This is usually approached by using a reference system, such as an optical motion capture system, to assess the signals' properties.However, this process raises the issue of synchronizing the signal and the reference data streams, as each sensor is subject to different latency, time drift, reference clocks and initialization timings.This paper presents an empirical quantification of the latency communication stages in a setup consisting of a Qualisys optical motion capture (mocap) system and a wireless microcontroller-based sensor device.We performed event-to-end tests on the critical components of the hybrid setup to determine the synchronization suitability.Overall, further synchronization is viable because of the near individual average latencies of around 25ms for both the mocap system and the wireless sensor interface.
Frequency Filtering for Data Augmentation in X-Ray Image Classification

A sufficiently large dataset is needed to solve tasks based on deep learning. However, using medical datasets for deep learning is challenging because their access is limited. In this paper, we propose a novel data augmentation paradigm that can be used in image classification of convolutional neural network-based approaches. As the frequency domain has more unique patterns than the spatial domain, more diverse patterns can be obtained by arbitrarily changing the frequency domain patterns of the given image. First, we assume that the meaningful patterns in the frequency domain are typically distributed in high-intensity regions in the Fourier spectrum. Next, we select the angle to reject meaningful patterns from the Fourier spectrum. Subsequently, we generate a novel mask pattern and remove specific frequency patterns in a fast Fourier transform image. Thereafter, we apply an inverse fast Fourier transform to the rejected frequency image to convert it back to the spatial domain. Our method achieves consistent performance improvements on X-ray images using various backbones. On average, we achieved 3.30%, 7.09%, 7.75%, 8.14%, and 4.91% improvements on the accuracy, precision, recall, F1-score, and AUC over the previous methods.
MLC PCM Techniques to Improve Nerual Network Inference Retention Time by 105X and Reduce Accuracy Degradation by 10.8X

We present three novel MLC PCM techniques – (1) device requirement balancing, (2) prediction-based MSB-biased referencing, and (3) bit-prioritized placement to address the MLC device challenges in neural network applications. Using measured MLC bit error rates, the proposed techniques can improve the MLC PCM retention time by 105 times while keeping the ResNet-20 inference accuracy degradation within 3% and reduce the accuracy degradation by 91% (10.8X) for CIFAR-100 dataset in the presence of temporal resistance drift.