Reviewerly/exHarmony huggingface dataset:
Mobile learning student worksheet based on socio-scientific-issues: Enhancing students’ scientific literacy skills in biology

The 21st-century science and technology rapid development requires suitable educational tools to meet the expected learning outcomes desired. This study aimed at producing a mobile learning student worksheet based on Socio-Scientific-Issues (SSI) which is feasible to use in biology teaching activity as well as finding out its effectiveness in enhancing students' scientific literacy skills. This study research and development (R&amp;D) used the ADDIE model. The effectiveness test of mobile learning student worksheet in enhancing students’ scientific literacy skills was done by using a one-group pretest-posttest design. The research findings indicated that the developed mobile learning student worksheet based on SSI is feasible to use in biology teaching activity and become the considerable tool in enhancing students’ scientific literacy skills based on the n-gain score of 0.75 (high category). Thus, it is suggested for science teachers, particularly biology teachers, to adopt this tool to support the learning activity they conduct.
PointerLock: Protecting Function Pointers with Access Control on Page

Since function pointers are closely related to software control flow and they are writable, attackers often take function pointers as the targets. To protect the function pointers, we propose a novel solution called PointerLock. To avoid being tampered with by the attacker, PointerLock restricts the writing operation on the function pointers with the support of access control on the page. PointerLock centralizes the application's function pointers to a few pages, and these pages are only readable by default. Only when the application want to modify the pointer, PointerLock makes the page writable where the function pointer is located in. This reduces the time window of attacking the function pointer. To prevent an attacker from tampering with other function pointers on the same page as function pointers, PointerLock performs the necessary checks on the page before they are used again. The prototype system of PointerLock was built in Fedora 21 with a 3.17.6 kernel. Two system calls are implemented in kernel space, and six interfaces are developed for users in the form of dynamic link libraries in user space. The tests show that PointerLock is capable of preventing the attacker from tampering with the function pointers, and it does not introduced an overheavy performance.
Efficiency of selective information dissemination within the system of information and communication support of university science (a case study of the Scientific Library of Novosibirsk State University of Economics and Management)

The author discusses the experience of the center for Information and Bibliographical Support of Learning and Science of the Scientific Library of Novosibirsk State University of Economics and Management in the methodology of selective information dissemination. The author examines the relevance of the method as applied in modern academic libraries and its efficiency for information and bibliographic support, as well as for information and communication support of university science. She also emphasizes the significance of stable communications within a university, and proves the necessity for improving the relevance of information provided through selective information dissemination system using not only feedback sheets but also through face-to-face communication. The concept of selective information dissemination is analyzed. The origins of the method are discussed. The author examines the target users to be acquired by the system and suggests that new relevant information and bibliographic products are needed. She concludes that today the system of selective information dissemination is an effective and demanded one. It is important for increasing the university’s reputation capital and user service performance of the scientific library.
Vehicle Feature Point Trajectory Clustering and Vehicle Behavior Analysis in Complex Traffic Scenes

Video-based analysis technology has a wide range of applications in intelligent transportation system (ITS). Vehicle segmentation and behavior analysis has become an important research area in traffic video analysis. To solve the problem of 2D video detection technology in actual traffic video scenes, a bottom-up analysis method is employed to study the related technical problems. Firstly, M-BRISK descriptor algorithm is proposed for describing local feature points, which based on the method of original BRISK. Secondly, a 3D feature analysis method based on rigid motion constraints for vehicle trajectory is proposed. With the result of camera calibration and the preset back-projection plane, the 2D trajectory points can be back-projected to the 3D space, and the back projection data of the 2D image can be reconstructed in 3D space. Thirdly, similarity measure method is proposed for achieving the trajectory clustering. The experimental results show that the proposed method not only accelerates the speed of clustering method, but also improves the accuracy of trajectory clustering at some extent. Moreover, the vehicle motion information contained in the trajectory data can be analyzed to recognize vehicle behavior. All of these provide an important data foundation for vehicle abnormal behavior detection and the identification of traffic status levels in traffic scenes.
Two-Stream AMTnet for Action Detection

In this paper, we propose Two-Stream AMTnet, which leverages recent advances in video-based action representation[1] and incremental action tube generation[2]. Majority of the present action detectors follow a frame-based representation, a late-fusion followed by an offline action tube building steps. These are sub-optimal as: frame-based features barely encode the temporal relations; late-fusion restricts the network to learn robust spatiotemporal features; and finally, an offline action tube generation is not suitable for many real-world problems such as autonomous driving, human-robot interaction to name a few. The key contributions of this work are: (1) combining AMTnet's 3D proposal architecture with an online action tube generation technique which allows the model to learn stronger temporal features needed for accurate action detection and facilitates running inference online; (2) an efficient fusion technique allowing the deep network to learn strong spatiotemporal action representations. This is achieved by augmenting the previous Action Micro-Tube (AMTnet) action detection framework in three distinct ways: by adding a parallel motion stIn this paper, we propose a new deep neural network architecture for online action detection, termed ream to the original appearance one in AMTnet; (2) in opposition to state-of-the-art action detectors which train appearance and motion streams separately, and use a test time late fusion scheme to fuse RGB and flow cues, by jointly training both streams in an end-to-end fashion and merging RGB and optical flow features at training time; (3) by introducing an online action tube generation algorithm which works at video-level, and in real-time (when exploiting only appearance features). Two-Stream AMTnet exhibits superior action detection performance over state-of-the-art approaches on the standard action detection benchmarks.
Combined Covers and Beth Definability (Extended Version)

In ESOP 2008, Gulwani and Musuvathi introduced a notion of cover and exploited it to handle infinite-state model checking problems. Motivated by applications to the verification of data-aware processes, we proved in a previous paper that covers are strictly related to model completions, a well-known topic in model theory. In this paper we investigate cover transfer to theory combinations in the disjoint signatures case. We prove that for convex theories, cover algorithms can be transferred to theory combinations under the same hypothesis (equality interpolation property aka strong amalgamation property) needed to transfer quantifier-free interpolation. In the non-convex case, we show by a counterexample that covers may not exist in the combined theories, even in case combined quantifier-free interpolants do exist. However, we exhibit a cover transfer algorithm operating also in the non-convex case for special kinds of theory combinations; these combinations (called `tame combinations') concern multi-sorted theories arising in many model-checking applications (in particular, the ones oriented to verification of data-aware processes).
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis

The aspect-based sentiment analysis (ABSA) task remains to be a long-standing challenge, which aims to extract the aspect term and then identify its sentiment orientation.In previous approaches, the explicit syntactic structure of a sentence, which reflects the syntax properties of natural language and hence is intuitively crucial for aspect term extraction and sentiment recognition, is typically neglected or insufficiently modeled. In this paper, we thus propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn). Additionally, we design a simple yet effective message-passing mechanism to ensure that our model learns from multiple related tasks in a multi-task learning framework. Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach, which significantly outperforms existing state-of-the-art methods. Besides, we achieve further improvements by using BERT as an additional feature extractor.
Comparative Analysis of Multiple Deep CNN Models for Waste Classification

Waste is a wealth in a wrong place. Our research focuses on analyzing possibilities for automatic waste sorting and collecting in such a way that helps it for further recycling process. Various approaches are being practiced managing waste but not efficient and require human intervention. The automatic waste segregation would fit in to fill the gap. The project tested well known Deep Learning Network architectures for waste classification with dataset combined from own endeavors and Trash Net. The convolutional neural network is used for image classification. The hardware built in the form of dustbin is used to segregate those wastes into different compartments. Without the human exercise in segregating those waste products, the study would save the precious time and would introduce the automation in the area of waste management. Municipal solid waste is a huge, renewable source of energy. The situation is win-win for both government, society and industrialists. Because of fine-tuning of the ResNet18 Network, the best validation accuracy was found to be 87.8%.
Developing local wisdom-based mobile science learning in manufacturing sasirangan fabric to improve ICT literacy facility of junior high school students

This study aimed to examine the feasibility, practicality, and effectiveness of the use of local wisdom-based mobile science learning in manufacturing sasirangan fabric to improve the Information and Communication Technology (ICT) literacy facility descriptively. The research model used was the 4D model consisting of Define, Design, Develop, and Disseminate steps. This Moodle-platform mobile learning contains teaching materials that are integrated with local wisdom, i.e. sasirangan fabric manufacturing (sasirangan m-learning). This mobile learning’s feasibility was assessed by media and material experts while practicality was assessed by science teachers. The instruments used consisted of product feasibility and practicality sheets, readability questionnaires, observation sheets, and ICT literacy facility questionnaires. Meanwhile, data analysis was doneusing the quantitative descriptive technique. Sasirangan m-learning was tested on 25 students of class VII I of SMP N 1 Banjarmasin in the 2018/2019 Academic Year. Data on ICT literacy facility is in the form of a percentage of task accomplishments and responses to the online learning in terms of information processing consisting of accessing, managing, integrating, evaluating, creating, and communicating information aspects. The result shows that the local wisdom-based mobile science learning in manufacturing sasirangan fabric that has been developed is feasible and practical to be used in science learning and is effective to improve the ICT literacy facility in junior high school students.
Meta-analysis on hate speech studies in South Korea

This research approaches the past 20 years of research on hate speech in Korea from a meta-analytical perspective and analyzes their tendencies and characteristics so as to navigate the landscape of academic discourse on hate speech. It is only recently that social attention has been turned to the issue of hate speech and it seems that Korea is only now moving from the stage of introducing and explaining the foreign issues on hate speech to discussing the situation in Korea, along with the quantitative increase of research. Out of the 62 academic articles analyzed in this chapter, legal studies composes about 74%, most of which delve into the issue of regulating hate speech. In the non-legal studies area, a variety of approaches have appeared since 2015 as online hate speech has risen as a critical social problem, and misogyny is a topic of particular interest. While hate speech is now recognized as an overt harm to the society, much of the current research have difficulty in defining hate speech and providing a consistent answer on how to appropriately respond to the issue. There is a need to conduct more academic research and to develop a shared understanding in cultural and institutional aspects of hate speech in Korean society.
Big Data and Class Imbalance in Medicare Fraud Detection

Traditionally, to detect Medicare fraud, a limited number of auditors, or investigators, are responsible for manually inspecting thousands of claims, but only have enough time to look for very specific patterns indicating suspicious behaviors. This chapter provides two case studies to demonstrate the effects of class imbalance with big data on the detection of fraud in the Medicare dataset with List of Excluded Individuals and Entities fraud labels. It utilizes three different datasets, with provider payment and utilization information, released by the Centers for Medicare and Medicaid Services: Medicare Provider Utilization and Payment Data: Physician and Other Supplier, Medicare Provider Utilization and Payment Data: Prescriber, and Medicare Provider Utilization and Payment Data: Referring Durable Medical Equipment, Prosthetics, Orthotics, and Supplies. The chapter considers these parts of Medicare because they cover a wide range of possible provider claims, the information is presented in similar formats, and they are publicly available.
Image denosing in underwater acoustic noise using discrete wavelet transform with different noise level estimation

In many applications, Image de-noising and improvement represent essential processes in presence of colored noise such that in underwater. Power spectral density of the noise is changeable within a definite frequency range, and autocorrelation noise function is does not like delta function. So, noise in underwater is characterized as colored noise. In this paper, a novel image de-noising method is proposed using multi-level noise power estimation in discrete wavelet transform with different basis functions. Peak signal to noise ratio (PSNR) and mean squared error represented performance measures that the results of this study depend on it. The results of various bases of wavelet such as: Daubechies (db), biorthogonal (bior.) and symlet (sym.), show that denoising process that uses in this method produces extra prominent images and improved values of PSNR than other methods.
Meta-heuristic Hybrid Algorithmic Approach for Solving Combinatorial Optimization Problem (TSP)

Solving and optimizing combinatorial problems require high computational power because of their exponential growth and requirement of high processing power. During this study, a hybrid algorithm (Genetic Ant Colony Optimization Algorithm) is proposed in comparison with standard algorithm (Ant Colony Optimization Algorithm). Further the parameters for Ant Colony Optimization Algorithm are instinctively tuned to different levels of all heuristics to obtain suboptimal level, then multiple crossovers and mutation operators are used alongside those selected parameters while generating results with hybrid algorithm. The main emphasis of the proposed algorithm is the selection and tuning of parameters, which is extremely influential in this case. The algorithm has been tested on six benchmarks of TSPLIB. The results were compared with standard ACO algorithm, the hybrid algorithm outperformed the standard ACO algorithm.
Trust Management as a Service: Enabling Trusted Execution in the Face of Byzantine Stakeholders

Trust is arguably the most important challenge for critical services both deployed as well as accessed remotely over the network. These systems are exposed to a wide diversity of threats, ranging from bugs to exploits, active attacks, rogue operators, or simply careless administrators. To protect such applications, one needs to guarantee that they are properly configured and securely provisioned with the (e.g., encryption keys) necessary to preserve not only the confidentiality, integrity and freshness of their data but also their code. Furthermore, these secrets should not be kept under the control of a single stakeholder - which might be compromised and would represent a single point of failure - and they must be protected across software versions in the sense that attackers cannot get access to them via malicious updates. Traditional approaches for solving these challenges often use ad hoc techniques and ultimately rely on a hardware security module (HSM) as root of trust. We propose a more powerful and generic approach to trust management that instead relies on trusted execution environments (TEEs) and a set of stakeholders as root of trust. Our system, PALAEMON, can operate as a managed service deployed in an untrusted environment, i.e., one can delegate its operations to an untrusted cloud provider with the guarantee that data will remain confidential despite not trusting any individual human (even with root access) nor system software. PALAEMON addresses in a secure, efficient and cost-effective way five main challenges faced when developing trusted networked applications and services. Our evaluation on a range of benchmarks and real applications shows that PALAEMON performs efficiently and can protect secrets of services without any change to their source code.
Analysis on College English Teaching Reform and Development Under the Background of Internet Plus

The advent of the Internet era has brought more teaching and learning resources to college English teaching, which will undoubtedly bring new opportunities to college English teaching.As long as it is properly used, the quality and efficiency of college English teaching will be constantly improved.Based on the background of Internet plus, this paper focuses on the college English teaching reform and development path.The author first analyses the necessity of college English teaching reform under the Internet background and the advantages of internet bring to college English, and then discuss the current situation of college English teaching under the background of internet plus, finally put forward the new path of development, aiming to promote better development of college English education.
An analysis of innovative Lean Method in foreign language learning

The question of how to teach and learn English has been the preoccupation of many scholars of English Language Teaching for more than a century.Since the emergence of grammar translation method, researchers, teachers, and academicians have been trying to find the best method for their learners.In other words, this process can be defined as a century of questioning.In fact, some of the methods have been successful to some extend in the mastery of language, but most of them unfortunately cannot even reach the promised lands of the fruitful learning, and none of them were completely suitable for all types of the learners.There are several reasons behind this problem, and one of them could be the students' contribution in the creation of these methods.It has been rarely questioned that how students feel about these methods employed in English classes, despite the fact that these methods, namely innovations, have been created for the purpose of students' mastery of foreign language learning.This study is mainly focused on using quantitative design conducted with 975 students and participants were randomly selected from 3 universities, 5 high schools, 2 middle schools and 1 primary school in Turkey and Iraq in order to analyze the suitability of English instruction from the perspective of learners towards innovative lean method.Within the context of the study, the students' attitudes and opinions towards innovative Lean Educational method were revealed and some recommendations were developed for especially language teachers and researchers.
[Papers] Interpretable Convolutional Neural Network Including Attribute Estimation for Image Classification

An interpretable convolutional neural network (CNN) including attribute estimation for image classification is presented in this paper. Although CNNs perform highly accurate image classification, the reason for the classification results obtained by the neural networks is not clear. In order to provide interpretation of CNNs, the proposed method estimates attributes, which explain elements of objects, in an intermediate layer of the network. This enables improvement of the interpretability of CNNs, and it is the main contribution of this paper. Furthermore, the proposed method uses the estimated attributes for image classification in order to enhance its accuracy. Consequently, the proposed method not only provides interpretation of CNNs but also realizes improvement in the performance of image classification.
COVID-19: Strategies for Allocation of Test Kits

With the increasing spread of COVID-19, it is important to systematically test more and more people. The current strategy for test-kit allocation is mostly rule-based, focusing on individuals having (a) symptoms for COVID-19, (b) travel history or (c) contact history with confirmed COVID-19 patients. Such testing strategy may miss out on detecting asymptomatic individuals who got infected via community spread. Thus, it is important to allocate a separate budget of test-kits per day targeted towards preventing community spread and detecting new cases early on. In this report, we consider the problem of allocating test-kits and discuss some solution approaches. We believe that these approaches will be useful to contain community spread and detect new cases early on. Additionally, these approaches would help in collecting unbiased data which can then be used to improve the accuracy of machine learning models trained to predict COVID-19 infections.