Reviewerly/exHarmony huggingface dataset:
The Development of English Writing Skills Through Techniques of Sentence Skeleton and Signpost Word Analysis for English Major Students

The purposes of this research were to develop English writing skills through techniques of sentence skeleton and signpost word analysis for English major students, and to compare the writing skills before and after the study. The sample consisted of 43 English major juniors at Faculty of Education, Ubon Ratchathani Rajabhat University, enrolling in the course of teaching and learning English I, the second semester, academic year 2017, gained by cluster sampling. The research instruments were a performance test of writing skills, and writing drills. The data analyzed by employing percentage, mean, standard deviation, and t-test. After the study, the students had better writing skills with 40.63 percent of average score than those with 14.80 percent before the study, the individual average score was at a weak level with 38.90 percent, while the small group&amp;rsquo;s was at a fair level with 69.78 percent, the individual writing skills were significantly higher than those before the study at the .01 level, and the small group writing skills were significantly higher than those of the individual&amp;rsquo;s at the .01 level.
Efficient Recognition and 6D Pose Tracking of Markerless Objects with RGB-D and Motion Sensors on Mobile Devices

This paper presents a system that can efficiently detect objects and estimate their 6D postures with RGB-D and motion sensor data on a mobile device. We apply a template-based method to detect the pose of an object, in which the matching process is accelerated through dimension reduction of the vectorized template matrix. After getting the initial pose, the proposed system then tracks the detected objects by a modified bidirectional iterative closest point algorithm. Furthermore, our system checks information from the inertial measurement unit on a mobile device to alleviate intensive computation for ease of interactive applications.
Protecting quantum correlations in presence of generalised amplitude damping channel: the two-qubit case

Abstract Any kind of quantum resource useful in different information processing tasks is vulnerable to several types of environmental noise. Here we study the behaviour of quantum correlations such as entanglement and steering in two-qubit systems under the application of the generalised amplitude damping channel and propose two protocols towards preserving them under this type of noise. First, we employ the technique of weak measurement and reversal for the purpose of preservation of correlations. We then show how the evolution under the channel action can be seen as an unitary process. We use the technique of weak measurement and most general form of selective positive operator valued measure (POVM) to achieve preservation of correlations for a significantly large range of parameter values.
Theorising on Information Cascades and Sequential Decision-making for Analysing Security Behaviour

Human behaviour is an ever-present aspect in information security and requires special attention when seeking to secure information systems. Information security behaviour is often based on an informed decision where information is obtained by previous experience and observation of the behaviour of others. In this research, the concept of sequential decision-making is contextualised in terms of information security behaviour. Information cascades, which are based on sequential decision-making, are theorised as a model to explain how decision-making (i.e. behaviour) takes place in terms of information security. A case study is presented to illustrate how behavioural threshold analysis can be employed as an instrument to evaluate the effect of information cascades and sequential decision-making on information security behaviour. The paper concludes by theorising on the applicability of the models and approaches that are presented in this research.
Speaking on Data’s Behalf: What Researchers Say and How Audiences Choose

Background: Bayesian statistics have become popular in the social sciences, in part because they are thought to present more useful information than traditional frequentist statistics. Unfortunately, little is known about whether or how interpretations of frequentist and Bayesian results differ. Objectives: We test whether presenting Bayesian or frequentist results based on the same underlying data influences the decisions people made. Research design: Participants were randomly assigned to read Bayesian and frequentist interpretations of hypothetical evaluations of new education technologies of various degrees of uncertainty, ranging from posterior probabilities of 99.8% to 52.9%, which have equivalent frequentist p values of .001 and .65, respectively. Subjects: Across three studies, 933 U.S. adults were recruited from Amazon Mechanical Turk. Measures: The primary outcome was the proportion of participants who recommended adopting the new technology. We also measured respondents’ certainty in their choice and (in Study 3) how easy it was to understand the results. Results: When presented with Bayesian results, participants were more likely to recommend switching to the new technology. This finding held across all degrees of uncertainty, but especially when the frequentist results reported a p value &gt;.05. Those who recommended change based on Bayesian results were more certain about their choice. All respondents reported that the Bayesian display was easier to understand. Conclusions: Presenting the same data in either frequentist or Bayesian terms can influence the decisions that people make. This finding highlights the importance of understanding the impact of the statistical results on how audiences interpret evaluation results.
A Dilated Recurrent Neural Network-Based Model for Graph Embedding

Graph embedding aims to preserve graphs into low-dimensional embedding space while preserving their properties. As thus, the embeddings can be easily exploited by downstream graph-based tasks. Most existing models of graph embedding strive to preserve some proximity properties of graphs such as the first/second-order proximity or higher-order proximity within a random walk sequence. However, the proximity relationship between different sequences has been rarely discussed, which is a significant property of graphs. For this reason, we propose a graph dilated recurrent neural network (G-DRNN) model to learn the inter-sequence proximity. Considering the inter-sequence proximity exists within subgraphs, we can also define it as subgraph-aware higher-order proximity. Especially, we note that a graph sequence consists of alternating nodes and edges and can be decomposed into a node subsequence and an edge sequence. To explore the special structure of a graph sequence, the G-DRNN exploits a skip connection network architecture to preserve the overall information of the graph sequence while maintaining the relative independence of each subsequence. To evaluate the effectiveness of the G-DRNN in multi-relational graphs, we empirically apply it to the popular research interest of knowledge graph embedding. The results demonstrate that the embeddings learned by the G-DRNN outperform previous approaches on both tasks of link prediction and node classification.
Data authentication algorithms

Data authentication algorithms represent the baseline on which advanced authentication protocols can be built. In the realm of cloud computing, IoT, and big data, data authentication algorithms play a vital role to secure the transmitted, stored, and exchanged data and information. These basic and advanced algorithms are categorized into four main types. Passwords and hash functions represent the simplest authentication schemes. Hash functions are used into the more complex constructions of authentication algorithms such as MACs and digital signatures. The choice of the underlined mathematical building blocks, such as ECs, contributed to the improvement of authentication algorithms; nowadays, we have algorithms with efficient computation performance and shorter cryptographic keys. Besides the cryptographic realm of authentication, biometric and steganography schemes have been used as other means of authentication algorithms where the featured of the user are the keys to authenticate him/her into the system. Below, we summarize the overall learned lessons and list some future trends in this area of research.
Sub-sampling Approach for Unconstrained Arabic Scene Text Analysis by Implicit Segmentation based Deep Learning Classifier

The text extraction from the natural scene image is still a cumbersome task to perform. This paper presents a novel contribution and suggests the solution for cursive scene text analysis notably recognition of Arabic scene text appeared in the unconstrained environment. The hierarchical sub-sampling technique is adapted to investigate the potential through sub-sampling the window size of the given scene text sample. The deep learning architecture is presented by considering the complexity of the Arabic script. The conducted experiments present 96.81% accuracy at the character level. The comparison of the Arabic scene text with handwritten and printed data is outlined as well.
Design of the Intelligent Education System Based on the Piano Introduction

In the era of the education informationization, it is necessary to collect the big data of the industries to realize the individualized music education and accelerate the development of the music industry. Regardless of the efficiency and effect of the piano learning, or from the perspective of the education inclusion and fairness, it is the general trend to use the Internet, the artificial intelligence and other technologies to carry out the music education. In the past, the piano learning was difficult to objectively demonstrate the effect of the piano learning by means of the science and technology, and there is a lack of the accurate effect analysis, which is one of the main reasons for the low efficiency of the piano learning and practicing.
SORT: SOcial HelpeR SelecTion Scheme for Mobile Question Answering Systems

Recent research has suggested that answers to the particular questions may be found when the questions are forwarded via suitable friends (or helpers) in a multi-hop manner. In mobile social network-based question answering systems, users are associated with limited resources; therefore, a light-weight scheme is crucial. However, improving the performance of the system with a light-weight scheme is challenging. To this end, we propose a distributed SOcial helpeR selecTion (SORT) scheme where all users share information with their 1-hop friends. SORT records and leverages the information to select and forward the questions to capable and cooperative helpers among the friends to improve the performance of the system. Our extensive trace-driven simulations reveal that, on average, SORT achieves higher response rate, higher best-answer rate, and lower response time by more than 14%, 13%, and 14%, respectively, in comparison to state-of-the-art helper selection schemes. Further, we observe that SORT performs consistently better than the state-of-the-art systems under a diverse number of system parameters.
ONLINE PROCTORED EXAMS: WHERE AND HOW ARE THEY USED? BASICS, PRACTICAL SCENARIOS AND TECHNICAL SOLUTIONS FOR ONLINE PROCTORING AT EUROPEAN UNIVERSITIES AND EDUCATIONAL INSTITUTIONS

ONLINE PROCTORED EXAMS: WHERE AND HOW ARE THEY USED? BASICS, PRACTICAL SCENARIOS AND TECHNICAL SOLUTIONS FOR ONLINE PROCTORING AT EUROPEAN UNIVERSITIES AND EDUCATIONAL INSTITUTIONS
Carts in the Hallway: Cataloging and Special Collections–A Partnership for Success

The purpose of the article is to demonstrate how Binghamton University Libraries has changed the workflows and channels of communication between Special Collections and the Technical Services Department. Over the last two years these departments have worked to foster cooperative decision making in order to increase understanding of project priorities and allow for more efficient cataloging, leading to greater accessibility of Special Collections items. The article also looks at how this relationship can be expanded upon in the future, so that cataloging/metadata librarians and Special Collections staff and librarians can work together to create better access to materials.
Using Corpus Linguistics to Investigate Mathematical Explanation

In this chapter we use methods of corpus linguistics to investigate the waysin which mathematicians describe their work as explanatory in their research papers.We analyse use of the words explain/explanation (and various related words andexpressions) in a large corpus of texts containing research papers in mathematics andin physical sciences, comparing this with their use in corpora of general, day-to-dayEnglish. We find that although mathematicians do use this family of words, such useis considerably less prevalent in mathematics papers than in physics papers or ingeneral English. Furthermore, we find that the proportion with which mathematiciansuse expressions related to ‘explaining why’ and ‘explaining how’ is significantlydifferent to the equivalent proportion in physics and in general English. We discusspossible accounts for these differences.
All for One: Frame-wise Rank Loss for Improving Video-based Person Re-identification

Person re-identification involves retrieving correct matches for a target image (query) from a set of gallery images, while video based re-identification extends this to the case of query and gallery videos. Typical video-based re-id methods ignore the temporal evolution of the intermediate representations of the video sequences. We propose a novel loss function, termed rank loss, to explicitly ensure that the learnt representations achieve enhanced performance and robustness as the sequence progresses and that better intermediate representations result in an improved final representation. Experiments indicate that the addition of rank loss indeed helps in improving the re-id performance while achieving performance comparable to state-of-the-art approaches.
A Survey of College Student Dormitory Lifestyle and Related Management Strategies

A systematic survey on the life-style of college undergraduate dormitory of a local public university finds that, most of the time, undergraduates “amuse themselves” in the dorms and mutual learning and communication time is relatively insufficient. Students in the dorms have a higher expectation on interpersonal relationship. However, in reality, there exists the problem that the affection between them is indifferent. Besides, when they come into conflicts, they are incapable of resolving the conflicts through a normal channel. Furthermore, students are relatively satisfied with the life service in dormitory management, while they are not satisfied with the hardware facilities and the rules and regulations. To improve the management level of college student dormitory and promote development of students, it is necessary for the department of college dormitory management to strengthen construction of the physical environment, develop an organic dormitory interpersonal network and establish the dormitory organizational structure and institutional norms in which students can participate. Above all, the department of dormitory management needs to establish the system of dormitory education to help students carry out effective education in life.
Person Re-identification for Estimating Bus Passenger Flow

A bus transportation system provides social and economic advantages and plays a key role in our daily lives. This study aims at utilizing videos recorded by cameras installed in buses to improve and optimize bus services, where the required basic and important information is bus passenger flow. We here consider the problem of estimating the bus passenger flow as person re-identification problem on a bus. Then, we propose a method for improving the identification accuracy of persons by using bus-specific properties. Results show that the proposed method is simple but effective in solving the person re-identification problem on buses.
When to turn to nature-inspired solutions for cyber systems

Nature-inspired algorithms have given us elegant solutions across a broad spectrum of design challenges. Efforts within cyber systems are equally replete with cases in which algorithms either adapted from, or at least inspired by, observations of natural biological systems, ranging from efficient packet routing, to anomaly detection, and beyond. We discuss how natural systems achieve robust and efficient solutions and the implications this ontology has for helping inform how and when it may be most fruitful to exploit them, and when instead we should probably forego analogy and instead build tailored tools from scratch. Further, we consider how it might be possible to move beyond this case-by-case analogy paradigm to exploit the deeper design elements that make natural systems successful at converging on sufficient solutions and then employ these more fundamental tools in purposeful design. In particular, we consider the design challenges faced when trying to borrow from nature to inform the areas of cyber security and resilience.
Multipartite states under elementary local operations

Multipartite pure states are equivalent under Stochastic Local Operations and Classical Communication (SLOCC) whenever they can be mapped into one another by Invertible Local Operations. It is shown that this is equivalent to the inter-convertibility through finite sequences of Elementary Local Operations. A multipartite version of the Gauss-Jordan elimination strategy is then obtained, enabling the analytical SLOCC-classification of previously unknown examples. It is argued that the problem of SLOCC-classification is equivalent to the problem of classifying the Multipartite Fully Reduced Forms that the coefficient matrix of a state can assume after being subjected to this Gauss-Jordan elimination procedure. The method is applied to examples of so-called LME and hypergraph states, showing new SLOCC-equivalences. Moreover, possible physical implications for states with a Dicke-like structure are sketched.
Single-frame three-dimensional imaging using spectral-coded patterns and multispectral snapshot cameras

We present an approach for single-frame three-dimensional (3-D) imaging using multiwavelength array projection and a stereo vision setup of two multispectral snapshot cameras. Thus a sequence of aperiodic fringe patterns at different wavelengths can be projected and detected simultaneously. For the 3-D reconstruction, a computational procedure for pattern extraction from multispectral images, denoising of multispectral image data, and stereo matching is developed. In addition, a proof-of-concept is provided with experimental measurement results, showing the validity and potential of the proposed approach.
Structural Model Analysis on the Emotional Labor, Emotional Dissonance, Job Engagement and Innovative Behavior in the Soccer Instructors

This study was to explore the relationship among the emotional labor, emotional dissonance, job engagement and innovative behavior in the soccer instructors. The survey was conducted on the registered instructors of the Korea Football Association. Except for eight questionnaires, final 292 out of 300 ones was statistically managed and SPSS 24.0 and LISREL 8.80 were used for statistical analysis. The result of this research was as follows.BR First, the emotional labor of the soccer instructors are significant to emotional dissonance of the soccer instructors. And the emotional labor of the soccer instructors are significant to job engagement of soccer instructors. Second, the emotional labor of the soccer instructors are significant to innovative behavior of soccer instructors. Third, the emotional dissonance of the soccer instructors are significant to innovative behavior of the soccer instructors. And the job engagement of the soccer instructors are significant to innovative behavior of soccer instructors.
The ristretto255 Group

This memo specifies a prime-order group, ristretto255, suitable forimplementing complex cryptographic protocols such as zero-knowledgeproofs. The ristretto255 group can be implemented using Curve25519,allowing existing Curve25519 implementations to be reused and extendedto provide a prime-order group.
Binary discrimination in two-level quantum systems via hypothesis testing

We investigate the discrimination of two candidates for an unknown parameter in quantum systems with continuous weak measurement, inspired by the application of hypothesis testing to distinguishing two Hamiltonians [A. H. Kiilerich and K. M\o{}lmer, Phys. Rev. A 98, 022103 (2018)]. Based on the measurement output and stochastic master equation, temporal evolutions of posterior probabilities of two hypotheses are given by Bayes' formula. The Bayes criterion is presented by the likelihood ratio conditioned on the outcome of measurements. Differently from the calculation method based on the maximum a posteriori criterion, the Bayes-criterion-based method for calculating the average probability of making errors is more suitable and efficient in the general situation of binary discrimination. Finally, an example of distinguishing two candidate Hamiltonians is given and the running times for calculating the average probability of error under the Bayes criterion and the maximum a posteriori criterion are compared to illustrate the feasibility of hypothesis testing for quickly distinguishing two candidates for the parameter to be estimated.
Learning natural language interfaces with neural models

Language is the primary and most natural means of communication for humans. The learning curve of interacting with various services (e.g., digital assistants, and smart appliances) would be greatly reduced if we could talk to machines using human language. However, in most cases computers can only interpret and execute formal languages.
Practical Safe Linux Kernel Extensibility

The ability to extend kernel functionality safely has long been a design goal for operating systems. Modern operating systems, such as Linux, are structured for extensibility to enable sharing a single code base among many environments. Unfortunately, safety has lagged behind, and bugs in kernel extensions continue to cause problems. We study three recent kernel extensions critical to Docker containers (Overlay File System, Open vSwitch Datapath, and AppArmor) to guide further research in extension safety. We find that all the studied kernel extensions suffer from the same set of low-level memory, concurrency, and type errors. Though safe kernel extensibility is a well-studied area, existing solutions are heavyweight, requiring extensive changes to the kernel and/or expensive runtime checks. We then explore the feasibility of writing kernel extensions in a high-level, type safe language (i.e., Rust) while preserving compatibility with Linux and find this to be an appealing approach. We show that there are key challenges to implementing this approach and propose potential solutions.
Visualizing Inferred Morphotactic Systems

We present a web-based system that facilitates the exploration of complex morphological patterns found in morphologically very rich languages. The need for better understanding of such patterns is urgent for linguistics and important for cross-linguistically applicable natural language processing. In this paper we give an overview of the system architecture and describe a sample case study on Abui [abz], a Trans-New Guinea language spoken in Indonesia.