Reviewerly/exHarmony huggingface dataset:
Opinion Recommendation Using Coverage for Adaptive Prediction

Opinion recommendation aims at consistently generating a text review and a rating score that a certain user would give to a product never seen before. Inputs driving recommendation are text reviews and ratings for this product contributed by other users as well as text reviews submitted by the user under consideration for other products. The aforementioned task faces the same problems emerging in text generation using neural networks, such as repetition, specificity. In this paper, coverage loss is used as a measure of repetition in the generated text review. It is experimentally demonstrated that such a measure can be used to calibrate rating prediction and significantly improve it.
Processing of ESM Data with ANN

Artificial neural networks are commonly used to solve almost any classification or pattern recognition problem in any imaginable area. This paper presents idea how artificial neural networks can help with processing data from passive surveillance systems to gain maximum possible information from collected data and eliminate inaccurate bearings. The first paragraph introduces reader into the topic of passive surveillance systems and declares why is proposed procedure necessary. Following paragraphs introduces artificial neural networks and their common applications, explains why is an artificial neural network suitable solution for presented problem and defines the way how data are prepared, processed and evaluated. The penultimate paragraph presents model situation with an application of proposed procedures on prepared simulated data also with a graphical representation. The final paragraph summarizes results of the presented approach not only on model situation and defines next steps of a research.
A Pluggable System to Enable Fractal Compression as the Primary Content Type for World Wide Web

This paper presents a comparative performance evaluation of three different image compression techniques for content type in World Wide Web. One is based on fractal compression technique and the other two are JPEG and Portable Network Graphics (PNG). It is due to the unavailability of fractal-based compression as an image type, a pluggable system to prove its feasibility and superior performance has been developed. Study includes the development of a test system to convert and upload the raw image as fractal compressed image to the major content delivery networks. A mobile application for downloading the rendered image is also developed for the client side system. The present system utilizes a Reinforcement Learning (RL) algorithm to reduce the encoding time required to compress an image by using classical Iterative Function System (IFS). The algorithm also employs modified Horizontal-Vertical (HV) partitioning scheme and upper bounded scaling, translation and shifting parameters. The empirical analysis proves that the usage of fractal compressed images can be a promising method for reducing the network traffic, and hence transmission bandwidth of content delivery networks.
Chapter 11. Measuring informativity

By applying data-driven methods based on information theory, this study adds to previous work on the development of the scientific register by measuring the informativity of alternative phrasal structures shown to be involved in change in language use in 20th-century Scientific English. The analysis based on data-driven periodization shows compounds to be distinctive grammatical structures from the 1920s onwards in Proceedings A of the Royal Society of London. Compounds not only increase in frequency, but also show higher informativity than their less dense prepositional counterparts. Results also show that the lower the informativity of particular items, the more alternative, more informationally dense options might be favoured (e.g., of-phrases vs. compounds) – striving for communicative efficiency thus being one force shaping the scientific register.
Exploitation of temporal structure in momentum-SGD for gradient compression

Distributed optimization has become the norm for training machine learning models on large datasets. Learning bigger models on such systems leads to the exchange of large-volume updates. For this reason, limits on network communication in a distributed system can bottleneck learning progress. While compression techniques have been introduced to reduce bit-rates, no methods have yet leveraged the temporal structure that exist in consecutive vector updates. An important example is distributed momentum-SGD where temporal correlation is enhanced by the low-pass-filtering effect of applying momentum. In this paper we design methods that leverage temporal correlation to reduce bit-rates in systems employing momentum-SGD. We demonstrate that a significant reduction in the volume of communication can be realized. Experiments with the ImageNet dataset show that our proposed methods offers up to 40% bit savings compared to widely used methods such as Scaled-sign and Top-K.
Impact of Infrastructure Service Quality on Residents’ Satisfaction in the United Arab Emirates (UAE), the Case of Ajman Emirate

This paper discusses the results of a field survey conducted by Ajman Statistics and Competitiveness Center, to assess the level of Ajman residents' satisfaction on the availed infrastructure.A sample of 1032 households, was randomly selected from a total of 95,531 Households, residing in the emirate of Ajman and has prior experience in utilizing the availed infrastructure.Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 22 and AMOS version 22. Structural Equation Modeling (SEM) analysis showed an acceptable model fit used to measure residents' satisfaction.Findings showed that accessibility, safety, and empathy dimensions correlated positively with residents' satisfaction, while reliability and responsiveness dimensions didn't suggest direct relationship with residents' satisfaction.Generally, 90% of Ajman residents were satisfied with infrastructure.Safety dimension had the highest proportion of satisfaction among residents, about 93%, while the accessibility dimension had the lowest satisfaction with only 88%.The findings of this study are expected to support the concerned decision-makers in improving infrastructure within the emirate of Ajman.
An Multi-Aspect Attentional Model To Capture Multistratal Influence In Social Group

The recommendation system is an effective means to handle “information overload”, and it is also one of the most common applications of big data technology. As an important sub-field in recommendation algorithms, social recommendation effectively enhances performance via using social relationships in online social network platforms. However, there are still two problems in the existing social recommendation models: Firstly, user's preference for item has multiple aspects of information, and its social neighbors only match or influence the target user's preferences in some aspects; Secondly, different friends have varied and dynamic influence on user at target item. To solve the problems, we propose a novel social recommendation model: SMAAM (Social-aware Multi-Aspect Attentional Model). We designed a multi-layer attention mechanism to capture the different levels of influence of social neighbors on users and their multiple aspects of views of items, and then select the most valuable friends and aspect information to model user preferences. The experimental results on two different public datasets explain that our model has more stable and excellent performance than some previously published models. In addition, we have also done some visualization of experimental process data to verify the effectiveness and correctness of the model on learning process and design ideas.
MinkowRadon: Multi-Object Tracking Using Radon Transformation and Minkowski Distance

The latest trend in multiple object tracking (MOT) is bending to utilize deep learning to improve tracking performance. With all advanced models such as R-CNN, YOLO, SSD, and RetinaNet, there will always be a time-accuracy trade-off which puts constraints to computer vision advancement. However, it is not trivial to solve those kinds of challenges using end-to-end deep learning models, adopting new strategies to enhance the aforementioned models are appreciated. In this paper we introduce a novel radon transformation based framework, which takes advantage of color space conversion and squeezes the MOT problem to signal domain using radon transformation. Afterwards, the inference of Minkowski distance between sequence of signals is used to estimate the objects' location. Adaptive Region of Interest (ROI) and thresholding criteria have been adopted to ensure the stability of the tracker. We experimentally demonstrated that the proposed method achieved a significant performance improvement in both The Multiple Object Tracking Accuracy (MOTA) and ID F1 (IDF1) with respect to previous state-of-the-art using two public benchmarks.
Knowledge Utilization and Code Reuse in Models Translation

An approach of designing expert system for model’s code reuse from existing knowledge is considered in this article. As a source of knowledge was taken apart a special module of multilanguage translation package – Multitranslator. This Multitranslator module itself allows to reuse models source codes in different modeling tools. After Multitranslator further development as a client-server application, we found some exceptional cases could cause unpredictable model translation results. All of the efforts to solve those cases lead us to add explicit software – an expert system based on CLIPS. It was suggested a way properly integrate it into translation part of Distributed Storage of Models complex and so took apart translation module of Multitranslator as a start to make sure that no exceptional cases can happen on this side. To properly use it the debugging interface was added that was sure to be made with dotNet due to Distributed Storage of Models compatibility issues. As basis of the expert system itself we chose message handling due to its OOP nature. The resulting version of expert system with such knowledge base shows expectable results and improves Distributed Storage of Models complex.
Optical encryption using CCRM camera

Compressive coded rotating mirror (CCRM) camera is a novel high-speed imaging system that operates using optical encoding and compression that achieves a fast and robust encryption on the data.
Manual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges

User needs inform designers and developers of essential functionalities for requirements engineering. In this work, we summarize key concepts and challenges relating to manual and automatic user needs detection methods. We discuss six challenges with manual and eight challenges with automated methods. Despite the promise of automated methods, the challenges imply that artificial intelligence and machine learning are not yet mature enough to replace manual methods, such as interviews and focus groups, for discovering user needs in requirements engineering.
STNet: Low-Complexity Neural Network Decoder With Network Pruning

In this letter, a novel and compact neural network called Sigmoid-Tanh Network (STNet) is proposed for channel decoding, which is only composed of sigmoid and tanh activation functions. To address the structural redundancy problem in long and short-term memory network (LSTM), the neurons in the STNet are redesigned with the most effective structure in the LSTM cell for decoding. To further reduce the computational complexity, we propose an automatic pruning method based on multiple layer sensitivity, which can effectively remove redundant weights in STNet decoder with slight performance loss. Simulation results show that the proposed STNet decoder achieves near-maximum likelihood (ML) performance with only 17.1% trainable parameters compared to LSTM. Moreover, our pruning method achieves comparable decoding performance when reducing 58.3% Floating-point operations (FLOPs) for STNet.
A Method of Dimensionality Reduction of Analog Circuit Fault Feature

To deal with dimensionality reduction of nonlinear analog circuit fault features, a method named local linear discriminant analysis (LLDA) was proposed. First, considering the local neighborhood of each data, the projection direction in the local space is found by making the neighbor samples of the same class as compact as possible and the neighbor samples of different classes as distant as possible. The final objective function is reconstructed by an alignment algorithm on the entire data space. Finally, a projection matrix is obtained by performing the standard eigenvalue decomposition for dimensionality reduction. The algorithm not only makes full use of the local discriminant information of data manifold to process the nonlinear data, but also keeps the class separate. The two-stage four-op amp low-pass filter circuit demonstrate the effectiveness of LLDA.
Meaning and Context in Computer Programs

When you look at a function program's source code, how do you know what it means? Is the meaning found in the return values of the function, or is it located inside the function body? What about the function name? Answering these questions is important to understanding how to share domain knowledge among programmers using the source code as the medium. The program is the medium of communication among programmers to share their solutions.
A memory-enhanced anomaly detection method for surveillance videos

Surveillance videos can capture anomalies in real scenarios and play an important role in security systems. Anomaly events are unpredictable, which reflect the unsupervised nature of the problem. In addition, it is difficult to construct a complete video dataset which contains all normal events. Based on the diversity of normal events, this paper proposes a memory-enhanced unsupervised method for anomaly detection. The proposed method reconstructs video events by combining prototype features and encoded features to detect anomaly events. Furthermore, a memory module is introduced to better store the prototype patterns of normal events. Experimental results in various benchmark datasets demonstrate the effectiveness and robustness of the proposed method.
Low-cost Single-Board-Computers and Learning-Sets and the Relation to the “Digital” Didactic Goals of the 21st Century

Abstract: This paper gives an overview of the existing single-board computers under 100$ on the market, which are more or less suitable to fulfil the digital competences of the curricula of individual school types from primary to high school. For this purpose, a quantitative analysis of single-board computers under 100$ available on the market was carried out and the characteristics of the relevant products were extracted from the technical documents of the products and binarized using a qualitative deductive meta-analysis. The resulting skill- and property- fulfilment was supplemented horizontally with the snapshot price information, combined, and ranked vertically. The resulting list of more or less suitable products was presented in a network graphic, taking into account a horizontal dynamic price weighting, in order to allow the decision-maker to take into account his own financial possibilities of possibly limited budgets in his decision. The study points out that the market for single-board computers is divided into 2 different product types and that the SOC variants can definitely replace a PC. Furthermore, it was found that there are currently no learning stets that match the price and content requirements of the curricula. The study concludes with a summary and an outlook.
IDOS: Improved D3DOCK on Spark

Virtual molecular docking is a computational method used in computer-aided drug discovery that calculates the binding affinity of a small molecule drug candidate to a target protein and greatly reduces the time and cost of suggesting new potential pharmaceuticals. D3DOCK is a suite of automated docking tools, it was developed for sensitively investigating the effects of halogen bond in drug discovery by based on Autodock Vina. In this study, we developed IDOS, a high-throughput and scalable virtual docking system. We use the open source Hadoop framework implementing the Spark paradigm for distributed computing on a private cloud platform. IDOS can work on a single node as well as on distributed nodes compared with the stand-alone version of D3DOCK, it performed almost linear acceleration on our Spark cluster, with parallel efficiency of more than 80%, and 5x to 6x speedup can be achieved on the cluster depending on the number of work nodes. Moreover, On account of the widely used of cloud computing, IDOS can be easily installed and employed for docking on condition that the IDOS docker image was upload to the docker hub shared warehouse. We also have developed the docking workflow with an MPI version on high-performance computers (HPC). However, compared with HPC system, mounting IDOS on a cheap and easy to obtain cloud computing cluster is obviously more conducive, which allows more researchers to do docking with it expediently. IDOS is freely available at https://github.com/codedinner/IDOS.
INTRODUCTION OF INNOVATIVE TEACHING TECHNOLOGIES IN THE HIGHER EDUCATIONAL INSTITUTIONS’ EDUCATIONAL PROCESS

The article considers innovative forms and methods of individualization of a students’ learning, which include: problem lecture, visualization lecture, seminar-dispute, educational discussion, brainstorming, control over the level of knowledge acquisition, portfolio method, problem presentation method, project method, problem-scientific teaching methods, research work of students, problem-based learning, e-course format, application of distance learning technologies. The effectiveness of the process of individualization of educational activities is ensured by the degree of implementation of the principle of dynamism and variability, the principle of stimulating independence and motivational support, the principle of supporting the student's personality and developing his autonomy, the principle of positive perspective and self-actualization, the principle of self-development and self-realization.
The Power of Analytics in Epidemiology for COVID 19

Mitigating the COVID-19 pandemic poses a series of unprecedented challenges, including predicting new cases and deaths, understanding the true prevalence, and allocating the different vaccines across regions. In this paper, we describe our efforts to tackle these issues. We first discuss the methods we developed for predicting cases and deaths using a novel ML based method we call MIT-Cassandra. MIT-Cassandra is currently being used by the CDC and is consistently among the top 10 methods in accuracy, often ranking 1st amongst all submitted methods. We then use this prediction to model the true prevalence of COVID 19 and incorporate this prevalence into an optimization model for fair vaccine allocation. The latter part of the paper also gives insights on how prevalence and exposure of the disease in different parts of the population can affect the distribution of different vaccine doses in a fairway. Finally, and importantly, our work has specifically been used as part of a collaboration with MIT's Quest for Intelligence and as part of MIT's process to reopen the institute.
Classification of Trends in Lecturer Research Fields Using Naive Bayes Method

Lecturers must contribute to publishing scientific papers following their respective research groups. Sometimes, the publications conducted by lecturers consist of various fields of research. So that lecturers need to determine their research interest in the research being carried out. The large number of lecturers' publications consisting of various research fields makes it difficult for lecturers to determine research interests. Based on these problems, this study tries to create a system that can determine the propensity of lecturers towards certain fields of research. The classification system applies the Naive Bayes method. The results of the classification are used to determine the tendency of each JTI POLINEMA lecturer towards a particular field of research. The results of the performance of the Naive Bayes classification model for the classification of research fields in the publication title show an accuracy of 57%, precision of 57%, recall of 57%, and an error value of 43%.
Chapter 1. Introduction

The Swedish FrameNet++ was designed to be several things. As a digital artifact, it is an integrated panchronic lexical macroresource, primarily for Swedish, but including several other languages, intended as a basic infrastructural component in Swedish language technology research and for developing natural language processing applications. As an activity, it is a long-term R&D initiative, initially aimed at bringing about this macroresource, and now at maintaining and extending it, at promoting its use in language technology research and application development, as well as ensuring that the results of this research and development in their turn are incorporated in the macroresource. As a product of research, it reflects both computational and linguistic approaches to lexicology, lexical semantics, and lexical typology.
On Integrating the Spirit of Volunteer Teaching in Western China into Higher Physical Education

The spirit of Volunteer teaching in Western China is the embodiment of patriotism.Based on literature review, this paper explores its spiritual connotation and value of the times, summarizes the necessity and way of introducing the spirit to higher physical education.The moral education of physical education courses needs to be promoted through a good integration of the spirit of volunteering to teach in the west into physical education, thereby more talents of service-oriented and high-quality can be cultivated in colleges and universities.
Does Managing Occupational Fraud Add Value?

The past few years have seen the emergence of new developments in the field of occupational fraud. These have manifested themselves in the forms of both insider trading and the misrepresentation of figures in financial statement reporting. In either case, the result is a loss to stakeholders. The proposed study sets out to establish whether the systemic management of occupational fraud can add value to the firm’s wider risk management practices. It will seek to establish how the nature of these value-adding factors can be identified and analysed.The methodology adopted by this research is the collection of primary data from risk man-agers and senior-level security officers of more than fifty organisations. The study-framework employs a quantitative research design via the application of quantitative data-analysis techniques. These include descriptive and demographic statistics, frequency analysis, correlation, and regression. The principal finding of the study are that:• the use of technology, artificial intelligence and blockchain, alongside government regulation, reinforces fraud risk management.• the removal of third parties adds value to the risk-management practices of the organisation concerned.• the use of whistleblowing is shown to be an effective risk-prevention measure.• the existence of ‘excessive trust’ relationships can increase the risk of occupational fraud.The overall conclusion is that the possibility of occupational fraud can be managed and significantly reduced. This can be achieved with the assistance of artificial intelligence, block-chain and whistleblowing.