Reviewerly/exHarmony huggingface dataset:
Multimode Bogoliubov transformation and Husimi’s Q-function

Abstract In this paper, we present numerical schemes for evaluating the matrix elements of Gaussian/non-Gaussian operators in the Fock state basis, which are identified as multivariate Hermite polynomials (MHPs). Using the integral transformation operator to perform the multimode Bogoliubov transformation, Husimi’s Q-functions of Gaussian/non-Gaussian operators are easily derived as the generating functions of MHPs.
Fg2seq: Effectively Encoding Knowledge for End-To-End Task-Oriented Dialog

End-to-end Task-oriented spoken dialog systems typically require modeling two types of inputs, namely, the dialog history which is a sequence of utterances and the knowledge base (KB) associated with the dialog history. While modeling these inputs, current state-of-the-art models typically ignore the rich structure in the knowledge graph or its intrinsic association with the dialog history. In this paper, we propose a Flow-to-Graph seq2seq model (FG2Seq) which can effectively encode knowledge by considering inherent structural information of the knowledge graph and latent semantic information from dialog history. Experiments on two publicly available task oriented dialog datasets show that our proposed FG2Seq achieves robust performance on generating appropriate system responses and outperforms the baseline systems.
TRUST AS A COGNITIVE BASE OF SOCIAL COHESION IN THE UNIVERSITY COMMUNITIES

The present article continues the cycle of the cognitive researches of the phenomenon of social cohesion in education, in particular, in the university communities. It contains the cognitive research of trust and its foundation as the central focus of social cohesion. The purpose of the study is to identify the level of trust which is connected with the social cohesion in university communities, to test the author’s questionnaire and to determinate the further steps for the trust enhancement in the educational community. Methods that were used in the study are the author’s questionnaire, math analytics etc. There were 196 people interviewed in both universities, among them 31 employees and 85 students of the National Pedagogical Dragomanov University and 33 employees and 47 students of the National University of Life and Environmental Sciences of Ukraine. According to the research results, the level of trust in each university community (as well as in common) was average, excluding some indicators. Although there were some differences between levels of trust of employees of these universities. We can assume that the quite sufficient average level or trust positively characterizes the attitude of employees and students to each other, reflects their readiness for mutual respect and support, acceptance of differences and tolerance etc. Also, the research highlights weak points of social interactions that form the base for further investigations and actions on the social cohesion development.
A New Device to Track and Identify people in a Multi-Residents Context

In recent years, technologies for monitoring people inside a house lead to the development of smart home. However, the vast majority of works deals only in monitoring the activities of a single inhabitant. Nevertheless, most of the people in the current context of ageing population does not live alone. Recognizing the activities performed by each inhabitant in a house is an important challenge. A first step to achieve this is to be able to distinguish where each inhabitant is in the house. In this paper, we present a new device to track and identify people in a multi-residents context. Experiments have been conducted to validate the reliability and accuracy of the proposed device.
Perfect codes in proper reduced power graphs of finite groups

Let Γ be a graph with vertex set V(Γ). A subset C of V(Γ) is a perfect code of Γ if C is an independent set such that every vertex in V(Γ)∖C is adjacent to exactly one vertex in C. A subset T of V(Γ) is a total perfect code of Γ if every vertex of Γ is adjacent to exactly one vertex in T. Let G be a finite group. The proper reduced power graph of G is the undirected graph whose vertex set consists of all nonidentity elements, and two distinct vertices x and y are adjacent if 〈x〉⊂〈y〉 or 〈y〉⊂〈x〉. In this paper, we give a necessary and sufficient condition for a proper reduced power graph to contain a perfect code. In particular, we determine all perfect codes of a proper reduced power graph provided that the proper reduced power graph admits a perfect code. Moreover, we give some necessary conditions for a proper reduced power graph to contain a total perfect code. As applications, we determine the abelian groups and generalized quaternion groups whose proper reduced power graphs admit a total perfect code. We also characterize all finite groups whose proper reduced power graphs admit a total perfect code of size 2.
A Comparative Study of Unstructured Data with SQL and NO-SQL Database Management Systems

This paper aims to establish a relative study between a relational Microsoft SQL Server database and a non-relational MongoDB database within the unstructured representation of data in JSON format.There is a great amount of work done regarding comparison of multiple database management applications on the basis of their performances, security etc., but we have limited information available where these databases are assessed on the basis of provided data.This study will mainly focus on looking at all the possibilities that both these database types offer us when handling data in JSON.We will accomplish this by implementing a series of experiments while taking into consideration that the subjected data does not require to be normalized; and therefore, evaluate the outcome to conclude the result.
Deep Induction: Induction Rules for (Truly) Nested Types

Abstract This paper introduces deep induction , and shows that it is the notion of induction most appropriate to nested types and other data types defined over, or mutually recursively with, (other) such types. Standard induction rules induct over only the top-level structure of data, leaving any data internal to the top-level structure untouched. By contrast, deep induction rules induct over all of the structured data present. We give a grammar generating a robust class of nested types (and thus ADTs), and develop a fundamental theory of deep induction for them using their recently defined semantics as fixed points of accessible functors on locally presentable categories. We then use our theory to derive deep induction rules for some common ADTs and nested types, and show how these rules specialize to give the standard structural induction rules for these types. We also show how deep induction specializes to solve the long-standing problem of deriving principled and practically useful structural induction rules for bushes and other truly nested types. Overall, deep induction opens the way to making induction principles appropriate to richly structured data types available in programming languages and proof assistants. Agda implementations of our development and examples, including two extended case studies, are available.
Learning Based Methods for Code Runtime Complexity Prediction

Predicting the runtime complexity of a programming code is an arduous task. In fact, even for humans, it requires a subtle analysis and comprehensive knowledge of algorithms to predict time complexity with high fidelity, given any code. As per Turing’s Halting problem proof, estimating code complexity is mathematically impossible. Nevertheless, an approximate solution to such a task can help developers to get real-time feedback for the efficiency of their code. In this work, we model this problem as a machine learning task and check its feasibility with thorough analysis. Due to the lack of any open source dataset for this task, we propose our own annotated dataset, (The complete dataset is available for use at https://github.com/midas-research/corcod-dataset/blob/master/README.md ) CoRCoD: Code Runtime Complexity Dataset, extracted from online coding platforms. We establish baselines using two different approaches: feature engineering and code embeddings, to achieve state of the art results and compare their performances. Such solutions can be highly useful in potential applications like automatically grading coding assignments, IDE-integrated tools for static code analysis, and others.
Factors Affecting Indonesian Pre-Service Teachers’ Use of m-LMS: A Mix Method Study

The purpose of this research is to investigate factors affecting Indonesian pre-service teachers’ (PSTs) teachers’ use of m-learning management systems (m-LMS) in higher education. The difference regarding the use of m-LMS based on some demographic information namely gender, university, year in university, and age are also reported as well as gaining an in-depth understanding of the use of m-LMS in Indonesian universities. To achieve this, two approaches, quantitative and qualitative, were applied. First, we developed and distributed a survey instrument to 210 students teachers based on the technology acceptance model (TAM). In addition, we interviewed 7 of the PSTs to obtain an in-depth understanding of the use of m-learning in their learning. Some statistical calculations were presented such as mean, standard deviation, Cronbach’s alpha, t-test, and ANOVA. Findings suggest that PSTs’ use m-LMS are related to their Perceived usefulness and perceived ease of use, Subjective norm and attitudes Self-efficacy and supporting condition. From these results, we recommend that teacher educators should target these factors within teacher development programs to prepare PSTs for successful use of m-LMS.
Love, Joy, Anger, Sadness, Fear, and Surprise: SE Needs Special Kinds of AI: A Case Study on Text Mining and SE

Do you like your code? What kind of code makes developers happiest? What makes them angriest? Is it possible to monitor the mood of a large team of coders to determine when and where a codebase needs additional help?
SMASH: A Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP

The growth of online video-on-demand consumption continues unabated. Existing heuristic-based adaptive bit-rate (ABR) selection algorithms are typically designed to optimise video quality within a very narrow context. This may lead to video streaming providers implementing different ABR algorithms/players, based on a network connection, device capabilities, video content, etc., in order to serve the multitude of their users' streaming requirements. In this paper, we present SMASH: a Supervised Machine learning approach to Adaptive Streaming over HTTP, which takes a tentative step towards the goal of a one-size-fits-all approach to ABR. We utilise the streaming output from the adaptation logic of nine ABR algorithms across a variety of streaming scenarios (generating nearly one million records) and design a machine learning model, using systematically selected features, to predict the optimal choice of the bitrate of the next video segment to download. Our evaluation results show that SMASH guarantees a high QoE with consistent performance across a variety of streaming contexts.
Selective Image Encryption Based on DCT, Hybrid Shift Coding and Randomly Generated Secret Key

Most of todayâ€™s techniques encrypt all of the image data, which consumes a tremendous amount of time and computational payload. This work introduces a selective image encryption technique that encrypts predetermined bulks of the original image data in order to reduce the encryption/decryption time and thecomputational complexity of processing the huge image data. This technique is applying a compression algorithm based on Discrete Cosine Transform (DCT). Two approaches are implemented based on color space conversion as a preprocessing for the compression phases YCbCr and RGB, where the resultant compressed sequence is selectively encrypted using randomly generated combined secret key.The results showed a significant reduction in image quality degradation when applying the system based on YCbCr over RGB, where the compression ratio was raised in some of the tested images to 50% for the same Peak Signal to Noise Ratio (PSNR). The usage of 1-D DCT reduced the transform time by 47:1 times comparedto the same transform using 2-D DCT. The values of the adaptive scalar quantization parameters were reduced to the half for the luminance (Y band) to preserve the visual quality, while the chrominance (Cb and Cr bands) were quantized by the predetermined quantization parameters. In the hybrid encoder horizontal zigzag,block scanning was applied to scan the image. The Detailed Coefficient (DC) coefficients are highly correlated in this arrangement- where DC are losslessly compressed by Differential Pulse Coding Modulation (DPCM) and theAccumulative Coefficients (AC) are compressed using Run Length Encoding (RLE). As a consequence, for the compression algorithm, the compression gain obtained was up to 95%. Three arrays are resulted from each band (DC coefficients, AC values, and AC runs), where the cipher is applied to some or all of those bulksselectively. This reduces the encryption decryption time significantly, where encrypting the DC coefficients provided the second best randomness and the least encryption/decryption time recorded (3 10-3 sec.) for the entire image. Although the compression algorithm consumes time but it is more efficient than the savedencryption time.
Comparative Study of using Data Mining Techniques for Bank Telemarketing Data

In this paper, a heuristic and comparative study is performed to assess several data mining techniques using the well-known Bank Marking data set. We discuss how data mining (DM) techniques can be utilized to achieve result that is more accurate. All selected DM methods have been evaluated using bank telemarketing for clients from direct marketing campaigns (phone calls) of a Portuguese banking institution. The Bank marking data set has been created using 16 extracted features. Selected DM techniques are: neural network (NN), support vector machine (SVM), decision trees (DTs), k-nearest neighbour (KNN), naive Bayes (NB) and logistic regression (LR). The experiments show that the LR is capable of providing best performance with the true positive rate reaching 90.20%.
Clustering of mixed-type data considering concept hierarchies: problem specification and algorithm

Abstract Most clustering algorithms have been designed only for pure numerical or pure categorical data sets, while nowadays many applications generate mixed data. It raises the question how to integrate various types of attributes so that one could efficiently group objects without loss of information. It is already well understood that a simple conversion of categorical attributes into a numerical domain is not sufficient since relationships between values such as a certain order are artificially introduced. Leveraging the natural conceptual hierarchy among categorical information, concept trees summarize the categorical attributes. In this paper, we introduce the algorithm ClicoT ( CL ustering mixed-type data I ncluding CO ncept T rees) as reported by Behzadi et al. (Advances in Knowledge Discovery and Data Mining, Springer, Cham, 2019) which is based on the minimum description length principle. Profiting of the conceptual hierarchies, ClicoT integrates categorical and numerical attributes by means of a MDL-based objective function. The result of ClicoT is well interpretable since concept trees provide insights into categorical data. Extensive experiments on synthetic and real data sets illustrate that ClicoT is noise-robust and yields well-interpretable results in a short runtime. Moreover, we investigate the impact of concept hierarchies as well as various data characteristics in this paper.
The Effectiveness of Social Media Application "Telegram Messenger" in Improving Students’ Reading Skills: A Case Study of EFL Learners at Ajloun University College/Jordan

With the advent of smart phones and new smart devices, the use of these mobile communication devices has become an integral part of our daily life. The study aims to discuss the influence of modern technological apps in developing students' skills such as telegram messenger channels on improving reading skills E-leaning, m-learning and techno-aids might help to fill the gap. The study is conducted on EFL learners at Ajloun University College. To sum up, the study has provided evidence to the effectiveness of Telegram® social media application in improving students' reading skills.
Integrity and Security in Cloud Computing Environment: A Review

Cloud computing is a newly developed concept that aims to provide computing resources in the most effective and economical manner. The fundamental idea of cloud computing is to share computing resources among a user group. Cloud computing security is a collection of control-based techniques and strategies that intends to comply with regulatory compliance rules and protect cloud computing-related information, data apps, and infrastructure. On the other hand, data integrity is a guarantee that the digital data are not corrupted, and that only those authorized people can access or modify them (i.e., maintain data consistency, accuracy, and confidence). This review presents an overview of cloud computing concepts, its importance in many applications, and tools that can be used for providing the integrity and security to the data located in the cloud environment.
The use of information and communication technologies in training ecology students

Increasing demand for experts capable of high-quality assessment of the impact of a particular planned activity on the environment involves a more effective use of information and communication technologies (ICT) in the educational process of training ecology students. The authors present a methodology for students to study the educational material of the “Environmental Impact Assessment” course by using ICT. The methodology and algorithm for using the Padlet interactive whiteboard, Mindomo mind maps, Easel infographics, Google Drive and Google Docs in lectures and practical lessons are described. Particular attention is paid to the preparation of a group project using ICT. Tested in the Ternopil Volodymyr Hnatiuk National Pedagogical University, the methodology allows to implement a number of tasks such as: promotion of intensification and effectiveness of teaching; acquisition of practical skills to quickly find the necessary information on various online resources for ecology students; simulation of the real procedure of environmental impact assessment in the classroom; development of the ability to professionally communicate with experts in other fields of knowledge or activity, etc. Examination of the results of using ICT to study the “Environmental Impact Assessment” course has shown a significant increase in informational literacy among ecology students and their individual professional growth.
OOP-AR: Learn Object Oriented Programming Using Augmented Reality

Object-Oriented Programming (OOP) is one of the challenging concept in computer science education especially for novice programmers. To help students understand this concept, this research try to blend OOP with Augmented Reality (AR). The reason because AR can give fun aspect to the learner, which can also help learner to focus longer without distraction. For this research purpose, the target audience is 20 university students with different ability in programming and augmented reality experience. The overall results of this research show that OOP-AR received positive feedback from users. It also proved that OOP-AR is a necessary product for students who need to improvise their knowledge in OOP and its concepts and gain interest in using AR application
Collecting Entailment Data for Pretraining: New Protocols and Negative Results.

Textual entailment (or NLI) data has proven useful as pretraining data for tasks requiring language understanding, even when building on an already-pretrained model like RoBERTa. The standard protocol for collecting NLI was not designed for the creation of pretraining data, and it is likely far from ideal for this purpose. With this application in mind, we propose four alternative protocols, each aimed at improving either the ease with which annotators can produce sound training examples or the quality and diversity of those examples. Using these alternatives and a simple MNLI-based baseline, we collect and compare five new 8.5k-example training sets. Our primary results are solidly negative, with our baseline MNLI-style dataset yielding good transfer performance, but none of our four new methods (nor the recent ANLI) showing any improvements on that baseline. However, we do observe that all four of these interventions, especially the use of seed sentences for inspiration, reduce previously observed issues with annotation artifacts.
Autonomic architecture for fault handling in mobile robots

Abstract This paper describes a generic autonomic architecture for use in developing systems for managing hardware faults in mobile robots. The method by which the generic architecture was developed is also described. Using autonomic principles, we focused on how to detect faults within a mobile robot and how specialized algorithms can be deployed to compensate for the faults discovered. We design the foundation of a generic architecture using the elements found in the MAPE-K and IMD architectures. We present case studies that show three different fault scenarios that can occur within the effectors, sensors and power units of a mobile robot. For each case study, we have developed algorithms for monitoring and analyzing data stored from previous tasks completed by the robot. We use the results from the case studies to create and refine a generic autonomic architecture that can be utilized for any general mobile robot setup for fault detection and fault compensation. We then describe a further case study which exercises the generic autonomic architecture in order to demonstrate its utility. Our proposal addresses fundamental challenges in operating remote mobile robots with little or no human intervention. If a fault does occur within the mobile robot during field operations, then having a self-automated strategy as part of its processes may result in the mobile robot continuing to function at a productive level. Our research has provided insights into the shortcomings of existing robot design which is also discussed.
Sentiment analysis of informal Malay tweets with deep learning

&lt;table width="593" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="387"&gt;&lt;p&gt;Twitter is an online microblogging and social-networking platform which allows users to write short messages called tweets. It has over 330 million registered users generating nearly 250 million tweets per day. As Malay is the national language in Malaysia, there is a significant number of users tweeting in Malay. Tweets have a maximum length of 140 characters which forces users to stay focused on the message they wish to disseminate. This characteristic makes tweets an interesting subject for sentiment analysis. Sentiment analysis is a natural language processing (NLP) task of classifying whether a tweet has a positive or negative sentiment. Tweets in Malay are chosen in this study as limited research has been done on this language. In this work, sentiment analysis applied to Malay tweets using the deep learning model. We achieved 77.59% accuracy which exceeds similar work done on Bahasa Indonesia.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;