Reviewerly/exHarmony huggingface dataset:
Enabling Efficient and Scalable Service Search in IoT With Topic Modeling: An Evaluation

Service search in IoT's large-scale, heterogeneous and multi-domain services space is a challenging task.It can take the time that may not be acceptable for many IoT applications and requires resources that may not be available in many IoT devices.A categorisation of these services into their application domains can reduce the search space and offer an efficient and scalable service search.Recently, in many fields, such as short text messages and categorisation of IoT service specifications, generative probabilistic models, like Topic modelling, are being used.Generally, IoT service descriptions are short and sparse.Existing work on IoT services categorisation is based on Latent Dirichlet Allocation (LDA), but it does not perform well in short and sparse texts.Also, IoT services categorisation has few specific issues, which are not well addressed by existing short texts-specific topic modelling approaches.In this paper, we identify these issues and quantitatively and qualitatively evaluate how well a set of selected short texts-specific topic modelling approaches perform as IoT service categorisers against these issues.The results show that these approaches do not perform well in a corpus of noisy APIs descriptions and heterogeneous service descriptions.Also, they do not support domain identification of services, which is essential in domainbased service search.We conclude that integrating an appropriate and comprehensive knowledge base (i.e., domain ontology) could minimise noise and address IoT's APIs and service descriptions' heterogeneity.More importantly, it can identify the domains of those APIs and services.
Psychological Correction of Parents' Attitude to Their Children with Special Educational Needs by Means of Art Therapy

The paper presents the results of empirical study of parents' attitudes towards their children with special educational needs. The following methods have been used: PARI method (E. Schaefer, R. Bell; adaptation by T. Nescheret), test-questionnaire of parental attitude of A. Varga and V. Stolin; questionnaire 'Analysis of family myth' by A. Nesterova. To clarify and deepen the analysis of research data, two samples of selected participants have been created: a) parents of children with special educational needs and b) parents of children without special educational needs. Two Google forms have been made with appropriate introductory questionnaires and research instructions. The analysis of the results obtained by the methods has revealed the following tendencies in the attitude of parents of children with special educational needs, in comparison with parents of other children: lower level of acceptance; greater concentration on the child and his/her control; a kind of inconsistency in the attitude to their children (simultaneous optimal emotional contact and excessive emotional distance); a sense of self-sacrifice and belief in its necessity. It has been proved, that the usage of different types and forms of art therapy (music, dance-movement, bibliotherapy, fairy tale therapy, phototherapy, film therapy, fine art therapy) helps to correct parents' attitude to their children with special educational needs. In addition, a number of recommendations for the use of art therapy to work with parents of children with special educational needs has been proposed.
Analysis of Key Generation Methods for Enable Secured Communication in the IoTs System

The open stack communication protocol of the internet of things always in security threats. The vulnerability of protocol faces a problem of man in middle attacks and loss of integrity of communication. Cryptography plays a vital role in security authentication on IoT based enable communication system. IoT devices' limited resource constraints cannot offer any security mechanism on the physical level and network level. The key generation methods uplift the security provision in IoT enables communication model. The process of key generation applied three types of cryptography algorithm, symmetric, asymmetric and hybrid algorithm. The complexity of key generation methods needs more computational time and memory; this is the limitation of IoT devices. So various authors proposed a lightweight key generation algorithm for security and authentication. This paper analyzed the performance of key generation methods based on different cryptography approaches and measured their security strength. For the analysis process, use MATLAB software and sample text data file for the communication of two parties. Some ADHOC methods apply to measure protocol weakness during the communication network compromised with a man in the middle attack. The variable key size also reflects the security strength of IoTs enable devices.
Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against <i>Pandemic</i>

Competitive board games have provided a rich and diverse testbed for artificial intelligence. This article contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all players to coordinate their different powers or pool their resources to overcome an escalating challenge posed by the board and a stochastic ruleset. This article focuses on the exemplary collaborative board game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pandemic</i> and presents a rolling horizon evolutionary algorithm designed specifically for this game. The complex way in which the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pandemic</i> game state changes in a stochastic but predictable way required a number of specially designed forward models, macroaction representations for decision making, and repair functions for the genetic operations of the evolutionary algorithm. Variants of the algorithm which explore optimistic versus pessimistic game state evaluations, different mutation rates, and event horizons are compared against a baseline hierarchical policy agent. Results show that an evolutionary approach via short-horizon rollouts can better account for the future dangers that the board may introduce, and guard against them. Results highlight the types of challenges that collaborative board games pose to artificial intelligence, especially for handling multiplayer collaboration interactions.
Distributed resource allocation with binary decisions via Newton-like Neural Network dynamics

This paper aims to solve a distributed resource allocation problem with binary local constraints. The problem is formulated as a binary program with a cost function defined by the summation of agent costs plus a global mismatch/penalty term. We propose a modification of the Hopfield Neural Network (HNN) dynamics in order to solve this problem while incorporating a novel Newton-like weighting factor. This addition lends itself to fast avoidance of saddle points, which the gradient-like HNN is susceptible to. Turning to a multi-agent setting, we reformulate the problem and develop a distributed implementation of the Newton-like dynamics. We show that if a local solution to the distributed reformulation is obtained, it is also a local solution to the centralized problem. A main contribution of this work is to show that the probability of converging to a saddle point of an appropriately defined energy function in both the centralized and distributed settings is zero under light assumptions. Finally, we enlarge our algorithm with an annealing technique which gradually learns a feasible binary solution. Simulation results demonstrate that the proposed methods are competitive with centralized greedy and SDP relaxation approaches in terms of solution quality, while the main advantage of our approach is a significant improvement in runtime over the SDP relaxation method and the distributed quality of implementation.
Evaluation of color image interpolation based on incompressible Navier Stokes technique

Color image interpolation encompasses reconstructing parts of a video or an image based on information from the neighbor. Technique involves the restoration of noised photos and animation or image denoising. The Navier-Stokes (NS) technique has been widely investigated as an essential research by image restoration. These NS equations contribute spectacular results for producing an animation as they augment reality. They can boost real-time video games to be more sensible than ever. In this paper, we present the Incompressible NS approach (INS) for color image interpolating. The method per se is based on fluid flow concept to circulate directed lines from the peripheral into the area to be interpolated. The image intensity represents stream function in a computational flow of 2D fluid dynamics. The algorithm is implemented to carry on lines regarding gradient vectors at the edge of the interpolating region. It uses the improvement of powerful numerical analysis. It is also proven as an innovative idea for easing problems in image analytics as well as computer vision.
Vehicle Detection in Congested Traffic Based on Simplified Weighted Dual-Path Feature Pyramid Network With Guided Anchoring

In modern life, traffic congestion is widespread in large and medium-sized cities in various countries. Multi-scale vehicle targets are densely distributed and occluded from each other in the images of crowded scenes. Vehicle detection in such scenarios is of great significance to urban traffic control, safety management and criminal investigation, but also has great challenges. Facing the special application in congested traffic, we propose Simplified Weighted Dual-path Network with Guided Anchoring framework to realize real-time vehicle detection. Firstly, a simplified weighted Dual-path Feature Pyramid Network (SWD-FPN) is used to improve the robustness of the model for multi-scale and partially occluded objects. Secondly, in order to improve the detection capability for vehicles with wide range of scale changes, the Guided Anchoring (GA) is applied to generate anchors of corresponding positions and scales according to the feature maps. Finally, for the challenge of vehicles intensive distribution, DIoU-soft NMS post-processing mechanism is introduced to reduce the missing alarm. Considering the class imbalance of vehicle detection in real-time traffic scenarios and the above improvements, multi-task loss is proposed for training. Ablation experiments are performed on UA-DETRAC dataset to further analyze the effect of different strategies on performance improvement. In addition, comparisons experiments on UA-DETRAC dataset and handcrafted Vehicles of Traffic (VOF) dataset are conducted to demonstrate the superiority of the proposed method over other state-of-the-art methods.
DEVELOPING CRITICAL THINKING IN READING COMPREHENSION OF TEXTS FOR SPECIFIC PURPOSES AT ALL LEVELS OF BLOOM'S TAXONOMY

This study tackles the role of the ESP teacher, course design, teaching techniques, and evaluation procedures. It further focuses on evaluating teaching techniques which should improve student reading comprehension, which operates at multiple levels, by applying Bloom’s six levels to in-class reading tasks. To accomplish this, twelve B2 level students engaged in activities that revolve around their reading of a text for specific purposes. Through these activities, teaching techniques were assessed and student comprehension was evaluated on each of the six levels. The activities used for research at level 1 were done in person in the classroom. The research activities for levels 2-6 were arranged through an eLearning platform, and students worked independently to complete them and return them to the teacher via the Internet. The research method used was action research. Based on the findings, it is aimed to offer answers and suggestions that may be useful for EAP and ESP teachers. The research has shown that using reading comprehension strategies at all levels of Bloom’s taxonomy in achieving comprehension is beneficial not only in language education, but also for development of the higher order thinking skills of adolescents. It equips them with the ability to think critically and creatively, and provides them with vital tools that they will use during their whole life.
Process Analysis Simulation

This interactive exercise emulates a process wherein a virtual Card is created in a series of steps. It can be used to expose students to ideas and concepts in process analysis, management, and improvement. It may be used in classes focusing on operations management, project management, and software development. The simulation is run on the project-management web application Trello. It allows for simultaneous, real-time observation and collaboration between players. Students can observe other students working at the same time they are performing specific tasks. This exercise is suitable for students in either full-time or executive MBA programs, as well as executive education programs.ExcerptUVA-OM-1718Dec. 16, 2020Process Analysis SimulationIntroductionA potential recruiter is looking to invite a team of high-performing individuals to a new software-development initiative they are initiating. Time and quality are of utmost importance to the recruiter, so they have developed a competition to test velocity and attention to detail. This task involves creating a set of virtual Cards. You and your team will be competing against several other teams for the positions. Production Process. . .
A New Semantic-Based Tool Detection Method for Robots

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms.
Smoothing and Clustering Guided Image Decolorization

In this paper, we propose a new image decolorization method based on image clustering and weight optimization. First, we smooth the color image and cluster it into several classes and get the class centers. Each center can represent a distinctive color in the image. Then the class centers are sorted according to their brightness measured by Euclidean norm. By assuming that the decolorized grayscale image is a linear combination of the three channels of the color image, we propose an optimization problem by forcing the sorted class centers to correspond to specified grayscale values satisfying uniform distribution. Numerically, the problem is solved by quadratic programming. Experiments on two popular data sets demonstrate that the proposed method is competitive with the state-of-the-art decolorization method.
ARCH-COMP20 Category Report:Hybrid Systems Theorem Proving

This paper reports on the Hybrid Systems Theorem Proving (HSTP) category in the ARCH-COMP Friendly Competition 2020. The characteristic features of the HSTP category remain as in the previous editions [MST+18, MST+19]: i) The flexibility of pro- gramming languages as structuring principles for hybrid systems, ii) The unambiguity and precision of program semantics, and iii) The mathematical rigor of logical reason- ing principles. The HSTP category especially features many nonlinear and parametric continuous and hybrid systems. Owing to the nature of theorem proving, HSTP again accommodates three modes: A) Automatic in which the entire verification is performed fully automatically without any additional input beyond the original hybrid system and its safety specification. H) Hints in which select proof hints are provided as part of the input problem specification, allowing users to communicate specific advice about the sys- tem such as loop invariants. S) Scripted in which a significant part of the verification is done with dedicated proof scripts or problem-specific proof tactics. This threefold split makes it possible to better identify the sources of scalability and efficiency bottlenecks in hybrid systems theorem proving. The existence of all three categories also makes it easier for new tools with a different focus to participate in the competition, wherever they focus on in the spectrum from fast proof checking all the way to full automation. The types of benchmarks considered and experimental findings with the participating theorem provers KeYmaera, KeYmaera X 4.6.3, KeYmaera X 4.8.0, Isabelle/HOL/Hybrid-Systems-VCs, and HHL Prover are described in this paper as well.
EFFECTIVENESS OF SMART INFOBOARD PC COMPONENTS AS AUGMENTED REALITY TEACHING AIDE FOR COMPUTER SYSTEM INTRODUCTORY COURSES ON POLYTECHNIC STUDENTS’ LEARNING

The concept of AR has been expanded in innovative education that has the potential to turn ordinary classes into exciting experiences as there is a variety of software and hardware that can be acquired and used to produce AR applications. Computer System Introduction Course is a course offered at the Department of Information and Communication Technology, Sultan Mizan Zainal Abidin Polytechnic (PSMZA), Dungun Terengganu aims to introduce students to the basic concepts of computers, hardware, and software installation. The purpose of this study was to identify the level of students' perceptions of the use of SIPC as a teaching aid and to identify the level of effectiveness of the use of SIPC as a teaching aid in the Computer System Introduction Course. A total of 36 students were involved as respondents. Questionnaire forms were used as research instruments. The quantitative analysis method is used to describe the mean score using SPSS version 26.0. The results show that the level of students' perception of the use of SIPC as a teaching aid is at the level of high mean score range of 4.51 and the level of effectiveness of the use of SIPC as a teaching aid is also at the level of high mean score range of 4.51. Overall, the results of the study found that the level of effectiveness of SIPC innovation as augmented reality teaching aid technology for the Computer System Introduction Course among PSMZA students is at a high level. This indicates that all the items studied support that SIPC as a teaching aid is very effective.
Social Learning During Online Learning? The Case for Japanese Language

Teaching language was once thought to be only possible to be done face-to-face. The pandemic has changed not only the mode of learning of language for learners, it has also changed the way people view language learning. One good thing that comes out from the pandemic is that people are forced to accommodate online communication. As a result, many have proven that language learning can be done successfully online. This study is done to investigate the perception of learners on their language instructors’ teaching the Japanese language via online. This study is rooted from two main theories -social learning theory and online engagement- to explain the sense of engagement learners perceive. The instrument used is a survey with 24 items and 5 Likert-scale. 433 students responded to the survey. Findings revealed that learners needed positive stimulus to give positive response in learning behaviour. Learners also learnt form modelling their instructors. Finally, they also learn well in an environment where there are supportive peers to help them cope with online engagements. Findings bear interesting implications towards the learning and teaching of language via online.
Methods of visual graph-analytical presentation and retrieval of scientific and technical texts

The technology of constructing and visualizing a semantic image of the full text of the document represented by the ontology as a system of three systems is offered: functional, conceptual and terminological.Objects and connections of the functional system correspond to the names of entities and relations extracted from the text; to objects of the conceptual system -descriptors of the thesauri of subject areas.The problem of the variable representation of entities at the sign level is solved using the rules for the formation of phrases of different lengths.Functional relationships are classified according to the taxonomy of functional relationships and are used to construct aspect projections of ontologies.As the data model of the ontology, a labeled directed graph is used, which includes nodes and arcs of different types, which makes it possible to formalize operations on ontologies.Constructing a display of set elements of ontology into graph elements in such way that elements of different sets of different systems are distinguishable, recognizable and depicted in different ways, allows to implement the principle of correspondence of the graphic image with the semantics of the visualized data.Based on the search tasks typology, metaphors for visualizing the ontology graph are proposed: the "pathfinding" metaphor, characterized by the construction of a directed chain of facts, and the "neighborhood analysis" metaphor, which is characterized by the study of the environment (context) of a fact.The technology and software for the construction and variant visualization of the ontology graph have been developed.Examples of using the proposed models for information retrieval through document texts are given.
FRAMEWORK FOR ACADEMIC WRITING CULTURE DEVELOPMENT WITH LANGUAGE STUDENTS

The paper focuses on the problem of academic writing culture development with language students. The article deals with the topicality of the issue under discussion. It reveals the gaps in academic writing teaching policy. Consequently, a new framework is offered to build the complete system for academic writing culture development. The author highlights one of the components of this system, namely terms of teaching academic writing in English. They are grouped: the general terms are common for teaching any type of writing; the specific ones are required in academic writing teaching precisely.
Evaluating the Health Literacy of Medical and Vocational School of Health Services Students and Affecting Factors

Amaç:
Robust Prediction Model for Multidimensional and Unbalanced Datasets

Data Mining is a promising field and is applied in multiple domains for its predictive capabilities. Data in the real world cannot be readily used for data mining as it suffers from the problems of multidimensionality, unbalance and missing values. It is difficult to use its predictive capabilities by novice users. It is difficult for a beginner to find the relevant set of attributes from a large pool of data available. The paper presents a Robust Prediction Model that finds a relevant set of attributes; resolves the problems of unbalanced and multidimensional real-life datasets and helps in finding patterns for informed decision making. Model is tested upon five different datasets in the domain of Health Sector, Education, Business and Fraud Detection. The results showcase the robust behaviour of the model and its applicability in various domains.