Reviewerly/exHarmony huggingface dataset:
Parsing Indonesian Sentence into Abstract Meaning Representation using Machine Learning Approach

Meaning Representation (AMR) provides many information of a sentence such as semantic relations, coreferences, and named entity relation in one representation. However, research on AMR parsing for Indonesian sentence is fairly limited. In this paper, we develop a system that aims to parse an Indonesian sentence using a machine learning approach. Based on Zhang et al. work, our system consists of three steps: pair prediction, label prediction, and graph construction. Pair prediction uses dependency parsing component to get the edges between the words for the AMR. The result of pair prediction is passed to the label prediction process which used a supervised learning algorithm to predict the label between the edges of the AMR. We used simple sentence dataset that is gathered from articles and news article sentences. Our model achieved the SMATCH score of 0.820 for simple sentence test data.
Modern Approaches to Creating Highly Undetectable Stegosystems (HUGO Systems)

This chapter's primary goal is to provide a comprehensive approach to the development of new highly undetectable stegosystems that greatly complicate their steganalysis. The authors propose several implementations of highly undetectable stegosystems, the so-called HUGO systems, using an integrated approach to their synthesis. This approach most fully considers the features of transmitting hidden messages over highly noisy communication channels. At the stage of embedding hidden messages, the authors suggest actively using their discrete transformations. The authors also propose increasing the secrecy of secret messages by converting them to a form that resembles natural noise. The authors use a discrete chaotic decomposition of the Arnold cat map (ACM) to do this. The authors also suggest using highly efficient noise-tolerant encoding and multi-threshold decoding to combat interference in the communication channel and an embedding algorithm. The authors also describe two original stegosystems ±HUGO and ⨁HUGO and test results confirming their effectiveness.
VEDBViz: The Visual Experience Database Visualization and Interaction Tool

Mobile, simultaneous tracking of both the head and eyes is typically achieved through integration of separate head and eye tracking systems because off-the-shelf solutions do not yet exist. Similarly, joint visualization and analysis of head and eye movement data is not possible with standard software packages because these were designed to support either head or eye tracking in isolation. Thus, there is a need for software that supports joint analysis of head and eye data to characterize and investigate topics including head-eye coordination and reconstruction of how the eye is moving in space. To address this need, we have begun developing VEDBViz which supports simultaneous graphing and animation of head and eye movement data recorded with the Intel RealSense T265 and Pupil Core, respectively. We describe current functionality as well as features and applications that are still in development.
MeTeaM—A method for characterizing mature software metrics teams

Metrics teams play an increasingly important role in handling data and information in modern software development organizations; they manage their companies’ measurement programs, collect and process data, and develop and distribute information products. Metrics teams can comprise several roles, and their set-up can differ between companies, as can the metrics maturity of host organizations. These differences impact the effectiveness and quality of a team’s measurement program. Our objective was to design and evaluate a model to describe the characteristics of a mature metrics team, which efficiently designs, develops, maintains, and evolves its organization’s measurement program. We conducted an action research study on four metrics teams of four distinct companies. We designed and evaluated a domain-specific model for assessing the maturity of metrics teams – MeTeaM – and also assessed the four metrics teams per se. Our results were two-fold: the creation of the metrics team maturity model MeTeaM and a template to assess metrics teams. Our evaluation showed that the model captures the characteristics of successful metrics teams and quantifies the maturity status of both the metrics teams and their host organizations. More mature metrics teams score higher in the MeTeaM model than less mature teams. The assessment provides less mature metrics teams with valuable insights on what factors to improve. Such insights can be shared with and acted upon successfully with their organizations.
Blockchain Technology and Its Implementation Challenges with IoT for Healthcare Industries

Currently, the electronic health records are stored digitally, and the systems are centralized on a small scale. Internet of Things (IoT) is not related to one thing/device or one aspect alone, rather it enfolds and connects various associated innovative things such as blockchain, artificial intelligence (AI), big data, cloud storage, and hyper-computing. Besides, there are almost no untouched aspects of social fields like education, research, e-commerce, healthcare, defense, transportation, etc., which does not get affected by IoT and its associative supporting technology. The concept of blockchain in healthcare sounds so amazing, but not been successfully implemented by any healthcare sector, and since there is no proven use case, nobody wants to take risk. Nevertheless, to make blockchain technology successful, multiple fund providers, healthcare researchers, and health ministries will have to work together for the transformation of new collaboration, because once it would be done, it is so going to benefit the end users. This review has enlightened to all the research communities about the use of blockchain and IoTs devices in healthcare industries with major challenges.
Characteristics recognition and soft multimedia system for Japanese machine translation and edge-driven hardware implementations

With the development of recent economic globalization, international exchanges and cooperation are increasingly frequent and in-depth. In this process, there is a serious obstacle, that is, language differences. Therefore, the development of high-quality and practical machine translation system is of great significance. Japan has a close relationship with China, so it is necessary to acquire and process Japanese information. Japanese translation is the basis of Japanese information processing, which plays an important role in cross language information retrieval, machine translation, information extraction and other practical applications. In recent years, machine translation has made remarkable progress, but there is still much room for improvement in the quality of translation. Multimedia assisted instruction is an important application of computer technology. Therefore, this paper realizes the feature recognition and hardware structure of Japanese machine translation; for the efficient implementation of the model, the edge-driven model is combined. The proposed model is implemented through the edge devices and the performance of the model is validated through the testing. The recognition accuracy is much higher than the traditional models and the robustness is higher.
A Zipfian Approach to Words in Contexts: The Cases of Modern English and Chinese

The system-level complexity of language has been thoroughly investigated in terms of Zipf’s law, whose quantitative features have proved to reflect text/language typology. This study extends the scope of Zipf’s law from the macroscopic scale of language to specific words in contexts, with the aim of examining its potential as an indicator of word typology. The focus is confined to the high-frequency words in English and Chinese as found in the FLOB and LCMC corpora. It has been found that the log–log rank-frequency distributions of contextual words of the words in question generally abide by the linear function y = ax+b. Moreover, it has been shown that an adjusted version of parameter a can help to distinguish the words in question’s classes. The contextual information as reflected by this Zipf-based index might be more important to the emergence of word classes of Chinese, which has no real inflection as a word-class indicator. From a Zipfian approach, the findings have preliminarily approved Saussure’s systems thinking regarding linguistic signs. Meanwhile, they may also contribute to such fields as usage-based linguistics.
A New Finite-Time Circadian Rhythms Learning Network for Solving Nonlinear and Nonconvex Optimization Problems With Periodic Noises

Nonlinear and nonconvex optimization problems are vital and fundamental problems in science and engineering fields. In this article, a novel finite-time circadian rhythms learning network (called FT-CRLN) is proposed for solving nonlinear and nonconvex optimization problems with periodic noises. Different from the traditional recurrent neural networks, the proposed FT-CRLN can suppress the periodic noise notably and achieve excellent convergence performance in solving nonlinear and nonconvex problems. The theoretical analysis and rigorous mathematical proof verify the superior convergence, high accuracy, and strong robustness of the proposed FT-CRLN. The simulation results demonstrate the effectiveness and robustness of the proposed FT-CRLN in solving nonlinear and nonconvex problems compared with other state-of-art neural networks.
Cyber Forensics on Internet of Things: Slicing and Dicing Raspberry Pi

Any device can now connect to the Internet, and Raspberry Pi is one of the more popular applications, enabling single-board computers to make robotics, devices, and appliances part of the Internet of Things (IoT). The low cost and customizability of Raspberry Pi makes it easily adopted and widespread. Unfortunately, the unprotected Raspberry Pi device—when connected to the Internet—also paves the way for cyber-attacks. Our ability to investigate, collect, and validate digital forensic evidence with confidence using Raspberry Pi has become important. This article discusses and presents techniques and methodologies for the investigation of timestamp variations between different Raspberry Pi ext4 filesystems (Raspbian vs. UbuntuMATE), comparing forensic evidence with that of other ext4 filesystems (i.e., Ubuntu), based on interactions within a private cloud, as well as a public cloud. Sixteen observational principles of file operations were documented to assist in our understanding of Raspberry Pi’s behavior in the cloud environments. This study contributes to IoT forensics for law enforcement in cybercrime investigations.
Application of binary employments during teaching of discipline physical education in establishments of higher education

Interdisciplinary integration is very important for modern learning and the creation of consideration not only in terms of interaction of users of knowledge of academic disciplines, but also as an integration of technologies, methods and forms of learning. Due to this, the achievement of success, and hence the result of educational activities.&#x0D; In the article possibilities and features of application of binary employments are considered within the framework of discipline "physical education" regardless of professional orientation of establishments of higher education. The use of binary employments is conducted with the aim of integration of educational disciplines. Such method of realization of employments allows to give to the bread-winner of higher education an evident idea about possibilities of application of knowledge and abilities from an area general disciplines in the professional activity. The literary review of modern scientific sources is conducted with the aim of exposure of features of work with students in the mode of binary employment, and also the most widespread directions of their application are considered within the limits of P.E. Reasonably, how employments of this type explain students to the improvement of physical qualities and to strengthening of the health. Basic advantages of the use of binary employments are marked as an alternative traditional employment after physical education for the students of different professional orientation.&#x0D; Binary training is one of the forms of realization of interdisciplinary connections, which allow to integrate knowledge from different areas to solve one problem, give the opportunity to apply the acquired knowledge from practice.
Background Subtraction based on Geometric - K mean Algorithm

Background Subtraction is widely used for detection of moving object, traffic management, and video surveillance even when the environmental condition is not favourable such as illumination problem, weather condition and fast moving object. Background subtraction methodology is extensively utilized in the detection of moving entity which is captured through a camera. Foundation for this methodology to identify the moving entity by observing the variation among the input frame and reference frame is described as background image. Fundamentally, background image is an illustration of section of images with no moving entity and that should be consistently modified to adjust with the changing illumination and geometric adjustments. Further composite prototypes were stretched the perception of background subtraction within the accurate significance. In this research, background modelling of Geometric Mean (GM) based lognormal distribution of each pixel is considered, followed by K-mean clustering algorithm is used to separate background from foreground. Finally to enhance the result weighted median filter is used. The proposed algorithm has been tested upon different data sets and the results shows better precision as compared to its ground truth by calculating sensitivity, specificity and accuracy.
TAPP: DNN Training for Task Allocation through Pipeline Parallelism Based on Distributed Deep Reinforcement Learning

The rapid development of artificial intelligence technology has made deep neural networks (DNNs) widely used in various fields. DNNs have been continuously growing in order to improve the accuracy and quality of the models. Moreover, traditional data/model parallelism is hard to expand due to communication bottlenecks and hardware efficiency issues. However, pipeline parallelism trains multiple batches, reducing training overheads, so that it can achieve better acceleration effect. Considering the complexity of solving the pipeline parallel task allocation problem in heterogeneous computing resources, in this paper, a task allocation in pipeline parallelism (TAPP) based on deep reinforcement learning, is proposed. In TAPP, the predictive network is trained by a policy gradient until it obtains the optimal pipeline parallel task allocation scheme and speeds up the model training. Experimental results show that, on average, the single-step training time of TAPP is decreased by 1.37 times and the proportion of communication time is reduced by 48.92%, compared with the data parallelism, bulk synchronous parallel (BSP).
Did you Understand this?

We investigate how problems in understanding text – specifically a word or a sentence – while filling in questionnaires are reflected in gaze behaviour. To identify text comprehension problems, while filling a questionnaire, and their correlation with the gaze features, we collected data from 42 participant. In a follow-up study (N=30), we evoked comprehension problems and features they affect and quantified users’ gaze behaviour. Our findings implies that comprehension problems could be reflected in a set of gaze features, namely, in the number of fixations, duration of fixations, and number of regressions. Our findings not only demonstrate the potential of eye tracking for assessing reading comprehension but also pave the way for researchers and designers to build novel questionnaire tools that instantly mitigate problems in reading comprehension.
Combining Deep Learning and Ontology to Reveal Video Sequences Semantics

As a semantic technology, an ontology is a formal hierarchical taxonomy of lexical terms including their syntactic and semantic relationships employed to represent the meaning conveyed by a knowledge domain.The huge increase in video data volumes creates a growing demand for efficient methods to reveal and manage their semantic content through an ontology.Therefore, the need for a system that allows efficient representation of video content is obvious.This paper proposes a method to extract ontology from video streams.It is based on Deep Learning technology to identify objects and movements in video scenes.All extracted features are stored in an OWL file.The OWL file will be used afterward to exploit video documents in many ways such as modeling, indexing, querying, and feeding ontology editors to visualize, elucidate and reason on the semantic structure.
“Emergency Distance Education” Model: How Normal Could The Projected New Normal Be?

In this opinion piece, the authors critically consider the transition to the ‘emergency model’ of distance education (DE), forced by the pandemic and associated restrictions to our daily life, paying special attention to its potential pitfalls. The authors argue in favour of more careful approach to DE design and implementation over the ‘one size fits all’ solution. The data from previous meta-analyses in the field of DE and technology integration in education are briefly summarized to provide research-based support for the following observations: (1) students’ academic achievements in DE are largely associated with the interactivity factor, which is also instrumental in preventing excessive drop-out rates; (2) the flexibility factor that largely predetermined the initial rise and rapid proliferation of DE should be maintained to avoid negative side-effects, including student’ dissatisfaction and drop-out; (3) pedagogical factors, imbedded in careful instructional design, outweigh technological affordances, especially since the latter require properly organized and managed infrastructure, adequate training for teachers an students, and sufficient time to be efficiently adopted in formal education to reveal its potential for successful teaching and learning; (4) vast variability of meta-analytical findings, even with the most favourable to DE average point estimates, do not only present educational system with pleasing promises, but also call for serious caution as the negative effect sizes are almost equally prevalent as the positive ones. In conclusion, the paper reminds educational practitioners and policy makers: what comes to life out of necessity does not necessarily present viable solutions in the long run.
Software Solutions for Recovery of the Lost Data from Mobile Phones, Tablets and Computers, Used in the Educational Process

It is already known that during the pandemic the educational process took place most of the time online or at most in a hybrid format. Within these activities, there are situations in which, in addition to the online teaching, it is necessary to conduct and store courses, homework or other teaching materials on various types of devices (phones, tablets or computers). When they are disconnected incorrectly or become corrupt, errors occur, and the information may no longer be accessed. In the case of the Windows operating system if the disconnection of the PCs is performed by the users in an erroneous way, after reconnecting the storage devices, they still have access to the data. In the case of the Linux operating system if the disconnection is not the professional one, most of the time there are chances that at reconnection, the data that was saved on that storage device cannot be any longer accessed. The current paper presents two methods for recovering the lost information: one for the educational data recovery, and one for recovering contacts, which are often important in the communication process. These methods involve techniques for saving educational information/contacts from destroyed, corrupt or inaccessible storage devices when they cannot be used normally. A first application, proposed for PCs, uses the Master File Table and tries to retrieve information, by using an algorithm implemented in the C++ language, with multiple advantages over the algorithms found in similar applications. The second software application is developed for the Android platform and it was created to make a backup to the contacts from the smartphone and save them in a text (.txt) file. The software applications presented in the paper are useful especially for those who own computers and phones from older generations.
Study on Image De-Noising and Technique

Noise in an image may be unwanted signals, elements or any natural involvement. De-noising the noisy image is a very crucial task, for which there are number of different methods, algorithms and transforms available. In this paper, a review of different types of image noise and methods of de-noising has been discussed. The merits and demerits of the techniques have also been discussed. The filters and domains for different types of noise is discuss like in Salt and Pepper NSM Filter show good result, Gaussian noise Linear and Non-Filter, Speckle noise spatial and frequency domain and Poisson ROF model use. Comparison of domain, filter and their result is done in different types of noise. The focus of the work is to identify the advantages and disadvantages of different algorithms.
Multiagent Simulation On Hide and Seek Games Using Policy Gradient Trust Region Policy Optimization

The hide and seek game is a game that implements a multi-agent system so that it will be solved by using multi-agent reinforcement learning. In this research, we examine how to apply policy gradient method, Trust Region Policy Optimization (TRPO) to solve the hide and seek game environment. We also examine the configuration of TRPO algorithm that gives the best performance and its comparison with the Vanilla Policy Gradient (VPG) algorithm. From the results of experiments conducted, we found that in general the VPG algorithm gives a better performance than the TRPO algorithm when tested in the same environment as the training environment. However, on the contrary, the TRPO algorithm gives better performance when tested in a different environment than that in the training environment.
Domains identification by the parameter values in multidimensional space

Abstract As a result of cluster and discriminant data analysis, the objects are distributed among different classes represented in the multidimensional space of parameter values. At the next stage, it is relevant to use the results obtained in various applications. The most frequently solved task is the diagnostics of a newly received object when the class of the object is determined through parameter values. In terms of a data view model, the problem can be reduced to determining a domain in parameter space, the new object belonging to this domain. The solution to this problem depends on the way the data analysis results are described and presented. Previously the graphic data model was considered to describe the domains separated by surfaces in the multidimensional space. Moreover, a constructive approach was proposed to describe an error in the representation of the boundaries, making it possible to take into account the experimental data errors. The mathematical model proposed in the given article is used to develop a domain identification algorithm by the parameter values in the multidimensional space. The proof of correctness and the estimation of computational complexity are provided for the algorithm. The results obtained make it possible to use the algorithm to calculate various characteristics of objects: state, intervals of existence, etc.