Reviewerly/exHarmony huggingface dataset:
Adaptive Eligibility Traces for Online Deep Reinforcement Learning

Deep reinforcement learning (DRL) is one of the promising approaches to make robots accomplish complicated tasks. Eligibility traces is well known as a online learning technique to improve sample efficiency in the traditional reinforcement learning with linear regressors, not DRL. This is because dependencies between parameters of deep neural networks would destroy the eligibility traces. To resolve this problem, this study proposes a new eligibility traces method that makes it possible to be applied even into DRL. The eligibility traces in DRL accumulate gradients computed based on the past parameters, which are different from that computed based on the latest parameters. Hence, the proposed method considers the divergence between the past and latest parameters to adaptively decay the eligibility traces. Instead of that divergence directly, Bregman divergences between outputs computed by the past and latest parameters, which are computationally feasible, are exploited. In benchmark tasks for DRL, the proposed method acquires the tasks stably in comparison to the cases without/with the standard eligibility traces.
An evaluation model of distance learning effect based on MOOC theory

In order to overcome the problem of fuzzy data in distance learning effect evaluation, a distance learning effect evaluation model based on MOOC theory is proposed. The vector of original time series and multi bit space analysis is constructed. The minimum embedding dimension of phase space reconstruction is extracted by using the false nearest neighbour algorithm, and the chaotic correlation dimension characteristics of data related to learners' learning are obtained. The input-output MOOC model is used to evaluate the effectiveness of decision-making units, and all decision-making units are arranged in a comprehensive way to realise the effect evaluation of distance learning. The experimental results show that the learning effect score of the designed model is basically consistent with the learning effect score given by the experts in the sample, the approximate error is less than 0.5, the training error is about 0.02, and the accuracy of the model reaches 90%.
A System Approach for Cognitive Learning in Digital Transformation of Education

This work is devoted to exploring the optimal resource (functional) state (ORS) of university students to improve the education quality and to evaluate the possibility of managing their psycho-emotional state by themselves in conditions of stress and cognitive load. Digitalization of education is characterized by technological breakthroughs, the processing of big data, the implementation of artificial intelligence, cloud technologies, machine learning development, the global educational landscape changing. It leads to a cognitive load increase, and in this regard, the development and experimental study of a model for monitoring and evaluating the ORS index of university students are relevant and strategically important. Algorithms for studying students’ ORS under stress and high cognitive load are considered. A model of ORS index monitoring has been developed based on the system approach and decomposition of psycho-emotional state, and tools for ORS studying have been proposed. A layout of a software model for the evaluation ORS index has been developed. The developed polyeffector model has no direct analogs. The hypothesis of the study has been experimentally confirmed: the assimilation of educational materials is by persons who are in an ORS compared to persons who are in a non-optimal resource (functional) state.
Heart Disease Misinformation Annotator: Towards a Generic, Fact-Based Annotation of Disease Misinformation

The health sector is affected by the spread of misinformation via social media or unreliable sources. Misinformation annotation or detection is usually based on behavioral features of texts rather than their semantic meaning. In this study, we propose a generic 'Disease Misinformation Annotator' framework for health misinformation annotation which can also used for basic misinformation detection, the framework also enables category detection based on some selected categories. We offer the usage of the factual semantics of sentences instead of annotating misinformation based on the reliability of the source or any other metadata. Data for this study were collected primarily from Twitter and article abstracts from the NCBI PubMed article database. A corpus of heart disease information was created and used for sentence classification tasks. We proposed an automatic annotation process to obtain a factually annotated data corpus and avoid manual labeling. The model was developed and its performance and usability were evaluated. Some paragraphs about heart disease with specific truth content were tested and our annotator detected satisfying results compared to a normal source reliability classifier. Category classification for 11 categories received an f1 score of 0.71. We found that mechanisms for annotating misinformation based on facticity are still in their infancy and we offered a way for clinicians to make fast annotations. However, our methods may help in future emergencies by facilitating the annotation of misinformation in health care, which then serves as input for detecting and correcting misinformation.
PROBLEMS OF TEACHING ELEMENTS OF DISCRETE MATHEMATICS TO FUTURE COMPUTER SCIENCE TEACHERS

The article deals with the problem of integrated teaching of discrete mathematics elements to the content of the main specialized disciplines in the preparation of future computer science teachers. The ways of solving some problems in the preparation of future computer science teachers on the elements of discrete mathematics and the need for its training are described. The analysis of scientific research of domestic and foreign scientists involved in the issues of teaching computer science in discrete mathematics is carried out. As a result of the research, the necessity of teaching computer science teachers the elements of discrete mathematics is scientifically substantiated. When considering the content of professional disciplines, the necessity of teaching elements of discrete mathematics is revealed, the rationale for including topics in the content of teaching by sections is given. The list of topics that can be considered in the content of specialized disciplines of discrete mathematics elements is described schematically. These topics are presented as an example of the content of specific disciplines and recommendations are given that it is necessary to specifically study the above, develop and present a teaching methodology.
DNA-Binding-Proteins Identification Based on Hybrid Features Extraction from Hidden Markov Model

DNA-binding proteins (DNA-BPs) identification is very important for genome annotation as they have many applied and research applications in biological, bio-physical, and bio-chemical effects of antibiotics and steroids on DNA. This paper presents a proposed approach for DNA-BPs identification. This approach is based on hybrid features extraction and Hidden Markov Model (HMM). The protein sequence was encoded into a digital sequence, then divided the digital sequence into sub-frames. From the HMM profile, four features’ groups were tested and compared each alone or combined; Amino Acid Composition (AAC), Auto Covariance Transformation (ACT), Ross-Covariance Transformation (CCT) and Best Tree Encoded (BTE). Random Forest (RF) classifier was used for matching the features between the enrolled features in the features database and the features of the tested sequence. The proposed approach has been tested using different four DNA-BPs datasets (PDB-1075, PDB-186, PDNA-543, and PDNA-316). The results show that the hybrid features extraction by combining all features gives the highest performance than using each features group alone. The accuracy of the proposed approach has been compared with the other available published identification methods for four tested DNA-BPs datasets. The results show that the proposed DNA-BPs identification approach provides higher accuracy than other published methods.
Wireless Technology and Impact on Society

This chapter brings an entirely different perspective—the social and cultural aspects of wireless technology, the devices and how it transforms society. It opens into an on-going debate, with opposition from conservative groups, yet they too admit the positive contribution of wireless. Perhaps different services that wireless offers, provide a bird’s eye view of its positive impact on society as a whole. Nations that barely had telecommunication infrastructure, are thriving today economically and socially, thanks to wireless technology. Businesses dealing with wireless services have adapted quite well, moving away from wired services to wireless, in order to remain as a business. Is even PBX (Personal Business Exchange) necessary for a business complex—after all the days of desk phone are numbered? Isn’t just WiFi network enough for the business complex—is a genuine question. Impact of wireless is felt on how people travel (navigation service, online reservation while traveling, online ticket for commuters etc.). How emergency services are rendered based on wireless devices, how wearable devices have impacted sports, are all visible to everyone today. Changes continue and the society seems to welcome wireless devices. It is important to note that wearables may include devices and antenna within the human body. For the elderly and some with chronic conditions, this has come as a boon—since medical personnel can keep a tab and provide help quickly when needed. Virtual reality emerged in game shows as entertainment, but its impact in training is recognized. There are clear indications of its growth due to wireless technology and its ability to make the trainee mobile, bringing their training closer to the real work environment. Outer space and satellite services not only fascinate Physicists but offer extensive support to aircraft and maritime services with reliable service from navigation satellites. Importance of GPS is so strong that a new generation of services with Galileo satellites now usher an era of interweaving this service into every mobile vehicle and cell phone. There are related services such as weather, indicating to pilots of an impending storm ahead, or warning ships and small vessels of an oncoming weather condition, while they are at sea. These are all highlighted. This chapter helps the student appreciate why wireless technology is not just about telecommunication, but a variety of services it replaced in society, both directly and indirectly. Smart phone can be viewed as a transformation tool, essential for business, entertainment and social life. This chapter helps everyone understand opposing points of view, since wireless has brought about a permanent change in the society, how everything is planned and executed.
Extracting Experiment Statistics, Conditions, and Topics from Scientific Papers with STEREO

We address the problem of extracting reports of statistics along with information about the experiment conditions and experiment topics from scientific publications. A common writing style for statistical results are the recommendations of the American Psychology Association (APA). In practice, writing styles vary as reports are not 100\% following APA-style or parameters are not reported despite being mandatory. In addition, the statistics are not reported in isolation but in context of experiment conditions investigated and the general experiment topic. We address these challenges by proposing a flexible pipeline STEREO based on wrapper induction and unsupervised aspect detection to extract experiment statistics, conditions, and topics. Thus, in contrast to existing rule-based tools like statcheck with a pre-defined set of rules, we learn rules via induction. Hierarchical wrapper induction is applied to learn rules to extract the reported statistics. Challenge here is to apply wrapper induction on an information extraction task without having formatting landmarks as they can be exploited in HTML pages. Result of step 1 is a set of extracted statistic reports together with sentences in which the reports were found. This is used as input to step 2 of STEREO, which has two parts. We extract experiment conditions using a grammar-based wrapper. Furthermore, we identify the experiment topic using an unsupervised attention-based aspect extraction approach adapted to our problem domain. We applied our pipeline to the over 100,000 documents in the CORD-19 dataset. It required only 0.25% of the CORD-19 corpus (about 500 documents) to learn statistics extraction rules that cover 95% of the sentences in CORD-19. The statistic extraction has 100% precision on APA-conform statistics, which is identical with statcheck. In addition, STEREO can extract non-APA writing styles with 95% precision, which statcheck does not support. Extracting non-APA conform statistics is important as they make more than 99% of all $113$k extracted statistics. We could extract in 46% the correct conditions from APA-conform reports (30% for non-APA). The best model for topic extraction achieves a precision of 75% on statistics reported in APA style $73% for non-APA conform). We conclude that STEREO is a good foundation for automatic statistic extraction and future developments for scientific paper analysis. Particularly the extraction of non-APA conform reports is important and allows applications such as giving feedback to authors about what is missing and could be changed. Finally, STEREO complements existing metadata extraction tools and can be integrated in a general scientific paper analysis pipeline.
Introduction

This chapter gives a short history of data privacy notions preceding differential privacy – the central idea of this Brief – and highlights the extent to it has been adopted.
Searching and Sorting

Searching and sorting are two elementary topics of computer science in the field of algorithms and data structures. Python provides efficient implementations for both of them and thus takes a lot of work off your shoulders. However, understanding the underlying algorithms helps in choosing the most suitable variant for a particular use case. I only skim over the topic of searching here since it is built in and does not offer a lot of variations, except for binary search, which is covered in section 9.1.1.
Machine‐Engineered Active Disorder for Digital Photonics (Advanced Optical Materials 7/2022)

A novel data-driven approach to examine “active disorder” for light is introduced by Jungmin Kim, Seungkyun Park, Sunkyu Yu, and Namkyoo Park (see article number 2102642). This work demonstrates a wave-oriented evaluation and design of active disorder by deep neural networks mapping the disorder state of a multilayer structure and the corresponding tunable optical responses. The result also provides an intriguing wave analogy of target control in network science.
La designazione dell’area cromatica del grigio nei dialetti italiani

"The Designation of the Chromatic Area of Grey in Italian Dialects. This article aims to study aspects of lexicalisation relating to the formation and usage of names designating ‘dark grey’ and ‘light grey’ in Italian dialects collected by the Italian Linguistic Atlas. These terms exist on the margins of the system of colour names present in dialects, and therefore the listing and analysis of the data allow us to note an interesting variety of linguistic solutions which compensate for the lack of a precise corresponding term in the dialect. From a geolinguistic point of view, the mapping of the data allows us to distinguish between shared areas covering the whole of Italy, and linguistic or cultural sub-areas with more specific features. Keywords: colour naming, Italian dialects, Atlante Linguistico Italiano, dark grey, light grey "
Image Retrieval Based on the Weighted and Regional Integration of CNN Features

The features extracted by convolutional neural networks are more descriptive of images than traditional features, and their convolutional layers are more suitable for retrieving images than are fully connected layers.The convolutional layer features will consume considerable time and memory if used directly to match an image.Therefore, this paper proposes a feature weighting and region integration method for convolutional layer features to form global feature vectors and subsequently use them for image matching.First, the 3D feature of the last convolutional layer is extracted, and the convolutional feature is subsequently weighted again to highlight the edge information and position information of the image.Next, we integrate several regional eigenvectors that are processed by sliding windows into a global eigenvector.Finally, the initial ranking of the retrieval is obtained by measuring the similarity of the query image and the test image using the cosine distance, and the final mean Average Precision (mAP) is obtained by using the extended query method for rearrangement.We conduct experiments using the Oxford5k and Paris6k datasets and their extended datasets, Paris106k and Oxford105k.These experimental results indicate that the global feature extracted by the new method can better describe an image.
Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling

The performance of cloud computing depends in part on job-scheduling algorithms, but also on the connection structure. Previous work on this structure has mostly looked at fixed and static connections. However, we argue that such static structures cannot be optimal in all situations. We introduce a dynamic hierarchical connection system of sub-schedulers between the scheduler and servers, and use artificial intelligence search algorithms to optimise this structure. Due to its dynamic and flexible nature, this design enables the system to adaptively accommodate heterogeneous jobs and resources to make the most use of resources. Experimental results compare genetic algorithms and simulating annealing for optimising the structure, and demonstrate that a dynamic hierarchical structure can significantly reduce the total makespan (max processing time for given jobs) of the heterogeneous tasks allocated to heterogeneous resources, compared with a one-layer structure. This reduction is particularly pronounced when resources are scarce.
Literature Review: Whistleblowing System Activist Simultancy, Big Data Analytics on Fraud Prevention

The issue of fraudulent practices in Indonesia has become one of the main problems of this nation because of the increasing prevalence of violations. This informs that fraud has really become an acute and systemic problem that is very dangerous and detrimental to the State and society, especially in small and developing countries such as Indonesia. (Dimant, E., &amp; Tosato, G., 2018). The development of the turbulence of human activity gives rise to criminal behavior such as fraudulent acts, one of which is in the form of financial crimes that are undergoing transformation (Rezaee, 2018; Indriani &amp; Terzaghi, 2017; Septriyani &amp; Handayani, 2018; Rusmana &amp; Tanjung, 2019).Several reports from various institutions and world organizations provide an overview of the shifts that have occurred during the pandemic(Huang, Y., 2018). This action is due to the fact that each actor is required to submit accurate and relevant financial information. The purpose of this study is to explore early detection and fraud prevention methods in a conceptual framework that combines elements of a whistleblowing system and big data analytics. In conclusion, there is a gap in the concept of a whistleblowing system and big data analytics in current practice to prevent it, as well as the auditor's perspective in making a proactive anti-fraud model. To explore this, it is necessary to synthesize information through a literature review from various information obtained in terms of generalizing information related to the problems that are the center topic in this study.
The Problem of Student-Centered Learning of IT Specialists for Education Sphere

The main purpose of this article is to determine the features of training IT specialists for creating the electronic educational environment in an economic university. The research methodology is based on the analysis of the basic category “computer communication” and modern approaches to the design of electronic space. The selected research method is a sociological survey conducted at the Plekhanov Russian University of Economics during the period of distance learning. The results of the study showed that computer communications for the educational space are most fully described based on the personality paradigm, in which a person is presented as an integral subject with cognitive, affective, praxeological, and motivational-value qualities. Therefore, the training of IT professionals should include an expanded range of disciplines that consider personality in different possible contexts. The training of IT specialists for higher education should proceed from the need to design complex electronic educational products that perform multiple functions, based on the unity of the competence and personal approaches in the system of modern education. The dynamics of higher education and the need for its constant adaptation to new realities and challenges requires the formation of innovative qualities of IT specialists. The originality of the work consists in determining the possibility of applying the principles of “emotional design” for the construction of an electronic educational space in an economic university. Training of IT specialists for the education system should take into account modern trends in electronic design, reflected in the paradigms of “emotional design”, which give the most complete picture of the individual as a subject of computer communications. The conclusions of the work are based on empirical research and sociological data, which are obtained by the authors.
Graph Attention Neural Network for Network-Structure Clustering an Application to Water-Network Partitioning

Partitioning a water distribution network into several district metered areas is beneficial for its management. Partitioning a network according to its node features and connections remains a challenge. A recent study has realized water network partitioning based on a simple objective. This study proposes an unsupervised clustering method for nodes based on a graph neural network, which uses graph attention technology to update node features based on the connections and a neural network to cluster nodes. The similarity between nodes located in each area and the balance of the total water demand between areas are optimized, and the importance of the boundary pipes is calculated to determine the installation position of flowmeters and valves. Three water distribution networks with different structures and sizes are used to verify the proposed model. The results show that the average location differences (ALDs) within the areas of the three networks completed by partitioning are 0.12, 0.07, and 0.06, and the total demand differences (TDDs) between areas are 0.13, 0.27, and 0.29, respectively. The ALD and TDD of the proposed method decreased by 6% and 55%, respectively, when compared to the traditional clustering method. Additionally, the proposed method for calculating the importance of boundaries provides an objective basis for boundary closure. When the same number of boundaries are closed, the comprehensive impact of the proposed method on the pipe network decreases by 17.1%. The proposed method can be used in practical applications because it ensures a highly reliable and interpretive water distribution network partitioning method.