Reviewerly/exHarmony huggingface dataset:
A Novel Hybrid Feature Selection Algorithm for Optimal Provisioning of Analytics as a Service

We propose a novel hybrid metaheuristic algorithm for optimal selection of features based on simulated annealing (SA), Hybrid Particle Swarm Optimization (HPSO), and Fuzzy K-Means (FKM) called FUFPS to optimally select the features which are more relevant to classify the instances in the dataset. FUFPS is designed to maximize the accuracy of the classifier which considers fitness measures such as intra-cluster similarity and inter-cluster similarity. Feature selection is a challenging task in Analytics as a Service (AaaS) where the dataset has much information that may or may not necessary to identify the pattern. Thus, the domain of data analytics requires a novel mechanism that can optimally select the features which are optimal enough to perform classification. Our proposed system uses various benchmarking datasets intending to select optimal features which can maximize accuracy, F-score and minimize the root mean square error and false-positive rate. The proposed FUFPS is tested and evaluated with other metaheuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO) and the results are proven to be convincing.
Peculiarities of Design of Modern Educational Space of Artistic Direction of Higher Education Institutions

The article analyzes the factors influencing the design of modern educational spaces in higher education institutions in the context of training future designers. The definition of the learning environment is provided as a set of specific material, communication and social conditions that make up the processes of teaching and learning. The division of physical learning environments into formal and informal is considered according to the functional purpose and form of interaction of the subjects of the educational process. The definition of formal learning environments in universities is provided. The interrelation of the organization of formal learning environments with the form and way of interaction, relevant to the current educational discipline, of the teacher with students and between students is considered. The functional features of the organization of the formal educational environment are determined. The needs of students that can be satisfied using informal environments in universities are established. The necessity of introduction of modern innovative technologies is considered. The list of factors is established that directly affect the design of the educational environment and which are largely caused by transformational changes in the educational process in Ukraine and the world.
Demonstrator Game Showcasing Indoor Positioning via BLE Signal Strength

For a non-technical audience, new concepts from computer science and engineering are often hard to grasp. In order to introduce a general audience to topics related to Industry 4.0, we designed and developed a demonstrator game. The Who wants to be a millionaire? style quiz game lets the player experience indoor positioning based on Bluetooth signal strength firsthand. We found that such an interactive game demonstrator can function as a conversation-opener and is useful in helping introduce concepts relevant for many future jobs.
Discovering Influential Twitter Authors Via Clustering And Ranking On Apache Storm

Nowadays several millions of people are throughout the day active, while hundreds of new accounts are created daily on social media. Thousands of short-length posts or tweets are posted on Twitter, a popular micro-blogging platform by a vast variety of authors and thus creating a widely diverse social content. The emerged diversity not only does indicate a remarkable strength, but also reveals a certain kind of difficulty when attempting to find Twitter’s authoritative and influencing authors. This work introduces a two-step algorithmic approach for discovering these authors. A set of metrics and features are, firstly, extracted from the social network e.g. friends and followers and the content of the tweets written by the author are extracted. Then, Twitter’s most authoritative authors are discovered by employing two distinct approaches, one which relies on probabilistic while the other applies fuzzy clustering. In particular, the former, initially, employs the Gaussian Mixture Model to identify the most authoritative authors and then introduces a novel ranking technique which relies on computing the cumulative Gaussian distribution of the extracted metrics and features. On the other hand, the latter combines the Gaussian Mixture Model with fuzzy c-means and subsequently the derived authors are ranked via the Borda count technique. The results indicate that the second scheme was able to find more authoritative authors in the benchmark dataset. Both approaches were designed, implemented, and executed on a local cluster of the Apache Storm framework, a cloud-based platform which supports streaming data and real-time scenarios.
IMPLEMENTATION OF THE "SMART ACTION" WEBSITE OF RESPIRATORY INFECTION IN SANITATION CLINIC ACTIVITIES AT COMMUNITY HEALTH CENTER SUMBANG 1 BANYUMAS DISTRICT

Some obstacles that may be encountered in implementing sanitation clinics are the limited number of personnel from the Community Health Center to operate sanitation clinics that sanitation clinic activities experience problems in terms of managing data into useful health information. Therefore, sanitation clinic activities are carried out on environment-based diseases like respiratory tract infections. This study uses Research and Development (RD) to develop a manual sanitation for environmental-based diseases, especially respiratory infections, based on the  AKSI PINTAR website. Data processing system   using the     website consists of input, process and output. The input stage on the AKSI PINTAR website is on the examination menu and the question form that appears on the AKSI PINTAR (literally means smart action). The process stage includes the process of editing, coding and entering and has a form of  AKSI PINTAR  website and the output of the data includes risk factors for the incidence of respiratory infections, patient behavior and environment are processed into tables, bar charts, pie charts and pictures (photos). Officers can save and print processed data. Officers can input and process sanitation clinic data both during the inspection   and the field inspection   In the implementation of data output, officers can output data in the form of tables of results of field inspections and inspections, bar and circle diagrams and photos of the results of visits that officers need in their reports.
Automatic Detection Method of Power Communication Network Breakpoint Data under Cloud Computing

This paper presents two methods based on neural network and clustering analysis are used to automatically detect break-point data of power communication network. It has a low recall rate and hinders the realization of power network planning objectives. In view of the above situation, an automatic detection method of power communication network break-point data under cloud computing is studied. This method is divided into four parts. Firstly, the connection model is constructed by the omni-directional construction mode of FP-tree micro-cellular, then the break-point data signal is collected by network tracker. In addition, the break-point data signal is filtered by wavelet filtering method, and finally the break-point data of power communication network is automatically detected by firefly algorithm. The results show that, compared with the two detection methods based on neural network and clustering analysis. Additionally, the recall rate of this detection method is increased by 3.1% and 2.27%, basically realizing the goal of power grid planning.
Applying Reversed Dictionaries to Improve the Automatic Morphological Analysis of Unknown Words

Lemmatization is an important step in many natural language processing tasks, information retrieval tasks, and information extraction tasks. In this paper, we present a lemmatization approach based on the modified analogy method which utilizes a reversed dictionary. The presented method is very efficient but simple to realize. Modifying the analogy method with fuzzy sets significantly improves the quality of the morphological analysis. We show (i) that the quality of this approach is comparable to state-of-the-art methods and (ii) that we can increase the accuracy of the unknown words analysis. Although the method was developed for the Russian language, it was successfully verified on the German language. Therefore, it is easily extensible to other highly inflected languages.
A New Content-Based Search Mechanism for Image Retrieval Search Engine

In the growing world of technology, where everything is available in just one click, the user expectations has increased with time. In the era of Search Engines, where Google, Yahoo are providing the facility to search through text and voice and image , it has become a complex work to handle all the operations and lot more of data storage is needed. It is also a time consuming process. In the proposed Image retrieval Search Engine, the user enters the queried image and that image is being matched with the template images . The proposed approach takes the input image with 15% accuracy to 100% accuracy to retrieve the intended image by the user. But it is found that due to the efficiency of the applied algorithm, in all cases, the retrieved images are with the same accuracy irrespective of the input query image accuracy. This implementation is very much useful in the fields of forensic, defense and diagnostics system in medical field etc. .
Hybrid Job and Resume Matcher

Information extraction from text data has always been a tricky and difficult task. This work follows a previous work regarding a designed system that matches job ads with resumes, then assigns them a scoring point. In this system, there are two main parts: information extraction and scoring. For the information extraction part, rule-based methods are efficient when the format of the resumes and job ads are known. In this paper, powerful and efficient methods for information extraction from the mixed resume format and job ads using machine learning and deep learning methods are proposed.
Bit-wise Autoencoder for Multiple Antenna Systems

We propose an end-to-end learned bit-wise autoen-coder neural network (NN) for open loop multiple-input multiple-output (MIMO) antenna based communication systems. The optimized transmit vector constellations are learned based on the number of transmit and receive antennas, the number of bits conveyed per channel use and the signal-to-noise ratio. By training through an i.i.d. complex Gaussian (i.e., Rayleigh ergodic) matrix channel, the system implicitly picks up constellation shaping gains “along the way”. We evaluate and analyze these gains in comparison to the single-input single-output (SISO) results shown in [1] for different symmetric and asymmetric MIMO configurations. We first show that the NN-based receiver is able to compete with the a posteriori probability (APP) receiver performance up to certain configuration settings. Then we perform an end-to-end optimization of the transmit vector constellations using the conventional APP receiver as the decoder part. Thereby, we also investigate an edge case where the number of bits per vector symbol is not a multiple of the number of transmit antennas. Finally, we examine the performance of the system if a non-optimal zero forcing (ZF) receiver is used for inference, while the vector constellation was optimized for the APP receiver during training. We show that constellations optimized for the APP receiver are not necessarily optimal for such sub-optimal receivers that operate with reduced complexity. To fix this, we propose a detector-aware training scheme to learn constellations that have been optimized for a specific sub-optimal receiver and, thus, achieve higher performance in inference.
Blockchain in public policy and governance

Blockchain is a new information technology that can be used in many areas of human activity. The fi rst and most famous example of the use of blockchain technology is the Bitcoin cryptocurrency. Today, cryptocurrency has transformed into a publicly recognized means of payment, a virtual currency that is accepted by large and small enterprises, corporations and services. Today, they conduct research and implement a number of projects using Blockchain technology in such areas as healthcare, media, electronic voting, fi le storage, smart contracts, insurance, the public sector (issuing passports, collecting taxes, registering land plots), etc. The IBM Corporation is researching Blockchain technology and is working on the creation of software, with the help of which partners will be able to conclude digital contracts that will be recorded in the global network. IBM is also implementing a project called Adept, which aims to track network-connected devices using Blockchain technology. The purpose of this article is to study the directions of using Blockchain in public policy.
Cognitive Shadowing for Learning Opponents in a Strategy Game Experiment

While non-player opponents in commercial video games often rely on simple artificial intelligence techniques, machine learning techniques that capture human strategies could make them more engaging. Cognitive Shadow is a prototype tool that combines several artificial intelligence techniques to continuously model human decision-making patterns during tasks that require categorical decision-making. The present study aims to assess the potential of Cognitive Shadow to create learning opponents that will counter the player's decisions in a strategy game, making it more challenging and engaging. The game developed to this end is a more complex version of rock-paper-scissors, set within the context of a wizards’ duel. Each participant (Player 1) took part in three game sessions of 12 battles (each including five rounds), only being told that they would face a non-player opponent. During Session 1, Cognitive Shadow was in learning mode, thus the non-player opponent (Player 2) chose its plays at random. During Session 2, Cognitive Shadow was active and helped counter participants’ decisions without their knowledge. Before Session 3, participants were informed that their opponent was using machine learning to anticipate and counter their strategy. The results showed that Player 2 was more effective with the help of Cognitive Shadow, having won significantly more battles in Sessions 2 and 3 than in Session 1. In addition, the level of engagement reported by human players increased significantly in Session 3. These results indicate that cognitive shadowing can be used in a strategy game to increase engagement when players are aware of the learning behavior.
ReMERT: Relational Memory-Based Extraction for Relational Triples

Relational triples extraction aims to detect entity pairs (subjects, objects) along with their relations. Previous work failed to deal with complex relationship triples, such as overlapping triples and nested entities, and lacked semantic representation in the process of extracting entity pairs and relationships. To mitigate these issues, we propose a joint extraction model called ReMERT, which first decomposes the joint extraction task into three interrelated subtasks, namely RSE (Relation-specific Subject Extraction), RM (Relational Memory) module construction and OE (Object Extraction). The first subtask is to distinguish all subjects that may be involved with target relations, the second is to retrieve target relational representation from RM module, and the last is to identify corresponding objects for each specific (s, r) pair. Additionally, RSE and OE subtasks are further deconstructed into sequence labeling problems based on the proposed hierarchical binary tagging scheme. Owing to the reasonable decomposition strategy, the proposed model can fully capture the semantic interdependency between different subtasks, as well as reduce noise from irrelevant entity pairs. Experimental results show that the proposed method outperforms previous work by 0.8% (F1 score), achieving a new state-of-the-art on Chinese DuIE datasets. We also adopt sufficient experiments and obtain promising results both in public English NYT and Chinese DuIE datasets.
The Use of Directing Technologies in the System of Additional Education

The study deploys various research methods. The theoretical methods include analysis, summarization, comparison, synthesis, and systematization of scientific and educational methodical literature to determine the current state of the problem under study. The empirical methods comprise purposeful observation, conversations, interviewing, questionnaire, and testing. In addition, a pedagogical experiment is conducted to test the experimental methods of using directing technologies in organizing the musical-pedagogical process in additional education institutions. Statistical methods are used in quantitative and qualitative processing of the study results, the objectivity of the results is tested using Pearson’s consistency criterion χ2.The need for the formation of a directing space of musical pedagogy as an important condition for improving the professional mastery of teachers in additional education is substantiated. The major directions for the implementation of directing technologies for the development of specialists’ competencies are revealed. The directing space created in the process of lessons and the demonstration of students’ achievements in performance contributes to students’ musical, aesthetic and spiritual, and moral development, the improvement of their performance skills, and the development of motivation for educational and creative and performance activity.
Defense Mechanism against Adversarial Attacks Based on Chaotic Map Encryption

During recent years, image classification through DNN has been applied to various fields, including payment security and image search. DNN in image classification is effective and convenient, yet susceptible to perturbations: non-targeted and targeted adversarial attacks against neural networks, such as FGSM and BIM respectively, exert modifications that are unrecognizable to naked eyes to image inputs, and will probably result in wrong classifications. To ensure the degree of safety of DNN image classification, researchers have been dedicated to the study of defense mechanisms to diminish or even eliminate the effects brought by adversarial attacks. Our proposed approach, aims at increasing the classifier's resistance to perturbations by adding a pseudo-random matrix key generated by Logistic Chaos. Our defense mechanism with Logistic Chaos-generated secret random key utilized 1 key with mere 3 elements and is of high generality. We show empirically that our approach is efficient against most attacks.
Improvement of Trade-Off Between Global and Local Search in Hybridization GA-PSO with Fuzzy Adaptive Acceleration Coefficients

In this paper, a new stochastic optimization methodology is proposed, resulting from the hybridization involving genetic algorithm (GA) and particle swarm optimization (PSO). This hybridization is performed with the goal of performing a search process with fast and non-premature convergence, through sharing and attenuating, respectively, the desirable and undesirable characteristics of GA and PSO. To avoid premature convergence and increase the speed of convergence, it is performed a parametric adaptation, through a fuzzy system, of the acceleration coefficients of the PSO; since through a correct selection of values, it is possible to perform an efficient trade-off between global and local search. Thus, through the proposed optimization methodology, it is possible to obtain an efficient optimization method which performs a search process with a fast and non-premature convergence.Noronha, Rodrigo Possidônio
Creative Narration as an Ideation Technique

Creative Narration is a technique that has the potential to enhance the initial design process of ideation in terms of collaboration and creativity. In this case study, the technique was used in the context of a distance creative (ideation) workshop to ideate on products in the specific field of Data Ethics. An initial assessment from this process, highlighting the strong and weak points of the technique, is discussed in this paper.
Low Illumination Image Enhancement Based on Gaussian Fusion Strategy

Under low illumination environment, the image captured by ordinary visual capture equipment has fuzzy details resulting in low recognition rate. Meanwhile, traditional enhancement algorithm has defects in noise amplification, color distortion and low contrast. Based on multiple exposure fusion principle and virtual exposure idea, this paper proposes an image enhancement algorithm based on Gaussian fusion strategy. The proposed algorithm first generates pseudo-multiple exposure maps sequences combined with a variety of emerging enhancement algorithms, where the adopted enhancement algorithms include camera response model, adaptive Sigmoid transfer function and homomorphic filtering enhancements. Subsequently, the Gaussian weighted fusion of the obtained sequence of pseudo-multiple exposure maps is performed. Finally, the resulting fusion image performs the contrast enhancement of the screened Poisson equation with the final enhancement result. The experimental results show that the algorithm enhancement results have superior data of objective image quality evaluation index which shows the effectiveness of the algorithm in image enhancement.
Why did Mendeleev not go to university? (Educational policy of Nicholas I)

The article examines the educational policy of the Russian Empire during the reign of Nicholas I. In particular, the history of the appearance of the order of Nicholas I on limiting the number of students at universities is considered. According to this order, the staff of students at the universities was “limited to 300 in each, with the prohibition of enrollment in students until the available number is not included in this legalized amount.” An exception was made only for medical faculties, since the army needed qualified doctors. This meant that in most universities in Russia the admission of students should be (and actually was) closed for several years. A particularly difficult situation has developed in the St. Petersburg and Moscow universities. That is why many talented young people, including D. I. Mendeleev, could not get a university education. The study shows that the educational policy in Nicholas Russia was ambiguous and contradictory. The emperor simultaneously wanted to get highly qualified officials and specialists who met the highest European requirements, but at the same time, considering universities as a source of social destabilization and ideological danger, he took measures that hindered the normal development of higher education (in primarily civic) in the country.
General Approximate Cross Validation for Model Selection

Cross-validation (CV) is a ubiquitous model-agnostic tool for assessing the error of machine learning. However, it has high complexity due to the requirement of multiple times of learner training especially in multimedia tasks with huge amounts of data. In this paper, we provide a unified framework to approximate the CV error for various common multimedia tasks such as supervised, semi-supervised and pairwise learning which requires training only once. Moreover, we study the theoretical performance of the proposed approximate CV and provide an explicit finite-sample error bound. Experimental results on several datasets demonstrate that our approximate CV has no statistical discrepancy from the original CV, but can significantly improve the efficiency, which is a great advantage in model selection.