Reviewerly/exHarmony huggingface dataset:
How Can Geographic Information in Text Documents be Visualized to Support Information Exploration in the Humanities?

Finding and selecting interesting and relevant information in large online digital text archives can be challenging. We tackle this information access problem from a geographic information science perspective using a case study exploring a semi-structured historical encyclopedia. We propose a three-pronged approach for this, based around (1) automatic retrieval of spatio-temporal and thematic information from digital text documents; (2) transformation of the extracted information to spatialize and visualize spatio-temporal and thematic structures; and (3) integration of the spatialized displays in an interactive web interface driven by a user-centered design and evaluation approach. We implemented an interactive spatialized network display to allow identification of spatio-temporal relationships hidden in the text archive, complemented by an interactive self-organizing map display to visualize thematic relationships in these text documents. We evaluated the utility and usability of the developed interface in a user study with digital humanities scholars. Empirical results show that the developed interface supports target users in the humanities uncovering latent spatio-temporal and thematic relationships and generated new insights through the spatialized text collection. Adopting this approach, we illustrate one avenue to addressing the information access problem in the digital humanities from a GIScience perspective.
A Logistics Network Distribution Algorithm Based on Deep Learning

With the rapid development of logistics industry, the position of outlets in logistics system is becoming more and more important. In the logistics system, the planning and layout are extremely important. Correct planning of the location can improve the efficiency of the resource effectively, which plays a crucial role in realizing the efficient operation of the logistics system. Limited by China's vast territory, large population, unbalanced regional economic development and other practical conditions, it is difficult to adapt the complex situation with traditional single-layer one-element logistics outlets model and single-layer multi-element logistics network model. There are certain limitations and deficiencies with traditional models to utilize logistics outlets resource. In this paper, the logistics problem is comprehensively sorted out and studied. Meanwhile, combining with the current situation of outlets layout, a multilayer multi-element logistics outlets layout model is proposed with the goal of minimizing transportation cost. Eventually, the location selection and logistics scale of the transfer center and basic outlets are determined by analyzing the layout condition with network model. In this paper, the data is trained by convolutional neural network . The logistics outlets layout problem is analogized with the minimum value calculation problem. After the comparison, the multi-layer multi-element logistics outlets model proposed in this paper can realize the distribution function with the lowest logistics cost.
An Biometric-Based Embedded System for E-Verification of Vehicle Users for RTO

In this fast-growing population and generation use of motor vehicles (Four wheelers, three wheelers, two wheelers, etc.) have rapidly increased. In this circumstances unauthorized use of vehicles without license, speeding of vehicle over the limits and theft is major concern for authorities like RTO. The available manual verification is misused. For this cause we propose a biometric embedded system approach which identifies and verifies an individual without any hard copy documents which also integrates all the legal proceedings with automated fine generation and actions necessary on the case at any checkpoints with the help of internet without any delayed manual interventions.
Active learning without unlabeled samples: generating questions and labels using Monte Carlo Tree Search

Classification of short texts (e.g. reviews, sentences) is a well-defined task, usually attempted in regimes where data is abundant. This is, however, not always the case. Limited data availability is very common in industrial settings and seriously hinders the performance of any classification task-it is not always obvious how to perform data augmentation. In this work, we apply the Recurrent Neural Network and Monte Carlo Tree Search (MCTS) to generate unlabelled questions. We use Human-In-the-Loop to help decide whether 1) the generated questions are meaningful or not 2) label them into correct categories. We show that generated data leads to improved classification performance in comparison to the vanilla dataset.
Mapping of Politically Active Groups on Social Networks of Russian Regions (On the Example of Karachay-Cherkessia Republic)

The article shows which segments constitute social and political activity in online social networks in the Karachay-Cherkessia Republic (KChR) and the width of their representation. The author’s technique allows to collect data on politically active groups of KChR. The segments of the social and political activity of the Republic on the social networks are shown. Eight main clusters of political activity in social networks of KChR were obtained by the author’s method of grain clustering. Each cluster was analyzed by social network analysis methods. The most influential persons and social movements are shown, and features of their network activity were investigated.
The Effect of Self-directed Learning Strategies on e-Learning Pre-learning of Nursing Students: Focusing on the Flow Experience

Purpose: The purpose of this study was to investigate the effect of self-directed learning strategies on e-learning pre-learning focusing on the flow experience.Methods: A structured self-report questionnaire was used to measure learning outcome achievement, pre-learning satisfaction, learning persistence, flow experience, and self-directed learning strategies. Data were collected from September 3~7, 2018. Participants were 150 nursing students studying at a university in D city. Data were analyzed using t-test, one-way ANOVA, Pearson correlation coefficients, and hierarchical multiple linear regression with the SPSS/WIN 23.0 program.Results: The flow experience had a partial mediating effect between self-directed learning strategies and pre-learning satisfaction (β=.73, &lt;i&gt;p&lt;/i&gt;&lt;.001). Also the flow experience had a partial mediating effect between self-directed learning strategies and learning outcome achievement (β=.17, &lt;i&gt;p&lt;/i&gt;=.020) and learning persistence (β=.24, &lt;i&gt;p&lt;/i&gt;=.001).Conclusion: Results suggest that flow experience has a positive effect as a mediating effect in the relationship between self-directed learning strategy and pre-learning satisfaction, learning outcome achievement, and learning persistence. Therefore, it is necessary to identify education methods that include flow experience with self-directed learning strategies.
An Iterated Local Search-Based Algorithm to Support Cell Nuclei Detection in Pap Smears Test

The focus of this work is on the detection of nuclei in synthetic images of cervical cells. Finding nuclei is an important step in building a computational method to help cytopathologists identify cell changes from Pap smears. The method developed in this work combines both the Multi-Start and the Iterated Local Search metaheuristics and uses the features of a region to identify a nucleus. It aims to improve the assertiveness of the screening and reduce the professional workload. The irace package was used to automatically calibrate all parameter values of the method. The proposed approach was compared with other methods in the literature according to recall, precision, and F1 metrics using the ISBI Overlapping Cytology Image Segmentation Challenge database (2014). The results show that the proposed method has the second-best values of F1 and recall, while the accuracy is still high.
A critical approach to technology-based risks in blockchain system

The blockchain technology has given a new shape to the world and disrupted a wide variety of technology-based industries. The disruptive characteristics of blockchain technology have molded the way of doing things. Since the importance of blockchain technology in business and social environment is increasing day by day, this study aimed to underline risks of blockchain technology within the network. Two strategies were made to infer the technology-based risk in the blockchain ecosystem. First, the study examined to find the technology-based risk at the point where a user executes the transaction from his/her computer ‘Node’. Second, this study attempts to figure out the security concerns within the web of the blockchain. After assessment of benefits and risks of this technology, this study concluded with the findings that blockchain is very much secure and trustable system which provides peer-to-peer transactions without needing any trusted third party, besides, once the information of the transaction is recorded, the system stores multiple copies of it, so there is no chance to change or delete it from the system. In the case of negligence, there is a possibility of forgery at the node, but numerous software has been developed for protecting the computer. The approach of the study is grounded in the literature review of journal articles, reports, and conference proceedings from authenticated sources via content analysis of the keywords such as blockchain, security, trust, technology-based risks. Blockchain technology is not associated with finance but it has high usage in other areas like health, education, supply chain, the music industry and many more to explore.
Generative Adversarial Networks-Based Image Compression for Consumer Photo Storage

We propose a new lossy image compression algorithm for images from consumer photo storage on the generative adversarial networks (GANs). It uses a pre-trained generator model as decoder and an algorithm called back-propagation-to-the-input as encoder. In compression process, this method could compress images with extremely small compression storage. Experiments demonstrate that this method surpasses the traditional ways in compression ratio and reconstruct representation.
Built-in Self-Evaluation of First-Order Power Side-Channel Leakage for FPGAs

Embedded and cyber-physical systems are pervading all aspects of our lives, including sensitive and critical ones. As a result, they are an alluring target for cyber attacks. These systems, whose implementation is often based on reconfigurable hardware, are typically deployed in places accessible to attackers. Therefore, they require protection against tampering and side-channel attacks. However, a side-channel resistant implementation of a security primitive is not sufficient, as it can be weakened by an adversary, aging, or environmental factors. To detect this, legitimate users should be able to evaluate the side-channel resistance of their systems not only when deploying them for the first time, but also during their entire service life. The most widespread and de facto standard methodology for measuring power side-channel leakage uses Welch's t-test. In practice, collecting the data for the t-test requires physical access to the device, a device-specific test setup, and the equipment for measuring the power consumption during device operation. Consequently, only a small number of cyber-physical systems deployed in the field can be tested this way and the tests to reevaluate the device resistance to side-channel attacks cannot be easily repeated. To address these issues, we present a design and an FPGA implementation of a built-in test for self-evaluation of the resistance to first-order power side-channel attacks. Once our test is triggered, the FPGA measures its own internal power-supply voltage and computes the t-test statistic in real time. Experimental results on two different implementations of the AES-128 algorithm demonstrate that the self-evaluation test is very reliable. We believe that this work is an important step towards the development of security sensors for the next generation of safe and robust cyber-physical systems.
Constructing an E-IoT System for Electric Company by Using 230MHz Band: A Case Study

State Grid Corporation proposed to build a “three-type two-network, world-class” strategic goal, of which the construction of ubiquitous electric Internet of Things (E-IoT) is an important content and a key link to future smart improvement. This paper builds an electric industry IoT system using the 230MHz band. The frequency band used by the system is described in detail. The 4.5G wireless access network construction plan is proposed to test the interference, multi-base station networking scenario, security, service docking and system stability projects near Nanrui. The test results of the grid network are given to verify the feasibility of the construction plan.
Quantum Markov Semigroups of Low Density Limit: the Generic Case

We investigate the structure of quantum Markov generators that describe the reduced dynamics of a test particle interacting with a dilute Bose gas in the low density limit. These generators, called low density limit type generators (LDL), differ from the weak coupling limit type generators (WCL) studied in [4, 5] because of the presence of the T-operator that describes the change in momentum of the gas particle due to collisions with the test particle. We propose a general definition of Markov generator of stochastic limit type that includes (almost) all generators arising in the stochastic limit approach and we prove that the associated semigroups as well as their invariant states (or weights) have a very special structure which extends the explicit representation for the generic quantum Markov semigroups of weak coupling limit type, due to Accardi, Hachicha and Ouerdiane [7]. We also investigate the structure of invariant states and give conditions on the T - operator for the existence of a unique invariant state and of equilibrium states.
Optimization of solder reflow processing and part design in thermoplastic optical interconnect components

Thermoplastic polyetherimides (ULTEM&trade; and EXTEM&trade; resins) have been used to produce complex optical lenses and ferrules that are integrated in optical interconnect sub-assemblies. Current thermoplastic optics must be added separately to prevent dimensional changes from the 260&deg;C peak temperature of a solder reflow process. This paper covers injection molding parameters and solder reflow conditions that influence optical properties and dimensional stability of solder-reflowable test components.
Design and Analysis of Modified Bully Algorithm for Leader Election in Distributed System

The distributed system concept has been continuously used in advanced technologies and in real-time applications since last few years. The distributed concept has become important part of the information technology, and it is frequently adapted in the current scenario with different high-tech concepts. Many researchers used various algorithms like Pre-Election Algorithm, Bully–FD Algorithm, Election Commission Algorithm, Coordinator Group Algorithm, Sandipan Basu Algorithm, Fault-Tolerant Algorithm, Leader Assistant Algorithm, Alternative Group Algorithm, etc. In the depth analysis of all these algorithms, it is found that Sandipan Basu Algorithm provided the best results. In this paper, we have designed and implemented a Modified Bully Algorithm and compared with Basic Bully Algorithm, Sandipan Basu Algorithm. In this work, we have carried out performance analysis of these algorithms by varying number of nodes.
Conjunction resolution is nonsyntactic, say paucals

Conjunctions of paucals trigger either paucal or plural agreement. The choice is semantic, not syntactic, and therefore argues against syntactic feature calculuses for conjunction resolution.
Graph Generation with a Focusing Lexicon

The major part of graph generation is deriving connections (links) between entities (nodes). In this paper, a novel technique RAKE-IDF is introduced for key-phrase extraction across multiple documents. There are two main parts to this work: First is a Contextual Lexicon Generation generation step (in this case, White Collar Crime Lexicon) and second is a text-to-graph generation process. The text-graph generation is an ensemble model of state of the art semantic role labeling models with our key-phrase extraction technique: RAKE-IDF. These two parts are combined by augmenting standard graph with lexicon-generation derived elements to ensure interesting components are represented.
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The p-rank of a Steiner quadruple system B is the dimension of the linear span of the set of characteristic vectors of blocks of B over GF(p). We derive a formula for the number of different Steiner quadruple systems of order v and given 2-rank r, r<v. Our result extends previous work on enumerating Steiner quadruple systems according to the rank of their codes over GF(2), mainly by V.A. Zinoviev and D.V. Zinoviev.
A Clustering Algorithm via Kernel Function and Locality Preserving Projections

This paper combines kernel function and Locality Preserving Projections (LPP) in a framework to improve the accuracy of clustering model. Specifically, we first use the kernel function to project each feature into high-dimensional kernel space so as to mine the nonlinear relationship of data. At the same time, the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm sparse regularization term is used for feature selection. Besides, LPP saves the local structure of data. Finally, the optimal clustering result is obtained by solving the objective function. Experimental results on six benchmark data sets show that our proposed method is superior to the compared methods in term of clustering tasks.
The Study of Warpage of an Embedded Substrate of the Flip Chip Chip Size Package

Here we present the investigation of warpage of the embedded trace substrate of the flip-chip chip size package (FCCSP-ETS). Factors of layer thickness, substrate dimension, difference of thermal expansion coefficient difference, Young's module difference, curing temperature, and curing time were investigated for minimizing the warpage of the FCCSP-ETS substrate.
Confidence Bound Minimization for Bayesian optimization with Student's-t Processes

Bayesian optimization seeks the global optimum of a black-box, objective function f (x), in the fewest possible iterations. Recent work applied knowledge of the true value of the optimum to the Gaussian Process probabilistic model typically used in Bayesian optimization. This, together with a new acquisition function called Confidence Bound Minimization, resulted in a Gaussian probabilistic posterior in which the predictions were no greater than the known maximum (and no less than for minimum). Our novel work applies Confidence Bound Minimization to Bayesian optimization with Student's-t Processes, a probabilistic alternative which addresses known weaknesses in Gaussian Processes - outliers' probability and the calculation of posterior covariance. The new model is applied to the problem of hyperparameter tuning for an XGBoost classifier. Experiments show superior regret minimization and predictive accuracy, versus the popular Expected Improvement acquisition function. Combining Confidence Bound Minimization with a transformed Student's-t Process probabilistic model and known optima produces superior training regret minimization and posterior predictions for the Six-Hump Camel(2D) and Levy(4D) benchmark problems, which do not fall below true minima.
Multiplatform System for Hand Gesture Recognition

This paper is focused on hand gestures and finger detection in still images and video sequences. The paper also contains a brief testing of different approaches to hand gesture detections as well as the realization of the platform independent application written in Python using OpenCV and PyTorch libraries, that can show a selected image or play a video sequence with highlighted recognized gestures.
RAN-PSO: Random Apollonian Network embedded Partical Swarm Optimization Method

Particle Swarm Optimization (PSO) has been developed as an important branch of Evolutionary Computation (EC). PSO algorithm originates from the simulation of the social community's intelligent behavior, in which the swarm's topology has the great effect on the optimization performance. Therefore, this paper proposes the PSO approach embedded with the Random Apollonian Network (RAN) model, which improves the topology and updates formula to form a new RAN-PSO optimization method. We test the RAN-PSO algorithm performance on nine typical test functions. Experimental results show that RAN-PSO can achieve higher accuracy with less time in practical optimization problems. In comparison with the standard PSO (Linearly Decreasing Weight Particle Swarm Optimization) and several other PSOs with different topologies, RAN-PSO is more stable and converges faster on our test sets. It can obtain a satisfactory extremum for the target function.
VR technologies in docudrama

The essay explores the implementation of VR technologies in film production, a development due to which audio-visual content, which is in high demand both in television and on the Internet, has taken a new direction, and which is a topical issue in contemporary film studies. VR technologies allow the viewer who sits on a swivel chair and wears a VR-helmet incorporating 360-degree rotating LCD monitors to watch different areas of action.&#x0D; A characteristic feature of VR content is a multi-sensory experience including sight, hearing, smell and touch. VR creates a digital reality with maximum sensory immersion. VR is different from cinema, theatre and 3D technologies: here the deception takes place not only at eye level but also at cerebral level.&#x0D; The essay argues that the use of VR technology is particularly successful in the genre of docudrama. A vivid confirmation of this argument is The Hermitage VR. An Immersion Into History (Russia,18 minutes). In this film, the dual nature of docudrama, which combines various elements of documentary and fiction cinema, allows to recreate historical eras, with the viewer becoming a witness to unique historic events. The films director Mikhail Antykov tells the history of the Hermitage Museum in a spectacular form, making the viewer empathize with the events they see through their VR glasses. The powerful artistic image is enhanced by the excellent acting of Konstantin Khabensky in the role of a mystical museum guide. Via facial expressions, gestures and gait, the actor conveys the emotions of a person walking through museum halls. Thoughtful re-enactment scenes representing various historical epochs, augmented by unusual camera angles, inventive lighting and music score, create a metaphor for time and give the viewer an illusion of participation in the unfolding events.&#x0D; Identifying the latest trends in film production, the essay demonstrates that VR technologies continue the evolution of screen arts which possess the potential to transform into an independent and profitable industry similar to traditional cinema. The author concludes that an increased interest in a national culture, little-known facts of history and the general historical heritage of a nation is a fertile source of content for the producers of VR-docudramas.