Reviewerly/exHarmony huggingface dataset:
Tecnología de la experiencia. Trata de personas // Technology of Experience. Human Trafficking

Este articulo intenta un acercamiento al pensamiento de lo que discursos juridicos y periodisticos corrientes llaman “trata de personas” desde una aproximacion estetico-tecnologica, como una tecnologia que busca producir una experiencia bien determinada de arrasamiento de los cuerpos. Primeramente, se hace una caracterizacion de los funcionamientos de esos discursos sobre la trata de personas y los efectos que producen para senalar que son incapaces de pensar el funcionamiento estetico-tecnologico de la tecnologia de la experiencia que aqui se postula. Al final del articulo se enumera una serie de elementos y mecanismos de lo que conformaria esa tecnologia de la experiencia. Como conclusion se ofrece un suplemento conceptual que buscaria caracterizar un mecanismo de no reconocimiento en el centro de esta tecnologia de la experiencia. Palabras clave: tecnologia, erata de personas, estetica, experiencia, retorica, sensibilidad This article is an attempt to approach what is currently called human trafficking among legal circles and journalistic discourses, from an aesthetic-technological perspective, as a technology that seeks to produce an experience of the obliteration of bodies. Firstly, we make a characterization of the way these discourses operate, as well as of their effects in order to indicate just how incapable of pondering aesthetic-technological functioning they are, as far as the technology experience postulated herein. In the final part the article, a series of elements and mechanisms of what would constitute that technology of experience is enumerated. In conclusion, a conceptual supplement is offered that seeks to characterize a mechanism of non-recognition at the center of this technology of experience.  Keywords: technology, human trafficking, aesthetics, experience, rhetoric, sensitivity .
Construction of Evaluation Functions for the Fifteen Puzzle by Learning Random Trials

In this paper, we propose an evaluation function for the fifteen puzzle using a neural network learning random trials of moves of the puzzle. Using the IDA* algorithm with the evaluation function, we were able to solve problems of the fifteen puzzle with about one-6,600th times as less search nodes as the well-known Manhattan distance based evaluation function in average. Comparing to our evaluation function to non-admissible evaluation function whose values are products of Manhattan evaluation function and a constant between 1.3 and 1.7, the computation time were reduced to one-fifth to one-hundredth. We used data sets generated by random trials of moves from the goal pattern of the fifteen puzzle. In addition to the data sets, we chose some patterns whose distance from the goal state is 20 or 22, and which emerges frequently in the trial sequences, and we generated random sequences from the patterns. Adding those sets to the original random sequences and giving the sets to the neural network for learning, we were able to make the evaluation function more effective.
Towards team performance visualization in collaborative systems

There are efforts aimed at the detection of performance indicators in collaborative systems. However, they face the challenge of their visual representation for the adequate interpretation of the user. For its part, the information display area has a wide range of techniques and theories for the visual representation of data. Therefore, this paper explores the process and visualization techniques to represent performance indicators in collaborative systems. Also, describes the results of an exploratory test from which there is a trace of a positive effect on users that visualize the team performance indicators.
Performance analysis of DE mutation schemes for constrained LSGO problems

Abstract Differential Evolution (DE) is one of the most popular evolution algorithms (EAs) for numerical optimization problems with various difficulty levels. Mutation scheme makes a strong influence in DE performance. It produces new trial vectors based on the current population to improve the previous best-found solution. In this paper, we have analyzed the performance of a wide range of various mutation schemes for early proposed ε-CC-SHADE algorithm. We have used scaled benchmark set of constrained large-scale global optimization (cLSGO) problems to investigate ε-CC-SHADE performance. The new benchmark set is based on IEEE CEC 2017 Competition on Constrained Real-Parameter Optimization. The experimental results have shown the dependence of EA performance on the selected mutation scheme for solving optimization problems with high dimension. Based on the numerical results, we have made conclusions for choosing mutation scheme to increase DE performance for cLSGO.
A New Advanced Perception For Social Media Website [Facebook]

Facebook play vital role in through out the world. In day to day life users of facebook was increasing. Facebook saves more number of information on his server in distributed level. But there was some drawback for user side i.e., people side. The drawback is authentication and security of user facebookaccount. Facebook's wise retieval outstanding burdens are very sundry: administrations require a wide range of kinds of models continuously practice. This diversity had a reflection at all layers of the system. Along with this it follows pipeline construction inorder to allow the flow of data from raw data into valid information. Computational essentials became so intensive everaging both GPU and CPU platforms for training and abundant CPU capacity for realtime inference. In the modification, a new system is implemented inorder to overcome the drawback of existing system. New implementation will give solution for authentication as well as data usage. When user login into their account using user id and password, system will initiate and turn on the camera and compare the face to verify whether that the person is authenticated person or not and an one time password alert system is generated for time based login to their facebook account and finally another one get alert for limited data usage.
A GPU-Based Hybrid jDE Algorithm Applied to the 2D-AB Protein Structure Prediction Problem

Protein Structure Prediction (PSP) problem is an open problem in bioinformatics and, as the problem scales, complexity and processing time increases. In this way, robust methods and massively parallel architectures are required. This paper presents a GPU-based hybrid algorithm, named cuHjDE, to handle the 2D-AB off-lattice PSP problem. The cuHjDE is composed of the jDE algorithm and the Hooke-Jeeves local search algorithm. An important feature present in the cuHjDE algorithm is the use of a crowding mechanism to avoid premature convergence promoting diversification in the search space. The proposed algorithm is compared with four state-of-the-art algorithms in both artificial and real proteins. Also, the impact of using GPU is analyzed. Experimental results point out that the proposed approach is competitive in artificial sequences and achieved new best results for all real sequence proteins.
Design of Personalized Smart Wellness Information Management System Through Wellness Data Analysis

Recently, the health concept is changing from simply being healthy to a proactive perspective of balancing physical and psychological factors. Consequently, interest in health-oriented wellness is increasing. Wellness, which can improve quality of life through continuous recording and management, is also continuously advancing. However, at present, classification and implementation of major wellness information in daily life has not yet been achieved. Therefore, in this study, a personalized smart wellness information management system is designed.
Schema-Based Visual Queries over Linked Data Endpoints

We present the option to use the schema-based visual query tool ViziQuer over realistic Linked Data endpoints. We describe the tool meta-schema structure and the means for the endpoint schema retrieval both from an OWL ontology and from a SPARQL endpoint. We report on a store of the endpoint-specific schemas and the options to support the schema presentation to the end-user both as a class tree within the environment and as external visual diagram.
Modernized RC4 encryption algorithm

Abstract A modernized RC4 encryption algorithm with implementation of the resulting algorithm in program for encrypting files is proposed. The program has simple and intuitive interface Testing showed that the modernized encryption algorithm is slower for 4 milliseconds than the standard RC4, which is explained by a more complex key expansion algorithm. Proposed algorithm can replace RC4 without substantial loss in file encryption.
Semi-supervised Classification Using Deep Learning

Semi-supervised learning considers the problem of classification when only a small subset of the observations have corresponding classes labels. Unfortunately, labeling a sufficiently large training data set will require human involvement, an expensive and time-consuming task. Recently, several papers in literature show that deep learning techniques are able to solve this problem. In this sense, this work proposes a Semi-supervised Deep Classification (SDC) method to solve the semi-supervised problem by using a Multilayer Perceptron (MLP) embedded to the labeling process. Extensive experiments on benchmark data sets validate the effectiveness and robustness of the proposed method.
Enlightenment of thematic knowledge Map Analysis of Human Resource Management practice to Engineering Management in China

Abstract Human resource is contained in the knowledge and ability of employees and employee relationship in the enterprise, and it is a competitive advantage that competitors can not imitate. Effective human resources management practice can promote organizational innovation. This study uses the knowledge graph research method to analyze the journals of the practice theme of human resource management in China. Taking the periodicals included in the core journals from 1994 to 2018 as the research object, using citespace software to visually analyze the existing literature from the literature author, the key words and so on, by drawing the scientific knowledge graph, the present situation and research hotspots of the practice theme of human resource management in China are identified. And this paper puts forward some suggestions on the optimization of human resource management in engineering management.
Research on the digital modeling method of the blast-fragmentation warhead with preformed fragments

In the design process of the blast-fragmentation warhead with preformed fragments, there are two key factors: the modeling and arrangement of the preformed fragments. These factors determine the accuracy of lethality assessment on the blast-fragmentation warhead with preformed fragments. Presently, there are some problems in the design process of the warhead, such as repeated modeling and low efficiency. This paper studies the modeling of blast fragmentation with preformed fragments. Based on the analysis of warhead structure and the technology of digital modeling, the idea of warhead modeling based on basic primitives and the arrangement method of preformed fragment modeling are proposed. In addition, by means of Visual C#, Direct X, and Direct3D, a digital modeling system based on the blast-fragmentation warhead with preformed fragments is developed, and then a warhead is taken as an example to realize the digital modeling. The case analysis shows that the digital modeling technology based on the blast-fragmentation warhead with preformed fragments completed the rapid and accurate modeling of the preformed fragment warhead, which lays the foundation for the accuracy of lethality assessment.
Adversarial Examples Against Image-based Malware Classification Systems

Malicious software, known as malware, has become urgently serious threat for computer security, so automatic mal-ware classification techniques have received increasing attention. In recent years, deep learning (DL) techniques for computer vision have been successfully applied for malware classification by visualizing malware files and then using DL to classify visualized images. Although DL-based classification systems have been proven to be much more accurate than conventional ones, these systems have been shown to be vulnerable to adversarial attacks. However, there has been little research to consider the danger of adversarial attacks to visualized image-based malware classification systems. This paper proposes an adversarial attack method based on the gradient to attack image-based malware classification systems by introducing perturbations on resource section of PE files. The experimental results on the Malimg dataset show that by a small interference, the proposed method can achieve success attack rate when challenging convolutional neural network malware classifiers.
Role of Augmented Reality and Virtual Reality in Digital World

In this day and age, we are limited by blends of advances. We know the vast majority of these innovations in our everyday lives. Bit by bit different advances are incorporated to give us new abilities and administrations. More often than not a PC is at the core of such joining. Augmented Reality is a PC based innovation that coordinates particular information and yield gadgets, enabling the client to communicate and try different things with a fake situation made in reality. Computer generated Reality can lead us to a fictional universe that looks precisely like our very own reality. Enlarged the truth is another advancing innovation thing in the physical world blended with computerized substance to expansion client experience and effortlessness. As expanded reality innovation enables us to make a virtual world over the physical world, this can be utilized to impart physical assets to numerous clients. The undertaking extension will be to give a stage to clients to share physical assets alongside powerful data to give improved blended reality experience. Expanded Reality is an innovation which joins virtual items and true situations. Computer generated reality and Augmented the truth is a well-known innovation utilized widely in different fields Engineering Education, Android Based Application, Share Cloud Database, Internet Of Things.
Requirements Engineering Challenges in the First 1000 Days of a Venture Capital-Backed US Startup

This tutorial focused on many competing aims, challenges, and trade-offs involved in leading and doing Requirements Engineering (RE) in a highly dynamic and uncertain environment of a venture-capital-backed New York City startup Fr8Hub (https://www.fr8hub.com) which runs a marketplace for over-the-road logistics services across North America. How to set up RE roles, responsibilities, and processes in such an environment? How and why did these change during the first 1000 days of this business? Which concepts, methods, and tools from RE research worked, and why? Which ones we tried and failed to get results from? How does RE - representations, models, and elicitation, representation, validation, verification methods - interface with corporate strategy, finance, product design, engineering, marketing, sales, and business intelligence? How do the priorities of speed and scale influence how RE is organized and done? Which research questions did this fieldwork lead to? I discussed these questions together with the audience during the tutorial, while showing actually applied RE methods, processes, and artifacts; I described actual situations, and found potential courses of action with the audience, described actual choices we made and their outcomes. Beyond the role of RE in those situations, the aim is to stimulate discussion about the use of long term fieldwork in RE research, what can be gained through it, and how this one worked out.
Exploiting Geographical Data to Improve Recommender Systems for Business Opportunities in Urban Areas

The rapid urban expansion of the world's major cities has directly impacted people's lives. In the urbanization process, it is common that business shops are open to attend the different needs and demands of the increasing number of citizens. This fact represents a business issue encouraging potential investments that could be harnessed to improve both urban economic environment and quality of urban life. However, many business opportunities are lost or not exploited properly due to the difficulty that investors, business owners, and marketers have to identify the right places where to open new stores. In this paper, we describe the implementation and evaluation of an approach to identify geographic areas with great potential to host business from a specific category. First, we adapt clustering algorithms to work with geographical data and, thus, partitioning a target city into business districts. Next, we use various recommendation algorithms to suggest the best categories for each business district. We conduct several experiments on Yelp data and our results show how geographical data and state-of-the-art algorithms can be used to mine business opportunities and predict adequate places to open new stores in urban areas.
Semi-supervised Regularized Coplanar Discriminant Analysis

Dimensionality Reduction is a widely used method of removing redundant features and data compression. Dimensionality reduction usually occurs in a supervised setting in which all the samples are labelled. However, aspects of spectral clustering and semi-supervised learning can be used in Dimensionality reduction to ensure minimum loss of important data while projecting the high-dimensional data into lower dimensions. We have proposed a novel framework called Semi-supervised Regularized Co-planar Discriminant Analysis (SRCDA) that creates a graph of labelled and unlabelled data and uses label propagation to predict the classes of the unlabelled data. Additionally, we introduce a regularized term which is used to prevent overfitting. The proposed algorithm is evaluated against several other state-of-the-art algorithms with benchmark datasets including PIE Face, ORL and Yale Dataset. The proposed algorithm shows higher accuracies compared to the other algorithms and can be used in real-life datasets where the unlabelled data is vastly greater than the labelled samples. We have also conducted a statistical significance test to verify the results obtained.
Cryptocurrencies: The Dragons of the Financial World

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A.I. Ziloti’s Educational Activity in St. Petersburg (According to Unpublished Correspondence Materials)

Collections of Russian libraries contain unique letters of pianist, teacher and conductor A.I. Ziloti (1863—1945). They demonstrate one of the facets of his talent as a musical public figure and educator. The correspondence of A.I. Ziloti with town governors is a unique phenomenon not only in his creative heritage, but also in the history of musical culture. The letters help to understand the musician’s worldview, artistic views, creative principles, giving as well a description of the cultural and historical context of the early 20th century period. The article aims to convey the importance of A.I. Ziloti’s correspondence for educational activities organization in St. Petersburg. The article identifies the letters’ specificity and studies A.I. Ziloti’s handwriting. For the first time, there are published his petitions addressed to the Director of the Imperial Thea­ters V.A. Telyakovsky and St. Petersburg town governors. The scientific analysis’ objectivity, connec­ted with comprehension of the value significance of A.I. Ziloti’s cultural ideas, requires to recognize the results of his musical and educational activities as his main achievements. Being an outstanding musician and teacher, A.I. Ziloti remains in the history of Russian culture, primarily, as a representative of musical education.
Collaborative Privacy for Web Applications

Real-time, online-editing web apps provide free and convenient services for collaboratively editing, sharing and storing files. The benefits of these web applications do not come for free: not only do service providers have full access to the users' files, but they also control access, transmission, and storage mechanisms for them. As a result, user data may be at risk of data mining, third-party interception, or even manipulation. To combat this, we propose a new system for helping to preserve the privacy of user data within collaborative environments. There are several distinct challenges in producing such a system, including developing an encryption mechanism that does not interfere with the back-end (and often proprietary) control mechanisms utilized by the service, and identifying transparent code hooks through which to obfuscate user data. Toward the first challenge, we develop a character-level encryption scheme that is more resilient to the types of attacks that plague classical substitution ciphers. For the second challenge, we design a browser extension that robustly demonstrates the feasibility of our approach, and show a concrete implementation for Google Chrome and the widely-used Google Docs platform. Our example tangibly demonstrates how several users with a shared key can collaboratively and transparently edit a Google Docs document without revealing the plaintext directly to Google.
Interactive Semantic Features Selection from Reviews for Recommendation

In recent years, reviews information has been effectively utilized by deep learning to improve the performance of the recommendation system and alleviate the problems of sparse data and cold start. However, there is much redundant information in the reviews that has a negative effect on the performance of the recommender system, which is ignored by most existing methods. In this paper, the Interactive Semantic Features Selection (ISFS) method is proposed to more effectively select the useful information from reviews based on attention mechanisms. Specifically, each word in reviews is interactively assigned a different weight according to the value of the semantic information it contains. Experiment results on real-world datasets show that ISFS outperforms baseline recommender systems on rating prediction tasks.
Clues About Bluffing in <i>Clue</i>: Is Conventional Wisdom Wise?

We have used the board game Clue as a pedagogical tool in our course on artificial intelligence to teach formal logic through the development of logic-based computational game-playing agents. The development of game-playing agents allows us to experimentally test many game-play strategies and we have encountered some surprising results that refine “conventional wisdom” for playing Clue. In this article, we consider the effect of the oft-used strategy wherein a player uses her/his own cards when making suggestions (i.e., “bluffing”) early in the game to mislead other players or to focus on acquiring a particular kind of knowledge. We begin with an intuitive argument against this strategy together with a quantitative probabilistic analysis of this strategy's cost to a player; both suggest “bluffing” should be detrimental to winning the game. We then present our counterintuitive simulation results from playing computational agents that “bluff” against those that do not that show “bluffing” to be beneficial. We conclude with a nuanced assessment of the cost and benefit of “bluffing” in Clue that shows the strategy, when used correctly, to be beneficial, and, when used incorrectly, to be detrimental.