Reviewerly/exHarmony huggingface dataset:
Short Text Tagging Using Nested Stochastic Block Model: A Yelp Case Study

From online reviews and product descriptions to tweets and chats, many modern applications revolve around understanding both semantic structure and topics of short texts. Due to significant reliance on word co-occurrence, traditional topic modeling algorithms such as LDA perform poorly on sparse short texts. In this paper, we propose an unsupervised short text tagging algorithm that generates latent topics, or clusters of semantically similar words, from a corpus of short texts, and labels these short texts by stable predominant topics. The algorithm defines a weighted undirected network, namely the one mode projection of the bipartite network between words and users. Nodes represent all unique words from the corpus of short texts, edges mutual presence of pairs of words in a short text, and weights the number of short texts in which pairs of words appear. We generate the latent topics using nested stochastic block models (NSBM), dividing the network of words into communities of similar words. The algorithm is versatile—it automatically detects the appropriate number of topics. Many applications stem from the proposed algorithm, such as using the short text topic representations as the basis of a short text similarity metric. We validate the results using inter-semantic similarity and normalized mutual information, which show the method is competitive with industry short text topic modeling algorithms.
How to Learn Quickly: An investigation of how to optimally train deep neural networks and its implications for human learning

This paper investigates the speed at which a deep neural network learns to play Tic- Tac- Toe as a function of the player's opponent's level of play. The goal of this work is not to find a faster deep learning algorithm or architecture, but rather to gain insight into the nature of adversarial learning and training. Do we learn faster by playing against an opponent of slightly worse, identical, better, or grandmaster ability?
Usability Measurement of Classroom Booking Information System Integrated with Course Scheduling Information System

Abstract The purpose of this research is to help users in the information systems department based on test results. The method used is qualitative research, by giving questionnaires, observations, and previous research. Based on preliminary test results, it is expected that this measurement system can help users in this case lecturers and secretariat in the information systems department to determine the reservation of available classrooms without having to order classes manually. Through the class booking information system it is expected that the secretariat can easily record class booking data and easily oversee the use of classrooms to avoid disputes between lecturers caused by the use of classrooms from other lecturers who are not entitled to classrooms.
The Ability Scoring Model of Software Support Engineers Based on Technical and Communication Skills

Abstract This study aims to determine the ability of a software support engineer both technical abilities and communication skills. To achieve these objectives, the research begins by determining questions through literature studies and interviews then validate these questions by involving 2 communication experts, 10 support managers, and 30 software support engineers from 3 IT companies ranging from a small business company, medium business company, and large business company. Validity testing is done through filling out a questionnaire to the support manager, communication expert and application support with weighting using a Likert scale. With this research, we succeed in creating the scoring model with a value of more than 90% confidence level to assess the ability of a software support engineer. This assessment model contains 5 questions about database capabilities, 5 questions about basic algorithms, and 10 questions about communication skills. Therefore, this scoring model has been able to represent the real capabilities of a software support engineer.
Smart Cloud Platform for Data Management in the Age of the Internet of Vehicles

Smart cars, with the emergence of the Internet of Vehicles (IoV), are expected to generate huge volumes of data at rates that typical data management systems will not be able to handle. Such data can be extremely useful to both analytics and machine learning applications. This paper discusses and demonstrates the process of architecting and building a scalable data management system for the IoV in a smart city environment, using Apache Spark, Apache Kafka and Apache Cassandra, which results in a scalable, resilient and fault-tolerant data management system that facilitates performing big data analytics and scalable machine learning on IoV data.
The Simulation Models in Grid Methods of Uniform Probing

The article provides an overview of modern problems of improving large-scale systems solved by means of using grid methods of uniform probing. It establishes a connection between approximate optimization algorithms for poorly formalized systems with the method of finite difference of mathematical physics. The finite difference method is known since the time of Euler, but began to develop rapidly only with the advent of high-speed computing. This method has also name of the grid method, widely used for solving partial differential equations. Suggested algorithms on the base of grid methods of uniform probing extend the capabilities of grid methods for the study of systems, which have in their description a set of non-differentiable functions. The appointment of the stages of grid methods of uniform probing with the use of simulation models to describe the investigated or projected systems is analyzed. A set of examples of approximate optimization of dynamic stochastic systems described by means of simulation statistical models demonstrate the features of the considered approaches. Models were checked for adequacy, and program implementations were created on the basis of high-speed procedures.
Impact of imbalanced data on the performance of software defect prediction classifiers

Abstract Software defect prediction plays an important role in analysing software quality and balancing software cost. However, it lacks suggestions for project managers and software engineers in selecting classifiers. Firstly, a method for building imbalanced distribution data is proposed. Then, Matthews correlation coefficient is used to measure the performance of different classifiers, and the coefficient of variation is utilised to evaluate the stability of classifiers on imbalanced distribution data. Finally, an experiment is conducted on 8 common classifiers and 12 publicly available and widely used data sets. Results show that NaiveBayes behaves steadily when the imbalance rate of data sets changes significantly. The experimental results provide a basis for project managers and software engineers to select classifiers.
Research on Synchronized Recognition System of Literary Works Based on Tesseract-OCR Model

Abstract Using mobile terminal for image and character recognition can solve the dilemma of the traditional large-scale scanning equipment, such as complicated steps and inconvenience in moving, and provide great convenience for mobile learning and office. As a direct and concrete manifestation of objective things, images are rich in information, which is incomparable in other ways. Therefore, the importance of images is self-evident. Recognition of graphics and text information of terminal equipment can solve the disadvantage of large-scale equipment, such as difficult to move and complicated use steps, and can make office and mobile learning more smoothly. Tesseract-OCR is powerful in open source OCR. Based on Tesseract-OCR model, this paper puts forward the corresponding improvement scheme for the defects such as irregular graphics and characters, and inconspicuous image and text recognition system, and carries out synchronous preview and recognition of image and text as much as possible. The graphic publication of literary works reflects readers’ impetuous reading mentality, the popular trend of literature trend and the management crisis of publishing houses.
An Improved Collaborative Filtering Algorithms Based on Maximum Entropy Model and Markov Process to Mine User Behavior

Abstract Aiming at problems of data sparsity and poor interpretability of recommendation reasons in user-based collaborative filtering algorithm, an improved collaborative filtering algorithm based on maximum entropy model and markov process is proposed to mine user behavior habits. Firstly, the maximum entropy model is established through the user-behavior matrix of collaborative filtering algorithm, and the user behavior is fuzzily and preliminarily mined. Secondly, fuzzy behavior information obtained by the maximum entropy model is introduced as the initial state of markov process, and an optimization model is set up according to the internal relation of user-behavior matrix, thus reaching the state probability transition matrix. Once the markov process reaches a stable state, the user’s final behavior habit is excavated. Finally, this user behavior is integrated into the recommendation process, so that the interpretability of the recommendation is enhanced. The experimental results show that the improved collaborative filtering algorithm can promote the recommendation accuracy.
Distributed Graph Perturbation Algorithm on Social Networks with Reachability Preservation

With the rapid development of social networks, the current scale of graph data continues to increase, and the performance of anonymous social network methods is limited. Node reachability query is essential in directed graphs, which can reflect the relationship between nodes and the direction of information dissemination. Aiming at the problem of the reachability of nodes between directed social network privacy technologies, this paper proposes a reachability preserving distribution perturbation (RPDP) algorithm, which is based on the distributed graph processing system GraphX. This algorithm first generates a Random Neighborhood Table (RNT) composed of four tuples for the nodes and then uses the message transmission of GraphX and “probe” mechanism. The proposed algorithm improves the disposal efficiency of the large-scale social network while maintaining the reachability of the nodes. Experiments based on the real social network data show that the proposed algorithm can keep the node reachability and deal with large-scale social network efficiently while protecting the character of the graph structure.
Intrinsically Motivated Active Perception for Multi-areas View Tasks

The target recognition of the human eye in real scenes is still far superior to any robot vision system. We believe that there are two major essential reasons. First, humans can observe the environment in which the object is located, get the probability of the object category. Second, human can use foveal to focus on the object and get more object detail features from the high resolution image containing the object and make it easier to identify. This paper proposes a novel method for searching and locating surrounding objects using a monocular Panning/Tilting/Zooming (PTZ) camera with free rotation and zoom functions. Our system is an active environment-aware vision system based on Intrinsic Motivation and capable of autonomously exploring the surroundings of the camera. At the same time, by combining the visual information of foveal field of view and context field of view, the visual system observes more details and make more accurate prediction, and overcomes the limitation of low-resolution image in target recognition. Finally, our experiment proved that the visual perception system incorporating the curiosity mechanism is superior to the common perception method in terms of time overhead and learning ability.
The Vision of Semantic Web

Knowledge discovery is a process that seeks new knowledge about an application domain. It consists of many steps, one of which is data mining, each aiming to complete a discovery task, and accomplished by the application of a discovery method [1]. On this subject, the Semantic Web (SW) is an effort to interchange unstructured data over the Web into a structured format that is processable not only by human beings but also by computers [2]. The SW creates a distributed framework to publish, query, and reuse information [3]. The key backbones of SW are ontologies and annotations that provide semantics for raw data are known as Resource Description Framework (RDF) data [4]. The RDF data can also be published over Linked Open Data (LOD) cloud. To highlight the vision of SW, we would like to run a tutorial. The main goal of this tutorial is to provide background knowledge about SW, RDF, Web Ontology Language (OWL), SPARQL, knowledge graph, graph databases, and SW-based applications. This tutorial will show how SW provides explicit models of the terminology of a domain to improve information access.
Multi-level Monte Carlo Simulation on Particle Filters and Extended Kalman Filters

The automation of vehicles depends on different observations that may represent the states in an inaccurate way. The probabilistic approach in the prediction and enhancement of measurement are already used in automation to model the noise and perceptual limitations in vehicles sensors. In this paper, we work on the statistical solutions of Extended Kalman filter and Particle filters that include noise in the prediction of the new state. We use different variance reduction technique as control variates to increase the precision of the simulation and make it statistically efficient.
Bifurcation analysis of the threshold of a solid-state laser

Abstract In this paper we describe the qualitative analysis of the dynamic model of a solid-state laser. Initially a brief description of the theory of dynamic systems is made, the part concerning the qualitative solutions of non-linear models, the obtaining of fixed points for second-order systems and the determination of the stability of these points. Later, the dynamic model of the solid-state laser is exposed, where all its inherent parameters and the behavior that follows will be described. Then, the qualitative analysis is developed where the model will be converted to dimensionless equivalent that contains two parameters that condense to all the others. The results obtained are verified with numerical analysis, there clarifies the veracity of the qualitative analysis in conjunction with the description of the behavior of the solid-state laser, to finish with the conclusions obtained from the whole development.
A Learning Method of Non-Differential SIC Fuzzy Inference Model Using Genetic Algorithm and Its Application to a Medical Diagnosis

There are typical learning methods for fuzzy inference models such as the steepest descent method, genetic algorithm, etc. It is generally impossible to apply the steepest descent method to fuzzy inference models with consequent fuzzy sets, such as Mamdani’s fuzzy inference models that use min and max operations. Therefore, genetic algorithm will be useful for the above model. However, the computational complexity of genetic algorithm is much larger than the steepest descent method. Also, since all input items are set to the antecedent parts in typical fuzzy inference model, the number of rules increases exponentially. Moreover, considering the computational complexity of genetic algorithm, it will not be necessarily suitable. On the other hand, Single Input Connected (SIC) fuzzy inference model sets the fuzzy rule of 1 input 1 output, so the number of rules can be reduced drastically. The consequent parts of the conventional SIC model were real number although linguistic interpretation is possible and easy to understand if the consequent parts are fuzzy sets. Therefore, we propose a new SIC model which extends real number of the consequent parts to fuzzy sets, and the fuzzy rules are derived by using genetic algorithm. In addition, it is applied to a medical diagnosis and compared with the conventional fuzzy inference models.
Review On privacy protection and intrusion avoidance for medical data sharing in cloud

With the advancement of wearable restorative gadgets remote wellbeing checking and old social insurance has become a prevalent application. The information gathered from persistent through wearable gadgets (like heartbeat, circulatory strain and so on) must be passed to application running in cloud to execute different administrations like master exhortation, crisis help and so forth. The information of patients when put away in cloud can be assaulted by gatecrashers and can be taken or debased. Existing arrangement depend on scrambling the information and putting away in cloud. By these arrangements can be assaulted and encryption keys can be broken and all information can be still taken. In this undertaking we propose a cloud let based answer for giving improved security to persistent human services information.
Designing of Application for Learning Sundanese Cultures

Abstract Sundanese culture is one of the cultures that is come form West Java region. Along with its development, not many students around West Java areas understand the Sundanese culture, if allowed to continue, Sundanese culture will lose its identity. As the development of information technology, a mobile application can be used as an application as learning media to help students and make students more interested in learning Sundanese culture as what they learn at school. This research aims to help to attract and motivate students to learn the Sundanese culture and provide entertainment aspects for students. The research method used was structured approach method and prototype development. The research design used was descriptive methods and actions. The method of data collection used was observation techniques, documentation, and interviews. System design tools used to describe the system model include use case diagrams and use case scenarios while for designing data dictionaries use were Use case diagrams, Activity diagrams, Sequence diagrams, and data dictionaries. The information system developed was using adobe flash CS6. The expectation is this application can stimulate students to learn Sundanese regional culture and can facilitate teachers in providing interactive lessons to stimulate students creativity.
ADVANTAGES OF IMPLEMENTING TRAINING TECHNOLOGIES IN THE MILITARY EDUCATION SYSTEM

Opportunities and perspectives of the use of distance learning technologies (DLT) in the process of professional training of military experts are considered in the paper. DLT allows to carry out educational process at a distance without losing its quality. DLT even improves quality due to individualization, active teaching methods, bilateral interaction between teacher and student (cadet). The distance learning form will be considered as an additional system that is used to supplement the academic disciplines taught by the Secondary Schools for applicants’ education in full-time education. The main aim of the using distance learning technologies at the Secondary School is providing students with access to electronic educational resources through the using modern information technologies and telecommunication networks. The educational process in distance learning for a cadet have to include all the main forms of the traditional organization at the educational process: lectures, seminars and practical classes, laboratory workshops, control system, research and independent work. All these forms of organization of the educational process will allow to implement a flexible combination of independent cognitive activity of students (cadets) in with different sources of information, operational and systematic interaction with the course teacher and group work of students (cadets).The advantages of using distance learning technologies in the training of military specialists are various, namely: flexibility, modularity, technological approach, increase motivation and self-motivation of students, new role of the teacher.The process of using remote technology is considered as an integral part of the assessment of the quality education and information during the educational process. The new possibilities of practical application of remote technologies in the organization educational process in the Secondary School are formulated in this paper.
Sixty Years of “Coherence Properties of Partially Polarized Electromagnetic Radiation”

This presentation, in part a tribute to Prof. Emil Wolf, the founder of this conference series and who died last year at age 96, will review the impact of his seminal 1959 paper on coherence and polarization, and its influence on modern research topics such as quantum information and quantum optics.
Parallel-Structure-based Transfer Learning for Deep NIR-to-VIS Face Recognition

This paper considers a heterogeneous face recognition problem, i.e., matching near-infrared (NIR) to visible (VIS) face images. The significant domain gap between the NIR and VIS modalities poses great challenges to accurate face recognition. To overcome the domain gap problem, previous works usually adopted a series structure to transfer high-level features. This paper proposes a Parallel-Structure-based Transfer learning method (PST), which fully utilizes multi-scale feature map information. Specifically, PST consists of two parallel streams of network, i.e., a source stream (S-stream) and a transfer stream (T-stream). S-stream is pre-trained on a large-scale VIS database, and its parameters are fixed. It preserves the discriminative ability learned from the large-scale source dataset. T-stream absorbs multi-scale feature maps from S-stream and transfers the NIR and VIS face embeddings to a unique feature space, which is agnostic to the input image modality. The proposed PST method achieves state-of-the-art performance on CASIA NIR-VIS 2.0 Database, the largest near-infrared face database.
Dynamic Multi-label Learning with Multiple New Labels

In a traditional multi-label learning task, an instance or object often has multiple labels. Previous works assume that the class labels are always fixed, i.e, the class labels in the test set are the same as that in the training set. Different from previous methods, we study a new problem setting where multiple new labels emerge in a dynamic environment. In this paper, we decompose the multiple labels pool to adjust the dynamic environment. The proposed method has several functions: classify instances on currently known labels, detect the emergence of several new labels then separate them using clustering, and construct a new classifier for each new label that works collaboratively with the classifier for known labels. Experimental results on publicly available data sets demonstrate that our method achieves superior performance, compared with the state-of-the-arts.
Construction of A Mobile Video Retrieval Dataset in the Cloud: Dos, Don’ts, and the Analysis

Currently, we are in an era when smartphones become everything even the best device to use for searching for a video from a video dataset using their camera. Designing an efficient dataset construction pipeline for the video retrieval system is not an obvious task and details of the process were often omitted in past literature. In this paper, we share our best practice of constructing a mobile video retrieval dataset. Every step from planning, developing mobile apps, recruiting workers, problems during and after the construction as well as the solutions are delineated in detail. In addition, we propose cloud architectures which facilitates the overall data collection process. This paper contributes to data science and computer vision research communities in two folds: first, it describes every step from planning to managing the dataset in detail, and second, it summarizes what should do and what should not: many of which are not obvious when planning for the dataset collection and during the data collection process.