Reviewerly/exHarmony huggingface dataset:
Maximum Entropy Principle based Document Ranking with Term Selection Analysis for Cross-Lingual Information Retrieval

Retrieval of proper information from the collection of available data on Internet in a cross-lingual platform is a tedious task. For the retrieval of information, the user specifies the required data in the way of query. In some cases, the query could not clearly define the required information specifically owing to the ambiguities and is solved by the use of Google translator. This paper presents a new Tamil-English cross lingual information retrieval (CLIR) where the queries in Tamil language are used to retrieve related documents from English language. The proposed model uses Maximum Entropy Principle based Document Ranking with Term Selection Analysis (MEPDR-TSA) for CLIR. The presented MEPDR model distributes the term frequency weights between the documents subjected to a provided query. In TSA, the term selection value is used to select the retrieved documents from each query. In addition, Google translator is used to translating the query into English and then again translate the retrieved English documents into Tamil language. The performance of the MEPDR-TSA model has been tested against several Tamil language based queries and the results are investigated interms of precision, recall, and F-score.
Maximum Expressive Number of Threshold Neural Networks

Expressive power of neural networks is one of important factors for understanding machine learning. It is wellknown that a deeper neural network also has much higher expressive power than shallower one experimentally, but the difference in expressive powers between activation functions and theoretical explanation have still not been clear. In this paper, we focus on the expressive power of neural networks with threshold activation function and study it by an index of expressive power named "expressive number". Expressive number gives us a type of theoretical limit of learning and enable us to compare expressive powers of different activation functions easily. In this paper, we find the exact value of the maximum expressive number of threshold neural networks, and prove it. Our result suggests deeper neural networks do not always have higher expressive power, and in some cases, rather shallower neural networks have higher expressive power than deeper one.
Decentralized Stock and Cryptocurrency Exchange

We propose a new blockchain-based framework for a completely decentralized stock market and bitcoin exchange in this paper. By proposing a groundbreaking framework utilizing blockchain to build a decentralized bitcoin and stock exchange network, this paper discusses the shortcomings of conventional centralized stock exchange platforms, High transaction costs, vulnerable centralized governance, and a lack of clarity in consumer behavior and algorithms are just a few of the problems. Blockchain technology consists of a large number of computer nodes that share a shared ledger securely without the need for intermediaries of any sort. The proposed blockchain-based solution addresses the disadvantages of the centralized stock exchange architecture by ensuring the integrity and security of the properties and orders of the owner, by self-enforcing intelligent agreements between parties, and by consensus algorithms, by achieving democratic and effective decisions on the execution and settlement of orders. Intelligent contracts are used in the proposed architecture to enforce the validation of the owner's rights as well as the proper execution and settlement of orders, reducing the need for a central authority to ensure that the stock exchange process is accurate. The proposed system proposes a hybrid platform that incorporates cryptocurrency and stock trading. The solution was tested for a subset of rules for the Stock Exchange by implementing a prototype in Ethereum.
Multi-attention mechanism for Chinese description of videos

Using natural language to describe videos is a hot topic in the field of natural language processing and computer vision. However, most of the video description tasks are to generate English descriptions now, rarely to generate Chinese descriptions. This paper explores the process of generating Chinese descriptions for videos. An improved model of video description is proposed in this paper, which combines multi-modal features and multi-attention mechanism. The model extracts video information from global features and fine-grained features and uses the multi-attention mechanism to focus more important video information in the decoding stage, which can further improve the richness and accuracy of the generated descriptions. The model is applied to the extended Chinese corpus of MSVD (Microsoft Research video description corpus), and the highest METEOR value obtained is still 9.6% higher than the best result of video Chinese description on MSVD found at present. The model also achieves an advanced result compared with many state-of-the-art methods in English environment.
Information system design of data bank population using Rapid Application Development

Abstract Population data is available from the results of structured individual data and aggregate data as a result of population registration and civil registration activities. In Indonesia, many high school students who are seventeen years old do not yet have a national identity card; this is due to their lack of knowledge of how registration to get a national identity card. The purpose of making this article is to provide an opportunity for students to make it easier to get their residence documents, such as making a National Identity Card. The methodology used is Rapid Application Development, with the stages of business modeling, data modeling, process modeling, application formation, testing, and turnover, wherein this article is limited only to the scene of the modeling process. The results of this study are in the form of a population data model in Web-based for Senior High School or Equivalents, and to make it easier for students to receive information about their residence documents such as making national Identity Cards.
Solving Nonlinear Equations Systems with a Two-Phase Root-Finder Based on Niching Differential Evolution

Solving nonlinear equation systems (NESs) is a challenging problems in numerical computation. Two goals should be considered for solving NESs. One is to locate as many roots as possible and the other is to improve the solving precision. In this work, in order to achieve these two goals, a two-phase niching differential evolution (TPNDE) is proposed. The innovation of TPNDE is mainly reflected in the following three aspects. First, a probabilistic mutation mechanism is designed to increase the diversity of the population. Second, a novel reinitialization procedure is proposed to detect converged individuals and explore more new regions that may contain global optima. Third, distribution adjustment local search (DALS) is proposed to deal with the isolated solution and enhance the convergence of the algorithm. To evaluate the performance of TPNDE, we test our TPNDE and 5 state-of-the-art algorithms on 20 NESs with diverse features. The experimental results show that the proposed TPNDE algorithm is better than the other methods.
FEATURES OF TEACHING CULTURAL STUDIES AT A CONSTRUCTION UNIVERSITY

The article is devoted to the issues of state policy in the field of humanitarization of education through an implementation of cultural studies into student training programs since the 1990s. The author draws attention to how the teaching of cultural studies contributes to the formation of a qualified specialist, raises the general cultural level of the graduate, called upon to solve socially significant problems. The author substantiates the necessity of teaching the discipline through the implementation of several methodological approaches aimed at the materials and specifics of the construction university. Due to the active use of axiological, thesaurus, semiotic and anthropological approaches to teaching cultural studies, students can master a wide range of knowledge, develop skills and abilities necessary for orientation in the modern sociocultural situation and personal self-realization, develop communication skills, choose a proper solution of professional problems. As a result of the study of cultural studies, the experience of previous generations is internalized into the spiritual world of young people, and sociocultural identification is strengthened. Studying the history of culture through the prism of the history of the city, architecture, combined with the general information of the course, helps students to find their bearings in the modern sociocultural situation more easily, to understand the meaning and origins of many cultural phenomena, to form a value attitude towards cultural objects and architectural heritage.
GCAN: A Group-Wise Collaborative Adversarial Networks for Item Recommendation

Recommendation System aims to provide personalized recommendation for different users. Recently, Generative Adversarial Networks based recommendation systems have attracted considerable attention. In previous research, GAN has shown potential and flexibility to learn latent features of users’ preferences. However, GANs are hard to train to converge and waste many processes of fulfilling empty data, especially when meeting with the data sparsity problem. In this paper, we propose a new group-wise framework, namely Group-wise Collaborative Adversarial Networks (GCAN) to solve the data sparsity problem and enable GAN to converge faster. We combine GAN with traditional collaborative filtering methods to generate recommendations (CAN), and then propose binary masking and sample shifting to achieve GCAN. Binary masking separates binary user-item interaction and abstracts group-wise relationship from these binary vectors, while sample shifting is designed to avoid incorrect learning process. A noise corruption parameter is then introduced with experiments to show the robustness of GCAN. We compare GCAN with other baseline methods on Yelp and SC dataset, where GCAN achieves the state-of-the-art performances for personalized item recommendation.
Research on Fast Inter-process Communication Algorithm

In recent years, the microkernel architecture has become an increasingly popular kernel architecture. Since most of the system services of the microkernel run as user-mode process, the user process needs to access system services through the kernel's communication mechanism to request other usermode service process to provide services. Therefore, the frequency and amount of inter-process communication in the microkernel is much larger than that in the monolithic kernel, causing the poor performance of the microkernel. In this paper, two exchange-based IPC communication algorithms are proposed to optimize the communication algorithm based on replication mode, which is commonly used in the microkernel. One is based on the algorithm of exchange of physical page of MMU, and the other is based on the algorithm of exchange of segment base address of MMU. These two exchange algorithms can achieve efficient transmission while using the independent address space to ensure the correctness of the transmitted data, greatly improving the efficiency of communication in the microkernel, thereby improving the performance of the microkernel.
Coordination Model Research Based on Command Decision Joint Intension

Abstract Command Decision Simulation (CDS) is not only an important and difficult issue in the field of simulation, but also a technique issue to be tackled in the course of collective Modeling and Simulation in Joint Operations Experiment Center. CDS can give support to all kinds of simulation. Using computer to realize the Blue side’s command decision automatic execution in the operation training simulation system can improve training effect. Using computer to realize the lower level command unit’s automatic command decision can help to reduce the number of controllers and liberate them from heavy work. It can also enlarge simulation scale and increase the freedom of the simulation entity especially the command entity, thus enhance the authenticity and credibility of the simulation. On the basis of the command decision structure and the Joint Intention Theory, the thesis puts forward the method of making use of Coordination Matrix (CM) to realize cooperation among the entities in CDS and constructs the calculation sketch of Task Execution, Task Monitoring and Task Replanning. At the same time, the thesis brings forward the CDS structure vision based on Partial Global Planning(PGP).As CM is an indispensable part of combat simulation, the theory and method of CDS will become an important part for the theory of Combat Simulation.
Feature Selection and Feature Manifold for Age Estimation

In recent years, a number of manifold learning techniques have been proposed in the literature to address the age estimation problem. In manifold methods, appearance features are projected onto a discriminant aging subspace and the age estimation is performed on the aging subspace. In these methods the manifold is learn from the gray intensity images. We propose a feature based discriminant manifold learning and feature selection scheme for robust age estimation. This paper also presents an experimental analysis of the manifold learning and feature selection schemes for age estimation. The exact age value is estimated by applying regression on the resultant feature vector. Experimental analysis on a large scale aging database MORPH-II, demonstrate the effectiveness of the proposed scheme.
Augmenting Humans in the Loop: Towards an Augmented Reality Object Labeling Application for Crowdsourcing Communities

Convolutional neural networks (CNNs) offer great potential for business applications because they enable real-time object recognition. However, their training requires structured data. Crowdsourcing constitutes a popular approach to obtain large databases of manually-labeled images. Yet, the process of labeling objects is a time-consuming and cost-intensive task. In this context, augmented reality provides promising solutions by allowing an end-to-end process of capturing objects, directly labeling them and immediately embedding the data in training processes. Consequently, this paper deals with the development of an object labeling application for crowdsourcing communities following the design science research paradigm. Based on seven issues and twelve corresponding meta-requirements, we developed an AR-based prototype and evaluated it in two evaluation cycles. The evaluation results reveal that the prototype facilitates the process of object detection, labeling and training of CNNs even for inexperienced participants. Thus, our prototype can help crowdsourcing communities to render labeling tasks more efficient.
Analysing the Adversarial Landscape of Binary Stochastic Networks

We investigate the robustness of stochastic ANNs to adversarial attacks. We perform experiments on three known datasets. Our experiments reveal similar susceptibility of stochastic ANNs compared to conventional ANNs when confronted with simple iterative gradient-based attacks in the white-box settings. We observe, however, that in black-box settings, SANNs are more robust than conventional ANNs against boundary and surrogate attacks. Consequently, we propose improved attacks against stochastic ANNs. In the first step, we show that using stochastic networks as surrogates outperforms deterministic ones, when performing surrogate-based black-box attacks. In order to further boost adversarial success rates, we propose in a second step the novel Variance Mimicking (VM) surrogate training, and validate its improved performance.
Develop the Ability to Learn by Extracurricular Activities through Regional Collaboration

In this paper, we report the results of education to foster the power of learning in the extracurricular activity “Smart City” project of Kanazawa Institute of Technology and International College of Technology in collaboration with local governments and local communities. Extracurricular activities play an important role in strengthening interdisciplinary activities using design processes to solve problems. In extracurricular activities, student can continue to learn about the same theme for about 4 years, so they can acquire a wide range of knowledge for the problem solving. The “Smart City” project aims at proposing safe and secure city planning through development of IoT devices. The project has the experience using GPS to detect the dangerous place of the traffic accident. Taking advantage of the experience, they made the “IoT smart Device” and mounted it to bicycles. When a bicycle equipped with an IoT device arrives at a tourist spot, it will automatically provide tourist information in English. We also planned and implemented a stamp rally that goes around 15 sightseeing spots in Kanazawa by bicycle in cooperation with “Machinori”, and evaluated IoT devices. This activity was effective in acquiring the learning ability needed in society.
A PROBABILISTIC <i>ℓ</i><sub>1</sub> METHOD FOR CLUSTERING HIGH-DIMENSIONAL DATA

In general, the clustering problem is NP-hard, and global optimality cannot be established for non-trivial instances. For high-dimensional data, distance-based methods for clustering or classification face an additional difficulty, the unreliability of distances in very high-dimensional spaces. We propose a probabilistic, distance-based, iterative method for clustering data in very high-dimensional space, using the ℓ 1 -metric that is less sensitive to high dimensionality than the Euclidean distance. For K clusters in ℝ n , the problem decomposes to K problems coupled by probabilities, and an iteration reduces to finding Kn weighted medians of points on a line. The complexity of the algorithm is linear in the dimension of the data space, and its performance was observed to improve significantly as the dimension increases.
Social Identity and Values of Managers, as a Factor in Increasing the Effectiveness of Corporate Training

Abstract The research explores the dynamics in social identity and values of managers through corporate learning experience. The sample comprised 80 managers aged 23-45fromUral businesses. In the course of the study, the sample of managers was divided into two groups: an experimental group, whose managers passed corporate training; and a control group, whose managers did not take part in corporate training. Corporate talent training was part of life-long learning (LLL) in the company. Through corporate training, managers got feedback on the level of their professional competencies and future opportunities for their growth. The research uncovered differences in the dynamics of social identity and values among managers of the experimental and control groups. The experimental group of managers featured an upward trend in social and role identity with status and resource roles and the choice of “career (success)” value through the learning experience and the obtained feedback in follow-up of the training. The control group failed to express any dynamics in social and role identity and choose any statistically significant values. It has been proved that corporate training and feedback encourages employees to drive their own professional competencies and improve their professional expertise with the value focus on building a career in the company. The dynamics in social identity and values of managers serves as a resource for changes reflecting the features of corporate culture.
Multi-job Merging Framework and Scheduling Optimization for Apache Flink

With the popularization of big data technology, distributed computing systems are constantly evolving and maturing, making substantial contributions to the query and analysis of massive data. However, the insufficient utilization of system resources is an inherent problem of distributed computing engines. Particularly, when more jobs lead to execution blocking, the system schedules multiple jobs on a first-come-first-executed (FCFE) basis, even if there are still many remaining resources in the cluster. Therefore, the optimization of resource utilization is key to improving the efficiency of multi-job execution. We investigated the field of multi-job execution optimization, designed a multi-job merging framework and scheduling optimization algorithm, and implemented them in the latest generation of a distributed computing system, Apache Flink. In summary, the advantages of our work are highlighted as follows: (1) the framework enables Flink to support multi-job collection, merging and dynamic tuning of the execution sequence, and the selection of these functions are customizable. (2) with the multi-job merging and optimization, the total running time can be reduced by 31% compared with traditional sequential execution. (3) the multi-job scheduling optimization algorithm can bring 28% performance improvement, and in the average case can reduce the cluster idle resources by 61%.
Tweet Length Matters: A Comparative Analysis on Topic Detection in Microblogs

Microblogs are characterized as short and informal text; and therefore sparse and noisy. To understand topic semantics of short text, supervised and unsupervised methods are investigated, including traditional bag-of-words and deep learning-based models. However, the effectiveness of such methods are not together investigated in short-text topic detection. In this study, we provide a comparative analysis on topic detection in microblogs. We construct a tweet dataset based on the recent and important events worldwide, including the COVID-19 pandemic and BlackLivesMatter movement. We also analyze the effect of varying tweet length in both evaluation and training. Our results show that tweet length matters in terms of the effectiveness of a topic-detection method.
Deep Learning Approach towards Solar Energy Forecast

The diffusion of solar energy is increasing at the exponential rate at the utility scale. The stochastic nature and its behavior are difficult to predict, thus forcing researchers to use the approach of deep learning. As the world is consuming more electricity and wasting the most vital aspect of the energy, the generation of solar energy is crucial for the balancing of the power grid and its optimization. For efficient operation and stability, it is essential to forecast solar energy. To do so, physical laws are applied for computation of the direction of the sun’s rays as well as its accumulated energy. The computational evaluation for the production of solar energy is quite challenging in the field of AI. There is a need to take into consideration certain factors like the positioning of the sun, fluctuation of the weather condition, and many more. It becomes challenging in cloudy weather because the movement of the clouds is unpredictable. This sometimes introduces computational error in the result, which may be ignored. In this chapter, an attempt is made to use the deep learning networks and models to capture the movement of the cloud pattern and how its impact will be on the generation of solar energy. In the proposed model using a deep learning network, the error rate reduces to 11%. Previous studies have shown an error rate of 21%. The 10% improvement in error rate will have a positive effect on the development of the solar energy industry in a healthier manner and reduce the cost (in USD) along with the reduction in the dependency on the emission of carbon dioxide.