Reviewerly/exHarmony huggingface dataset:
Multi-linguistic analysis of the uniqueness of scientific and educational texts based on lexically related components

Abstract The article presents computer modeling and the development of algorithmic and information tools to support multilingual analysis of the uniqueness of scientific and educational texts using the technique of lexically related components. This new educational and communication methodology allows the formation of interlanguage and intralingual associative links. The authors carried out a theoretical and informational analysis of the communication structure and information base in computer modeling of the process of multilingual analysis of the uniqueness of texts. The development of algorithmic and information tools for supporting multilingual analysis of the uniqueness of texts on the basis of lexically related components with the formation of interlanguage and intra-lingual associative links has been carried out. As a result of the work, a new method of computer modeling of multilingual analysis of the uniqueness of texts is proposed, focused on the formation of intralingual and interlanguage associative links. To implement the proposed methodology in the process of multilingual analysis of the uniqueness of texts, the structure of frequency lexically related dictionaries has been developed as a means of information and terminological support.
Where to Cut and Paste: Data Regularization with Selective Features

Deep convolutional neural networks are continually evolving through various effective training methods such as data augmentation. Among data augmentation methods, regional dropout or replacement strategies such as [3], [4], [5] have been proved effective in recognition and localization performance. However, such methods suffer from unintended content corruption like informative pixel loss. For example, cutting and pasting a random patch may consist of areas that are not important and even if a new cutout patch consists of informative pixels, it could be pasted at useful locations of input covering the interest of the object. Therefore, this operation can cause too much or meaningless regularization. Motivated by this, we propose a new data augmentation method strategy, called FocusMix, which exploits informative pixels based on proper sampling techniques. Through experiments, we analyzed and compared various data augmentation methods to provide improvements and effectiveness of FocusMix. Finally, we have shown that FocusMix results in improvements in performance compared to other data augmentation methods.
Frame definability, canonicity and cut elimination in common sense modal predicate logics

Abstract The paper presents semantically complete Hilbert-style systems for some variants of common sense modal predicate logic proposed by van Benthem and further developed by Seligman. The paper also investigates frame definability in the logics and shows what axiom schema is canonical in the logics. In addition to these semantic investigations on the logics, the paper provides the sequent calculi for some of the logics which enjoy cut elimination theorem.
A Study Aimed for Drones that Carry Human

In this paper, as a first step, we prototyped a small machine and measured its characteristics. The prototyped small machine was able to lift 10 kg. As the second step, we examined the increase in size and examined the capacity of each part of the drone that can produce a static thrust of 140 kgf that can carry a person. It was shown that this can be achieved by connecting multiple LiPo batteries in series.
Reference Service Process: A Normalized Cross-Over Service Collaboration Paradigm

The deep integration and innovation of cross-over services across the boundaries of different industries, organizations and individuals will provide developers with multidimensional, high-quality and valuable cross-over services, which has become an important innovation approach for the development of modern service industry. With the further development of this trend, services in the form of processes play an increasingly important role in the field of service computing research. However, with the rise of digitization and the escalation of cross-industry, cross-over participants face two tough questions: (l) What kind of service process can solve the complex business we face.(2) How can we access these service processes quickly and conveniently. In this paper, we propose the concept of reference service process to solve the problems above. The reference service process is a normalized cross-over service collaboration paradigm. On the one hand, reference service process of different functional topics can solve most complex businesses, eliminating the need for developers to design service processes. On the other hand, each reference service process establishes a mapping relationship with multiple general service processes, which solves the problem of selection by automatically selecting the optimal service process for developers.
TSCREC: Time-sync Comment Recommendation in Danmu-Enabled Videos

In recent years, Time-sync Comment (TSC), as known as “Danmaku” or “Danmu”, has been increasingly recognized as a valuable representation of video being incorporated into the process of generating video or highlight recommendation. However, little work has studied how to recommend proper TSC when users are watching the video. Providing some suggestions for users when they want to post TSCs can not only enhance the real-time interactions among users within videos, but also motivate users to post more TSCs which could be useful for video or highlight recommendation in return. In order to accomplish the TSC recommendation task, we extract candidates from existing comments sent by other users. Specifically, we propose a model, namely “TSCREC”, which uses a bidirectional Gated Recurrent Unit to capture the semantic meaning of existing comments and assigns scores for comments by a Multi-Layer Perceptron. Furthermore, considering the importance of correlations among comments, we also take similarities among comments as features. In addition, we design a set of novel evaluation metrics which combines n-grams overlapping and ranking order to measure the quality of the recommendation list. We conduct experiments on realworld datasets, and the results show that our TSCREC model outperforms baseline approaches.
Specifying and Model Checking Workflows of Single Page Applications with TLA<sup>+</sup>

Single Page Applications (SPAs) are different than hypertext-based web applications in that their workflow is not defined by explicit links, but rather implicitly by changes of their widgets' states. The workflow may hence be hard to track. We present an approach to specifying and model checking SPAs with TLA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> . Our approach makes it easier to document and to track the workflow of SPAs and to find potential design flaws.
Densely connected network with improved pyramidal bottleneck residual units for super-resolution

Recent studies have shown that super-resolution can be significantly improved by using deep convolution neural network. Although applying a larger number of convolution kernels can extract more features, increasing the number of feature mappings will dramatically increase the training parameters and time complexity. In order to balance the workload among all units and maintain appropriate time complexity, this paper proposes a new network structure for super-resolution. For the sake of making full use of context information, in the structure, the operations of division (S) and fusion (C) are added to the pyramidal bottleneck residual units, and the dense connected methods are used. The proposed network include a preliminary feature extraction net, seven residual units with dense connections, seven convolution layers with the size of 1×1 after each residual unit, and a deconvolution layer. The experimental results show that the proposed network has better performance than most existing methods.
A Comparative Study of Centroid and Medoid based Categorical Data Clustering Methods for Solving Cold-start Recommendation Problem

One efficient solution to solve the cold-start recommendation problem is by exploiting the user demographic information using a clustering method. As the user demographic information contains categorical data, the choice of the clustering method to be used must naturally suitable to the particular data characteristic. There are two popular heuristic categorical data clustering algorithms, i.e., centroid and medoid based. This paper conducts a comparative study towards the implementation of K-Modes of the centroid-based method and K-Approximate Modal Haplotype (K-AMH) of the medoid-based method for solving the cold-start recommendation problem. The experiment results on the MovieLens dataset show that K-AMH achieves the average performance increase of 0.51% in terms of Precision and 0.40% in terms of Normalized Discounted Cumulative Gain (NDCG) to K-Modes. Yet, K-Modes is more lenient to use due to its scalability.
Arbitrary-Oriented Ship Detection Based on Feature Filter and KL Loss

Although deep learning has been dominant in the field of target detection, there are still some challenges in the field of detection of ships: horizontal boundary box contains too much redundancy and noise in the scene of large scale and dense arrangement of ships, and there are many interferences in remote sensing optical images that affect the bounding box regression. Recent neural network for ship detection is generally used to extract the pixel information in the image to achieve rapid positioning and boundary box regression, or to process the dataset, so as to train a more robust network. However, these methods do not focus on the original feature map and loss functions of the target, which directly lead to the effects of ship positioning and regression. In this paper, we propose a ship detection network based on feature filter and Kullback-Leibler (KL) divergence loss function. In this paper, we propose to use mask filters on the output of the region of interest (ROI) network to remove the noise around the rotating bounding box, which is conducive to the regression of the rotating bounding box and angle in the second stage. In order to get a better bounding box, we propose to use KL loss function for regression of bounding box parameters. With the optimization of KL loss, the bounding box can better surround the ship. We carried out experiments on our own remote sensing ship image dataset for ship detection, it contains 5,126 remote sensing satellite images and more than 23800 ships in 6 categories. The dataset contains a variety of scenarios and challenges. Experiments show that the method is accurate and effective, and the bounding box has good wrapping property.
Mixed Reality Application Prototype to Explore the Real World in Real Time Through Third Person Point of View

Mixed reality is a technology that combines virtual objects and imagery of real objects simultaneously. As technology develops, ways to observe human behavior have also developed, such as projecting users virtually so that they can see their environment through a third person perspective. There is still no application that provides such functionality. For that reason, this research was developed to simulate the experience of exploring reality through a third person perspective. The prototype was later compared with its first person counterpart. The evaluation on the comparison, which was done through a subjective questionnaire taken by 8 people, resulted in the first person perspective as a winning option. Even though the third person perspective benefited the user's surrounding awareness, its performance was less superior to its first person counterpart. Nevertheless, more observable experiments were suggested to open possibility for the third person perspective to perform better in the mixed reality environment.
State Estimation Method Using Radio Wave Intensity of BLE Beacon Installed Inside Object

With the spread of the Internet of things, a variety of sensors have been attached to home appliances, and it has become possible to obtain life-logs based on the information obtained from these sensors. Life-logs are easy to collect for home appliances that have sensors, but are difficult to collect for home appliances, doors, chairs, and other furniture without special sensors. Previously, acceleration sensors and Wi-Fi radio waves have been used to estimate the state of such home appliances or furniture. However, the estimation accuracy greatly depends on the presence or absence of movement and the size of the estimation object. Therefore, we propose a method to place Bluetooth low energy beacons directly into objects such as home appliances or furniture. This method allows for state estimation based on the radio wave intensity of the beacon, which changes depending on the state of the object. The method was applied to state estimation of the opening or closing of a refrigerator, the opening or closing of a safe, and the occupancy of a chair, and the estimation accuracy was confirmed. Our results showed that the method could estimate the opening or closing of the refrigerator with 99.2% accuracy, the opening or closing of the safe with 93.8% accuracy, and the occupancy of the chair with 98.9% accuracy.
Training fuzzy inference system-based classifiers with Krill Herd optimization

In recent years, researchers have been able to access many novel metaheuristic algorithms inspired by natural phenomena. One such bio-inspired optimization routine is Krill Herd Algorithm (KHA). In this study, a new approach for modification of membership function parameters in a fuzzy inference system (FIS) is demonstrated. Here, the main intent is to compare KHA optimization with other heuristic and metaheuristic algorithms, as a means to train FIS structures. The proposed FIS training method has been designed to serve as a fuzzy classifier. Hence, benchmark data sets extracted from the University of California, Irvine (UCI) Machine Learning Repository were applied, while Classification Errors and Sum of Squared Errors were used as measures for evaluation criteria. The obtained results led to the conclusion that the utilization of KHA provides promising performance, especially in the case of imbalanced data—whether in terms of the classification measures or the time required for an adequate FIS training.
DEVELOPMENT OF TARUMANAGARA UNIVERSITY TRACER STUDY INFORMATION SYSTEM

Abstract A tracer study is a graduate or alumni survey that attempts to track the activities of former students of an academic institution. Tracer Study results can be utilized by universities to find out the success of the educational process that has been done towards their students. In the current tracer study at Tarumanagara University, the data collected has not been optimized for the use of detailed data accreditation and reporting purposes. The process in this application program begins with alumni filling out their identity and a questionnaire. The data that has been entered by the alumni will then be processed so the data can be used for data reporting. The purpose of making this application program is to collect alumni data and establish alumni data reports that can be used for university accreditation on QS Stars accreditation and BAN-PT accreditation. The system development method used in developing this system is the System Development Life Cycle with Waterfall model. This program is designed based on the web using the PHP programming language and using the Laravel Framework. Based on testing conducted on the application and evaluation programs that have been carried out, the program can assist users in managing alumni data and tracer studies as well as producing the required reports.
Assessment of digital equipment of higher educational institutions

Abstract The article considers the assessment of the digital equipment of higher educational institutions. As part of the work carried out, the indicators of the digital equipment of higher educational institutions were considered, which indicate that there is one personal computer for four students and a computer with Internet access for five students. In libraries of higher educational institutions, this indicator is lower, here there is one personal computer with Internet access for about six students, while in private educational institutions this indicator is one personal computer for three students. At the end of the study, mechanisms were proposed to increase the level of digital equipment in higher education institutions.
Multi-Agent Feature Learning and Integration for Mixed Cooperative and Competitive Environment

At present, most of the centralized training with decentralized execution (CTDE) multi-agent reinforcement learning (MARL) algorithms have good results in the research of homogeneous scenarios. Heterogeneous multi-agent scenarios with different roles, cooperation modeling and credit assignment problems lead difficulty to learn effective collective strategies. In this paper, we propose a method of feature learning and feature integration about cooperation. Specifically, in the aspect of feature learning, through graph attention network, the relationship between agents is simplified to graph adjacency matrix representation, so that their feature vectors have relationship attributes. At the same time, for feature integration, we use batch normalization (BN) method to concatenate trained feature. We expect that agent relations can be modeled by end-to-end design. Meanwhile, attention mechanism can enhance the communication between interrelated agents. Through the experiments, our method has a significant result on improving the cooperative-competitive scenario of heterogeneous multi-agent. Moreover, we can visualize the output to analyze the reasonable collaborative and emphases attack policy.
Hardware-Assisted Isolation Technologies: Security Architecture and Vulnerability Analysis

Hardware-assisted isolation technology provide a Trusted Execution Environment (TEE) for the Trusted Computing Base (TCB) of a system. Since there is no standardization for such systems, many technologies using different approaches have been implemented over time. Before selecting or implementing a TEE, it is essential to understand the security architecture, features and analyze the technologies with respect to the new security vulnerabilities (i.e. Micro-architectural class of vulnerabilities). These technologies can be divided into two main types: 1) Isolation by software virtualization and 2) Isolation by hardware. In this paper, we discuss technology implementation of each type i.e. Intel SGX and ARM TrustZone for type-1; Intel ME and AMD Secure Processor for type-2. We also cover the vulnerability analysis against each technology with respect to the latest discovered attacks. This would enable a user to precisely appreciate the security capabilities of each technology.
An IND-CCA2 Attack Against the 1st- and 2nd-Round Versions of NTS-KEM

This paper presents an IND-CCA2 attack against the 1st- and 2nd-round versions of NTS-KEM, i.e., the versions before the update in December 2019. Our attack works against the 1st- and 2nd-round specifications, with a number of decapsulation queries upper-bounded by $$n-k$$ and an advantage lower-bounded by roughly $$0.5(n-k)t/n^2$$ , where n, k, and t stand for the code length, code dimension, and the designed decoding capacity, for all the three parameter sets of NTS-KEM. We found that the non-reference implementations are also vulnerable to our attack, even though there are bugs. There are also bugs in the reference implementations, but in a way invulnerable to our attack.
WITHDRAWN: Energy efficient resource allocation for a sustainable computing environment

Cloud computing is a highly scalable infrastructure and also cost effective for managing High Performance Computing (HPC) system and Web applications. It provides software, platform and Infrastructure services. As the demand of the cloud infrastructure is increased day by day, the consumed energy by the data centers is also hugely increased. The world’s highest electricity consumer is data center. It leads to the problem of high carbon emissions. It affects the environment. High carbon emission problem is the impact of Cloud computing on the environment. So the utilization of energy in an efficient way is more important. Here heuristic technique is used to solve this problem.
Particle Filtering Under General Regime Switching

In this paper, we consider a new framework for particle filtering under model uncertainty that operates beyond the scope of Markovian switching systems. Specifically, we develop a novel particle filtering algorithm that applies to general regime switching systems, where the model index is augmented as an unknown time-varying parameter in the system. The proposed approach does not require the use of multiple filters and can maintain a diverse set of particles for each considered model through appropriate choice of the particle filtering proposal distribution. The flexibility of the proposed approach allows for long-term dependencies between the models, which enables its use to a wider variety of real-world applications. We validate the method on a synthetic data experiment and show that it outperforms state-of-the-art multiple model particle filtering approaches that require the use of multiple filters.
Block-based masking region relocation and detection method for image privacy masking

The widespread use of CCTV and various image devices has become a primary cause of privacy invasion, because these are possible to record, share and leak privacy images without owners' consent. The image privacy masking is one of technology for privacy prevention, and its necessity has been increased owing to need of personal information protection and social safety in these days [1]. In this paper, we propose a block-based masking region relocation and detection method to overcome the shortage of the meta-data typed privacy region information sharing method of restorable image masking service. This method is based on the histogram difference between the original image and the masked image. It analyzes the histogram feature of images, and relocates the masking region information from the coordinate system to the block system. Therefore, it set and detect the masking region without additional information such as meta-data and the original image. For demonstration the feasibility of our approach, we used the real-world database and the experimental results show the applicability of the real privacy masking service.
Application and Use of ICT in University Library Services: A Panacea for Conventional Library Services

In this study, Information and Communication Technology has been treated as a concept. Components, types and characteristics of ICT have also been discussed together with the areas for ICT application in university libraries. The researchers discussed in details the emergence state-of-the-art library services mostly in university libraries and at the end drawn conclusion with regard to Information and Communication Technology (ICT).
A proposal for preparation of cluster states with linear optics*

Measurement-based quantum computation in an optical setup shows great promise towards the implementation of large-scale quantum computation. The difficulty of measurement-based quantum computation lies in the preparation of cluster state. In this paper, we propose the method of generating the large-scale cluster state, which is a platform for measurement-based quantum computation. In order to achieve more complex quantum circuits, the preparation protocol of N -photon cluster state will be proposed as a generalization of the preparation of four- and five-photon cluster states. Furthermore, our proposal is experimentally feasible.