Reviewerly/exHarmony huggingface dataset:
Peculiarities of Mathematical Training of Sociologist in the Process of Training

The article deals with the problem and directions of improving the professional education of social specialists in the aspect of mathematical training. The author notes that mathematical training in the process of teaching a sociologist is important, first of all, for understanding the directions of scientific and professional activity. The author marked the directions of scientific and professional activity. The author points out that one of the most important functions is to conduct predictive studies in the field of pedagogy and build models based on them. The author noted the priority areas of the mathematics course for specialties of the social profile. The author identified the problems of organizing the training of future sociologists, which are factors that reduce the effectiveness of training future sociologists. The author noted further directions of scientific research in this matter.
Perencanaan Arsitektur Sistem Informasi Pelayanan Kesejahteraan Sosial Menggunakan EAP

Dinas Sosial Kabupaten Garut is currently still experiencing difficulties in managing information related to social welfare services, so it is necessary to strive for better information management, one form of implementation is by implementing a computer-based information system and technology. In information technology there are three interconnected parts, namely: hardware, software, and databases. Planning an integrated information system architecture and in accordance with the organization's business goals is one of the efforts to improve services in a better direction so that they can compete with other organizations. This study discusses the architectural planning of social welfare service information systems using Enterprise Architecuter Planning (EAP) which was developed and integrated with The Open Group Architecture Framework (TOGAF at Dinas Sosial Kabupaten Garut, where the discussion is focused on four stages, namely: business architecture planning, data architecture planning, application architecture planning, and technology architecture planning. The results of the research that has been carried out are recommendations for the development of information system architecture to support the improvement of public service performance and system integration into all parts of the organization.
PRESENCE OF PRICE CLUSTERING AND PSYCHOLOGICAL BARRIERS IN BITCOIN MARKET

In the last year, the market of Bitcoin experienced one of the most turbulent times of its existence.In the last year, there were bull markets, bear markets, and sideways markets.This paper deals with the most apparent virtual currency, Bitcoin and the evidence of price clustering and the presence of psychological barriers of investors.The primary objective of this paper is to find out if the last digit and last two digits of the maximum (or minimum) price of Bitcoin tend to cluster around digit nine (or zero).Paper uses price clustering to determine if the theory of behavioural finance also implies conditions of the Bitcoin market, namely if investors in Bitcoin tend to see the resistance line when the maximum price ends with digit nine and if the minimum price with last digit 0 signals the support line.Results of the 2191 observations showed robust results in terms of support line, however not so unambiguously in the case of resistance line.The paper suggests that Bitcoin is changing its reputation as a solely speculative asset and transforming into a long-term investment strategy.
A Method for Reducing the Register Effect for Modular Data Formats

The substantiation of the author's method of reducing redundancy from the register effect when placing the components of a tuple representing a numerical value in a modular format designed for storage, transmission and processing in modular arithmetic in a specialized SIMD processor of parallel structure is given. Modular coding allows to obtain parallel execution of ring operations in independent computing paths. This, according to Amdahl's law, accelerates the execution of the computational process on multiprocessor computing systems or on multiple cores. Modular data formats are not consistent with the binary bit grid of a multiprocessor computer. In homogeneous binary registers designed to display modulo deductions, redundancy occurs because not all possible binary combinations in a digital register are used to display data. The method is based on the redistribution of the redundancy of digital registers used to display the components of the modular tuple, which allows to reduce to zero the register effect and redundancy of the representation of the components of the tuple. This makes it possible to obtain a dense packing of components of vector modular formats in homogeneous digital registers, which makes the development of SIMD architecture computers processing data in computer modular formats promising. The simulation results allow us to obtain mutually simple bases of the modular number system that meet the conditions of a new patented method for the complete elimination of redundancy.
Awareness and Utilization of Emerging Technologies in E-Businesses

Rapid developments in digital technologies such as artificial intelligence and big data are changing how businesses run today. In order to improve the process and product quality, decision making, and management at operational, tactical, and strategic levels, emerging technologies need to be adopted by businesses. This chapter explores the utilization and awareness of blockchain, artificial intelligence, cloud computing, big data, and internet of things (IoT) technologies in e-businesses in Turkey with a survey study. From 46 data points, the results indicate that the most utilized two technologies are cloud computing and big data, with 82.6% and 67.3%, respectively. The least adopted ones are the IoT and blockchain technologies. The authors specified that the survey participants' awareness of the blockchain's potential usage areas in the e-business domain is considerably low.
Virtual Reality Technology

Digital technologies of virtual reality (VR) are swiftly immersed into the material associations area and inflating it with novel means of fulfilling business goals. The higher intricacy of managing various and fast-advancement business models might lead innovators in applying highly developed technologies such as VR. It refers to technologies in scripting the perceived reality. Considering the advancement of VR technology, several business sectors have initiated to wake up to the probable use of VR. Scrutinizing the historical background and application results, it turned out that VR applications are an efficacious and motivation enhancing alternative to typical tools in business process amelioration. This chapter fills the literature niche by discussing the implications and suggesting future lines of research in lending a hand to organizations to promote their business activity by applying VR technologies.
Vessel and Lung Segmentation-Based Hierarchical FFD Framework for Non-rigid Thoracic CT Registration

Medical image registration is an important and fundamental task for medical image analysis. Due to the large-scale deformation in the lung, thoracic CT registration is more challenging. In this paper, we proposed a vessel and lung segmentation-based hierarchical registration method for non-rigid thoracic CT registration. We used the vessel segmentation to augment the CT image to highlight the local features and the lung segmentation was used to constrain the similarity metrics function to make the registration framework focus the deformation in the lung. The normalized cross-correlation (NCC) was used as the global similarity metrics and the normalized gradient field (NGF) was used for lung. In addition, the hierarchical registration strategy was used to register images from coarse to fine. The ablation experiments were shown that our proposed method played a positive role in the registration processing. Compared to other registration methods, our method achieves a competitive result. We achieved 1.22 mm accuracy in the Dirlab dataset.
Research on Target Grouping Method Based on Graph Neural Network

With the application of a large number of battlefield observation sensors, the battlefield target data obtained by the command and control system has shown a surge trend. The scale of target information data is large, problems such as high dimensionality and complex structure have brought new challenges to the target grouping technology. The traditional clustering method has been unable to effectively target grouping in high-dimensional battlefield target data. Aiming at the dilemma of current target grouping technology, in order to help commanders get different types of target groups, this paper constructs a target grouping algorithm based on graph neural network, which combines graph convolutional neural network and clustering algorithm, and uses self-supervised mechanism to optimize the model. Through the verification experiment of the target clustering model on 4 high-dimensional data sets, three clustering effect evaluation indicators are established, and compared with the two methods of K-means and AE+k-means, it proves that our method is more efficient and intelligent.
Enhancing RUP security for the OCB mode

Authenticated encryption(AE) is the most widely used encryption, for it guarantee both integrity and confidentiality. Among oceans of AE algorithms, OCB is recognized as the best choice for its performance on platform supporting AES-NI instructions. But the problem is that the OCB is not robust, which is to say the security of OCB broke when it face complicated situation such as nonce-misuse or release of unverified plaintext. Although many new AE algorithm has been submitted to the CAESAR competition and the ongoing lightweight AEAD competition to solve these problem, their computational overhead is high. And in consideration of the fact that it is costly to change algorithm on machines which has already deployed OCB, we focus on fixing the RUP security along with nonce misuse property, with acceptable time-performance tradeoff.To solve this problem, we introduce two authenticated encryption schemes, named as OCB-RUP and nmOCB-RUP ,which combining the OCB mode with a tweakable blockcipher of variable tweakable length. We give a security proof of our schemes, and a optimized implementation using AES-NI and PLMULQDQ instructions. We give a comparison of performance between our schemes and the AES-OCB implementation in OpenSSL. Our measurement shows that OCB-RUP is only 34% slower than AES-OCB, and nmOCB-RUP, the more slower one achieve encryption under one cycle per byte. We conclude that our schemes is a practical option to enhance the OCB mode, providing RUP security and nonce misuse resistance at low cost. Both of the schemes achieve encryption at under one cycle per byte.
Improved Related-Key rectangle Attack on GIFT

GIFT is an important lightweight block cipher widely used in lightweight algorithms, and the security of GIFT64 against rectangle attacks are evaluated. Based on Boomerang Connectivity Table (BCT)of S-boxes, the model to search for optimal r-round related-key boomerang characteristic of the form r = X +1+ X for block ciphers is established with Mixed Integral Linear Programming (MILP) method, and an optimal 19-round related-key boomerang characteristic of this form is found for GIFT-64. Furthermore, a 25-round related-key rectangle attack on GIFT-64 is presented by adding 3 rounds before and 3 rounds after the characteristic. Compared with the 25-round related-key rectangle attack proposed by Ji et al., our attack decreases time complexity by a factor of about 2<sup>13</sup>.
Apla<sup>+</sup>: a Practical Service-oriented Programming Language for Service Orchestration

Service-oriented paradigm utilizes simple small-scale services as fundamental elements for developing applications which are complex large-scale services. efficiently orchestrating services in a platform-independent and dynamic way is still desired as the service-oriented computing depicting. We concentrate on modeling the characteristics of services under the view of clearly distinguishing between service and resource providing service, then propose a practical and effective service-oriented programming language Apla<sup>+</sup> based on PAR. The language emphasizes on not only combining services provided by others, but also developing atomic services. Meanwhile, a fascinating architecture is raised to invoke various resources which remain unchanged on account of establishing the relationship between the resources and the services. Then both the service provider and the service consumer pay attention on their concerns via separating service orchestration and atomic services development, and services can be modeled conveniently through providing a higher level of abstraction. Finally we also give a case study to illustrate the feasibility of this language.
Adaptive Robust Nonlinear Filter Algorithm for Time Synchronization

This paper adopts a fusion algorithm to deal with different noise estimates and outlier processing in nonlinear time-synchronized system. In the framework of the unscented Kalman filter, the statistical characteristic parameters of measurement noise are estimated based on the variational Bayesian process. Combined with the maximum a posterior(MAP) estimation method, we use the measured noise parameters obtained by the variational Bayesian method to estimate the process noise and simultaneously realize the real-time adaptive estimation of the two kinds of noise statistical properties. A judgment method based on the likelihood function of innovation is embodied in the algorithm to make real-time judgments on the time deviation outlier generated in the synchronization process, and smooth the outlier through the window sliding average. In the nonlinear time synchronization process where the noise is unknown and the measurement process contains outliers, the algorithm can adaptively complete the synchronization estimation of the covariance matrix of noise, and can effectively judge and process the clock deviation outliers in the synchronization process.
Design and Implementation of New Generation Substation Secondary Simulation Automatic Test System

In order to improve the efficiency of New Generation intelligent substation transformation and expansion, the overall design scheme of the secondary simulation automatic test system of intelligent substation, as well as the key technologies and key functions of the system is introduced. The design and implementation methods of three functional modules of intelligent terminal simulation, merged unit simulation and automatic closed-loop test are expounded. The research results have been applied in several substations.
Improved Mayfly Optimization Algorithm with Cooperation

The mayfly optimization algorithm (MA) is a stochastic, population-based optimization technique that can be applied to a wide range of problems. To improve the optimization performance of mayfly optimization algorithm, a variation on the mayfly algorithm, called the improved mayfly optimization algorithm with cooperation, or MAC, was proposed in this paper, employing cooperative behavior. This is achieved by using multiple male populations to optimize different components of the solution vector cooperatively. Application of the MAC on several benchmark optimization problems shows a marked improvement in performance over the original algorithm.
Human Computer Interaction Method for Feeding Demands of the Disabled and the Sick Based on Micro Facial Posture Capture

There are a large number of elderly, disabled, and bedridden people who can not eat independently in China. With the development of science and technology, many eating auxiliary robot systems have been studied at home and all over the world. However, most of these systems lacks humanized humancomputer interaction relying only on based buttons, joysticks or human voice. Such methods can not be well applied to groups with limited limbs, weak body and tired mind. In this paper, we propose a food demand recognition method for the disabled and the sick based on micro facial posture capture. This method can capture the key points of face and face in real-time and accurately, so as to identify whether the user is eating, ready for new food or refusing to eat the food picked up by the robot, and optimize the human-computer interaction method. The effectiveness of the proposed method is proved by experiments.
Reading between the lines: A hybrid RNN architecture to detect prejudice in movies

Watching movies is considered the most popular pastime today. Keeping in mind research that has indicated that adolescents imbibe social behavior from movies, we propose a hybrid deep learning model to detect the degree of prejudice (sexism, racism, ableism, xenophobia, homophobia etc) in movies. Using a stacked bi-LSTM and XGBoost model to make predictions, we compared the percentage of prejudice in movies from two time periods (1975-2000 and 2001-2020). Further, we compared animated movies from production houses like Disney, Pixar with movies often rated R or PG-18 by Motion Picture Content Rating Boards. The results showed that the percentage of prejudice has been significantly reduced in the newer decades, and that, surprisingly, movies intended for a younger target audience, on average, perpetuate more prejudice than those meant for late teenagers and adults.
Post-Training Quantization for Longformer with Chunkwise Quantization Granularity and Optimized Percentile

Transformer-based models have been verified successful in many natural language processing and computer vision tasks. Because of computational complexity, many efficient transformer variants have been proposed, including the Long-former, which aims for long document processing. In this paper, we present an effective post-training quantization scheme for Longformer. Based on sliding window attention in Longformer, we propose chunkwise quantization. It can decrease quantization noise caused by significant gaps between ranges of different windows. Besides, to reduce quantization noise caused by clipping, we optimize percentile value by minimizing mean squared error between the original and quantized matrixes. The quantization scheme is evaluated on the TriviaQA task, and the performance is comparable to the float32 model. In addition, it is important that the quantization scheme can be extended to other efficient transformer-based models.
Checklist Screening Similarity Matching Algorithm Based on Decision Fusion

In this paper, we propose the algorithm of checklist screening based on decision fusion. First, it Converts unstructured checklist data to structured, regularizes structured checklist data such as extracting index words and removing modifiers, and creates Solr-index based on the customer entity name. Then we calculate the adjustment Levenshtein-Distance(LD) similarity matching score and Longest-Common-Subsequence(LCS) similarity matching score between the customer to be screened and the checklist data respectively. Finally, decision fusion results can be gained by fusing the two scores. The experimental results show that hit-rate and accuracy of the decision fusion algorithm which uses LD algorithm and LCS algorithm to calculates similarity is higher than single algorithm, and the Solr-index checklist data is effectively reduced, also the checklist screening performance is improved.
Accurate and Visual Video Recommendation Based on Deep Neural Network

Video recommendation is vital for a video platform, which provides its users with videos they may be interested in. In this paper, we integrate users' ratings of videos in the video platform and community and crucial information data such as video category, director/actor, predict users' preference for videos through deep neural network, which could improve the accuracy of personalized recommendation. In addition, we use weighted force-directed Graph to show the relationship among users, videos, directors, and other elements, which could display the visualization of data elements and recommended results. Extensive experiments are conducted on three video datasets, and the experimental results demonstrate that the proposed method is more effective than several other recommendation methods.
A traditional medicine intellectual property protection scheme based on Hyperledger Fabric

Due to its decentralized trust mechanism, blockchain is increasingly used as a trust intermediary for multi-party cooperation to reduce the cost and risk of maintaining centralized trust nowadays. And as the requirements for privacy and high throughput, consortium blockchain is widely used in data sharing and business cooperation in practical application scenarios. Nowadays, the protection of traditional medicine has been regarded as human intangible cultural heritage in recent years, but this kind of protection still faces the problem that traditional medicine prescriptions are unsuitable for disclosure and difficult to protect. Hyperledger is a consortium blockchain featuring authorized access, high throughput, and tamper-resistance, making it ideal for privacy protection and information depository in traditional medicine protection. This study proposes a solution for intellectual property protection of traditional medicine by using a blockchain platform to record prescription iterations and clinical trial data. The privacy and confidentiality of Hyperledger can keep intellectual property information safe and private. In addition, the author proposes to invite the Patent Offices and legal institutions to join the blockchain network, maintain users' properties and issue certificates, which can provide a legal basis for rights protection when infringement occurs. Finally, the researchers have built a system corresponding to the scheme and tested the system. The test outcomes of the system can explain the usability of the system. And through the test of system throughput, under low system configuration, it can reach about 200 query operations per second, which can meet the application requirements of relevant organizations and governments.
Frequency Domain Approximation in Deep Hashing

In the field of deep hashing, we introduce a gradient approximation estimation method for sign function. This method expands the sign function by frequency domain approximation. The classic deep hash methods generally use sigmoid series function to make the activation value close to -1 and 1 to realize the hashing of the content. We expand the sign by Fourier expansion. Improvements have been made in most of current deep hashing methods, like HashNet, DHN, ADSH, GreedyHash and CSQ. We further analyze the interpretability of frequency domain approximation. In the training process of neural network, the mapping function for input and output will be fitted gradually from low frequency to high frequency. Therefore, compared with the traditional approximation of sign function using sigmoid series function which just look like original sign function, we use Fourier expansion directly to achieve perfect approximation in the low-frequency part of the function and ignore the high-frequency part. We do experiments with five learning hash methods on four image retrieval datasets. The hash method aims to generate hash values with closer Hamming distance for similar images. The experimental results show that our method achieved better performance by replacing the original binarization function in the field of hash simply.