Reviewerly/exHarmony huggingface dataset:
Historical Method in Bibliography Science by E.K. Bespalova

The scientific legacy of Emilia Konstantinovna Bespalova, well-known Russian bibliographer, theorist and historian of bibliography, includes more than 200 works. She laid her own line in theoretical and methodological understanding of bibliography science and activity. “Formation of Bibliographic Thought in Russia (Up to the 60s of the 19th century)” is the last fundamental work of E.K. Bespalova; it describes the philosophical and methodological explication of bibliography as a naturally occurring phenomenon of information nature. There was formed unique method of analysis that considered historical and bibliographic facts in the context of professionalization of bibliographic activity. The analysis of bibliographic phenomena applied by E.K. Bespalova can be generally described as combination of modern theoretical knowledge on bibliography, methodology of system-activity approach and philosophy of historical process. The historical-theoretical method of studying bibliographic activity at different stages of its development allows a modern researcher to see the institutional significance of bibliographic processes as one of the full-fledged components of the global information picture. Analyzing the initial, original object of bibliographic activity — a book, the scientist proves that it was the process of replication and therefore the need to create the secondary structure of a book in a form of title page, which made a book to be the “book”. Through the concept of “book” E.K. Bespalova also traces interaction and sequential connection of three systems — “knowledge”, “book” (“document”) and “bibliographic document”. From the point of view of cognitive potential of the history of bibliography, bibliographical guide is of historical and theoretical interest being the result of activity and the object of desobjectivation in it of the conceptual theoretical-methodological and historical representations of authors, composers and doers of the history of bibliography. As the main differentiation of bibliographic products, Bespalova puts forward the division into timer bibliographic subsystems (reflection of current, retrospective, prospective primary flow) and chorographic subsystems that restrict documentary flows by the territorial and linguistic principle. The historical method by E.K. Bespalova reveals a wide range of theoretical foundations that enrich modern bibliography science.
MLND: A Weight-Adapting Method for Multi-label Classification Based on Neighbor Label Distribution

In multi-label classification, each training sample is associated with a set of labels and the task is to predict the correct set of labels for the unseen instance. Learning from the multi-label samples is very challenging due to the tremendous number of possible label sets. Therefore, the key to successful multi-label learning is exploiting the label correlations effectively to facilitate the learning process. In this paper, we analyze the limitations of existing methods that add label correlations and propose MLND, a new method which extracts the label correlations from neighbors. Specifically, we take neighbor’s label distribution as new features of an instance and obtain the label’s confidence according to the new features. Nevertheless, the neighbor information is unreliable when the intersection of nearest neighbor samples is small, so we use information entropy to measure the uncertainty of the neighbor information and combine the original instance features with the new features to perform multi-label classification. Experiments on three different real-world multi-label datasets validate the effectiveness of our method against other state-of-the-art methods.
BVOT: self-tallying boardroom voting with oblivious transfer

A boardroom election is an election with a small number of voters carried out with public communications. We present BVOT, a self-tallying boardroom voting protocol with ballot secrecy, fairness, and dispute-freeness. BVOT works by using a multiparty threshold homomorphic encryption system in which each candidate is associated with a set of masked primes. Each voter engages in an oblivious transfer with an untrusted distributor: the voter selects the index of a prime associated with a candidate and receives the selected prime in masked form. The voter then casts their vote by encrypting their masked prime and broadcasting it to everyone. By hiding the mapping between primes and candidates, BVOT provides voters with insufficient information to carry out effective cheating. The threshold feature prevents anyone from computing any partial tally—until everyone has voted. In contrast to some existing boardroom voting protocols, BVOT does not rely on any zero-knowledge proof; instead, it uses oblivious transfer to assure ballot secrecy and correct vote casting. Also, BVOT can handle multiple candidates in one election. BVOT prevents cheating by hiding crucial information: an attempt to increase the tally of one candidate might increase the tally of another candidate.
CMWMF: Constant Memory Architecture of Weighted Mode/Median Filter for Extremely Large Label Depth Refinement

In this study, we propose a constant memory hardware architecture that can support weighted mode, median, and joint bilateral filters, which is referred to as CMWMF. This work aims to meet the high memory and computation requirements of processing depth maps with a large number of depth candidates. In the proposed architecture, we leverage the geometry smoothing characteristic of natural images to reduce the static random access memory (SRAM) size for hardware implementation. The architecture preserves a constant number of disparity values instead of depending on the label count and size of the local supporting window. A novel weighted median search procedure is proposed, which assigns a computation to each input cycle, thereby rendering the process hardware friendly. An index-checking technique is proposed to process out-of-order joint histograms. We adopted the above-mentioned techniques in our architecture as they consume a constant SRAM size and supports multiple types of filters. As a result, this architecture reduces the SRAM size by 92.4% with a negligible decrease in performance. According to our analysis on the KITTI, and Middlebury datasets, and with actual depth cameras, the preserved information is sufficient. The proposed architecture is one of the most suitable depth refinement architectures for scenarios having a large number of depth candidates.
Study of customer behavior in online B2B shopping

The main goal of our study is to analyze behavior of the online shopping customers in a particular company, which is distributor of electronic components for industrial production. To reach the main goal we perform a data analysis of the data obtained from the companýs online sales process by using data mining technique - mining association rules. The purpose of the analysis is to identify and describe the significant association rules in order to provide information to support management decision making.
Goal setting using the SMART method in the disciplines of the professional cycle of the direction "Information systems and technologies" as a mechanism of self-realization

A distinctive feature of modern higher education, based on the activity approach, is the orientation of the entire educational process towards diagnosed goals. These goals are formed as educational results presented in the actions of the students themselves. It should be noted that the skill of goal setting is not formed spontaneously in the course of training — it is the result of a specially organized process, therefore the development of a methodology for teaching goal setting is one of the urgent problems in pedagogy. Goal setting is the process of creating a system of goals, correlating them with each other and choosing priority goals, and this is of particular importance for graduates of the IT direction of training. The development of the ability to set goals contributes to a clear vision of the prospects and opportunities for personal and professional self-realization of the subject. By the third year, students of the “Information Systems and Technologies” direction have already accumulated a certain amount of knowledge, but the most laborious thing in the course of studying the disciplines of the professional cycle is acquiring the professional ability to set goals correctly. A study of the literature and different approaches to solving this problem led the authors to develop a training session. The training for the development of the personality’s ability to set goals was developed on the basis of the SMART method. The technique was tested at the Department of Informatics and Applied Mathematics of Bratsk State University for three years and gave positive results. The advantage of the training is that the impact is carried out on all components of the ability to set goals. The formation of positive dynamics to goal-setting after passing the educational training has been proved.
A Unified Spatio-Temporal Description Model of Environment for Intelligent Vehicles

Beneficial from fusion of various external sensors such as LiDAR and camera, intelligent vehicles are able to be informed of their surroundings with details in real time. The characteristics of each type of sensors determine its specificity in raw data, leading to inconformity of content, precision, range and timing, which eventually increases the complexity of environment descriptions and effective information extraction for further applications. In order to efficiently describe the environment, as well as remain the diversity of necessary information, we propose a unified description model independent of specific sensors for intelligent vehicle environment perception, containing 3D positions, semantics and time. The spatio-temporal relationship between different types of collected data are established to express all the elements in the same system. As a potential implementation scheme, we take advantages of LiDAR point cloud and color images to acquire the positions and semantics of the environment components. Semantic segmentation based on convolutional neural networks (CNN) provides rich semantics for the model, which could also be enhanced with the fusion of LiDAR point cloud. We validate the effectiveness of our proposed model on the KITTI dataset and our own collected data and exhibit qualitative results.
An Improved Image Stitching Method Based on Seed Region Growth and Poisson Fusion

In order to address the problem of ghosting and gap in the field of image stitching, we proposed a novel image stitching method based on seed region growth algorithm and Poisson fusion. Firstly, PSO (Particle Swarm Optimization) is used to improve the accuracy of image registration. Then we combine the improved seed region growth algorithm with Poisson fusion to implement image stitching. Experimental results illustrate outstanding performance of our method by comparing with one famous image stitching tool: PTGui and one existing image stitching method. Our method can effectively solve the problem of ghosting and gap in the process of image stitching, and avoid objects distortion.
ECAT: an Enhanced Confidence-aware Trust-based recommendation system

One of the most widely employed recommendation approaches is collaborative filtering. There are some limitations to this approach, such as the cold-start user problem. Trust-based recommendation approaches are solutions to prevent traditional collaborative filtering-based approaches generating a poor recommendation. We have introduced a unique recommendation approach in our conference paper, called Confidence-aware Trust (CAT), which considers a confidence estimate in both direct and indirect trust calculations. However, the CAT recommendation approach does not involve some vital aspects, namely: the number of neighbors of active users/ target items, consistency in rating values, and the level of confidence in the predicted rating value as well. To address these aspects, we further propose an innovative approach, called an Enhanced Confidence-aware trust (ECAT), which improves the CAT recommendation approach. The Movielens dataset was evaluated. The experimental results show an improved performance by ECAT over its counterparts with respect to the accuracy of recommendations, especially tackle the issue of cold -start users.
Unique Stego Key Generation from Fingerprint Image in Image Steganography

The concept of image steganography involves concealing secret message, using stego key and cover image. The stego key is used both at the sender side and the receiver side, for insertion as well as for extraction of secret message. Thus, the stego key and the insertion algorithm are connected closely in image steganography. The process of insertion requires a unique method of embedding data in the original cover image for generating the stego image, such that it is difficult for the hacker to know the presence of secret data. This process in turn depends on the stego key to a large extent. On the other hand, biometrics has been accepted as one of the strongest ways of confirming the identity of any individual. This paper focuses both on biometrics and steganography for the secret transmission of data. Thus, a unique key has been generated from the fingerprint image, as a part of biometrics, where the special features of the image have been extracted. The special features and their corresponding locations have been found. These locations are used for data embedding in the cover image, thus paving the path for a unique way of insertion of secret bits.
A Multi-secret Sharing Scheme with Combiner Identification Authentication

Verifiable secret sharing is a hot topic in the field of secret sharing, but many verifiable secret sharing can not share more than one secret at the same time, moreover, some existing verifiable secret sharing schemes can not achieve the fairness of secret reconstruction in asynchronous environment. In this paper, an improved verifiable multi-secret sharing scheme is proposed by using one-way Hash function and Lagrange interpolation formula. In the scheme we introduce combiner verification and recognition and make the combiner and participants authenticate each other. Participants can check whether secret share submission requests from effective combiner is true, and combiner can detect and identify the participant. Our scheme has the following characteristics: Multiple Secrets can be reconstructed at the same time, and no safe channel is needed between the secret distributor and the participants, most importantly, our scheme adds validation to the combiner by the participants, making it impossible for an attacker to obtain information by impersonating the combiner.
Image Super-Resolution Network Based on Prior Information Fusion

Research in the field of image super-resolution in recent years has shown that convolutional neural networks are conducive to improving the quality of image restoration. In the deep network, simply increasing the number of network layers cannot effectively improve the quality of image restoration, but increases the difficulty of training. Therefore, in this paper, we propose a new model. By using multi-layer convolution, the image segmentation map based on image texture is modulated into the network, and the attention mechanism is used to adjust the feature output of each layer. The output of each layer is used as hierarchical feature for global feature fusion. Finally, the attention mechanism is used to fuse the hierarchical features to improve the quality of image restoration.
Performance Comparison of Parallel PSO-GA Algorithm with Dynamic Parameter Adjustment Using Type-1 and Interval Type-2 Fuzzy Systems

This paper reveals a comparison between different optimization methods with dynamical adjustment of parameters using Type-1 and interval Type-2 Fuzzy logic systems. In addition, the use of migration blocks in the PSO and Genetic Algorithm (GA) optimization methods is used to help each other to reach the best possible solution, which is also investigated in detail. The hybrid PSO-GA approach offers good simulation results outperforming individually the PSO and GA methods.
Electronic Cash System based on Multi-Bank Mobile Divisible, Recoverable and Transferable

Multi-bank mobile divisible, recoverable and transferable electronic cash system adapts on mobile terminals for e-business that can work in multi-banks and cannot confine from the bank which distributes the electronic cash. My thesis discusses on the protocol of the establishing account, the withdrawal protocol, the pay protocol, the deposit protocol, renew agreement of the electronic cash, the transferable agreement is based upon elliptic curve cryptography (ECC). The detachable electronic cash does not need bank by the usage of a electronic cash, so the bank will not turn into the bottleneck of e-business. This system is fitter for mobile micro-payment terminals with localization of storage, operand capacity, power supply and network bandwidth, which can satisfy the daily necessities of business. The new protocol can prevent from double spending, non-frameability, eavesdropping, tampering and "perfect crime" effectively as well. It can secured against mis-identity attacks, secured against two-layer anonymity attacks, secured against linking attacks.
Steganography Application Using Combination of Movements in a 2D Video Game Platform

Steganography represents the art of hiding information within a harmless medium such as digital images, video, audio, etc. Its purpose is to embed and transmit a message without raising suspicion to a third party or attacker who wishes to obtain that secret information. This research aims to propose a methodology with steganography using as a cover object a 2D platform video game. The experimentation model followed consists of using the combination of horizontal and vertical movements of the enemies by applying the numbering in base 5 or quinary where each character of the message is assigned a quinary digit. In the proposal for improvement the video game is set with 20 enemies per level along the map. The concealment is divided into 3 phases from the choice of the message, allocation of quinary values and generation of the videogame level. Finally, the limitations found will be presented based on experimentation.
Short Read Alignment Based on Maximal Approximate Match Seeds

Sequence alignment is a critical step in many critical genomic studies, such as variant calling, quantitative transcriptome analysis (RNA-seq), and metagenomic sequence classification. However, the alignment performance is largely affected by repetitive sequences in the reference genome, which extensively exist in species from bacteria to mammals. Aligning repeating sequences might lead to tremendous candidate locations, bringing about a challenging computational burden. Thus, most alignment tools prefer to simply discard highly repetitive seeds, but this may cause the true alignment to be missed. Using maximal approximate matches (MAMs) as seeds is an option, but MEMs seeds may fail due to sequencing errors or genomic variations in MEMs seeds. Here, we propose a novel sequence alignment algorithm, named MAM, which can efficiently align short DNA sequences. MAM first builds a modified Burrows-Wheeler transform (BWT) structure of a reference genome to accelerate approximate seed matching. Then, MAM uses maximal approximate matches (MAMs) seeds to reduce the candidate locations. Finally, MAM applies an affine-gap-penalty dynamic programming to extend MAMs seeds. Experimental results on simulated and real sequencing datasets show that MAM achieves better performance in speed than other state-of-the-art alignment tools. The source code is available at https://github.com/weiquan/mam.
Relationship Between Facial Recognition, Color Spaces, and Basic Image Manipulation

TensorFlow and OpenCV are open source libraries used for machine learning and image manipulation, respectively. Using these libraries, we develop a Python-based program that utilizes trained convolutional neural networks for facial recognition. As needs for object recognition increases in various applications such as autonomous vehicles, medical imaging, and facial recognition, it is useful to know what effect color spaces may have on image recognition models. The purpose of our project is to run basic tests with simple facial recognition models to verify which model performs the best when inputs are subject to uniform image manipulation. Three models with identical network architectures are trained with RGB, HSV, and LAB color space images. Images are then uniformly darkened and brightened in the RGB color space before being converted to the separate color spaces and run in the models trained earlier. We found that the RGB color space model responds the best to image manipulations of this kind using our dataset. The HSV color space model had the second-best performance and the LAB color space model suffered significantly. All three models performed worse when images were darkened as opposed to brightened. Answers are proposed as to why some color space models perform poorly. The paper will also look at performance metrics while training the neural network for different runtime environments when using Google Colab.
A Gait Analysis of a Virtual Reality Inverse Treadmill

Many VR “simulated walking locomotion systems” have been developed to allow for movement in virtual environments in a manner similar to how people move in the real world. Similarly, several solutions that could be classified as “inverse treadmills” (in which the motion of running is translated to devices attached to the foot) exist. However, in their current state, these devices either require a large amount of space and equipment, or make sacrifices in mimicking the motions of natural movement, such as requiring the user to move their feet while seated, or be running against an incline. This paper details the theory and development of a 1 to 1 locomotive system comprising a full scale inverse treadmill that is affordably replicable and more closely approximates the human gait. Developments required to make the system more accurately reflective of human motion are discussed.
Toward U-Net-based GANs for Diverse Facial Image Synthesis from Sketch

Face physical changes may result from aging, surgery, or disguise. The criminal suspects conceal their identity with false appearances such as wearing a wig, glasses, beard and mustache. This research benefits the generation of various fictitious appearances of the suspects or facial changes of lost persons. The technique of GANs is applied for synthesizing a color image from a sketch. The output image can be varied in five facial attributes with a toggle: bald, makeup, straight hair, wearing glasses, beard and mustache. The approach enhances the Generator of StarGan2 with the U-Net architecture. The experiments were carried out to evaluate the performance of the proposed model compared to that of StarGan2. FID scores are used for measuring the quality of the generated images. The FID scores measured on the test data reported about 40% less than that of the baseline model and the synthesized images with varied facial attributes look natural and realistic.