Reviewerly/exHarmony huggingface dataset:
Kidney segmentation from computed tomography images using U-Net and batch-based synthesis

The article presents an innovative technique of kidney detection in computed tomography images. The proposed system is based on a batch-based synthesis algorithm, never used in medical image processing before. The manuscript presents key points: U-Net-based initial segmentation, tracking system and image completion. The proposed system's main concept is to remove the kidney region from an image based on the previous image mask and calculate the difference between the original image and the image with the removed kidney. The developed system was compared to the patterns obtained by specialists from medical centers. Numerical tests were carried out confirming kidney detection's high effectiveness, which equals <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$90.63\pm 4.59\ \%$</tex> . The system's high performance and low hardware requirements compared to typical techniques based on deep learning systems are its main advantages. Numerical experiments supplied performance analysis and comparison with an alternative solution based on U-Net image semantic segmentation. A measurable effect of the developed techniques is the concept of the original and practical fully automatic kidney detection and segmentation system in computed tomography images.
Dynamic Planning Networks

We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, that combines model-based and model-free aspects for online planning. Our architecture learns to dynamically construct plans using a learned state-transition model by selecting and traversing between simulated states and actions to maximize information before acting. DPN learns to efficiently form plans by expanding a single action-conditional state transition at a time instead of exhaustively evaluating each action, reducing the number of state-transitions used during planning. We observe emergent planning patterns in our agent, including classical search methods such as breadth-first and depth-first search. DPN shows improved performance over existing baselines across multiple axes.
Attentive Relation Network for Object based Video Games

Deep reinforcement learning algorithms have made great progress in video games. However, there are still some problems, such as sample inefficiency and poor generalization. In this paper, we highlight that these problems are partially caused by the inability of convolutional neural networks (CNNs) to reason with the underlying relations between the objects in the image observations. Based on this point, we try to alleviate these problems in a more efficient and explainable way, including learning the representations of objects and reasoning the relations between them with a relation network (RN). Each pixel in the feature maps is treated as an object and our model explicitly learns the relations between object pairs. The relations are summarized through an attention mechanism and then fed into the downstream fully-connected layers. In the experiments, our model is compared with baseline models in three typical object based Atari games. Under the same hyperparameter settings, our model still achieves better sample efficiency and generalization capability. Further studies throw light on the impact of hyperparameters and verify the interpretability of the model.
A Repeated Mistake is a Choice: Considering Security Issues and Risks in Quantum Computing from Scratch

Quantum computing is set to revolutionize the world of software engineering. Currently, quantum paradigms and languages are being developed that are bringing the physical principles of quantum information closer to solving problems in multiple application domains. These paradigms and languages will continue to evolve and bring quantum computing closer to software engineering, and to an increasingly massive use of it. Unfortunately, as quantum software engineering advances in bringing these new capabilities closer to widespread industry exploitation, attackers will turn the spotlight on quantum software vulnerabilities, on what will be the new battlefront in the world of cybersecurity. In this paper, we approach this security from scratch, starting with an introduction to the current state of quantum programming languages. Then, some current hardware limitations are presented and how these affect quantum software engineering. Followed, we make an overview on the analysis of the quantum languages in terms of security together with some of the research made up to date. As a conclusion, we express the necessity to avoid the many times made mistake of not including security considerations from the first steps in the design of computing paradigms. An error that is repeated a second time is not an error; it is, indeed, a choice.
Noise Quality and Super-Turing Computation in Recurrent Neural Networks

Noise and stochasticity can be beneficial to the performance of neural networks. Recent studies show that optimized-magnitude, noise-enhanced digital recurrent neural networks are consistent with super-Turing operation. This occurred regardless of whether true random or sufficiently long pseudo-random number time series implementing the noise were used. This paper extends prior work by providing additional insight into the degrading effect of shortened and repeating pseudo-noise sequences on super-Turing operation. Shortening the repeat length in the noise resulted in fewer chaotic time series. This was measured by autocorrelation detected repetitions in the output. Similar rates of chaos inhibition by the shortening of the noise repeat lengths hint to an unknown, underlying commonality in noise-induced chaos among different maps, noise magnitudes, and pseudo-noise functions. Repeat lengths in the chaos-failed outputs were predominately integer multiples of the noise repeat lengths. Noise repeat lengths only marginally shorter than output sequences cause the noise-enhanced digital recurrent neural networks to repeat and, thereby, fail in being consistent with chaos and super-Turing computation. This implies that noise sequences used to improve neural network operation should be at least as long as any sequence it produces.
A Topical Keywords Fusion Based on Transformer For Text Summarization

In recent years, the model of sequence to sequence based on attention mechanism have made impressive progress in abstractive document summarization. Unfortunately, as abstractive summarization research is in a primitive stage, the performance of these models is still far from standard. In this paper, we propose a novel method called Abstractive Summarization with Transformer and Topical Keywords(ASTTK). This method augments the attention sequence-to-sequence model in two aspects. First, we use the Transformer as feature extractor to improve the performance of the model, which alleviates the poor parallel computing ability or poor remote feature capture ability of the traditional model. Second, we creatively integrate the topical keyword information, which too makes the generated summary more close to the standard summary. Experiments on data show that the model has a significant improvement in ROUGE index and readability, which shows that the proposed model can integrate keyword information effectively and generate core abstracts.
Low-Resource Neural Machine Translation with Neural Episodic Control

Reinforcement Learning (RL) has been proved to alleviate metric inconsistency and exposure deviation in training-evaluation of neural machine translation (NMT), but the sample efficiency is limited by sampling methods (Temporal-Difference (TD) or Monte-Carlo (MC)), and still cannot compensate for the inefficient non-zero rewards caused by insufficient data sets. In addition, RL rewards can only be effective when the model parameters are basically determined. Therefore, we proposed episodic control reinforcement learning method, which obtains the model with basically determined parameters through the knowledge transfer, and records the historical action trajectory by introducing semi-tabular differentiable neural dictionary (DND), the model can quickly approximate the real state-value according to samples reward when updating policy. We verified on CCMT2019 Mongolian-Chinese (Mo-Zh), Tibetan-Chinese (Ti-Zh), and Uyghur-Chinese (Ug-Zh) tasks, and the results showed that the quality was significantly improved, which fully demonstrated the effectiveness of the method.
VDGAN: A Collaborative Filtering Framework Based on Variational Denoising with GANs

Generative Adversarial Networks (GANs) effectively capture the true posterior distribution. When applied to Collaborative Filtering (CF), GANs can generate a recommendation list through implicit feedback. However, the discriminators in the existing GANs-based CF methods are not utilized fully, and the generators perform poorly on sparse data mining. In this paper, we propose an improved collaborative filtering framework based on variational denoising for GANs (VDGAN). Specifically, VDGAN integrates the variational encoder and the self-attention mechanism into the GANs. By using the positive-negative sampling mechanism to add specific noise to the input data, the variational encoder obtains a robust feature matrix and improves the sparse data processing capability of the generator. In VDGAN, the denoising generator reconstructs the user-items interaction matrix through the feature matrix. And the discriminator is composed of the self-attention mechanism to obtain the explicit features of user preferences, which extends the ability of the discriminator. Furthermore, reinforcement learning replaces the traditional objective function of GANs, which better optimizes the generator and further improves the recommendation accuracy of the model. From our comprehensive experiments on three real-world datasets, we demonstrate that the performance of VDGAN significantly outperforms the state-of-the-art methods based on GANs and Auto-Encoders.
Economic Impacts of Post Pandemic Cyber Crimes – A Global Overview

Cybercrimes have become as common as petty crimes and at every level we can face situations that make us vulnerable to cybercrimes through links, text messages on phones, scam phone calls etc. that demand to sensitive information and misuse them. The 2019-2021 Corona Virus (SARS-CoV-2) Pandemic has shown a drastic increase in cybercrimes. As use of technology, remote access work and communication rise, increase in digital process increase, it has also made people vulnerable to many different types of cyber-attacks such as phishing, malwares and social engineering. It has also resulted in commercial theft of intellectual property. This paper tries to throw light on economic loses that have occurred due to cybercrimes.
Rentcomp - Website Design for Renting Computer in Information System Laboratory XYZ University

The rapid development of technology has been used almost evenly in any field, more specifically in the field of education. Learning support software in the world of education is also increasingly numerous and varied. One of the efforts to equalize the use of supporting software made by XYZ University is to provide a paid application license on student e-mails. but there are still many students who have computers that are not yet capable of using the available applications. This study aims to analyze and design a web-based computer rent application system at the XYZ University information system laboratory. This research uses the stages of analysis and waterfall design version of the satzinger. The design includes making activity diagrams, use case diagrams, use case descriptions, system sequence diagrams, data dictionaries, entity relationship diagrams, and user interfaces. Apart from the design, this research also reached the implementation stage and application testing after implementation. Then, it was found that the results of this analysis and design helped students or lecturers as users and the secretariat of the lab as the admin to record and record computer borrowing data that occurred in the information system laboratory.
Design and Implementation of ShenTong Database Comparison Tool based on ODBC under Kylin Platform

At present, there is no effective ShenTong database comparison software under the domestic Kylin operating system platform. The database comparison is mainly performed manually, which may cause heavy workload and high error rate. This article mainly studies the ShenTong database content comparison method based on Unix ODBC interface under the Kylin operating system platform, designs a software-based comparison method using ODBC driver, and implements the ShenTong database comparison software by executing SQL scripts or statements, which can compare two database instances, show the difference of tables and records, edit and synchronize the instances. At present, the software has been successfully applied to the maritime tracking and control software system, which greatly improved the work efficiency.
Autonomous Landing of a Quadcopter on a Stationary Platform

This work proposes an algorithm for the autonomous landing of a quadcopter on a stationary platform in environments with low visibility, including wind intensity and light disturbance factors unpredictable. The quadcopter, equipped with a camera facing the ground and a small computer, detects the given symbol on the platform, predicts a suitable trajectory using a nonlinear motion model, and follows the calculated trajectory. As a result, the quadcopter could touch down the platform at the height of 4 meters, the highest altitude of a quadcopter could achieve to the same type of low configuration designs. In details, the experiments have been done ten times in each case of environmental conditions with two different structured models: the proposed model and the remake reference model. While the reference model shares the moderate figures in terms of landing ability and algorithm speed: 270 centimeters with ten milliseconds respectively, the proposed model leads to accuracy for touching down the base is 90%, as well as staying a stable range of flight time between 16 seconds and 53 seconds. Also, the results show the feasibility of the proposed algorithm when the system remains a high performance 174 FPS, together with around eight milliseconds in the scan cycle time.
Library Services for the Population of the BAM Construction Areas (1970s—1980s)

The historical experience of library services for the population, accumulated in the areas of new economic development of the late Soviet era, needs to be generalized. At the same time, the processes of formation and development of libraries in sparsely populated territories of Siberia and the Far East have not received sufficient coverage in the special literature. This paper examines the features of the libraries organization in the construction areas of the Baikal-Amur Mainline (BAM) in the period of the 1970s — 1980s. The research materials include archival documents, most of which are introduced into research discourse for the first time, as well as oral stories of participants in the all-Union construction project, collected by the author in the BAM regions. The libraries formation in the mainline construction zone was carried out at an accelerated pace within the framework of the state network of the Ministry of Culture of the RSFSR and the trade union system of transport builders. As a result, the provision of library services to builders was higher than in neighboring regions. However, the accessibility of libraries remained low, because the construction areas were characterized by a low population density, poor development of transport and infrastructure facilities, personnel shortage, and insufficient material and technical support. At first, the book collections were acquired spontaneously from the number of the publications donated or collected from the local population. As soon as the library systems stabilized, the central organizations “Soyuzkniga” and “Roskniga” became the main sources of book supplies, but they could not meet the reader’s demand of the mainline builders. The BAM had formed a special readership, which was distinguished by a high educational level, cultural and linguistic diversity, and the shaped reader’s interests and needs. This was due to the careful selection of those arriving at the construction site as part of Komsomol detachments. Along with their main functions, the libraries were a place for communication and leisure for workers, which compensated for the lack of other social and cultural institutions. Due to the weakness of the Party and state control, they had greater independence in cultural and mass work, which was reflected in the widespread distribution of different associations and library clubs, the acquaintance of readers with the works of authors banned by the Soviet censorship, and the development of amateur literary creativity.
Bilingual Lexical Induction for Sinhala-English using Cross Lingual Embedding Spaces

Bilingual lexicons are an important resource in Natural Language Processing (NLP). Such resources are scarce for Low Resource languages (LRLs) such as Sinhala. However, research on Bilingual Lexical Induction (BLI) on low resource settings is limited. This paper presents the first-ever implementation of BLI for the Sinhala-English language pair. Following the recently introduced VecMap model, we map the vectors of words belonging to both Sinhala and English into a shared vector space and measure the Cross Lingual (CL) similarity between the words. The closest English word for a given Sinhala word in this CL vector space is taken as the corresponding similar word. Currently, there is no detailed evaluation with respect to the size and the nature of the dataset used to create the word vectors, type of the evaluation dictionary, or the technique used to create the word vectors. This paper presents a comprehensive analysis of how these factors affect BLI for Sinhala and English languages and shows that the BLI results have a heavy dependency on these factors.
A Multi-feature Fusion Method for Tibetan Sentence Similarity Calculation

Sentence similarity calculation has an important position in natural language processing and is the basis of text similarity calculation, therefore its accuracy affects the performance of natural language processing. In order to solve the problem of Tibetan sentence similarity calculation, this paper analyzes the contribution of sentence edit distance, sentence structure, sentence length, word order, and the same word to sentence similarity, designs a multi-feature fusion model for Tibetan sentence similarity calculation, proposes a multi-feature fusion method for Tibetan sentence similarity calculation. Experimental results show that the proposed method has achieved good results in Tibetan sentence similarity calculations.
Investigation on chromatic aberrations and its potential for application in depth measurement

Chromatic aberrations are mostly supressed in typical industrial lenses to ensure a well-defined colour image. Beside the main wavelengths which are recognizable for the human eye, multispectral cameras can sample with a more detailed chromatic (spectral) resolution. This leads to a partial wavelength depended unsharpness in the images. If the imaging system can be well defined out of these imperfections, a depth information can be calculated. In this paper the theoretical model as well as a method for the 3D-reconstruction out of different colour (spectral) channels will be discussed.
Load Balancing Issues in Cloud Computing

Load balancing is done to distribute the network traffic over various servers. This is done so that one server does not get overloaded and its resources do not run out. This also increases the efficiency of the application and it also increases the availability of the application for users. This is done by a load balancer. It is a device used for load balancing that is for distributing traffic over the server. In today’s world cloud computing is growing day by day and the major concern is to increase the efficiency and performance of applications, and load balancing is one way to increase the performance. But there are several issues involved in load balancing. This paper discusses those issues involved in load balancing and how load balancing is done in different types of applications.
Efficient Use of DDR Bandwidth and Space for Rendering 3D Graphics

Latest mobile device processors like Snapdragon have multicore CPU and a mobile GPU embedded on a single chip, which enables implementation of high-end 3D graphics application into the mobile devices. As a result, the emerging demand in on rendering on power and memory budget. Texture Processing is a DDR intensive operation Therefore, it becomes important to find ways of reducing DDR BW usage by Texture processors especially in mobile devices. In this paper we propose a novel method to effectively use the available DDR space on a mobile device by calculating the bounding box for all the textures and loading the texture grids which has maximum coverage percentage and storing those texture in the memory. Manhattan benchmark was used for graphics simulation and for a single frame the average texture coverage for top 5 APIs was found to be 62.81% by using texture gridding logic. Next, 30 consecutive frames were used to analyze the texture reusage in multiple frames using PSNR value and we proposed a novel algorithm based on PSNR value to effectively store the texture in DDR.
Evaluation of Cyber Security Management in Light of the Technology Acceptance Model

The main objective of the research is to investigate the correlation and influence between cyber security management and the technology acceptance model in the Iraqi Ministry of Interior. The data was collected from (150) respondents represented by an intentional sample of the Directorate of Systems and Informatics and the Information Technology Department in the General Directorate of Human Resources Management and for data analysis, statistical programs (AMOS, SPSS) were employed. By adopting appropriate statistical methods, the results of the research showed that the Ministry of Interior, whenever it tries to pay attention to cyber security management, the more it is reflected in the level of technology acceptance. In other words, cyber security management has a fundamental and influential role in the extent of acceptance of the technology that will be adopted within the ministry.
Ranking Schemas by Focus: A Cognitively-Inspired Approach

The main goal of this paper is to evaluate knowledge base schemas, modeled as a set of entity types, each such type being associated with a set of properties, according to their focus. We model the notion of focus as “the state or quality of being relevant in storing and retrieving information”. This definition of focus is adapted from the notion of “categorization purpose”, as first defined in cognitive psychology. In turn, this notion is formalized based on a set of knowledge metrics that, for any given focus, rank knowledge base schemas according to their quality. We apply the proposed methodology on a large data set of state-of-the-art knowledge base schemas and we show how it can be used in practice (Data and scripts are available at https://github.com/knowdive/Focus).
EUROPEAN AUGMENTED REALITY TRAINING NEEDS

The report of AROMA project (AROMA project consortium, 2018) summarizes a detailed study performed within project partner countries (Belgium, Czech Republic, Greece, Malta, Romania, Spain and Sweden) aimed at identifying the training needs connected with augmented reality technology (AR) and entrepreneurial skills and mapping an awareness about the AR technology. For the project, the research also serves as a first step to identify gaps that need to be addressed to offer a holistic syllabus integrating AR with selected skills and competencies. Keywords: augmented reality, competencies for entrepreneurship, vocational education and training.