Reviewerly/exHarmony huggingface dataset:
Analysis and Implementation of the Enigma Machine

The Enigma Machine is considered as powerful cipher machine, it managed to protect the messages of German for a long time. This study synthesizes and examines the current literature on Enigma Machine and implements the Enigma algorithm using Java. The history, structure, and cryptographic algorithms of the Enigma machine are presented and analysed. The Virtual Enigma Machine imitates the processes of encryption and decryption, and shows the changes of rotors. Each letter of message passes through the plugboard and rotors, and then is exchanged to another letter based on different keys. The experiment result demonstrates that the Enigma Machine had the state-of-art mathematics in that time and the complex mechanical design. In that time it had many undoubtedly unique advantages. Thus, the Enigma Machine is an unquestionable milestone in classical cryptography. However, as a classical cipher, it cannot be denied that there are many shortages of the Enigma Machine that can cause significant security problems, such as when the Enigma Machine is working, there is only one rotor will rotate. The cracking of these flaws led to the realization of the flaws of classical ciphers and contributed to the advancement of technology and the development of cryptography. The process by which it was broken has also provided researchers with valuable historical lessons to guide the design and use of modern ciphers. As widely used and excellent classical cipher, the Enigma Machine was ultimate used for evil by Nazi Germany, and the analysis and cracking of the Enigma code are excellent examples of showing the public that cryptography can have a huge effect on real life.
New Media Teaching System Based on Data Mining Technology

The advent of the information age has brought a profound change in the field of education. Through the practical effects of data mining technology in various industries, it is found that data mining, sorting, and analysis of large amounts of data can provide an effective reference for industry decision makers. The introduction of data mining technology in the education field to study the new media teaching system can effectively improve the learning effect and teaching management level of students. This article investigates students' attitudes towards using the new media teaching system for teaching, and finds that 64.67% of the students are very willing to use the new media teaching system in teaching, 21.33% of the students' attitude is willing, and only a few students' attitude is unwilling, accounting for 4%. In response to this analysis result, a wired system and a wireless system for new media teaching were designed to improve the quality of teaching.
The Information Bank: Information Authenticity with Simultaneous Accountability and Anonymity in the Digital Era

Humans have struggled with the authenticity of information in physical formats since ancient times when information breached the temporal and spatial immediacy dictated by speech. Secondhand consumption of information was riddled with source uncertainty as forgeries proved too tempting for malefactors to resist. With the onset of the digital information era, forgeries have proved even more pervasive and insidious. Encouragingly, new cryptographic tools have enabled digital information to be secured and authenticated to a greater degree than was possible with physical media such as paper. These complications with physical and digital information mirror similar headaches about money: authenticity, ownership/source, and accountability. Drawing from the solutions provided by monetary banks, we introduce the idea of an information bank. This proposed information bank acts as a intermediary between the individual and the physically distal “others” who may want to participate in the individual’s information. This decentralized, non-governmental information bank can guarantee the authenticity of the individual producer of information while simultaneously providing both the option to preserve the individual’s anonymity and ensuring that individual’s accountability for that information. We examine the implementation mechanics and implications of this information bank and explore possible scenarios and applications for this type of information management for society in journalism and politics.
Improvement of Disparity Measurement of Stereo Vision Algorithm Using Modified Dynamic Cost Volume

Interest in a stereo vision system was increasing and applied in several critical applications such as image processing in medical, virtual reality, autonomous navigation and many more. It is challenging to establish accurate correspondences due to several factors such as low texture pixels, illumination variations of environment, inaccurate discontinuities, mismatched and blurry boundaries reflected due to poor segmentation techniques. This work presents an improvement framework of the stereo vision system to measure the depth measurement using modified dynamic cost volume in the matching cost stage. A modified Birchfield Thomas (BT) metric with dynamic histogram is used to provide the cost volume in the matching cost step, while four-way scanline Semiglobal (SGM) used in the cost aggregation to aggregate the cost volume. Winners-Take-All (WTA) optimisation used for disparity selection and bilateral-median filter applied in refinement stage. Based on the dataset of standard Middlebury, the framework developed in this work has better accuracy and outperformed several other state-of-the-art algorithms.
Stareview Review Management

Reviews are the backbone for every growing industry, To manage reviews and keeping track of records can be a overwhelming task. Managing performance of individuals can be a task to tackle, and improving the respective performances can be managed when there is a dedicated business tool for it. Having good management of reviews and ratings for products and individuals and by analyzing pros and cons can surely services in future. During this research, we kept in mind to manage all this and automate things as much as possible, we analyzed sentiments of reviews for staff for some statistical data for each individual and made a review management application that are portrayed within the later a component of paper. NLP models have been measured by criterion like confusion matrices and a easy to use web application has been implemented for convenient uses
A Software Metamodel Restricted to Key Aspects of a Software System for Developers and a Method to Keep Manually Drawn Diagrams Up-to-Date and Correct

Context In many areas of engineering, it has become common to edit and display models using software. An exception to this is the area of software development itself. The high complexity of software systems makes it difficult to create useful diagrams automatically, whereas manual diagrams are often very work-intensive, especially if they are detailed and need to be kept up to date.Objective Develop metamodels and visualization techniques to support software developers. Especially for use cases where many different software techniques have to be handled.Method The tools SAP2Moose and Moose2Model use concepts that address the mentioned problems. These concepts are presented and discussed.Results A metamodel is described that contains only a few different elements and can be used for different types of software. A diagram technique is presented where certain elements are added manually and additional elements are displayed automatically as a logical fish-eye view with multiple foci.Conclusion The metamodel can be used to represent very different types of software in a common model. The described technique for drawing diagrams can be used to manually create diagrams that can easily be kept up to date after changes in the software.
Differentiated Attention with Multi-modal Reasoning for Video Question Answering

It is extremely challenging to infer the answers for long questions and complex videos. Video question answering not only needs to capture clues from questions but also reasonably infer the certain clip or frame in videos. In the paper, we propose a novel method to understand the entire video and reasonably infer the answer to the question. We utilize a traditional attention mechanism combined with the multi-head structure to construct a differentiated attention module. Different from existing methods, our method is dedicated to obtaining differentiated features. Videos on our method are split into a few video clips, and there is a great overlap between video clips. Thus, simple using the self-attention mechanism to aggregate features will lead to excessive redundancy in the captured features. To tackle this issue, we propose a differentiated attention module consists of traditional attention mechanism and multi-head structure to focus on the core semantics and decode different clips or phrases. In addition, we also apply the differentiated attention block on question aggregation and video clues reasoning. We use different query attention loss (DQALoss) to solve the problem of question requiring stronger differentiation. Meanwhile, we propose to utilize the multi-modal factorized bilinear pooling method to solve multi-modal features reasoning and interaction. Our experiment shows that the proposed method outperforms existing methods on TGIF-QA datasets by large margins. The experimental results show the effectiveness of our method.
Accelerating Backward Aggregation in GCN Training With Execution Path Preparing on GPUs

The emerging <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Graph Convolutional Network</i> (GCN) has been widely used in many domains, where it is important to improve the efficiencies of applications by accelerating GCN trainings. Due to the sparsity nature and exploding scales of input real-world graphs, state-of-the-art GCN training systems (e.g., GNNAdvisor) employ graph processing techniques to accelerate the message exchanging (i.e., aggregations) among the graph vertices. Nevertheless, these systems treat both the aggregation stages of forward and backward propagation phases as all-active graph processing procedures that indiscriminately conduct computations on all vertices of an input graph. In this article, we first point out that in a GCN training problem with a given training set on an input graph, its aggregation stages of backward propagation phases (called as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">backward aggregations</i> in this article) can be equivalently converted to partially-active graph processing procedures, which conduct computations on only partial vertices of the input graph. By leveraging such a finding, we propose an execution path preparing method that collects and coalesces the graph data used during different training layers of backward aggregations, and constructs their corresponding sub-graphs (called as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">execution paths</i> in this article) as inputs to conduct the backward training on GPUs. Further, we propose a structural-aware strategy for the execution paths to compute their optimal group sizes, so as to gain as high as possible performances on GPUs during the backward aggregations. The experiment results by conducting GCN training in typical real-world graphs show that compared with GNNAdvisor, our approach improves the performance of backward aggregations by up to 5.68x on NVIDIA P100 GPU, and up to 6.57x on NVIDIA V100S GPU
Mining Latent Semantic Correlation inspired by Quantum Entanglement

Text representation learning is the cornerstone of solving downstream problems in Natural Language Processing (NLP). However, mining the potential explanatory factors or semantic associations behind data, rather than simply representing the superficial co-occurrence of words, remains a non-trivial challenge. To this end, we seek inspiration from the Quantum Entanglement (QE) which can effectively provide a complete description for the nature of realities and a globally-determined intrinsic correlation of considered objects, thus proposing a novel representation learning hypothesis called the Latent Semantic Correlation (LSC), namely the implicit internal coherence between the semantic space and its corresponding category space. To construct a multi-granularity representation from sememes to words, phrases, sentences, and higher-level LSC, we implement a QE-inspired Network (QEN) under the constraints of quantum formalism and propose the Local Semantic Measurement (LSM) and Extraction (LSE) for effectively capturing probability distribution information from the entangled state of a bipartite quantum system, which has a clear geometrical motivation but also supports a well-founded probabilistic interpretation. Experimental results conducted on several benchmarking classification tasks prove the validity of the LSC hypothesis and the superiority of QEN.
Design and Implementation of English Intelligent Learning System from The Perspective of Distributed Cognition

From the perspective of distributed theory, there is no effective learning method combining mobile communication technology with digital learning. Therefore, this paper designs an intelligent college English learning system based on mobile platform. Based on the analysis of the theory of distributed cognitive learning and the basic model of mobile learning, the physical framework of college English mobile learning is provided. Then the design is based on eclipse IDE, the server is based on J2EE technology and MVC mode, and the client adopts Android SDK. According to learners' knowledge level and Ebbinghaus forgetting curve rule, the intelligent recommendation module can push learners' learning content personalized. The final running test results of the system prove the feasibility of the system, and it can provide the mobile learning function with stable running, perfect function and friendly interaction.
Recovering Semantic Traceability between Requirements and Source Code Using Feature Representation Techniques

Requirement traceability is essential for software development and maintenance, thereby effectively recovering the requirements traceability has become an important issue for requirement engineering. With the development of software systems, it is always unrealistic to maintain traceability links between requirements and source code manually. Therefore, researchers have proposed information retrieval-based approaches to recover the links automatically. Although these methods reduce human labor, they do not fully extract the specific features, resulting in poor traceability accuracy. In this paper, we propose an approach to recovering traceability between requirements and source code, which combines word embedding and self-attention model to extract features and generate text vectors. These technologies make full use of the semantic information of the context and feature representation. In addition, the paper discusses the impact of code content and comments on the results and improves the results on weight. Finally, the proposed approach is compared with the commonly-used baselines, and the study results show that the proposed approach outperforms others.
Experiment-Supported Mobile Application for Monitoring Human Activities Using Neural Networks

During the last years, people have been interested to understand the human nature by creating smart algorithms and systems which can detect or recognize activities performed by humans. In this paper, we propose the implementation of a smart system able to recognize and monitor human’s activities. The system implemented as an application running on a smartphone using Android OS. The mobile application determines what activity is performed by a person and stores the information into an internal database. The database can be used to generate statistics. By using statistics, the users can check the records of their daily activities. Our mobile application can detect 4 types of activities: standing, sitting, walking and jogging. For determining these activities, we used AI software implemented in PyTorch. The AI inputs consist in coordinates read from the smartphone’s accelerometer. To evaluate the performance, we tested the neural network in 2 different modes: using a testing set and when it is part of the mobile application (someone performs an activity and the application detects it).
Scientific schools of universities and character-building work

Scientific schools shape the image of universities, while the activity of the scientific schools enables sustainable and dynamic development of the universities. This paper is devoted to the role of scientific schools in characterbuilding work in the context of leading aerospace engineering universities of Russia. The paper stresses the value of building the students’ character based on the example of the older senior counterparts capable of transferring their experience and teaching the cutting-edge methods of educational and scientific work. Among these methods, the paper highlights a historical approach in the description of the logic and evolution of design and manufacturing solutions and the method of size effect, allowing in-depth comprehension of the true scale and composition of complex aerospace equipment. Aerospace universities of Russia implement these methods with special study laboratories called demonstration halls. Having unique specimens of spacecrafts in these laboratories helps preserve the historical memory of the forefront research of Russian scientists and engineers. Practical classes in such laboratories are a fine example of transferring the scientific legacy and positively affecting the motivation for professional growth.
STIF: Semi-Supervised Taxonomy Induction using Term Embeddings and Clustering

In this paper, we developed a semi-supervised taxonomy induction framework using term embedding and clustering methods for a blog corpus comprising 145,000 posts from 650 Ukraine-related blog domains dated between 2010-2020. We extracted 32,429 noun phrases (NPs) and proceeded to split these NPs into a pair of categories: General/Ambiguous phrases, which might appear under any topic vs. Topical/Non-Ambiguous phrases, which pertain to a topic’s specifics. We used term representation and clustering methods to partition the topical/non-ambiguous phrases into 90 groups using the Silhouette method. Next, a team of 10 communications scientists analyzed the NP clusters and inducted a two-level taxonomy alongside its codebook. Upon achieving intercoder reliability of 94%, coders proceeded to map all topical/non-ambiguous phrases into a gold-standard taxonomy. We evaluated a range of term representation and clustering methods using extrinsic and intrinsic measures. We determined that GloVe embeddings with K-Means achieved the highest performance (i.e. 74% purity) for this real-world dataset.
Pedagogical Conditions Actualizing the Potential of the Karakalpak Traditions of Labor Education in the Content of Theoretical, Methodological and Practical Training of Students

Abstract: The article reveals the main labor qualities of the Karakalpaks, highlights the set of pedagogical conditions and traditions of labor education in the content of theoretical, methodological and practical training of students; influence of different genres and types of activities (games, fairy tales, songs, stories, sayings, etc.) for the labor education of children; in the course of the research, the main labor qualities of the Karakalpaks were determined.
An Adaptive Rate Allocation Scheme for Time-Varying Graph Signal Quantization

To address the communication resource limitations the uplink data in some distributed networks suffers from, quantization enables these graph signals to realize compression. However, the compression process is accompanied by quantization errors, which pose threat to the communication quality. In addition, in real-world scenarios, graph signals tend to evolve with time, where the information loss would be larger without adaption to the evolvement. To tackle the above problems, we first propose an adaptive rate allocation scheme, which allocates rate to each quantizer under a total rate constraint, for time-varying graph signals. Along with the smoothness of graph signals at the same time instant and the rate-distortion features of scalar quantization, the smoothly evolving characteristics of time-varying graph signals are leveraged to adaptively adjust the allocated rate with time to reduce quantization distortion of uplink data incurred by the time-varying feature. Simulation results demonstrate the superiority of distortion performance of the proposed rate allocation scheme on both synthetic and real-world graphs.
Towards Explainable Search in Legal Text

Assume a non-AI expert user like a lawyer using an AI driven text retrieval (IR) system. A user is not always sure why a certain document is at the bottom of the ranking list although it seems quite relevant and is expected at the top. Is it due to the proportion of matching terms, semantically related topics, or unknown reasons? This can be confusing and leading to lack of trust and transparency in AI systems. Explainable AI (XAI) is currently a vibrant research topic which is being investigated from various perspectives in the IR and ML community. While a major focus of the ML community is to explain a classification decision, a key focus in IR is to explain the notion of similarity that is used to estimate relevance rankings. Relevance in IR is a complex entity based on various notions of similarity (e.g. semantic, syntactic, contextual) in text. This is often subjective and ranking is an estimation of the relevance. In this work, we attempt to explore the notion of similarity in text with regard to aspects such as semantics, law cross references and arrive at interpretable facets of evidence which can be used to explain rankings. The idea is to explain non-AI experts that why a certain document is relevant to a query, for legal domain. We present our preliminary findings, outline future work and discuss challenges.
Data privacy and utility trade-off based on mutual information neural estimator

In the era of big data and the Internet of Things (IoT), data owners need to share a large amount of data with the intended receivers in an insecure environment, posing a trade-off issue between user privacy and data utility. The privacy utility trade-off was facilitated through a privacy funnel based on mutual information. In this article, we propose a privacy funnel which is using mutual information neural estimator (MINE) to optimize the privacy utility trade-off by estimating mutual information. Firstly, we estimate mutual information in a training way for data with unknown distributions and make the result a measure of privacy and utility. Secondly, we optimize the privacy utility trade-off by optimizing the mutual information added noise as an encoding process and minimizing cross-entropy mutual information between published data and non-sensitive data as a decoding process. Finally, simulations are conducted comparing our methodology to the Kraskov, Stögbauer, and Grassberger (KSG) estimation obtained by k-nearest neighbor as well as information bottleneck in the traditional method. Our results clearly demonstrate that the designed framework has better performance and attains convergence quicker in the scenario where enormous volumes of data are handled, and the largest data utility obtained by the MINE for a given privacy threshold is even better.