Reviewerly/exHarmony huggingface dataset:
Innovations for Data Center Network Optimization

Data centers are at the core of cloud computing, and the data center network forms the basis of data center functioning. Increasing challenges, however, are faced by the data center network, as the production as well as requests for big data are flooding into data centers. Many innovative techniques are proposed in the research of data center networking for these challenges, in which specific problems are identified and practical steps are demonstrated. In this paper, we summarize these innovations from three aspects, namely, communication performance, monitoring and management, and virtualization. Our work is expected to assist researchers to catch up with the most recent and advanced developments.
SOTIF Requirement Analysis Based on STPA

With the increasing number of electrical and electronic systems in automobiles, safety issues are particularly important. Safety of the intended functionality is an important part of it, and it solves the safety risks caused by avoiding intended functions. This paper takes the active obstacle avoidance function of the autonomous driving truck as an example, through the STPA analysis method, analyzes the intended functional safety requirements corresponding to the front-view camera, and lays the foundation for the subsequent validation and verification of the requirements.
Distance Education: Prerequisites of Its Emergence and Development

At a time of digital transformation, the international market of electronic education has rapidly developed. It can be explained by a number of reasons. Electronic format of teaching and learning above all helps to minimize numerous negative barriers in education: financial, geographic, and physical. Students also get possibility to study at places comfortable for them, to decide convenient time for learning, and to determine pace of their studies. Besides, students can use the Internet to search for necessary information, to study learning materials and so on. Thus, mutualism of educational process and online space is becoming an integral actual of contemporary education. The present article has revealed the prerequisites of emergence and development of distance education, and determined the stages of its emergence. In addition to that, we have specified the terms of “distance education” and “online education,” and their distinctive features have been determined as well. Also, the article covers the process of transformation from novelty to innovation in educational medium.
Research on water quality prediction model based on echo state network

Using artificial neural network (ANN) to solve the problem of time series water quality prediction has become increasingly mature. In this paper, through the study of leaky-integral echo state neural network (Leaky ESN), combined with the historical water quality data of Dongzhen Reservoir in Fujian Province, a single-day water quality prediction model was constructed, and the Bayesian optimization algorithm was used to realize the automatic optimization of hyper-parameters in the network. On this basis, multi-day prediction models were constructed by further improving the network, which used the historical water quality data of the previous 7 days to predict the water quality of the next 3 days, 5 days and 7 days. Then the prediction models were applied to the water quality prediction of the study. The experimental results show that the single-day prediction model with Bayesian optimization has high accuracy. The multi-day prediction models can also achieve good prediction effect, and have more practical application value. They are more suitable for early warning of water quality.
Short Answer Automatic Scoring Based on Multi-model Dynamic Collaboration∗

Short answer automatic scoring is a research hotspot of text semantic matching. Education text contains the multidisciplinary characteristics. Each discipline can be subdivided into a small field. Researchers use a large-scale and hyper-parametric deep learning model to deal with these text. This method results in difficult reproduction and high training costs. In this paper, we propose a Multi-model Dynamically Cooperation Semantic Matching (MMDCSM) algorithm which combines multiple models through rule collaboration. We use LCQMA dataset and Xiyou ASAG dataset to experiment MMDCSM algorithm. Xiyou ASAG dataset is created by Xi'an University of Posts and Telecommunication. The results show that MMDCSM algorithm can effectively improve the accuracy and reduce the complexity of related training in the short answer automatic scoring task.
A Modified Algorithm of Visual Quality Providing in Lossy Compression of Remote Sensing Images

The development of remote sensing technology leads to a huge number of acquired images needed to be stored and/or transferred. Lossy compression is an essential mean to reduce the image size and, thus, to improve the efficiency of storing and transferring. Meanwhile, a certain quality (including visual quality) must be provided in lossy compression since this quality determines the efficiency of further image processing and/or analysis. However, the task to provide a desired quality in lossy image compression quickly and accurately is not trivial. In this paper, we propose a modified lossy compression scheme able to guarantee a desired quality with high accuracy in two steps. The quality is evaluated in terms of both a conventional metric PSNR and a human visual system (HVS)-based metric PSNR-HVS-M. The experiments are conducted for discrete cosine transform (DCT)-based AGU coder for which we reach the max variance of errors equal to 0.124 dB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and the biases less than 0.27 dB. This result proves that the average rate-distortion curve drawn from the remote sensing category better fits remote sensing data. The combination scheme of derivative and tangent gives better parameter predictions and helps reduce the final error.
Effective actor-centric human-object interaction detection

While Human-Object Interaction(HOI) Detection has achieved tremendous advances in recent, it still remains challenging due to complex interactions with multiple humans and objects occurring in images, which would inevitably lead to ambiguities. Most existing methods either generate all human-object pair candidates and infer their relationships by cropped local features successively in a two-stage manner, or directly predict interaction points in a one-stage procedure. However, the lack of spatial configurations or reasoning steps of two- or one- stage methods respectively limits their performance in such complex scenes. To avoid this ambiguity, we propose a novel actor-centric framework. The main ideas are that when inferring interactions: 1) the non-local features of the entire image guided by actor position are obtained to model the relationship between the actor and context, and then 2) we use an object branch to generate pixel-wise interaction area prediction, where the interaction area denotes the object central area. Moreover, we also use an actor branch to get interaction prediction of the actor and propose a novel composition strategy based on center-point indexing to generate the final HOI prediction. Thanks to the usage of the non-local features and the partly-coupled property of the human-objects composition strategy, our proposed framework can detect HOI more accurately especially for complex images. Extensive experimental results show that our method achieves the state-of-the-art on the challenging V-COCO and HICO-DET benchmarks and is more robust especially in multiple persons and/or objects scenes.
Alliance chain management system and methods for personal files based on improved multi-layer PBFT

The alliance chain and consensus mechanism are the focus of current blockchain research. The challenges faced by the alliance blockchain are performance and availability. This paper provides an alliance chain management system and methods based on improved multi-layer Practical Byzantine Fault Tolerance(PBFT), including node management module, level division module and consensus module, to improve performance and availability. The node management module is used for the system to manage node creation, level assignment and node deletion. The hierarchical division module is used in the system to classify all nodes according to their levels. The consensus module is used to combine the PBFT consensus algorithm, multi-layer nodes and incentive mechanism to achieve optimization. Furthermore, this paper uses the modules in a personal files system to highlight its usability. Finally, performance experiments show that our design effectively reduces the total consensus delay and the number of consensus communications, solved the shortcomings of the sharp decline in the performance of the alliance chain when the number of nodes increases, and improves its performance and availability.
Improvement of the Probabilistic Roadmap Planner Algorithm for Mobile Robots

The probabilistic Road Map (PRM) algorithm is one of the path planning algorithms for mobile robots based on random nodes. Owing to the uneven node distribution, motion planning is not highly effective. In severe cases, it may even lead to path planning failure. In addition, the obstacles between the path generated by the algorithm and the environment are too close, which makes the algorithm not safe enough. In this article, the two aspects of the mobile robot PRM algorithm are improved. By improving the uniformity of node distribution in the map, the proportion of effective nodes is increased. The calculation time, trajectory length, and calculation amount of the algorithm have been improved to improve the efficiency of the PRM algorithm. The expansion algorithm is used to expand the size of obstacles in the map, thereby avoiding the collision between the mobile robot and the environment. The improved algorithm is simulated in Matlab software, and the results show that the improved algorithm in this paper can effectively improve the efficiency and safety of the PRM algorithm in path planning. Through the improvement, the PRM algorithm is expected to provide better services and be applied to the field of mobile robots and manipulators.
Grapheme to Phoneme Conversion for Malayalam Speech Using Encoder-Decoder Architecture

The two key components of Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) systems are language modeling and acoustic modeling. The language model generates a lexicon, which is a pronunciation dictionary. A lexicon can be created using a variety of approaches. For low-resource languages, rule-based methods are typically employed to build the lexicon. However, because the corpus is often tiny, this methodology does not account for all possible pronunciation variances. As a result, low-resource languages like Malayalam require a method for developing a comprehensive lexicon as the corpus grows. In this work, we explored deep learning based encoder-decoder models for grapheme-to-phoneme (G2P) conversion in Malayalam. Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) encoder models with varying embedding dimensions were used to create the encoder model. The performance of the deep learning models used for G2P conversion was measured using the Word Error Rate (WER) and Phoneme Error Rate (PER). With 1024 embedding dimensions, the encoder using the BiLSTM model had the maximum accuracy of 98.04% and the lowest PER of 2.57% at the phoneme level, and the highest accuracy of 90.58% and the lowest WER of 9.42% at the word level.
CleanLeaf Table: Preventing the Spread of COVID-19 through Smart Surfaces

One mechanism for the spread of the COVID-19 virus is through contaminated surfaces, e.g. tables in cafes, trains or public libraries. This mechanism may be prevented by cleaning the table surface between each use. In practice, this can be optimized by directing arriving users to clean tables and highlighting to staff which surfaces require cleaning. One approach to achieve this is through making the table surface itself smart and indicate its clean or dirty status. In the CleanLeaf table we demonstrate two approaches to integrating indication in a table surface, LEDs under a thin wood veneer and electrochromic displays. Rather than explicit light emitting display based signage, the design demonstrates calm computing with the display forming an integral part of the surface design.
Partially identified Babassu cipher and attack on it

In the present article, we consider a new class of one-pad ciphers. We define its encryption algorithm as a partial function with two arguments. The first argument is a key, which is a random and equiprobable sequence of natural language letters. The length of the key is N. The second argument is a coherent (readable) text of length L, where L < N. The partiality feature of the encryption function does not allow us to apply C. Shannon's mathematical model. For this class of ciphers, we present two operational modes. We chose positions (numbers) of letters in a given key with the encrypted coherent text in the first mode. The positions (numbers) of letters represent the encrypted text. If there is not enough key information to encrypt the whole text, we may repeat the encryption using the next chosen key. To illustrate cryptographic capabilities of this mode, we widely use a widely known perfect cipher secrecy concept of C. Shannon as a prototype to the concept of perfect partial secrecy. Newly presented concepts allow us to build a hypothesis stating that it is impossible to decrypt the presented cipher without finding the information about its key. The second operational mode of the cipher consists of multiple usage of the key to save the key information. We also calculate the complexity of finding the key.
Exploring Potential Gestures for Controlling an Eye-Tracker Based System

Body gestures could be used as an intuitive interaction method with computerized systems. Previous studies explored gesture-based interaction mostly with digital displays, thus there is no standard set of gestures for a system that lacks a display. In this work we conducted a pilot study to explore the potential of using gestures to control an eye tracker based mobile museum visitors’ guide. Our objective was to identify a user-defined set of gestures for controlling the mobile guide. In this work we present the preliminary results of the experiment and discuss the participants’ suggestions and concerns for using this type of interaction.
A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series

In this paper, a novel hybrid method called DMHS-GMDH is presented to predict the time series of COVID-19 outbreaks. In this way, a new version of Harmony Search (HS) algorithm, named Double Memory HS (DMHS), is designed to optimize the structure of a Group Method of Data Handling (GMDH) type neural network. We conduct a series of experiments by applying proposed method on real COVID-19 dataset to forecast new cases and deaths of COVID-19. The statistical analysis indicates that the DMHS-GMDH algorithm on average provides better results than other competitors and the results demonstrate how our approach at least improves coefficient of determination and RMSE by 21% and 45%, respectively.
Blockchain E-voting Scheme Based on SM9 Partial Blind Signature

Aiming at the problems of user privacy leakage, digital signature denial and tracking in the current digital signature electronic voting system, a blockchain electronic voting scheme based on SM9 partial blind signature is proposed. The use of partial blind signature can effectively resist signature denial and tracking. The problem is to introduce a trusted third party to prevent attackers from tampering with information, and at the same time use blockchain technology to replace voting and vote counting functions, which solves the drawbacks of the centralized system. The security analysis proves that the solution can meet the security requirements of the electronic voting protocol, improve the security and voter’s sense of trust in the voting system, and reduce the voting cost.
Research on online course teaching quality evaluation index system in Colleges and Universities

At present, colleges and universities evaluate courses in different ways, mainly reflected in the differences of schools, majors and regions. They use different indicators to evaluate teaching quality and the weight of differences. This article introduces the problems existing in the current online teaching quality evaluation, such as unscientific evaluation index weight and imperfect evaluation system, this paper preliminarily determines the weight index by using the analytic hierarchy process, and redistributes the weight index by using the fuzzy quotient space and rough set theory. Finally, a complete set of online course quality evaluation index system is constructed, and a specific example is used to verify the correctness of this method.
Machine Learning Based SPAM Message Classification System using Blockchain Technology

Short Message Service (SMS) is one of the most common communication media for mobile devices. Although there are many alternatives to mobile text communication such as Whatsapp or LINE based online, SMS is still very commonly used because it only requires mobile number information and mobile signals available. Because of the ease of disseminating information with this SMS media, many people use SMS to send unimportant or even harmful messages to mobile device users. This unimportant SMS is referred to as SPAM. The number of SPAM messages that enter a person’s mobile phone filled the inbox with non-essential information, covered important information, wasted user time, and hindered communication. Therefore, a system that can automatically classify SPAM is required. Data obtained from this classification system may contain sensitive user information such as mobile numbers. Therefore, a SPAM SMS classification storage system with a blockchain platform is created. In this work, we use Hyperledger Composer to create our blockchain. Blockchain technology is used because it is an immutable and secure data storage system that can maintain the confidentiality of sensitive information that may be contained in SPAM messages.
Performance Analysis of Image Inpainting using K- Nearest Neighbor

Image inpainting is a method that focuses to fill the disappeared regions to restore the images back to provide a complete meaningful image. However, the classical methods still struggle with solving the data loss problem because of object and its background. This research analyzes the performance of inpainting model using K-Nearest Neighbor (KNN)-based search method with Bhattacharya distance and the patch extraction is performed using the concept of Bhattacharya distance. Here, the analysis is done by varying the patch size in KNN-based search model for the considered image depending on the percentage of scratch. Moreover, the performance is estimated by considering metrics like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Second-Derivative-like Measure of Enhancement (SDME), Universal image Quality Index (UQI), Multi-Scale Structural Similarity (MS-SSIM) and Mean Square Error (MSE). From the analysis, it is clear that the KNN + Bhattacharya distance provides effective performance with respect to evaluation metrics and also shows higher efficiency when compared with any other traditional approaches. When the percentage of scratch is 100%, the MSE attained by KNN + Bhattacharya distance with patch size 20x20 is 0.190, MS- SSIM is 0.925, PSNR is 29.01, SDME is 69.02, SSIM is 0.914, UQI is 0.190 for dataset 3.
Multimodal Biometric Person Identification System Based on Speech and Keystroke Dynamics

In this paper, user data from two different unimodal biometric modalities are combined for providing diversified decision and persistent authentication. Each unimodal biometric is suffering from some drawbacks, which greatly affects the robustness and integrity of the security system. This work proposes a multimodal behavioral based biometric system utilizing both the keystroke (typing timings) and acoustic (speech) traits, which identify the user using pretrained deep learning models. Features from the two modalities are combined using weighted linear method of feature-level fusion, which are later trained by a deep learning convolution neural network (CNN) classifier model. Results clearly depicts that this proposed system achieves a better accuracy than other classifier techniques such as long short-term memory (LSTM) and backpropagation neural network (BPNN).
Could we rely on credit swap hedging as a substitute for insurer blockchain technology involvement?

Could we rely on credit swap hedging as a substitute for insurer blockchain technology involvement? This paper is to develop a two-stage contingent claim model to answer the question. We have three main results. First, blockchain helps the policyholder protection when blockchain involvement is relatively large-scale, helps the insurer's survival probability when blockchain involvement is relatively small-scale and helps enable greater efficiency. Second, credit swap hedging adversely affects policyholder protection and increases a higher likelihood of insurer survival. The life insurance company could regard credit swap hedging as a strategic substitute for blockchain involvement. Third, capital regulation makes the insurer more prone to blockchain involvement, thereby accelerating the transformation of the insurance system.
Study of fuzzy expert systems towards prediction and detection of fraud case in health care insurance

The size of this issue is sufficiently enormous to focus on it issue for wellbeing structures. Unseemly installments by protection associations or outsider payers happen due to mistakes, fraud and misrepresentation. Determined to pinpoint has been some property/loss protection inclusion that incorporates a medical consideration part. Fraud and misuse happen at many focuses in the medical services framework. Specialists, clinics, nursing homes, symptomatic offices, clinical gear providers, and lawyers have been referred to in tricks to dupe the framework. Medical care assumes a vital part in human existence and it should be reasonable. The rate of health care coverage extortion continues to expand each year, particularly during coronavirus. Extortion and maltreatment on clinical cases turned into a basic issue for the health care coverage industry. We have tracked down that large numbers of the distortions planned to bring about an unapproved advantage were made in the Medical health care coverage. Manual identification of fraud in medical coverage is an exceptionally intense undertaking. In this paper, Fuzzy expert system has been carried out to recognize medical coverage frauds. The point is to recognize extortion asserts precisely inside a brief timeframe. Extortion in medical coverage is truly exceptional and exorbitant for the two policyholders and insurance agencies in all areas of the protection area.
Mirror, Mirror on the Wall: Exploring Ubiquitous Artifacts for Health Tracking

While fitness trackers are increasingly popular among users, recent studies have shown that the health benefits of wearing a tracker are not apparent. The need to explicitly retrieve data can lead to limited benefits. Understanding how users can access, understand, and reflect on their data can lead to building systems that benefit our wellbeing. In this work, we explore the feasibility of using ubiquitous artifacts for unobtrusive feedback in health tracking. We evaluated a concept based on design dimensions for personal visualization on a smart mirror in a user study. Our design puts emphasis on the temporality of presented data. Participants found the visualizations comprehensive, rating cardiac and inertial data most useful as well as approved the different levels of temporal aggregation. Our work contributes findings on how to represent health-related data with ubiquitous artifacts to increase users’ awareness.