Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: Voice O Braille

This research has a social value which aims to leverage inclusiveness of the visually impaired, in common forums, social discussion forums where any kind of knowledge is imparted through speech. It contains three main components like Voice to Text, Text to Braille and Text to Voice. Its sub-components be like save the converted text as notes, listen to the stored notes. It overcomes the difficulty of documenting/storing the content spoken by converting it into Braille.
Blockchain-Enabled Targeted Information Dissemination Framework in Vehicular Networks

Leveraging the Internet of Vehicles (IoV) to accurately and timely disseminate traffic information, especially for emergency information, is an important way to improve traffic safety and efficiency. However, privacy and security issues in the information transmission process will seriously affect the reliability of the information dissemination system. In this paper, we propose a decentralized targeted information dissemination framework in vehicular networks based on blockchain techniques. In this framework, the information collected by users is uniformly judged and identified by Roadside Units (RSUs), and finally packaged and uploaded to the blockchain that makes the system run more stable and reliable. Further more, we utilize reputation evaluation algorithm to comprehensively process the collected information, and we design the corresponding reputation management system to stimulate more users to participate in the system and provide as accurate information as possible. Extensive simulation results reveal that the proposed framework is effective and feasible in collecting, filtering, conducting, and disseminating in vehicular networks.
A Survey on Current Semantic level Algorithms for improving Performance in CBIR

Abstract This paper expounds a complete review of recent topical methodological attainments in the investigation region of CBIR. Many decades, Images are being utilized in variety of applications counting with Education, Healthcare, Defense, Engineering and Entertainment etc. Commonly, the amount of images across the world has developed in an enormous way. Consequently, in enormous and wide-ranging group, retrieving images in effective and efficient way is a challenging constraint. To elucidate this situation, diverse practices are implemented. This review comprises numerous publications to pronounce the study features of its region related to extraction of content features in different sized of the vectors, distance matrices, connect bridge between complexities and added with objective of the applications improvement such as precision and recall rate. Moreover, established on the demand of dissimilar applications and recent methodology, limited capable research directions are submitted.
Models and Methodologies for Automated Creating of Webpage Mobile Versions

Abstract There are still many outdated web pages on the Internet that are difficult to browse with small screen mobile devices. Software tools are needed to aid mobile users by generating mobile versions of web pages, depending on the parameters of the specific mobile device, which is used. The main aim of this paper is to present a technique, a conceptual and computer model of an application for automated generation of mobile pages. In the following are considered methodologies for redesign and dynamic design of web pages. A five-layer architecture of the Mobiles Online application is presented. It includes database layer, database management layer, business logic layer, web services layer and presentation layer.
Classification of medical images of patients with Covid-19 using transfer learning technology of convolutional neural network

Abstract The paper shows an approach to solving the problem of classifying X-ray images of the chest part of a healthy person and with the presence of COVID-19. The method is a trainable convolutional neural network. The results obtained allow improving existing approaches and methods in the field of classification of medical images with COVID-19, as well as obtaining an auxiliary mechanism for detecting COVID-19 in patients.
Research on the Construction of Intelligent Learning System Based on Big Data

Abstract Internet technology and online learning are in a period of high integration. The learner-centered online learning environment is no more restricted by distance. Facing the large amount of learning resources on the Internet, learners often have no choice. The problem cannot be solved in time, so it is imperative to build an intelligent learning system that according to the characteristics of learners. This article analyses the design of an intelligent learning system based on big data, and analyses the key points of the fit between big data and personalized learning features. According to the design principles of intelligent learning system based on big data, it is divided into four categories: learning strategies, learning tools, learning resources, and learning activities. To build an intelligent learning system in three dimensions. Then the recommended mode of resources in the intelligent learning system is introduced in detail. According to the above design ideas of the intelligent learning system based on big data, a personalized learning platform is constructed, and the structure, functions, and development technology of the platform are elaborated.
Determinants of the Bitcoin Performance during the COVID-19 Pandemic Period: How Can We Measure the Fear?

Purpose – This paper tries to contribute to financial modelling and decision-making in investment strategies by presenting that sentiment analysis using Google trends can explain the performance of Bitcoin (BTC) during the COVID-19 pandemic period (1/1/2020-26/3/2021). The information from social media regarding people’s interest could be beneficial for investors and practitioners. Design/methodology/approach – We employ a code which measures and indexes the Google searches for terms related to COVID-19 and BTC. These indices are included in a GARCH(1,1) model as explanatory variables in order to enable us to identify the variables that influenced the BTC performance during the COVID-19 period.Findings – The empirical findings show that COVID-19 fear increases risk aversion and has a negative impact on BTC prices. The increased interest regarding BTC, as expressed by Google searches, has a positive impact on the BTC prices. However, when this information arrives with a 1-day lag, the impact is negative because the increased interest of the previous day has already been incorporated in the previous day’s BTC price. Research limitations/implications – This study focuses on the number of Google searches. Further research on sentiment analysis should be done using textual analysis. This will allow for more accurate sentiment analysis and thus more accurate financial models which will include not only economic variables, but behavioral also. Practical implications – This note could be useful for scholars and practitioners who are interesting in financial modelling, investments, and decision-making. The methodological approach can be applied in any asset class, e.g. stocks, bonds, currencies etc., thus, there are no significant limitations to our research.
Mutex Propagation for SAT-based Multi-agent Path Finding

Multi-agent path finding (MAPF) is the problem of planning a set of non-colliding paths for a set of agents so that each agent reaches its individual goal location following its path. A mutex from classical planning is a constraint forbidding a pair of facts to be both true or a pair of actions to be executed simultaneously. In the context of MAPF, mutexes are used to rule out the simultaneous occurrence of a pair of agents in a pair of locations, which can prune the search space. Previously mutex propagation had been integrated into conflict-based search (CBS), a major search-based approach for solving MAPF optimally. In this paper, we introduce mutex propagation into the compilation-based (SAT-based) solver MDD-SAT, an alternative to search-based solvers. Our experiments show that, despite mutex propagation being computationally expensive, it prunes the search space significantly so that the overall performance of MDD-SAT is improved.
Threat Assessment for Power Industrial Control System Based on Descriptive Vulnerability Text

With the development of information technology and Power Internet of Things, the increasing number of networked terminals presents new network security threats. Industrial control equipment and systems have vulnerabilities in hardware, software and firmware, and advanced persistent threat against ICS has become more complex and diverse. However, most of the research are aimed at the detection of single vulnerability, and lack attack mechanism analysis and threat assessment for attack chain. We proposed a threat assessment method based on vulnerability descriptive text and described the attributes of attack samples according to the classification results in attack targets, methods and consequences. We constructed attack graphs based on attack sample attributes and cyber-physical topology to quantitatively evaluate the feasibility and benefits of each attack path from vulnerability capabilities and the impact of devices in the physical world. Finally, we took the substation device status monitoring system as an example to verify the feasibility of this method.
Of ‘Authorless Works’ and ‘Inventions without Inventor’ – The Muddy Waters of ‘AI Autonomy’ in Intellectual Property Doctrine

Artificial intelligence (AI) has entered all areas of our life, including creative production and inventive activity. Modern AI is used, inter alia, for the production of newspaper articles; the generation of weather, company, and stock market reports; the composition of music; the creation of visual arts; and pharmaceutical and medicinal research and development. Despite the exponential growth of such real- world scenarios of artificial creativity and inventiveness, it is still unclear whether the output of creative and inventive AI processes – i.e., AI-generated ‘works’ and ‘inventions’ – should be protected under cop- yright or patent law. Current doctrine largely denies such protection on the grounds that no human crea- tor exists in cases where AI functions autonomously in the sense of being independent of and uncontrolled by humans. More recently, both the European Parliament and the EU Commission have put the topic on their agenda. Interestingly, their positions seem to contradict each other – one in favour of, one against creating new instruments of protection for AI-generated output. This and the rising debate in legal schol- arship (with equally contradictory positions) invites more analysis. A closer look at the doctrinal founda- tions and economic underpinnings of ‘work without author’ and ‘invention without inventor’ scenarios reveals that neither the law as it stands nor scholarly debate is currently up to the challenges posed by AI creativity and inventiveness.
Inference Attacks on Physical Layer Channel State Information

In Physical Layer Security, knowing the reciprocal state information of the legitimate terminals' wireless channel is considered a shared secret. Although questioned in recent works, the basic assumption is that an eavesdropper, residing more than half of a wavelength away from the legitimate terminals, is unable to even obtain estimates that are correlated to the state information of the legitimate channel. In this work, we present a Machine Learning based attack that does not require knowledge about the environment or terminal positions, but is solely based on the eavesdropper's measurements. It still successfully infers the legitimate channel state information as represented in impulse responses. We show the effectiveness of our attack by evaluating it on two sets of real world ultra wideband channel impulse responses, for which our attack predictions can achieve higher correlations than even the measurements at the legitimate channel.
Generative Data Augmentation for Vehicle Detection in Aerial Images

Scarcity of training data is one of the prominent problems for deep networks which require large amounts data. Data augmentation is a widely used method to increase the number of training samples and their variations. In this paper, we focus on improving vehicle detection performance in aerial images and propose a generative augmentation method which does not need any extra supervision than the bounding box annotations of the vehicle objects in the training dataset. The proposed method increases the performance of vehicle detection by allowing detectors to be trained with higher number of instances, especially when there are limited number of training instances. The proposed method is generic in the sense that it can be integrated with different generators. The experiments show that the method increases the Average Precision by up to 25.2% and 25.7% when integrated with Pluralistic and DeepFill respectively.
Evolution of Applied Cryptography

This chapter briefly explains the evolution of applied cryptography by reflecting on its origins and its ongoing evolution. The objective of this chapter is to provide a basic insight into how applied cryptography has evolved whilst acknowledging the contributions of the inventors and security researchers that have further advanced the discipline. Indeed, applied cryptography and encryption are not the sum of one inventor or invention, they are the product of thousands of years of applied research. The final sections of the chapter explore the growing public demand for encryption and the paradox encryption creates for ransomware victims.
Improvement Suggestions by Industry based on Assessment Data Analysis of Smart Factories

The diagnostic evaluation model for the smart factory operation system, established as a national standard (KS X 9001-3) in 2016, consists of 46 items in 10 categories. The level of smart factories has been quantitatively and qualitatively diagnosed based on KS for 49 companies in the manufacturing sector, and this paper analyzed the data of this diagnosis. It proposed the characteristics that could be based on the smart factory construction (specialization) project by industry, and the priority promotion and convergence improvement areas among the evaluation areas to improve the level of smart factories. This paper can be utilized in updating reference models by industry for the development of smart factories, improving the level diagnosis models of smart factories, establishing policies related to building smart factories, or establishing directions for improvement of companies.
EVISClass: a new evaluation method for image data stream classifiers

Methods for image data stream classification need to update their model constantly and many of these perform this in a supervised way. However, these studies evaluate the performance of their methods assuming that all labels will be available immediately after classification, which is not consistent with various real-world application scenarios. This article proposes a new evaluation method for image data stream classifiers that allows for the exploration of different issues present in real-world applications, such as the emergence of new classes, the evolution of existing classes, and delayed image labels after classification. Through an analysis of the experimental results, we verified that the proposed evaluation method allowed the identification of the issues that most impact the accuracy of the image classifier, indicating a need to direct efforts in carrying out future works to develop strategies to mitigate these issues.
Placing Conditional Disclosure of Secrets in the Communication Complexity Universe

In the conditional disclosure of secrets (CDS) problem (Gertner et al. in J Comput Syst Sci, 2000) Alice and Bob, who hold n-bit inputs x and y respectively, wish to release a common secret z to Carol, who knows both x and y, if and only if the input (x, y) satisfies some predefined predicate f. Alice and Bob are allowed to send a single message to Carol which may depend on their inputs and some shared randomness, and the goal is to minimize the communication complexity while providing information-theoretic security. Despite the growing interest in this model, very few lower-bounds are known. In this paper, we relate the CDS complexity of a predicate f to its communication complexity under various communication games. For several basic predicates our results yield tight, or almost tight, lower-bounds of $$\Omega (n)$$ or $$\Omega (n^{1-\epsilon })$$ , providing an exponential improvement over previous logarithmic lower-bounds. We also define new communication complexity classes that correspond to different variants of the CDS model and study the relations between them and their complements. Notably, we show that allowing for imperfect correctness can significantly reduce communication—a seemingly new phenomenon in the context of information-theoretic cryptography. Finally, our results show that proving explicit super-logarithmic lower-bounds for imperfect CDS protocols is a necessary step towards proving explicit lower-bounds against the communication complexity class $$\text {AM}^{\text {cc}}$$ , or even $$\text {AM}^{\text {cc}}\cap \text {co-AM}^{\text {cc}}$$ —a well known open problem in the theory of communication complexity. Thus imperfect CDS forms a new minimal class which is placed just beyond the boundaries of the “civilized” part of the communication complexity world for which explicit lower-bounds are known.
9.2: <i>Invited Paper:</i> The Evaluation and Testing on Visual Fatigue and Comfort Between Different Types of VR HMDS and Their Corresponding Pad or Phone

In the same indoor lighting conditions, we compare the differences in visual fatigue and comfort between VR devices and smartphone or tablets by the changes in visual fatigue, comfort and visual function of several different VR devices and their corresponding phone or pad before and after watching the same content. The test task is watching the same video, and the test task time for each condition is 40 minutes. The main measures indexes include critical fusion frequency, contrast sensitivity, stereoscopic function, diopter, accommodation and visual fatigue perception scale, etc. One group of test users is students aged 21‐35, and the other group is students aged 9‐15. The results show that there are no significant differences in critical fusion frequency, the contrast sensitivity and the stereoscopic visual indicators between the glasses VR device and the corresponding smartphone screen. But there are significant differences in critical fusion frequency before and after the task ( ps &lt;0.05), indicating that visual fatigue occurred after the task. Whereas the other smartphone device and pad after watching video have produced a certain degree of eye tightness and headache. However, the differences between two samples in subjective visual fatigue, comfort, refractive degree and accommodation, contrast sensitivity and so on are not significant. In terms of critical fusion frequency, the decrease in critical fusion frequency of smartphone devices is greater than that of Pad, indicating that the use of smartphone devices has a greater impact on users' ability to identify critical fusion frequency than the use of iPad. The results show that there are a lot of developments in the technology of VR‐HMDs, and their effects on users' visual fatigue and comfort are comparable to those of tablet devices, but they are still different from tablet devices in some aspects.
Dynamic scaling for low-precision learning

In recent years, distributed deep learning is becoming popular in industry and academia. Although researchers want to use distributed systems for training, it has been reported that the communication cost for synchronizing gradients can be a bottleneck. Using low-precision gradients is a promising technique for reducing the bandwidth requirement. In this work, we propose Auto Precision Scaling (APS), an algorithm that can improve the accuracy when we communicate gradients by low-precision floating-point values. APS can improve the accuracy for all precisions with a trivial communication cost. Our experimental results show that for both image classification and segmentation, applying APS can train the state-of-the-art models by 8-bit floating-point gradients with no or only a tiny accuracy loss (<0.05%). Furthermore, we can avoid any accuracy loss by designing a hybrid-precision technique. Finally, we propose a performance model to evaluate the proposed method. Our experimental results show that APS can get a significant speedup over the state-of-the-art method. To make it available to researchers and developers, we design and implement a high-performance system for customized precision Deep Learning(CPD), which can simulate the training process using an arbitrary low-precision customized floating-point format. We integrate CPD into PyTorch and make it open-source to the public1.
Research on the prediction model of elderly fall

As the world’s aging process accelerates, the issue of elderly safety is about to become a serious social problem. The elderly are prone to falls due to physiological reasons such as decreased physical function, weakened balance and coordination ability, and poor vision. The study of fall prediction models can predict the impending fall behavior in time before the fall, and have enough time to remind the elderly to adjust or take corresponding protective measures. Reduce the damage caused by falls to the human body, reduce the medical expenses caused by falls, and enhance the confidence of the elderly to live independently. This article gives a detailed overview of the research on the wearable device-based fall prediction system, and introduces the entire process of falling. According to the work flow of the wearable device fall detection system, it includes data collection, data preprocessing, feature extraction, and discrimination algorithms. Several aspects of the current research work are introduced, and the existing research results are classified, compared and statistically analyzed to provide meaningful reference and reference for subsequent research work. Finally, a fall prediction model based on an improved ConvLSTM is proposed.
Semantic Label Prediction of Mammography Based on CC and MLO Views

Mammography image usually contains two views in different orientations-CC and MLO. In current computer-aided mammography label prediction systems, most models either assess information from only a single view which increases the false-positive rate or comprehensively evaluate information from four views of two breasts without distinguishing between the two breasts. In this paper, we propose a semantic label prediction model of mammography based on co-consideration of CC and MLO views. Firstly, a DCN is used to classify and enhance dense images. Secondly, a MLLN is designed to generate semantic labels of mammography by fusing the features of CC view and MLO view. The experiment shows that our model improves the prediction mAP of 7.3% compared to the single view model and 23.3% compared to the four-view model, respectively.
Application Layer Throughput Control for Video Streaming over HTTP2

Video streaming is one of the most popular applications on the Internet nowadays. With the increasing number of Internet-connected devices, it is common to have multiple streaming clients on the same network connection, such as in e-learning and video games. In this paper, we propose a novel application layer throughput management framework for multiple streaming clients sharing the same network bottleneck. To our best knowledge, this is the first framework that includes a throughput estimation scheme and an application layer based throughput control scheme which utilizes the HTTP2's Flow Control mechanism. Experimental results show that the proposed framework can effectively control throughput of individual clients. Specifically, the bandwidth is no longer disputed by the clients and throughput of each client is allocated according to the assigned ratio.