Reviewerly/exHarmony huggingface dataset:
An Authenticated Keyword Searchable Conditional Proxy Re-encryption Scheme in Cloud Services

The internet and mobile platform together are playing a vital role in making people to keep and manage their data conveniently using cloud services. This made cloud servers to manage larger volume of data where data confidentiality has to be taken into consideration. The major issue is to overcome the tradeoff between satisfactory mobility and security and privacy in cloud environment. A long time the researches in this field focused on encryption and decryption techniques to protect data owners' privacy and data confidentiality. These encryption and decryption techniques made another essential requirement to be focused such as searching on encrypted data. This leads to the evolution of techniques for searching on encrypted data. Further in another direction conditional proxy re-encryption scheme where cipher text satisfying certain keyword vector set determined by the owner can be re-encrypted by a proxy server started getting attention. However, the combination of both searching and hierarchical conditional proxy re-encryption has been attempted recently. In this paper the above scheme is analyzed and proved that the data owner authentication offered by the scheme is fails to satisfy the requirement of authentication property. Additionally an extended version of the scheme is proposed which ensures data owner authentication. The proposed scheme is analyzed and proved based on widely used BAN logic and AVISPA.
Ananlysis of DDoS Attacks In Cloud

A Denial of Service (DoS) is a computer crime where machines and other resources are made unavailable to its legitimate users by flooding the resources with unwanted requests when connected to the Internet. Since most of the functions of the organization are automated and stored in servers, security must be taken care of at the highest level. Distributed Denial of Service (DDoS) is a major threat to the network. Since the network is the basic requirement in cloud computing or other latest technologies and DoS being distributive in nature should be addressed properly. This paper gives the comparison of a healthy network with that of DDoS network in order to define the declination of the performance of a cloud network under this attack. The DDoS network shows how the performance deteriorates in this particular scenario. OPNET MODELER 14.5 is used as a simulation tool.
A Super-Resolution Model for Improving the Precision of Wafer Mark Alignment

Abstract Wafer bonding machine uses industrial camera to recognize marks on top wafer and bottom wafer, compute deviation, and move top wafer to align the bottom wafer. The alignment precision mainly depends on the camera resolution, high resolution industrial camera is expensive, while classical image up-sampling methods such as bicubic interpolation don’t have good effect. To improve the alignment precision, a super-resolution model is proposed. Main component of this model is convolutional neural network. The first two convolutional layers are to extract feature on wafer image, the next convolutional layer is used for nonlinear mapping, and the final one outputs super-resolution image. Peak Signal to Noise Ratio (PSNR) is used to evaluate the similarity of super resolution image and the target high resolution image. It’s proved by experiments that the super-resolution model has better effect than classical image interpolation methods. This research result can also be applied to other equipment using industrial cameras.
Information-aware Message Passing Neural Networks for Graph Node Classification

In this paper, we present an information-aware message passing neural network (MPNN) to improve the effectiveness of weight calculation in the message passing process for node classification problems on a graph. Related researches are theoretically analyzed to introduce a special information interchanging scheme with precise weight generation. Compared to fix weighted message passing in convolutional networks (GCN) and neural network computed message coefficient in graph attention networks (GAT), the proposed information-aware message passing neural network takes advantage of node feature and its hidden representations, which indicate the amount of information should be passed and how to absorb the message between node neighbours. This exact message-passing method with self-learning for weight on each dimension of feature achieves better accuracy for node classification tasks in graph neural networks. The proposed method is evaluated by semi-supervised graph node classification task on citation network dataset. Obtained results show the correctness of the proposed method which are more accurate than that of GCN and GAT result.
Field programmable gate array‐based all‐layer accelerator with quantization neural networks for sustainable cyber‐physical systems

Summary Low‐Bit Neural Network (LBNN) is a promising technique to enrich intelligent applications running on sustainable Cyber‐Physical Systems (CPS). Although LBNN has the advantages of low memory usage, fast inference and low power consumption, Low‐bit design requires additional computation units and may cause large accuracy drop. In this paper, we approach to design Field Programmable Gate Array (FPGA)‐based LBNN accelerator to support sustainable CPS. First, we propose a method to quantize the neural networks into 2‐bit weights, 8‐bit activations and 8‐bit biases with few accuracy loss. The mapping function is presented to approximate discrete space of weights gradually and quantize the activations and biases through the improved straight‐through estimator. Second, we design the bitwise FPGA‐based accelerator to speed up the LBNN. Different from traditional accelerating techniques (mainly focused on convolution layer), the dataflows of fully connected layer, pooling layer and convolution layer are considered to accelerate all layers of neural networks. The 2×8 bitwise multiplier implemented by AND/XOR operation is devised to replace 32×32‐bit multiplication unit, which can bring faster inference and lower power consumption. We conduct extensive experiments on benchmarks of MNIST, CIFAR‐10, CIFAR‐100 and ImageNet to evaluate the efficiency of our approach. The LBNN obtained by our quantization method can save 93.75% memory with 2.26% accuracy loss on average compared with original networks. The FPGA‐based accelerator achieves a peak performance of 427.71 GOPS under 100 MHz working frequency, which outperforms previous approaches significantly.
Objective quality assessment of synthesized images by local variation measurement

Due to the rapid development of free-viewpoint television (FVT), Depth-Image-Based Rendering (DIBR) technology has been widely used to synthesize images of virtual view-points. However, the types of distortions in the synthesized images are different from those of natural images, such as discontinuity, flickering, stretching, etc. To measure the distortion occurred in the synthesized images, we propose a full-reference (FR) quality assessment method by local variation measurement consisting of three-modules. Firstly, since the distortion in the synthesized image mainly occurs in the region with high-frequency structure information, the Neutrosophic domain is employed to evaluate the degradation of local image structure. Secondly, by considering that the texture of the synthesized image might be damaged due to the warping of 2D image or the loss of information in the occlusion region, we evaluate the visual quality of local texture by using the features obtained from frequency domain. Thirdly, to measure the stretching distortion which is unique in the synthesized image, the visual quality of extracted stretching area is measured by entropy. Finally, a pooling operation is used to combine the quality scores of the three modules to obtain the final predicted quality score. Experimental results show that the performance of the proposed algorithm is competitive with state-of-the-art FR and no-reference image quality assessment metrics.
Adaptive Panoramic Video Multicast Streaming with Limited FoV Feedback

Virtual reality (VR) provides an immersive 360-degree viewing experience and has been widely used in many areas. However, the transmission of panoramic video usually places a large demand on bandwidth; thus, it is difficult to ensure a reliable quality of experience (QoE) under a limited bandwidth. In this paper, we propose a field-of-view (FoV) prediction methodology based on limited FoV feedback that can fuse the heat map and FoV information to generate a user view. The former is obtained through saliency detection, while the latter is extracted from some user perspectives randomly, and it contains the FoV information of all users. Then, we design a QoE-driven panoramic video streaming system with a client/server (C/S) architecture, in which the server performs rate adaptation based on the bandwidth and the predicted FoV. We then formulate it as a nonlinear integer programming (NLP) problem and propose an optimal algorithm that combines the Karush–Kuhn–Tucker (KKT) conditions with the branch-and-bound method to solve this problem. Finally, we evaluate our system in a simulation environment, and the results show that the system performs better than the baseline.
GWAD: Greedy Workflow Graph Anomaly Detection Framework for System Traces

System traces are a collection of time-stamped messages recorded by the operating system while the system is running. Analysis of these traces is crucial for tasks such as system fault finding. Moreover, detecting anomalies in system behavior becomes crucial in safety-critical and time-sensitive systems where delayed detections can lead to catastrophic outcomes. Therefore, we focus on developing a lightweight and explainable approach for safety-critical time-sensitive systems.Given a set of system traces under normal conditions and anomalous conditions, trace-based anomaly detection aims at classifying the trace as anomalous or not. In this work, we introduce GWAD, a greedy workflow graph framework for anomaly detection, a novel greedy graph construction approach for both offline and online anomaly detection in system traces. Our approach utilizes both sequence of occurrence of events and the time interval between their occurrences in learning the normal system behavior. We propose two approaches, first for offline classification of the trace as anomalous or normal using the event occurrence workflow graphs and secondly an online streaming algorithm that monitors the events as they occur in real-time for detecting anomalies increasing system resilience. Our approach also provides reasoning for the cause of anomalous behavior. We show that GWAD is better than traditional state-of-the-art models. The paper shows the technical feasibility and viability of GWAD through multiple case studies using traces from a field-tested hexacopter.
Multi-modal actuation with the activation bit vector machine

Research towards a new approach to the abstract symbol grounding problem showed that through model counting there is a correspondence between logical/linguistic and coordinate representation in the visuospatial domain. The logical/verbal description of a spatial layout directly gives rise to a coordinate representation that can be drawn, with the drawing reflecting what is described. The main characteristic of this logical property is that it does not need any semantic information or ontology apart from a separation into symbols/words referring to relations and symbols/words referring to objects. Moreover, the complete mechanism can be implemented efficiently on a brain inspired cognitive architecture, the Activation Bit Vector Machine (ABVM), an architecture that belongs to the Vector Symbolic Architectures. However, the natural language fragment captured previously was restricted to simple predication sentences, with the corresponding logical fragment being atomic Context Logic (CLA), and the only actuation modality leveraged was visualization. This article extends the approach on all three aspects: adding a third category of action verbs we move to a fragment of first-order Context Logic (CL1), with modalities requiring a temporal dimension, such as film and music, becoming available. The article presents an ABVM generating sequences of images from texts.
Analysis of Concentrations of Loans by Using Book Circulation Data in Korea University Library

In this paper, data of almost 8 million loans of books recorded for 15 years by the Korea University Library are analyzed by using big data analytic techniques. During this period, book circulation decreased with an average annual rate of decline of 4.4%. The use factor of books in each Dewey decimal classification (DDC) class was evaluated to measure how efficiently books were used by library users. Loan frequencies of books were analyzed and meaningful results regarding loan concentrations and the half-lives of books were obtained. It was observed that 50% of the total loans in each year were for 20% of all borrowed books in that year. This phenomenon will be called the 20/50 loan rule, and the set of the top 20% most borrowed books, whose cumulative loan frequencies reach 50% of total loans, will be called a core collection. The 20/50 loan rule shows the loan concentration of library books. The extent of loan concentration gets stronger if loans for two or more consecutive years are concerned. It was found that with high probability, books in a core collection at a specific year are also categorized as a core collection in next years. Moreover, books categorized as a core collection in consecutive years have longer half-lives compared with all other circulating books.
Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors

In this paper we address the problem of automatic sensor composition for servicing human-interpretable high-level tasks. To this end, we introduce multi-level distributed intelligent virtual sensors (multi-level DIVS) as an overlay framework for a given mesh of physical and/or virtual sensors already deployed in the environment. The goal for multi-level DIVS is two-fold: (i) to provide a convenient way for the user to specify high-level sensing tasks; (ii) to construct the computational graph that provides the correct output given a specific sensing task. For (i) we resort to a conversational user interface, which is an intuitive and user-friendly manner in which the user can express the sensing problem, i.e., natural language queries, while for (ii) we propose a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation. Finally, we evaluate and demonstrate the feasibility of our approach in the context of a smart city setup.
Online Integration of SQL and No-SQL Databases using RestAPIs: A Case on 2 furniture e-Commerce Sites

Database technology is one of the key elements in every internet-based system. As technology develops and the need for fast and large data exchange arises, a type of NoSQL or unstructured database emerges. SQL and relational databases have table forms, while NoSQL has a format of document-oriented stores. Integration issue came as a challenge when the two different databases were used on the same software. The challenge is increasingly complicated when the database used becomes the resource of online websites. In this study, we present a case which based on 2 online e-Commerce furniture websites that. These both online websites will merge their product data into 1 new database. This research combines two different databases using Web services to retrieve data in each database and a synchronization system for the process of checking and storing data into a new database. All merging components are stored on cloud storage services. Every batching process in the synchronization system is done online and automatically uses Cron. This research provides the results of data integration testing on web services and measurement of the synchronization system execution time. After testing 2 web services used, all passed the data integration test. Then measurements are made on the synchronization system by looking at the displayed execution time. The results of measurements using 100 to 1000 data and different conditions, the execution time obtained from 4 to 5 seconds for each data in the measurement increases to 500 data. The results of this study indicate that the integration of data from 2 different databases using web services and synchronization systems is tolerable and suitable for the amount of data under 10000 data with the condition of the database already filled.
Research and Design of Moving Target Detection and Tracking System Based on Web Server

We present an algorithm to improve the detection and tracking of moving target. Combined with the ORB feature detection operator, the center of the target is estimated, and a kind of anti-shadow interference and good matching target detection algorithm is designed. Combined the adaptive filter, a motion object tracking algorithm that resists background color interference and resists short-term occlusion is designed. This paper implements target tracking on embedded ARM-Linux system, completes the design of image acquisition module, image preprocessing design, transplantation of moving target detection and tracking algorithm, and design of video transmission module based on RTP protocol. Finally, web client is designed to view the system's tracking effect. The experimental results show that the system algorithm has a certain ability of anti-illumination and background color interference, and it has adaptability to the short-term occlusion of the target and improves the algorithm detection accuracy, increasing processing time accordingly, decreasing tracking error, with good real-time and accuracy.
WITHDRAWN: Binary sequence using chaotic map for cryptogrphic applications

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Factors that influence academics’ intention to use mobile-based assessment in Higher Education in South Pacific

This Research Full paper presents finding of factors influencing the use of mobile-based assessments in higher education. With the growing popularity of mobile learning in higher education, there exists a potential of using mobile devices to deliver various types of assessments. The study aimed to find out factors influencing the adoption of mobile-based assessments, perspectives on types of assessments suitable to deliver such assessments and challenges faced by academics and students in South Pacific in using these assessments. An online survey was created using the case study approach with a blend of quantitative and qualitative questions assisted by Technology acceptance (TAM) and unified theory of acceptance and use of technology (UTAUT) models. The factors from TAM and UTAUT considered for this study were perceived usefulness, behavioral intension to use, perceived trust and attitude by introducing output quality and social influence as external variables. The participants (n = 35) comprised of science, business and economics, arts, law, education and technical and further education academics from a higher education institute in the South Pacific. The study revealed that focusing on mobile assessment quality design and trust are the important factors for academics to accept mobile-based assessments in higher education. Furthermore, game-based, self-and peer, classroom polling, formative, adaptive and personalized assessments were the types of assessment preferred by academics to be delivered through the mobile devices. The major challenges identified for academics were connectivity, lack of skills in designing effective mobile-based assessments, lack of e-teaching pedagogy and student cheating issues. For students' the major challenges identified by academics were internet connectivity, device availability, distractions and device competency, readability and disability issues.
Application of Information Measuring Systems for Development of Engineering Skills for Cyber-Physical Education

The new industrial revolution opening the way to the digital world requires further development of engineering education. Future engineers should obtain skills in the area of development of cyber physical (intellectual) systems. In National Research Nuclear University MEPhI we have examples of implementation of new engineering courses and programs for cyber physical education. In this paper we discuss the problems and results of application of information measuring systems which main purpose is for research and development needs, for educational tasks.
A framework for sentiment analysis in customer product reviews using machine learning

Perceiving and Analysis of textual information created from different online life stages have gotten one of the most necessities in the present Big Data period. The consequence of such investigation helps in numerous vital organizations to increase clear experiences about their plans of action and to in the long run take essential business-situated choices to improve their organizations. In this paper, an endeavor is made to perform a notion examination on the conveyed processing system utilizing the many Machine Learning (ML) models and Hadoop-based Spark programming model. The current methodologies towards slant investigation are restricted to just a couple of brands and their items. In this way, to coordinate the learning capacities with conveyed processing models on huge literary information, we built up the proposed system that prescribes the item to clients as per client's element prerequisites gathered as the enormous printed information. The investigation executed the Gaussian Naive Bayes (GNB) and Random Forest (RF) on the Spark Big Data examination stage to process immense printed information. The trial results have indicated that the two calculations produce better effectiveness over different strategies while handling large opinion datasets.