Reviewerly/exHarmony huggingface dataset:
Security Enhancement of E-Healthcare System in Cloud Using Efficient Cryptographic Method

The electronic patient record (EPR) data sharing is a critical and major challenging issue in cloud environment. The processing chain of EPR mainly includes record collection, record storage, record sharing, etc. Conventional healthcare system often requires the delivery of EPR to the cloud which involves user-sensitive information and causes correspondence vitality utilization. Practically, EPR data sharing is a critical and challenging issue. In this work, we proposed a novel technique healthcare system by effectively handling the EPR data in cloud storage. The management of keys has consistently been a security threat of healthcare system; anyway, its belonging has been intensified after presentation of the cloud; that is why key management needs to be performed effectively. Our novel approach can only be done by securing the process of key management. This is the best way to guarantee that touchy data is not weak when it is setting off to the cloud. Furthermore, keys should be mutually made sure about, and the recovery cycle ought to be troublesome and tedious to ensure that information can never be gotten to without authorization. Finally, in order to protect the healthcare data from criminal attackers by our approach, our experiments are demonstrated.
Bridging the trust gaps in biometrics

Biometric systems promise to transform our lives by enhancing capabilities to rapidly detect, analyse and respond to human features and behaviours. Yet while we are at the dawn of the AI age, the potential applications of biometric tools have already triggered unease in the public and heightened scrutiny from policymakers. These concerns have emerged against the backdrop of a global ‘techlash’ fuelled by growing anxiety over privacy breaches, loss of data rights and disinformation.
The United States and Government-Provided Internet Access

While Syria provides a stark example of state repression and censorship, this chapter provides an interesting contrast: by focusing on the United States and efforts by state governments to provide high-speed Internet access to citizens. The author Enrique Armijo notes that these efforts, while laudable and in line with the Access to Knowledge movement, nevertheless come with major caveats and potential threats to freedom of expression. Armijo discusses three models of Internet access provision: a public utility model, a public–private partnership model, and a third model where a city owns the Internet infrastructure, while ISPs utilize the infrastructure to provide access for an agreed-upon fee. The author argues, based on different case studies, that efforts to provide public Internet access often come with restrictions to free expression, which are enforced through various “terms of service” from the government in conjunction with the local network providers. He instead argues that provision of free Internet access by government actors should be in line with constitutional rights afforded to citizens such as free expression. Otherwise, Armijo warns that such “free access” could end up curtailing citizens’ free speech and make it easy for governments to surveil and censor citizens.
Open Science in Spain: Towards a Coordinated Strategy

Research is being transformed by transparency, collaboration, public engagement and shareability, which are key elements of the Open Science (OS) movement. Open Access (OA), one of its main areas of action, aims to make all research freely available. Benefits of OA have already triggered a shift toward its implementation at the European and international level, with funders creating new platforms to support an ecosystem of open publications and data. Despite remarkable early contributions by Spain in terms of OS pilot initiatives and specifically OA publication performance, the latter has declined by more than a third since 2016. Moreover, no new indicators have been put forward since, even though openness remains to be deemed a strength. In this policy memo, we examine policy options to support OS in the country, with a focus on OA. These could be structured by a National Strategy for Openness, including actions to ensure OA for all publicly funded research, standardization of procedures, and the re-design of assessment criteria to incorporate reproducibility of outputs, knowledge dissemination and transfer.
Computational performance of heterogeneous ensemble frameworks on high-performance computing platforms

To enable efficient computations on rapidly growing big data, a variety of high-performance computing (HPC) platforms, such as traditional multi-processor systems, Hadoop and cloud computing systems, have been developed. On the analytics side of big data, several innovative machine learning methods have been developed to enable the extraction of accurate and actionable knowledge from large datasets. In particular, heterogeneous ensemble algorithms, which are designed to aggregate an unrestricted variety and number of analytical models, have performed well for a variety of prediction problems. However, the performance of these algorithms in terms of computational metrics, such as time requirement, disk space consumption and memory usage, on these HPC platforms has not been systematically examined yet. Here, we address this gap in knowledge by implementing these algorithms and systematically assessing their computational performance on traditional HPC and Hadoop platforms. Our results show that these implementations used the resources, especially disk space and memory, consistent with the respective designs of the platforms. Furthermore, due to the iterative nature of the heterogeneous ensemble computations, the traditional HPC system executed them faster than Hadoop, since an in-memory design is better suited for them than a disk-based one. Overall, our study sheds new light on the computational performance of ensemble algorithms and software frameworks on two prominent HPC platforms, and offers a systematic methodology for conducting similar assessments for other data analytics methods as well. Basic source code of our heterogeneous ensemble implementations, as well as the HPC performance assessments, are available at https://github.com/GauravPandeyLab/HPC-Ensemble.
Research and Implementation of Multi-modal Video Retrieval System Based on Deep Learning

Abstract At present, keyword-based video retrieval is gradually difficult to adapt to the needs of the rapid development of the Internet due to its strong subjectivity and huge workload. As a result, multi-modal video retrieval based on deep learning has appeared. This retrieval method can conduct video retrieval through multiple methods such as text, image, and video, which fully meets the different retrieval needs of different users, and significantly improves the accuracy and effectiveness of video retrieval. Based on this, this article discusses in detail the design of a multi-modal video retrieval system based on deep learning, analyzes and designs each functional module of the system to provide reference for future related work.
Mining Events through Activity Title Extraction and Venue Coupling

In this paper, we discuss the challenges to construct an event/activity search engine through activity title extraction and venue recognition as well as relation coupling. While distant supervision is a common technique to speed up training data preparation, it does not always work on social network. For activity titles, they may contain other entities such as person, venues and temporal expressions, which could be much longer than general named entities (such as person names). Venue recognition is another challenge, as they could be an address, a specific point-of-interest like an restaurant or organization name, or across a neighborhood. Another problem is how to determine the venue and correct date of the event when multiple venues or temporal expressions are recognized in a message. In this paper, a sequential pattern mining approach is applied to discover rules for coupling the recognized place names with the activity title in discussion. The experimental result shows that our approach is beneficial and practical for locating social activity venues.
Indexing-Based Peculiarity Extrication from Biomedical Images Using Deep Learning

Biomedical image classification is an important task to generate classification maps for deadly diseases like cancer, tumor, etc. In this paper, a novel technique for classification of biomedical images is proposed. The proposed technique takes raw biomedical image as input and applies indexing to retrieve the parameters used to classify different diseases based on deep learning. The proposed Indexing-Based Peculiarity Extrication from biomedical images using deep learning (IBPE) is evaluated using GLCM, Gabor filter, fuzzy clustering means and SVM. To achieve an effective biomedical image classification, this framework system isolates its work in various stages; these phases are important to give the better classification accuracy. Evaluation proves the efficiency of the proposed IBPE with an achieved accuracy of 96.7 over 92.5% of the existing method.
Research on GIS Monitoring System in Operation Based on Rail Robot

GIS is prone to various defects and faults after putting into operation, and the failure rate is high. At present, the inspection methods of GIS in operation include traditional inspection mode, two-dimensional code inspection mode, radio frequency identification technology mode and robot inspection mode. However, the current inspection robot has the disadvantages of weak adaptability to complex environment, insufficient inspection scope, low efficiency and low intelligence. Therefore, this paper designs a GIS monitoring system based on the rail robot. By installing all kinds of sensors on the rail robot, with the help of binocular vision and mechanical arm, the omni-directional live detection of GIS in transit can be realized.
Establishing reliability and validity of an instrument to measure digital literacy practices and perceptions in higher education

The purpose of this study was to develop a reliable and valid instrument to explore the perceptions and practices of university professors and instructors with regards to digital literacy practices in the field of education. The Exploratory Factor Analysis (EFA) revealed four factors with eigenvalues greater than one. The 20-item instrument explained 65.08 % of the variance in relationship patterns among items: 36.49 % — F1: “Digital literacies help me teach”, 11.02 % — F2: “I have digital literacy competency and resources”, 8.99 % — F3: “Digital literacies support student learning”, and 8.58 % — F4: “Students are competent with digital literacy”. The instrument was deemed as reliable in determining faculty perceptions and practices.
A Sampling-Based Method for Highly Efficient Privacy-Preserving Data Publication

The data publication from multiple contributors has been long considered a fundamental task for data processing in various domains. It has been treated as one prominent prerequisite for enabling AI techniques in wireless networks. With the emergence of diversified smart devices and applications, data held by individuals becomes more pervasive and nontrivial for publication. First, the data are more private and sensitive, as they cover every aspect of daily life, from the incoming data to the fitness data. Second, the publication of such data is also bandwidth-consuming, as they are likely to be stored on mobile devices. The local differential privacy has been considered a novel paradigm for such distributed data publication. However, existing works mostly request the encoding of contents into vector space for publication, which is still costly in network resources. Therefore, this work proposes a novel framework for highly efficient privacy-preserving data publication. Specifically, two sampling-based algorithms are proposed for the histogram publication, which is an important statistic for data analysis. The first algorithm applies a bit-level sampling strategy to both reduce the overall bandwidth and balance the cost among contributors. The second algorithm allows consumers to adjust their focus on different intervals and can properly allocate the sampling ratios to optimize the overall performance. Both the analysis and the validation of real-world data traces have demonstrated the advancement of our work.
THE MOVEMENT OF HISTORICALLY INFORMED PERFORMANCE IN THE CONTEXT OF THE CULTURE OF THE XX–XXI CENTURY

The hermeneutical method is the basis for performing interpretation, because the author's musical text is the initial condition for its implementation. Over the last century, new musical forms and performing practices have emerged, where there is a synthesis of the heritage of many stylistic eras. Modern trends in performance art can be divided into three global independent areas – actualization, authenticity and avant- garde. The orientation in modern performance is directed both at the historical authenticity of the performance and at the questions of the subjective potency of musical creativity. There is an affirmation of a new paradigm, which consists in increasing the role of the intellectual principle, in striving to study a musical work in the context of the epoch that created it, in clearing the traditions of performance from later layers. Due to the growing interest of musicians in Renaissance and Baroque music, the number of soloists and groups performing Western European works written before the middle of the XVIII century is increasing. Over the course of the century, there has been a change in the status of authentic performance from individual to universal and social, because it not only organizes the experience of the community of musicians, but also forms the consciousness of a whole generation of culture. Due to the fact that a person is aware of the loss of the integrity, traditional art forms undergo transformations and destruction, which caused the need to appeal to the Baroque culture. The reception of the Baroque culture promotes the restoration of cultural identity, warns against multicultural fragmentation, and creates conditions for establishing links between cultural eras. The Baroque aesthetic is becoming one of the defining markers of popular culture. Attempts to adequately reproduce musical impressions of past eras among performers and interest in them among the public is a significant characteristic of modern society. A comprehensive study of authentism as a direction allows us to understand the context and functions of music for the cultural types of that time.
CNN based watermarking technique for authentication of valid subscribers

The use of Watermarking has become very important technique for security and authentication and also many more as we are that plays a important role in providing very good security and authentication for image transmission and also in the reception, In the case of broadcasting and general transmission of confidential secret information. The deep learning techniques with multiple layers having multiple waits. The deep learning techniques are adding Intelligence (AI) to the existing models, In the recent past it has presented great success proficiencies for number of tasks, like visual investigation, speech detection, natural language processing and etc. In this process there have come up many usable models. Building up a creation level profound learning structure is a non-insignificant assignment, which requisites a lot of preparing information, amazing processing assets, and human algorithmic. Therefore, it is a very importance which can actually harm human exploration. Ill-conceived recreating, conveyance, and the induction of exclusive profound learning models can prompt copyright encroachment and it is financial mischief to demonstrate makers. Hence, it is vital for devises method to ensure the licensed innovation of profound learning model.
Drawing and Recognising Simple Shapes with Real-Time Feedback Using Pattern Recognition

Pattern recognition is a mature but exciting and fast-developing field concerning computer vision, image processing, shape drawing, and text analysis. The shape pattern can be recognised easily using this technique. Some children have difficulties in identifying shapes. Therefore, through shape drawing exercises, it helps children to understand better. This project aims to develop a mobile application, assisting children in practising drawing using pattern recognition. It can identify shape types by matching the shape pattern with the given input. It will classify the information provided based on the feature extraction using the Freeman Chain Code. Then, each shape pattern is recognised using regular expression tools. Functionality testing has been conducted on this application with an accuracy of 80%. The application will encourage children to draw more by giving feedback on the exercise that they do. It may assist children in learning a new, better way of drawing shape accurately while improving children’s fine motor skills.
Improving Model Training by Periodic Sampling over Weight Distributions

Optimizing deep neural networks using gradient-based techniques such as SGD and Adam have some well documented problems including the high volatility, low stability and non-monotonicity of performance improvements during training. One approach that addresses these problems in convex settings is Polyak-Ruppert Averaging (PRA) which averages the model weights distributions over gradient updates. But this technique (and its variants) do not scale for non-convex settings and their use for large neural networks involves a tradeoff between increased stability and optimal convergence. In this paper, we introduce techniques centered around periodic sampling of model weights that improve upon gradient update methods (minibatch SGD, Momentum, Adam) for a variety of vision problems (classification, detection, segmentation). Compared to existing PRA approaches, the proposed algorithms provide better, faster and more robust convergence and training performance with only a slight increase in computation time. Importantly, these techniques are independent of the neural network model, gradient optimization methods and existing optimal training policies, and converge in a less volatile fashion with performance improvements that are approximately monotonic. We conduct a variety of experiments to quantify these improvements and identify scenarios where these techniques could be more useful.
Exploring Time-Series Through Force-Directed Timelines

Temporal datasets are a product of many scientific disciplines and analyzing the events that they describe may help provide valuable insight into their respective research subjects and help move towards solutions to existing problems. Time-series analysis is still an open problem which prompts new solutions, particularly the discovery of patterns across complex temporal networks. Visualization has proven to be a valuable tool in the analysis of such datasets, with the emergence of new models such as Time Curves, which distorts timelines to position time points based on their similarity, creating visualizations that highlight behavior patterns. In this paper, we further explore time-series functionally and aesthetically by revising the dynamic Time Curves models in CroP, a visualization tool with coordinated multiple views. Firstly, we propose the additional of new visual elements and interactive functions, coordinated with a network visualization to help discover and understand temporal patterns across complex datasets. Secondly, we visually explore time-series through Time Paths, a parameter-based force-directed layout that can dynamically transform the original model to either highlight small data variations or reduce visual noise in favor of overall patterns.
Using audio noise for generating random key stream

Abstract Steam ciphers systems are widely used in cryptography applications due to low possibility of propagating errors, ease of implementation and high processing speed. A stream cipher is an encryption process where random binary stream keys combine with the plaintext to produce cipher text. The stream cipher systems encrypt bits of plaintext individually by adding bits from a key stream to plaintext bits. The security of a cipher text fully depends on the key stream. Therefore, generating a long unpredictable sequence of binary bits used as the encryption key is the main topic in stream cipher systems. This paper focuses on generating a stream of binary keys using audio noise and subjected the generated keys to several local randomness tests such as; frequency test, serial test, Poker test and Autocorrelation test. The randomness tests show that the generated keys contain good statistical characteristics and high security makes them suitable to be used in stream cipher systems.
Electroencephalography-Based Neuromarketing Using Pegasos on Partition Membership Data

Neuromarketing exists as a manipulation trick to map customer perceptions and desires. Through this method, companies can market their products more effectively. This study examines it by using machine learning. The data were recorded using electroencephalography from 30 respondents. A video was used to simulate the respondents. It displays product images. Then they gave an assessment of the picture presented, whether they liked it or not. For automation, we build a system that classifies based on the Pegasos method. This method was also combined with the propositionalization approach. It aims to form partition membership data. Based on the experiments that have been carried out, the combination of these two methods has much higher performance compared to the support vector machine (SVM) method. Its accuracy reaches 93.30%. It shows that the combination of the two ways is a suitable method in the classification process, especially on the neuromarketing data used.
A Master-Slave Chain Model for Multiple Blockchains

In recent years, blockchain has attracted great attention because of decentralization, tamper resistance and traceability. However, a single blockchain is less able to handle a large number of transactions. Besides, interoperability among different blockchains remains a continuing challenge. In this paper, we propose a master-slave chain model (MSCM) for multiple blockchains, which provides flexibility and scalability for the blockchain system. In the model, atomic cross-blockchain transactions are enabled by the proposed cross-blockchain protocol. We also introduce a blockchain router algorithm to optimize the communications among different blockchains. The experimental results support the usefulness of MSCM as a means of performance improvement.