Reviewerly/exHarmony huggingface dataset:
ADOBSVM: Anomaly detection on block chain using support vector machine

Bitcoins are cryptocurrencies that make use of blockchain technology, which consists of network nodes and permanent ledgers of events. Theft and illicit actions are referred to as abnormalities in the financial network. Members of the network want to figure out what's up as quickly as possible to avoid conflicts in the community and maintain the network's integrity. Using an unconstrained machine learning technique, this research aims to discover anomalies in bitcoin transactions. Anomaly detectors play a critical role in defending networks and systems from unexpected threats, typically by identifying as well as screening problems efficiently. Various strategies have been developed over the years, all with the goal of lowering the number of false positives. Furthermore, no proposals so far talked about blockchain-based system-based attacks were considered. The Support Vector Machine (SVM) for anomalies is described in this article. In comparison to other existing approaches, SVM provides good security with a shorter execution time. The effectiveness of the SVM approach is evaluated using attack detection rate, error rate, execution time, and power consumption; and it is compared to existing methods. According to experimental results, the best results from existing approaches are obtained using SVM.
Detecting double H.266/VVC compression with the same coding parameters

The detection of video double compression with the same coding parameters is a difficult problem in video forensics, since the recompression traces are extremely slight in this case. Although a series of methods have been proposed, detection of Versatile Video Coding (H.266/VVC) double compression with the same coding parameters is rarely reported. To address this issue, we propose a novel algorithm for the detection of H.266/VVC double compression with the same coding parameters in this paper. We first analyze the generation of H.266/VVC double compression traces, and explore the variation of coding modes caused by multiple compressions. Then we define Minimum Unit Mapping (MUM) and Subunit Prediction Mapping (SPM) to facilitate feature construction. The strength of the variation in the number of coding unit (CU) in each compression is calculated to form CU partition modes based feature set. We also use the consistency ratios of adjacent prediction mode pairs in the horizontal, vertical, major diagonal, and minor diagonal directions after multiple compressions to obtain prediction modes based feature set. These feature sets are further concatenated into a fusion feature for detection. With the thorough comparison to the state-of-the-art algorithms, it is verified that the proposed algorithm outperforms other current works in the field. Besides, the proposed method is robust against various encoding configurations.
Improving the management and security of COVID 19 diagnostic test data with a digital platform in resource-limited settings: The case of PlaCARD in Cameroon

During the COVID 19 pandemic, round-the-clock demand for COVID -19 laboratory tests exceeded capacity, placing a significant burden on laboratory staff and infrastructure. The use of laboratory information management systems (LIMS) to streamline all phases of laboratory testing (preanalytical, analytical, and postanalytical) has become inevitable. The objective of this study is to describe the architecture, implementation, and requirements of PlaCARD, a software platform for managing patient registration, medical specimens, and diagnostic data flow, as well as reporting and authentication of diagnostic results during the 2019 coronavirus pandemic (COVID -19) in Cameroon. Building on its experience with biosurveillance, CPC developed an open-source, real-time digital health platform with web and mobile applications called PlaCARD to improve the efficiency and timing of disease-related interventions. PlaCARD was quickly adapted to the decentralization strategy of the COVID 19 testing in Cameroon and, after specific user training, was deployed in all COVID 19 diagnostic laboratories and the regional emergency operations center. Overall, 71% of samples tested for COVID 19 by molecular diagnostics in Cameroon from 05 March 2020 to 31 October 2021 were entered into PlaCARD. The median turnaround time for providing results was 2 days [0-2.3] before April 2021 and decreased to 1 day [1- 1] after the introduction of SMS result notification in PlaCARD. The integration of LIMS and workflow management into a single comprehensive software platform (PlaCARD) has strengthened COVID 19 surveillance capabilities in Cameroon. PlaCARD has demonstrated that it can be used as a LIMS for managing and securing test data during an outbreak.
New Probe Design for Hardware Characterization by ElectroMagnetic Fault Injection

ElectroMagnetic Fault Injection requires two main devices, a pulse generator and an electromagnetic (EM) injection probe. To improve security characterizations, inductive probes need to be optimized as regards the pulse waveform and field distribution. The improvement of these parameters leads to greater electromotive forces generated in the integrated circuits, which reinforces the efficiency of the attacks by fault injections. This paper presents a complete model of the electromagnetic field induced at the target surface according to Biot and Savart's law. This work defines the relevant design parameters of the new probes that are used for hardware characterization of the target components. These new probes reduce the voltage fault threshold required to disturb the execution of a program in the target. This paper also presents results related to the optimization of spatial resolution and inductive power at the target circuit surface. Understanding these phenomena then helps to implement countermeasures and best secure the strategies of the key IC components.
A personalized recommendation method under the cloud platform based on users’ long-term preferences and instant interests

Rich consumer online text data are embedded in the cloud platform. Using new technologies has become a central issue for acquiring consumer preference, analyzing consumer demand, and performing personalized recommendation services. In order to recommend the cloud platform services efficiently and accurately, this paper proposes a personalized recommendation model referred to as Residual bi-directional Recurrent Neural Network with Dual Attentive mechanism (BiRDA) for the service recommend to cloud platforms, by combining users' long-term preferences with instant interest. The proposed recommender prototype is summarized as follows. (1) Analyzing the relationship between long-term preferences and instant interests based on co-opetition theory. (2) Extracting users' online text data from the cloud platform. (3) Deriving the product attribute words of user preference using an analysis of online text data. (4) Product attribute words are transformed into the form of word vectors. (5) The word vector is input into the Residual bi-directional Recurrent Neural Network (Res-BiRNN) to make the prediction. On the one hand, the long-term preference is expressed by the user's field of expertise (i.e., answer content). On the other hand, the even interest is expressed by the user's changing interest (i.e., question data). (6) Assigning different weights to long-term preferences and instant interest using the dual attention mechanism to output predictions. (7) Generating recommendation lists for users based on the predicted values. Accordingly, BiRDA is compared with five state-of-the-art recommendation methods (i.e., DREAM, BINN, SHAN, Caser, and DeepMove), as well as six variants of the BiRDA model, Using users' Q&A datasets from NiorcngeCDS cloud platform, XMAKE cloud platform, and Asksubarme cloud platform as examples. The experiments show that the proposed method is more efficient and accurate than the other models. Therefore, the study offers some important insights into allowing a large number of resources under the cloud platform to be fully utilized and provides a novel idea for the construction of the cloud platform front-end.
OpExHAN: opinion extraction using hierarchical attention network from unstructured reviews

In recent decades, sellers and merchants have asked their customers to share their opinion on the products at online marketplace. Analysis of the massive amounts of reviews for a potential customer is an immense challenge, to decide whether to purchase a product or not. In this paper, a hierarchical attention network-based framework is presented to resolve this challenge. In this proposed framework, the Amazon's Smartphone review dataset is preprocessed using NLP approaches and then applied Glove embedding to extract word vector representation of reviews which identifies contextual information of words. These word vectors are fed into the hierarchical attention network, which produces vectors at the word and sentence levels. Bi-GRU model encodes the words and sentences into hidden vectors. Finally, reviews are classified into five opinion classes like extremely positive, positive, extremely negative, negative and neutral. Furthermore, to perform the experiments with the proposed method, the dataset is divided into three parts such as 80% train, 10% validation and 10% test. Experiments reveal that the proposed framework outperforms baseline methods in terms of accuracy, precision and recall. The OpExHAN model achieved admirable results like 94.6% accuracy, 91% both precision and recall after a lot of hyper-parameter experimentation.
Does the realized distribution-based measure dominate particular moments? Evidence from cryptocurrency markets

This study investigates the relationship between the daily returns of cryptocurrencies and their realized performance measure and moments (variance, skewness, and kurtosis). Because the cryptocurrency returns are non-Gaussian distributions and perform bubble-like behaviors, we adopt the performance measure proposed by Schnytzer and Westreich (2013) to capture all informational content related to the distribution of a return. First, the empirical results revealed that there is a positive relationship between the realized performance measure and daily returns. Furthermore, this realized performance measure dominates the realized variance, skewness, and kurtosis in terms of affecting and predicting Bitcoin and Ethereum daily returns.
An Intelligent Secure Monitoring Phase in Blockchain Framework for Large Transaction

Blockchain is the key concept for security purposes for digital applications. But, in some cases, the effectiveness of the malicious behavior has degraded the security function of the blockchain. So, to enrich the blockchain process prediction and to neglect the malicious event from the data broadcasting medium is very important. So, the current research article intends to develop an efficient monitoring strategy based on incorporating deep features. Hence, the designed paradigm is termed as Lion-based Convolutional Neural Model (LbCNM) with serpent encryption. Before performing the encryption process, the novel LbCNM parameters have been activated to monitor the data process channel in the blockchain environment. Here, the malicious behaviors were estimated by incorporating the known and unknown user behavior in the Lion fitness model. During the execution, the fitness formulation of Lion is acted in the classification layer of the convolutional model. Once the present malicious characteristics have been detected, it is neglected from the data broadcasting channel. Hereafter, the transactional data has been encrypted and stored in the specific cloud. The planned strategy is verified in the python platform. The successful performance of the LbCNM with serpent has been analyzed with some key parameters like confidential rate, accuracy, data overhead, and processing time.
MemSweeper: virtualizing cluster memory management for high memory utilization and isolation

Memory caches are critical components of modern web services that improve response times and reduce the load on backend databases. In multi-tenant clouds, several instances of caches compete for memory. The current state-of-the-art is to statically allocate memory for cache instances (e.g., based on cost-tier) but such allocation tends to be sub-optimal as memory demands of instances often vary with time and not known apriori. We propose MemSweeper, which dynamically manages memory between cache instances. MemSweeper uses a novel, score-based metric and an associated algorithm to identify cache instances whose working sets fit well within their allocated memory and thus can relinquish a portion of the memory without suffering appreciable loss in their hit rates. Using a combination of synthetic and production traces on a real implementation, we show that MemSweeper achieves 74% improvement (on average) in the miss rate of critical tenants without degrading the performance of other tenants.
Blockchain-Enabled Decentralized Network Management in 6G

The Internet has evolved from a fault-tolerant infrastructure to support both social networking and a semantic web for machine users. Trust in the data, and the infrastructure, has become increasingly important as cyber threats and privacy concerns rise. Communication services become increasingly delivered through virtualized, software-defined infrastructures, like overlays across multiple infrastructure providers. Increasing recognition of the need for services to be not only fault-tolerant but also censorship-resistant while delivering an increasing variety of services through a complex ecosystem of service providers drives the need for decentralized solutions like blockchains. Service providers have traditionally relied on contractual arrangements to deliver end-to-end services globally. Some of the contract terms can now be automated through smart contracts on blockchains. This is a complex distributed environment with multiple actors and resources. Blockchains are not only proposed for use at a business services level but also in the operation of the network infrastructure including dynamic spectrum management, SDN and resource management, metering and IoT services. Traditional approaches to network management have relied on client–server protocols and centralized architectures. Digital transformation at both network operators and many of their customers has led to a software-defined infrastructure for communication services, based on virtualized network functions. Decentralized approaches for network management have gained increasing attention from researchers. The operators increased need for mechanisms to assure trust in data, operations and commercial transactions while maintaining business continuity through software and equipment failures, and cyberattacks provide further motivations for blockchain-based approaches.
A Novel Counting Sort for Real Numberswith Linear Time Complexity

Counting sort is a method that counts the occurrence of numbers with different key values. It sorts the numbers according to their key values (smaller integer) and mapped sorted value as an index of the array. This sort mainly uses to sort the integer number in linear time. It requires total O (N+K) execution time to sort the array of N elements, where K is the biggest number in the array. This paper presents a novel approach of counting sort with linear time complexity which is capable of sorting the real numbers as well as integer numbers. In this we have considered the small precision after the decimal because the large precision takes extra memory space. The proposed algorithm works for real numbers without affecting the time complexity of existing/original counting sort.
CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts Based on Historical Data

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.
Analysis of the Correlation between College Music Education and Public Mental Health Based on Deep Learning

With the continuous changes of college music education and public mental health problems, conventional solutions are no longer sufficient. At this time, we adopted a method based on deep learning to study them. The experiments showed that ① deep learning can be well integrated into two. Among them, the rapport rate between them reached 91%, and the sometimes-unstable state of the system was solved by optimizing the model. Finally, through the evaluation algorithm, the overall score of the model was obtained to fluctuate between 85 and 90. ② According to the data in the figures and tables, it can be concluded that university music education under deep learning has received more attention in my country, and the investment amount has also increased from 2.4 billion to 3.7 billion. The highest music education activities in universities have become 55% of concerts and 41% of musical instrument experience classes, and finally, my country's public mental health problem has become a country with the lowest level in the world.
Confidentiality in medical images through a genetic-based steganography algorithm in artificial intelligence

Nowadays, image steganography has an important role in hiding information in advanced applications, such as medical image communication, confidential communication and secret data storing, protection of data alteration, access control system for digital content distribution and media database systems. In these applications, one of the most important aspects is to hide information in a cover image whithout suffering any alteration. Currently, all existing approaches used to hide a secret message in a cover image produce some level of distortion in this image. Although these levels of distortion present acceptable PSNR values, this causes minimal visual degradation that can be detected by steganalysis techniques. In this work, we propose a steganographic method based on a genetic algorithm to improve the PSNR level reduction. To achieve this aim, the proposed algorithm requires a private key composed of two values. The first value serves as a seed to generate the random values required on the genetic algorithm, and the second value represents the sequence of bit locations of the secret medical image within the cover image. At least the seed must be shared by a secure communication channel. The results demonstrate that the proposed method exhibits higher capacity in terms of PNSR level compared with existing works.
(De-)constructing New Zealand PM Jacinda Ardern’s initiative to wear the hijab after the Christchurch terrorist attack

Abstract This paper examines the discursive construction of Prime Minister Jacinda Ardern after she wore a veil following the unprecedented terror attack in two mosques in the city of Christchurch in 2019. The articles analyzed are collected from three main newspapers published in New Zealand’s three main cities. Analyzed using principles and tools from Critical Discourse Studies (CDS) and Argumentum Model of Topics (AMT) more specifically, the articles reveal key arguments from both sides i.e. in support of and against this unparalleled initiative on behalf of a political leader in the western world. On the one hand, the majority of the news articles under analysis portray PM’s initiative as one that attempts to mitigate tension and conflict while bringing solidarity to a grief-stricken nation; and thus, are based on the ‘locus from ontological implications’. While the perspectives that oppose the initiative vary from explicit racist ones that present Muslims as threat to Zealand, to those who strongly believe such actions support the women oppression. Through a (de-)construction of the arguments opposing this move, the findings demonstrate that the opposition towards PM’s initiative is based on two main loci namely the ‘locus from correlates’ and the ‘locus from termination and setting up’
Fungible and non-fungible tokens with snapshots in Java

Abstract Many blockchain applications exchange tokens, such as bitcoin and ether, or implement them through smart contracts. A trend in blockchain is to apply standards for token interoperability, unchanged, from platform to platform, easing the design challenges with trusted and widely-used specifications. However, the exploitation of the target language semantics can result in technological advantages and more efficient contracts. This paper presents a re-engineering of OpenZeppelin’s implementation of the ERC-20 and ERC-721 standards in Takamaka, a Java framework for programming smart contracts. It describes a sound solution to the issue about the types allowed for the token holders and a novel implementation for making snapshots of tokens, based on tree maps, that is possible in Java, but not in Solidity, more efficient than the literal translation in Java from Solidity, within the Java virtual machine. Moreover, it applies to ERC-721 as well, where a snapshot mechanism was previously missing. The same snapshot mechanism can also be applied beyond the smart contracts for tokens.