Reviewerly/exHarmony huggingface dataset:
A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

High-utility itemset mining (HUIM) can consider not only the profit factor but also the profitable factor, which is an essential task in data mining. However, most HUIM algorithms are mainly developed on a single machine, which is inefficient for big data since limited memory and processing capacities are available. A parallel efficient high-utility itemset mining (P-EFIM) algorithm is proposed based on the Hadoop platform to solve this problem in this paper. In P-EFIM, the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce framework. Then the ordered itemsets are renumbered, and the low-utility itemsets are pruned to improve the dataset utility. In the Map phase, the P-EFIM algorithm divides the task into multiple independent subtasks. It uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure load-balancing. Furthermore, the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce phase. Experiments are performed on eight datasets, and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth, which is also HUIM algorithm based on the Hadoop framework.
Compositional Confluence Criteria

We show how confluence criteria based on decreasing diagrams are generalized to ones composable with other criteria. For demonstration of the method, the confluence criteria of orthogonality, rule labeling, and critical pair systems for term rewriting are recast into composable forms. We also show how such a criterion can be used for a reduction method that removes rewrite rules unnecessary for confluence analysis. In addition to them, we prove that Toyama's parallel closedness result based on parallel critical pairs subsumes his almost parallel closedness theorem.
Journalists' Perceptions Towards Digital Media Training in Jordanian Media Organizations

The study aimed to reveal the perceptions of Journalists' Perceptions toward digital media Training in Jordanian Media organizations. The study sample consisted of journalists working in Jordanian media organizations. The study used the descriptive exploratory approach and concluded that there is an effect of applying digital media tools according to the perceptions of journalists, and they focused on the reasons why most Jordanian media organizations are not ready to employ digital media techniques. In addition, the study indicated that there were Jordan journalists possessed various skills in digital media, including using social networks for research, writing, publishing news stories, and editing content.
Technologies as a Mediator between Creator and Audience in Postmodern Art Practices

The article analyzes the interaction between creator and audience that occurs in the new modes of art practices. Within this field, experimenting with technology is accompanied by emergence of new art forms and genres. Thus, it seems appropriate to view technologies as a mediator in the dynamic process of communication between the creator and viewer. In this context, a number of innovative projects were considered that emerged in the art practices of the recent decades. The choice of the projects is determined by the specific expressive means and by the features of interaction of these means on the basis of contemporary technologies. Creation of these projects resulted from the ideas and achievements of the 1970s–1990s. At first, it was "pure" experimentation, aimed at expanding the range of new expressive components and testing them. Afterwards, modern art products created within this direction are mature and complete. To evoke the interest of the audience, the use of multimedia and installations based on interactive technologies are employed. Technologies stimulate further evolution of art, i.e. they contribute to forming the new genres, and trends. It was concluded that technologies constitute a toolkit that de facto serves as a catalyst enabling practical realization of the artistic polylogue within the creative process: from the point when the idea is conceived, through its fulfillment in the creator’s design and up to its presentation when its communicative models of influence aimed at the art communities are activated.
Extrapolative Controlled Sequence Generation via Iterative Refinement

We study the problem of extrapolative controlled generation, i.e., generating sequences with attribute values beyond the range seen in training. This task is of significant importance in automated design, especially drug discovery, where the goal is to design novel proteins that are \textit{better} (e.g., more stable) than existing sequences. Thus, by definition, the target sequences and their attribute values are out of the training distribution, posing challenges to existing methods that aim to directly generate the target sequence. Instead, in this work, we propose Iterative Controlled Extrapolation (ICE) which iteratively makes local edits to a sequence to enable extrapolation. We train the model on synthetically generated sequence pairs that demonstrate small improvement in the attribute value. Results on one natural language task (sentiment analysis) and two protein engineering tasks (ACE2 stability and AAV fitness) show that ICE considerably outperforms state-of-the-art approaches despite its simplicity. Our code and models are available at: https://github.com/vishakhpk/iter-extrapolation.
Emulating two qubits with a four-level transmon qudit for variational quantum algorithms

Using quantum systems with more than two levels, or qudits, can scale the computation space of quantum processors more efficiently than using qubits, which may offer an easier physical implementation for larger Hilbert spaces. However, individual qudits may exhibit larger noise, and algorithms designed for qubits require to be recompiled to qudit algorithms for execution. In this work, we implemented a two-qubit emulator using a 4-level superconducting transmon qudit for variational quantum algorithm applications and analyzed its noise model. The major source of error for the variational algorithm was readout misclassification error and amplitude damping. To improve the accuracy of the results, we applied error-mitigation techniques to reduce the effects of the misclassification and qudit decay event. The final predicted energy value is within the range of chemical accuracy. Our work demonstrates that qudits are a practical alternative to qubits for variational algorithms.
Hierarchical memories: Simulating quantum LDPC codes with local gates

Constant-rate low-density parity-check (LDPC) codes are promising candidates for constructing efficient fault-tolerant quantum memories. However, if physical gates are subject to geometric-locality constraints, it becomes challenging to realize these codes. In this paper, we construct a new family of [[N,K,D]] codes, referred to as hierarchical codes, that encode a number of logical qubits K = Omega(N/\log(N)^2). The N-th element of this code family is obtained by concatenating a constant-rate quantum LDPC code with a surface code; nearest-neighbor gates in two dimensions are sufficient to implement the corresponding syndrome-extraction circuit and achieve a threshold. Below threshold the logical failure rate vanishes superpolynomially as a function of the distance D(N). We present a bilayer architecture for implementing the syndrome-extraction circuit, and estimate the logical failure rate for this architecture. Under conservative assumptions, we find that the hierarchical code outperforms the basic encoding where all logical qubits are encoded in the surface code.
The TraceTogether Matrix Has You – Surveillance, Rationalisation and Tactics of Governance in Singapore’s COVID-19 App

In the heat of the COVID-19 pandemic, Singapore rolled out TraceTogether; a contact-tracing mobile app that uses proximity sensing to track the movements of its population. TraceTogether was initially voluntary, and used solely for contact tracing. By December 2020, the system became mandatory. This sparked a mass adoption that made TraceTogether possibly the most successful application in Singapore’s Smart Nation initiative. When it emerged in January 2021 that the data had been used by the police for criminal investigation, images of a totalitarianism sprang to mind, where technology permits the state an invasive awareness of the movement of individuals. In this paper, we defer from common arguments that Singaporeans are intrinsically trusting of the government or have been conditioned to accept ‘Big Brother’ modes of surveillance. Instead, we argue that the success of TraceTogether reflects a Singapore society that, through the rationalisation of surveillance, willingly participates in their own surveillance. In uncovering the genealogy of media discourse that surrounds TraceTogether, we highlight that it is the regular practice of voluntary surveillance, of subscribing oneself to the apparatuses of state control, rather than specific technologies, that characterises the Singapore surveillance state. We describe a matrix of reason, layered-on and normalised through media discourse, that exemplifies what Foucault has termed ‘governmentality’, which asserts a government’s power of control not over, but within, citizens.
Improving Vocabulary Reading Skills with Word Card and Picture Card for Moderate Intellectual Disabilities

Teaching reading functional vocabulary to students with intellectual disability (ID’s) is a challenge for teachers in special schools. They face many obstacles in teaching vocabulary which are caused by various factors, especially student characteristics and learning structures that are less practical and tend to be academic. This study aims to measure the effectiveness of using word cards and picture cards to improve vocabulary reading skills in moderate ID’s students. The type of research applied is single subject research, with multiple baseline across subject designs. The research subjects were two moderately ID’s students in grade 5 and grade 2 SLB-C1 Bhakti Luhur Malang. An instructor and an observer played an important role in this study. The experimental instrument used is Lesson Plan which consists of three units, each teaching two vocabularies. Data analysis is carried out by performing graphical inspections that focus on trend, latency, and level changes. The effectiveness was confirmed by Percentage of All Non-overlapping Data (PAND) in the intervention condition against the baseline-1 condition. The results showed that with the intervention, the target behavior in the form of vocabulary understanding had a trend of grades rising gradually touching a high level of score, and settling at a high level in the baseline-2 condition. The average PAND for subject-1 reached 80.55 percent (effective), and subject-2 reached 96.67 percent (very effective).
Quantum computing reduces systemic risk in financial networks

Abstract In highly connected financial networks, the failure of a single institution can cascade into additional bank failures. This systemic risk can be mitigated by adjusting the loans, holding shares, and other liabilities connecting institutions in a way that prevents cascading of failures. We are approaching the systemic risk problem by attempting to optimize the connections between the institutions. In order to provide a more realistic simulation environment, we have incorporated nonlinear/discontinuous losses in the value of the banks. To address scalability challenges, we have developed a two-stage algorithm where the networks are partitioned into modules of highly interconnected banks and then the modules are individually optimized. We developed a new algorithms for classical and quantum partitioning for directed and weighed graphs (first stage) and a new methodology for solving Mixed Integer Linear Programming problems with constraints for the systemic risk context (second stage). We compare classical and quantum algorithms for the partitioning problem. Experimental results demonstrate that our two-stage optimization with quantum partitioning is more resilient to financial shocks, delays the cascade failure phase transition, and reduces the total number of failures at convergence under systemic risks with reduced time complexity.
ARNLI: ARABIC NATURAL LANGUAGE INFERENCE ENTAILMENT AND CONTRADICTION DETECTION

Natural Language Inference (NLI) is a hot topic research in natural language processing, contradiction detection between sentences is a special case of NLI. This is considered a difficult NLP task which has a big influence when added as a component in many NLP applications, such as Question Answering Systems, text Summarization. Arabic Language is one of the most challenging low-resources languages in detecting contradictions due to its rich lexical, semantics ambiguity. We have created a data set of more than 12k sentences and named ArNLI, that will be publicly available. Moreover, we have applied a new model inspired by Stanford contradiction detection proposed solutions on English language. We proposed an approach to detect contradictions between pairs of sentences in Arabic language using contradiction vector combined with language model vector as an input to machine learning model. We analyzed results of different traditional machine learning classifiers and compared their results on our created data set (ArNLI) and on an automatic translation of both PHEME, SICK English data sets. Best results achieved using Random Forest classifier with an accuracy of 99%, 60%, 75% on PHEME, SICK and ArNLI respectively.
A model of decoy system based on dynamic attributes for cybercrime investigation

The object of research are decoys with dynamic attributes. This paper discusses the impact of decoys involving blockchain technologies on the state of information security of the organization and the process of researching cybercrime. This is important because most cybercrimes are detected after the attacker gains access to sensitive data. Through systematic analysis of the literature focused on assessing the capabilities of decoy and blockchain technologies, this work identifies the main advantages of decoys that utilize blockchain technology. To assess the effectiveness of attacker detection and cybercrime analysis, controlled experiments were conducted using a blockchain-based decoy system that we developed aimed at determining network performance. As part of the study reported here, a technique is proposed to detect cybercrime using decoys based on blockchain technology. This technique is based on the fact that the attributes of the system change dynamically. Such a technique has made it possible to obtain a system model that solves the task of detecting decoys by intruders. In addition, the developed scheme reduces the load in contrast to the conventional fixed solution. The results indicate that the response time of services is significantly reduced in the environment of decoys with dynamic attributes. For example, Nginx's response time in a static host is twice as high as dynamic, and an Apache dynamic server can still respond to an intruder's attack even if a static server fails. Therefore, the results reported in the article give grounds to assert the possibility of using the solution in the infrastructure of information systems at the public and private levels
Study And Modeling of Question Answer System Using Deep Learning Technique of AI

In this paper, the different QA system types, the theoretical foundation for deep learning models, the metaheuristic optimization techniques, and the performance assessment metrics are discussed. A suggested architecture for a question-and-answer system that takes a deep learning approach is shown here. The study also covers the constraints and factors to take into account regarding the aforementioned system.
Auto Deep Learning-based Automated Surveillance Technique to Recognize the Activities in the Cyber-Physical System

In recent days, the Internet of Things (IoT) plays a significant role and increasing in rapid usage in various applications. As IoT is being developed for cyber-physical systems in the specific domain of e-health care, military, etc. Based on real-time applications, security plays a vital role in certain activities in educational institutions. In the institutions, there are multiple videos are collected and stored in the data repositories. Those datasets are developed specifically for certain activities and no other datasets are developed for academic activities. As there is a large number of videos and images are collected and considered, advanced technologies like, deep learning and IoT are used to perform certain tasks. In this paper, a Auto Deep learning-based Automated Identification Framework (DLAIF) is proposed to consider and reconsider the activities based on image pre-processing, model can be trained through the proposed GMM model and then predication to make an effective surveillance process based on HMM. This proposed process makes to recognize the activities through EM and log Likelihood for cyber-physical systems. In the performance analysis, the proposed model efficiency can be determined through Accuracy detection, False Positive rate and F1 Score requirement. Then calculating the accuracy is more effective for the proposed model compared to other existing models such as BWMP and LATTE.
Novel Load Balancing Optimization Algorithm to Improve Quality-of-Service in Cloud Environment

Scheduling cloud resources calls for allocating cloud assets to cloud tasks. It is possible to improve scheduling outcomes by treating Quality of Service (QoS) factors as essential constraints. However, efficient scheduling calls for improved optimization of QoS parameters, and only a few resource scheduling algorithms in the available literature do so. The primary objective of this paper is to provide an effective method for deploying workloads to cloud infrastructure. To ensure that workloads are executed efficiently on available resources, a resource scheduling method based on particle swarm optimization was developed. The proposed method's performance has been measured in the cloud. The experimental results prove the efficiency of the proposed approach in reducing the aforementioned QoS parameters. Several metrics of algorithm performance are used to gauge how well the algorithm performs.
Retraction: Recent trends, challenges, and future aspects of P2P energy trading platforms in electrical-based networks considering blockchain technology: A roadmap toward environmental sustainability

Please find the retraction statement below:Javed H, Irfan M, Shehzad M, Abdul Muqeet H, Akhter J, Dagar V and Guerrero JM (2022) Recent Trends, Challenges, and Future Aspects of P2P Energy Trading Platforms in Electrical-Based Networks Considering Blockchain Technology: A Roadmap Toward Environmental Sustainability. Front. Energy Res. 10:810395. doi: 10.3389/fenrg.2022.810395The journal retracts the 18 March 2022 article cited above.Following concerns regarding the originality of the article, an investigation was conducted in accordance with Frontiers’ policies. The investigation determined an unacceptably high level of similarity with an article published by Tushar et al. (2020). This retraction was approved by the Chief Editors of Frontiers in Energy Research and the Chief Executive Editor of Frontiers.The authors have not agreed to the retraction.
The effect of Blockchain technology as a moderator on the relationship between big data and the risk of financial disclosure (analytical study in the Egyptian and Iraqi stock exchange)

This paper aims to explain the impact of Blockchain Technology as a moderator in the relationship between Big Data and The Risk of Financial Disclosure to address the Risk of Financial Disclosure. Therefore, this technique was used to reduce the risks of disclosing financial and accounting data for various companies. The estimated population size of accounting information systems and information technology professionals is more than 300 in the Egyptian and Iraqi Stock Exchange. The results indicate that all the direct hypotheses that reflect the relation between Big Data characteristics and FDR are supported which is less than 0.05. There is no impact of Volume on Financial Disclosure Risk with a p-value=0.074. the indirect effect of Blockchain Technology on the relationship between (VEL, VER and VOL) and Financial Disclosure Risk was significant with a p-value of 0.048,0.024,0.001 respectively, which is less than 0.05 and does not support the relationship between VAR and Financial Disclosure Risk a p-value of 0.735. Then, we recommend state-of-the-art studies on the use of blockchain for big data applications in different vertical domains such as smart cities, Financial transactions, smart transportation, and smart bank accounts. For a better understanding, some representative blockchain-big data projects are also presented and analysed. Finally, challenges and future directions are discussed to further drive research in the countries of the Middle East. This paper presents the novel solutions associated with Big Data with Financial Disclosure Risks that can be addressed by Blockchain technology. As Well as present the motivations behind the use of blockchain for big data. We show that blockchain has great potential for facilitating big data analytics such as control of dirty data, enhanced security and transparency, enhanced quality of data, the management of data sharing and, Addressing risk financial disclosure
Cross-modal Retrieval based on Shared Proxies

Abstract Inconsistency of distribution and representation across different data modalities makes measuring cross-modal similarities a very difficult problem. Learning a common space that is semantically discriminative and modality invariant is the main challenge in cross-modal retrieval. Existing solutions usually employ pairwise or triplet data relationships to learn the common space, which can only capture the data similarity locally but would be unable to effectively characterize the global geometry of the common embedding space, and thus would limit the performance of cross-modal retrieval. In this paper, we introduce a shared proxy solution to cross-modal retrieval. We propose to incorporate the principles of shared proxy with neighbourhood component analysis to learn a common space for different modalities in which the distance between a sample’s representation and its corresponding proxy is minimized while the distances between a sample’s representation and the proxies not belonging to the sample are maximized. We propose the Cross-mOdal proXy learnIng (COXI) framework which integrates a cross-modal shared proxy loss, a discriminative loss and a modality invariant loss for supervised cross-modal retrieval. Extensive experiments on benchmark datasets clearly shows that COXI outperforms state of the art cross-modal retrieval techniques. Code is available on https://github.com/LigangZheng/COXI.
Implementation of AES Algorithm

Now a day’s large number of internet and wireless communication users has led to an increasing demand of security measures and devices for protecting the user data transmitted over the unsecured network so that unauthorized persons cannot access it . As we share the data through wireless network it should provide data confidentiality, integrity and authentication. The symmetric block cipher plays a major role in the bulk data encryption. Advanced Encryption Standard (AES) provides data security. AES has the advantage of being implemented in both hardware and software. Hardware implementation of the AES has lot of advantage such has increased throughput and better security level. Hardware Implementation for 128 bit AES (Advanced Encryption Standard) encryption and Decryption has been made using VHDL. The proposed algorithm for encryption and decryption module will functionally verified using modelsim, will be synthesize using Quartus 2 using Altera FPGA platform and analyze the design for the power, Throughput &amp; area.