Reviewerly/exHarmony huggingface dataset:
Image Compression Using Stochastic-AFD Based Multisignal Sparse Representation

Adaptive Fourier decomposition (AFD) is a newly developed signal processing tool that can adaptively decompose any single signal using a Szeg&#x00F6; kernel dictionary. To process multiple signals, a novel stochastic-AFD (SAFD) theory was recently proposed. The innovation of this study is twofold. First, a SAFD-based general multi-signal sparse representation learning algorithm is designed and implemented for the first time in the literature, which can be used in many signal and image processing areas. Second, a novel SAFD based image compression framework is proposed. The algorithm design and implementation of the SAFD theory and image compression methods are presented in detail. The proposed compression methods are compared with 13 other state-of-the-art compression methods, including JPEG, JPEG2000, BPG, and other popular deep learning-based methods. The experimental results show that our methods achieve the best balanced performance. The proposed methods are based on single image adaptive sparse representation learning, and they require no pre-training. In addition, the decompression quality or compression efficiency can be easily adjusted by a single parameter, that is, the decomposition level. Our method is supported by a solid mathematical foundation, which has the potential to become a new core technology in image compression.
porter meets williamson in 2022: governance value analysis and its implications in a world of digital technologies

In this chapter we briefly describe the Governance Value Analysis model introduced by Ghosh and John (1999) that links firm-specific strategic considerations into the design of value creating and value claiming governance choices in both inter-firm and intra-firm settings. We provide a brief review of the relevant research that explicates the links tying strategic positioning choices and non-imitable resource profiles to the transactional attributes and governance choices observed in a variety of substantive settings. We close the chapter with forward-looking ideas that expand on how the GVA model could be used to shed light on important problems that arise in the value chain in the current technological and business environment replete with significant advances in Information and Communication Technologies (ICT) like the Internet of Things (IoT), smart contracts, and crypto assets. We explore the implications of GVA into three key features of such digitized transactions - their scale and scope, their measurement intensity, and their interaction with institutional environments and legal systems.
Programmable implementation of time-area-efficient Elliptic Curve Cryptography for entity authentication

In the ever increasing wireless reliant technologies, communications and devices, the demand for effective security with low hardware and high speed is wanting in the cryptography domain. Among the various cryptographic algorithms, the Elliptic Curve Cryptography (ECC) provides an attractive solution for this demand. In this paper, the Remote Keyless system Authentication process using the ECC has been implemented in Field Programmable Gate Array (FPGA). The designed ECC processor supports 256-bit point multiplication and point addition on the Koblitz curve secp256k1. The scalar multiplication has been performed with the faster multiplier Urdhva Tiryagbhyam. Additionally, pipelining has been incorporated in order to speed up the multiplication process of the processor. The proposed ECC processor performs single point multiplication of 256-bit in 1.2062ms with a maximum clock frequency of 192.5MHz, which provides 212.23kbps throughput and occupies 8.23k slices in Virtex-7 FPGA. Incorporating pipeline in scalar multiplication improves the maximum clock frequency up to 15.12%, reduces time consumption by 22.36%, which in turn increases the throughput by 22.36%. The proposed pipelined Vedic multiplier based ECC processor outperforms the existing designs in terms of area, operating frequency, area-delay product and throughput. Hence, the implementation of the proposed method offers time-area-efficient fast scalar multiplication with effective hardware utilization without any compromise in security level.
Measurement of Entangled States Before, During, and After a Proposed Entangled Two-Photon Molecular Excitation

Entangled photons are predicted to linearize and enhance the probability of multiphoton processes. We construct an interferometer to measure the quantum state before, during, and after an entangled excitation and distinguish it from one-photon processes. Comments and questions should be directed to the Optica Conference Papers staff (tel: +1 202.416.6191, e-mail: cstech@optica.org ).
Sequre: a high-performance framework for rapid development of secure bioinformatics pipelines

Genomic data leaks are irreversible. Leaked DNA cannot be changed, stays disclosed indefinitely, and affects the owner's family members as well. The recent large-scale genomic data collections [1], [2] render the traditional privacy protection mechanisms, like the Health Insurance Portability and Accountability Act (HIPAA), inadequate for protection against the novel security attacks [3]. On the other hand, data access restrictions hinder important clinical research that requires large datasets to operate [4]. These concerns can be naturally addressed by the employment of privacy-enhancing technologies, such as a secure multiparty computation (MPC) [5]–[10]. Secure MPC enables computation on data without disclosing the data itself by dividing the data and computation between multiple computing parties in a distributed manner to prevent individual computing parties from accessing raw data. MPC systems are being increasingly adopted in fields that operate on sensitive datasets [11]–[13], such as computational genomics and biomedical research [14]–[22].
Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor's algorithm, weighted sum optimization problems in data processing and machine learning, and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM's superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands' superposed integer states. We analyze performance, and identify the most effective approximation depths for quantum add and quantum multiply within the given context. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges - relevant to signed, unsigned, modular and non-modular versions - that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. We analyze performance trends in our results and speculate on possible future development within this computational paradigm.
On the Quality of Experience of Content Sharing in Online Education and Online Meetings

The turn of the decade introduced a new era of global pandemics to the world through the appearance of COVID-19, which is still an active crisis at the time of this paper. As a countermeasure, the phenomena of home office and online education became not only widely available, but also mandatory in many countries. However, the performance, reliability and general usability of such real-time activities may be severely affected by unfavorable network conditions. In both contexts, content sharing is now a common practice, and the success of the related use cases may fundamentally depend on it. In this paper, we present our surveys and subjective studies on the Quality of Experience of content sharing in online education and online meetings. A total of 6 surveys and 5 experiments are detailed, addressing topics of student experience, user interface settings, sharing options of lecturers and employees of the private sector, the perceivable effects of network impairments and the related long-term adaptation, the rubber band effect of slide sharing, the overall perceived quality and the separate quality aspects of media loading times, and the preference between visual quality, average frame rate and frame rate uniformity. The findings of the subjective studies do not characterize the use cases of the investigated topics on a general, widely-applicable level, as only a single online platform is involved throughout the experiments. However, their experimental configurations are reinforced by comprehensive surveys and many results indicate statistically significant differences between the selected test conditions.
Interactive Dashboard with Visual Sensing and Zero-Shot Learning Capabilities

These days, technology is growing rapidly, and the market has been introduced with lots of fascinating ways to interact with computers. The advancement of deep learning models and hardware technology also enables more applications with fancy features to be built. The importance of hand gesture recognition has increased due to the prevalence of touchless applications. However, developing an efficient recognition system needs to overcome the challenges of hand segmentation, local hand shape representation, global body configuration representation, and a gesture sequence model. This paper proposed an interactive dashboard that could react to hand gestures. This is also an initiative of the Tunku Abdul Rahman University College (TAR UC) Smart Campus project. Deep learning models were investigated in this research and the optimal model was selected for the dashboard. In addition, 20BN Jester Dataset was used for the dashboard development. To set up a more user-friendly dashboard, the data communication stream between the captured input stream and commands among the devices will also be studied. As to achieve higher responsiveness from the dashboard, evaluation on data communication protocols which were used to pass the input data included in the study. Keywords: Computer Vision, Human-Computer Interaction (HCI), Gesture Detection, Real-time systems, Feature Extraction
What news sparks interest on YouTube? A study of news content uploaded by India's top five Hindi news networks

Purpose The consumption of news from social media is the new trend, still news channels are the authentic source to transmit relevant news to audiences. Social media has gradually left an impact on the audience but the news channels have upgraded and providing various news services online on social media websites. The present study aims to study the type of news videos uploaded by the top five Hindi TV news channels on their YouTube channels with an aim to see which type of videos spark interest for YouTube viewers. Design/methodology/approach By applying the techniques of content analysis, sentiment analysis and text mining the study aims to measure the average sentiments, top words and the trend of selected popular terms in the comments on uploaded news videos by the top five Hindi news channels over a period of one year. Findings Results of the study indicate that the news channels are uploading more news videos about crime and investigation, politics, health and protests while uploading fewer news videos covering travel, science and technology, and religion. While the viewers of the participating news channels are more interested in giving their thoughts or opinions in the form of comments on news videos concerning crime, politics, protests and health or that these videos inspire conversation on YouTube. Research limitations/implications The findings might be of interest to content managers of news channels to understand the interest of their audience. Originality/value The study's distinctiveness resides in the approach utilised to collect data and analyse the results in order to better understand the online behaviour of news channel audiences. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2022-0007
The model of fast face recognition against age interference in deep learning

In order to overcome the low recognition efficiency of traditional anti-age face recognition methods, the paper proposes a new anti-age-disturbing face recognition modelling method based on deep learning. Firstly, build a standard face recognition information database and use this as the matching standard for face recognition. Then, construct a deep learning convolutional neural network, install the propagation process and training strategy of deep learning. Lastly, build an age discrimination model, a loss function and an objective function and solve it. On this basis, the facial features are extracted, after matching with the data in the standard database and similarity calculation, the final rapid facial recognition result is obtained. Experimental results show that the highest recognition accuracy of the designed face recognition model is 99.2%, and the recognition speed of the designed model is faster.
The Role of Social Media Forensics in Digital Forensics

Social media forensics collects evidence from social media sites such as Facebook, WhatsApp, TikTok, and Snapchat to identify criminals. This paper discusses social media crimes such as hacking, photo morphing, shopping scams, cyberbullying, and link baiting. The paper deliberates the social media forensics techniques such as evidence collection, storing, analyzing, and preserving; the paper discusses the process of forensics examination in social media forensics. The paper examines the social media forensics tools such as WebPreserver, make a Website Hub, Pipl Search, TinEye, and TweetBeaver and discusses the applications of each device. The paper concludes by discussing the future of social media forensics.
Sociocultural Inclusion: Conceptual Foundations and Development Trends

The formation of inclusive processes in Russian culture sphere is becoming more and more noticeable every year. The article systematizes theoretical and methodological foundations on which modern inclusive practices used in the field of culture are based. It also identifies further development trends in this area. The content of basic concepts of sociocultural inclusion is revealed — the social model of disability, universal design, principles of participation and the accessible environment in a cultural institution. Attention is paid to examples of networking in the field of sociocultural inclusion as a necessary condition for the development of effective and systematic work of cultural institutions with new audiences, in particular, with visitors with ASD in other mental characteristics. Examples of main vectors of cooperation in the field of sociocultural inclusion are given: the involvement of experts with ASD on the creation of special conditions in public spaces, interaction with educational organizations and charitable foundations, which contributes to the emergence of new inclusive programs, the creation of an inter-institutional expert project. It is noted that the main trend in the further development of sociocultural inclusion at this stage is the development of criteria for assessing the effects of introducing practices into the sphere of culture aimed at the integration people with disabilities.
Quality Evaluation and Satisfaction Analysis of Online Learning of College Students Based on Artificial Intelligence

In order to better study the quality and satisfaction of online learning of college students, this paper analyzes and researches online learning of college students based on relevant theories of artificial intelligence. Through the traditional machine learning method to evaluate the quality of online learning, the deep learning theory is applied to the satisfaction analysis of college students'’ online learning. The results show that different statistical indexes have different influences on traditional machine learning, but they all show a gradually decreasing trend. The main reason for the different degrees of influence is that the emphasis of different statistical indexes is different, and the order from large to small is MAE &gt; RMSE &gt; MAPE &gt; TIC. Statistical indicators can better describe the first stage of test data, while the corresponding quality indicators can better characterize the second stage of test data. It indicates that statistical and quality indexes should be considered comprehensively to analyze the test data accurately. The increase of evaluation indexes based on traditional machine learning can improve the evaluation indexes of online learning quality of college students. And the improvement of statistical indicators and evaluation factors can promote the accuracy of online learning quality evaluation of college students. Based on the theory of artificial intelligence, the quality and satisfaction of online learning of college students are analyzed and evaluated by using the traditional machine learning method and deep learning method, respectively. Relevant research can provide a research basis for artificial intelligence in online learning methods of college students.
A Novel Oracle-Aided Industrial IoT Blockchain: Architecture, Challenges, and Potential Solutions

Smart contract over the blockchain accelerates the deployment of decentralized Industrial Internet of Things (IIoT) applications. However, the usability of smart contracts is constrained to the data on the blockchain, and has no direct access to the off-chain environment. Consequently, smart contract cannot directly serve as a carrier for the IIoT applications that rely on intensive data exchange and task computation. To cope with this issue, we first propose an oracle-aided IIoT blockchain (OIB) system to facilitate the deployment of smart contract based IIoT applications. Specifically, a distributed oracle network is employed to not only bridge the off-chain environment and smart contracts, but also extend the computing capability of smart contracts. Then, we study important properties of the OIB system, that is, liveness, safety, and fairness. In particular, we propose a new challenge of fairness to guarantee that each oracle can reap positive utility (i.e., individual rationality), and a new challenge of liveness to meet delay constraints of oracle services (i.e., delay-sensitivity). To address these two challenges, we develop an auction based incentive mechanism for the data-feeding oracles to achieve individual rationality as well as incentive compatibility, and a delay-sensitive matching based incentive mechanism for the computing oracles under specific delay constraints. Simulation results corroborate that the proposed auction based incentive mechanism guarantees not only truthfulness but also individual rationality, and show that the matching based incentive mechanism achieves smaller delay than the random selection scheme used in the Decentralized Oracle Service (DOS) network.
Phishing Website Detection using Deep Learning

Phishing attack is a type of cyber-attack where attacker sends fraudulent (spoofed, fake or deceptive) messages designed to lure a human victim to give away personal information or credentials or to deploy malicious software in victim's infrastructure like ransomware. As Internet usage is increasing day by day so are the cyber-crimes and scams. Phishing is one of the latest sophisticated techniques used by the scammers. Phishing website detection can help the users to avoid falling victim to these attacks. Although phishing websites are disguised as a legitimate one, fortunately they have some identifiable features. We have proposed a supervised learning approach using deep learning algorithms to detect phishing websites. We have achieved 94.8% accuracy using standard neural network model and achieved 93.6% accuracy with CNN (Conv2D) model. We have used a dataset downloaded from University of California, Irvine machine learning repository.
Optical Parametric Amplifier Detection for Quantum Illumination

Quantum illumination (QI) is a technique exploiting the quantum entanglement between the transmitted signal and the idler to enhance the detection performance of a quantum radar over the classical radar. Here, we propose the double optical parameter amplifier (OPA) detector for QI so that the effect of entanglement between the idler and the returned signal beams is maximized. We show here how even when a weak returned signal is embedded in a large background noise, the probability of detection can be significantly increased.
PHISHING URL DETECTION WEBSITE

Phishing websites are one of the types of internet security issues that target human vulnerabilities rather than software flaws. It's the technique of luring online users in order to get sensitive information like usernames and passwords. We present an intelligent technique for detecting phishing websites in this research. The technology works as an add on to an internet browser, notifying the user when it finds a phishing website. The system is based on supervised learning, which is a type of machine learning. We chose the Random Forest approach because of its high categorization performance. Our goal is to develop a higher performing classifier by examining the characteristics of phishing websites and combining them in the best way possible.
The impact of customer experience in achieving continuous improvement, an exploratory study in a number of private banks in the governorate of Dohuk

The aim of the research deals with identifying the impact of the customer’s experience in achieving continuous improvement. The research objective deals with a group of leading organizations in the field of providing banking services and facilities to the beneficiaries, and a number of Dohuk banks were selected, as the research sample amounted to (40) male and female employees in the surveyed banks. The descriptive and analytical approach was reached, and a number of conclusions were reached, the most important of which is the customer experience, an important role in encouraging banks to benefit from customers' experiences in improving the quality of services they provide.