Reviewerly/exHarmony huggingface dataset:
Hierarchical Block Chain-Based Authentication Management Scheme for IoT Devices

The evolution of sensor-based IoT technologies has presented the research community with new service exigencies such as service availability in a decentralized fashion, immutability and network transparency by utilizing available scarce resources. Adopting conventional security mechanisms on these devices has exposed devices and the networks to a myriad of attacks, hence in order to overcome these challenges a hybrid block chain-based security solution along with black widow optimization-based clustering is adopted for IoT networks. The comparison of security and the analysis of the newly developed model confirms the improvement of quantity of messages that each node in the network handles, packet loss rate and throughput, when simulated using network simulator.
Using Hidden Markov Model to Predict the Potential Intent of User's Gaze Behavior

Study between visual gaze behavior and implied intent, it provides a new idea for exploring the human-computer interaction mode of non-verbal communication. Applying hidden Markov model to implicit intention inference of visual gaze, find predictive models for both efficiency and accuracy, reduction of lag in training models using historical data. The subjects' gaze data were collected, based on the hidden Markov model, two different gaze patterns are constructed, the model parameters are trained by Baum- Welch algorithm, next, the viterbi algorithm is used to solve the maximum probability hidden state sequence, then, the implicit intent prediction of the gaze behavior. On this basis, the architecture of the model and its effectiveness are verified, the relationship between human intention and gaze behavior is further discussed.
Learning Depth for Multi-View Stereo with Adversarial Training

The deep learning-based Multi-View Stereo (MVS) methods have shown excellent performance in depth estimation. However, due to the heavy computational burden caused by 3D convolution, most of the existing methods are difficult to be applied to high-resolution scenarios. In this paper, a novel method of learning depth for multi-view stereo with adversarial training is proposed, which exploits the Wasserstein generative adversarial network (GAN) to overcome shortcomings of the original GAN. The proposed network mainly consists of the encoder, the generator, and the discriminator sub-network. With the help of the proposed loss function, the proposed network can merge more global information provided by the discriminator of GAN and local information provided by the generator. This can capture more details and estimate an accurate depth map in high-resolution scenarios. It also significantly reduces memory consumption by exploiting WGAN instead of performing 3D convolution. Extensive experiments validate the proposed method, which can achieve on par or even better performance than the state-of-the-art.
The Impact of GDPR Regulations on Cyber Security Effectiveness Whilst Working Remotely

In light of the industry 4 evolution, the concerns regarding the data privacy seemed to rise due to the vast platforms and the adverse impacts that its data protection policies can have on people using it. In 2018, the European Union imposed a regulation called the general data privacy regulation (GDPR). The regulations aimed towards the protection of digital data that was transferred outside of Europe. The regulations focused on the safety of the digital data of users, the security of their data in various digital sites that they are continually sharing. They also aimed to ensure that the privacy laws concerning digital data were vigilant in modifying their rules as the digital technological changes occur in the digital world. The final aim of the GDPR is to certify that regulated legislation is unified in every region of Europe and are being followed and implemented by the digital platforms operating in Europe. As the Pandemic took hold from late 2019, there are questions arising in Europe as how to evaluate the effect of pandemic and the GDPR impact it will have as COVID 19 has forced many to go into a lockdown and as a result work remotely. The research aims to evaluate and measure the impact of GDPR on cyber security effectiveness whilst working remotely.
Towards Energy Efficient Cloud Computing: Research Directions and Methodological Approach

Cloud computing has revolutionized the IT industry by allowing the supply of elastic computing tools on demand as pay-as-you-go services such as SaaS, PaaS, and IaaS (Beloglazov, Energy-efficient management of virtual machines in data centers for cloud computing. PhD thesis, 2013; Farahnakian et al., Energy and performance: management of virtual machines: provisioning, placement, and consolidation. 2016). Cloud computing has contributed to the reinventing of the way technology resources such as servers, storage, private networks, application platforms, and software services are procured and provisioned. A cloud consumer only requires an Internet connection to connect to a cloud service provider to use these services without placing undue pressure on the resources of their computer or mobile device. A medium-sized enterprise may outsource the demand for computing resources to the Cloud and escape high front-end costs that could be spent on private computational resources and thereby save costs that may arise from hardware and software maintenance and upgrades. A giant enterprise, meanwhile, can also create its own private cloud data center to satisfy the demand for computing services and enhance the process of resource management and provisioning. However, cloud computing infrastructure requires massive energy, sometimes even exceeding the energy requirements of a mid-size town. This chapter discusses the need for energy efficiency and the methodological approach needed to achieve energy efficiency.
Session-based Personalized Recommender System for Online Shopping

The recommendation system (RS) plays an important role in aiding e-commerce customers in overcoming information overload. In the last decades, the recommendation system has made significant advances. In specific, user-dynamic behavioural context concerning the item. Modelling user-behavioural context is complex and has been employed by state-of-the-art methods. However, this model fails to address the several problems occurring in future real-time online shopping portal environment. This paper aimed at addressing the issues of online recommendation considering continuous data (i.e. streaming environment) in an e-commerce environment. Customers and products may rely on a certain unexpected scenario and can arise variously at varied rates. This paper presents an efficient RS by continuously identifying (i.e. using session-specific information) and adapts to changing scenarios in customer items views, customer favourites (i.e. preferences) and product portrayals. Further shows the significance of proposed time-centric prediction/recommendation (TCP) when compared with the existing recommendation model.
Teaching Reform of Broadcasting and Presenter under the Background of Artificial Intelligence

Abstract With the continuous development of modern science and technology, artificial intelligence has entered an unprecedented period of rapid development. It is profoundly changing all social strata, and with further changes, artificial intelligence has developed rapidly. In recent years, artificial intelligence has been used in broadcasting and hosting field for many times, which has attracted the attention of domestic and foreign media. The anchor of artificial intelligence is still constantly updated and iterated, It is gradually replacing a lot of simple and highly repetitive work. The media industry and academic circles are worried whether the artificial intelligence anchor will replace the broadcasting and hosting profession? Based on this reason, under the background of artificial intelligence, this paper discusses the educational reform of broadcasting and hosting art in Colleges and universities. It is found that 54% of the students are willing to learn the second type of broadcasting and hosting subject, and act as their own, 10% of the students hold an attitude of unwillingness, and the remaining 36% are indifferent.
Is Blockchain Becoming More Centralized? Evidence on Collusion in the Ethereum Blockchain

Decentralization is a unique selling point of blockchain and its applications. However, the novel property has been both theoretically and empirically challenged. This research provides the empirical evidence on centralization in the consensus layer, where mining power is exploited by collusion. Here, collusion refers to certain interactions between blockchain validators and users. Through bribing, validators may execute transactions according to the intention of bribers. We demonstrate the participants of collusion are centralized in a small group, and both users with public names and anonymous users are involved. Potential collusion is correlated with some blockchain-specific factors, e.g., Ether volatility and Tether return. Besides, interlinks exist between collusion and regional stock markets, e.g., Japan and the United States.
Similarity Distance-Based Kernel Canonical Correlation Analysis for Multiview Data Representation

Gupta, Surendra Thakar, Urjita Tokekar, SanjivData from different views of a problem can better understand the problem and enabling us to find a better solution. Integrating multiview data in one common featured space may improve the performance of the decision system. Different views may contain either similar or complementary data, and they are often not directly comparable. Therefore in this paper, similarity distance-based kernel canonical correlation analysis (KSDCCA) has been proposed. In this proposed approach, multiple data is nonlinearly transformed into a common subspace such that nonlinear complementary information between views contributes more in the common subspace. The proposed approach also used locality preserving projections (LPP) to preserve the single view’s local manifold structure in the common subspace. It is evident from experimental results based on real-world data sets that the KSDCCA approach performs around 0–14% better than existing canonical correlation analysis-based methods. Thus, it is a more effective and promising approach for solving real-life problems where consideration of nonlinear complementary information in multiple views is essential.
Dehazing algorithms based on credibility fusion

The mainstream of existing dehazing algorithms can be arranged into two categories: algorithms based on restoration and algorithms based on image enhancement. But the advantages and disadvantages of the two categories are converse. Combining and compensating their characteristics, two fusion-based dehazing methods are designed in this paper, namely Weber Contrast Weighting Method and Credibility-Fusion Adversarial Network Method, with which the dehazing visual effect, details and recognizabilities of dehazed images can all be enhanced. At the same time, the thoroughness of dehazing is also maintained. Compared with the rigid combination in existing papers, this paper fuses softly at each pixel. Experiments suggest that both algorithms proposed in this paper achieve good results and outperform the original algorithms.
Data Anonymization With Diversity Constraints

Recent privacy legislation has aimed to restrict and control the amount of personal data published by companies and shared with third parties. Much of this real data is not only sensitive requiring anonymization but also contains characteristic details from a variety of individuals. This diversity is desirable in many applications ranging from Web search to drug and product development. Unfortunately, data anonymization techniques have largely ignored diversity in its published result. This inadvertently propagates underlying bias in subsequent data analysis. We study the problem of finding a diverse anonymized data instance where diversity is measured via a set of diversity constraints. We formalize diversity constraints, and study their fundamental problems of satisfiability, implication, and validation. We show that determining the existence of a diverse, anonymized instance can be done in PTIME, and we present a clustering-based algorithm, along with optimizations to improve performance. We conduct extensive experiments using real and synthetic data showing the effectiveness of our techniques, and improvement over existing baselines. Our work aligns with recent trends towards responsible data science by coupling diversity with privacy-preserving data publishing.
Impact Factors and Best Practices to Improve Effort Estimation Strategies and Practices in DevOps

Effort estimation plays an important role in the software development process by supporting the decision-making process for the stakeholders. DevOps has become a widely used software engineering practice with the collaboration of the development and operational teams. This paper addresses the factors that affect the effort estimation strategies and practices in DevOps based software development in Sri Lanka. This study explains the research approach, generation of the conceptual model and the quantitative data analysis process in detail. A survey is conducted among the software professionals who are working in DevOps-based software development in the Sri Lanka IT industry and a detailed data analysis is performed using statistical techniques to identify the reliability, correlation and significance of the considered factors. With an extensive analysis the independent variables namely, exploration, communication, and technology stack are identified as highly impacted factors to the effort estimation in DevOps-based software development. We also provide recommendations for the effort estimation strategies and practices; hence the managerial decision can be made for the improvements of the development process.
A Complexity Measure for Binary Classification Problems Based on Lost Points

Complexity measures are focused on exploring and capturing the complexity of a data set. In this paper, the Lost points (LP) complexity measure is proposed. It is obtained by applying k-means in a recursive and hierarchical way and it provides both the data set and the instance perspective. On the instance level, the LP measure gives a probability value for each point informing about the dominance of its class in its neighborhood. On the data set level, it estimates the proportion of lost points, referring to those points that are expected to be misclassified since they lie in areas where its class is not dominant. The proposed measure shows easily interpretable results competitive with measures from state-of-art. In addition, it provides probabilistic information useful to highlight the boundary decision on classification problems.
Translate2Classify: Machine Translation for E-Commerce Product Categorization in Comparison with Machine Learning &amp; Deep Learning Classification

Product categorization is a necessary feature of e-commerce websites since it ensures that the websites retrieve related items from the product taxonomy tree accurately. In traditional product categorization methods, machine learning and deep learning classification algorithms are frequently applied. These cater towards product categorization by taking input and then categorising it in one of the predefined categories. In this paper, we propose a machine translation-based solution for e-commerce product categorization. We convert the natural language description of a product into a token sequence that reflects the root leaf taxonomy of the product category. In the experiment, three e-commerce product datasets (Flipkart, Walmart, Amazon) have been combined to substantiate the applicability of the natural language models implemented. We demonstrate that ensembling sequence-to-sequence neural networks and the transformer model outperforms state-of-the-art product categorization algorithms in terms of predicted accuracy. In addition, the accuracy comparison for machine learning classification (KNN, Random Forests, SVM), deep learning classification (LSTM, BERT) and neural machine translation models (Seq2Seq, Seq2Seq + Transformer) is shown to validate ensembling the two elements as a better method. In conclusion, we illustrate that attentional sequential models generate product category labels without supervised constraints.
Semantic Face Completion Based on DCGAN with Dual-Discriminator

Image completion refers to utilizing image residual information to repair the missing part. In order to solve the problem that the existing models cannot learn the deep features of the image, resulting in inaccurate artifacts in the repaired details, and the inconsistency between the repaired part and the adjacent pixels, we propose a semantic face completion method based on DCGAN with dual discriminator in this paper. We improve DCGAN with dual discriminator to ensure the consistency of local part and global image. In addition, we utilize the VGG16 network to help learn the deep features of the image to make the repaired result clearer and more realistic at the pixel level. Therefore, our method generates images by optimizing the generators with three kinds of loss functions, which are composed of dual image reconstruction loss, dual adversarial loss, and dual image feature reconstruction loss. Experiments on celebA datasets show that the model can get reasonable repairing results, and PSNR and SSIM are higher than baseline on most test datasets.
Dynamic Patch Convolution (DPConv)

Abstract Lightweight Convolutional Neural Networks (CNNs) due to the small amount of calculation and performance degradation, budget constraints depth (number of convolution layers), and width of CNN (number of channels), resulting in limited representation capabilities. To solve this problem, this paper proposes a new dynamic block convolution design to increase the depth or width of the model complexity without increasing the network. Instead of using a single convolution kernel in each layer, dynamic block convolution dynamically aggregates multiple parallel convolution kernels based on input-related attention. Assembling multiple kernels is not only computationally efficient, but also due to the small size of the kernel, but also has more representation capabilities. These kernels are aggregated in a non-linear manner through attention. The two methods of spatial dimension block and multi-head are used in the traditional CNN attention design. The image is divided into small images and then multiple attention mechanism weights are used for each small image. Compared with the traditional CNN attention, the experimental effect is better.
Translation Accuracy Evaluation of English Complex Long Sentences Based on Multi-Label Clustering Algorithms

In the traditional sense, the translation evaluation of English complex long sentences is often limited to the idea of whether or how to realize the semantic transformation of the original text, so many phenomena that have nothing to do with language but directly affect the translation evaluation are not included in the field of vision and can be interpreted. In order to solve the above problems, a multi-label clustering algorithm is proposed to evaluate the translation accuracy of English complex long sentences. The multi-label clustering algorithm is introduced into the translation evaluation activities to carry out the translation and detection parameters of complex long sentences. The comprehensive description, the accuracy of generalization and the rationality of interpretation lay a solid foundation for English translation activities.
Understanding Generated RTN as an Entropy Source for True Random Number Generators

Future secure communication relies on the true Random number Generator (TRNG), of which the random source is of most importance for offering high entropy. The random telegraph noise (RTN) in nano-scaled devices show excellent randomness, however, RTN-based TRNG suffer from relatively large area and low speed. Here we introduce our recently-proposed solutions to tackle these two issues. Then we performed systematic analysis based on Monte Carlo simulation to understand the impact of design parameters on its practical performance, shedding light on the practical circuit design.
Interference between Total Solar Radiation and Cloud Cover over Baghdad City

Abstract Clouds greatly affect the elements of climate, energy balance, and solar radiation, which has increased the interest of many researchers in trying to find the best relationships and formulas that link these variables. In this work, the data of the European Center for Medium-Range Weather Forecasts (ECMWF) was relied on. The research aims to find the overlap between Cloud Cover (Low, Middle, High, and Total), (LCC, MCC, HCC, and TCC) respectively, with Total Solar Radiation (TSR) of Baghdad city, for the period (1981-2013), the work was carried out with the monthly and annual data of the (Low, Middle, High, and Total) cloud cover and the total solar radiation falling on a horizontal surface. And by using the correlation coefficient Spearman rho test (rs) to find the strength of the relationship between total solar radiation and cloud cover, it was found that there is an inverse relationship between the total solar radiation falling on a horizontal surface and the (Low, Middle, High, and Total) cloud cover.
Addictive digital experiences: the influence of artificial intelligence and more-than-human design

Due to the novel design paradigm brought about by artificial intelligence (AI), addictive technology is becoming more common and harder to resist.In order to counterbalance the rise of technological addiction, an effort should be made by all the actors involved in the complex world of addictive digital experiences.This paper focuses on the role of designers.It is based on a literature review on addiction to digital experiences based on the evolution of human-machine interactions, highlighting challenges brought by AI within the emerging "more-than-human" perspective.The introduction of a systemic perspective that considers new meeting points between datasets and algorithms can help designers understand how their products can lead to addiction and how addiction is created in the context of "AI factories".By reinforcing their ethical approach and treating AI as a design material, UX designers can become the bearers of a more responsible mindset and rehumanize addictive technology.
Quantum corralling

We propose a robust and efficient way to store and transport quantum information via one-dimensional discrete time quantum walks. We show how to attain an effective dispersionless wave packet evolution using only two types of local unitary operators (quantum coins or gates), properly engineered to act at predetermined times and at specific lattice sites during the system's time evolution. In particular, we show that a qubit initially localized about a Gaussian distribution can be almost perfectly confined during long times or sent hundreds lattice sites away from its original location and later almost perfectly reconstructed using only Hadamard and $\sigma_x$ gates.
Partnerships to offer advanced learning for all students

Out-of-school programs can offer personalized and meaningful learning experiences that deepen students’ knowledge and cater to their passions and interests, but it has been a painful reality that many such programs have been available only to students and families with privilege and resources. Karen Hammerness, Anna MacPherson, Preeti Gupta, Tramia Jackson, and Rachel Chaffee describe how the Science Research Mentoring Consortium in New York City gives students from underserved communities opportunities to engage in STEM activities alongside researchers at sites throughout the city. Students with a strong interest in and aptitude for science take classes after school or on weekends and conduct their own research with the support of mentors. The authors share the principles that undergird the program and that can be applied to similar mentoring partnership programs in other disciplines.