Reviewerly/exHarmony huggingface dataset:
Decorrelated spectral regression: An unsupervised dimension reduction method under data selection bias

In manipulating high-dimensional data arising from real applications, dimension reduction methods have been proven to be effective and necessary. Nevertheless, the widespread selection bias, induced in the process of data collection, has long been unaware and its negative effects have been ignored. The accidental spurious correlation between the features brought by selection bias will deteriorate the performance of dimension reduction approaches. It is vital to remove such bias and consider its influence on dimension reduction. In this paper, we propose a novel decorrelated spectral regression algorithm for unsupervised dimension reduction on data with sample selection bias. In this method, the sample weights of the global feature distribution are learned by introducing the decorrelation regularizer. The learned weights are then combined with the regression model to obtain the dimension reduction mapping. Thus, the dimension reduction results are less affected by the accidental spurious correlation of data caused by selection bias. In addition, we also propose the updating rules of the parameters in our algorithm. A large number of experimental results on real-world datasets show that our method has achieved a significant improvement, indicating that it is effective and necessary to remove the accidental spurious correlation between features caused by selection bias in dimension reduction.
Immersive Experiences and XR: A Game Engine or Multimedia Streaming Problem?

Recent improvements in extended reality (XR) technology created an increase in XR products and solutions in the industry while raising new requirements for new or improved architectural concepts. This need can be particularly complex as XR applications often relate both to 3D geometric rendering and multimedia paradigms. This article outlines the main concepts relevant to XR, both from a game engineering and a multimedia streaming system perspective. XR requires new metadata and media/game orchestration to allow complex interaction between users, objects, and (volumetric) multimedia content, which also results in new requirements on synchronization (i.e., for global object state and positioning). Furthermore, the article presents the functional blocks needed in new XR system architectures and how they will glue both (game and media) spaces together. The discussion of functional components and architecture relates to the ongoing activities in relevant standardization bodies like Khronos, Moving Picture Experts Group (MPEG) and The 3rd Generation Partnership Project (3GPP). To make XR successful in the long term, the industry needs to agree on interoperable solutions and how to merge both game and media paradigms to allow complex multiuser XR applications.
Credit Card Fraud Detection Using Machine Learning Based on Support Vector Machine

The usage of machine learning algorithm in the detection of fraudulent transactions is becoming more common. Most application systems, on the other hand, only catch fraudulent activityafter it has already taken place, rather than in real time. Detecting fraud is difficult because there are considerably fewererroneous transactions than legitimate ones. This data imbalance necessitates methods other than machine learning to handle it. Quantum machine learning (QML) has been used to develop detection frame work, which was then, implemented using SVMs supplemented with quantum annealing solvers. A total of twelve machine learning algorithms have been applied to testQML's detection capability, and their results have been compared to those of the QML application ontwo datasets: a non-time series of Israeli credit card transactions and a time series of Israeli bank loanapplications. The results reveal that, using the bank loan dataset, the quantum augmented SVM over takes the others in relationships of both speed and accuracy. The detection accuracy is comparable to those that use Israel credit card transaction data.By the detection time can be greatly improved for both data sets by using feature selection, although the increase in accuracy is minor. QML applications on time series data with significant imbalance have been shown to have promise, whereas standard machine learning methodologies have been shown to have worth when dealing withnon-time series data, as these results show. This research sheds light on how to choose the besttechnique for various datasets while keeping in mind the trade-offs between speed, accuracy, and price.
Diagonalize three-dimensional nonlinear chaotic map to encrypt color image

In this paper, multidimensional chaos systems called a trilinear chaotic system for encryption the color image is used. Combining the multi-dimensional chaotic system with encryption algorithms improves the security. The trilinear system is built from logistic and cubic maps and generates six completely random bijections T1x,T2x,T2y,T3x,T3y, and T3z. The map T1x randomly shuffles the image pixels, the two maps T2x and T2y scramble the position of the pixels, and the other three maps change the pixel values. The correlations among the three components of RGB components are reduced, and the security of the algorithm increases. The simulation results demonstrated that the procedure has very large key spaces and a high level of security.
Obscurity-Quantified Curriculum Learning for Machine Translation Evaluation

The pre-trained language model has been developed for evaluating the quality of machine translation. It achieves state-of-the-art results. However, building a model for the evaluation of machine translation still faces the following challenges: 1) large scale of the training data affects the speed of the optimization; 2) the varied quality of the training data makes the optimization process unstable. To alleviate the issues of data learning, curriculum learning is proposed to rearrange the training sequence following an “easy-to-hard” process. However, the definition of difficulty can not be directly applied to the training data used in the machine translation evaluation. Hence, we propose an obscurity-quantified curriculum learning framework for this task. Specifically, the obscurity of each training example can be measured from multiple perspectives, including the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">difficulty of ranking</i> , the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzziness of reference</i> , the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complexity of text</i> , and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliability of judgement</i> . To incorporate the obscurity measurements, we also design a dynamic learning strategy to guide the training process from instances with low obscurity to those with high-obscurity. Experimental results show that our proposed methods yield remarkable improvements on the segment-level WMT2019 and WMT2020 Metrics Shared Tasks compared to other baseline methods.
What Will the Future of Cybersecurity Bring Us, and Will It Be Ethical? The Hunt for the Black Swans of Cybersecurity Ethics

Although the ethics of cybersecurity might seem to be simple, the matter can be surprisingly complicated.This paper discusses the results of an extensive study aimed at uncovering the anticipated, emerging ethical issues related to cybersecurity.First, it discusses the ''strong signals'', i.e., the ''mainstream'' worries and concerns.Then, it uncovers the ''weak signals'' -the hidden, less-discussed concerns, which may still define the upcoming future of the ethics of cybersecurity.The results of the study are also compared to the outcomes of a similar experiment conducted two years ago, in order to see if the upcoming ethical dilemmas anticipated back then have in fact become a reality.
Human Micro-Expression: A Novel Social Behavioral Biometric for Person Identification

The reliance on Online Social Networks (OSN) for both formal and informal social interactions has dramatically changed the way people communicate.In this paper, a novel Social Behavioral Biometric (SBB), human micro-expression, is introduced for person identification.An emotion detection model is developed to extract emotion probability scores from person's writing samples posted on Twitter.The corresponding emotion-progression features are extracted using an original technique that turns users' microblogs into emotion-progression signals.Finally, a novel social behavioral biometric system that leverages rank-level weighted majority voting to achieve an accurate person identification is implemented.The proposed system is validated on a proprietary benchmark dataset consisting of 250 Twitter users.The experimental results convincingly demonstrate that the proposed social behavioral biometric, human microexpression, possesses a strong distinguishable ability and can be used for person identification.The study further reveals that the proposed social behavioral biometric outperforms all the original SBB traits.
Universal and automatic elbow detection for learning the effective number of components in model selection problems

We design a Universal Automatic Elbow Detector (UAED) for deciding the effective number of components in model selection problems. The relationship with the information criteria widely employed in the literature is also discussed. The proposed UAED does not require the knowledge of a likelihood function and can be easily applied in diverse applications, such as regression and classification, feature and/or order selection, clustering, and dimension reduction. Several experiments involving synthetic and real data show the advantages of the proposed scheme with benchmark techniques in the literature.
Sentiment Analysis of Literary Texts vs. Reader's Emotional Responses

Sentiment analysis is a relevant task in natural language processing, which is often conducted on Internet texts to analyze reviews of products, services, posts, and comments on social media. Unlike most studies, our work focuses on literary texts in the Russian language written during the emotionally charged period of the early 20th century, when a change in the Russian political system and a fundamental shift in society's way of life occurred as a result of socio-economic upheavals such as wars and revolutions. It is assumed that literary texts from this period should also be rich in emotional vocabulary. The main goal of the research described in this article is to study the correlation between the results of sentiment analysis, performed on the material of Russian short stories using several automatic methods, and the average expert evaluation of the emotions that these same texts evoke in modern readers. The results of the study contribute to understanding the evaluative component of literary works vocabulary and can also be used to build recommendation systems aimed at selecting literary texts for readers.
embServe: Embedded Services for Constrained Devices

Implementing, managing and updating large-scale complex IoT applications is still a non-trivial challenge. Service Oriented Architectures (SOA) are increasingly used for this purpose, given their well-defined communication interfaces and configurable service-to-service links that ease the implementation of complex data flows. However, these architectures usually stop at the edge, not supporting far-edge resource-constrained devices. This paper proposes embServe, a new service-oriented framework that enables SOA on networks of constrained devices. It allows users to deploy services compiled independently of the base application and configure their connections using standard IoT protocols and data models. We compared a reference implementation against traditional approaches. Results show that embServe reduces resource overhead with a subtle impact on computing performance while improving application response time and uptime.
Integrating Digitalization Role to Play in Financial Management

Financial management, a crucial part of enterprises, is the corporate strategy, supervision & direction of financial activity. Industrial revolution 4.0 innovations should be employed in a number of crucial industries, including risk evaluation, fraud prevention, private equity, internet banking, personalized bond schemes, retaining customers, virtual agents, as well as other important industries. According to past study, there have only been a few studies on the integration of Industry 4.0 technologies, technology including robotic process automation (RPA) in finance, artificial intelligence (AI) under finance, use of blockchain in finance. This study reviewed the role of Industry 4.0 technologies in financial management in detail. Based upon analysis, the study findings concluded that technology such as assessment of risk, digitized finance, digital auditing, identity protection, or AI & blockchain may all help manage credit risk utilizing real-time information. In the future work, we will be studying the role of digitalization in financial management in real-time for concluding its impacts and advantages.
Blockchain: Its Applications And Challenges

Blockchain was initially designed as a means of enabling the trust-less cryptocurrency Bitcoin, but as more industries and stakeholders view the technology as a viable alternative to existing business solutions and a potential disruptor of mature industries, it has since moved beyond its original intent. A blockchain can be used in a wide variety of ways in real time. The functioning of almost all sectors of the economy depends on handling an infinite number of tasks. As a result, in recent years, blockchain has become a popular technique in not just cryptocurrency, but also healthcare, energy, insurance, supply chain management, cybersecurity, and copyrights. Here, we examine the numerous applications of blockchain and how it can improve management in the sectors the technology is being applied to.
Analyzing the difference between ReactJS and AngularJS

There is a large demand for front-end technologies. Many Frontend frameworks are available, but the two most used frontend frameworks are ReactJS and AngularJS. So, there is so much hassle in choosing the suitable framework for a particular project. Developers want the best framework to design the UI of the web pages and make web pages attractive. So, it is required to identify the best framework for a particular project. A better frontend framework leads to a better webpage or better user experience will indirectly help an organization’s growth. For this analysis, we will build a frontend webpage in both the frameworks and then use the google developer tool to analyze the performance of both webpages built in React and Angular. We will compare both frameworks based on various parameters like popularity, learning curve, SEO, and performance. This will give the final results and help us to decide which frontend framework is best for developers for a particular project.
Text to Image Generator with Latent Diffusion Models

Generating images from natural language text descriptions is an impressive feat of deep learning. Diffusion models have enabled state-of-the-art synthesis results for various types of data, including images, by breaking down the image generation process into successive denoising autoencoder applications. However, these models can be computationally expensive and time-consuming to train and infer due to their reliance on pixel space. This paper introduces a novel approach to training diffusion models with limited computational resources while still maintaining quality and flexibility using pre-trained autoencoders. We have developed a methodology that strikes a near-perfect equilibrium between simplifying complexity and maintaining intricate details, enhancing visual accuracy. We also propose a cross-attention layer in our model design to enable high-resolution synthesis using a convolutionbased approach for conditioned inputs like text and bounding boxes. Our latent diffusion model exhibits exceptional performance in multiple image synthesis tasks such as text image synthesis, super-resolution, and unconditional picture generation while reducing computational requirements significantly compared to pixel-based models.
Intelligent Food Recommendation System Based on Nutritional Information and Preferences of Customers

The growing concern for human being is about their health and resilience. People are unaware about their health conditions and what type of food they should eat in that health condition. People tend to overlook the internal structure of the food they eat and don’t pay attention to how many calories they should take in a day. This project motivates us to find out what food items and number of calories we should eat in various health conditions. This paper introduces a study of the food recommendation system. Foods can be grouped into categories based on calories, nutrients, price, vitamins, diseases, and other factors. Alternatively, restaurants can be identified based on distance from the user, price range, food offered, ratings and reviews. This paper highlights several parameters for food process making which is a very time-consuming process. A digital menu containing comprehensive information about food items. The digital menu will help customers to make decision on food very easily. This project includes a table booking service, recipes of food items, and food ordering service for the customers.
Deep Neural Network-based Crime Scene Detection with Frames

The field of computer vision stands to benefit significantly from automated crime scene detection. In this work, we demonstrate the application of DNN (Deep Neural Network) to identify a knife, blood, and gun in a picture and forecast whether a crime has occurred. Improving the accuracy of the system's detection capabilities was a top priority for us since we wanted to make the system as helpful as possible. To get a detection result, DNN's Non-linearity ReLu (Rectified Linear Unit), a Convolutional Neural Network Layer, a Fully Connected Layer, and a Dropout Function are employed. Additionally, this research makes use of a Dropout Function. To get the outcomes we want from Neural Networks, we use the open-source platform TensorFlow. Our system has a test accuracy of about 92.1% for the datasets that are currently accessible, which puts it in a very competitive position compared to other systems specifically created for this task.
Detection of Malicious Social Bots with reinforcement learning technique with URL Features in Twitter Network with KNN in comparison with RNN

Aim: The objective of the analysis is the Real time identification of spiteful Social Bots Utilize reinforcement learning technique with URL aspects in Twitter networking with Novel K-Nearest Neighbours in comparison with Recurrent Neural Network. Materials and Methods: Realtime noticing of destructive general bots operates reinforcement knowledge technique with uniform resource locator factor in Tweet web work with K-Nearest Neighbours in comparison with Recurrent Neural Network. In this case, pre-test power analysis was done with 80% and the test size for the two groups are 20 and each group having the size of 10. Result: Real-time classification of suspicious spammers in the Online social network using reinforcement learning technique method and Webpage capabilities with K-Nearest Neighbours with the accuracy 93.89% and then compared with Recurrent Neural Network with the accuracy of 92.11% respectively. There is a statistical significance variance in the precision for two methods 0.007(p<0.05) with the use of independent sample test. Conclusion: To conclude that KNN algorithm performs significantly better than the RNN algorithm.
Data-driven ML Approaches for the concept of Self-healing in CWN, Including its Challenges and Possible Solutions

The idea of a self-organizing network, or SON for short, was developed to facilitate the autonomous deployment and administration of cellular networks. SON capabilities have the potential to improve service quality, increase network performance, and lower operating and capital expenditures (OPEX and CAPEX). Self-healing refers to a network paradigm in which faults in intended networks are repaired through the execution of a predetermined set of steps, and recompense. SON self-heals as a network paradigm. SON requires self-healing. It is now widely acknowledged that data-driven machine learning is an effective method for infusing networks with intelligence and enabling them to repair themselves. However, there are significant obstacles to overcome before practical implementations of machine-learning approaches for self-healing may be developed. In the first part of this essay, we will begin by dividing these difficulties into Data imbalance, data insufficiency, data cost insensitivity, data reaction time, multi-source data fusion, and data latency are the five main areas of concern. Then, after identifying these difficulties, we provide viable technological solutions to overcome them. In addition, A cost-sensitive defect detection case study employing unbalanced data shows the approaches' feasibility and efficiency
Library Management System Using Artificial Intelligence

Artificial intelligence, which has resuscitated shrewd libraries, is one of the principal powers driving the improvement of present day civilisation. This exposition makes sense of the hidden association between artificial intelligence (simulated intelligence) and savvy libraries, examinations how computer based intelligence is utilized with regards to brilliant libraries, and gives instances of how computer based intelligence further develops library administrations. Artificial intelligence is an expansive, complex area of exploration that may be hard for nonexperts to comprehend. However, its definitive goal is to make PC systems with artificial intelligence, which clearly has significant ramifications for librarianship. In the event that we are to propel the investigation of canny systems, we should have an exhaustive comprehension of simulated intelligence advances. Through the ongoing utilization of computer based intelligence advancements, countless model shrewd library systems for classifying, ordering, data recovery, reference, and different purposes have been grown; notwithstanding, just few these systems have formed into items or systems that are utilized in the standard activities of libraries.
Mode sensitive SWG-based Phase Shifter for Optical Processors

We experimentally demonstrate mode-based phase shift monitoring for optical processors employing a subwavelength grating-based phase shifter enabling a 12% temperature coefficient difference between two modes. The second mode extinction ratio increases from 2.2 dB for a conventional phase shifter design to 29.45 dB.