Reviewerly/exHarmony huggingface dataset:
Data Offloading in the Internet of Vehicles Using a Hybrid Optimization Technique

The Internet of Vehicles (IoV) is utilized for collecting enormous real time information driven traffics and alert drivers depending on situations.In recent times, all smart vehicles are developed with IoT devices.These devices communicate with a radio access unit (RAU) at road side.Moreover, a 5G system is equipped with a base station and connection interfaces that use optic fiber for their effective communication.For a fast mode of communication, the IoV must offload its data to the nearest edge nodes.The main problem with the IoV is that it generates enormous data which is offloaded randomly during the journey.This data exceeds the memory of the edge or road side unit (RSU) devices.This feature also causes substantial energy usage and high storage cost.To overcome the above issues, hybrid optimization techniques are suggested to offload the data with an energy efficient destination.In this research, the Clustered Block Chain Based Grasshopper Optimizer (BC-GAO) Based Task Offloading is implemented for a green IoV.The block chain provides a secure environment for sharing extensive data.The IoV data are clustered according to the vehicle location, and the grasshopper optimization is employed in selecting the RSU to offload the data.The result for the proposed technique is evaluated using OPNET Modulator and MATLAB.The simulated results are compared with the other existing techniques such as Random Offloading, Full Offloading, Mobility Aware Task Offloading, and the Lyapunov-based Dynamic Offloading Decision algorithm.The proposed algorithm computed 100 tasks in 33.27 s by consuming 19.23 joule energy, a value which is lower than all other existing techniques.
The Basics of Quantum Computing for Chemists

The rapid and successful strides in quantum chemistry in the past decades can be largely credited to a conspicuous synergy between theoretical and computational advancements. However, the architectural computer archetype that enabled such a progress is approaching a state of more stagnant development. One of the most promising technological avenues for the continuing progress of quantum chemistry is the emerging quantum computing paradigm. This revolutionary proposal comes with several challenges, which span a wide array of disciplines. In chemistry, it implies, among other things, a need to reformulate some of its long established cornerstones in order to adjust to the operational demands and constraints of quantum computers. Due to its relatively recent emergence, much of quantum computing may still seem fairly nebulous and largely unknown to most chemists. It is in this context that here we review and illustrate the basic aspects of quantum information and their relation to quantum computing insofar as enabling simulations of quantum chemistry. We consider some of the most relevant developments in light of these aspects and discuss the current landscape when of relevance to quantum chemical simulations in quantum computers.
Towards Optimal Technological Solutions for Central Bank Digital Currencies

Abstract In this chapter, we provide a historic overview of the origin and definitions of Central Bank Digital Currencies (CBDCs), by examining relevant research dating back to the 1990s. We find that digital versions of sovereign money accessible by the private sector were motivated by advancements and challenges emerging from the private sector itself. We present the factors that necessitate their issuance, and especially focus on financial stability, monetary policy, and the increased competition in payments leading to threats in financial and monetary sovereignty. Finally, we assess the appeal of the various technical options for CBDCs against what has emerged as their universally desirable features.
CGST: Provably Secure Lightweight Certificateless Group Signcryption Technique Based on Fractional Chaotic Maps

In recent years, there has been a lot of research interest in analyzing chaotic constructions and their associated cryptographic structures.Compared with the essential combination of encryption and signature, the signcryption scheme has a more realistic solution for achieving message confidentiality and authentication simultaneously.However, the security of a signcryption scheme is questionable when deployed in modern safety-critical systems, especially as billions of sensitive user information is transmitted over open communication channels.In order to address this problem, a lightweight, provably secure certificateless technique that uses Fractional Chaotic Maps (FCM) for group-oriented signcryption (CGST) is proposed.The main feature of the CGST-FCM technique is that any group signcrypter may encrypt data/information with the group manager (GM) and have it sent to the verifier seamlessly.This implies the legitimacy of the signcrypted information/data is verifiable using the public conditions of the group, but they cannot link it to the conforming signcrypter.In this scenario, valid signcrypted information/data cannot be produced by the GM or any signcrypter in that category alone.However, the GM is allowed to reveal the identity of the signcrypter when there is a legal conflict to restrict repudiation of the signature.Generally, the CGST-FCM technique is protected from the indistinguishably chosen ciphertext attack (IND-CCA).Additionally, the computationally difficult Diffie-Hellman (DH) problems have been used to build unlinkability, untraceability, unforgeability, and robustness of the projected CGST-FCM scheme.Finally, the security investigation of the presented CGST-FCM technique shows appreciable consistency and high efficiency when applied in real-time security applications.INDEX TERMS Certificateless group signcryption scheme (CGSS), fractional chaotic maps (FCM), provably secure scheme, authentication, Diffie-Hellman (DH) problem, wireless security networks.
Single-element real-time interferometric system for measuring dynamic temperature field of liquid medium

Temperature plays an important role in biomedicine and industrial production. In this study, we demonstrate a simple and reliable real-time interferometric system based on a single-element for measuring the dynamic temperature field of liquid media. In the proposed system, the reference beam and the object beam enter the beam splitter cube with a splitting ratio of 50:50 to produce interference fringes. The interference light arrives at the observation plane of a CMOS camera after being attenuated, which is capable of monitoring the change process of space carrier phase shift distribution in real time. The common optical path design eliminates the environmental disturbance. The phase shift of the interferograms as the optical path difference between the reference beam passing through liquid and the object beam passing through air is calculated by fast Fourier transform. The temperature changes of 50% polyacrylamide hydrogel (25–65 °C), 4% NaCl (25–65 °C), and ethanol (20–60 °C) were measured by our proposed system. During the experiment, we successfully measured the dynamically changing temperature field of deionized water subjected to a heating and cooling process (25–40 °C). The proposed system offers the advantages of compact structure, high efficiency, long-term stability, and low cost, and it is expected to assist non-contact measurements and biomedical research, making it a viable reference candidate for the field of environmental ecology and medicine.
A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets

A machine can understand human activities, and the meaning of signs can help overcome the communication barriers between the inaudible and ordinary people. Sign Language Recognition (SLR) is a fascinating research area and a crucial task concerning computer vision and pattern recognition. Recently, SLR usage has increased in many applications, but the environment, background image resolution, modalities, and datasets affect the performance a lot. Many researchers have been striving to carry out generic real-time SLR models. This review paper facilitates a comprehensive overview of SLR and discusses the needs, challenges, and problems associated with SLR. We study related works about manual and non-manual, various modalities, and datasets. Research progress and existing state-of-the-art SLR models over the past decade have been reviewed. Finally, we find the research gap and limitations in this domain and suggest future directions. This review paper will be helpful for readers and researchers to get complete guidance about SLR and the progressive design of the state-of-the-art SLR model
Automated Deep Learning: Neural Architecture Search Is Not the End

Deep learning (DL) has proven to be a highly effective approach for developing models in diverse contexts, including visual perception, speech recognition, and machine translation. However, the end-to-end process for applying DL is not trivial. It requires grappling with problem formulation and context understanding, data engineering, model development, deployment, continuous monitoring and maintenance, and so on. Moreover, each of these steps typically relies heavily on humans, in terms of both knowledge and interactions, which impedes the further advancement and democratization of DL. Consequently, in response to these issues, a new field has emerged over the last few years: automated deep learning (AutoDL). This endeavor seeks to minimize the need for human involvement and is best known for its achievements in neural architecture search (NAS), a topic that has been the focus of several surveys. That stated, NAS is not the be-all and end-all of AutoDL. Accordingly, this review adopts an overarching perspective, examining research efforts into automation across the entirety of an archetypal DL workflow. In so doing, this work also proposes a comprehensive set of ten criteria by which to assess existing work in both individual publications and broader research areas. These criteria are: novelty, solution quality, efficiency, stability, interpretability, reproducibility, engineering quality, scalability, generalizability, and eco-friendliness. Thus, ultimately, this review provides an evaluative overview of AutoDL in the early 2020s, identifying where future opportunities for progress may exist.
Saliency Detection with Moving Camera via Background Model Completion

Detecting saliency in videos is a fundamental step in many computer vision systems. Saliency is the significant target(s) in the video. The object of interest is further analyzed for high-level applications. The segregation of saliency and the background can be made if they exhibit different visual cues. Therefore, saliency detection is often formulated as background subtraction. However, saliency detection is challenging. For instance, dynamic background can result in false positive errors. In another scenario, camouflage will result in false negative errors. With moving cameras, the captured scenes are even more complicated to handle. We propose a new framework, called saliency detection via background model completion (SD-BMC), that comprises a background modeler and a deep learning background/foreground segmentation network. The background modeler generates an initial clean background image from a short image sequence. Based on the idea of video completion, a good background frame can be synthesized with the co-existence of changing background and moving objects. We adopt the background/foreground segmenter, which was pre-trained with a specific video dataset. It can also detect saliency in unseen videos. The background modeler can adjust the background image dynamically when the background/foreground segmenter output deteriorates during processing a long video. To the best of our knowledge, our framework is the first one to adopt video completion for background modeling and saliency detection in videos captured by moving cameras. The F-measure results, obtained from the pan-tilt-zoom (PTZ) videos, show that our proposed framework outperforms some deep learning-based background subtraction models by 11% or more. With more challenging videos, our framework also outperforms many high-ranking background subtraction methods by more than 3%.
How to Understand Masked Autoencoders

"Masked Autoencoders (MAE) Are Scalable Vision Learners" revolutionizes the self-supervised learning method in that it not only achieves the state-of-the-art for image pre-training, but is also a milestone that bridges the gap between visual and linguistic masked autoencoding (BERT-style) pre-trainings. However, to our knowledge, to date there are no theoretical perspectives to explain the powerful expressivity of MAE. In this paper, we, for the first time, propose a unified theoretical framework that provides a mathematical understanding for MAE. Specifically, we explain the patch-based attention approaches of MAE using an integral kernel under a non-overlapping domain decomposition setting. To help the research community to further comprehend the main reasons of the great success of MAE, based on our framework, we pose five questions and answer them with mathematical rigor using insights from operator theory.
Survey of Sentence Scoring Techniques for Extractive Text Summarization

With the growing amount of text data being generated from multiple sources over the past few years, it has become essential to effectively summarize this information. At present, it is estimated that there are over sixty trillion pages on the web. This is definitely more than any individual could hope to read or understand. Each year, there are approximately 600,000–1,000,000 books published in the US alone. These numbers demonstrate that it is next to impossible for any human to be able to consume all the information. Through text summarization, proper insights into the data can be gained. We need tools and techniques that can sift through this large amount of data and provide us with a brief summary of the information we are trying to learn and understand. Tools that can provide a detailed yet concise summary of the source text have become the need of the hour. Hence, text summarization has found its application in multiple field such as search engines, business analysis, market review and academia. This paper is an attempt to summarize some of the commonly used sentence scoring methods for text summarization. It presents a review of their algorithm, their effectiveness and their shortcomings.
Angular Margin-Mining Softmax Loss for Face Recognition

Face recognition methods have been significantly improved in recent years owing to the advances made in loss functions.Typically, loss functions are designed to enhance the separability power by concentrating on hard samples in mining-based approaches or by increasing the feature margin between different classes in margin-based approaches.However, margin-based methods lack the utilization of informative hard sample, and mining-based methods also fail to learn the latent correlations between classes.Moreover, there are no methods that simultaneously consider the effects of hard samples and feature margin through the same shape of feature angular margin.Therefore, this paper introduces the Angular Margin-Mining Softmax (AMM-Softmax) loss function, which adaptively emphasizes hard samples while also increasing the decision margins.The proposed AMM-Softmax loss function introduces a linear angular margin for hard samples, enabling the direct optimization of the geodesic distance margin and maximization of class separability.Furthermore, the proposed AMM-Softmax loss function is computationally efficient and can be easily converged by rapidly switching from the hard samples to easy samples.The results of the extensive experimental analyses conducted on popular benchmarks demonstrate the superiority of the proposed AMM-Softmax loss function over the existing state-of-the-art methods. INDEX TERMS Deep convolutional neural network, face recognition
Fully non-positive-partial-transpose genuinely entangled subspaces

Genuinely entangled subspaces are a class of subspaces in the multipartite Hilbert spaces that are composed of only genuinely entangled states. They are thus an interesting object of study in the context of multipartite entanglement. Here we provide a construction of multipartite subspaces that are not only genuinely entangled but also fully non-positive-partial-transpose (NPT) in the sense that any mixed state supported on them has non-positive partial transpose across any bipartition. Our construction originates from the stabilizer formalism known for its use in quantum error correction. To this end, we first introduce a couple of criteria allowing to assess whether any state from a given non-trivial stabilizer subspace is genuinely multipartite entangled. We then use these criteria to construct genuinely entangled stabilizer subspaces for any number of parties and arbitrary local dimension and conjecture them to be of maximal dimension achievable within the stabilizer formalism. At the same time, we prove that every genuinely entangled subspace is fully NPT in the above sense, which implies a quite surprising fact that no genuinely entangled stabilizer subspace can support PPT entangled states.
Feature Extraction and Opinion Mining of Gujarati Language text

The field of opinion mining has gained much popularity in last few years. Many new techniques and methods are being developed in different languages like English, Hindi etc. However, it is observed that there is no significant progress in the field of Opinion Mining for languages like Gujarati. The presented work uses a deep learning approach for the Opinion Mining of Gujarati language text. The paper also discusses feature extraction which is one of the most important steps in machine learning or deep learning method.
An Approach to Solar Radiation Prediction Using ARX and ARMAX Models

In recent years, Brazilian meteorological networks have introduced numerous automatic stations to monitor global solar radiation at hourly intervals. Historically, large-scale climate data measurement has supported aviation and agricultural activities. The need for a good mathematical model to adequately describe a process is a great challenge, since the performance of control and simulation systems can significantly impact both system operation and/or automation and system planning. The design of control systems based on predictive models should allow for describing the dynamic behavior of the process or system under realistic conditions, as well as finding the simplest possible model to optimize the computational resources. The present work sought to predict solar radiation levels via ARX and ARMAX linear mathematical modeling. During the simulations, global horizontal radiation was defined as input, while the following parameters were outputs: extraterrestrial normal radiation, infrared horizontal radiation, extraterrestrial horizontal radiation, direct normal radiation, and diffuse horizontal radiation. It must be noted that a new simulation was performed for each variable. The use of linear modeling (ARX and ARMAX) to predict solar radiation data was efficient for extraterrestrial normal, infrared, and extraterrestrial horizontal radiation with the mean square error equal to 2.51, 1.40 and 7.15%, respectively.
Local Texture Estimator for Implicit Representation Function

Recent works with an implicit neural function shed light on representing images in arbitrary resolution. However, a standalone multi-layer perceptron shows limited performance in learning high-frequency components. In this paper, we propose a Local Texture Estimator (LTE), a dominant-frequency estimator for natural images, enabling an implicit function to capture fine details while reconstructing images in a continuous manner. When jointly trained with a deep super-resolution (SR) architecture, LTE is capable of characterizing image textures in 2D Fourier space. We show that an LTE-based neural function achieves favorable performance against existing deep SR methods within an arbitrary-scale factor. Furthermore, we demonstrate that our implementation takes the shortest running time compared to previous works.
User Information Enhanced Knowledge Graph Convolutional Networks for Recommender Systems

To deeply excavate the information contained in user data and better alleviate cold start problem, we propose KGCN++, a user information enhanced knowledge graph convolutional networks model for recommender system, which simulates nerves&#x2019; influence diffusion of information of user and items in a unified framework. The model combines user information and user-item interaction information into a user knowledge graph, which advances KGCN by injecting high-order user potential information reflected in user graph and high-order influence of items reflected in item knowledge graph. In addition, we have designed the influence factors of different items on users, which are used to characterize the weight of items on the user&#x2019;s information dissemination, so as to distinguish users with different information. Through extensive experiments on real-world datasets, we demonstrate that KGCN++ achieves substantial gains in a variety of ecenarios, including movie and book recommendation, better performance compared to the other methods.
A brief and understandable guide to pseudo-random number generators and specific models for security

The generation of random sequences is the basis of simulation and can be used in many different areas such as Statistics, Computer Science, Systems Management and Control, Biology, Particle Physics, Cryptography or Cyber-Security, among others. It is crucial that the numbers generated were random or at least, behave as such. The fundamental statistical properties required for such sequences are randomness and independence and, from a cryptographic perspective, unpredictability. There is a variety of methods to generate these sequences. The main ones are physical and arithmetic methods. In this work, a detailed study of the main arithmetic methods is carried out. On the other hand, the necessity of secure sequence generation will be analyzed and new lines of ongoing research focusing applications in Internet of Things and new generator designs will be described.
Cyber Psychological Case Studies of Sextortion for Identifying the Accused in the Offences Committed on Social Media

Human behaviors can never fail to astound society. People behave as they want in their privacy and sometimes it can be weirdest, dark, and taboo. Sex is not a routine and normal topic in the conversation of Indian society. Therefore, people are very secretive about their sexual beliefs, thoughts, and sexuality. Whenever anybody does anything where she/he knows that their act is not going to be known by others people tend to involve in such acts. This is the exact angle that blackmailers in sextortion have identified and they get success too in duping people. They threaten people to viral their videos or chat in the sexual act. But, the Majority of the crimes committed on social media go unreported or untraced. Due to lack of procedural setups to identify the accuses. especially in the cases of sextortion where name and shame in the society are attacked of any individual. The present research paper aims to provide a simple procedural method to identify the accused in the offences committed on social media. The authors of the research paper have chatted with the 11 blackmailers of sextortion on social media and the authors were able to identify the location of the accused. The research paper is attempting to explain the dynamics of crime and victimization of sextortion from a cyber psychological perspective.