Reviewerly/exHarmony huggingface dataset:
Colon + Hyphen + Right Paren: At the Origins of Face Semiotics from Smileys to Memes

The article engages with the semiotics of "stylized faces" in online communication, focusing on "smileys." It reconstructs the origins of both emoticons and emojis, outlining how they differ functionally (paralinguistic signs vs. narrative figures) and commenting on their pragmatics, with regard to the issue of literacy as related to generational fruition. A chronology is provided of the first tokens of smileys in written communication, both before and after the Internet. By relying upon the anthropology of the face dating back to prehistory, the issues of iconism and universality are discussed, supporting the view that there is a strong cultural, conventional component in face depiction, varying diachronically (emoticons versus emojis) and diatopically (emoticons versus kaomojis, i.e., Japanese emoticons). Emoticons and emojis are regarded as prominent examples of intermedia, working at the intersection of written word and image. Finally, stylized digital faces are set in the broader framework of Internet memes, thus discussing the dichotomy between structural memes (the focus is on the formula) and iconic memes (the focus is on the image and, thus, the face). Throughout the text, great care is devoted to the philology of sources, some of them being presented in this form for the first time.
An analysis of the Word Adjacency Network method—Part 2—A true understanding of the method

Abstract This second of a two-part examination of the Word Adjacency Network (WAN) method considers the idea behind the method, which is that authors place function words in proximity to each other in such distinctive ways that the distinctions can be used for authorship attribution. By doing control experiments, it shows that the attributions made by the method are not due to distinctive word adjacencies at all, contrary to the claim of its inventors. It shows that the method is in fact another word-counting method, the counting hidden by superfluously complicated mathematics. It concludes that the claim that the placements of function words in a text can be used for attributing its authorship remains unsubstantiated. It also demonstrates that the claimed theoretical basis for the method, that WAN are Markov chains, is false.
Artificial Intelligence and VR Environment Design of Digital Museum Based on Embedded Image Processing

This article first introduces the development of embedded image processing system, puts forward the overall design of the system structure, constructs the development environment of software system and hardware, and introduces in detail the choice of embedded image processing system and artificial intelligence embedded operating system in the industry, the choice of objective lens, and the choice and installation of the light source in the system. According to the system requirements, the design, function, and working method of the GPIO peripheral circuit are introduced in detail, and the circuit diagram and PCB diagram of the GPIO peripheral expansion circuit are provided. Based on this, this article focuses on the technical realization of 3D landscape modeling technology, 3D landscape rotation technology, digital museum dynamic scene fusion technology, interactive animation perception over time, real visual virtual landscape, and virtual roaming scene perception in detail. The 3D landscape modeling technology and the modeling method of digital museum environment in virtual reality in this article are used to analyze and study the general concept of VR environment design in virtual reality environment and make it based on different characteristic methods of different modeling. The overall structure and theoretical basis of the VR environment in the digital museum are based on technical solutions, such as process modeling and scene structure. This technical solution has been verified in practical applications and is achievable. Regarding image recording, this paper proposes an improved image-based recording method. In terms of image coupling, this article uses an improved progressive input and output algorithm that can use CRML to create simple dynamic scenes, which can create clear and vivid animation effects, so that the viewer has a good experience.
Covert Timing Channels Detection Based on Image Processing Using Deep Learning

With the development of the Internet, covert timing channel attacks have increased exponentially and ranking as a critical threat to Internet security. Detecting such channels is essential for protection against security breaches, data theft, and other dangers. Current methods of CTC detection have shown low detection speeds and poor accuracy. This paper proposed a novel approach that used deep neural networks to improve the accuracy of CTC detection. The traffic inter-arrival times are converted into colored images; then, the images are classified using a CNN that automatically extracts the image's features. The experimental results demonstrated that the proposed CNN model achieved better performance than other detection models.
A Quantitative Evaluation Method of Software Usability Based on Improved GOMS Model

The Goal, Object, Model, and Selection Rules (GOMS) model plays an important role in user interface (UI) designs for evaluating the software usability. The vanilla GOMS has loosely defined the basic operators and their associated cost. In this paper, we propose an improved GOMS model to quantitatively estimate the software usability. Firstly, it emphasis that the complexity of mouse positioning is not a constant. It is affected by the size of the background area during positioning. Therefore, we introduce a novel complexity metric of mouse positioning. Then, based on GOMS, we put forward the concept of business operation complexity, which is used to evaluate the system usability by the sum of the operation complexity. Finally, we conduct a case study by applying our method to the software usability evaluation of Invoicing Systems, which demonstrates that our method is capable of giving reasonable advices for the UI designers
AFFECTIVE-COGNITIVE INDICATOR OF EMOTIONAL INTELLIGENCE FORMEDNESS IN HIGH SCHOOLERS WITH INTELLECTUAL DISABILITIES

The aim is to determine the peculiarities of the affective-cognitive indicator of emotional intelligence in high schoolers with intellectual disabilities. Materials and methods: The study, which was conducted during 2019-2020, involved 76 high schoolers in the age of 14-17 who studied in the 7-9th grades in Kyiv schools (Ukraine) (45 high schoolers with intellectual disabilities and 31 high schoolers with normative development). The diagnosis of the high schoolers’ ability to perceive, understand and identify emotions; to assimilate emotions in thoughts, to stimulate thought processes with the help of emotions was carried out. The Mayer-Salovey-Caruzo Emotional Intelligence Test, adapted for children with intellectual disabilities, was used. Results: Insufficient formedness of the affective-cognitive indicator of emotional intelligence in high schoolers with intellectual disabilities has been determined: a decrease in the level of perception of emotions by expression; superficial perception of expressive features, vagueness of ideas about them; insufficient differentiation of the constituent elements of the emotional model; difficulties of orientation in a set of emotional signs of different modalities; lack of understanding of social emotions and the content of a moral act. Conclusions: It has been proven that the low state of formedness of the affective-cognitive indicator of emotional intelligence results in significant difficulties that arise during interaction with each other, and this affects primarily the further socialization and integration into society.
Digital Competence of Future Researchers: Empirical Research of PhD Students of Ukrainian University

The article analyzes the experience of Jisc, which provides digital solutions for education and research in the UK, which became the basis for additional research on scaling the Jisc Reseacher model for the formation of digital competence of graduate students in higher education in different countries. The digital competence of the PhD students researcher of a particular educational institution is considered as a factor influencing the quality of education and the readiness of PhD students for its development. The result of the study is to determine the readiness of PhD students of Borys Grinchenko Kyiv University to acquire and develop their own digital competence of the researcher. The readiness to acquire and develop digital competence of graduate students both at the level of resource provision and basic digital competence and motivation of future researchers was confirmed by conducting a survey of the experimental group of graduate students of the 1st year of study. To identify general or specific problems for graduate students based on the analysis of average group values for each group of Jisc Reseacher competencies, unformed digital competencies of researchers were identified and the author’s interpretation of the causes and prospects of development was given.
A Novel Security Framework for Medical Data in IoT Ecosystems

The Internet of Things (IoT) is integrated with the healthcare system to create an intelligent hospital ecosystem. Protecting the privacy and security of medical data is a central problem in such an ecosystem. This article proposes a novel yet simple security framework to ensure the confidentiality of medical data during the communication between IoT hops. The proposed framework utilizes the concept of encryption to ensure the security. It essentially involves inducing confusion among pixels by employing image pixel scrambling achieved by spiralizing the medical data matrix, followed by a diffusion process using matrix exponentials and pseudorandom number generation by sequential chaotic maps. A reliable decryption scheme is finally designed to obtain the original data from the encrypted data. Experimental results and analyzes demonstrate the efficiency and robustness of the proposed framework and, thus, making it more suitable for the IoT ecosystem.
Study of Architecture and Issues in Services of Cloud Computing

Cloud computing, which takes over the computational and database volume of assistance to supply flexibility to users, infrastructure and platforms manages by the service providers for convenient of user so that which applications handled by user, is basis of the distribution of assets to ensure stability and flexibility for the buyer use to consummate. Cloud computing is fundamental service depending on the web. The purpose of this research is discover the several utilization of techniques and few specific security feature those are related to cloud computing.
Grammars for free

Ad hoc parsers are everywhere: they appear any time a string is split, looped over, interpreted, transformed, or otherwise processed. Every ad hoc parser gives rise to a language: the possibly infinite set of input strings that the program accepts without going wrong. Any language can be described by a formal grammar: a finite set of rules that can generate all strings of that language. But programmers do not write grammars for ad hoc parsers---even though they would be eminently useful. Grammars can serve as documentation, aid program comprehension, generate test inputs, and allow reasoning about language-theoretic security. We propose an automatic grammar inference system for ad hoc parsers that would enable all of these use cases, in addition to opening up new possibilities in mining software repositories and bi-directional parser synthesis.
Waveform Selection for Radar Tracking in Target Channels With Memory via Universal Learning

In tracking radar, the sensing environment often varies significantly over a track duration due to the target's trajectory and dynamic interference. Adapting the radar's waveform using partial information about the state of the scene has been shown to provide performance benefits in many practical scenarios. Moreover, radar measurements generally exhibit strong temporal correlation, allowing memory-based learning algorithms to effectively learn waveform selection strategies. This work examines a radar system which builds a compressed model of the radar-environment interface in the form of a context-tree. The radar uses this context tree-based model to select waveforms in a signal-dependent target channel, which may respond adversarially to the radar's strategy. This approach is guaranteed to asymptotically converge to the average-cost optimal policy for any stationary target channel that can be represented as a Markov process of order <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$U &lt; \infty$</tex> , where the constant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$U$</tex> is unknown to the radar. The proposed approach is tested in a simulation study, and is shown to provide tracking performance improvements over two state-of-the-art waveform selection schemes.
Research on Comprehensive Energy Efficiency Control and Optimization Technology for the Internet of Things for Three-Dimensional “Common Intelligence” Visualization

This article uses digital twins and edge computing technologies to design virtual models in the digital space and establish the mapping relationship between digital virtual and physical entities to achieve interconnected and interactive digital &#x201C;twin mirroring&#x201D; physical models, building multi-source integration, autonomous comprehensive perception, and globalization An integrated small and micro park integrated energy IoT system architecture with optimal coordination, virtualized management and control of physical resources, and deep decoupling of software and hardware. Finally, the typical design of edge computing and edge cloud collaboration of actual power distribution business is listed.
Design of Surgical Remote Virtual Biomedical Laboratory under 5G environment

This paper designs a surgical remote virtual training scheme under 5G environment. It uses computer to establish a virtual environment to simulate the surgical training scene in the laboratory. In such environment, the trained medical staff perform various operations by setting objects interactively. Then a collision pre detection method using Sphere-AABB composite hierarchical bounding box method is proposed. The whole operation process applies 5G+4K/VR live CDN architecture to complete information transmission, so as to ensure no high bandwidth and low delay of media stream. OpenGL + VRML is adopted to test the scheme in this paper. The results show that the VR interactive experimental platform has convenient flexibility and scalability, and the scene FPS value meets the requirements of real-time, which can generate a good learning process experience for users.
Content-Based Recommender System for Similar Products in E-Commerce

The recommendation system aims to provide the best suggestion to the user by analysing the buyer’s interest. Most of the time, we can get good product as the recommender(s) already has/have much knowledge about the product. So, this work is about the development of a recommender system for similar products in E-commerce with two particular cases of book recommendation and movie recommendation. This work is based on content-based recommendation technique. We have experimented the content-based techniques using book data set, namely BookCrossing.csv and movie review data set IMDB_Top250Engmovies2 _OMDB_Detailed.csv. For book recommendation, we have used pivot table for finding correlation coefficient. For movie recommendation, we have implemented TF-IDF-based recommender system using CountVectorizer function of scikit-learn and cosine similarity for getting good recommendation. Our system recommends most similar books by calculating the correlation coefficient for the book entered by the user and recommends top ten most similar movies using cosine similarity for a movie entered by user. This system can be helpful for people to find books and movies of their interest. This system can reduce the effort to put on physically for getting recommendation from people as it is an automatic system for recommendation.
Trust in Context: The Impact of Regulation on Blockchain and DeFi

Trust is a key resource in financial transactions. Traditional financial institutions, and novel blockchain-based decentralized financial services (DeFi) rely on fundamentally different sources of trust and confidence. The former relies on heavy regulation, trusted intermediaries, clear rules (and restrictions) on market competition, and long standing informal expectations on what banks and other financial intermediaries are supposed to do or not to do. The latter rely on blockchain technology to provide confidence in the outcome of rules encoded in protocols and smart contracts. Their main promise is to create confidence in the way the blockchain architecture enforces rules, rather than to trust banks, regulators, markets. In this article, we compare the trust architectures surrounding these two financial systems. We provide a deeper analysis of how proposed regulation in the blockchain space affects the code- and confidence-based architectures which so far have underwrote DeFi. We argue that despite the solid safeguards and guarantees which code can offer, the confidence in DeFi is still very much dependent on more traditional trust-enhancing mechanisms, such as code governance, and anti-fraud regulation to address some of the issues which currently plague this domain, and which have no immediate, purely software-based solutions. What is more, given the risks of bugs or scams in the DeFi space, regulation and trusted intermediaries may need to play a more active role, in order for DeFi to gain the trust of the next generation of users.
Paradigm Shift of Machine Learning to Deep Learning in Side Channel Attacks - A Survey

A side-channel attack is a sort of computer security attack depending on data obtain from program implementation rather than flaws in the program itself. Additional data sources that might be exploited include timing data, power consumption, electromagnetic leakage, and even sound. Since the 1990's, when side channel attacks were first introduced, a lot of effort is done in improving their effectiveness and efficiency. Many machine learning methods were designed for side-channel attacks. The researchers have recently noticed a growing trend in the SCA community to use deep learning techniques, which has resulted in more precise side-channel studies, even when countermeasures are in effect. Machine learning algorithms, on the other hand, have the drawback of requiring human engineering to function and some performance fluctuations in some cases. Recent research has focused on using deep learning techniques to extract characteristics from data automatically. Concepts of side channel attacks, machine learning, deep learning, and current breakthroughs in deep learning-based side-channel attacks are described in this paper. This discussion provides an overview of contemporary machine and deep learning research in the context of SCA. Machine Learning is not completely negated, or Deep Learning is not completely supported but their competitiveness and trend is discussed as a baseline to new challenges in context of side channel attack. Thus, a paradigm shift of ML to DL in SCAs is the focus of this paper.
Multistage Pixel-Visibility Learning With Cost Regularization for Multiview Stereo

Multiple-view stereo has potential applications in robotic operations and autonomous driving (unstructured environment construction, visual servo). With assisted depth information, inertial navigation systems can achieve precise navigation. It is, especially suitable for GPS failures in complex environments. Accurate depth estimation is a challenge in low-textured or occluded regions. To alleviate the inference of incorrect depth, a multi-stage pixel-visibility learning-based stereo network is presented in this paper. Its improvements are as follows: 1) a new content-adaptive cost volume aggregation mechanism based on neighboring pixel-wise visibility is designed to effectively produce more accurate and smoother depth map predictions in the object boundary. 2) global convolution block and boundary refinement block are developed to regularize its cost volume, they can learn the inherent constraints of feature matching correspondence and effectively mitigate the depth estimation uncertainty in low-textured regions. 3) a new loss function is designed to measure the uncertainty of predicted probability distribution and enhance the reliability of depth map inference. Experimental results on the indoor DTU datasets and the outdoor Tanks & Temples datasets indicate that our method can achieve superior performance and has a powerful generalization ability, which is comparable to state-of-the-art works. Note to Practitioners—Multiple-view stereo (MVS) can estimate dense 3D representations of scenes, which is widely used in autonomous driving, robotic navigation, virtual reality (VR), and augmented reality (AR). Aiming at the problem of incorrect depth inference in low-textured or occluded regions, this work proposes a novel multi-stage depth prediction method based on neighboring pixel-wise visibility. Our method cannot only achieve accurate depth estimation for robot perception but also make no concession to real-time performance. It is clear that the proposed method has good potential in 3D reconstruction, robotic navigation, and VR/AR fields to provide accurate depth estimation in real-time with limited memory consumption.
CRFlex: A Flexible and Configurable Cryptographic Hardware Accelerator for AES Block Cipher Modes

This paper presents a System-on-Chip (SoC) implementation of a cryptographic hardware accelerator supporting multiple AES based block cypher modes, including the more advanced CMAC, CCM, GCM and XTS modes. Furthermore, the proposed design implements in hardware advanced features for AES key secure storage. A flexible interface allows the communication between the hardware accelerator and the chosen processor and makes this implementation suitable to be easily integrated into a generic embedded system. The system has been prototyped and characterized on a Xilinx Zynq 7000 platform. Synthesis results on a 7 nm CMOS Standard-Cell library are proposed too, showing competitive performances and resource usage respect to the State of Art and assessing the portability in different technology libraries of the proposed design. Furthermore, power consumption data are extracted to prove the suitability of the hardware acceleration also in the case of power-constrained devices.
Decision Boundary-aware Data Augmentation for Adversarial Training

Adversarial training (AT) is a typical method to learn adversarially robust deep neural networks via training on the adversarial variants generated by their natural examples. However, as training progresses, the training data becomes less attackable, which may undermine the enhancement of model robustness. A straightforward remedy is to incorporate more training data, but it may incur an unaffordable cost. To mitigate this issue, in this paper, we propose a deCisiOn bounDary-aware data Augmentation framework (CODA): in each epoch, the CODA directly employs the meta information of the previous epoch to guide the augmentation process and generate more data that are close to the decision boundary, i.e., attackable data. Compared with the vanilla mixup, our proposed CODA can provide a higher ratio of attackable data, which is beneficial to enhance model robustness; it meanwhile mitigates the model's linear behavior between classes, where the linear behavior is favorable to the standard training for generalization but not to the adversarial training for robustness. As a result, our proposed CODA encourages the model to predict invariantly in the cluster of each class. Experiments demonstrate that our proposed CODA can indeed enhance adversarial robustness across various adversarial training methods and multiple datasets.