Reviewerly/exHarmony huggingface dataset:
Video and Image Steganography

Video steganography, a technique for embedding secret information within video sequences, has gained prominence in the domain of covert communication. By exploiting the inherent redundancy and high capacity of video data, this approach enables secure data transmission without raising suspicion. This paper delves into the intricacies of AES-based video steganography, with a particular focus on the integration of the Advanced Encryption Standard (AES) algorithm. The AES algorithm, renowned for its robust encryption capabilities, serves as a cornerstone of this steganographic approach. By encrypting the secret data prior to embedding it within the video frames, an additional layer of security is introduced, safeguarding the confidentiality of the hidden information
A bifunctional superconducting cell as flux qubit and neuron

Josephson digital or analog ancillary circuits are an essential part of a large number of modern quantum processors. The natural candidate for the basis of tuning, coupling, and neromorphic co-processing elements for processors based on flux qubits is the adiabatic (reversible) superconducting logic cell. Using the simplest implementation of such a cell as an example, we have investigated the conditions under which it can optionally operate as an auxiliary qubit while maintaining its "classical" neural functionality. The performance and temperature regime estimates obtained confirm the possibility of practical use of a single-contact inductively shunted interferometer in a quantum mode in adjustment circuits for q-processors.
MEMORIES BY VARVARA MUZIL-BOROZDINA ABOUT A.N. OSTROVSKY

A.N. Ostrovsky not only wrote plays, but also staged them on stage himself. And since he lived in Moscow, the Maly Theater naturally came into his field of vision. Ostrovsky wrote plays for actors and their benefit performances. He loved actors, although sometimes he suffered incredibly from their poorly learned roles. The names of actors are often found in the playwright’s letters and in his thoughts about the theater. In turn, the actors loved Ostrovsky not only as the author of plays, but also as a person. This is clearly seen from the memoirs of the Maly Theater actress V.P., published here for the first time. Muzil-Borozdina. These memoirs were written by the actress already at a very respectable age for those times. But time could not extinguish the sincerity of her feelings, their spontaneity. The actress writes about Ostrovsky as if he were somewhere nearby, he was alive in her memory. This emotion gives the published memories the aroma of a bygone time.
Automated Detection of Persuasive Content in Electronic News

Persuasive content in online news contains elements that aim to persuade its readers and may not necessarily include factual information. Since a news article only has some sentences that indicate persuasiveness, it would be quite challenging to differentiate news with or without the persuasive content. Recognizing persuasive sentences with a text summarization and classification approach is important to understand persuasive messages effectively. Text summarization identifies arguments and key points, while classification separates persuasive sentences based on the linguistic and semantic features used. Our proposed architecture includes text summarization approaches to shorten sentences without persuasive content and then using classifiers model to detect those with persuasive indication. In this paper, we compare the performance of latent semantic analysis (LSA) and TextRank in text summarization methods, the latter of which has outperformed in all trials, and also two classifiers of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). We have prepared a dataset (±1700 data and manually persuasiveness-labeled) consisting of news articles written in the Indonesian language collected from a nationwide electronic news portal. Comparative studies in our experimental results show that the TextRank–BERT–BiLSTM model achieved the highest accuracy of 95% in detecting persuasive news. The text summarization methods were able to generate detailed and precise summaries of the news articles and the deep learning models were able to effectively differentiate between persuasive news and real news.
Scene Text Recognition via Dual-path Network with Shape-driven Attention Alignment

Scene text recognition (STR), one typical sequence-to-sequence problem, has drawn much attention recently in multimedia applications. To guarantee good performance, it is essential for STR to obtain aligned character-wise features from the whole-image feature maps. While most present works adopt fully data-driven attention-based alignment, such practice ignores specific character geometric information. In this paper, built upon a group of learnable geometric points, we propose a novel shape-driven attention alignment method that is able to obtain character-wise features. Concretely, we first design a corner detector to generate a shape map to guide the attention alignments explicitly, where a series of points can be learned to represent character-wise features flexibly. We then propose a dual-path network with a mutual learning and cooperating strategy that successfully combines CNN with a ViT-based model, leading to further accuracy improvement. We conduct extensive experiments to evaluate the proposed method on various scene text benchmarks, including six popular regular and irregular datasets, two more challenging datasets (i.e., WordArt and OST), and three Chinese datasets. Experimental results indicate that our method can achieve superior performance with a comparable model size against many state-of-the-art models.
UCD Sprint: A Fast Process to Involve Users in the Design Practices of Software Companies

Several studies have shown that involving users in design processes is a key factor in understanding user needs and essential for designing computer systems capable of providing valuable user experiences. However, many practitioners do not emphasize user needs sufficiently and neglect user involvement in software design and development. The UCD Sprint is a recently proposed, step-by-step, cost-effective process that focuses on involving users in design activities. This article reports a mixed-method study in which, for the first time, practitioners working in companies used the UCD Sprint; the study’s main objective was to investigate the impact of the UCD Sprint on practitioners’ design practices. The results show that, among the various activities associated with this process, those that involved users were particularly appreciated by practitioners. Thus, the UCD Sprint can enhance the innovativeness of company design practices; it enables practitioners to obtain sufficient information to create systems that fit users’ needs.
Regulatory Choices and Legal Disputes in the Fight Against COVID-19 Infections in the Workplace: A Comparison of Vaccine Mandates in the Italian and US Contexts

This is a comparative study of two countries’ legal and policy actions to stem the spread of COVID-19 in the workplace, focusing on Italy and the United States (US). Both nations sustained great loss of life and high infection rates during the first years of the pandemic. This chapter examines how Italy and the US approached COVID-19 vaccine mandates for workers. Of particular interest are the regulatory choices made, including the choice not to regulate, and the consequences of those choices on the employment relationship. Additionally revealing are the legal grounds upon which regulatory actions were challenged, and how courts balanced the interests at stake. Finally, the way in which the debates over workplace vaccine mandates were framed illuminate national culture and the extent to which each country views labor rights as human rights. To provide context for these insights, this chapter examines convergence and divergence in the two countries’ initial responses to the global health emergency presented by COVID-19, and the way in which workplace vaccine mandates were initially embraced.
Understanding the Axioms and Assumptions of Logical Mathematical Systems through Raster Images: Application to the Construction of a Likert Scale

This article presents different artistic raster images as a resource for correcting misconceptions about different laws and assumptions that underlie the propositional systems of binary logic, Łukasiewicz’s trivalent logic, Peirce’s trivalent logic, Post’s n-valent logic, and Black and Zadeh’s infinite-valent logic. Recognizing similarities and differences in how images are constructed allows us to deepen, through comparison, the laws of bivalence, non-contradiction, and excluded middle, as well as understanding other multivalent logic assumptions from another perspective, such as their number of truth values. Consequently, the first goal of this article is to illustrate how the use of visualization can be a powerful tool for better understanding some logic systems. To demonstrate the utility of this objective, we illustrate how a deeper understanding of logic systems helps us appreciate the necessity of employing Likert scales based on the logic of Post or Zadeh, which is the second goal of the article.
Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach

Abstract Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty in obtaining an adequate number of cases and quality data. Another issue is improving the learning process, which can be done by actively introducing experts into the learning loop, in what is known as human-in-the-loop (HITL) ML. We describe an ML model based on a neural network in which HITL techniques were used to resolve the data bottleneck problem for the treatment of pancreatic cancer. We first augmented the dataset using synthetic cases created by a generative adversarial network. We then launched an active learning (AL) process involving human experts as oracles to label both new cases and cases by the network found to be suspect. This AL process was carried out simultaneously with an interactive ML process in which feedback was obtained from humans in order to develop better synthetic cases for each iteration of training. We discuss the challenges involved in including humans in the learning process, especially in relation to human–computer interaction, which is acquiring great importance in building ML models and can condition the success of a HITL approach. This paper also discusses the methodological approach adopted to address these challenges.
Prototype to Investigate the Extent to Which Words with Specific Attributes Can Be Retrieved Using Granular Metadata

Despite the growth in digital text collections, the ability to retrieve words or phrases with specific attributes is limited, for example, to retrieve words with a specific meaning within a specific section of a text. Many systems work with coarse bibliographic metadata. To enable fine-grained retrieval, it is necessary to encode texts with granular metadata. Sample texts were encoded with granular metadata. Five categories of metadata that can be used to capture additional data about texts were used, namely, morphological, syntactic, semantic, functional and bibliographic. A prototype was developed to parse the encoded texts and store the information in a database. The prototype was used to test the extent to which words or phrases with specific attributes could be retrieved. Retrieval on a detailed level was possible through the prototype. Retrieval using all five categories of metadata was demonstrated, as well as advanced searches using metadata from different categories in a single search. This article demonstrates that when granular metadata is used to encode texts, retrieval is improved. Relevant information can be selected, and irrelevant information can be excluded, even within a text.
The Elat Timna area, southern Israel: a unique geological window into 800 million years of geological evolution

The Elat/Timna area in Southern Israel provides an unparalleled opportunity for geological field studies due to its unique geology, spanning from the Neoproterozoic to the Holocene. The well-exposed rocks document the variability and evolution of three major processes: the creation of the Arabian-Nubian Shield during the Neoproterozoic, the accumulation of the Cambrian to Eocene sedimentary sequence, and the Miocene to Recent evolution of the Dead Sea Transform, an active plate boundary. The area’s geological diversity and extensive documentation of its geology have made it a particularly important study site for geoscience students. Recently the Geological Survey of Israel published new 1:50,000 scale geological maps of the area, updated by additional fieldwork and presented in a series of reports. This paper presents a brief geological overview and a description of six key locations, complete with maps and photos that are a showcase document to the area’s geological uniqueness as an excellently-exposed window into 800 million years of Neoproterozoic to Holocene evolution at the NE African plate.
Stationary, Markov, stochastic processes with polynomial conditional moments and continuous paths

We are studying stationary random processes with conditional polynomial moments that allow a continuous path modification. Processes with continuous path modification are important because they are relatively easy to simulate. One does not have to care about the distribution of their jumps which is always difficult to find. Among those processes with the continuous path are the Ornstein–Uhlenbeck process, the Gamma process, the process with Arcsin or Wigner margins and the Theta functions as the transition densities and others. We give a simple criterion for the stationary process to have a continuous path modification expressed in terms of skewness and excess kurtosis of the marginal distribution.
End-to-end data security with DMaya on IPFS: keyless secured private swarm for the closed user group

The interplanetary File system (IPFS) provides data communication using plaintext over distributed and decentralized systems. Up till now, the security of data has mostly been established using keys. To facilitate classified data communication within a Closed User Group (CUG), services of IPFS private swarm are required to render additional content encryption. Currently, there is no inherent security built into IPFS. This paper presents an application DMaya on IPFS to provide keyless encryption on IPFS based on the Quasigroup-based Secret Sharing Scheme (QSS). IPFS is used as a storage medium, irrespective of the nature of data. Data security in decentralized mode with IPFS is provided with DMaya. The classified data of all types (i.e. video data, audio data, text data, and image data) when subjected to DMaya application, are fragmented into smaller fragments then processed by the probabilistic Quasigroup-based Secret Sharing (QSS) Scheme. DMaya keyless encryption generates the metadata tag file at the source node. The recipient node within the same private IPFS swarm, on receipt of metadata tag file reconstructs the original data with DMaya application.
Local imperceptible adversarial attacks against human pose estimation networks

Deep neural networks are vulnerable to attacks from adversarial inputs. Corresponding attack research on human pose estimation (HPE), particularly for body joint detection, has been largely unexplored. Transferring classification-based attack methods to body joint regression tasks is not straightforward. Another issue is that the attack effectiveness and imperceptibility contradict each other. To solve these issues, we propose local imperceptible attacks on HPE networks. In particular, we reformulate imperceptible attacks on body joint regression into a constrained maximum allowable attack. Furthermore, we approximate the solution using iterative gradient-based strength refinement and greedy-based pixel selection. Our method crafts effective perceptual adversarial attacks that consider both human perception and attack effectiveness. We conducted a series of imperceptible attacks against state-of-the-art HPE methods, including HigherHRNet, DEKR, and ViTPose. The experimental results demonstrate that the proposed method achieves excellent imperceptibility while maintaining attack effectiveness by significantly reducing the number of perturbed pixels. Approximately 4% of the pixels can achieve sufficient attacks on HPE.
Job Assistant: Job Preparation and Community Engagement Application with AI Assistant

This paper describes the offers provided by our application for Job seekers. It offers how wide range of Job listings is provided with interactive community platform and AI Assistant to give personalized guidance &amp; support to make your Job Preparation a journey a breeze. This research project focuses on the significance of job search websites and the importance of efficient user logins on these platforms. Job placements have become increasingly crucial, as many people's livelihoods rely on them. Job seekers and recruiters both rely on job portals to discover the right match for their requirement. Job portals provide a list of companies for them
ESS MS-G3D: extension and supplement shift MS-G3D network for the assessment of severe mental retardation

Abstract Automated mental retardation (MR) assessment is potential for improving the diagnostic efficiency and objectivity in clinical practice. Based on the researches on abnormal behavior characteristics of patients with MR, we propose an extension and supplement shift multi-scale G3D (ESS MS-G3D) network for video-based assessment of MR. Specifically, all videos are collected from clinical diagnostic scenarios and the skeleton sequence of human body is extracted from videos through an advanced pose estimation model. To solve the shortcomings of existing behavior characteristic learning methods, we present: (1) three G3D styles, enable the network to have different input forms; (2) two G3D graphs and two extension graphs, redefine and extend the graph structure of spatial–temporal nodes; (3) two learnable parameters, realize adaptive adjustment of graph structure; (4) a shift layer, enable the network to learn global features. Finally, we construct a three-branch model ESS MS-STGC, which can capture the discriminative spatial–temporal features and explore the co-occurrence relationship between spatial and temporal domains. Experiments in clinical video data set show that our proposed model has good performance in MR assessment and is superior to the existing vision-based methods. In two-classification task, our model with joint stream achieves the highest accuracy of $$94.63\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>94.63</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> in validation set and $$89.13\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>89.13</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> in test set. The results are further improved to $$96.52\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>96.52</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$93.22\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>93.22</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , respectively, by utilizing multi-stream fusion strategy. In four-classification task, our model obtains Top1 accuracy of $$78.84\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>78.84</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and Top2 accuracy of $$91.34\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>91.34</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> in test set. The proposed method provides a new idea for clinical mental retardation assessment.
Bit duplication technique to generate hard quadratic unconstrained binary optimization problems with adjustable sizes

Summary Quadratic unconstrained binary optimization (QUBO) is a combinatorial optimization to find an optimal binary solution vector that minimizes the energy value defined by a quadratic formula of binary variables in the vector. The main contribution of this article is to propose the bit duplication technique that can specify the number of duplicated bits, so that it can generate hard QUBO problem with adjustable sizes. The idea is to duplicate specified number of bits and then to give constraints so that the corresponding two bits take the same binary values. By this technique, any QUBO problem with bits is converted to a hard QUBO problem with bits . We use random QUBO problems, N‐Queen problems, traveling salesman problem and maximum weight matching problems for experiments. The performance of QUBO solvers including Gurobi optimizer, Fixstars Amplify AE, OpenJij with SA, D‐Wave samplers with SA, D‐Wave hybrid and ABS2 QUBO solver are evaluated for solving these QUBO problems. The experimental results show that only a small scale of duplicated bits can make QUBO problems harder. Hence, the bit duplication technique is a potent method to generate hard QUBO problems and generated QUBO problems can be used as benchmark problems for evaluating the search performance of QUBO solvers.
Lens: Experiencing Multi-level Page Tables at Close Quarters

Practical understanding of the working of different sub-systems of the operating system (OS) is crucial to develop a comprehensive understanding of computing systems. Virtual memory mechanisms such as virtual to physical memory translation using multi-level page tables are commonly found in most computing devices, ranging from hand-held devices to desktops and servers. From the pedagogy perspective, one of the primary challenges in explaining virtual to physical address translation through practical demonstrations is the dependency of virtual memory sub-system on the underlying hardware architecture. Furthermore, complicated, diverse, and ever-evolving nature of the multi-level page table support for address translation presents non-trivial challenges for educators to enable a smooth journey for the students from conceptual understanding to hands-on programming experience.
Perspectives on Computer Science Curricula 2023 (CS2023)

This panel examines Computer Science Curricula 2023 (CS2023) from different perspectives. All panelists serve on the CS2023 steering committee and have an intimate understanding of CS2023. The moderator will lay out its overall vision and structure while panelists will emphasize three major perspectives of CS education: software development fundamentals; systems development; and the increased role of societal, ethical, and professional aspects crucial to a modern CS graduate. Strong interdependencies exist between these perspectives, along with tensions arising from how much can be squeezed into a tight undergraduate CS curriculum. Attendees will take home an understanding of the approach taken by the CS2023 task force, the constraints on curriculum design, and how best to use the CS2023 guidelines to educate the next generation of CS graduates.
A Tool based Experiment to Teach Elicitation and Specification of Virtual Reality Product Requirements

Students need to understand the assessment of requirements correctness while building software systems. It helps produce products that meet the stakeholder objectives. This poster illustrates an experiment conducted as a tool-based collaborative assignment for assessing the correctness of software products using a Virtual Reality (VR) application as part of a CS300-level course at our University.