Reviewerly/exHarmony huggingface dataset:
Choice Trees: Representing Nondeterministic, Recursive, and Impure Programs in Coq

This paper introduces ctrees, a monad for modeling nondeterministic, recursive, and impure programs in Coq. Inspired by Xia et al.'s itrees, this novel data structure embeds computations into coinductive trees with three kind of nodes: external events, and two variants of nondeterministic branching. This apparent redundancy allows us to provide shallow embedding of denotational models with internal choice in the style of CCS, while recovering an inductive LTS view of the computation. ctrees inherit a vast collection of bisimulation and refinement tools, with respect to which we establish a rich equational theory. We connect ctrees to the itree infrastructure by showing how a monad morphism embedding the former into the latter permits to use ctrees to implement nondeterministic effects. We demonstrate the utility of ctrees by using them to model concurrency semantics in two case studies: CCS and cooperative multithreading.
Unrealizability Logic

We consider the problem of establishing that a program-synthesis problem is unrealizable (i.e., has no solution in a given search space of programs). Prior work on unrealizability has developed some automatic techniques to establish that a problem is unrealizable; however, these techniques are all black-box, meaning that they conceal the reasoning behind why a synthesis problem is unrealizable. In this paper, we present a Hoare-style reasoning system, called unrealizability logic for establishing that a program-synthesis problem is unrealizable. To the best of our knowledge, unrealizability logic is the first proof system for overapproximating the execution of an infinite set of imperative programs. The logic provides a general, logical system for building checkable proofs about unrealizability. Similar to how Hoare logic distills the fundamental concepts behind algorithms and tools to prove the correctness of programs, unrealizability logic distills into a single logical system the fundamental concepts that were hidden within prior tools capable of establishing that a program-synthesis problem is unrealizable.
ABCD technology- AI, Blockchain, Cloud computing and Data security in Islamic banking sector

The banking system gives the groundwork of current business. A well-working financial system is essential to an advanced economy, and banks fill significant roles for society. They should subsequently be secure. Artificial intelligence (AI) is steeply affecting innovation and skill. The financial industry is without a doubt to be the one to take on Artificial Intelligence. The Islamic banking sector is adopting this technology to evaluate vulnerabilities, perceive and prevent payment fraud, further develop against anti-money laundering (AML) processes, and play out extra administrative know-your-client (KYC) checks. Blockchain technology is centre, basic innovation with promising application possibilities in the banking industry. This could enable associations to settle and screen transactions more effortlessly than conventional methods like those of SWIFT. The emergence of cloud computing has modified the part that the Internet plays in a range of associations and organizations. Banks are slowly attempting to embrace cloud technologies to meet their varied prerequisites and to produce a versatile and light financial environment competent of rapidly response to arising business needs. For banks, physical and digital security along-with clients own data are vital to guaranteeing that banks can keep the cash and personal information they are entrusted with and the people who consistently are available in the establishment safe. Although these technologies have rapidly evolved and has the potential to revolutionise the banking industry. Some of these are fairly new concept and are therefore users should be aware of considerable risks and challenges linked with it. Thus, a systematic literature review was conducted to examine the challenges associated with these technologies, along-with the suggested solutions to mitigate them or lessen them.
Dutch public health professionals’ perspectives and needs regarding citizen involvement in COVID-19 contact tracing through digital support tools: an exploratory qualitative study

Contact tracing (CT) is an important, but resource-intensive tool to control outbreaks of communicable diseases. Under pandemic circumstances, public health services may not have sufficient resources at their disposal to effectively facilitate CT. This may be addressed by giving cases and their contact persons more autonomy and responsibility in the execution of CT by public health professionals, through digital contact tracing support tools (DCTS-tools). However, the application of this approach has not yet been systematically investigated from the perspective of public health practice. Therefore, we investigated public health professionals' perspectives and needs regarding involving cases and contact persons in CT for COVID-19 through DCTS-tools.Between October 2020 and February 2021, we conducted online semi-structured interviews (N = 17) with Dutch public health professionals to explore their perspectives and needs regarding the involvement of cases and contact persons in CT for COVID-19 through DCTS-tools, in the contact identification, notification, and monitoring stages of the CT-process. Interviews were audio recorded and transcribed verbatim. A thematic analysis was performed.Four main themes related to Dutch public health professionals' perspectives and needs regarding involving cases and contact persons in CT for COVID-19 through DCTS-tools emerged from the data: 'Distinct characteristics of CT with DCTS-tools'; 'Anticipated benefits and challenges of CT for COVID-19 with DCTS- tools'; 'Circumstances in CT for COVID-19 that permit or constrain the application of DCTS-tools'; and 'Public health professionals' needs regarding the development and application of DCTS-tools for CT'. Public health professionals seem to have a positive attitude towards involving cases and contact persons through DCTS-tools. Public health professionals' (positive) attitudes seem conditional on the circumstances under which CT is performed, and the fulfilment of their needs in the development and application of DCTS-tools.Dutch public health professionals seem positive towards involving cases and contact persons in CT for COVID-19 through DCTS-tools. Through adequate implementation of DCTS-tools in the CT-process, anticipated challenges can be overcome. Future research should investigate the perspectives and needs of cases and contact persons regarding DCTS-tools, and the application of DCTS-tools in practice.
Leveraging angular distributions for improved knowledge distillation

Knowledge distillation as a broad class of methods has led to the development of lightweight and memory efficient models, using a pre-trained model with a large capacity (teacher network) to train a smaller model (student network). Recently, additional variations for knowledge distillation, utilizing activation maps of intermediate layers as the source of knowledge, have been studied. Generally, in computer vision applications, it is seen that the feature activation learned by a higher capacity model contains richer knowledge, highlighting complete objects while focusing less on the background. Based on this observation, we leverage the dual ability of the teacher to accurately distinguish between positive (relevant to the target object) and negative (irrelevant) areas. We propose a new loss function for distillation, called angular margin-based distillation (AMD) loss. AMD loss uses the angular distance between positive and negative features by projecting them onto a hypersphere, motivated by the near angular distributions seen in many feature extractors. Then, we create a more attentive feature that is angularly distributed on the hypersphere by introducing an angular margin to the positive feature. Transferring such knowledge from the teacher network enables the student model to harness the higher discrimination of positive and negative features for the teacher, thus distilling superior student models. The proposed method is evaluated for various student-teacher network pairs on four public datasets. Furthermore, we show that the proposed method has advantages in compatibility with other learning techniques, such as using fine-grained features, augmentation, and other distillation methods.
Peritext: A CRDT for Collaborative Rich Text Editing

Conflict-Free Replicated Data Types (CRDTs) support decentralized collaborative editing of shared data, enabling peer-to-peer sharing and flexible branching and merging workflows. While there is extensive work on CRDTs for plain text, much less is known about CRDTs for rich text with formatting. No algorithms have been published, and existing open-source implementations do not always preserve user intent. In this paper, we describe a model of intent preservation in rich text editing, developed through a series of concurrent editing scenarios. We then describe Peritext, a CRDT algorithm for rich text that satisfies the criteria of our model. The key idea is to store formatting spans alongside the plaintext character sequence, linked to a stable identifier for the first and last character of each span, and then to derive the final formatted text from these spans in a deterministic way that ensures concurrent operations commute. We have prototyped our algorithm in TypeScript, validated it using randomized property-based testing, and integrated it with an editor UI. We also prove that our algorithm ensures convergence, and demonstrate its causality preservation and intention preservation properties.
Imagining Caring Futures for Frontline Health Work

Care workers are increasingly using digital technology in their daily lives, for monitoring, financial compensation, training, coordination, and more. State and corporate actors have invested significant resources to enable this digital shift, particularly during the COVID-19 pandemic. However, care work has remained chronically underpaid, and continues to rely on women from minoritized and marginalized backgrounds. Our paper examines how care workers carefully navigate digitization, precarity, and complex social relationships, in an attempt to care for their communities and each other. We analyze the emerging digital ecosystem for frontline health workers in India during the COVID-19 pandemic where these dynamics have been highly visible. Our research draws attention to four interconnected ways in which workers practiced care, by directing their efforts towards survival, resilience, advocacy, and/or resistance. We suggest these also as care orientations that can be adopted by researchers and practitioners, to critically reflect on and direct technology design towards enabling more caring futures, for (and with) workers and communities.
Modeling and Simulation of Tennis Serve Image Path Correction Optimization Based on Deep Learning

The traditional tennis serve training model has been deeply ingrained in the current training. For athletes, in order to master the essentials of technical movements proficiently, they must practice for a long time and repeatedly according to the guidance of coaches. Effectively correcting the image path of tennis serve can improve the level of tennis training and competition. When correcting the image path of the tennis serve, it is necessary to mark the corners of the error points according to the characteristics of rotating multidimensional characteristics of the serving action. The traditional method adopts the critical node control method to realize the extraction of limb features and complete the path correction of the serving image. The error points of the process are marked, which reduces the accuracy of the path correction. Based on the deep learning model, this paper proposes an optimization modeling method for tennis serve image path correction and builds a visual feature acquisition system for tennis serve action based on remote video monitoring. The processing method designs the edge segmentation algorithm for the collected visual images and, on this basis, marks the corner points of the error points of the serving action and realizes the optimal modeling of the path correction of the tennis serving image. In this paper, the DLT algorithm and MSDLT algorithm are compared first, and then, the DLT algorithm is compared with the algorithm in this paper. The results show that the success rate of this method is about 92%, while the success rate of DLT algorithm is only about 82%. This algorithm has obvious advantages. The method in this paper is used to correct the action shape of the tennis serving action, which has better real time and accuracy and superior performance and can accurately track the visual edge information feature points of the player during the serving process.
Explaining the effect of artificial intelligence on the technology acceptance model in media: a cloud computing approach

Purpose The purpose of this paper is to explain the effect of the technology acceptance model in the media environment by using the mediating role of artificial intelligence and the cloud computing approach. Design/methodology/approach After reviewing the theoretical foundations, a conceptual model framework and research hypotheses were formed. The statistical population of the study included managers, deputies and experts from the National Iranian Oil Company, and a statistical sample of 368 people was selected by simple random sampling. Findings The results of structural equation modelling with PLS 2.0 software show a positive and significant effect on the artificial intelligence variable in the technology acceptance model with the cloud approach. Artificial intelligence has opened a new space in the digital world, especially in the media, so that its profound impact is quite evident and has affected people’s lives. Originality/value The acceleration of various technologies has severely challenged the approach of organizations, especially the media. The media environment with word of the technologies of the Industry 4.0, especially cloud computing technology, has changed the ways of accessing and using products and services.
Virtual Agents with Augmented Reality in Digital Healthcare

Virtual agents benefit from reducing the staffing required in current services, such as customer care and education. This benefit also can be expected in digital healthcare by providing on-demand services and covering limited human resources of medical staff. Therefore, in this study, we explore how virtual agents can be more effective in the context of digital healthcare. For this, an experiment has been conducted to find the user experience's effectiveness according to the different appearances, zoomorphic or professional, and the effect of the Augmented Reality function on digital healthcare virtual agents. In the experiments, 80 participants tested four applications implemented with different appearances of a virtual agent with AR functions. As a result, it was found that the AR function had a positive effect in terms of intention to use and reduced anxiety. In addition, it was observed that the zoomorphic appearance had a positive effect on perceived enjoyment, perceived sociability, and social influence. Based on the results of this study, it is expected that when the advantages of AR functions and the advantages of zoomorphic appearance are combined, it can effectively influence the implementation design of digital healthcare virtual agents.
Perturb and optimize users’ location privacy using geo-indistinguishability and location semantics

Abstract Location-based services (LBS) are capable of providing location-based information retrieval, traffic navigation, entertainment services, emergency rescues, and several similar services primarily on the premise of the geographic location of users or mobile devices. However, in the process of introducing a new user experience, it is also easy to expose users’ specific location which can result in more private information leakage. Hence, the protection of location privacy remains one of the critical issues of the location-based services. Moreover, the areas where humans work and live have different location semantics and sensitivities according to their different social functions. Although the privacy protection of a user’s real location can be achieved by the perturbation algorithm, the attackers may employ the semantics information of the perturbed location to infer a user’s real location semantics in an attempt to spy on a user’s privacy to certain extent. In order to mitigate the above semantics inference attack, and further improve the quality of the location-based services, this paper hereby proposes a user side location perturbation and optimization algorithm based on geo-indistinguishability and location semantics. The perturbation area satisfying geo-indistinguishability is thus generated according to the planar Laplace mechanism and optimized by combining the semantics information and time characteristics of the location. The optimum perturbed location that is able to satisfy the minimum loss of location-based service quality is selected via a linear programming method, and can be employed to replace the real location of the user so as to prevent the leakage of the privacy. Experimental comparison of the actual road network and location semantics dataset manifests that the proposed method reduces approximately 37% perturbation distance in contrast to the other state-of-the-art methods, maintains considerably lower similarity of location semantics, and improves region counting query accuracy by a margin of around 40%.
Mobile shopping decision comfort using augmented reality: the effects of perceived augmentation and haptic imagery

Purpose Virtual try-on apps (VTOs) allow consumers to examine fashion and furniture items in usage context without going to a physical store. But the adoption of such apps has varied across product categories, and research on user acceptance of AR marketing has been fragmented. The current study aims to develop and test a general model that explains the formation of decision comfort (DC) in the majority of AR try-on experiences for mobile shopping. Design/methodology/approach After reviewing 30 VTOs available on the iOS app store, the authors chose the Wanna Kicks sneaker shopping VTO as the most representative to test their hypotheses for AR try-on in general. Overall, 178 online consumers performed a sneaker shopping task on their mobile devices, and their responses were analyzed with the partial least squares method. Findings The study confirmed the key role of perceived augmentation in leading to DC via a utilitarian and a hedonic path. These effects were attenuated for younger users, and haptic imagery only had a utilitarian impact. Scholars should pay more attention to the variable of age, while managers should act quickly to enhance the basic AR affordances of mobile try-on apps. Originality/value This is the first study of a VTO in the footwear category and with a model that tests age as a moderating variable between antecedents and consumer responses.
On-chip generation of hybrid polarization-frequency entangled biphoton states

We demonstrate a chip-integrated semiconductor source that combines polarization and frequency entanglement, allowing the generation of entangled biphoton states in a hybrid degree of freedom without post-manipulation. Our AlGaAs device is based on type-II spontaneous parametric downconversion in a counterpropagating phase-matching scheme in which the modal birefringence lifts the degeneracy between the two possible nonlinear interactions. This allows the direct generation of polarization–frequency entangled photons at room temperature and telecom wavelength, and in two distinct spatial modes, offering enhanced flexibility for quantum information protocols. The state entanglement is quantified by a combined measurement of the joint spectrum and Hong–Ou–Mandel interference (raw visibility <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mn>70.1</mml:mn> <mml:mi>%</mml:mi> <mml:mo>±</mml:mo> <mml:mn>1.1</mml:mn> <mml:mi>%</mml:mi> </mml:mrow> </mml:math> ) of the biphotons, allowing to reconstruct a restricted density matrix in the hybrid polarization–frequency space.
Integrating Educational Components into the Metaverse

The prospect of integrating educational component into the “metaverse” has been analyzed, and the origin story of the idea is traced from the ancient religious, philosophical and psychological concepts. It is proposed: (1) to create a “metaverse” as a developmental ecosystem, taking into account the laws of personality development; (2) to involve not only figurative but also abstract thinking to expand the way of describing the “metaverse” through abstract schemes in addition to images; and (3) to introduce moral dimension into the assessment of possible “metaverse” impact on users’ mind and body, as well as on the real world. It is assumed that modeling a digital learning environment based on the internal laws of human development will optimize the entire process. The built-in educational component will provide a virtual learning environment with developmental capacity vastly superior to simulated reality. An innovative Eco-Humanistic Developmental Model is proposed for integration. It focuses on human existential, social and career metasenses of self-development, synergistic interaction, and competencies development. In contrast to the situational approach, the process is modeled in its existential integrity. It is based on the idea of human development as a living self-developing system in interaction with the surrounding world as a developmental environment. In the process of balancing actual needs, personal abilities and conditions of environment, the synergetic development of all three components takes place. As a result, not only cognitive and personality development is optimized, but there is also an awareness of interdependence and mutual development of self and environment. The concept, structure, strategy, mechanisms and tools of innovative e-learning courses based on Eco-Humanistic principles are presented. To confirm the efficiency of the proposed approach, the results of research within the framework of a long-term developmental experiment are presented. The prospect of multidisciplinary research is revealed.
Fundamental Results of Cyclic Codes over Octonion Integers and Their Decoding Algorithm

Coding theory is the study of the properties of codes and their respective fitness for specific applications. Codes are used for data compression, cryptography, error detection, error correction, data transmission, and data storage. Codes are studied by various scientific disciplines, such as information theory, electrical engineering, mathematics, linguistics, and computer science, to design efficient and reliable data transmission methods. Many authors in the previous literature have discussed codes over finite fields, Gaussian integers, quaternion integers, etc. In this article, the author defines octonion integers, fundamental theorems related to octonion integers, encoding, and decoding of cyclic codes over the residue class of octonion integers with respect to the octonion Mannheim weight one. The comparison of primes, lengths, cardinality, dimension, and code rate with respect to Quaternion Integers and Octonion Integers will be discussed.