Reviewerly/exHarmony huggingface dataset:
A Fusion-Based Dense Crowd Counting Method for Multi-Imaging Systems

Dense crowd counting has become an essential technology for urban security management. The traditional crowd counting methods mainly apply to the scene with a single view and obvious features but cannot solve the problem with a large area and fuzzy crowd features. Therefore, this paper proposes a crowd counting method based on high and low view information fusion (HLIF) for large and complex scenes. First, a neural network based on an attention mechanism (AMNet) is established to obtain a global density map from a high view and crowd counts from a low view. Then, the temporal correlation and spatial complementarity between cameras are used to calibrate the overlap areas of the two images. Finally, the total number of people is calculated by combining the low-view crowd counts and the high-view density map. Compared to single-view crowd counting methods, HLIF is experimentally more accurate and has been successfully applied in practice.
Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal

This work studies the multi-weather restoration problem. In real-life scenarios, rain and haze, two often co-occurring common weather phenomena, can greatly degrade the clarity and quality of the scene images, leading to a performance drop in the visual applications, such as autonomous driving. However, jointly removing the rain and haze in scene images is ill-posed and challenging, where the existence of haze and rain and the change of atmosphere light, can both degrade the scene information. Current methods focus on the contamination removal part, thus ignoring the restoration of the scene information affected by the change of atmospheric light. We propose a novel deep neural network, named Asymmetric Dual-decoder U-Net (ADU-Net), to address the aforementioned challenge. The ADU-Net produces both the contamination residual and the scene residual to efficiently remove the contamination while preserving the fidelity of the scene information. Extensive experiments show our work outperforms the existing state-of-the-art methods by a considerable margin in both synthetic data and real-world data benchmarks, including RainCityscapes, BID Rain, and SPA-Data. For instance, we improve the state-of-the-art PSNR value by 2.26/4.57 on the RainCityscapes/SPA-Data, respectively. Codes will be made available freely to the research community.
Web Framework

The purpose of this paper is to demonstrate how to extend a web framework (WF) for developing web apps that can meet the diverse needs of different fields. This work aims to provide computer science senior project students with an opportunity to work on a wide range of projects using Apache, HTML, CSS, JavaScript, PHP, and MySQL. The paper presents a WF that manages jokes, based on the book PHP MySQL: Novice to Ninja (6th ed.) by T. Butler and K. Yank. The WF is interactive, responsive, secure, password-protected and database-driven. It consists of generic and specific components that use a MySQL database on the web server-side to store jokes. The specific components can be modified or added to create custom apps. Admin users can log in with their email and password and perform various actions (view, add, delete and edit) on jokes and categories, depending on their level of permission. Admins with full permissions can also edit the permissions of other registered accounts and access the Manage Categories, Manage Jokes, and Manage Authors pages. The original WF is enhanced and expanded for the senior project starting point by adding the following features: a hierarchical main menu with admin menus as the second level, a highlighted selected menu item, responsive design - accessibility on all devices, improved password security (minimum of 8 characters, 1 lowercase letter, 1 uppercase letter, 1 digit, 1 special character), a two-factor authentication security subsystem, cascaded deletion and uploaded and retrieved binary files (images and documents). In conclusion, the project is designed and implemented following the best practices and tested on a web hosting server provider.
Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio

Lasso regression methods include a penalty function expressed in terms of a norm defined in the space of model coefficients. The norm plays a key role as regards the way coefficients can become irrelevant in the model. For models with a compositional covariate, the norm should be coherent with the Aitchison geometry. The proposed method is based on a newly-defined compositional norm called L1 pairwise logratio. The novel approach allows one to construct an appropriate basis through a sequential binary partition for discriminating between balances that influence the response variable and those that have no effect. This generalised Lasso regression scheme is illustrated with the analysis of a geochemical data set.
Chapter 10. Domain-adapting and evaluating machine translation for institutional German in South Tyrol

Building on a prior small-scale study on machine translation adaptation in the same language pair and domain (De Camillis 2021), this chapter reports on a (i) domain-adaptation, (ii) quality assessment, and (iii) automatic legal terminology evaluation experiment for legal South Tyrolean German. After collecting, sentence-aligning, and cleaning the LEXB parallel corpus, we used this bilingual resource to domain-adapt a ModernMT engine. Performance improvements were measured in terms of automatic quality metrics. The machine-translation of South Tyrolean legal terms was evaluated using an ad hoc automatic terminology evaluation tool. We observed a significant boost in performance and term accuracy in the output of the ModernMT adapted engine, but the improvement in legal terminology translation was deemed unsatisfactory.
Chapter 2. Light Verb Constructions in English-Spanish translation

Light Verb Constructions (LVCs) are combinations of a partially delexicalized verb and a noun indicating an action or an event (e. g., give a description ). Studies modelled on the Meaning-Text theory and qualia roles of the Generative Lexicon model propose that LVCs combine nouns and verbs according to shared underlying, underspecified semantic features. This paper explores these features’ role in translating English LVCs into Spanish. Data come from the parallel corpus P-ACTRES 2.0, including fiction and nonfiction materials. Results indicate that translation choices for LVCs with have, take, make/do , and give are governed by the same notions of transference, inception, and volition that determine their combinatorial compatibility. Register also seems to influence the choice. This information may prove significant to machine translation, bi/multilingual writing support, and post-editing.
A Graphical and Qualitative Review of Literature on AI-based Cyber-Threat Intelligence (CTI) in Banking Sector

Cyber threats have become a threat to the banking industry, and resulting in the business attempting to implement artificial intelligence strategies while build resilient cyber-defense systems. This is done to ensure that unauthorized access, which leads to cyber-attacks, is severely limited. The credit trade is undergoing significant technical change. Because of this, crucial to comprehend implications a cyber threat, as well as how technologies implementation that is artificial intelligence will revolutionize entire sector. Paper aims at examining how AI affects cyber threat intelligence in the commerce subdivision. A graphical and qualitative analysis of available publications, primarily conference papers, was carried out. Despite being widely used in India and the United States, there are still few studies in the AI field. Furthermore, no study found that African banks used AI for cyber threat intelligence.
DifFuzzAR: automatic repair of timing side-channel vulnerabilities via refactoring

Abstract Vulnerability detection and repair is a demanding and expensive part of the software development process. As such, there has been an effort to develop new and better ways to automatically detect and repair vulnerabilities. DifFuzz is a state-of-the-art tool for automatic detection of timing side-channel vulnerabilities, a type of vulnerability that is particularly difficult to detect and correct. Despite recent progress made with tools such as DifFuzz, work on tools capable of automatically repairing timing side-channel vulnerabilities is scarce. In this paper, we propose DifFuzzAR, a tool for automatic repair of timing side-channel vulnerabilities in Java code. The tool works in conjunction with DifFuzz and it is able to repair 56% of the vulnerabilities identified in DifFuzz’s dataset. The results show that the tool can automatically correct timing side-channel vulnerabilities, being more effective with those that are control-flow based. In addition, the results of a user study show that users generally trust the refactorings produced by DifFuzzAR and that they see value in such a tool, in particular for more critical code.
CUSTOMIZING EXPERIENCES: EXPANDING THE BOUNDARIES BEYOND GAME’S MECHANICS

Since the advent of the internet the gaming market has undergone a significant transformation, embracing global publishing and sales, as well as the exponential growth of online gaming. However, the appeal of this market extends beyond the games themselves, as it offers users immersive experiences not only as players but also as creators and customizers of their in-game characters. This research paper centres around the hypothesis that games have the potential to foster interaction and provide experiences that go beyond their mechanics and design, leveraging the customization options available to players. To explore this concept, an online survey was conducted, supported by a theoretical framework drawing from the fields of design, media, and psychology. The survey aimed to investigate the prevalence of customization practices, examining how players acquire customizations for games and how these personalization enhance their overall gaming experience. The findings underscored the significance of customization as a prominent aspect of gaming and revealed that players derive enjoyment from engaging in this activity, considering it an integral part of their gaming journey. These findings suggest that customization can serve as a valuable tool to enhance players' sense of attachment to the game universe.
Effects of training on Libyan bank employees’ performance mediated by job satisfaction and motivation

The main objective of this research was to investigate the effects of training on employees’ job performance mediated by their organizational commitment in Libyan banks. A quantitative re-search design has been adopted to conduct this study by investigating 426 valid responses col-lected from face to face questionnaire survey. The respondents were selected from 45 branches of banks located in Tripoli of Libya using simple random sampling method. IBM SPSS (version 25) and SmartPLS (version 4) statistical programs have been used to analyze the data and vali-date the proposed hypotheses. This study finds that training has a positive and significant effect on employees’ organizational commitment, whereas it has no effect on employees’ job perfor-mance. Furthermore, employees’ organizational commitment has a full mediation effect be-tween training and employees’ job performance. The outcome from this research has contribut-ed to new practical insights. The study concludes that Libyan banks that are seeking to enhance their employees' performance through training practices should integrate to evaluate their job performance and specifically focus on their organizational commitment attributes.
Effects of training on Libyan bank employees’ performance mediated by organizational commitment

The main objective of this research was to investigate the effects of training on employees’ job performance mediated by their organizational commitment in Libyan banks. A quantitative re-search design has been adopted to conduct this study by investigating 427 valid responses col-lected from face to face questionnaire survey. The respondents were selected from 45 branches of banks located in Tripoli of Libya using simple random sampling method. IBM SPSS (version 25) and SmartPLS (version 4) statistical programs have been used to analyse the data and vali-date the proposed hypotheses. This study finds that training has a positive and significant effect on employees’ organizational commitment, whereas it has no effect on employees’ job perfor-mance. Furthermore, employees’ organizational commitment has a full mediation effect be-tween training and employees’ job performance. The outcome from this research has contribut-ed to new practical insights. The study concludes that Libyan banks that are seeking to enhance their employees' performance through training practices should integrate to evaluate their job performance and specifically focus on their organizational commitment attributes.
Nudge goes to Silicon Valley: designing for the disengaged and the irrational

An array of software apps, from fitness to finance, enrolls behavioral economics, and economists, in their product designs, value propositions, or else sales pitches, to make products more engaging and to afford users new capabilities in their daily lives.Drawing on 30 interviews with product strategists, designers, and user researchers who work on these self-styled 'behavior change apps,' this paper empirically studies the behavioral economic proposition and its operationalization in routine practices of software development and design.Setting aside the behavioral addiction and manipulation frame that critical work on app design typically summons, I approach behavioral economics applications as market work and tease out the different, co-existing logics of attachment between products and their users, that emerge from how market actors decide what product to build, which features to have and how to design the user experience.In doing so, I show that strictly focusing on the frequency of repeated interaction is also empirically inadequate.The product is rather strategized, developed, and designed to become something that the user 'cannot do without,' not because it is addictive, but because it is made indispensable to the distributed action universe of the behavioral problem that it addresses.
Performance Analysis of Endorsement in Hyperledger Fabric Concerning Endorsement Policies

As a typical representative of a permissioned blockchain system, Hyperledger Fabric has garnered substantial attention in recent years. Despite the application and promotion of Hyperledger Fabric in fields such as smart grid and smart healthcare, challenges persist with respect to its performance, especially its transaction latency. High latency always discourages Hyperledger Fabric from latency-sensitive applications. In this paper, we focus on the execution phase of the unique Execute-Order-Validate architecture of Hyperledger Fabric and conduct a comprehensive analysis of its endorsement policies. We summarize three theorems and give corresponding mathematical proofs based on the definition of the endorsement policy. To better analyze the generation of latency during the endorsement process, we further developed a theoretical model using queuing theory. Subsequently, we conducted multiple experiments on Hyperledger Fabric v2.0 for performance evaluation. With the experiment results, we discuss the reasons for the performance differences between different endorsement strategies and between equivalent logical expressions. Eventually, this paper offers some suggestions for endorsement policy selection which can provide a reference for the application of Hyperledger Fabric in the actual production environment.
Interpretable Multi-Channel Capsule Network for Human Motion Recognition

Recently, capsule networks have emerged as a novel neural network architecture for human motion recognition owing to their enhanced interpretability compared to traditional deep learning networks. However, the characteristic features of human motion are often distributed across distinct spatial dimensions and existing capsule networks struggle to independently extract and combine features across multiple spatial dimensions. In this paper, we propose a new multi-channel capsule network architecture that extracts feature capsules in different spatial dimensions, generates a multi-channel capsule chain with independent routing within each channel, and culminates in the aggregation of information from capsules in different channels to activate categories. The proposed structure endows the network with the capability to independently cluster interpretable features within different channels; aggregates features across channels during classification, thereby enhancing classification accuracy and robustness; and also presents the potential for mining interpretable primitives within individual channels. Experimental comparisons with several existing capsule network structures demonstrate the superior performance of the proposed architecture. Furthermore, in contrast to previous studies that vaguely discussed the interpretability of capsule networks, we include additional visual experiments that illustrate the interpretability of the proposed network structure in practical scenarios.
Human–Computer Interaction: Comparison of Different Contributions Based on Opinion and Survey

Human–computer interaction (HCI) has a long and contentious history of “technology.” The academic and consistent status of HCI is regularly called into question, resulting in hollow rhetoric. In the 1970s, it grew quickly from research laboratories, coinciding with the term of the Decennial conferences. Since its inception, there have been concerns regarding his standing as a normal academic object, on which many of them have concentrated their attention. In this paper, a comparative study was conducted between the main parameters of human–computer interaction, which are analytical and theoretical contribution. Three types of discoveries are described here: the first is conceptual, the second is operational, and the third is analytical. HCI is widely recognised as an interdisciplinary study topic. It takes years and years for new types of knowledge and information to arise, integrate, and stabilise in HCI. Several sorts of study findings have evolved over time, and the gaps in this research were identified using opinion datasets and survey contributions. This makes it easy to comprehend how much human–computer interaction is feasible in real time.
Quantum Reliability

Quantum technology has led to increasingly sophisticated and complex quantum devices. Assessing their reliability (quantum reliability) is an important issue. Although reliability theory for classical devices has been well developed in industry and technology, a suitable metric on quantum reliability and its loss has not been systematically investigated. Since reliability loss depends on the process, quantum fidelity does not always fully depict it. This study provides a metric of quantum reliability by shifting the focus from state distinguishing to trajectory distinguishing. In contrast to the conventional notion of classical reliability, which is evaluated using probabilistic measurements of binary logical variables, quantum reliability is grounded in the quantum probability amplitude or wave function. This research provides a universal framework for reliability theory encompassing both classical and quantum devices. It offers a new perspective on quantum engineering by elucidating how intensely the real quantum process that a device undergoes influences its performance.
A phase error compensation method for three-dimensional shape measurement using improved iteration algorithm

AbstractThe binary defocusing has been extensively studied in the three-dimensional measurement. But if the projector is slightly defocused, the binary fringes after defocusing still contain high-order harmonics compared with the ideal sinusoidal fringes, so the phase error caused by the nonlinear response is not negligible. In this paper, two models are proposed to calculate and compensate phase error, which include a double-precision iterative compensation model (DPICM) and a dual-domain iterative compensation model (DDICM). These two models obtain accurate phase errors by fusing the phases at different precision and domains. DPICM is composed of the double precision method and the improved iterative algorithm, and DDICM consists of the dual-domain method and the improved iterative algorithm. This improved iterative algorithm is used to compensate phase error, which can improve phase accuracy. DPICM and DDICM reduce the RMS error by 25.5% and 13.5% respectively.KEYWORDS: Three-dimensional shape measurementphase error compensationiteration algorithmphase shifting profilometry Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work is supported by International Cooperation Foundation of 10.13039/501100003807 Jilin Province (Grant Nos. 20210402074GH) and the 111 Project of China (Grant Nos. D21009 and D17017).
Framing Texts and Images: Critical and Posthumous Editions in the Digital Single Market

Abstract Although Art. 14 of the Directive on Copyright in the Digital Single Market (CDSM Directive) was introduced mostly to deal with issues connected with non-original photographs of public domain works, it also impacts the protection of posthumous and critical editions. Whenever a work of visual art that belongs in the public domain becomes the object of an edition, Art. 14 CDSM Directive excludes its protection under neighbouring rights. In the case of complex works, embedding original contributions belonging to different genres, Art. 14 CDSM Directive applies only if visual elements are predominant in the given work and as long as it is not possible to exploit the work of visual art without using other contributions embedded in the complex work. At the same time, Art. 14 CDSM Directive indirectly confirms that an edition may be protected by copyright if it is original. The particularly low threshold of originality required makes the presence of copyright quite common in three-dimensional and two-dimensional reproductions of works in the public domain that can qualify as editions. Then, with reference to textual works, the limits of originality are challenged: although there is little doubt that the person preparing a critical edition faces choices, they may not necessarily be free and creative. Editorial interference with the text can be compared to findings made by historians during their research and, as such, remain unprotected by copyright. Such an edition may still be protected within the regime of the neighbouring right in those few member states that decided to implement it. Eventually, as is the case nowadays with many aspects of human activity, the future of IP protection of various editions will become murkier due to the advent of new artificial intelligence technologies. For example, models are already being developed to automate the arduous and difficult process of transcription of manuscripts, as well as to ensure appropriate restoration of works of visual art. In particular, the further rapid development of artificial intelligence may cause the neighbouring rights in editions to become at least partially obsolete if the process of preparation of editions becomes devoid of human input. And this would have an impact on the copyright presence as well.
Multi-channel free space optical convolutions

Convolutional layers are a critical feature of modern neural networks and require significant com-putational resources. In response, optical accelerators have been developed as a low-energy, high-bandwidth approach for performing large-scale convolutions. We extend these methods to act on many input channels each with their own set of convolutional kernels. We simulate the performance of this system with ray-tracing and evaluate its performance.