Reviewerly/exHarmony huggingface dataset:
Digital Transformation 4.0: Integration of Artificial Intelligence &amp; Metaverse in Marketing

The main objective of digitalizing the fashion industry was to attain sustainability through the use of numerous digital tools while streamlining operations, production, and business of tangible products for the real world. But the recent creation of the metaverse, a parallel virtual reality universe, has given rise to new possibilities for digital clothing. The integration of artificial intelligence and the metaverse, as well as the analysis of vast volumes of data to uncover important information, are all included in the notion of digitalization. In the fashion industry, artificial intelligence is increasingly being used to forecast trends by analysing consumer behaviour, preferences, and emotions. Over the years, the concept of the metaverse and artificial intelligence has been the subject of academic studies in a variety of fields ranging from literature to art, music to education. AI and the metaverse are two of the most prominent technologies of the twenty-first century. Each can improve people's lives in a variety of ways, as well as many industries and working processes. AI and the metaverse can be used in a variety of industries, including operations, fashion, management, marketing, and education. The purpose of this paper is to investigate the impact of integrating artificial intelligence and the metaverse in the fashion industry, specifically in the marketing sector. The method used in this paper is a descriptive literature review delivered by reviewing as many journals as possible from 2014 to 2023. The results of this research provide both researchers and technology developers with brief information about the research area. Furthermore, recommendations for future research on technology integration have been made.
NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation

With the progress of 3D human pose and shape estimation, state-of-the-art methods can either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases. However, they cannot obtain robustness and mesh-image alignment at the same time. In this work, we present NIKI (Neural Inverse Kinematics with Invertible Neural Network), which models bidirectional errors to improve the robustness to occlusions and obtain pixel-aligned accuracy. NIKI can learn from both the forward and inverse processes with invertible networks. In the inverse process, the model separates the error from the plausible 3D pose manifold for a robust 3D human pose estimation. In the forward process, we enforce the zero-error boundary conditions to improve the sensitivity to reliable joint positions for better mesh-image alignment. Furthermore, NIKI emulates the analytical inverse kinematics algorithms with the twist-and-swing decomposition for better interpretability. Experiments on standard and occlusion-specific benchmarks demonstrate the effectiveness of NIKI, where we exhibit robust and well-aligned results simultaneously. Code is available at https://github.com/Jeff-sjtu/NIKI.
Role of Corporate Social Responsibility in Corporate Reputation via Organizational Trust and Commitment

Purpose: The conceptual framework based on a comprehensive literature review hypothesized that the perceived CSR of an organization may lead to the development of trust and commitment among the employees, which in turn may lead to the building of the corporate reputation of the organization. Along with that, the moderating effects of HRM practices and organizational justice have also been investigated in the given relationship of corporate social responsibility (CSR) with its given mediators i.e., “organizational trust” and “organizational commitment”. Methodology: The target population of the study was comprised of the management and teaching faculty of educational institutions. The random sampling technique was employed to carry out an empirical study of 380 samples of employees. The data collected were analyzed by using the Smart PLS 3 software. The model was tested, and all the hypotheses were accepted. Findings: A positive relationship has been observed between CSR investments and corporate reputation. Furthermore, empirical results show that the employee’s commitment and the level of trust towards the organization serve as partial mediators between CSR practices and corporate reputation. The results showed that all of the hypotheses were accepted. Conclusion: This study is intuitive and empirically substantiates the selection of organizational justice and HRM practices as moderating variables between the observed CSR activities and its given mediators.
Solving Peg Solitaire with Bidirectional BFIDA*

We present a novel approach to bidirectional breadth-first IDA* (BFIDA*) and demonstrate its effectiveness in the domain of peg solitaire, a simple puzzle. Our approach improves upon unidirectional BFIDA* by usually avoiding the last iteration of search entirely, greatly speeding up search. In addition, we provide a number of improvements specific to peg solitaire. We have improved duplicate-detection in the context of BFIDA*. We have strengthened the heuristic used in the previous state-of-the-art solver. Finally, we use bidirectional search frontiers to provide a stronger technique for pruning unsolvable states. The combination of these approaches allows us to improve over the previous state-of-the-art, often by a two-orders-of-magnitude reduction in search time.
A Hierarchical Approach to Generating Maps Using Markov Chains

In this paper we describe a hierarchical method for procedurallygenerating maps using Markov chains. Ourmethod takes as input a collection of human-authoredtwo-dimensional maps, and splits them into high-leveltiles which capture large structures. Markov chains arethen learned from those maps to capture the structure ofboth the high-level tiles, as well as the low-level tiles.Then, the learned Markov chains are used to generatenew maps by first generating the high-level structure ofthe map using high-level tiles, and then generating thelow-level layout of the map. We validate our approachusing the game Super Mario Bros., by evaluating thequality of maps produced using different configurationsfor training and generation.
Reversible denoising and lifting based color component transformation for lossless image compression

Abstract An undesirable side effect of reversible color space transformation, which consists of lifting steps (LSs), is that while removing correlation it contaminates transformed components with noise from other components. Noise affects particularly adversely the compression ratios of lossless compression algorithms. To remove correlation without increasing noise, a reversible denoising and lifting step (RDLS) was proposed that integrates denoising filters into LS. Applying RDLS to color space transformation results in a new image component transformation that is perfectly reversible despite involving the inherently irreversible denoising; the first application of such a transformation is presented in this paper. For the JPEG-LS, JPEG 2000, and JPEG XR standard algorithms in lossless mode, the application of RDLS to the RDgDb color space transformation with simple denoising filters is especially effective for images in the native optical resolution of acquisition devices. It results in improving compression ratios of all those images in cases when unmodified color space transformation either improves or worsens ratios compared with the untransformed image. The average improvement is 5.0–6.0% for two out of the three sets of such images, whereas average ratios of images from standard test-sets are improved by up to 2.2%. For the efficient image-adaptive determination of filters for RDLS, a couple of fast entropy-based estimators of compression effects that may be used independently of the actual compression algorithm are investigated and an immediate filter selection method based on the detector precision characteristic model driven by image acquisition parameters is introduced.
Knowledge Management in the Design of Safety-Critical Systems

Knowledge management in the design of safety-critical systems addresses the question of how designers can share, capitalize and re-use knowledge in an effective and reliable way. Knowledge management is situated in groups, organizations and communities, playing different roles in the design process. Design of safety-critical systems has specific properties, such as dealing with complexity, traceability, maturity of knowledge, interaction between experts, awareness of the status of information, and trust in knowledge. Documentation is of crucial importance in design processes, ensuring that these properties are taken care of in a proper and reliable way. However, writing is not an easy task for engineers and support is needed. Several knowledge management solutions, both tools and organizational set-ups, are available to support design work, such as active notification of changes, personal and team work-spaces, active design documents and knowledge portal solutions.
Performance Analysis of Planning Portfolios

In recent years the concept of sequential portfolio has become an important topic to improve the performance of modern problem solvers, such as SAT engines or planners. The PbP planner and more recently Fast Downward Stone Soup are successful approaches in Automated Planning that follow this trend. However, neither a theoretical analysis nor formal definitions about sequential portfolios have been described. In this paper, we focus on studying how to evaluate the performance of planners defining a baseline for a set of problems. We present a general method based on Mixed-Integer Programming to define the baseline for a training data set. In addition to prior work, we also introduce a short empirical analysis of the utility of training problems to configure sequential portfolios.
Supporting User Requirements and Preferences in Cloud Plan Selection

With the cloud emerging as a successful paradigm for conveniently storing, accessing, processing, and sharing information, the cloud market has seen an incredible growth.An ever-increasing number of providers offer today several cloud plans, with different guarantees in terms of service properties such as performance, cost, or security.While such a variety naturally corresponds to a diversified user demand, it is far from trivial for users to identify the cloud providers and plans that better suit their specific needs.In this paper, we address the problem of supporting users in cloud plan selection.We characterize different kinds of requirements that may need to be supported in cloud plan selection and introduce a very simple and intuitive, yet expressive, language that captures different requirements as well as preferences users may wish to express.The corresponding formal modeling permits to reason on requirements satisfaction to identify plans that meet the constraints imposed by requirements, and to produce a preference-based ranking among such plans.
A Convex Approach to Superresolution and Regularization of Lines in Images

We present a new convex formulation for the problem of recovering lines in degraded images.Following the recent paradigm of super-resolution, we formulate a dedicated atomic norm penalty and we solve this optimization problem by means of a primal-dual algorithm.This parsimonious model enables the reconstruction of lines from lowpass measurements, even in presence of a large amount of noise or blur.Furthermore, a Prony method performed on rows and columns of the restored image, provides a spectral estimation of the line parameters, with subpixel accuracy.
Survey of Websites and Web Application Security Threats Using Vulnerability Assessment

Nowadays information has become anasset to many institutions and as a result these institutions have become targets for people with malicious intents to attack these institutions.The web is now an important means of transacting business and without security, websites cannot thrive in today's complex computer ecosystem as there are new threats emerging as old ones are being tackled.Vulnerability assessment of websites is one of the means by which security can be improved on websites.This research seek to study and use vulnerability assessment as a tool to improve security by identifying vulnerabilities and proposing solutions to solve the security issues.Assessment was done on 5 web hosts belonging to different institutions in Ghana.Nmap, Nikto and Nessus were the tools used for the assessment, the assessment was carried out in four stages, and the first stage in the assessment was planning which involved activities and configurations performed before the actual assessment.The second stage was information gathering which involved obtaining information about the targets necessary to help identify vulnerabilities.This was followed by vulnerability scanning to identify vulnerabilities on the target hosts.The results indicated all the five hosts had security flaws which needed to be addressed.In all 16 vulnerabilities were identified on host 1, 8 vulnerabilities were identified on host 2, 15 vulnerabilities on host 3, 4 vulnerabilities on host 4 and 10 vulnerabilities on host 5.After the vulnerabilities were identified, a solution was proposed to mitigate the security flaws identified.
Towards a Taxonomy of Musical Metacreation: Reflections on the First Musical Metacreation Weekend

The Musical Metacreation Weekend (MuMeWe), a series of four concerts presenting works of metacreation, was held in June 2013. These concerts offered an opportunity to review how different composers and system designers are approaching generative practices. We propose a taxonomy of musical metacreation, with specific reference to works and systems presented at MuMeWe, in an effort to begin a discussion of methods of measuring metacreative musical works. The seven stages of this taxonomy are loosely based upon the autonomy of the system, specifically in its ability to make high-level musical decisions during the course of the composition and/or performance. We conclude with a discussion of how these levels could be interpreted, and the potential difficulties involved in their specification and terminology.
Quantum coherence and path-distinguishability of two entangled particles

An interference experiment with entangled particles is theoretically analyzed, where one of the entangled pair (particle 1) goes through a multi-slit before being detected at a fixed detector. In addition, one introduces a mechanism for finding out which of the n slits did particle 1 go through. The other particle of the entangled pair (particle 2) goes in a different direction, and is detected at a variable, spatially separated location. In coincident counting, particle 2 shows n-slit interference. It is shown that the normalized quantum coherence of particle 2, $\mathcal{C}_2$, and the path-distinguishability of particle 1, $\mathcal{D}_{Q1}$, are bounded by an inequality $\mathcal{D}_{Q1} + \mathcal{C}_2 \le 1$. This is a kind of {\em nonlocal} duality relation, which connects the path distinguishability of one particle to the quantum coherence of the other.
PRACTICA. A Virtual Reality Platform for Specialized Training Oriented to Improve the Productivity

With the proliferation of Virtual reality headset that are emerging into a consumer-oriented market for video games, it will open new possibilities for exploiting the virtual reality (VR).Therefore, the PRACTICA project is defined as a new service aimed to offering a system for creating courses based on a VR simulator for specialized training companies that allows offering to the students an experience close to reality.The general problem of creating these virtual courses derives from the need to have programmers that can generate them.Therefore, the PRACTICA project allows the creation of courses without the need to program source code.In addition, elements of virtual interaction have been incorporated that cannot be used in a real environment due to risks for the staff, such as the introduction of fictional characters or obstacles that interact with the environment.So to do this, artificial intelligence techniques have been incorporated so these elements can interact with the user, as it may be, the movement of these fictional characters on stage with a certain behavior.This feature offers the opportunity to create situations and scenarios that are even more complex and realistic.This project aims to create a service to bring virtual reality technologies closer and artificial intelligence for non-technological companies, so that they can generate (or acquire) their own content and give it the desired shape for their purposes.
Towards a Semantic Measure of the Execution Time in Call-by-Value lambda-Calculus

We investigate the possibility of a semantic account of the execution time (i.e. the number of β v -steps leading to the normal form, if any) for the shuffling calculus, an extension of Plotkin's call-by-value λ -calculus.For this purpose, we use a linear logic based denotational model that can be seen as a nonidempotent intersection type system: relational semantics.Our investigation is inspired by similar ones for linear logic proof-nets and untyped call-by-name λ -calculus.We first prove a qualitative result: a (possibly open) term is normalizable for weak reduction (which does not reduce under abstractions) if and only if its interpretation is not empty.We then show that the size of type derivations can be used to measure the execution time.Finally, we show that, differently from the case of linear logic and call-by-name λ -calculus, the quantitative information enclosed in type derivations does not lift to types (i.e. to the interpretation of terms).To get a truly semantic measure of execution time in a call-by-value setting, we conjecture that a refinement of its syntax and operational semantics is needed.
A Robust Multi-Circle Detector Based on Horizontal and Vertical Search Analysis Fitting with Tangent Direction

In this paper, we propose an efficient Multi-Circle detector which follows the fixed search order. The method makes use of horizontal and vertical search to realize circle detection, which is named as HVCD. First, this method computes edge areas in a given image. The edge areas could be divided into some regions by means of region growing. Each of regions could be efficiently searched to achieve not only one-pixel wide edges but edge segments as well. Next, the candidate circles can be extracted from every edge segment. Finally, the circle candidates could be validated with the help of Helmholtz principle. Experimental results demonstrate that HVCD could effectively detect circles on synthetic and natural images on the one hand; on the other hand, HVCD here could solve the weakness in the process of circle Hough transform implementation and EDcircles implementation.
Benchmarking semantic reasoning on mobile platforms: Towards optimization using OWL2 RL

Mobile hardware has advanced to a point where apps may consume the Semantic Web of Data, as exemplified in domains such as mobile context-awareness, m-Health, m-Tourism and augmented reality. However, recent work shows that the performance of ontology-based reasoning, an essential Semantic Web buil ding block, still leaves much to be desired on mobile platforms. This presents a clear need to provide developers with the ability to benchmark mobile reasoning performance, based on their particular application scenarios, i.e., including reasoning tasks, process flows and datasets, to establish the feasibility of mobile deployment. In this regard, we present a mobile benchmark framework called MobiBench to help developers to benchmark semantic reasoners on mobile platforms. To realize efficient mobile, ontology-based reasoning, OWL2 RL is a promising solution since it (a) trades expressivity for scalability, which is important on resource-constrained platforms; and (b) provides unique opportunities for optimization due to its rule-based axiomatization. In this vein, we propose selections of OWL2 RL rule subsets for optimization purposes, based on several orthogonal dimensions. We extended MobiBench to support OWL2 RL and the proposed ruleset selections, and benchmarked multiple OWL2 RL-enabled rule engines and OWL reasoners on a mobile platform. Our results show significant performance improvements by applying OWL2 RL rule subsets, allowing performant reasoning for small datasets on mobile systems.
Integrated Analysis of Performance and Resources in Large-Scale Quantum Computing

To see the feasibility of a large-scale quantum computing, it is required to accurately analyze the performance and the quantum resource. However, most of the analysis reported so far have focused on the statistical examination, i.e., simply calculating the performance and resource based on individual data, and even worse usually only a few components have been considered. In this work, to achieve more exact analysis, we propose an integrated analysis method for a practical quantum computing model with three components (\textit{algorithm}, \textit{error correction} and \textit{device}) under a realistic quantum computer system architecture. To implement the above method, we develop a quantum computing framework composed of three functional layers: compile, system and building block. This framework can support, for the first time, the mapping of quantum algorithm from physical qubit level to system architecture level with a given fault-tolerant scheme. Therefore, the proposed method can measure the effect of dynamic situation when the quantum computer practically runs. By using our method, we found that Shor algorithm to factorize 512-bit integer requires $8.78\times 10^ 5$ hours. We also show how the proposed method can be used for analyzing optimal concatenation level and code distance of fault-tolerant quantum computing.
Similarity Domains Machine for Scale-Invariant and Sparse Shape Modeling

We present an approach to extend the functionality and the use of kernel machines in image processing applications. We introduce a novel way to design spatial kernel machines with spatial properties and demonstrate how those newly introduced spatial properties enhance the possibilities of the use of kernel machines in image processing applications as a proof of concept. In this paper, we demonstrate four particular extensions: 1) how to model shapes efficiently with spatially computed kernel parameters in a geometrically scalable way; 2) how to visualize the kernel parameters precisely and intuitively on binary 2D shapes; 3) how to construct a one-class classifier from the binary classifier in a straightforward manner without re-training; and 4) how to use the computed kernel parameters for filtering. The existing literature on kernel machines mostly focuses on estimating the optimal kernel parameters via additional cost function(s). In this paper, instead of employing an additional cost function to estimate the kernel-related parameters, we investigate on an analytical solution to predict the actual kernel parameters locally and show how to build a spatial kernel machine with our analytical approach. Classical kernel machines do not perform well on precise shape modeling with a low number of support vectors as demonstrated in this paper. However, we demonstrate and visualize that our analytical approach provides a natural means to relate the kernel parameters to the 2D shapes for sparse shape modeling, where the shape boundary represents the decision boundary. For that, we incorporate the selected kernel function's geometric properties as an additional constraint into the classifier's optimization problem by defining an easy-to-explain and intuitive concept: similarity domains. In our experiments, we study and demonstrate how the resulting new kernel machine enhances the capabilities of the classical kernel machines with applications on shape modeling, (geometrically) scaling the non-linear decision boundary at various scales and precise visualization of the kernel parameters in 2D images.