Reviewerly/exHarmony huggingface dataset:
The Discrete Cosine Transform and Its Impact on Visual Compression: Fifty Years From Its Invention [Perspectives]

Compression is essential for efficient storage and transmission of signals. One powerful method for compression is through the application of orthogonal transforms, which convert a group of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${N}$</tex-math></inline-formula> data samples into a group of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${N}$</tex-math></inline-formula> transform coefficients. In transform coding, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${N}$</tex-math></inline-formula> samples are first transformed, and then the coefficients are individually quantized and entropy coded into binary bits. The transform serves two purposes: one is to compact the energy of the original <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${N}$</tex-math></inline-formula> samples into coefficients with increasingly smaller variances so that removing smaller coefficients have negligible reconstruction errors, and another is to decorrelate the original samples so that the coefficients can be quantized and entropy coded individually without losing compression performance. The Karhunen–Loève transform (KLT) is an optimal transform for a source signal with a stationary covariance matrix in the sense that it completely decorrelates the original samples, and that it maximizes energy compaction (i.e., it requires the fewest number of coefficients to reach a target reconstruction error). However, the KLT is signal dependent and cannot be computed with a fast algorithm.
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-Constrained Computing

Federated learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence, along with emerging security and privacy threats. Federated learning (FL) enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users’ sensitive data, the performance of the FL process can be threatened and reach a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of the FL process, the integration of blockchain for FL environments has drawn increasing attention from academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristics. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms suitable for the FL process and discuss their tradeoffs for a limited resource budget. Furthermore, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role in blocking those cyberattacks. To this end, we highlight some potential solutions for the coupling of blockchain and FL that can offer high levels of reliability, data privacy, and distributed computing performance.
Automatic prediction of developers’ resolutions for software merge conflicts

In collaborative software development, developers simultaneously work in parallel on different branches that they merge periodically. When edits from different branches textually overlap, conflicts may occur. Manually resolving conflicts can be tedious and error-prone. Researchers proposed tool support for conflict resolution, but these tools barely consider developers' preferences. Conflicts can be resolved by: keeping the local version only KL, keeping the remote version only (KR), or manually editing them (ME). Recent studies show that developers resolved the majority of textual conflicts by KL or KR. Thus, we created a machine learning-based approach RPredictor to predict developers' resolution strategy (KL, KR, or ME) given a merge conflict. We did large-scale experiments on the historical resolution of 74,861 conflicts. Our experiments show that RPredictor achieved 63% F-score for within-project prediction and 46% F-score for cross-project prediction. Compared with other classifiers, RPredictor provides the highest effectiveness when using a random forest (RF) classifier. Finally, we proposed a variant technique RPredictor v, which enables developers to customize its prediction conservativeness. For a highly conservative setting, RPredictor v achieved 34% effort saving while minimizing the risk of producing incorrect prediction labels.
Cyber-Resilience Evaluation Methods Focusing on Response Time to Cyber Infringement

Though multilevel, in-depth information protection systems are employed to defend against unknown cyber threats, vulnerabilities in these systems are frequently exploited by cyberattacks. As a result, it becomes challenging to comprehensively counter these attacks within a constrained time frame. When a cyberattack is detected, immediate measures are necessary to prevent widespread damage and maintain the system’s regular functioning. Possessing sustainable cyber-resilience capabilities, which can promptly restore the system to its pre-attack state, is crucial. In this paper, a cyber-defense activity optimization procedure is introduced, drawing on the failure recovery time of the information system, aiming to enhance both the response and recovery phases of cyber resilience. Through training, the response time for various types of cyberattack was determined. Notably, a decrease in response time by 17.8% compared to the baseline was observed. By optimizing response times and integrating them with sustainable cyber-resilience assessment activities, a robust framework is presented for evaluating an organization’s overall cyber-defense stance. Research on the cyber combat capability index, dissecting the response time for each distinct cyber-defense activity, is planned for future endeavors.
Optimal control of quantum state preparation and entanglement creation in two-qubit quantum system with bounded amplitude

Abstract We consider the optimal control problem in a two-qubit system with bounded amplitude. Two cases are studied: quantum state preparation and entanglement creation. Cost functions, fidelity and concurrence, are optimized over bang-off controls for various values of the total duration, respectively. For quantum state preparation problem, three critical time points are determined accurately, and optimal controls are estimated. A better estimation of the quantum speed limit is obtained, so is the time-optimal control. For entanglement creation problem, two critical time points are determined, one of them is the minimal time to achieve maximal entanglement (unit concurrence) starting from the product state. In addition, the comparisons between bang-off and chopped random basis (CRAB) are made.
Sfnet: Faster and Accurate Semantic Segmentation Via Semantic Flow

Abstract In this paper, we focus on exploring effective methods for faster and accurate semantic segmentation. A common practice to improve the performance is to attain high-resolution feature maps with strong semantic representation. Two strategies are widely used: atrous convolutions and feature pyramid fusion, while both are either computationally intensive or ineffective. Inspired by the Optical Flow for motion alignment between adjacent video frames, we propose a Flow Alignment Module (FAM) to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high-resolution features effectively and efficiently. Furthermore, integrating our FAM to a standard feature pyramid structure exhibits superior performance over other real-time methods, even on lightweight backbone networks, such as ResNet-18 and DFNet. Then to further speed up the inference procedure, we also present a novel Gated Dual Flow Alignment Module to directly align high-resolution feature maps and low-resolution feature maps where we term the improved version network as SFNet-Lite. Extensive experiments are conducted on several challenging datasets, where results show the effectiveness of both SFNet and SFNet-Lite. In particular, when using Cityscapes test set, the SFNet-Lite series achieve 80.1 mIoU while running at 60 FPS using ResNet-18 backbone and 78.8 mIoU while running at 120 FPS using STDC backbone on RTX-3090. Moreover, we unify four challenging driving datasets (i.e., Cityscapes, Mapillary, IDD, and BDD) into one large dataset, which we named Unified Driving Segmentation (UDS) dataset. It contains diverse domain and style information. We benchmark several representative works on UDS. Both SFNet and SFNet-Lite still achieve the best speed and accuracy trade-off on UDS, which serves as a strong baseline in such a challenging setting. The code and models are publicly available at https://github.com/lxtGH/SFSegNets .
Revisiting the Role of Institutional Structures in the Relationship Between Trade Openness and Poverty Reduction in Sub-Saharan Africa

ABSTRACTThe study investigates the conditioning effect of institutions in the relationship between trade openness and poverty reduction in Sub-Saharan African economies. Employing generalized method of moments, it was revealed that institutions influence the relationship between trade openness and poverty reduction in SSA. Thus, to reduce poverty in the region, SSA economies must first start by opening their economies to international trade. This could be achieved by reducing import and export taxes, focusing more on exporting value-added products and providing relevant training and information for importers and exporters. However, such attempts will be more fruitful in the presence of sound institutions.KEYWORDS: Sub–Saharan Africainstitutional structurespoverty reductiontrade openness Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data on the Human Development Index employed as a measure for poverty reduction is openly available at https://hdr.undp.org/en/content/human-development-index-hdi. Also, data on the institutional structures that support the study's findings are publicly available from the Worldwide Governance Indicators (WGI) at https://databank.worldbank.org/source/worldwide-governance-indicators. Again, data on trade openness, FDI, GDP per capita, inflation, and unemployment that support the findings of this study are openly available from the World Development Indicators (WDI) at https://databank.worldbank.org/source/world-development-indicators. Data on the Financial Development Index is available openly at https://data.imf.org/?sk=F8032E80-B36C-43B1-AC26-493C5B1CD33B. Finally, data on trade freedom is openly available at https://www.heritage.org/index.
Tourists’ Value Co-Creation With Service Robots: Antecedents and Mechanisms

Recent research on the use of robotics in hospitality and tourism has called attention to tourists’ post-adoption behavior, including value co-creation. Guided by the stimulus–organism–response (SOR) framework, a mixed-method study was conducted to explore the antecedents and mechanisms of tourists’ value co-creation behavior with robots. Qualitative interviews revealed robots’ anthropomorphism and economic rewards as tourists’ key concerns regarding robot-provided services. Subsequently, partial least squares structural equation modeling was employed to investigate how robot service attributes influenced tourist–robot co-creation, including participation and citizenship behaviors, and enhancing tourists’ experiences at tourist attractions. Findings indicated that three types of perceived experiential value fully mediated the relationships between robots’ degree of anthropomorphism and tourists’ value co-creation behavior, as well as between economic rewards and participation behavior. However, the relationship between economic rewards and citizenship behavior was partially mediated. Theoretical and practical implications are discussed.
On the Structure of Quantum Markov Chains on Cayley Trees Associated with Open Quantum Random Walks

Quantum Markov chains (QMCs) and open quantum random walks (OQRWs) represent different quantum extensions of the classical Markov chain framework. QMCs stand as a more profound layer within the realm of Markovian dynamics. The exploration of both QMCs and OQRWs has been a predominant focus over the past decade. Recently, a significant connection between QMCs and OQRWs has been forged, yielding valuable applications. This bridge is particularly impactful when studying QMCs on tree structures, where it intersects with the realm of phase transitions in models naturally arising from quantum statistical mechanics. Furthermore, it aids in elucidating statistical properties, such as recurrence and clustering. The objective of this paper centers around delving into the intricate structure of QMCs on Cayley trees in relation to OQRWs. The foundational elements of this class of QMCs are built upon using classical probability measures that encompass the hierarchical nature of Cayley trees. This exploration unveils the pivotal role that the dynamics of OQRWs play in shaping the behavior of the Markov chains under consideration. Moreover, the analysis extends to their classical counterparts. The findings are further underscored by the examination of notable examples, contributing to a comprehensive understanding of the outcomes.
Rise and fall of entanglement between two qubits in a non-Markovian bath

We analyze the dynamics of quantum correlations between two qubits coupled to a linear chain of oscillators. The chain mediates interactions between the qubits and acts as a non-Markovian reservoir. The model is amenable to an analytical solution when the initial state of the chain is Gaussian. We study the dynamics of the qubits' concurrence starting from a separable state and assuming that the chain spectrum is gapped and the chain is initially in a thermal state. We identify three relevant regimes that depend on the strength of the qubit-chain coupling in relation to the spectral gap. These are (i) the weak-coupling regime, where the qubits are entangled at the asymptotics, (ii) the strong-coupling regime, where the concurrence can exhibit collapses followed by revivals with exponentially attenuated amplitude, and (iii) the thermal damping regime, where the concurrence rapidly vanishes due to the chain's thermal excitations. In all cases, if entanglement is generated, this occurs after a finite time has elapsed. This time scale depends exponentially on the qubits' distance and is determined by the spectral properties of the chain. Entanglement irreversible decay, on the other hand, is due to the dissipative effect induced by the coupling with the chain and is controlled by the coupling strength between the chain and qubits. This study unravels the basic mechanisms leading to entanglement in a non-Markovian bath and allows one to identify the key resources for realizing quantum coherent dynamics of open systems.
Best practices for sign language technology research

Abstract Research on sign language technology (SLT) has steadily increased in recent decades, and yet, common mistakes and pitfalls have significantly hindered progress in the field. The purpose of this paper is to examine some of the most prominent issues and suggest practical steps to overcome them, outlining the best practices to consider when conducting SLT research. These practices cluster around the five following issues: (1) knowledge of the specific sign language at the centre of the research and of sign languages more generally; (2) involving deaf people at the centre of research, including researchers who are themselves deaf; (3) motivations of the researcher and the relationship to the views of the sign language community; (4) what sign language data needs to be considered; (5) full recognition of the challenges posed by such research.
Organizational Transformation Projects

The implementation of organizational transformation projects (simply project) in the contexts of complex and dynamic businesses (or other application domains), need an optimal transformation framework and a holistic RoGCSC. The RoGCSC uses measurable cybersecurity and governance security risk (secRisk) critical factors, which are mitigated and tuned, to ensure project's successful evolution and predict/block cyber (or classical) crimes/misdeeds. The actual exponential rise of cybercrimes has become a major concern for countries, enterprises, and citizens; and that obliges projects to integrate polymathic-holistic security strategies. Actual cyberspace's resilience, control, and security concepts are siloed, insufficient, chaotic, and concentrate only on platforms' infrastructural aspects. Actual concepts focus on isolated hackers, where financial predators are the ones behind major cybercrimes. Global cybercrimes are closely related to global events and phenomena, like financial greediness, insecurity, conflicts, terrorism, pandemics, and societal crisis.
A Password-Based Mutual Authentication Protocol via Zero-Knowledge Proof Solution

Password-based authentication is the most common strategy despite it having considerable problems. Password can be stealthily observed, its management is costly where users have to change their passwords regularly. With the appearance of Quantum Computing, the classic authentication models are threatened to be hacked. In this work, we propose a secure authentication scheme based on the zero-knowledge model. In the proposed scheme, the verifier generates random numbers $$r_1$$ and $$r_2$$ and mixes them with the stored password; after extracting $$r_1$$ , the user proves the possession of its password using $$r_1$$ and another random number $$r_3$$ . Therefore, both the prover and verifier authenticate each other with only a single message, which provides a lightweight protocol with robust resistance against known attacks. The proposal can resist quantum computer attacks if we use a post-quantum key-establishment algorithm to exchange the password for one-time.
Privacy-preserving multi-party deep learning based on homomorphic proxy re-encryption

Deep learning is an important branch of artificial intelligence. However, the first task of deep learning is to collect data, which will seriously threaten user data privacy. The existing dual server privacy-preserving deep learning schemes operate under the assumption that two semi-honest servers will not collude, but this security assumption may be too robust. This paper proposes a privacy-preserving multiparty deep learning scheme based on a homomorphic proxy re-encryption scheme, which can resist collusion between the semi-honest servers. Introducing a fog node with high response and low latency characteristics as a proxy and leveraging a one-way homomorphic proxy re-encryption scheme to convert the user-end ciphertext to server-end ciphertext reduces the possibility of privacy leakage due to cooperation between two servers or participants and servers. To avoid a rise in the rounds of interaction caused by the increase in the number of participants, a multi-party random numbers aggregation method is proposed based on verifiable secret sharing. Ensuring the sensitive data remains undisclosed while enhancing the global model's precision. Theoretical analysis and experimental evaluation have both demonstrated that this privacy-preserving deep learning scheme is equipped to handle multiple keys, resist collusion attacks, and achieve higher accuracy.
Analysis of Unsupervised Consumption Anomaly Detection in Sports Facilities using Artificial Intelligence-Based Data Analytics: A Case Study

Sports facilities have exceptionally high energy demand due to the extensive operational requirements and high-occupancy seasonal rates. Towards promoting efficient energy usage and minimal losses, consumption anomaly detection in sports facilities is addressed in this work using Artificial intelligence (AI)-based analytics approaches. Traditional AI-based data analytics approaches are applied in a practical context for a local sports complex. The actual unlabeled operation data of the facility are used and a case-specific comparative analysis of the various approaches is presented where AI-based data labeling is used. The characteristics of the different algorithms are contextually discussed. It was found that the size and distribution of the training datasets influence the performance of the different algorithms. This study represents preliminary findings on the topic with a promising potential for further research.
Automated Reverse Engineering of the Technology-Induced Software System Structure

Evolving system architectures can be complex and difficult to understand, leading to problems such as poor maintainability. Automated reverse engineering of system structure models from source code can mitigate these problems and facilitate architectural decisions. However, identifying components with their interfaces can be challenging because components are often implemented in different frameworks and interconnected in complex ways. Our approach aims to create software models using reusable concept descriptions for reverse engineering. We use structural-level mapping rules to reconstruct the static system structure from source code, assuming that the technology used can determine the components with their interfaces and deployment. We evaluate our approach on four public reference systems. The analyses show that technology-specific rules already provide good results, but the addition of project-specific rules further improves reverse engineering.
ZeroLess-DARTS: Improved Differentiable Architecture Search with Refined Search Operation and Early Stopping

Differentiable architecture search (DARTS) method has gained noticeable popularity in neural architecture search (NAS) domain as it reduces the required search time compared to reinforcement learning and evolutionary based NAS algorithms. However, some further studies have indicated that the search algorithm of DARTS may be suboptimal, and its performance may deteriorate over the search process. In this paper, we provided a detailed performance analysis of the DARTS search algorithm (on the CIFAR10 image classification task) from different aspects such as changes in accuracies of derived architectures at each search epoch, the trend of changes in strengths of different operations over successive epochs, and the number of skip connections per normal cells. We propose ZeroLess-DARTS that considerably improves original DARTS performance on the CIFAR10 dataset (Cohen's D = 2.213), by refining the operation space in the search procedure and introducing an early stopping criterion. We show that our approach is generalizable to time series classification tasks by evaluating the performance of our model on one-dimensional ECG signals for WCT (Wide Complex Tachycardia) classification (Cohen's D = 1.706).
Data protection beyond data rights: governing data production through collective intermediaries

This paper explores how four commonly proposed collective data intermediaries – data trusts, decentralised autonomous organisations, data cooperatives and data unions – have been envisioned and enacted by their proponents.