Reviewerly/exHarmony huggingface dataset:
Unsupervised feature selection based on variance–covariance subspace distance

Subspace distance is an invaluable tool exploited in a wide range of feature selection methods. The power of subspace distance is that it can identify a representative subspace, including a group of features that can efficiently approximate the space of original features. On the other hand, employing intrinsic statistical information of data can play a significant role in a feature selection process. Nevertheless, most of the existing feature selection methods founded on the subspace distance are limited in properly fulfilling this objective. To pursue this void, we propose a framework that takes a subspace distance into account which is called "Variance-Covariance subspace distance". The approach gains advantages from the correlation of information included in the features of data, thus determines all the feature subsets whose corresponding Variance-Covariance matrix has the minimum norm property. Consequently, a novel, yet efficient unsupervised feature selection framework is introduced based on the Variance-Covariance distance to handle both the dimensionality reduction and subspace learning tasks. The proposed framework has the ability to exclude those features that have the least variance from the original feature set. Moreover, an efficient update algorithm is provided along with its associated convergence analysis to solve the optimization side of the proposed approach. An extensive number of experiments on nine benchmark datasets are also conducted to assess the performance of our method from which the results demonstrate its superiority over a variety of state-of-the-art unsupervised feature selection methods. The source code is available at https://github.com/SaeedKarami/VCSDFS.
<scp>AI</scp> and formative assessment: The train has left the station

Abstract In response to Li, Reigh, He, and Miller's commentary, Can we and should we use artificial intelligence for formative assessment in science , we argue that artificial intelligence (AI) is already being widely employed in formative assessment across various educational contexts. While agreeing with Li et al.'s call for further studies on equity issues related to AI, we emphasize the need for science educators to adapt to the AI revolution that has outpaced the research community. We challenge the somewhat restrictive view of formative assessment presented by Li et al., highlighting the significant contributions of AI in providing formative feedback to students, assisting teachers in assessment practices, and aiding in instructional decisions. We contend that AI‐generated scores should not be equated with the entirety of formative assessment practice; no single assessment tool can capture all aspects of student thinking and backgrounds. We address concerns raised by Li et al. regarding AI bias and emphasize the importance of empirical testing and evidence‐based arguments in referring to bias. We assert that AI‐based formative assessment does not necessarily lead to inequity and can, in fact, contribute to more equitable educational experiences. Furthermore, we discuss how AI can facilitate the diversification of representational modalities in assessment practices and highlight the potential benefits of AI in saving teachers’ time and providing them with valuable assessment information. We call for a shift in perspective, from viewing AI as a problem to be solved to recognizing its potential as a collaborative tool in education. We emphasize the need for future research to focus on the effective integration of AI in classrooms, teacher education, and the development of AI systems that can adapt to diverse teaching and learning contexts. We conclude by underlining the importance of addressing AI bias, understanding its implications, and developing guidelines for best practices in AI‐based formative assessment.
High-On-Off-Ratio Beam-Splitter Interaction for Gates on Bosonically Encoded Qubits

A SNAIL-based coupler is used to devise a programmable beam splitter interaction between two bosonic modes with over an octave separation in frequency; the method is further applied as a primitive for constructing two-qubit bosonic gates.
Unlocking the Power of Digital Commons: Data Cooperatives as a Pathway for Data Sovereign, Innovative and Equitable Digital Communities

Network effects, economies of scale, and lock-in-effects increasingly lead to a concentration of digital resources and capabilities, hindering the free and equitable development of digital entrepreneurship, new skills, and jobs, especially in small communities and their small and medium-sized enterprises (“SMEs”). To ensure the affordability and accessibility of technologies, promote digital entrepreneurship and community well-being, and protect digital rights, we propose data cooperatives as a vehicle for secure, trusted, and sovereign data exchange. In post-pandemic times, community/SME-led cooperatives can play a vital role by ensuring that supply chains to support digital commons are uninterrupted, resilient, and decentralized. Digital commons and data sovereignty provide communities with affordable and easy access to information and the ability to collectively negotiate data-related decisions. Moreover, cooperative commons (a) provide access to the infrastructure that underpins the modern economy, (b) preserve property rights, and (c) ensure that privatization and monopolization do not further erode self-determination, especially in a world increasingly mediated by AI. Thus, governance plays a significant role in accelerating communities’/SMEs’ digital transformation and addressing their challenges. Cooperatives thrive on digital governance and standards such as open trusted application programming interfaces (“APIs”) that increase the efficiency, technological capabilities, and capacities of participants and, most importantly, integrate, enable, and accelerate the digital transformation of SMEs in the overall process. This review article analyses an array of transformative use cases that underline the potential of cooperative data governance. These case studies exemplify how data and platform cooperatives, through their innovative value creation mechanisms, can elevate digital commons and value chains to a new dimension of collaboration, thereby addressing pressing societal issues. Guided by our research aim, we propose a policy framework that supports the practical implementation of digital federation platforms and data cooperatives. This policy blueprint intends to facilitate sustainable development in both the Global South and North, fostering equitable and inclusive data governance strategies.
SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking

Vision-based object tracking has boosted extensive autonomous applications for unmanned aerial vehicles (UAVs). However, the dynamic changes in flight maneuver and viewpoint encountered in UAV tracking pose significant difficulties, e.g., aspect ratio change, and scale variation. The conventional cross-correlation operation, while commonly used, has limitations in effectively capturing perceptual similarity and incorporates extraneous background information. To mitigate these limitations, this work presents a novel saliency-guided dynamic vision Transformer (SGDViT) for UAV tracking. The proposed method designs a new task-specific object saliency mining network to refine the cross-correlation operation and effectively discriminate foreground and background information. Additionally, a saliency adaptation embedding operation dynamically generates tokens based on initial saliency, thereby reducing the computational complexity of the Transformer architecture. Finally, a lightweight saliency filtering Transformer further refines saliency information and increases the focus on appearance information. The efficacy and robustness of the proposed approach have been thoroughly assessed through experiments on three widely-used UAV tracking benchmarks and real-world scenarios, with results demonstrating its superiority. The source code and demo videos are available at https://github.com/vision4robotics/SGDViT.
A Novel Approach for Anomaly Detection in Time-Series Data using Generative Adversarial Networks

Data anomalies are found using anomaly detection. Generative adversarial networks (GANs) can produce synthetic data and learn complex patterns. Temporal dependencies and relevant features are needed to identify timeseries anomalies. A GAN is trained to detect anomalies in timeseries data. The model performs better with synthetic data. Traditional ML models struggle with the difficulties of anomaly identification in time-series data due to issues such as dealing with high dimensionality, capturing temporal correlations, and detecting infrequent events with skewed class distributions. This study tested machine learning techniques for anomaly identification in time-series data using the Yahoo! Webscope S5 dataset. In terms of F1 score (0.912), precision (0.S99), recall (0.925), AUC-ROC (0.976), TP rate (0.925), and FP rate (0.045), the suggested approach beat the baseline methods. The suggested method uses GANs to generate synthetic data for anomaly identification in time-series data.
Revealing the Barriers of Blockchain Technology for Supply Chain Transparency and Sustainability in the Construction Industry: An Application of Pythagorean FAHP Methods

Blockchain technology has emerged as a promising solution to enhance supply chain transparency and sustainability in the construction industry. However, the widespread adoption of blockchain faces several barriers that need to be identified and understood. The construction industry faces significant challenges regarding supply chain transparency and sustainability. Current practices lack visibility, leading to difficulties in tracing material origins, tracking movement, and ensuring compliance. To fill this gap, this study employed a three-phase approach. In the first phase, a comprehensive literature review identified 37 potential barriers. Subsequently, expert discussions were held to refine the list, ultimately selecting 15 barriers of utmost importance. In the second phase, data were collected from 17 experts representing academia and industry. Finally, in the last phase, the collected data were analyzed using the Pythagorean fuzzy analytical hierarchical process (AHP) methodology. The findings revealed that the “transparency range” category was the most critical barrier, closely followed by “inadequate access to institutional finance”. Surprisingly, the study identified the “security environment” as the most significant barrier. These results offer construction companies, policymakers, and other industry stakeholders a comprehensive understanding of blockchain adoption’s challenges. With this knowledge, stakeholders can design effective strategies and policies to address these barriers. Moreover, the research highlights the importance of considering uncertainty in decision making when assessing technology adoption, making the findings applicable beyond the construction industry.
Quantum-enhanced Markov chain Monte Carlo

Quantum computers promise to solve certain computational problems much faster than classical computers. However, current quantum processors are limited by their modest size and appreciable error rates. Recent efforts to demonstrate quantum speedups have therefore focused on problems that are both classically hard and naturally suited to current quantum hardware, such as sampling from complicated-although not explicitly useful-probability distributions1-3. Here we introduce and experimentally demonstrate a quantum algorithm that is similarly well suited to current hardware, but which samples from complicated distributions arising in several applications. The algorithm performs Markov chain Monte Carlo (MCMC), a prominent iterative technique4, to sample from the Boltzmann distribution of classical Ising models. Unlike most near-term quantum algorithms, ours provably converges to the correct distribution, despite being hard to simulate classically. But like most MCMC algorithms, its convergence rate is difficult to establish theoretically, so we instead analysed it through both experiments and simulations. In experiments, our quantum algorithm converged in fewer iterations than common classical MCMC alternatives, suggesting unusual robustness to noise. In simulations, we observed a polynomial speedup between cubic and quartic over such alternatives. This empirical speedup, should it persist to larger scales, could ease computational bottlenecks posed by this sampling problem in machine learning5, statistical physics6 and optimization7. This algorithm therefore opens a new path for quantum computers to solve useful-not merely difficult-sampling problems.
A fast <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1623" altimg="si3.svg"><mml:mrow><mml:mi>O</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>N</mml:mi><mml:mo class="qopname">lg</mml:mo><mml:mi>N</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math> time hybrid clustering algorithm using the circumference proximity based merging technique for diversified datasets

Clustering has been widely employed for extracting intrinsic groups because of its low reliance on domain knowledge. Though several clustering techniques have been developed in the literature, the majority of them become inefficient due to their dependence on user-defined parameters or inability to cluster multi-scale datasets. To handle these issues, several hybrid clustering algorithms which combine the advantages of partitional, hierarchical, and graph-based clustering techniques have been developed. Hybrid clustering algorithms first partition the data into smaller clusters and then merge them into genuine clusters. However, most of them incur a quadratic time complexity which limits their application for large datasets. The proposed work presents a two-step fast hybrid clustering algorithm based on partitions and efficient merging to reduce the computational cost by maintaining the clustering quality. In the first step, the dispersion of the data is considered to produce balanced partitions. Then graph circumference proximity-based merging technique is proposed to merge the sub-clusters. The overall computational complexity of the algorithm is O(NlgN) where N is the number of data points, ignoring the dimension. To the best of our knowledge, this is the fastest known graph neighborhood-based hybrid clustering algorithm. Experimental results on various diversified datasets exhibit a significant improvement in the running time as well as cluster quality and robustness against noise.
On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot

Software engineering research has always being concerned with the improvement of code completion approaches, which suggest the next tokens a developer will likely type while coding. The release of GitHub Copilot constitutes a big step forward, also because of its unprecedented ability to automatically generate even entire functions from their natural language description. While the usefulness of Copilot is evident, it is still unclear to what extent it is robust. Specifically, we do not know the extent to which semantic-preserving changes in the natural language description provided to the model have an effect on the generated code function. In this paper we present an empirical study in which we aim at understanding whether different but semantically equivalent natural language descriptions result in the same recommended function. A negative answer would pose questions on the robustness of deep learning (DL)-based code generators since it would imply that developers using different wordings to describe the same code would obtain different recommendations. We asked Copilot to automatically generate 892 Java methods starting from their original Javadoc description. Then, we generated different semantically equivalent descriptions for each method both manually and automatically, and we analyzed the extent to which predictions generated by Copilot changed. Our results show that modifying the description results in different code recommendations in ∼46% of cases. Also, differences in the semantically equivalent descriptions might impact the correctness of the generated code (±28%).
GTxChain: A Secure IoT Smart Blockchain Architecture Based on Graph Neural Network

With the expansion of scale, the Internet of Things (IoT) suffers more and more security threats, and vulnerability and sensitivity to attacks are also increasing. As a distributed and secure network architecture, Blockchain is suitable for protecting the security and privacy of the IoT. In this article, we propose a secure smart blockchain IoT architecture based on Graph Neural Networks (GNN) named GTxChain, using a distributed intelligent prophecy machine to obtain off-chain data and construct the transaction data structure of the blockchain through the blockchain-directed acyclic graph (DAG). In the off-chain transaction and off-chain storage part, we use the lightning network, improved IPFS and GNN to obtain transaction information and continuously update the blockchain network and blockchain for transaction verification and other operations. GTxChain employs an IPFS storage architecture to enhance user privacy and reduce data processing time. Compared to other blockchain architectures, it improves by 10.51% and has better efficiency and stability in terms of Merkle-proof time overhead. Experimental results show that the GTxChain architecture can effectively ensure the IoT’s trustworthiness, security, and privacy (TSP).
Rethinking Searchable Symmetric Encryption

Symmetric Searchable Encryption (SSE) schemes enable keyword searches over encrypted documents. To obtain efficiency, SSE schemes incur a certain amount of leakage. The vast majority of the literature on SSE considers only leakage from one component of the overall SSE system, the encrypted search index. This component is used to identify which documents to return in response to a keyword query. The actual fetching of the documents is left to another component, usually left unspecified in the literature, but generally envisioned as a simple storage system matching document identifiers to encrypted documents.This raises the question: do SSE schemes actually protect the security of data and queries when considered from a system-wide viewpoint? We answer this question in the negative. We do this by introducing a new inference attack that achieves practically efficient, highly scalable, accurate query reconstruction against end-to-end SSE systems. In particular, our attack works even when the SSE schemes are built in the natural way using the state-of-the-art techniques (namely, volume-hiding encrypted multi-maps) designed to suppress leakage and protect against previous generations of attack.A second question is whether the state-of-the-art leakage suppression techniques can instead be applied on a system-wide basis, to protect both the encrypted search index and the encrypted document store, to produce efficient SSE systems. We also answer this question in the negative. To do so, we implement SSE systems using those state-of-the-art leakage suppression methods, and evaluate their performance. We show that storage overheads range from 100× to 800× while bandwidth overheads range from 20× to100×, as compared to a naïve baseline system.Our results motivate the design of new SSE systems that are designed with system-wide security in mind from the outset. In this regard, we show that one such SSE system due to Chen et al. (IEEE INFOCOM 2018), with provable security guarantees based on differential privacy, is also vulnerable to our new attack.In totality, our results force a re-evaluation of how to build end-to-end SSE systems that offer both security and efficiency.
Path planning for dual UAVs cooperative suspension transport based on artificial potential field-A* algorithm

Unmanned Aerial Vehicles (UAVs) are frequently utilized for transferring and delivering. Dual- or multi-quadrotor cooperative transportation schemes are required for heavy loads. In this work, focusing on the path planning for a dual-quadrotor cooperative transport system, a unique artificial potential field-A*(APF-A*) algorithm is studied to fulfill the safe and effective cooperative suspension transport. APF-A* algorithm proposed the strategy of host-wingman cooperative transportation based on the APF theory, and optimizes the heuristic function by introducing the concept of repulsive force to maintain the quadrotor flying at a safe distance from the obstacle. In addition, APF-A* algorithm promises to increase search efficiency while identifying the optimal path, and it can reduce the swing angle motion of loads to enhance the security of transportation. The coordinated flight control and path planning of quadrotor UAVs are simulated in AirSim environment and tested in flight experiments. The results demonstrate that the algorithm is capable to plan safe and reliable paths, meanwhile changing formation and avoid collision when the wingman is close to obstacles. In comparison to the regular A* algorithm, the APF-A* algorithm proposed in the paper performs better in path planning for dual-quadrotor cooperative transport system, reducing the search time and the number of search grids by nearly 60% and 53.75%, respectively.
Optimization techniques for Multi-Robot Task Allocation problems: Review on the state-of-the-art

In the last years, Multi-Robot Systems (MRS) have experienced considerable recognition due to various possible real-world applications. Multi-Robot Task Allocation (MRTA) is among the most interesting MRS problems. This problem concerns the situation when a set of given tasks must be performed by a team of mobile robots with the intention of optimizing an objective function (e.g., minimizing the mission time). This paper aims to present MRTA applications and categorizes methods into market-based, behavior-based, and optimization-based approaches. The paper focus on the latter and review several works in order to point out their advantages and limitations and to identify possible future research opportunities. Furthermore, a statistical analysis is provided to identify the most used methods and the evolution of the topic over the years.
Left Gaze Bias Between LHT and RHT: A Recommendation Strategy to Mitigate Human Errors in Left- and Right-Hand Driving

Driver errors, such as distraction, perceptual blindness, and incorrect control manipulation, can either cause road accidents or reduce driving performance in daily driving tasks. Several works in literature have illustrated perceptual blindness and distraction are associated with insufficient attention to those activities vital for safe driving. Also, inappropriate driving-related eye movements may subsequently result in manipulation errors, such as inappropriate control of the throttle or steering wheel. Although many studies have examined drivers' visual performance, there have been few attempts to compare left-hand traffic (LHT) and right-hand traffic (RHT) conditions. Even driving in the same driving scene, different eye movement patterns may also be induced due to drivers' left gaze bias. Motivated by this human factor, this research investigates potential human errors in driving from the perspective of eye movements (i.e., saccades and fixations) and corresponding control manipulations. Firstly, a driving simulator is used to investigate the discrepancy of eye movements in identical driving scenes between LHT and RHT. Secondly, visual regions and feature sensitivities are spatially correlated by comparing the gaze distribution from the central to peripheral visual fields for both LHT and RHT. Besides, considering the violation of traffic rules are likely the cause of potentially fatal driving eye movement and control mistakes, a recommendation strategy is proposed to alleviate visual errors by utilizing a neural network inspired by the driver gaze map. The experiments in both LHT and RHT driving tasks demonstrate improvements in the alleviation of human errors by importing the proposed eye movement recommendation strategy.