Reviewerly/exHarmony huggingface dataset:
TAPInspector: Safety and Liveness Verification of Concurrent Trigger-Action IoT Systems

Trigger-action programming (TAP) is a popular end-user programming framework that can simplify the Internet of Things (IoT) automation with simple trigger-action rules. However, it also introduces new security and safety threats. A lot of advanced techniques have been proposed to address this problem. Rigorously reasoning about the security of a TAP-based IoT system requires a well-defined model and verification method both against rule semantics and physical-world features, e.g., concurrency, rule latency, extended action, tardy attributes, and connection-based rule interactions, which has been missing until now. By analyzing these features, we find 9 new types of rule interaction vulnerabilities and validate them on two commercial IoT platforms. We then present TAPInspector, a novel system to detect these interaction vulnerabilities in concurrent TAP-based IoT systems. It automatically extracts TAP rules from IoT apps, translates them into a hybrid model by model slicing and state compression, and performs semantic analysis and model checking with various safety and liveness properties. Our experiments corroborate that TAPInspector is practical: it identifies 533 violations related to rule interaction from 1108 real-world market IoT apps and is at least 60000 times faster than the baseline without optimization.
An Adaptive Hardware Architecture using Quantized HOG Features for Object Detection

This article presents an adaptive hardware architecture for high-performance object detection using Histogram of Oriented Gradient (HOG) features in combination with Supported Vector Machines (SVM). This architecture can adapt to various bit-width representations of HOG features by using the quantization technique. The HOG features can be represented from 8 bits to 4 bits to remove the bubble in the processing pipeline and reduce the memory footprint. As a result, the overall throughput is robustly increased as the number of bits decreases. Moreover, we propose a new cell-reused strategy to speed up the system throughput and reduce memory footprint. The proposed architecture has been implemented in TSMC 65nm technology with a maximum operating frequency of 500MHz and throughput of 3.98Gbps. The total hardware area cost is about 167KGEs and 212kb SRAMs.
Scalability Solutions in Blockchain-Supported Manufacturing: A Survey

Researchers in the field of smart manufacturing have recognized the benefits of blockchain technology, which solves the trust problem in the open network without relying on any trusted third party. Blockchain technology enables interaction between otherwise competing manufacturing entities to satisfy increasing customer demands in a trustful way. However, existing blockchain networks are facing limitations, which are defined by the trade-off between scalability, decentralization, and security. The scalability of the blockchain network is defined as the ability of the network to support an increasing load of transactions and it is lower compared to the non-blockchain systems. In order to omit the effects of the limitations, scalability solutions are being presented. This research reviews the literature in the field of blockchain-supported manufacturing concerning scalability solutions. The selected literature has been reviewed and classified according to the type of scalability solution. For each type of scalability solution, the main features of the concepts and connection between blockchain technology and manufacturing system are highlighted and discussed. The main findings of the study are that Layer 1 scalability solutions are better represented in the literature and are predominating in the case of general smart manufacturing systems, whereas Layer 2 scalability solutions are better represented in the case of specific smart manufacturing systems. Based on insights obtained from the presented analysis, future directions and open issues regarding the scalability limitations and solutions in blockchain-supported manufacturing are presented.
Predicting shallow water dynamics using echo-state networks with transfer learning

In this paper we demonstrate that reservoir computing can be used to learn the dynamics of the shallow-water equations. In particular, while most previous applications of reservoir computing have required training on a particular trajectory to further predict the evolution along that trajectory alone, we show the capability of reservoir computing to predict trajectories of the shallow-water equations with initial conditions not seen in the training process. However, in this setting, we find that the performance of the network deteriorates for initial conditions with ambient conditions (such as total water height and average velocity) that are different from those in the training dataset. To circumvent this deficiency, we introduce a transfer learning approach wherein a small additional training step with the relevant ambient conditions is used to improve the predictions.
Video Super-Resolution Method Using Deformable Convolution-Based Alignment Network

With the advancement of sensors, image and video processing have developed for use in the visual sensing area. Among them, video super-resolution (VSR) aims to reconstruct high-resolution sequences from low-resolution sequences. To use consecutive contexts within a low-resolution sequence, VSR learns the spatial and temporal characteristics of multiple frames of the low-resolution sequence. As one of the convolutional neural network-based VSR methods, we propose a deformable convolution-based alignment network (DCAN) to generate scaled high-resolution sequences with quadruple the size of the low-resolution sequences. The proposed method consists of a feature extraction block, two different alignment blocks that use deformable convolution, and an up-sampling block. Experimental results show that the proposed DCAN achieved better performances in both the peak signal-to-noise ratio and structural similarity index measure than the compared methods. The proposed DCAN significantly reduces the network complexities, such as the number of network parameters, the total memory, and the inference speed, compared with the latest method.
A novel density-based outlier detection method using key attributes

Outlier detection has attracted extensive attention in medical, financial, telecommunications and other fields. Although many related technologies have been proposed, most of them are faced with the problems of the neighborhood size of an object is difficult to determine and the distance in high-dimensional space is unreliable. To overcome these weaknesses, we propose a novel density-based outlier detection method that introduces the concept of Minimum the Sum of Edge Set and other related definitions in key attributes space. Based on the stability of Reverse Minimum the Sum of Edge Set, the proposed method can adaptively select the parameter representing the neighborhood size. In addition, some properties of the proposed local outlier factor are derived. Experiments on synthetic and real-world datasets demonstrate that our method is more effective than the existing outlier detection approaches.
From Augmented Reality Location-based Games To the Real-world Metaverse

The real-world metaverse can provide users with a hybrid experience blending the physical with the virtual through augmented reality (AR) technologies. In academia, the research of the metaverse is in its infancy, with prior works focused on technological and computational aspects of the concept. Still, a firm understanding of the lived user experiences of hybrid spaces is a significant gap in the literature. Further, Location-based games (LBG) have been started to be viewed as proto-metaverse applications with the potential to transition into real-world metaverse applications; however, little work explores how developers and researchers may facilitate this change. This report presents my research proposal to address the two lacunae mentioned above. First, I discuss state-of-the-art and present my research agenda. This is followed by presenting some initial results from my previous studies and discussing my future work.
Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural Networks

Autonomous vehicles produce high data rates of sensory information from sensing systems. To achieve the advantages of sensor fusion among different vehicles in a cooperative driving scenario, high data-rate communication becomes essential. Current strategies for joint radar-communication (JRC) often rely on specialized hardware, prior knowledge of the system model, and entail diminished capability in either radar or communication functions. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge of the system model and a tunable performance balance, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help an intelligent vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a generalized form of the Markov Decision Process (MDP). We identify deep reinforcement learning algorithms (DRL) and algorithmic extensions suitable for solving our JRC problem. For multi-agent scenarios, we introduce a Graph Neural Network (GNN) framework via a control channel. This framework enables modular and fair comparisons of various algorithmic extensions. Our experiments show that DRL results in superior performance compared to non-learning algorithms. Learning of inter-agent coordination in the GNN framework, based only on the Markov task reward, further improves performance.
Grasping decentralized finance through the lens of economic theory

Abstract In this viewpoint article, we provide an analysis of the value proposition of decentralized finance (DeFi) and its limitations using a simple stylized model of collateralized lending. DeFi uses a decentralized ledger to run smart contracts that automatically enforce the terms of a lending contract and safeguard the collateral. DeFi can lower the costs associated with intermediated lending and improve financial inclusion. Limitations are the volatility of crypto collateral and stablecoins used for settlement, the possible incompleteness of smart contracts and the lack of a reliable oracle. A proper infrastructure reducing such limitations could improve the value of DeFi.
Presence and Immersion: A Tale of Two Cities

Highlights: 1) the "sense of presence" and "immersion" are logically distinct but related concepts which to date have often been confused an used synonymously 2) "immersion" may be conceptualized as an objective property of technology or the extent to which a user feels immersed3) "presence", in turn, is a perceptual illusion in which the user has the impression of "being there" in the virtual environment4) for a better terminological stringency, we suggest using the term "immnersiveness" to describe the technology and "immersion" to indicate the level to which a user feels immersed5) also, we suggest conceptualizing presence as a consequence of immersive technology which is mediated by user factors (demographics, attention, motion sickness etc.)
Optical Speech Recovery From Desktop Speakers

In this article, we show that desktop speakers’ internal (electrical circuitry) and external (reflective diaphragm) design may expose users to confidential information leakage. We demonstrate that these flaws are present in billions of devices produced by global manufacturers and discuss countermeasures.
Improved Decision Based Adaptive Threshold Median Filter for Fingerprint Image Salt and Pepper Noise Denoising

This paper proposes an Improved Decision Based Adaptive Threshold Median Filter(IDBATMF) for fingerprint image salt and pepper noise denoising, which can more effectively remove the salt and pepper noise in fingerprint images and preserve the details of the image. The algorithm first detects noise, introduces the minimum absolute brightness difference to reflect the difference between candidate noise pixels and surrounding non-noise pixels, and innovatively sets a linear threshold, which is adapted to the local noise density and compared with the minimum absolute brightness difference to distinguish noise pixels. When removing noise, the window size used is determined by the extreme pixel density in the window, and the median of the non-extreme pixel in the window is used for replacement. The methods mentioned in this paper are compared with Standard Median Filter (SMF), Adaptive Median Filter (AMF), Modified Adaptive Median Filter (MAMF), Switching Median Filter (SWMF), Adaptive Switching Median Filter (ASMF), the Decision Based Algorithm (DBA), and the Modified Decision Based Algorithm (MDBA). The experimental results show that, compared with the existing methods, the method proposed in this paper better considers the local characteristics of the image, and has better processing effect under various noise densities.
Opportunities for additional education in the formation of social competence and self-realization of children and youth

The article is devoted to the study of the pedagogical possibilities of additional education in creating conditions for the formation of the social competence of students and the versatile self-realization of the younger generation. Solving the problems associated with the education of responsible, active and competent citizens today determines the priority lines for the development of educational policy in the Russian Federation. The article reflects the results of the theoretical analysis of modern pedagogical literature on the issues of determining the possibilities of additional education in solving the identified urgent problems and revealing the pedagogical potential of this type of education in the upbringing of the younger generation. The proposed materials can be used in the process of teaching students in the areas of training: "Organization of work with youth", "Pedagogical education", "Psychological and pedagogical education", levels of higher education - bachelor's, master's in the process of mastering students of disciplines based on the study of the subject area associated with further education.
A Study on Beginner Korean Speaking Education Using Metaverse: Focusing on Metaverse Platform ZEP

The purpose of this study is to propose an effective teaching method for speaking classes for beginner Korean learners by using metaverse, which has a high sense of reality and interaction and resolves spatial limitations. For this, the metaverse platform ZEP was selected and the value of educational use was thoroughly reviewed. In this study, 10 maps that can be used in &lt;Applied Research for the International Standard Curriculum of Korean Language (2017)&gt; and beginner textbooks of D University Institute of Korean Language(DIKL) were selected, and practical educational methods to improve speaking ability were presented. This study is meaningful in that it examined the educational use of the metaverse platform ZEP, which has not been addressed in Korean language education, and prepared an educational plan to improve the speaking ability of beginner Korean learners by supplementing the shortcomings of online real-time and face-to-face classes with metaverse.
All you need is logs: improving code completion by learning from anonymous IDE usage logs

In this work, we propose an approach for collecting completion usage logs from the users in an IDE and using them to train a machine learning based model for ranking completion candidates. We developed a set of features that describe completion candidates and their context, and deployed their anonymized collection in the Early Access Program of IntelliJ-based IDEs. We used the logs to collect a dataset of code completions from users, and employed it to train a ranking CatBoost model. Then, we evaluated it in two settings: on a held-out set of the collected completions and in a separate A/B test on two different groups of users in the IDE. Our evaluation shows that using a simple ranking model trained on the past user behavior logs significantly improved code completion experience. Compared to the default heuristics-based ranking, our model demonstrated a decrease in the number of typing actions necessary to perform the completion in the IDE from 2.073 to 1.832.
Embodied nonlinear dynamics of cognitive performance.

Study participants are typically unable to generate binary button-press sequences that pass as classically random sequences, such as from successive "fair coin" flips. Instead, their sequences repeat or alternate between responses too often. These deviations from randomness are commonly explained in terms of limitations or idiosyncrasies in cognitive processing. This article tests a novel hypothesis that randomness departures in participant-generated binary sequences are driven by coordination dynamics; alternating and repeating sequences are related to bimanual coordination attractors. Participants (N = 128) were asked to generate sequences that were representative of a random sequence, by successively pressing either of two buttons across 1,600 trials. Statistical analyses identify the binary button-press dynamics with a discrete sine-circle version of the Haken, Kelso, Bunz bimanual coordination model. Permutation analyses revealed the most common one- to five-trial subsequences were identified with the most dynamically stable coordinative relationships, consistent with bimanual coordination predictions. The sequences were consistent with scaling noises. Thus, participants' sequences departed from classical randomness by virtue of membership in a more inclusive category of variability that subsumes classical randomness. Recurrence quantification analysis revealed the mixture of stochasticity and determinism in the sequences was better approximated by the sine-circle model than by phase-randomized surrogate data sets that preserved both the power spectral densities and distributions of each participant's sequence. A relationship between randomness production and two-alternative forced-choice performance is established that constrains response time distribution models. The article's organization illustrates a nonreductive approach to inference for cognitive systems, inspired by statistical physics concepts such as renormalization group theory and universality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Visible-Infrared Person Re-Identification With Modality-Specific Memory Network

Visible-infrared person re-identification (VI-ReID) is challenging due to the large modality discrepancy between visible and infrared images. Existing methods mainly focus on learning modality-shared representations by embedding images from different modalities into a common feature space, in which some discriminative modality information is discarded. Different from these methods, in this paper, we propose a novel Modality-Specific Memory Network (MSMNet) to complete the missing modality information and aggregate visible and infrared modality features into a unified feature space for the VI-ReID task. The proposed model enjoys several merits. First, it can exploit the missing modality information to alleviate the modality discrepancy when only the single-modality input is provided. To the best of our knowledge, this is the first work to exploit the missing modality information completion and alleviate the modality discrepancy with the memory network. Second, to guide the learning process of the memory network, we design three effective learning strategies, including feature consistency, memory representativeness and structural alignment. By incorporating these learning strategies in a unified model, the memory network can be well learned to propagate identity-related information between modalities and boost the VI-ReID performance. Extensive experimental results on two standard benchmarks (SYSU-MM01 and RegDB) demonstrate that the proposed MSMNet performs favorably against state-of-the-art methods.
Robust multi-view subspace enhanced representation based on collaborative constraints and HSIC induction

The design of effective multi-view subspace clustering (MSC) algorithms has recently garnered significant research attention. Herein, to effectively improve the recognition performance and anti-noise interference ability of an MSC model, we propose a novel MSC algorithm, termed as robust multi-view subspace enhancement representation, based on collaborative constraints and a Hilbert–Schmidt independence criterion (HSIC) induction method. To mine the complementary information between different views, we apply the HSIC as a diversity regularization term. Specifically, to enhance the diagonal block structure of a subspace representation, a new sparse constraint is introduced on the product of itself and the transpose of the subspace representation matrix in a multi-view subspace learning model. Furthermore, hypergraph regularization and a low-rank idea are considered to capture the local geometric structure and clean data. In addition, to optimize our model, we adopt an augmented Lagrangian multiplier method and discuss the convergence of the model. Extensive experiments on six challenging datasets reveal that the proposed method achieves a highly competent objective performance with and without noisy views, as compared with several state-of-the-art multi-view clustering methods.
Influencing Factors of Online Course Learning Intention of Undergraduates Majoring in Art and Design: Mediating Effect of Flow Experience

Using technology to support learning is a major trend in the development of modern education. In the face of COVID-19, online courses play an important role. During the COVID-19 pandemic, a large number of online art design courses have been developed in China. Art design courses involve many practical investigation and skill operation links, which is a challenge of online learning. In order to explore the influencing factors of online course learning intention of art and design majors, this study compiled a questionnaire, conducted an online survey on 1,057 Chinese art and design majors through the Wenjuanxing questionnaire survey platform, and conducted structural equation analysis and hierarchical regression analysis on the sample data by using SPSS and AMOS analysis software. Based on Information Technology Acceptance Model and immersion theory, this study focuses on the relationship between information characteristics, immersion experience and learning intention of art and design online courses. The results show that the antecedent variables such as professionalism, interactivity, interest, and ease of use of online courses have a positive impact on College Students’ user immersion experience and learning intention; Immersion experience positively affects learning intention; Immersion experience plays a mediating role between online course information characteristics and learning intention.