Reviewerly/exHarmony huggingface dataset:
Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

Currently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona, 149 cases of tracking, and 203 cases of arcing were collected. Noise from ultrasound data was removed before uploading it as a training process to the ANN engine, which used the extreme learning machine (ELM) model. The developed AI-based switchgear faults classification system was designed and incorporated with the feature of scalability and can be tested and replicated for other switchgear conditions. A customized graphical user interface (GUI), Ultrasound Analyzer System (UAS), was also developed, to enable users to obtain the switchgear condition or classification output via a graphical interface screen. Hence, accurate decision-making based on this analysis can be made to prioritize the urgency for the remedial works.
A Systematic Review on Language Learning Strategies for Speaking Skills in a New Learning Environment

&lt;p style="text-align: justify;"&gt;Speaking skills have always been regarded as the most difficult skill out of the four skills that learners should master in language learning. The importance of learning speaking skills has also been highlighted by many researchers in recent years. With the use of technology in this modern era, learners can learn speaking skills through various resources. Therefore, there is a need to identify the learning strategies used by learners for speaking skills in the new learning environment. In this review, the most frequently used strategies in language learning for speaking skills has been examined. The articles reviewed were from 2017 until 2021 across two databases, ERIC and Google Scholar. The PRISMA 2020 methodology is used in the review. From the review, it was found that meta-cognitive and cognitive strategies were most frequently used for learning speaking skills, followed by compensation and social strategies. The least used strategies by learners were memory and affective strategies. The findings help teachers to look for the most suitable teaching method in the current learning environment. For future research, an extensive review of the use of learning strategies for other learning skills could be carried out.&lt;/p&gt;
Securing Federated Learning: A Covert Communication-Based Approach

Federated Learning Networks (FLNs) have been envisaged as a promising paradigm to collaboratively train models among mobile devices without exposing their local privacy data. Due to the need for frequent model updates via wireless links, FLNs are vulnerable to various attacks (e.g., eavesdropping attacks, replay attacks, inference attacks, and jamming attacks). Balancing privacy protection with efficient distributed model training is a key challenge for FLNs. Existing countermeasures incur high computation costs and are only designed for specific attacks on FLNs. In this article, we bridge this gap by proposing the Covert Communication-based Federated Learning (CCFL) approach. Based on the emerging communication security technique of covert communication which hides the existence of wireless communication activities, CCFL can degrade attackers' capability of extracting useful information from the FLN training protocol, which is a fundamental step for most existing attacks, and thereby holistically enhances the privacy of FLNs. We experimentally evaluate CCFL extensively under real-world settings in which the FL latency is optimized under given security requirements. Numerical results demonstrate the significant effectiveness of the proposed approach in terms of both training efficiency and communication security.
Versatile and Sustainable Timed-Release Encryption and Sequential Time-Lock Puzzles (Extended Abstract)

Timed-release encryption (TRE) makes it possible to send messages “into the future” such that a pre-determined amount of time needs to pass before a message can be accessed. Malavolta and Thyagarajan (CRYPTO’19) recently introduced an interesting variant of TRE called homomorphic time-lock puzzles (HTLPs), making TRE more versatile and greatly extending its applications. Here one considers multiple independently generated puzzles and the homomorphic evaluation of a circuit over these puzzles. Solving the so obtained puzzle yields the output of a circuit evaluated on the messages locked by the original puzzles.We observe that viewing HTLPs more abstractly gives rise to a simple generic construction of homomorphic TRE (HTRE) that is not necessarily based on sequential squaring, but can be instantiated based on any TLP, e.g., from the LWE assumption (via randomized encodings). This construction has slightly different properties, but provides essentially the same functionality for applications. It makes TRE versatile and can be used beyond HTRE, for instance to construct timed-release functional encryption. Interestingly, it achieves a new “solve one, get many for free” property, which supports that an arbitrary number of independently time-locked (homomorphically evaluated) messages can all be obtained simultaneously after solving only a single puzzle. This puzzle is independent of the number of time-locked messages and thus achieves optimal amortized cost.Moreover, we define and construct sequential TLPs as a particularly useful generalization of TLPs and TRE. Such puzzles can be solved sequentially in a way that solving a puzzle additionally considers the previous solution and the time required to solve the puzzle is determined by the difference in the time parameters. When instantiated from sequential squaring, this allows to realize public “sequential squaring services”, where everyone can time-lock messages, but only one entity needs to perform the computations required to solve puzzles. Thus, this removes the burden of wasting computational resources by every receiver and makes TRE economically and ecologically more sustainable.
An improved geometrical calibration method for stereo deflectometry by using speckle pattern

System geometrical calibration is critical for accurate stereo deflectometry. Conventional calibration approaches involve displaying checkerboard, circle, or fringe patterns on a systemic LCD screen, and the system parameters are calculated via reflection using a markerless mirror at three different locations. These approaches introduce form errors into the system because of defocus images. When using fringe patterns, the calibration procedure becomes more complex for acquiring a plethora of fringe patterns in two orthogonal directions, and ambient light influences the accuracy of the unwrapped phase map. In this study, we propose a novel calibration method for a stereo deflectometry system using a speckle pattern to improve the system measurement accuracy with a flexible calibration procedure. The proposed method includes an improved similarity evaluation function based on KAZE features to improve the accuracy of feature matching between images with perspective transformation, and a coarse-to-precise algorithm is applied to suppress the defocusing effect and refine the matched set. The initial geometrical parameters are calculated based on the refined matching results. The final procedure optimises all geometrical parameters into a single cost function using the Levenberg–Marquardt algorithm. Experiments using a stereo deflectometry system demonstrated that the proposed method achieves high accuracy with while being simple and flexible.
Distributed and Autonomous Aerial Data Collection in Smart City Surveillance Applications

The massive growth of Smart City and Internet of Things applications enables safety and security. The data those are produced from surveillance cameras in aerial devices such as unmanned aerial networks (UAVs) are needed to be transferred to ground stations for secure data analysis. When the scale of network is relatively large compare to the wireless communication coverage of device, it is not always available to transmit the data to the ground stations, thus distributed and autonomous algorithms are essentially desired. Based on the needs, we propose a novel algorithm that is for collecting surveillance data under the consideration of mobility and flexibility of UAV networks. Due to the battery limitation in UAVs, we selectively collect data from the UAVs by setting rules under the consideration of distance and similarity. As a sequence, the UAV devices have to compete for a chance to get data processing. For this purpose, this paper designs a Myerson auction-based deep learning algorithm to leverage the UAV’s revenue compare to traditional second-price auction while preserving truthfulness. Based on simulation results, we verify that our proposed algorithm achieves desired performance improvements.
Brain-Inspired Search Engine Assistant Based on Knowledge Graph

Search engines can quickly respond to a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine search keywords and open a large number of web pages to find and summarize answers. Many research works of question and answering (Q&A) system attempt to assist search engines by providing simple, accurate, and understandable answers. However, without original semantic contexts, these answers lack explainability, making them difficult for users to trust and adopt. In this article, a brain-inspired search engine assistant named DeveloperBot based on knowledge graph is proposed, which aligns to the cognitive process of humans and has the capacity to answer complex queries with explainability. Specifically, DeveloperBot first constructs a multilayer query graph by splitting a complex multiconstraint query into several ordered constraints. Then, it models a constraint reasoning process as a subgraph search process inspired by a spreading activation model of cognitive science. In the end, novel features of the subgraph are extracted for decision-making. The corresponding reasoning subgraph and answer confidence are derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate answers and answer confidences with high accuracy. We implement a prototype and conduct a user study to evaluate whether and how the direct answers and the explanations provided by DeveloperBot can assist developers' information needs.
Contrastive Learning Based Stain Normalization Across Multiple Tumor in Histopathology

Generative adversarial network (GAN) has been a prevalence in color normalization techniques to assist deep learning analysis in H&E stained histopathology images. The widespread adoption of GAN has effectively released pathologists from the heavy manual workload in the conventional template image selection. However, the transformation might cause significant information loss, or generate undesirable results such as mode collapse in all likelihood, which may affect the performance in the succeeding diagnostic task. To address the issue, we propose a contrastive learning method with a color-variation constraint, which maximally retains the recognizable phenotypic features at the training of a color-normalization GAN. In a self-supervised manner, the discriminative tissue patches across multiple types of tumors are clustered, taken as the salient input to feed the GAN. Empirically, the model is evaluated by public datasets of large cohorts on different cancer diseases from TCGA and Camelyon16. We show better phenotypical recognizability along with an improved performance in the histology image classification.
From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation

Despite recent advancements, deep neural networks are not robust against adversarial perturbations. Many of the proposed adversarial defense approaches use computationally expensive training mechanisms that do not scale to complex real-world tasks such as semantic segmentation, and offer only marginal improvements. In addition, fundamental questions on the nature of adversarial perturbations and their relation to the network architecture are largely understudied. In this work, we study the adversarial problem from a frequency domain perspective. More specifically, we analyze discrete Fourier transform (DFT) spectra of several adversarial images and report two major findings: First, there exists a strong connection between a model architecture and the nature of adversarial perturbations that can be observed and addressed in the frequency domain. Second, the observed frequency patterns are largely image- and attack-type independent, which is important for the practical impact of any defense making use of such patterns. Motivated by these findings, we additionally propose an adversarial defense method based on the well-known Wiener filters that captures and suppresses adversarial frequencies in a data-driven manner. Our proposed method not only generalizes across unseen attacks but also excels five existing state-of-the-art methods across two models in a variety of attack settings.
Singularity-Exponent-Domain Image Feature Transform

Combining the generalized fractal theory and the time-frequency distribution, the image feature decomposition in the singularity exponent domain is studied in this paper. With the theoretical derivation and quantitative analysis, the singularity-exponent-domain image feature transform (SIFT) method is proposed to analyze and process images from new feature dimensions. If one derives from the generalized fractal characteristics of the image, the two-dimensional frequency variables of the 2D time-frequency transform of the image can be used to estimate the two-dimensional singularity power spectrum (SPS) in the space dimension. As a consequence, it leads to the SPS distribution of the original image in the spatial domain, i.e., SIFT images. Based on the SIFT, the feature transform images with different singularity exponent and feature curves of singularity power spectrum with respect to different physical regions can thus be obtained. The SIFT is rigorously derived from the 2D-SPS and the Pseudo Wigner-Ville distribution (PWVD). In addition, the feature images based on the SIFT is proved to be the SNR independence in the GWN background. In order to validate the effectiveness of feature extraction, the proposed methodology is tested on the breast ultrasound images, the visual images, and the synthetic aperture radar (SAR) images. Furthermore, the SAR target detection method based on the SIFT images is proposed, and the experiment results indicate that the proposed algorithm is superior in performance to the traditional CFAR or 2D-SPS method. In fact, this new SIFT is promising to provide a technical approach for image feature extraction, target detection, and recognition.
Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions

Cooperative multi-robot teams need to be able to explore cluttered and unstructured environments while dealing with communication dropouts that prevent them from exchanging local information to maintain team coordination. Therefore, robots need to consider high-level teammate intentions during action selection. In this letter, we present the first Macro Action Decentralized Exploration Network (MADE-Net) using multi-agent deep reinforcement learning (DRL) to address the challenges of communication dropouts during multi-robot exploration in unseen, unstructured, and cluttered environments. Simulated robot team exploration experiments were conducted and compared against classical and DRL methods where MADE-Net outperformed all benchmark methods in terms of computation time, total travel distance, number of local interactions between robots, and exploration rate across various degrees of communication dropouts. A scalability study in 3D environments showed a decrease in exploration time with MADE-Net with increasing team and environment sizes. The experiments presented highlight the effectiveness and robustness of our method.
Iterative joint classifier and domain adaptation for visual transfer learning

Current available supervised classifiers cannot generalize across various domains due to distribution mismatch among them. Domain adaptation and transfer learning algorithms are proposed to tackle domain shift problem that originates from different data collection conditions. In this paper, we propose a transfer learning framework called iterative joint classifier and domain adaptation for visual transfer learning (ICDAV), which utilizes the balanced maximum mean discrepancy to better transfer knowledge across domains. Also, for learning a robust classifier against domain shift, a set of graph manifold regularizer and modified joint probability maximum mean discrepancy are simultaneously exploited to capture the domain structures and adapt the distribution of projected samples during the model learning process. Variety of experiments on several public datasets indicates that our approach achieves remarkable performance on visual domain adaptation and transfer learning tasks.
SCB-HC-ECC–Based Privacy Safeguard Protocol for Secure Cloud Storage of Smart Card–Based Health Care System

The advent of the internet has brought an era of unprecedented connectivity between networked devices, making one distributed computing, called cloud computing, and popular. This has also resulted in a dire need for remote authentication schemes for transferring files of a sensitive nature, especially health-related information between patients, smart health cards, and cloud servers via smart health card solution providers. In this article, we elaborate on our proposed approach for such a system and accomplish an informal analysis to demonstrate the claim that this scheme provides sufficient security while maintaining usability.
Linguistically Routing Capsule Network for Out-of-distribution Visual Question Answering

Generalization on out-of-distribution (OOD) test data is an essential but underexplored topic in visual question answering. Current state-of-the-art VQA models often exploit the biased correlation between data and labels, which results in a large performance drop when the test and training data have different distributions. Inspired by the fact that humans can recognize novel concepts by composing existed concepts and capsule network’s ability of representing part-whole hierarchies, we propose to use capsules to represent parts and introduce "Linguistically Routing" to merge parts with human-prior hierarchies. Specifically, we first fuse visual features with a single question word as atomic parts. Then we introduce the "Linguistically Routing" to reweight the capsule connections between two layers such that: 1) the lower layer capsules can transfer their outputs to the most compatible higher capsules, and 2) two capsules can be merged if their corresponding words are merged in the question parse tree. The routing process maximizes the above unary and binary potentials across multiple layers and finally carves a tree structure inside the capsule network. We evaluate our proposed routing method on the CLEVR compositional generation test, the VQA-CP2 dataset and the VQAv2 dataset. The experimental results show that our proposed method can improve current VQA models on OOD split without losing performance on the in-domain test data.
Cybersecurity Program Accreditation

The Curricular Guidelines for Post-Secondary Degree Programs in Cybersecurity (CSEC 2017) released by the Joint Task Force of the ACM, IEEE Computer Society, AIS and IFIP have begun to define the academic field of cybersecurity. Increasing numbers of institutions are now offering full-fledged undergraduate cybersecurity degree programs. However, as the CSEC 2017 guidelines do not define what constitutes a standalone undergraduate cybersecurity degree, the computing community needs to agree on parameters of what such a degree ought to be. To this end, ABET has created flexible accreditation criteria for cybersecurity programs guided extensively by CSEC 2017 and community input. These criteria were used to conduct pilot accreditation visits of four distinct cybersecurity programs in 2017-18. This panel discusses benefits, challenges, and costs of cybersecurity program accreditation. The panelists, who represent the first four ABET-accredited cybersecurity programs, will discuss major curricular features of their programs, their motivation for participating in the pilot accreditation, the effort in preparing for accreditation, and the benefits anticipated both for their programs and the larger computing community due to program accreditation
CloudExpert: An intelligent system for selecting cloud system simulators

During the last decade, the research community has developed different simulation tools to model and study cloud systems. However, current cloud simulators focus on specific features that typically do not fully cover all aspects of the cloud infrastructure. The ever-growing number of existing simulators increases the difficulty to properly choose the most appropriate one. Moreover, in certain situations, these simulators must be combined to analyze the features required by the user, which leads to investing a considerable time and effort for their selection. In this paper, we propose CloudExpert, an intelligent system based on metamorphic testing that selects the most appropriate simulator covering the features of interest for the user. In contrast to our previous work, where metamorphic testing is applied to improve models representing a cloud, in this work we analyse the underlying features of several well-known cloud simulators to generate metamorphic rules, which are applied to represent the properties of the simulator. To show the applicability of CloudExpert, we conducted an empirical study where the adequacy of six well-known cloud simulators was analyzed. In this experiment, CloudExpert recommended the most appropriate simulator for eight scenarios involving different aspects of the cloud (energy, storage, network, memory, CPU) and simulator performance; and could also identify strengths and weaknesses of these simulators. Then, we further validated CloudExpert in two different ways. Firstly, the effectiveness of CloudExpert was measured using different faulty cloud simulators. Secondly, we designed a questionnaire based on the results provided by CloudExpert for some of the scenarios of the first experiment. The questionnaire was answered by eight experts in cloud simulation, confirming the usefulness of the tool.