Reviewerly/exHarmony huggingface dataset:
Assessing the Usefulness of a Visual Programming IDE for Large-Scale Automation Software

Industrial control applications are usually designed by domain experts instead of software engineers. These experts frequently use visual programming languages based on standards such as IEC 61131-3 and IEC 61499. The standards apply model-based engineering concepts to abstract from hardware and low-level communication. Developing industrial control software is challenging due the fact that such systems are usually one-of-a-kind systems that have to be maintained for many years. These challenges, together with the growing complexity of control software, require very usable model-based development environments for visual programming languages. However, so far only little empirical research exists on the practical usefulness of such environments, i.e., their usability and utility. In this paper, we discuss common control software maintenance tasks and tool capabilities based on existing research and show the realization of these capabilities in 4diac IDE. We first performed a walkthrough of the demonstrated capabilities using the cognitive dimensions of notations framework from the field of human-computer interaction. We then improved the tool and conducted a user study involving ten industrial automation engineers, who used 4diac IDE in a realistic control software maintenance scenario. Our findings demonstrate how the usefulness of IDEs can be successfully investigated using a multi-phase approach that includes a walkthrough and a user study. We discuss lessons learned and derive general implications with respect to large-scale applications for developers of IDEs that we deem applicable in the context of (visual) model-based engineering tools.
Chaotic state of matter search with elite opposition based learning: A new hybrid metaheuristic algorithm

Abstract In this article, the exploration and stochastic property of elite opposition‐based learning and chaotic maps are utilized to introduce a hybridized metaheuristic optimization technique. Both the techniques are combined with state of matter search optimization to enhance its capability of locating global minima. The proposed hybrid algorithm is tested on various benchmark functions and compared with the state of mater search optimization to verify its efficiency. The results show that the proposed hybrid algorithm gives better convergence for various benchmark functions.
Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection

OSN platforms are under attack by intruders born and raised within their own ecosystems. These attacks have multiple scopes from mild critiques to violent offences targeting individual or community rights and opinions. Negative publicity on microblogging platforms, such as Twitter, is due to the infamous Twitter bots which highly impact posts’ circulation and virality. A wide and ongoing research effort has been devoted to develop appropriate countermeasures against emerging “armies of bots”. However, the battle against bots is still intense and unfortunately, it seems to lean on the bot-side. Since, in an effort to win any war, it is critical to know your enemy, this work aims to demystify, reveal, and widen inherent characteristics of Twitter bots such that multiple types of bots are recognized and spotted early. More specifically in this work we: (i) extensively analyze the importance and the type of data and features used to generate ML models for bot classification, (ii) address the open problem of multi-class bot detection, identifying new types of bots, and share two new datasets towards this objective, (iii) provide new individual ML models for binary and multi-class bot classification and (iv) utilize explainable methods and provide comprehensive visualizations to clearly demonstrate interpretable results. Finally, we utilize all of the above in an effort to improve the so called Bot-Detective online service. Our experiments demonstrate high accuracy, explainability and scalability, comparable with the state of the art, despite multi-class classification challenges.
Multi-Robot Gaussian Process Estimation and Coverage: Deterministic Sequencing Algorithm and Regret Analysis

We study the problem of multi-robot coverage over an unknown, nonuniform sensory field. Modeling the sensory field as a realization of a Gaussian Process and using Bayesian techniques, we devise a policy which aims to balance the tradeoff between learning the sensory function and covering the environment. We propose an adaptive coverage algorithm called Deterministic Sequencing of Learning and Coverage (DSLC) that schedules learning and coverage epochs such that its emphasis gradually shifts from exploration to exploitation while never fully ceasing to learn. Using a novel definition of coverage regret which characterizes overall coverage performance of a multi-robot team over a time horizon T , we analyze DSLC to provide an upper bound on expected cumulative coverage regret. Finally, we illustrate the empirical performance of the algorithm through simulations of the coverage task over an unknown distribution of wildfires.
End-to-End Semi-supervised Learning for Differentiable Particle Filters

Recent advances in incorporating neural networks into particle filters provide the desired flexibility to apply particle filters in large-scale real-world applications. The dynamic and measurement models in this framework are learnable through the differentiable implementation of particle filters. Past efforts in optimising such models often require the knowledge of true states which can be expensive to obtain or even unavailable in practice. In this paper, in order to reduce the demand for annotated data, we present an end-to-end learning objective based upon the maximisation of a pseudo-likelihood function which can improve the estimation of states when large portion of true states are unknown. We assess performance of the proposed method in state estimation tasks in robotics with simulated and real-world datasets.
Learning Contextual Transformer Network for Image Inpainting

Fully Convolutional Networks with attention modules have been proven effective for learning-based image inpainting. While many existing approaches could produce visually reasonable results, the generated images often show blurry textures or distorted structures around corrupted areas. The main reason is due to the fact that convolutional neural networks have limited capacity for modeling contextual information with long range dependencies. Although the attention mechanism can alleviate this problem to some extent, existing attention modules tend to emphasize similarities between the corrupted and the uncorrupted regions while ignoring the dependencies from within each of them. Hence, this paper proposes the Contextual Transformer Network (CTN) which not only learns relationships between the corrupted and the uncorrupted regions but also exploits their respective internal closeness. Besides, instead of a fully convolutional network, in our CTN, we stack several transformer blocks to replace convolution layers to better model the long range dependencies. Finally, by dividing the image into patches of different sizes, we propose a multi-scale multi-head attention module to better model the affinity among various image regions. Experiments on several benchmark datasets demonstrate superior performance by our proposed approach.
Deep learning based decomposition for visual navigation in industrial platforms

Abstract In the heavy asset industry, such as oil &amp; gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in such GPS denied environments is very time consuming due to the complexity of the environment and the large amount of equipment. To address this challenge we investigate an alternative approach to study the navigation problem based on visual imagery data instead of current ad-hoc methods where engineering drawings or large CAD models are used to find equipment. In particular, this paper investigates the combination of deep learning and decomposition for the image retrieval problem which is central for visual navigation. A convolutional neural network is first used to extract relevant features from the image database. The database is then decomposed into clusters of visually similar images, where several algorithms have been explored in order to make the clusters as independent as possible. The Bag-of-Words (BoW) approach is then applied on each cluster to build a vocabulary forest. During the searching process the vocabulary forest is exploited to find the most relevant images to the query image. To validate the usefulness of the proposed framework, intensive experiments have been carried out using both standard datasets and images from industrial environments. We show that the suggested approach outperforms the BoW-based image retrieval solutions, both in terms of computing time and accuracy. We also show the applicability of this approach on real industrial scenarios by applying the model on imagery data from offshore oil platforms.
Precise Runtime Analysis for Plateau Functions

To gain a better theoretical understanding of how evolutionary algorithms (EAs) cope with plateaus of constant fitness, we propose the $n$-dimensional Plateau$_k$ function as natural benchmark and analyze how different variants of the $(1 + 1)$ EA optimize it. The Plateau$_k$ function has a plateau of second-best fitness in a ball of radius $k$ around the optimum. As evolutionary algorithm, we regard the $(1 + 1)$ EA using an arbitrary unbiased mutation operator. Denoting by $\alpha$ the random number of bits flipped in an application of this operator and assuming that $\Pr[\alpha = 1]$ has at least some small sub-constant value, we show the surprising result that for all constant $k \ge 2$, the runtime $T$ follows a distribution close to the geometric one with success probability equal to the probability to flip between $1$ and $k$ bits divided by the size of the plateau. Consequently, the expected runtime is the inverse of this number, and thus only depends on the probability to flip between $1$ and $k$ bits, but not on other characteristics of the mutation operator. Our result also implies that the optimal mutation rate for standard bit mutation here is approximately $k/(en)$. Our main analysis tool is a combined analysis of the Markov chains on the search point space and on the Hamming level space, an approach that promises to be useful also for other plateau problems.
Pistis: Issuing Trusted and Authorized Certificates With Distributed Ledger and TEE

The security of HTTPS fundamentally relies on SSL/TLS certificates issued by Certificate Authorities (CAs), which, however, are vulnerable to be compromised to issue unauthorized certificates (i.e., certificates issued without domains’ permission). Current countermeasures such as Certificate Transparency (CT) can only detect unauthorized certificates rather than preventing them. In this article, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pistis</small> , a framework for issuing authorized and trusted certificates with the distributed ledger and Trusted Execution Environment (TEE) technology. In <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pistis</small> , TEE nodes validate whether the domain in a requested certificate passes the domain ownership validation (i.e., under corresponding applicants’ control) and submit attested results to a smart contract in the distributed ledger. The smart contract issues a certificate to the applicant when an attested result shows a pass. Therefore, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pistis</small> can ensure its issued certificates are authorized due to the domain ownership validation mechanism in the TEE. Furthermore, as the issued certificates are stored in a Merkle Patricia Tree (MPT) in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pistis</small> , they are trusted and can be verified by a normal user easily. The security of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pistis</small> is formally proved in the Universally Composable (UC) framework. Compared with state-of-the-art, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pistis</small> avoids potential damages by preventing unauthorized certificates from issuing.
GeCoAgent: A Conversational Agent for Empowering Genomic Data Extraction and Analysis

With the availability of reliable and low-cost DNA sequencing, human genomics is relevant to a growing number of end-users, including biologists and clinicians. Typical interactions require applying comparative data analysis to huge repositories of genomic information for building new knowledge, taking advantage of the latest findings in applied genomics for healthcare. Powerful technology for data extraction and analysis is available, but broad use of the technology is hampered by the complexity of accessing such methods and tools. This work presents GeCoAgent, a big-data service for clinicians and biologists. GeCoAgent uses a dialogic interface, animated by a chatbot, for supporting the end-users’ interaction with computational tools accompanied by multi-modal support. While the dialogue progresses, the user is accompanied in extracting the relevant data from repositories and then performing data analysis, which often requires the use of statistical methods or machine learning. Results are returned using simple representations (spreadsheets and graphics), while at the end of a session the dialogue is summarized in textual format. The innovation presented in this article is concerned with not only the delivery of a new tool but also our novel approach to conversational technologies, potentially extensible to other healthcare domains or to general data science.
Swarm-Based Extreme Learning Machine Models for Global Optimization

Extreme Learning Machine (ELM) is popular in batch learning, sequential learning, and progressive learning, due to its speed, easy integration, and generalization ability.While, Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence, high time and space complexity.In ELM, the hidden layer typically necessitates a huge number of nodes.Furthermore, there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem, the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms "Salp Swarm Algorithm (SSA-ELM), Grasshopper Algorithm (GOA-ELM), Grey Wolf Algorithm (GWO-ELM), Whale optimization Algorithm (WOA-ELM) and Moth Flame Optimization (MFO-ELM)".These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data, that describes the energy use of a single residence over a fouryear period.In the hidden layer, Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13% accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication, the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.
Contrastive Disentangled Meta-Learning for Signer-Independent Sign Language Translation

Sign language translation aims at directly translating a sign language video into a natural sentence. The majority of existing methods take the video-sentence pairs labeled by multiple specific signers as training and testing samples. However, such setting does not fit in with the real-world applications. A practicable sign language translation system is supposed to provide accurate translation results for unseen signers. In this paper, we mainly attack the signer-independent setting and focus on augmenting the generalization ability of translation model. To adapt to the challenging setting, we propose a novel framework called contrastive disentangled meta-learning (CDM), which develops several improvements in both deep architecture and training mode. Specifically, based on the minimax entropy objective, a disentangled module with adaptive gated units is developed to decouple the signer-specific and task-specific representation in the encoder. Besides, we facilitate the frame-word alignments by leveraging contrastive constraints between the obtained task-specific representation and the decoding output. The disentangled and contrastive modules could provide complementary information for each other. As for the training mode, we encourage the model to perform well in the simulated signer-independent scenarios by finding the generalized learning directions in the meta-learning process. Considering that vanilla meta-learning methods utilize the multiple specific signers insufficiently, we adopt a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration. Extensive experiments on the benchmark dataset RWTH-PHOENIX-Weather-2014T(PHOENIX14T) show that CDM could achieve competitive results compared with the state-of-the-art methods.
Quantum Circuit Architecture Optimization for Variational Quantum Eigensolver via Monto Carlo Tree Search

The advent of Noisy Intermediate-Scale Quantum (NISQ) devices provides crucial promise for the development of quantum algorithms.Variational quantum algorithms have emerged as one of the best hopes to utilize NISQ devices.Among these is the famous Variational Quantum Eigensolver (VQE), where one trains a parameterized and fixed quantum circuit (or an ansatz) to accomplish the task.However, VQE also suffers from some serious challenges, which are training difficulty and accuracy reduction due to deep quantum circuit and hardware noise.To be motivated by these issues, we propose a runtime and resource efficient scheme, monto-carlo tree (MCT) search based quantum circuit architecture optimization, where the ansatz is built in the variable form.Our approach first models the search space with a MCT and regards it as a supernet, where we make use of layers dependence to reduce the size of the search space .Second, a two-stage scheme is proposed for the search space training where weight sharing and warm-up strategies are employed to avoid huge computation cost.Training results are stored in nodes of the MCT for future decisions and a hierarchical node selection is presented to obtain an optimal ansatz.As a proof of principle, we carry out a series of numerical experiments for condensed matter and quantum chemistry in the quantum simulator with and without noise.Consequently, our scheme can be efficient to mitigate trainability and accuracy issues by minimizing the ansatz depth and the number of entanglement gates.
Charting the Path: Requirements and Constraints for Technology-Supported Walking Meetings

Traditional meetings involve extensive sitting, which negatively impacts the health of attendees. Understanding how technology can facilitate integrating physical activity into the workplace, such as in walking meetings, is vital to improving workplace wellbeing. To that end, we applied a mixed-method approach to explore requirements and opportunities for walking meetings. We conducted an online questionnaire and a series of interviews with early adopters of walking meetings and created design fictions based on their feedback. We evaluated the design fictions with a second questionnaire and garnered additional feedback from the original early adopters. Based on our findings, we derived four dimensions associated with walking meetings: practical, environmental, social, and cognitive facets. We define attributes, challenges, and opportunities within these dimensions which are important for designing systems that support walking meetings. Our work identifies key considerations for developing systems that integrate physical activity into communication activities.
3D Reconstruction of Unstructured Objects Using Information From Multiple Sensors

The Structure-from-Motion (SfM) algorithm is widely used for point cloud reconstruction. However, one drawback of conventional SfM based methods is that the obtained final point sets may contain holes and noise, which could degrade the estimation of reconstructed objects especially for smooth surfaces with few features. The other drawback is the accuracy and speed of SfM based methods depend on the uncertain number of images. To overcome these limitations, this paper proposes a novel 3D reconstruction method for unstructured objects based on the structure from motion in combination with the structured light, in which the point sets of structured light and the point sets of structure from motion can come from different target objects. Since the two point sets coming from multiple sensors do not scale well for register, making it difficult to find corresponding points, a scaled principal component analysis algorithm is proposed for the registration to overcome the impact due to large scale variance. With a large scale factor, a recalculated registration center is proposed via feature region segmentation to achieve point cloud registration again. The two point sets are matched using the proposed optimization method to complete 3D reconstruction. Surface reconstruction is performed using the Poisson algorithm to obtain a smooth surface. The proposed method is tested on some simple structured objects and real-life data of complex unstructured objects collected using range sensors. Compared with several state-of-the-art algorithms, experimental results confirm its potential for surface reconstruction from depth data calculated from the two sets.
Neurological injury from virtual reality mishap

Consumer virtual reality systems are becoming increasingly popular with the increasing availability of devices and gamified technologies. Self-sustained injury risks exist with the use of this technology in the uncontrolled home environment, however, the public awareness of these risks may not be recognised. We present a case of a low- impact virtual reality fall resulting in spinal cord injury, hypoglossal nerve injury, vertebral artery dissection and traumatic brain injury.
ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection

Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks. It, however, is also a challenging step that requires specialized expertise in security and code representation. To this end, we aim to develop a general, practical, and programming language-independent model capable of running on various source codes and libraries without difficulty. Therefore, we consider vulnerability detection as an inductive text classification problem and propose ReGVD, a simple yet effective graph neural network-based model for the problem. In particular, ReGVD views each raw source code as a flat sequence of tokens to build a graph, wherein node features are initialized by only the token embedding layer of a pre-trained programming language (PL) model. ReGVD then leverages residual connection among GNN layers and examines a mixture of graph-level sum and max poolings to return a graph embedding for the source code. ReGVD outperforms the existing state-of-the-art models and obtains the highest accuracy on the real-world benchmark dataset from CodeXGLUE for vulnerability detection. Our code is available at: https://github.com/daiquocnguyen/GNN-ReGVD.