Reviewerly/exHarmony huggingface dataset:
System security assurance: A systematic literature review

System security assurance provides the confidence that security features, practices, procedures, and architecture of software systems mediate and enforce the security policy and are resilient against security failure and attacks. Alongside the significant benefits of security assurance, the evolution of new information and communication technology (ICT) introduces new challenges regarding information protection. Security assurance methods based on the traditional tools, techniques, and procedures may fail to account new challenges due to poor requirement specifications, static nature, and poor development processes. The common criteria (CC) commonly used for security evaluation and certification process also comes with many limitations and challenges. In this paper, extensive efforts have been made to study the state-of-the-art, limitations and future research directions for security assurance of the ICT and cyber–physical systems (CPS) in a wide range of domains. We conducted a systematic review of requirements, processes, and activities involved in system security assurance including security requirements, security metrics, system and environments and assurance methods. We highlighted the challenges and gaps that have been identified by the existing literature related to system security assurance and corresponding solutions. Finally, we discussed the limitations of the present methods and future research directions.
Intelligent Optimization Algorithm-Based Path Planning for a Mobile Robot

The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.
Deep Temporal Model-Based Identity-Aware Hand Detection for Space Human–Robot Interaction

Hand detection is a crucial technology for space human-robot interaction (SHRI), and the awareness of hand identities is particularly critical. However, most advanced works have three limitations: 1) the low detection accuracy of small-size objects; 2) insufficient temporal feature modeling between frames in videos; and 3) the inability of real-time detection. In the article, a temporal detector (called TA-RSSD) is proposed based on the SSD and spatiotemporal long short-term memory (ST-LSTM) for real-time detection in SHRI applications. Next, based on the online tubelet analysis, a real-time identity-awareness module is designed for multiple hand object identification. Several notable properties are described as follows: 1) the hybrid structure of the Resnet-101 and the SSD improves the detection accuracy of small objects; 2) three-level feature pyramidal structure retains rich semantic information without losing detailed information; 3) a group of the redesigned temporal attentional LSTM (TA-LSTM) is utilized for three-level feature map modeling, which effectively achieves background suppression and scale suppression; 4) low-level attention maps are used to eliminate in-class similarity between hand objects, which improves the accuracy of identity awareness; and 5) a novel association training scheme enhances the temporal coherence between frames. The proposed model is evaluated on the SHRI-VID dataset (collected according to the task requirements), the AU-AIR dataset, and the ImageNet-VID benchmark. Extensive ablation studies and comparisons on detection and identity-awareness capacities show the superiority of the proposed model. Finally, a set of actual testing is conducted on a space robot, and the results show that the proposed model achieves a real-time speed and high accuracy.
Towards quantum simulations in particle physics and beyond on noisy intermediate-scale quantum devices

We review two algorithmic advances that bring us closer to reliable quantum simulations of model systems in high-energy physics and beyond on noisy intermediate-scale quantum (NISQ) devices. The first method is the dimensional expressivity analysis of quantum circuits, which allows for constructing minimal but maximally expressive quantum circuits. The second method is an efficient mitigation of readout errors on quantum devices. Both methods can lead to significant improvements in quantum simulations, e.g. when variational quantum eigensolvers are used. This article is part of the theme issue 'Quantum technologies in particle physics'.
Evaluation of argument strength in attack graphs: Foundations and semantics

An argumentation framework is a pair made of a graph and a semantics. The nodes and the edges of the graph represent respectively arguments and relations (e.g., attacks, supports) between arguments while the semantics evaluates the strength of each argument of the graph. This paper investigates gradual semantics dealing with weighted graphs, a family of graphs where each argument has an initial weight and may be attacked by other arguments. It contains four contributions. The first consists of laying the foundations of gradual semantics by proposing key principles on which evaluation of argument strength may be based. Foundations are important not only for a better understanding of the evaluation process in general, but also for clarifying the basic assumptions underlying semantics, for comparing different (families of) semantics, and for identifying families of semantics that have not been explored yet. The second contribution consists of providing a formal analysis and a comprehensive comparison of the semantics that have been defined in the literature for evaluating arguments in weighted graphs. As a third contribution, the paper proposes three novel semantics and shows which principles they satisfy. The last contribution is the implementation and empirical evaluation of the three novel semantics. We show that the three semantics are very efficient in that they compute the strengths of arguments in less than 20 iterations and in a very short time. This holds even for very large graphs, meaning that the three semantics scale very well.
Using Motion History Images With 3D Convolutional Networks in Isolated Sign Language Recognition

Sign language recognition using computational models is a challenging problem that requires simultaneous spatio-temporal modeling of the multiple sources, i.e. faces, hands, body, etc. In this paper, we propose an isolated sign language recognition model based on a model trained using Motion History Images (MHI) that are generated from RGB video frames. RGB-MHI images represent spatio-temporal summary of each sign video effectively in a single RGB image. We propose two different approaches using this RGB-MHI model. In the first approach, we use the RGB-MHI model as a motion-based spatial attention module integrated into a 3D-CNN architecture. In the second approach, we use RGB-MHI model features directly with the features of a 3D-CNN model using a late fusion technique. We perform extensive experiments on two recently released large-scale isolated sign language datasets, namely AUTSL and BosphorusSign22k. Our experiments show that our models, which use only RGB data, can compete with the state-of-the-art models in the literature that use multi-modal data.
Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection

The modern era, with information overload, has compelled strategic decision makers to obtain assistance from artificial intelligence- (AI) based decision support systems (DSS). Data-driven DSSs powered by powerful AI algorithms can instantly categorize unstructured data, assign context, and produce meaningful insights from such data. This paper presents an automated media monitoring system that can analyze unstructured global events reported in online news, channels, government websites, and major social media to produce significant insights with explainable AI. Using this innovative system, a decision maker can focus on global events requiring urgent attention since it segregates millions of unnecessary data by using the presented methodology involving entity detection, sentiment analysis, and anomaly detection. The system was designed, deployed, and tested during June 2, 2021 – September 1, 2021. During this 92-day period, it connected to 2,397 distinct types of news sources and automatically fetched 22,425 major event descriptions from 192 countries. Then, to assign meaning and context to the unstructured event descriptions, the system performed AI-based entity detection and sentiment analysis of these global events. The proposed system is sufficiently robust to detect anomalies instantly from 2.76 × 108404 possible scenarios and provides detailed explanations by using natural language descriptions along with dynamic line charts and bar charts to portray detailed reasoning. The entity detection algorithm had a F1-score of 0.994 and the anomaly detection algorithm had an area under curve score of 0.941, establishing the proposed system with explainable AI as the most accurate, robust media monitoring system according to the literature.
Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts

Media coverage has a substantial effect on the public perception of events.Nevertheless, media outlets are often biased.One way to bias news articles is by altering the word choice.The automatic identification of bias by word choice is challenging, primarily due to the lack of a gold standard data set and high context dependencies.This paper presents BABE, a robust and diverse data set created by trained experts, for media bias research.We also analyze why expert labeling is essential within this domain.Our data set offers better annotation quality and higher inter-annotator agreement than existing work.It consists of 3,700 sentences balanced among topics and outlets, containing media bias labels on the word and sentence level.Based on our data, we also introduce a way to detect bias-inducing sentences in news articles automatically.Our best performing BERT-based model is pre-trained on a larger corpus consisting of distant labels.Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F 1 -score of 0.804, outperforming existing methods.
A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.
Aspect-Controllable Opinion Summarization

Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them.In this paper, we propose an approach that allows the generation of customized summaries based on aspect queries (e.g., describing the location and room of a hotel).Using a review corpus, we create a synthetic training dataset of (review, summary) pairs enriched with aspect controllers which are induced by a multi-instance learning model that predicts the aspects of a document at different levels of granularity.We fine-tune a pretrained model using our synthetic dataset and generate aspect-specific summaries by modifying the aspect controllers.Experiments on two benchmarks show that our model outperforms the previous state of the art and generates personalized summaries by controlling the number of aspects discussed in them.
Steering a Robotic Wheelchair Based on Voice Recognition System Using Convolutional Neural Networks

Many wheelchair people depend on others to control the movement of their wheelchairs, which significantly influences their independence and quality of life. Smart wheelchairs offer a degree of self-dependence and freedom to drive their own vehicles. In this work, we designed and implemented a low-cost software and hardware method to steer a robotic wheelchair. Moreover, from our method, we developed our own Android mobile app based on Flutter software. A convolutional neural network (CNN)-based network-in-network (NIN) structure approach integrated with a voice recognition model was also developed and configured to build the mobile app. The technique was also implemented and configured using an offline Wi-Fi network hotspot between software and hardware components. Five voice commands (yes, no, left, right, and stop) guided and controlled the wheelchair through the Raspberry Pi and DC motor drives. The overall system was evaluated based on a trained and validated English speech corpus by Arabic native speakers for isolated words to assess the performance of the Android OS application. The maneuverability performance of indoor and outdoor navigation was also evaluated in terms of accuracy. The results indicated a degree of accuracy of approximately 87.2% of the accurate prediction of some of the five voice commands. Additionally, in the real-time performance test, the root-mean-square deviation (RMSD) values between the planned and actual nodes for indoor/outdoor maneuvering were 1.721 × 10−5 and 1.743 × 10−5, respectively.
Touching Our Breathing through Shape-Change: Monster, Organic Other, or Twisted Mirror

We report on a soma design process, where we designed a novel shape-changing garment—the Soma Corset. The corset integrates sensing and actuation around the torso in tight interaction loops. The design process revealed how boundaries between the garment and the wearer can become blurred, leading to three flavours of cyborg relations. First, through the lens of the monster, we articulate how the wearer can adopt or reject the garment, resulting in either harmonious or disconcerting experiences of touch. Second, it can be experienced as an organic “other”-with its own agency-resulting in uncanny experiences of touch. Through mirroring the wearer’s breathing, the garment can also be experienced as a twisted version of one’s own body. We suggest that a gradual sensitisation of designers-through soma design and reflection on the emerging human-technology relations-may serve as a pathway for uncovering and articulating novel, machine-like, digital touch experiences.
A span-sharing joint extraction framework for harvesting aspect sentiment triplets

Aspect sentiment triplet extraction (ASTE) is the most recent subtask of aspect-based sentiment analysis (ABSA), and it aims to provide a complete solution for ABSA by extracting the triplets of aspect terms, opinion terms and their associated sentiments. However, previous pipeline approaches to ASTE suffer from error propagation in different subtasks, and previous tagging-based joint extraction approaches fail to deal with one-to-many and many-to-one relationship between aspect terms and opinion words in a sentence. To address this, we propose a span-sharing joint extraction (SSJE) framework to extract aspect sentiment triplets from sentences in an end-to-end fashion. In this framework, an aspect term and the corresponding opinion term(s), combined with the sentiment on the aspect, are identified simultaneously in the last step to avoid the error propagation. At the same time, all possible candidate spans are enumerated to be shared by aspect terms and opinion terms to deal with the problem of multiple-word entities and the complex relationship between aspect terms and opinion terms. To verify SSJE’s effectiveness, we conduct extensive experiments on two benchmark datasets. The experimental results demonstrate that the proposed SSJE achieves state-of-the-art performances not only for ASTE tasks but also for extracting aspect-opinion pairs.
Demystifying Metaverse as a New Paradigm of Enterprise Digitization

As Facebook takes on metaverse as its new business infrastructure and company image, this new development blows a swirling wind through the IT industry. However, metaverse as a new enterprise paradigm is still quite unfamiliar to many technical people, letting along most business managers. In this paper, we give an overview of metaverse as a potential business platform and propose a framework for enterprise digitization. In particular, we clarify several critical concepts such as blockchainization, gamification, tokenization, and virtualization in relation to four Ps of marketing mix: People, Place, Product, and Process; although as many as ten Ps have been proposed, four of them are sufficient for our purposes.
Digital Pathology Implementation in Private Practice: Specific Challenges and Opportunities

Digital pathology (DP) is being deployed in many pathology laboratories, but most reported experiences refer to public health facilities. In this paper, we report our experience in DP transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a private practice setting and how to overcome these issues. We started our implementation in 2020 and we are currently scanning 100% of our histology cases. Pre-existing sample tracking infrastructure facilitated this process. We are currently using two high-capacity scanners (Aperio GT450DX) to digitize all histology slides at 40×. Aperio eSlide Manager WebViewer viewing software is bidirectionally linked with the laboratory information system. Scanning error rate, during the test phase, was 2.1% (errors detected by the scanners) and 3.5% (manual quality control). Pre-scanning phase optimizations and vendor feedback and collaboration were crucial to improve WSI quality and are ongoing processes. Regarding pathologists' validation, we followed the Royal College of Pathologists recommendations for DP implementation (adapted to our practice). Although private sector implementation of DP is not without its challenges, it will ultimately benefit from DP safety and quality-associated features. Furthermore, DP deployment lays the foundation for artificial intelligence tools integration, which will ultimately contribute to improving patient care.
A CMIP6 assessment of the potential climate change impacts on solar photovoltaic energy and its atmospheric drivers in West Africa

Abstract Many solar photovoltaic (PV) energy projects are currently being planned and/or developed in West Africa to sustainably bridge the increasing gap between electricity demand and supply. However, climate change will likely affect solar power generation and the atmospheric factors that control it. For the first time, the state-of-the-art CMIP climate models (CMIP6) are used to investigate the potential future evolution of solar power generation and its main atmospheric drivers in West Africa. A multi-model analyses carried out revealed a decrease of solar PV potential throughout West Africa in the 21st century, with an ensemble mean reduction reaching about 12% in southern parts of the region. In addition, the variability of future solar PV production is expected to increase with a higher frequency of lower production periods. The projected changes in the solar PV production and its variability are expected to be predominant in the June to August season. We found the decrease in the solar PV potential to be driven by a decrease of surface irradiance and an increase of near-surface air temperature. However, the decrease of the surface irradiance accounted for a substantially larger percentage of the projected solar PV potential. The decrease in surface irradiance was further linked to changes in both cloud cover and aerosol presence, although generally much more strongly for the former.
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer

Knowledge graph completion aims to address the problem of extending a KG with missing triples. In this paper, we provide an approach GenKGC, which converts knowledge graph completion to sequence-to-sequence generation task with the pre-trained language model. We further introduce relation-guided demonstration and entity-aware hierarchical decoding for better representation learning and fast inference. Experimental results on three datasets show that our approach can obtain better or comparable performance than baselines and achieve faster inference speed compared with previous methods with pre-trained language models. We also release a new large-scale Chinese knowledge graph dataset AliopenKG500 for research purpose. Code and datasets are available in https://github.com/zjunlp/PromptKG/tree/main/GenKGC.