Reviewerly/exHarmony huggingface dataset:
Application-scoped Access Control for the Construction Industry

The construction industry is characterized by its extensive and dynamic collaborations between contractors providing various services and expertise. In such eco-systems, the secure sharing of information, data and equipment challenges the access control needs to be application agnostic. Furthermore, it needs fine-grained access policies including means for abstraction to ease administration, and support for delegated authorization in Service-Oriented Architecture (SOA) based systems. In this paper, we explore the use of delegated access using OAuth 2.0 with Attribute-Based Access Control (ABAC) for the collaborative sharing of equipment at construction sites. In particular, we investigate the use of contextual attributes to capture the dynamic aspects, such as location and urgency, in the booking of construction lifts. Through this study, we propose a solution based on the IoT Application-scoped Access Control as a Service (IAACaaS) architecture model combined with NIST Next Generation Access Control (NGAC). We present an architecture for a general Identity and Access Management (IAM) system for the construction industry, and provide a design and guide for implementation of this architecture in terms how key functionalities should be captured as reusable micro-services. Moreover, we describe how these micro-services can be combined to make the system a general and reusable solution providing access control for collaborative sharing of data, information and equipment at construction sites.
About the professional activity of an informatics teacher in the context of digital transformation of education

The article highlights and describes the main stages passed by the Russian secondary school on the path of digital transformation of education: computerization, informatization, early digitalization. The features of the content of teaching informatics and the activities of an informatics teacher within the framework of each of the above stages are given. The main trends in the development of the content of the school informatics course are highlighted, new areas of subject knowledge included in the school informatics course are described: robotics, information security, cloud and mobile technologies, virtual reality, augmented reality, intelligent methods of data processing and analysis. It is considered how the role of an informatics teacher at school is changing at the present stage of the digital transformation of education.
A Comparative Study of Transformers on Word Sense Disambiguation

Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this regard, numerous NLP systems have leveraged the power of neural network-based architectures to incorporate sense information in embeddings, resulting in Contextualized Word Embeddings (CWEs). Despite this progress, the NLP community has not witnessed any significant work performing a comparative study on the contextualization power of such architectures. This paper presents a comparative study and an extensive analysis of nine widely adopted Transformer models. These models are BERT, CTRL, DistilBERT, OpenAI-GPT, OpenAI-GPT2, Transformer-XL, XLNet, ELECTRA, and ALBERT. We evaluate their contextualization power using two lexical sample Word Sense Disambiguation (WSD) tasks, SensEval-2 and SensEval-3. We adopt a simple yet effective approach to WSD that uses a k-Nearest Neighbor (kNN) classification on CWEs. Experimental results show that the proposed techniques also achieve superior results over the current state-of-the-art on both the WSD tasks.
Leveraging pre-trained Segmentation Networks for Anomaly Segmentation

Employing representations generated by large-scale training in a transfer-learning setting achieves state-of-the-art anomaly segmentation results when applied to the visual inspection task. Current approaches, however, focus exclusively on features of pre-trained classification networks, which are known to posess lower spatial resolution than segmentation or object detection networks. In our work, we investigate whether features extracted from pre-trained segmentation networks can be used to further improve anomaly segmentation performance in the transfer-learning setting. To this end, we apply state-of-the-art transfer-learning methods to encoder-decoder based segmentation networks. Results show that the encoders of pre-trained segmentation networks yield improved anomaly segmentation performance compared to their pre-trained classification counterparts. However, no consistent improvements can be observed yet regarding the decoders of the pre-trained segmentation networks. Together, this demonstrates that pre-trained segmentation networks can be used to further improve transfer-learned anomaly segmentation performance and that additional research is required to fully unleash their potential.
Modelling and Simulation of Quantum Key Distribution using OptSim

We present a simulation approach to creating a symmetric key material with a Quantum Key Distribution (QKD) protocol. We also give an applied cryptographic purpose to realise quantum cryptography with standard programs. The QKD methodology guarantees a perfectly random secret key by encoding the quantum states of photons with a set of specific polarization angles shared between a transmitter and receiver participant via a quantum channel. This paper presents consistent theoretical results and demonstrates a feasible QKD standard BB84 protocol simulation with optical software and visuals for each QKD phase.
Optimized Path Planning by Adaptive RRT* Algorithm for Constrained Environments Considering Energy

Optimized path planning of robots are necessary for the industries to thrive towards greater flexibility and sustainability. This paper proposes an implementation of a collision-free path with the shortest distance. The novelty of the work presented is the new ARRT*(Adaptive Rapidly exploring Random Tree Star) algorithm, which is modified from the RRT*(Rapidly exploring Random Tree Star). In a constraint environment, RRT* algorithms tend to fail when searching for suitable collision-free paths. The proposed ARRT* algorithm gives an optimized feasible collision-free paths in a constraint environment. The feasibility to implement RRT* and ARRT* in a Multi Agent System as a path agent for online control of robots is demonstrated. We have created a digital twin simulated environment to find a collision-free path based on these two algorithms. The simulated path is tested in real robots for feasibility and validation purpose. During the real time implementation, we measured the following parameters: the algorithm computation time for generating a collision-free path, move along time of the path in real time, and energy consumed by each path. These parameters were measured for both the RRT* and the ARRT* algorithms and the test results were compared. The test results were showing that ARRT* performs better in a constrained environment. Both algorithms were tested in a Plug and Produce setup and we find that the generated paths for both algorithms are suitable for online path planning applications.
A Study of Differentially Private Machine Learning in Healthcare

The field of Machine Learning (ML) has been engaged in intensive research for a while to build an efficient and effective intelligent system for the early identification of chronic diseases such as cancer and diabetes and has recently seen some promising findings. The bulk of the initiatives are aimed at classifying illness onset and minimizing cases of maltreatment. As a supervised learning problem, its accuracy is mostly determined by the training data, which is labeled data on actual patients that is highly privacy-sensitive. Privacy leakage can occur at any point in the machine learning lifecycle, from model training through model deployment, and can lead to a membership inference attack, model inversion attack, and reconstruction attack. As a result, safeguarding users' privacy is critical in healthcare issues, but little has been done to satisfy this demand. In this paper, we propose differential privacy-based Logistic Regression and Naive Bayes models on breast cancer classification and diabetes prediction. We evaluate the two models using the popular Wisconsin Diagnostic Breast Cancer (WDBC) dataset, and Pima Indians Diabetes dataset and depict the privacy requirement and model accuracy trade-off.
A Sentiment Classification from Review Corpus using Linked Open Data and Sentiment Lexicon

The sentiment analysis approach has become an essential customer analysis as a result of the fast growth of internet technology and social media. Several studies have detailed the efficacy of several sentiment classifications, ranging from lexicon-based to machine learning approaches. While lexicon-based approaches have limitations due to the limited number of terms in dictionaries and labeled data, machine learning approaches are frequently flawed because it requires massive datasets to train each domain dataset. This paper presents a framework and algorithms that bridge the gap between the lexicon and linked open data methods (DBpedia) for resolving semantic conflicts and improving SentiWordNet's sentiment score to obtain higher performance. Furthermore, we also evaluate our sentiment classification using precision, recall, and F-measure metrics, which have values of 0.76, 0.91, and 0.82, respectively.
Federated cooperative detection of anomalous vehicle trajectories at intersections

Nowadays mobile positioning devices, sensor technologies, machine learning methods and cloud computing allow an efficient collection, processing and analysis of dynamic position information (trajectories), which can be used for various applications. Currently, data owners (e.g. vehicle producers or service operators) are reluctant to share data due to data privacy rules or because of the risk of sharing information with competitors, which could jeopardize the data owner's competitive advantage. In this paper, we compare various state-of-the-art anomaly detection methods like one-class support vector machine, isolation forest and bidirectional generative adversarial networks towards the detection of abnormal vehicle trajectories at intersections solving one-class classification problem with unsupervised learning algorithms. The experiments show that the selected models allow to identify anomalies with 98%-99% accuracy for the centralized approach. However, data exists mostly fragmented in form of isolated islands throughout the whole industry. Thus, we focus on a general concept of collaborative learning which provides with an increasing number of partners more accurate anomaly detection models than local models of each partner without exchanging raw data, but only synchronizing model parameters. We propose a federated learning algorithm for an one-class support vector machine model towards the detection of anomalies. The federated collaborative approach allows to construct comparable accuracy with the centralized approach, reduces the local data labeling load and keeps individual data private.
MGD: A Utility Metric for Private Data Publication

Differential privacy has been accepted as one of the most popular techniques to protect user data privacy. A common way for utilizing private data under DP is to take an input dataset and synthesize a new dataset that preserves features of the input dataset while satisfying DP. A trade-off always exists between the strength of privacy protection and the utility of the final output: stronger privacy protection requires larger randomness, so the outputs usually have a larger variance and can be far from optimal. In this paper, we summarize our proposed metric for the NIST “A Better Meter Stick for Differential Privacy” competition [26], MarGinal Difference (MGD), for measuring the utility of a synthesized dataset. Our metric is based on earth mover distance. We introduce new features in our metric so that it is not affected by some small random noise that is unavoidable in the DP context but focuses more on the significant difference. We show that our metric can reflect the range query error better compared with other existing metrics. We introduce an efficient computation method based on the min-cost flow to alleviate the high computation cost of the earth mover’s distance.
Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrations

Natural language programming is a promising approach to enable end users to instruct new tasks for intelligent agents. However, our formative study found that end users would often use unclear, ambiguous or vague concepts when naturally instructing tasks in natural language, especially when specifying conditionals. Existing systems have limited support for letting the user teach agents new concepts or explaining unclear concepts. In this paper, we describe a new multimodal domain-independent approach that combines natural language programming and programming-by-demonstration to allow users to first naturally describe tasks and associated conditions at a high level, and then collaborate with the agent to recursively resolve any ambiguities or vagueness through conversations and demonstrations. Users can also define new procedures and concepts by demonstrating and referring to contents within GUIs of existing mobile apps. We demonstrate this approach in PUMICE, an end-user programmable agent that implements this approach. A lab study with 10 users showed its usability.
A single-pose series sphere-based calibration method for camera-projector structured light system

Camera-projector structured light sensor plays a crucial part in three-dimensional (3D) reconstruction and robot vision fields. Calibration of the camera-projector structured light system is essential for further measurement. The common-used plane-based calibration method is always not flexible due to the inevitable manual adjustment of multi-pose plane. In this paper, a single-pose series sphere-based calibration method is proposed, which mainly includes two steps. The first step is to calibrate the camera’s intrinsic parameters. An improved calibration method based on the image of absolute conic (IAC) is proposed in this step to achieve higher calibration accuracy, and the minimum number of spheres used in this improved method is analyzed. The second step is to calibrate the projector’s intrinsic and extrinsic parameters. In order to achieve the 3D coordinates of feature points on the spherical surface, an accurate method based on the back projection theory is proposed in this step. Finally, calibration experiment for a camera-projector structured light system is implemented, and the experimental result demonstrates that the proposed method can achieve desired flexibility, robustness and accuracy.
On Optimal Test Signal Design and Parameter Identification Schemes for Dynamic Takagi-Sugeno Fuzzy Models Using the Fisher Information Matrix

Abstract This paper is concerned with the analysis of optimization procedures for optimal experiment design for locally affine Takagi-Sugeno (TS) fuzzy models based on the Fisher Information Matrix (FIM). The FIM is used to estimate the covariance matrix of a parameter estimate. It depends on the model parameters as well as the regression variables. Due to the dependency on the model parameters good initial models are required. Since the FIM is a matrix, a scalar measure of the FIM is optimized. Different measures and optimization goals are investigated in three case studies.
Development of QoS-aware agents with reinforcement learning for autoscaling of microservices on the cloud

Microservices play an essential role in cloud application scalability. When demand increases on a microservice-based application, the microservices need to be scaled to sustain the demand without degrading the application performance. At the same time, cloud platforms need to maintain Quality of Service (QoS) for deployed cloud applications. Current microservices autoscaling technologies such as Kubernetes Horizontal Pod Autoscaler (HPA) require identifying specific scaling metrics in addition to very good knowledge of the application resource usage. Those technologies do not provide a built-in auto scaling based on QoS constraints. In this work, we present an intelligent micro services auto scaling module using Reinforcement Learning (RL) agents. The RL agents are trained and validated on microservices for disaster management real time systems with response time as QoS constraint. Our RL agents deployed on Google cloud can identify the scaling metrics, provide microservices auto scaling, and enhance the response time compared to the default Kubernetes intelligently and autonomously. The RL agents serve as an extendible plug-in module to Kubernetes HP A for auto scaling micro services in the cloud while adhering to QoS constraints autonomously. The intelligent module is flexible to accommodate other types of QoS and provides a cost-effective solution to cloud applications auto scaling in areas of limited resources.
Applying Computation Intelligence for Improved Computer Vision Capabilities

While computers remain undoubtedly the most successful innovation of the modern-day world, it has helped us envision new dreams and aspirations which can be fulfilled with computer vision. Its ability of artificially sensing the external world coupled with its precision, accuracy, and quality-driven output has resulted in the use of software to cater to the upcoming and ever-growing needs, reducing human dependence on the tasks by self-maneuvering them. A path-breaking development arose with the attempt to utilize machine learning for recognition of patterns on real-time situations which opened a horizon of new opportunities for human talent to dwell on in the form of computer vision. Computer vision capabilities have set open another arena for development and improvement as well as revenue generation by working on computer vision capabilities; it often comes with its own challenges including difficulty in pattern recognition, efficient handling of variations in pose, illumination, expression, and occlusion. With the upgraded and enhanced techniques along with the implementation of computerized intelligence, efforts are being made to arrive at solutions to minimize the problems associated with computer vision explaining the processes like image segmentation, filtering, pattern recognition, and its applications involving several components such as memory, retrieval, reasoning, recognition, estimation, and coordination along with other senses in a manner that it serves a tool to aid the mankind.
The application of Deep Reinforcement Learning in Coordinated Control of Nuclear Reactors

Abstract The nuclear reactor control system plays a crucial role in the operation of nuclear power plants. The coordinated control of power control and steam generator level control has become one of the most important control problems in these systems. In this paper, we propose a mathematical model of the coordinated control system, and then transform it into a reinforcement learning model and develop a deep reinforcement learning control algorithm so-called DDPG algorithm to solve the problem. Through simulation experiments, our proposed algorithm has shown an extremely remarkable control performance.
Intelligent Document Processing Method Based on Robot Process Automation

This paper discusses the importance of document format information in document understanding and the latest research progress in the field of document content understanding, document classification, document format checking and error correction, document interoperability and document intelligent processing, and proposes a method of document intelligent processing based on robotic process automation. The relationship between document understanding and intelligent processing of information in different document formats and layouts is briefly explained.
Ageing population supported by ambient-assisted living in the Kingdom of Saudi Arabia

The number of elderly people worldwide (defined as those aged 60 years and older) was 202 million in 1950; this number has since quadrupled to reach 901 million and is expected to triple again by 2100. The rate of growth of the ageing population is rapidly increasing, and the cost of their care will be a challenging issue in the future. In particular, the number of elderly people in the Kingdom of Saudi Arabia (KSA) is increasing rapidly, from 5% of the total population in 2015 to a forecasted 20.9% by 2050. Clearly, the cost of taking care of elderly people is already a challenge, but it will be very difficult to meet in the future, when it will lead to a much higher expenditure on healthcare facilities. Elderly people are vulnerable to a decline in their health, and they do not wish to live as they did in the 1970s to 1990s. Instead, their desire is to live independently in their own homes and continue to practice normal activities. In fact, Saudi culture is changing, and the independent children generation tend not to live with their parents as was common in the past. A conceptual model using Ambient Assisted Living (AAL) to provide a Home Proactive Monitoring System (AALHPMS) was developed that supports the stakeholders in adopting AAL technologies using Smart sensors and particularly the use of Smart watches for monitoring heartbeat. To share and disseminate knowledge, a website (AgeinginKSA.org) was designed and implemented based on eight phase standards. A detailed description of this AgeinginKSA website is provided, and this website was tested using different website testers, and overall, it passed all the tests. The implementation of a specially designed website, AgeinginKSA, makes the system developed by this research easily and widely accessible, particularly for aged people, and for adoption throughout KSA.
Privacy-Preserving Keystroke Analysis using Fully Homomorphic Encryption & Differential Privacy

Keystroke dynamics is a behavioural biometric form of authentication based on the inherent typing behaviour of an individual. While this technique is gaining traction, protecting the privacy of the users is of utmost importance. Fully Homomorphic Encryption is a technique that allows performing computation on encrypted data, which enables processing of sensitive data in an untrusted environment. FHE is also known to be “future-proof” since it is a lattice-based cryptosystem that is regarded as quantum-safe. It has seen significant performance improvements over the years with substantially increased developer-friendly tools. We propose a neural network for keystroke analysis trained using differential privacy to speed up training while preserving privacy and predicting on encrypted data using FHE to keep the users' privacy intact while offering sufficient usability.
1921–2021: 100 years of macroseismic studies at BCSF

The French Central Seismological Bureau (BCSF) was set up in Strasbourg in July 1921 by a governmental decision.Since then, BCSF is in charge of the macroseismic survey for the French territories.One of its main tasks is to determine macroseismic intensity in the different localities after each felt earthquake.This paper shows how the way of collecting macroseismic information evolved with time through the analysis of questionnaires sent to various dissemination networks and by using different methods and macroseismic scales.In context of a renewal of interest in macroseismic data, we present their main uses, as well as some perspectives for the forthcoming years.The quality of macroseismic data, together with the improvement of the analytical methods and the accessibility to BCSF data, in particular those from our archives, remain at the heart of our concerns and objectives.