Reviewerly/exHarmony huggingface dataset:
Developing a Risk Analysis Strategy Framework for Impact Assessment in Information Security Management Systems: A Case Study in IT Consulting Industry

Organizations must be committed to ensuring the confidentiality, availability, and integrity of the information in their possession to manage legal and regulatory obligations and to maintain trusted business relationships. Information security management systems (ISMSs) support companies to better deal with information security risks and cyber-attacks. Although there are many different approaches to successfully implementing an ISMS in a company, the most important and time-consuming part of establishing an ISMS is a risk assessment. The purpose of this paper was to develop a risk assessment framework that a company followed in the information technology sector to conduct the risk assessment process to comply with International Organization for Standardization (ISO) 27001. The findings analyze the conditions that force organizations to invest in protecting information and the benefits they can derive from this process. In particular, the paper delves into a multinational IT consulting services company that undertakes and implements large business support installation and customization projects. It explains the risk assessment process and the management of the necessary configurations so that its functions are acceptable and in line with information security standards. Finally, it presents the difficulties and challenges encountered.
A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks

Currently, law enforcement and legal consultants are heavily utilizing social media platforms to easily access data associated with the preparators of illegitimate events. However, accessing this publicly available information for legal use is technically challenging and legally intricate due to heterogeneous and unstructured data and privacy laws, thus generating massive workloads of cognitively demanding cases for investigators. Therefore, it is critical to develop solutions and tools that can assist investigators in their work and decision making. Automating digital forensics is not exclusively a technical problem; the technical issues are always coupled with privacy and legal matters. Here, we introduce a multi-layer automation approach that addresses the automation issues from collection to evidence analysis in online social network forensics. Finally, we propose a set of analysis operators based on domain correlations. These operators can be embedded in software tools to help the investigators draw realistic conclusions. These operators are implemented using Twitter ontology and tested through a case study. This study describes a proof-of-concept approach for forensic automation on online social networks.
Stereoscopic 3D teaching material usability analysis for interactive boards

Abstract The Ministry of National Education of Turkey equipped all schools with interactive boards (IBs) within the scope of the FATİH project. The need for appropriate materials has arisen for the effective use of IBs in educational activities. Three‐dimensional (3D) technologies are also among these materials. It has been determined in the study that IBs have the necessary hardware to run stereoscopic 3D (S3D) training materials, but the panel has not got an S3D imaging feature. Therefore, only the anaglyph S3D imaging method can be applied to IBs. Thus, an anaglyph S3D training material was prepared for the interaction of the skeletal system and interactive 3D material design for IBs with its effects in education was investigated. Besides, S3D imaging methods with features of IBs were examined. A Likert‐type scale was developed to measure the usability of the training material on IBs and the material was evaluated by 20 experts. The data were analyzed by the SPSS statistical program and the results were interpreted. According to the results, educational material seems to be positive in terms of image characteristics, content, navigation, and ease of use, font sizes were moderate for readability, the feedback process and the help menu were moderately effective.
Cost-Efficient Blockchain-Based Access Control for the Internet of Things

Blockchain-based access control (BBAC) has been highly promising to prevent unauthorized resource access in the Internet of Things (IoT). However, maintaining BBAC can be potentially expensive due to the storage cost of the blockchain. To address this issue, we propose a layered BBAC architecture by combining blockchain with blockchain oracle and tamper-proof decentralized storage (e.g., IOTA). The proposed architecture consists of three main layers: a blockchain layer, which provides distributed and trustworthy access control, a storage layer, which stores meta data (e.g., subject/object attributes and policies) used in the access control of the blockchain layer, and an oracle layer, which works as a bridge to help transfer data between the blockchain and decentralized storage. This architecture achieves robust, auditable, and cost-efficient access control by migrating the meta data from the blockchain to the decentralized storage while keeping the fascinating tamper-proof feature of the blockchain. We implement and evaluate this architecture in terms of time and monetary cost to demonstrate its feasibility and superiority over existing ones.
Deep neural networks with attention mechanism for monocular depth estimation on embedded devices

Monocular depth estimation (MDE) predicts pixel-level depth from a single image and plays a vital role in image sensing. MDE has made progress due to the usage of deep neural networks (DNNs). However, current MDE methods fail to provide satisfactory depth, as they rarely modeled dependencies among convolution channels and ignored location relationships in DNNs. Additionally, they are commonly slow for inference on embedded devices due to high computation complexity. To tackle these problems, we propose a novel encoder–decoder network (EDNet) for fast MDE inference on diverse embedded devices. Specifically, (1) we design an encoder to re-explore new features, and then model nonlinear and dynamic dependencies among convolution channels based on an attention mechanism. (2) We propose a decoder containing four plug-and-play blocks to individually extract image features, model dependencies among convolution channels, learn location relationships, and adjust the channels. (3) We optimize EDNet with inference engines to match MDE with different embedded system architectures. Experiments confirm that our RMSE (root mean square error) is at least lower by 3.7% and 5.0% than that of state-of-the-art models on the NYU-Depth-v2 and KITTI datasets, respectively. The optimized EDNet simultaneously improves the accuracy, inference speed, and visualized results of MDE on different embedded devices.
Violence Detection Using One-Dimensional Convolutional Networks

Violence detection in surveillance video processing is a useful capability helping discover abnormal events in a variety of places. Utilizing methods considering the accuracy and complexity simultaneously can provide systems suitable for real-time applications. In this paper, the traditional approach of extracting temporal features has been investigated, while by exploiting one-dimensional convolutional networks, a new approach is proposed, which extracts these features across consecutive frames properly. This low-complexity convolutional-based approach represents a series of frames with a robust feature vector, which can be applied for real-time applications. The experimental results on Hockey, ViolentFlow reveal the efficiency of this proposed method.
Technical Communication as Assemblage

This article offers a theoretical intervention into the work on posthumanism in technical and professional communication (TPC), an intervention that encourages the field to recognize relationships between objects and users in different ways. Our intervention draws on the work of Deleuze and Guattari to reimagine how TPC tends to think about the concept of assemblage. We apply this other view in makerspaces, illustrating what it buys us for practice and theory in complex sociotechnical contexts.
A Labelling Semantics and Strong Admissibility for Weighted Argumentation Frameworks

Abstract Argumentation Theory provides tools for both modelling and reasoning with controversial information and is a methodology that is often used as a way to give explanations to results provided using machine learning techniques. In this context, labelling-based semantics for Abstract Argumentation Frameworks (AFs) allow for establishing the acceptability of sets of arguments, dividing them into three partitions: in, out and undecidable (instead of classical Dung acceptable and not acceptable sets). This kind of semantics have been studied only for classical AFs, while the more powerful weighted and preference-based frameworks have not been studied yet. In this paper, we define a novel labelling semantics for Weighted Argumentation Frameworks (WAFs), extending and generalizing the crisp one, and we provide some insights towards a definition of strong admissibility for WAFs.
Robotics services at home support

This article proposes a robotic system that aims to support the elderly, to comply with the medication regimen to which they are subject. The robot uses its locomotion system to move to the elderly and through computer vision detects the packaging of the medicine and identifies the person who should take it at the correct time. For the accomplishment of the task, an application was developed supported by a database with information about the elderly, the medicines that they have prescribed and the respective timetable of taking. The experimental work was done with the robot NAO, using development tools like MySQL, Python, and OpenCV. The elderly facial identification and the detection of medicine packing are performed through computer vision algorithms that process the images acquired by the robot’s camera. Experiments were carried out to evaluate the performance of object recognition, facial detection, and facial recognition algorithms, using public databases. The tests made it possible to obtain qualitative metrics about the algorithms’ performance. A proof of concept experiment was conducted in a simple scenario that recreates the environment of a dwelling with seniors who are assisted by the robot in the taking of medicines.
Single-step and Multi-step Time Series Prediction for Urban Temperature Based on LSTM Model of TensorFlow

In this paper, a temperature prediction model based on long - term memory network (LSTM) is developed by modeling urban temperature data. The fluctuations of temperature changes are usually nonstationary fluctuations and many meteorological factors need to be considered in the analysis. At the same time, the temperature data is very sensitive to time series. The LSTM network is often used to deal with time series data problems, and has the advantage of being able to remember information for a long time to predict temperature. The model based on TensorFlow is mainly introduced to predict the future temperature by learning the historical temperature samples. Compared with other time series data prediction methods, it has more accurate prediction effect.
Human-in-the-Loop Low-Shot Learning

We consider a human-in-the-loop scenario in the context of low-shot learning. Our approach was inspired by the fact that the viability of samples in novel categories cannot be sufficiently reflected by those limited observations. Some heterogeneous samples that are quite different from existing labeled novel data can inevitably emerge in the testing phase. To this end, we consider augmenting an uncertainty assessment module into low-shot learning system to account into the disturbance of those out-of-distribution (OOD) samples. Once detected, these OOD samples are passed to human beings for active labeling. Due to the discrete nature of this uncertainty assessment process, the whole Human-In-the-Loop Low-shot (HILL) learning framework is not end-to-end trainable. We hence revisited the learning system from the aspect of reinforcement learning and introduced the REINFORCE algorithm to optimize model parameters via policy gradient. The whole system gains noticeable improvements over existing low-shot learning approaches.
Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling

Abstract Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs). It is a pivotal step for integrating KGs to increase knowledge coverage and quality. Recent years have witnessed a rapid increase of EA frameworks. However, state-of-the-art solutions tend to rely on labeled data for model training. Additionally, they work under the closed-domain setting and cannot deal with entities that are unmatchable. To address these deficiencies, we offer an unsupervised framework that performs entity alignment in the open world. Specifically, we first mine useful features from the side information of KGs. Then, we devise an unmatchable entity prediction module to filter out unmatchable entities and produce preliminary alignment results. These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment. Finally, the progressive learning framework gradually improves the quality of structural embeddings and enhances the alignment performance. Furthermore, noticing that the pseudo-labeled data are of various qualities, we introduce the concept of confidence to measure the probability of an entity pair of being true and develop a confidence-based unsupervised EA framework . Our solutions do not require labeled data and can effectively filter out unmatchable entities. Comprehensive experimental evaluations validate the superiority of our proposals .
Coverage Path Planning for Robotic Quality Inspection With Control on Measurement Uncertainty

The optical scanning gauges mounted on the robots are commonly used in quality inspection, such as verifying the dimensional specification of sheet structures. Coverage path planning (CPP) significantly influences the accuracy and efficiency of robotic quality inspection. Traditional CPP strategies focus on minimizing the number of viewpoints or traveling distance of robots under the condition of full coverage inspection. The measurement uncertainty when collecting the scanning data is less considered in the free-form surface inspection. To address this problem, a novel CPP method with the optimal viewpoint sampling strategy is proposed to incorporate the measurement uncertainty of key measurement points (MPs) into free-form surface inspection. At first, the feasible ranges of measurement uncertainty are calculated based on the tolerance specifications of the MPs. The initial feasible viewpoint set is generated considering the measurement uncertainty and the visibility of MPs. Then, the inspection cost function is built to evaluate the number of selected viewpoints and the average measurement uncertainty in the field of views of all the selected viewpoints. Afterward, an enhanced rapidly exploring random tree algorithm is proposed for viewpoint sampling using the inspection cost function and CPP optimization. Case studies, including simulation tests and inspection experiments, have been conducted to evaluate the effectiveness of the proposed method. Results show that the scanning precision of key MPs is significantly improved compared with the benchmark method.
Improve Exploration of Arithmetic Optimization Algorithm by Opposition-based Learning

An improved version of the arithmetic optimization algorithm (AOA) based on the opposition-based learning (OBL) strategy called OBLAOA is proposed in this paper. The proposed OBLAOA algorithm consists of two stages, and in the second stage we adds OBL to update the AOA population in each iteration. The improved AOA is compared with the original AOA by using 12 benchmark functions in different dimensions to validate the improvement on exploration with the OBL. Eventually ,we get a conclusion that the OBLAOA is committed to take both candidate solutions and their opposite solutions into consideration, which shows greater opportunity to reach the global optimal and faster convergence acceleration than AOA.
Primary teachers’ professional learning during a COVID-19 school lockdown

Background The COVID-19 crisis forced education providers around the world to cancel contact teaching in schools as part of measures to limit contact between people and to slowdown the spread of the virus. The rapid and unexpected transition to distance teaching in the beginning of the pandemic posed an unprecedented challenge for teachers and placed a significant demand on their informal professional learning. This study focused on Finnish primary teachers’ professional learning domains and activities during the first weeks of Finland’s COVID-19 school lockdown.Purpose The aim was to understand what and how primary teachers learnt during the beginning of the distance teaching period.Methods Semi-structured interviews were conducted individually with 20 Finnish primary school teachers, all of whom had been delivering distance teaching from the beginning of the lockdown, during the fourth week of the distance teaching period. Two separate qualitative analyses were undertaken.Findings The first analysis indicated that teachers’ professional learning involved more than progress in the most self-evident and apparent technological knowledge domain: learning was also firmly connected to pedagogical knowledge and coping skills. The second analysis identified the individual learning activities, such as learning by doing, experimenting and considering one’s own teaching practice, and revealed three levels of collective learning activities (with a close colleague, in the school community and in larger online communities) carried out with the aim of improving distance teaching.Conclusions The study draws attention to primary teachers’ multifaceted professional learning domains and the activities entered into during the rapid shift from contact to distance teaching. It highlights that even in emergency circumstances, teachers’ individual and collective learning processes are interrelated and supplementary to each other. Educators’ hard-earned understanding achieved in relation to distance teaching should be nurtured and refined to further benefit and support the profession.
FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment

In this paper we develop FaceQgen <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Publicly available in: https://github.com/uam-biometrics/FaceQgen, a No-Reference Quality Assessment approach for face images based on a Generative Adversarial Network that generates a scalar quality measure related with the face recognition accuracy. FaceQgen does not require labelled quality measures for training. It is trained from scratch using the SCface database. FaceQgen applies image restoration to a face image of unknown quality, transforming it into a canonical high quality image, i.e., frontal pose, homogeneous background, etc. The quality estimation is built as the similarity between the original and the restored images, since low quality images experience bigger changes due to restoration. We compare three different numerical quality measures: a) the MSE between the original and the restored images, b) their SSIM, and c) the output score of the Discriminator of the GAN. The results demonstrate that FaceQgen's quality measures are good estimators of face recognition accuracy. Our experiments include a comparison with other quality assessment methods designed for faces and for general images, in order to position FaceQgen in the state of the art. This comparison shows that, even though FaceQgen does not surpass the best existing face quality assessment methods in terms of face recognition accuracy prediction, it achieves good enough results to demonstrate the potential of semi-supervised learning approaches for quality estimation (in particular, data -driven learning based on a single high quality image per subject), having the capacity to improve its performance in the future with adequate refinement of the model and the significant advantage over competing methods of not needing quality labels for its development. This makes FaceQgen flexible and scalable without expensive data curation.
Hybrid heuristic-based key generation protocol for intelligent privacy preservation in cloud sector

Cloud Computing is defined as a set of software and hardware that are used together for delivering different kinds of cloud services based on user's demand. Cloud computing secures a major role in the Information Technology (IT) industry for accessing the services at any place around the world. On the other hand, there are increasing vulnerabilities and threats in the cloud environment due to the rise in popularity and demands in cloud computing services. Data privacy and integrity are the major issues in cloud computing while storing data in various geographical locations. So, it is necessary to consider the data privacy and integrity factors in a cloud computing environment. It is difficult to construct a common platform to interact in the cloud environment. So, it is essential for implementing the security solutions, which need to provide confidentiality while exchanging the data. The main intention of this paper is to design and develop a novel artificial intelligence approach for handling the privacy preservation problem in the cloud sector. Through the process of data sanitization, the sensitive data is hidden, so that it cannot be accessed by unauthorized users. To perform the sanitization process, the heuristic-based key generations play a vital role, and here, it is solved by considering a multi-objective function with constraints like the “degree of modification, hiding ratio, and information preservation ratio”. This multi-objective problem is solved by the adoption of novel Probability Switch searched Butterfly-Moth Flame Optimization (PS-BMFO). Finally, the restoration is also performed by the same PS-BMFO-based key generation. The analysis results confirm that the suggested model preserves privacy and ensures the integrity of the user's data against unauthorized parties. From the experimental analysis, the proposed PS-BMFO gives 12.5%, 10%, 30.7%, and 12.5% enriched than GWO, JA, MFO, and BOA, respectively. Therefore, the statistical analysis shows that the developed data privacy preservation model with suggested PS-BMFO performs better than the other conventional algorithms.
Attacks on Data-Driven Process Monitoring Systems: Subspace Transfer Networks

With the rapid development of information technology, intelligent upgrading of the manufacturing industry has broken the closed environment of traditional industrial control systems (ICS); thus, the information security of ICS has been seriously threatened. As part of ICS, the process monitoring system (PMS) is heavily subject to external risks. Data-driven PMSs have been widely used as initial lines of defense to ensure ICS safety. Once the PMS is under attack, the consequences on the whole ICS will be unimaginable. Unfortunately, the safety issues of the PMS have received inadequate attention. This article reveals PMS’s vulnerabilities through effective attacks. A novel method called subspace transfer network (STN) is proposed to conduct adversarial and poisoning attacks on the PMS simultaneously. Then the attack task flow is defined and explained to make online adversarial attacks and data poisoning on PMS. Meanwhile, aiming at two poisoning goals, targeted and untargeted attacks of STN are designed, respectively. Finally, the PMS’s fragility is verified in two industrial benchmarks.