Reviewerly/exHarmony huggingface dataset:
What is required for the truth of a general conditional?

General conditionals, if p then q, can be used to make assertions about sets of objects. Previous studies have generally found that people judge the probability of one these conditionals to be the conditional probability of q given p, P(q|p). Two experiments investigated the qualitative relation between the exhaustive possibilities, p & q, p & ¬q, ¬p & q, and ¬p & ¬q, and truth and possibility judgements about general conditionals. In Experiment 1, for truth judgements, people evaluated a general conditional as "true" in sets containing p & q cases but no p & ¬q, and "true" judgements depended only on P(q|p). In Experiment 2, for possibility judgements, people's responses implied that only p & q cases have to be possible in a set for a general conditional to be true of the set. Our results add to earlier findings against representing a general conditional as the material conditional of extensional logic, and they provide novel disconfirmation of two recent proposals: the modal semantics of revised mental model theory and certain inferentialist accounts of conditionals. They supply new support for suppositional theories of conditionals.
Character Recognition of Arabic Handwritten Characters Using Deep Learning

Optical character recognition (OCR) is used to digitize texts in printed documents and camera images. The most basic step in the OCR process is character recognition. The Arabic language is more complex than other alphabets, as the cursive is written in cursive and the characters have different spellings. Our research has improved a character recognition model for Arabic texts with 28 different characters. Character recognition was performed using Convolutional Neural Network models, which are accepted as effective in image processing and recognition. Three different CNN models have been proposed. In the study, training and testing of the models were carried out using the Hijja data set. Among the proposed models, Model C with a 99.3% accuracy rate has obtained results that can compete with the studies in the literature.
Security Risk Analysis in IoT Systems through Factor Identification over IoT Devices

IoT systems contribute to digital transformation through the development of smart concepts. However, the IoT has also generated new security challenges that require security tools to be adapted, such as risk analysis methodologies. With this in mind, the purpose of our study is based on the following question: Which factors of IoT devices should be considered within risk assessment methodologies? We have addressed our study with a 4-phase design-research methodology (DRM) that allows us, based on systematic literature review, to experiment and draw upon expert judgment; as a final product, we obtain a risk assessment methodology based on the characteristics of IoT devices. At the end of this study, we establish seven main constructs—Organization, Risk Behaviors, Dependency, Attack Surface, Susceptibility, Severity and Uncertainty—over which security risk in IoT systems can be evaluated.
EE-ISAC—Practical Cybersecurity Solution for the Energy Sector

A recent survey of cybersecurity assessment methods proposed by the scientific community revealed that their practical adoption constitutes a great challenge. Further research that aimed at identifying the reasons for that situation demonstrated that several factors influence the applicability, including the documentation level of detail, the availability of supporting tools, and the continuity of support. This paper presents the European Energy Information Sharing and Analysis Centre (EE-ISAC)—a cybersecurity platform for the energy sector that has been adopted by multiple organisations. The platform facilitates sharing information about cybersecurity incidents, countermeasures, and assessment results. Prospectively, it is envisaged to be integrated with the threat intelligence platform that enables real-time situational awareness. By considering both fault and attack scenarios together, threat awareness can be mapped onto operational contexts to prioritise decisions and responses. This paper analyses EE-ISAC’s approach based on the conceptual applicability framework developed during the research, to improve the applicability and usefulness of this platform for energy sector participants and to identify areas that require further development.
Document-level event argument linking as machine reading comprehension

Document-level event argument linking aims to find global event arguments to fill an event’s semantic role, which is a challenging task owing to the appearance of long contexts and the issue of data sparsity. In this paper, we study a new formulation to address the above challenges in document-level EAL, by explicitly framing the task as a machine reading comprehension (MRC) problem. In this formulation, argument extraction is viewed as a question answering procedure. To better transfer each semantic role into a question, we propose a back-translation based query generation method, which can effectively generate well-formed questions without adopting huge human effort. Moreover, to better capture the non-local dependencies between triggers and arguments, we devise a dependency-guided question answering process, which can explore the underlying structure of the document to boost learning. The extensive experiments on a benchmark have justified the effectiveness of our approach. Particularity, our approach achieves substantially improvement over previous methods, leading to +5.7% in F1 in the full argument linking setting. Moreover, our approach is particular data-efficient and demonstrates superior performance in the data-low scenario with limited training data.
Towards improved FAIRness of the ThermoML Archive

The ThermoML Archive is a subset of Thermodynamics Research Center (TRC) data holdings corresponding to cooperation between NIST TRC and five journals: Journal of Chemical Engineering and Data (ISSN: 1520-5134), The Journal of Chemical Thermodynamics (ISSN: 1096-3626), Fluid Phase Equilibria (ISSN: 0378-3812), Thermochimica Acta (ISSN: 0040-6031), and International Journal of Thermophysics (ISSN: 1572-9567). Data from initial cooperation (around 2003) through the 2019 calendar year are included. The archive has undergone a major update with the goal of improving the FAIRness and user experience of the data provided by the service. The web application provides comprehensive property browsing and searching capabilities; searching relies on a RESTful API provided by the Cordra software for managing digital objects. JSON files with a schema derived from ThermoML are provided as an additional serialization to lower the barrier to programmatic consumption of the information, for stakeholders who may have a preference of JSON over XML. The ThermoML and JSON files for all available entries can be downloaded from data.nist.gov (https://data.nist.gov/od/id/mds2-2422).
A feature selection method with feature ranking using genetic programming

Feature selection is a data processing method which aims to select effective feature subsets from original features. Feature selection based on evolutionary computation (EC) algorithms can often achieve better classification performance because of their global search ability. However, feature selection methods using EC cannot get rid of invalid features effectively. A small number of invalid features still exist till the termination of the algorithms. In this paper, a feature selection method using genetic programming (GP) combined with feature ranking (FRFS) is proposed. It is assumed that the more the original features appear in the GP individuals' terminal nodes, the more valuable these features are. To further decrease the number of selected features, FRFS using a multi-criteria fitness function which is named as MFRFS is investigated. Experiments on 15 datasets show that FRFS can obtain higher classification performance with smaller number of features compared with the feature selection method without feature ranking. MFRFS further reduces the number of features while maintaining the classification performance compared with FRFS. Comparisons with five benchmark techniques show that MFRFS can achieve better classification performance.
Efficient Driver Anomaly Detection via Conditional Temporal Proposal and Classification Network

Detecting driver inattentive behaviors is crucial for driving safety in a driver monitoring system (DMS). Recent works treat driver distraction detection as a multiclass action recognition problem or a binary anomaly detection problem. The former approach aims to classify a fixed set of action classes. Although specific distraction classes can be predicted, this approach is inflexible to detect unknown driver anomalies. The latter approach mixes all distraction actions into one class: anomalous driving. Because the objective focuses on finding the difference between safe and distracted driving, this approach has better generalization in detecting unknown driver distractions. However, a detailed classification of the distraction is missing from the predictions, meaning that the downstream DMS can only treat all distractions with the same severity. In this work, we propose a two-phase anomaly proposal and classification framework [driver anomaly detection and classification network (DADCNet)] robust for open-set anomalies while maintaining high-level distraction understanding. DADCNet makes efficient allocation of multimodal and multiview inputs. The anomaly proposal network first utilizes a subset of the available modalities and views to suggest suspicious anomalous driving behavior. Then, the classification network employs more features to verify the anomaly proposal and classify the proposed distraction action. Through extensive experiments in two driver distraction datasets, our approach significantly reduces the total amount of computation during inference time while maintaining high anomaly detection sensitivity and robust performance in classifying common driver distractions.
Control of a DC motor using feedback linearization and gray wolf optimization algorithm

The aim of this study is to investigate nonlinear DC motor behavior and to control velocity as output variable. The linear model is designed, but as it is experimentally verified that it does not describe the system well enough it is replaced by the nonlinear one. System’s model has been obtained taking into account Coulomb and viscous friction in the firmly nonlinear environment. In the frame of the paper the dynamical analysis of the nonlinear feedback linearizing control is carried out. Furthermore, a metaheuristic optimization algorithm is set up for finding the coefficient of the proportional-integral feedback linearizing controller. For this purpose Gray wolf optimization technique is used. Moreover, after the introduction of the control law, analysis of the pole placement and stability of the system is establish. Optimized nonlinear control signal has been applied to the real object with simulated white noise and step signal as disturbances. Finally, for several desired output signals, responses with and without disruption are compared to illustrate the approach proposed in the paper. Experimental results obtained on the given system are provided and they verify nonlinear control robustness.
Study on the detection of heavy metal lead (Pb) in mussels based on near-infrared spectroscopy technology and a REELM classifier

It is toxic to consume mussels polluted by heavy metal Pb for humans. A quick, accurate, and non-destructive method based on near-infrared reflection spectroscopy (NIRS) has been investigated for the detection of Pb-polluted mussels in this paper. The spectral data of non-polluted and Pb-polluted mussels in the range of 950–1700 nm is acquired. A wavelength selection algorithm based on information measures of neighborhood rough set is applied to select optimal wavelengths. A sparse extreme learning machine (ELM) based on residual errors (REELM) is established as a classifier to detect Pb-polluted mussels. The average accuracy of 50 randomly assigned test datasets reaches 99.27% for detecting Pb-polluted mussels. For the class imbalance problem and mislabeled samples in the presence of practical applications, the detection performance of the proposed model has been analysed. The experimental results show that, compared with the traditional ELM and randomly pruned ELM, the REELM model is proven to be superior. The results indicate that NIRS combined with pattern recognition method has great potential to detect Pb pollution in mussels. This research is of significant importance in terms of the evaluation of edible quality and safety of mussels.
A Framework of the Training Module for Untrained Observers in Usability Evaluation Motivated by COVID-19: Enhancing the Validity of Usability Evaluation for Children’s Educational Games

The usability evaluation of educational games is an important task, especially for children. By applying Jakob Nielsen’s ten heuristics, most of the HCI designs can be evaluated, but when educational games are involved, where the user being observed is a child between the ages of six and eight, many questions arise. Is the observer trained well enough to observe the child’s reactions to the game with regard to its memorability, learnability, ease of use, and enjoyment? Will it be necessary for the observer to have a training session exploring the game before evaluating a child? Our research suggests that a training module designed to train an untrained facilitator (observer) in how to evaluate four usability dimensions (learnability, memorability, ease of use, and enjoyment) would be very useful. The usability evaluation data was collected by observing users playing generic educational games, using the Mann–Whitney U test, which was conducted by two groups of observers, one trained and one untrained. This was then reviewed, and a distinct difference was found between the results of evaluations in the two groups, thus validating the importance of training for an observer.
Unsupervised Balanced Hash Codes Learning With Multichannel Feature Fusion

Unsupervised hashingalgorithms are widely used in large-scale <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">remote sensing images</i> (RSIs) retrieval task. However, existing RSI retrieval algorithms fail to capture the multichannel characteristic of multispectral RSIs and the balanced property of hash codes, which lead the poor performance of RSI retrieval. To tackle these issues, we develop an unsupervised hashing algorithm, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">variational autoencoder balanced hashing</i> (VABH), to leverage multichannel feature fusion and multiscale context information to perform RSI retrieval task. First, multichannel feature fusion module is designed to extract RSI feature information by leveraging the multichannel properties of multispectral RSI. Second, multiscale learning module is developed to learn the multiscale context information of RSIs. Finally, a novel objective function is designed to capture the discrimination and balanced property of hash codes in the hashing learning process. Comprehensive experiments on diverse benchmark have well demonstrated the reasonableness and effectiveness of the proposed VABH algorithm.
Automated classification of hand gestures using a wristband and machine learning for possible application in pill intake monitoring

Thanks to the increased interest towards health and lifestyle, a larger adoption in wearable devices for activity tracking is present among the general population. Wearable devices such as smart wristbands integrate inertial units, including accelerometers and gyroscopes, which can be utilised to perform automatic classification of hand gestures. This technology could also find an important application in automatic medication adherence monitoring. Accordingly, this study aims at comparing the performance of several Machine-Learning (ML) and Deep-Learning (DL) approaches for the automatic identification of hand gestures, with a specific focus on the drinking gesture, commonly associated to the action of oral intake of a pill-packed medication.A method to automatically recognize hand gestures in daily living is proposed in this work. The method relies on a commercially available wristband sensor (MetaMotionR, MbientLab Inc.) integrating tri-axial accelerometer and gyroscope. Both ML and DL algorithms were evaluated for both multi-gesture (drinking, eating, pouring water, opening a bottle, typing, answering a phone, combing hair, and cutting) and binary gesture (drinking versus other gestures) classification from wristband sensor signals. Twenty-two participants were involved in the experimental analysis, performing a 10 min acquisition in a laboratory setting. Leave-one-subject-out cross validation was performed for robust performance assessment.The highest performance was achieved using a convolutional neural network with long- short term memory (CNN-LSTM), with a median f1-score of 90.5 [first quartile: 84.5; third quartile: 92.5]% and 92.5 [81.5;98.0]% for multi-gesture and binary classification, respectively.Experimental results showed that hand gesture classification with ML/DL from wrist accelerometers and gyroscopes signals can be performed with reasonable accuracy in laboratory settings, paving the way for a new generation of medical devices for monitoring medical adherence.
Implementation of TRNG with SHA-3 for hardware security

Random Number Generators (RNGs) are the solution for cryptographic applications to enhance hardware security. These RNGs ought to have three specific properties unpredictability, aperiodic, and good statistical criteria. This brief presents a True Random Number Generator (TRNG) based on Ring oscillators' jitter with MLFSR. The MLFSR is augmented with a set of prime primitive polynomials, Boolean, and non-linear functions to attain a non-linear, unpredictable, and extended sequence period. Paper mainly focused on achieving high randomness by integrated the TRNG with a new promising crypto engine Keccak as a post-processing block; leads to extensively more security in data transfer, encryption keys, data authenticity of ICs, and IoT based applications. The RNG design is coded in Verilog HDL and implemented on the FPGA Zed board. Hashing is performed with a throughput of 2.4Gbps at 100 MHz either with RNG data or SHA data. Evaluated the randomness of the generated non-deterministic bitstreams (10 Mb) using the NIST 800–22 & Diehard test suite and successfully passed.
An Effective Big Data Sharing Prototype Based on Ethereum Blockchain

Because of its many advantages, big data has been extending to various domains of science, health, education, and commerce. Despite its many applications, big data sharing typically suffers from some key issues, such as user control, lack of incentives, cost, and the right of data. This paper proposes a decentralized big data sharing prototype to improve the applications and services of big data. The method makes use of Ethereum blockchain and related technologies to systematically recommend the implementation guidelines. The research provides a detailed description of the design and implementation of each sublayer of a big data system. As the method is based on blockchain technology, the key technical points are properly addressed in each of the layers. For evaluation, relevant data were collected, and functional testing was performed. A comparison was performed about the sharing frequency and blockchain consensus performance of similar platforms. The dual mining node of the proposed prototype succeeded in processing 1366 blocks and 300 messages. A comparatively satisfactory file access time in the range of 10 m to 20 s and file transmission time between 100 m and 200 s were achieved. The results obtained show that this prototype can effectively verify the feasibility of the model, the layered architecture, and the related sharing mechanism. For the functional and performance testing, practical projects were implemented and evaluated. The promising results obtained testify that the research offers a theoretical background for innovative research in the domain and specialized guidelines for practical implementation.
Analysis of Breast Cancer using Machine Learning Methods

Breast Cancer is considered to be very severe in women but mortality rate of cancer can be minimized by early diagnosis and screening of cancer. The timely identification of cancer at initial stage can enhance the survival rate significantly. Breast cancer occurs from different part of breast tissue. In this paper, study of breast cancer identification is performed using various machine learning techniques. The precise findings can be helpful in predicting malignant or benign cancer. Machine learning technique and computer vision is applied for extracting the features and developing the optimize model by doing hyperparameter of certain values. The analysis is performed using support vector machine and various quality measures are computed such as precision, F1-score and accuracy. The observation shows that attained results are prominent.
Is Class-Incremental Enough for Continual Learning?

The ability of a model to learn continually can be empirically assessed in different continual learning scenarios. Each scenario defines the constraints and the opportunities of the learning environment. Here, we challenge the current trend in the continual learning literature to experiment mainly on class-incremental scenarios, where classes present in one experience are never revisited. We posit that an excessive focus on this setting may be limiting for future research on continual learning, since class-incremental scenarios artificially exacerbate catastrophic forgetting, at the expense of other important objectives like forward transfer and computational efficiency. In many real-world environments, in fact, repetition of previously encountered concepts occurs naturally and contributes to softening the disruption of previous knowledge. We advocate for a more in-depth study of alternative continual learning scenarios, in which repetition is integrated by design in the stream of incoming information. Starting from already existing proposals, we describe the advantages such class-incremental with repetition scenarios could offer for a more comprehensive assessment of continual learning models.
Evidence for round number effects in cryptocurrencies prices

This paper analyses the relationship between price clustering and trade volume in the Ether, Ripple and Litecoin cryptocurrencies. We examine at which digits price clustering exists and study the behaviour at different price levels and time frames. By using recent data to provide an updated view of price clustering in the cryptocurrency market, we find a remarkable level of price clustering at round prices: 5.29%, 2.84% and 2.97% for Ether, Ripple and Litecoin for every one minute at open prices, respectively. This paper reaffirms the negotiation hypothesis by finding that price clustering appears at prices at which traded volume is higher.
Foundational issues in conceptual engineering: Introduction and overview

This is the introduction to the Special Issue 'Foundational Issues in Conceptual Engineering'. The issue contains contributions by James Andow, Delia Belleri, David Chalmers, Catarina Dutilh Novaes, Eugen Fischer, Viktoria Knoll, Edouard Machery and Amie Thomasson. We, the editors, provide a brief introduction to the main topics of the issue and then summarize its contributions.