Reviewerly/exHarmony huggingface dataset:
What Is the Difference? Investigating the Self-Report of Wellbeing via Conversational Agent and Web App

Adopting speech as their mode of interaction, speech-enabled conversational agent (CA) systems hold the potential to enable more natural and engaging self-report experiences than traditional media (e.g., pen and paper, web, or smartphone systems). Our recent research concerns the potential design and use of CAs to support mental health and wellbeing. In this article, we present findings from a study during which 22 individuals with affective disorders (e.g., depression, bipolar disorder) used either a speech-enabled CA or web app to self-report their emotional wellbeing. Analysis of users’ experiences and engagement with the system for daily self-report suggests that despite many technical limitations, users rated self-reporting via CA as a significantly novel and attractive experience yielding similar rates of engagement to a traditional web-based method.
American Sign Language: Detection and Automatic Text Generation

In our daily life, communication is crucial. For people who are hard of hearing (deaf or/and mute) sign language is the means of their communication. Nonetheless, many people are still unaware of sign languages, resulting in a communication gap. To improve communication between deaf-mutes and the hearing majority, this paper proposes an ASL (American Sign Language) detection system for 26 alphabets and three assisted signs, which can detect ASL captured by a standard computer camera, then generate an automatic text. SSD-MobileNet is used as the object detection model. The proposed system achieved precision and recall of 82.8% and 85.5% respectively.
Recommendations for Discipline-Specific FAIRness Evaluation Derived from Applying an Ensemble of Evaluation Tools

From a research data repositories' perspective, offering research data management services in line with the FAIR principles is becoming increasingly important. However, there exists no globally established and trusted approach to evaluate FAIRness to date. Here, we apply five different available FAIRness evaluation approaches to selected data archived in the World Data Center for Climate (WDCC). Two approaches are purely automatic, two approaches are purely manual and one approach applies a hybrid method (manual and automatic combined). The results of our evaluation show an overall mean FAIR score of WDCC-archived (meta)data of 0.67 of 1, with a range of 0.5 to 0.88. Manual approaches show higher scores than automated ones and the hybrid approach shows the highest score. Computed statistics indicate that the test approaches show an overall good agreement at the data collection level. We find that while neither one of the five valuation approaches is fully fit-for-purpose to evaluate (discipline-specific) FAIRness, all have their individual strengths. Specifically, manual approaches capture contextual aspects of FAIRness relevant for reuse, whereas automated approaches focus on the strictly standardised aspects of machine actionability. Correspondingly, the hybrid method combines the advantages and eliminates the deficiencies of manual and automatic evaluation approaches. Based on our results, we recommend future FAIRness evaluation tools to be based on a mature hybrid approach. Especially the design and adoption of the discipline-specific aspects of FAIRness will have to be conducted in concerted community efforts.
Robust Latent Common Subspace Learning for Transferable Feature Representation

This paper proposes a novel robust latent common subspace learning (RLCSL) method by integrating low-rank and sparse constraints into a joint learning framework. Specifically, we transform the data from source and target domains into a latent common subspace to perform the data reconstruction, i.e., the transformed source data is used to reconstruct the transformed target data. We impose joint low-rank and sparse constraints on the reconstruction coefficient matrix which can achieve following objectives: (1) the data from different domains can be interlaced by using the low-rank constraint; (2) the data from different domains but with the same label can be aligned together by using the sparse constraint. In this way, the new feature representation in the latent common subspace is discriminative and transferable. To learn a suitable classifier, we also integrate the classifier learning and feature representation learning into a unified objective and thus the high-level semantics label (data label) is fully used to guide the learning process of these two tasks. Experiments are conducted on diverse data sets for image, object, and document classifications, and encouraging experimental results show that the proposed method outperforms some state-of-the-arts methods.
Highlights from 25 Years of the Geo<i>PT</i> Programme: What Can be Learnt for the Advancement of Geoanalysis

Data submitted over the past 25 years to Geo PT , the highly successful proficiency‐testing programme for the geochemical analysis of geological materials, organised by the International Association of Geoanalysts, provide a valuable resource that permits detailed investigation of contrasting results associated with different sample preparation and measurement principles. Highlighted issues include the following: recurring problems with the dissolution of the refractory minerals zircon and chromite, which produce a large dispersion in data obtained when acid digestion is involved; issues related to different XRF sample preparation methods, whereby a significant divergence of pressed powder pellet results compared with those from fused glass discs is observed; high relative dispersion of data both at low mass fractions, and those higher than normally found in silicate rocks, due to incomplete method validation, in particular due to an overconfidence in estimating reporting limits and to the employment of limited working ranges. In addition, an example for Sr in an ancient pegmatite with extremely high Rb abundance is presented, where ICP‐MS results amount to only to a third of the XRF results, severely underestimated due to the disregard of the radiogenic ingrowth from 87 Rb decay. Recommendations are made both for improving data quality and the selection of test materials for future Geo PT rounds.
Gold and minerals of the heavy fraction of alluvial sediments in the Pokyu river (western slope of the Volsko-Vymskaya ridge, Middle Timan)

The article presents novel results of studies of the mineral composition of placer samples, morphological and chemical study of native gold from alluvial deposits in the Pokyu River (Middle Timan). The main minerals in the heavy fraction of concentrates are garnet and ilmenite. Most samples contain gold from occasional particles to certain grades. Small (0.25–1.0 mm) medium-rounded gold of plate and tabular form prevails. The only impurity is silver, the content of which is 0.5–27 wt. %. Almost all gold particles have a high-grade rim. The gold contains microinclusions of galena and gersdorffite.
SCM-IoT: An Aproach for Internet of Things Services Integration and Coordination

Today, new applications demand an internet of things (IoT) infrastructure with greater intelligence in our daily use devices. Among the salient features that characterize intelligent IoT systems are interoperability and dynamism. While service-oriented architectures (SOA) offer a well-developed and standardized architecture and protocols for interoperability, answering whether SOA offers enough dynamism to merge IoT with artificial intelligence (AI) is still in its beginnings. This paper proposes an SOA model, called SCM-IoT (service composition model for IoT), for incorporating AI into IoT systems, addressing their coordination by a mediator offering services for storage, production, discovery, and notification of relevant data for client applications. The model allows IoT systems to be incrementally developed from three perspectives: a conceptual model, platform-independent computational model, and platform-dependent computational model. Finally, as a case of study, a domotic IoT system application is developed in SCM-IoT to analyze the characteristics and benefits of the proposed approach.
Application of the Improved Clustering Algorithm in Operating Room Nursing Recommendation under the Background of Medical Big Data

The nursing work in the operating room has the characteristics of long time, strong technicality, and heavy work, which have an important influence on the quality of the operation. Operating room nursing recommendations based on data mining technology can solve a series of practical problems in clinical nursing and nursing management. This paper selects the clustering algorithm in commonly used data mining technology as the research object and actually analyzes the impact of this algorithm in operating room nursing recommendations. At this stage, there is little research on data mining technology in the field of nursing in China. This paper aims to provide new ideas for the field of nursing research by exploring the actual application in the field of nursing.
Single-Trace Clustering Power Analysis of the Point-Swapping Procedure in the Three Point Ladder of Cortex-M4 SIKE

In this paper, the recommended implementation of the post-quantum key exchange SIKE for Cortex-M4 is attacked through power analysis with a single trace by clustering with the k-means algorithm the power samples of all the invocations of the elliptic curve point swapping function in the constant-time coordinate-randomized three point ladder. Because each sample depends on whether two consecutive bits of the private key are the same or not, a successful clustering (with $$k=2$$ ) leads to the recovery of the entire private key. The attack is naturally improved with better strategies, such as clustering the samples in the frequency domain or processing the traces with a wavelet transform, using a simpler clustering algorithm based on thresholding, and using metrics to prioritize certain keys for key validation. The attack and the proposed improvements were experimentally verified using the ChipWhisperer framework. Splitting the swapping mask into multiple shares is suggested as an effective countermeasure.
Which French speech recognition system for assistant robots?

Artificial intelligence-based speech recognition systems are already available and capable of recognizing the French language. Still, it is quite time-consuming to compare which one will be effective for an assistant robot. The study aims to select the best French-language speech recognition system with the least error in a real environment. In this paper, we present related works on how an Automatic Speech Recognition (ASR) system works, the models used by each of its components, several open-source French datasets, and the frequently used evaluation techniques. Next, we compare deep learning-based speech recognition APIs and pre-trained models for French on two different datasets using the Word Error Rate (WER) metric. The experimental results reveal that Google's Speech-to-Text API outperforms the other systems, namely VOSK API, Wav2vec 2.0, QuartzNet, and Speech Brain's Convolutional, Recurrent, and Fully-connected Networks (CRDNN) model.
Cumulative markedness effects and (non-)linearity in phonotactics

How do grammars assess the well-formedness of words with multiple phonotactic violations? Certain models predict that as the strength of phonotactic restrictions decrease, forms that violate multiple restrictions should be less acceptable than expected, in a pattern we term super-linear cumulativity. We test this prediction using a series of Artificial Grammar Learning experiments, in which we vary the number of exceptions to phonotactic patterns in artificial languages. We find that super-linear cumulativity is indeed observed in the conditions with the weakest restrictions. Strikingly, participants exhibit super-linear cumulativity even when the trained language does not contain evidence for it.
Study protocol: a survey exploring patients’ and healthcare professionals’ expectations, attitudes and ethical acceptability regarding the integration of socially assistive humanoid robots in nursing

Population ageing, the rise of chronic diseases and the emergence of new viruses are some of the factors that contribute to an increasing share of gross domestic product dedicated to health spending. COVID-19 has shown that nursing staff represents the critical part of hospitalisation. Technological developments in robotics and artificial intelligence can significantly reduce costs and lead to improvements in many hospital processes. The proposed study aims to assess expectations, attitudes and ethical acceptability regarding the integration of socially assistive humanoid robots into hospitalised care workflow from patients' and healthcare professionals' perspectives and to compare them with the results of similar studies.The study is designed as a cross-sectional survey, which will include three previously validated questionnaires, the Technology-Specific Expectation Scale (TSES), the Ethical Acceptability Scale (EAS) and the Negative Attitudes towards Robots Scale (NARS). The employees of a regional clinical centre will be asked to participate via an electronic survey and respond to TSES and EAS questionaries. Patients will respond to TSES and NARS questionaries. The survey will be conducted online.Ethical approval for the study was obtained by the Medical Ethics Commission of the University Medical Center Maribor. Results will be published in a relevant scientific journal and communicated to participants and relevant institutions through dissemination activities and the ecosystem of the Horizon 2020 funded project HosmartAI (grant no. 101016834).06 May 2021.December 2021.
Modeling of Hyperparameter Tuned Hybrid CNN and LSTM for Prediction Model

The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers.Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors.Anticipating stock market trends is a difficult endeavor that requires a lot of attention, because correctly predicting stock prices can lead to significant rewards if the right judgments are made.Due to non-stationary, noisy, and chaotic data, stock market prediction is a huge difficulty, and as a result, investors find it difficult to invest their money in order to make a profit.In order to predict stock market movements, a number of strategies have been established.Earlier studies based on statistical models and machine learning techniques have been focused on short term stock price prediction.In this aspect, this study designs a novel hyperparameter tuned hybrid convolutional neural network with long short term memory (HPT-HCLSTM) for stock price prediction.The proposed HPT-HCLSTM technique encompasses three different processes namely pre-processing, prediction, and parameter optimization.The HPT-HCLSTM technique employs the HCLSTM technique for the prediction of stock prices.In addition, teaching and learning based optimization (TLBO) algorithm is applied for the hyperparameter optimization of the HCLSTM technique and thereby results in minimal error values.In order to demonstrate the enhanced prediction performance of the HPT-HCLSTM technique, a wide range of simulations were carried out and the results highlighted the better performance of the HPT-HCLSTM technique under several aspects.The HPT-HCLSTM technique is found to be a proper tool for forecasting stock prices.the HPT-HCLSTM technique has showcased better performance with the increased R2 value of 0.9154.
Web-Based Online Judge System for Online Programming Education

Due to COVID-19, many educational institutions are using non-face-to-face online education services for programming lectures. However, it is not easy for one person to manage various features, which are scoring the assignment and practice, problem feedback, and so on, in the online programming education service. This paper develops an educational system for programming education using an open source-based online judge system for easy management of instructors. In addition, it defines and implements an academic interface.
Designing an Improved Deep Learning-Based Classifier for Breast Cancer Identification in Histopathology Images

Cancer is a rampant phenomenon caused by uncontrollable cells that grow and spread throughout the body. Invasive Ductal Carcinoma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> is the most common type of breast cancer, which can be fatal for females if not detected early. As a result, prompt diagnosis is critical to maximizing surveillance rates and, in the meantime, minimizing long-term mortality rates. Nowadays, modern computer vision and deep learning techniques have transformed the medical image analysis arena. Computer vision application in medical image analysis has provided us with remarkable results, enhanced accuracy, and reduced costs. The main purpose of designing a new algorithm to detect unusual patches of breast images, was to acquire both high accuracy and low computational cost, simultaneously. Therefore, a novel architecture has been designed by utilizing Xception and MobileNetV2.This new algorithm achieves 93.4% balanced accuracy and 94.8% for F1-Score, which outperforms previously published algorithms for identifying IDC histopathology images that use deep learning techniques.
A new hough transform operated in a bounded cartesian coordinate parameter space

In this paper, a new Hough transform is proposed to detect lines with geometric meanings. In the standard Hough transform, lines are parameterized by the length and orientation of the normal vector from the origin to the line. The parameter space of the standard Hough transform is presented in a polar coordinate system. Geometric measurements such as distance and angle are not suitable in polar coordinate space due to the inconsistency of the axes. To deal with this problem, a Hough transform which is carried out in a bounded Cartesian coordinate parameter space is developed. The lines are represented by their perpendicular feet with respect to a fixed point. Since the parameter space is in Cartesian coordinate space, it is intuitive to the researchers. When detecting the peaks in the parameter space, distance and angle constraints can be applied. The effectiveness of the proposed method has been illustrated by two applications, that is, lane line detection and convex polygon extraction. The results show that the proposed Cartesian coordinate Hough transform is suitable for detecting meaningful lines.
From Big to Small: Adaptive Learning to Partial-Set Domains

Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirement of identical class space shared across domains hinders applications of domain adaptation to partial-set domains. Recent advances show that deep pre-trained models of large scale endow rich knowledge to tackle diverse downstream tasks of small scale. Thus, there is a strong incentive to adapt models from large-scale domains to small-scale domains. This paper introduces Partial Domain Adaptation (PDA), a learning paradigm that relaxes the identical class space assumption to that the source class space subsumes the target class space. First, we present a theoretical analysis of partial domain adaptation, which uncovers the importance of estimating the transferable probability of each class and each instance across domains. Then, we propose Selective Adversarial Network (SAN and SAN++) with a bi-level selection strategy and an adversarial adaptation mechanism. The bi-level selection strategy up-weighs each class and each instance simultaneously for source supervised training, target self-training, and source-target adversarial adaptation through the transferable probability estimated alternately by the model. Experiments on standard partial-set datasets and more challenging tasks with superclasses show that SAN++ outperforms several domain adaptation methods.
Intelligent Accounting System Structure and Intelligent Accounting Algorithm Based on Computer Vision

With the development of artificial intelligence, more and more companies have begun to get involved in the field of computer vision. At present, the theoretical research on intelligent accounting system at home and abroad has been quite mature. The optimization and upgrade of accounting algorithms through computer vision will effectively reduce the operating costs of group companies. With the reengineering of corporate financial processes and the optimized configuration brought by information technology, the management model innovation brought by new technology will be the use of intelligence The focus of attention of group companies in the accounting system structure model. Computer vision has been a hot spot in the current intelligent research. Some scholars have combined intelligent accounting with computer vision. Based on the analysis of financial business processes, the processes that have the characteristics of repetitive operations and rule determination in corporate shared services are passed through computers. Visual technology to improve efficiency. The experimental results of this article show that the intelligent accounting system structure and intelligent accounting algorithm based on computer vision have increased the financial accounting efficiency of enterprises by 16.8%, and the lack of compound talents that improve the combination of enterprise finance and information technology and Low level of intelligence, etc. Based on optimization goals and principles, the performance evaluation, forecasting and tax management process of the operation management module of the intelligent accounting center are optimized. Through the mutual coupling of optimization modules and computer vision intelligent technology.
Automatic compiler/interpreter generation from programs for Domain-Specific Languages: Code bloat problem and performance improvement

Using advanced AI approaches, the development of Domain-Specific Languages (DSLs) can be facilitated for domain experts who are not proficient in programming language development. In this paper, we first addressed the aforementioned problem using Semantic Inference. However, this approach is very time-consuming. Namely, a lot of code bloat is present in the generated language specifications, which increases the time required to evaluate a solution. To improve this, we introduced a multi-threaded approach, which accelerates the evaluation process by over 9.5 times, while the number of fitness evaluations using the improved Long Term Memory Assistance (LTMA) was reduced by up to 7.3%. Finally, a reduction in the number of input samples (fitness cases) was proposed, which reduces CPU consumption further.