Reviewerly/exHarmony huggingface dataset:
Building an ICCN Multimodal Classifier of Aggressive Political Debate Style: Towards a Computational Understanding of Candidate Performance Over Time

Understanding the implications of aggressive political debate style amid corrosive modes of campaign politics requires fine-grained analyses of political performance, attending to multiple communication modalities. Politicians’ facial expressions, emotional tone, and speech content can all independently convey aggression and dominance, and often work in combination for purposes of emphasis. Yet micro-coding individual visual, tonal, and verbal features across more than a handful of debate segments becomes extremely labor intensive, hampering research, especially historical, longitudinal, and cross-cultural work. To address this limitation, we develop a novel multimodal classifier using an Interaction Canonical Correlation Network (ICCN) that incorporates video and audio features with speech coding of candidate debate performance, trained on a 20% sample of 10-second segments from each of the first televised U.S presidential debates between 1980 and 2020. In the analysis, we demonstrate this classifier can accurately detect aggression by political candidates in U.S. debates. We sharpen its performance by distinguishing between debate eras characterized by lower and higher levels of aggression and validate the approach by comparing the performance of unimodal with multimodal classification. This classifier opens new avenues for computational social science research, including explaining candidate behavior within debates at a larger scale and across different eras.
Compact entanglement concentration for three-electron-spin W states with error-detected parity-check gates

Abstract We present a compact entanglement concentration protocol (ECP) for unknown less-entangled three-electron-spin W states, resorting to the interaction rules between the circularly polarized photon and cavity-quantum-dot (QD) system. In the first step of our ECP, the parties utilize two less-entangled three-electron-spin systems not only to obtain one partially entangled three-electron-spin system with two unknown parameters if the odd-parity occurs with the parity-check gate (PCG) but also to get one entangled two-electron-spin system if the even-parity occurs. By exploiting the above three-electron-spin and two-electron-spin systems as the resource for the second step of our ECP, the parties can obtain a standard three-electron-spin W state if the odd parity occurs. Meanwhile, the systems in the even-parity instance can be used as the resource in the next round of our ECP. As the imperfect performances originated from the side leakage and the limited coupling strength of the cavity-QD system can be reflected by clicking the single-photon detectors, the fidelity of the PCG is unit, in principle, immune to strong coupling-strength restriction. Moreover, the success of our ECP not only is heralded by the detectors but also its efficiency further is improved by repeating the operation processes. Therefore, our ECP is useful in the quantum communication network.
Discovering agents

Causal models of agents have been used to analyse the safety aspects of machine learning systems. But identifying agents is non-trivial – often the causal model is just assumed by the modeller without much justification – and modelling failures can lead to mistakes in the safety analysis. This paper proposes the first formal causal definition of agents – roughly that agents are systems that would adapt their policy if their actions influenced the world in a different way. From this we derive the first causal discovery algorithm for discovering the presence of agents from empirical data, given a set of variables and under certain assumptions. We also provide algorithms for translating between causal models and game-theoretic influence diagrams. We demonstrate our approach by resolving some previous confusions caused by incorrect causal modelling of agents.
A low-cost real-time IoT human activity recognition system based on wearable sensor and the supervised learning algorithms

Activity recognition systems can detect human physical activities to support the assessment of health conditions. Among approaches of activity recognition systems were researched and implemented, the wearable systems based on accelerometers and machine learning classifiers offer one of the most viable solutions. These systems are cheap, comfortable, easy to use, with high recognition accuracy. The major challenge in this classification problem is required directly performed in a low-performance microcontroller. In this manuscript, an optimal time frame of an activity, a feature set, and a simple machine learning model were proposed to build a low-cost and responsive recognition system in real-time. The proposed device was verified on both public data and our experiment data. An excellent recognition rate resulted in 99.2% on the recorded dataset for four critical daily activities (standing, sitting, running, and walking).
Data analysis of physical recovery and injury prevention in sports teaching based on wearable devices

Athletes can also cause damage to some parts of their body during training, so specialized preparation activities should be carried out before athlete training to reduce the damage caused to the athlete's body, allowing the stressed parts to move and distribute the load. Excessive recovery has a significant effect on improving the performance level of the athletes studied and preventing sports injuries. This article studies the data analysis of body recovery and injury prevention in physical education teaching based on wearable devices. Real time collection of students' exercise data, including indicators such as exercise volume, heart rate, steps, distance, etc., by wearing wearable devices. By using Internet of Things technology to transmit data to cloud servers, data analysis and mining techniques are used to process the data and study issues related to body recovery and injury prevention. Specifically, this article adopts methods such as time series analysis, machine learning algorithms, and artificial neural networks to analyze the relationship between exercise data and body recovery and injury prevention, providing scientific guidance and support for physical education teaching. This method can monitor students' exercise data in real-time, predict the risk of physical recovery and injury, and provide corresponding prevention and guidance suggestions.
Scalable Educational Question Generation with Pre-trained Language Models

The automatic generation of educational questions will play a key role in scaling online education, enabling self-assessment at scale when a global population is manoeuvring their personalised learning journeys. We develop EduQG, a novel educational question generation model built by adapting a large language model. Our extensive experiments demonstrate that EduQG can produce superior educational questions by further pre-training and fine-tuning a pre-trained language model on the scientific text and science question data.
Professional digital competence (PDC) in teacher education – teacher candidates reasoning about programming when involved in problem-solving activities with digital tools

The increased emergence of digital tools in educational contexts have changed the conditions for teaching and learning which, in turn, has meant a need for the development of professional digital competence (PDC) among teachers worldwide. Simultaneously, and aligned with this, programming has become an increasingly important part of schooling in many parts of the world and a step toward developing computational thinking. In this paper we investigate teacher candidates reasoning about programming when involved in problem-solving activities with digital tools. The unit of analysis is programming activities conducted by a group of 17 teacher candidates in a Swedish primary teacher education. The research question posed is: How do digital tools co-determine teacher candidates’ reasoning about programming and its pedagogical application during problem-solving activities with digital tools designed for teaching? Drawing from a sociocultural approach, the results of the present study imply that the teacher candidate’s appropriation of knowledge is co-determined by the digital tool itself and as such it does not provide any deeper understanding for its pedagogical use.
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In this paper, we consider a fractional-order octonion-valued Hopfield neural network. Based on the fixed point theorem and analytical technique, we first establish the existence and uniqueness of Sp-almost periodic solutions of the network by direct method. Then, we take the fractional-order octonion-valued Hopfield neural network as the driving system and introduce the corresponding response system to study their finite-time synchronization. Finally, we use an example to illustrate the validity of our results. Even when the neural network under consideration degenerates into real-valued one, our results are completely new.
Gigapixel end-to-end training using streaming and attention

Current hardware limitations make it impossible to train convolutional neural networks on gigapixel image inputs directly. Recent developments in weakly supervised learning, such as attention-gated multiple instance learning, have shown promising results, but often use multi-stage or patch-wise training strategies risking suboptimal feature extraction, which can negatively impact performance. In this paper, we propose to train a ResNet-34 encoder with an attention-gated classification head in an end-to-end fashion, which we call StreamingCLAM, using a streaming implementation of convolutional layers. This allows us to train end-to-end on 4-gigapixel microscopic images using only slide-level labels. We achieve a mean area under the receiver operating characteristic curve of 0.9757 for metastatic breast cancer detection (CAMELYON16), close to fully supervised approaches using pixel-level annotations. Our model can also detect MYC-gene translocation in histologic slides of diffuse large B-cell lymphoma, achieving a mean area under the ROC curve of 0.8259. Furthermore, we show that our model offers a degree of interpretability through the attention mechanism.
The effectiveness of social elements in virtual reality tourism: A mental imagery perspective

In this study, we introduced Stimulus-Organism-Response (S–O-R) theory, social perceptions, and para-social interaction concepts to construct a path model from the perspective of mental imagery. The model is designed to reveal the influence of virtual social elements on virtual reality (VR) tourism effects, including attachment to VR and visit intention. We tested three versions of VR travel environments independently using a head-mounted display (HMD) and an integrated eye-tracking device. A total of 254 participants took part in the experiment. We analyzed the experimental data using variance analysis and PLS-SEM analysis. Our results show that both the quantity (multiple vs. few) and dynamism (dynamic vs. static) of virtual social elements positively affect consumers' social perceptions and formation of mental imagery. Additionally, mental imagery serve as a conduction mechanism that positively affects VR tourism. Our findings may have important implications for marketers regarding approaches to improving VR tourism by incorporating social elements into the VR preview environment.
Safety Verification for Neural Networks Based on Set-Boundary Analysis

Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their behaviors should undergo rigorous guarantees before deployment in practice. In this paper, we propose a set-boundary reachability method to investigate the safety verification problem of NNs from a topological perspective. Given an NN with an input set and a safe set, the safety verification problem is to determine whether all outputs of the NN resulting from the input set fall within the safe set. In our method, the homeomorphism property of NNs is mainly exploited, which establishes a relationship mapping boundaries to boundaries. The exploitation of this property facilitates reachability computations via extracting subsets of the input set rather than the entire input set, thus controlling the wrapping effect in reachability analysis and facilitating the reduction of computation burdens for safety verification. The homeomorphism property exists in some widely used NNs such as invertible NNs. Notable representations are invertible residual networks (i-ResNets) and Neural ordinary differential equations (Neural ODEs). For these NNs, our set-boundary reachability method only needs to perform reachability analysis on the boundary of the input set. For NNs that do not feature this property with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property and then abandon these subsets for reachability computations. Finally, some examples demonstrate the performance of the proposed method.
Artificial Intelligence in Education: Developing Competencies and Supporting Teachers in Implementing AI in School Learning Environments

Implementing digital technology, and especially artificial intelligence, in schools is becoming an increasingly significant challenge for society. Digital services supported by artificial intelligence are becoming more prevalent in all aspects of social life, including schools. The project “Innovative Learning Environments Supported by Digital Technologies” aims to introduce artificial intelligence as a support for transforming teaching in a way that prepares learning opportunities for students, where they can acquire knowledge and develop digital competencies. Teachers need knowledge and tools to assess whether AI-supported activities are appropriate for achieving their goals and enabling the transformation of teaching. Within the project, we have tested three models to assist teachers in implementing digital technology and AI. Based on the analysis of submitted good practice cases, we found that the most suitable scale for teachers in the project “Innovative Learning Environments Supported by ICT” is the RAT scale.
Nonlinear MPC for Full-Pose Manipulation of a Cable-Suspended Load using Multiple UAVs

In this work, we propose a centralized control method based on nonlinear model predictive control to let multiple UAVs manipulate the full pose of an object via cables. At the best of the authors knowledge this is the first method that takes into account the full nonlinear model of the load-UAV system, and ensures all the feasibility constraints concerning the UAV maximumum and minimum thrusts, the collision avoidance between the UAVs, cables and load, and the tautness and maximum tension of the cables. By taking into account the above factors, the proposed control algorithm can fully exploit the performance of UAVs and facilitate the speed of operation. Simulations are conducted to validate the algorithm to achieve fast and safe manipulation of the pose of a rigid-body payload using multiple UAVs. We demonstrate that the computational time of the proposed method is sufficiently small (<100 ms) for UAV teams composed by up to 10 units, which makes it suitable for a huge variety of future industrial applications, such as autonomous building construction and heavy-load transportation.
Optimizing the Hyperparameter Tuning of YOLOv5 for Breast Cancer Detection

This research aims to find the best use optimizer for the task while reducing training time. We optimized the YOLOv5s model and focused on three optimizers, including the Stochastic Gradient Descent (SGD) optimizer, Adaptive Moment Estimation (Adam) optimizer, and Adam with Weight Decay Regularization (AdamW) optimizer. This research utilized 1,471 mammogram images from National Cancer Institute and Udonthani Cancer Hospital, Thailand. A dataset of mammograms was labeled into six classes, including Masses Benign, Masses Malignant, Calcifications Benign, Calcifications Malignant, Associated Features Benign, and Associated Features Malignant, to classify the results accurately. We found that the SGD optimizer outperformed the others, with a mean average precision (mAP) of 0.91, a precision of 0.92, a recall of 0.85, and the shortest training time of about 5.453 hr.
On the influence of the quality of pseudo-labels on the self-supervised speaker verification task: a thorough analysis

One of the most widely used self-supervised (SS) speaker verification (SV) system training methods is to optimize the speaker embedding network in a discriminative fashion using clustering algorithm (CA)-driven Pseudo-Labels (PLs). Although the PL-based SS training scheme showed impressive performance, recent studies have shown that label noise can significantly impact performance. In this paper, we have explored various PLs driven by different CAs and conducted a fine-grained analysis of the relationship between the quality of the PLs and the SV performance. Experimentally, we shed light on several previously overlooked aspects of the PLs that can impact SV performance. Moreover, we could observe that the SS-SV performance is heavily dependent on multiple qualitative aspects of the CA used to generate the PLs. Furthermore, we show that SV performance can be severely degraded from overfitting the noisy PLs and that the mixup strategy can mitigate the memorization effects of label noise.
A Structural Equation Approach and Modelling of Pre-service Teachers' Perspectives of Cybersecurity Education

Abstract The increasing technology reliance in today’s world has amplified the need for cybersecurity education for all. Hence, initiatives regarding the integration of cybersecurity education within the pre-college level have begun to emerge in recent times. However, limited research exists regarding in-service or pre-service teachers’ perspectives on this phenomenon. More importantly, the need to understand pre-service teachers’ perceptions; since their perceptions may significantly influence how the prospective teachers respond to cybersecurity issues and also affect their behavior toward learning and promoting cybersecurity education in the future. Consequently, in order to gain insight into how candidates entering the teaching profession regard cybersecurity, 451 pre-service teachers were sampled in a major public university in Lesotho. The prospective teachers recruited across various departments in the faculty of education responded to an online survey that comprised 33 items gauged from five constructs which include personal cybersecurity awareness, perceived self-efficacy of learning cybersecurity, personal relevance of cybersecurity knowledge, behavioral intention towards learning cybersecurity and actual learning of cybersecurity. We analyzed the response from the survey by utilizing the structural equation modelling approach. Our findings showed that our hypothesized model was mostly accepted. The result suggests that practitioners and researchers in the related field need to raise the pre-service teacher’s behavioral intention to learn cybersecurity by helping them realize the implication for their personal lives and society. We discussed our findings in relation to the proposed research model and highlighted the implication for teacher education programs. Finally, the article concludes with limitations and identifies future research agenda.
Blockchain-enabled supply chain management: integrated impact on firm performance and robustness capabilities

Purpose This study aims to proposed that blockchain helps the organization improve supply chain (SC) performance by improving integration, agility and security through real-time information sharing, end-to-end visibility, transparency, data management, immutability, irrevocable information and cyber-security platforms. Design/methodology/approach This study has made an initial effort toward proposing a framework that shows the problems and challenges for the O&amp;G SC under its segments (upstream, midstream and downstream) and provides the interlink among blockchain properties for SCM problems. SC managers were selected for survey questionnaires from the Pakistan O&amp;G industries. Findings This study analyzes the impact of blockchain-enabled SC on firm performance with an understanding of the SC robustness capabilities as a mediator. The result revealed that the SC manager believes that the blockchain-enabled SC has a positive and significant on firm performance and robustness capabilities. Research limitations/implications Blockchain technology is reflected as high-tech to support the firm process, responses and methods. The technology helps eliminate bottlenecks, avoid uncertainties and improve decision-making, leading to improved SC functions. This study guides managers about the potential problems of existing SC and how blockchain solves SC problems more effectively. Originality/value The oil and gas (O&amp;G) sectors are neglected by researchers, and there are limited studies on O&amp;G supply chain management (SCM). Additionally, no empirical evidence suggests implementing blockchain for O&amp;G as a solution for potential problems. Furthermore, present the roadmap to other industries those having complex SC networks for the implication of blockchain to improve the SC performance.
Time–Frequency Multiscale Convolutional Neural Network for RF-Based Drone Detection and Identification

Due to recent technological advancements and significant decreases in their costs, drones are gaining popularity rapidly. With drones becoming readily accessible to the public, the need for reliable detection and identification systems for drone networks is becoming more critical. We propose a time–frequency multiscale convolutional neural network-based deep learning model for the detection and identification of drones, which learns features from both raw and frequency domain drone radio frequency signals. The performance of the proposed network is evaluated on a publicly accessible database, and it outperforms state-of-the-art methods proposed for radio frequency-based drone detection and identification using deep neural networks.
A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

The growth of the online review phenomenon, which has expanded from specialised trade magazines to end users via online platforms, has also increasingly involved the cultural heritage of countries, a source of tourism and growth driver of local economies. Unfortunately, this has been paralleled by the emergence and spread of the phenomenon of fake reviews, against which the scientific world has developed language models capable of distinguishing them from the truthful. The application of such models, often based on deep neural networks with transformer-type architectures, is however limited by the availability of local language data sets for specific domains, useful for both training and verification. The purpose of this article is twofold. Firstly, a new data set was created in the Italian language, generally considered low-resource, relating to the domain of cultural heritage in Italy, by collecting reviews available online, reorganising them in the form of a data set usable by the language models. Secondly, a baseline of results for the detection of misleading reviews was constructed by exploiting two widely used language models, namely BERT and ELECTRA. The performance achieved is interesting, around 95% accuracy and F1 score, using data set splits between training and testing of 80/20 and 90/10. In addition, SHAP was used as a tool to support the explicability of AI models: in this way, it was possible to show the usefulness of sentiment analysis as a support for the recognition of deceptiveness.