Reviewerly/exHarmony huggingface dataset:
Student use of a quantum simulation and visualization tool

Knowledge of quantum mechanical systems is becoming more important for many science and engineering students who are looking to join the emerging quantum workforce. To better prepare a wide range of students for these careers, we must seek to develop new tools to enhance our education in quantum topics. We present initial studies on the use of one of these such tools, Quantum Composer, a 1D quantum simulation and visualization tool developed for education and research purposes. In particular, we conducted five think-aloud interviews with students who worked through an exercise using Quantum Composer that focused on the statics and dynamics of quantum states in a single harmonic well system. Our results show that Quantum Composer helps students to obtain the correct answers to the questions posed, but additional support is needed to facilitate the development of student reasoning behind these answers. We also show that students are able to focus only on the relevant features of Quantum Composer to achieve the task.
Explainable Multitask Shapley Explanation Networks for Real-Time Polyp Diagnosis in Videos

Colorectal cancer is mostly caused by colorectal polyps, which can be prevented through polyp diagnosis using colonoscopy. The current computer-aided decision-making methods suffer from a variety of drawbacks, including inaccurate polyp classification, poor real-time performance, and poor interpretability. To address these issues, we propose an explainable multitask Shapley explanation networks (EMSEN) that can perform real-time explainable multitasks such as polyp detection and classification in colonoscopy videos. The EMSEN accepts two multimodal inputs of different light sources, and outputs the polyp location, classification type, and diagnosis results according to the real-time colonoscopy video, where efficient channel attention (ECA) Mechanism-based network and Shapley explanation networks (ShapNet) are designed to improve the feature extraction performance and model interpretability, respectively. Extensive experiment studies are conducted to verify the efficiency and effectiveness of the proposed method by comparing with the experts and state-of-the-art methods. The results demonstrate that the developed method performs the best, which achieves competitive diagnosis performance.
EEG-Video Emotion-Based Summarization: Learning With EEG Auxiliary Signals

Video summarization is the process of selecting a subset of informative keyframes to expedite storytelling with limited loss of information. In this article, we propose an EEG-Video Emotion-based Summarization (EVES) model based on a multimodal deep reinforcement learning (DRL) architecture that leverages neural signals to learn visual interestingness to produce quantitatively and qualitatively better video summaries. As such, EVES does not learn from the expensive human annotations but the multimodal signals. Furthermore, to ensure the temporal alignment and minimize the modality gap between the visual and EEG modalities, we introduce a Time Synchronization Module (TSM) that uses an attention mechanism to transform the EEG representations onto the visual representation space. We evaluate the performance of EVES on the TVSum and SumMe datasets. Based on the rank order statistics benchmarks, the experimental results show that EVES outperforms the unsupervised models and narrows the performance gap with supervised models. Furthermore, the human evaluation scores show that EVES receives a higher rating than the state-of-the-art DRL model DR-DSN by 11.4% on the coherency of the content and 7.4% on the emotion-evoking content. Thus, our work demonstrates the potential of EVES in selecting interesting content that is both coherent and emotion-evoking.
Libraries for Tomorrow: The Use of ICT and Space Transformation in Some Academic Libraries in Ghana

Purpose-the aim of the study was to examine transformations in academic library spaces in Ghana Design/Methodology/approach-the quantitative approach was adopted for this study. Data was collected using Google Forms. A total of 98 librarians from five academic libraries in Ghana representing less than 10% of all librarians in academic libraries in Ghana. Data collected was analysed using the R 4. 0 programming Software and presented using charts and tables. Findings- Eighty-one percent (81%) of the participants agreed that their libraries were moving from ‘book-centered’ to ‘technology supported and learning centered’ libraries engineered by emerging technologies and the changing demand of academic library users. Again, it was revealed that librarians had moderate to low skills in providing advanced technological support to users in creating, capturing, editing, and sharing multimedia (video, audio, text, images) for their research, teaching, and learning purposes. Finally, results from the multinomial regression analysis with p values showed that availability of IT utilities, usability and skills of librarians were significant revealing that they have effects on the levels of library space transformation. Practical implications- The capabilities of librarians should be improved form undertaking basic computing task to providing advanced support to their users. Stakeholder and academic library management should encourage their young librarian to further their education and engage in continuous professional development programmes. Libraries should develop strategies to secure the necessary funding to implement IT facilities. Originality-The study used quantitative methods to study transformations in academic library spaces, a grey researched area in Ghana.
Switching Gaussian-heavy-tailed distribution based robust Gaussian approximate filter for INS/GNSS integration

In inertial navigation system and global navigation satellite system (INS/GNSS) integration, the practical stochastic measurement noise may be non-stationary heavy-tailed distribution due to outlier measurements induced by multipath and/or non-line-of-sight receptions of the original GNSS signals. To address the problem, a new switching Gaussian-heavy-tailed (SGHT) distribution is presented, which models the measurement noise with the help of switching between the Gaussian and the an existing heavy-tailed distribution. Then, utilizing two auxiliary parameters satisfying categorical and Bernoulli distributions respectively, we construct the SGHT distribution as a hierarchical Gaussian presentation. Furthermore, applying variational Bayesian inference, a novel SGHT distribution based robust Gaussian approximate filter is derived. Meanwhile, to reduce the computational complexity of the filtering process, an improved fixed-point iteration method is designed. Finally, the simulation of integrated navigation for an aircraft illustrates effectiveness and superiority of the proposed filter as compared the existing robust filters.
Gamification in training with next generation AI- virtual reality, animation design and immersive technology

Multimedia tools and immersive technology have enormous potential for actively engaging students and improving learning quality. This research aims into the use of virtual reality (VR) in physical training and sports rigorous training to give students a new perspective on college training and educational training, as well as to improve the professional level and training excellence of college athletes. We present a revolutionary Virtual Reality-based Physical Training (VR-PT) technology for effective virtual reality instruction on a digital application. The semi-supervised learning framework was used to implement movement inputs and interactive reality methods. Virtual simulation and differentiated selection techniques based on Q statistical data are used first to identify sports students with excellent independent learning capacity, followed by the classifier's neighbour reliability. Experiment results indicate that this strategy can successfully support physical training practices while also improving students' learning performance. The effectiveness of students in athletics has increased by 30%. Simultaneously, two-thirds of people believe their involvement in athletic training has increased by 80%, and 90% of college coaches believe that using VR technology throughout physical training is extremely important for improving the technical aspect as well as the reliability of college sports training.
Holding Your Hand on the Danger Button

Phishing emails continue to be a major cause of cybersecurity breaches despite the development of technical measures designed to thwart these attacks. Most phishing studies have investigated desktop email platforms, but the use of mobile devices for email exchanges has soared in recent years, especially amongst young adults. In this paper, we explore how the digital platform (desktop vs. mobile) influences users' phish detection strategies. Twenty-one young adults (18-25 years) were asked to rate the legitimacy of emails using a live inbox test while using a think-aloud protocol on both platforms. Our results suggest that a lack of knowledge about key defence information on the mobile platform results in weak phish detection. We discuss the implications of these findings and offer design recommendations to support effective phish detection by smartphone users.
Tailor My Zwift

Physical activity is entering the virtual realm. Zwift is an at-home cycling system that is enjoying increasing popularity, yet the specifics of the experience of a virtual cyclist have not been studied to date. Building virtual sports systems can make physical activity accessible to more diverse user groups. To understand how and why users engage in virtual cycling, we conducted n=22 interviews with Zwift users. Through charting the motivations behind using Zwift, we determined that it allowed users to engage in a range of cycling activities traditionally reserved for professional cyclists. Our work reports on key motivations and identifies five key strategies which Zwift uses to create an engaging virtual sports experience. Further, we discuss how Zwift creates a world of virtual professionalism. Our findings offer a structured understanding of the experience of Zwift which can be used to inspire the design of future virtual amateur sports systems.
Blind Quality Prediction for View Synthesis Based on Heterogeneous Distortion Perception

The quality of synthesized images directly affects the practical application of virtual view synthesis technology, which typically uses a depth-image-based rendering (DIBR) algorithm to generate a new viewpoint based on texture and depth images. Current view synthesis quality metrics commonly evaluate the quality of DIBR-synthesized images, where the DIBR process is computationally expensive and time-consuming. In addition, the existing view synthesis quality metrics cannot achieve robustness due to the shallow hand-crafted features. To avoid the complicated DIBR process and learn more efficient features, this paper presents a blind quality prediction model for view synthesis based on HEterogeneous DIstortion Perception, dubbed HEDIP, which predicts the image quality of view synthesis from texture and depth images. Specifically, the texture and depth images are first fused based on discrete cosine transform to simulate the distortion of view synthesis images, and then the spatial and gradient domain features are extracted in a Two-Channel Convolutional Neural Network (TCCNN). Finally, a fully connected layer maps the extracted features to a quality score. Notably, the ground-truth score of the source image cannot effectively represent the labels of each image patch during training due to the presence of local distortions in view synthesis image. So, we design a Heterogeneous Distortion Perception (HDP) module to provide effective training labels for each image patch. Experiments show that with the help of the HDP module, the proposed model can effectively predict the quality of view synthesis. Experimental results demonstrate the effectiveness of the proposed model.
LESCA: LightwEight Stream Cipher Algorithm for emerging systems

Recently, there has been a dire need for lightweight cryptographic solutions, which exhibit low computational complexity and require few resources. In this paper, we present LESCA, a novel dynamic key-dependent lightweight stream cipher, which consists of two main functions, a typical round function based on cryptographic primitives, and a function that updates these primitives. The update is performed in a selective (partial) manner while encrypting a block, and in full after each δ blocks/iterations. As such, LESCA consumes minimal resources and introduces a very low latency. The originality of this solution stems from the fact that the cryptographic primitives get updated even when encrypting the same message. Several performance and security tests were performed to confirm that the proposed cipher is robust and efficient, especially for limited devices and real-time applications. The proposed cipher achieves a high throughput; for example, when implemented on a Raspberry Pi (RPI4) device, LESCA provides an enhancement of at least 343% when compared to the Advanced Encryption Standard (AES), 72% over a recent one-round cipher scheme, and 43% improvement compared to a recent LORCA stream cipher that outperforms the Simon and Speck algorithms.
Mitigating the effect of dataset shift in clustering

Dataset shift is a relevant topic in unsupervised learning since many applications face evolving environments, causing an important loss of generalization and performance. Most techniques that deal with this issue are designed for data stream clustering, whose goal is to process sequences of data efficiently under Big Data. In this study, we claim dataset shift is an issue for static clustering tasks in which data is collected over a long period. To mitigate it, we propose Time-weighted kernel k-means, a k-means variant that includes a time-dependent weighting process. We do this via the induced ordered weighted average (IOWA) operator. The weighting process acts as a gradual forgetting mechanism, prioritizing recent examples over outdated ones in the clustering algorithm. The computational experiments show the potential Time-weighted kernel k-means has in evolving environments.
Recover User’s Private Training Image Data by Gradient in Federated Learning

Exchanging gradient is a widely used method in modern multinode machine learning system (e.g., distributed training, Federated Learning). Gradients and weights of model has been presumed to be safe to delivery. However, some studies have shown that gradient inversion technique can reconstruct the input images on the pixel level. In this study, we review the research work of data leakage by gradient inversion technique and categorize existing works into three groups: (i) Bias Attacks, (ii) Optimization-Based Attacks, and (iii) Linear Equation Solver Attacks. According to the characteristics of these algorithms, we propose one privacy attack system, i.e., Single-Sample Reconstruction Attack System (SSRAS). This system can carry out image reconstruction regardless of whether the label can be determined. It can extends gradient inversion attack from a fully connected layer with bias terms to attack a fully connected layer and convolutional neural network with or without bias terms. We also propose Improved R-GAP Alogrithm, which can utlize DLG algorithm to derive ground truth. Furthermore, we introduce Rank Analysis Index (RA-I) to measure the possible of whether the user's raw image data can be reconstructed. This rank analysis derive virtual constraints Vi from weights. Compared with the most representative attack algorithms, this reconstruction attack system can recover a user's private training image with high fidelity and attack success rate. Experimental results also show the superiority of the attack system over some other state-of-the-art attack algorithms.
Camera-LiDAR Fusion Method with Feature Switch Layer for Object Detection Networks

Object detection is an important factor in the autonomous driving industry. Object detection for autonomous vehicles requires robust results, because various situations and environments must be considered. A sensor fusion method is used to implement robust object detection. A sensor fusion method using a network should effectively meld two features, otherwise, there is concern that the performance is substantially degraded. To effectively use sensors in autonomous vehicles, data analysis is required. We investigated papers in which the camera and LiDAR data change for effective fusion. We propose a feature switch layer for a sensor fusion network for object detection in cameras and LiDAR. Object detection performance was improved by designing a feature switch layer that can consider its environment during network feature fusion. The feature switch layer extracts and fuses features while considering the environment in which the sensor data changes less than during the learning network. We conducted an evaluation experiment using the Dense Dataset and confirmed that the proposed method improves the object detection performance.
MCIC: Multimodal Conversational Intent Classification for E-commerce Customer Service

Conversational intent classification (CIC) plays a significant role in dialogue understanding, and most previous works only focus on the text modality. Nevertheless, in real conversations of E-commerce customer service, users often send images (screenshots and photos) among the text, which makes multimodal CIC a challenging task for customer service systems. To understand the intent of a multimodal conversation, it is essential to understand the content of both text and images. In this paper, we construct a large-scale dataset for multimodal CIC in the Chinese E-commerce scenario, named MCIC, which contains more than 30,000 multimodal dialogues with image categories, OCR text (the text contained in images), and intent labels. To fuse visual and textual information effectively, we design two vision-language baselines to integrate either images or OCR text with the dialogue utterances. Experimental results verify that both the text and images are important for CIC in E-commerce customer service.
Aspect-Specific Context Modeling for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims at predicting sentiment polarity (SC) or extracting opinion span (OE) expressed towards a given aspect. Previous work in ABSA mostly relies on rather complicated aspect-specific feature induction. Recently, pretrained language models (PLMs), e.g., BERT, have been used as context modeling layers to simplify the feature induction structures and achieve state-of-the-art performance. However, such PLM-based context modeling can be not that aspect-specific. Therefore, a key question is left under-explored: how the aspect-specific context can be better modeled through PLMs? To answer the question, we attempt to enhance aspect-specific context modeling with PLM in a non-intrusive manner. We propose three aspect-specific input transformations, namely aspect companion, aspect prompt, and aspect marker. Informed by these transformations, non-intrusive aspect-specific PLMs can be achieved to promote the PLM to pay more attention to the aspect-specific context in a sentence. Additionally, we craft an adversarial benchmark for ABSA (advABSA) to see how aspect-specific modeling can impact model robustness. Extensive experimental results on standard and adversarial benchmarks for SC and OE demonstrate the effectiveness and robustness of the proposed method, yielding new state-of-the-art performance on OE and competitive performance on SC.
Cloud-Assisted Privacy Protection Energy Trading Based on IBS and Homomorphic Encryption in IIoT

The decentralized and tamper-proof features of blockchain technology can solve the problems of low compatibility, poor flexibility, and single point of failure in the traditional Industrial Internet of Things (IIoT). However, the transparency of the blockchain ledger makes the privacy disclosure of users a huge security risk. Given the privacy leakage problem exposed in the existing energy trading scheme based on the blockchain, this paper creatively proposes a privacy protection scheme for IIoT energy trading based on an identity-based signature (IBS) and homomorphic encryption. On the premise of satisfying the transaction traceability and verifiability, this scheme uses IBS technology to provide an anonymous mechanism for energy trading nodes and utilizes Paillier homomorphic encryption to prevent the disclosure of transaction amounts. To meet the high-concurrency and high-throughput energy trading requirements in IIoT, moreover, the proposed scheme combines the off-chain storage with cloud assistance and the off-chain transaction based on PCN to reduce redundant data written into the blockchain and to improve the concurrent trading efficiency, respectively. The security analysis and performance evaluation results show that the proposed scheme can realize the dual privacy protection of identities and transaction amounts in the trading process at the cost of reasonable calculation.
Carrier frequency by axial displacements of lens and grating in a double-aperture common-path interferometer

Double-aperture common-path interferometer (DACPI) is built on a 4f optical imaging system. It consists of two lenses, two apertures at input plane, and a grating at Fourier plane. As known, a carrier frequency is introduced when first lens or grating is displaced axially. Nevertheless, the effect when both displacements are present is not described yet. In this manuscript, we describe the interrelation existing among the axial displacements of the first lens and, grating. Additionally, with least squares method, we find with high accurate the initial position of the first lens and grating, for calibrating the 4f optical system. It is important to note that no tilt between the two beams is requested to produce this effect, and it turns out to be simpler, and more accurate than other existing methods. This proposal will be discussed and illustrated in detail with experimental results.
An approximation of the Bayesian state observer with Markov chain Monte Carlo propagation stage

The state estimation problem for nonlinear systems with stochastic uncertainties can be formulated in the Bayesian framework, where the objective is to replace the state completely by its probability density function. Without the restriction to selected system classes and disturbance properties, the Bayesian estimator is particularly interesting for highly nonlinear systems with non-Gaussian noise. The main limitations of Bayesian filters are the significant computational costs and the implementation problems for higher dimensional systems. The present paper introduces a piecewise linear approximation of the Bayesian state observer with Markov chain Monte Carlo propagation stage and kernel density estimation. These methods are suitable for the prediction of multivariate probability density functions. The piecewise linear approximation and the proposed algorithms can increase the estimation performance at reasonable computational cost. The estimation performance is demonstrated in a benchmark comparing the Bayesian state observer with an extended Kalman filter and a particle filter.