Reviewerly/exHarmony huggingface dataset:
Applicability of an Immersive Virtual Reality Exercise Training System for Office Workers during Working Hours

Virtual reality is a computer-generated simulation of a real or imaginary three-dimensional environment that has entered our lives, particularly for gaming. Lately, it has been permeating into many aspects of our everyday life, such as exercise. It is important to ascertain whether exercise in an immersive virtual reality environment can be accepted from employees and lead to positive outcomes for them. The aim of this exploratory study was to examine the acceptance, future adoption, interest/enjoyment and usability of an immersive virtual reality system for exercise training by office workers during breaks within their working hours. A total of 40 female employees participated in the study with a mean age of 42.58 years (SD 10.77). Participants were requested to complete two sequential 15-min dual task cycling sessions corresponding to two experimental conditions. The first, condition A, involved cycling in a virtual environment, wearing a virtual reality head mounted display, and responding to cognitive tasks by answering multiple choice questions-on a screen, using a joystick. The second, condition B, involved cycling on a static bicycle and simultaneously responding to cognitive tasks by answering multiple choice questions in a real environment. After completion of the two conditions, participants responded to a series of scales regarding each of the experimental conditions and to a semi-structured interview. The results showed that participants noted a significant preference for the immersive virtual reality exercise, condition A, compared to condition B (bike only); and their acceptance, interest/enjoyment, usability and intention for future use were high. The qualitative data showed increased intention for future use, feelings of control and presence and most of the participants did not encounter any difficulties or require extra help to understand the immersive virtual reality system. Overall, exercising during working hours with an immersive virtual reality exercise system was well perceived by office workers and applicable. However, the effects of the immersive virtual reality training system on physical and mental health and the employees' adherence to the exercise program should be tested with a longer intervention program.
Exploring the factors that influence the cybersecurity behaviors of young adults

Young adults aged between 18 and 30 are likely to encounter increasing cyber threats. Understanding the cybersecurity behaviors of young adults, and identifying the measures and factors that can help reduce cyber threats is thus crucial. Since the existing studies have not sufficiently explored these factors, this study adopted a socio-behavioral perspective. It employed the primary constructs of the theory of planned behavior (TPB) with other factors, including perceived awareness and knowledge of cyber threats, to predict young adults' behavioral intent to practice cybersecurity behaviors. Data were collected from a random sample of 1581 young adults studying at Technical and Vocational Training Corporation (TVTC) colleges in Saudi Arabia through an online survey and were analyzed using the least-squares partial structural equation modeling (SEM). The results revealed that attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC) strongly influenced young adults’ intentions to practice cybersecurity behavior (IPC). Also important for IPC was the perceived awareness of the consequences of the risks of cyber threats and the need for cybersecurity behavior (PCST). Moreover, while PCST and IPC were directly related to practicing cybersecurity behaviors, PBC was not. Future studies may benefit from examining cultural, and socio-demographic aspects that may influence CSB. • Planned behavior theory is used to model cybersecurity practices online. • A nuanced understanding of young adults' cybersecurity behaviors is obtained. • Factors are found that affect young adults' motivation to adopt certain behaviors. • The value of cyber risk awareness of in predicting online behavior is shown. • Knowledge of cyber threats is shown to enhance security practices.
IntegrateCF: Integrating explicit and implicit feedback based on deep learning collaborative filtering algorithm

• We proposed a novel recommendation system based on collaborative filtering. • It is a combination of explicit (Intra & Inter) and implicit feedback interaction couplings. • It solves the cold start and sparsity problems of collaborative filtering methods. Due to the expansion of e-business, the availability of products on the internet has massively increased. Finding suitable stuff from the vast array of products available on the internet is a time-consuming task. Collaborative Filtering (CF) is the most effective recommendation method for providing users with the ability to identify relevant content and, therefore, increase engagement. However, CF has several flaws, including data sparsity and cold start problems. These are ongoing research questions that pose major hurdles to the precision of the algorithms. Therefore, in this work, a novel neural recommendation model is proposed based on non-independent and identically distributed (Non-IID) for CF by incorporating explicit and implicit coupling interaction. The explicit interactions consist of two models, namely Intra-coupling interactions within users and items, and Inter-coupling interactions between different users and items concerning the attributes of users and items. The Intra-coupled model learns using deep learning convolutional neural networks and is combined with the Inter-coupled model. Besides explicit coupling interactions, we present a Generalized Matrix Factorization Bias (GMFB) model that systematically trains the implicit user-item coupling. Finally, we combined with explicit and implicit coupling interactions within and between users and items accompanying the extra information about users and items under a framework called “IntegrateCF.” Extensive experiments on two large real-world datasets have shown that the proposed model performs better than existing methods.
The practice of developing the academic cloud using the Proxmox VE platform

Cloud technologies provide users with efficient and secure tools for data management, computing, storage and other services. The article analyzes the projects for the introduction of cloud technologies in education and identifies the main advantages and risks in creating a cloud infrastructure for the university. Such startups contribute to the formation of a new paradigm of education. It involves the virtualization of education, the introduction of mobile and blended learning, ie the combination of cloud computing with modern learning concepts. In this paper, we highlight our experience in improving the academic cloud for the training of a bachelor's degree in computer science. This is through the integration of the Proxmox VE platform into existing computing power by deploying the Proxmox VE system. In the study, we reveal some technical and methodological aspects of the organization of the educational process using this corporate cloud platform. The scheme of the organization of physical components of cloud infrastructure (nodes, virtual networks, routers, domain controller, VPN server, backup system of students' virtual machines) is given. All characteristics of this environment and possibilities of their application are studied.
Improving fault localization and program repair with deep semantic features and transferred knowledge

Automatic software debugging mainly includes two tasks of fault localization and automated program repair. Compared with the traditional spectrum-based and mutation-based methods, deep learning-based methods are proposed to achieve better performance for fault localization. However, the existing methods ignore the deep semantic features or only consider simple code representations. They do not leverage the existing bug-related knowledge from large-scale open-source projects either. In addition, existing template-based program repair techniques can incorporate project specific information better than deep-learning approaches. However, they are weak in selecting the fix templates for efficient program repair. In this work, we propose a novel approach called TRANSFER, which leverages the deep semantic features and transferred knowledge from open-source data to improve fault localization and program repair. First, we build two large-scale open-source bug datasets and design 11 BiLSTM-based binary classifiers and a BiLSTM-based multi-classifier to learn deep semantic features of statements for fault localization and program repair, respectively. Second, we combine semantic-based, spectrum-based and mutation-based features and use an MLP-based model for fault localization. Third, the semantic-based features are leveraged to rank the fix templates for program repair. Our extensive experiments on widely-used benchmark De-fects4J show that TRANSFER outperforms all baselines in fault localization, and is better than existing deep-learning methods in automated program repair. Compared with the typical template-based work TBar, TRANSFER can correctly repair 6 more bugs (47 in total) on Defects4J.
Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect

Knowledge Tracing (KT), which aims to assess students' dynamic knowledge states when practicing on various questions, is a fundamental research task for offering intelligent services in online learning systems. Researchers have devoted significant efforts to developing KT models with impressive performance. However, in existing KT methods, the related question difficulty level, which directly affects students' knowledge state in learning, has not been effectively explored and employed. In this paper, we focus on exploring the question difficulty effect on learning to improve student's knowledge state assessment and propose the DIfficulty Matching Knowledge Tracing (DIMKT) model. Specifically, we first explicitly incorporate the difficulty level into the question representation. Then, to establish the relation between students' knowledge state and the question difficulty level during the practice process, we accordingly design an adaptive sequential neural network in three stages: (1) measuring students' subjective feelings of the question difficulty before practice; (2) estimating students' personalized knowledge acquisition while answering questions of different difficulty levels; (3) updating students' knowledge state in varying degrees to match the question difficulty level after practice. Finally, we conduct extensive experiments on real-world datasets, and the results demonstrate that DIMKT outperforms state-of-the-art KT models. Moreover, DIMKT shows superior interpretability by exploring the question difficulty effect when making predictions. Our codes are available at https://github.com/shshen-closer/DIMKT.
Conversational Information Seeking

Conversational information seeking (CIS) involves interaction sequences between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. CIS recently attracted significant attention and advancements continue to be made. This tutorial follows the content of the recent Conversational Information Seeking book authored by several of the tutorial presenters. The tutorial aims to be an introduction to CIS for newcomers to CIS in addition to the recent advanced topics and state-of-the-art approaches for students and researchers with moderate knowledge of the topic. A significant part of the tutorial is dedicated to hands-on experiences based on toolkits developed by the presenters for conversational passage retrieval and multi-modal task-oriented dialogues. The outcomes of this tutorial include theoretical and practical knowledge, including a forum to meet researchers interested in CIS.
V2P

Multi-modal Product Summary Generation is a new yet challenging task, which aims to generate a concise and readable summary for a product given its multi-modal content, e.g., its long text description and image. Although existing methods have achieved great success, they still suffer from three key limitations: 1) overlook the benefit of pre-training, 2) lack the representation-level supervision, and 3) ignore the diversity of the seller-generated data. To address these limitations, in this work, we propose a Vision-to-Prompt based multi-modal product summary generation framework, dubbed as V2P, where a Generative Pre-trained Language Model (GPLM) is adopted as the backbone. In particular, to maintain the original text capability of the GPLM and fully utilize the high-level concepts contained in the product image, we design V2P with two key components: vision-based prominent attribute prediction, and attribute prompt-guided summary generation. The first component works on obtaining the vital semantic attributes of the product from its image by the Swin Transformer, while the second component aims to generate the summary based on the product's long text description and the attribute prompts yielded by the first component with a GPLM. Towards comprehensive supervision over the second component, apart from the conventional output-level supervision, we introduce the representation-level regularization. Meanwhile, we design the data augmentation-based robustness regularization to handle the diverse inputs and improve the robustness of the second component. Extensive experiments on a large-scale Chinese dataset verify the superiority of our model over cutting-edge methods.
Discovering repetitive code changes in python ML systems

Over the years, researchers capitalized on the repetitiveness of software changes to automate many software evolution tasks. Despite the extraordinary rise in popularity of Python-based ML systems, they do not benefit from these advances. Without knowing what are the repetitive changes that ML developers make, researchers, tool, and library designers miss opportunities for automation, and ML developers fail to learn and use best coding practices.
Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction

Relation prediction on knowledge graphs (KGs) aims to infer missing valid triples from observed ones. Although this task has been deeply studied, most previous studies are limited to the transductive setting and cannot handle emerging entities. Actually, the inductive setting is closer to real-life scenarios because it allows entities in the testing phase to be unseen during training. However, it is challenging to precisely conduct inductive relation prediction as there exists requirements of entity-independent relation modeling and discrete logical reasoning for interoperability. To this end, we propose a novel model ConGLR to incorporate context graph with logical reasoning. Firstly, the enclosing subgraph w.r.t. target head and tail entities are extracted and initialized by the double radius labeling. And then the context graph involving relational paths, relations and entities is introduced. Secondly, two graph convolutional networks (GCNs) with the information interaction of entities and relations are carried out to process the subgraph and context graph respectively. Considering the influence of different edges and target relations, we introduce edge-aware and relation-aware attention mechanisms for the subgraph GCN. Finally, by treating the relational path as rule body and target relation as rule head, we integrate neural calculating and logical reasoning to obtain inductive scores. And to focus on the specific modeling goals of each module, the stop-gradient is utilized in the information interaction between context graph and subgraph GCNs in the training process. In this way, ConGLR satisfies two inductive requirements at the same time. Extensive experiments demonstrate that ConGLR obtains outstanding performance against state-of-the-art baselines on twelve inductive dataset versions of three common KGs.
Complementarity-aware cross-modal feature fusion network for RGB-T semantic segmentation

RGB-T semantic segmentation has attracted growing attention because it makes a model robust towards challenging illumination. Most existing methods fuse RGB and thermal information in an equal manner along spatial dimensions, which results in feature redundancy and affects the discriminability of cross-modal features. In this paper, we propose a Complementarity-aware Cross-modal Feature Fusion Network (CCFFNet) including a Complementarity-Aware Encoder (CAE) and a Three-Path Fusion and Supervision (TPFS). The CAE, which consists of cascaded cross-modal fusion modules, can select complementary information from RGB and thermal features via a novel gate and fuse them by a channel-wise weighting mechanism. TPFS not only iteratively performs Three-Path Fusion (TPF) to further enhance cross-modal features, but also supervise the training of CCFFNet along three branches by Three-Supervision (TS). Extensive experiments are carried out and the results demonstrate that our model outperforms the state-of-the-art models by at least 1.6% mIoU on MFNet dataset and 2.9% mIoU on PST900 dataset, respectively. And a single-modality-based model can be easily applied to multi-modal semantic segmentation when plugging our CAE.
Facilitating authentic contextual EFL speaking and conversation with smart mechanisms and investigating its influence on learning achievements

This study introduced an app, called Smart UEnglish, for helping EFL conversation practices in authentic contexts. These conversation practices were categorized into 'designed talk' and 'free talk', based on the content of an English textbook and authentic ambient environment that includes such things as transportation, weather and scenic descriptions, respectively. The mechanisms in Smart UEnglish were designed in consideration of flexible, sustainable and adaptive conversations to improve English-speaking and lexical resources. Statistical analysis results show that the learning achievement of the experimental group is significantly higher than that of the control group. Moreover, both 'designed talk' and 'free talk' have significant positive correlations with learning achievement. Further analysis with stepwise multiple regression found that 'designed talk' plays the most important role and can significantly influence learning achievement. This is because 'designed talk' can motivate them to practice what they learned in class during their daily conversations and consolidate the understanding and applying of English vocabularies and sentence patterns through 'designed talk'. According to interview records, participants also perceived that using Smart UEnglish for conversation practice in authentic contexts can stimulate their interests and ideas to think and practice more useful English conversation. Hence, Smart UEnglish can effectively help English-speaking and conversation in authentic contexts anytime and anywhere.
High-capacity reversible data hiding in encrypted 3D mesh models based on multi-MSB prediction

As a new generation of digital media for covert transmission, three-dimension (3D) mesh models are frequently used and distributed on the network. Facing the huge massive of network data, it is urgent to study a method to protect and store this large amounts of data. In this paper, we proposed a high capacity reversible data hiding in encrypted 3D mesh models. This method divides the vertices of all 3D mesh into "embedded sets" and "prediction sets" based on the parity of the index. In addition, the multiple most significant bit (Multi-MSB) prediction reserved space is used to adaptively embed secret message, and the auxiliary information is compressed by arithmetic coding to further free up redundant space of the 3D mesh models. We use the majority voting system(MSV) principle to restore the original mesh model with high quality. The experimental results show that our method achieves a higher embedding capacity compared with state-of-the-art RDH-ED methods on 3D mesh models and can restore the original 3D mesh models with high quality.
Augmented language model with deep learning adaptation on sentiment analysis for E-learning recommendation

Sentiment Analysis is considered as an important research field in text mining, and is significant in recommendation systems and e-learning environments. This research proposes a new methodology of e-learning hybrid Recommendation System Based on Sentiment Analysis (RSBSA) by leveraging tailored Natural Language Processing (NLP) and Convolutional Neural Network (CNN) techniques, to recommend appropriate e-learning materials based on learner’s preferences. Integration is done on fine-grained sentiment analysis models, to classify text reviews of e-content posted on e-learning platform. Two enhanced language models based on ‘Continuous Bag of Word’ and ‘Skip-Gram’ are introduced. Moreover, three resilient language models based on the hybrid language techniques are developed to produce a superior vocabulary representation. These models were trained using various CNN models to predict ratings of resources from online reviews provided by learners. To accomplish this, a customizable dataset ‘ABHR-1′ is used, which is derived from e-content' reviews with corresponding ratings labeled [1–5]. The proposed models are evaluated and tested using ABHR-1 and two public datasets. According to the simulation results, Multiplication-Several-Channels-CNN model outperformed other models with an accuracy of 90.37 % for fine-grained sentiment classification on 5 discrete classes and the empirical results are compared.
Impact of the tilt angle, inverter sizing factor and row spacing on the photovoltaic power forecast accuracy

The growth of the installed photovoltaic (PV) capacity calls for accurate power forecasts, which are commonly calculated from irradiance forecasts using physical model chains. This study analyses the impact of the PV plant design parameters on the additional error of the irradiance-to-power conversion using three approaches. First, PV power curves are plotted for different design parameters to illustrate the amplification of errors and to enhance the understanding of the presented phenomena. Second, six forecast accuracy metrics are calculated as a function of the design parameters for the irradiance observation and forecast data of ten stations from five climate zones and two forecast providers. Third, the Pareto-optimal tradeoff between the annual energy production and the absolute forecast errors are obtained by the NSGA-II multi-objective optimization algorithm. The results reveal that the design parameters highly influence the PV power forecast errors, which is important to consider in all studies presenting PV power forecast verification. A tilt angle of 45° increases the error metrics up to 49% compared to a horizontal plane, and undersizing the inverters by a 1.5 sizing factor lead to an average error decrease up to 25%. Including the expected forecast errors as an objective during their design optimization process is important to maximize the net revenues of PV plants, especially if the unit imbalance costs are more than 50% of the electricity prices.