Reviewerly/exHarmony huggingface dataset:
Source-Free Domain Adaptation via Target Prediction Distribution Searching

Abstract Existing Source-Free Domain Adaptation (SFDA) methods typically adopt the feature distribution alignment paradigm via mining auxiliary information (eg., pseudo-labelling, source domain data generation). However, they are largely limited due to that the auxiliary information is usually error-prone whilst lacking effective error-mitigation mechanisms. To overcome this fundamental limitation, in this paper we propose a novel Target Prediction Distribution Searching (TPDS) paradigm. Theoretically, we prove that in case of sufficient small distribution shift, the domain transfer error could be well bounded. To satisfy this condition, we introduce a flow of proxy distributions that facilitates the bridging of typically large distribution shift from the source domain to the target domain. This results in a progressive searching on the geodesic path where adjacent proxy distributions are regularized to have small shift so that the overall errors can be minimized. To account for the sequential correlation between proxy distributions, we develop a new pairwise alignment with category consistency algorithm for minimizing the adaptation errors. Specifically, a manifold geometry guided cross-distribution neighbour search is designed to detect the data pairs supporting the Wasserstein distance based shift measurement. Mutual information maximization is then adopted over these pairs for shift regularization. Extensive experiments on five challenging SFDA benchmarks show that our TPDS achieves new state-of-the-art performance. The code and datasets are available at https://github.com/tntek/TPDS .
Differential privacy optimal control with asymmetric information structure

Abstract A linear‐quadratic optimal control is investigated in this article under the differential privacy (DP) philosophy to trade off the performance and privacy of sensitive information, where the two controllers have asymmetric information structure and some prescribed signal needs to be tracked. Note that the system output and tracking signal are always sensitive and easy to be filched by adversaries; thus the DP methodology is explored to protect them. Under DP Gaussian mechanism, the optimal linear controllers are first studied for finite‐horizon and infinite‐horizon problems. Then, the bounds of mean‐square error of steady‐state Kalman filter estimator is provided, and the DP parameter design will be guided that characterizes the privacy of sensitive information. As the DP Gaussian noise will degrade the controlled performance, the degraded performance is quantitatively calculated. Finally, a numerical example is given that shows the efficiency of obtained results.
Digitally nudging users to explore off-profile recommendations: here be dragons

Abstract In many application domains of recommender systems, e.g., on media streaming sites, one main goal of the provider of the recommendation service is to increase the engagement of users by helping them discover new types of content they like. Standard collaborative filtering algorithms by design often lead to a certain level of discovery. Nonetheless, in certain domains, it may be helpful to more actively promote content to users beyond their past preference profile (“off-profile”) and thereby help users explore new content. However, when showing such off-profile content to users in combination with more familiar content, the new content items may be overlooked. In this research, we explore to what extent digital nudging , i.e., subtly directing user choices in a specific direction, can help to raise the attention and interest of users for off-profile content. We conducted a user study ( $$N=1064$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1064</mml:mn> </mml:mrow> </mml:math> ) on a real-world social book recommendation app. We find that users who are nudged towards recommended books of their non-preferred genres significantly more often put these off-profile books on their reading lists, thus confirming the effectiveness of digital nudging in this application. However, we also found that digital nudges may negatively impact the users’ beliefs and attitudes towards the system and a more limited intention to use the system in the future. As a result, we find that digital nudging in recommendations, while effective in the short run, must be done with due care, keeping an eye on the overall quality perceptions by users and potentially harmful long-term effects.
The impact of innovative work behavior, perceived Leadership 4.0, and corporate social responsibility on sustaining banking industry performance in Nigeria within the 4IR Era

Despite the increase in business performance research, only some studies have examined the combination of innovative work behavior, Leadership 4.0, and corporate social responsibility as performance factors in Nigeria’s banking industry in the current 4IR. This study aims to sustain performance in the banking industry of Nigeria. Four hundred (400) bank employees were randomly selected for this study from a sample of cooperating banks (Zenith Bank Plc, Guarantee Trust Bank Plc, United Bank for Africa Plc, and First Bank of Nigeria Plc) in the Nigerian states of Oyo and Lagos. One Hundred (100) participants were chosen from each bank. Additionally, the survey was given out to randomly chosen bank employees using structured questionnaires. Participants were selected using a simple random sampling technique; 386 of the 400 surveys were appropriate for analysis. To do the analysis, SPSS version 29 was used. According to the study’s findings, innovative work behavior had a 77% influence on performance variance within the banking industry in the current 4IR, Leadership 4.0 had an 88% influence, and corporate social responsibility had a 71% influence. Accordingly, the results show that more significant innovation in work behavior, adoption of Leadership 4.0, and involvement in CSR significantly predict the maintenance of performance in the Nigerian banking industry. Additionally, the findings indicate that adopting Leadership 4.0 predicts a more significant variance in performance in the banking business, followed by demonstrating innovative work behavior and involvement in corporate social responsibility. AcknowledgmentThe author thanks Professor Wilfred Ukpere and the Department of Industrial Psychology and People Management (University of Johannesburg) for funding and publishing this study.
Wavelet-Based Gesture Recognition Method for Human-Machine Interaction in Aviation

Human-machine interaction (HMI) has been an important research area in many scientific fields for many years with the development of sensor technologies. With the general use of unmanned aerial vehicles (UAVs) in recent years, the gesture recognition concept for HMI has become a considerable study in UAVs. They can be used in different domains and for different purposes. The main motivation for this paper is to create as simple a human-machine interface design for UAVs as possible. The vector-based features can be arranged in low memory size and the low-complexity decision process on them can be aimed. The gesture recognition problem for HMI in UAVs can be modeled as a pattern recognition problem in a multi-sensor system with vectorial data from various types of sensors. The speed of the action and the magnitude of the action can be different even within the same class of gesture action. In other words, user-independent gesture action recognition is difficult due to large intra-class differences in gesture action patterns. This paper introduces the wavelet-based vectorial feature extraction and the discriminant function-based decision in the supervised-based reduced vectorial dimension. According to experimental studies, the promising method is put forward for gesture recognition in the control of UAVs.
Streamlining room scheduling and managing library events using LibCal

AbstractThis article explains how one academic library met greater demand than ever before by streamlining the room reservation process for both patrons and staff and implementing an events calendar and other tools to make library happenings more visible. Like many academic libraries, the College of Charleston’s main library, Addlestone Library, contains a mix of spaces used by students, faculty, and staff from across campus, and library faculty and staff. The library had used LibCal, the popular library room booking software from Springshare, to efficiently manage study rooms and other patron-forward library spaces since 2013. Spaces reserved primarily by library staffers were booked using legacy Outlook calendars, a system that had become increasingly difficult to manage. By thoughtfully reorganizing the library’s LibCal Spaces module, the library incorporated all reservable library spaces into a single platform and streamlined the room booking process for all users. The project reduced staff time spent managing room bookings and setups, made it easier for staff to find information about what is going on in the library, and facilitated the relaunch of an events calendar on the library website.Keywords: LibCalevent calendarroom bookingreservationslibrary spaceslibrary websitelibrary staffersstaff training Disclosure statementThe author reports there are no competing interests to declare.Additional informationNotes on contributorsGretchen ScronceGretchen Scronce is a research and instruction librarian and virtual services coordinator for the College of Charleston Libraries.
Secondary School Teachers' Attitudes Toward Information Technology

Every aspect of human existence has been touched by information technology, and it has the ability to contribute to education and training—especially distance learning—to make it a cutting-edge learning environment. The need for new technology in the teaching and learning process is growing. With sound and unmatched feasibility for information exchange, communication, and discovery to support teaching and learning, the information age evolves into an era of knowledge. Information technologies support opportunities for knowledge exchange on a global scale. These can help by giving teachers and students access to the most recent knowledge and information. According to Haag (1998), information technologies are a "set of tools that can help provide the accurate and appropriate information that is necessary for effective teaching and learning"
Messaging App Using Flutter

The aim of this project is to build a real-time messaging app using modern technologies. Unlike most chat apps available in the market, the present system will focus on developers and will try to increase their productivity are coding and archive view. The main features of the project are completely open source. The users will be able to dig into the code to learn what happens after the scenes or contribute to the source code. It was therefore within this intent to write pure codes, which could be measured by following the most popular patterns and principles of each language and relevant libraries. The main objective of this project is going to build an application through which Instant mobile app and web applications are used for communication between the users. The applications initiate message sending via internet, making it free of cost for the users. Sending messages can be in the form of a text message or it can be a voice call.
Current Usage Areas of Deepfake Applications with Artificial Intelligence Technology

The concept of artificial intelligence has gained significant prominence throughout history, persisting as a subject of regular discussion and exploration in contemporary times. Artificial intelligence (AI) has been a recurring theme in numerous literary works and films, with its projected significance in future contexts. This thematic exploration of AI has been a subject of creative endeavors spanning several decades. In recent years, deepfake technology has emerged as a prominent subject of interest within the realm of artificial intelligence. Deepfake technology is widely recognized as an artificial intelligence and deep learning-based innovation. Numerous deepfake applications have had a big impact on the public recently. In addition to the production of manipulation films targeting individuals of high popularity, it is evident that deepfake technology possesses many potential applications across several domains. The objective of this study is to explore the potential applications of deepfake technology across many domains. Deepfake technology was examined in the study by concentrating on the concept of deep learning and referencing artificial intelligence technology. The study involved the classification of the many applications of deepfake technology by conducting a comprehensive literature analysis and analyzing examples of its usage in diverse domains. Based on the findings of the study, it is possible to categorize the significant applications of deepfake technology into four distinct groups. The previously mentioned categories include arts and entertainment, advertising and marketing, the film industry, political communication, and media.
Few-Shot Air Object Detection Network

Focusing on the problem of low detection precision caused by the few-shot and multi-scale characteristics of air objects, we propose a few-shot air object detection network (FADNet). We first use a transformer as the backbone network of the model and then build a multi-scale attention mechanism (MAM) to deeply fuse the W- and H-dimension features extracted from the channel dimension and the local and global features extracted from the spatial dimension with the object features to improve the network’s performance when detecting air objects. Second, the neck network is innovated based on the path aggregation network (PANet), resulting in an improved path aggregation network (IPANet). Our proposed network reduces the information lost during feature transfer by introducing a jump connection, utilizes sparse connection convolution, strengthens feature extraction abilities at all scales, and improves the discriminative properties of air object features at all scales. Finally, we propose a multi-scale regional proposal network (MRPN) that can establish multiple RPNs based on the scale types of the output features, utilizing adaptive convolutions to effectively extract object features at each scale and enhancing the ability to process multi-scale information. The experimental results showed that our proposed method exhibits good performance and generalization, especially in the 1-, 2-, 3-, 5-, and 10-shot experiments, with average accuracies of 33.2%, 36.8%, 43.3%, 47.2%, and 60.4%, respectively. The FADNet solves the problems posed by the few-shot characteristics and multi-scale characteristics of air objects, as well as improving the detection capabilities of the air object detection model.
Trolling and Victims' Perception of Trolling

Online trolling is the practise of engaging in dishonest, harmful, or disruptive behaviour in a social setting over the internet without any apparent gain. If a target falls for their trick, the trolling gets worse for even more heinous enjoyment. Trolling may be distinguished from other forms of online antisociality by its dishonest and “pointless” disruptive features, in contrast to which the perpetrators' names and objectives are usually more transparent. Trolling has adverse negative effects on mental health. Therefore, there is an urgent need to address this issue. This chapter attempted to look into the causes of trolling which is damaging the mental health of people and why people become victims of trolling. Secondly, it assessed whether the damage in mental health vary with age or not. Thematic analysis was conducted on the transcribed data and the findings were discussed.
A Synergistic Bidirectional LSTM and N-gram Multi-channel CNN Approach Based on BERT and FastText for Arabic Event Identification

Event extraction from texts continues to pose a challenge for many NLP systems. This article presents a novel neural network architecture that can extract and classify events from Arabic sentences. The model combines word representations and Part-Of-Speech (POS) tags and uses a bidirectional LSTM layer and a dual combined convolutional neural network. The first layer of the network focuses on sentence representations, while the second layer focuses on POS representations. The model takes advantage of both N-gram character features from FastText and contextual representations from bidirectional encoder representations from transformers. This combination proves to be successful, as evidenced by the good results obtained from evaluating the model on the Arabic TimeML corpus. Our results show that combining both contextual and N-gram representations outperforms the traditional skip-gram model.
A comparative study on the development of Chinese and English abilities of Chinese primary school students through two bilingual reading modes: human-AI robot interaction and paper books

To address the challenges encountered by Chinese primary school students, particularly left-behind and migrant children, who exhibit a preference for animations, video games, and short videos over reading books and struggle with Chinese-English bilingual skills, this study introduces an educational robot AI-assisted method for simultaneous bilingual reading. To assess the effectiveness of this method, a 6-month Chinese-English bilingual extracurricular reading comparative experiment was conducted involving 85 grade 5 students from two classes in a primary school in Hangzhou, China. The AI-assisted class freely read 100 bilingual/English electronic picture books and 200 Chinese electronic classic serial picture books by employing the AI-assisted human-computer interactive electronic reading mode of the "Educational Robot+Audio Electronic Picture Book+Character-play Based Reading." In contrast, the paper book group read the same content presented in the traditional paper book format, following the "regular independent reading" mode. Post-experimental analyses were conducted employing t-tests and MANCOVA and the results revealed that: the primary factors influencing reading effectiveness are the choice of reading materials, reading tools, and reading mode, while reading time does not emerge as the principal influencing factor. Furthermore, students in the AI class demonstrated significant enhancements in bilingual reading motivation, reading amount, reading comprehension, independent learning ability, pronunciation proficiency, and test scores compared to their peers in the paper book class. The AI-assisted reading mode utilizing educational robots garnered positive feedback from teachers, parents, and students. It offers the potential to effectively substitute parental involvement in parent-child reading and English tutoring, while also enabling the simultaneous acquisition of bilingual proficiency in both Chinese and English. This approach proves to be highly effective, cost-efficient, and convenient, particularly for enhancing children's foreign language abilities. Moreover, it fosters positive reading habits and independent learning skills among primary school students, contributes to the establishment of lofty aspirations, and enhances bilingual performance. Overall, this innovative mode offers an effective means of facilitating children's acquisition of bilingualism and foreign language skills, as well as promoting reading education.
“I Feel My Abs”: Exploring Non-standing VR Locomotion

Virtual Reality (VR) games and experiences predominantly have the users interact while standing or seated. However, this only represents a fraction of the full diversity of human movement. In this paper, we explore a novel non-standing approach to VR locomotion where the user performs locomotion movements in the air or only slightly touching the ground with their feet. For instance, the user may lie supine on the ground, reminiscent of the Bicycle Crunch, a core training movement common in Pilates and other forms of bodyweight exercise. Although this cannot generally replace traditional VR locomotion, it provides two benefits that we believe can be of use for specific application domains such as VR exergames: First, the user's lower body movement is not impeded by a small real-life space, allowing versatile navigation of large virtual worlds using walking, running, strafing, and jumping. Second, we allow new ways to activate parts of the body that remain passive in most existing VR interactions. We describe and discuss four different variants of the approach, and investigate two prototypes further in a qualitative user study, to better understand their strengths, weaknesses, and application potential.
Physics-Informed Machine Learning for Power Grid Frequency Modeling

Toward unlocking the mysteries of power grid dynamics, a physics-inspired machine learning model reveals hidden dependencies and enables precise probabilistic predictions in the power system of continental Europe.
ExpanStick: Augmenting Gamepad Thumbstick using Force at the Limit

We present ExpanStick, a novel thumbstick that augments video game interaction using the force that the user applies at the physical limit of the gamepad. Gamers frequently manipulate a thumbstick leaning on the limit, or physical boundary around it, either to apply maximum input or to secure physical support, which makes the contact force an easily accessible input channel. The ExpanStick preserves the form factor and usability of the thumbstick, while expanding its input space. We built the ExpanStick and inserted it in a commercial gamepad as a proof-of-concept prototype. Using the prototype, we conducted a design workshop with console gamers to design the ExpanStick game interaction. Through demonstration of the interactions, we found the early use experience with the ExpanStick is positive and well accepted by the game players.
On Enhancement of Text Classification and Analysis of Text Emotions Using Graph Machine Learning and Ensemble Learning Methods on Non-English Datasets

In recent years, machine learning approaches, in particular graph learning methods, have achieved great results in the field of natural language processing, in particular text classification tasks. However, many of such models have shown limited generalization on datasets in different languages. In this research, we investigate and elaborate graph machine learning methods on non-English datasets (such as the Persian Digikala dataset), which consists of users’ opinions for the task of text classification. More specifically, we investigate different combinations of (Pars) BERT with various graph neural network (GNN) architectures (such as GCN, GAT, and GIN) as well as use ensemble learning methods in order to tackle the text classification task on certain well-known non-English datasets. Our analysis and results demonstrate how applying GNN models helps in achieving good scores on the task of text classification by better capturing the topological information between textual data. Additionally, our experiments show how models employing language-specific pre-trained models (like ParsBERT, instead of BERT) capture better information about the data, resulting in better accuracies.