Reviewerly/exHarmony huggingface dataset:
Multi-user and multi-language collaborative speech translation system and method in the era of big data

In the rapid development of information technology and network technology, the cultural knowledge of human society blends with each other, and people have more and more ways to obtain information. Faced with the continuous growth and various types of information composed of different languages, researchers have proposed to use machine translation technology to build auxiliary translation system, which can play an important role in daily learning and life work. After understanding the research and application status of speech translation system in the era of big data, this paper mainly explores the architecture of multi-user and multi-language collaborative speech translation system according to the application characteristics of multi-language assisted translation system in the new era, and puts forward practical application methods from the perspective of long-term development of the industry, so as to let the world economy and culture communicate with each other. Fully demonstrate the translation efficiency and application quality of the design system.
Development SAT Recommendations and Community Service in Web Based Technology

The presence of information technology in the world really helps society in general and students, in various ways that support the learning process. One of the important pieces of information that students need is Student Activity Transcript (SAT) points and KKN hours. At Bina Nusantara University, every student is required to fulfill a minimum of 120 SAT points and 30 hours of community service. However, students sometimes experience difficulties in obtaining information and recommendations regarding the SAT and KKN. Therefore, this research focuses on designing a website-based information application that provides SAT recommendations and Community Service. The aim of designing this information application is to create a website that helps students find information about the SAT and community service, as well as recommendations for activities to fulfill the minimum SAT points and community service hours. After developing this application, more than 88% can help students and 90.5% easily register for SAT and the community is more reliable and useful for students.
Artificial intelligence and the law: can we and should we regulate AI systems?

Artificial intelligence (AI) deployment raises social and legal concerns, prompting legislators and legal scholars to explore regulating this technology. This book chapter contextualizes AI as a further technological step in the history of law and technology research, dating back to the 1940s with Lee Loevinger's introduction of jurimetrics. The authors examine past interactions of law and technology, the definition dilemma related to the concept of AI, how legal scholars have defined AI and whether they have identified regulatory needs, and assess the EU Commission's Artificial Intelligence Act (AIA) proposal. The AIA serves as an example of using technology as justification for new regulatory policies, even if their rationales are unrelated to the technology itself. The chapter highlights the long-standing relationship between law, technology and society and suggests a critical take on the AI definition dilemma as well as the way the AIA regulates AI.
A Complexity Measure for Data Classification Based on KNN with Dynamic Optimal K-Value Finding

The outcome of data classification is affected not only by the goodness of the classifier, but also by the complexity of the data itself. As a result, quantifying the complexity of the data itself can serve as a reference point for evaluating categorization models. Current approaches to quantifying data complexity overlook the significance of local data complexity in favor of a global viewpoint. In this research, we present a KNN-based data classification complexity measure with dynamic optimisation k-value (C2M_kNN) that gives greater weight to border sample classification difficulty. First, using dynamic optimization, the best k-value for k-Nearest-Neighbors is determined for each dataset. The samples that have a significant impact on classification complexity are next filtered by the kNN algorithm, and the classification complexity of the data is finally assessed. Based on created and real datasets, C2M_kNN performs better in experiments than 11 traditional data categorization complexity metrics.
The Use of Text Mining in New Media Studies: A Systematic Review

Text mining is a method that relies on the text as the source and aims to get the data through the text. This method has a strong potential in obtaining real-time findings and effective results in terms of the evaluation of large data sets. It is valuable to understand the people and the whole picture by using such powerful and effective methods obtained from rich sources like new media. In this context, the aim of this study is to define the basic tendencies and the changes that happened in the studies on new media and text mining in the last decade. This study analyses 82 articles published on Google Scholar between 2013 and 2023. The articles were analysed using a systematic analysis method which contains specific criteria in accordance with the aim of this research.
RADAR/INS TIGHTLY-COUPLED INTEGRATION FOR LAND VEHICLE NAVIGATION

Abstract. Multisensor systems are essential for autonomous navigation applications to achieve reliable accuracy. Integrating the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) is the most common integration scheme. However, this integration is unreliable in different scenarios since the GNSS signal may deteriorate in downtown areas or suffer from a blockage in underground and indoor areas. Therefore, other sensors are integrated with INS to compensate for GNSS outages. This paper proposes a novel algorithm for radar/INS tightly-coupled integration for autonomous navigation applications. This algorithm is applied in multiple steps. Radar data analysis is the first and most crucial step to remove the noisy data and the outliers and keep the useful objects. Then, data association is done to match the detected objects between radar frames. The tightly-coupled integration is performed at the measurement level through an Extended Kalman Filter (EKF), where the distance between the INS and the detected objects can be predicted from the INS and measured from the radar. Real data was collected from four Frequency Modulated Continuous Wave (FMCW) radar units in Calgary's suburban areas and Toronto's downtown area. The proposed algorithm was tested and assessed by introducing simulated GNSS single outages with different durations. The results show an enhancement in the vehicle's position by about 94% to 96%.
A Resource-Efficient Multi-Function Embedded Eye Tracker System Implemented on FPGA

In this paper, a multi-function embedded eye tracker system (ETS) has been presented and FPGA implementation of that has been described. The image processing algorithm is based on morphological operations to detect centers of pupil (PC) and corneal glint (GC) so that gaze of the user can be estimated by establishing a mapping between obtained PC-GC vector and screen. Moreover, the proposed ETS is capable to detect pupil size which is vitally important in understanding consciousness of the user since it varies in response to light condition. Taking advantage of optimum system design and parallel signal processing on high-speed digital units of the FPGA, the processing time of 1.8 ms was achieved that allows operation of the system up to 500 fps. The results of test with 176 × 120 images demonstrate maximum vertical or horizontal error of ±1 pixel. Experimental results imply versatility of the proposed low-power and high-accuracy ETS for real-time and accurate applications such as medical diagnosis and human-computer interface.
READING CORNER IMPLEMENTATION TO IMPROVE CHILDREN'S ENGLISH LITERACY IN KAMPUNG LITERASI 26 ILIR

One of the skills that must be possessed by the younger generation in the Society 5.0 era is communicating in English. Literacy activities can improve English as a basic skill for communication. Kampung Literasi 26 Ilir tried to provide English literacy facilities for children with a reading corner to hone their ability to understand analytical, critical, and reflective information. The study aims to describe the implementation of a reading corner to improve children's English literacy. It used a descriptive analysis with a case study technique. Data was obtained from observation. The result is that the implementation of a reading corner to improve children's English literacy is carried out with reading assistance and comprehension of English reading content for children aged 4-6 years and facilitating reading activities for children aged 7-11 years. At the end of the activity, children aged 7-11 years analyzed and criticized the content of the reading and reflected on the results of their reading by presenting. Reading corners can improve children's English literacy
Social Presence Mediates Audience Behavior Effects on Social Stress in Virtual Public Speaking

Public speaking is a near universally anxiety inducing social situation. Applications recreating social interactions with autonomous agents in virtual reality have been proposed as tools to alleviate public speaking anxiety, recreating exposure therapy methods with virtual audiences. To be efficient, such applications rely on the precise induction of controlled amounts of social stress. We reviewed the literature that studied the effect of virtual audience behaviors on participants’ stress levels, and found contradictory results that we attempt to explain in this article. We examine those studies and propose that social presence is one of the important factors mediating the effect of audience behavior on stress levels, and through social presence we can explain the different effects of audience behavior on stress observed in past studies. We conducted a study to test this theory, and expected that high social presence would lead to larger difference in stress induced by positive and negative audiences, as opposed to lower social presence which would attenuate the effect of audience behavior. We compared two display mediums, VR headsets to induce high social presence and wall projection to induce low social presence. We find an interaction effect of social presence and audience behavior on subjectively reported stress levels, but not on physiological measures.
STUDYING THE INFLUENCE OF USING SOCIAL NETWORKS ON HIGH SCHOOL STUDENTS THROUGH AN OVERVIEW OF RESEARCH IN SOME COUNTRIES AND VIETNAM

In the context of strong development of science and technology, more and more social networking platforms are appearing, which has greatly impacted all areas of human activity and daily life, especially young people and high school student. Researching the effects of using social networks is one of the common problems of modern society around the world in general and Vietnam in particular. Therefore, researching the effects of social networks on high school students is extremely urgent. The article addresses research directions on the effects of using social networks on high school students through understanding concepts, tools, methods, content and general findings on positive and negative effects of social networks on high school students in some countries around the world and in Vietnam. Thereby, we propose some solutions to improve the effectiveness of using social networks for high school students.
Time Series Forecasting of Cryptocurrency Prices with Long Short-Term Memory Networks

The rapid evolution of cryptocurrencies has brought transformative changes to the financial landscape. Cryptocurrency prices, characterized by their inherent volatility, pose challenges for precise forecasting. This study introduces a novel approach to cryptocurrency price forecasting, leveraging Long Short-Term Memory (LSTM) networks, known for discerning temporal dependencies within time series data. Motivated to enhance prediction accuracy, this research investigates the effectiveness of LSTM networks in capturing complexities inherent in cryptocurrency price movements. The proposed methodology involves meticulous data collection and preprocessing, utilizing an extensive dataset from Kaggle. This dataset forms the foundation for predictive modeling and facilitates an in-depth analysis of cryptocurrency price dynamics. Exploratory data analysis, including visualization techniques, and a dedicated Time Series Analysis precede the implementation of predictive models, such as LSTM networks. Results and evaluation showcase promising outcomes, emphasizing the models' precision, accuracy, and explanatory power. The Mean Absolute Error (MAE) of 0.0177 underscores the precision achieved in predicting cryptocurrency prices, while the Mean Squared Error (MSE) of 0.00066 and the R² Score of 0.9486 attest to our models' overall accuracy and explanatory power. This research significantly contributes to understanding cryptocurrency forecasting by incorporating LSTM networks, paving the way for advancements in this evolving domain.
&lt;b&gt;Analysis of Knowledge Management, Organizational Ambidexterity and Financial Innovation in Banking Sector&lt;/b&gt;

The banking industry is the backbone of an economy. The competition in the banking sector and the threat of a third financial crisis require banks to become more effective and efficient. It may be accomplished through financial innovation. The primary purpose of the research is to develop a framework for fostering financial innovation through evaluating the relationship among knowledge management (KM), organizational ambidexterity (OAMB), and level of financial innovation (LoFI) in the banking sector and to develop a framework. To achieve the research aim, a survey was carried out among the commercial banks of Pakistan. The findings of this research are based on 383 responses from Pakistan. The collected data was analyzed using structural equational modeling. Findings indicated that KM significantly influenced OAMB and LoFI, and OAMB significantly influenced LoFI. Moreover, organizational climate (OC) moderated the relationship between KM and OAMB, and environmental dynamism moderated the relationship between OAMB and LoFI. The researcher also analyzed the relationship between OAMB, KM, and LoFI and found that OAMB mediated the relationship between KM and LoFI. Based on the research findings, the researcher suggested that banks should enhance the level of KM and OAMB as these two things increase LoFI. Banks should also foster OC in a way that may enhance learning and sharing. Likewise, banks should also critically appraise the external environment to increase LoFI.
Rainfall Prediction Using Naïve Bayes and K-Means Algorithms in Malang Region

Rain is beneficial for agriculture and also for water reserves, but rain can also have negative impacts such as floods, disease outbreaks and other disasters if not handled properly, therefore to overcome or avoid the problem of excessive rainwater falling It is better to make predictions as an effort to prevent disasters that will occur. Rain will be able to be handled well if you are able to predict when the rain will fall, so that early mitigation can be carried out. With a prediction method using the Naïve Bayes algorithm, rainfall can be predicted because this method uses a probability approach for each data. The result of this method is that it is able to predict the fall of rain by 80%, so the Naïve Bayes method is able to predict when it will rain accurately, where this value comes from 10 test data results for the Malang Raya area. The incorrect value is 20% which is relatively small and this value is quite accurate as a basis for the probability of rain in the Malang Raya area.
Improving Students’ Grammar Mastery of Noun Clauses By Using Teacher’s Correction Technique

This study aims to know the improvement of students’ grammar mastery by using Teacher Correction technique. The research method used was a Classroom Action Research (CAR). The research data included the results of the pretest and posttest regarding noun clauses. The data collection techniques used were classroom observation, interviews, pretest and posttest. The results of this study showed that the average score of students was 24.6 in pretest, increased from to in Cycle I to 39.2 in 69.2 II. It indicated that the use of Teacher Correction technique could improve students' understanding in mastering noun clauses. This technique can be one of effective ways for teachers or lecturers to teach noun clauses.
intCC: An efficient weighted integrative consensus clustering of multimodal data

High throughput profiling of multiomics data provides a valuable resource to better understand the complex human disease such as cancer and to potentially uncover new subtypes. Integrative clustering has emerged as a powerful unsupervised learning framework for subtype discovery. In this paper, we propose an efficient weighted integrative clustering called intCC by combining ensemble method, consensus clustering and kernel learning integrative clustering. We illustrate that intCC can accurately uncover the latent cluster structures via extensive simulation studies and a case study on the TCGA pan cancer datasets. An R package intCC implementing our proposed method is available at https://github.com/candsj/intCC.
Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues

A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications.
Communication and Energy Efficient Wireless Federated Learning With Intrinsic Privacy

Federated Learning (FL) is a collaborative learning framework that enables edge devices to collaboratively learn a global model while keeping raw data locally. Although FL avoids leaking direct information from local datasets, sensitive information can still be inferred from the shared models. To address the privacy issue in FL, differential privacy (DP) mechanisms are leveraged to provide formal privacy guarantee. However, when deploying FL at the wireless edge with over-the-air computation, ensuring client-level DP faces significant challenges. In this paper, we propose a novel wireless FL scheme called private federated edge learning with sparsification (PFELS) to provide client-level DP guarantee with intrinsic channel noise while reducing communication and energy overhead and improving model accuracy. The key idea of PFELS is for each device to first compress its model update and then adaptively design the transmit power of the compressed model update according to the wireless channel status without any artificial noise addition. We provide a privacy analysis for PFELS and prove the convergence of PFELS under general non-convex and non-IID settings. Experimental results show that compared with prior work, PFELS can improve the accuracy with the same DP guarantee and save communication and energy costs simultaneously.
DIR-YOLOv5: a real-time drone-perspective infrared object detection method based on YOLOv5

With the advancement of drone technology, object detection from the perspective of drones has found extensive applications in various fields, including surveillance, search operations, and reconnaissance tasks. Currently, most drones in the market are equipped with visible light imagers, while some high-end drones are equipped with infrared imaging detectors capable of performing infrared object detection tasks. Infrared imaging utilizes a passive imaging mode, enabling it to detect thermal radiation emitted by objects. As a result, it offers the distinct advantage of continuous operation without being restricted by daylight conditions. In comparison to visible imaging, infrared imaging uses longer wavelengths and possesses a certain level of penetration capability through clouds and smoke. Consequently, infrared object detection represents a significant research area within the field of object detection. However, detecting infrared objects, especially small ones, remains challenging due to the complexity of background information, lower resolution compared to visible images, and the lack of shape and texture information in infrared images. In response to these challenges, this study proposes a real-time drone-perspective infrared (IR) object detection method based on the YOLOv5 framework, known as DIR-YOLOv5. To effectively address the challenge of infrared vehicles occupying fewer pixels in the drone’s perspective image and making objects difficult to detect, the coordinate attention (CA) for feature enhancement is introduced. we also introduce a Spatial-Channel dynamic and query-aware sparse attention mechanism (SCBiFormer), which is optimized based on BiFormer. Additionally, we redefine the loss function as the Repulsion Loss function to tackle the problem of infrared vehicle objects gathering and overlapping occlusion in scenarios like parking lots. Furthermore, we expand the ISVD infrared image object detection dataset to include multiple scenarios and conduct experiments using this dataset. The experimental results demonstrate the excellent performance of the proposed method in infrared image object detection tasks, showing improved object detection accuracy and reduced false detection rate compared to current mainstream methods.