Miracle-dz/UniFAR-FTUs huggingface dataset:
ROD-WGAN hybrid: A Generative Adversarial Network for Large-Scale Protein Tertiary Structures

The tertiary structures of proteins play a critical role in determining their functions, interactions, and bonding in molecular chemistry. Proteins are known to demonstrate natural dynamism under various physiological conditions, which enables them to adjust their tertiary structures and effectively interact with the surrounding molecules. The present study utilized the remarkable progress made in Generative Adversarial Networks (GANs) to generate tertiary structures that accurately mimic the inherent attributes of actual proteins, which includes the backbone conformation as well as the local and distal characteristics of proteins. The current study has introduced a robust model, ROD-WGAN hybrid, that is able to generate large-scale tertiary protein structures that greatly mimic those found in nature. We have made several noteworthy contributions in pursuit of this objective by integrating the ROD-WGAN model with a hybrid function that incorporates adversarial loss, perceptual loss, distance-by-distance loss, and structural similarity loss. Through this innovative approach, we achieved remarkable results, particularly in the generation of protein structures of considerable length. The ROD-WGAN hybrid model has transcended the limitations inherent in the ROD-WGAN model, demonstrating its capability in the successful synthesis of high-quality proteins, characterized by a length of up to 256 amino acids. This accomplishment serves as a demonstrating the effectiveness and potential of our proposed methodologies. Furthermore, these generated protein structures serve as a valuable resource for data augmentation in crucial applications such as molecular structure prediction, inpainting, dynamics, and drug design. Interested individuals can access the data, code, and trained models at https://github.conmena01/ROD-WGAN-and-ROD-WGAN-hybird-models.
POSSIBLE BACK PRELATERAL MERGERS IN OKLAHOMA

Prelateral mergers occur in American English with a loss of vowel distinction before /l/, often between tense-lax pairs (/il-ɪl/ feel-fill , /el-ɛl/ sale-sell , /ul-ʊl/ pool-pull ). This study examined non-low back/ central prelateral vowels in Oklahoma, where plain counterparts /u, o, ʊ, ʌ/ are fronted and earlier work found /ul/-lowering. Formants (F1, F2) from 113 adults reading words and passages were measured mid-vowel. All four prelaterals were backed, without /ul/-lowering but with /ʊl/ varying in height between /ol/ and /ul/, and /ʌl/ varying between plain /ʌ/ and /ol/ or higher. While many speakers overlapped /ʊl/ and/or /ʌl/ with /ol/, none showed definitive three-way /ʊl-ʌl-ol/ merger. Instead, a correlation between /ʊl/- and /ʌl/-raising suggests a chain shift that might allow only one /ol/-merger per speaker – or two lax-tense mergers (/ʊl-ul/ pull-pool , /ʌl-ol/ hull-hole ). However, wide individual variation is not well explained by demographics, leaving the direction and social meaning of these changes unsettled.
Implementation of the A* Algorithm in a First Aid Educational Game Application for Elementary School Students

First aid, immediate assistance for individuals facing illness, injury, or accidents, plays a crucial role in saving lives, preventing physical disabilities, and providing comfort before being referred to a healthcare center. This research presents comprehension and cultivates learning interest by developing an educational first-aid game in the Indonesian language, utilizing Object-Oriented Programming (OOP) concepts and the $\mathrm{A}^{*}$ algorithm. The selection of the $\mathrm{A}^{*}$ algorithm is based on its efficiency in optimizing pathfinding through a heuristic-based approach. By employing heuristics, the number of nodes requiring evaluation can be significantly reduced, enabling Non-Playable Characters (NPC) or the ambulance character to swiftly and effectively determine the best routes for providing aid. This efficiency is particularly critical in illustrating the context of first aid, where every second has a profound impact on safety and health. The functionality of the game operates effectively, demonstrated by the successful independent $\mathrm{A}^{*}$ path testing. Additionally, the results from beta testing indicate a user satisfaction level of 92%, affirming the value of this game as a viable option for children or students unable to engage in extracurricular first aid activities at school. Essentially, this game facilitates the interactive, efficient, and comprehensible delivery of first aid knowledge, providing substantial benefits to society.
PolyLite: A Lightweight Transformer With Multi-Scale Attention for Colorectal Polyp Segmentation

The urgent need for early detection and intervention in colorectal cancer highlights the importance of automating colorectal polyp detection. Despite the use of deep learning techniques for polyp detection from medical images, these methods often struggle with issues such as skewed data distribution and class imbalances. In this study, we introduce PolyLite, a lightweight transformer-based model designed to balance performance and efficiency. PolyLite features a custom SegFormer encoder that takes advantage of its lightweight design, and a decoder that uses a multi-scale attention network (MA-NET) to achieve an optimal balance between accuracy and efficiency. To improve accuracy and reduce false negatives, we incorporate a reverse attention layer that effectively counteracts input mask attention during training. This refocuses the model's efforts to better learn non-polyp regions. PolyLite undergoes a two-stage training process, beginning with pre-training on the ImageNet dataset and then fine-tuning on the Kvasir-SEG dataset. We conduct rigorous evaluations on three complex datasets: CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB. Empirical results demonstrate PolyLite's superiority over existing techniques, as evidenced by its performance in terms of Intersection over Union and Dice scores. This validates its effectiveness and scalability in polyp segmentation, making complex and computationally intensive architectures unnecessary.
A Visual Study of Automotive Augmented Reality Head Up Display Warning Signals

Based on the driver's demand for warning signs presented by augmented reality head-up display devices during night-time driving, this study investigates the recognition efficiency of the driver's visual warning messages for automotive collision warning signs during night-time driving under the design of different size coding, flicker coding and transparent coding forms. Through the accuracy and reaction time indexes in the experiment, the optimal visual display strategy for visual recognition performance in the context of collision warning is explored to provide a design basis and experimental reference for the display of warning messages in the interface of augmented reality environments, so as to make the visual design of the warning messages more in line with the human cognitive and psychological characteristics, in order to improve the driver's performance and reduce the driver's psychological burden.
Examining the impact of experiment-centric pedagogy on students’ critical thinking, test anxiety, and motivation while using hands-on technology through pre- and post-activity questionnaires

This study aims to summarize the positive effects of Experiment-Centric Pedagogy (ECP) hands-on tools on students’ ability to think critically, test anxiety, and motivation rate. Using survey-monkey and IBM SPSS, data was gathered, examined, and cleaned. Both remote and classroom lab experiments were used in this study. Thirty undergraduate physics students served as the subjects. The results include analyzed data, photographs, and feedback from students gathered through observation and oral defense of their reports. The study demonstrated that the introduction of the ECP hands-on device changed the learning environment, resulting in (1) an increase in the positive impact of the survey constructs of critical learning and test anxiety, (2) an enhancement of learning enthusiasm, and (3) an increase in motivation rate that enhances their academic performance. There was a significant difference in the pre- and post-test scores of critical thinking and test anxiety (p<0.05). All these observations account for the high impact of ECP on students’ critical thinking, test anxiety, and motivation via hands-on devices.
Low Latency of Object Detection for Spiking Neural Network

Spiking Neural Networks (SNNs), as a third-generation neural network, are well-suited for edge AI applications due to their binary spike nature. However, when it comes to com-plex tasks like object detection, SNN s often require a substan-tial number of time steps to achieve high performance. This limitation significantly hampers the widespread adoption of SNN s in latency-sensitive edge devices. In this paper, our fo-cus is on generating highly accurate and low-latency SNNs specifically for object detection. Firstly, we systematically derive the conversion between SNNs and ANNs and analyze how to improve the consistency between them: improving the spike firing rate and reducing the quantization error. Then we propose a structural replacement, quantization of ANN acti-vation and residual fix to allevicate the disparity. We evalu-ate our method on challenging dataset MS COCO, PASCAL VOC and our spike dataset. The experimental results show that the proposed method achieves higher accuracy and lower latency compared to previous work Spiking-YOLO. The ad-vantages of SNNs processing of spike signals are also demon-strated.
A Review on AI-Enabled Congestion Control Schemes for Content Centric Networks

Content centric networks (CCN) offer more advantages over conventional TCP/IP networks in areas like content distribution. However, congestion control functionalities in CCN present challenges such as detecting congestion, over-reducing windows for non-congested paths, and addressing fairness issues. Most existing studies employed congestion control mechanisms similar to those in TCP. In addition, the existing mechanisms were based on conventional optimization rules to adjust the rate at which Interest packets are sent to request data from downstream nodes. However, such existing mechanisms do not consider the changes in network status and caching strategy due to multi-path and multi-source transmission. Moreover, they are based on assumptions about link bandwidth. In this paper, we study the problem of congestion control in CCN and discuss its challenges. In addition, we review the existing congestion control schemes in CCN based on machine learning. Finally, we highlight the open research issues to spur further investigations.
Instashoppers Users’ Concerns in UAE: Trust and Security Issues

Instagram has transformed over the years from a simple platform for friends to share stories to a platform for advertising and shopping. The platform now provides personalized user recommendations and direct marketing due to AI and big data developments. As a result, it’s essential to understand the primary issues people have while using Instagram for shopping. 128 Instashoppers were surveyed in order to gain further insight into these concerns. The results showed that users’ top concerns are the security of their personal and financial information and the presence of fake sellers. Yet the study also discovered that some elements, including a legitimate UAE trade license, positive word-of-mouth recommendations, and a well-designed page layout, can make buyers feel more comfortable when shopping with Instagram sellers. This study points out the significance of additional research on consumer behaviour and the role of social media platforms in the marketplace. Moreover, the study discusses important research implications and limitations.
SpaceWire Based Reconfiguration and Redundancy Management of COTS Based Highly Integrated Onboard Computer

This paper proposes a dynamic reconfiguration and redundancy solution for a next generation of COTS based Highly Integrated Computer System (CHICS) based on Xilinx Multiple Processor System on Chip (MPSoC) acting as a processor and Polarfire Field Programmable Gate Array (FPGA) acting as a supervisor using purely Commercial-Off-The-Shelf (COTS) components. The proposed architecture uses warm redundancy with dual lanes, nominal and redundant, each lane comprising of a processor and a supervisor. They are interconnected via SpaceWire on a compact PCI back plane making a CHICS pair. In each lane, the supervisor is responsible for monitoring the system and performing reconfiguration in case of a failure. Additionally, we have the option of switching between the nominal and redundant lanes seamlessly in case of a critical fault. The proposed solution is robust, efficient, and cost-effective, making it suitable for use in space applications targeting missions for Low Earth Orbits (LEO). In this paper we present the algorithm for reconfiguration along with the redundancy switchover and discuss the metrics obtained during the reconfiguration process.
Comparative analysis of multi-loss functions for enhanced multi-modal speech emotion recognition

In recent years, multi-modal analysis has gained significant prominence across domains such as audio/speech processing, natural language processing, and affective computing, with a particular focus on speech emotion recognition (SER). The integration of data from diverse sources, encompassing text, audio, and images, in conjunction with classifier algorithms has led to the realization of enhanced performance in SER tasks. Traditionally, the cross-entropy loss function has been employed for the classification problem. However, it is challenging to discriminate the feature representations among classes for multi-modal classification tasks. In this study, we focus on the impact of the loss functions on multi-modal SER rather than designing the model architecture. Mainly, we evaluate the performance of multi-modal SER with different loss functions, such as cross-entropy loss, center loss, contrastive-center loss, and their combinations. Based on extensive comparative analysis, it is proven that the combination of cross-entropy loss and contrastive-center loss achieves the best performance for multi-modal SER. This combination reaches the highest accuracy of 80.27% and the highest balanced accuracy of 81.44% on the IEMOCAP dataset.
Analytical Review of Methods for Observer Metameric Failure Reduction

CIE Standard Colorimetric Observers model (viewing angles of 2 degrees and 10 degrees) have served industry well for many decades. At the present stage, the workflow of colour imaging based on Standard Colorimetric Observer is close to not acceptable. For achieving wide gamut, it is necessary to realize very narrow Spectral Power Distribution (SPD) functions of display. Narrow SPD functions lead to the fact that the use of a Standard Colorimetric Observer model gives the metameric effect failure. In this paper an analytical review of existing methods for the observer metameric effect failure reduction, examples of which are the physiologically based Observer Model CIE 2006 (not a standard), the Categorical Observer model and correction for a set of target observers are given.
Changes in shortwave and longwave radiative fluxes as observed at BSRN sites and simulated with CMIP5 models

The high precision Baseline Surface Radiation Network (BSRN), established in the early 1990s, allows the monitoring of surface radiative fluxes and their changes with unprecedented accuracy. To investigate changes in the longwave spectrum, the longest records of downward longwave radiation currently available from BSRN have been analyzed. From 25 records covering altogether 353 years, an overall mean increase in downward longwave radiation of +2.0 Wm−2 per decade since the early 1990s was obtained. Thereby, three quarter of these BSRN sites showed increasing trends (19 sites in total, 9 of them significant), while one quarter indicated decreasing trends (6 sites, 3 significant). This change in downward longwave radiation quantitatively agrees very well with the respective change calculated by the latest generation of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). In their simulations, including all known relevant climate forcings the downward longwave radiation shows.
Work-family conflict, personality, and safety behaviors among high-speed railway drivers

Abstract Based on conservation of resources theory, the present study investigated the influence of high-speed railway drivers’ work–family conflict on their supervisors’ ratings of their safety behaviors and its boundary conditions. The sample consisted of 624 high-speed railway drivers and 62 of their direct supervisors from August 2016 to June 2017 in China. The results revealed that safety compliance and safety participation were negatively related to work–family conflict. Moreover, the study identified two boundary conditions: the influence of work–family conflict on high-speed railway drivers’ safety behaviors was weaker when they have higher levels of conscientiousness and extroversion. Theoretically, the framework has enriched theories of work–family conflict and personality traits in safety-critical industries. New and valid criteria are suggested for the selection and training of high-speed railway drivers.
Knowledge management for the sustainable development of the semi-arid region in Northeastern Brazil

ABSTRACT: The objective of this study was to analyze the knowledge management perception in managers of a public university in the Brazilian Northeastern Semiarid Region, especially their tactical and strategic processes to produce knowledge about this region. The analysis was conducted according to the perspective of administrators at this university with a view to the sustainable development of the region. This was a qualitative descriptive study. All data were collected through structured interviews, using a questionnaire based on the Knowledge Management Diagnosis as a structuring model. Results showed that Knowledge Management is still a maturing practice. However, knowledge management is making institutional changes feasible and generating organization capacity to implement solutions and promote the sustainable development of the semi-arid region in the Northeastern Brazil.
Middle School Teachers’ Instructional Practices to Maximize Learning Using integrated STEM

STEM fields are considered the building blocks toward the United States’ leadership position in the world; however, the current STEM pipeline problem can pose a serious challenge to it. Considering integrated STEM (iSTEM) as an instructional strategy that could help increase students’ interest in STEM education, this study explored middle school teachers’ conceptualization of iSTEM and their instructional practices around iSTEM in classrooms. Four middle school STEM teachers from different schools and districts in a mid-west state took part in the study. An instrumental case study, utilizing a phenomenographic lens, was used to explore the research question guiding this work. Data were collected and analyzed using qualitative research strategies. Five core themes (conceptual learning, reflection, teacher engagement, student engagement, and context set-up) emerged from the data. Findings from this study revealed that while teachers conceptualize iSTEM as the integration of interdisciplinary concepts and knowledge to situate problems in the real-world context, students are more inclined to learn STEM concepts by actively engaging in hands-on iSTEM activities.
Factors influencing early school dropout: student’s perspective Factores

Reducing early school dropout rates continues to be a priority line of action for education systems worldwide. In this sense, it seems appropriate to advance in the understanding of the factors that predispose students to make this decision, which can have disruptive effects on both personal and social levels. Sensitive to its importance, this work aims to identify factors that influence the intention to drop out of school early. To do so, we adopted a quantitative methodological approach through a survey procedure. Its application took place in the Autonomous Community of La Rioja (Spain), where the problem of early school leaving is a key area of work in its political agenda. The survey was carried out by administering an ad hoc questionnaire to the population of students in the last years of compulsory education and the first year of non-compulsory education. The participating sample consisted of 1157 students. The results indicate that the usefulness attributed to the study activity and the perceived relative ease of obtaining the academic qualification are two factors of significant influence on the intention to drop out of school early. Additionally,
EXPLORING GLOBAL AND LOCAL ARTICULATION RATE ENTRAINMENT IN TYPICAL AND ATYPICAL SPEAKERS

Entrainment, in which talkers adapt their speech to become more similar to their interlocutor’s, seems to be a persistent phenomenon among typical speakers. However, it is not clear how this behaviour is affected when one speaker has difficulty controlling their speech production, for example because they stutter. We explore whether typical speakers entrain their Articulation Rate differently to people who stutter than to fluent speakers. Twenty participant pairs (10 typical-typical, 10 typical-atypical) participated over Zoom, completing a solo picture description, and three rounds of a spot-the-differences Diapix task in interaction. Local entrainment was defined as turn-by-turn entrainment. Global entrainment was investigated by comparing the Articulation Rate of speakers of a pair between Diapix rounds, and the solo picture description. Results indicate that speakers entrained locally. No evidence for global entrainment nor differences between the two groups were found. Overall, this suggests speakers entrain locally to both typical and atypical speakers.
"KRIECH NICHT DA REIN!" - A NEW CORPUS OF NATURALISTIC MISPERCEPTION OF GERMAN MISHEARD SUNG SPEECH

Naturally-occurring misperception [1] can help establish the ecological validity of laboratory findings of speech perception and generate new hypotheses. In this study, we report on a corpus of misheard German sung speech which contains instances of misperception reported by individuals. We validated the corpus by examining segmental confusions, and word mis-segmentation. Approximately 1,000 segment confusions were found. Our naturalistic segment confusions were significantly correlated with acoustic distances ( r = 0.559) and with speech-in-noise-induced confusions in an experimental study (vowel: r = 0.364; consonant: r = 0.210). Our mis-segmentation patterns only partially confirmed the rhythmic segmentation hypothesis [2] and findings from previous studies. While boundaries inserted before strong syllables created content words following the preferred rhythmic properties of German, we find an unexpected amount of boundary deletion before strong syllables, resulting in nonce percepts which might reflect the expectation of listeners with neologisms in lyrics [3].