Reviewerly/exHarmony huggingface dataset:
V2X communication Technology Identification Using Residual Neural Network

Signal identification is a critical topic in new communication systems, especially for cognitive radio to have an optimal sharing of radio resources. Some existing techniques, used to identify and classify wireless communication signals, show strong performance, but their sensitivity to noise levels or high computational complexity pose challenges. In this paper, we propose to use a Deep Learning technique, based on Residual Neural Networks (ResNet) to detect and classify V2X (vehicle-to-everything) signals. Three V2X communication technologies are studied and evaluated: ITS-G5, 4G, and 5G. The proposed model offers robust performance compared to a classical CNN-1D model even for the low SNR value.
Development of Smart Games Themes The Beauty of Diversity in My Country for Grade IV SD/MI students

Based on the results of observations at UPT SDN Sumberdiren 02, it is known that thelearning media used so far are still less varied, so creative and interesting media areneeded. At that school the Smart Game Media had never been used. The purpose ofthis study is to explain the development of Smart Games for SD/MI students. Theresearch design used is Research and Development (R&amp;D). The development modelused is the Borg and Gall model proposed by Sugiyono which is limited to the seventhstage, namely product revision. The results showed that the assessments of mediaexperts, linguists and material experts were respectively 85.5%, 87% and 80%.Student response 89%. Students' reading interest increased by 99%, from the averageresult of 2.63 to 5.27. The results of this study have reached the criteria set out inprevious studies, namely 9.3%. 87% student interest in reading questionnaire. So it isknown that Smart Games can increase the learning interest of SD/MI student
Image memorability modulates image recognition, but not image localization in space and time

Observers can determine whether they have previously seen an image with over 80% accuracy, even after seeing hundreds of images for just 2-3 seconds each. This “Massive Memory” for WHAT we have seen is accompanied by smaller but still massive memories for WHERE and WHEN the item was seen (Spatial Massive Memory - SMM and Temporal Massive Memory - TMM). Recent studies have shown that certain images are more easily remembered than others (i.e., that some have higher “memorability”). Will memorability influence SMM and TMM as well? 48 viewers saw approximately 310 images each, sequentially presented at random locations in a 7x7 grid. One group of observers saw high memorability images. The other group saw low memorability images. At the presentation of each image, the participant was instructed to indicate whether it was new or old. If the image was categorized as old, observers clicked on a spot in the grid to mark where they thought they saw it the first time, and on a spot on a time line to mark when they saw it, otherwise they clicked a “new” button. Replicating prior work, data show that high memorability images are remembered better than low memorability images (Avg d’ 2.7 vs 1.8, p<0.0001). Intriguingly, we found no significant difference between the average SMM of images in the high and low memorability conditions (p=0.0860 with ‘bad’ observers removed, p=0.61 otherwise). Similarly, there was no apparent effect of memorability on TMM (p=0.2981). This suggests that properties that make images memorable do not also help us remember where and when they were presented, though a negative result of this sort should be replicated before accepting this conclusion.
Objective Metrics Definition for QoE Assessment for Extended Reality Applications

The increasing advancement of Virtual and Augmented Reality technologies opens new research perspectives in various fields. While traditional multimedia content quality assessment has been extensively investigated, different issues need to be addressed for these novel technologies. In particular, evaluating the quality of experience must consider several aspects, involving, on the one hand, the quality of the displayed multimedia content and, on the other hand, human factors. Due to its inherent subjectivity, defining objective metrics for Quality of Experience is complex. This paper aims to frame the problem of objective Quality of Experience assessment and propose research directions to be pursued in this field.
Individual differences in image preferences: a personalized image enhancement method

INTRODUCTION: A person's light sensitivity varies depending on lighting conditions and the state of their sensory system. Yet, current image enhancement algorithms do not consider individual differences, instead offering a "one size fits all". For example, histogram equalization improves the visibility of nighttime images by modifying the luminance histogram to the full scale between black and white. Here, we developed a new algorithm that enhances images according to individual preferences and demonstrate its efficacy in an experiment. METHODS: Phase 1 - image adjustment: for each image (5 in daylight and 13 in nighttime), participants (n=12) moved the mouse in the x and y directions to simultaneously adjust two parameters, the mean and variance of a gaussian function that determined the luminance and contrast of the image. Images were adjusted twice-when presented alone or presented near the histogram equalized image. Phase 2 – discrimination: we tested whether the selected parameters in phase 1 improved the visibility of the adjusted image. Each adjusted image was compared to the original image or to the histogram equalized image, and the participant selected the image that appeared to have more details. RESULTS: Phase 1: Image parameters varied across image type and participants. Importantly there was a high correlation between the first and second image adjustment both across images and participants. These finding suggests individual difference in image enhancement preferences. Phase 2: With daylight images, participants choices did not differ from chance, that is they chose adjusted or comparison images randomly. However, when nighttime images were presented, participants preferred the adjusted images significantly more than the original and the histogram equalized images (88% and 74%, respectively). Our study quantifies variation in image enhancement preferences across neurotypical individuals, demonstrates the efficacy of our method, and that individual differences should be considered in future applications of image processing.
Feedback Driven Multi Stereo Vision System for Real-Time Event Analysis

2D cameras are often used in interactive systems. Other systems like gaming consoles provide more powerful 3D cameras for short range depth sensing. Overall, these cameras are not reliable in large, complex environments. In this work, we propose a 3D stereo vision based pipeline for interactive systems, that is able to handle both ordinary and sensitive applications, through robust scene understanding. We explore the fusion of multiple 3D cameras to do full scene reconstruction, which allows for preforming a wide range of tasks, like event recognition, subject tracking, and notification. Using possible feedback approaches, the system can receive data from the subjects present in the environment, to learn to make better decisions, or to adapt to completely new environments. Throughout the paper, we introduce the pipeline and explain our preliminary experimentation and results. Finally, we draw the roadmap for the next steps that need to be taken, in order to get this pipeline into production.
Vection, presence, and cybersickness in a virtual reality driving simulation

Illusory self-motion (or “vection”) in virtual environments is often accompanied by feelings of motion sickness (or “cybersickness”), limiting the accessibility of virtual reality (VR) applications. We investigated the effects of three factors (speed, body posture, and alignment between the real and virtual reference frames) on vection, cybersickness, and sense of presence in a VR driverless car simulation. We used an HTC Vive head-mounted display to present participants with laps generated in iRacing (a driving simulator) and manipulated driving speed (60mph vs. 120mph), body posture (sitting upright or lying reclined 30 degrees backward from vertical), and the virtual reference frame (aligned with real world, or misaligned by 30 degrees in pitch). Participants (N = 24) experienced eight 60-second laps around a virtual race track (one lap per condition) and reported vection (magnitude, onset time, duration), cybersickness, and presence after each lap. 3-way repeated measures ANOVAs indicated a significant main effect of driving speed on vection magnitude, vection onset time, vection duration, and presence (Fs > 7.67, ps < .05). Vection was stronger in magnitude, faster in onset time, and longer in duration in the fast driving conditions. Participants also felt more present in the fast driving conditions. Furthermore, 3-way ANOVAs indicated a significant interaction between body posture and the virtual reference frame on vection magnitude (F = 4.85, p < .05), such that vection was stronger when the virtual reference frame was parallel with the participants' viewpoint. Additionally, 3-way ANOVAs indicated a significant interaction between driving speed and reference frame alignment on cybersickness, such that driving speed had a greater effect on cybersickness when the virtual and real reference frames were aligned (F = 6.44, p < .05). Notably, neither driving speed nor reference frame alignment had a main effect on cybersickness.
Motion Trajectory Based Human Hand Tracking for Sign Language Recognition

Sign Language is the only formal way of communication for the mute persons and the hearing impaired. Developed systems that can recognize these signs provide an interface between signers and non{signers by which the meaning of sign can be interpreted. The aim of this project work was to design a user independent framework for automatic recognition of Sign which can recognize various one handed dynamic isolated sign and interpreting their meaning. The proposed approach consists majorly of three steps: preprocessing, feature extraction and recognition. We proposed system which we have compared different approaches of image processing for Motion trajectory based human hand tracking for sign language recognition.
How Blind is Inattentional Blindness in Mixed Hybrid search?

In day-to-day visual search tasks, we may search for instances of multiple types of targets (e.g., searching for specific road signs while also scanning for pedestrians, animals, and traffic cones). In the lab, the “mixed hybrid search task” is a model system developed to study such tasks, where you are looking for general categories of items (e.g., things you don’t want to hit with your car) alongside specific items (e.g., the sign for your exit). Previous hybrid visual search studies have shown that observers are much more likely to miss more general “categorical” targets than specific targets, even though it is quite clear that categorical and specific items are equally likely to be attended in this paradigm. If an item is attended but missed, do observers have any access to the information that may have been accumulating about that target? Twelve participants searched arrays for two specific items (e.g., this shoe and this table) while also searching for unambiguous instances of two categorical target types (e.g., ANY animal and ANY car). In order to look for the existence of sub-threshold information about missed targets, we borrowed methods from the inattentional blindness literature. We asked two, 2AFC questions after every miss error and after 5% of target-absent trials. Question 1: Do you think you missed an item? Question 2: If you did miss something, which of these two items was it? On trials where participants asserted that they had NOT missed an item (“No” to question one), participants correctly selected the right item ~63% of the time against a 50% chance level (p<0.018). Interestingly, this ability to identify the missed target was only seen following missed categorical targets, not missed specific targets. Knowledge about the target’s identity can lurk behind after that target is missed.
Evaluation of heavy metals in soil from automobile mechanic Village Dutse, Jigawa State, Nigeria

Soil contamination by heavy metals is a worldwide environmental problem. Some heavy metals pollutants are released from the activities in the auto-mechanic workshops and have various effects on human health as well as the environment. It is important to determine the presence and concentration level of the heavy metals in the soil. This research was aimed to determine the level of some selected heavy metals and physicochemical parameters of the soil sample. The soil samples were digested with nitric and hydrochloric acid and heavy metals were analyzed from digested samples using atomic absorption spectrometer (AAS). Results of the atomic absorption spectrophotometric (AAS) analysis of soil samples revealed that sample is contaminated with Ni, Zn, Mn, Fe and Cu with concentrations level (mg/kg) range between (0.75 - 5.55), (0.30 - 0.40), (1.00 -1.42) (0.80 - 1.30) and (0.05 - 0.60), respectively. Other metals such as As, Cd, Cr also contaminated the soil with concentrations (mg/kg) ranged between (0.12-0.76), (0.13-01.13) and (0.11-1.30), respectively. The results were compared with the standard set by National Environmental Standards and Regulations Enforcement Agency (NESREA). The research concluded that the anthropogenic activities at auto-mobile workshops contaminate soil with heavy metals which may have a direct or indirect effect on human. Further studies should be conducted to ascertain the effects of those metals on human, plants, groundwater, and environment.
Advanced Logic Gates for FPGAs

Since the 80s of the 20th century, FPGAs have been continuously progress. Logic elements have gone from the simplest 4-bit LUT to factory logic and adaptive logic modules that implement the functions of 8 elements. The main resources of the FPGA are Logic Elements. There may be a different number of such elements in different types of FPGAs. The number of logic elements is constantly increasing and reaches several million in the latest FPGAs. Nevertheless, there is always room for perfection. With this development we should expect a further increase in “logical power”. The authors suggest several ways to improve logic elements that can be applied in FPGAs of the next generations. The development and further application of Advanced Logic Gates for FPGAs can be considered as an important direction in the various fields in which it is used. The authors of the article investigate such a subject as an advanced logic element and its possible application. There is some progressive proposals are outlined here.
ASIM: Explicit Slot-Intent Mapping with Attention for Joint Multi-intent Detection and Slot Filling

The accurate analysis of a user’s natural language statement, including their potential intentions and corresponding slot tags, is crucial for cognitive intelligence services. In real-world applications, a user’s statement often contains multiple intentions, and most existing approaches either mainly focus on the single-intent research problems or utilizes an overall encoder directly to capture the relationship between intents and slot tags, which ignore the explicit slot-intent mapping relation. In this paper, we propose a novel Attention-based Slot-Intent Mapping Method (ASIM) for joint multi-intent detection and slot filling task. The ASIM model not only models the correlation among sequence tags while considering the mutual influence between two tasks but also maps specific intents to each semantic slot. The ASIM model can balance multi-intent knowledge to guide slot filling and further increase the interaction between the two tasks. Experimental results on the MixATIS dataset demonstrate that our ASIM model achieves substantial improvement and state-of-the-art performance.
Model Autoregressive Integrated Moving Average (ARIMA) untuk Peramalan Curah Hujan di Kota Surabaya

Surabaya is the largest city in the province of East Java and is also the center of the provincial government. In the city of Surabaya the dry season is from May to October and the rainy season is from November to April. Heavy rain usually occurs between December and January. One of the negative impacts caused by excessive rainfall in the city of Surabaya is flooding. The method is often used to predict rainfall in the city of Surabaya, it is Autoregressive Integrated Moving Average (ARIMA). ARIMA models is forecasting model analysis data of single time series or univariate models. The purpose of this study is to forecast the daily rainfall in the city of Surabaya with a ARIMA model
Pengembangan Multimedia Interaktif Mata Pelajaran Informatika Pada Kurikulum Sekolah Penggerak Untuk Siswa Kelas X SMA

Learning informatics sub-material algorithms and programming in the driving school curriculum has a change from visual programming to textual language in class IX, and in programming learning in class X students will learn the concepts of coding and procedural programming. In learning infomatics is still monotonous because the explanation of the material still uses e-books and books that have been provided by the government about Algorithms and Programming. Therefore, the author is interested in designing interactive multimedia, so that students better understand the learning material that is packaged interactively.This type of research is research and development or what is known as Research and Development (R&amp;D). Based on the results of the material assessment from teachers of informatics subjects of SMA N 2 Padang and media experts lecturers of the Department of Curriculum and Educational Technology of UNP, there is a procurement of validation with the suitability of the material calculated from the average validator of 4.79 with the criteria of "very valid". There are two validators with the criteria of "very appropriate" with the value obtained from validator I of 4.62 and validator II of 4.87. For practicality analysis, it was taken through a questionnaire of responses from class X SMA N 2 Padang students related to the use of multimedia which was calculated to reach an average score of 4.62, so that the results of the practice could be known with the criteria of "very practical".
LeCAR: Leveraging Context for Enhanced Automotive Specification Retrieval

In the domain of automotive manufacturing, specification documents represent intricate descriptions detailing every aspect of a product, design, or service. Conventionally, these specifications demand the deployment of expert teams to manually identify crucial data from the extensive documentation. The need to automate the extraction of candidate information from these documents is increasingly pressing in this industry. This research encounters two central challenges: Firstly, the queries for the specifications input by users are typically concise and ambiguous; secondly, not every word in a query carries the same significance. In response to these challenges, we propose LeCAR, which exploits contextual data to clarify query sentences and concentrate the search scope. Our experiments validate that the proposed method outperforms existing techniques that employ pre-trained language models, all without necessitating additional training data.
Recommender Systems: An Overview, Challenges and Optimization Strategies, Future Scope

In an era characterized by information overload and the proliferation of digital content, recommender systems (RS) have emerged as indispensable tools for assisting users in navigating the vast landscape of choices. These systems employ sophisticated algorithms and methodologies to anticipate user preferences and provide tailored suggestions, thereby enhancing user experience and engagement across a diverse range of online platforms. They have made giant leaps of progress in accuracy and performance. Despite the revolutionary potential, recommender systems are not immune to challenges that can affect their efficiency. The key challenges include data sparsity-The uneven distribution of data regarding user-item interactions, cold start, scalability, ever changing priorities of user making the previous results obsolete .In This paper we set a stage for meticulous review of the RS and their applications in various fields ,the taxonomies involved in RS, discussing the two primary categories : collaborative filtering and content-based filtering. This paper at the later stage exposes us to the challenges in RS, the metrics used to evaluate the impact of the strategies used to overcome these challenges. Furthermore, this paper also envisions the future of RS which may open new research directions in this domain. Index: introduction, Types of RS, Challenges in RS, Optimization Strategies, Evaluation Metrics, Case Studies, Future Directions, Conclusion, References. Appendices.
Production technology research of naked-eye 3D map

Naked-eye 3D map is based on the theories of DEM (Digital Elevation Model) and lentricular grating imaging，which users can get three-dimensional experience without the aid of software and hardware systems. The hierarchical method on depth of field and plane displacement are the key techniques and used to reduce the production cost. This article briefly introduces the three-dimensional display principle of Naked-eye 3D maps, elaborates on the production process of Naked-eye 3D maps, and finally summarizes the printing and post-printing processing methods of Naked-eye 3D maps.
Law of large numbers and central limit theorem for ergodic quantum processes

A discrete quantum process is represented by a sequence of quantum operations, which are completely positive maps that are not necessarily trace preserving. We consider quantum processes that are obtained by repeated iterations of a quantum operation with noise. Such ergodic quantum processes generalize independent quantum processes. An ergodic theorem describing convergence to equilibrium for a general class of such processes has been recently obtained by Movassagh and Schenker. Under irreducibility and mixing conditions, we obtain a central limit type theorem describing fluctuations around the ergodic limit.
Incorporating neuro-inspired adaptability for continual learning in artificial intelligence

Continual learning aims to empower artificial intelligence with strong adaptability to the real world. For this purpose, a desirable solution should properly balance memory stability with learning plasticity, and acquire sufficient compatibility to capture the observed distributions. Existing advances mainly focus on preserving memory stability to overcome catastrophic forgetting, but it remains difficult to flexibly accommodate incremental changes as biological intelligence does. Here, by modelling a robust Drosophila learning system that actively regulates forgetting with multiple learning modules, we propose a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity, and accordingly coordinates a multi-learner architecture to ensure solution compatibility. Through extensive theoretical and empirical validation, our approach not only enhances the performance of continual learning, especially over synaptic regularization methods in task-incremental settings, but also potentially advances the understanding of neurological adaptive mechanisms. Continual learning is an innate ability in biological intelligence to accommodate real-world changes, but it remains challenging for artificial intelligence. Wang, Zhang and colleagues model key mechanisms of a biological learning system, in particular active forgetting and parallel modularity, to incorporate neuro-inspired adaptability to improve continual learning in artificial intelligence systems.