Reviewerly/exHarmony huggingface dataset:
Rainfall's Symphony: Understanding its Influence on Communication Systems in Nigeria

This study investigates the significant issue of rain-induced signal attenuation in satellite communication, specifically focusing on television receive-only (TVRO) stations operating in the Ku band across diverse Nigerian locations. Utilizing a comprehensive dataset of point rain rate distribution and the ITU rain attenuation model, the study comprehensively assesses how rainfall impacts signal quality. The findings highlight that southern regions consistently display high decibel (dB) values, indicating increased susceptibility to signal disruptions during heavy rainfall, while a comparative analysis between two key satellites, EUTELSAT 36B and INTELSAT 20, consistently favors the former in terms of signal resilience during adverse weather conditions. In contrast, northern regions generally exhibit lower dB values, suggesting a higher degree of signal resilience during rainfall events. These insights underscore the importance of considering location-specific and satellite-specific factors when designing satellite communication systems, emphasizing the need for robust infrastructure and strategic satellite selection to mitigate the effects of rain-induced attenuation. This study provides valuable guidance to engineers and service providers, enabling them to make informed decisions to minimize signal disruptions and enhance overall network reliability, particularly in regions susceptible to rain-induced attenuation.
Paintings in naked-eye virtual reality: a parallax view between the surface and volumetric depth

Abstract This article explores a subgenre of naked-eye virtual reality (VR) video that features two-dimensional paintings in a three-dimensional space and is circulated on the Chinese video streaming website Bilibili. In contrast to traditional VR, which requires the spectator to wear a head-mounted display, naked-eye VR offers a stereoscopic view on the screen, eliminating the need for VR glasses. The apparent incongruity between the screen’s physical flatness and the volumetric depth of the painting becomes even more pronounced when the naked-eye VR image emphasises the painting’s inherent two-dimensionality. Employing an interdisciplinary humanities’ method that connects media archaeology, film studies, art history, and the field of science, technology, and society, this paper delves into this distinctive juxtaposition between the painting’s flatness and the volumetric depth characteristic of naked-eye VR, a subgenre that remains mostly uncharted in the field of film and media studies. Taking a media archaeological perspective, this paper introduces a parallax media history, suggesting that the aesthetics of VR can be traced not only to the stereoscope but also to scroll paintings, which are paradoxically defined by their flat compositions. In addition, naked-eye VR references pictorial traditions by generating a spatial illusion that leads spectators to feel as if they are delving beneath the surface of a painting, transitioning from spatial extensivity on the x-y axis to perceived depth along the z-axis. This oscillation between surface and depth is engendered by the horizontal parallax rooted in binocular disparity, thereby positioning naked-eye VR as a “parallax media.” In analysing the illusion created by naked-eye VR, this paper proposes a paradigmatic shift in the framework of media study from conceiving of an image as a mirror to experiencing it as a door, representing the oscillation between volumetric depth and two-dimensional surface.
The visual content of neural networks as a return to the third-order simulacrum

This article is part of a larger study of design as a cultural phenomenon. In this part of the study, the author examines the process that is currently taking place in the semiotic structure of design, associated with the active introduction of neural networks into the creation of visual content. Artificial intelligence products stylistically take the design away from the fourth-order simulacrum (from the flat design style) and return the design to using the third-order simulacrum as the main iconic form.The object of the research is the transformation of the semiotic design system. The subject of the study is a return to the third–order simulacrum in modern design. The purpose of the study is to show and try to explain how in modern design there is a return to the use of a third-order simulacrum. The research method is a semiotic analysis of modern design based on the methodology of R. Barth's semiotic analysis. The study is also based on J. Baudrillard's theory of semiosis in hyperreality and three orders of simulacra. The author sees the philosophical justification of the art of neural networks in the concept of flat ontologies. The study of the semiotic structure of design allows us to see that the logic of design development carries this phenomenon through successive stages of semiosis associated with a decrease in meaning and a diminution of being. In its development, the design consistently uses first index signs, a second-order simulacrum, then a third-order simulacrum as the main sign form. Nowadays, the main iconic form in design has become a fourth-order simulacrum. Next, design had to either end as a profession and phenomenon, or move into a new cultural paradigm that was not related to simulation. However, unexpected transformations have begun to occur in design, due to the active involvement of non–human agents - neural networks – in the creation of visual content. Neural network products are a typical example of a third-order simulacrum. Thanks to the use of neural networks, modern design finally acquires the vector of transhumanism and closes in simulation.
An Attention-Averaging-Based Compression Algorithm for Real-Time Transmission of Ship Data via Beidou Navigation System

The real-time transmission of ship status data from vessels to shore is crucial for live status monitoring and guidance. Traditional reliance on expensive maritime satellite systems for this purpose is being reconsidered with the emergence of the global short message communication service offered by the BeiDou-3 navigation satellite system. While this system presents a more cost-effective solution, its bandwidth is notably insufficient for handling real-time ship status data. This inadequacy necessitates the compression of such data. Therefore, this paper introduces an algorithm tailored for real-time compression of sequential ship status data. The algorithm is engineered to ensure both accuracy and the preservation of valid data range integrity. Our methodology integrates quantization, predictive coding employing an attention-averaging-based predictor, and arithmetic coding. This combined approach facilitates the transmission of succinct messages through the BeiDou Navigation System, enabling the live monitoring of ocean-going vessels. Experimental trials conducted with authentic data obtained from ship monitoring systems validate the efficiency of our approach. The achieved compression rates closely approximate theoretical minimum values. Consequently, this method exhibits substantial promise for the real-time transmission of parameters across various systems.
IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

Abstract The recognizing underwater targets is a crucial component of autonomous underwater vehicle patrols and detection efforts. In the process of visual image recognition in real underwater environment, the spatial and semantic features of the target often appear to different degrees of loss, and the scarcity of specific types of underwater samples leads to unbalanced data on categories. This kind of problem makes the target features appear weak and seriously affects the accuracy of underwater target recognition. Traditional deep learning methods based on data and feature enhancement cannot achieve ideal recognition effect. Based on the above difficulties, this paper proposes an improved feature enhancement network for weak feature target recognition. Firstly, a multi-scale spatial and semantic feature enhancement module is constructed to extract the feature information of the extraction target accurately. Secondly, this paper solves the influence of target feature distortion on classification through multi-scale feature comparison of positive and negative samples. Finally, the Rank &amp; Sort Loss function was used to train the depth target detection to solve the problem of recognition accuracy under highly unbalanced sample data. Experimental results show that the recognition accuracy of the proposed method is 2.28% and 3.84% higher than that of the existing algorithms in the recognition of underwater fuzzy and distorted target images, which demonstrates the effectiveness and superiority of the proposed method.
The Development and Evaluation of an Animated Video for Pre- and Postoperative Instructions for Patients with Osteoarthritis—A Design Science Research Approach

Osteoarthritis (OA) is a condition in the hip or knee joints that develops during a long period of time and sometimes needs hip or knee joint replacement surgery when pain gets too intense for the patient. This paper describes how an animated video for pre- and postoperative instructions for patients with osteoarthritis was designed. The design science research (DSR) approach was followed by creating a web-based animated video. The web-based animated video is used to support surgical departments with education for patients suffering from OA. In the web-based animated video, information about OA surgical treatment and its pre- and post-arrangements was included. The relevance, the rigor, and the design cycles were focused on, with some iterations of and improvements in the animations. Even after implementation, there was a feedback-loop with comments from the surgeons and their patients. Moreover, as more departments will use the web-based animated video, they want to make their special mark on it, so that further changes will be made. This paper presents the design and successful implementation of an animated video for pre- and postoperative instructions for patients with osteoarthritis, tightly linked to the patient journey and the workflow of healthcare professionals. The animated video serves not only as a tool to improve care but also as a basis for further scientific research studies.
Genre: generative multi-turn question answering with contrastive learning for entity–relation extraction

Abstract Extractive approaches have been the mainstream paradigm for identifying overlapping entity–relation extraction. However, limited by their inherently methodological flaws, which hardly deal with three issues: hierarchical dependent entity–relations, implicit entity–relations, and entity normalization. Recent advances have proposed an effective solution based on generative language models, which cast entity–relation extraction as a sequence-to-sequence text generation task. Inspired by the observation that humans learn by getting to the bottom of things, we propose a novel framework, namely GenRE, Generative multi-turn question answering with contrastive learning for entity–relation extraction. Specifically, a template-based question prompt generation first is designed to answer in different turns. We then formulate entity–relation extraction as a generative question answering task based on the general language model instead of span-based machine reading comprehension. Meanwhile, the contrastive learning strategy in fine-tuning is introduced to add negative samples to mitigate the exposure bias inherent in generative models. Our extensive experiments demonstrate that GenRE performs competitively on two public datasets and a custom dataset, highlighting its superiority in entity normalization and implicit entity–relation extraction. (The code is available at https://github.com/lovelyllwang/GenRE ).
Analysis of Coordination Mechanisms during Collaborative Problem-Solving on an Interactive Tabletop Display

Abstract Coordination is an important aspect of group work. Previous studies have shown how collocated multi-user interfaces, such as interactive tabletops, support coordination by providing a shared space that enhances workspace awareness. However, only little is known about the coordination processes that occur during problem-solving on such shared devices and how the design of features and interaction techniques can impact coordination behaviour. In this paper we analyse users’ coordination mechanisms during joint manipulation tasks in Orbitia, an interactive tabletop-based problem-solving activity. The proposed design integrates a series of “breaches” seeking to challenge participants’ collaboration by confronting them to different limitations. We report on a case study with five groups of three users ( N = 15), jointly solving tasks while facing different challenges related to the activity interface and the environment. By analysing 135 min of video material along with their transcripts, we identified nine different coordination mechanisms that relate to either coordination of information or coordination of actions. By exploring the occurrences of these mechanisms, we found that sharing unsolicited task-relevant information is a common coordination behaviour that can be observed at interactive tabletops, and that with breaches, more explicit coordination in the form of direct requests, orders, or shadowing is used.
A novel Cosine-Cosine chaotic map-based video encryption scheme

Abstract The surge in online activities has led to the increasing popularity of sharing video data across diverse applications, including online education tutorials, social networking, video calling, and OTT platforms. Encryption prevents unauthorized access to the transmitted data over unreliable channels. The well-known features of chaos theory such as random behaviour, unpredictability, and initial parameters dependency facilitate its use in cryptography. Many security issues are faced by chaos-based cryptosystems because of their less complexity. Hence, a new Cosine-Cosine chaotic map characterized by intricate chaotic behaviour is designed in the current study. Additionally, we formulate an original video encryption scheme employing this Cosine-Cosine chaotic map. The encryption process involves five steps, beginning with the segmentation of the original video into frames based on its frame rate. In the second phase, a 384 bits pseudorandom key is generated that is further divided into three subkeys of 128 bits each. The novel Cosine-Cosine chaotic map-based sequence is generated. In the fourth step, red, green, and blue components are encrypted using the pseudorandom key and the chaotic sequence. In the last step, we combine encrypted frames to get cipher video. The security analysis validates that the proposed encryption protects against eavesdropping.
The Serbian Sign Language Alphabet: A Unique Authentic Dataset of Letter Sign Gestures

Language barriers and the communication difficulties of individuals with developmental disabilities are two major causes of communication problems that societies worldwide encounter. A particularly challenging group is hearing-impaired people who have difficulties with communication, reading, writing, learning, and social interactions, which have a substantial impact on their quality of life. This article focuses on detailing a Serbian Sign Language alphabet database and the method for creating it in order to provide a foundation for answering the various societal challenges of persons who use the Serbian language. In front of a computer camera, 41 people performed Serbian Sign Language sign movements that replicated the Serbian alphabet for this study’s aims. Hand and body key points were identified using the recorded video clips, and the numerical values of the identified key points were then stored in a database for further processing. In total, 8.346 video clips of people making recognized hand gestures were gathered, processed, classed, and archived. This paper provides a thorough technique that may be applied to comparable tasks and details the process of constructing a dataset based on Serbian Sign Language alphabet signs. This dataset was created using custom-made Python 3.11 software. Data regarding dynamic video clips that capture entire subject movement were incorporated into this dataset to fill in the gaps in other similar efforts based on static photographs. Thus, the purpose of this investigation is to employ innovative technology to support the community of hearing-impaired people in areas such as general inclusion, education, communication, and empowerment.
Modern Trends in Multi-Agent Systems

The term “multi-agent system” is generally understood as an interconnected set of independent entities that can effectively solve complex and time-consuming problems exceeding the individual abilities of common problem solvers [...]
Removing Coding and Inter Pixel Redundancy in Image Compression

The digital image plays an important role in today’s digital world. Storing and transmitting digital images efficiently is a challenging job. There are lots of techniques for reducing the size of digital pictures. This paper adapts the following method. The digital technique is separated into high and low resolutions. The low intensity and high intensity pixels single-handedly is dense and decompressed using three diverse algorithms to hit upon out the occurrence of low down intensity pixels in the picture. Totally six algorithms are experienced by means of benchmark images and the most excellent scheme is selected for concluding compression. A Comparison is made between the results obtained using these techniques and those obtained using JPEG 2000.
Air Writing Detection and Recognition

Air Writing, a groundbreaking concept, revolutionizes the act of writing by allowing users to create characters or words in free space through hand or finger movements and coloured light. Unlike traditional pen-and-paper methods, this approach replaces pen-up and pen-down motions with colour shifts or light toggling to indicate the beginning and end of characters or words. The Air Handwriting Recognition project combines computer vision object tracking with handwriting recognition using machine learning techniques. Using a computer's webcam, the system tracks the characters users write in the air, following a user-selected colour with the aid of a mask. These tracked movements are then transcribed onto a virtual canvas, mimicking a plain whiteboard. The resulting canvas image serves as input for the recognition model, employing machine learning to interpret air-written words and characters. The integration of colour-based tracking and advanced recognition algorithms ensures the avoidance of plagiarism, making Air Handwriting Recognition a cutting-edge solution for hands-free writing in the digital realm.. A brief history of CNN and other approaches to characters detection and recognition are discussed in this paper
Pronoun interpretation in Italian

Abstract We explore potential effects of prosody on pronoun interpretation in Italian, building on previous research which has shown that second language learners/users (L2ers) assign non-target interpretations to overt pronouns. We investigate effects of contrastive stress and pause, proposing that these will result in changes to default antecedent preferences for overt and null pronouns, for L2ers and for native speakers. An experiment was conducted, involving English-speaking L2ers of Italian and Italian native speakers. Participants were presented with auditory stimuli like Lorenzo ha scritto a Roberto quando Ø/lui si è trasferito a Torino ‘Lorenzo wrote to Roberto when (he) moved to Turin’ and indicated their preferred antecedent for the pronoun. Overt versus null pronouns, presence versus absence of stress on overt pronouns, and presence versus absence of pause between clauses were manipulated. The results yielded significant differences for antecedent choices between null and overt pronouns, consistent with earlier literature. In addition, stress was significant for both groups. Implications of a prosodic approach to ambiguity resolution are discussed.
A Literature Survey on Online Voting System using Face Recognition

This paper presents a creative technique for overseeing modernizing the larger part rule cycle through the mix of face certification improvement into an internet projecting a surveying structure framework. By utilizing facial certification calculations, occupants can safely certify their characters from a decent ways, guaranteeing the uprightness and security of the discretionary association. The proposed structure means to overhaul straightforwardness, accuracy, and straightforwardness in fair strategies, tending to stresses related with character check and fake exercises. Through thorough testing and underwriting, this plan offers a promising development for driving the capacity and reliability of web based projecting a surveying structure systems in contemporary social orders
Exploiting PlanetScope Imagery for Volcanic Deposits Mapping

During explosive eruptions, tephra fallout represents one of the main volcanic hazards and can be extremely dangerous for air traffic, infrastructures, and human health. Here, we present a new technique aimed at identifying the area covered by tephra after an explosive event, based on processing PlanetScope imagery. We estimate the mean reflectance values of the visible (RGB) and near infrared (NIR) bands, analyzing pre- and post-eruptive data in specific areas and introducing a new index, which we call the ‘Tephra Fallout Index (TFI)’. We use the Google Earth Engine computing platform and define a threshold for the TFI of different eruptive events to distinguish the areas affected by the tephra fallout and quantify the surface coverage density. We apply our technique to the eruptive events occurring in 2021 at Mt. Etna (Italy), which mainly involved the eastern flank of the volcano, sometimes two or three times within a day, making field surveys difficult. Whenever possible, we compare our results with field data and find an optimal match. This work could have important implications for the identification and quantification of short-term volcanic hazard assessments in near real-time during a volcanic eruption, but also for the mapping of other hazardous events worldwide.
Hyperspectral low altitude UAV target tracking algorithm based on deep learning and improved KCF

This article presents a novel target tracking algorithm for hyperspectral low altitude UAV, combining deep learning with an improved Kernelized Correlation Filter (KCF). Initially, an image noise reduction method based on principal component analysis with Block-Matching 3D (BM3D), is employed to process redundant information. Subsequently, an image fusion method is utilized to merge the processed hyperspectral image and the high-resolution panchromatic band image to obtain a high spatial resolution image for target enhancement. Following this, YOLOv5 is used to detect the coordinate information of the UAV target in the current frame. Then, The KCF algorithm is used for target tracking in the current frame using kernel correlation filtering. Finally, the Discriminative Scale Spatial Tracker (DSST) is employed to determine the scale information to achieve a multi-scale tracking effect. The experimental results demonstrate that the algorithm presented in this paper surpasses CSK, HLT, and the conventional KCF algorithm in hyperspectral UAV datasets. On average, there is a significant increase in accuracy which is over 17% when using our algorithm.