Reviewerly/exHarmony huggingface dataset:
A Secure Image Steganography Using Shark Smell Optimization and Edge Detection Technique

The stegangraphic system supply premium secrecy and ability of conserving the mystery information from gaining stalked or cracked. The suggested method consists of three phases which are edge detection, embedding and extraction. This paper concentrated on three basic and significant parts which are payload, quality, and security also introduces a new steganography method by using edge detection method and shark smell optimization to effectively hide data with in images. Firstly, to promote the hiding ability and to realize altitude standard of secrecy the mystery message is separated into four parts and the cover image is masked and also divided into four sections, then the edge detection algorithm and shark smell optimization is performed on each section respectively. Edge prospectors were utilized to produce edge pixels in every section to hide mystery message and attain the best payload. To increase security, the shark smell optimization is used to select the best pixels among edge pixels based on its nature in motion, then reflect these pixels above original carrier media. Finally the mystery message bits are hidden in the selected edge pixels by using lest significant bit technique. The experimental outcomes appreciated utilizing several image fitness appreciation fashion, it displays best hiding ability, achieve higher image quality with least standard of deformation and provide altitude standard of secrecy, also the results shows that the suggested method exceeds previous approaches in idioms of the PSNSR, MSE also demonstrate that the mystery information cannot be retrieved of the stego image without realizing the algorithms and the values of parameters that are used in hidden process
Analysis of potentially toxic elements from selected mechanical workshops using the geo-accumulation index and principal component analysis in Omu-Aran Community, Nigeria

Bioaccumulation of potentially toxic elements in soil threatens public health and the ecosystem. This study aims to assess the concentration of potentially toxic elements (chromium (Cr), lead (Pb), iron (Fe), arsenic (As), and cadmium (Cd)) in selected automobile workshop premises in Omu-Aran, Nigeria. Forty-eight samples were collected at a depth (15 cm) in six locations, including a control point. Acid digestion was carried out to prepare the soil samples before assessing their concentration via an atomic absorption spectrophotometer. Geo-accumulation index (Igeo) was used to classify the level of contamination. Statistical analysis, which includes principal component analysis (PCA) and Pearson's correlation, was also determined. The difference in concentration was determined using ANOVA. In the study area, the lowest observed concentration values for Cr, Pb, Fe, As, and Cd, which are 0.246 ± 0.002 mg/kg, 0.178 ± 0.001 mg/kg, 90.715 ± 0.038 mg/kg, 0.012 ± 0.004 mg/kg, and 0.078 ± 0.004 mg/kg, respectively, are relatively higher than observed for the control. The observed potentially toxic elements fall within three Igeo based on Muller's interpretation; heavily to extremely contaminated (Cd), moderately to heavily contaminated (Pb, Cr, and As), and uncontaminated to moderately contaminated (Fe). PCA shows that two principal components (PC) account for up to 91.052% of the original mean dataset variability. PC1 explains 67.723% of the total variance associated with Cd, Cr, Fe, Pb, and As, indicating anthropogenic is the primary source of these potentially toxic elements. The PC2 accounted for 23.329%, with Pb and As significant contributors. Cadmium contamination of soil was the most influential, with an Igeo value ranging from 4 to 5. Residents in the polluted region face considerable health risks from potentially toxic elements.
SOCRAR: Semantic OCR through Augmented Reality

To enable people to interact more efficiently with virtual and physical services in their surroundings, it would be beneficial if information could more fluently be passed across digital and non-digital spaces. To this end, we propose to combine semantic technologies with Optical Character Recognition on an Augmented Reality (AR) interface to enable the semantic integration of (written) information located in our everyday environments with Internet of Things devices. We hence present SOCRAR, a system that is able to detect written information from a user’s physical environment while contextualizing this data through a semantic backend. The SOCRAR system enables in-band semantic translation on an AR interface, permits semantic filtering and selection of appropriate device interfaces, and provides cognitive offloading by enabling users to store information for later use. We demonstrate the feasibility of SOCRAR through the implementation of three concrete scenarios.
Analysis of Korean-to-English Machine Translation Systems’ Treatment of Passives

This study explores how Korean-to-English machine translation (MT) systems (e.g., Google Translator, NAVER Papago) deal with Korean passive structures. Cross-linguistically, Korean and English passives show different ways to construct passive-voice sentences from active structure. English passives including with be+p.p. may have a ‘by phrase’ after the verb, which perform the agent of the action. However, Korean has three different passive structures and a rich repertoire of affixes, including case markers, which play critical roles in the production and comprehension of passives, complicating translation to English. Moreover, the grammatical constructions that express passive voice in Korean and English differ in terms of frequency in both written and spoken contexts. To explore production accuracy by two MTs, 213 Korean passive sentences were translated into English passives. This finding shows that current Korean-to-English MTs almost always produce translations that are correct in meaning when they generally translate Korean passives into English passives. They also translate Korean passives into English active voice constructions at significant rates.
A Monocular Camera Bird-Eye-View Generation using Lane Markers Prior

Retinotopy expresses the mapping of location preservation in the mechanism of human vision. However, a perception of the semantics of observed images in a scene is computed later in the hierarchy of neural information processing. Moreover, the utility of the perception to use it for planning is another challenging task as the human perception of objects in a scene is a putative orthographic projection. A monocular view from a front camera mounted on an autonomous vehicle captures a perspective projection view. Another challenge is in the use of a monocular camera in motion planning where navigation in the absence of any prior location information is required. This work suggests a solution of mapping the perspective view in a bird-eye-view (BEV) which is close to the orthographic projection. Furthermore, it suggests the use of the same processed information in an image to generate waypoints for maneuvering of the vehicle in absence of location information. The proposed technique uses clues inherent in the road infrastructure images using lane detection and utilizes a conventional computer vision’s inverse perspective mapping technique to generate BEV. The proposed technique is tested with the video dataset captured in urban and highway traffic to show its efficacy and its utility in motion planning.
Research on Hand-eye Calibration Method Based on Binocular Camera

With a three-dimensional calibration ball as the calibration object, the binocular camera was fixed at the end of six-axis mechanical arm, the movement of the mechanical arm was controlled by the Eye-in-hand calibration method. The robotic arm is kept in different positions to acquire the point cloud data of standard ball through binocular camera, further calculate the 3-D coordinates of the calibration ball in space. The homogeneous transformation equation was used to establish a mathematical model, and the calibration matrix was solved. The final calibration error was within 0.2mm.
Rating YouTube Videos: An Improvised and Effective Approach

YouTube is one of the best sources of video information on the Internet. While it serves as the best media for creators to communicate to a broad audience, it has become less user-friendly over the past few years. Some official changes to the YouTube app have triggered many global audiences. One significant change that took place in the past year was the removal of the dislike count from every YouTube video. Without a dislike count, the current YouTube rating system has become ineffective. The proposed work recommends more user-friendly methods over the current inadequate rating system. Some previous researchers like Alhujaili and Rawan Fahad ( Alhujaili, R.F., Yafooz, W.M.: Sentiment analysis for youtube videos with user comments. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)) have already given insights on how sentiment analysis can be used on video comments to know the fairness of the video. So, the authors try to create a robust rating system that primarily uses sentiment analysis to provide fair ratings to every video depending on comment sentiment and would be easier to embed in the official YouTube app as a plugin. This rating system even helps to detect clickbait videos to a certain extent, making it much better than the previous systems.
Supporting Children's Collaborative Authoring: Practicing Written Literacy While Composing Oral Texts

This paper presents the theory, design and evaluation of a new type of computer-supported collaborative interface intended to help young children practice certain oral language skills critical for later written literacy acquisition. Based on a theory of “emergent literacy”, this paper describes a toy – TellTale – designed to let young children create, share and edit oral language in a way similar to how they will eventually create written language. Two user studies were conducted. The first suggests that paired children of different SES use different social and linguistic strategies to establish cohesion and that purely syntactic measures of narrative coherence are not sensitive enough to describe children's collaborative language play. A second pilot study investigated how groups of children used TellTale. Although results are not conclusive, TellTale also seems to be an engaging interface for group authorship.
Retracted: Investigating the interactive audio‐visual course mode for college English using virtual reality and artificial intelligence

Abstract Retraction: [Yanfeng Ma, Investigating the interactive audio‐visual course mode for college English using virtual reality and artificial intelligence, IET Software 2023 ( https://doi.org/10.1049/sfw2.12088 )]. The above article from IET Software , published online on 9 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Optimasi Sistem Tata Air pada Daerah Irigasi Rawa (Food Estate) Dadahup Kabupaten Kapuas Provinsi Kalimantan Tengah

The Swamp Irrigation Area (DIR) of Dadahup District is a former Peat Land Development (PLG) with an area of around 21,226 Ha. The water management system at DIR Dadahup utilizes changes in sea tides as a source of water to the land and drainage to drain water out of the land. However, in its implementation, DIR Dadahup experienced several problems such as flooding during the rainy season and experiencing drought during the dry season. These conditions indicate the need for land management and water management systems to overcome existing problems. The purpose of this study was to optimize the water management system for irrigation by optimizing the functions and benefits of swamp land in Dadahup, Kapuas Regency, Central Kalimantan Province. Optimization of the water system is carried out by modeling using the HEC-RAS program. Flow modeling with HEC-RAS in irrigation canals can be simulated into two scenarios, namely scenarios using gates and scenarios with doors and pumps for the rainy and dry seasons. Based on the results of the modeling analysis that has been carried out, the surface elevation during the dry season is in the range of +0.06 ms/d + 0.77 m, which is below the average land elevation (+0.8 m MSL). experience drought because the water in the canals cannot irrigate the land. The water level during the rainy season is in the range of +0.82 ms/d +1.53 m, which is above the average land elevation. It can be concluded that irrigation canals cannot accommodate the overflow of water that occurs at the planned discharge so that the land is flooded. The scenario model is suitable for the Dadahup DIR land use, namely with the door and pump scenario, the water level elevation for the dry season and the rainy season can reach the target of +1.0 m, then the water level elevation for the rainy season can also reach the target of +0, 7m.
Detection Model of Hangul Stroke Elements: Expansion of Non-Structured Font and Influence Evaluation by Stroke Element Combinations

With the increase of various media, fonts continue to be newly developed. In Korea, numerous ‘Hangul’ fonts are also being developed, and as a result, the need for research on determining the similarity between fonts is emerging. For example, when creating a document, the font to be used must be downloaded from each computing environment. However, this is a very cumbersome process. If there is a font that is not supported in the system, the above problem can be easily solved by recommending the most similar font that can replace it. According to this need, we conducted various prior studies for similar font recommendations. As a result, we developed a ‘stroke element’ that exists in each consonant and vowel in Korean font and developed a font recommendation model using a stroke element. However, there is a limitation in that the existing research was studied only for the structured fonts corresponding to the printed type. Additionally, the font size was not considered in the font recommendation. In this study, two experiments were conducted to expand the font recommendation model by supplementing the limitations of existing studies. First, in order to enable similar font recommendations based on the stroke element even in fonts with various shapes, the font was classified according to the shape, and the stroke elements in each classification were detected. Second, when the font sizes were different, the change in the font recommendations result based on the stroke element was analyzed. In conclusion, we found that it was necessary to find a plan to extract stroke elements for font recommendation of fonts that do not belong to standard fonts. In addition, since the influence of the stroke element varies depending on the size of the font, we propose a stroke element weight model that can be used for recommendation by reflecting it.
Information structure in Korean: What's new and what's old?

• The association between morphological marking and IS needs to be understood flexibly. • Topicality is sufficient for reference point; reference point is necessary for topicality. • Focus/contrastiveness are defined on the availability of an open/closed alternative set. • - nun/-un is polyfunctional; it can either encode a topic or contrastiveness.
AI-enhanced Multifunctional Haptic Rings for Cross-space Perception and Sensation in Metaverse

Advancements of virtual reality technology pave the way for developing wearable devices to enable somatosensory sensation, which can bring more comprehensive perception and feedback in metaverse-based virtual society.Here, we propose augmented tactile-perception and haptic-feedback rings with multimodal sensing and feedback capabilities.This highly integrated ring consists of the triboelectric and pyroelectric sensor for tactile and temperature perception, and the vibrator and nichrome heater for vibro-and thermo-haptic feedback.All these components integrated on the ring can be directly driven by a custom wireless platform of low power consumption for wearable/portable scenarios.By fusing the multimodal sensing and feedback functions, an interactive metaverse platform with cross-space perception capability is successfully achieved, giving people a face-to-face like immersive virtual social experience.
Drone Based Object Detection using AI

Aerial surveillance plays an important role for security applications. It can be further used to monitor borders, restricted zones and critical infrastructure. With the help of drones one can perform surveillance and get the exact location of various objects. Aerial object detection comes with many challenges like the object size which can be as low as 20×20 pixels. Images taken from satellites are hundreds of megapixels. Traditional methods like Histogram of oriented gradients (HOG) and Scale invariant feature transformation (SIFT) were used to extract features from the objects. Then these features were given to machine learning classifier like logistic regression, Support vector machine (SVM) and Random forest (RF) for detection and classification. However, the issue with these methods is that they are highly inaccurate and generated many false detections and misclassifications too. With the evolution of Graphics processing units (GPU) and the introduction of convolutional neural networks (CNN) as well as Deep Learning algorithms situation got changed. Now, it is possible to extract more information and provide better accuracy. In this paper for object detection You only look once version 4 (YOLOv4) is used which is one of the state-of-the-art algorithms. It uses Darknet 53 which is a type of CNN as a backbone for feature extraction. In this work the YOLOv4 based proposed system detect and localize vehicles present in the restricted zone and then geotag them.
Online Psychological Counseling Chatbot for Seniors

We introduce an online psychological counseling chatbot that uses the Task-Oriented Dialogue (TOD) system and the open-domain dialogue (ODD) system to communicate and recommend content as an emotion recognition result. If you use the TOD system, which is a dialogue system for a specific purpose, and the ODD system, which is a system that conducts dialogues without a purpose, you can conduct conversations more naturally. In this paper, the TOD system is used in inducing emotional conversation and providing content according to emotion, and the ODD system is used in the process of emotional conversation. This helps people conduct more natural conversations, and finally helps recommend content through emotional analysis.
Ultra-High Resolution Image Segmentation with Efficient Multi-Scale Collective Fusion

Ultra-high resolution image segmentation has at-tracted increasing attention recently due to its wide applications in various scenarios such as road extraction and urban planning. The ultra-high resolution image facilitates the capture of more detailed information but also poses great challenges to the image understanding system. For memory efficiency, existing methods preprocess the global image and local patches into the same size, which can only exploit local patches of a fixed resolution. In this paper, we empirically analyze the effect of different patch sizes and input resolutions on the segmentation accuracy and propose a multi-scale collective fusion (MSCF) method to exploit information from multiple resolutions, which can be end-to-end trainable for more efficient training. Our method achieves very competitive performance on the widely-used DeepGlobe dataset while training on one single GPU.
Kinematic Synthesis for 3D Signatures

This paper proposes a method to generate the synthetic kinematic of signatures in 3D. The analysis of 3D signatures is becoming a hot topic due to the irruption of commercial off-the-shelf devices for easy acquisition of 3D movements. However, the novelty of this technology reveals the scarce publicly available signatures in 3D, which hinder their de-velopment. A solution is the synthesis of Signatures in 3D. As a first step, this paper synthesizes the kinematics of 3D signatures based on the Kinematic Theory of Rapid Movements and its associated Sigma-Lognormal model in 3D. To evaluate the method, we regenerate signature databases with synthetic speed profiles in all genuine and forgeries found in two 3D signature databases. Then, we analyze the similarities in the performance of a signature verifier when real and synthetic signatures are used in random and skilled forgeries experiments.
Only frequency domain diffractive deep neural networks

Diffractive deep neural networks (D2NNs) have demonstrated their importance in performing various all-optical machine learning tasks such as classification and segmentation. However, current D2NNs can only detect spatial domain intensity information. They cannot solve problems that rely on frequency information, such as laser linewidth compression. We propose a new D2NN architecture that fully exploits frequency domain information. We demonstrate that only frequency domain D2NN (OF-D3NN) can be trained using deep learning algorithms and be successfully integrated into a free-space optical communications system (FSO) for information recovery.
Applied ecosemiotics: Ontological basis and conceptual models

Addressing ecological problems requires an ontological position that sees environmental processes simultaneously as semiotically mediated and rooted in ecosystems. For example, problems such as the effects of global environmental change, animals that are coping with urbanized environments and human migration due to environmental degradation bond sign processes and the flows of matter and energy. Ecosemiotics provides a robust conceptual framework for studying these topics. The toolbox proposed in this paper combines the ecosemiotic sphere as a general concept for ecosemiotic research; activity centers, which are loci in the ecosemiotic sphere with distinct identities and dynamics, bringing along change; relations between activity centers that are the primary object of ecosemiotic study. The proposed framework gathers some specific concepts, such as, among others, affordance, semiotic pollution, ecological code, meaning transfer, semiocide. I will argue that ecosemiotics holds a good potential for analyzing crucial contemporary environmental problems.
Elimination of optical phase sensitivity in a shift, scale, and rotation invariant hybrid opto-electronic correlator via off-axis operation

The hybrid opto-electronic correlator (HOC) uses a combination of optics and electronics to perform target recognition. Achieving a stable output from this architecture has previously presented a significant challenge due to a high sensitivity to optical phase variations, limiting the real-world feasibility of the device. Here we present a modification to the architecture that essentially eliminates the dependence on optical phases, and demonstrate verification of the proposed approach. Experimental results are shown to agree with the theory and simulations, for scale, rotation and shift invariant image recognition. This approach represents a major innovation in making the HOC viable for real-world applications.