Reviewerly/exHarmony huggingface dataset:
The ringed residual u-net with non-natural regions feature for image splicing forgery detection and localization

Authors:
raw_author_name: Qi Wang
raw_affiliation_string: School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China
raw_author_name: TongWei Lu
raw_affiliation_string: Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, China

Abstract: Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-to-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-to-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack.
Avoiding Illegal Land Registry using Blockchain

Authors:
raw_author_name: None Chandan M
raw_affiliation_string: Global Academy of Technology, Bengaluru, India

Abstract: The Land Registry system is an essential department for any government system, who maintains the crucial information about the land like ownership, transaction history and many more. There are various escape clauses in existing framework that raise the possibilities of misdirection and questions. To resolve this issue, we have accompanied an answer of carrying out blockchain in the land vault framework. A safe stage for land library framework utilizing Blockchain has been anticipated. Blockchain here is utilized as partner electronic record of computerized records and exchanges that are scrambled utilizing cryptography[1]. This framework targets concocting a model for secure and consistent land enlistment framework upheld blockchain innovation, which can make simple to dispose of the provisos in the ongoing area vault framework. Our framework gives one-of-a-kind character to each land, enlisted and checked for the land proprietor which can't be altered or repeated in this way, this gives secure exchanging of land. The land proprietor will be furnished with the ID when the land is enrolled in our framework and will be moved to the next client once he sells his territory. So, nobody can imitate as other client and there will no opportunities for deceitful or trick
A Survey of Health Care Chatbot for Patient Support

Authors:
raw_author_name: None Mayank Kumar
raw_affiliation_string: Global Academy of Technology, Bengaluru, India

Abstract: This literature survey delves into the evolving realm of healthcare chatbots, analyzing a variety of research papers on AI chatbot systems. The papers cover diverse objectives, methodologies, and applications within healthcare, including microservice architectures for chronic patient support, disease prediction, herbal remedies, and mental health support. Common threads across the papers include the adoption of natural language processing, machine learning, and AI markup languages. Ethical considerations, such as data privacy and consent, are identified as crucial aspects. Some papers focus on specific medical domains, while others propose comprehensive frameworks integrating IoT and health knowledge graphs. The survey highlights challenges like unified semantic approaches for patient data and underscores the ongoing need for research to address gaps, enhance chatbot intelligence, and ensure ethical deployment in real-world healthcare scenarios
Relevancy transformation operators in the form of polynomial functions

Authors:
raw_author_name: Yiming Tang
raw_affiliation_string: Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, School of Computer and Information, Hefei University of Technology, Hefei, P.R.China

Abstract: Relevancy transformation operators (RET operators) have been widely used in fuzzy systems modelling and the construction of weighted aggregation functions. Several construction methods of RET operators based on different aggregation functions such as t-norm, t-conorm and copula, have been proposed. In this paper, the attention is paid to the expression of RET operators, which is an important feature from an application the point of view. Polynomial RET operators are introduced as those RET operators in the form of polynomial functions of two variables. A complete characterisation of polynomial RET operators of degree less than 4 are presented.
Information extraction from automotive reports for ontology population

Authors:
raw_author_name: Hamid Ahaggach
raw_affiliation_string: LIB Laboratory, University of Burgundy, Dijon, France; Syartec Company, Aix-en-Provence, France
raw_author_name: Lylia Abrouk
raw_affiliation_string: LIB Laboratory, University of Burgundy, Dijon, France; MISTEA, University of Montpellier, INRAE, Institut Agro, France
raw_author_name: Eric Lebon
raw_affiliation_string: Syartec Company, Aix-en-Provence, France

Abstract: In this paper, we showcase our research on the use of ontologies and information extraction for the purpose of modeling damages incurred on car bodies. With the increasing use of technology in the automotive industry, it is important to have a standardized and efficient way of documenting and analyzing car damage reports. Most existing reports are unstructured, and there is a lack of standardization in describing the damage. To address this issue, we have developed a domain ontology for car damage modeling (OCD),11 industryportal.enit.fr/ontologies/OCD,22 github.com/OntologyCarDamage/OCD and proposed an end-to-end system to extract information from French automotive reports. The information extraction process involves using named entity recognition (NER) and relationship extraction (RE) techniques to identify and extract relevant information from the reports. Then, the extracted information is used to populate the OCD ontology, allowing a structured and standardized representation of the damage information. The proposed system was tested on a real dataset of automotive reports and showed promising results.
A Literature Survey on SpeakSmart: AI – Enhanced language learning guide

Authors:
raw_author_name: None Praveen P B
raw_affiliation_string: Global Academy of Technology, Bangalore, Karnataka, India
raw_author_name: None Dr. Kavita Patil
raw_affiliation_string: Global Academy of Technology, Bangalore, Karnataka, India

Abstract: This extensive review of the literature examines the pervasive impact of artificial intelligence (AI) on language learning and teaching, covering innovative uses like real-time language practice, personalized language learning, and automated testing methods in the teaching of English as a foreign language (EFL). The survey covers a wide range of topics, such as the use of Natural Language Processing (NLP) to extract Business Process Models in Business Process Management (BPM), the deployment of English-language chat-bots operating on the WeChat platform that are based on transfer learning, and the incorporation of NLP tools and virtual reality in the e-learning platform Exills. Additionally, it delves into cutting-edge technologies such as chat-bots for language learning, low-resource spoken language learning applications with AI support, and the broad application of AI in EFL teaching, tackling issues and offering encouraging results. The poll also addresses the use of AI in language learning tools, focusing on transparency and ethical issues while utilizing chat-bots, machine translation, speech recognition, and AI-generated content. The study highlights the importance of exposure to different cultures, community interaction, and multilingual voice recognition in creating a comprehensive language learning environment. It also highlights the limitations and enormous possibilities of integrating AI into language instruction.
Integrating Artificial Intelligence into Cybersecurity Curriculum: New Perspectives

Authors:
raw_author_name: Mark Finlayson
raw_affiliation_string: Florida International University
raw_author_name: A. Uluagac
raw_affiliation_string: Florida International University

Abstract: Abstract As societies rely increasingly on computers for critical functions, the importance of cybersecurity becomes ever more paramount. Even in recent months there have been attacks that halted oil production, disrupted online learning at the height of COVID, and put medical records at risk at prominent hospitals. This constant threat of privacy leaks and infrastructure disruption has led to an increase in the adoption of artificial intelligence (AI) techniques, mainly machine learning (ML), in state-of-the-art cybersecurity approaches. Oftentimes, these techniques are borrowed from other disciplines without context and devoid of the depth of understanding as to why such techniques are best suited to solve the problem at hand. This is largely due to the fact that in many ways cybersecurity curricula have failed to keep up with advances in cybersecurity research and integrating AI and ML into cybersecurity curricula is extremely difficult. To address this gap, we propose a new methodology to integrate AI and ML techniques into cybersecurity education curricula. Our methodology consists of four components: i) Analysis of Literature which aims to understand the prevalence of AI and ML in cybersecurity research, ii) Analysis of Cybersecurity Curriculum that intends to determine the materials already present in the curriculum and the possible intersection points in the curricula for the new AI material, iii) Design of Adaptable Modules that aims to design highly adaptable modules that can be directly used by cybersecurity educators where new AI material can naturally supplement/substitute for concepts or material already present in the cybersecurity curriculum, and iv) Curriculum Level Evaluation that aims to evaluate the effectiveness of the proposed methodology from both student and instructor perspectives. In this paper, we focus on the first component of our methodology - Analysis of Literature and systematically analyze over 5000 papers that were published in the top cybersecurity conferences during the last five years. Our results clearly indicate that more than 78% of the cybersecurity papers mention AI terminology. To determine the prevalence of the use of AI, we randomly selected 300 papers and performed a thorough analysis. Our results show that more than 19% of the papers implement ML techniques. These findings suggest that AI and ML techniques should be considered for future integration into cybersecurity curriculum to better align with advancements in the field.
InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction from Multi-view RGB-D Images

Authors:
raw_author_name: Yinghao Huang
raw_affiliation_string: Max Planck Institute for Intelligent Systems, Tübingen, Germany
raw_author_name: Omid Taheri
raw_affiliation_string: Max Planck Institute for Intelligent Systems, Tübingen, Germany
raw_author_name: Michael J. Black
raw_affiliation_string: Max Planck Institute for Intelligent Systems, Tübingen, Germany
raw_author_name: Dimitrios Tzionas
raw_affiliation_string: University of Amsterdam, Amsterdam, The Netherlands

Abstract: Abstract Humans constantly interact with objects to accomplish tasks. To understand such interactions, computers need to reconstruct these in 3D from images of whole bodies manipulating objects, e.g., for grasping, moving and using the latter. This involves key challenges, such as occlusion between the body and objects, motion blur, depth ambiguities, and the low image resolution of hands and graspable object parts. To make the problem tractable, the community has followed a divide-and-conquer approach, focusing either only on interacting hands, ignoring the body, or on interacting bodies, ignoring the hands. However, these are only parts of the problem. On the contrary, recent work focuses on the whole problem. The GRAB dataset addresses whole-body interaction with dexterous hands but captures motion via markers and lacks video, while the BEHAVE dataset captures video of body-object interaction but lacks hand detail. We address the limitations of prior work with InterCap, a novel method that reconstructs interacting whole-bodies and objects from multi-view RGB-D data, using the parametric whole-body SMPL-X model and known object meshes. To tackle the above challenges, InterCap uses two key observations: (i) Contact between the body and object can be used to improve the pose estimation of both. (ii) Consumer-level Azure Kinect cameras let us set up a simple and flexible multi-view RGB-D system for reducing occlusions, with spatially calibrated and temporally synchronized cameras. With our InterCap method we capture the InterCap dataset, which contains 10 subjects (5 males and 5 females) interacting with 10 daily objects of various sizes and affordances, including contact with the hands or feet. To this end, we introduce a new data-driven hand motion prior, as well as explore simple ways for automatic contact detection based on 2D and 3D cues. In total, InterCap has 223 RGB-D videos, resulting in 67,357 multi-view frames, each containing 6 RGB-D images, paired with pseudo ground-truth 3D body and object meshes. Our InterCap method and dataset fill an important gap in the literature and support many research directions. Data and code are available at https://intercap.is.tue.mpg.de .
Popularity-Debiased Graph Self-Supervised for Recommendation

Authors:
raw_author_name: Shanshan Li
raw_affiliation_string: College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China
raw_author_name: Jingfeng Guo
raw_affiliation_string: College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China
raw_author_name: Mingyue Qi
raw_affiliation_string: Hebei Reading Information Technology Co., Ltd., Shijiazhuang 050000, China; The Big Data and Social Computing Research Center, Hebei University of Science and Technology, Shijianzhuang 050018, China
raw_author_name: Yutong Jia
raw_affiliation_string: College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China

Abstract: The rise of graph neural networks has greatly contributed to the development of recommendation systems, and self-supervised learning has emerged as one of the most important approaches to address sparse interaction data. However, existing methods mostly focus on the recommendation’s accuracy while neglecting the role of recommended item diversity in enhancing user interest and merchant benefits. The reason for this phenomenon is mainly due to the bias of popular items, which makes the long-tail items (account for a large proportion) be neglected. How to mitigate the bias caused by item popularity has become one of the hot topics in current research. To address the above problems, we propose a Popularity-Debiased Graph Self-Supervised for Recommendation (PDGS). Specifically, we apply a penalty constraint on item popularity during the data enhancement process on the user–item interaction graph to eliminate the inherent popularity bias. We generate item similarity graphs with the popularity bias removed to construct a self-supervised learning task under multiple views, and we design model optimization strategies from the perspectives of popular items and long-tail items to generate recommendation lists. We conduct a large number of comparison experiments, as well as ablation experiments, on three public datasets to verify the effectiveness and the superiority of the model in balancing recommendation accuracy and diversity.
Quantum algorithms for state preparation and data classification based on stabilizer codes

Authors:
raw_author_name: Pejman Jouzdani
raw_affiliation_string: General Atomics, 3550 General Atomics Court, San Diego, California 92121, USA
raw_author_name: Eduardo R. Mucciolo
raw_affiliation_string: Department of Physics, University of Central Florida, Orlando, Florida 32816, USA

Abstract: Quantum error correction (QEC) is a way to protect quantum information against noise. It consists of encoding input information into entangled quantum states known as the code space. Furthermore, to classify if the encoded information is corrupted or intact, a step known as syndrome detection is performed. For stabilizer codes, this step consists of measuring a set of stabilizer operators. In this paper, inspired by the QEC approach, and specifically stabilizer codes, we propose a prototype quantum circuit model for classification of classical data. The core quantum circuit can be considered as a quantum perceptron where the classification is based on syndrome detection. In this proposal, a quantum perceptron is realized by one stabilizer as part of a stabilizer code, while a quantum neural network (QNN) layer is realized by a stabilizer code which consists of many stabilizers. The concatenation of stabilizer codes results in complex QNNs. The QNN is trained by performing measurements and optimization of a set of parametrized stabilizers. We demonstrate the concept numerically. In this paper we also consider the first challenge to most applications of quantum computers, including data classification, which is to load data into the memory of the quantum computer. This loading amounts to representing the data as a quantum state, i.e., quantum state preparation. An exact amplitude encoding algorithm requires a circuit of exponential depth. We introduce an alternative recursive algorithm which approximates amplitude encoding with only a polynomial number of elementary gates. We name it the recursive approximate-scheme algorithm.
HaCk: Hand Gesture Classification Using a Convolutional Neural Network and Generative Adversarial Network-Based Data Generation Model

Authors:
raw_author_name: M. Pramod
raw_affiliation_string: Department of Computer Science & Engineering, Nalla Malla Reddy Engineering College, Hyderabad 500088, Telangana, India
raw_author_name: Anjan Bandyopadhyay
raw_affiliation_string: School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, Odisha, India
raw_author_name: Saurav Mallik
raw_affiliation_string: Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Pharmacology & Toxicology, The University of Arizona, Tucson, MA 85721, USA

Abstract: According to global data on visual impairment from the World Health Organization in 2010, an estimated 285 million individuals, including 39 million who are blind, face visual impairments. These individuals use non-contact methods such as voice commands and hand gestures to interact with user interfaces. Recognizing the significance of hand gesture recognition for this vulnerable population and aiming to improve user usability, this study employs a Generative Adversarial Network (GAN) coupled with Convolutional Neural Network (CNN) techniques to generate a diverse set of hand gestures. Recognizing hand gestures using HaCk typically involves a two-step approach. First, the GAN is trained to generate synthetic hand gesture images, and then a separate CNN is employed to classify gestures in real-world data. The evaluation of HaCk is demonstrated through a comparative analysis using Leave-One-Out Cross-Validation (LOO CV) and Holdout Cross-Validation (Holdout CV) tests. These tests are crucial for assessing the model’s generalization, robustness, and suitability for practical applications. The experimental results reveal that the performance of HaCk surpasses that of other compared ML/DL models, including CNN, FTCNN, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models. Specifically, the improvement percentages for the LOO CV Test are 17.03%, 20.27%, 15.76%, 13.76%, 10.16%, 5.90%, and 15.90%, respectively. Similarly, for the Holdout CV Test, HaCk outperforms HU, ZM, GB, GB-ZM, GB-HU, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models, with improvement percentages of 56.87%, 15.91%, 13.97%, 24.81%, 23.52%, 17.72%, 15.72%, 12.12%, 7.94%, and 17.94%, respectively.
Merging of Neural Networks

Authors:
raw_author_name: Vladimír Boža
raw_affiliation_string: Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia

Abstract: Abstract We propose a simple scheme for merging two neural networks trained with different starting initialization into a single one with the same size as the original ones. We do this by carefully selecting channels from each input network. Our procedure might be used as a finalization step after one tries multiple starting seeds to avoid an unlucky one. We also show that training two networks and merging them leads to better performance than training a single network for an extended period of time.
Improving Learners’ Self-Efficacy in Performing Design Reviews with Virtual Reality

Authors:
raw_author_name: Fadi Castronovo
raw_author_name: SeyedReza RazaviAlavi

Abstract: Research on the use of innovative technology, such as virtual reality (VR), in Architecture, Engineering, and Construction (AEC) education, has been growing in the past twenty years. However, such research still requires robust investigation, as few studies have adopted educational psychology theories and rigorous methods. Furthermore, most of the research has focused on the cognitive effects of VR and additional research is also needed to investigate the affective effects, such as motivation, engagement, and self-efficacy. This study aims to evaluate the effects of technology decision-making between immersive virtual reality (IVR) and non-immersive virtual reality (nIVR) setups for a learning activity on AEC learners. For this, three hypotheses are formulated and tested on 165 UK students. Based on the results, both the designed IVR and nIVR learning activities had significant positive effects on learners’ self-efficacy and user experience with no significant difference between IVR and nIVR delivery. With this research, the authors contribute to the growing literature on VR implementation in AEC classrooms by showcasing a study founded on educational psychology theory and by using a rigorous research methodology. Furthermore, this study illustrates the effects that IVR and nIVR have on students’ affective learning and opens the possibility of new research in the field.