Reviewerly/exHarmony huggingface dataset:
Copy-Move Forgery Detection Based on Keypoint Clustering and Similar Neighborhood Search Algorithm

Copy-move is one of the most commonly used methods of tampering with digital images.Keypoint-based detection is recognized as effective in copy-move forgery detection (CMFD).This paper proposes an efficient CMFD method via clustering SIFT keypoints and searching the similar neighborhoods to locate tampered regions.In the proposed method, the keypoints are clustered based on scale and color, grouped into several smaller clusters and matched separately, which reduce the high time complexity caused in matching caused by the high dimensionality of SIFT.In order to locate the tampered regions accurately at pixel level finally, a novel localization algorithm is designed to compare the similar neighborhoods of matching pairs by two similarity measures, and mark the tampered regions in pixels iteratively.We experimented on three different image data sets including kinds of tampering means to compare and verify the effectiveness and robustness of proposed method.The experimental results show that the proposed method is superior to existing state-of-art methods in terms of matching time complexity, detection reliability and forgery location accuracy.
BioMove: Biometric User Identification from Human Kinesiological Movements for Virtual Reality Systems

Virtual reality (VR) has advanced rapidly and is used for many entertainment and business purposes. The need for secure, transparent and non-intrusive identification mechanisms is important to facilitate users’ safe participation and secure experience. People are kinesiologically unique, having individual behavioral and movement characteristics, which can be leveraged and used in security sensitive VR applications to compensate for users’ inability to detect potential observational attackers in the physical world. Additionally, such method of identification using a user’s kinesiological data is valuable in common scenarios where multiple users simultaneously participate in a VR environment. In this paper, we present a user study (n = 15) where our participants performed a series of controlled tasks that require physical movements (such as grabbing, rotating and dropping) that could be decomposed into unique kinesiological patterns while we monitored and captured their hand, head and eye gaze data within the VR environment. We present an analysis of the data and show that these data can be used as a biometric discriminant of high confidence using machine learning classification methods such as kNN or SVM, thereby adding a layer of security in terms of identification or dynamically adapting the VR environment to the users’ preferences. We also performed a whitebox penetration testing with 12 attackers, some of whom were physically similar to the participants. We could obtain an average identification confidence value of 0.98 from the actual participants’ test data after the initial study and also a trained model classification accuracy of 98.6%. Penetration testing indicated all attackers resulted in confidence values of less than 50% (&lt;50%), although physically similar attackers had higher confidence values. These findings can help the design and development of secure VR systems.
Meta-Transfer Learning Through Hard Tasks

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, typical meta-learning models use shallow neural networks, thus limiting its effectiveness. In order to achieve top performance, some recent works tried to use the DNNs pre-trained on large-scale datasets but mostly in straight-forward manners, e.g., (1) taking their weights as a warm start of meta-training, and (2) freezing their convolutional layers as the feature extractor of base-learners. In this paper, we propose a novel approach called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">meta-transfer learning (MTL)</i> , which learns to transfer the weights of a deep NN for few-shot learning tasks. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">meta</i> refers to training multiple tasks, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transfer</i> is achieved by learning scaling and shifting functions of DNN weights (and biases) for each task. To further boost the learning efficiency of MTL, we introduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hard task (HT) meta-batch</i> scheme as an effective learning curriculum of few-shot classification tasks. We conduct experiments for five-class few-shot classification tasks on three challenging benchmarks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mini</i> ImageNet, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tiered</i> ImageNet, and Fewshot-CIFAR100 (FC100), in both supervised and semi-supervised settings. Extensive comparisons to related works validate that our MTL approach trained with the proposed HT meta-batch scheme achieves top performance. An ablation study also shows that both components contribute to fast convergence and high accuracy.
Apply transfer learning to cybersecurity: Predicting exploitability of vulnerabilities by description

Thousands of software vulnerabilities are archived and disclosed to the public each year, posing severe cybersecurity threats to the whole society. Predicting the exploitability of vulnerabilities is crucial for decision-makers to prioritize their efforts and patch the most critical vulnerabilities. Software vulnerability descriptions are accessible features in early stage and contain rich semantic information. Therefore, descriptions are wildly used for exploitability prediction in both industry and academia. However, comparing with other corpora, the size of vulnerability description corpus is too small to train a comprehensive Natural Language Processing (NLP) model. To gain a better performance, this paper proposes a framework named ExBERT to accurately predict if a vulnerability will be exploited or not. ExBERT essentially is an improved Bidirectional Encoder Representations from Transformers (BERT) model for exploitability prediction. First, we fine-tune a pre-trained BERT using collected domain-specific corpus. Then, we design a Pooling Layer and a Classification Layer on top of the fine-tuned BERT model to extract sentence-level semantic features and predict the exploitability of vulnerabilities. Results on 46,176 real-word vulnerabilities have demonstrated that the proposed ExBERT framework achieves 91.12% on accuracy and 91.82% on precision, outperforming the state-of-the-art approach with 89.0% on accuracy and 81.8% on precision.
Improving the service industry with hyper-connectivity: IoT in hospitality

Purpose Internet of Things (IoT) adoption is a differentiating factor in the hospitality industry which facilitates the integration of the digital and real world. This paper aims to explore academic research and practical applications of IoT in the hospitality domain to help identify opportunities and challenges with implementing the technology for creating competitive advantages and service operations process improvements. Design/methodology/approach This paper uses previous works and exemplars to demonstrate the use of IoT in hospitality. Academic indexing websites such as Google Scholar and ScienceDirect are used to search for related terms. Whitepapers, IoT project websites of service providers and media coverage are accessed to collect information. Related work is investigated by classifying into major categories of hospitality. Findings Hospitality is one of the leading industries that has adopted IoT to create innovative services, but this topic has not been investigated deeply. A comprehensive study is needed to give guidance to decision-makers and helps to design better services by presenting practical and potential benefits. Practical implications The IoT will usher in great opportunities in hospitality by enabling novel applications for customization and personalization of the services. Operational processes will be redefined for efficiency and speed. It will alter the expectations and servicescape; thus, its integration will be vital in terms of competitiveness and success. Originality/value This study provides a comprehensive overview of IoT research and applications in the hospitality domain. It contributes to better understanding of recent trends and potentials. A holistic approach was used instead of focusing on a single sector which enables the consideration of all aspects of the topic. Theoretical support in addition to technical aspects, challenges and concerns are offered to the reader.
Efficient Estimation of Nepali Word Representations in Vector Space

Word representation is a means of representing a word as mathematical entities that can be read, reasoned and manipulated by computational models. The representation is required for input to any new modern data models and in many cases, the accuracy of a model depends on it. In this paper, we analyze various methods of calculating vector space for Nepali words and postulate a word to vector model based on the Skip-gram model with NCE loss capturing syntactic and semantic word relationships.&#x0D; This is an attempt to implement a paper by Mikolov on Nepali words.
Findings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion

Hope is considered significant for the well-being, recuperation and restoration of human life by health professionals. Hope speech reflects the belief that one can discover pathways to their desired objectives and become roused to utilise those pathways. To encourage research in natural language processing towards positive reinforcement approach, we created a hope speech detection dataset. This paper reports on the shared task of hope speech detection for Tamil, English, and Malayalam languages. The shared task was conducted as a part of the EACL 2021 workshop on Language Technology for Equality, Diversity, and Inclusion (LT-EDI-2021). We summarize here the datasets for this challenge which are openly available at https://competitions.codalab.org/competitions/27653, and present an overview of the methods and the results of the competing systems. To the best of our knowledge, this is the first shared task to conduct hope speech detection.
Image Augmentation Techniques for Mammogram Analysis

Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods' performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.
Attention-based residual autoencoder for video anomaly detection

Abstract Automatic anomaly detection is a crucial task in video surveillance system intensively used for public safety and others. The present system adopts a spatial branch and a temporal branch in a unified network that exploits both spatial and temporal information effectively. The network has a residual autoencoder architecture, consisting of a deep convolutional neural network-based encoder and a multi-stage channel attention-based decoder, trained in an unsupervised manner. The temporal shift method is used for exploiting the temporal feature, whereas the contextual dependency is extracted by channel attention modules. System performance is evaluated using three standard benchmark datasets. Result suggests that our network outperforms the state-of-the-art methods, achieving 97.4% for UCSD Ped2, 86.7% for CUHK Avenue, and 73.6% for ShanghaiTech dataset in term of Area Under Curve, respectively.
Image encryption scheme based on blind signature and an improved Lorenz system

In this paper, a new improved Lorenz system (ImproLS) is designed with a better behavior due to a larger positive Lyapunov exponent. Then, an asymmetric image encryption scheme is presented based on blind signature and the ImproLS. First, the sender uses the public key from the receiver to produce the cipher keys in conjunction with the Rivest–Shamir–Adleman (RSA) algorithm. Then, a new mathematical model is established to convert both the plain keys and the cipher keys to generate the initial values for ImproLS. After doing preprocess to the secret plain image, its statistical characteristics is changed greatly. Then, by employing permutation and diffusion operations, the cipher image can be obtained and sent to the receiver and the signer. Second, the signer encrypts the digital signature image using Arnold transform in conjunction with the RSA algorithm, and applies the discrete wavelet transform (DWT) to the cipher image. The scrambled signature image is then embedded into the wavelet domain of cipher image blindly. Then, the cipher image containing signature is sent to the receiver for authentication. Third, the receiver uses the private key to check the signature and recover the cipher image. After comparing the decrypted images from both the sender and the signer, the receiver can judge the source of the secret plain image. Experimental results show that the information entropy is high and closer to the theoretical value. In addition, both unsigned and signed cipher images can effectively resist salt and pepper attack, and clipping attack.
Image super-resolution with an enhanced group convolutional neural network

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://github.com/hellloxiaotian/ESRGCNN.
Simple low-light image enhancement based on Weber–Fechner law in logarithmic space

In an environment with poor illumination, such as indoor, night, and overcast conditions, the image information can be seriously lost, which affects the visual effect and degrades the performance of machine systems. However, existing methods such as retinex-based method, dehazing model-based method, and machine learning-based method usually have high computational complexity or are prone to color distortion, noise amplification, and halo artifacts. To balance the enhancement effect and processing speed, this paper applies the Weber–Fechner law to the grayscale mapping in logarithmic space and proposed an adaptive and simple color image enhancement method based on the improved logarithmic transformation. In the framework, the brightness component is extracted from the scene of the low-light image using Gaussian filtering after color space conversion. The image is logarithmically transformed by adaptively adjusting the parameters of the illumination distribution to improve the brightness of the image. The color saturation is hence compensated. The proposed algorithm adaptively reduces the impact of non-uniform illumination on the image, and the enhanced image is clear and natural. Our experimental results demonstrate improved performance to the existing image enhancement approaches.
Gemiverse: The blockchain-based professional certification and tourism platform with its own ecosystem in the metaverse

The traditional tourism industry is facing severe challenges and it urgently needs digital technology to improve the quality of service experience and storage security. Blockchain, as an emerging technology, brings new opportunities to the tourism industry as it provides a trusted platform for travel companies and tourists. This paper aims to address the challenges and for a real-world prototype development, it has proposed Gemiverse, a blockchain-based professional certification and travel platform that offers specialized solutions to meet challenges and focuses on building immersive experiences. Finally, three development stages of Gemiverse and conduct scene application tests is proposed.
BrokerChain: A Cross-Shard Blockchain Protocol for Account/Balance-based State Sharding

State-of-the-art blockchain sharding solutions, say Monoxide, can induce imbalanced transaction (TX) distributions among all blockchain shards due to their account deployment mechanisms. Imbalanced TX distributions then cause hot shards, in which the cross-shard TXs may experience an unlimited length of confirmation latency. Thus, how to address the hot-shard issue and how to reduce cross-shard TXs become significant challenges of blockchain state sharding. Through reviewing the related studies, we find that a cross-shard TX protocol that can achieve workload balance among all shards and simultaneously reduce the number of cross-shard TXs is still absent from the literature. To this end, we propose BrokerChain, which is a cross-shard blockchain protocol devised for the account/balance-based state sharding. Essentially, BrokerChain exploits fine-grained state partition and account segmentation. We also elaborate on how BrokerChain handles cross-shard TXs through broker accounts. The security issues and other properties of BrokerChain are analyzed substantially. Finally, we conduct comprehensive evaluations using both a cloud-based prototype and a transaction-driven simulator. The evaluation results show that BrokerChain outperforms other solutions in terms of system throughput, transaction confirmation latency, the queue size of transaction pool, and workload balance.
Blockchain for Industry 5.0: Vision, Opportunities, Key Enablers, and Future Directions

Industry 4.0 have witnessed a paradigm shift from cyber-physical systems (CPS) that aims at massive automation, towards a more customer-driven approach. The shift has been attributed to the design of hyper-cognitive systems, integration of virtual and extended reality, digital machinery prototyping and twin designs, trusted machine boundaries, collaborative robots, and artificial intelligence (AI)-based supply chains. This new wave, termed Industry 5.0, is expected to leverage massive production with user-centric customization outside the scope of Industry 4.0 ecosystems. Industry 5.0 is expected to assist diverse industrial verticals like healthcare, smart farming, drones, smart grids, and supply chain production ecosystems. However, data is shared among multiple heterogeneous networks, spanning different authoritative domains. Thus, trusted and secured data transfer is crucial to synergize and secure the industrial perimeters. Blockchain (BC) is a preferred choice as a security enabler to Industry 5.0 ecosystems owing to its inherent property of immutability, chronology, and auditability in industrial systems. Limited works are proposed that present the vision and holistic view of BC-assisted Industry 5.0 applications. The article presents a first-of-its-kind survey on BC as a security enabler in Industry 5.0. Based on a descriptive survey methodology and research questions, we presented the key drivers, and potential applications, and propose an architectural vision of BC-based Industry 5.0 in diverse applicative verticals. The survey intends to present solutions that would assist industry practitioners, academicians, and researchers to drive novel BC-assisted solutions in Industry 5.0 verticals.
Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network

The purpose is to improve the teaching and learning efficiency of college Innovation and Entrepreneurship Education (IEE). Firstly, from the perspective of aesthetic education, this work designs the teacher and student sides of the Computer-aided Instruction (CAI) system. Secondly, the CAI model is implemented based on the weight sharing and local perception of the Convolutional Neural Network (CNN). Finally, the performance of the CNN-based CAI model is tested. Meanwhile, it analyses students' IEE experience under the proposed CAI model through a case study of Music Majors from Xi'an Conservatory of Music. The experimental data show that the CNN-based CAI model can respond quickly and stably when users access different functional modules, such as webpage browsing. The proposed CAI model increases students' entrepreneurial interest, skills, and knowledge by 55.62, 57.32, and 72.12%, respectively. Students' entrepreneurial practice ability has been improved by over 50.00%; such an increase in entrepreneurial practice ability has also shown individual differences. Thus, the proposed Music Majors-oriented IEE-infiltrated CAI model based on CNN improves students' entrepreneurial practice ability and reflects the positive experience of Music Majors on IEE. The finding provides references for the step-by-step identification of the CNN-based CAI model and has certain guiding significance for analyzing the effect of college IEE.
Private blockchain-based encryption framework using computational intelligence approach

Electronic Health monitoring system has performed an essential role in managing healthcare monitoring. E-health can provide effective and valuable facilities for the patients to monitor. Though, there are protection disputes in the current E-Health system. The current e-health system, on the other hand, has security issues. Malevolent doctors may work together with cloud Storage Service Providers (CSPs) to interfere with patients' electronic health records (EHRs) or promptly leak EHR matter to other enemies for income. (EHRs). The malevolent doctors may conspire with the Patient Healthcare Monitoring Service Provider (PHMSP) to manipulate with the patients'. For profit, EHRs or directly divulge the EHR content of EHRs to other opponents. Block-chain has recently appeared as one of the most powerful methods in the protection and secrecy fields. It is assumed to be the promised security approach that will eventually replace the security challenges in existing e-health monitoring systems. Encryption in blockchain refers to technical methods that make accessing encrypted data difficult for unauthorized resources. This research proposed a blockchain-based encryption framework to provide security-based solutions using a computational intelligence methodology. The proposed approach provides better results in terms of 0.93 in the training phase and 0.91 in the validation accuracy.