Reviewerly/exHarmony huggingface dataset:
A novel copy-move forgery detection algorithm via two-stage filtering

With the wide application of simple image editing software, forgery images have become severe social problems with extremely damaging effects. The copy-move forgery technique is commonly used to temper an image by copying some regions of an image and pasting them somewhere in the same image. In this paper, a novel algorithm is proposed for the detection of copy-move forgery. First, the keypoints of the input image are computed using a keypoint extraction algorithm. Second, a keypoint-matching algorithm is used to match similar keypoints as the candidate keypoint pairs. Third, a proposed novel Two-Stage Filtering algorithm, including the Grid-Based Filter and the Clustering-Based Filter, is applied to filter out most of the false matching keypoint pairs. Subsequently, image matting is achieved by the Delaunay triangulation algorithm so that the marked areas indicate the forgery regions. Three copy-move forgery datasets are used to compare the performances of the proposed algorithm with some state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm's overall performance is superior to other solutions for detecting copy-move forgery images.
Al-SPSD: Anti-leakage smart Ponzi schemes detection in blockchain

Blockchain provides a decentralized environment for applications and information systems in various fields. It is an innovative revolution for the traditional Internet. However, without proper regulatory mechanisms, the blockchain technology has gradually become a hotbed of criminal activities, such as Ponzi scheme that brings huge economic losses to people. To maintain the security of the blockchain system, the machine learning technique, which can detect smart Ponzi schemes automatically has recently received extensive attention. However, the existing method has potential target leakage and prediction shift problems when dealing with category features and calculating gradient estimates. Besides, they also ignore the imbalance and repeatability of smart contracts, which often causes the model to overfit. In this paper, we introduce a novel method for detecting smart Ponzi schemes in blockchain. Specifically, we first expand the dataset of smart Ponzi schemes and eliminate the unbalanced dataset via data enhancement. Then, we leverage ordered target statistics (TS) to handle the category features of smart contract without target leakage. Finally, we propose an anti-leakage smart Ponzi schemes detection (Al-SPSD) model based on the idea of ordered boosting. Experimental results show that our proposal outperforms the competitive methods and is effective and reliable in detecting smart Ponzi schemes. Al-SPSD achieves 96% F-score and detects about 1,621 active smart Ponzi schemes in Ethereum.
Universal Representation Learning from Multiple Domains for Few-shot Classification

In this paper, we look at the problem of few-shot image classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples. Recent methods use various adaptation strategies for aligning their visual representations to new domains or select the relevant ones from multiple domain-specific feature extractors. In this work, we present URL, which learns a single set of universal visual representations by distilling knowledge of multiple domain-specific networks after co-aligning their features with the help of adapters and centered kernel alignment. We show that the universal representations can be further refined for previously unseen domains by an efficient adaptation step in a similar spirit to distance learning methods. We rigorously evaluate our model in the recent Meta-Dataset benchmark and demonstrate that it significantly outperforms the previous methods while being more efficient.
A lightweight key management scheme for key-escrow-free ECC-based CP-ABE for IoT healthcare systems

The rapid increase in the implementation of Internet of Things (IoT) solutions in diverse healthcare sectors raises the IoT in the healthcare market. The real-time health monitoring to manage the chronic diseases is likely to drive the demand of IoT in healthcare. Moreover, the enhancement in communication technologies like real-time data transmission has improved the patients' confidence in managing the chronic disease and medication dosage. Although IoT in healthcare makes a positive impact on patients and healthcare providers, however, it gets the challenges like data security and privacy. One means of sustaining security in IoT based healthcare system is through key management and at the same time an effective security mechanism for outsourced data to obtain fine-grained access control is the Ciphertext Policy-Attribute Based Encryption (CP-ABE). However, the overhead of complex decryption operations and key-escrow problem of CP-ABE hinders its applicability in IoT. Hence, in this work, we design a lightweight key management mechanism for the CP-ABE scheme using Elliptic Curve Cryptography (ECC). The novelty in this scheme is that irrespective of the secret key generation by the semi-trusted authority (honest, but curious to know the secret information), it is not capable to decrypt any message using these keys until and unless it has an additional private key of the receiver. For a constraint environment like IoT, the applicability of CP-ABE has two major issues: complex decryption operations and key-escrow problem. Hence, in this paper, a lightweight CP-ABE scheme for IoT based health care system using ECC has been designed. The proposed key management mechanism in the CP-ABE scheme is key-escrow free as well as significantly reduces the decryption overhead of the data receiver. The performance analysis shows that the proposed scheme is more effective as compared to the existing competing schemes.
Effects of Incorporating Augmented Reality into a Board Game for High School Students’ Learning Motivation and Acceptance in Health Education

In traditional schools, where education and teaching tend to be subject-oriented, the standardization of the teaching materials of health education courses would be obscurely related to know-how of daily life. This frustrates the learners from developing the awareness of engagement, thereby decreasing their willingness to acquire new information or skill. Therefore, in this study, a board game assimilating augmented reality (AR) into health education is presented. It associates the card game, slides, and learning sheets gamification teaching model with the learning experience; and proposes the efficacy of the board games mingled with augmented reality to enhance the motivation in learning and confidence in technology. In this experiment for a health education board game, 52 high school students participated in this experiment. There were 25 in the experimental group (with AR) and 27 in the control group (without AR). The IMMS (instructional material motivation survey) and the TAM (technology acceptance model) are applied to acquire quantitative data for examination. The findings are as follows: (1) The acceptance was significantly affected by the integration of AR into the health education board game and (2) the learning motivation was significantly affected by the integration of AR into the health education board game.
Effectiveness of online and blended learning from schools: A systematic review

Abstract This systematic analysis examines effectiveness research on online and blended learning from schools, particularly relevant during the Covid‐19 pandemic, and also educational games, computer‐supported cooperative learning (CSCL) and computer‐assisted instruction (CAI), largely used in schools but with potential for outside school. Eight research databases were searched. Studies which were non‐school, before 2000, not in English, without data and duplicates were removed, leaving 1355 studies: online 7%, blended 13%, CSCL 7%, games 26% and CAI 47%. Overall, digital technology was more effective (better) than regular instruction in 85% of studies, 8% the same and 3% worse. Blended learning was considerably better than online learning. CAI was the most effective, with games and CSCL coming after blended learning, but of course CAI was not searched for and these were not widely used outside of schools. Primary and early years/kindergarten were most effective (87% better) and secondary/high next (80%). Although science and mathematics were the most popular subjects, English as a foreign language interventions were the most effective, then writing and STEM, thinking, arts/music, humanities, health and science, reading and maths, foreign languages and English in that order. Overall, females did better than males. ‘Low ability’ children and second language learners did especially well. Disadvantaged and special educational needs/disabled students did slightly worse. Self‐efficacy was highly related to better outcomes. The limitations/strengths of the research were discussed and linked back to previous literature, a critical analysis made, and detailed implications for practitioners, policy makers and future researchers outlined. Digital technology's main advantage may be the possibility for enhanced task flexibility and learner autonomy, encouraging greater self‐regulation. However, this may not be an advantage for all students.
A Qualitative Analysis of Implementing E-Learning during the COVID-19 Lockdown

The existing literature evidences the potential of the e-learning methodology, although some call it into question. Our study aimed to analyse the real scope of applying this methodology type to a lockdown situation like that caused by COVID-19. It could provide the scientific and educational community with useful novel information on e-learning and its real adequacy for schools in pre-university educational stages. This qualitative study was designed using questionnaires with open-ended questions for students and semistructured interviews for teachers, management board members, and families of students of Primary and Secondary Education. The scripts of data collection tools were made ad hoc. The thematic analysis was carried out in accordance with the study dimensions by relating the access and use of technological resources in classrooms, implementing the e-learning methodology during face-to-face teaching, and finally knowing the effects of its application during remote teaching in the lockdown situation. The results clearly show a need to adapt and adjust the implementation of this methodology by considering not only its specificities, but also a combination of e-learning and traditional teaching methodologies can help to introduce information and communication technologies (ICT) into classrooms. To conclude, including such a methodology in the educational context can be argued for, provided that specific training is received to allow the potential of both online and traditional teaching to be leveraged.
Quantum circuit representation of Bayesian networks

Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems to perform various types of analysis such as probabilistic prediction, risk analysis, and system health monitoring, which can become computationally expensive in large-scale systems. While demonstrations of true quantum supremacy remain rare, quantum computing applications managing to exploit the advantages of amplitude amplification have shown significant computational benefits when compared against their classical counterparts. We develop a systematic method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits. The marginal probabilities associated with root nodes (nodes without any parent nodes) are represented using rotation gates, and the conditional probability tables associated with non-root nodes are represented using controlled rotation gates. The controlled rotation gates with more than one control qubit are represented using ancilla qubits. The proposed approach is demonstrated for three examples: a 4-node oil company stock prediction, a 10-node network for liquidity risk assessment, and a 9-node naive Bayes classifier for bankruptcy prediction. The circuits were designed and simulated using Qiskit, a quantum computing platform that enables simulations and also has the capability to run on real quantum hardware. The results were validated against those obtained from classical Bayesian network implementations.
Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning

Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people’s lives, as well as the availability of vast amounts of digital cooking recipes and food images to train machine learning models. In this work, we revisit existing approaches for cross-modal recipe retrieval and propose a simplified end-to-end model based on well established and high performing encoders for text and images. We introduce a hierarchical recipe Transformer which attentively encodes individual recipe components (titles, ingredients and instructions). Further, we propose a self-supervised loss function computed on top of pairs of individual recipe components, which is able to leverage semantic relationships within recipes, and enables training using both image-recipe and recipe-only samples. We conduct a thorough analysis and ablation studies to validate our design choices. As a result, our proposed method achieves state-of-the-art performance in the cross-modal recipe retrieval task on the Recipe1M dataset. We make code and models publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides

Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC on biopsy and surgical resection slides under a microscope remains a heavily specialized, error-prone, and time-consuming task for pathologists. In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal. In addition to the whole-slide classification pipeline, we visualized the identified indicative regions and features on slides for classification by reprocessing patch-level classification results to ensure the explainability of our diagnostic model. We evaluated our model on independent test sets of 78 surgical resection whole slides and 79 biopsy slides from our tertiary medical institution, and 917 surgical resection slides from The Cancer Genome Atlas (TCGA) database. The average area under the curve (AUC) of our classifier on the internal resection slides, internal biopsy slides, and external TCGA slides is 0.98 (95% confidence interval (CI): 0.97-1.00), 0.98 (95% CI: 0.96-1.00) and 0.97 (95% CI: 0.96-0.98), respectively. Our results suggest that the high generalizability of our approach across different data sources and specimen types. More importantly, our model has the potential to assist pathologists by (1) automatically pre-screening slides to reduce false-negative cases, (2) highlighting regions of importance on digitized slides to accelerate diagnosis, and (3) providing objective and accurate diagnosis as the second opinion.
Public Blockchains for Resource-Constrained IoT Devices—A State-of-the-Art Survey

Although blockchain offers a lot of benefits for the Internet-of-Things (IoT) network in terms of security, there are a number of barriers to its widespread adoption. Since many IoT networks consist of battery-powered devices with limited computing capabilities, one of those adoption barriers is the IoT end node itself. Several articles provided a general survey of the blockchain integration with IoT applications. However, a detailed analysis of this integration is missing, possible challenges are omitted, and no solutions that would enable the integration are presented. Furthermore, none of the surveys focused on reviewing public blockchains for IoT devices with low computational capabilities and limited energy resources. Therefore, the aim of this article is to provide the results of an in-depth study that covers challenges faced by existing public blockchains with IoT device integration. This article looks into resource-constrained IoT device classification and the wireless communication protocols used by them. It examines challenges that are required to be overcome for the successful integration of resource-constrained IoT end nodes with existing public blockchains. It then looks at the proposed solutions and their feasibility. A detailed review of possible security attacks on an IoT blockchain network is provided. As a result, a list of important characteristics that a public blockchain should possess in order to be integrated with a resource-constrained IoT network is proposed. Finally, the requirements for the IoT end nodes themselves are specified.
Evaluation of Performance and Security of Proof of Work and Proof of Stake using Blockchain

Storing information in Blockchain has become in vogue in the Technical and Communication Industry with many major players jumping into the bandwagon. Two of the most prominent enablers for Blockchain are “Proof of Work” and “Proof of Stake”. Proof of work includes the members solving the complex problem without having a particular need for the solution (except as evidence, of course), which absorbs a large number of resources in turn. The proof of stake doesn’t require as many resources to enable Blockchain secure information store. Both methodologies have their advantages and their shortcomings. The article attempts to review the current literature and collate the results of the study to measure the performance of both the methodologies and to arrive at a consensus regarding either or both methodologies to implement Blockchain to store data. Post reviewing the performance aspects and security features of both Proofs of Stake and Proof of Work the reviewer attempts to arrive at a secure and better performing blended Blockchain methodology that has wide industry practical application.
Longitudinal self-supervised learning

Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscience, we avoid them in our analysis by combing factor disentanglement with self-supervised learning to identify changes and consistencies across the multiple MRIs acquired of each individual over time. Specifically, we propose a new definition of disentanglement by formulating a multivariate mapping between factors (e.g., brain age) associated with an MRI and a latent image representation. Then, factors that evolve across acquisitions of longitudinal sequences are disentangled from that mapping by self-supervised learning in such a way that changes in a single factor induce change along one direction in the representation space. We implement this model, named Longitudinal Self-Supervised Learning (LSSL), via a standard autoencoding structure with a cosine loss to disentangle brain age from the image representation. We apply LSSL to two longitudinal neuroimaging studies to highlight its strength in extracting the brain-age information from MRI and revealing informative characteristics associated with neurodegenerative and neuropsychological disorders. Moreover, the representations learned by LSSL facilitate supervised classification by recording faster convergence and higher (or similar) prediction accuracy compared to several other representation learning techniques.
Toward Blockchain-Enabled Supply Chain Anti-Counterfeiting and Traceability

Existing product anti-counterfeiting and traceability solutions across today’s internationally spanning supply chain networks are indeed developed and implemented with centralized system architecture relying on centralized authorities or intermediaries. Vulnerabilities of centralized product anti-counterfeiting solutions could possibly lead to system failure or susceptibility of malicious modifications performed on product records or various potential attacks to the system components by dishonest participant nodes traversing along the supply chain. Blockchain technology has progressed from simply being a use case of immutable ledger for cryptocurrency transactions, to a programmable interactive environment of developing decentralized and reliable applications addressing different use cases globally. Key areas of decentralization, fundamental system requirements, and feasible mechanisms of developing decentralized product anti-counterfeiting and traceability ecosystems utilizing blockchain technology are identified in this research, via a series of security analyses performed against solutions currently implemented in supply chain industry with centralized architecture. The decentralized solution will be a secure and immutable scientific data provenance tracking and management platform where provenance records, providing compelling properties on data integrity of luxurious goods, are recorded and verified automatically across the supply chain.
Human–Robot Interaction through Eye Tracking for Artistic Drawing

In this paper, authors present a novel architecture for controlling an industrial robot via an eye tracking interface for artistic purposes. Humans and robots interact thanks to an acquisition system based on an eye tracker device that allows the user to control the motion of a robotic manipulator with his gaze. The feasibility of the robotic system is evaluated with experimental tests in which the robot is teleoperated to draw artistic images. The tool can be used by artists to investigate novel forms of art and by amputees or people with movement disorders or muscular paralysis, as an assistive technology for artistic drawing and painting, since, in these cases, eye motion is usually preserved.
Application of mixed reality for improving architectural design comprehension effectiveness

Extended Reality (XR) technologies such as AR, VR and MR have influenced many industries, including architecture. Even though they are all capable of creating immersive digital worlds, the only one capable of merging the real world with a holographic 3D modelby letting the user interact intuitively and naturally with the project is MR. In this paper, 42 participants were divided into two groups and analyzed an original architectural renovation design. They assessed the effectiveness of design review using Mixed Reality (MR) versus traditional 2D methods. The results show that MR based design review can effectively communicate 85% of the information to the client versus 70% provided by 2D media. At the same time, it has the potential to enhance the client's comprehension of the aesthetic characteristics of materials, giving the possibility to replace physical samples during the finishing stage of construction.