Reviewerly/exHarmony huggingface dataset:
CNN Based Image Resizing Detection and Resize Factor Classification for Forensic Applications

This paper investigates the forensic problem of resizing detection along with determining the factor by which the image underwent resizing in the uncompressed scenario. In many forensic applications, there is a need to understand the life history of the image under analysis since multimedia data is slowly becoming admissible as evidence in the court of law. The work reported in the paper is a novel attempt in this direction where a convolutional neural network is utilized with twin objectives. Firstly, to detect the presence of resizing by capturing the forensic clues left behind by the resizing operation and secondly, to determine by what factor the uncompressed image was resized which is a blind estimation. Experimental simulation utilizing the raise dataset shows very high accuracy scores for the proposed method. To check the robustness of the proposed network, an adversarial attack, namely Carlini and the Wagner attack, is a white-box attack aiming towards system failure. To the best of our knowledge, such a detailed forensic analysis with one of the adversarial attacks to validate the security of the proposed method has never been reported in the literature and is the novel contribution of the work.
Smart Contract-Based Access Control Through Off-Chain Signature and On-Chain Evaluation

Access control is essential in computer security systems to regulate the access to critical or valuable resources. Conventional access control models mainly rely on a centralized and trusted server to mediate each attempted access from client to resources, which face serious challenges of single point of failure and lack of transparency. In this brief, we propose a smart contract-based access control framework, which enables the owner to achieve resource access control in a reliable, auditable and scalable way. An access control contract is deployed on blockchain to manage attribute-based access policies of resources flexibly and make access decisions for clients credibly. A set of attributes is distributed to the clients through off-chain signatures signed by the owner to determine their privileges, without consuming the expensive on-chain storage space. Finally, we implement an experimental prototype on Ethereum test network and perform extensive experimental and theoretical analysis to evaluate its scalability and efficiency.
A novel cooling hole inspection method for turbine blade using 3D reconstruction of stereo vision

Abstract Cooling holes (number 100 ∼ 200, diameter 0.3 ∼ 1.2 mm) are important heat dissipation structures of a turbine blade in aero-engine. Due to the small sizes and similar contours of cooling holes, it is difficult to extract and match their features by traditional stereo reconstruction methods. This paper proposes a novel cooling hole inspection method for turbine blades utilizing 3D reconstruction technique of stereo vision, which combines the stereo vision principle with the invariance of cross-ratio. The feature points of contours are extracted and matched by calculating two intersection points of circular contours and a line through the center points of adjacent cooling holes. Additionally, the 3D points corresponding to the feature points are reconstructed, by which, the diameters of cooling holes can be calculated successfully. Moreover, measurement experiments verify the effectiveness and accuracy of the proposed method. The experimental results show that the average errors of the standard circle ruler and tiny hole sample are within 0.05 mm, which satisfies the inspection requirement of cooling holes.
V-Shuttle: Scalable and Semantics-Aware Hypervisor Virtual Device Fuzzing

With the wide application and deployment of cloud computing in enterprises, virtualization developers and security researchers are paying more attention to cloud computing security. The core component of cloud computing products is the hypervisor, which is also known as the virtual machine monitor (VMM) that can isolate multiple virtual machines in one host machine. However, compromising the hypervisor can lead to virtual machine escape and the elevation of privilege, allowing attackers to gain the permission of code execution in the host. Therefore, the security analysis and vulnerability detection of the hypervisor are critical for cloud computing enterprises. Importantly, virtual devices expose many interfaces to a guest user for communication, making virtual devices the most vulnerable part of a hypervisor. However, applying fuzzing to the virtual devices of a hypervisor is challenging because the data structures transferred by DMA are constructed in a nested form according to protocol specifications. Failure to understand the protocol of the virtual devices will make the fuzzing process stuck in the initial fuzzing stage, resulting in inefficient fuzzing.
Human-Centered Explainable Artificial Intelligence for Marine Autonomous Surface Vehicles

Explainable Artificial Intelligence (XAI) for Autonomous Surface Vehicles (ASVs) addresses developers’ needs for model interpretation, understandability, and trust. As ASVs approach wide-scale deployment, these needs are expanded to include end user interactions in real-world contexts. Despite recent successes of technology-centered XAI for enhancing the explainability of AI techniques to expert users, these approaches do not necessarily carry over to non-expert end users. Passengers, other vessels, and remote operators will have XAI needs distinct from those of expert users targeted in a traditional technology-centered approach. We formulate a concept called ‘human-centered XAI’ to address emerging end user interaction needs for ASVs. To structure the concept, we adopt a model-based reasoning method for concept formation consisting of three processes: analogy, visualization, and mental simulation, drawing from examples of recent ASV research at the Norwegian University of Science and Technology (NTNU). The examples show how current research activities point to novel ways of addressing XAI needs for distinct end user interactions and underpin the human-centered XAI approach. Findings show how representations of (1) usability, (2) trust, and (3) safety make up the main processes in human-centered XAI. The contribution is the formation of human-centered XAI to help advance the research community’s efforts to expand the agenda of interpretability, understandability, and trust to include end user ASV interactions.
Fused Contextual Data With Threading Technology to Accelerate Processing in Home UbiHealth

According to the ubiquitous computing paradigm, dispersed computers within the home environment can support the residents’ health by being aware of all the developing and evolving situations. The context-awareness of the supporting computers stems from the data acquisition of the occurring events at home. In some cases, different sensors provide input of identical type, thereby raising conflict-related issues. Thus, for each type of input data, fusion methods must be applied on the raw data to obtain a dominant input value. Also, for diagnostic inference purpose, data fusion methods must be applied on the values of the available classes of multiple contextual data structures. Dempster-Shafer theory offers the algorithmic tools to efficiently fuse the data of each input type or class. The employment of threading technology accelerates the computational process and carrying out benchmarks on publicly available data set, is shown to be more efficient. Thus, threading technology proved promising for home UbiHealth applications by lowering the number of required cooperating computers.
DyGNN: Algorithm and Architecture Support of Dynamic Pruning for Graph Neural Networks

Recently, graph neural networks (GNNs) have achieved great success for graph representation learning tasks. Enlightened by the fact that numerous message passing redundancies exist in GNNs, we propose DyGNN, which speeds up GNNs by reducing redundancies. DyGNN is supported by an algorithm and architecture co-design. The proposed algorithm can dynamically prune vertices and edges during execution without accuracy loss. An architecture is designed to support dynamic pruning and transform it into performance improvement. DyGNN opens new directions for accelerating GNNs by pruning vertices and edges. DyGNN gains average $2\times$ speedup with accuracy improvement of 4% compared with state-of-the-art GNN accelerators.
A traceability method of biofuel production and utilization based on blockchain

Biofuel is one of the hot research directions in energy field. However, the data of the production and utilization of biofuels has not been widely valued. Blockchain is a decentralized data management technology, which can provide traceable data storage for the production and utilization of biofuels. As the number of blockchain applications of biofuels grows, security, efficiency and privacy have become the main concerns of the users. In order to address the security, efficiency and privacy challenges in biofuels system, we propose an efficient blockchain model based on post-quantum cryptography. First, in order to resist quantum computer attacks in biofuels system, we present a post-quantum cryptographic scheme based on multivariate signatures. Second, in order to improve the privacy protection in biofuels system, we propose a blind signature based on the post-quantum cryptographic scheme. Third, in order to improve the efficiency in biofuels system, we propose an efficient payment model. The blockchain model based on post-quantum cryptography is evaluated and the results show that the biofuels system is efficient. Besides, the new biofuels system is secure against both traditional computer attacks and quantum computer attacks.
Fault identification method for distribution network based on parameter optimized variational mode decomposition and convolutional neural network

In order to extract the fault characteristics of the distribution power system automatically, a new method for fault identification based on parameter optimized variational mode decomposition (VMD) and convolutional neural network (CNN) is proposed. First, the VMD is used to decompose electric signals of the main transformer, including the current of the low-voltage inlet line, bus voltage, and so on. The obtained intrinsic mode functions should be arranged in sequence from low to high according to the central frequency. The VMD can obtain the time-frequency matrices. In order to decide the decomposition layers of the VMD adaptively, the partial mean of multi-scale permutation entropy is used to optimize it. Then, the time-frequency matrixes can be converted into pixel matrices by pseudo-colour coding. The pixel matrices which are appropriately compressed can be used as input of CNN. The CNN can autonomously extract the eigenvectors of fault. Finally, the classification algorithms of support vector machine (SVM), naive Bayesian classifier (NBC), extreme learning machine (ELM), and random forest (RF) are used to train and test the feature vectors extracted by CNN to verify the effectiveness of the method. The experiments show that the proposed method has excellent stability, fast convergence speed, and high precision. The technique can effectively complete the fault identification of the distribution network.
Secure Continuous-Variable Quantum Key Distribution with Machine Learning

Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. However, the common quantum hacking strategies and countermeasures inevitably increase the complexity of practical CV systems. Machine-learning techniques are utilized to explore how to perceive practical imperfections. Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied.
Experiences of information literacy and mobile technologies amongst undergraduates in times of COVID. A qualitative approach

Purpose The purpose of this article is to analyse the social science students' perceptions of information literacy (IL) and the use of mobile technologies (MTs) before and during the COVID-19 pandemic. Design/methodology/approach Exploratory focus group methodology involving 18 Information Science and Education undergraduates. Findings Students believe that, above all, an information literate person must know how to search for information. Being able to recognise acceptable levels of IL and MT competencies/skills enabled them to detect the scant contribution of the university to IL and the poor support from the library. They routinely use information and communication technologies (ICTs) in the classroom for assignments, presentations, searching and administrative tasks. Conversely, they consider their teachers' competency in the use of mobile devices for academic tasks could be improved. The increase in these shortcomings during the COVID-19 pandemic has allowed students to discover most teachers' attitudinal and technical limitations. Students' suggestions mostly point to improving platforms, teaching methods, teacher motivation and teachers/librarians interaction. Research limitations/implications Research is limited to several IL/MT-related concepts, one university and two degrees. It could be applied in other contexts, larger samples and other stakeholders such as librarians and teachers. Practical implications Need for more IL and TM instruction for students/teachers and greater interaction amongst them and with the library. The authors suggest promoting interdisciplinary seminars on the importance of IL and awareness sessions on ICT for learning. Originality/value This case study addresses students' critical/exceptional experience triggered by the pandemic.
InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive media

Interactive Machine Learning offers a method for designing movement interaction that supports creators in implementing even complex movement designs in their immersive applications by simply performing them with their bodies. We introduce a new tool, InteractML, and an accompanying ideation method, which makes movement interaction design faster, adaptable and accessible to creators of varying experience and backgrounds, such as artists, dancers and independent game developers. The tool is specifically tailored to non-experts as creators configure and train machine learning models via a node-based graph and VR interface, requiring minimal programming. We aim to democratise machine learning for movement interaction to be used in the development of a range of creative and immersive applications.
Book Recommendation System

The main purpose of a recommendation system is that it will suggest items to users easily making their life easier. Today the quantity of facts with inside the net increase very hastily and those want few instruments to seek out and access appropriate data. One of such tools is named recommendation system. Recommendation systems propose products to the users which are most relevant to that particular user. Nowadays, online book marketing websites compete with one another in a variety of ways. One of the most powerful methods for increasing benefit and retaining customers is a recommendation framework, which can recommend books that are of interest to the customer. So the fundamental reason for this project is to support folks that have an interest in reading and to influence those individuals who are inculcating the habit of reading. By building a book recommendation system we tend to aim to assist people opt for the proper book that interests them and so encouraging them to read more. With the assistance of data sets and machine learning we believe we will choose the right book for someone supported their interests and also the data from several different readers. therefore here we use a collaborative filtering method.
Amortized Bayesian Model Comparison With Evidential Deep Learning

Comparing competing mathematical models of complex processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way to perform model comparison and extract useful metrics for guiding decisions. However, many interesting models are intractable with standard Bayesian methods, as they lack a closed-form likelihood function or the likelihood is computationally too expensive to evaluate. In this work, we propose a novel method for performing Bayesian model comparison using specialized deep learning architectures. Our method is purely simulation-based and circumvents the step of explicitly fitting all alternative models under consideration to each observed dataset. Moreover, it requires no hand-crafted summary statistics of the data and is designed to amortize the cost of simulation over multiple models, datasets, and dataset sizes. This makes the method especially effective in scenarios where model fit needs to be assessed for a large number of datasets, so that case-based inference is practically infeasible. Finally, we propose a novel way to measure epistemic uncertainty in model comparison problems. We demonstrate the utility of our method on toy examples and simulated data from nontrivial models from cognitive science and single-cell neuroscience. We show that our method achieves excellent results in terms of accuracy, calibration, and efficiency across the examples considered in this work. We argue that our framework can enhance and enrich model-based analysis and inference in many fields dealing with computational models of natural processes. We further argue that the proposed measure of epistemic uncertainty provides a unique proxy to quantify absolute evidence even in a framework which assumes that the true data-generating model is within a finite set of candidate models.
LightFed: An Efficient and Secure Federated Edge Learning System on Model Splitting

With the integration of Artificial Intelligence (AI) and Internet of Things (IoT), the Federated Edge Learning (FEL), a promising computing framework is developing. However, there are still unsolved issues on communication efficiency and data security due to the huge models and unreliable transmission links. To address these issues, this paper proposes a novel federated edge learning system, called LightFed, where the edge nodes upload only vital partial local models, and successfully achieve lightweight communication and model aggregation. First, a novel model aggregation method Model Splitting and Splicing (MSS) and a Selective Parameter Transmission (SPT) scheme are proposed. By detecting the updating gradients of local parameters and filtering significant parameters, selective rotated transmission and efficient aggregation of local models are achieved. Second, a Training Filling Model (TFM) is proposed to infer the total data distribution of edge nodes, and train a filling model to mitigate the unbalanced training data without violating the data privacy of individual users. Moreover, a blockchain-powered confusion transmission mechanism is proposed for defending the attacks from external adversaries and protecting the model information. Finally, extensive experimental results demonstrate that our LightFed significantly outperforms the existing FEL systems in terms of communication efficiency and privacy security.
<scp>ParsiNLU</scp>: A Suite of Language Understanding Challenges for Persian

Abstract Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of language understanding tasks—reading comprehension, textual entailment, and so on. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5k new instances across 6 distinct NLU tasks. Additionally, we present the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.1
HTD: Heterogeneous Task Decoupling for Two-Stage Object Detection

Decoupling the sibling head has recently shown great potential in relieving the inherent task-misalignment problem in two-stage object detectors. However, existing works design similar structures for the classification and regression, ignoring task-specific characteristics and feature demands. Besides, the shared knowledge that may benefit the two branches is neglected, leading to potential excessive decoupling and semantic inconsistency. To address these two issues, we propose Heterogeneous task decoupling (HTD) framework for object detection, which utilizes a Progressive Graph (PGraph) module and a Border-aware Adaptation (BA) module for task-decoupling. Specifically, we first devise a Semantic Feature Aggregation (SFA) module to aggregate global semantics with image-level supervision, serving as the shared knowledge for the task-decoupled framework. Then, the PGraph module performs progressive graph reasoning, including local spatial aggregation and global semantic interaction, to enhance semantic representations of region proposals for classification. The proposed BA module integrates multi-level features adaptively, focusing on the low-level border activation to obtain representations with spatial and border perception for regression. Finally, we utilize the aggregated knowledge from SFA to keep the instance-level semantic consistency (ISC) of decoupled frameworks. Extensive experiments demonstrate that HTD outperforms existing detection works by a large margin, and achieves single-model 50.4%AP and 33.2% APs on COCO test-dev set using ResNet-101-DCN backbone, which is the best entry among state-of-the-arts under the same configuration. Our code is available at https://github.com/CityU-AIM-Group/HTD.
Model-Free Reinforcement Learning for Lexicographic Omega-Regular Objectives

We study the problem of finding optimal strategies in Markov decision processes with lexicographic \(\omega \)-regular objectives, which are ordered collections of ordinary \(\omega \)-regular objectives. The goal is to compute strategies that maximise the probability of satisfaction of the first \(\omega \)-regular objective; subject to that, the strategy should also maximise the probability of satisfaction of the second \(\omega \)-regular objective; then the third and so forth. For instance, one may want to guarantee critical requirements first, functional ones second and only then focus on the non-functional ones. We show how to harness the classic off-the-shelf model-free reinforcement learning techniques to solve this problem and evaluate their performance on four case studies.