Reviewerly/exHarmony huggingface dataset:
Sign Language Recognition Using CNN and CGAN

There is a long drawn communication barrier between normal people and deaf-mute community. Sign language is a major tool of communication for hearing impaired people. The goal of this work is to develop a Convolutional Neural Network (CNN) based Indian sign language classifier. CNN models with combination of different hidden layers are analysed and the model giving highest accuracy is selected. Further synthetic data is generated using Conditional Generative Adversarial Network (CGAN), in order to improve classification accuracy.
Offline/Online lattice-based ciphertext policy attribute-based encryption

Attribute Based Encryption (ABE) is a new public key encryption scheme which allows users to encrypt and decrypt messages based on user attributes, and it can enforce fine grained access control over the encrypted data. However, encryption and key generation computational costs grow with the complexity of the access policy or number of attributes, which makes it unsuitable for mobile device scenario. To address this problem, we propose an offline/online lattice-based ciphertext policy attribute-based encryption scheme under the Ring Learning with Errors (R-LWE) assumption where the computations for the algorithms are split into two phases: offline and online. In the process of generating the private key, the offline stage generates the intermediate private key before the attributes are known, and the online stage generates the corresponding private key based on the attributes. In the encryption process, the offline stage generates the intermediate ciphertext before the plaintext message and access policy are known, and preprocesses the complex computations required for encryption. After the online stage knows the plaintext message and access policy, it only needs to perform a small amount of simple computations to generate the final ciphertext. Compared with the Learning with Errors (LWE) based attribute-based encryption scheme, our scheme has better computational performance. Compared with the regular lattice based attribute based encryption scheme, our scheme is suitable for mobile devices. Compared with the attribute-based encryption schemes based on bilinear pairing, our scheme is resistant against quantum computing attacks.
A Hardware Security-Monitoring Architecture Based on Data Integrity and Control Flow Integrity for Embedded Systems

As technology evolves, embedded systems access more networks and devices, which means more security threats. Existing security-monitoring methods with a single parameter (data or control flow) are not effective in detecting attackers tampering with the data or control flow of an embedded system. However, simply overlaying multiple security methods will result in excessive performance overhead for embedded systems. In this paper, we propose a novel hardware security-monitoring architecture that extracts DI (data integrity) digests and CFI (control flow integrity) tags to generate reference information when the program is offline. To monitor the indirect jumping behavior, this paper maps the legal target addresses into the bitmap, thus saving the search time. When the program is loaded, the reference information and the bitmap are safely loaded into the on-chip memory. The hardware monitoring module designed in this paper will check the DI summary and CFI tags in real time while executing the program. The architecture proposed in this paper has been implemented on the Xilinx Virtex 5 FPGA platform. Experimental results show that, compared with existing protection methods, the proposed approach in this paper can effectively detect multiple tampering-type attacks on the data and control flow of the embedded system, with a performance overhead of about 6%.
Towards deep-learning-based image enhancement for optical camera-based monitoring system of laser powder bed fusion process

With the increasing demand of quality assurance and reliability of additive manufacturing (AM), the demand of development of advanced in-situ monitoring systems is increased to monitor the process behavior. Optical-based camera monitoring systems are proved as the effective ways to observe part surface layer wise. For certain camera-based monitoring system, the coverage of the build platform and the resolution of the images are always a trade-off. In the low-resolution images, detailed features (e.g. scan vector) are often lost. Super resolution (SR) algorithms are often discussed in the literature, but there are no specific applications in AM area. In this paper, the authors present a U-Net-based super-resolution (SR) algorithm to enhance details of monitoring image of the optical camera for the LPBF process. A test setup was built in the laboratory to generate high-resolution images for training. To have precise original images for the validation, low-resolution images were downscaled and blurred from high-resolution images. SR results were evaluated by peak signal to noise ratio (PSNR) and plausibility of details. The SR algorithm shows the ability to reconstruct detailed features from low-resolution images for LPBF process.
Shall AI moderators be made visible? Perception of accountability and trust in moderation systems on social media platforms

This study examines how visibility of a content moderator and ambiguity of moderated content influence perception of the moderation system in a social media environment. In the course of a two-day pre-registered experiment conducted in a realistic social media simulation, participants encountered moderated comments that were either unequivocally harsh or ambiguously worded, and the source of moderation was either unidentified, or attributed to other users or an automated system (AI). The results show that when comments were moderated by an AI versus other users, users perceived less accountability in the moderation system and had less trust in the moderation decision, especially for ambiguously worded harassments, as opposed to clear harassment cases. However, no differences emerged in the perceived moderation fairness, objectivity, and participants confidence in their understanding of the moderation process. Overall, our study demonstrates that users tend to question the moderation decision and system more when an AI moderator is visible, which highlights the complexity of effectively managing the visibility of automatic content moderation in the social media environment.
Fact Checking with Insufficient Evidence

Abstract Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions only when there is sufficient evidence and otherwise indicate when it is not enough. To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions. First, we conduct an in-depth empirical analysis of the task with a new fluency-preserving method for omitting information from the evidence at the constituent and sentence level. We identify when models consider the remaining evidence (in)sufficient for FC, based on three trained models with different Transformer architectures and three FC datasets. Second, we ask annotators whether the omitted evidence was important for FC, resulting in a novel diagnostic dataset, SufficientFacts1, for FC with omitted evidence. We find that models are least successful in detecting missing evidence when adverbial modifiers are omitted (21% accuracy), whereas it is easiest for omitted date modifiers (63% accuracy). Finally, we propose a novel data augmentation strategy for contrastive self-learning of missing evidence by employing the proposed omission method combined with tri-training. It improves performance for Evidence Sufficiency Prediction by up to 17.8 F1 score, which in turn improves FC performance by up to 2.6 F1 score.
Design of vehicle certification schemes in IoV based on blockchain

Abstract Because of a large number of vehicles in Internet of Vehicle(IoV), distributed nodes and complex driving environment, data security and certification speed are easily affected. Blockchain enables different devices that do not trust each other to work together, maintain the general state in the process of information dissemination and sharing, and protect the privacy of devices. However, at present, the speed of vehicle certification in IoV is slow, and the use of idle resources is not considered. To address this problem, this paper provides a blockchain-based vehicle identity verification scheme by using a hybrid identity code verification method to ensure the nodes in the network securely share information. Meanwhile, a task processing algorithm based on time window is proposed to optimize the utilization of idle resources. In addition, the method is evaluated by simulation experiment, and the designed scheme can reduce malicious behavior of a registered vehicle in the network, and can shorten the processing task delay.
Alleviating the over-smoothing of graph neural computing by a data augmentation strategy with entropy preservation

The Graph Convolutional Networks (GCN) proposed by Kipf and Welling is an effective model to improve semi-supervised learning of pattern recognition, but faces the obstacle of over-smoothing, which will weaken the representation ability of GCN. Recently some works are proposed to tackle above limitation by randomly perturbing graph topology or feature matrix to generate data augmentations as input for training. However, these operations inevitably do damage to the integrity of information structures and have to sacrifice the smoothness of feature manifold. In this paper, we first introduce a novel graph entropy definition as a measure to quantitatively evaluate the smoothness of a data manifold and then point out that this graph entropy is controlled by triangle motif-based information structures. Considering the preservation of graph entropy, we propose an effective strategy to generate randomly perturbed training data but maintain both graph topology and graph entropy. Extensive experiments have been conducted on real-world datasets and the results verify the effectiveness of our proposed method in improving semi-supervised node classification accuracy compared with a surge of baselines. Beyond that, our proposed approach could significantly enhance the robustness of training process for GCN.
A process-aware approach for blockchain-based verification of academic qualifications

The global trend of credentialism as well as increased mobility in studies and work bring new requirements regarding the verification of academic qualifications. So far, traditional nostrification processes face challenges related to high process cycle times, elevated fees, and fraud incidents. In the higher education domain, blockchain-based approaches have emerged to revitalize the verification of academic qualifications, but they mostly rely on closed concepts offering their facility to a limited circle of entities. As a result, the verification of degrees is not publicly available to any third party. Moreover, in most cases, a concise process-aware approach describing the intended functionality with appropriate models is missing; this absence raises ambiguity and trust issues in the implemented application. This paper presents the design and early implementation of the VerDe (Verified Degrees) platform; a proposed blockchain-based application for registration and verification of academic qualifications. The novelty of the approach presented in this paper is the implementation of VerDe as a decentralized application utilizing Business Process Model & Notation (BPMN). This work demonstrates that usage of BPMN constructs provides an efficient method in addressing blockchain usability and complexity issues and facilitates the design and implementation of blockchain-based applications. The benefits of deploying VerDe in conjunction with BPMN are an efficient and transparent facility able to handle mobility consequences, detect fraud, and overcome administrative barriers by offering the verification capability to any third-party, and employing a custom-made token that emulates the European Credit Transfer and Accumulation System (ECTS).
Quantum algorithms for SVD-based data representation and analysis

Abstract This paper narrows the gap between previous literature on quantum linear algebra and practical data analysis on a quantum computer, formalizing quantum procedures that speed-up the solution of eigenproblems for data representations in machine learning. The power and practical use of these subroutines is shown through new quantum algorithms, sublinear in the input matrix’s size, for principal component analysis, correspondence analysis, and latent semantic analysis. We provide a theoretical analysis of the run-time and prove tight bounds on the randomized algorithms’ error. We run experiments on multiple datasets, simulating PCA’s dimensionality reduction for image classification with the novel routines. The results show that the run-time parameters that do not depend on the input’s size are reasonable and that the error on the computed model is small, allowing for competitive classification performances.
Bringing the Metaverse to Higher Education

This chapter provides a case study on the use of a virtual world as the predominant mechanism for interacting with university students. In the Autumn Semester 2020, 49 engineering students partook in weekly seminars with teachers and each other on a fantastical virtual teaching island, known as Nottopia. The majority of students accessed the virtual world on desktop computers, although four routinely used their own VR headsets. Survey data indicated that the student experience was extremely positive and motivation to learn was very high. Interviews, together with video observations, established that five themes dictated the positive outcome, namely the design of the space, the nature of the integral content, the prevailing group dynamics, the role of avatars, and the magical interactions possible. These themes are realised in an initial design framework.
Static–Dynamic coordinated Transformer for Tumor Longitudinal Growth Prediction

Accurate prediction of the tumor's future imaging features can provide its complete growth evolution and more detailed clinical parameters. The existing longitudinal models tend to lose detailed growth information and make it difficult to model the complete tumor development process. In this paper, we propose the Static-Dynamic coordinated Transformer for Tumor Longitudinal Growth Prediction (SDC-Transformer). To extract the static high-level features of tumors in each period, and to further explore the dynamic growth associations and expansion trend of tumors between different periods. Aiming at the insensitivity to local pixel information of the Transformer, we propose the Local Adaptive Transformer Module to facilitate a strongly coupled status of feature images, which ensures the characterization of tumor complex growth trends. Faced with the dynamic changes brought about by tumor growth, we introduce the Dynamic Growth Estimation Module to predict the future growth trend of the tumor. As a core part of SDC-Transformer, we design the Enhanced Deformable Convolution to enrich the sampling space of tumor growth pixels. And a novel Cascade Self-Attention is performed under multi-growth imaging to obtain dynamic growth relationships between periods and use dual cascade operations to predict the tumor's future expansion trajectories and growth contours. Our SDC-Transformer is rigorously trained and tested on longitudinal tumor data composed of the National Lung Screening Trial (NLST) and collaborative Shanxi Provincial People's Hospital. The RMSE, Dice, Recall, and Specificity of the longitudinal prediction results reach 11.32, 89.31%, 90.57%, and 89.64%, respectively. This result shows that our proposed SDC-Transformer model can achieve accurate longitudinal prediction of tumors, which will help physicians to establish specific treatment plans and accurately diagnose lung cancer. The code will be released soon.
Optical image encryption scheme based on quantum s-box and meaningful ciphertext generation algorithm

With the development of quantum computers, difficult computational problems of classical cryptosystems could potentially become solvable. Hence, due to the powerful computing power of quantum computers, all current cryptographic algorithms based on computational complexity may become ineffective. Furthermore, most of the ciphertexts generated by traditional encryption schemes are noise-like, and such ciphertexts are easily suspected by cryptanalysts during transmission. To solve these problems, a novel image encryption scheme is proposed by visually meaningful ciphertext generation and quantum walks (QWs). The proposed scheme is composed of three phases: (I) Permutation, (II) Diffusion, and (III) Visually meaningful ciphertext generation. In phase I, the quantum substitution box (s-box) is firstly constructed by QWs, and the original image is permutated by the quantum s-box to reduce the correlation between adjacent pixels. In phase II, the permutated quantum image is diffused by a key image, which is constructed by the probability matrix generated by the same QWs. In phase III, the diffused image is disguised as a meaningful image to avoid the suspicions of cryptanalysts. Compared to traditional encryption algorithms, the introduction of quantum technology effectively improves the security of the cryptosystem. Compared to previous quantum encryption methods, our algorithm generates meaningful ciphertexts that can further enhance security by preventing cryptanalysts from suspecting noise-like ciphertexts. Thus, our encryption scheme is secure even in the future quantum computer age. Besides, the decryption time of our proposed scheme can also be effectively reduced since the decryption process can be optically implemented, partially. Therefore, real-time scenarios can be satisfied. Simulation results and numerical analysis show the effectiveness of the proposed scheme.
Network Delay Measurement with Machine Learning: From Lab to Real-World Deployment

Artificial Intelligence (AI) continues to impact all facets of technology including Instrumentation and Measurement (I&M) with much effort spent on developing I&M systems assisted by machine learning (ML), especially deep learning [1]. While these ML-assisted I&M systems show promising results in a lab environment, there is always the question of how well they will perform in the real world. In fact, concerns about the real-world performance of ML is not exclusive to I&M but an inherent property of ML in general, because ML is data driven and its performance will change if the data distribution changes in the real world. In this article, we present a case study of developing in the lab an ML-assisted I&M system, specifically a network delay predictor, and deploying it in the real world, achieving 93% accuracy.
An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection

To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO uses a new transformation strategy that enables flexible switching between search and development, enabling it to jump out of local optima. We replace the original HHO exploitation process with improved differential perturbation and a greedy strategy to improve its global search capability. We tested it in experiments against seven algorithms using single-peaked, multi-peaked, hybrid, and composite CEC2017 benchmark functions, and IGHHO outperformed them on optimization problems with different feature functions. We propose new objective functions for the problem of data imbalance in FS and apply IGHHO to it. IGHHO outperformed comparison algorithms in terms of classification accuracy and feature subset length. The results show that IGHHO applies not only to global optimization of different feature functions but also to practical optimization problems.
Metapath and syntax-aware heterogeneous subgraph neural networks for spam review detection

Spam Review Detection is a subclass of text classification that aims to distinguish genuine reviews from spam reviews (e.g., irrelevant reviews, deceptive reviews, machine-generated reviews, and non-review messages). Previous studies have focused on review text analysis, abnormal behavior detection, and intrinsic relationship identification. However, these methods ignore different fraudulent camouflages and writing styles. In this paper, we instead design a Spam detection model Metapath-based Subgraph Aggregated Neural Network (Spam-MSANN) integrating three metapath-based subgraphs (i.e. User-Item Subgraph, Review Subgraph, and User-Review-Item Subgraph) and syntactic information to enhance the relevant representation of the review information with subgraph aggregation operations. Experimental results on benchmarking datasets (i.e. YelpCHI and Amazon) demonstrate that our Spam-MSANN model significantly improves the state-of-the-art models. Specifically, Spam-MSANN outperforms 11 of the 12 advanced benchmark models on these two datasets, which further manifests that the fusion of different metapath-based subgraphs and syntactic information is adequate for the spam review detection task.
Novel event‐triggered distributed state estimation algorithm for nonlinear systems over wireless sensor networks

Abstract This article focuses on the event‐triggered distributed state estimation for nonlinear dynamic systems over wireless sensor networks, where whether measurement should be transmitted from the sensor to the corresponding local estimator depends on a predesigned event‐triggered mechanism. To obtain a better estimation performance while saving the communication energy consumption, a novel event‐triggered nonlinear state estimator is designed by approximating the true posterior probability density function with minimum Kullback–Leibler divergence when the measurement is not transmitted. After local estimation results are produced in every local estimator through applying the cubature rule, according to a communication protocol and covariance intersection fusion strategy, an event‐triggered distributed cubature Kalman filtering (EDCKF) algorithm is developed. Compared with algorithms based on weighted average consensus, the proposed algorithm eliminates the disagreement between local estimators and obtains its best performance within a limited time. Moreover, sufficient conditions are obtained to prove the stability of the EDCKF algorithm. Simulation results are provided to demonstrate the effectiveness and superiority of the proposed estimator and algorithm.