Reviewerly/exHarmony huggingface dataset:
Proof of Delivery Smart Contract for Performance Measurements

The growth of the enterprise blockchain research supporting supply chain management calls for investigations of their impact and mindfulness of their design, use cases, and pilots.With a blockchain design for the Proof of Delivery (PoD) process management, this paper contributes to learning about performance measurement and the transaction costs implications during the development and application of smart contracts.An experimental design science approach is applied to develop an open-source blockchain to explore ways to make the delivery processes more efficient, the proof of delivery more reliable, and the performance measurements more accurate.The theory of Transaction Costs is applied to evaluate the cost implications of the adoption of smart contracts in the management of the PoD.The findings show that smart contracts make the delivery processes more efficient and proof of delivery more reliable.Yet, the methods and metrics are too complex and qualitative, limiting the smart contract's capability to measure performance.Our findings indicate potential transaction costs reduction by implementing a blockchainbased performance measurement.The complexities of the delivery process and proof of delivery call for precontractual steps to identify the processes and performance metrics to design blockchains.Smart contracts need further development and digital aids to handle qualitative inspections and proof of delivery generation during the delivery process.The blockchain requires the system's capacity to record off-chain transactions, such as in case of disputes resolutions.The authors extended blockchain research beyond the theoretical level, designing an open-source blockchain for supply chain management within the use case, pilot design, and case study.
Fog-based Self-Sovereign Identity with RSA in Securing IoMT Data

In the healthcare applications, Internet of Medical Things (IoMT) comforts the communication processes between the medical devices and the humans <i>via</i> wireless network. Moreover, this communication helps both the physicians and the patients to contact remotely for the diagnosis of the disease’s wearable devices sensor signals. However, IoMT system violates the privacy preserving of Patient’s Health Record (PHR) as well as self-sovereign identity of patient. In this regard, security action should be taken. Previous techniques used in IoMT are in lack of data consistency, confidentiality, and inaccessible of data. To overcome these issues, the fog computing-based technology is used in order to handle the health data of the patient for digital identity management. Self-Sovereign Identity empowers the patient to handle and control their own data. This paper proposes Fog Computing based Self-Sovereign Identity with Rivest, Shamir, Adleman (RSA) for IoMT (FCSSI+RSA-IoMT). For that, it keeps the Patient Health Record (PHR) securely and effectively using RSA to make the identification and authentication procedures at ease. Besides, PHR is used to store the sensitive information regarding identity and privacy of the patient. Additionally, it records the diagnosis history of patient by the doctors and radiology images. Therefore, the result evaluation proves that, for 100 users, the proposed technique takes only 631 secs and also the throughput performance is better when compared to the existing techniques.
Extending Multi-MSB Prediction and Huffman Coding for Reversible Data Hiding in Encrypted HDR Images

Reversible data hiding in encrypted images can simultaneously enhance the image privacy and preserve the message security for the purpose of covert communication and cloud data management. The algorithm extends the multi-MSB prediction and Huffman coding to propose the first reversible data hiding in encrypted HDR images in the Radiance RGBE format. Because of the high similarity among the exponent channel values of neighboring pixels, we directly applied multi-MSB prediction in our proposed preprocessing procedure, yielding considerably increased embedding capacity. We also proposed a novel image encryption method to maintain the characteristics of the images. Subsequently, a distortion-free data hiding algorithm, namely the homogeneity index modification algorithm, was added to further increase embedding capacity. The experimental results demonstrate the feasibility of the proposed algorithm and its ability to increase embedding capacity, embedding rate, and image privacy, support two data hiders, and enable reversibility and separability.
Robust Zero Watermarking Algorithm for Medical Images Using Local Binary Pattern and Discrete Cosine Transform

Thanks to the rapid development of the telemedicine, medical treatment has turned in great convenience. However, as the internet network can be addressed by any user, the medical images cannot avoid the defects of suffering from information leakage and require information encryption techniques. To protect the privacy of patients in medical images, a robust watermarking method, which combines medical images with watermark information, is proposed in this paper. First, the low-frequency feature vector of the medical image is extracted by combining local binary pattern (LBP) and transformation with discrete cosine transform (DCT). Second, hash sequence is implemented on to convert feature vector into binary sequences. At last, the medical image is embedded with hash sequence and watermark preprocessed by chaotic encryption technology using the zero-watermark technology. The experimental results show that the method can encrypt the medical images' watermark effectively and has good robustness against geometric attacks.
A Light Transformer-Based Architecture for Handwritten Text Recognition

Transformer models have been showing ground-breaking results in the domain of natural language processing. More recently, they started to gain interest in many others fields as in computer vision. Traditional Transformer models typically require a significant amount of training data to achieve satisfactory results. However, in the domain of handwritten text recognition, annotated data acquisition remains costly resulting in small datasets compared to those commonly used to train a Transformer-based model. Hence, training Transformer models able to transcribe handwritten text from images remains challenging. We propose a light encoder-decoder Transformer-based architecture for handwriting text recognition, containing a small number of parameters compared to traditional Transformer architectures. We trained our architecture using a hybrid loss, combining the well-known connectionist temporal classification with the cross-entropy. Experiments are conducted on the well-known IAM dataset with and without the use of additional synthetic data. We show that our network reaches state-of-the-art results in both cases, compared with other larger Transformer-based models.
A Comparison Between Various Human Detectors and CNN-Based Feature Extractors for Human Activity Recognition via Aerial Captured Video Sequences

Human detection and activity recognition (HDAR) in videos plays an important role in various real-life applications.Recently, object detection methods such as "you only look once" (YOLO), faster region based convolutional neural network (R-CNN), and EfficientDet have been used to detect humans in videos for subsequent decision-making applications.This paper aims to address the problem of human detection in aerial captured video sequences using a moving camera attached to an aerial platform with dynamical events such as varied altitudes, illumination changes, camera jitter, and variations in viewpoints, object sizes and colors.Unlike traditional datasets that have frames captured by a static ground camera with medium or large regions of humans in these frames, the UCF-ARG aerial dataset is more challenging because it contains videos with large distances between the humans in the frames and the camera.The performance of human detection methods that have been described in the literature are often degraded when input video frames are distorted by noise, blur, illumination changes, and the like.To address these limitations, the object detection methods used in this study were trained on the COCO dataset and evaluated on the publicly available UCF-ARG dataset.The comparison between these detectors was done in terms of detection accuracy.The performance evaluation considers five human actions (digging, waving, throwing, walking, and running).Experimental results demonstrated that EfficientDetD7 was able to outperform other detectors with 92.9% average accuracy in detecting all activities and various conditions including blurring, addition of Gaussian noise, lightening, and darkening.Additionally, deep pre-trained convolutional neural networks (CNNs) such as ResNet and EfficientNet were used to transfer learning from the ImageNet dataset to the UCF-ARG dataset and to extract highly informative features from the detected and cropped human patches.The extracted spatial features were utilized by Long Short-Term Memory (LSTM) to consider temporal relations between features for human activity recognition (HAR).Experimental results found that the EfficientNetB7-LSTM was able to outperform existing HAR methods in terms of average accuracy (80%), average precision (83%), average recall (80%), average F1 score (80%), average false negative rate (FNR) (20%), average false positive rate (FPR) (4.8%), and average Area Under Curve (AUC) (94%).The outcome is a robust HAR system which combines EfficientDetD7, and EfficientNetB7 with LSTM for human detection and activity classification, respectively.
E-VAT: An Asymmetric End-to-End Approach to Visual Active Exploration and Tracking

The development of visual tracking systems is becoming a major goal for the Robotics community. Most of the works dealing with this topic focus exclusively on passive tracking, where the target is confined within the camera’s field of view. Only a minority propose active approaches, capable not only of identifying the object to be tracked but also of producing motion control actions to maintain visual contact with it. However, all the methods introduced so far assume that the target is initially in the immediate proximity of the tracker. This represents an undesirable constraint on the applicability of these techniques, and it is to overcome this limitation that we propose a novel End-to-End Deep Reinforcement Learning based system, capable of both exploring the surrounding environment to find the target and then of tracking it. To do this, we develop a network consisting of two sub-components: i) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Target-Detection Network</i> , which detects the target in the camera’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">field-of-view</i> , and ii) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Exploration and Tracking Network</i> , which employs this information to switch between the exploration policy and the tracking policy with the goal of exploring the environment, finding the target and finally tracking it. Through different experiments, we demonstrate the effectiveness of our approach and its superior performance with respect to current state-of-the-art (SotA) methods.
A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

Biogeography-based optimization (BBO) cannot effectively solve high-dimensional global optimization problems due to its single migration mechanism and random mutation operator.To overcome these limitations, this paper propose a dual BBO based on sine cosine algorithm (SCA) and dynamic hybrid mutation, named SCBBO.Firstly, the Latin hypercube sampling method is innovatively used to improve the initial population ergodicity.Secondly, a nonlinear transformation parameter and a inertia weight adjustment factor are designed into the position update formula of SCA to make SCBBO suitable for high dimensional environments.Then, a dynamic hybrid mutation operator is designed by combining Laplacian and Gaussian mutation, which helps the algorithm to escape from local optima and balance the exploration and exploitation.Finally, the dual learning strategy is integrated, so the convergence accuracy is further improved by generating dual individuals.Meanwhile, A sequence convergence model is established to prove the algorithm can converge to the global optimal solution with probability 1.Compared with other state-of-the-art evolutionary algorithms, SCBBO effectively improves the optimization accuracy and convergence speed for high-dimensional optimization problems.To further show the superiority of SCBBO, the performance is compared on 1000, 2000, 5000 and 10000 dimensions, respectively.The comparsions show that SCBBO's optimization results on these dimensions are basically the same.SCBBO also applied to engineering design problems, and the simulation results demonstrate that the proposed method is also effective on constrained optimization problems.
Memory-Efficient Deformable Convolution Based Joint Denoising and Demosaicing for UHD Images

This paper introduces deformable convolution in deep learning based joint denoising and demosaicing (JDD), which yields more adaptable representation and larger receptive fields in features extraction for a superior restoration performance. However, the deformable convolution generally leads to considerable computational load and irregular memory access bottleneck, limiting its extensive deployment on edge devices. To address this issue, we develop grouping strategy and assign independent offsets to each kernel group to reduce the computation latency while keeping the accuracy. Motivated by the exploration for aggregate distribution characteristics of deformable offsets, we present the offset sharing methodology to simplify the memory access complexity of deformable convolution. As for hardware acceleration, we specially design a novel deformable matrix multiplication workflow incorporated with a deformable memory mapping unit to boost the computational throughput. The verification experiments on FPGA demonstrate that the proposed deformable convolution based JDD can restore 4K Ultra High Definition (UHD) images at 70FPS and yields significant promotion in visual effect and objective quality assessment.
Cybersecurity Attack-Resilience Authentication Mechanism for Intelligent Healthcare System

People focus on the intelligent healthcare system expecting sufficient medical facilities even from a remote location. However, to make the healthcare system more trustworthy, secure access control plays a vital role in resisting several cyber-attacks. Security threat on medical data is highly sensitive since it is associated with life risks. When a user accesses a healthcare system through the Internet, the main concern is preventing unauthorized access to vital healthcare-related data. Traditional authentication (based on a password, smartcard, and/or biometric) provides a solution to allow such kind of application only to the authorized users. But once a user is authenticated, and he/she is idle for a while, the chances of security threat are reasonably high. This article has designed an intelligent user recognization mechanism for continuous monitoring of the user’s activity throughout the session. We have made the system user-friendly through single sign-on and eliminated trusted third-party dependence to avoid data breaches. Once the authentication is performed, then in a later stage, the continuous monitoring mechanism based on machine learning techniques helps to decide the user’s unique behavior allowing the application server to make better decisions regarding the user’s authenticity in the current session. The security analysis shows that the proposed system is more secure than the traditional authentication mechanisms.
Instance-Rotation-Based Surrogate in Genetic Programming With Brood Recombination for Dynamic Job-Shop Scheduling

Genetic programming (GP) has achieved great success for learning scheduling heuristics in dynamic job-shop scheduling (JSS). In theory, generating a large number of offspring for GP, known as brood recombination, can improve its heuristic generation ability. However, it is time consuming to evaluate extra individuals. Phenotypic characterization-based surrogates with K-nearest neighbors have been successfully used for GP to preselect only promising individuals for real fitness evaluations in dynamic JSS. However, sample individuals used by surrogate are from only the current generation, since the fitness of individuals across generations is not comparable due to the rotation of training instances. The surrogate cannot accurately estimate the fitness of an offspring that is far away from all the limited sample individuals at the current generation. This article proposes an effective instance-rotation-based surrogate to address the above issue. Specifically, the surrogate uses the samples extracted from individuals across multiple generations with different instances. More importantly, we propose a fitness mapping strategy to make the fitness evaluated by different instances comparable. The results show that the GP with brood recombination and the proposed surrogate can significantly improve the quality of scheduling heuristics. The results also reveal that the proposed algorithm has successfully reduced the number of omitted promising offspring due to the higher accuracy of the surrogate. The samples in the new surrogate spread better in the phenotypic space, and the nearest neighbor tends to be closer to the predicted offspring. This makes the estimated fitness more accurate.
Edge-Assisted Public Key Homomorphic Encryption for Preserving Privacy in Mobile Crowdsensing

Mobile crowdsensing (MCS) is becoming an increasingly important topic due to rapid proliferation of mobile apps where participants’ anonymity is a pivotal requirement with direct impacts on their safety and well being. There are two main challenges in crypto-based privacy-preserving aggregation in MCS, namely, participants joining or leaving the crowd randomly at will, and adversaries injecting fake data. The conventional approach for preserving privacy is to provide blanket anonymity to all, including adversaries, which enables the latter to cause harm without being identified. In addition, with the proliferation of edge servers, there is a need to develop edge-assisted MCS, which would be more efficient in terms of less back-haul traffic and less delay as compared to cloud-assisted-only MCS. In this paper, we present an efficient edge-assisted MCS scheme which preserves the participants’ privacy and anonymity, protects the service against adversaries, and can be used to verify that aggregation is free of anomalies. Our scheme is transparent to the join-and-leave problem; and its computational cost and communication overhead are small and fixed, i.e., it is insensitive to crowd count. We utilize group signature for source authentication to identify and block adversaries that cause harm while providing anonymity to ordinary participants.
AMR-DA: Data Augmentation by Abstract Meaning Representation

Meaning Representation (AMR) is a semantic representation for NLP/NLU.In this paper, we propose to use it for data augmentation in NLP.Our proposed data augmentation technique, called AMR-DA, converts a sample sentence to an AMR graph, modifies the graph according to various data augmentation policies, and then generates augmentations from graphs.Our method combines both sentence-level techniques like back translation and token-level techniques like EDA (Easy Data Augmentation).To evaluate the effectiveness of our method, we apply it to the English tasks of semantic textual similarity (STS) and text classification.For STS, our experiments show that AMR-DA boosts the performance of the state-of-the-art models on several STS benchmarks.For text classification, AMR-DA outperforms EDA and AEDA and leads to more robust improvements. 1
Code Smell Detection Using Classification Approaches

Code smells combine software features that indicate a code or design-related problem that can increase the difficulty of the software and make it complicated to recognize, develop, and maintain the software. Code smell detection helps us to make code accurate and effective. In this study, five classification models are applied. To complete this study, four datasets of code smell are used. To pick the finest feature in the existing dataset, a feature selection method (FST) is used. The ten-iteration cross-validation method is applied to calculate the execution of the model predicted. In this document, the random forest model for the Feature-envy dataset achieves an accuracy of 0.9912%.
Privacy-Preserving and Low-Latency Federated Learning in Edge Computing

Edge computing has been widely used in recent years for bringing services closer to end users, resulting in faster response for applications. However, the sensitive information that leaves the data owner is at risk of being disclosed because the service provider is generally honest-but-curious. Federated learning (FL) is a popular method for preserving privacy by transferring the model from the edge node to local devices and training on the local data set. Nonetheless, the training parameter that communicates between local mobile devices and the edge node may contain the original data and be guessed by adversaries. In order to address the privacy threats, we propose the PL-FedIPEC scheme in this article, which is a privacy-preserving and low-latency FL method that transmits parameters encrypted with the improved Paillier, a homomorphic encryption algorithm, to protect the privacy of end devices without transmitting data to the edge node. Our method introduces the improved Paillier encryption, which brings a new hyperparameter and previously computes multiple random intermediate values in the key generation phase so that the time for the encryption phase has a significant reduction. With this new algorithm, the time for model training is decreased, and the sensitive information is in ciphertext format and cannot be analyzed. To evaluate the efficiency of our proposed scheme, we conduct extensive experiments and the results validate and demonstrate that our scheme with the improved Paillier algorithm can achieve the same accuracy as the original Paillier algorithm and the baseline FedAVG algorithm. At the same time, our method can save a massive amount of time when training the learning model with various settings.
VSOIQE: A Novel Viewport-Based Stitched 360° Omnidirectional Image Quality Evaluator

With the rapid development of virtual reality (VR), 360° omnidirectional images and videos have drawn wide attention. However, the quality assessment of 360° omnidirectional images is a challenging task, especially when the panoramic image contains multiple stitching distortions. We propose a viewport-based stitched 360° omnidirectional image quality evaluator (VSOIQE), by first extracting the features of salient and stitching viewports, and then inferring the overall perceptual quality via multiple linear regression (MLR). Comprehensive image attributes including edge, color, shape and information entropy are considered in the framework. Experimental results on two benchmark databases demonstrate the superiority of the proposed metric over both the state-of-the-art quality models designed for 2D images and the quality models developed for 360° omnidirectional images.