Reviewerly/exHarmony huggingface dataset:
Detecting the Traceability Issues in Supply chain Industries using Blockchain Technology

Today, agriculture remains the most important economic activity in the world. Food care is a major concern for the whole society. For agriculture-based supply chain management, traceability is considered as the major requirement. Traceability helps in providing quality assurance and transparency to the end user. The antiquated traceability system has the complications of centralized systems, non-transparency of information, unpredictable data, and the very easy formation of false data. The World Health Organization (WHO) reports that one out of ten people is affected by having tainted food. Globalization involving different food production processes and automation pushes the food supply chain into a complicated web. Numerous advancements have been investigated lately to determine food vulnerability and carry productivity to food related issues. One of the expected advances is block chain, which is as of now being effectively executed in monetary areas, for example, bit coin. Block chain can also be used in the food supply chain industries to improve traceability and help in the danger decrease of fakes and different types of unlawful occupation. This paper presents a systematic survey on agrifood supply chain traceability issues using various block chain platforms such as Ethereum and Hyperledger, as well as IOT techniques such as barcodes, RFID, NFC, and QR codes. The performance of several consensus algorithms is also investigated.
Invoking blockchain technology in e-government services: a cybernetic perspective

Purpose This paper aims to provide insights into the integration of blockchain technology in e-government services. Design/methodology/approach The article invokes an exploratory approach to emphasize the possibilities of integrating blockchain technology in e-government services. A cybernetic model is detailed in the paper for bridging the gulf between blockchain and e-government. Findings The integration of blockchain technology in e-government services is capable of enhancing the efficiency and effectiveness of service delivery. Furthermore, this integration would facilitate in maintaining the privacy of the online transactions. Originality/value Hitherto, studies have focused on the blockchain technology in many sectors; however, the integration and utility of blockchain technology for the government sector have remained unexplored. The current study seeks to fill this gap.
TAAM: Topic-aware abstractive arabic text summarisation using deep recurrent neural networks

Abstractive text summarisation is essential to producing natural language summaries with main ideas from large text documents. Despite the success of English language-based abstractive text summarisation models in the literature, they are limitedly supporting the Arabic language. Current abstractive Arabic summarisation models have several unresolved issues, a critical one of which is syntax inconsistency, which leads to low-accuracy summaries. A new approach that has shown promising results involves adding topic awareness to a summariser to guide the model by mimicking human awareness. Therefore, this paper aims to enhance the accuracy of abstractive Arabic summarisation by introducing a novel topic-aware abstractive Arabic summarisation model (TAAM) that employs a recurrent neural network. Two experiments were conducted on TAAM: quantitative and qualitative. Based on a quantitative approach using ROUGE matrices, the TAAM model achieves 10.8% higher accuracy than other existing baseline models. Additionally, based on a qualitative approach that captures users’ perspectives, the TAAM model is capable of producing a coherent Arabic summary that is easy to read and captures the main idea of the input text.
Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO

The quality of asymptomatic corona virus disease 2019 (COVID-19) computed tomography (CT) image is reduced due to interference from Gaussian noise, which affects the subsequent image processing. Aiming at the problem that asymptomatic COVID-19 CT image often have small flake ground-glass shadow in the early lesions, and the density is low, which is easily confused with noise. A denoising method of wavelet transform with shrinkage factor is proposed. The threshold decreases with the increase of decomposition scale, and it reduces the misjudgment of signal points. In the advanced stage, the range of lesions increases, with consolidation and fibrosis in different sizes, which have similar gray value to the CT images of suspected cases. Aiming at the problems of low contrast and fuzzy boundary in the traditional wavelet transform, the threshold function based on the optimization of parameters combined with the improved particle swam optimization (PSO) is proposed, so that the parameters of wavelet threshold function can change adaptively according to the lung lobe and ground-glass lesions with fewer iterations. The simulation results show that the paper method is significantly better than other algorithms in peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and mean absolute error (MSE). For example, aiming at the early asymptomatic COVID-19, compared with the comparison methods, the PSNR under the proposed method has increased by about 5 dB, the MSE has been greatly reduced, and the SNR has increased by about 6.1 dB. It can be seen that the denoising effect under the proposed method is the best.
Moving Robots in Unknown Environments Using Potential Field Graphs

The purpose of this research paper is to introduce a new navigation algorithm for Robot Operating System (ROS) based robots which will allow complete autonomous traversal in any given indoor environment. Turtle bot3 burger bot is the sample robot chosen for this project. This will be equipped with a Light Detection and Ranging (LIDAR) scanner with the default settings, ultrasound, Intel Real Sense camera, etc. Environment information gathered via these sensors will be sent into a remote computer for processing since the raspberry pie micro-controller with the turtle bot burger is not enough to carry out a high computation-intensive task. Once the information is received series of tasks will be carried out by the remote personal computer before executing the newly developed navigation algorithm. The data from the LIDAR scanner help to generate a 2-dimensional(2D) potential field graph. Then it will later be used for path planning by navigation algorithm. Furthermore, the Real sense camera is used for object boundary detection to produce more accurate data for the navigation algorithm. Once all the data proceed, data will return to the robot, where the navigation algorithm will start execution in parallel with a recovery behavior algorithm and scan algorithm. The recovery behavior algorithm is responsible for guiding the robot to face away from an obstacle in an event where the predetermined path is obstructed. Furthermore, it is responsible for maintaining accurate data in the cost map. Subsequently, the scanning algorithm will be used to generate maps.
Self-Supervised Object Distance Estimation Using a Monocular Camera

Distance estimation using a monocular camera is one of the most classic tasks for computer vision. Current monocular distance estimating methods need a lot of data collection or they produce imprecise results. In this paper, we propose a network for both object detection and distance estimation. A network-based on ShuffleNet and YOLO is used to detect an object, and a self-supervised learning network is used to estimate distance. We calibrated the camera, and the calibrated parameters were integrated into the overall network. We also analyzed the parameter variation of the camera pose. Further, a multi-scale resolution is applied to improve estimation accuracy by enriching the expression ability of depth information. We validated the results of object detection and distance estimation on the KITTI dataset and demonstrated that our approach is efficient and accurate. Finally, we construct a dataset and conduct similar experiments to verify the generality of the network in other scenarios. The results show that our proposed methods outperform alternative approaches on object-specific distance estimation.
Augmented reality surgical navigation system based on the spatial drift compensation method for glioma resection surgery

The number of patients who suffer from glioma has been increasing, and this malignancy is a serious threat to human health. The mainstream treatment for glioma is surgical resection; therefore, accurate resection can improve postoperative patient recovery.Many studies have investigated surgical navigation guided by mixed reality, with good outcomes. However, the limitations of mixed reality, such as spatial drift caused by environmental changes, limit its clinical application. Therefore, we present a mixed reality surgical navigation system for glioma resection. Preoperative information can be fused precisely with the real patient with the spatial compensation method to achieve clinically suitable accuracy.A head-mounted device was used to display virtual information, and a markerless spatial registration method was applied to precisely align the virtual anatomy with the real patient preoperatively. High-accuracy preoperative and intraoperative movement and spatial drift compensation methods were used to increase the positional accuracy of the mixed reality-guided glioma resection system when the patient's head is fixed to the bed frame. Several experiments were designed to validate the accuracy and efficacy of this system.Phantom experiments were performed to test the efficacy and accuracy of this system under ideal conditions, and clinical tests were conducted to assess the performance of this system in clinical application. The accuracy of spatial registration was 1.18 mm in the phantom experiments and 1.86 mm in the clinical application.Herein, we present a mixed reality-based multimodality-fused surgical navigation system for assisting surgeons in intuitively identifying the glioma boundary intraoperatively. The experimental results indicate that this system has suitable accuracy and efficacy for clinical usage.
Sign Language Recognition using Sensor and Vision Based Approach

Sign language is one of mode for Communication which conveys the ideas non-verbally. It is gift for speech and hear impaired community. A Sign Language Recognition system has made this community close to regular communication. This system convert's the signs or gestures to a form which is easily understood. System is trained by using images of letters. As technology improved many different methods have evolved to create this system. In this study, two main methods for implementing sign language recognition are i) Sensor-based approach ii) Vision-based approach are studied. In image-based, cameras are used to capture the images of signs and use image processing to identify the sign in image and then algorithm that interpret the meaning of that sign. The sensor-based approach a glove is constructed using sensors that will track the sign made by hand. In this paper translation of sign to speech, methods to SLR, sign language interpretation, techniques to map features and popular methods are studied.
Evaluating Human–Robot Interaction Algorithms in Shared Autonomy via Quality Diversity Scenario Generation

The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring diverse scenarios of humans and robots interacting in simulation can improve understanding of the robotic system and avoid potentially costly failures in real-world settings. We formulate this problem as a quality diversity (QD) problem, of which the goal is to discover diverse failure scenarios by simultaneously exploring both environments and human actions. We focus on the shared autonomy domain, in which the robot attempts to infer the goal of a human operator, and adopt the QD algorithms CMA-ME and MAP-Elites to generate scenarios for two published algorithms in this domain: shared autonomy via hindsight optimization and linear policy blending. Some of the generated scenarios confirm previous theoretical findings, while others are surprising and bring about a new understanding of state-of-the-art implementations. Our experiments show that the QD algorithms CMA-ME and MAP-Elites outperform Monte-Carlo simulation and optimization-based methods in effectively searching the scenario space, highlighting their promise for automatic evaluation of algorithms in human–robot interaction.
An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task

Many real-world classification problems such as fraud detection, intrusion detection, churn prediction, and anomaly detection suffer from the problem of imbalanced datasets. Therefore, in all such classification tasks, we need to balance the imbalanced datasets before building classifiers for prediction purposes. Several data-balancing techniques (DBT) have been discussed in the literature to address this issue. However, not much work is conducted to assess the performance of DBT. Therefore, in this research paper we empirically assess the performance of the data-preprocessing-level data-balancing techniques, namely: Under Sampling (OS), Over Sampling (OS), Hybrid Sampling (HS), Random Over Sampling Examples (ROSE), Synthetic Minority Over Sampling (SMOTE), and Clustering-Based Under Sampling (CBUS) techniques. We have used six different classifiers and twenty-five different datasets, that have varying levels of imbalance ratio (IR), to assess the performance of DBT. The experimental results indicate that DBT helps to improve the performance of the classifiers. However, no significant difference was observed in the performance of the US, OS, HS, SMOTE, and CBUS. It was also observed that performance of DBT was not consistent across varying levels of IR in the dataset and different classifiers.
Hiding patients’ medical reports using an enhanced wavelet steganography algorithm in DICOM images

In the latest years, there is an increasing emphasis on DICOM medical images, especially in digital medical images transmission to e-health services. Besides, the confidentiality and protection of patients’ secure information need more attention and research. In this paper, we propose an enhanced medical image steganographic algorithm to hide patients’ secure information in their medical images. The proposed algorithm has a higher level of confidentiality while maintaining a higher quality of the medical image and a higher embedding capacity and robustness. The proposed algorithm has two main steps. The medical cover image is decomposed using an integer wavelet filter in the first step. Moreover, the patients’ confidential information is processed using the Arithmetic Coding (AC) and Data Encryption Standard (DES) algorithms to be compressed and encrypted before of the embedding process. In the second step, the insignificant coefficients are selected from the integer wavelet high-frequency subbands of the transformed medical image, and finally, both the target cover bits and the secret bits are grouped during the injection process. The performance of our algorithm was evaluated using different evaluation metrics. The obtained results show that our algorithm outperforms existing ones, with a higher embedding capacity, and a lower imperceptibility. The proposed algorithm can be applied in hospitals and health care canters worldwide.
Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition

Wearable devices and smartphones that are used to monitor the activity and the state of the driver collect a lot of sensitive data such as audio, video, location and even health data. The analysis and processing of such data require observing the strict legal requirements for personal data security and privacy. The federated learning (FL) computation paradigm has been proposed as a privacy-preserving computational model that allows securing the privacy of the data owner. However, it still has no formal proof of privacy guarantees, and recent research showed that the attacks targeted both the model integrity and privacy of the data owners could be performed at all stages of the FL process. This paper focuses on the analysis of the privacy-preserving techniques adopted for FL and presents a comparative review and analysis of their implementations in the open-source FL frameworks. The authors evaluated their impact on the overall training process in terms of global model accuracy, training time and network traffic generated during the training process in order to assess their applicability to driver's state and behaviour monitoring. As the usage scenario, the authors considered the case of the driver's activity monitoring using the data from smartphone sensors. The experiments showed that the current implementation of the privacy-preserving techniques in open-source FL frameworks limits the practical application of FL to cross-silo settings.
Decentralized Graph Federated Multitask Learning for Streaming Data

In federated learning (FL), multiple clients connected to a single server train a global model based on locally stored data without revealing their data to the server or other clients. Nonetheless, the current FL architecture is highly vulnerable to communication failures and computational bottlenecks at the server. In response, a recent work proposed a multi-server federated architecture, namely, a graph federated learning architecture (GFL). However, existing work assumes a fixed amount of data at clients and the training of a single global model. This paper proposes a decentralized online multitask learning algorithm based on GFL (O-GFML). Clients update their local models using continuous streaming data while clients and multiple servers can train different but related models simul-taneously. Furthermore, to enhance the communication efficiency of O-GFML, we develop a partial-sharing-based O-GFML (PSO-GFML). The PSO-GFML allows participating clients to exchange only a portion of model parameters with their respective servers during a global iteration, while non-participating clients update their local models if they have access to new data. In the context of kernel regression, we show the mean convergence of the PSO-GFML. Experimental results show that PSO-GFML can achieve competitive performance with a considerably lower communication overhead than O-GFML.
User Preference for Navigation Instructions in Mixed Reality

Current solutions for providing navigation instructions to users who are walking are mostly limited to 2D maps on smartphones and voice-based instructions. Mixed Reality (MR) holds the promise of integrating navigation instructions directly in users&#x2019; visual field, potentially making them less obtrusive and more expressive. Current MR navigation systems, however, largely focus on using conventional designs such as arrows, and do not fully leverage the technological possibilities. While MR could present users with more sophisticated navigation visualizations, such as in-situ virtual signage, or visually modifying the physical world to highlight a target, it is unclear how such interventions would be perceived by users. We conducted two experiments to evaluate a set of navigation instructions and the impact of different contexts such as environment or task. In a remote survey (n = 50), we collected preference data with ten different designs in twelve different scenarios. Results indicate that while familiar designs such as arrows are well-rated, methods such as avatars or desaturation of non-target areas are viable alternatives. We confirmed and expanded our findings in an in-person virtual reality (VR) study (n = 16), comparing the highest-ranked designs from the initial study. Our findings serve as guidelines for MR content creators, and future MR navigation systems that can automatically choose the most appropriate navigation visualization based on users&#x2019; contexts.
FLaMAS: Federated Learning Based on a SPADE MAS

In recent years federated learning has emerged as a new paradigm for training machine learning models oriented to distributed systems. The main idea is that each node of a distributed system independently trains a model and shares only model parameters, such as weights, and does not share the training data set, which favors aspects such as security and privacy. Subsequently, and in a centralized way, a collective model is built that gathers all the information provided by all of the participating nodes. Several federated learning framework proposals have been developed that seek to optimize any aspect of the learning process. However, a lack of flexibility and dynamism is evident in many cases. In this regard, this study aims to provide flexibility and dynamism to the federated learning process. The methodology used consists of designing a multi-agent system that can form a federated learning framework where the agents act as nodes that can be easily added to the system dynamically. The proposal has been evaluated with different experiments on the SPADE platform; the results obtained demonstrate the benefits of the federated system while facilitating flexibility and scalability.
A high-capacity and reversible patient data hiding scheme for telemedicine

The quality and efficiency of telemedicine make progress successfully due to the launch of Electronic Medical Record (EMR). However, EMR suffers information security problems such as unauthorized access, data disclosure, and tampering in telemedicine transmission. To ensure security for sensitive EMR during telemedicine transmission, a novel high-capacity and reversible data hiding scheme is proposed to conceal EMR into the medical images using rectangular predictors and optimal strategy. The clinic original image is interpolated into the cover image in which interpolated pixels are predicted by rectangular predictor to facilitate reversibility and high payload for data hiding scheme. Based on the classification idea, the proposed rectangular predictor calculates the weighted factor via local correlation to protect edges and textures reducing the appearance of common interpolation defects like blurring, jaggies, and zippers. The binary secret message is converted into a series of secret symbols in base-T notational system to balance image quality and embedding capacity, in which optimal base-T is selected adaptively by the content length of the EMR. The EMR is embedded into the cover image via finding the optimal pixel modification value in liner area for lower distortion. In addition, a fragile watermark, as a discriminator of whether medical information has been tampered during transmission, is also hidden in the cover image. Abundant experimental results demonstrate that the proposed method is superior over state-of-the-art techniques in terms of payload and image quality. High payload of 2.25 bpp for PSNR 42 dB is achieved.