Reviewerly/exHarmony huggingface dataset:
The New Generation of Contact Tracing Solution: The Case of Morocco

Nowadays, the entire world is struggling to adapt and survive the global pandemic. Moreover, most countries had a hard time keeping up with the new mutations of COVID-19. Therefore, taking preventive measures to control the spreading of the virus, including lockdowns, curfews, social distancing, masks, vaccines, is not enough to stop the virus. However, using the new technologies to adapt the prevention measures and enhance the existing ones will be more efficient. Most countries have already developed their non-pharmaceutical interventions measures (NPIs), mainly contacts tracing solutions at the pandemic beginning. Using those mobile applications, the authorities were able to reduce the spreading of the virus. Nevertheless, the virus is evolving, mutating, and becoming more and more dangerous to survive. Therefore, these mobile applications have become less effective in facing the constant changes of the pandemic situation. To that end, the need for enhancing and evolving contact tracing became more urgent. The goal here is to control the spread of the new variants and keep up with the rapid changes happening around the world. In this paper, we will present a detailed view of the new solution built to take contact tracing to a new level, empowered by the Bluetooth Low Energy technology for communication, advanced encryption method for security and data privacy, as well as secured storage and data management to have a system capable of slowing the COVID-19 variants from spreading and save lives.
Blockchain for Proposal Management

In business world, proposals are used to sell a company’s products or services and provide solutions in response to a need. Companies use a proposal management process to manage and coordinate the tasks required to submit proposals. The proposal management process includes a well-defined set of tasks, but inherently has its challenges when applying the process to the dynamic business environment. As a form of digital trust and a platform for immutable records, blockchain technology has a lot to offer in solving these problems. This paper describes how blockchain technology can help solve the challenges in proposal management. Our work presented in this paper includes the design and implementation of two blockchain platforms. The first is a decentralized autonomous organization that coordinates the proposal management workflow. The second is a decentralized application that manages document registration activities. These use cases act as two examples of how blockchain technology can contribute to proposal management.
CoAP and MQTT: Characteristics and Security

There is no doubt that Internet of Things (IoT) has a significant impact on many aspects of our lives including how we live, drive, irrigate, the way we consume energy and the way we manage our confidential and personal data. Data is generated and gathered from different lightweight IoT gadgets and smart devices using two widely protocols; Constrained Application Protocol (CoAP) and Message Queuing Telemetry Transport (MQTT). These protocols are based on Publish/Subscribe model. Nevertheless, as the use of these emerging protocols increase, the risk of attacks increases as well. Indeed, these communications come up with many security vulnerabilities. In this paper, we describe these two emerging messaging protocols to address the needs of the lightweight IoT nodes, we discuss protocols and techniques used to manage security in CoAP and MQTT, we reveal some of security limitations and issues, we conclude with some future directions.
A new construction of shearlets

In order to achieve optimally sparse approximations of signals exhibiting anisotropic singularities, the shearlet systems that are systems of functions generated by one generator with dilation, shear transformation and translation operators applied to it were introduced. In this paper, we will construct the shearlet systems that are not only Parseval frames for [Formula: see text] but they are also obtained from an [Formula: see text]-MRA associated with wavelet multiresolution, and by using this approach, we obtain the corresponding filters for these systems. For this purpose, the tensor product of the corresponding wavelet scaling function and a compact support bump function is used to construct the scaling function associated with the shearlet.
A SVM Approach for Assessing Traffic Congestion State by Similarity Measures

Nowadays, we are seeing more and more problems related to the analysis of large data acquired in CCTV systems. Among these, the evaluation of traffic jams, with the aim of detecting and predicting anomalies, is a perfect illustration. The use of support vector machines (SVM) has alleviated the problems of classification, detection and prediction of anomalies in several areas [1, 2]. In this paper, we propose an approach to assessing traffic jam conditions based on similarity measures to produce better classification and prediction of abnormal events in real time. By considering standardized datasets PETS (Performance Evaluation of Tracking and Surveillance). The results obtained allow the events to be classified by a logistic regression at a rate of 99.88%.
Apply Trust Computing and Privacy Preserving Smart Contracts to Manage, Share, and Analyze Multi-site Clinical Trial Data

Multi-site clinical trial systems face security challenges when streamlining information sharing while protecting patient privacy. In addition, patient enrollment, transparency, traceability, data integrity, and reporting in clinical trial systems are all critical aspects of maintaining data compliance. A Blockchain-based clinical trial framework has been proposed by lots of researchers and industrial companies recently, but its limitations of lack of data governance, limited confidentiality, and high communication overhead made data-sharing systems insecure and not efficient. We propose $$\textsf{Soteria}$$ , a privacy-preserving smart contracts framework, to manage, share and analyze clinical trial data on fabric private chaincode (FPC). Compared to public Blockchain, fabric has fewer participants with an efficient consensus protocol. $$\textsf{Soteria}$$ consists of several modules: patient consent and clinical trial approval management chaincode, secure execution for confidential data sharing, API Gateway, and decentralized data governance with adaptive threshold signature (ATS). We implemented two versions of $$\textsf{Soteria}$$ with non-SGX deploys on AWS blockchain and SGX-based on a local data center. We evaluated the response time for all of the access endpoints on AWS Managed Blockchain, and demonstrated the utilization of SGX-based smart contracts for data sharing and analysis.
Forward Oriented Programming: A Meta-DSL for Fast Development of Component Libraries

In the software development industry, domain specific languages (DSLs) are constantly gaining traction when it comes to developing software for specific application domains. However, building efficient DSL libraries is an extremely complex task that requires extensive programming skills, deep domain understanding, and the ability to generalize and model concepts. In this work, we introduce a meta-DSL aiming to assist software library development, called forward oriented programming (FOP). This combines lazy evaluation and aspect oriented programming principles to align crosscutting component inputs and alter execution outcomes depending on usage in subsequent code. Theoretical analysis shows that FOP component combination logic is learnable by developers and simplifies source code. We realize the paradigm with a Python package, called \textit{pyfop}, and conduct a case study on library development to compare it with similar functional and object oriented implementations. In the study, source code quality metrics demonstrate reduced time and effort to write code, and increased comprehensibility. Furthermore, shared inputs are parsed without acting at distant parts of code.
Feasibility of Using Feature Entropy Reducation to Auto-Catch Response Frames of Video Ultrasound Data for Deep Learning Model to Detect Breast Tumors

Background: The AI research of breast tumors has mostly based on static images, which were selected by doctors during scanning. This is unclear whether it represents the best diagnosis image of breast tumor.Purpose: To explore the method of capturing complementary responsibility frames of video data by using feature entropy subtraction. Establish the feature entropy breast network (FEBrNet), which can independently extract video responsibility frames for breast tumor diagnosis.Materials and Methods: Based on feature entropy subtraction method, and inherit from the optimal combination of the pre-training backbone and weights of full connection layers, FEBrNet was constructed. Compare the diagnostic effectiveness of FEBrNet 's responsibility frame model, doctor selected frame, 5-frame interval method selected frame, all video frames, senior ultrasound and mammography doctors.Results: The training and testing set showed that the AUC of the responsibility frame is higher than that of the doctor frame selection, fixed interval frame selection method, all video frames, senior ultrasound and mammography doctor, indicating that the frame selection level of FEBrNet can reach the frame selection level of senior experts.Conclusion: The complementary responsibility frame of video data can be captured by using the feature entropy subtraction method and used for the construction an artificial intelligence model; FEBrNet model independently extracts video responsibility frames for breast tumor diagnosis, which is better than the diagnosis level of doctors' responsibility frames, interval method, and all video frames, as well as the diagnosis level of senior ultrasound and mammography doctors. The diagnostic efficiency of the complementary responsibility frame construction model for breast tumors is higher than that of static images stored in clinical work.Funding Information: This project was supported by Commission of Science and Technology of Shenzhen(CYJ20190806151807192). Declaration of Interests: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Ethics Approval Statement: This research was approved by the Ethics Committee of Shenzhen Peopleâ€™s Hospital, and each patient signed an informed consent form.
Comparing Blockchain Bridges: Poly Network vs IBC

Blockchain is a system where connected nodes work together to perform various activities within the system, and all of them maintain their own copy of a system ledger that stores transactions. With the emergence of new blockchain networks and the massive growth of users on the network the need for inter blockchain communication is now at an all-time high. At first glance the two existing bridging protocols, the Poly Network and the Inter Blockchain Communication protocol (IBC), seem to be completely different, but a closer inspection reveals a connection between them that will most likely be seen in future blockchain bridges as well. This paper discusses their similarities and differences and ends by making a prediction about future blockchain bridges.
Asymmetric Bidirectional Controlled Quantum Teleportation of Three- and Four-Qubit States

In this paper, we theoretically realize bidirectional controlled quantum teleportation by using ten-qubit entangled state method. This paper uses a case to introduce the specific process of realizing quantum teleportation: Alice sends an unknown four-qubit GHZ state to Bob, and Bob sends an arbitrary three-qubit GHZ state to Alice. In addition, Charlie controls the transfer to ensure the integrity of the protocol. A ten-qubit quantum channel is constructed and used in this paper. Then, the unitary matrix transformation is used to complete the communication protocol. The research results show that the communication protocol constructed in this paper is more efficient than most communication protocols.
Multisensor and Multitarget Tracking Based on Generalized Covariance Intersection Rule

Distributed multitarget tracking (MTT) is suitable for sensors with limited field of view (FoV). Generalized covariance intersection (GCI) fusion is used to solve the MTT problem based on label probability hypothesis density (PHD) filtering in this paper. Because the traditional GCI fusion only has good fusion performance for the targets in the intersection of each sensor’s FoV, and the targets outside the intersection range would be lost, this paper redivides the Gaussian components according to the FoV and distinguishes the Gaussian components of the targets inside and outside the intersection. GCI fusion is sensitive to label inconsistency between different sensors. For label fusion in the intersection region, the best match of labels is found by minimizing label inconsistency index, and then GCI fusion is performed. Finally, the feasibility and effectiveness of the proposed fusion method are verified by simulation, and its robustness is proved. The proposed method is obviously superior to local sensor and traditional GCI algorithm.
Real-time optimal protocol prediction of quantum key distribution using machine learning

Purpose The purpose of optimal protocol prediction and the benefits offered by quantum key distribution (QKD), including unbreakable security, there is a growing interest in the practical realization of quantum communication. Realization of the optimal protocol predictor in quantum key distribution is a critical step toward commercialization of QKD. Design/methodology/approach The proposed work designs a machine learning model such as K-nearest neighbor algorithm, convolutional neural networks, decision tree (DT), support vector machine and random forest (RF) for optimal protocol selector for quantum key distribution network (QKDN). Findings Because of the effectiveness of machine learning methods in predicting effective solutions using data, these models will be the best optimal protocol selectors for achieving high efficiency for QKDN. The results show that the best machine learning method for predicting optimal protocol in QKD is the RF algorithm. It also validates the effectiveness of machine learning in optimal protocol selection. Originality/value The proposed work was done using algorithms like the local search algorithm or exhaustive traversal, however the major downside of using these algorithms is that it takes a very long time to revert back results, which is unacceptable for commercial systems. Hence, machine learning methods are proposed to see the effectiveness of prediction for achieving high efficiency.
A Systematic Literature Review and Delphi Study on Agile Software Development Challenges

Agile software development is one of the methodologies used by organizations to manage software development projects. The method allows processes to adapt quickly and respond to customer expectations. Nevertheless, agile method utilization may involve challenges in software development projects. It is crucial to identify the relevant challenges of agile software development to attain project success. Extant literature has limited examination concerning project setting and the categorization of agile challenges. This study explores agile challenges in the external software development project and expert perspectives for possible categorization. This study aims to map agile software development challenges using a systematic literature review (SLR) and the Delphi method to address the gap. Nineteen challenges and five categories are identified from prior studies using the SLR. The Delphi method’s result suggests five categories of challenges and improves the challenge concepts from previous studies. Finally, this study can serve as a starting point to examine which group of challenges substantially impacts in information system industry.
PointerScope: Understanding Pointer Patching for Code Randomization

Various fine-grained randomization schemes have been designed to increase the entropy of process space, while none of them can rise from an academic exercise to industrial deployment like Address Space Layout Randomization (ASLR). One of the critical reasons is the incorrectness of randomization caused by the mismatch between their pointer collection capabilities and the high accuracy requirements of the pointer patching task. In this article, we present PointerScope, an accurate compile-time pointer collection scheme deriving from a group of novel observations. The success of PointerScope relies on the complete tracing of the pointer generation process, including the compilation chain from compiler to static linker and the interface specification between them. From this view, PointerScope identifies four types of pointer-related static linker behaviors and clarifies five types of inherent addressing modes in the x86-64 architecture. The vague understanding of them causes the Compiler-assisted Code Randomization (CCR) to incorrectly collect pointers and patch them to the wrong values after randomization. Further, we measure the pointer collection capability of augmented binary analysis, the experimental results show that they can mitigate challenges from the traditional binary analysis by the given premises, but additional heuristics still need to be designed to support the fine-grained randomization.
Focal Loss Improves Performance of High-Sensitivity C-Reactive Protein Imbalanced Classification

Chronic inflammation has been shown to be associated with cardiovascular disorders, atherosclerosis, and colorectal adenoma. Using a high-sensitivity assay, we can detect levels of High-Sensitivity C-Reactive Protein (HSCRP), which in turn, yields an understanding of systemic low-grade chronic inflammation. Prediction of HSCRP has historically been performed to determine association with other factors that impact its prediction. To our knowledge, it is generally not performed for prediction itself. Here, we utilize Focal Loss Logistic Regression, a variation of log-loss Logistic Regression to achieve increased performance of HSCRP classification. With the use of this model, one can perform imputation of HSCRP in the case of missing data. It also can be utilized for medical professionals as a screen to determine if an HSCRP test should be performed.
Data Augmentation Methods For Object Detection and Segmentation In Ultrasound Scans: An Empirical Comparative Study

In ultrasound imaging, sonographers are tasked with analyzing scans for diagnostic purposes; a challenging task, especially for novice sonographers. Deep Learning methods have shown great potential in their ability to infer semantics and key information from scans to assist with these tasks. However, deep learning methods require large training sets to accomplish tasks such as segmentation and object detection. Generating these large datasets is a significant challenge in the medical domain due to the high cost of acquisition and annotation. Therefore, data augmentation is used to increase the size of training datasets to create the needed variability for deep learning models to generalize. These augmentation methods try to mimic differences among scans that result from noise, tissue movement, acquisition settings, and others. In this paper, we analyze the effectiveness of general augmentation methods that perform color, rigid, and non-rigid geometric transformation, to empirically analyze and compare their ability to improve the performance of three segmentation architectures on three different ultrasound datasets. We observe that non-rigid geometric transformations produce the best performance improvement.
Hyperparameter for Deep Learning Applied in Mammogram Image Classification

Deep Learning has become increasingly frequent in the studies and analysis of medical images. Advances relevant to this area of research improve computer-aided diagnostic systems and help physicians' routine when providing a second opinion. Breast cancer is one of the types most common cancer among women worldwide. Early diagnosis of breast cancer can facilitate treatment and help saves lives. Mammography is the most widely used exam in the clinical routine to diagnose breast cancer. The analysis of the mammogram requires a specialist with experience in medical imaging. Deep Learning and Machine Learning techniques can collaborate computationally with this task. Adapting the hyperparameters provided to deep learning architectures helps improve the results in analyzing and classifying mammogram images. This paper presents a deep learning-based approach to classifying mammogram image regions of interest (ROIs). This approach includes transfer learning, hyperparameter and fine-tuning, and an ensemble with the models that showed the best results. The process demonstrated promising results, with the ensemble reaching 92% accuracy in the classification of mammogram ROIs of the test set and the area under the curve (AUC) value of 0.97 for the best model.
Evaluation of Relevance-Driven Compression of Regular Cataract Surgery Videos

In recent years, the utilization intensity and thus the demand for storing cataract surgery videos for different purposes has increased. Hospitals continuously improve their technical recording equipment, i.e., cameras, to enhance the post-operative processing efficiency of the recordings. However, afterward, the videos are stored on hospitals' internal data servers in their original size, which leads to a massive storage consumption. In this paper, we propose a relevance-based compression scheme. First, we perform a user study with clinicians to define the relevance rates of regular cataract surgery phases. Then, we compress different phases based on the determined relevance rates, using different encoding parameters and two coding standards, namely H.264/AVC and AV1. Afterward, the medical experts evaluate the visual quality of the encoded videos. Our results show a storage-saving potential for H.264/AVC of up to 95.94% and up to 98.82% for AV1, excluding idle phases (no tools are visible).
TSEUnet: A 3D neural network with fused Transformer and SE-Attention for brain tumor segmentation

Brain tumor segmentation of 3D magnetic resonance (MR) images is of great significance for brain diagnosis. Although the U-Net and its variants have achieved outstanding performance in medical image segmentation, there still exist some challenges somewhat due to the fact that the CNN based models are powerful in extracting local features but are powerless in capturing global representations. To tackle this problem, we propose a 3D network structure based on the nnUNet, named TSEUnet. In this network, the transformer module is introduced in the encoder in a parallel interactive manner so that both local features and global contexts can be efficiently extracted. Moreover, SE-Attention is also incorporated in the decoder to enhance the meaningful information and improve the segmentation accuracy for brain tumor area. In addition, we propose a post-processing method to further improve the brain tumor segmentation. Experiments on the BRATS 2018 dataset show that our proposed TSEUnet achieves better performance on brain tumor segmentation as compared with the state-of-the-art methods.