Reviewerly/exHarmony huggingface dataset:
Intel Software Guard Extensions in Internet of Things Scenarios: A Systematic Mapping Study

Due to the Internet of Things (IoT) devices' processing and memory constraints, the processing and analysis of data acquired by such devices are generally performed in a fog or cloud environment, which offers more processing power. When delegating data processing to third parties, it is necessary to ensure their confidentiality and their owners' privacy, which can be achieved using a Trusted Execution Environment, such as Intel SGX. In this paper, we present a systematic mapping study to review recent works related to the use of Intel SGX architecture in IoT scenarios. We conduct the study by selecting 35 papers published between 2017 and 2020 and providing a comprehensive overview of the application scenarios and solutions when combining Intel SGX and the IoT paradigm.
Development of a mobile based birth and funeral event planning application in Bahrain

In decades, the demand for event planning and management fields with specialty niches has extremely increased. Event planning is an important process towards the successful execution of an event as clients struggle to get suitable event managers and service providers for their events. Classification of events is a wide subject at which managerial expertise differs for different event types. Thus, understanding the event type is crucial for efficient collection of client's requirements aiming for client's satisfaction. This research is aimed at developing a mobile application “Emotive Events”, acting as an event planner that maintains time and budget for a private event including funeral considering multiple religions and birth parties and addresses many features such as checklists creation, guest list, religion-based services, online payments, a collaboration of service providers/services that most existing applications typically lack all in one place. The theoretical framework is based on a systematic literature review (SLR) and web content analysis (CA) techniques. For the mobile application development, an Agile system development life cycle (SDLC) methodology was employed at which two questionnaires were conducted, one for collecting user and application requirements, while the other for the mobile application's evaluation. The research outcomes recognized the shortcomings of the currently used event planning applications, informed the requirements of the “Emotive event” mobile application which revealed 86.23% of acceptance in terms of application's usability.
Face-to-Face, Emergency Remote Teaching and ELearning: A Study of Students’ Performance

during the spring semester of the academic year 2019/2020, the COVID-19 pandemic broke out and most higher education institutions were locked down and converted to Emergency Remote Teaching. The following semester the medical situation did not improve and therefore institutions continued with distance education. Following a quantitative methodology to analyze the data collected from the last three semesters, we examine the effects of Emergency Remote Teaching in spring 2020 semester and the differences in applications and results of this mode of education with those of fall 2020 semester. For more realistic comparison we analyze results of spring 2019 and fall 2020 with results of fall 2019 semester where the whole teaching process was carried out in traditional face-to-face mode.
Multiscale Aggregate Networks with Dense Connections for Crowd Counting

The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo'10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.
Improving Representation Consistency with Pairwise Loss for Masked Face Recognition

Given the coronavirus disease (COVID-19) pandemic, people need to wear masks to protect themselves and reduce the spread of COVID, which brings new challenge to the traditional face recognition task. Since features like the nose and mouth, which are well distinguishable, are hidden under the mask, traditional methods are no longer simply applicable, even though they once achieved a high degree of accuracy. In response to this problem, the Masked Face Recognition Challenge & Workshop (MFR) was held in conjunction with the International Conference on Computer Vision (ICCV) 2021. This article details a method that combining the classic ArcFace and pairwise loss to target the new masked face recognition task. So far, our method has achieved the second place in the competition.
One-Stage Tree: end-to-end tree builder and pruner

Abstract Decision trees have favorable properties, including interpretability, high computational efficiency, and the ability to learn from little training data. Learning a decision tree is known to be NP-complete. The researchers have proposed many greedy algorithms such as CART to learn approximate solutions. Inspired by the current popular neural networks, soft trees that support end-to-end training with back-propagation have attracted more and more attention. However, existing soft trees either lose the interpretability due to the continuous relaxation or employ the two-stage method of end-to-end building and then pruning. In this paper, we propose One-Stage Tree to build and prune the decision tree jointly through a bilevel optimization problem. Moreover, we leverage the reparameterization trick and proximal iterations to keep the tree discrete during end-to-end training. As a result, One-Stage Tree reduces the performance gap between training and testing and maintains the advantage of interpretability. Extensive experiments demonstrate that the proposed One-Stage Tree outperforms CART and the existing soft trees on classification and regression tasks.
A novel mutual aid Salp Swarm Algorithm for global optimization

Abstract Salp Swarm Algorithm is a new intelligent optimization algorithm. Because of it is fewer control parameters and convenient operation, it has attracted the attention of researchers from all circles. However, due to the lack of complex iterative process, it has some disadvantages, such as low optimization precision and poor population diversity in the late iteration. To solve these problems of Salp Swarm Algorithm, we proposed a Salp Swarm Algorithm based on mutual learning mechanism. In this article, the improved Salp Swarm Algorithm uses the iteration factor of tangent change to update the population position, which balances the global exploration and local development ability of the algorithm. At the same time, the introduction of mutual learning mechanism in the local development stage solves the problem of poor population diversity in the later iteration of Salp Swarm Algorithm, and improves the convergence accuracy of the algorithm. Finally, 23 classical and CEC2014 benchmark functions are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the improved Salp Swarm Algorithm has better optimization accuracy and stability compared with the algorithm of Salp Swarm, Moth Flame Optimization, Grasshopper Optimization, and Ant Lion Optimization.
Covid-19 Face Mask Prediction Using Machine Learning Techniques

Entire world including India is going through a pandemic that has arisen due to the outbreak of COVID-19. Medicines and Vaccine for Covid-19 are still under developmental stage. Wearing a Face Mask is the best viable option for humans to prevent the spread of infection due to Corona virus. As a result, controlling government agencies may want to know the percentage of people wearing masks during a period as well as which group of people are most likely to wear masks when they go outside. To help answer these questions, this paper introduces a model that can classify faces among masked faces and unmasked faces using Python 3.0 Language. In the present face detecting model, Vietnam based mask classifier dataset, CelebA dataset, WiderFace dataset and MAFA datasets are used for achieving better results. Single Stage Headless Face Detector (SSH) is successfully implemented to segregate human faces with or without mask. Experimental results with the Mask Classifier model show that it can achieve about 96.5% accuracy during testing stage. Selected on road going people video is tested successfully where the present model clearly segregated human faces with and without mask. The present model is useful to safeguard people from spread of Covid-19 virus in public places.
Improved Sensing Strategies for Low Altitude Non Cooperative Sense and Avoid

Very low altitude non-cooperative sense and avoid is considered as an essential component of multi-layered mitigation strategies for collision hazard within the UTM/U-Space framework. However, it currently represents a challenging problem with open issues concerning performance trade-offs and technological options. In fact, low altitude operations emphasize sensing challenges such as ground clutter removal for radar, and below-the-horizon detection for optical sensors. These issues have a significant impact in scenarios involving small UAS, which may be characterized by low detectability though potentially generating relatively fast encounter geometries. As a contribution to this framework, this paper proposes some improved sensing strategies for these scenarios. The first idea is to optimize architectural and algorithmic trade-offs by accounting for the possible closure rates corresponding to near collision scenarios. This approach can be used to develop adaptive sensing strategies, as well as to select different sensors to cover different areas of the aircraft field of regard. The concept is demonstrated in numerical analyses focused on the computation of probability of missed and false conflict detection. Then, with regards to purely visual architectures, the possibility to extract and use shape-based ranging information to improve conflict detection performance is investigated through flight tests with small UAS. Finally, the paper describes first experimental activities conducted with a compact collision avoidance radar.
Formal Verification and Analysis of a Pseudo Random Number Generator

Formal verification and analysis of a crypto hardware requires a formal specification, formal proof of equivalence of the specification with the hardware realization. Pseudo Random Number Generator hardware in Verilog RTL or equivalent has an entropy source for random seed, crypto algorithms and processing unit, authenticated access depicting static behavior; and dynamic finite state machines (FSM) for data flow control, fault/error checks and recovery. This paper focusses on a unified, transitive, compositional formal verification and analysis of a FPGA based PRNG with statistical methods, quantitative physical measurements based analysis, symbolic logical equivalence and model checks, and properties verification.
Synchronization in Fractional Discrete Neural Networks Using Linear Control Laws

This work studies the synchronization of the fractional order discrete neural networks based on h-fractional difference operator. In addition, using simple linear control, it has been confirmed that two chaotic fractional discrete neural network achieve synchronized dynamics. Finally, numerical simulations are given in order to illustrate the results.
Supervised Machine Learning Strategies for Investigation of Weird Pattern Formulation from Large Volume Data Using Quantum Computing

Quantum machine learning [QML] is a new formulation on quantum hardware platform will try to achieve more enhanced data analysis and prediction which classical computer will not be able to generate. Classical computers are having computational limitations in terms of large volume data processing. Quantum machine are not to replace classical machines but quantum computers will solve operational difficulties of classical machines in terms of computational time. Quantum machine learning accelerates the supervised, unsupervised, and reinforcement learning methods. Classical ML methods such as SVM, PCA, Clustering, Neural networks are giving promising results but classical machines are inadequate to perform certain computations. Proposed Quantum machine learning would result in complex and weird patterns. Quantum support vector machine(QSVM) is a method used in supervised learning for classification and regression. QSVM uses high-dimensional feature space possibly on infinite dimension called as enhanced feature space for generating hyperplane. This hyperplane will classify non-linear and complex data on multiclass domain to achieve improved accuracy in less computational time. This paper investigates various strategies for quantum enhanced machine learning algorithm in supervised learning method.Nivelkar, Mukta Bhirud, S. G.
Literature Review on Breast Cancer Diagnosis using 3D Images: Methods and Performance Analysis

Breast cancer is becoming one of the leading causes of mortality in women all over the world. The advanced engineering of Artificial Intelligence techniques and natural image classification approaches is mostly employed to classify breast images. The image classification permits the physicians and the doctor the next chance, and this, in turn, saves the time of the physician and the doctor. Apart from several publications on classifying breast images, only some of the review papers are present that offer a clear definition of the “approaches to breast cancer image classification, feature extraction and selection methods, classification measurement parameterizations, and image classification results” with the help of 3D images. Machine learning (ML) seems to be an important portion of medical imaging research. These are developed over the years from manual seeded inputs to automatic initializations. The enhancements in the area of ML lead to various self-reliant and intelligent "Computer-Aided Diagnosis (CAD) systems" since the learning capability of the ML techniques is increasing in a constant manner. Several automated techniques are developing with deep feature representations and learning. The modern enhancements of ML with extensive and deeper representation techniques called Deep Learning (DL) techniques have created an important effect on enhancing the diagnosing ability of these systems. The main objective is to undergo a critical review on diverse contributions related to breast cancer detection that could be highly advantageous for dealing with 3D mammogram images. This paper plans to make a review on different algorithmic categorizations, datasets used, simulation platforms, and performance metrics. Furthermore, the research gaps and challenges of the breast cancer diagnosis using 3D images are elaborated, encouraging the upcoming researchers to give tremendous concern on new contributions.
A New Method for Designing Lightweight S-Boxes With High Differential and Linear Branch Numbers, and its Application

Bit permutations are efficient linear functions often used for lightweight cipher designs. However, they have low diffusion effects, compared to word-oriented binary and maximum distance separable (MDS) matrices. Thus, the security of bit permutation-based ciphers is significantly affected by differential and linear branch numbers (DBN and LBN) of nonlinear functions. In this paper, we introduce a widely applicable method for constructing S-boxes with high DBN and LBN. Our method exploits constructions of S-boxes from smaller S-boxes and it derives/proves the required conditions for smaller S-boxes so that the DBN and LBN of the constructed S-boxes are at least 3. These conditions enable us to significantly reduce the search space required to create such S-boxes. Using the unbalanced- Bridge and unbalanced- MISTY structures, we develop a variety of new lightweight S-boxes that provide not only both DBN and LBN of at least 3 but also efficient bitsliced implementations including at most 11 nonlinear bitwise operations. The new S-boxes are the first that exhibit these characteristics.
Visitor Artwork Ambient and how Making New Functions of Cultural Heritage by Using Augmented Reality within an Ambient Intelligence

The recent massive development of technology has radically changed the relationship between humans and their reality. Engineering sciences are literally re-shaped the world where people are living, and consequently how humans use technology. In this context, classical frameworks, and methodologies to provide Cultural Heritage experiences are poorly equipped to tackle the dynamic and heterogeneous needs of visitors. In this paper, we propose a reference framework that aims to enhance the interaction between visitors, artworks and the ambient, by the mediation of technology. Stemming from the proposed reference architecture, we also discuss a series of open challenges, which we believe represent relevant research directions in the nearest future.
Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm

For many years, there was an increasing necessity for being capable of the identification of a person based on his/her voice. Judges, law enforcement agencies, detectives, and lawyers, wanted to be able to use the forensic authentication of voice for investigating a suspect or confirming a judgment of guilt or innocence. This study aims to design and build a comprehensive identification of the forensic speakers for the Arabic language. The suggested system has been utilized for the recognition of forensic speaker's isolated words for purposes of identification. It comprises two stages; the first stage is training the sentence of the forensic speaker in the case where it is not previously processed and stored; the second stage is testing; it is applied in the case where the sentence of the forensic speaker has been previously processed and stored. Every one of the phases involves utilizing audio features (standard division, mean, amplitude, and zero-crossing), pre-processing with the use of the MFCC, Hamming Window, vector quantization, and data mining classification approaches. The proposed system implementation provides removal of the noise in spoken sentences, processing speech sentences prior to the storing, and a correct classification with the use of a number of algorithms of data mining classification such as the Logistic Model Tree (LMT), and K-nearest neighbor (KNN) algorithms. KNN being given the highest accuracy of 91.53% and 94.56% respectively.
A Compact Representation of Indoor Trajectories

In this article, we present a system that combines indoor positioning with a compression algorithm for trajectories in the context of a nursing home. Our aim is to gather and effectively represent the location of the residents and caregivers along time, while allowing for efficient access to those data. We briefly show the system architecture that enables the automatic tracking of user’s movements and consequently the gathering of their locations. Then, we present indRep, our compact representation to handle positioning data using grammar-based compression, and provide two basic operations that enable pseudorandom access to the data. Finally, we include experiments that show that indRep is competitive with well-known general-purpose compressors in terms of compression effectiveness and also provides fast access to the compressed data. We expect both features would enable exploitation functionalities even in computers with rather low computational resources.
Secure and Privacy-Preserving Lightweight Blockchain for Energy Trading

Renewable energy is gaining wide attention to address the issues, such as depleting resources and environmental degradation, linked with the current non-renewable energy. However, storing renewable energy is challenging. Consequently, energy trading is required to transfer the surplus amount of renewable energy from the prosumers to the consumers. The current centralized energy trading system is less secure and involves large transmission losses and high overheads. The decentralization, replication, immutability, and transparency characteristics of blockchain show a promising potential to address the inherent issues of centralized energy trading. However, the existing blockchain-based energy trading suffers from the drawbacks such as limited scalability, high energy loss, and low transaction throughput. In this paper, we propose a secure and privacy-preserving lightweight blockchain for energy trading that reduces the computational and communication overhead compared to the bitcoin network. This is by clustering the prosumers and perform the energy trading through a cluster head. Our experimental results show the potential of our architecture.
Traceable and Intelligent Supply Chain based on Blockchain and Artificial Intelligence

Abstract Smart manufacturing focuses on maximizing the capabilities to increase multiple objectives, like cost, delivery, and quality, in manufacturing enterprises. This requires implementing product development lifecycle, production system lifecycle, and business cycle for supply chain management. In short, a considerable amount of data is generated in a given manufacturing enterprise. Likewise, progress has been made to adopt blockchain in financial industries, but the adoption is slow in non-financial sectors. The article elaborates a methodology for the realization of a traceable and intelligent supply chain. First, the methodology elaborates on the realization of traceability of enterprise entities, which are an integral part of the supply chain. In this case, each participating stakeholder of the supply chain is required internally to realize a smart manufacturing system with an extension to write critical control data to the blockchain (i.e., a subset of process data). Artificial Intelligence (AI) is being adopted in most industries. A supply chain stakeholder has access to its data and can employ AI to derive new insights. The data available with the stakeholder provides a narrow context. With blockchain, all the stakeholders have access to the data from other stakeholders. Subsequently, the insights derived by a stakeholder will be more meaningful. This will assist in realizing an intelligent supply chain.
Halal Blockchain Digital Strategy using Peppard And Ward Strategic Formulation Approach

Digital technology usages are becoming more important in today's business environment, including at sharia business environment. It concludes ability to adapt for change that imperishable to anyone. Therefore, for the same arguments, there are not enough research findings and discussion regarding on how to leverage Blockchain ecosystems (especially for sharia business environments) incorporate with Digital Strategy Plan formulation. This is a question that the researcher wants to explore using a systemic literature review technique and specific case studies from sharia business applications and environments. The result of this will attempt to propose the convergent of digital strategy plan formulation and halal blockchain ecosystems concepts.