Reviewerly/exHarmony huggingface dataset:
Embedded Payload Solutions in UAVs for Medium and Small Package Delivery

Investigations about the feasibility of delivery systems with unmanned aerial vehicles (UAVs) or drones have been recently expanded, owing to the exponential demand for goods to be delivered in the recent years, which has been further increased by the COVID-19 pandemic. UAV delivery can provide new contactless delivery strategies, in addition to applications for medical items, such as blood, medicines, or vaccines. The safe delivery of goods is paramount for such applications, which is facilitated if the payload is embedded in the main drone body. In this paper, we investigate payload solutions for medium and small package delivery (up to 5 kg) with a medium-sized UAV (maximum takeoff of less than 25 kg), focusing on (i) embedded solutions (packaging hosted in the drone fuselage), (ii) compatibility with transportation of medical items, and (iii) user-oriented design (usability and safety). We evaluate the design process for possible payload solutions, from an analysis of the package design (material selection, shape definition, and product industrialization) to package integration with the drone fuselage (possible solutions and comparison of quick-release systems). We present a prototype for an industrialized package, a right prism with an octagonal section made of high-performance double-wall cardboard, and introduce a set of concepts for a quick-release system, which are compared with a set of six functional parameters (mass, realization, accessibility, locking, protection, and resistance). Further analyses are already ongoing, with the aim of integrating monitoring and control capabilities into the package design to assess the condition of the delivered goods during transportation.
Blockchain applications for the Internet of Things: Systematic review and challenges

Technological advancements have always been influencing our lives. Recently, the Internet of Things (IoT) and Blockchain (BC) are emerging as potentially disruptive technologies. Whereby, the IoT is a system of inter-related devices with unique identifiers for data sharing and device management and control. IoT is based on the integration of traditional technologies including embedded systems, wireless sensor networks, control systems, and automation. While, the concept of IoT is continuously evolving with the convergence of multiple technologies including real-time analytics, machine learning, commodity sensor, and embedded systems. On the other hand, BC technology is a distributed ledger used to maintain the transaction logs of a network, and it has started revolutionizing data provenance, storage, secure, and traceable transaction management systems. There is limited use of blockchain technology for a fully decentralized, untrusted, and secure environment in the field of IoT. This article reviews the current state-of-art blockchain technology and its current utilization in different application domains of IoT. Furthermore, it presents the use of blockchain technology with digital ledger technology (DLT) and IoT. Similarly, the notable challenges of BC and IoT integration are presented. To the best of our knowledge, there is no such SLR available that provides a comprehensive review in this domain. Applying blockchain to solve IoT problems improves IoT security. Moreover, a taxonomy of application domains is presented, which can be integrated with BC and IoT. The article identifies and discusses open research issues and challenges that need to be addressed to harness the potential of BC technology for IoT.
A comprehensive study towards high-level approaches for weapon detection using classical machine learning and deep learning methods

Surveillance systems do not give a rapid response to deal with suspicious activities such as armed robbery in public places. Consequently, there is a need for technology that can recognize criminal activities from Closed Circuit Televisions (CCTV) footage without the need of human help. Various high-performance computing algorithms have been developed but are limited to specific conditions. In this paper, we have identified gaps between existing technologies for weapon detection. The automatic detection of guns/weapons could help in the investigation of crime scenes. A new and difficult area of study is identifying the specific type of firearm used in an attack known as intra-class detection. The study examines and classifies the strengths and shortcomings of several existing algorithms using classical machine learning and deep learning approaches, employed in the detection of different kinds of weapons. We have thoroughly compare and analyze the performance of several recent state-of-the-art methods on different datasets along with their future scope. We observed that deep learning techniques beat traditional machine learning techniques in terms of speed and accuracy.
Applications of augmented reality for construction productivity improvement: a systematic review

Purpose Despite the significance of the construction industry to the nation's economic growth, there is empirical evidence that the sector is lagging behind other industries in terms of productivity growth. The need for improvements inspired the industry's stakeholders to consider using emerging technologies that support the enhancement. This research aims to report augmented reality applications essential for contractors' productivity improvement. Design/methodology/approach This study systematically reviewed academic journals. The selection of journal articles entailed searching Scopus and Web of Science databases. Relevant articles for reviews were identified and screened. Content analysis was used to classify key applications into six categories. The research results were limited to journal articles published between 2010 and 2021. Findings Augmented reality can improve construction productivity through its applications in assembly, training and education, monitoring and controlling, interdisciplinary function, health and safety and design information. Originality/value The research provides a direction for contractors on key augmented reality applications they can leverage to improve their organisations' productivity.
FAIR Equivalency with Regulatory Framework for Digital Health in Uganda

Abstract This study explores the possibility of opening a policy window for the adoption of the FAIR Guidelines— that data be Findable, Accessible, Interoperable, and Reusable (FAIR)—in Uganda's eHealth sector. Although the FAIR Guidelines were not mentioned in any of the policy documents relevant to Uganda's eHealth sector, the study found that 83% of the documents mentioned FAIR Equivalent efforts, such as the adoption of the National Identification Number (NIN) as a unique identifier in Uganda's national Electronic Health Management Information System (eHMIS) (findability), the planned/ongoing integration of various information systems (interoperability), and the alignment of various projects with international best practices/standards (reusability). A FAIR Equivalency Score (FE-Score), devised in this study as an aggregate score of the mention of the equivalent of FAIR facets in the policy documents, showed that the documents at the core of Uganda's digital health/eHealth policy have the highest score of all the documents analysed, indicating that there is a degree of alignment between Uganda's National eHealth Vision and the FAIR Guidelines. Therefore, it can be concluded that favourable conditions exist for the adoption and implementation of the FAIR Guidelines in Uganda's eHealth sector. Hence, it is recommended that the FAIR community adopt a capacity building strategy through organisations with a worldwide mandate, such as the World Health Organization, to promote the adoption of the FAIR Guidelines as part of international best practices.
Hyperparameter Optimization on CNN Using Hyperband on Tomato Leaf Disease Classification

Convolutional Neural Network (CNN) has been successfully applied to image classification, one of which is plant or leaf disease. However, choosing the optimal architecture and hyperparameters is a challenge in its implementation. The purpose of this study was to optimize the Convolutional Neural Network (CNN) hyperparameter on the classification of tomato leaf diseases. In this research, optimization of hyperparameter Convolutional Neural Network (CNN) using Hyperband on Tomato Leaf Disease Detection dataset. The dataset consists of 10,000 training data and 1,000 testing data with 10 classes. In the training data, the distribution of the dataset is 80% for training data and 20% for data validation. This study uses the Keras-Tuner library which aims to optimize two hyperparameters, namely the number of dense neurons and the learning rate. The best hyperparameter value resulting from hyperparameter optimization is 128 for the number of dense neurons and 0.001 for the learning rate. The proposed method succeeded in achieving an accuracy value of 95.690% in the training phase and 88.50% in the validation phase. These results were obtained from model training of 50 epochs. In addition, the model testing obtained an accuracy value of 88.60%. Therefore, hyperparameter optimization on Convolutional Neural Network (CNN) using Hyperband can be an alternative in choosing the optimal architecture and hyperparameters.
RF Fingerprinting of LoRa Transmitters Using Machine Learning with Self-Organizing Maps for Cyber Intrusion Detection

In this paper, a novel unsupervised machine learning (ML) algorithm is presented for the expeditious RF fingerprinting of LoRa modulated chirps. Identification based on received signal strength indicator (RSSI) alone is unlikely to yield a robust means for sensor authentication within critical infrastructure deployment. Here, an unsupervised ML algorithm is used to rapidly train an artificial neural network (ANN) matrix creating self-organizing maps (SOMs) for each authentic transmitter and a potential rogue node. A general classifier can be trained on the SOMs for precisely profiling each transmitter as either genuine or rogue. By means of experimental validation, this methodology demonstrated cent-percent success in recognizing each transmitter, either being a real or a rogue node.
Precueing Object Placement and Orientation for Manual Tasks in Augmented Reality

When a user is performing a manual task, AR or VR can provide information about the current subtask (cueing) and upcoming subtasks (precueing) that makes them easier and faster to complete. Previous research on cueing and precueing in AR and VR has focused on path-following tasks requiring simple actions at each of a series of locations, such as pushing a button or just visiting. We consider a more complex task, whose subtasks involve moving to and picking up an item, moving that item to a designated place while rotating it to a specific angle, and depositing it. We conducted two user studies to examine how people accomplish this task while wearing an AR headset, guided by different visualizations that cue and precue movement and rotation. Participants performed best when given movement information for two successive subtasks and rotation information for a single subtask. In addition, participants performed best when the rotation visualization was split across the manipulated object and its destination.
Investigating the customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction

Abstract Artificial intelligence (AI) applications are observed in different industries including the services, where the impressive changing pace is boosted by the technological advances in AI. The use of AI in the daily work of individuals and in shaping the relationship between firms and customers has resulted in increased efficiency and a higher degree of fascination in interaction. There are, however, customers' fears related to the use of AI. Therefore, understanding how AI affects customer trust is worth investigating. This study focuses on the AI applications and their impact on communication quality and customer trust. The features of AI applications investigated in this study include anthropomorphism, empathy response and interaction. A model was first empirically validated through Confirmatory Factor Analysis and tested using Covariance‐Based Structural Equation Modelling. Data were then collected from a survey of 507 customers of two banks and two big telecommunication firms in Vietnam. Empirical results indicate that anthropomorphism and interaction do not play critical roles in generating customer trust in AI unless they create communication quality with customers. The ability of empathy response makes customers trust in various service situations. AI application seems to be a good solution for service firms to enhance their relationship with customers in difficult circumstances, including a pandemic crisis. This study delivers implications for enhancing AI adoption in the service firms that intend to innovate their future operations.
Automatic detection of indoor occupancy based on improved YOLOv5 model

Indoor occupancy detection is essential for energy efficiency control and Coronavirus Disease 2019 traceability. The number and location of people can be accurately identified and determined through classroom surveillance video analysis. This information is used to manage environmental equipment such as HVAC and lighting systems to reduce energy use. However, the mainstream one-stage YOLO algorithm still uses an anchor-based mechanism and couples detection heads to predict. This results in slow model convergence and poor detection performance for densely occluded targets. Therefore, this paper proposed a novel decoupled anchor-free VariFocal loss convolutional network algorithm DFV-YOLOv5 for occupancy detection to tackle these problems. The proposed method uses the YOLOv5 algorithm as a baseline. It uses the anchor-free mechanism to reduce the number of design parameters needing heuristic tuning. Afterwards, to reduce the coupling of the model, speed up the model's convergence ability, and improve the model detection performance, the detection head is decoupled based on the YOLOv5 model. It can resolve the conflict between classification and regression tasks. In addition, we use the VariFocal loss to assign more weights to difficult data points to optimize the class imbalance problem and use the training target q to measure positive samples, treating positive and negative samples asymmetrically. The total loss function is redesigned, the L1 loss is increased, and the ablation experiment verifies the effect of the improved loss. By applying a hybrid activation function of the sigmoid linear unit and rectified linear unit, we improved the model's nonlinear representation and reduced the model's inference time. Finally, a classroom dataset was constructed to validate the occupancy detection performance of the model. The proposed model was compared with mainstream target detection models regarding average mean precision, memory allocation, execution time, and the number of parameters on the VOC2012, CrowdHuman and self-built datasets. The experimental results show that the method significantly improves the detection accuracy and robustness, shortens the inference time, and proves the practicality of the algorithm in occupancy detection compared with the mainstream target detection model and related variants of the model.
News Text Classification Method Based on the GRU_CNN Model

The convolutional neural network can extract local features of text but cannot capture structure information or semantic relationships between words, and a single CNN model’s classification performance is low, whereas GRU can effectively extract semantic information and global structure relationships of text. To address this problem, this paper proposes a news text classification method based on the GRU_CNN model, which combines the advantages of CNN and GRU. The model first trains word vectors as the embedding layer with the Word2vec model and then extracts semantic information from text sentences with the GRU model. Following that, this model employs the CNN model to extract crucial semantic information features and finally completes the classification through the Softmax layer. The experimental results reveal that the GRU_CNN hybrid model outperforms single CNN, LSTM, and GRU models in terms of classification effect and accuracy.
A systematic literature review on web service clustering approaches to enhance service discovery, selection and recommendation

With the advancement of web 2.0 and the development of the Internet of Things (IoT), all tasks can be handled with the help of handheld devices. Web APIs or web services are providing immense power to IoT and are working as a backbone in the successful journey of IoT. Web services can perform any task on a single click event, and these are available over the internet in terms of quantity, quality, and variety. It leads to the requirement of service management in the service repository. The well-managed and structured service repository is still challenging as services are dynamic, and documentation is limited. It is also not a piece of cake to discover, select and recommend services easily from a pool of services. Web service clustering (WSC) plays a vital role in enhancing the service discovery, selection, and recommendation process by analyzing the similarity among services. In this paper, with a systematic process total of 84 research papers are selected, and different state-of-the-art techniques based on web service clustering are investigated and analyzed. Furthermore, this Systematic Literature Review (SLR) also presents the various mandatory and optional steps of WSC, evaluation measures, and datasets. Research challenges and future directions are also identified, which will help the researchers to provide innovative solutions in this area.
Entropic uncertainty and quantum correlations dynamics in a system of two qutrits exposed to local noisy channels

Abstract We address the dynamics of the lower bound of geometric quantum discord and quantum-memory-assisted entropic uncertainty in a two-qutrit system when exposed to classical channels characterized by power-law (PL) and random telegraph (RT) noises. The system-channel coupling strategy is examined in two contexts: common qutrit-environment (CQE) and different qutrit-environment (DQE) configurations. We show that the geometric quantum discord functions remain anti-correlated with entropic uncertainty and decline as uncertainty appears in the system. The rate of entropic uncertainty appearance seems more prevalent than the decline rate of quantum discord function, suggesting that uncertainty causes the quantum correlations to fade in quantum systems. We find that non-local correlations estimated by the lower bound of geometric quantum discord are not destroyed even at the maximum entropic disorder and entropic uncertainty. In addition, the efficacy of entropic uncertainty and the lower bound is strongly influenced by the state’s purity factor, with the former being more robust at higher purity values and the latter at lower purity values. All the parameters impact entropic uncertainty, however, the mixedness of the state is noticed to greatly alter the generation of quantum memory. Besides, PL noise caused Markovian behavioral dynamics, and the RT noise allowed non-Markovian dynamics, while the latter remains more resourceful for the quantum correlations preservation and entropic uncertainty suppression. We also demonstrate how to model longer quantum correlations and provide optimal parameter settings for suppressing the dephasing and entropic uncertainty effects.
Efficient Query-based Black-box Attack against Cross-modal Hashing Retrieval

Deep cross-modal hashing retrieval models inherit the vulnerability of deep neural networks. They are vulnerable to adversarial attacks, especially for the form of subtle perturbations to the inputs. Although many adversarial attack methods have been proposed to handle the robustness of hashing retrieval models, they still suffer from two problems: (1) Most of them are based on the white-box settings, which is usually unrealistic in practical application. (2) Iterative optimization for the generation of adversarial examples in them results in heavy computation. To address these problems, we propose an Efficient Query-based Black-Box Attack (EQB 2 A) against deep cross-modal hashing retrieval, which can efficiently generate adversarial examples for the black-box attack. Specifically, by sending a few query requests to the attacked retrieval system, the cross-modal retrieval model stealing is performed based on the neighbor relationship between the retrieved results and the query, thus obtaining the knockoffs to substitute the attacked system. A multi-modal knockoffs-driven adversarial generation is proposed to achieve efficient adversarial example generation. While the entire network training converges, EQB 2 A can efficiently generate adversarial examples by forward-propagation with only given benign images. Experiments show that EQB 2 A achieves superior attacking performance under the black-box setting.
An effectiveness analysis of transfer learning for the concept drift problem in malware detection

Malware classification is a task that has acquired importance due to the increase in malware distribution. In the literature, the application of machine learning techniques is proposed to tackle this task because machine learning models may be able to detect new malware variants more effectively than traditional signature-based solutions. Nonetheless, there are some difficulties in the application of machine learning in this field, particularly the presence of concept drift, that must be addressed by keeping models up to date in order to detect new threats. In this research, we carry out an evaluation of the performance of transfer learning techniques on the problem of malware detection over different time horizons and on several learning settings. We carry out experiments on unbalanced data with different file types to better reflect additional challenges in malware detection. Our goal is to determine whether transfer learning may be helpful to solve the concept drift problem, and construct models that can detect new malware by using the information obtained from past data.
Training elementary school teachers-to-be at Computer Science lessons to evaluate e-tools

The study purpose is to develop methodological support for students’ training for evaluation e-tools for young learners and to check its effectiveness experimentally. The module “Expert evaluation of the quality of e-tools for young learners” is offered for teachers-to-be. The determination of the weighting factor of each criterion by expert evaluations was organized. Educational principles, correlation e-tool content with the curriculum, interactivity, multimedia, assistance system, ergonomic requirements are mentioned. On the basis of the criterion rank, the significance of each criterion was calculated. The indicators to determine the level of preliminary expert evaluations of e-tools are proposed. The results are calculated with nonparametric methods of mathematical statistics, in particular, Pearson’s criterion χ2. The conclusion is the expert evaluation has different activity stages, gradually becoming a common phenomenon. Training teachers-to-be for e-tool expert evaluation at Computer Science, Mathematics, English is a complex process.