Reviewerly/exHarmony huggingface dataset:
360° videos in education – A systematic literature review on application areas and future potentials

Abstract As a teaching and learning medium, 360° videos offer new teaching-learning experiences. Through the possibility of immersion, individual 360° panoramic images, multi-perspective viewing options and interaction possibilities, they extend the advantages of conventional video technology. To understand the potential of using 360° video technology for educational processes, a systematic literature review analyzed previous scientific articles ( N = 44) about the interdisciplinary use of 360° videos according to PRISMA guidelines. In the systematic literature selection, particular emphasis was placed on the conceptual distinction between virtual reality and 360° videos. By the authors, 360° videos are understood as a specific video format that has characteristics of virtual reality but is to be distinguished from virtual reality by the necessary real recording situation without programmed virtual environments. The results show a use of 360° videos mainly for three teaching-learning purposes: presentation and observation of teaching–learning content, immersive and interactive theory–practice mediation, and external and self-reflection. Combined with the added value of conventional video technology and other immersive technology such as virtual reality, five added value categories for its use as a teaching–learning medium were identified: To increase learning motivation and interest, to learn in authentic and realistic learning scenarios, for immersive and interactive learning experiences, for multi-perspective observation opportunities and for individual learning. These consisted primarily of positive motivational effects for authentic or immersive learning experiences.
Blockchain Adoption in Food Supply Chains: A Systematic Literature Review on Enablers, Benefits, and Barriers

Globalization of the food supply chain (FSC) has brought significant challenges to the food system, such as fraud, safety, security, and quality issues, due to information asymmetry. Globalization also increases the difficulty and complexity of solving these problems to improve FSC efficiency. Blockchain technology (BCT) has proven to have the capability to transform FSC based on its potential benefits. However, studies of BCT adoption in FSC are relatively new and scarce. This study systematically reviews the current state of research in the space of BCT and FSCs. In carrying out the research, a systematic literature review (SLR) was deployed using two prominent databases, Scopus and Business Source Complete (EBSCO), covering articles from 2016 to 2021, with 52 articles synthesized to identify blockchain enablers, benefits, and barriers. Based on this review, a conceptual framework was developed for BCT adoption within the FSC. The study identified scalability, interoperability, high cost, lack of expertise, and regulations as the most likely barriers to BCT adoption. It contributes to the body of knowledge by providing insights into BCT adoption in the FSC and offers evidence-based direction for other industries to build their BCT strategies.
Unified Terrestrial/Aerial Motion Planning for HyTAQs via NMPC

In this study, an NMPC-based planning approach is proposed for Hybrid Terrestrial and Aerial Quadrotors (HyTAQs) to achieve safe and efficient hybrid locomotion in unknown environments. The approach ensures bimodal dynamic feasibility and fully exploits the high efficiency and accessibility of HyTAQs. Equipped with the complementarity constraints for hybrid dynamics, the unified NMPC enables simultaneous optimization of the full-state trajectory and the inherent locomotion mode without the computational burden of mixed-integer programming. Also, the uncertainty bounds are evaluated along the nominal trajectory in a receding horizon manner and incorporated within the geometry constraints for the safety and robustness. Further, the bimodal energy efficiency is fully considered by the topology-guided path searching, achieving more efficient mode selection. Then, a compact unified terrestrial-aerial planner is implemented on the HyTAQ and validated through various comparisons and navigation tests. Experimental results verify the high efficiency and robustness of the proposed approach.
Reputation Systems: A framework for attacks and frauds classification

Reputation and recommending systems have been widely used in e-commerce, as well as online collaborative networks, P2P networks and many other contexts, in order to provide trust to the participants involved in the online interaction. Based on a reputation score, the e-commerce user feels a sense of security, leading the person to trust or not when buying or selling. However, these systems may give the user a false sense of security due to their gaps. This article discusses the limitations of the current reputation systems in terms of models to determine the reputation score of the users. We intend to contribute to the knowledge in this field by providing a systematic overview of the main types of attack and fraud found in those systems, proposing a novel framework of classification based on a matrix of attributes. We believe such a framework could help analyse new types of attacks and fraud. Our work was based on a systematic literature review methodology.
Genetic algorithm based probabilistic model for agile project success in global software development

Software development organizations implement agile methods in global software development (GSD) to leverage benefits in terms of low development cost, continuous project delivery, and high-quality product. Despite the benefits, using the agile process in the GSD organizations is not a straightforward task due to inhibit additional risk factors that could lead agile projects to failure. GSD organizations are continuously improving their process management activities to improve the rate of agile project success. Project success could be improved if the project manager or organization management has preliminary information about the derailment of the project features. This work aims to investigate the most influential agile project features in determining the project outcomes and develop a cost-effective, and effort-based prediction model to improve the probability of successful completion in the globally distributed environment. To do so, a nature-inspired optimization algorithm i.e., genetic algorithm (GA), has been employed to achieve these goals. In the proposed model, GA considers the probability of success with respect to cost to determine probable project outcomes. An efficacy measure is formulated as a fitness function in GA which maximizes the success of agile project outcomes relative to cost. The optimization model has been tested with two different prediction models i.e., Naive Bayes classifier (NBC) and logistic regression (LR). We performed the experiment on data gathered through the survey administered from the globally distributed agile projects. The results demonstrate that prediction models calculate the efficacy for best solutions as 0.531 and 0.5850 for NBC and LR, respectively. Moreover, the ranking of each project feature based on their relative cost identified using NBC and LR have more similarities. The t test results are significant, i.e., t = 6.068, p = 0.001 < 0.005, which indicates that no significant differences have been observed between the ranking assigned by two different methods ( NBC and LR). The results reveal that the developed prediction model based on identified eight agile project features that the GSD organization management and agile team need to focus more on to facilitate cost-effective successful implementation of agile projects.
Performance analysis of Kmeans with modified initial centroid selection algorithms and developed Kmeans9+ model

Partition the sample space to the user requirement is an easy and efficient cluster method in many applications. Kmeans is one of the simple and efficient partition algorithms used in many cluster solutions. We suggest Multivariant Silhouette method to predict the cluster count value for Kmeans algorithm. The work proposes three new algorithms for initial seed selection. The Kmeans algorithm is modified using statistical measure Mean, Median, Partition centre for the cluster centroid calculation. In conventional Kmeans algorithm, the samples are compared with all the partition centroids to decide the inclusion of a sample in a cluster. In Kmeans9+ algorithm, the samples are compared only with the centroids of current and eight nearest neighbouring cluster partitions. It reduces the needless comparisons of samples with cluster centroids and improve the efficiency of the algorithm. The performance of created algorithms is evaluated using data from UCI and KBPE SSLC. Cluster efficiency analysis is done using the cluster evaluation indices such as Silhouette and Dunn Index. Nine nearest neighbour uniform partition cluster model Kmeans9+ improve the performance of the K means algorithm and obtain the natural cluster results with minimum iterations.
Simulation and implementation of two-layer oscillatory neural networks for image edge detection: bidirectional and feedforward architectures

Abstract The growing number of edge devices in everyday life generates a considerable amount of data that current AI algorithms, like artificial neural networks, cannot handle inside edge devices with limited bandwidth, memory, and energy available. Neuromorphic computing, with low-power oscillatory neural networks (ONNs), is an alternative and attractive solution to solve complex problems at the edge. However, ONN is currently limited with its fully-connected recurrent architecture to solve auto-associative memory problems. In this work, we use an alternative two-layer bidirectional ONN architecture. We introduce a two-layer feedforward ONN architecture to perform image edge detection, using the ONN to replace convolutional filters to scan the image. Using an HNN Matlab emulator and digital ONN design simulations, we report efficient image edge detection from both architectures using various size filters (3 × 3, 5 × 5, and 7 × 7) on black and white images. In contrast, the feedforward architectures can also perform image edge detection on gray scale images. With the digital ONN design, we also assess latency performances and obtain that the bidirectional architecture with a 3 × 3 filter size can perform image edge detection in real-time (camera flow from 25 to 30 images per second) on images with up to 128 × 128 pixels while the feedforward architecture with same 3 × 3 filter size can deal with 170 × 170 pixels, due to its faster computation.
Obstacle avoidance for environmentally-driven USVs based on deep reinforcement learning in large-scale uncertain environments

This paper focuses on obstacle avoidance for the environmentally-driven unmanned surface vehicles (USVs) in large-scale and uncertain environments. A novel speed adaptive robust obstacle avoidance (SAROA) approach is proposed with the deep reinforcement learning (DRL). A feature enhanced dynamic training method for the DRL is proposed, which significantly improves the sampling efficiency and accelerates convergence. The sensory cues, the executed action and the reward feedback function are properly designed for reinforcement learning to realize robust obstacle avoidance in uncertain environment. Moreover, the obstacle perception domain and the line-of-sight (LOS) based target tracking method are proposed to enable the USVs to avoid collision as well as to follow the path in large-scale environment. In addition, regarding that the environmentally-driven USV is normally super under-actuated and its speed is uncontrollable, a speed adaptive zone is proposed to adapt the obstacle avoidance policies to various navigation speeds, which significantly improves the adaptability and robustness of the proposed obstacle avoidance strategy. Extensive obstacle avoidance tests for the environmentally-driven USV with different navigation speed are conducted, which demonstrates that the DRL based SAROA approach shows excellent practicability and robustness for the environmentally-driven USVs in large-scale and uncertain environments.
Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images

Computerized identification of lymph node metastasis of breast cancer (BCLNM) from whole-slide pathological images (WSIs) can largely benefit therapy decision and prognosis analysis. Besides the general challenges of computational pathology, like extra-high resolution, very expensive fine-grained annotation, etc., two particular difficulties with this task lie in (1) modeling the significant inter-tumoral heterogeneity in BCLNM pathological images, and (2) identifying micro-metastases, i.e., metastasized tumors with tiny foci. Towards this end, this paper presents a novel weakly supervised method, termed as Prototypical Multiple Instance Learning (PMIL), to learn to predict BCLNM from WSIs with slide-level class labels only. PMIL introduces the well-established vocabulary-based multiple instance learning (MIL) paradigm into computational pathology, which is characterized by utilizing the so-called prototypes to model pathological data and construct WSI features. PMIL mainly consists of two innovatively designed modules, i.e., the prototype discovery module which acquires prototypes from training data by unsupervised clustering, and the prototype-based slide embedding module which builds WSI features by matching constitutive patches against the prototypes. Relative to existing MIL methods for WSI classification, PMIL has two substantial merits: (1) being more explicit and interpretable in modeling the inter-tumoral heterogeneity in BCLNM pathological images, and (2) being more effective in identifying micro-metastases. Evaluation is conducted on two datasets, i.e., the public Camelyon16 dataset and the Zbraln dataset created by ourselves. PMIL achieves an AUC of 88.2% on Camelyon16 and 98.4% on Zbraln (at 40x magnification factor), which consistently outperforms other compared methods. Comprehensive analysis will also be carried out to further reveal the effectiveness and merits of the proposed method.
Leveraging Blockchain for sustainability and supply chain resilience in e-commerce channels for additive manufacturing: A cognitive analytics management framework-based assessment

E-commerce channels for additive manufacturing (ECAM) is an interface of democratizing manufacturing for large scale customized and flexible production. ECAM facilitates exchange of goods, services and information between key stakeholders viz. users, designers, additive manufacturer and customers. However, these systems are prone to threats viz. identity issues, currency exchange, innovation and ideation theft etc. Thus, a trusted technology is required to operationalize ECAM. Extant research suggests that Blockchain renders trust, sustainability and Supply Chain Resilience (SCRes) to host systems. However, leveraging Blockchain for a sustainable and SCRes ECAM has been negligible in the extant research. We use a Cognitive Analytics Management framework to assess the role of Blockchain in enhancing SCRes and Sustainability in ECAM. Value-Focussed Thinking is used in the Cognitive dimension to converge Blockchain Attributes with dimensions of SCRes and Sustainability. Fuzzy Cognitive Maps in Analytics dimension quantifies the impact of Blockchain adoption in ECAM. The practical contributions of this work are 1) encompassing stakeholder cognition for a sustainable and SCRes ECAM using blockchain 2) insights into design, coherence and integration aspects of blockchain for ECAM 3) a demonstration of a holistic and robust approach for optimal and shared value creation for stakeholders.
Discrete equilibrium optimizer combined with simulated annealing for feature selection

This paper proposes a binary adaptation of the recently proposed meta-heuristic, Equilibrium Optimizer (EO), called Discrete EO (DEO), to solve binary optimization problems. A U-shaped transfer function is used to map the continuous values of EO into the binary domain. To further improve the exploitation capability of DEO, Simulated Annealing (SA) is used as a local search procedure and the combination is named as DEOSA. The proposed DEOSA algorithm is applied to 18 well-known UCI datasets and compared with a wide range of algorithms. The results are statistically validated using Wilcoxon rank-sum test and Friedman test. In order to test the scalability and robustness of DEOSA, it is additionally tested over seven high-dimensional Microarray datasets and 25 binary Knapsack problems. The results evidently demonstrate the superiority and merits of DEOSA when solving binary optimization problems.
MONWS: Multi-Objective Normalization Workflow Scheduling for Cloud Computing

Cloud computing is a prominent approach for complex scientific and business workflow applications in the pay-as-you-go model. Workflow scheduling poses a challenge in cloud computing due to its widespread applications in physics, astronomy, bioinformatics, and healthcare, etc. Resource allocation for workflow scheduling is problematic due to the computationally intensive nature of the workflow, the interdependence of tasks, and the heterogeneity of cloud resources. During resource allocation, the time and cost of execution are significant issues in the cloud-computing environment, which can potentially degrade the service quality that is provided to end users. This study proposes a method focusing on makespan, average utilization, and cost. The authors propose a task’s dynamic priority for workflow scheduling using MONWS, which uses the min-max algorithm to minimize the finish time and maximize resource utilization by calculating the dynamic threshold value for scheduling tasks on virtual machines. When the experimental results were compared to existing algorithms, MONWS achieved a 35% improvement in makespan, an 8% increase in maximum average cloud utilization, and a 4% decrease in cost.
HDM-RRT: A Fast HD-Map-Guided Motion Planning Algorithm for Autonomous Driving in the Campus Environment

On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a motion planning algorithm for autonomous vehicles on campus, called HD-Map-guided rapidly-exploring random tree (HDM-RRT). In our algorithm, A collision risk map (CR-Map) that quantifies the collision risk coefficient on the road is combined with the Gaussian distribution for sampling to improve the efficiency of algorithm. Then, the node optimization strategy of the algorithm is deeply optimized through the prior information of the CR-Map to improve the convergence rate and solve the problem of poor stability in campus environments. Three experiments were designed to verify the efficiency and stability of our approach. The results show that the sampling efficiency of our algorithm is four times higher than that of the Gaussian distribution method. The average convergence rate of the proposed algorithm outperforms the RRT* algorithm and DT-RRT* algorithm. In terms of algorithm efficiency, the average computation time of the proposed algorithm is only 15.98 ms, which is much better than that of the three compared algorithms.
Resource optimizing federated learning for use with IoT: A systematic review

Recently, Federated Learning (FL) has been explored as a new paradigm that preserves both data privacy and end-users knowledge while reducing latency during model training. While often applied between a central server and the edge devices, FL deals with a wide range of end-user applications and devices. Given the Internet of Things (IoT) current popularity, its relevance and its penetration in several new application domains, this work examines the new challenges faced when combining IoT with the classic FL model. The limited resources of IoT edge devices require careful adaptation of the way how FL should be structured in this scenario. In addition, since FL is a distributed paradigm that shares deep learning artifacts through a network, there are also communication issues reminiscent to IoT networks that need special consideration. Thus, it is necessary to optimize the use of both processing and communication resources when considering the use of IoT edge devices as part of a FL. This paper systematically reviews current advances and steps taken towards dealing with resource optimization when using FL as part of IoT scenarios. We examine the published works over the last ten years and discuss the main goals, scenarios, and developed solutions to solve the problems encountered. We also present the main metrics used to quantify the effectiveness and to evaluate the performance of existing IoT based architectures with FL support.
Blockchain Enabled Biometric Security in Intemet-of-Medical-Things (IoMT) Devices

The Internet of Medical Things (IoMTs) are critical components in wearable healthcare applications such as bio-systems, rehabilitation robots, activity identification, to name a few. Traditional security measures are unable to protect data from harmful attackers. Patients' privacy may be endangered as a result of eavesdropping, and the diagnosis of life-threatening diseases may be delayed as a result of IoMT device malfunction caused by attacks such as Denial of Service (DoS) attacks. Blockchain has emerged as a possible answer to these problems. The benefits of adopting blockchain based into the healthcare ecosystem include decentralisation, dependability, efficiency, security, and privacy, to name a few. Biometric systems have been rapidly gaining traction in a variety of industries in recent years, and they continue to provide enhanced security for access control systems. In comparison to traditional procedures, security methods based on biometric features give more trustworthiness while requiring less processing. As a result, biometric traits are used in current healthcare systems to implement security in healthcare. This research work proposes a secure blockchain based architecture for IoMT based healthcare applications that can provide decentralized and secure communication while taking into account the constraints listed above. Bio-keys, in combination with biometric authentication are also offered as a way to further enhance the security.
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 In-ternational Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 differ-ent countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and at-tract solutions that deal with detecting face morphing at-tacks while protecting people's privacy for ethical and le-gal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to out-performing the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://github.com/marcohuber/SYN-MAD-2022.