Reviewerly/exHarmony huggingface dataset:
Smart Home Technologies and Services for Geriatric Rehabilitation

This text consists of nine chapters and is the product of 25 contributors, nine reviewers, and two editors. The book offers a toolbox for health care stakeholders involved in decision-making for the design, development, and implementation of smart home solutions. The book is designed for rehabilitation students and engineers as well as those with an interest for geriatric rehabilitation that includes biomedical engineers, electrical engineers, computer scientists, software developers. The body of this text consists of roughly 185 pages (counting all tables and the inserted 35-page appendix 1), and there are an additional 41 pages of references. With less than 130 pages of written text, this book is quite expensive and is primarily a review of the literature, rather than a “toolbox.” Furthermore, in these references, one would expect more than one reference to RESNA, the Rehabilitation Engineering and Assistive Technology Society of North America! One would also expect more than zero attributions to the two editors. It is also the experience of this reviewer that some mention of patient therapy would involve music therapy, but the word “music” Not recommended.
Automated Analysis of Low-Field Brain MRI in Cerebral Malaria

Abstract A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate to low-resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low-field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM.
Hybrid Convolutional-Transformer framework for drone-based few-shot weakly supervised object detection

Drone delivery is becoming a new trend in the logistics system , but few researches are developed in this field. Locating the target buildings in the drone camera is a crucial technique. However, it is difficult to collect extensive drone-view images and their bounding box annotations for supervised training. Therefore, we address this problem by formulating it as a weakly supervised task and using small amount of category labels as supervision. To extract representative features of cross-view and cross-device images, we propose a Hybrid Convolutional-Transformer (HCT) framework for detection given the very few image-level annotated images. To better evaluate the proposed method in the realistic drone delivery task, we build a drone-view object detection dataset based on the University-1652 benchmark by annotating bounding boxes of target buildings. Extensive experimental results demonstrate the effectiveness of the proposed method. • A new drone-view detection dataset for evaluation. • A Hybrid Convolutional-Transformer network for drone-view detection. • The proposed method achieves SOTA performance.
Trello as a Tool for the Development of Lifelong Learning Skills of Senior Students

The article focuses on the problem of development of the lifelong learning skills of senior students in secondary school of general education. The model of interaction between the subjects of learning in the Trello project management system is developed. The foreign literature learning environment in the Trello system is developed. The experience of using the Trello project management system in the process of teaching foreign literature in 10th and 11th grades is described. It has been found that according to the lifelong learning approach a person has to possess skills: goal setting, self-regulation, motivation, time management, self-directed learning, collaboration skills, and self-development. It was pointed out that there are common approaches to project management and educational process management. The use of online systems for collaborative work in the educational process in the secondary school of general education is suggested. Online systems for teamwork that support Kanban methodologies for project management have been analyzed. To confirm the effectiveness of the implementation of the Trello project management system as a tool for the development of lifelong learning skills of senior students, an experiment was conducted: criteria, indicators, levels, and the state of formation of lifelong learning skills of senior students were determined; statistical analysis of the results of the experiment was carried out. The effectiveness of the implementation of the Trello project management system as a tool of the formation of the lifelong learning skills of senior students has been experimentally proven. The study can be applied in educational institutions interested in using agile methods in teaching.
Interactive, Collaborative and Multi-user Augmented Reality Applications in Primary and Secondary Education. A Systematic Review

Augmented reality is a technology that enhances human perception with additional, artificially generated sensory inputs. This creates new experiences enriching human vision by combining natural with digital elements. Augmented reality development dates back to the early sixties but it is only in the last decade, thanks to improvements to hardware and software, when it has begun to be rapidly incorporated in several fields, including education. This study presents a systematic review of the literature on the use of augmented reality applications in primary and secondary schools, with a specific focus on collaborative, multi-user and interactive applications. The aim of the study is to investigate the characteristics of such applications, the processes that led to their adoption, and their effectiveness in enhancing the learning experience. This study synthesises a set of 100 publications from 2015 to 2020 and performs a qualitative analysis of their content. The review describes the current state of the art in research in augmented reality for education and provides future research lines, as well as trends for the future of such applications in educational settings, analysing the relevance of the multi-user interaction challenge within the augmented reality ecosystem.
DeTRM: Decentralised Trust and Reputation Management for Blockchain-based Supply Chains

Blockchain has the potential to enhance supply chain management systems by providing stronger assurance in transparency and traceability of traded commodities. However, blockchain does not overcome the inherent issues of data trust in IoT enabled supply chains. Recent proposals attempt to tackle these issues by incorporating generic trust and reputation management methods, which do not entirely address the complex challenges of supply chain operations and suffers from significant drawbacks. In this paper, we propose DeTRM, a decentralised trust and reputation management solution for supply chains, which considers complex supply chain operations, such as splitting or merging of product lots, to provide a coherent trust management solution. We resolve data trust by correlating empirical data from adjacent sensor nodes, using which the authenticity of data can be assessed. We design a consortium blockchain, where smart contracts play a significant role in quantifying trustworthiness as a numerical score from different perspectives. A proof-of-concept implementation in Hyperledger Fabric shows that DeTRM is feasible and only incurs relatively small overheads compared to the baseline.
Work Experience as a Factor in Cyber-Security Risk Awareness: A Survey Study with University Students

The emergence of the COVID-19 pandemic in early 2020 has transformed how individuals work and learn and how they can apply cyber-security requirements in their, mostly remote, environments. This transformation also affected the university student population; some needed to adjust to new remote work settings, and all needed to adjust to the new remote study environment. In this online research study, we surveyed a large number of university students (n = 798) to understand their expectations in terms of support and help for this new remote work and study environment. We also asked students to report on their practices regarding remote location and Wi-Fi security settings, smart home device usage, BYOD (bring your own device) and personal device usage and social engineering threats, which can all lead to compromised security. A key aspect of our work is a comparison between the practices of students having work experience with the practices of students having no such additional experience. We identified that both the expectations and the level of cyber-security awareness differ significantly between the two student populations and that cyber-security awareness is increased by work experience. Work experience students are more aware of the cyber-security risks associated with a remote environment, and a higher portion of them know the dedicated employee whom they can contact in the event of incidents. We present the organizational security practices through the lens of employees with initial work experience, contributing to a topic that has so far received only limited attention from researchers. We provide recommendations for remote study settings and also for remote work environments, especially where the existing research literature survey results differ from the findings of our survey.
Application of Improved Manta Ray Foraging Optimization Algorithm in Coverage Optimization of Wireless Sensor Networks

For the shortcomings of the manta ray foraging optimization (MRFO) algorithm, like slow convergence speed and difficult to escape from the local optimum, an improved manta ray foraging algorithm based on Latin hypercube sampling and group learning is proposed. Firstly, the Latin hypercube sampling (LHS) method is introduced to initialize the population. It divides the search space evenly so that the initial population covers the whole search space to maintain the diversity of the initial population. Secondly, in the exploration stage of cyclone foraging, the Levy flight strategy is introduced to avoid premature convergence. Before the somersault foraging stage, the adaptive t-distribution mutation operator is introduced to update the population to increase the diversity of the population and avoid falling into the local optimum. Finally, for the updated population, it is divided into leader group and follower group according to fitness. The follower group learns from the leader group, and the leader group learns from each other through differential evolution to further improve the population quality and search accuracy. 15 standard test functions are selected for comparative tests in low and high dimensions. The test results show that the improved algorithm can effectively improve the convergence speed and optimization accuracy of the original algorithm. Moreover, the improved algorithm is applied to wireless sensor network (WSN) coverage optimization. The experimental results show that the improved algorithm increases the network coverage by about 3% compared with the original algorithm, and makes the optimized node distribution more reasonable.
Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks

Ensemble Learning is an effective method for improving generalization in machine learning. However, as state-of-the-art neural networks grow larger, the computational cost associated with training several independent networks becomes expensive. We introduce a fast, low-cost method for creating diverse ensembles of neural networks without needing to train multiple models from scratch. We do this by first training a single parent network. We then create child networks by cloning the parent and dramatically pruning the parameters of each child to create an ensemble of members with unique and diverse topologies. We then briefly train each child network for a small number of epochs, which now converge significantly faster when compared to training from scratch. We explore various ways to maximize diversity in the child networks, including the use of anti-random pruning and one-cycle tuning. This diversity enables "Prune and Tune" ensembles to achieve results that are competitive with traditional ensembles at a fraction of the training cost. We benchmark our approach against state of the art low-cost ensemble methods and display marked improvement in both accuracy and uncertainty estimation on CIFAR-10 and CIFAR-100.
Learning Token-Based Representation for Image Retrieval

In image retrieval, deep local features learned in a data-driven manner have been demonstrated effective to improve retrieval performance. To realize efficient retrieval on large image database, some approaches quantize deep local features with a large codebook and match images with aggregated match kernel. However, the complexity of these approaches is non-trivial with large memory footprint, which limits their capability to jointly perform feature learning and aggregation. To generate compact global representations while maintaining regional matching capability, we propose a unified framework to jointly learn local feature representation and aggregation. In our framework, we first extract local features using CNNs. Then, we design a tokenizer module to aggregate them into a few visual tokens, each corresponding to a specific visual pattern. This helps to remove background noise, and capture more discriminative regions in the image. Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention. Finally, different visual tokens are concatenated to generate a compact global representation. The whole framework is trained end-to-end with image-level labels. Extensive experiments are conducted to evaluate our approach, which outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets.
An Interactive Explanatory AI System for Industrial Quality Control

Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in industry, where transparency and comprehensibility of decisions are crucial. Therefore, we aim to extend the defect detection task towards an interactive human-in-the-loop approach that allows us to integrate rich background knowledge and the inference of complex relationships going beyond traditional purely data-driven approaches. We propose an approach for an interactive support system for classifications in an industrial quality control setting that combines the advantages of both (explainable) knowledge-driven and data-driven machine learning methods, in particular inductive logic programming and convolutional neural networks, with human expertise and control. The resulting system can assist domain experts with decisions, provide transparent explanations for results, and integrate feedback from users; thus reducing workload for humans while both respecting their expertise and without removing their agency or accountability.
The (Gami)fictional Ego-Center: Projecting the Location of the Self Into an Avatar

A rich body of research suggests that self-associated stimuli are preferentially processed and therefore responses to such stimuli are typically faster and more accurate. In addition, people have an understanding of what they consider their "Self" and where it is located, namely near the head and upper torso-further boosting the processing of self-related stimuli if they are presented near the felt location of the self. We were interested in whether the same mechanism can be found when people transfer their "Self" into a static avatar. We investigated this in two studies with N = 33 and N = 39 young, healthy adults, respectively. Taken together, the results showed that (i) people indeed show enhanced processing for self-avatar-related stimuli and (ii) that self-associations are stronger if the to-be-associated stimuli are closer to the avatar's upper torso-suggesting some kind of a projected location of the self in the avatar. This implies that attention is not equally distributed across the avatar. Beyond a theoretical level, this also has implications for practical use. For example, digital games opting for a non-traditional user interface where information is displayed on or in the direct vicinity of the character should take this effect into account when choosing which information to present where (i.e., present the most crucial piece of information close to the self-center of the avatar).
Hill-Climb-Assembler Encoding: Evolution of Small/Mid-Scale Artificial Neural Networks for Classification and Control Problems

The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks which, in spite of the great success of deep architectures, are still in use, for example, in robotic systems. The paper analyses the influence of different mechanisms incorporated into HCAE on the effectiveness of evolved neural networks and compares it with a number of rival algorithms. In order to verify the ability of HCAE to evolve effective small/mid-scale neural networks, both feed forward and recurrent, it was tested on fourteen identification problems including the two-spiral problem, which is a well-known binary classification benchmark, and on two control problems, i.e., the inverted-pendulum problem, which is a classical control benchmark, and the trajectory-following problem, which is a real problem in underwater robotics. Four other neuro-evolutionary algorithms, four particle swarm optimization methods, differential evolution, and a well-known back-propagation algorithm, were applied as a point of reference for HCAE. The experiments reported in the paper revealed that the evolutionary approach applied in the proposed algorithm makes it a more effective tool for solving the test problems than all the rivals.
The compact genetic algorithm struggles on Cliff functions

The compact genetic algorithm (cGA) is a non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In this paper, we investigate the cGA on the Cliff function for which it has been shown recently that non-elitist evolutionary algorithms and artificial immune systems optimize it in expected polynomial time. We point out that the cGA faces major difficulties when solving the Cliff function and investigate its dynamics both experimentally and theoretically around the Cliff. Our experimental results indicate that the cGA requires exponential time for all values of the update strength K. We show theoretically that, under sensible assumptions, there is a negative drift when sampling around the location of the cliff. Experiments further suggest that there is a phase transition for K where the expected optimization time drops from nΘ(n) to 2Θ(n).
Cutting Simulation in Unity 3D Using Position Based Dynamics with Various Refinement Levels

Augmented and Virtual Reality-based surgical simulations have become some of the fastest-developing areas, due to the recent technological advances and changes, in surgical education. Cutting simulation is a crucial part of the virtual surgery simulation in which an incision operation is performed. It is a complex process that includes three main tasks: soft body simulation, collision detection and handling, and topological deformation of the soft body. In this paper, considering the content developer’s convenience, the deformable object simulation, using position-based dynamics (PBD), was applied in the Unity 3D environment. The proposed algorithm for fast collision detection and handling between the cutting tool and the deformable object uses a sweep surface. In case of incision, the algorithm updates the mesh topology by deleting intersected triangles, re-triangulation, and refinement. In the refinement part, the boundary edges threshold was used to match the resolution of new triangles to the existing mesh triangles. Additionally, current research is focused on triangle surface meshes, which help to reduce the computational costs of the topology modifications. It was found that the algorithm can successfully handle arbitrary cuts, keeping the framerate within interactive and, in some cases, in the real-time.
MFA: A Smart Glove with Multimodal Intent Sensing Capability

At present, virtual-reality fusion smart experiments mostly employ visual perception devices to collect user behavior data, but this method faces the obstacles of distance, angle, occlusion, light, and a variety of other factors of indoor interactive input devices. Moreover, the essence of the traditional multimodal fusion algorithm (TMFA) is to analyze the user’s experimental intent serially using single-mode information, which cannot fully utilize the intent information of each mode. Therefore, this paper designs a multimodal fusion algorithm (hereinafter referred to as MFA, Algorithm 4) which focuses on the parallel fusion of user’s experimental intent. The essence of the MFA is the fusion of multimodal intent probability. At the same time, this paper designs a smart glove based on the virtual-reality fusion experiments, which can integrate multichannel data such as voice, visual, and sensor. This smart glove can not only capture user’s experimental intent but also navigate, guide, or warn user’s operation behaviors, and it has stronger perception capabilities compared to any other data glove or smart experimental device. The experimental results demonstrate that the smart glove presented in this paper can be widely employed in the chemical experiment teaching based on virtual-reality fusion.