Reviewerly/exHarmony huggingface dataset:
Blockchain Technology for Privacy Protection in Healthcare Industry

This study proposes establishing a model for patient data privacy protection, considering the numerous potential breaches beyond the data owner’s control in response to the rapid growth of the Indonesian healthcare industry, bolstered by technological advancements. With its immutable nature, enhanced security features, and potential for peer-to-peer distribution, blockchain technology is uniquely positioned to revolutionize healthcare data management. By creating a blockchain-based model, this research aims to elucidate the necessity of data owner consent for data entry and alterations, ensuring transparent recording for the data owners. Furthermore, it seeks to safeguard the data flow, thus presenting the potential to serve as a national benchmark for data privacy in the Indonesian health sector.
HAC-POCD: Hardware-Aware Compressed Activity Monitoring and Fall Detector Edge POC Devices

Edge Point of Care (POC) devices are crucial for human activity recognition (HAR) and fall detection because they enable real-time analysis and fast intervention, which can greatly improve outcomes in situations of patient and elderly monitoring. The emergence of Artificial Intelligence (AI) has sparked renewed enthusiasm for integrating AI algorithms into low-power embedded systems, broadening the potential applications of the POC devices. This paper introduces HAC-POCD, a system for multimodal human activity recognition and fall detection that processes different modalities of complementary images, designs deep neural network (DNN) models, and employs model compression techniques including knowledge distillation and low bit-width quantization with memory-aware considerations to fit models within lower memory hierarchy levels, reducing latency and enhancing energy efficiency on resource-constrained edge devices. With compact inference model of 58 KB, we achieved 95.6% accuracy on HAR case-study. Our compact inference model, deployed on resource constrained hardware, GAPuino and Raspberry Pi 4, demonstrated low latencies within milliseconds and very high energy efficiency.
A Comparative Study of Sensing Technologies for Automatic Detection of Home Elopement

This paper presents four prototype implementations of a low-cost automatic system dedicated to home exit detection. The prototypes employ Bluetooth Low Energy beacon and Wi-Fi technology to identify the person of interest and alarm the caregiver about elopement event but differ in sensing technologies utilized to detect the event. We experimentally evaluate the technologies in the same environment and compare the results in terms of accuracy, response time and implementation cost.
ATHENA: a GPU-based Framework for Biomedical 3D Rigid Image Registration

3D image registration is one of the most complex algorithms employed in medical imaging applications. Software solution struggles to reach high accuracy in a reasonable time, therefore this work presents ATHENA, a framework for rigid 3D Image Registration, exploiting heterogeneous architecture for acceleration, and also providing support for memory-constrained devices. Moreover, ATHENA presents a tool for automatically generating misalignent between volumes, to perform a robustness analysis on different kinds of distortions. We compared ATHENA with SimpleITK, a well-known software library, and with a software version of the proposed algorithm, achieving a top speedup of 18.1× and 53.7× respectively.
Improvement of YOLOv7 with Attention Modules for Urinary Sediment Particle Detection

Urinary sediment analysis is crucial for assessing kidney health. Traditional machine learning techniques treat urine sediment particle detection as an image classification problem, but it is challenging due to low contrast and weak edges. To improve detection and focus on particles, attention-based urinary sediment detectors have been developed. This paper proposes a YOLOv7-based urinary sediment detection with attention modules, enhancing feature extraction and reducing background noise by locating attention modules in backbone part. Experimental results obtained from a urinary sediment dataset demonstrate that our proposed models outperform both the original YOLOv7 and YOLOv5s-CBL, which is a state-of-the-art urinary sediment detector, in terms of recall and mAP. Our models achieve significantly higher recall scores, surpassing the original YOLOv7 by 12.4% and the compared detector by 4.3%. Moreover, our models exhibit improved mAP scores, surpassing the original YOLOv7 by 7.4% and the compared detector by 1.6%.
CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms

DSP holds significant potential for important applications in Deep Neural Networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that aims to address this gap. By predicting the latency of operators on both CPU and DSP, CD-Sched automatically schedules the computation of operators to the appropriate computing device. This scheduling optimization accelerates the computation of individual operators and ultimately improves the overall training time of neural networks. In end-to-end training tasks, CD-Sched can significantly reduce the overall training time, with an average reduction of approximately 10.77%.
Recommendation System Based on Temporal Knowledge Graph Path Reasoning

Reasoning based on Knowledge graph has been studied and used for explanation recommendations because it can provide a clear explanation. However, the current recommendation methods based on Knowledge graphs use static Knowledge graphs, without considering the time information that has a significant impact on recommendation. In this paper, we propose a method of path recommendation reasoning on the temporal Knowledge graph. This method uses the time information of user and item interaction to provide better recommendations with more reasonable explanations. First, we use a representation learning method that has a good performance effect on the temporal Knowledge graph to code entities and relations, and then use the path reasoning method based on Reinforcement learning to recommend according to the embedded representation of entities and relationships. We conducted extensive experiments on three real-world datasets. The results indicate that the proposed method is effective in NDCG@K Recall@K Precision@K and HR@K Four evaluation indicators are superior to existing models.
"Application research of incorporating structured meteorological data into the \"Tianqing\" universal component"

This article aims to study the integration of multi-source structured meteorological data in the meteorological big data cloud platform (Tianqing), and explore the importance of universal components in meteorological data integration. It reviews the current status and technical challenges of meteorological data integration, and designs a universal component based on the “Tianqing” framework. The component has functions of data cleaning, organization, and visualization, and can effectively integrate multi-source structured meteorological data. Through the verification of practical application examples, the effectiveness and feasibility of the component are demonstrated, providing new ideas and methods for meteorological data integration.
Research on the Comprehensive Application of Cloud Technology and Web 3.0

The widespread application of cloud technology in the Internet makes cloud computing, cloud storage, cloud desktop, etc. widely used in various fields. The proposal of Web 3.0 provides new ideas for people to better utilize the Internet. The comprehensive application of cloud technology and web 3.0 has proposed a new idea for comprehensively utilizing the advantages of internet resources and cloud computing. This article analyzes the characteristics of cloud technology and web 3.0, and proposes an application concept of the "cloud technology+web 3.0" model based on this. Taking the construction of practical bases as an example, the advantages of this model are elaborated.
Stimulus Motion: A Basic Influence Factor in Visual Search

Background: Stimulus motion constitutes fundamental visual attributes within visual search tasks. Nevertheless, a comprehensive exploration of its impacts on visual search performance and the potential mechanisms governing these effects remains absent in the existing literature. Objective: The aim of the review is to fill the gap in the existing literature by reviewing the impact of stimulus motion on visual search performance. Method: This review discussed extant studies from two perspectives: stimulus motion and visual search performance, and theoretical models explaining underlying influential mechanisms. Results: The effects of stimulus motion on search performance differ under various types of conditions. Motion's influence on search performance varies depending on the specific type of motion; it can either have a negative or a positive effect. In cases of uniform motion, performance decreases as velocity increases under high-velocity conditions. However, motion's impact is negligible when velocity is low. Three theoretical models, including feature integration theory, Guided Search Model and visual salience theory have been introduced to explain how stimulus motion could influence the performance of visual search. Conclusion: We concluded from this review that stimulus motion may enhance or impair search performance under different conditions. This review will provide valuable references for practice and further research in the field of visual search.
Uncovering Self-Supervised Learning: From Current Applications to Future Trends

Deep neural networks require large amounts of labelled data for training so that the models can be applied more accurately to specific tasks. However, in reality, obtaining data with detailed labelling is quite expensive and difficult. In contrast, unlabelled data is available in abundance. Self-supervised learning is a machine learning method in which unlabelled data acts as a supervisory signal for learning using its own information. The method tries to make full use of the large amount of unlabelled data by capturing deeper, higher-order features in unlabelled data. In this paper, we first review several typical design ideas of self-supervised learning, summarising them into three main categories based on their training methods: end-to-end, memory bank and momentum. Then, the current state of research and current challenges faced by self-supervised learning algorithms for common tasks in NLP and CV are summarised in segments. Finally, the future research directions of self-supervised learning algorithms are envisioned based on these challenges. Through this paper, we have gained a deeper understanding of the design ideas of the three systems of self-supervised learning and their advantages and disadvantages. The research direction is indicated according to the difficulties encountered in the practical application of self-supervised learning, which makes it more promising on large-scale unlabelled datasets.
Artificial Intelligence Technology Applied to the Smart System of Physical Education Classroom

Abstract: Due to the impact of a new technological revolution, it makes the teaching mode of SSC more operable and popular, and the continuous development of information technology will also promote the popularity of smart sports classroom(SSC). New technologies are constantly being upgraded, future life is bound to be more intelligent, and information-based learning will become the norm, so that they can keep themselves in a continuous state of learning, remain open to new technologies, and better teach students sports knowledge in the way of smart classroom(SC). This paper creates a sports SC teaching system based on artificial intelligence(AI) technology, which uses deep neural network for face recognition, completes classroom attendance, and then realizes smart sports teaching through the Internet, mobile terminal, and cloud platform. By analyzing the teaching effect of AI technology in the sports SC again, we found that students' satisfaction with the teaching mode was as high as 87.7%, and the teaching mode could improve the awareness of smart learning and smart learning ability.
APP Navigation based on AR Technology

"ARrive" is a navigation APP that combines the existing real-time navigation and AR navigation and is conducive to commercial development, inherits the good functions of the current navigation products, enriches them, develops new models, and brings outstanding user experience. Different from the current market navigation, "ARrive" is an AR-based, real-time navigation supplemented navigation version, focusing on helping users compare business information more clearly and make choices, save time, and solve the problem of navigation accuracy. In addition, two strangers who use "ARrive" in a sea of people can socialize by rubbing shoulders, liking each other's steps and trajectory that day, and adding friends; In addition, AR night running event users can make friends who love sports during the event; After AR navigation plans the route, the pet displayed on the guide guides the user to their destination. The addition of these new features is designed to encourage users to explore and practice a fun and healthy lifestyle, and it is believed that after a period of optimization, the product can be taken to a new level.
Global Trends of Innovation and Elite Dominance in Rentier States

Abstract Chapter 3 applies a global quantitative, longitudinal analysis, assessing variables important to creative insecurity. The statistical analysis examines data from 2007 to 2016 and indicates how the degree of business elite market dominance and institutional performance in resource-wealthy countries affects innovation, which is a necessary component to achieve the demographic dividend.
Interactive Topic Modeling for the Broadcasting Media

Broadcasting companies produce large amounts of text and audiovisual content. Extracting meaningful insights from these sources requires efficient analysis methods, which are often only palatable to data scientists. Even in large organizations there is a critical knowledge gap: media experts manually curate work to derive insights, which is very time consuming, while engineers can use advanced data science methods but lack the domain expertise to derive key insights from the data. We propose to bridge this knowledge gap with INTEX, a human-in-the-loop interactive topic modeling application. We designed INTEX considering non-technical media experts as the main stakeholders of the application. A user evaluation shows that INTEX enables domain experts to extract and explore topics in an intuitive and efficient manner. Our work illustrates how complex applications can be made more accessible by hiding low-level details and linking these to high-level interpretations.
Comparing a Mid-air Two-Hand Pinching Point-and-Click Technique with Mouse, Keyboard and TouchFree

Some of our daily activities are performed by interacting with public touchscreens, such as food kiosks, bank tellers and newsstands. Nonetheless, the physical contact with these screens that are used by different people may be considered unhygienic. To avoid contact, some screens already integrate one-hand contactless interaction technologies, i.e. Leap Motion Controller, though they may lead to arm fatigue and slow performance. We present LeapPointer, a mid-air two-hand pinching point-and-click technique. Specifically, this technique relies on a Leap Motion device to track both hands, and proposes a new software tool that allows bimanual selection through pointing and pinching gestures. A user study was performed to compare LeapPointer with two other techniques: the common mouse/keyboard and the current UltraLeap’s TouchFree technique. Task completion time and accuracy as well as subjective data were gathered. The analysis of these data suggested that LeapPointer is significantly faster than the other touchless technique although less accurate. Self-reported fatigue was less with LeapPointer than with TouchFree.
Proposal of a set of heuristics for evaluation of wearable interfaces usability

Wearable technologies aim to merge computational elements into the user's daily life through integration with clothing. This type of technology distances itself from traditional computer models by its variety of communication options with the user and its need to integrate into clothing without drawing too much attention. Since the model diverges from conventional interfaces, there is a need to create a new set of heuristics to verify the quality of the interaction concerning usability. This paper discusses the problems related to wearable technologies and presents a set of formulated heuristics to meet this product type. It also offers the results of applying this set in a real scenario with a commercial device to validate it.
Remember Then Recommend: Critically Engaging Spell Checker Algorithms and Other Text Recommender Systems as Memory Infrastructures

Preview this article: Remember Then Recommend: Critically Engaging Spell Checker Algorithms and Other Text Recommender Systems as Memory Infrastructures, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/ce/86/1/collegeenglish32661-1.gif
Impact of Explanations for Trustworthy and Transparent Artificial Intelligence

Trust is a fundamental aspect in the interaction between humans and artificial intelligence (AI). Building and maintaining trust requires designing AI systems that are transparent, explainable and trustworthy, and providing appropriate feedback to users to ensure that they can understand the systems behaviour. This work aims at evaluating the impact of different explanations (local and global) on human’s trust and understanding of a facial expression recognizer. Results show that explanations are appreciated when present, but when no explanations are given, users apply their own mental model on how does the system work and trust it if their experience using it is positive.
Whitepaper: Remote usability laboratory to perform user and expert evaluations

This paper presents a new open source web platform called RUXLAB, to perform user and heuristic evaluation techniques in which is conceived as a Remote Usability Laboratory. The paper describes the technical architecture of the system based on the C4 model for visualising software architecture, as well as its key features, including the ability to customize both systems already developed with the inclusion of plugins that will increase the number features of user testing. The paper also discusses the potential benefits of their use on the field of the usability, creating a network of collaborators with the aim to share their usability studies as long as the working plan of development during the next years, with the aim to add more collaborators to the open source community.
Explainable activity recognition for the elderly

In this work, we present the recognition of daily activities of elderly people at their home environment, as a way to help this collective preserve their autonomy. To do so, we have taken three different neural networks with near state-of-the-art performance for human activity recognition and trained them on a big dataset of elderly people activities at home environments: ETRI-Activity3D. Then, we have explained the results adapting LIME to work with video input, to further validate the models and to help future users understand the models’ predictions. In addition, we have separated the explanations in space and time as a way of reducing the required computation time. Our main objective is that through the deployment of our work on a real environment, abnormal (such as repetitions or omissions) or alarming (like falls) activities can be discovered and help elderly people accordingly.
Design and development of the LATILL platform for retrieving adequate texts to foster reading skills in German

Reading and comprehending information in different languages is becoming increasingly important in our interconnected world. However, teaching such abilities can be challenging, and pre-packaged materials found in textbooks and readers may not always meet the needs of individual learners. To address this, the LATILL project aims to enhance the reading competencies of young Europeans by providing foreign language teachers with digital tools that enable them to select level-appropriate and engaging texts for their students. The project proposes a platform to ease the search and analysis of German literature based on specific topics and CEFR levels, along with additional tools and materials for working with authentic texts. The platform's features include generative AI in creating new elements (translations, simplifications, and generated images) and the development of text bundles, making it easier for teachers to find, share, and use appropriate materials. The initial user evaluations of the platform's prototype are promising, with the pilot users finding their features helpful and innovative.
Virtual Reality Skills Acquisition in Simulated Radiation Emergency Response Learning

Virtual reality technology is an increasingly accessible technology with great potential for use in emergency training, particularly for professionals such as firefighters, military personnel, and healthcare staff who often face stressful situations. However, many of these individuals may not have experience with virtual reality, making it difficult to integrate this technology into their training. This novelty effect can hinder their ability to effectively learn and perform in virtual environments. To overcome this, studies have suggested using tutorials to familiarize users with the virtual environment and improve their performance. In this context, an interactive tutorial is proposed to guide users on how to interact with a virtual environment depicting a radiation emergency. The efficacy of this tutorial will be evaluated by measuring the performance achieved by the user in a simulated radiation emergency.
Is It Worth the Hassle? Moving from One-directional to Multi-directional on Fitts' Law Evaluation

Based on the ISO 9241-411 standard to evaluate the efficiency and effectiveness of a pointing device, this article analyses the importance in the choice of either the one-directional point-select test or the multi-directional tapping test proposed for such a purpose. Traditionally, the multi-directional test is the chosen de facto to evaluate non one-directional pointing-devices such as mice, joysticks, trackballs or touchpads. However, the multi-directional test takes longer to complete, and the participants perceive it as more difficult. We conducted a user study to analyze the effect on throughput of using each of the tests while evaluating a mouse and a touchpad. The results show that while the multi-directional test takes longer and its error rate is lower, there is no significant difference on throughput. Our findings suggest that the one-directional test should not always be discarded de facto in front of the multi-directional test when evaluating the throughput of a pointing device.
Kalman Filter for Dynamic Imaging Based on Complex Empirical Covariances

Kalman filter (KF) is a priori unsuitable for the estimation from sample covariance matrices as they cannot be formulated analytically as a function of state parameters to be estimated. In this work, we propose a novel KF adapted to sample covariance matrices under the unconditional signal model. It is evaluated on simulated data representative of a dynamic radio astronomy framework, considering multiple uncorrelated sources and Gaussian noise. The results show that our method is capable of effectively tracking moving sources in complex scenes with greater accuracy than a KF regularized in a standard way, i.e., without proper formalization of the noise model.