Reviewerly/exHarmony huggingface dataset:
Enhancing Biomedical Education with Real-Time Object Identification through Augmented Reality

This paper introduces a mobile augmented reality (AR) platform aimed at training first and second-year STEM majors in college, specifically focusing on the identification and usage of laboratory instruments. The platform addresses the challenges faced by students in introductory STEM courses and utilizes computer vision and AR technologies to create an engaging and learner-centric teaching environment. The platform incorporates features such as a knowledge test, guided navigation using Google Map API, and the ability to build a library of laboratory instruments through augmented reality. Developed using the Unity 3D Game engine and ARkit-XR plugin, the platform currently identifies six laboratory instruments in the biology department, with plans for expansion. The target audience comprises freshman biology majoring and minoring students at the university. The application will be integrated as a pre-lab activity during the semester, requiring the use of specialized equipment. The effectiveness of the platform will be evaluated through a user study conducted within introductory biology courses. This mobile AR platform caters to a specific market niche, addressing the existing resource gap at the university and aiming to enhance the learning experience for biomedical students. Future research will focus on expanding the platform to include chemistry and physics labs while incorporating additional functionalities to deepen students' understanding of laboratory instruments and improve the overall learning environment.
Hand Gesture Wheelchair Control Based on the Wrist Rotation with Agglomerative Hierarchical Clustering Method

A person with disabilities is in a state of self-limitation, which can be physical, cognitive, mental, sensory, emotional, and other, so they need assistive devices. A wheelchair is a tool used to help people with walking difficulties. The use of conventional wheelchairs and electric wheelchairs with a joystick is uncomfortable for a person who has a problem with fingers and toes. This study provides an alternative method to control an electric wheelchair using wrist rotation to solve the limitations of conventional wheelchairs and enhances mobility for individuals without fingers. Two flex sensors and one gyro sensor is attached to the hand to detect five wrist rotation for forward, backward, turn right, turn left, and normal (stop). An Agglomerative Hierarchical Clustering (AHC) is implemented to recognize the wrist rotation. There are 450 training data for each wrist rotation with 100% accuracy. The developed algorithms is tested for real implementation with six adults operating the wheelchair along the trajectory. The result shows that this method is available to control the electric wheelchair. Finally, this intelligent electric wheelchair system controlled by wrist rotation based on the AGHC method can be used as an alternative to help persons with a disability in moving, especially those with problems with their fingers.
Multi-AUV Static Target Search Based on Improved PSO

In recent years, with the development of AUVs, AUVs have been more applied in military and civilian fields, and the cooperative operation of multiple AUVs has become one of the research hotspots by virtue of its high efficiency and strong robustness in performing tasks. For the problem that it is difficult to fully cover the regional static target search in limited time, Voronoi diagram and biological competition model are used to realize the reasonable allocation of multiple AUVs and search area, and convert the multiple AUV search into single AUV search to simplify the problem and improve the search efficiency. In the search path planning within the sub-region, for the problems that the traditional particle swarm algorithm is easy to fall into local extrema and not applicable to continuous space, an improved particle swarm algorithm is proposed, combining the discrete particle swarm idea and adjusting the algorithm parameter factors to adapt the discrete space while accelerating the jump out of local extrema and improving the probability of detecting the target and the environment. The simulation results show that compared with the pure random route and the standard particle swarm route, the detection probability of target and environment is improved by 54.3% and 7.7%, respectively, which proves its effectiveness.
Multiscale Spatial Patterns in Giant Dike Swarms Identified Through Objective Feature Extraction

Abstract Dike swarms are ubiquitous on terrestrial planets and represent the frozen remnants of magma transport networks. However, spatial complexity, protracted emplacement history, and uneven surface exposure typically make it difficult to quantify patterns in dike swarms on different scales. In this study, we address this challenge using the Hough transform (HT) to objectively link dissected dike segments and analyze multiscale spatial structure in dike swarms. We apply this method to swarms of three scales: the Spanish Peaks, USA; the Columbia River Flood Basalt Group (CRBG), USA; the Deccan Traps Flood Basalts, India. First, we cluster dike segments in HT space, recognizing prevalent linearly aligned structures that represent single dikes or dike packets, with lengths up to 10 − 30 x the mapped mean segment length. Second, we identify colinear and radial dike segment mesoscale structures within each data set, using the HT to segment swarms into constituent spatial patterns. We show that for both the CRBG and Deccan Traps, a single radial or circumferential swarm does not well characterize the data. Instead, multiple and sometimes overlapping mesoscale linear and radial features are prevalent suggesting a complex history of crustal stresses. The HT can provide useful insights in a variety of geologic settings where many quasi‐linear features, at any scale, are superimposed spatially.
Speaker Profiling Based on the Short-Term Acoustic Features of Vowels

Speech samples can provide valuable information regarding speaker characteristics, including their social backgrounds. Accent variations with speaker backgrounds reflect corresponding acoustic features of speech, and these acoustic variations can be analyzed to assist in tracking down criminals from speech samples available as forensic evidence. Speech accent identification has recently received significant consideration in the speech forensics research community. However, most works have utilized long-term temporal modelling of acoustic features for accent classification and disregarded the stationary acoustic characteristics of particular phoneme articulations. This paper analyzes short-term acoustic features extracted from a central time window of English vowel speech segments for accent discrimination. Various feature computation techniques have been compared for the accent classification task. It has been found that using spectral features as an input gives better performance than using cepstral features, with the lower filters contributing more significantly to the classification task. Moreover, detailed analysis has been presented for time window durations and frequency bin resolution to compute short-term spectral features concerning accent discrimination. Using longer time durations generally requires higher frequency resolution to optimize classification performance. These results are significant, as they show the benefits of using spectral features for speaker profiling despite the popularity of cepstral features for other speech-related tasks.
Retrieving Chinese Questions and Answers Based on Deep-Learning Algorithm

Chinese open-domain reading comprehension question answering is a task in the field of natural language processing. Traditional neural network-based methods lack interpretability in answer reasoning when addressing open-domain reading comprehension questions. This research is grounded in cognitive science’s dual-process theory, where System One performs question reading and System Two handles reasoning, resulting in a novel Chinese open-domain question-answering retrieval algorithm. The experiment employs the publicly available WebQA dataset and is compared against other reading comprehension methods, with the F1-score reaching 78.66%, confirming the effectiveness of the proposed approach. Therefore, adopting a reading comprehension question-answering model based on cognitive graphs can effectively address Chinese reading comprehension questions.
Enhancing Software Project Monitoring with Multidimensional Data Repository Mining

Software project development and maintenance activities have been reported in various repositories. The data contained in these repositories have been widely used in various studies on specific problems, e.g., predicting bug appearance, allocating issues to developers, and identifying duplicated issues. Developed analysis schemes are usually based on simplified data models while issue report details are neglected. Confronting this problem requires a deep and wide-ranging exploration of software repository contents adapted to their specificities, which differs significantly from classical data mining. This paper is targeted at three aspects: the structural and semantic exploration of repositories, deriving characteristic features in value and time perspectives, and defining the space of project monitoring goals. The considerations presented demonstrate a holistic image of the project development process, which is useful in the assessment of its efficiency and identification of imperfections. The original analysis introduced in this work was verified using open source and some commercial software project repositories.
Extreme Learning Machine-Enabled Coding Unit Partitioning Algorithm for Versatile Video Coding

The versatile video coding (VVC) standard offers improved coding efficiency compared to the high efficiency video coding (HEVC) standard in multimedia signal coding. However, this increased efficiency comes at the cost of increased coding complexity. This work proposes an efficient coding unit partitioning algorithm based on an extreme learning machine (ELM), which can reduce the coding complexity while ensuring coding efficiency. Firstly, the coding unit size decision is modeled as a classification problem. Secondly, an ELM classifier is trained to predict the coding unit size. In the experiment, the proposed approach is verified based on the VVC reference model. The results show that the proposed method can reduce coding complexity significantly, and good image quality can be obtained.
Improvement of a Rapid Method of Detecting Gasoline Detergency Based on the Image Recognition

The detergency of motor gasoline is closely related to vehicle exhaust emissions and fuel economy. This paper proposed an improved method for the rapid detection of gasoline detergency based on the deposit images of test gasoline on aluminum plates produced by a multichannel gasoline detergency simulation test (MGST). The detection algorithm system was structured to recognize the deposit plate images by computer vision based on the convolutional neural networks (CNNs). Compared with the traditional simulation test, the improved MGST method resulted in significant reductions in fuel consumption, cost, and test time. The performance of three transfer learning models (Inception-ResNet-V2, Inception-V3, and ResNet50-V2) and a customized CNN was evaluated in the detection algorithm system, and their detection accuracies reached 94, 94, 88, and 82%. Inception-RsNet-V2 was selected due to its higher accuracy and better robustness. Based on the model interpretation, it is evident that the model undergoes feature extraction from the sediment deposits on the deposit plate. Subsequently, it employed the acquired deposit features to accurately detect gasoline samples that failed to meet detergency standards. This approach was proved to be effective in enhancing the detection process and ensuring reliable results for gasoline detergency evaluation. It is beneficial to environmental protection regulators for managing market gasoline detergency and urban mobile source pollution. In addition, a deposit plate image database should be established to further improve the detection model performance during the environmental regulation.
ComAvg: Robust decentralized federated learning with random committees

Federated learning (FL) has been widely used in IoT applications. However, FL is vulnerable to various attacks in its each phase. Existing defense in federated learning mainly focus on the centralized setting. And centralized parameter-server settings require a trusted third party to collect and distribute model parameters. However, the requirement of a trusted third party cannot always be satisfied in many cases. Meanwhile, the centralized settings suffer from the inherent vulnerability of single-point-of-failure (SPOF), in which the whole system cease to function once the parameter server is broken. Therefore, decentralized federated learning has gain great attention recently. Existing conventional defense strategies are mostly designed for the centralized parameter-server architecture. The problem is that these conventional defense strategies cannot cope with new challenges occurred in highly decentralized settings of FL. Firstly, in a trustless setting, malicious participants can cause breakdown to the whole system on the communication level by disrupting model exchanges. Secondly, current defensive methods cannot effectively identify and rule out malicious participants. In either case, a harmful bias hurts the performance even if malicious participants do not perform model-level attacks. Therefore, defensive strategies for the decentralized-manner FL are in urgent need. To this end, we propose a committee-based FL system, named ComAvg, under a trustless setting. ComAvg provides a general coordination scheme for robust aggregation of distributed learning. With reliability assessment scheme to expel abnormal participants and fortified classic model exchange methods, the conventional centralized methods of FL can be easily modified into decentralized versions to cope with the two challenges aforementioned. Finally, we implement a prototype of ComAvg and perform various groups of evaluations on its robustness. The prototype-based evaluation results and theoretical analysis show that the proposed ComAvg is effective against model attacks such as sign-flipping and communication-level isolating attacks.
Using Blockchain to Decentralize Product Development

Digital technologies such as IoT, Cloud, and mobile computing have enabled product innovation, consumer interactions, and efficiency. Blockchain technology has altered how organizations work, providing a paradigm shift in resilience, security, and efficiency. Blockchain enables agile value chains, product innovations, and technological fusion. Every company considers research and product development expensive. Investments are risky, especially if the new product version differs considerably from their current portfolio. Firms must share high investment risks through joint ventures or cooperative product development. However, for collaborative product development, trust and transparency must be vital. Decentralizing design data interchange, funding, and marketing with correct crediting of contributions is one of the methods for multiple-organization collaboration. Blockchains technologies can be employed for such collaborations. This study explores how blockchain can decentralize product development worldwide to meet security, scalability, efficiency, and incentivization criteria.
The Lifespan of Human Activity Recognition Systems for Smart Homes

With the growing interest in smart home environments and in providing seamless interactions with various smart devices, robust and reliable human activity recognition (HAR) systems are becoming essential. Such systems provide automated assistance to residents or to longitudinally monitor their daily activities for health and well-being assessments, as well as for tracking (long-term) behavior changes. These systems thus contribute towards an understanding of the health and continued well-being of residents. Smart homes are personalized settings where residents engage in everyday activities in their very own idiosyncratic ways. In order to provide a fully functional HAR system that requires minimal supervision, we provide a systematic analysis and a technical definition of the lifespan of activity recognition systems for smart homes. Such a designed lifespan provides for the different phases of building the HAR system, where these different phases are motivated by an application scenario that is typically observed in the home setting. Through the aforementioned phases, we detail the technical solutions that are required to be developed for each phase such that it becomes possible to derive and continuously improve the HAR system through data-driven procedures. The detailed lifespan can be used as a framework for the design of state-of-the-art procedures corresponding to the different phases.
Iconicity bias and duration.

Descriptions of durational relations can be ambiguous, for example, the description "one meeting happened during another" could mean that one meeting started before the other ended, or it could mean that the meetings started and ended simultaneously. A recent theory posits that people mentally simulate descriptions of durational events by representing their starts and ends along a spatial axis, that is, an iconic representation of time. To draw conclusions from this iconic mental model, reasoners consciously scan it in the direction of earlier to later timepoints. The account predicts an iconicity bias: People should prefer descriptions that are congruent with an iconic scanning procedure-descriptions that mention the starts of events before the ends of events-over logically equivalent but incongruent descriptions. Six experiments corroborated the prediction; they show that iconicity biases in temporal reasoning manifest in cases when reasoners consciously evaluate the durations of events. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Syntactic complexity in German Easy Language

Complex events are usually expressed through language to an equally complex degree. If an event has an origin, causal sentences are usually used to express this connection. These, in turn, consist of at least two parts expressing the two factors of causality – cause and effect. However, this direct transfer of complexity does not follow the syntactic rules of Easy Language. The guidelines of German Easy Language propose a prohibition of multipart sentences and indicate how to convert them into autonomous individual sentences. The assumption is that so-called complex sentences cannot be understood by the primary target group and that it means too much effort for the working memory. According to the rules and guidelines, restructuring into individual sentences should reduce this workload (cf. Bredel/Maaß 2016a, b). But this assumption is mainly based on theoretical approaches.
Competences for Easy Language translation

Easy Language is a means to overcome communication barriers enabling people with special communicative needs to participate independently in various areas of life. In order to meet these needs, translators for Easy Language need to have substantial expert knowledge that will be discussed in this article.
Language Models for Everyone—Responsible and Transparent Development of Open Large Language Models

Large language and multimodal models are revolutionising many aspects of human work and creativity, with broad potential not only as chatbots and for information retrieval but as interaction points and integrators of large technical systems. However, these technologies’ significance comes with societal challenges regarding accessibility, applicability, alignment, and inclusion. The development of open-source large language models may lead to more transparent and responsible use of such technologies. Here, we discuss the importance of open language models, their challenges, and how they were managed in a specific use case.
Oral cancer detection using transfer learning-based framework from histopathology images

Oral cancer is a serious worldwide health problem. It might be seen in the face, oral glands, neck, or mouth of the patient. Cancer detection utilizing histopathology images aids in easing and forecasting abnormality. Furthermore, it delivers better outcomes if biological procedures are implemented appropriately, however, there are large opportunities for human errors and blunders during physical examinations. The advancement of deep learning techniques may open the door to a better detection of oral cancer from histopathology images, which will be advantageous to lab staff and medical professionals. Our study presents an in-depth analysis of 10 pre-trained deep convolutional neural network models using transfer learning approach for the detection of oral cancer. The experiment was performed for two classes, i.e., normal and oral squamous cell carcinoma. The results show that the VGG19 model with data augmentation was able to attain the highest classification accuracy of 96.26% using the transfer learning technique. We have also introduced an approach by integrating the VGG19 pre-trained model with a custom naïve inception block. This fusion of two well-known models harnesses their complementary strengths and results in a more robust architecture. The incorporation of the inception block addresses limitations observed in the VGG19 framework, such as vanishing gradient issues and excessive computational requirements. The proposed model for detection of oral cancer has undergone a thorough block-wise fine tuning. Our results demonstrate the superior performance of our deep learning architecture compared to existing literature, highlighting its potential to enhance the detection and diagnosis of oral cancer.
Smart - Contract Privacy Preservation Mechanism

Sensitive records stored in the cloud such as healthcare records, private conversation and credit card information are targets of hackers and privacy abuse. Current information and record management systems have difficulties achieving privacy protection of such sensitive records in a secure, transparent, decentralized and trustless environment. The Blockchain technology is a nascent and a promising technology that facilitates data sharing and access in a secure, decentralized and trustless environment. The technology enables the use of smart contracts that can be leveraged to complement existing traditional systems to achieve security objectives that were never possible before. In this paper, we propose a framework based on Blockchain technology to enable privacy-preservation in a secured, decentralized, transparent and trustless environment. We name our framework SmartCoAuth. It is based on Ethereum Smart Contract functions as the secure, decentralized, transparent authentication and authorization mechanism in the framework. It also enables tamper-proof auditing of access to the protected records. We analysed how SmartCoAuth could be integrated into a cloud application to provide reliable privacy-preservation among stakeholders of healthcare records stored in the cloud. The proposed framework provides a satisfactory level of data utility and privacy preservation.