Reviewerly/exHarmony huggingface dataset:
Annotation Scheme for Named Entity Recognition and Relation Extraction Tasks in the Domain of People with Dementia

The number of people suffering from dementia increases with the increase of the aging population. This, in turn, is associated with the need of innovative methods for supporting the family carers of people with dementia (PwD) as the burden of taking care of their relatives results in increased stress and health conditions. Technological solutions could potentially help caregiver to cope with stressful situations caused by the behavior of PwD. In particular, we envision an ontology-based chatbot system that provides recommendations to family carers for coping with the challenging behavior of PwD. The ontology is developed in a semi-automated manner, through manual modeling of domain knowledge and through automated knowledge extraction from textual sources. The challenge in the automated ontology learning is the lack of annotated language resources for this domain. Therefore, in this work we propose an annotation scheme for the extraction of entities and relationships for the domain of agitation and dyadic relationship as a part of stability of care arrangements for PwD. We use the annotation scheme to annotate a textual corpus of informal texts collected from forums for family carers of PwD and we calculate the inter-rater reliability. The results show a moderate overlapping between the annotators. The proposed annotation scheme could serve as a template for providing ground truth for textual datasets in the scarcely annotated domain of PwD.
Know Your Library: How the libjpeg Version Influences Compression and Decompression Results

Introduced in 1991, libjpeg has become a well-established library for processing JPEG images. Many libraries in high-level languages use libjpeg under the hood. So far, little attention has been paid to the fact that different versions of the library produce different outputs for the same input. This may have implications on security-related applications, such as image forensics or steganalysis, where evidence is generated by tracking small, imperceptible changes in JPEG-compressed signals. This paper systematically analyses all libjpeg versions since 1998, including the forked libjpeg-turbo (in its latest version). It compares the outputs of compression and decompression operations for a range of parameter settings. We identify up to three distinct behaviors for compression and up to six for decompression.
OCR-oriented Master Object for Text Image Captioning

Text image captioning aims to understand the scene text in images for image caption generation. The key issue of this challenging task is to understand the relationship between the text OCR tokens and images. In this paper, we propose a novel text image captioning method by purifying the OCR-oriented scene graph with themaster object. The master object is the object to which the OCR is attached, which is the semantic relationship bridge between the OCR token and the image. We consider the master object as a proxy to connect OCR tokens and other regions in the image. By exploring the master object for each OCR token, we build the purified scene graph based on the master objects and then enrich the visual embedding by the Graph Convolution Network (GCN). Furthermore, we cluster the OCR tokens and feed the hierarchical information to provide a richer representation. Experiments on the TextCaps validation and test dataset demonstrate the effectiveness of the proposed method.
LockedDown: Exploiting Contention on Host-GPU PCIe Bus for Fun and Profit

The deployment of modern graphics processing units (GPUs) has grown rapidly in both traditional and cloud computing. Nevertheless, the potential security issues brought forward by this extensive deployment have not been thoroughly investigated. In this paper, we disclose a new exploitable side-channel vulnerability that ubiquitously exists in systems equipped with modern GPUs. This vulnerability is due to measurable contention caused on the host-GPU PCIe bus. To demonstrate the exploitability of this vulnerability, we conduct two case studies. In the first case study, we exploit the vulnerability to build a cross-VM covert channel that works on virtualized NVIDIA GPUs. To the best of our knowledge, this is the first work that explores covert channel attacks under the circumstances of virtualized GPUs. The covert channel can reach a speed up to 90 kbps with a considerably low error rate. In the second case study, we exploit the vulnerability to mount a website fingerprinting attack that can accurately infer which web pages are browsed by a user. The attack is evaluated against popular browsers like Chrome and Firefox on both Windows and Linux, and the results show that this fingerprinting method can achieve up to 95.2% accuracy. In addition, the attack is evaluated against Tor browser, and up to 90.6% accuracy can be achieved.
A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field

The head-mounted eye-tracking technology is often used to manipulate the motion of servo platform in remote tasks, so as to achieve visual aiming of servo platform, which is a highly integrated human-computer interaction effect. However, it is difficult to achieve accurate manipulation for the uncertain meanings of gaze points in eye-tracking. To solve this problem, a method of classifying gaze points based on a conditional random field is proposed. It first describes the features of gaze points and gaze images, according to the eye visual characteristic. An LSTM model is then introduced to merge these two features. Afterwards, the merge features are learned by CRF model to obtain the classified gaze points. Finally, the meaning of gaze point is classified for target, in order to accurately manipulate the servo platform. The experimental results show that the proposed method can classify more accurate target gaze points for 100 images, the average evaluation values Precision = 86.81%, Recall = 86.79%, We = 86.79%, these are better than relevant methods. In addition, the isolated gaze points can be eliminated, and the meanings of gaze points can be classified to achieve the accuracy of servo platform visual aiming.
“There are a LOT of moral issues with biowearables” ... Teaching Design Ethics through a Critical Making Biowearable Workshop

There has been an increasing focus on teaching youth about design ethics as part of technical literacy. Biowearables are an emerging technology in which devices worn on children's bodies are used to track, monitor and provide feedback about their biological processes. In this paper we describe an online critical making workshop designed to enable students in middle school years to develop technical literacy skills that include reflection on issues related to design ethics. We investigated if and how our workshop enabled eleven youth, aged 12-14, to reflect through processes of making their own biowearable, on potential negative impacts of biowearables on their developing senses of identity, agency, autonomy and authenticity. The workshop elements included facilitated activities using custom created biowearable-tangible kit and ethics cards. Through qualitative coding and thematic analysis of moments of reflection captured with video, chat, and design journals we gathered evidence of the feasibility of promoting critical making as a means to cultivate technical literacy in youth. Our findings suggest the potential of teaching design ethics through critical making workshops and reveal a range of ways that reflection on ethical issues can be supported during making. We interpret our empirical evidence to further explore how workshop elements supported, or failed to support, learning outcomes and generalize our interpretations to propose preliminary guidance about workshop mechanisms that might be used to support ethical reflection during making.
Invisible Work in Two Frontline Health Contexts

Frontline health workers provide essential services for their communities, but much of their work remains invisible—undervalued and underappreciated. Examining this invisible work ensures new technologies do not amplify or reinforce inequitable power structures, especially as governments and organizations push to digitize health work processes. We build on a burgeoning conversation by studying how invisible work manifests and how this invisibility can be challenged in two contexts of frontline health: home health aides in New York City, USA and Accredited Social Health Activists (ASHAs) in Uttar Pradesh, India. We highlight three shared manifestations of invisible work: (1) work done outside of the workers’ boundaries (2) work done to gain and share knowledge and (3) work done to manage relationships. These common categories are experienced differently in the two contexts, raising nuances to consider when designing technology for frontline health workers. We discuss these nuances and other tensions through concrete examples of how workers can escalate feedback and conflicts, quantify implicit expertise about patients, or build more awareness of their situation. Our paper guides the creation of technologies that take into account a more comprehensive understanding of the frontline health workers’ processes and highlight more of their contributions.
FECC-Net: A Novel Feature Enhancement and Context Capture Network Based on Brain MRI Images for Lesion Segmentation

In recent years, the increasing incidence of morbidity of brain stroke has made fast and accurate segmentation of lesion areas from brain MRI images important. With the development of deep learning, segmentation methods based on the computer have become a solution to assist clinicians in early diagnosis and treatment planning. Nevertheless, the variety of lesion sizes in brain MRI images and the roughness of the boundary of the lesion pose challenges to the accuracy of the segmentation algorithm. Current mainstream medical segmentation models are not able to solve these challenges due to their insufficient use of image features and context information. This paper proposes a novel feature enhancement and context capture network (FECC-Net), which is mainly composed of an atrous spatial pyramid pooling (ASPP) module and an enhanced encoder. In particular, the ASPP model uses parallel convolution operations with different sampling rates to enrich multi-scale features and fully capture image context information in order to process lesions of different sizes. The enhanced encoder obtains deep semantic features and shallow boundary features in the feature extraction process to achieve image feature enhancement, which is helpful for restoration of the lesion boundaries. We divide the pathological image into three levels according to the number of pixels in the real mask area and evaluate FECC-Net on an open dataset called Anatomical Tracings of Lesions After Stroke (ATLAS). The experimental results show that our FECC-Net outperforms mainstream methods, such as DoubleU-Net and TransUNet. Especially in small target tasks, FECC-Net is 4.09% ahead of DoubleU-Net on the main indicator DSC. Therefore, FECC-Net is encouraging and can be relied upon for brain MRI image applications.
An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model

Abstract Hand gestures are the nonverbal communication done by individuals who cannot represent their thoughts in form of words. It is mainly used during human‐computer interaction (HCI), deaf and mute people interaction, and other robotic interface applications. Gesture recognition is a field of computer science mainly focused on improving the HCI via touch screens, cameras, and kinetic devices. The state‐of‐art systems mainly used computer vision‐based techniques that utilize both the motion sensor and camera to capture the hand gestures in real‐time and interprets them via the usage of the machine learning algorithms. Conventional machine learning algorithms often suffer from the different complexities present in the visible hand gesture images such as skin color, distance, light, hand direction, position, and background. In this article, an adaptive weighted multi‐scale resolution (AWMSR) network with a deep embedded hybrid convolutional neural network and long short term memory network (hybrid CNN‐LSTM) is proposed for identifying the different hand gesture signs with higher recognition accuracy. The proposed methodology is formulated using three steps: input preprocessing, feature extraction, and classification. To improve the complex visual effects present in the input images, a histogram equalization technique is used which improves the size of the gray level pixel in the image and also their occurrence probability. The multi‐block local binary pattern (MB‐LBP) algorithm is employed for feature extraction which extracts the crucial features present in the image such as hand shape structure feature, curvature feature, and invariant movements. The AWMSR with the deep embedded hybrid CNN–LSTM network is applied in the two‐benchmark datasets namely Jochen Triesch static hand posture and NUS hand posture dataset‐II to detect its stability in identifying different hand gestures. The weight function of the deep embedded CNN‐LSTM architecture is optimized using the puzzle optimization algorithm. The efficiency of the proposed methodology is verified in terms of different performance evaluation metrics such as accuracy, loss, confusion matrix, Intersection over the union, and execution time. The proposed methodology offers recognition accuracy of 97.86% and 98.32% for both datasets.
Construction and Application of a Human-Computer Collaborative Multimodal Practice Teaching Model for Preschool Education

This paper adopts the multimodal approach of human-computer collaboration to conduct an in-depth study and analysis of the practical teaching model of preschool education, and applies the designed model to the actual teaching process. The application of multimodal theory to preschool teaching is chosen to theoretically help expand the research scope of multimodal theory and enrich the research of preschool teaching, and practically help break through the previous single-modal teaching model, further enrich the theoretical guidance of preschool teaching, and improve the quality of preschool classroom teaching. Then, from the perspective of human-machine synergy, this paper analyzes the advantages of artificial intelligence technology and teachers in the English classroom, puts forward the new roles of teachers and learners in the human-computer cooperation teaching environment, and discusses the significance and value of applying the four main modules of human-computer cooperation teaching, human-computer gesture mapping and human-computer cooperation manipulator control in the preschool classroom. According to the physical structure of hand joints, the human hand joint angles are obtained through the inverse kinematic solution, and the human hand joint angles correspond to the dexterous manipulator one by one so that the dexterous manipulator can be controlled to imitate the human hand to complete flexible gesture movements and realize the vision-based collaborative human-machine control of the dexterous manipulator. Combined with Gagne's nine teaching events, a model of the English teaching process based on human-computer collaboration was constructed. Based on this model, the "EasyDotWise English Teaching System" was designed to combine the basic lesson types of preschool classroom teaching and the secondary objectives of the English curriculum standards, including "reading text-reading aloud evaluation," "playing speech-sound recognition," and "presenting text-selection." We designed and implemented three types of teaching activities: "reading text-reading aloud assessment," "playing phonetic sounds-sound identification," and "presenting text-comprehension selection."
Research of Consumption Behavior Prediction Based on Improved DNN

In terms of the low accuracy and unsatisfactory effect of traditional prediction models for consumption behavior, in the study of deep learning DNN model, a consumption behavior prediction model based on rDNN model is proposed. By choosing the appropriate function as the activation function of the model, the random sampling method is used to select negative samples of consumer behavior data to determine the N/P ratio, which improves the DNN model. Based on the improved DNN model, a consumer behavior prediction model based on the rDNN model is constructed. The results show that when the tanh function is used as the activation function and the ratio of N/P is 3, the rDNN model has the best prediction effect on consumption behavior, with AUC value of 0.8422 and the fastest operation efficiency of 434.36 s. Compared with traditional prediction models, DNN, and KmDNN deep learning models, the proposed model has more reliable prediction results and can be used to predict actual consumption behavior.
Fast Fusion of Hyperspectral and Multispectral Images: A Tucker Approximation Approach

Hyperspectral super-resolution based on coupled Tucker decomposition has been recently considered in the remote sensing community. The state-of-the-art approaches did not fully exploit the coupling of information contained in hyperspectral and multispectral images of the same scene. This paper proposes a new algorithm that overcomes this limitation. It accounts for both the high-resolution and the low-resolution information in the model by solving a set of least-squares problems. In addition, we provide exact recovery conditions for the super-resolution image in the noiseless case. Our simulations show that the proposed algorithm achieves very good reconstruction quality with a very low computational complexity.
Color Matching in Children’s Room Based on Computer Interactive Experience

In order to improve the effect of color matching in children’s room, this paper studies the color matching of children’s room combined with computer interactive experience technology. Moreover, this paper analyzes the basic knowledge of colorimetry, the principle of color measurement equipment, the basic concepts and mathematical models of color conversion, equipment color characteristic correction, and color gamut matching used in color management research and analyzes the color perception in the process of computer interaction experience. The color design method based on hue harmony is a color harmony design method extracted on the basis of the basic color system and Chevrel’s theory of color harmony. The experimental research shows that the indoor color matching system for children’s room based on computer interactive experience proposed in this paper has a good color matching effect.
A new speech corpus of super-elderly Japanese for acoustic modeling

The development of accessible speech recognition technology will allow the elderly to more easily access electronically stored information. However, the necessary level of recognition accuracy for elderly speech has not yet been achieved using conventional speech recognition systems, due to the unique features of the speech of elderly people. To address this problem, we have created a new speech corpus named EARS (Elderly Adults Read Speech), consisting of the recorded read speech of 123 super-elderly Japanese people (average age: 83.1), as a resource for training automated speech recognition models for the elderly. In this study, we investigated the acoustic features of super-elderly Japanese speech using our new speech corpus. In comparison to the speech of less elderly Japanese speakers, we observed a slower speech rate and extended vowel duration for both genders, a slight increase in fundamental frequency for males, and a slight decrease in fundamental frequency for females. To demonstrate the efficacy of our corpus, we also conducted speech recognition experiments using two different acoustic models (DNN-HMM and transformer-based), trained with a combination of data from our corpus and speech data from three conventional Japanese speech corpora. When using the DNN-HMM trained with EARS and speech data from existing corpora, the character error rate (CER) was reduced by 7.8% (to just over 9%), compared to a CER of 16.9% when using only the baseline training corpora. We also investigated the effect of training the models with various amounts of EARS data, using a simple data expansion method. The acoustic models were also trained for various numbers of epochs without any modifications. When using the Transformer-based end-to-end speech recognizer, the character error rate was reduced by 3.0% (to 11.4%) by using a doubled EARS corpus with the baseline data for training, compared to a CER of 13.4% when only data from the baseline training corpora were used.
Social Agriculture: Examining the Affordances of Social Media for Agricultural Practices

This paper examines the experiences and perspectives of Kenyans who use social media platforms as part of their agricultural livelihoods. Through a mixed-methods study of 324 survey respondents and 81 interviews, we present data that demonstrates the significance and shape of "social agriculture" in the Kenyan agricultural landscape. We complement previous ICT4D/HCI4D literature that has primarily focused on purpose-built agricultural platforms through a novel focus on farmers' appropriation of existing social media platforms to enter the agricultural sector and diversify agricultural livelihoods. Our study highlights new insights into the growing phenomenon of using social media platforms for agriculture practice, including how these platforms afford particular practices around the buying and selling of produce and information on social media platforms. We also identify challenges around trust and online abuse and describe the strategies employed by participants to counter them. Lastly, we build on our findings to highlight the affordances and constraints of using social media platforms, thus contributing to the field an initial conceptualization of social agriculture as a space of commerce. We offer eight design considerations for both technology designers and international development stakeholders to strengthen the potential for social platforms to afford social agricultural practices that enrich individual lives and livelihoods.
Quality Evaluation Method of College Sports Long Jump Training Course Based on Genetic Algorithm

In order to improve the accuracy of the quality evaluation of college sports long jump training courses, this paper studies the quality evaluation method of college sports long jump training courses based on a genetic algorithm. According to the purpose of college long jump training course evaluation, basic principles of teaching quality evaluation and classroom teaching evaluation standards constructs the establishment process of evaluation index system and calculates the weight of long jump training course quality evaluation index based on AHP method, so as to provide index basis for subsequent quality evaluation; The quality evaluation model of the long jump training course is constructed based on the genetic algorithm. Through cross operation and mutation operation based on adaptive mutation probability, the high accuracy evaluation of college sports long jump training course quality is realized. The experimental results show that the evaluation accuracy of this design method is higher than 97.21%, and the evaluation satisfaction is higher than 97.15%, which significantly improves the accuracy of the university sports long jump training courses and has practical significance in the course evaluation.