Reviewerly/exHarmony huggingface dataset:
Human-Like Rule Learning from Images Using One-Shot Hypothesis Derivation

Unlike most computer vision approaches, which depend on hundreds or thousands of training images, humans can typically learn from a single visual example. Humans achieve this ability using background knowledge. Rule-based machine learning approaches such as Inductive Logic Programming (ILP) provide a framework for incorporating domain specific background knowledge. These approaches have the potential for human-like learning from small data or even one-shot learning, i.e. learning from a single positive example. By contrast, statistics based computer vision algorithms, including Deep Learning, have no general mechanisms for incorporating background knowledge. This paper presents an approach for one-shot rule learning called One-Shot Hypothesis Derivation (OSHD) based on using a logic program declarative bias. We apply this approach to two challenging human-like computer vision tasks: 1) Malayalam character recognition and 2) neurological diagnosis using retinal images. We compare our results with a state-of-the-art Deep Learning approach, called Siamese Network, developed for one-shot learning. The results suggest that our approach can generate human-understandable rules and outperforms the deep learning approach with a significantly higher average predictive accuracy.
Hardware Countermeasures Benchmarking against Fault Attacks

The development of differential fault analysis (DFA) techniques and mechanisms to inject faults into cryptographic circuits brings with it the need to use protection mechanisms that guarantee the expected level of security. The AES cipher, as a standard, has been the target of numerous DFA techniques, where its security has been compromised through different formulations and types of fault injections. These attacks have shown vulnerabilities of different AES implementations and building blocks. Consequently, several solutions have been proposed that provide additional protection to cover the identified vulnerabilities. In this paper, an extensive analysis has been carried out covering the existing fault injection techniques, the types of faults, and the requirements needed to apply DFA. Additionally, an analysis of the countermeasures reported in the literature is also presented, considering the protection provided, the type of faults considered, and the coverage against fault attacks. The eight different types of fault that allow us to perform DFAs on the AES cipher have been differentiated, as well as the vulnerabilities of the cipher. On the other hand, two comparisons have been made considering frequency penalty vs. area and fault coverage vs. area and frequency overhead. A metric has been proposed to compare the fault coverage of all the proposed solutions. To conclude, a final analysis is presented discussing the key aspects when choosing a particular solution and the possible development of new countermeasures to provide further protection against DFA.
Few-Shot User-Definable Radar-Based Hand Gesture Recognition at the Edge

Technological advances and scalability are leading Human-Computer Interaction (HCI) to evolve towards intuitive forms, such as through gesture recognition. Among the various interaction strategies, radar-based recognition is emerging as a touchless, privacy-secure, and versatile solution in different environmental conditions. Classical radar-based gesture HCI solutions involve deep learning but require training on large and varied datasets to achieve robust prediction. Innovative self-learning algorithms can help tackling this problem by recognizing patterns and adapt from similar contexts. Yet, such approaches are often computationally expensive and hardly integrable into hardware-constrained solutions. In this paper, we present a gesture recognition algorithm which is easily adaptable to new users and contexts. We exploit an optimization-based meta-learning approach to enable gesture recognition in learning sequences. This method targets at learning the best possible initialization of the model parameters, simplifying training on new contexts when small amounts of data are available. The reduction in computational cost is achieved by processing the radar sensed data of gestures in the form of time maps, to minimize the input data size. This approach enables the adaptation of simple convolutional neural network (CNN) to new hand poses, thus easing the integration of the model into a hardware-constrained platform. Moreover, the use of a Variational Autoencoders (VAE) to reduce the gestures’ dimensionality leads to a model size decrease of an order of magnitude and to half of the required adaptation time. The proposed framework, deployed on the Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Neural Compute Stick 2 (NCS 2), leads to an average accuracy of around 84% for unseen gestures when only one example per class is utilized at training time. The accuracy increases up to 92.6% and 94.2% when three and five samples per class are used.
Foreign language education and value formation of a personality: Analysis of contemporary trends and developments (review)

Introduction. The article considers personal development through intercultural communication. The purpose of the study is to analyze and summarize approaches to researching the content of foreign language education with the main focus on the value formation of a personality. Materials and Methods. The research methodology comprises a comparative analysis and generalization of research interpretations and methodological approaches to intercultural language teaching and learning. The study follows the basic principles of philosophy of education, which considers education in its value, system, procedural and productive characteristics, taking into account the interdisciplinary factors of the development of the education system. The comparative analysis is based on scholarly literature on the problem in the fields of philosophy and history of education, linguistics, cultural and education studies. Results. The authors reveal the modern dimension in studying the content of foreign language education with the main focus on the value-based formation of a personality in the following main contexts: (1) foreign language education as a holistic process of teaching and learning and moral education; (2) foreign language education as an integrated process of developing a linguistic personality capable of dialogue and communication; (3) foreign language education as an integrated process of learning world culture. Conclusions. In conclusion, the authors summarize the main content of foreign language education with an emphasis on value formation of a personality.
The collapse of neoliberal concept of education in the COVID-19 era and the prospects for post-COVID education

Introduction. The authors investigate the problem of value changes occurring in the higher education system. The COVID-19 pandemic, which spread around the world in early 2020, has had a serious impact on higher education. It has led to the need to revise the axiological foundations of modern education. The purpose of this article is to analyze the neoliberal concept of higher education in the modern world and identify trends for the future post-COVID education. Materials and Methods. The materials included international research articles published between 2020 and 2021 and devoted to the subject under study, as well as a sociological study conducted in the Republic of Kazakhstan and investigating well-being and value orientations of students during the COVID-19 pandemic. A sample of 1,000 university students from all 14 regions in Kazakhstan was interviewed. The main proportion of students was retained in the sample (by gender, language of instruction, nationality, years of study and degree programmes). The study was conducted using the Google Forms application. The main research methods were content analysis of scholarly literature (more than 40 international sources), as well as analysis based on the results of the sociological survey of students in Kazakhstan. Results. The authors revealed that if at the very beginning researchers paid attention to such negative aspects of the pandemic as poor technical equipment, Internet failures, unpreparedness of academic staff and students for online learning, the growth of student debts due to job loss, etc., then it soon became clear that the pandemic affected not only the external (economic, technical) side of education, but also its very essence. Today, researchers all over the world are critically analyzing the foundations of modern higher education - neoliberal concepts that have dominated in different countries since the late twentieth century and the development of globalization. It is shown that the current global educational crisis has not been caused by the pandemic, but by the unaccounted negative consequences of the deep penetration of neoliberalism ideas into society (market relations, uncontrolled competition, weakening of the role of the state, weak support for social sectors of the economy, etc.). The authors argue that societies with a developed system of neoliberal higher education experience more stress than developing, traditional ones like Kazakhstan, since the basis of education of the former is mainly the market and monetary-market relations, the basis of the latter is moral, intangible values of education. Conclusions. Exploring the prospects of post-secondary education based on international research investigations, the authors have put forward the ideas about the need to abandon neoliberalism in favor of greater humanization, fundamentalization and ecologization of education, strengthening state control and financing. The authors consider this as the only possible form for progressive development of higher education after the pandemic.
Motion Blur Kernel Rendering Using an Inertial Sensor: Interpreting the Mechanism of a Thermal Detector

Various types of motion blur are frequently observed in the images captured by sensors based on thermal and photon detectors. The difference in mechanisms between thermal and photon detectors directly results in different patterns of motion blur. Motivated by this observation, we propose a novel method to synthesize blurry images from sharp images by analyzing the mechanisms of the thermal detector. Further, we propose a novel blur kernel rendering method, which combines our proposed motion blur model with the inertial sensor in the thermal image domain. The accuracy of the blur kernel rendering method is evaluated by the task of thermal image deblurring. We construct a synthetic blurry image dataset based on acquired thermal images using an infrared camera for evaluation. This dataset is the first blurry thermal image dataset with ground-truth images in the thermal image domain. Qualitative and quantitative experiments are extensively carried out on our dataset, which show that our proposed method outperforms state-of-the-art methods.
Anomaly detection for hydropower turbine based on variational modal decomposition and hierarchical temporal memory

Hydropower generation has become an important guarantee for social and economic evolution. The hydropower turbine is the main power device of hydropower generation. And the anomaly state of the turbine is important to the operation and maintenance of the turbine. The complex signals measured by the hydropower turbine sensor are decomposed into the sub signals employing variational modal decomposition (VMD). The accuracy of anomaly detection is improved by applying VMD. A hydropower turbine anomaly detection application combining VMD and hierarchical temporal memory (HTM) is developed. Specificities of spinning-tree, cluster, HTM and the proposed method are 0.730, 0.690, 0.820, 0.945, respectively. Accuracy are 0.872, 0.873, 0.931, 0.959, respectively, and F1 scores are 0.923, 0.924, 0.959, 0.976, respectively. The proposed method can be applied to other anomaly detection applications.
Understanding How Sensory Changes Experienced by Individuals with a Range of Age-Related Cognitive Changes Can Affect Technology Use

Clinical researchers have identified sensory changes people with age-related cognitive changes, such as dementia and mild cognitive impairment, experience that are different from typical age-related sensory changes. Technology designers and researchers do not yet have an understanding of how these unique sensory changes affect technology use. This work begins to bridge the gap between the clinical knowledge of sensory changes and technology research and design through interviews with people with mild to moderate dementia, mild cognitive impairment, subjective cognitive decline, and healthcare professionals. This extended version of our ASSETS conference paper includes people with a range of age-related cognitive changes describing changes in vision, hearing, speech, dexterity, proprioception, and smell. We discuss each of these sensory changes and ways to leverage optimal modes of sensory interaction for accessible technology use with existing and emerging technologies. Finally, we discuss how accessible sensory stimulation may change across the spectrum of age-related cognitive changes.
A Practical Guide to Logical Access Voice Presentation Attack Detection

Voice-based human-machine interfaces with an automatic speaker verification (ASV) component are commonly used in the market. However, the threat from presentation attacks is also growing since attackers can use recent speech synthesis technology to produce a natural-sounding voice of a victim. Presentation attack detection (PAD) for ASV, or speech anti-spoofing, is therefore indispensable. Research on voice PAD has seen significant progress since the early 2010s, including the advancement in PAD models, benchmark datasets, and evaluation campaigns. This chapter presents a practical guide to the field of voice PAD, with a focus on logical access attacks using text-to-speech and voice conversion algorithms and spoofing countermeasures based on artifact detection. It introduces the basic concept of voice PAD, explains the common techniques, and provides an experimental study using recent methods on a benchmark dataset. Code for the experiments is open-sourced.
Generalizable Person Re-identification via Self-Supervised Batch Norm Test-Time Adaption

In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. As an important tool of regularizing the distribution, batch normalization (BN) has been widely used in existing methods. However, they neglect that BN is severely biased to the training domain and inevitably suffers the performance drop if directly generalized without being updated. To tackle this issue, we propose Batch Norm Test-time Adaption (BNTA), a novel re-id framework that applies the self-supervised strategy to update BN parameters adaptively. Specifically, BNTA quickly explores the domain-aware information within unlabeled target data before inference, and accordingly modulates the feature distribution normalized by BN to adapt to the target domain. This is accomplished by two designed self-supervised auxiliary tasks, namely part positioning and part nearest neighbor matching, which help the model mine the domain-aware information with respect to the structure and identity of body parts, respectively. To demonstrate the effectiveness of our method, we conduct extensive experiments on three re-id datasets and confirm the superior performance to the state-of-the-art methods.
Just Rank: Rethinking Evaluation with Word and Sentence Similarities

Word and sentence embeddings are useful feature representations in natural language processing.However, intrinsic evaluation for embeddings lags far behind, and there has been no significant update since the past decade.Word and sentence similarity tasks have become the de facto evaluation method.It leads models to overfit to such evaluations, negatively impacting embedding models' development.This paper first points out the problems using semantic similarity as the gold standard for word and sentence embedding evaluations.Further, we propose a new intrinsic evaluation method called EvalRank, which shows a much stronger correlation with downstream tasks.Extensive experiments are conducted based on 60+ models and popular datasets to certify our judgments.Finally, the practical evaluation toolkit is released for future benchmarking purposes. 1
RETRACTED: Sports highlight recognition and event detection using rule inference system

Computer vision in sport is a very interesting application. People spend a lot of time watching sports videos because this is one of the best field of entertainment. Sports video broadcasts generally take a lot of time, ranging from two to four hours. However, the interesting part happens for just a few minutes. Detecting the highlighted event in a sport will be useful for people who like to watch only the prominent events section instead of watching the whole video broadcast. Event detection will give precise details about the action that occurred for a particular time, but the detection of highlighted events is more complex. This is due to the fact that a sports video contains collections of events. Among them, segregation of the required event is a time-consuming process but it requires more knowledge about the sport as well as processing time. Hence, a novel work is proposed focused on identifying the location of the functional object using agglomerative clustering and annotating the event highlights automatically by means of the rule inference mechanism. The SHRED (Sports Highlight Recognition and Event Detection) system achieves an overall accuracy of about 97.38% relative to other state-of-art methods in event class annotation.
Implementing an institution-wide electronic lab notebook initiative

To strengthen institutional research data management practices, the Indiana University School of Medicine (IUSM) licensed an electronic lab notebook (ELN) to improve the organization, security, and shareability of information and data generated by the school's researchers. The Ruth Lilly Medical Library led implementation on behalf of the IUSM's Office of Research Affairs.This article describes the pilot and full-scale implementation of an ELN at IUSM. The initial pilot of the ELN in late 2018 involved fifteen research labs with access expanded in 2019 to all academic medical school constituents. The Ruth Lilly Medical Library supports researchers using the electronic lab notebook by (1) delivering trainings that cover strategies for adopting an ELN and a hands-on demo of the licensed ELN, (2) providing one-on-one consults with research labs or groups as needed, and (3) developing best practice guidance and template notebooks to assist in adoption of the ELN. The library also communicates availability of the ELN to faculty, students, and staff through presentations delivered at department meetings and write-ups in the institution's newsletter as appropriate.As of August 2021, there are 829 users at IUSM. Ongoing challenges include determining what support to offer beyond the existing training, sustaining adoption of the ELN within research labs, and defining "successful" adoption at the institution level. By leading the development of this service, the library is more strongly integrated and visible in the research activities of the institution, particularly as related to information and data management.
Stay Safe! Safety Precautions for Walking on a Conventional Treadmill in VR

Conventional treadmills are used in virtual reality (VR) applications, such as for rehabilitation training or gait studies. However, using the devices in VR poses risks of injury. Therefore, this study investigates safety precautions when using a conventional treadmill for a walking task. We designed a safety belt and displayed parts of the treadmill in VR. The safety belt was much appreciated by the participants and did not affect the walking behavior. However, the participants requested more visual cues in the user&#x0027;s field of view.
SHAP-Based Interpretable Object Detection Method for Satellite Imagery

There is a growing need for algorithms to automatically detect objects in satellite images. Object detection algorithms using deep learning have demonstrated a significant improvement in object detection performance. However, deep-learning models have difficulty in interpreting the features for inference. This difficulty is practically problematic when analyzing earth-observation images, which are often used as evidence for public decision-making. In addition, for the same reason, it is difficult to set an explicit policy or criteria to improve the models. To deal with these challenges, we introduce a feature attribution method that defines an approximate model and calculates the attribution of input features to the output of a deep-learning model. For the object detection models of satellite images with complex textures, we propose a method to visualize the basis of inference using pixel-wise feature attribution. Furthermore, we propose new methods for model evaluation, regularization, and data selection, based on feature attribution. Experimental results demonstrate the feasibility of the proposed methods for basis visualization and model evaluation. Moreover, the results illustrate that the model using the proposed regularization method can avoid over-fitting and achieve higher performance, and the proposed data selection method allows for the efficient selection of new training data.
Role of Dynamic Affordance and Cognitive Load in the Design of Extended Reality based Simulation Environments for Surgical Contexts

In this paper, HCI-based design criteria for the Extended Reality (XR) based training environments. XR is an umbrella term used to describe Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) training environments are presented. The design criteria explored in the paper help lay a foundation for the creation of Human Centric XR environments to train users in an orthopedic surgical procedure. The HCI-based perspective presented in the paper investigates the criteria such as affordance and cognitive load during the design. The paper focuses on the design of XR based environments based on the participatory design approach and information-centric modeling. Further, the paper delineates the implementation of the XR based environments and a set of attributes that guide and influence the content of the environments. Testing and assessment strategy has also been described integrating the design thinking to the assessment and providing insights into the impact of such HCI-based criteria on participants&#x0027; acquisition of skills and knowledge during interactions with the XR environments.