Reviewerly/exHarmony huggingface dataset:
Design And Development of Cost-Effective Exergames For Activity Incrementation

In the modern era of computing and mobile technology, the human lifestyle is affected by technology, and people are adopting habits that promote sedentary behaviour, including watching TV, playing video games, using computers, and mobile phones, leading to increased periods when people are not actively engaging in physical activity. The combination of Virtual reality and Exergame has been recently introduced and used, and this is a combination that this paper takes an interest in. This research paper shows the design and development of the Happy Child exergame, which, along with the design, the cost of the device has been taken into consideration to be more accessible to the public. Happy Child consists of a mobile application (APP), an Arduino board as the main processor which, with the two types of sensors (Accelerometer and temperature), will send the user data and the players BMI to the user personal computer and after playing each designed game which uses Unity to develop send back to Arduino and store on using the mobile phone. Happy child Design shows the successful implementation and its ability to use as the Exergame to increase people's activity.
Automatic Text Simplification for German

The article at hand aggregates the work of our group in automatic processing of simplified German. We present four parallel (standard/simplified German) corpora compiled and curated by our group. We report on the creation of a gold standard of sentence alignments from the four sources for evaluating automatic alignment methods on this gold standard. We show that one of the alignment methods performs best on the majority of the data sources. We used two of our corpora as a basis for the first sentence-based neural machine translation (NMT) approach toward automatic simplification of German. In follow-up work, we extended our model to render it capable of explicitly operating on multiple levels of simplified German. We show that using source-side language level labels improves performance with regard to two evaluation metrics commonly applied to measuring the quality of automatic text simplification.
Experimental and numerical study of interlaminar shear property and failure mechanism of none-felt needled composites

• The mode Ⅱ interlaminar shear strength of none-felt needled composites increased by up to 108.6%. • Double cohesive zone model was established to predict the mode Ⅱ interlaminar shear property. • The in-plane tensile strength and modulus improved by 19%–21% and 11%–20% respectively. • The interlaminar shear and in-plane tensile properties of the needle-punched composite were simultaneously improved. The none-felt needling technology improved the interlaminar peeling and in-plane tensile property of the needled fabric. Following the previous research, this paper proposed a study on the interlaminar shear property of the none-felt needled composites by using experiments and numerical methods, and compared with common felt/base cloth needled composites. Result showed that none-felt needled composites had great enhancement in interlaminar shear strength, increased by up to 108.6%. Double cohesive zone models were established to predict the mode Ⅱ interlaminar shear property of none-felt needled composites. The simulation of interlaminar shear strength and the surface strain cloud graph were well close to the experimental results. Moreover, tensile and bending properties were also characterized. The in-plane tensile strength and modulus increased by up to 21% and 20% respectively. The bending strength and modulus improved by 3%–7% and 4%–13%. The none-felt needling technology realized the simultaneous enhancement of interlaminar shear and in-plane tensile properties of needled composites, which may further broaden the application fields of needled composites.
A Study on Social Engineering Attacks in Cybersecurity

Due to improvements in data communication technology, humans can communicate with each other in the world instantly. Private and confidential information available on virtual community and e-services is not protected because of the absence of security procedures. Therefore, communication systems are more exposed and can be breached by malevolent users by means of social engineering attacks. These attacks are achieved by misleading people or organizations into performing activities that are advantageous to attackers or giving secret information like transaction codes, unique identification numbers, medical archives, and passwords. Social engineering attack is one of the major challenges in the field of security since it misuses the human inclination toward trust. This paper presents a detailed review on social engineering attacks, taxonomies, detection approaches, and prevention measures.
Consistent Representation Learning for Continual Relation Extraction

Continual relation extraction (CRE) aims to continuously train a model on data with new relations while avoiding forgetting old ones.Some previous work has proved that storing a few typical samples of old relations and replaying them when learning new relations can effectively avoid forgetting.However, these memory-based methods tend to overfit the memory samples and perform poorly on imbalanced datasets.To solve these challenges, a consistent representation learning method is proposed, which maintains the stability of the relation embedding by adopting contrastive learning and knowledge distillation when replaying memory.Specifically, supervised contrastive learning based on a memory bank is first used to train each new task so that the model can effectively learn the relation representation.Then, contrastive replay is conducted of the samples in memory and makes the model retain the knowledge of historical relations through memory knowledge distillation to prevent the catastrophic forgetting of the old task.The proposed method can better learn consistent representations to alleviate forgetting effectively.Extensive experiments on FewRel and TACRED datasets show that our method significantly outperforms state-of-the-art baselines and yield strong robustness on the imbalanced dataset.
Real-time two-photon interference from distinct molecules on the same chip

Scalability and miniaturization are hallmarks of solid-state platforms for photonic quantum technologies. Still a main challenge is two-photon interference from distinct emitters on chip. This requires local tuning, integration, and novel approaches to understand and tame noise processes. A promising platform is that of molecular single-photon sources. Thousands of molecules with optically tunable emission frequency can be easily isolated in solid matrices and triggered with pulsed excitation. We here discuss Hong–Ou–Mandel interference experiments using several couples of molecules within few tens of microns. Quantum interference is observed in real time, enabling the analysis of local environment effects at different time scales.
Data-driven RRAM device models using Kriging interpolation

A two-tier Kriging interpolation approach is proposed to model jump tables for resistive switches. Originally developed for mining and geostatistics, its locality of the calculation makes this approach particularly powerful for modeling electronic devices with complex behavior landscape and switching noise, like RRAM. In this paper, a first Kriging model is used to model and predict the mean in the signal, followed up by a second Kriging step used to model the standard deviation of the switching noise. We use 36 synthetic datasets covering a broad range of different mean and standard deviation Gaussian distributions to test the validity of our approach. We also show the applicability to experimental data obtained from TiOx devices and compare the predicted vs. the experimental test distributions using Kolmogorov-Smirnov and maximum mean discrepancy tests. Our results show that the proposed Kriging approach can predict both the mean and standard deviation in the switching more accurately than typical binning model. Kriging-based jump tables can be used to realistically model the behavior of RRAM and other non-volatile analog device populations and the impact of the weight dispersion in neural network simulations.
Self-Semantic Contour Adaptation for Cross Modality Brain Tumor Segmentation

Unsupervised domain adaptation (UDA) between two significantly disparate domains to learn high-level semantic alignment is a crucial yet challenging task. To this end, in this work, we propose exploiting low-level edge information to facilitate the adaptation as a precursor task, which has a small cross-domain gap, compared with semantic segmentation. The precise contour then provides spatial information to guide the semantic adaptation. More specifically, we propose a multi-task framework to learn a contouring adaptation network along with a semantic segmentation adaptation network, which takes both magnetic resonance imaging (MRI) slice and its initial edge map as input. These two networks are jointly trained with source domain labels, and the feature and edge map level adversarial learning is carried out for cross-domain alignment. In addition, self-entropy minimization is incorporated to further enhance segmentation performance. We evaluated our framework on the BraTS2018 database for cross-modality segmentation of brain tumors, showing the validity and superiority of our approach, compared with competing methods.
Optimal Transport for Unsupervised Denoising Learning

Recently, much progress has been made in unsupervised denoising learning. However, existing methods more or less rely on some assumptions on the signal and/or degradation model, which limits their practical performance. How to construct an optimal criterion for unsupervised denoising learning without any prior knowledge on the degradation model is still an open question. Toward answering this question, this work proposes a criterion for unsupervised denoising learning based on the optimal transport theory. This criterion has favorable properties, e.g., approximately maximal preservation of the information of the signal, whilst achieving perceptual reconstruction. Furthermore, though a relaxed unconstrained formulation is used in practical implementation, we prove that the relaxed formulation in theory has the same solution as the original constrained formulation. Experiments on synthetic and real-world data, including realistic photographic, microscopy, depth, and raw depth images, demonstrate that the proposed method even compares favorably with supervised methods, e.g., approaching the PSNR of supervised methods while having better perceptual quality. Particularly, for spatially correlated noise and realistic microscopy images, the proposed method not only achieves better perceptual quality but also has higher PSNR than supervised methods. Besides, it shows remarkable superiority in harsh practical conditions with complex noise, e.g., raw depth images. Code is available at https://github.com/wangweiSJTU/OTUR.
MsKAT: Multi-Scale Knowledge-Aware Transformer for Vehicle Re-Identification

Existing vehicle re-identification (Re-ID) methods usually suffer from intra-instance discrepancy and inter-instance similarity. The key to solving this problem lies in filtering out identity-irrelevant interference and collecting identity-relevant vehicle details. In this paper, we aim to design a robust vehicle Re-ID framework that trains a model guided by knowledge vectors yet is able to disentangle the identity-relevant features and identity-irrelevant features. Toward this end, we propose a novel Multi-scale Knowledge-Aware Transformer (MsKAT) to build a knowledge-guided multi-scale feature alignment framework. First, we construct a Knowledge-Aware Transformer (KAT) to interact with semantic knowledge and visual feature. KAT mainly includes State elimination Transformer (SeT) to eliminate state (camera, viewpoint) interference and Attribute aggregation Transformer (AaT) to gather attribute (color, type) information. Second, to learn the knowledge-guided sample differences, we propose to encourage the separation of identity-relevant features and identity-irrelevant features by a Knowledge-Guided Alignment loss ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{KGA}$ </tex-math></inline-formula> ). Specifically, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{KGA}$ </tex-math></inline-formula> suppresses the difference between knowledge-guided positive pairs and the similarity between knowledge-guided negative pairs. Third, with the multi-scale settings of KAT and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{KGA}$ </tex-math></inline-formula> , our model can capture knowledge-guided visual consistency features at different scales. Extensive evidence demonstrates our approach achieves new state-of-the-art on three widely-used vehicle re-identification benchmarks.
Predicting A New Hotel Rating System by Analysing UGC Content from Tripadvisor: Machine Learning Application to Analyse Service Robots Influence

Industry 4.0 tools permit computerized creation measures, and Artificial Intelligence (AI) approaches are pivotal in investigating the travel industry. Applying these devices to decipher User Generated Content (UGC) is fundamental to understand better client’s necessities, opinions, and assumptions regarding tourism services. Through this research, an exploratory analysis of results is developed through Machine Learning Models to understand better the role played by robot and traveler typologies on the rating given to hotels considering TripAdvisor reviews of 74 hotels. The purpose of this exploratory research is to develop a methodology focused on analyzing online reviews related to service robots in hotels using Machine Learning techniques to train the data collected from TripAdvisor. Preliminary results show a link between the hotel rating given in TripAdvisor and the robot typology implemented in hotels.
Fast Healthcare Interoperability Resources (FHIR) in a FAIR Metadata Registry for COVID-19 Research

Adopting international standards within health research communities can elevate data FAIRness and widen analysis possibilities. The purpose of this study was to evaluate the mapping feasibility against HL7® Fast Healthcare Interoperability Resources® (FHIR)® of a generic metadata schema (MDS) created for a central search hub gathering COVID-19 health research (studies, questionnaires, documents = MDS resource types). Mapping results were rated by calculating the percentage of FHIR coverage. Among 86 items to map, total mapping coverage was 94%: 50 (58%) of the items were available as standard resources in FHIR and 31 (36%) could be mapped using extensions. Five items (6%) could not be mapped to FHIR. Analyzing each MDS resource type, there was a total mapping coverage of 93% for studies and 95% for questionnaires and documents, with 61% of the MDS items available as standard resources in FHIR for studies, 57% for questionnaires and 52% for documents. Extensions in studies, questionnaires and documents were used in 32%, 38% and 43% of items, respectively. This work shows that FHIR can be used as a standardized format in registries for clinical, epidemiological and public health research. However, further adjustments to the initial MDS are recommended – and two additional items even needed when implementing FHIR. Developing a MDS based on the FHIR standard could be a future approach to reduce data ambiguity and foster interoperability.
Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases

In mammography, calcifications are one of the most common signs of breast cancer. Detection of such lesions is an active area of research for computer-aided diagnosis and machine learning algorithms. Due to limited numbers of positive cases, many supervised detection models suffer from overfitting and fail to generalize. We present a one-class, semi-supervised framework using a deep convolutional autoencoder trained with over 50,000 images from 11,000 negative-only cases. Since the model learned from only normal breast parenchymal features, calcifications produced large signals when comparing the residuals between input and reconstruction output images. As a key advancement, a structural dissimilarity index was used to suppress non-structural noises. Our selected model achieved pixel-based AUROC of 0.959 and AUPRC of 0.676 during validation, where calcification masks were defined in a semi-automated process. Although not trained directly on any cancers, detection performance of calcification lesions on 1,883 testing images (645 malignant and 1238 negative) achieved 75% sensitivity at 2.5 false positives per image. Performance plateaued early when trained with only a fraction of the cases, and greater model complexity or a larger dataset did not improve performance. This study demonstrates the potential of this anomaly detection approach to detect mammographic calcifications in a semi-supervised manner with efficient use of a small number of labeled images, and may facilitate new clinical applications such as computer-aided triage and quality improvement.
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief

Although pretrained language models (PTLMs) contain significant amounts of world knowledge, they can still produce inconsistent answers to questions when probed, even after specialized training.As a result, it can be hard to identify what the model actually "believes" about the world, making it susceptible to inconsistent behavior and simple errors.Our goal is to reduce these problems.Our approach is to embed a PTLM in a broader system that also includes an evolving, symbolic memory of beliefs -a BeliefBank -that records but then may modify the raw PTLM answers.We describe two mechanisms to improve belief consistency in the overall system.First, a reasoning component -a weighted MaxSAT solver -revises beliefs that significantly clash with others.Second, a feedback component issues future queries to the PTLM using known beliefs as context.We show that, in a controlled experimental setting, these two mechanisms result in more consistent beliefs in the overall system, improving both the accuracy and consistency of its answers over time.This is significant as it is a first step towards PTLM-based architectures with a systematic notion of belief, enabling them to construct a more coherent picture of the world, and improve over time without model retraining.
Word Embeddings and Its Application in Deep Learning

Word embedding in simple term can be defined as representing text in form of vectors. Vector representations of text help people in finding similarities, because contextual words that seem to appear nearby regularly use to appear in close proximity in vector space. The motivating factor behind such numerical representation of text corpus is that it can be manipulated arithmetically just like any other vector. Deep learning along with neural network is not new at all, both the concepts are prevalent around the decades but there was a major tailback of unavailability and accessibility of computation power. Deep learning is now effectively being used in Natural Language Processing with the improvement in techniques like word embedding, mobile enablement and focus on attention. The paper will discuss about the two popular model of word embedding (Word2Vec model) can be used for deep learning and will also compare them. The implementation steps of Skip gram model are also discusses in the paper. The paper will also discuss challenging issues for Word2Vce model.
Dismantling DST80-based Immobiliser Systems

Car manufacturers deploy vehicle immobiliser systems in order to prevent car theft. However, in many cases the underlying cryptographic primitives used to authenticate a transponder are proprietary in nature and thus not open to public scrutiny. In this paper we publish the proprietary Texas Instruments DST80 cipher used in immobilisers of several manufacturers. Additionally, we expose serious flaws in immobiliser systems of major car manufacturers such as Toyota, Kia, Hyundai and Tesla. Specifically, by voltage glitching the firmware protection mechanisms of the microcontroller, we extracted the firmware from several immobiliser ECUs and reverse engineered the key diversification schemes employed within. We discovered that Kia and Hyundai immobiliser keys have only three bytes of entropy and that Toyota only relies on publicly readable information such as the transponder serial number and three constants to generate cryptographic keys. Furthermore, we present several practical attacks which can lead to recovering the full 80-bit cryptographic key in a matter of seconds or permanently disabling the transponder. Finally, even without key management or configuration issues, we demonstrate how an attacker can recover the cryptographic key using a profiled side-channel attack. We target the key loading procedure and investigate the practical applicability in the context of portability. Our work once again highlights the issues automotive vendors face in implementing cryptography securely.
A Survey on Adversarial Examples in Deep Learning

Adversarial examples are hot topics in the field of security in deep learning. The feature, generation methods, attack and defense methods of the adversarial examples are focuses of the current research on adversarial examples. This article explains the key technologies and theories of adversarial examples from the concept of adversarial examples, the occurrences of the adversarial examples, the attacking methods of adversarial examples. This article lists the possible reasons for the adversarial examples. This article also analyzes several typical generation methods of adversarial examples in detail: Limited-memory BFGS (L-BFGS), Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Iterative Least-likely Class Method (LLC), etc. Furthermore, in the perspective of the attack methods and reasons of the adversarial examples, the main defense techniques for the adversarial examples are listed: preprocessing, regularization and adversarial training method, distillation method, etc., which application scenarios and deficiencies of different defense measures are pointed out. This article further discusses the application of adversarial examples which currently is mainly used in adversarial evaluation and adversarial training. Finally, the overall research direction of the adversarial examples is prospected to completely solve the adversarial attack problem. There are still a lot of practical and theoretical problems that need to be solved. Finding out the characteristics of the adversarial examples, giving a mathematical description of its practical application prospects, exploring the universal method of adversarial example generation and the generation mechanism of the adversarial examples are the main research directions of the adversarial examples in the future.