Reviewerly/exHarmony huggingface dataset:
Lightweight Privacy-Preserving Data Sharing Scheme for Internet of Medical Things

Internet of Medical Things (IoMT) is a kind of Internet of Things (IoT) that includes patients and medical sensors. Patients can share real-time medical data collected in IoMT with medical professionals. This enables medical professionals to provide patients with efficient medical services. Due to the high efficiency of cloud computing, patients prefer to share gathering medical information using cloud servers. However, sharing medical data on the cloud server will cause security issues, because these data involve the privacy of patients. Although recently many researchers have designed data sharing schemes in medical domain for security purpose, most of them cannot guarantee the anonymity of patients and provide access control for shared health data, and further, they are not lightweight enough for IoMT. Due to these security and efficiency issues, a novel lightweight privacy-preserving data sharing scheme is constructed in this paper for IoMT. This scheme can achieve the anonymity of patients and access control of shared medical data. At the same time, it satisfies all described security features. In addition, this scheme can achieve lightweight computations by using elliptic curve cryptography (ECC), XOR operations, and hash function. Furthermore, performance evaluation demonstrates that the proposed scheme takes less computation cost through comparison with similar solutions. Therefore, it is fairly an attractive solution for efficient and secure data sharing in IoMT.
Recognition of Heavily Accented and Emotional Speech of English and Czech Holocaust Survivors Using Various DNN Architectures

The Malach Project [] verified the possibility of using automatic speech recognition (ASR) methods to search for information in large multilingual archives of Holocaust testimonies. After the end of the MALACH project, in which we participated, we continued to work on the completion and implementation of the project’s objectives with priority for two languages - Czech and English. We have developed and implemented a full-text search system that can be used by experts and by the general public in the MALACH Centre for Visual History and Jewish Museum in Prague. ASR is a key technology that ensures the functioning of the whole information retrieval process. To ensure the highest quality searches, we are constantly striving to develop this technology using the state-of-the-art methods. The article presents the latest results obtained in extensive experiments using various DNN architectures in the ASR of the English and Czech MALACH archives. The paper is therefore one of the first responses to M. Picheny’s call [] to the speech community to reconsider this very difficult task of recognizing strongly emotional and heavily accented speech of Holocaust survivors.
Using Virtual Reality Technology to Construct Computer-Aided Animation Material Development

with the improvement of computer science and technology, modern fashion design has become the product of the combination of designers, computer application and art design.3D CAD design based on virtual technology has become a technical means and is widely used in the field of fashion.This paper mainly studies how to apply virtual technology to 3D CAD Hanfu design.Starting from the principle of garment CAD, this topic first introduces the function and application method of 3D virtual design LookStailor X, the establishment of 3D human model, the establishment of garment model, style line drawing, the conversion from 3D garment piece to 2D garment piece, etc.Then, the virtual design of Hanfu is carried out through parametric 3D human modeling, 3D garment generation and physical simulation.Finally, through the actual research, this paper compares and analyzes the clothing under the three modes, demonstrates the advantages of 3D virtual clothing design with specific research figures, and predicts the market prospect and economic contribution of 3D virtual clothing design.
Cross-Modal Coherence for Text-to-Image Retrieval

Common image-text joint understanding techniques presume that images and the associated text can universally be characterized by a single implicit model. However, co-occurring images and text can be related in qualitatively different ways, and explicitly modeling it could improve the performance of current joint understanding models. In this paper, we train a Cross-Modal Coherence Model for text-to-image retrieval task. Our analysis shows that models trained with image–text coherence relations can retrieve images originally paired with target text more often than coherence-agnostic models. We also show via human evaluation that images retrieved by the proposed coherence-aware model are preferred over a coherence-agnostic baseline by a huge margin. Our findings provide insights into the ways that different modalities communicate and the role of coherence relations in capturing commonsense inferences in text and imagery.
YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews

Current TSA evaluation in a cross-domain setup is restricted to the small set of review domains available in existing datasets.Such an evaluation is limited, and may not reflect true performance on sites like Amazon or Yelp that host diverse reviews from many domains.To address this gap, we present YASO -a new TSA evaluation dataset of open-domain user reviews.YASO contains 2215 English sentences from dozens of review domains, annotated with target terms and their sentiment.Our analysis verifies the reliability of these annotations, and explores the characteristics of the collected data.Benchmark results using five contemporary TSA systems show there is ample room for improvement on this challenging new dataset.YASO is available at github.
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking

This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem.Previous methods require numerous training examples to estimate the accurate performance of architectures, although the actual goal is to find the distinction between "good" and "bad" candidates.Here we do not resort to performance predictors.Instead, we propose a performance ranking method (RankNAS) via pairwise ranking.It enables efficient architecture search using much fewer training examples.Moreover, we develop an architecture selection method to prune the search space and concentrate on more promising candidates.Extensive experiments on machine translation and language modeling tasks show that RankNAS can design high-performance architectures while being orders of magnitude faster than state-ofthe-art NAS systems.
Consistent Segmentation of Longitudinal Brain MR Images with Spatio-Temporal Constrained Networks

Accurate and consistent segmentation of longitudinal brain magnetic resonance (MR) images is of great importance in studying brain morphological and functional changes over time. However, current available brain segmentation methods, especially deep learning methods, are mostly trained with cross-sectional brain images that might generate inconsistent results in longitudinal studies. To overcome this limitation, we present a novel coarse-to-fine spatio-temporal constrained deep learning model for consistent longitudinal segmentation based on limited labeled cross-sectional data with semi-supervised learning. Specifically, both segmentation smoothness and temporal consistency are imposed in the loss function. Moreover, brain structural changes over time are summarized as age constraint, to make the model better reflect the trends of longitudinal aging changes. We validate our proposed method on 53 sets of longitudinal T1-weighted brain MR images from ADNI, with an average of 4.5 time-points per subject. Both quantitative and qualitative comparisons with comparison methods demonstrate the superior performance of our proposed method.
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation

The emotion recognition in conversation (ERC) task aims to predict the emotion label of an utterance in a conversation. Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC. Although existing researchers have proposed various methods of speaker interaction modeling, they cannot explore dynamic intra- and inter-speaker dependencies jointly, leading to the insufficient comprehension of context and further hindering emotion prediction. To this end, we design a novel speaker modeling scheme that explores intra- and inter-speaker dependencies jointly in a dynamic manner. Besides, we propose a Speaker-Guided Encoder-Decoder (SGED) framework for ERC, which fully exploits speaker information for the decoding of emotion. We use different existing methods as the conversational context encoder of our framework, showing the high scalability and flexibility of the proposed framework. Experimental results demonstrate the superiority and effectiveness of SGED.
OCR error correction for Vietnamese handwritten text using neural machine translation

OCR post-processing is an important step for improving the quality of OCR output texts. Long short-term memory (LSTM) is a deep learning model, which has wide-range applications in many domains like time series prediction, natural language processing and speech recognition. In this paper, we propose an OCR error correction model using neural machine translation with bidirectional LSTM networks at syllable level. Vietnamese OCR text dataset for the model evaluation is outputted from an OCR engine based on the attention-based encoder-decoder (AED) model taking input of handwritten text in the benchmark database of the ICFHR 2018 Vietnamese online handwritten text recognition competition. The experimental results show that the proposed model helps decrease the word error rate in the OCR output texts of the above AED model by about 2%. The model performance is also discussed and compared to the other baseline methods in the competition.
Write, Attend and Spell

Text entry on a smartwatch is challenging due to its small form factor. Handwriting recognition using the built-in sensors of the watch (motion sensors, microphones, etc.) provides an efficient and natural solution to deal with this issue. However, prior works mainly focus on individual letter recognition rather than word recognition. Therefore, they need users to pause between adjacent letters for segmentation, which is counter-intuitive and significantly decreases the input speed. In this paper, we present 'Write, Attend and Spell' (WriteAS), a word-level text-entry system which enables free-style handwriting recognition using the motion signals of the smartwatch. First, we design a multimodal convolutional neural network (CNN) to abstract motion features across modalities. After that, a stacked dilated convolutional network with an encoder-decoder network is applied to get around letter segmentation and output words in an end-to-end way. More importantly, we leverage a multi-task sequence learning method to enable handwriting recognition in a streaming way. We construct the first sequence-to-sequence handwriting dataset using smartwatch. WriteAS can yield 9.3% character error rate (CER) on 250 words for new users and 3.8% CER for words unseen in the training set. In addition, WriteAS can handle various writing conditions very well. Given the promising performance, we envision that WriteAS can be a fast and accurate input tool for smartwatch.
Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security Applications

X-ray baggage inspection has been widely used for maintaining airport and transportation security. Towards automated inspection, recent deep learning-based methods have attempted to detect hazardous objects directly from X-ray images. Since it is challenging to collect a large number of training images from real-world environments, most previous learning-based methods rely on image synthesis for training data generation. However, these methods randomly combine foreground and background images, restricting the effectiveness of synthetic images for object detection. To solve this problem, in this paper, we propose a learning-based X-ray image synthesis method for object detection. Specifically, for each foreground object to be synthesized, we first estimate positions difficult to detect by the object detector. These positions and their corresponding confidence values are then used to construct a difficulty map, which is used for sampling the target foreground position for image synthesis. The performance analysis using various state-of-the-art object detectors shows that the proposed synthesis method can produce more useful training data compared with the conventional random synthesis method.
A Comparative Analysis of LSB, MSB and PVD Based Image Steganography

Steganography is the art and science of hiding information by embedding data into cover media. Numerous techniques are designed to provide the security for the communication of data over the Internet. A good steganographic algorithm is recognized by the performance of the techniques measured with the support of the performance metrics among which are PSNR, MSE, SSIM, robustness and capacity to hide the information in the cover image. In this paper a comparative analysis of Least Significant Bit (LSB), Most Significant Bit (MSB) and Pixel Value Differencing (PVD) image steganography in grayscale and colored images was performed. Three different cover images was used to hide secret message. A comparative performance analysis of LSB, MSB and PVD methods used in image steganography was performed using peak signal to noise ratio (PSNR), Mean square error (MSE) and Structural Similarity index (SSIM) as performance metrics. LSB technique gives higher PSNR and SSIM values than MSB and PVD method with lower MSE than the other two techniques. Future research can be geared towards investigating the embedding capacity, security, and computational complexity of each technique. Keywords: Least Significant Bit (LSB), Most Significant Bit (MSB), Pixel value differencing (PVD), PSNR, SSIM and MSE,
Sub-pixel position estimation algorithm based on Gaussian fitting and sampling theorem interpolation for wafer alignment

Wafer alignment is the core technique of lithographic tools. Image-processing-based wafer alignment techniques are commonly used in lithographic tools. An alignment algorithm is used to analyze the alignment mark image for obtaining the mark position. The accuracy and speed of the alignment algorithm are very important for guaranteeing the overlay and throughput of lithographic tools. The most commonly used algorithm in image-processing-based alignment techniques is the self-correlation method. This method has a high accuracy, but the calculation is complex, and the calculation speed is slow. In this paper, we propose a sub-pixel position estimation algorithm based on Gaussian fitting and sampling theorem interpolation. The algorithm first reconstructs the alignment signal by sampling theorem interpolation and then obtains the sub-pixel position of the mark by Gaussian fitting. The accuracy and robustness of the algorithm are verified by testing the simulated marks and experimentally captured marks. The repeat accuracy can reach 1/100 pixels, which is in the same level with the self-correlation method. The calculation speed is highly improved compared with the self-correlation method, which needs only about 1/3 of even short calculation time.
Attitude towards ICT: a statistical analysis of gender differences in Spanish higher education teachers

The use of ICT resources in the teaching and learning process, as well as in the field of educational research, requires positive attitudes from all higher education teachers.Therefore, the purposes of this study are: (1) to measure both the level of attitude of teachers towards technology related to the teaching and learning process and the level of attitude towards technology related to research in both genders; and (2) to know if there are differences in the attitudes of teachers according to their gender.For this, an ex post facto design with 867 higher education teachers from the Spanish territory was used.An instrument created by Romero-Martínez et al. (2020) was composed of a total of 18 items, classified into three dimensions: affective, cognitive and behavioural attitudes towards technology in the teaching and learning process; affective attitudes towards technology in educational research; and cognitive attitudes towards technology in educational research.In general, the results showed that the teachers perceived a very good attitude towards technology (3.9/5), being higher in the male gender.Specifically, significant differences were found between both genders in the global attitude towards technology, as well as in the affective attitudes dimension towards ICT research.The conclusions derived from this study can help to develop training actions focused on improving the most unfavourable attitudes of each gender.
Prediction of dogecoin price using deep learning and social media trends

INTRODUCTION: Cryptocurrency is a digital, decentralized form of money based on blockchain technology, which makes it the most secure method of making a transaction.There has been a huge increase in the number of cryptocurrencies in the past few years.Cryptocurrencies such as Bitcoin and Ethereum have become an interesting subject of study in fields such as finance.In 2021, over 4,000 cryptocurrencies are already listed.There are many past studies that focus on predicting the price of cryptocurrencies using machine learning, but the majority of them only focused on Bitcoin.Moreover, the majority of the models implemented for price prediction only used the historical market prices, and do not utilize social signals related to the cryptocurrency.OBJECTIVES: In this paper, we propose a deep learning model for predicting the prices of dogecoin cryptocurrency.The proposed model is based on historical market price data as well as social trends of Dogecoin cryptocurrency. METHODS:The market data of Dogecoin is collected from Kaggle on the granularity of a day and for the same duration the verified tweets have also been collected with hashtags "Dogecoin" and "Doge".Experimental results show that the proposed model yields a promising prediction of future price of Dogecoin, a cryptocurrency that has recently become the talk of the town of the crypto market.RESULTS: Minimum achieved RMSE in predicted price of Dogecoin was 0.02 where the feature vector consisted of OCVP (Open, Close, Volume, Polarity) values from combined dataset.RESULTS: Experimental results show that the proposed approach performs efficiently.s a major factor in the future price of Dogecoin.
Cloud Native Architecture and Design Patterns

The Pattern Language is an organized and coherent set of patterns, each of which describes a problem and the core of a solution that can be used in many ways within a specific field of expertise.
Non-verbal Aspects of Collaboration in Virtual Worlds: a CSCW Taxonomy-development Proposal Integrating the Presence Dimension

Virtual worlds, particularly those able to provide a three-dimensional physical space, have features that make them suitable to support collaborative activities. These features distinguish virtual worlds from other collaboration tools, but current taxonomies of the field of Computer-Supported Cooperative Work do not account for several distinctive features of virtual worlds, namely those related with non-verbal communication. We intended to find out how the use of an avatar, gestures, spatial sounds, etc., influence collaboration in order to be able to include non-verbal communication in taxonomies of the field Computer-Supported Cooperative Work. Several cases of collaboration in virtual worlds are analysed, to find the impact of these non-verbal characteristics of virtual worlds. We proposed adding the concept of Presence to taxonomies of Computer-Supported Cooperative Work and contribute with guidance for future taxonomy development that includes it as a new dimension. This new dimension of Presence is subdivided into &amp;quot;avatar&amp;quot; and &amp;quot;physical space&amp;quot; subdimensions. In turn, these are divided into &amp;quot;physical appearance&amp;quot;, &amp;quot;gestures, sounds and animations&amp;quot; and &amp;quot;focus, nimbus and aura&amp;quot;; &amp;quot;environment&amp;quot; and &amp;quot;objects / artefacts&amp;quot;. This new taxonomy-development proposal may contribute to inform better design of virtual worlds in support of cooperative work.
How to (Legally) Keep Secrets from Mobile Operators

Secure-channel establishment allows two endpoints to communicate confidentially and authentically. Since they hide all data sent across them, good or bad, secure channels are often subject to mass surveillance in the name of (inter)national security. Some protocols are constructed to allow easy data interception . Others are designed to preserve data privacy and are either subverted or prohibited to use without trapdoors.
A State-Domain Robust Chi-Square Test Method for GNSS/INS Integrated Navigation

Aiming at abrupt faults in GNSS/INS integrated systems in complex environments, classical fault detection algorithms are mostly developed from the measurement domain. A robust chi-square test method based on the state domain is proposed in this paper. The fault detection statistic is built based on the difference between the prior state estimation and the posterior state estimation in Kalman filtering. To improve the calculation stability, singular value decomposition (SVD) is used to factor the covariance matrix of the difference. The relevant formulas of the proposed method were theoretically derived, and the relationship between the proposed method and the existing innovation chi-square test method was revealed. The proposed method was compared with state-of-the-art chi-square test methods and verified by GNSS/INS integrated navigation experiments using simulation data and real data. The experimental results show that the proposed method (a) directly works in the state domain, (b) does not require the known real system state, (c) has computational efficiency and good robustness, and (d) accurately detects abrupt faults.