Reviewerly/exHarmony huggingface dataset:
Estimation of full-dose 4D CT perfusion images from low-dose images using conditional generative adversarial networks

CT Perfusion (CTP) imaging is one of the most common regimes for evaluation of acute ischemic stroke patients. The CTP imaging protocol typically involves the rapid acquisition of several frames of the brain volume over ~1 minute, following contrast administration. Therefore, it is associated with a relatively high radiation dose. The ability to reduce this dose, while maintaining the accuracy of image-based stroke analysis is highly desirable. However, a reduction in dose is accompanied by an increase in noise, which can compromise computation of important haemodynamic parameters during stroke analysis. In this paper, we investigate the feasibility of using 3D conditional generative adversarial networks (3D c-GANs) to achieve CTP dose reduction while preserving image quality. We simulated low-dose CTP images corresponding to tube currents of 100 mAs and 45 mAs for 18 positive acute stroke subjects and applied a 3D c-GANs model to estimate the standard dose CTP images from the simulated low-dose images. We also compared two different strategies for handling the 4D nature of the CTP data in the 3D c-GANs model. Qualitatively, the results showed excellent agreement between the estimated low-noise images and the true images. Quantitative assessment also showed good performance of the model associated with high peak signal-to-noise ratio (PNSR) around 40 dB, normalized mean squared error (NMSE) close to zero, and structural similarity index (SSIM) close to 1. By stacking the original data rather than concatenating all volumes, the results were improved by 1.05 dB PNSR, 0.005 NMSE, and 0.01 SSIM at the simulated exposure of 100mAs, and by 0.93dB PNSR, 0.019 NMSE at the simulated tube current of 45 mAs. The results show good promise for dose reduction in CTP, however we are currently performing full stroke modelling analysis on the synthetic images to validate the method.
Embedding Framework for Identifying Ambiguous Words in Code-Mixed Social Media Text

Now a day's text on social media contains codeswitched and code-mixed contents. These contents are widely used by people to express their opinions on any topic in the languages known to them. Her code-mixing technique is analyzed to find the words which can be used both in Hindi and in English, having different contexts. This leads to word sense ambiguity problem as one word can have a different meaning when it used in context to other words in a sentence. As Hindi Roman and English language exhibit word sense ambiguity, and resolving this ambiguity is a current research issue using the machine learning model. Here character embedding features are used for the representation of each word written in code-mixed content. The proposed method was developed for identifying context words by classifying the intent for using the ambiguous word in code mixed sentence. A well-known hierarchical LSTM model is used in the paper for context-based sub-word-level ambiguity detection to identify the language of the word. The work on Language Identification in the code-mixed text using character-based embedding for processing ambiguous word is a novel approach and shows promising results.
Nonce reuse/misuse resistance authentication encryption schemes for modern TLS cipher suites and QUIC based web servers

Authentication Encryption with Associated Data (AEAD) is a scheme that preserves the integrity of both the cipher text and authenticated data. In AEAD, cipher suites like GCM_SIV and AES_GCM_SIV provides the message integrity through nonce-based authentication encryption technique. The problem of n once-based authentication encryption is the repetition of nonce in two different messages that violates message integrity property when the number of message blocks is maximized to 232. This paper verifies the maximum limit of nonce usefulness and proves better security bounds attained in GCM_SIV and AES_GCM_SIV using nonce-reuse/misuse resistance authentication encryption (NRMR-AE) technique. The NRMR-AE resistance property achieves better security bounds and performance even when the nonces are repeated in different messages. But nonce repetition in NRMR-AE property reduces the number of message encryption and message length (in blocks) in GCM_SIV and AES_GCM_SIV AEAD methods used in QUIC(Quick UDP Internet Communications) and TLS Cipher suites which is found to be a greater drawback. This paper increases the number of messages encrypted even with maximum number of nonce repetition ensuring that the message length in AES_GCM_SIV meets the standard NIST bound 2-32.
Genetic Algorithm-Based PCA Classification for Imbalanced Dataset

The real-world dataset such as defects in products, credit card frauds, disease diagnosis and natural calamity occurrence is large and often imbalanced. In this paper, a genetic algorithm (GA)-based error classification for imbalanced dataset is proposed. Conventionally, principle component analysis (PCA) was applied for dataset processing and error identification. The approach produced a binary form result for errors present in a dataset. In imbalanced dataset, it is important to determine the location and error percentage in dataset. This is achieved by GA where the dataset is selected at random and compared with the original dataset for error location identification. Furthermore, the sample patch size selected at different levels makes it easier to process different dataset size. The proposed GA detects error location and increases processing time of imbalanced dataset.
Generalized Deduplication: Lossless Compression by Clustering Similar Data

This paper proposes generalized deduplication, a concept where similar data is systematically deduplicated by first transforming chunks of each file into two parts: a basis and a deviation. This increases the potential for compression as more chunks can have a common basis that can be deduplicated by the system. The deviation is kept small and stored together with an identifier to its chunk, e.g., hash of a chunk, in order to recover the original data without errors or distortions. This paper characterizes the performance of generalized deduplication using Golomb-Rice codes as a suitable data transform function to discover similarities across all files stored in the system. Considering different synthetic data distributions, we show in theory and simulations that generalized deduplication can result in compression factors of 300 (high compression), i.e., 300 times less storage space, and that this compression is achieved with 60,000 times fewer data chunks inserted into the system compared to classic deduplication (compression gains start earlier). Finally, we show that the table/registry to recognize similar chunks is 10,000 times smaller for generalized deduplication compared to the table in classic deduplication techniques, which will result in less RAM usage in the storage system.
Reproducible Visualization of Raw Fisheries Data for 113 Species Improves Transparency, Assessment Efficiency, and Monitoring

Modern survey and fishery observation programs generate vast quantities of data. However, regulatory agencies often lack the capacity to translate those data into effective monitoring information. Here, we describe an approach of automated visualization of raw fisheries data and demonstrate it with a report on 113 groundfish species in Pacific Canadian waters. Our implementation consists of two pages per species that show standardized visualizations of temporal trends and spatial distributions of commercial catches and survey indices, along with analyses of age, size, maturity, and growth. The approach facilitates discussions on stock assessment and survey prioritization, increases transparency about data holdings, and makes data available for regular review by interested parties. We encourage other agencies to consider similar approaches for their own data.
Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors

Finger motion tracking has a number of applications in user-interfaces, sports analytics, medical rehabilitation and sign language translation. This paper presents a system called FinGTrAC that shows the feasibility of fine grained finger gesture tracking using low intrusive wearable sensor platform (smart-ring worn on the index finger and a smart-watch worn on the wrist). Such sparse sensors are convenient to wear but cannot track all fingers and hence provide under-constrained information. However application specific context can fill the gap in sparse sensing and improve the accuracy of gesture classification. This paper shows the feasibility of exploiting such context in an application of American Sign Language (ASL) translation. Non-trivial challenges arise due to noisy sensor data, variations in gesture performance across users and the inability to capture data from all fingers. FinGTrAC exploits a number of opportunities in data preprocessing, filtering, pattern matching, context of an ASL sentence to systematically fuse the available sensory information into a Bayesian filtering framework. Culminating into the design of a Hidden Markov Model, a Viterbi decoding scheme is designed to detect finger gestures and the corresponding ASL sentences in real time. Extensive evaluation on 10 users shows a detection accuracy of 94.2% for 100 most frequently used ASL finger gestures over different sentences.
LSTM Based Semi-Supervised Attention Framework for Sentiment Analysis

With the rapid development of Internet technology and social media, people are accustomed to making comments on the Internet. Sentiment analysis, as an efficient technique, has been used by researchers in the tasks of analysing the sentiment polarity under these comments. To better achieve this target, the fundamental challenge is how to extract the feature and build a proper mechanism to learn them. A lot of word embedding based deep learning models for sentiment analysis are proposed in the literature. And the semi-supervised learning methods make it possible to use both labelled and unlabelled data for this kind of task. Furthermore, the attention mechanism proposed in recent years has achieved great accomplishments for natural language processing (NLP) tasks since it helps to capture the important information of the documents. In this paper, inspired by these works, we proposed a long short term memory (LSTM) based semi-supervised attention framework for sentiment analysis tasks, which is composed of an unsupervised attention based LSTM encoder-decoder and an attention based supervised LSTM model attached by a Softmax layer. The unsupervised part worked for attaining the high dimensional representation of the documents, and the supervised part extracted feature and enhanced the important parts for classification. Experimental study on commonly used datasets has demonstrated its ability for sentiment analysis tasks.
Semantic Segmentation based Hand Gesture Recognition using Deep Neural Networks

The ability to discern the shape of hands can be a vital issue in improving the performance of hand gesture recognition. Segmentation itself is a very challenging problem having various constraints like illumination variation, complex background etc. The objective of the paper is to incorporate the perception of semantic segmentation into a classification problem and make use of the deep neural models to achieve improved results. This paper utilizes the UNET architecture to obtain the semantically segmented mask of the input, which is then given to a VGG16 model for classification. Here the top classifier layer of the VGG16 model is replaced with a classifier designed specifically for classifying the gestures at hand. The Brazilian Sign Language database used in the paper contains about 9600 images. Data augmentation process is used in preprocessing to generate sufficient number of training images for the aforementioned CNN-based models. A significant and improved average recognition rate of 98.97% is achieved through inherent feature learning capability of CNN and refined segmentation for 34 classes.
Analytical Approach towards Prediction of Diseases Using Machine Learning Algorithms

Healthcare is a human right and in this complex technology driven world, healthcare industry is equipped with modern technology for the solution of disease but struggles when it comes to prevent them beforehand. Machine learning can transform healthcare industry. Machine Learning provides a wide scope of apparatuses, strategies and structures to address difficulties like electronic record the executives, information combination, PC supported judgments and disease expectation. This research paper aims to predict disease accurately according to the symptoms of patients and helps doctor in better diagnosis, further reducing the cost of treatment and improving quality of life. It includes the comparative study of the outcomes and time required for analysis and prediction of disease by various machine learning algorithms and contribute towards research in healthcare department.
Emerging trends: Subwords, seriously?

Abstract Subwords have become very popular, but the BERT a and ERNIE b tokenizers often produce surprising results. Byte pair encoding (BPE) trains a dictionary with a simple information theoretic criterion that sidesteps the need for special treatment of unknown words. BPE is more about training (populating a dictionary of word pieces) than inference (parsing an unknown word into word pieces). The parse at inference time can be ambiguous. Which parse should we use? For example, “electroneutral” can be parsed as electron-eu-tral or electro-neutral, and “bidirectional” can be parsed as bid-ire-ction-al and bi-directional. BERT and ERNIE tend to favor the parse with more word pieces. We propose minimizing the number of word pieces. To justify our proposal, a number of criteria will be considered: sound, meaning, etc. The prefix, bi-, has the desired vowel (unlike bid) and the desired meaning (bi is Latin for two, unlike bid, which is Germanic for offer).
Classification of Activities of Daily Living Based on Depth Sequences and Audio

In this paper, we propose a non-intrusive daily activity recognition system to monitor elders who are living alone. Our contributions are to collect a dataset of activities of daily living consist of RGB, depth, silhouette, skeleton and audio data streams and to develop a system to recognize 20 different activities using depth image sequences and audio data. We have trained two separate Neural Networks to recognize activities from human silhouette features and Short Time Fourier Transform features extracted from depth images and audio data. Predictions from the two Neural Networks are fed into a fusion model and the final activity classification is done. The proposed activity recognition system can be used in assisted living systems to enhance the quality of the lifestyle of the elders who are living alone.
A novel publicly delegable secure outsourcing algorithm for large-scale matrix multiplication

Computation of complex mathematical problems are always a challenge of resource constrained clients. A client can outsource the computations to resource abundant cloud server for execution. But this arrangement brings many security and privacy challenges. In this paper, we have presented a secure a nd efficient algorithm for general computation and scientific problem i.e. matrix multiplication. The proposed algorithm is inspired by the existing algorithm, but we believe that it is imperative to improve the algorithm to enable secure outsourcing of computation. The previous state-of-the art algorithm for matrix multiplication is vulnerable to the Cipher-Text Only Attack (COA) along with Chosen Cipher-Text Attack (CCA) and Known Plain-Text Attack (KPA) and reveal information about the client's data. Hence fails the security requirements of the outsourcing algorithm. The proposed work retains the efficiency benefit of state-of-the-art algorithm, additionally defended the client data against (COA) along with (CCA) and Known Plain-Text Attack (KPA).
Determinants Affecting Economic Growth – Evidence for Western Balkans Countries

The purpose of this paper is to find out if selected determinants have any effect on the economic growth rate using the strong balanced panel data for the Western Balkan countries for the period 2001-2017, and the data used are on an annual basis, which in total there are 102 observation periods. For the realization of the paper, secondary data and an advanced dynamic approach were used, such as pooled OLS methods, fixed and random effects model, to test economic growth rate as dependent variable, and explanatory variables such as working remittances to GDP, exports to GDP, imports to GDP, foreign direct investment to GDP and inflation rate. From the generated outputs, it is true to say that working remittances to GDP, exports to GDP, and imports to GDP have an effect that influences economic growth, respectively GDP growth. Even though foreign direct investment to GDP and inflation rate does not have a significant effect on economic growth, respectively GDP growth.&#x0D; &#x0D; Keywords: Economic growth; macroeconomic determinants; panel data.&#x0D; JEL code: O47, O11, C23&#x0D;
Stimme: A Chat Application for Communicating with Hearing Impaired Persons

This paper presents an assistive technology framework which furnishes a communication interface in the form of a chat application for communicating between hearing impaired and ordinary persons using a mobile phone. It converts the voice messages of the ordinary person into texts. The framework then transforms the texts into a combination of specially designed chat acronyms. Finally, it relates the combination to a set of tactile feedback for the hearing impaired person. Morse code is used to provide the feedback. Empirical evidence suggests that our framework is both an acceptable and a viable approach for communicating with hearing impaired persons.
Reflectance-Guided, Contrast-Accumulated Histogram Equalization

Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously. To address this problem, we propose a histogram equalization-based method that adapts to the data-dependent requirements of brightness enhancement and improves the visibility of details without losing the global contrast. This method incorporates the spatial information provided by image context in density estimation for discriminative histogram equalization. To minimize the adverse effect of non-uniform illumination, we propose defining spatial information on the basis of image reflectance estimated with edge preserving smoothing. Our method works particularly well for determining how the background brightness should be adaptively adjusted and for revealing useful image details hidden in the dark.
Cyclon Language First Grade App: Technological Platform to Support the Construction of Citizen and Democratic Culture of Science, Technology and Innovation in Children and Youth Groups

This article shows the construction of software applications Cyclon Language First Grade App, like a strategy in which communities of practice, learning, knowledge, innovation and transformation are generated, understood as a transversal process, where collaborative, problematizing learning is encouraged, by critical inquiry, permanent interaction, cultural negotiations and the dialogue of knowledge, typical of the pedagogical proposal of the Ondas program. It is summarized in the following aspects: “Building an identity that incorporates the recognition of science and technology as a constituent element of everyday culture both in individuals and in the communities and institutions of which they are part, involving various sectors of society: productive, social, political, state and in the various territorial areas: local, departmental and national. Development of forms of organization oriented to the appropriation of values that recognize a cultural identity around science and technology in the aspects mentioned in the previous point. This implies models of participation, social mobilization and public recognition of scientific and technological activity. On the other hand, the incorporation of the research activity in the elementary and middle school involves the development of national, departmental and local financing mechanisms; in such a way that children and young people can develop their abilities and talents in a favorable environment of both social recognition and economic conditions. Development of a methodological strategy supported by ICT that helps the Colombian population to recognize and apply both individually and collectively, science and technology through research activities designed according to the characteristics of the scientific method. “The appropriation of ICTs as a constitutive part of the citizen and democratic culture of the CT + I and the construction of virtual reality as central to the process of knowledge democratization.
Artificial Bandwidth Extension Using Conditional Variational Auto-encoders and Adversarial Learning

Artificial bandwidth extension (ABE) algorithms have been developed to estimate missing highband frequency components (4-8kHz) to improve quality of narrowband (0-4kHz) telephone calls. Most ABE solutions employ deep neural networks (DNNs) due to their well-known ability to model highly complex, non-linear relationship between narrowband and highband features. Generative models such as conditional variational auto-encoders (CVAEs) are capable of modelling complex data distributions via latent representation learning. This paper reports their application to ABE. CVAEs, form of directed, graphical models, are exploited to model the probability distribution of highband features conditioned on narrowband features. While CVAEs are trained with the standard mean square criterion (MSE), their combination with adversarial learning give further improvements. When compared to results obtained with the baseline approach, the wideband PESQ is improved significantly by 0.21 points. The performance is also compared on an automatic speech recognition (ASR) task on the TIMIT dataset where word error rate (WER) is decreased by an absolute value of 0.3%.
Pseudo Likelihood Correction Technique for Low Resource Accented ASR

With the availability of large data, ASRs perform well on native English but poorly for non-native English data. Training nonnative ASRs or adapting a native English ASR is often limited by the availability of data, particularly for low resource scenarios. A typical HMM-DNN based ASR decoding requires pseudo-likelihood of states given an acoustic observation, which changes significantly from native to non-native speech due to accent variation. In order to improve the performance of a native English ASR on non-native English data, we, in this work, propose a DNN-based pseudo-likelihood correction (PLC) technique, in which a non-native pseudo-likelihood vector is mapped to match its native counterpart. Instead of correcting all elements of a non-native pseudo-likelihood vector, a loss function is proposed to correct only top few of them. Experiments with one native and multiple Indian English corpora show an improvement of WER by ~12% and over ~5% using the proposed PLC technique unadapted and adapted native English ASR respectively, when recognition is performed on an Indian English corpus different from that used for both PLC and adaptation. Experiments with upto 2 hours of parallel native and non-native English data reveal that, PLC performs better than adaptation for all unseen cases considered.