Reviewerly/exHarmony huggingface dataset:
Handling Double Intensifiers in Feature-Level Sentiment Analysis Based on Movie Reviews

In the beginning of the information age Web 1.0, whatever content which was available on the Internet was used only for the purpose of reading. But, things changed after Web 2.0 as people started sharing what they think about a particular product or an object. This related information generated by user became of paramount importance when it started affecting the way other people think of the same objects and this formulated the need of opinion mining or sentiment analysis by different organizations. Opinion mining or sentiment analysis is the process of determining the positive or negative inclination of a piece of written information. Feature-based opinion mining works upon the individualistic features of an opinion. This paper proposed a robust algorithm for handling the issues of double intensifiers when expressed in an opinion especially in negative opinion by performing feature-based opinion mining on movie reviews and discusses over future aspects of the same.
Mobile Telemedicine Systems with Artificial Medical Intelligence

We consider mobile telemedicine systems with artificial medical intelligence (MTMS with AMI) the best solution for mass screening of the population in rural, remote and hard-to-access areas. In Russia, more than 110,000 settlements with about 33 million people are not provided with healthcare services. The situation is barely better in other localities, primarily in the district healthcare setting which lacks enough doctors, or their qualification level is insufficient. The article describes the design of a telemedicine system with AMI based on modern telecommunications (cellular and mobile-satellite services) and a variety of AMIs for mammography, fluorography, cardiography, etc. The project has been developed and partially implemented by the MIPT and Vector Radio Company over the past 5 to 7 years. An artificial medical intelligence for automated analysis of fluorograms, designed as a cloud service, is described in detail, and so are the results of its testing.
Large Data Flow Graphs in Limited GPU Memory

The size of a GPU's memory imposes strict limits both on the complexity of neural networks and the size of the data samples that can be processed. This paper presents methods to efficiently use GPU memory by the TensorFlow <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> machine learning framework for processing large data flow graphs of neural networks. The proposed techniques make use of swapping data stored in GPU memory to and from CPU memory, data compression, and serialization of computation. The data flow graph is modified by inserting nodes for data transfer and compression, and by defining control dependencies for serializing the execution of graph nodes. The locations for additional nodes and control dependencies are determined algorithmically by the analysis of the graph's topology and the complexity of operations implemented by the graph's nodes. Our experiments show the capability to process 3D-Unet [1] on 192 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -sized images with batch size 4 and ResNet [2] models with more than 7 fold the maximum batch size.
Visibility of quantization errors in reversible JPEG2000

Image compression systems that exploit the properties of the human visual system have been studied extensively over the past few decades. For the JPEG2000 image compression standard, all previous methods that aim to optimize perceptual quality have considered the irreversible pipeline of the standard. In this work, we propose an approach for the reversible pipeline of the JPEG2000 standard. We introduce a new methodology to measure visibility of quantization errors when reversible color and wavelet transforms are employed. Incorporation of the visibility thresholds using this methodology into a JPEG2000 encoder enables creation of scalable codestreams that can provide both near-threshold and numerically lossless representations, which is desirable in applications where restoration of original image samples is required. Most importantly, this is the first work that quantifies the bitrate penalty incurred by the reversible transforms in near-threshold image compression compared to the irreversible transforms.
Learning a Faceted Customer Segmentation for Discovering new Business Opportunities at Intel

For sales and marketing organizations within large enterprises, identifying and understanding new markets, customers and partners is a key challenge. Intel's Sales and Marketing Group (SMG) faces similar challenges while growing in new markets and domains and evolving its existing business. In today's complex technological and commercial landscape, there is need for intelligent automation supporting a fine-grained understanding of businesses in order to help SMG sift through millions of companies across many geographies and languages and identify relevant directions. We present a system developed in our company that mines millions of public business web pages, and extracts a faceted customer representation. We focus on two key customer aspects that are essential for finding relevant opportunities: industry segments (ranging from broad verticals such as healthcare, to more specific fields such as “video analytics”) and functional roles (e.g., “manufacturer” or “retail”). To address the challenge of labeled data collection, we enrich our data with external information gleaned from Wikipedia, and develop a semi-supervised multi-label, multi-lingual deep learning model that parses customer website texts and classifies them into their respective facets. Our system scans and indexes companies as part of a large-scale knowledge graph that currently holds tens of millions of connected entities with thousands being fetched, enriched and connected to the graph by the hour in real time, and also supports knowledge and insight discovery. In experiments conducted in our company, we are able to significantly boost the performance of sales personnel in the task of discovering new customers and commercial partnership opportunities.
WATAPI: Composing Web API Specification from API Documentations through an Intelligent and Interactive Annotation Tool

The number of web APIs grows rapidly. Each API provider offers API documentations which comes with diversity and complexity of structures. In order to understand a large number of diverse web APIs, we employ deep learning that extracts key objects of a large number of web APIs and produce a unified API specification (Open API Specification). However, the unified API specification is not a simple task due to heterogeneous API documentations and it is required additional human evaluation to adjust incorrect information which produced by machine. In this paper, we introduce WATAPI which represents an automation tool for representing machine learning outcomes and adding a user as a humanin-the-loop to transparently interact with complex machinelearning components to process diverse API documentations. WATAPI allows the user to annotate API documentations and/or adjust the automated annotation process which produced by machine-learning models' predictions. WATAPI is also capable to perform as a semi-automated annotation tool where it records user's interactions. WATAPI is able to automatically apply user's interaction of one API annotation to another API documentations with a similar structure. The user's annotation adjustment provides feedback to machinelearning components for improving the accuracy of extracting Open API Specifications.
Performance Evaluation of CAESAR Authenticated Encryption on SROS2

With the expansion of Industry 4.0, the factory automation (FA) system has become more complex due to wireless communication between multiple devices. Therefore, short-term development and secure communication are required for development cost and cyber-security issues. For this reason, a secure robot operating system 2 (SROS2) was developed. SROS2 is an open-source platform applicable to industrial robots. SROS2 achieves short-term development with various libraries and implements encryption and authentication by embedding advanced encryption standard - Galois/counter mode (AES-GCM). On the other hand, AES-GCM has been reported to be vulnerable to spoofing. Therefore, a secure encryption algorithm to replace AES-GCM is required. In addition, the system must be able to operate without problems after replacement. For these reasons, this study embeds secure authenticated encryption instead of AES-GCM in SROS2. The results of an evaluation experiment on the latency of the encryption algorithm showed that Ascon is more effective than AES-GCM.
$k$-Means-Lite++: The Combined Advantage of Sampling and Seeding

The k-means-lite algorithm was recently introduced to address the poor scalability of the popular k-means algorithm. This paper modifies k-means-lite by applying D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> sampling, the state-of-the-art seeding technique employed by k-means++, to each sample in the former. K-means-lite estimates its final centroids by applying standard k-means to the combination of all centroids obtained from initial application of k-means to a few (say, five) samples. Although k-means-lite achieves drastic efficiency gain, the five-sample implementation trades off some of the standard algorithm's accuracy. One way to improve its accuracy is to increase the number of samples. But it may be difficult to determine a priori what performance level is attainable, or the number of samples required to attain such performance. These would vary per problem. In this paper, we show experimentally that without increasing the sample size or number of samples in k- means-lite, our modified algorithm, named k-means-lite++, addresses this problem and is even more accurate than the standard k-means. For the cases tested, k-means-lite++ matches the accuracy of k-means++, which is widely considered the state-of-the-art k-means algorithm. The extra cost of sample seeding is compensated for by faster convergence. So, although drastically more accurate, k-means-lite++ has efficiency that is comparable to k-means-lite's. Thus, the proposed algorithm may also be viewed as a solution to scalability issues associated with k-means++'s sequential sampling.
Promising Areas for Advanced Training of Subject Teachers in the Training System for Participants of the All-Russian Olympiad for Schoolchildren

The article describes the problem of the current state of the all-Russian Olympiad of schoolchildren: formed accessible creative environment for children with maximum coverage of students in the school stage of the all-Russian Olympiad in all subjects in all schools of the country, on the one hand, and the lack of systematic training with coverage of all subject teachers in all schools of regions of Russia, on the other hand. &#x0D; The structure and approximate content of the standard training program for subject teachers as school mentors on the specifics of preparing students to participate in the all-Russian Olympiad are presented.
Dangerous Driving Behavior Detection with Attention Mechanism

In order to reduce the incidence of traffic accidents caused by dangerous driving, a dangerous driving behavior recognition model based on convolutional neural network (CNN) and long short-term memory network (LSTM) is proposed. Aiming at the problem of low accuracy of the network model identification, the algorithm is optimized by introducing the unsupervised attention mechanism. The model focuses on a specific visual area and improves the recognition accuracy of the algorithm to some extent by integrating the attention weighted module and the convolution LSTM. The experimental results show that the detection accuracy and detection rate of the algorithm are improved compared with the Two-Stream method and C3D behavior recognition algorithm in the dangerous driving behavior recognition task.
Domain Specific word Embedding Matrix for Training Neural Networks

The text represents one of the most widespread sequential models and as such is well suited to the application of deep learning models from sequential data. Deep learning through natural language processing is pattern recognition, applied to words, sentences, and paragraphs. This study describes the process of creating a pre-trained word embeddings matrix and its subsequent use in various neural network models for the purposes of domain-specific texts classification. Embedding words is one of the popular ways to associate vectors with words. Creating a word embedding matrix maps imply well semantic relationship between words, which can vary from task to task.
Representation Learning for Style and Content Disentanglement with Autoencoders

Many approaches have been proposed to disentangle the style and content from image representations. Most existing methods aim to create new images from the combinations of separated style and content. These approaches have shown impressive results in creating new images in such a way as to swap or mix features, but they have not shown the results of disentanglement respectively. In this paper, to show up the results of disentanglement on each, we propose a two-branch autoencoder framework. Our proposed framework, which incorporates content branch and style branch, enables complementary and effective end-to-end learning. To demonstrate our framework in a qualitative assessment, we show the disentanglement results of style and content respectively in handwritten digit datasets: MNIST-M and EMNIST-M. Furthermore, for the quantitative assessment, we examined our method to the classification task. In this experiment, our disentangled content images from digit datasets can achieve competitive performance in classification tasks. Our code is available at https://github.com/najaemin92/disentanglement-pytorch .
Neuromorphic computing through photonic integrated circuits

The identification of neuromorphic computing as a highly promising alternative computing system has been emerged from its potential to increase rapidly the computational efficiency that is currently restricted by Moore's law end. First electronic neuromorphic chips like IBM's TrueNorth and Intel's Loihi revealed a tremendous performance improvement in terms of computational speed and density; however, they are still operating in MHz rates. To this end, neuromorphic photonic integrated circuits can further increase the computational speed and density, having a large portfolio of components with GHz-bandwidth and low-energy. Herein, we present an all-optical sigmoid activation function as well as a single-λ linear neuron. The all-optical sigmoid activation function comprises a Semiconductor Optical Amplifier-Mach-Zehnder Interferometer (SOA-MZI) configured in differentially-biased scheme followed by an SOA. Its thresholding capabilities have been experimentally demonstrated with 100psec optical pulses. Then, we introduce an all-optical phase-encoded weighting scheme and we experimentally demonstrate its linear algebra operational credentials by the means of a typical IQ modulator operated at 10Gbaud/s.
Mixed Bandwidth Acoustic Modeling Leveraging Knowledge Distillation

Training of mixed bandwidth acoustic models have recently been realized by incorporating special Mel filterbanks. To fit information into every filterbank bin available across both narrowband and wideband data, these filterbanks pad zeros at high frequency ranges of narrowband data. Although these methods succeed in decreasing word error rates (WER) on broadband data, they fail to improve on narrowband signals. In this paper, we propose methods to mitigate these effects with generalized knowledge distillation. In our method, specialized teacher networks are first trained on lossless acoustic features with full scale Mel filterbanks. While training student networks, privileged knowledge from these teacher networks is then used to compensate for missing information at high frequencies introduced by the special Mel filterbanks. We show the benefit of the proposed technique for both narrowband (10% relative WER improvement) and wideband data (7.5% relative WER improvement) on the Aurora 4 task over traditional methods.
Gegenbauer-Based Image Descriptors for Visual Scene Recognition

Visual scene recognition is an important problem in artificial intelligence with applications in areas such as autonomous vehicles, visually impaired people assistance, augmented reality, and many other pattern recognition areas. Visual scene recognition has been tackled in recent years by means of image descriptors such as the popular Speeded-Up Robust Features (SURF) algorithm. The problem consists in analyzing the scenes in order to produce a compact representation based on a set of so called regions of interest (ROIs) and then finding the largest number of matches among a dataset of reference images that include non-affine transformations of the scenes. In this paper, a new form of descriptors based on moment invariants from Gegenbauer orthogonal polynomials is presented. Their computation is efficient and the produced feature vector is compact, containing only a couple dozens of values. Our proposal is compared against SURF by means of the recognition rate computed on a set of two hundred scenes containing challenging conditions. The experimental results show no statistically significant difference between the performances of the descriptors.
Scalable Document Image Information Extraction with Application to Domain-Specific Analysis

Document images are ubiquitous, but existing methods mainly focus on the text reading but not information understanding. In this paper, we propose a novel document image information extraction framework with application to domain-specific analysis. Key gains of our system result from the modularized implementation of the document analysis modules needed for different document analysis problems. Further, we provide an efficient text recognition approach that makes a trade-off between performance and running speed for document images and a novel information extraction method with both visual and semantic information. Our framework is scalable and customizable, and only a few annotations of the keyword-content mapping is needed towards domain-specific document analysis.
Continuous Gait Authentication Against Unauthorized Smartphone Access Through Naïve Bayes Classifier

Rayani, Praveen Kumar Changder, SuvamoyContinuous authentication is one of the emerging feature to identify the genuine user of the smartphone passively and prevent information theft. Earlier works on entry-point authentication failed to identify the genuine user of the smartphone and were resilient to information theft. So, we observed that theft of personal information from a smartphone might not be the loss of confidential information. However, we identified that loss of sensitive information through an internal attack causes the user vulnerable to attack and blackmail from an anonymous user. In this paper, we proposed an algorithm on the continuous gait authentication to work against unauthorized smartphone access. The continuous gait authentication uses accelerometer and gyroscope data to classify user patterns via naïve Bayes. We systematically evaluated proposed work with fifteen users and three behavioral postures of the smartphone. Finally, we addressed open research challenges existed in continuous gait authentication of the smartphone.
Facial Expression Recognition Using Merged Convolution Neural Network

In this paper, a merged convolution neural network (MCNN) is proposed to improve the accuracy and robustness of real-time facial expression recognition (FER). Although there are many ways to improve the performance of facial expression recognition, a revamp of the training framework and image preprocessing renders better results in applications. When the camera is capturing images at high speed, however, changes in image characteristics may occur at certain moments due to the influence of light and other factors. Such changes can result in incorrect recognition of human facial expression. To solve this problem, we propose a statistical method for recognition results obtained from previous images, instead of using the current recognition output. Experimental results show that the proposed method can satisfactorily recognize seven basic facial expressions in real time.
Quantum Distributed Complexity of Set Disjointness on a Line

Given \( x,y\in \lbrace 0,1\rbrace ^n \) , Set Disjointness consists in deciding whether \( x_i=y_i=1 \) for some index \( i \in [n] \) . We study the problem of computing this function in a distributed computing scenario in which the inputs \( x \) and \( y \) are given to the processors at the two extremities of a path of length \( d \) . Each vertex of the path has a quantum processor that can communicate with each of its neighbours by exchanging \( \operatorname{O}(\log n) \) qubits per round. We are interested in the number of rounds required for computing Set Disjointness with constant probability bounded away from \( 1/2 \) . We call this problem “Set Disjointness on a Line”. Set Disjointness on a Line was introduced by Le Gall and Magniez [ 14 ] for proving lower bounds on the quantum distributed complexity of computing the diameter of an arbitrary network in the CONGEST model. However, they were only able to provide a lower bound when the local memory used by the processors on the intermediate vertices of the path is severely limited. More precisely, their bound applies only when the local memory of each intermediate processor consists of \( \operatorname{O}(\log n) \) qubits. In this work, we prove an unconditional lower bound of \( \widetilde{\Omega }\big (\sqrt [3]{n d^2}+\sqrt {n} \, \big) \) rounds for Set Disjointness on a Line with \( d + 1 \) processors. This is the first non-trivial lower bound when there is no restriction on the memory used by the processors. The result gives us a new lower bound of \( \widetilde{\Omega } \big (\sqrt [3]{n\delta ^2}+\sqrt {n} \, \big) \) on the number of rounds required for computing the diameter \( \delta \) of any \( n \) -node network with quantum messages of size \( \operatorname{O}(\log n) \) in the CONGEST model. We draw a connection between the distributed computing scenario above and a new model of query complexity. In this model, an algorithm computing a bi-variate function \( f \) (such as Set Disjointness) has access to the inputs \( x \) and \( y \) through two separate oracles \( {\mathcal {O}}_x \) and \( {\mathcal {O}}_y \) , respectively. The restriction is that the algorithm is required to alternately make \( d \) queries to \( {\mathcal {O}}_x \) and \( d \) queries to \( {\mathcal {O}}_y \) , with input-independent computation in between queries. The model reflects a “switching delay” of \( d \) queries between a “round” of queries to \( x \) and the following “round” of queries to \( y \) . The information-theoretic technique we use for deriving the round lower bound for Set Disjointness on a Line also applies to the number of rounds in this query model. We provide an algorithm for Set Disjointness in this query model with round complexity that matches the round lower bound stated above, up to a polylogarithmic factor. This presents a barrier for obtaining a better round lower bound for Set Disjointness on the Line. At the same time, it hints at the possibility of better communication protocols for the problem.
Heuristics and Metaheuristic Optimization Algorithms

The concept of optimization is widely used to locate the optimum solution for different mathematical problems.