Reviewerly/exHarmony huggingface dataset:
Document-Level Neural Machine Translation with Associated Memory Network

Standard neural machine translation (NMT) is on the assumption of document-level context independent. Most existing document-level NMT methods are satisfied with a smattering sense of brief document-level information, while this work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings, which is capable of sufficiently modeling deeper and richer document-level context. The proposed document-aware NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end. Experiments show that the proposed method significantly improves the translation performance over strong baselines and other related studies.
Vision-based Road Segmentation for Intelligent Vehicles using Deep Convolutional Neural Networks

Intelligent Transportation Systems are crucial to enhance traffic security and reduce traffic violations. Recent developments in artificial intelligence have also motivated researchers to work on these systems. In order to reduce the risk of road accidents, accurately determining the driving area is important. On the other hand, automated road segmentation is challenging because of different weather, light, road conditions, etc. Also, the roads’ design characteristics may be different and may include very small and large features. In this paper, a vision-based road segmentation system is presented. To apply this system, a CNN architecture based on consecutive triple filter size has been designed. The consecutive triple filter size approach avoids missing both small and large features. Proposed CNN architecture consists of an encoder and a decoder subnetwork. Preliminary experimental results achieved 95.84% IoU, 97.86% TPR, and 2.34% FPR indexes for road segmentation tasks on the CamVid database. This study presents a low-cost, reliable, and basic road segmentation system for intelligent vehicles. Also, it is a step toward a more complex system to alert the driver about dangerous situations. Owing to the proposed system, a successful road segmentation is possible using a simple camera.
Nonlinear State Estimation Using Adaptive Gaussian Filters with One-Step Randomly Delayed Measurements

This paper presents two adaptive sigma point Gaussian filters based on unscented transformation (UT) rule and cubature rule for one-step randomly delayed measurements when process noise covariance remains unknown due to parametric uncertainty. The proposed filter is designed to give satisfactory estimation performance by carrying out auto tuning of unknown element of process noise covariance. Efficacy of the adaptive Gaussian filters for one-step randomly delayed measurements is demonstrated by comparing root mean square error (RMSE), average RMSE (ARMSE), and percentage of track loss with corresponding non-adaptive filters. Monte Carlo (MC) simulations demonstrated the effectiveness and consistency of the proposed filters.
Analysis of Membership Function in the Implementation of Neuro-Fuzzy System for Prediction of Depressive Lexicons

The soft computing environment does not stop making predictions to save lives. Its calculations, though not precise can still provide solutions to the complex scenarios of computational problems. Over the years, robustness and the accuracy of results became the remarkable challenge despite of the huge number of studies relating to it. In this case, the Neuro fuzzy system is one amongst the model proven to be used for prediction and has been applied to various fields of education, business, engineering and even in health. The Neuro fuzzy has its capability to adopt various strategies like the mix and match approach with different parameters involved during the experimentation. However, there is still a lack of support as to how this membership function could be able to produce a precise result that leads to different views towards soft computing. In this work, we gave emphasize on how the membership function works with NFS, which is essential in preparing data towards the prediction of depressive lexicons. Classifiers like Naïve Bayes, Simple Logistics, and Neuro fuzzy through the Multilayer Perceptron (MLP) of Weka tool have been used for the experimentation of the study. This study recommends to use the process of the analysis of membership function and carefully validate parameters from the first layer towards the third layer of the NFS since the strength of the membership function actively happens here.
A Detection and Tracking Combined Network for Long-Term Tracking

Long-term tracking algorithms need to track targets stably in long videos. Compared with short-term tracking, long-term tracking faces more complex challenges. Such as the background brightness distributed in the entire gray level, wide range of target scale changes, more frequent motion blur and occlusion and other challenges. Existing long-term tracking algorithms cannot solve these challenges well, and easily cause tracking box drift, which significantly decrease the accuracy and robustness of the algorithms. In this paper, we design a detection and tracking combined network that can implement a global instance search for the tracked target. We integrate deformable convolution into the siamese network to solve the target deformation problem during long-term tracking. Besides, guided anchor is adopted in RPN to generate more sparse and accurate proposals, which can decrease the interference of the background. We design the cascaded RCNN with template information to filter out proposals and refine coordinates. And finally, select the highest confidence proposal as the final tracking box. Experiments show that our method has a stronger discriminative ability and can get more accurate tracking boxes compared with other advanced trackers. On several long-term tracking benchmarks, our method has achieved excellent performance.
Three-dimensional Medical Image Segmentation with SE-VNet Neural Networks

research-article Share on Three-dimensional Medical Image Segmentation with SE-VNet Neural Networks Authors: Sheng Lu Xi'an University of Posts and Telecommunications, China Xi'an University of Posts and Telecommunications, ChinaView Profile , Jungang Han Xi'an University of Posts and Telecommunications, China Xi'an University of Posts and Telecommunications, ChinaView Profile , Jiantao Li Chinese PLA General Hospital, China Chinese PLA General Hospital, ChinaView Profile , Liyang Zhu Xi'an University of Posts and Telecommunications, China Xi'an University of Posts and Telecommunications, ChinaView Profile , Jiewei Jiang Xi'an University of Posts and Telecommunications, China Xi'an University of Posts and Telecommunications, ChinaView Profile , Shaojie Tang Xi'an University of Posts and Telecommunications, China Xi'an University of Posts and Telecommunications, ChinaView Profile Authors Info & Claims IMIP '21: 2021 3rd International Conference on Intelligent Medicine and Image ProcessingApril 2021 Pages 14–20https://doi.org/10.1145/3468945.3468948Online:27 September 2021Publication History 0citation62DownloadsMetricsTotal Citations0Total Downloads62Last 12 Months62Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Robust Automatic Speech Recognition for Call Center Applications

This paper is focused on developing an Automatic Speech Recognition (ASR) system robust against different noisy scenarios. ASR systems are widely used in call centers to convert telephone recordings into text transcriptions which are further used as input to automatically evaluate the Quality of the Service (QoS). Since the evaluation of the QoS and the customer satisfaction is performed by analyzing the text resulting from the ASR system, this process highly depends on the accuracy of the transcription. Given that the calls are usually recorded in non-controlled acoustic conditions, the accuracy of the ASR is typically decreased. To address this problem, we first evaluated four different hybrid architectures: (1) Gaussian Mixture Models (GMM) (baseline), (2) Time Delay Neural Network (TDNN), (3) Long Short-Term Memory (LSTM), and (4) Gated Recurrent Unit (GRU). The evaluation is performed considering a total of 478,6 h of recordings collected in a real call-center. Each recording has its respective transcription and three perceptual labels about the level of noise present during the phone-call: Low level of noise (LN), Medium Level of noise (ML), and High Level of noise (HN). The LSTM-based model achieved the best performance in the MN and HN scenarios with \(22,55\%\) and \(27,99\%\) of word error rate (WER), respectively. Additionally, we implemented a denoiser based on GRUs to enhance the speech signals and the results improved in 1,16% in the HN scenario.
Communicating Cybersecurity Vulnerability Information: A Producer-Acquirer Case Study

The increase in both the use of open-source software (OSS) and the number of new vulnerabilities reported in this software constitutes an increased threat to businesses, people, and our society. To mitigate this threat, vulnerability information must be efficiently handled in organizations. In addition, where e.g., IoT devices are integrated into systems, such information must be disseminated from producers, who are implementing patches and new firmware, to acquirers who are responsible for maintaining the systems. We conduct an exploratory case study with one producer of IoT devices and one acquirer of the same devices, where the acquirer integrates the devices into larger systems. Through this two-sided case study, we describe company roles, internal and inter-company communication, and the decisions that need to be made with regard to cybersecurity vulnerabilities. We also identify and discuss both challenges and opportunities for improvements, from the point of view of both the producer and acquirer.
Quantum Based Deep Learning Models for Pattern Recognition

The machine learning model influences the pattern recognition based on extraction of relative patterns, but not enough capable of efficiently processing the data set that needs layered interaction. So, deep learning model takes the advantage of artificial neural network for processing the data in layered abstraction by exploring massive parallelism, although the classical implementations of such model may not be competent due to the processing and storage of large neural networks. Current research explores quantum computation potentials and utilizes its significance for supporting inherent parallelism, as the machine perform exponential operations in single step of execution. The possible classical designs of pattern recognition using deep learning are Hopfield network and Boltzmann machine and their equivalent quantum models can remain effective and overcome the processing limitations of classical model by incorporating Grover’s search method and quantum Hebbian learning. The aim of writing this article is to introduce the necessity of deep learning model, an emergence of quantum computations, discussion over requisites of data preprocessing techniques, then to propose the classical equivalent quantum deep learning model along with the algorithms, complexity comparison and speedup analysis followed by conclusive aspects that proves effectiveness of quantum deep learning model and future works.
Blockchain in Philanthropic Management

In the world of cutting-edge technology, a buzzword that has been thrown around quite often is blockchain. It has gained prominence in a wide array of fields ranging from the money transfer industry to that of the healthcare sector. The primordial reason for this varied use is the concept of smart contract that makes it intangible and less susceptible to errors. Using this platform of blockchain, the authors propose an avant-garde method of contributing donations to charities through a pivotal body, namely the trust. This application is paramount in the sense that it binds the advancements made on the technological frontier with the social aspect of life, that is, charities in the form of NGO's or other such beneficiaries. This enforces the scrupulous way of donating, ensuring that the money does not get consumed by outsiders or any third-party bodies because ultimately, safeguarding the needs and necessities of the lesser privileged folks is the primary goal, which should be addressed with immediate effect.
FEATURES OF THE OLYMPIAD IN MATHEMATICS IN THE FRAMEWORK OF NETWORK INTERACTION OF EDUCATIONAL ORGANIZATIONS

The article presents the features of the organization and conduct of the Olympiad work in mathematics based on the network interaction of various educational organizations. The network interaction of educational organizations in the article is considered as a complex mechanism for centralizing educational resources, contributing to the active involvement of several educational organizations at once in a single educational process, overcoming the considerable territorial remoteness of various educational organizations. There are small educational organizations that have limited material, technical, methodological and human resources for organizing and conducting, on the basis of creating a unified information and educational environment, various events, in particular Olympiads. Each educational organization included in a single network has access to all its aggregate resources and thereby increases its own teaching and educational potential, and students receive a wide range of educational services, due to which each of them can build their own individual educational route. The article also discusses the main directions of organizing and holding the Olympiad in mathematics based on the network interaction of educational organizations of different levels using TRIZ pedagogy (theory of inventive problem solving), LEGO pedagogy (development and formation of the student's personality based on design technology, or modeling). As the main tasks of organizing mathematics olympiads based on the technology of network interaction of educational organizations, the article discusses: increasing students' interest in mathematical disciplines, the formation of creative thinking, the development of the ability to solve non-standard problems, the dissemination of experience in using innovative models of organizing and holding mathematics olympiads. popularization of the Olympiad work among students and teachers of educational organizations of the Republic of North Ossetia-Alania.
Target-oriented Opinion Words Extraction via graph convolutional network

Abstract Target-oriented opinion word extraction (TOWE) is a sequence labelling subtask of aspect-level sentiment analysis (ABSA), which aims to extract corresponding opinion words for a given sentence and opinion target. In view of the existing related work, the structural information of sentences is not fully considered. In this study, we explore the integration of neural network and dependency tree to handle the TOWE and propose a model based on graph convolution network (GCN). The model uses the long short-term memory network (LSTM) to learn the semantic features of sentences. On this basis, the model uses the GCN to model the sentence structure through dependency tree and capture the syntactic dependency relationship between the target word and the opinion word. The experimental results show that GCN can effectively improve the performance of TOWE. The F1 values of the model on the semeval-rest14 and semeval-laptop14 reached 82.60% and 74.45%.
An algorithm for Vehicle Distance and Speed Estimation

In this paper, an algorithm for vehicle distance and speed estimation is proposed. In particular, the estimation is performed using video frames obtained from the cameras, located on the front and rear of the car. Moreover, the vehicle detection is performed using a Viola-Jones algorithm, while a Median flow is used for detected vehicle tracking. The speed of the vehicle is calculated using a distance of the detected vehicle and a change in the detected bounding box width between two consecutive frames. The proposed algorithm performance is tested on different car brands, different weather conditions, and different car distances. The testing results of the proposed method show promising results.
Outlier Detection Using One-Class Classification

Outlier detection helps users to identify the infrequent observations. Here, we propose a one-class classification model to detect outliers using autoencoder with deep SVDD. The support vector data description has been introduced to address the problem of outlier detection as it creates the smallest possible sphere around the given data points. The hybrid approach of using autoencoders with deep SVDD helps to achieve better results as autoencoders decrease data dimensions by training how to remove the noise present in the given data and compress and encode data which is given to deep SVDD for classification. Our result has been experimented on CIFAR10 complex dataset.
Analysis and improvement of Boolean chaos robustness to noise

Boolean chaos generated by autonomous Boolean networks has been successfully applied to random number generation, physical unclonable function, gene regulation and reservoir computing, because it can generate complex dynamics with very simple structure. Influences of noise on autonomous Boolean networks chaotic system in delay time parameter spaces are analyzed by simulation and experiments in this article. It is found that the influences of noise on Boolean chaotic system is two-sided. What is new is that noise may lead to chaos instability, i.e., the noise transforms the original chaotic outputs into periodic outputs, which means the Boolean chaos lacks robustness to noise. Then like other chaotic systems noise contributes to Boolean chaotic unpredictability. The physical origin of noise induced Boolean chaos instability is analyzed that noise may easily change delay time parameter no longer a chaotic parameter in discontinuous chaotic parameter spaces. In addition, simulation and experiments on FPGA show that the increase of the number of nonlinear elements of the chaotic system can improve the Boolean chaos robustness to noise. This research is significant to the design of autonomous Boolean networks.
Research on the Algorithm of Detection for Small and Weak Target Based on the Mechanism of High-Resolution Amplification

Abstract Aiming at the problem that the existing target detection framework based on big data and deep learning has poor recognition effect for small and weak targets with low resolution in complex scenes, in this paper, we improve the small targets detection accuracy and speed, based on the FCOS algorithm for target detection and the mechanism of high resolution amplification. The basic principle is to increase amplification precision gain regression network (R-NET) and amplification region selection algorithm, on the basis of enhanced learning. The former learns the correlation between coarse and fine detection, and the latter calculates the information gain of the amplified region. The results show that this method can detect small and weak targets in complex environment.
DBAM: Dense Boundary and Actionness Map for Action Localization in Videos via Sentence Query

Action localization in videos via sentence query remains a very challenging problem because of the semantic misalignment and the structural misalignment. With the observation that activities should be localized with both the local keywords of query sentence and the global information of whole video, we propose a novel method named Dense Boundary and Actionness Map (DBAM). This method trains a self-attention model to evaluate the importance of each word in the query sentence. Then it constructs a two-dimensional visual feature map for each candidate moment after video encoding. The visual feature map is cross-modal concatenated with the semantic feature and then DBAM directly performs convolution over the feature map to predict two-dimensional actionness map, starting map and ending map for candidate moments. The three maps are fused to generate proposals. We evaluate DBAM on the two challenging public benchmarks Charades-STA and TACoS and it outperforms the state-of-the-art by a large margin.
Person Re-identification with Limited Supervision

Person re-identification is the problem of associating observations of targets in different non-overlapping cameras. Most of the existing learning-based methods have resulted in improved performance on standard re-identification benchmarks, but at the cost of time-consuming and tediously labeled data. Motivated by this, learning person re-identification models with limited to no supervision has drawn a great deal of attention in recent years.
Development of Goal-Oriented Dialogue Systems for Customer Services in Automotive Industry

Being the largest sector in Turkey in the field of export, the automotive sector is one of the most important sectors in Turkey. Companies that manage their customer relations through live support channels are faced with problems such as long waiting times, access through limited channels, the live support team losing time while accessing the inquiry tools, and the inability of the customers to convey a problem through visual channels that cannot be verbally expressed. This paper introduces a Goal-Oriented Dialogue System (GODS) for Customer Services in the Automotive Industry. Although there exist some studies for in-car dialogue systems, this is the first study for GODS in the mentioned field, and we think that the results obtained in this paper on Turkish will shed light on future studies for other languages. Our paper defines 6 general Intents and 16 Entities in the automotive customer services and 8 Dialog act classes. The paper also provides the ways to extend the system for emerging intent classes in live data and for the needs of different service companies, as well as the experimental results on a preliminary data-set.
Extracting Textual Features from Video Streaming Services Publications to Predict their Popularity

The Internet's popularization has increased the amount of content produced and consumed on the Web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract most users' attention. For example, in the data explored in this paper, only 6% of the most popular videos are responsible for 85% of the total views. Finding out in advance which videos will be popular is not trivial, specially because of the large amount of influencing variables. Nevertheless, a tool with this ability would be of great value to help dimensioning network infrastructure and to properly recommend new content to users. In this work, we propose two approaches to obtaining features to classify the popularity of a video before it is published. The first one builds upon predictive attributes defined by feature engineering. The second leverages word embeddings from the descriptions and titles of the videos. We experiment with the proposed approaches on a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of both engineered features and the embeddings using Random Forest machine learning algorithm reached the best result, with an accuracy of 87%.