Reviewerly/exHarmony huggingface dataset:
The Leap Speaker Recognition System for NIST SRE 2018 Challenge

The NIST Speaker Recognition Evaluation (SRE) 2018 challenge comprises an open evaluation of the text independent speaker verification task. This paper summarizes the LEAP speaker verification systems submitted to the NIST SRE 2018. For all the speaker verification approaches, the front-end feature extraction involved the use of neural embeddings from a time delay neural network (TDNN) trained on a speaker discrimination task. These features, called x-vectors, are used in multiple ways for speaker verification task. In the first approach, the x-vectors with pre-processing and dimensionality reduction, are used with probabilistic linear discriminant analysis (PLDA) scoring. The second approach applies a speaker diarization scheme on the test segments containing multiple talkers before speaker verification scoring based on PLDA. The third system uses a local pairwise LDA model for pre-processing the x-vectors which are then scored using a Gaussian back-end. With experiments on the SRE 2018 database, we show that most of the systems achieved noticeable improvements over the NIST baseline in terms of the primary cost metric. Using a system fusion of the various approaches, we obtain significant improvements over the NIST official baseline (average relative improvements of 19.7% and 20.1% for the development and evaluation set respectively).
Pixel-level Texture Segmentation Based AV1 Video Compression

Modern video coding standards use hybrid coding techniques to remove spatial and temporal redundancy. However, efficient exploitation of statistical dependencies measured by a mean squared error (MSE) does not always produce the best psychovisual result. In this paper, we propose a pixel-level texture segmentation approach based on visual relevancy to improve the coding efficiency of newly developed AV1 video codec. Our method performs semantic segmentation and combines regions with similar texture in a video frame. These texture regions are then reconstructed using a motion model at the decoder instead of inter-frame prediction. A Convolutional Neural Networks based semantic segmentation combined with post-processing generates pixel-level texture masks that are more accurate compared to block-based texture masks in our previous work. We show that for many standard test sets, the proposed method achieves significant data rate reductions with improved visual quality.
Dialogue State Tracking with Convolutional Semantic Taggers

In this paper, we present our novel approach to the 6th Dialogue State Tracking Challenge (DSTC6) track for end-to-end goal-oriented dialogue, in which the goal is to select the best system response from among a list of candidates in a restaurant booking conversation. Our model uses a convolutional neural network (CNN) for semantic tagging of each utterance in the dialogue history to update the dialogue state, and another CNN for predicting the best system action template. Our model is competitive with the top two submissions to the challenge, achieving 100% precision on subtasks 1 and 2 with a CNN rather than an LSTM for action selection, and a CNN for slot-value tagging, instead of an LSTM or CRF.
Improving the Prediction of Therapist Behaviors in Addiction Counseling by Exploiting Class Confusions

In this work we address the problem of joint prosodic and lexical behavioral annotation for addiction counseling. We expand on past work that employed Recurrent Neural Networks (RNNs) on multimodal features by grouping and classifying subsets of classes. We propose two implementations: One is hierarchical classification, which uses the behavior confusion matrix to cluster similar classes and makes the prediction based on a tree structure. The second is a graph-based method which uses the result of the original classification just to find a certain subset of the most probable candidate classes, where the candidate sets of different predicted classes are determined by the class confusions. We make a second prediction with simpler classifier to discriminate the candidates. The evaluation shows that the strict hierarchical approach degrades performance, likely due to error propagation, while the graph-based hierarchy provides significant gains.
Angularly continuous light‐field format: Concept, implementation, and evaluation

Abstract In this paper, we present the concept, the implementation, and the evaluation of our novel angularly continuous light‐field format. We compared the subjective visual performance of our format with the perspective camera format through a series of subjective and objective tests. In our extensive subjective study, we used multiple absolute and comparative rating scales and various visual stimuli with different contents and angular resolutions. The perceived quality was assessed by a total of 36 test participants, who were separated based on their scientific expertise. The objective quality was evaluated through the degradations caused by three well‐known compression methods. The obtained results indicate that our light‐field format may outperform the conventional format, and it generally can provide at least equivalent visual quality. Furthermore, these findings open the way for data size optimization, without compromising the achieved level of perceived quality.
Fast hardware architecture for fixed-point 2D Gaussian filter

This study presents the hardware architecture for 16-bit, 5 × 5 fixed-point 2D Gaussian kernel. Two filters are proposed, one using generalized kernel and other using separable kernel. The quality analysis of both the filters are obtained for different metrics and it is observed that the generalized filter achieves the best performance compared to all the existing methods. The proposed filters are evaluated on different images and its performance is similar to that of the original filter, evident from the difference in values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) of proposed filters to that of the original filter. Based on the principle of Distributed Arithmetic (DA), two hardware architectures, Generalized Filter Architecture (GFA) and Separable Filter Architecture (SFA) are proposed. SFA achieves an improvement in area, delay, power, Area Delay Product (ADP) and Area Power Product (APP) compared to GFA. GFA, on the other hand, achieves high performance compared to the SFA model. The performance of both the architectures are analyzed in terms of max speed and frames per second metric and it is shown that the proposed architectures achieve significantly better performance compared to all the existing techniques.
Peak Detection and Baseline Correction Using a Convolutional Neural Network

Peak detection and localization in a noisy signal with an unknown baseline is a fundamental task in signal processing applications such as spectroscopy. A current trend in signal processing is to reformulate traditional processing pipelines as (deep) neural networks that can be trained end-to-end. A trainable algorithm for baseline removal and peak localization can serve as an important module in such a processing pipeline. In practical applications, one of the most successful approaches to joint baseline suppression and peak localization is based on the continuous wavelet transform: We re-formulate this as a convolutional neural network (CNN) followed by a non-linear readout layer. On a synthetic benchmark we demonstrate that with sufficient training data, the CNN approach consistently outperforms the optimized continuous wavelet method by means of adapting to the spectral peak shape, noise level, and characteristics of the baseline. The CNN approach to peak localization shows great promise, as it can more efficiently leverage data to outperform the current state of the art, and can readily be extended and incorporated as a module in a larger neural network architecture.
An Efficient Deep Learning Model to Infer User Demographic Information From Ratings

Obtaining demographics of online users is of great significance to Internet service providers and advertisers. Most previous works used standard machine learning methods to infer user demographics from handcrafted features. This has two disadvantages. First, the handcrafted features are usually not robust and rely too much on expert experience. Second, these low-capacity models can neither model the complex non-linear relationship between users nor recognize interdependencies among items. To address these problems, we propose a DEep REtentive learning frameworK (DEREK) for demographic information prediction. Specifically, we introduce a heuristic data generation method that can alleviate data sparsity in order to use rating data more efficiently than the handcrafted features. Moreover, the retention blocks based on high-capacity deep neural networks are designed to extract a share representation from input rating data. Finally, the DEREK can simultaneously infer different demographic attributes through end-to-end multi-label learning architecture. The extensive experimental results on Movielens 100-k and Movielens 1-M data sets have demonstrated the superiority of the proposed DEREK compared with standard machine learning methods (logistic regression and SVM).
Real-Time Distraction Detection Based on Driver's Visual Features

Driver's distraction has been listed as the leading contributing factor to traffic accidents for the past decades. This paper focuses on developing an approach to detect distraction real time by analyzing driver's visual feature from the face region. The proposed approach uses visual features such as movement of eye and head to extract critical information to detect driver attention states and to classify it as either attentive or distracted. Deviation of eye center and head from their standard position for a period of time is considered to be useful cues for detecting lack of attention in this approach. At first face detection is performed after which region of interest (ROI) - eye and head region, are extracted using facial landmarks and lastly, head and eye movements are detected to classify attention state. To evaluate the system performance, we conducted an experiment in a real driving environment with subjects having different characteristics. Our system achieved on average 92% accuracy in detecting attention state for all tested scenarios.
Application of Ensemble Techniques in Predicting Object-Oriented Software Maintainability

While prior object-oriented software maintainability literature acknowledges the role of machine learning techniques as valuable predictors of potential change, the most suitable technique that achieves consistently high accuracy remains undetermined. With the objective of obtaining more consistent results, an ensemble technique is investigated to advance the performance of the individual models and increase their accuracy in predicting software maintainability of the object-oriented system. This paper describes the research plan for predicting object-oriented software maintainability using ensemble techniques. First, we present a brief overview of the main research background and its different components. Second, we explain the research methodology. Third, we provide expected results. Finally, we conclude summary of the current status.
ICT in STEM Education in Bulgaria

Contemporary information and communication technology (ICT) and its applications are not only becoming an integral part of the everyday life of our society but also standard in most of the schools across the globe. Furthermore, the technology integration offers a qualitative transformation of all components of educational process. As innovative tools are getting an important requisite for all kind of educational institutions, the researchers should be aware of their influence on teaching and learning processes. This paper presents parts of the outcomes from a massive survey aimed at investigation of ICT integration in Bulgarian schools. In order to assess the acceptance of technology resources and their institution-wide adoption, it is important to understand teachers and students’ perceptions on the technology-enhanced teaching. It is essential to find out to what extent the teachers’ practice is in line with the expectations of the students. The research of dynamics of this issue helps to better understand the processes and provide valuable guidance for the effective use of innovative tools in a learning context. Here we focus especially on the teaching practice in science, technology, engineering, and mathematics (STEM) subjects in secondary education, where many different innovative tools are widely implemented.
Action recognition using dynamic hierarchical trees

Hierarchical models have shown their effectiveness for action recognition. However, most of the existing hierarchy construction methods fail to model the complex motion patterns in videos, and thus are vulnerable to the interference of the inevitable noise in action videos. Therefore, we propose a Dynamic Hierarchical Tree (DHT) model to characterize such complex motion for better recognition performance. First, a minimum maximum DTW (mmDTW) is developed to produce more stable atomic actions by limiting the minimum and maximum lengths of atomic actions. Then an aggregation method is utilized to construct a DHT for each video by merging atomic actions from bottom to top. Not only the similarity between frames but also the compatibility of dynamic evolution between frames and segments is exploited for the mmDTW and the aggregation process, making the DHTs more suitable for modeling actions in videos. Finally, a k-Nearest Neighbors Edge Pairs (kNNEP) kernel is proposed to compare two DHTs by using the mean similarity of k nearest neighbors edge pairs.
Tuning Distance Metrics and K to Find Sub-categories of Minority Class from Imbalance Data Using K Nearest Neighbours

Data classification by the machine learning algorithms is one of the most studied topic within data mining. Machine learning classifiers suffers significantly while learning from the imbalance data. Within imbalance class data, a class covers a very small portion of the whole dataset also known as minority class or the positive class, generally are the most significant. The issue that plays the key responsibility for these degradation's of performance is primarily the distribution of different class of samples within the dataset. It is the presence of local sub-concepts from different classes within the domain of another class. Recent time, a number of studies have concentrated on categorizing the minority class into several sub-categories or sub-concepts based on their logical neighbourhood. For this purpose-these studies have utilized methods like K nearest neighbour and kernel method. This study aimed at improving the categorization process of the minority class by incorporating an idea of using dataset specific distance function for the categorization process. The major objective of this study is to choose a distance metric among five distance metrics and a k value for a specific dataset, in which the classification performance is most optimal. For the analysis purpose five datasets are chosen.
Chapter 9 AI Revolution: How Data Can Identify and Shape Consumer Behavior in Ecommerce

Data has become one of the most significant instruments in e-commerce innovation. Benefits to the entire society can be summarized as following: from the government's perspective - to assess the impact of e-commerce to the economy; for merchants - to understand consumers' needs; and for consumers - to be offered with the right product he/she is looking for. The digital revolution in the past five years has shown the need to offer more differentiated services than the physical stores, when consumers are not able to try and touch products. It is for this reason that e-commerce has continuously developed and transformed Research Online, Purchase Offline into a true experience. Considering the future of e-commerce is to enhance economic development and growth, this research will discuss the disruption of Research and Development through big data. The core objective of this research is to propose a predictive model to deeply understand consumer behavior by analyzing new regulations and transaction records.
An LSTM Enabled Dynamic Stackelberg Game Theoretic Method for Resource Allocation in the Cloud

Resource allocation is essential in cloud computing because it affects the performance, functionality, and development of cloud services. Resource allocation in the cloud based on economic and pricing approaches has the potential to increase the infrastructure suppliers' revenue and improve the service providers' efficiency and quality of services. However, in practice, information may be imperfect, and resulting resource allocation is not fair. Therefore, there is a need for a more appropriate model which could leverage imperfect information. In this paper, we propose an imperfect information based game theoretic method for resource allocation in the cloud. In this method, both an information control strategy and an LSTM model are used to predict the market status and to optimize bidding strategy for achieving maximal profits while maintaining fairness and profits for tenants with huge demands. The simulation results demonstrate the feasibility of the simulation framework as well as the effectiveness of the proposed method.
LL-based query answering over RDF databases

We present a method based on top-down parsing techniques for evaluating context-free path queries on RDF Graph Databases. The syntax of the query language is based on SPARQL. The language extends SPARQL by allowing the use of non-terminal symbols of a context-free grammar to specify paths on the graph. In this manner, the language subsumes the definition of regular graph patterns present in SPARQL. Our query evaluator takes an RDF graph, a context-free grammar and a declarative query, and produces tuples of values. The query evaluator proceeds in two stages: Firstly, the RDF graph is enriched with edges representing paths which correspond to strings derived by the grammar. We show that this algorithm is correct and presents a cubic worst-case run-time complexity on the number of nodes in the graph, which is an improvement over some previous work. The second stage of the evaluator uses the produced graph to identify tuples of values defined by a declarative query. In order to validate our approach, we conducted experiments by using some popular ontologies as well as synthetic databases. We compare performance results of our method with some related work.
Fast variational quantum algorithms for training neural networks and solving convex optimizations

Variational hybrid quantum classical algorithms to optimizations are important applications for near-term quantum computing. This paper proposes two quantum algorithms (the second one is variational) for training neural networks. Both of them obtain exponential speedup at the number of samples and polynomial speedup at the dimension of the samples over classical training algorithms. Moreover, the proposed quantum algorithms return the classical information of the training weight so that the outputs can be used directly to solve other problems. For practicality, we draw the quantum circuits to implement the two algorithms. Finally, as an inspiration, we show how to apply the variational algorithm to achieve speedup at the number of constraints in solving convex optimization problems.
Validation in Fluid Dynamics and Related Fields

A brief description of fluid dynamics is presented for a general audience. Within the context of fluid dynamicsFluid dynamics practice, the distinction is made between general (weak) models and specific (strong) models. Three historical options concerning a working definition of validation are briefly considered. Current validation practice in fluid dynamics is described based mostly on ASME V&V 20-2009, an ANSI Standard document. Model form uncertainty and other epistemic uncertainties, while sometimes important in model applications, are argued not to be important issues in validation. The weakest link in validation practice is claimed to be the reluctance, by both experimentalists and computationalists, to go beyond use of nominal set point dataData . This is clarified by the paradigm of experiments designed specifically for model validation. Coding features that facilitate model validation are described. Counter arguments are given to claims, based on extrapolation of the philosophy of falsificationismFalsificationism , that validation is impossible even in principle.
Local experts finding using user comments in location‐based social networks

Abstract The opinions of local experts in the location‐based social network are of great significance to the collection and dissemination of local information. In this paper, we investigated in‐depth how the user comments can be used to identify the local expert over social networks. We first illustrate the existences of potential local experts in a social network using a scored model by considering the personal profiles, comments, friend relationship, and location preferences. Then, a multidimensional model is proposed to evaluate the local expert candidates and a local expert discovery algorithm is proposed to identify local experts. Meanwhile, a scoring algorithm is proposed to train the weights in the model. Finally, an expert recommendation list can be given based on the score ranks of the candidates. Experimental results demonstrate the effectiveness of proposed model and algorithms.
Embedding Linguistic Features in Word Embedding for Preposition Sense Disambiguation in English—Malayalam Machine Translation Context

Preposition sense disambiguation has huge significance in Natural language processing tasks such as Machine Translation. Transferring the various senses of a simple preposition in source language to a set of senses in target language has high complexity due to these many-to-many relationships, particularly in English-Malayalam machine translation. In order to reduce this complexity in the transfer of senses, in this paper, we used linguistic information such as noun class features and verb class features of the respective noun and verb correlated to the target simple preposition. The effect of these linguistic features for the proper classification of the senses (postposition in Malayalam) is studied with the help of several machine learning algorithms. The study showed that, the classification accuracy is higher when both verb and noun class features are taken into consideration. In linguistics, the major factor that decides the sense of the preposition is the noun in the prepositional phrase. The same trend was observed in the study when the training data contained only noun class features. i.e., noun class features dominates the verb class features.
GazeVisual: A Practical Software Tool and Web Application for Performance Evaluation of Eye Tracking Systems

The concept and functionalities of a software tool developed for in depth performance evaluation of eye gaze estimation systems is presented. The software, GazeVisual has capabilities for quantitative, statistical, and visual analysis of eye gaze data as well as generation of static and dynamic visual stimuli for sample gaze data collection. This is a first of its kind cross-platform tool for gaze data analysis and evaluation. This software is made freely available to the eye gaze research and development community to provide a common framework for estimating the quality and reliability of data from eye tracking systems, especially those implemented in consumer electronics (CE) applications. The feasibility of using this software is tested through case studies which show that the software can handle eye gaze datasets obtained from several different consumer grade eye trackers. GazeVisual operates consistently, irrespective of the platform, algorithm or hardware of the eye trackers. In addition, the GazeVisual software capabilities are also made accessible via a Web-based application enabling performance evaluation of eye tracker data over a cloud-based platform.