Reviewerly/exHarmony huggingface dataset:
Voice command recognition in intelligent systems using deep neural networks

In this article, we focus on the isolated voice command recognition for autonomous man-machine and intelligent robotic systems. We propose to create a grammar model for a small testing command set with self-loops for each state to return blank symbols for noise and-of-vocabulary words. In addition, we use single arc connected beginning and ending of the grammar in order to filter unknown commands. As a result, the grammar is resistant to distortions and unexpected words near or inside of command. We implemented the proposed approach using Finite State Transducers in the Kaldi framework and examined it using self-recorded noised data with various level of signal-to-noise ratio. We compared recognition accuracy and average decision-making time of our approach with the state-of-the-art continuous speech recognition engines based on language models. It was experimentally shown that our approach is characterized by up to 60% higher accuracy than conventional offline speech recognition methods based on language models. The speed of utterance recognition is 3 times higher than speed of traditional continuous speech recognition algorithms.
Readiness of the education manager to provide the organizational development of institutions (based on the sociological research)

The article contains the analysis of scientific approaches to understanding of organizational development in the field of educational institutions management as a complex of successive educational changes. Scientific researches on the change of education managers’ behavior in the course of changes are presented.Types of education managers attitude to changes and their quantitative expression were determined and revealed based on the data of an empirical research. Almost half of the surveyed education managers are represented by such types as “traditionalist” and “realist”, which indicates the insufficient orientation of education managers in introducing innovative changes in educational institutions. This indicates a low level of education managers’ readiness to provide the organizational development of educational institutions. The results obtained can be used by the government in developing and implementing the programs for the professional development of education managers. The authors offer the most practical tools for implementing the concept of organizational development. It is proposed to distinguish between special requirements for both professional and personal qualities of education managers.
Improving representation learning in autoencoders via multidimensional interpolation and dual regularizations

Autoencoders enjoy a remarkable ability to learn data representations. Research on autoencoders shows that the effectiveness of data interpolation can reflect the performance of representation learning. However, existing interpolation methods in autoencoders do not have enough capability of traversing a possible region between two datapoints on a data manifold, and the distribution of interpolated latent representations is not considered.To address these issues, we aim to fully exert the potential of data interpolation and further improve representation learning in autoencoders. Specifically, we propose the multidimensional interpolation to increase the capability of data interpolation by randomly setting interpolation coefficients for each dimension of latent representations. In addition, we regularize autoencoders in both the latent and the data spaces by imposing a prior on latent representations in the Maximum Mean Discrepancy (MMD) framework and encouraging generated datapoints to be realistic in the Generative Adversarial Network (GAN) framework. Compared to representative models, our proposed model has empirically shown that representation learning exhibits better performance on downstream tasks on multiple benchmarks.
Interpreting, Axiomatising and Representing Coherent Choice Functions in Terms of Desirability.

Choice functions constitute a simple, direct and very general mathematical framework for modelling choice under uncertainty. In particular, they are able to represent the set-valued choices that appear in imprecise-probabilistic decision making. We provide these choice functions with a clear interpretation in terms of desirability, use this interpretation to derive a set of basic coherence axioms, and show that this notion of coherence leads to a representation in terms of sets of strict preference orders. By imposing additional properties such as totality, the mixing property and Archimedeanity, we obtain representation in terms of sets of strict total orders, lexicographic probability systems, coherent lower previsions or linear previsions.
Beyond Structural Causal Models: Causal Constraints Models

Structural Causal Models (SCMs) provide a popular causal modeling framework. In this work, we show that SCMs are not flexible enough to give a complete causal representation of dynamical systems at equilibrium. Instead, we propose a generalization of the notion of an SCM, that we call Causal Constraints Model (CCM), and prove that CCMs do capture the causal semantics of such systems. We show how CCMs can be constructed from differential equations and initial conditions and we illustrate our ideas further on a simple but ubiquitous (bio)chemical reaction. Our framework also allows to model functional laws, such as the ideal gas law, in a sensible and intuitive way.
Personal Identification Based on Deep Learning Technique Using Facial Images for Intelligent Surveillance Systems

Identity recognition is a very important task in intelligent surveillance systems.Today, identity recognition systems have achieved high accuracy and widely used in specific application areas such as recognition system based on retina imaging in immigration inspection, civil security and citizen management.In these systems, human is required to be submissive for data acquisition to identify themselves.However, the automated monitoring systems are required to be active for information retrieval and human is passively monitored in this situation.In this kind of approach, human recognition is still a challenging task for the overall system performance.This study proposes a solution for human identification based on the human face recognition in images extracted from conventional cameras at a low resolution and quality.Our proposed approach for human identification is based on a deep learning method for feature extraction and classification for human identification using a similarity estimation.This approach was evaluated on some standard databases which are available online and also on our own collected dataset.The results from the comparison to the state of the art approach illustrate that our proposed approach achieves high accuracy and is suitable for practical applications.
Accuracy analysis of an orthogonally arranged four-camera 3D digital image correlation system

To reduce the uncertainty region of a three-dimensional (3D) position, a four-camera 3D digital image correlation (3D-DIC) system was built by orthogonally arranging two sets of two-camera DIC systems. The theoretical model proposed herein revealed the relationship between 3D coordinates and system parameters and the propagation of the matching error to the position error. Numerical simulation and experiment were conducted to verify the theory. The simulation and experimental results indicated that the 3D position error of the four-camera system was smaller than that of the two-camera DIC system. The present contribution proves the feasibility of using four-camera DIC systems to improve measurement accuracy.
Differentially Private Obfuscation of Facial Images

The pervasiveness of camera technology in every-day life begets a modern reality in which images of individuals are routinely captured on a daily basis. Although this has enabled many benefits, it also infringes on personal privacy. To mitigate the loss of privacy, researchers have investigated methods of facial obfuscation in images. A promising direction has been the work in the k-same family of methods which employ the concept of k-anonymity from database privacy. However, there are a number of deficiencies of k-anonymity which carry over to the k-same methods, detracting from their usefulness in practice. In this paper, we first outline several of these deficiencies and discuss their implications in the context of facial obfuscation. We then develop the first framework to apply the formal privacy guarantee of differential privacy to facial obfuscation in generative machine learning models for images. Next, we discuss the theoretical improvements in the privacy guarantee which make this approach more appropriate for practical usage. Our approach provides a provable privacy guarantee which is not susceptible to the outlined deficiencies of k-same obfuscation and produces photo-realistic obfuscated output. Finally, while our approach provides a stronger privacy guarantee, we demonstrate through experimental comparisons that it can achieve comparable utility to k-same approaches in the context of preservation of demographic information in the images. The preservation of such information is of particular importance for enabling effective data mining on the obfuscated images.
PRTIRG: A Knowledge Graph for People-Readable Threat Intelligence Recommendation

People-Readable Threat Intelligence (PRTI) recommender Systems aim to address the problem of information explosion of PRTIs and make personalized recommendation for users. In general, PRTI is highly condensed, and consists of security items, network entities and emerging hacker organizations, attacks, etc. PRTI may also contain many Machine-Readable Threat Intelligence (MRTI). However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among PRTIs. Under this scenario, the existing generic knowledge graphs will introduce too much noise and can not consider the entity relationship in terms of the attack chain. To solve the problems above, in this paper, we propose a knowledge graph for People-Readable Threat Intelligence recommendation (PRTIRG) and incorporates knowledge graph representation into PRTI recommender system for click-through prediction. The key components of PRTIRG are the denoising entity extraction module and the knowledge-aware long short-term memory neural network (KLSTM). Through extensive experiments on real-world datasets, we demonstrate that the PRTIRG is more effective and accurate than baselines.
Attention-Based Fusion for Outfit Recommendation

This paper describes an attention-based fusion method for outfit recommendation which fuses the information in the product image and description to capture the most important, fine-grained product features into the item representation. We experiment with different kinds of attention mechanisms and demonstrate that the attention-based fusion improves item understanding. We outperform state-of-the-art outfit recommendation results on three benchmark datasets.
Online Clustering for Novelty Detection and Concept Drift in Data Streams

Data streams are related to large amounts of data that can continuously arrive with a probability distribution that may change over time. Depending on the changes in the data distribution, different phenomena can occur, like new classes can appear or concept drift can occur in existing classes. Machine Learning algorithms have been often used to model this data. New classes are patterns that were not seen during the training of the current classification model, but appear after some time. Concept drift occurs when the concepts associated with a dataset change as new data arrive. This paper proposes a new algorithm based on kNN that uses micro-clusters as prototypes and incrementally updates the micro-clusters or creates new micro-clusters when novelties are detected. In the online phase, each instance close to a micro-cluster is considered an extension of the micro-cluster, being used to adapt the model to concept drift. The proposed algorithm is experimentally compared with a state-of-the-art classifier from the data stream literature and one baseline. According to the experimental results, the proposed algorithm increases the predictive performance over time by incrementally learning changes in the data distribution.
Traffic Accident Severity Prediction Using Naive Bayes Algorithm - A Case Study of Semarang Toll Road

Abstract A traffic accident was one of the leading cause of death in Indonesia. Toll Road is one of the places where traffic accidents occur. In 2007-2017 there were 501 accidents at Semarang Toll Road. Accident in Semarang Toll Road has a variety of severity. The most severe case is death. A traffic accident can lead to death. One of the ways to decrease the number of the accident was decreased the severity of the accident. This achieved by making a prediction model. The prediction model can predict the severity of the accident based on the attribute affecting the severity of the accident. In this research, Days, Type of Road, Weather, Condition of Road, Time of the accident, Sex of Driver, and Type of Vehicle were chosen as attributes to make prediction model of accident severity. Naive Bayes algorithm was used to make the model which can predict accident severity. The result was an accident prediction model with an accuracy of 39.49% to predict accident severity and the probability of an accident.
An End-to-End Text-Independent Speaker Verification Framework with a Keyword Adversarial Network

This paper presents an end-to-end text-independent speaker verification framework by jointly considering the speaker embedding (SE) network and automatic speech recognition (ASR) network. The SE network learns to output an embedding vector which distinguishes the speaker characteristics of the input utterance, while the ASR network learns to recognize the phonetic context of the input. In training our speaker verification framework, we consider both the triplet loss minimization and adversarial gradient of the ASR network to obtain more discriminative and text-independent speaker embedding vectors. With the triplet loss, the distances between the embedding vectors of the same speaker are minimized while those of different speakers are maximized. Also, with the adversarial gradient of the ASR network, the text-dependency of the speaker embedding vector can be reduced. In the experiments, we evaluated our speaker verification framework using the LibriSpeech and CHiME 2013 dataset, and the evaluation results show that our speaker verification framework shows lower equal error rate and better text-independency compared to the other approaches.
Morpheus: A Neural Network for Jointly Learning Contextual Lemmatization and Morphological Tagging

In this study, we present Morpheus, a joint contextual lemmatizer and morphological tagger.Morpheus is based on a neural sequential architecture where inputs are the characters of the surface words in a sentence and the outputs are the minimum edit operations between surface words and their lemmata as well as the morphological tags assigned to the words.The experiments on the datasets in nearly 100 languages provided by SigMorphon 2019 Shared Task 2 organizers show that the performance of Morpheus is comparable to the state-of-theart system in terms of lemmatization.In morphological tagging, on the other hand, Morpheus significantly outperforms the SigMorphon baseline.In our experiments, we also show that the neural encoder-decoder architecture trained to predict the minimum edit operations can produce considerably better results than the architecture trained to predict the characters in lemmata directly as in previous studies.According to the SigMorphon 2019 Shared Task 2 results, Morpheus has placed 3 rd in lemmatization and reached the 9 th place in morphological tagging among all participant teams.
Sentence Level Sentiment Analysis from News Articles and Blogs using Machine Learning Techniques

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Wavelet Based Interval Varying Algorithm for Optimal Non-Stationary Signal Denoising

This paper reviews wavelet-based denoising techniques and presents an effective new algorithm using adaptive threshold method, processing of detailed and approximate coefficient, the optimal choice of level of decomposition for noise reduction in non-stationary signals. The method is based on computing of orthogonal wavelet transform and level-dependent estimation of the threshold. The proposed algorithm is tested with different wavelet bases: Haar, Daubechies; Cubic B-splines; Symplet; Coinflet; biorthogonal wavelets and a comparative analysis is performed. The proposed algorithm was applied to the real signals and has the task of assessing the application of different wavelet bases, threshold techniques, decomposition levels, and times to execute the procedures for accurate and fast-track procedures to reduce interference. The proposed algorithm can be used for single channel real non-stationary signals. For the purpose of comparative analysis of different methods a software program was created by the author, with graphical user interface, that implements the basic denoising techniques, enables the setting of the decomposition level, the wavelet basis, the size of the test signal, and calculates the evaluation characteristics of the denoising process. The software application enables the testing of new denoising algorithms, allows the addition of other types of noise and the investigation of their effects on the signals and is realized in the MATLAB development environment. The proposed results demonstrate that the presented algorithm is suitable for denoising of non-stationary real signals.
Am I Floating or Not? : Sensitivity to Eye Height Manipulations in HMD-based Immersive Virtual Environments

Eye height manipulations have previously been found to affect judgments of object size and egocentric distance in both real and immersive virtual environments. In this short paper we report the results of an experiment that explores people's sensitivity to various offsets of their eye height in VR using a forced-choice task in a wide variety of different architectural models. Our goal is to better understand the range of eye height manipulations that can be surreptitiously employed under different environmental conditions.
Automated design process for hybrid regression modeling with a one-class SVM

Abstract The accuracy of many regression models suffers from inhomogeneous data coverage. Models loose accuracy because they are unable to locally adapt the model complexity. This article develops and evaluates an automated design process for the generation of hybrid regression models from arbitrary submodels. For the first time, these submodels are weighted by a One-Class Support Vector Machine, taking local data coverage into account. Compared to reference regression models, the newly developed hybrid models achieve significant better results in nine out of ten benchmark datasets. To enable straightforward usage in data science, an implementation is integrated in the open source MATLAB toolbox SciXMiner.
Hardware Implementation for Haze Removal With Adaptive Filtering

Haze removal is useful in computational photography and computer vision applications. Single image haze removal is still a challenge for real-time embedded systems. In general, dark channel prior approach can effectively remove haze from a single image. However, the halo effect is the major problem of dehazing. In this study, adaptive filtering, which is easy for VLSI design, is introduced to refine the transmission map and improve the result of haze removal. A hardware architecture for our haze removal method is proposed to achieve the real-time requirement. A specific hardware component is designed to compute lengths of the paths and adaptive structuring element (ASE). The hardware architecture for haze removal is implemented in 180 nm CMOS technology and occupies 5.46 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with 143 K bit on-chip memory. The design can operate at 177MHz and support for HD (1280×720) 192 frame/s. Experiment results show that the hardware implementation is suitable in embedded system for real-time HD applications.
N-Storm: Efficient Thread-Level Task Migration in Apache Storm

Apache Storm is a widely used stream processing system, but it only supports offline task scheduling. Recently, some online task scheduling approaches were proposed to offer task migrations in Storm at runtime. However, most of them have to cost more than 10 seconds during a task migration, because they employed a Worker-level scheme to stop or start Workers (implemented as processes in Storm). When an Executor within a Worker needs migrating, Storm has to kill the Worker, leading to the stop and restart of all Executors in the Worker. This process-level scheme introduces unnecessary stop and restart of Executors and Workers, resulting in poor performance of task migrations. Aiming to solve this problem, we propose N-Storm (Non-stop Storm). Instead of using the process-level migration scheme, N-Storm employs a thread-level scheme for task migrations. Particularly, we add a key/value store on each Worker node to make Workers be aware of the changes of the task schedule, so that the Workers on a Worker node can manage their Executors at runtime, i.e., killing existing Executors or starting new Executors. With this mechanism, we can avoid the unnecessary stop of Executors and Workers during a task migration, and thus improve the performance of task migrations. We further propose two optimizations to make N-Storm efficient for multiple tasks migrations. Our experimental results on a real Storm cluster show that N-Storm is able to reduce the stop time, increase the system throughput, and save significant migration time, compared with Storm and other approaches.