Reviewerly/exHarmony huggingface dataset:
A Proposed Authentication Approach Based on Voice and Fuzzy Logic

Conventional authentication methods which depend on what the user knows that is usually a password do not guarantee sufficient protection to the information system, because they do not depend on the identity of the authenticated user. The proposed intelligent authentication makes use of digital identity which relies on the concept “who is the user,” and this identity consists of user biometric (voice) attributes, like Mel-frequency cepstral coefficient (MFCC). The proposed authentication approach has two options for feature matching; the first option uses dynamic time warping (DTW) as crisp matching to find the distance between two samples of voice to recognize the identity of an authenticated user (speaker) using MFCC features. The second option uses the fuzzy inner product between fuzzy feature vectors to compute the approaching values between two voice samples using the same features. The experiments are implemented using (ELSDSR) data set, in order to recognize the identity of claimed user. The accuracy of recognition using MFCC with fuzzy vectors was better than recognition using MFCC with dynamic time warping (DTW), and the accuracy of the recognition was 95.45% when using fuzzy logic in MFCC features matching technique.
Toward Next Generation of Autonomous Systems with AI

I discuss a growing area of research in autonomous driving systems and overview what this means in terms of their testing. I also discuss implications for autonomous decision making systems of the future. (This paper is expected to serve as the basis of my plenary talk at IJCNN 2019.).
Learning Unsupervised Word Mapping via Maximum Mean Discrepancy

Cross-lingual word embeddings aim at capturing common linguistic regularities of different languages. Recently, it has been shown that these embeddings can be effectively learned by aligning two disjoint monolingual vector spaces through a simple linear transformation (word mapping). In this work, we focus on learning such a word mapping without any supervision signal. Most previous work of this task adopts adversarial training or parametric metrics to perform distribution-matching, which typically requires a sophisticated alternate optimization process, either in the form of minmax game or intermediate density estimation. This alternate optimization process is relatively hard and unstable. In order to avoid such sophisticated alternate optimization, we propose to learn unsupervised word mapping by directly minimize the maximum mean discrepancy between the distribution of the transferred embedding and target embedding. Extensive experimental results show that our proposed model can substantially outperform several state-of-the-art unsupervised systems, and even achieves competitive performance compared to supervised methods. Further analysis demonstrates the effectiveness of our approach in improving stability.
How to Measure Relative Bias in Media Coverage?

Abstract Mainstream media are regularly accused of being biased or of peddling “fake news”. But are the accusations fair? Does a supposedly “partisan” newspaper like the New York Times really differ in the tone of its coverage from a “neutral” newswire service like Reuters? Siddhartha Dalal, Berkay Adlim and Michael Lesk investigate
Towards a General and Efficient Linked-List Hash Table on GPUs

Hash table is a fundamental indexing data structure, and extensively used in many applications. Recently, several hash table implementations have been proposed on GPUs. However, they have two pitfalls. Firstly, their synchronization approaches can only ensure the atomicity among individual operations. This makes them can hardly be used in some real-world applications (e.g., online transaction processing, OLTP), which require groups of operations to be executed atomically. Secondly, they use either a GPU thread or a warp of GPU threads to process an operation at a time (named thread-and warp-level execution respectively). The former one incurs more branch divergences, while the latter one requires all threads within a warp to be active. To this end, we (1) propose a readers-writer lock based, bucket-level synchronization to realize the atomicity among both individual and groups of operations, and (2) combine thread-and warp-level executions to optimize system performance. Moreover, we optimize the data structure of linked-list with respect to GPU architectures. Putting them together, we realize a general and efficient linked-list hash table on GPUs, named GEL-HASH. Compared with existing GPU hash table implementations, GEL-HASH can easily support more real-world applications. Moreover, we compare GEL-HASH with the state-of-the-art hash table implementation on GPUs (slab hash) using applications that are well supported by the existing implementations. The results show that, for small data scales (numbers of key-value pairs stored in a hash table) GEL-HASH outperforms slab hash by up to 3.05X, while for large data scales GEL-HASH exhibits less than 20% performance slowdown.
The Architecture of Subsystem for Eliminating an Uncertainty in Assessment of Information Objects’ Semantic Content Based on the Methods of Incomplete, Inconsistent and Fuzzy Knowledge Processing

The study considers different levels of the architecture of subsystem for eliminating uncertainty in assessment of the information objects’ semantic content based on the methods for processing incomplete, inconsistent and fuzzy knowledge. It analyzes the possibility of their joint operation on the basis of multilevel functional, informational and logical interaction. The variant of architecture based on a single integration platform is proposed. The results of analysis of relationships between the different levels of architecture for obtaining new knowledge related to the detection of unwanted, doubtful and harmful information features under uncertainty are discussed.
Hardware-Based Principal Component Analysis for Hybrid Neural Network Trained by Particle Swarm Optimization on a Chip

This paper presents a hybrid architecture for a neural network (NN) trained by a particle swarm optimization (PSO) algorithm. The NN is implemented on the hardware side while the PSO is executed by a processor on the software side. In addition, principal component analysis (PCA) is also applied to reduce correlated information. The PCA module is implemented in hardware by the SystemVerilog programming language to increase operating speed. Experimental results showed that the proposed architecture had been successfully implemented. In addition, the hardware-based NN trained by PSO (NN-PSO) program was faster than the software-based NN trained by the PSO program. The proposed NN-PSO with PCA also obtained better recognition rates than the NN-PSO without-PCA.
CSSD: Cascade Single Shot Face Detector

Face detection has achieved great success with the development of convolution neural network. However, it remains a challenging problem to detect small and blurred faces in unconstrained environment. This paper presents a novel cascade single-shot face detector, named Cascade Single Shot Face Detector (CSSD), which introduces novel cascade classification and regression network in an anchor-based face detector to reject false positives and improve location accuracy. We have contributed in the following three aspects: 1) proposing a feature enchanted and scale-invariable face detection architecture to process faces with different scales; 2) regressing bounding boxes of faces in two steps with a cascade method; 3) filtering negative anchors online after anchor refinement and rebalancing puzzle negative anchors and positive anchors with rate of 3:1. As a consequence, our method achieves state-of-the-art detection performance on FDDB and WIDER FACE dataset.
Analyzing the impact of data representations in classification problems using clustering

This work presents an investigation about how to better represent output data labels to be used in supervised training of classifiers. The posed hypothesis is that grouping cohesive patterns into clusters and assigning them sub-labels, may improve the classifier performance. We used 12 benchmark datasets to test our hypothesis. First, we create the clusters, and when appropriate, new sub-labels were generated, according to Fuzzy-CMeans and Silhouette score thresholds. After that, Multilayer Perceptrons were employed to model each dataset with cluster generated sub-labels, obtaining promising results. From results, we observed that in cases where the sub-labels were used, the accuracy increased with statistical significance with p=0.05 in 22 cases and remained statistically equivalent in 14 cases, presenting no decrease in accuracy.
The Technique of Multi-aspect Evaluation and Categorization of Malicious Information Objects on the Internet

Under the influence of rapid development in the sphere of information technologies, rises the challenge related to detection of malicious information sources on the Internet. To solve this we can use machine learning methods as one of the most popular and powerful tools designed to identify dependencies between input (observed) data and output (desired) results. This article presents a methodology which is aimed at multi-level processing of input data about malicious information objects on the Internet and providing their multi-aspect assessment and categorization using machine learning methods. The purpose of the investigation is to improve the efficiency of the detecting process of malicious information on the Internet using the examples of Web-pages classification.
Developing Security Solutions for Telemedicine Applications

Telemedicine is defined as the means of providing healthcare for people from a distance by the use of telecommunication and information technology. This technology is mainly useful in overcoming the obstacles of distance and provide enhancement in the access of medical services that would not be easily available in different rural areas. Telemedicine security includes issues such as confidentiality, integrity, and authentication that are also present in other systems involving information and data. Maintaining integrity of data stored and used is a huge problem for medical applications because it contains more sensitive medical records of patients which can cause severe ill effects on slight modification. In order to resolve the confidentiality and integrity issues of telemedicine applications, medical image encryption and watermarking comes into play. The security issues in telemedicine applications is to be given higher importance and thus choosing a reliable and effective approach or framework is more essential.
Feature-Level Attention Based Sentence Encoding for Neural Relation Extraction

Relation extraction is an important task in NLP for knowledge graph and question answering. Traditional relation extraction models simply concatenate all the features as neural network model input, ignoring the different contribution of the features to the semantic representation of entities relations. In this paper, we propose a feature-level attention model to encode sentences, which tries to reveal the different effects of features for relation prediction. In the experiments, we systematically studied the effects of three strategies of attention mechanisms, which demonstrates that scaled dot product attention is better than others. Our experiments on real-world dataset demonstrate that the proposed model achieves significant and consistent improvement in the relation extraction task compared with baselines.
Image Digital Zoom Based Single Target Apriltag Recognition Algorithm in Large Scale Changes on the Distance

Aiming at the problems of the existing Apriltag detection and recognition algorithm, such as complex targets, low efficiency and poor recognition accuracy, an improved single Apriltag recognition algorithm based on image digital zoom is proposed in this paper. The algorithm achieves automatic digital zoom by changing the area ratio of a single Apriltag in the field and the display resolution of the view, which effectively simplifies the recognition target, reduces the complexity of the algorithm, and improves the efficiency of target detection and recognition. The experimental results show that the algorithm proposed in this paper can realize the high efficiency and high accuracy of Apriltag detection and recognition under different distances in large scale, and the continuity of position data output can be ensured before and after zoom, which can meet the real-time and accuracy requirements of UAV positioning during landing.
Exploiting Randomness in Deep Learning Algorithms

The recent surge of interest in using deep neural networks for real-world tasks has led to training complex networks with billions of parameters that use enormous amounts of training data. Performing backpropagation in these deep networks is time consuming and requires large amount of resources that is usually limited by the underlying hardware. In order to move towards agile deep learning, we are motivated to exploit randomness in the networks. In this work, we explore the effects of utilizing random weights in convolutional neural networks. This is achieved through random initialization of weights and by freezing them. The training occurs only in the output layer. We also propose a novel weight distribution method based on the sum of sinusoids for random convolutional neural networks. Our experiments show that by leaving the weights random in convolutional neural networks relatively high performance can be achieved for MSTAR and CIFAR-10 datasets.
Solving Chinese Character Puzzles Based on Character Strokes

Chinese character puzzles are popular games in China. To solve a character puzzle, people need to fully consider the meaning and the strokes of each character in puzzles. Therefore, Chinese character puzzles are complicated and it can be a challenging task in natural language processing. In this paper, we collect a Chinese character puzzles dataset (CCPD) and design a Stroke Sensitive Character Guessing (SSCG) Model. SSCG can consider the meaning and strokes of each character. In this way, SSCG can solve Chinese character puzzles more accurately. To the best of our knowledge, it is the first work which tries to handle the Chinese character puzzles. We evaluate SSCG on CCPD. The experiment results show the effectiveness of the SSCG.
A Kernel Discriminant Information Approach to Non-linear Feature Selection

Feature selection has become a de facto tool for analyzing high-dimensional data, especially in bioinformatics. It is effective in improving learning algorithms' scalability and facilitating feature generalization or interpretability by removing noise and redundancy. Our focus is placed on the paradigm of supervised feature selection, which aims to find an optimal feature subset to best predict the target. We propose a nonlinear approach for finding a feature subset that achieves the highest inter-class separability in terms of the kernel Discriminant Information (KDI) measure. Theoretically, we prove the existence of good prediction hypotheses for feature subsets with high KDI value. We also establish the equivalency between maximizing the KDI statistic and minimizing a functional dependency measure of label variable on data. Moreover, we asymptotically prove the concentration property of the optimal feature subset found by maximizing the KDI measure. Practically, we provide an efficient gradient optimization algorithm for solving the KDI feature selection problem. We evaluate the proposed method based on 19 benchmark datasets in various domains, and demonstrates a noticeable improvement against state-of-the-art baselines on the majority of classification and regression tasks. Notably, our method is robust to the choice of hyper-parameters, works well with various downstream classifiers, has competitive computational complexity among the kernel based methods considered, and scales well the large-scale object recognition dataset, with generalization enhancement on CIFAR.
PEDAGOGICAL TRAINING OF LEARNING ENGINEER

Digital technologies command determines the information and professional competence of a teacher. The first competences help him to organize a modern educational process, others provide access to the latest professional information. Studies show that serious complex strategies for the use of digital technologies are beginning to emerge, and a teacher is becoming a leader in future education. Digital technologies are becoming more complicated and there is an urgent need for new professionals - learning engineers who have to provide new strategies in education. The paper considers a complex of open distance courses for the training of teachers to use new educational technologies. The courses use the results of the annual polls of the world's pedagogical community to identify popular pedagogical instruments. They form teachers’ personal knowledge mastery. This contributes to the development of a personal learning environment and a teacher's personal learning network. The learning engineer's training system should consist of software engineering and the proposed teaching block. The last block includes sections for the formation of skills of personal knowledge mastery, the design of a distance course, the development of the structure, organization of distance and blended learning, expertise of the distance course. All sections of the pedagogical unit were tested in open distance courses at the Research Laboratory of Distance Learning at NTU "KhPI". During 2013-2018 open distance learning courses were learnt by more than 2,600 teachers of Ukraine and 370 of them successfully completed the training. The next step should be to integrate efforts with IT specialists, identify competencies of the learning engineer and prepare a training program for a specialist in the future.
FORMATION OF FUTURE PRIMARY SCHOOL TEACHERS’ DIGITAL AND TECHNOLOGICAL COMPETENCES

The article discusses some aspects of using digital technology on technology lesson in primary school. Under current conditions of national education development the problem of future primary school teachers’ digital and technological competences formation remains insufficiently investigated in the context of integrated approach to the educational process. Authors propose an example of a conducted lesson using cloud services and technologies for motivating students in the process of studying new material using augmented reality, online board Padlet, infographics, conducting formative assessment, tests; assessment by means of online tests using QR-codes. The article also aims to help teachers to understand the technologies and methods of their application under modern educational conditions. The conducted lesson helps to build the knowledge of future primary school teachers about paper origami and kusudama techniques and to present self-made products using ICT for messaging and collaborative learning while solving learning, inquiry-based and practical tasks. The authors have found and implemented innovative approaches to solve the problem of formation and development of digital and technological competencies of a future primary school teacher by integrating the educational process in the higher education institution. The expediency of using digital technologies in the training of educational recipients responds to the tendency of restructuring the educational process in accordance with the Concept of the New Ukrainian School and taking into account modern innovations in the field of providing educational services.
Approach for generating high accuracy machine learning model for high resolution geochemical map completion using remote sensing data: case study of Arizona, USA

Complete high resolution geochemical maps are strongly needed for mineral exploration; however, the previously proposed methods for making geochemical maps have low accuracy. In this research, we propose a new algorithm called sample density based mixture interpolation (SADBAMIN) for high resolution geochemical map completion using remote sensing data. In the SADBAMIN algorithm, first, according to the measured copper data density on the map, the map is classified into two parts: the area for training (T area) and the area waiting to be predicted (P area). The two areas are classified by the edge of the data point set’s alpha shape. In the T area, a triangle area among three neighbourhood points is interpolated by using the kriging model. Then, remote sensing data, including advanced spaceborne thermal emission and reflection radiometer (ASTER) data, digital elevation model (DEM) data, and geophysics (magnetic) data, and copper geochemical data at all measured and partial randomly selected interpolated points are applied as training data to construct a random forest regression model. By considering the relationship between interpolation reliability and distance, a penalty on data selection probability of going into training data is given. Finally, by inputting the remote sensing data in the P area to the model, the copper data in this area can be obtained, and the completed map comprises these two parts. We use 16,000 measured points, 10-fold cross-validation, and root mean squared error (RMSE) for model evaluation. We achieved an RMSE of 293 ppm, while the RMSE of the previously proposed method is 347 ppm.
PDMF: Parallel Dictionary Motif Finder on Multicore and GPU

Unknown motif finding in a set of DNA sequences is an important step of understanding the functionality of a group of genes and it requires accuracy and efficiency. We propose and present high-performance computation models for accelerating an efficient combinatorial approach of finding motif, which uses tree based bypassing and hash based heuristics to skip unnecessary computations with keeping the maximum accuracy. The computation models are designed for multicore processors and GPU and implemented with OpenMP and CUDA, respectively. To achieve the maximum efficiency, we also developed efficient heterogeneous computation models, in which multicore processor(s) and GPU collaborate. The collection of the resulting products is named PDMF and tested on a couple of HPC systems for performance. Our experimental results showed that the multicore version (PDMFm) achieved average 4.63x and 8.87x speedups over the serial version on a couple of systems with 4 cores and 16 cores, respectively. The GPU version (PDMFg) achieved average 41.48x and 9.95x speedups over the serial version and PDMFm on a system with a 4-core host CPU and a GPU. The best heterogeneous version showed ~1.4x speedup over the baseline GPU version.
On implementation of decision tree induction in cloud platforms

In this article, the importance of cloud computing in the modern world is considered. The influence of cloud computing on modern information technologies is analyzed. The components of cloud platforms are described. The decision tree induction algorithms are considered. The main platforms of cloud computing have been analyzed, which enable implementation of algorithms for induction of decision tree. There is offered software implementation of one simplified decision tree algorithm for categorized data on cloud platform. Examples of decision tree construction for medical research are presented and analyzed.