Reviewerly/exHarmony huggingface dataset:
Deep Subclass Linear Discriminant Analysis For Multimodal Feature Space Learning

In this work, we target a known problem in representation learning that is: beyond coarse classification, how can we better model fine-grained categorization? To address this problem, we introduce Deep Subclass Linear Discriminant Analysis (DeepSDA), which utilizes intra-class variation and inter-class similarity during training. We could achieve multimodal classification by maximizing the ratio of between-subclass scatter matrix and within-subclass scatter matrix. We maximize the eigenvalues along the discriminative eignevector directions. Hence the deep neural network is able to learn more discriminative representation space and thus has higher class separation in the linearly separable latent space. We show that DeepSDA leads to significant improvements on diverse fine-grained categorization and attribute learning benchmarks.
Monitoring Routine Changes From Daily Smartphone Usage

The detection of user routine changes from smartphone sensor data is investigated in this study. A smartphone application is used to record multi-modal sensor data. A dataset from 60 users was used for activity classification. Anomaly detection was performed on these activities to detect and characterise abnormal behavioural changes. A Multi-task Multilayer Perceptron Neural Network was used for activity classification. Four different anomaly detection architectures were compared, using two weeks of data for training. An accuracy of 65.7 percent was achieved for activity classification of the 14 most common human activities. A One-class Support Vector Machine yielded the best results for the anomaly detection, with an accuracy of 76.8 percent. These preliminary results show a potential of the proposed methods to detect and characterise changes in human routine.
Genuine Spontaneous vs Fake Spontaneous Speech: In Search of Distinction

The paper examines the forensic problem of identifying genuine spontaneous speech as opposed to its imitation (quasi-spontaneous speech) during the interrogation of a suspect or in the process of testifying. We sought to establish criteria for distinguishing between genuine and fake spontaneous speech. In the course of the linguistic experiment three different types of speech (prepared, quasi-spontaneous, spontaneous) were recorded from six participants belonging to three professional groups. Recordings were examined in terms of disfluencies, constructions of conversational syntax and speech rate. The paper reports the initial results based on the performed analysis.
Image Texture Analysis based on Color Level Methods using Neural Network

Texture and colour are very important elements in image analysis. This article provides advanced research on the rapid acquisition and classification of coloured textures. In our research we use the principle of Grey Level Co-occurrence Matrix (GLCM) and its modifications. The main idea was to propose a method to describe the combined elements, precisely colours and textures. This work consists of our previous research on the Colour-level co-occurrence matrices (CLCM). The CLCM method is modified by a one-dimensional GLCM approach. The texture classification process is performed using a robust vector machine auxiliary classifier. We also conducted several experiments with convolutional neural networks (CNN) with 15 layers and AlexNet with 25 layers. Finally, all evaluations and comparisons of the colour texture acquisition results for all methods used are given. All algorithms were tested on two different known colour texture databases (Outex and Vistex databases).
The New Era of Healthcare: Smart Healthcare Assistant

This paper provides an outline of our work in providing an efficient and accurate Smart Healthcare Assistant at the user’s fingertips. The new era gives access to all forms of data, but to what extent can it be considered reliable? One of the biggest issues with such vast availability is that people, today, have substituted everything with such search engines, even professional consultants. The idea for our smart assistant is to provide accurate and definitive information to its users for analyzing symptoms and directing the patients to the right department of doctors for their treatment. Often, even such access to know-the-call requires patients to get recommendations from the informed or many trips to the hospital. But, with a trustworthy source like an authorized smart healthcare assistant such luxury can meet reliability. The implementation of each algorithm ought to be done with consideration to all factors in relation to user and health, while generating data in regular practice, therefore, working out a productive cycle. This model explores a still work in progress, but assures the readers of the potential of technology in providing valuable healthcare assistance.
False Positive Removal for 3D Vehicle Detection With Penetrated Point Classifier

Recently, researchers have been leveraging LiDAR point cloud for higher accuracy in 3D vehicle detection. Most state-of-the-art methods are deep learning based, but are easily affected by the number of points generated on the object. This vulnerability leads to numerous false positive boxes at high recall positions, where objects are occasionally predicted with few points. To address the issue, we introduce Penetrated Point Classifier (PPC) based on the underlying property of LiDAR that points cannot be generated behind vehicles. It determines whether a point exists behind the vehicle of the predicted box, and if does, the box is distinguished as false positive. Our straightforward yet unprecedented approach is evaluated on KITTI dataset and achieved performance improvement of PointRCNN, one of the state-of-the-art methods. The experiment results show that precision at the highest recall position is dramatically increased by 15.46 percentage points and 14.63 percentage points on the moderate and hard difficulty of car class, respectively.
Handloom Design Generation Using Generative Networks

This paper proposes deep learning techniques of generating designs for clothing, focused on handloom fabric and discusses the associated challenges along with its application. The capability of generative neural network models in understanding artistic designs and synthesizing those is not yet explored well. In this work, multiple methods are employed incorporating the current state of the art generative models and style transfer algorithms to study and observe their performance for the task. The results are then evaluated through user score. This work also provides a new dataset ”Neural-Loom” for the task of the design generation.
An application of Miller Moreno groups to establishment protocol Non commutative cryptography

The protocol of Ko K., Lee S., are improved and generalized on base of metacyclic and non-metacyclic p-groups G of type Miller-Moreno (minimal non-abelian group). We show that the decomposition problem in this group is intractable. It is proved that the conjugacy problem in this group is also intractable. Further, we have constructed an algorithm of generating (designing) of common key for a non-commutative group with two mutually commuting subgroups has been constructed by us.
Design guidelines for micro information radiators to increase seniors' safety in urban space

Senior citizens face challenges during activities in urban space. To help and motivate them pursuing outside activities, we propose a network of (micro and macro) information radiators to increase their feeling of safety. In this paper we first collect guidelines and relevant aspects for the design of micro information radiators. Then we summarize our own experiences from a project designing smart urban objects - particularly giving an overview of design guidelines for input and output interaction of micro information radiators.
PMAC++: Incremental MAC Scheme Adaptable to Lightweight Block Ciphers

This paper presents a new incremental parallelizable message authentication code (MAC) scheme adaptable to lightweight block ciphers for memory integrity verification. The highlight of the proposed scheme is to achieve both incremental update capability and sufficient security bound with lightweight block ciphers, which is a novel feature. We extend the conventional parallelizable MAC to realize the incremental update capability while keeping the original security bound. We prove that a comparable security bound can be obtained even if this change is incorporated. We also present a hardware architecture for the proposed MAC scheme with lightweight block ciphers and demonstrate the effectiveness through FPGA implementation. The evaluation results indicate that the proposed MAC hardware achieves 3.4 times improvement in the latency-area product for the tag update compared with the conventional MAC.
Towards Ontology Based Data Extraction for Organizational Goals Metrics Indicator

In this paper, we proposed a measurement framework to evaluate the quality of results at organizational goals level. We proposed metrics indicators to guarantee and optimize the usage of data to extract useful information in organization. The framework is flexible to change without affecting things around because the framework is applicable in any domains. We discuss the review of the problems, proposed solution to the problems and draw a conclusion of this paper.
Calculation of electromagnetic field for very fast transient process in GIS

Abstract The calculation method of electromagnetic field generated by very fast transient process in GIS is studied. The VFTC is calculated, and the mathematical model of direct wire electromagnetic radiation is established according to the structure of GIS. The space electromagnetic field is calculated with VFTC as excitation source. The non-stationary VFTC simulation waveform is decomposed in combination with Hilbert yellow method. The electromagnetic field intensity of each decomposed waveform is calculated on the basis of mathematical model of direct wire electromagnetic radiation. Then the electromagnetic field intensity of fast transient process is reconstructed. The results show that the intensity of electromagnetic field decreases exponentially with the increase of distance. The method proposed solves the theoretical calculation of complex electromagnetic field for unsteady VFTC waveform that contains complex frequency components with transient characteristics in very fast transient process. That has theoretical guiding significance for the electromagnetic field calculation of very fast transient phenomenon in GIS.
An UML Based Approach to Find out Software Reliability Prediction Metrics

As we know that in Software Industry a software with high reliability is preferred that is, why Software Reliability plays a crucial role in the Software Industry. Also as we know in Software Engineering there are plenty of models for development and deployment. Among the models, UML Diagrams are most preferred since it requires low knowledge of the system. So, in this paper we proposed a method to an “Early Detection of Software Reliability” in order to work towards making the software more reliable considering if there is a possibility of system failures. In this method we will know if there will be changes necessary to the UML diagrams or if the proposed diagram are feasible. This helps the designer to move to the next stage. As a result the analysis of UML's for the software reliability will help. This leads to making the software more reliable and feasible for the end users.
Hybrid Gray-Scale and Fuzzy Morphological/Linear Perceptrons Trained By Extreme Learning Machine

Morphological perceptrons (MPs) belong to the class of morphological neural networks (MNNs) whose neuronal aggregation functions are drawn from mathematical morphology (MM). Most MNN models including MPs employ operators of gray-scale mathematical morphology as aggregation functions. Recently, a hybrid morphological/linear perceptron (HMLP) appeared in the literature. This neural network model combines the approximation capabilities of the two-layer perceptron having sigmoid activation functions with the capability of the MP to represent non-differentiable functions. For a number of reasons, that include the non-differentiability of morphological operators, it is advantageous to train HMLPs using extreme learning machine (ELM). The fact that gray-scale MM is closely related to fuzzy MM based on Lukasiewicz operators motivated us to introduce hybrid fuzzy morphological/linear perceptrons (FMLPs) and train them using ELM in this paper. We compare the performances of the HMLP and the FMLP with the ones of some related models in a number of well-known classification problems.
The Construction of an Effective Multi-modal English Oral Output Teaching Mode in the Cloud Environment

In view of the current inefficient oral English teaching and low quality of learners' oral output, this paper attempts to apply information technology in the English education, to build an effective English oral output teaching mode under the guidance of multimodal discourse theory. This paper first clarifies three concepts: multimodal discourse, effective teaching, and the cloud environment. Then it puts forward three components for the construction of a new oral English teaching mode from three aspects: making the teaching process more effective, applying multi-modal teaching activities and building a cloud environment for oral English teaching.
Exploring polyrhythms, polymeters, and polytempi with the universal grid sequencer framework

Polyrhythms, Polymeters, and Polytempo are compositional techniques that describe pulses which are desynchronous between two or more sequences of music. Digital systems permit the sequencing of notes to a near-infinite degree of resolution, permitting an exponential number of complex rhythmic attributes in the music. Exploring such techniques within existing popular music sequencing software and notations can be challenging to generally work with and notate effectively. Step sequencers provide a simple and effective interface for exploring any arbitrary division of time into an even number of steps, with such interfaces easily expressible on grid based music controllers.
MG-Hybrid: A Strongly Connected Components Detection Algorithm using Multiple GPUs

Detection of strongly connected component (SCC) on the GPU has become a fundamental operation to accelerate graph computing. Existing SCC detection methods on multiple GPUs introduce massive unnecessary data transformation between multiple GPUs. In this paper, we propose a novel distributed SCC detection approach using multiple GPUs plus CPU. Our approach includes three key ideas: (1) segmentation and labeling over large-scale datasets; (2) collecting and merging the segmented SCCs; and (3) running tasks assignment over multiples GPUs and CPU. We implement our approach under a hybrid distributed architecture with multiple GPUs plus CPU. Our approach can achieve device-level optimization and can be compatible with the state-of-the-art algorithms. We conduct extensive theoretical and experimental analysis to demonstrate efficiency and accuracy of our approach. The experimental results expose that our approach can achieves 11.2×, 1.2×, 1.2× speedup for SCC detection using NVIDIA K80 compared with Tarjan's, FB-Trim, and FB-Hybrid algorithms respectively.
Self-adaptation of Workflow Business Software to the User’s Requirements and Behavior

The main goal of the presented paper is to propose a method for adaptation of the user interface of workflow software to increase its efficiency, reduce the number of errors, and improve its UX. The authors assumed that the system adaptation will be achieved by application to intelligent methods for modeling user as well as system. In order to do this, a special tool for data gathering has been designed and in the next steps of the research, this tool will be also implemented in a real environment. A unique value of the paper is that after many years of theoretical research, the first attempt to implement a practical solution for self-adapting and the personalized interface for workflow systems was done.
Stage Selection for Machine Combination Optimization in Multi-Process Production System

In a multi-process production system, the yield rate of the final products depends not only on the performance of each machine but also on the combination of machines at different stages. In the previous study, it was demonstrated that field-aware factorization machines (FFM) can estimate the yield rates achieved by unused machine combinations and identify important machine pairs with high accuracy. However, when the number of stages is large and the data of used machine combinations is not enough, the prediction accuracy will be low. Hence, we proposed important stage selection methods. Through two numerical examples, we confirmed that RMSE between the actual and predicted values of yield rates decreased by up to 66 % in comparison with a model using all stages.
The Method of Rapidly Improving Pass Rate of CET-4 Based on Gaussian/ Steed Variance

With the rapid development of globalization, English becomes a de facto standard for communication. College English Test Band 4(CET-4) in China becomes a necessary process for undergraduates, and every college endeavors to improve CET-4 pass rates for students. Therefore, we developed and built a novel approach for improving pass rates with Gaussian variance and Steed variance. The results show that students are classified in either normal or abnormal categories according to the Pearson correlation coefficient. Thus, college administration can easily focus on the students with the greatest potential and improve their CET-4 performance for the future. Our research reveals that improving CET-4 pass rate for the future is a valuable avenue for education and will inspire more practice and research.
Machine translation testing via pathological invariance

Due to the rapid development of deep neural networks, in recent years, machine translation software has been widely adopted in people's daily lives, such as communicating with foreigners or understanding political news from the neighbouring countries. However, machine translation software could return incorrect translations because of the complexity of the underlying network. To address this problem, we introduce a novel methodology called PaInv for validating machine translation software. Our key insight is that sentences of different meanings should not have the same translation (i.e., pathological invariance). Specifically, PaInv generates syntactically similar but semantically different sentences by replacing one word in the sentence and filter out unsuitable sentences based on both syntactic and semantic information. We have applied PaInv to Google Translate using 200 English sentences as input with three language settings: English→Hindi, English→Chinese, and English→German. PaInv can accurately find 331 pathological invariants in total, revealing more than 100 translation errors.
Approximating the Anticover of a String

The k-anticover of a string S is a set of distinct k-length substrings such that every index in S is contained in one of these substrings. The existence of an anticover indicates a lack of structure in S. It was recently proven by Alzamel et al. [] that finding whether or not a k-anticover exists is \(\mathcal {NP}\)-Hard for \(k \ge 3\).In this paper, we extend the definition to provide three optimization versions for the k-anticover problem. We provide efficient approximation algorithms for these problems.
Uncertainty of Phone Voicing and Its Impact on Speech Synthesis

While unit selection speech synthesis is not at the centre of research nowadays, it shows its strengths in deployments where fast fixes and tuning possibilities are required. The key part of this method is target and concatenation costs, usually consisting of features manually designed. When there is a flaw in a feature design, the selection may behave in an unexpected way, not necessarily causing a bad quality speech output. One of such features in our systems was the requirement on the match between expected and real units voicing. Due to the flexibility of the method, we were able to narrow the behaviour of the selection algorithm without worsening the quality of synthesised speech.
Systematic study on dimensionality reduction in the gesture phase segmentation problem

In this paper, we present the results obtained in a systematic study related to dimensionality reduction on gesture windowed data. The aim of the study was to analyze the effects that such reduction causes on the performance of classification models used to implement the gesture phase segmentation task. Piecewise Aggregate Approximation was used to implement the dimensionality reduction and k-Nearest Neighbors was used to implement the classification models. The results showed that the dimensionality reduction can improve the classification models' performance both by decreasing the complexity of their decision space and by improving the quality of the data.
Detection of Obstacle Features Using Neural Networks with Attention in the Task of Autonomous Navigation of Mobile Robots

This article describes the design process of a software package for image recognition of a mobile robot camera using neural networks with attention, which allows to identify the probability of a robot colliding with obstacles standing in its way. A key feature of this software is using a dataset that is prepared without manual labeling of all obstacles and the probability of a collision.Currently, an important task in mobile robotics is the need to use numerous heuristics and deterministic algorithms in control programs along with neural networks. The use of a single neural network that solves all the tasks of scene analysis (the so-called “end-to-end” solution) is impossible for several reasons: the high complexity of the training samples due to the large parameter space of the environment of the robot and the insufficient formalization of these parameters, as well as the computational complexity of machine learning algorithms, which is critical for mobile robots with strict energy requirements. Therefore, the development of a universal algorithm (end-to-end) is a laborious process.The article describes a method that allows to use weakly formalized parameters of the robot environment for training convolutional neural networks with attention using the obstacle recognition task. At the same time, weak formalization reduces the time-consuming process of manual data labeling due to automatically generated datasets in the NVIDIA Isaac environment, and the attention mechanism allows increasing the interpretability of the analysis results.
Construcción y gestión de contenido digital y analógico en las escuelas: cuestiones pedagógicas y didácticas

Society in which we live has been imbued with innovations that have profoundly changed lifestyles, communication, socialization, work and, above all, learning. The vision of education in the age represents a shared path of cultural, organizational, social and institutional innovation that wants to give new energy to democratic processes, new connections, new capacities in the democratic school. In this essay, the author, from the deep link between technology, education and teaching, describe a vision of the as an enabling tool, connector and driver of change both in school and in society, focusing on the role that new technologies can have in the school environment for the creation of a new life project for natives. At the center of this new world in which natives are always connected through the network, a 3.0 school can and should have a fundamental role as a laboratory for a new democracy in which the skills of digital citizenship are increasingly more essential to face the spirit of an increasingly intelligent society.