Reviewerly/exHarmony huggingface dataset:
Investigating Issues with Machine Learning for Accent Classification

Abstract Speech recognition has become a widely researched topic for decades, and there were already some successful products which has been put into commercial use, like Siri. However, sometimes it is hard to distinguish the word because words of different accents have different pronunciations. For instance, in Japanese English /r/ is usually pronounced as /l/. Therefore, it is natural to think that speech recognition could be divided into two parts. First attach a label to the audio about the accent, then recognize the contents based on the regular pattern of that accent. In this paper, we researched on several characteristics including voice onset region(VOR), vowels and formants to distinguish British English and American English. By applying both linear neural network and neural network with nonlinear classifications and two hidden layers(NN2HL), the accuracy rate reaches 86.67%, which is very satisfying.
Implementation of mappings between the description logic and the binary relational data model on the RDF level

This paper is dedicated to the data integration problem. In article the task of practical implementation of mappings between description logic and a binary relational data model is discussed. This method was formulated earlier at a theoretical level. A practical technique to test mapping engines using RDF is provided in the current paper. To transform the constructs of the description logic ALC and its main extensions into RDF triplets the OWL 2-to-RDF mappings are used. To convert RDB to RDF graph, the R2R Mapping Language (R2R ML) was chosen. The mappings DL ALC and its main extensions to the RDF triplets are described in the publication. The mapping of the DL axioms into an RDF triplet also is considered in the publication. The main difficulties in describing DL-to-RDF transformations are given in the corresponding section. For each constructor of concepts and roles a corresponding expression in OWL 2 and its mapping into the RDF triplet. A schematic representation of the resulting RDF graph for each mapping is created. The paper also provides an overview of existing methods that relate to the use of RDF when mapping RDB to ontology and vice versa.
Semantic Information Retrieval using String Ontology in Music Domain using Protege5.0

Retrieval of the semantic data becomes a time consuming and a highly tedious task. Ontology plays a maj or role in the retrieval of semantic data. In this paper the researcher has presented the methodology of music domain ontology construction using protegé 5.0 which is the best and the most commonly used Editor, intended to enhance information retrieval. The researcher has developed the string_ ontology in music domain using protege 5.0 and the working of ontology has been tested using the Descriptive Logic ontology query language. The novelty of this paper is that the researcher has constructed this string ontology in music domain from scratch and no such ontology has been constructed earlier.
Using Metamodels to Improve Model-Based Testing of Service Orchestrations

Online model-based testing is one of the most suitable techniques to assess the proper behavior of service orchestrations. However, the diverse panorama in terms of modeling languages and test case generation tools is a limitation to widespread adoption. We advocate that the application of Model-Driven Engineering principles as meta-modeling and model transformation can cope with this problem, improving the interoperability of artifacts in the test case generation process, thus bringing benefits in case of agile development processes, where system and technology evolution is frequent. In this paper, we present our contribution to this idea, introducing i) a reference metamodel, which stores the business process behavior and the information to generate input models for testing tools, and ii) transformations from orchestration languages towards testing tools. The proposed approach is implemented in a testing framework and evaluated on a case study where multiple orchestrations are expressed in two languages. Also, the paper presents how test cases are appropriately generated and successfully executed, starting from an orchestration model as a consequence of successful transformations.
Construction Method of User Mental Model in Interaction Design

Through the analysis of user research methods and the combination of user research methods and the three-layer theory of emotional design, this paper proposes a new mental model construction method, namely the Hierarchical Elicitation Approach. Taking the information architecture design of the emergency monitoring platform as an example, this paper uses the Hierarchical Elicitation Approach to measure the performance rules of users’ minds in the emergency monitoring platform, designs the construction experiments of the macro-mental model and meta-mental model of the system, and optimizes the design according to the experimental results. Moreover, Cogtool is used to analyze the effect of optimization design and verify the application value of the Hierarchical Elicitation Approach in the study of user’s mind.
Detecting objects and people and tracking movements in a video using tensorflow and deeplearning

With the advent of the digital age and more specifically videos, a huge amount of data is produced every day such as television archiving, video surveillance, etc. Faced with the need to keep control over this content, to be able to analyze it, classify it and many other applications, the need for algorithms capable of performing this task efficiently and quickly is undeniable. The proposed approach permits the analysis of video sequences using deep learning and TensorFlow technologies. The proposed approach splits video in set of images, detects objects/entities present in these images and stores their descriptions into a standard XML file. With an algorithm we developed, we're able track motion that animated entities in the video sequences.
DeskWalk

We propose DeskWalk, which is an exercise system that replaces key inputs with body movements. Prolonged sitting significantly compromises health. Previous studies have shown that walking and standing during sedentary work can reduce the negative effects of prolonged sitting. However, these movements interrupt the work of the user. DeskWalk allows a user to exercise during his/her work. It recognizes the body movements using stretch sensors attached to the lower limbs and replaces them with preassigned key inputs. In preliminary experiments, we determined three target movements that involved muscle activities equivalent to walking. The average F-value of the motion recognition rate was 0.99. We implemented a system to measure steps using a smartphone and to perform exercises similar to the walking motion with DeskWalk.
Lexical semantic and derivational features of the names of persons during the coronavirus pandemic

This article reviews the new lexical material related to the coronavirus pandemic. The empirical material was collected in the electronic database Integrum. The subject of the collected material are names of persons which were used in electronic mass media (newspapers, magazines, blogs, social networks). The volume of the lexical corpus is about 90 neolexemes. The collected corpus of innovations counts more than 600 words. The aim of the article is to describe the material from lexical and semantic, word-formation points of view, to demonstrate a language game.
Partial Dynamic Reconfiguration for efficient Adaptive Implementation of a Video Shot Boundary Detection System

In this paper, we propose a dynamic implementation, on FPGA, of a video shot boundary detection system based on the Local Histogram descriptor (LH) and the Color Structure Descriptor (CSD). Different versions of these descriptors have been implemented: three versions of the CSD (32, 16 and 8 quantization levels) and two versions for the LH (8 and 4 quantization levels). The versions of the obtained hardware modules can be loaded according to the requirements of the application in different possible Partial Reconfigurable Region (PRR) in the FPGA circuit. The objective is to optimize the hardware resources by adapting each PRR to the size of the hardware task. A partitioning adapted to the different scenarios and versions of the implemented modules has been proposed. We have used the ZedBoard, which is a low-cost development board for the Xilinx Zynq-7000 SOC. A significant gain of hardware resources occupancy rate was obtained by this implementation.
Information Retrieval Approach Using Semiotic Models Based on Multi-layered Semantic Graphs

The paper presents a novel method of information retrieval using semiotic models based on multilayer semantic graphs. The layers correspond to the level of detail of the extracted semantic information. The key developments underlying the method are described. Firstly, it is a graph model, which can be automatically built on the basis of general linguistic dictionaries or specialized encyclopedias. Using formalized text sources allows us to build a graph in a reasonable amount of time. The graph vertices are presented by canonical forms of words. Three types of connections are used as graph edges: association, definition, and synonymy. A method of automatic selection of a connection type based on the analysis of the dictionary entry structure is proposed. Secondly, an approach to using the semantic graph as a flexible basis for complex information retrieval systems is described. Information about existing connections between lexical units can be interpreted in different ways. Experiments show that, depending on the task, information about synonymous or associative relationships may come to the fore. In general, data extracted from dictionaries are reliable and complete enough to use the presented graph as a framework for information retrieval systems.
Multiple Kernel Learning via Ensemble Artifice in Reproducing Kernel Hilbert Space

In this paper, we propose a novel ensemble model of multiple kernels called Multiple Kernel Learning via Ensemble Artifice in Reproducing Kernel Hilbert Space (MKLEA) by minimizing unified ensemble loss in multiple Reproducing Kernel Hilbert Spaces (RKHSs). Different from the previous Multiple Kernel Learning (MKL) methods which attempt to seek a linear combination of basis kernels as a unified kernel; our MKLEA aims to find multiple solutions in corresponding RKHSs simultaneously. To achieve this goal, the multiple individual kernel losses are integrated into a unified ensemble loss. Therefore, each individual model can co-optimize to learn their optimal parameters in the ensemble framework. Experimental results on several UCI classification and computer vision datasets demonstrate that our proposed MKLEA model achieves best classification performances among comparative MKL methods, such as SimpleMKL, Matrix-Regularized MKL, SpicyMKL and GMKL.
DeepIoT: Deep Learning Based Symbol Detection for Spatially Undersampled Internet of Things

With the explosive growth of the Internet of Things (IoT), a massive number of IoT devices are deployed so as to realize a variety of advanced applications, i.e., environmental monitoring and smart traffic. There are two main characteristics in these typical applications, namely the massive connectivity and the sporadic transmission. The massive connectivity usually leads the IoT system to be spatially undersampled, since the number of devices is much larger than the number of receiver antennas, which brings difficulties and challenges to symbol detection. Fortunately, the sporadic transmission in IoT communication introduces sparsity into transmitted symbols, thanks to which we are able to perform symbol detection even in a spatially undersampled scenario. In this paper, we attempt to incorporate deep learning (DL) into the symbol detection of spatially undersampled IoT with sporadically transmitting devices. Specifically, we propose a novel DL-based detector named DeepIoT that employs a variant autoencoder network to recover both the indices of active devices and their transmitting symbols by using only the received signal. Simulation results show that the DeepIoT can outperform various existing methods and has only a 1.5- 2dB signal-to-noise ratio (SNR) loss compared to the optimal maximum likelihood (ML) detector.
Chaining up Time: Bitcoin and Long-Term Validity of PKI Timestamp Tokens

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A Control Strategy for the Safe Navigation of UAVs Among Dynamic Obstacles using Real-Time Deforming Trajectories

Unmanned aerial vehicles (UAVs) have become very popular in many military and civilian applications where they are required to fly autonomously through unknown environments to perform some tasks without human interaction. Safe strategies are needed to control the motion of UAVs, and navigation decisions should be based mainly on onboard sensors observations for a fully autonomous operation. Developing such strategies is very challenging when considering dynamic environments with moving obstacles and other non-cooperative UAVs as usual path planning methods can be computationally unreliable. Hence, this work proposes a safe navigation control strategy based on the sense-and-avoid paradigm to allow for a reliable solution computational-wise. Bezier splines are used to generate motion paths which are manipulated in realtime reacting to current sensors observations resulting in a deformation-like effect to navigate safely around obstacles. The performance of the proposed approach is evaluated using the Gazebo simulator within a ROS framework, and good results were obtained for different simulation scenarios.
Liver Segmentation using Fast-Generalized Fuzzy C-Means (FG-FCM) from CT Scans

Liver tumors are one of the leading deaths causes across the world. The early diagnosis of those tumors will be beneficial in reducing mortality rates. Liver computer-aided diagnosis (CAD) is used to assist radiologist decision. Accurate liver segmentation is an essential stage in CAD systems. This paper proposed an automatic, unsupervised liver segmentation based on Fast-Generalized Fuzzy C-Means (FG-FCM). This paper's primary concern is to accurately segment the liver from the rest of the abdomen organs on computed tomography (CT) scans. The proposed method was tested and evaluated using three benchmark datasets, MICCAI-Sliver07, LiTS17, and 3Dircadb, with 250 subjects. Experimental results show the proposed method's reliability with an accuracy of 94.70%, sensitivity of 93.81%, a specificity of 91.90%, a Dice similarity coefficient of 92.51%, and an area under the curve of 93.09%. The proposed method is advantageous in terms of noise robustness and in-homogeneity correction when compared with FCM.
Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems

In this paper we discuss the trade-off between accuracy and efficiency in distributed machine learning (ML) systems and analyze its resulting policy considerations. This trade-off is in fact quite common in multiple disciplines, including law and medicine, and it applies to a wide variety of sub-fields within computer science. Accuracy and efficiency trade-offs have unique implications in ML algorithms because, being probabilistic in nature, such algorithms generally exhibit error tolerance. After describing how the trade-off takes shape in real-world distributed computing systems, we show the interplay between such systems and ML algorithms, explaining in detail how accuracy and efficiency interact particularly in distributed ML systems. We close by making specific calls to action for approaching regulatory policy for the emerging technology of real-time distributed ML systems.
A Modeling Method to Evaluate the Finger-Click Interactive Task in the Virtual Environment

A modeling method is introduced in this paper to evaluate the finger-click interactive task in the virtual environment. The problem that the finger-click interactive task in the virtual environment currently cannot be quantitatively evaluated has been solved. The method mainly includes five processes: analysis, decomposition, configuration, acquisition, and evaluation. The proposal of this method can help relevant practitioners design the interactive process of user’s finger-click interactive task in the virtual environment more scientifically, optimize the interactive efficiency.
Continuous Experimentation with Product-Led Business Models: A Comparative Case Study

Context. Continuous experimentation is used by many companies to improve their products with users data. In this study, the efficacy of continuous experimentation as used in two different types of business models (product-led or sales-led). Method. Two case companies with a product-led business model and three companies with sales-led business model were compared against each other. 14 interviewees were selected from the cases. Results. Having a product-led business model enabled four different drivers to continuous experimentation: 1) development and sales & marketing integration, 2) improved prioritization, 3) decreased feature bloat, and 4) product measurability. Conclusions. The takeaway message is that a company must be structured in the right way to obtain benefits from experimentation.
Phase Shift Strategies in Phase Shifting Time Averaging Interferometry

Abstract Classical time-averaging interferometry technique is widely used for MEMS/MOEMS dynamic behavior investigations. It provides useful information on resonant vibration frequencies and mode shapes in the form of Bessel fringe images. To evaluate the information on objects’ vibration amplitude distribution, one needs to further process Bessel fringe images (besselograms). Among different available approaches to besselograms processing, Temporal Phase Shifting is of particular interest since it provides the most accurate results. However, it requires an additional cumbersome correction routine via specially designed look-up-tables fitted to the calculation algorithm used. In this paper, we numerically investigate the possibility of reducing Bessel images argument (phase) evaluation error by different phase shifting strategies. Two different 5 step algorithms are investigated for that purpose. Additionally, a quick and robust correction procedure based on evaluated phase distribution is presented. It allows us to speed up the calculations significantly. Moreover, such an approach is much less susceptible to noise in comparison to a look-up-table solution.
Fine-Grained Evaluation for Entity Linking

The Entity Linking (EL) task identifies entity mentions in a text corpus and associates them with an unambiguous identifier in a Knowledge Base. While much work has been done on the topic, we first present the results of a survey that reveal a lack of consensus in the community regarding what forms of mentions in a text and what forms of links the EL task should consider. We argue that no one definition of the Entity Linking task fits all, and rather propose a fine-grained cate-gorization of different types of entity mentions and links. We then re-annotate three EL benchmark datasets-ACE2004, KORE50, and VoxEL-with respect to these categories. We propose a fuzzy recall metric to address the lack of consensus and conclude with fine-grained evaluation results comparing a selection of online EL systems.
Exploring the Potential of Low-Bit Training of Convolutional Neural Networks

Convolutional neural networks (CNNs) have been widely used in many tasks, but training CNNs is time consuming and energy hungry. Using the low-bit integer format has been proved promising for speeding up and improving the energy efficiency of CNN inference, while CNN training can hardly benefit from such a technique because of the following challenges: 1) the integer data format cannot meet the requirements of the data dynamic range in training, resulting in the accuracy drop; 2) the floating-point data format keeps sizeable dynamic range with much more exponent bits, thus using it results in higher accumulation power than using the integer data format; and 3) there are some specially designed data formats (e.g., with group-wise scaling) that have the potential to deal with the former two problems but common hardware platforms cannot support them efficiently. To tackle all these challenges and make the training phase of CNNs benefit from the low-bit format, we propose a low-bit training framework for CNNs to pursue a better tradeoff between accuracy and energy efficiency: 1) we adopt element-wise scaling to increase the dynamic range of data representation, which significantly reduces the quantization error; 2) group-wise scaling with hardware friendly factor format is designed to reduce the element-wise exponent bits without degrading the accuracy; and 3) we design the customized hardware unit that implements the low-bit tensor convolution arithmetic with our multilevel scaling data format. Experiments show that our framework achieves a superior tradeoff between the accuracy and the bit-width than previous low-bit training studies. For training various models on CIFAR-10, using 1-bit mantissa and 2-bit exponent is adequate to keep the accuracy loss within 1%. On larger datasets like ImageNet, using 4-bit mantissa and 2-bit exponent is adequate. Through the energy consumption simulation of the whole network, we can see that training a variety of models with our framework could achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.9\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10.2\times $ </tex-math></inline-formula> higher energy efficiency than full-precision arithmetic.
A Subjective Quality Assessment of Objects Based on Visual Attention

Subjective assessment of visual quality for the evaluation of the most attentive object of a video scene is very crucial for developing and validating any objective measures. In this paper, we present a subjective evaluation scheme for a visual attentive object in a video frame. A total of 223 viewers were asked to identify objects which attract most visual attention in a particular scene, for 20 such different scenes. The viewer’s opinion score was rated as per the ITU recommended 5-point discrete scale and was composed with an objective model for visual attention as developed by Itti’s et al. The trajectory of viewer’s choice decided by mean opinion score(MOS) and that of Itti’s model indicate that the objective model is best suitable for salient visually attentive objects, but not for moving objects. The reason may be attributed to the fact of feature selection for the objective model.