Reviewerly/exHarmony huggingface dataset:
Spoken Word and Speaker Recognition Using MFCC and Multiple Recurrent Neural Networks

Identification of spoken word and speaker has been featured in many kinds of research. The problem or obstacle that persists is in the pronunciation of a particular word. So it is the noise that causes the difficulty of words to be identified. Furthermore, every human has different pronunciation habits and is influenced by several variables, such as amplitude, frequency, tempo, and rhythmic. This study proposed the identification of spoken sounds by using specific word input to determine the patterns of the speaker and spoken using Mel-frequency Cepstrum Coefficients (MFCC) and Multiple Recurrent Neural Networks (RNN). The Mel coefficient of MFCC is used as an input feature for identifying spoken words and speakers using RNN and Long Short Term Memory (LSTM). Multiple RNN works spoken word and speaker in parallel. The results obtained by multiple RNN have an accuracy of 87.74%, while single RNNs have 80.58% using Adam of new data. In order to test our model computational regularly, the experiment tested K-fold Cross-Validation of datasets for spoken and speakers with an average accuracy of 86.07%, which means the model to be able to learn on the dataset without being affected by the order or selection of test data.
Research of Virtual Reality Technology in Home Design

Abstract This paper mainly discusses the application of virtual reality technology in home design. Through virtual reality technology, we can have more realistic experience and switch freely in each space, so that customers can feel the future home personally. Built by importing household design, real 3D model, combined with the virtual reality modelling language (VRML) generated with interactive link the prototype of the object, to quickly build furniture design based on the technology of web 3D virtual reality display system. System combined with rich interactive methods, adjust and contrast design, advance awareness the real effect of household is decorated, improve customer satisfaction; Combination scheme design, the system can also be used for virtual simulation practice. Engineering practice proved that the system development is simple, high efficiency and can greatly accelerate the household decoration engineering design schedule and save time cost, and has high value in engineering practice. This article provides a new Angle of view to parse the virtual reality technology in the application of home design
Implementing Tablets in Norwegian Primary Schools. Examining Outcome Measures in the Second Cohort

This study examines the implementation of tablets in primary schools in Norway. The outcome measures in the study are external for the intervention and are recorded data from national tests (National reading, arithmetic and English Tests, Classes 5, 8, and 9, National Mapping Tests for reading and arithmetic, Classes 1–3, and the 2014–2017 National Pupil Survey). The entire study ( N = 15, 708) relies on an explanatory, sequential mixed-methods design and in this study we examine the quantitative effects of this implementation. The results indicate that in several school areas tablets have rather limited effect on pupils learning outcome. However, there were both some negative- and positive effects of tablets on several of the outcome measures. It seems that tablets contribute more positively to boys’ school achievements than to girls’ school achievements. The effect of introducing tablets is significantly positive for boys in fifth grade in English (as in the first cohort from 2015/2016). This might also be linked to a “spill over-effect” from outside school learning where the significantly positive results for boys in fifth grade in English can be interpreted as a sign of the times where English language immersion in leisure time (e.g., gaming, youtube, etc.) among boys are continuously developing. For boys, we also find positive significant effects on well-being, common rules, and assessment for learning, while in girls we find positive and significant effects on mastering, teacher support, and assessment for learning. From the study, we find some tendencies that when the use of tablets is supported by teachers who have high digital competence, their use seems to have a small equalizing effect between the school achievements of boys and girls. However, we cannot rule out that a grade effect and informal learning may also have an impact on the results, and we therefore request that the results be read with this reservation.
A Practical Approach to Testing Random Number Generators in Computer Algebra Systems

This paper has a practical aim. For a long time, implementations of pseudorandom number generators in standard libraries of programming languages had poor quality. The situation started to improve only recently. Up to now, a large number of libraries and weakly supported mathematical packages use outdated algorithms for random number generation. Four modern sets of statistical tests that can be used for verifying random number generators are described. It is proposed to use command line utilities, which makes it possible to avoid low-level programming in such languages as C or C++. Only free open source systems are considered.
Optimal fusing of video sequence images

In this paper, we consider the problem of multi-frame super-resolution. We propose an information technology of optimal (in terms of mean square error) multiplexing of video signals. A field of interpolation error dispersions is formed for each previously enlarged frame before the fusion procedure. The values of this field are used to calculate the optimal weighting coefficients of every sample during the formation of the resulting image with a higher spatial resolution. A series of experiments was carried out on a test video set with a statically mounted camera to examine the proposed method.
Forecast Method of Track Irregularity of Heavy-haul Railway Based on BP Neural Network

Abstract The track irregularity is an important factor for the transport safety of heavy-haul railway. Limited by the technique of data mining and analysis, the data obtained by rail inspection vehicles cannot fully identify the status of track irregularity. In this paper, based on the characteristics of track irregularity, the BP neural network is used to predict the geometric irregularity parameters of heavy-haul railway tracks, identify the status of track irregularity, and provide support for the decision-making of maintenance strategy. In order to further verify the accuracy of the BP neural network, the single and double hidden layer networks are established to predict 20 sets of the 7 indicators of track irregularity. According to the prediction results, the mean square errors of the single and double hidden layer networks are 0.064 and 0.051, respectively. The result shows that the multi-hidden layer BP neural network has higher accuracy, which provides a new idea for the research on the prediction model of track irregularity.
An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features

Feature selection has devoted a consistently great amount of effort to dimension reduction for various machine learning tasks. Existing feature selection models focus on selecting the most discriminative features for learning targets. However, this strategy is weak in handling two kinds of features, that is, the irrelevant and redundant ones, which are collectively referred to as noisy features. These features may hamper the construction of optimal low-dimensional subspaces and compromise the learning performance of downstream tasks. In this study, we propose a novel multi-label feature selection approach by embedding label correlations (dubbed ELC) to address these issues. Particularly, we extract label correlations for reliable label space structures and employ them to steer feature selection. In this way, label and feature spaces can be expected to be consistent and noisy features can be effectively eliminated. An extensive experimental evaluation on public benchmarks validated the superiority of ELC.
Post-quantum signature schemes for efficient hardware implementation

Post-quantum cryptosystems have attracted a great interest, from researchers, latest. This work introduces two new forms of the hidden discrete logarithm problem and three new post-quantum signature schemes. The finite non-commutative associative algebras of two types are used as the algebraic support of the proposed cryptoschemes: i) containing a global two-sided unit and ii) containing a large set of global left-sided units. The illustrated FPGA implementation results, show the efficiency of the proposed cryptographic schemes, in hardware approaches. Detailed comparisons, with other security hardware implementations, are also presented.
TIPS: <i>Ti</i>me-aware <i>P</i>ersonalised <i>S</i>emantic-based query auto-completion

With the rapid growth of the Internet, search engines play vital roles in meeting the users’ information needs. However, formulating information needs to simple queries for canonical users is a problem yet. Therefore, query auto-completion, which is one of the most important characteristics of the search engines, is leveraged to provide a ranked list of queries matching the user’s entered prefix. Although query auto-completion utilises useful information provided by search engine logs, time-, semantic- and context-aware features are still important resources of extra knowledge. Specifically, in this study, a hybrid query auto-completion system called TIPS ( Time-aware Personalised Semantic-based query auto-completion) is introduced to combine the well-known systems performing based on popularity and neural language model. Furthermore, this system is supplemented by time-aware features that blend both context and semantic information in a collaborative manner. Experimental studies on the standard AOL dataset are conducted to compare our proposed system with state-of-the-art methods, that is, FactorCell, ConcatCell and Unadapted. The results illustrate the significant superiorities of TIPS in terms of mean reciprocal rank (MRR), especially for short-length prefixes.
NNBench-X

The tremendous impact of deep learning algorithms over a wide range of application domains has encouraged a surge of neural network (NN) accelerator research. Facilitating the NN accelerator design calls for guidance from an evolving benchmark suite that incorporates emerging NN models. Nevertheless, existing NN benchmarks are not suitable for guiding NN accelerator designs. These benchmarks are either selected for general-purpose processors without considering unique characteristics of NN accelerators or lack quantitative analysis to guarantee their completeness during the benchmark construction, update, and customization. In light of the shortcomings of prior benchmarks, we propose a novel benchmarking methodology for NN accelerators with a quantitative analysis of application performance features and a comprehensive awareness of software-hardware co-design. Specifically, we decouple the benchmarking process into three stages: First, we characterize the NN workloads with quantitative metrics and select the representative applications for the benchmark suite to ensure diversity and completeness. Second, we refine the selected applications according to the customized model compression techniques provided by specific software-hardware co-design. Finally, we evaluate a variety of accelerator designs on the generated benchmark suite. To demonstrate the effectiveness of our benchmarking methodology, we conduct a case study of composing an NN benchmark from the TensorFlow Model Zoo and compress these selected models with various model compression techniques. Finally, we evaluate compressed models on various architectures, including GPU, Neurocube, DianNao, and Cambricon-X.
Credal sets representable by reachable probability intervals and belief functions

Belief functions and reachable probability intervals are theories based on imprecise probabilities that generalize classical probability theory. On the one hand, belief functions have been commonly used to deal with uncertainty and in the combination of information provided by different sources. On the other hand, reachable probability intervals have high expressive power, are easy to manage, and can be efficiently computed. Belief functions are not generalizations of reachable probability intervals, and the converse is also not verified. In this work, we study under which conditions a reachable set of probability intervals is associated with a belief function, as well as the properties that a belief function has to satisfy to be representable by a reachable set of probability intervals.
A Light Field Sparse and Reconstruction Framework for Improving Rendering Quality

We present a sparse reconstruction light field (SRLF) framework using compressed sensing theory. Light field signals have sparsity in some specific phenomena, such as nonocclusions, smooth surfaces and texture uniformity. We study the light field sparsity to analyze the sparse basis and measurement matrix of the light field in a complicated scene. This extends previous work on light field sampling that considered either spatial or angular dimensions, which can be used to control the sampling rate of the light field. Furthermore, the sparse Bayes learning (SBL) algorithm is applied to the reconstruction of sparsely sampled light fields. We derive a learning machine for the light field SBL algorithm, which can improve the rendering quality based on a given set of captured multiview images. The proposed SRLF compares favorably with state-of-the-art light field sampling and reconstruction techniques. The innovation of the SRLF is to use compressed sensing theory to further reduce the light field sampling rate. We conduct a detailed derivation of the mathematical theory of light field sparseness.
User Authentication leveraging behavioral information using Commodity WiFi devices

User authentication is a major area of interest within the field of Human Computer Interaction (HCI). Meanwhile, it prevents unauthorized accesses to certain the security of data. Personal Identification Number (PIN) and biometrics are the main approaches for identifying the user on the basis of his/her identity. However, PIN can be easily leaked to others, and biometrics usually require specialized devices. In this paper, we prototype our system, a new method for user authentication by leveraging commodity WiFi. The basic methodology is to explore the typing habit of users from Channel State Information (CSI). The design and implementation of our system face two challenges, i.e. extracting keystroke features from wireless channel data and authenticating the user via typing habit from the corresponding keystroke features. For the former, we capture signal fluctuations caused by the micro movements like typing and extract the keystroke features on channel response obtained from commodity WiFi devices. For the latter, we design a computational intelligence driven mechanism to authenticate users from the corresponding keystroke feature. We prototype our system on the low-cost off-the-shelf WiFi devices and evaluate its performance in real-world experiments. We have explored four classifiers including K Nearest Neighbor(KNN), Support Vector Machine (SVM), Random Forest, and Decision Tree for recognizing users. Empirical results show that KNN provides the best performance, i.e., 85.2% authentication accuracy, 12.8% false accept rate, and 11.2% false reject rate on average over 9 participants.
Recent developments in histogram libraries

Boost.Histogram, a header-only C++14 library that provides multidimensional histograms and profiles, became available in Boost 1.70. It is extensible, fast, and uses modern C++ features. Using template metaprogramming, the most efficient code path for any given configuration is automatically selected. The library includes key features designed for the particle physics community, such as optional under- and overflow bins, weighted increments, reductions, growing axes, thread-safe filling, and memory-efficient counters with high-dynamic range. Python bindings for Boost.Histogram are being developed in the Scikit-HEP project to provide a fast, easy-to-install package as a backend for other Python libraries and for advanced users to manipulate histograms. Versatile and efficient histogram filling, effective manipulation, multithreading support, and other features make this a powerful tool. This library has also driven package distribution efforts in Scikit-HEP, allowing binary packages hosted on PyPI to be available for a very wide variety of platforms. Two other libraries fill out the remainder of the Scikit-HEP Python histogramming effort. Aghast is a library designed to provide conversions between different forms of histograms, enabling interaction between histogram libraries, often without an extra copy in memory. This enables a user to make a histogram in one library and then save it in another form, such as saving a Boost.Histogram in ROOT. And Hist is a library providing friendly, analyst-targeted syntax and shortcuts for quick manipulations and fast plotting using these two libraries.
Learning network event sequences using long short‐term memory and second‐order statistic loss

Abstract Modeling temporal event sequences on the vertices of a network is an important problem with widespread applications; examples include modeling influences in social networks, preventing crimes by modeling their space–time occurrences, and forecasting earthquakes. Existing solutions for this problem use a parametric approach, whose applicability is limited to event sequences following some well‐known distributions, which is not true for many real life event datasets. To overcome this limitation, in this work, we propose a composite recurrent neural network model for learning events occurring in the vertices of a network over time. Our proposed model combines two long short‐term memory units to capture base intensity and conditional intensity of an event sequence. We also introduce a second‐order statistic loss that penalizes higher divergence between the generated and the target sequence's distribution of hop count distance of consecutive events. Given a sequence of vertices of a network in which an event has occurred, the proposed model predicts the vertex where the next event would most likely occur. Experimental results on synthetic and real‐world datasets validate the superiority of our proposed model in comparison to various baseline methods.
Developing an Ontology for Implementation of Lean Construction Methods in Construction Projects

Although lean methods have been used on construction projects for over three decades, the adoption of such methods into the project delivery process varies in formal planning and implementation. This makes it challenging to benchmark the performance of projects implementing lean construction methods. Preliminary analysis of literature combined with a content analysis of expert interviews with lean practitioners suggested that there is an opportunity to improve the awareness, understanding, and education of lean methods. As a result, the adoption of lean methods would also improve. However, to improve the awareness, understanding, and education of lean methods, there is also the need to develop consistent terminology that can be used. Having identified this need, further investigation of various knowledge representation techniques resulted in the identification of domain ontologies as a mechanism to organize the domain of lean construction methods. This paper presents the summary of the various approaches and tasks adopted from ontological methodologies to outline the process for developing the lean construction methods ontology and the ontology structure. The paper concludes with future research directions focused on documentation of the knowledge within the ontology, ontology evaluation using expert interviews, and ontology application using project case studies.
A propositionalization method of multi-relational data based on Grammar-Guided Genetic Programming

The propositionalization process tries to find distinctive features of the examples in a database to transform such relational data into a simpler representation. More informative features have a positive impact on the classification capabilities of the learning algorithms. In this work, we propose a new propositionalization method, which generates complex Boolean attributes using Grammar-Guided Genetic Programming (G3P). The generated attributes are compound formulas that combine word items coming from a Bag-of-Words (BoW) representation using Boolean operators. The proposal was assessed against three state-of-the-art simple-instance and multiple-instance propositionalization methods. The experimental results show that the proposed method achieves an improvement in terms of classification accuracy and a considerable reduction in the dimensionality of the resulting datasets.
CAN-GRU: A Hierarchical Model for Emotion Recognition in Dialogue

Emotion recognition in dialogue systems has gained attention in the field of natural language processing recent years, because it can be applied in opinion mining from public conversational data on social media. In this paper, we propose a hierarchical model to recognize emotions in the dialogue. In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN). Convolution is used to capture n-gram information and attention mechanism is used to obtain the relevant semantic information among words in the utterance. In the second layer, a GRU-based network helps to capture contextual information in the conversation. Furthermore, we discuss the effects of unidirectional and bidirectional networks. We conduct experiments on Friends dataset and EmotionPush dataset. The results show that our proposed model(CAN-GRU) and its variants achieve better performance than baselines.
A Cognitive Approach to Teaching Italian Prepositions to Polish Students

The present work aims to show a variety of teaching methods of Italian prepositions to Polish students. The study is based on cognitive linguistics, or, more specifically, Cognitive Grammar (Langacker, 1987) and one its key notions—imagery. One of the difficulties in learning Italian prepositions by Polish students is the differences between the two linguistic systems, which express the same relations by means of different lexical structures. An application of cognitive linguistics in teaching can, in many cases, help students understand the sources of these differences and, consequently, learn the uses of prepositions in Italian despite the fact that the corresponding Polish structures frequently do not include prepositions. The most common methods of teaching Italian prepositions, while not always effective, are based on the classification of objects and adverbials which a given preposition introduces. This research aims to present a different means of explaining the link created by a preposition based on human cognitive abilities. The chapter will examine several examples of phrases and sentences containing prepositional expressions that cause considerable difficulties for Polish students. Finally, it will highlight the need to improve the classical methods of teaching prepositions, making an important contribution to the field of language teaching.