Reviewerly/exHarmony huggingface dataset:
Attribute Bagging-Based Extreme Learning Machine

Extreme learning machine (ELM) is a fast training scheme of single-hidden-layer feedforward neural network. How to further improve the prediction stability and accuracy of ELM in an ensemble learning way becomes one of the hot research topics in the filed of supervised learning. This paper proposes an attribute bagging-based ELM (AB-ELM) which is an ensemble learning system for classification and regression tasks by training the base ELMs on random samples of attributes instead of the entire attribute set. AB-ELM uses the sampling with replacement method to get the multiple randomized attribute subsets so as that the different data subsets can be constructed for the training of base ELMs. After obtaining a set of base ELMs, the weighted averaging method and the weighted voting method are used to generate a combination output, where the weight considers the information amount of training data subset. The relationship between the size of attribute subsets and the size of base ELMs is also discussed in AB-ELM. On 4 classification and 4 regression data sets, we verify the training and testing performances of AB-ELM in comparison with the classical ELM and the voting based ELM (V-ELM). The experimental results show that AB-ELM obtains the better prediction stability and accuracy than the classical ELM and V-ELM and thus demonstrate the effectiveness of AB-ELM.
Software Visualization Tool for Evaluating API Usage in the Context of Software Ecosystems: A Proof of Concept

Software Ecosystem (SECO) is a consolidated research area in software engineering, emerging as a paradigm for understanding dynamics and relationships among software systems that collaborate with each other to achieve their goals. Understanding the ecosystem and how its elements interact is essential for software evolution, especially for those that provide functions and services for other systems, such as software APIs. Once an API is being used by different software, future changes need to be made in a systematic and appropriate manner, considering the whole ecosystem. However, there is a lack of formal and effective ways for APIs evaluation in the context of SECO. Thus, in this paper, we present Ecolyzer, a prototype tool that aims to support the analysis of API usage considering its ecosystem through interactive visualization. To demonstrate the feasibility of our tool, we conducted a proof of concept (PoC) using an open-source platform API. The results obtained with Ecolyzer are useful and show that the prototype meets the goals described for the accomplishment of this work.
Design of a Convolutional Neural Network Instruction Set Based on RISC-V and Its Microarchitecture Implementation

The success of Convolution Neural Network (CNN) in computer vision presents a continuing challenge on performance requirement in both training and inference processes. Various software optimization has been examined towards existing hardware devices such as CPU and GPU to meet the computation needs; however, the performance gap between ideal and reality will keep going if there is short of hardware support. In this paper, we propose a customized CNN processor by extending the RISC-V instruction set. We have added six primary instructions by analyzing and abstracting the characteristics of conventional CNN models. The target micro-architecture has been upgraded accordingly to exploit the parallelism in the massive data access. We evaluated our work on the broadly used CNN model, LeNet-5, on Field Programmable Gate Arrays (FPGA) for the correctness validation. Comparing to traditional x86 and MIPS ISAs, our design provides a higher code density and performance efficiency.
Unconstrained Arabic Scene Text Analysis using Concurrent Invariant Points

Text in natural scene image portrays rich semantic information that plays an important role in content analysis. However, apart from Arabic text in documents, the text in natural scene images exhibit much higher diversity and variability, especially in uncontrolled circumstances. In this paper, a hybrid feature extraction approach is presented to detect extremal region of Arabic scene text. The binary image and image mask are considered as a variant of input image and look for concurrent extremal regions in both images. After determination of conjoined extremal points, the scale invariant technique is applied to consider those invariant points which are common in both images based on their coordinate positions. To evaluate the performance, a multidimensional long short term memory (LSTM) network is adapted and obtained 94.21% accuracy for word recognition on unconstrained Arabic scene text recognition (ASTR) dataset.
Efficient Salient Object Detection Model with Dilated Convolutional Networks

Introduction of Fully Convolutional Networks (FCNs) has made record progress in salient object detection models. However, in order to retain the input resolutions, deconvolutional networks with unpooling are applied on top of FCNs. This will cause the increase of the computation and network model size in segmentation task. In addition, most deep learning based methods always discard effective saliency prior knowledge completely, which are shown effective. Therefore, an efficient salient object detection method based on deep learning is proposed in our work. In this model, dilated convolutions are exploited in the networks to produce the output with high resolution without pooling and adding deconvolutional networks. In this way, the parameters and depth of the network are decreased sharply compared with the traditional FCNs. Furthermore, manifold ranking model is explored for the saliency refinement to keep the spatial consistency and contour preserving. Experimental results verify that performance of our method is superior with other state-of-art methods. Meanwhile, the proposed model occupies the less model size and fastest processing speed, which is more suitable for the wearable processing systems.
The Construction and Discussion of Smart Learning Environment in the Context of 5G

The deep combination of information technology and education has provided a complete technical support and deep foundation for smart education. With the advent of 5G, the smart learning environment based on the smart education system will be greatly developed. This paper presents the concepts and technical characteristics of 5G, explains the content of smart learning environment, analyzes the technical composition of smart learning environment in the context of 5G, and probes into the challenges faced by smart learning environment in 5G environment. The purpose of this paper is to provide new ideas for creating a smart learning environment in the context of 5G.
Promising new Techniques for Computer Network Traffic Classification: A Survey

This paper aims to give an overview of the application of image processing to network traffic analysis, including a description of the essence of the most important works in the last 15 years. The importance of efficient, automated analysis of network traffic is growing especially today, when huge volumes of diverse data need to be quickly processed. With the rapid development of artificial intelligence in the field of image processing, it seems logical to use it for analyzing network traffic image data. Recent results on this topic are very promising.
Lsragan: Generating Multifarious Color Photographes From Sketch

Image translation is to translate source domain image into target domain image. Generative Adversarial Networks (GANs) have achieved appealing performances on one-to-one image translation. The task of sketch translating to real-world images is a one-to-many task. In this paper, a new translation network, named Latent Space Regularization Attention GAN (LSRAGAN), is proposed to generate multifarious color photographes from sketch. We add Gaussian prior and latent variable from target domain to generator to realize multi-modal mapping. Attention weighting is implemented on ResNet blocks in generator. L1-constrained multi-layer perceptual loss is incorporated in conditional GAN. In addition, we present a latent space regularization (LSR) loss to force generator pay attention to the influence of latent code vector, which makes the generated images more diverse. Experiments demonstrate that our method outperforms state-of-the-arts in both quantitative and visual performances.
Scalable and approximate program dependence analysis

Program dependence is a fundamental concept to many software engineering tasks, yet the traditional dependence analysis struggles to cope with common modern development practices such as multi-lingual implementations and use of third-party libraries. While Observation-based Slicing (ORBS) solves these issues and produces an accurate slice, it has a scalability problem due to the need to build and execute the target program multiple times. We would like to propose a radical change of perspective: a useful dependence analysis needs to be scalable even if it approximates the dependency. Our goal is a scalable approximate program dependence analysis via estimating the likelihood of dependence. We claim that 1) using external information such as lexical analysis or a development history, 2) learning dependence model from partial observations, and 3) merging static, and observation-based approach would assist the proposition. We expect that our technique would introduce a new perspective of program dependence analysis into the likelihood of dependence. It would also broaden the capability of the dependence analysis towards large and complex software.
Stein Variational Gradient Descent with Variance Reduction

Probabilistic inference is a common and important task in statistical machine learning. The recently proposed Stein variational gradient descent (SVGD) is a generic Bayesian inference method that has been shown to be successfully applied in a wide range of contexts, especially in dealing with large datasets, where existing probabilistic inference methods have been known to be ineffective. In a large-scale data setting, SVGD employs the mini-batch strategy but its mini-batch estimator has large variance, hence compromising its estimation quality in practice. To this end, we propose in this paper a generic SVGD-based inference method that can significantly reduce the variance of mini-batch estimator when working with large datasets. Our experiments on 14 datasets show that the proposed method enjoys substantial and consistent improvements compared with baseline methods in binary classification task and its pseudo-online learning setting, and regression task. Furthermore, our framework is generic and applicable to a wide range of probabilistic inference problems such as in Bayesian neural networks and Markov random fields.
ICT-competency Training for Foreign Language Teachers of Electrical Engineering Curricula

The present paper considers the professional activities modification of foreign language teachers at engineering universities within ICT-based environment, including the new demands for their competencies. The modular content and methodical aspects of a professional development course "ICT in FL training for electrical engineering students" is presented.
A color map to compare reactions tools in interactive systems

In this paper we study whether visualizations based on color maps can encourage the intuitive interpretation of detailed descriptions, as the ones proposed in the formal model UPRISE, designed to analyze interactive system components, such as reaction tools, which allow users to provide reactions. We carried out a between-subjects experiment where 56 participants had to group 6 systems according to similarity using either color maps or textual descriptions. Results showed that color maps seem to favour inter-user agreement in comparison and grouping tasks.
Evaluation of Voice Mimicking Using I-Vector Framework

Intentional speech modifications by mimicking cause degradation in speaker verification systems. Improving the robustness of speaker verification systems against most competent mimicked speech is extremely important. The fusion of mel-frequency cepstral coefficient (MFCC)-based i-vectors and phonation-based features is utilized to identify the most competent imitator who imitates a target voice in the proposed work. Two scoring mechanisms namely, deep neural network (DNN) and dynamic time warping (DTW) has been employed. The phonation features are computed from the characteristics related to perturbation measures of fundamental frequency and amplitude of speech The proposed system evaluates the competence of artists in mimicking voices target voices and ranks them according to the metric obtained from the scoring mechanism. The results of the scoring frameworks are evaluated through a perception test conducted on twenty listeners. If the artist with the highest mean opinion score is identified as rank-1 by the proposed system, a hit occurs. The performance is evaluated using top X-hit criteria on a mimicry dataset. On the top-2 rate, the DNN-based experiment reports an accuracy of 72.72% with i-vector-phonation feature fusion.
Intelligent Video Computer Tracking System

This paper describes the target tracking system development. This system based on neural network, which help to select necessary method of target tracking.
Should I Stay or Should I Grow? A Dynamic Self-Governed Growth for Determining Hidden Layer Size in a Multilayer Perceptron

A novel dynamic self-governed growth algorithm inspired from population dynamics is introduced in a Multi-Layer Perceptron (MLP). This allows the inclusion of a carrying capacity, which is the maximum population of hidden units that can be sustained in a single hidden layer. The inclusion of this constraint in combination with population dynamics provides a built-in mechanism for a dynamic growth rate. The proposed approach is used in parallel with direct performance feedback from the network to modulate the growth rate of the hidden layer. This algorithm incrementally adds units to the hidden layer up to a point where the complexity of the task no longer requires further addition. The MLP is extended with the growing algorithm and its adaptability is tested by subjecting the network to increasing levels of task complexity for the n-bit problem. Using fixed rules that dictate both the size of a fixed layer MLP (fMLP) and the upper bound carrying capacity of the growing MLP (gMLP), the resulting topologies are directly compared for the n-bit problem. In short, the results suggest that even if an upper boundary of the carrying capacity is set by a fixed rule, the growing algorithm is capable of converging to less than the predicted number of units required for solving the given task. With the majority of trials growing to the same number of hidden units regardless of the rule used. This effect is consistent across the specified rules and levels of task complexity for the n-bit problem.
IoT4Fun rapid prototyping tools for Toy User Interfaces

A Toy User Interface (ToyUI) is a setup that combines smart toy components with other hardware and software components to enhance the player’s experience. We propose a novel Rapid Prototyping Tool to facilitate the development of smart toy components so that creative teams can focus on design improvements. IoT4Fun is a hardware toolkit that supports distributed data collection through wireless communication and contactless identification. It can support a range of play and interface features and limit personal data acquisition by collecting motion tracking information. We compare IoT4Fun single and modular toolkits by embedding them into ToyUI prototypes. The modular toolkit demonstrates more appropriate than the single-board version to design a variety of smart toy components when embedded into different shapes and sizes. A technical assessment compares battery consumption and identifies vulnerabilities of the modular toolkit. Ongoing improvements cover miniaturization, robustness, and designing a secure Application Programming Interface for easier development.
A Novel Global Energy and Local Energy-Based Legendre Polynomial Approximation for Image Segmentation

Active contour model (ACM) is a powerful segmentation method based on differential equation. This paper proposes a novel adaptive ACM to segment those intensity inhomogeneity images. Firstly, a novel signed pressure force function is presented with Legendre polynomials to control curve contraction. Legendre polynomials can approximate regional intensities corresponding to evolving curve. Secondly, global term of our model characterizes difference of Legendre coefficients, and local energy term characterizes fitting evolution curve of interested region. Final contour evolution will minimize the energy function. Thirdly, a correction term is employed to improve the performance of curve evolution according to the initial contour position, so wherever the initial contour being in the image, the object boundaries can be detected. Fourthly, our model combines the advantages of two classical models such as good topological changes and computational simplicity. The new model can classify regions with similar intensity values. Compared with traditional models, experimental results show effectiveness and efficiently of the new model.
Multivariate Top-Coding for Statistical Disclosure Limitation

One of the most challenging problems for national statistical agencies is how to release to the public microdata sets with a large number of attributes while keeping the disclosure risk of sensitive information of data subjects under control. When statistical agencies alter microdata in order to limit the disclosure risk, they need to take into account relationships between the variables to produce a good quality public data set. Hence, Statistical Disclosure Limitation (SDL) methods should not be univariate (treating each variable independently of others), but preferably multivariate, that is, handling several variables at the same time. Statistical agencies are often concerned about disclosure risk associated with the extreme values of numerical variables. Thus, such observations are often top or bottom-coded in the public use files. Top-coding consists of the substitution of extreme observations of the numerical variable by a threshold, for example, by the 99th percentile of the corresponding variable. Bottom coding is defined similarly but applies to the values in the lower tail of the distribution. We argue that a univariate form of top/bottom-coding may not offer adequate protection for some subpopulations which are different in terms of a top-coded variable from other subpopulations or the whole population. In this paper, we propose a multivariate form of top-coding based on clustering the variables into groups according to some metric of closeness between the variables and then forming the rules for the multivariate top-codes using techniques of Association Rule Mining within the clusters of variables obtained on the previous step. Bottom-coding procedures can be defined in a similar way. We illustrate our method on a genuine multivariate data set of realistic size.
Automatic Policy Decomposition through Abstract State Space Dynamic Specialization

Significant progress has been made recently in deep reinforcement learning in the development of options. This idea consists in learning policies (or macro of actions) for sub-goals. An important bottleneck of this approach is that these options are often available as actions everywhere in the state space, hence, potentially enlarging the action space to search for the optimal policy. In this paper, we propose to use the fact that the state space is rarely fully connected, but instead has regions of highly connected states with fewer links between those regions. Our proposed model extends deep Q-Learning network (DQN) by splitting the top layers into multiple heads each specializing in learning the dynamics of a particular region of the state space as well as the optimal policy for that region. The state prediction quality of each head is used to determine which head is the local expert, rating its contribution to the current state's policy. We show that this approach is able to learn something similar to options and generalized value function, providing a promising alternative to the current approach.
The Pedagogy of User Experience: Methods, Tools, and Approaches

Teaching user experience (UX) is complex and challenging. UX is interdisciplinary and claimed by a variety of fields, Technical and Professional Communication being one. While many UX positions include skills and responsibilities that align with skills traditionally taught in technical communicator programs, not all programs have broadened to include an explicit focus on UX. This workshop brings together academics and practitioners interested in sharing and expanding their understanding of teaching UX. By focusing on teaching challenges and sharing methods, tools, and approaches, the goals of this workshop are to create connections and community to sustain ongoing collaboration to improve UX pedagogy and provide participants with resources and examples they can implement in their courses and programs.
Improving Generalization Performance of Adaptive Learning Rate by Switching from Block Diagonal Matrix Preconditioning to SGD

Deep Neural Networks (DNNs) are widely used for various applications. Although adaptive learning rate algorithms are attractive for DNN training, their theoretical performance remains unclear. In fact, published analyses consider only simple optimization settings such as convex optimization, none of which are applicable to DNN training. This paper proposes TSO-ALRA, a two-stage optimizer using an adaptive learning rate algorithm; it is based on a full analysis of two approaches that do suit DNNs: parameter updates along geodesics on the statistical manifold and covariance structure of gradients. Our analysis reveals that the diagonal approximation used by existing adaptive learning rate algorithms inevitably degrades their efficiency. In addition, our analysis suggests that adaptive learning rate algorithms suffer drops in generalization performance in the last phase of training. To overcome these problems, TSO-ALRA combines an effective approximation technique and a switching strategy. Our experiments on several models and datasets show that TSO-ALRA efficiently converges with high generalization performance.
Term Weight Algorithm Oriented Terms: Low Frequency Rather Than Little Occurrences

Term weight algorithms based on inverse document analysis are widely used in the expression of characteristic information for text. According to the finding that frequently occurring terms always cover less feature information for the text, the terms with lower frequency will be endowed higher weight. However, the terms with little occurrences always display unimportant information or even error information, such as rare terms and misspelled terms. To tackle such a problem, this paper proposed a novel term weight algorithm that focuses on the terms with low frequency rather than little occurrences. With the statistics based on non-homogeneous compression of term frequency, the action of terms with concerned frequency will be highlighted. And logarithmic function combined with the number of terms with the same frequency is utilized to weight the terms with different frequency based on different compression intervals. Comparing with TF-IDF and SIF, the proposed approach has a similar performance with SIF and a little better than TF-IDF. According to the difference among such methods, a finding shows that the term with a low frequency rather than little occurrences may dominate the feature information of the text.