Reviewerly/exHarmony huggingface dataset:
Extracting user requirements from online reviews for product design: A supportive framework for designers

With the development of social networks and online shopping sites, we can easily obtain valuable feedback from users. The crucial question is how to utilize customer feedback for supporting the development of product design in the early phases. For product design, understanding user needs or user requirements would help designers design a better product for users. Therefore, user requirements is considered as an important role in product design. This paper proposes a framework for assessing user requirements from websites to support designers. They key idea is to extract user requirements from online customer reviews and represent them into an appropriate form for designers. We show that a support system consisting of feature aspect extraction, opinion summarization, and sentiment classification would be an useful tool for product design. Experimental results on a the data collected from the Amazon website show that supporting of opinion extraction techniques would be useful for designers in product design.
Towards High-Quality Intrinsic Images in the Wild

We address the intrinsic image decomposition problem for separating an image into its intrinsic images, i.e, a reflectance layer and a shading layer. Although this problem has been studied for decades, it remains a significant challenge, particularly for real-world images. In this paper, we present a novel method for estimating high-quality intrinsic images for real-world images. Our method is built upon two observations on real-world images: (i) reflectance is generally sparse and there are limited number of reflectance values in an image; (ii) shading usually has locally smooth transition. Based on the two observations, we formulate the decomposition problem into an optimization framework, where we encourage the reflectance sparseness by globally confining the number of reflectance discontinuities among neighboring pixels using an L_0 norm, and utilize a total variation for maintaining locally smooth shading. We employ two benchmark datasets and perform various experiments to evaluate our method. Experimental results show that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.
A Multifaceted Surrogate Model for Search-Based Procedural Content Generation

This paper proposes a framework for the procedural generation of level and ruleset components of games via a surrogate model that assesses their quality and complementarity. The surrogate model combines level and ruleset elements as input and gameplay outcomes as output, thus constructing a mapping between three different facets of games. Using this model as a surrogate for expensive gameplay simulations, a search-based generator can adapt content toward a target gameplay outcome. Using a shooter game as the target domain, this paper explores how parameters of the players' character classes can be mapped to both the level's representation and the gameplay outcomes of balance and match duration. The surrogate model is built on a deep learning architecture, trained on a large corpus of randomly generated sets of levels, classes, and simulations from game playing agents. Results show that a search-based generative approach can adapt character classes, levels, or both toward designer-specified targets. The model can thus act as a design assistant or be integrated in a mixed-initiative tool. Most importantly, the combination of three game facets into the model allows it to identify the synergies between levels, rules, and gameplay and orchestrate the generation of the former two toward desired outcomes.
A Communication Channel Density Estimating Generative Adversarial Network

Autoencoder-based communication systems use neural network channel models to backwardly propagate message reconstruction error gradients across an approximation of the physical communication channel. In this work, we develop and test a new generative adversarial network (GAN) architecture for the purpose of training a stochastic channel approximating neural network. In previous research, investigators have focused on additive white Gaussian noise (AWGN) channels and/or simplified Rayleigh fading channels, both of which are linear and have well defined analytic solutions. Given that training a neural network is computationally expensive, channel approximation networks-and more generally the autoencoder systems-should be evaluated in communication environments that are traditionally difficult. To that end, our investigation focuses on channels that contain a combination of non-linear amplifier distortion, pulse shape filtering, intersymbol interference, frequency-dependent group delay, multipath, and non-Gaussian statistics. Each of our models are trained without any prior knowledge of the channel. We show that the trained models have learned to generalize over an arbitrary amplifier drive level and constellation alphabet. We demonstrate the versatility of our GAN architecture by comparing the marginal probability density function of several channel simulations with that of their corresponding neural network approximations.
Maximizing the Expected Information Gain of Cognitive Modeling via Design Optimization

To ensure robust scientific conclusions, cognitive modelers should optimize planned experimental designs a priori in order to maximize the expected information gain for answering the substantive question of interest. Both from the perspective of philosophy of science, but also within classical and Bayesian statistics, it is crucial to tailor empirical studies to the specific cognitive models under investigation before collecting any new data. In practice, methods such as design optimization, classical power analysis, and Bayesian design analysis provide indispensable tools for planning and designing informative experiments. Given that cognitive models provide precise predictions for future observations, we especially highlight the benefits of model-based Monte Carlo simulations to judge the expected information gain provided by different possible designs for cognitive modeling.
Internet-Marketing in the Sphere of Higher Education

Long before this time, universities adhered to a certain strategy, which implied special criteria for admission, but at the same time limited marketing approaches and communications, which significantly influenced the methodology itself and had a unidirectional nature of development. With the gradual introduction of various changes in the technological sphere, higher educational institutions strengthened their positions, rethinking their strategies, began to adapt more to changing conditions and began to apply marketing approaches to meet the needs of clients in information and communication. The changed approach was obligatory for survival of the universities in the demand-side market. The article discusses the essence of the use of marketing in the field of educational services of higher education institutions, also analyzes the means and tools of marketing, allowing the university to consolidate its position in the market and increase competitiveness. The features of the promotion of educational services and the potential of marketing in the field of higher professional education are being advanced.
State-of-the-art quantum computing simulators: Features, optimizations, and improvements for D-GM

Quantum computing is strongly limited by physical implementations of quantum computers. Currently, development of quantum computing algorithms is carried out by analytic or simulation procedures while quantum computers are not widely available. Although quantum computing simulation is parallel by nature, spatial and temporal complexity are major performance hazards. The exponential increase in memory and global access to quantum states in simulations limit not only the number of qubits but also quantum transformations. Considering these scenarios, six quantum simulators are studied, in order to find out their main features and simulation results considering implementations classified in single/multiple processor and accelerator architectures. The main strategies used to improve performance in these architectures provide relevant parameters for the development of the new extension of the Distributed Geometric Machine environment proposed in this paper. Shor's and Grover's algorithms are simulated and compared to previous D-GM results and to LIQUi|⟩'s simulator, showing improvements as relative speedups up to 22.2 × in relation to the previous version and up to 910.46 × when compared to LIQUi|⟩.
Text-Attentional Conditional Generative Adversarial Network for Super-Resolution of Text Images

Text in natural scene images are often faced with low-resolution problem, which brings significant difficulties to many text-related tasks such as text detection and recognition. In this paper, we propose a novel text-attentional Conditional Generative Adversarial Network (cGAN) model for text image super-resolution (SR). The model enhances the original cGAN by introducing effective channel and spatial attention mechanisms based on the proposed Residual Dense Channel Attention Block and text/non-text segmentation information, which focus the model on the text regions instead of the background of the image to learn more effective representations of text and achieve better text super-resolution result. The proposed model achieves state-of-the-art performances on public text image super-resolution dataset.
DV8: Automated Architecture Analysis Tool Suites

This paper present our tool suite called DV8. The objective of DV8 is to measure software modularity, detect architecture anti-patterns as technical debts, quantify the maintenance cost of each instance of an anti-pattern, and enable return on investment analyses of architectural debts. Different from other tools, DV8 integrates data from both source code and revision history. We now elaborate on each of DV8's capabilities.
Zernike moments-based fingerprint recognition using weighted-support vector machine

Moments play an important role in image analysis and invariant pattern recognition. There are two types: orthogonal moments and non-orthogonal moments. Orthogonal moments perform better than non-orthogonal moments, they have properties such as robustness to image noise and geometrical invariant properties such as scale, rotation and translation. In this paper, an improvement in fingerprint recognition is done by using the Non-subsampled contourlet transform (NSCT) and the Zernike moments (ZMs). NSCT decomposes the fingerprint images into NSCT subbands. Thereafter, ZMs are used to evaluate the features of fingerprint images. Thereafter, feature selection technique is applied to select potential features from the obtained features using coefficient of determination. Thereafter, a well-known weighted-support vector machine is also used to train and test the evaluated features. Extensive experiments reveal that the proposed technique achieves significant improvement over the existing techniques in terms of accuracy, sensitivity, specificity, [Formula: see text]-measure, kappa statistics and execution time.
On the polynomiality of finding <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e37" altimg="si4.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:msup></mml:math>DMDGP re-orders

In Cassioli et al. (2015), the complexity of finding KDMDGP re-orders was stated to be NP-complete by inclusion, which fails to provide a complete picture. In this paper we show that this problem is indeed NP-complete for K=1, but it is in P for each fixed K≥2.
A Performance Comparison of Container Networking Alternatives

In recent years, containerization technologies have rapidly gained momentum in the industry for the purpose of managing and deploying applications and services at the expense of virtual machines. Despite its numerous benefits, several challenges still remain to be addressed from the inter-container communication perspective, notably as existing container networking fails to meet the requirements of container-based applications in terms of isolation, portability and performance. As a result, trade-offs between these properties must be considered, typically resulting in performance being sacrificed. In this article, we investigate the impact of using RDMA (Remote Direct Memory Access) for inter-container communication. To this end, we perform a comparison between various relevant inter-container solutions with RoCE. The experimentation results demonstrate that RDMA-based networking solutions outperform existing networking solutions in terms of throughput, latency, and CPU overhead.
Comparative study of socio-economic barriers to development of digital competences during formation of human capital in Russian Arctic

Abstract The article is dedicated to analysis of problems and barriers hindering the development of human capital in the Arctic region, starting from the stage when skills and abilities, in particular those connected to digital competences and information and communication technologies, are acquired by future professionals throughout modern higher education. We have presented the results for a target group comprising students of the Murmansk Arctic State University (Apatity Branch). We have carried out a survey of the target group, including via the online GoogleForms service, analyzing the answers given by the respondents. Analysis of the results allowed to identify the socio-economic barriers hindering the development of digital competences. We have provided the results of comparative analysis with the previously obtained characteristics of students of Peter the Great St. Petersburg Polytechnic University, finding both similarities and differences in certain aspects of formation of digital competences of the respondents of the two given regions.
Image Blur Classification and Unintentional Blur Removal

Blur is a general image degradation caused by low-quality cameras or intentional photographing for highlighting moving or salient objects. However, most blur classifiers just classify images into blur and sharp, which cannot distinguish the intentional blurred images from the unintentional blurred ones. Some unintentional blurred images are too valuable to discard directly. In this paper, we propose a robust image blur classifier to classify images into sharp, intentional blur, and unintentional blur. The basic idea of identifying the blur of a pixel being intentional or unintentional is that whether the blur occurs on a salient and semantic meaningful object. This inspired us to employ cues of blur, saliency, and semantic segmentation. We use spatial pyramid pooling to extract global features. Then, a random forest is used to conduct classification. We further detect the unintentional blur pixels by incorporating the cues into a conditional random field (CRF). The intentional blur image can be generated by pasting the deblurred unintentional blur regions back to the blur image. We conduct image blur classification on UBICD dataset and unintentional blur removal on different types of unintentional blur images. The experimental results show superior performance of image blur classification and the promising results of unintentional blur removal of our method.
Distance Distribution in Reed-Solomon Codes

Let Fq be the finite field of q elements. In this paper we obtain bounds on the following counting problem: given a polynomial f (x) ∈ Fq[x] of degree k + m and a non-negative integer r, count the number of polynomials g(x) ∈ Fq[x] of degree at most k - 1 such that f (x) + g(x) has exactly r roots in Fq. Previously, explicit formulas were known only for the cases m = 0, 1, 2. As an application, we obtain an asymptotic formula on the list size of the standard Reed-Solomon code [q, k, q - k + 1] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> .
Communication Network in an Agile Distributed Software Development Team

Challenges in implementing Agile Distributed Software Development (ADSD) are well documented. A primary challenge lies in achieving the necessary degree of cooperation and collaboration, which are founded on good communication. Agile methodology historically relies on frequent face-to-face communication, which is infeasible in distributed development. Recent work suggests that current communication tools can support practices that accommodate ADSD. We present an experience report of a distributed team and describe its interpersonal communication network, which is notably more limited when connections are across an eight-hour time difference. As a limited study, we provide context to allow better comparisons with other work.
Automatic Modulation Classification Based on Bispectrum and CNN

Automatic modulation classification (AMC) is crucial for the subsequent analysis and processing of modulated signals. In this paper, we propose an AMC method based on bispectrum and convolutional neural network (CNN) Alexnet model (Bispectrum-Alexnet) after studying the latest achievements in related fields. The proposed Bispectrum-Alexnet method feeds the CNN with amplitude spectrums of bispectrum (ASB) belonging to different signals, enabling it to learn and extract deep features automatically from those images, and finally obtain the ideal label. To present our work more clearly, firstly, we analyze the feasibility of using ASB for classification, especially focusing on the physical meaning of ASB. Secondly, an introduction to the complete algorithm implementation is given, including bispectrum estimation and CNN architecture. Finally, this method was designed to distinguish among 7 popular modulation signals (BPSK, 2ASK, 2FSK, 4FSK, 8FSK, LFM, OFDM) to validate the recognition performance. Simulation results show that the overall accuracy ratio can reach 97.7% when the symbolic signal to noise ratio is no less than 5dB.
Single Image Dehazing Using Dark Channel Fusion and Haze Density Weight

Aiming at the defect that many traditional single image dehazing algorithms fail in sky areas and suffer from the darkness and residual artifacts, a method of single image dehazing based on dark channel fusion and haze density weight is proposed in this paper. Firstly, the method of dark channel fusion, which can effectively ensure that the pixel intensity of the sky areas in the dark channel map is as low as that of non-sky areas, is proposed to approximate the dark channel operation. Considering that the haze density is different in different regions of the hazy image, we then calculate the haze density weight to estimate reliable transmission. For obtaining more accurate transmission and atmospheric light, we utilize the guided filter to refine them. Consequently, the haze-free image can be restored by the atmospheric scattering model. Experimental results show that the proposed method can obviously eliminate the color cast and distortion phenomenon in sky regions and the details of restored image become more distinct after removing the residual artifacts. Moreover, the brightness, visibility and contrast are improved to a large degree.
HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images

We propose an automatic method for generating high-quality annotations for depth-based hand segmentation, and introduce a large-scale hand segmentation dataset. Existing datasets are typically limited to a single hand. By exploiting the visual cues given by an RGBD sensor and a pair of colored gloves, we automatically generate dense annotations for two hand segmentation. This lowers the cost/complexity of creating high quality datasets, and makes it easy to expand the dataset in the future. We further show that existing datasets, even with data augmentation, are not sufficient to train a hand segmentation algorithm that can distinguish two hands. Source and datasets are publicly available at the project page.
Performance Evaluation of Proof-of-Work and Collatz Conjecture Consensus Algorithms

Blockchain is the underlying technology of Bitcoin that allows a peer-to-peer distributed ledger with security and immutability. The core of a blockchain is the consensus mechanism that sets the rule for nodes in handling the shared data. Implementation of the consensus algorithm depends on the nature of targeted business environment. In this research, the performance of two consensus algorithms, Proof-of-Work (PoW) and Proof-of-Collatz Conjecture (PCC), are studied in the context of a private blockchain. A quantitative analysis on the execution time, deployment time, and latency time are done for 1, 10, 100, 1000, and 10000 transactions and the results are presented. The results shows that PCC takes only (1/1000) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> of the execution time that is required for PoW for these different sets of transactions. In addition, these timings are recorded for ten repeated executions for the same sets of transactions, and found that PCC has a nearly consistent execution time.
Scene Text Recognition via Gated Cascade Attention

Scene text recognition is very challenging due to the complex background, low resolution, perspective distortion and curved placement, etc. Most of the state-of-the-art methods adopt the attention-based encoder-decoder framework, and usually get suboptimal recognition performance for challenging text images due to the misalignment between attention region and target character region. In this paper, a novel module, named Gated Cascade Attention Module (GCAM), is proposed to increase the alignment precision of attention in a cascade way. Moreover, a channel and spatial attention module is introduced into the encoder to extract more discriminative features for text recognition. By assembling these two modules, a novel scene text recognizer is developed, and extensive experiments demonstrate it can achieve state-of-the-art results on multiple benchmarks of regular and irregular text images.
Modeling the Clause-Level Structure to Multimodal Sentiment Analysis via Reinforcement Learning

In this paper, we propose a novel approach to multimodal sentiment analysis with focus on both textual and acoustic modalities. Especially, we utilize deep reinforcement learning to explore the clause-level structure in an utterance. On the basis, we perform multimodal interactions at clause-level to model hierarchical interactive representation for multimodal senitment analysis. Detailed evaluation on two benchmark datasets demonstrates the great effectiveness of our approach over several state-of-the-art baselines.
Inferring Netflix User Experience from Broadband Network Measurement

Netflix is the largest video-streaming provider in the world today, with over 148 million subscribers and accounting for over 20% of broadband traffic in most developed countries. Internet Service Providers (ISPs) are acutely aware of the need to provide good video streaming experience to viewers, but are poorly equipped to measure and monitor per-stream quality. In this paper, we measure and analyze Netflix playback data from multiple households, develop a practical and scalable method to correlate network activity with client playback behavior, and provide a means for ISPs to infer per-stream Netflix experience from broadband traffic patterns. Our specific contributions are: (1) We develop a measurement tool for collecting network flow activity and client playback metrics, deploy it in 9 households and our lab to gather data for about 8000 Netflix video streams under various network conditions, and release the data to the public; (2) We analyze our data to highlight correlation between active flows and video playback phase, and between network chunk transfers and playback buffer health, during both regular-play and trick-play of video; (3) We develop a method for the ISP to infer Netflix user experience in terms of buffer fill-time, video bitrate and throughput, and detect playback buffer depletion and quality degradation events. ISPs can use our methods to measure, monitor, and manage Netflix user experience in real-time.