Reviewerly/exHarmony huggingface dataset:
Accelerating Stochastic Gradient Descent Based Matrix Factorization on FPGA

Matrix Factorization (MF) based on Stochastic Gradient Descent (SGD) is a powerful machine learning technique to derive hidden features of objects from observations. In this article, we design a highly parallel architecture based on Field-Programmable Gate Array (FPGA) to accelerate the training process of the SGD-based MF algorithm. We identify the challenges for the acceleration and propose novel algorithmic optimizations to overcome them. By transforming the SGD-based MF algorithm into a bipartite graph processing problem, we propose a 3-level hierarchical partitioning scheme that enables conflict-minimizing scheduling and processing of edges to achieve significant speedup. First, we develop a fast heuristic graph partitioning approach to partition the bipartite graph into induced subgraphs; this enables to efficiently use the on-chip memory resources of FPGA for data reuse and completely hide the data communication between FPGA and external memory. Second, we partition all the edges of each subgraph into non-overlapping matchings to extract the maximum parallelism. Third, we propose a batching algorithm to schedule the execution of the edges inside each matching to reduce the memory access conflicts to the on-chip RAMs of FPGA. Compared with non-optimized FPGA-based baseline designs, the proposed optimizations result in up to 60× data dependency reduction, 4.2× bank conflict reduction, and 15.4× speedup. We evaluate the performance of our design using a state-of-the-art FPGA device. Experimental results show that our FPGA accelerator sustains a high computing throughput of up to 217 billion floating-point operations per second (GFLOPS) for training very large real-life sparse matrices. Compared with highly-optimized GPU-based accelerators, our FPGA accelerator achieves up to 12.7× speedup. Based on our optimization methodology, we also implement a software-based design on a multi-core platform, which demonstrates 1.3× speedup compared with the state-of-the-art multi-core implementation.
Are the Blockchain-Based Patents Sustainable for Increasing Firm Value?

Blockchain technology is changing conventional online transaction systems by eliminating payment gateway firms. The blockchain technology is highly attractive and has earned a lot of attention from investors and firms. To protect blockchain technology, firms acquire a patent of blockchain for enhancing the value of their blockchain technology. However, the sustainable value for a patent of blockchain has not been clearly explored. For this reason, our research attempted to explore the relationship between a patent of blockchain and firm value. We used a real options theory and built robust empirical tests based on United State Patents and Trademark Office (USPTO) data. We collected the patents of blockchains from 2014 to 2018 and matched financial data from the Compustat database. In total, we found 153 panel observations. Our results suggest that a firm’s patent of blockchain originality and t-1 lagged effects for a firm’s patent of blockchain generality are positively associated with firm value in general. In addition, the sustainable value for the patent of blockchain affects firms differently based on their industry. We found that the sustainable value for the patent of blockchain originality was positively and exclusively associated with the software industry, while the sustainable value for the patent of blockchain generality was positively and exclusively associated with the hardware industry.
Predicting Louisiana Public High School Dropout through Imbalanced Learning Techniques

This study is motivated by the magnitude of the problem of Louisiana high school dropout and its negative impacts on individual and public well-being. Our goal is to predict students who are at risk of high school dropout, by examining Louisiana administrative dataset. Due to the imbalanced nature of the dataset, imbalanced learning techniques including resampling, case weighting, and cost-sensitive learning have been applied to enhance the prediction performance on the rare class. Performance metrics used in this study are F-measure, recall and precision of the rare class. We compare the performance of several machine learning algorithms such as neural networks, decision trees and bagging trees in combination with the imbalanced learning approaches using an administrative dataset of size of 366k+ from Louisiana Department of Education. Experiments show that application of imbalanced learning methods produces good results on recall but decreases precision, whereas base classifiers without regard of imbalanced data handling gives better precision but poor recall. Overall application of imbalanced learning techniques is beneficial, yet more studies are desired to improve precision.
Multiscale Multipath Ensemble Convolutional Neural Network

Most convolutional neural network (CNN) models require large parameter quantity, high computational consumption, and deeper layers to achieve better performance, which limit their further applications. For improvement, a lightweight multiscale multipath ensemble CNN (MSME-CNN) is proposed. First, the shallow-layer and deep-layer convolution output features are directly concatenated to form a multipath ensemble mode, which can not only avoid gradient vanishing but also fuse the multilayer features. In addition, each path of the network is composed of multiscale low-rank convolution kernels in parallel. This structure can extract multiscale features of the input and improve the feature extraction ability of the network. Meanwhile, the convolution kernel low-rank approximation can effectively compress the model complexity. Furthermore, the proposed sparse connection mechanism of the convolution kernel helps to reduce the complexity, so that higher classification accuracy can be obtained with less parameters and computational load. Finally, the linear sparse bottleneck structure is used to fuse multiscale features and compress the convolution channel, which further improves the network performance. Experiments of four commonly used image recognition datasets verify the superiority of MSME-CNN to several baseline models.
An Adaptive Method for Vein Recognition Enhancement Using Deep Learning

Authentication methods based on some human traits including fingerprints, face, and palm prints, have developed significantly, and currently are sophisticated enough to be reliably considered for identification purposes. As state-of-the-art techniques, computational methods have recently been introduced to extract vein patterns for identification purposes. However, these methods suffer in a lack of image enhancement which the vein pattern cannot be uncovered properly. To address image enhancement weaknesses, this paper proposes a new image enhancement method based on deep learning, which can improve the robustness of vein recognition and feature extraction. Making use of deep neural network techniques, a sparse auto-encoder algorithm is applied to develop an adaptive method for image enhancement. We use a pair of synchronised colour and near-infrared images for verifying the outcome of the proposed method and measure the length of veins. The experimental results on 605 colour images and 605 infrared images from 66 forearms, 66 thighs, 33 chests, 33 ankles, and 50 random internet images show that the proposed enhancement method significantly improves the feature extraction procedure.
Single-Image Dehazing Using Color Attenuation Prior Based on Haze-Lines

In this paper, we propose a new single-image dehazing method for synthetic and real-world hazy images. Based on the color attenuation prior, this proposed dehazing method improves it in two aspects. First, we estimate the atmospheric light with the haze-lines prior, which is based on the observation that pixel values of a hazy image can be modeled as lines in the RGB color space that intersects at the air-light. Second, the dynamic scattering coefficient, which is an exponential function of image depth, is proposed to replace the constant scattering coefficient. Experimental results demonstrate that the dehazed image of proposed algorithm is clearer and more natural than that of the color attenuation prior. The proposed algorithm can effectively improve the effect of dehazing.
Value of data meets IT security – assessing IT security risks in data-driven value chains

Digitalization forces manufacturing companies to shift towards customer-oriented, highly data-driven forms of value creation. This results in a changing IT security risk landscape as data becomes an attractive target for adversaries leading to an increasing number of attacks. In order to successfully protect data, it is essential that it is assessed in an integrated manner. Although IT security and data-based value creation have been studied by large research bodies, the existing literature fails to provide guidance on IT security risk analysis in data-based value chains. To contribute to the closure of this research gap, we propose a modeling approach which allocates different data types to value activities and analyses the data types in relation to the properties of relevant IT security risks. The evaluation, conducted with industry experts, reveals that it is not only a company's primary assets that are of concern but also less important data types subject to significant levels of exposure that bear considerable IT security risks.
Data Augmentation Based on Vowel Stretch for Improving Children's Speech Recognition

Prolongation is a speech disfluency that lengthens some portions of speech utterances. It is frequently observed in children's spontaneous speech, while it is rare in read speech. To make acoustic models more robust to children's spontaneous speech, collecting a large amount of children's speech data containing prolongation is usually required, which is very impractical in many cases. To tackle this problem, we propose a novel data augmentation method that virtually generates additional data by simulating prolongation. The method inserts pseudo frames into specific positions of speech utterances to simulate prolongation. The acoustic features of the inserted frames are calculated from the original frames on both sides. This is based on our analysis that many of vowels are actually stretched in children's spontaneous speech. Our proposed procedure can generate partially stretched utterances with low computational costs, unlike a conventional speed or tempo perturbation method that extends and shrinks entire utterances at a uniform rate. The effectiveness of the proposed method were confirmed with the experiments of acoustic model adaptations, in which our proposed method focusing on vowel stretch showed consistent improvement compared with conventional speed and tempo perturbation approach.
Entangled Simultaneity Versus Classical Interactivity in Communication Complexity

In 1999 Raz demonstrated a partial function that had an efficient quantum two-way communication protocol but no efficient classical two-way protocol and asked whether there existed a function with an efficient quantum one-way protocol, but still no efficient classical two-way protocol. In 2010 Klartag and Regev demonstrated such a function and asked whether there existed a function with an efficient quantum simultaneous-messages protocol, but still no efficient classical two-way protocol. In this work we answer the latter question affirmatively and present a partial function Shape that can be computed by a protocol sending entangled simultaneous messages of poly-logarithmic size, and whose classical two-way complexity is lower bounded by a polynomial.
Estimation of linear motion in dense crowd videos using Langevin model

Expert and intelligent systems are highly popular for designing cross-domain autonomous systems. Computer vision aided by expert decision-making systems are widely used to automate tasks that are normally carried out manually. For example, in the traditional way of traffic monitoring, signals are either controlled through predefined set-ups or with the help of visual observations. Even though some of the modern cities employ sensor-based surveillance at large, full automation is still far from the desirable accuracy. This is more challenging for monitoring high-density crowds that often cause unwanted situations due to sudden changes in dynamics. It has been shown in this paper that the movement of a dense crowd can be approximated using well-known physics-based models. Such a modeling can help to understand the overall crowd behavior. In accomplishing this, we have introduced a computer vision guided expert system with the help of a Langevin equation-based force model to represent the linear flow of the crowd, particularly in situations when the density is high. One of the primary contributions of our proposed model is its computational efficiency, particularly when a timely decision can help to avoid unwanted situations. Our proposed three-term force model is capable of predicting the positions of the group of key-points in a video frame leading to a significant computational gain. We have carried out several experiments on publicly available videos as well as our own videos to validate the claims in terms of accuracy as well as computational gain. It has been observed that the proposed physics-based model outperforms the existing systems with a 4−6% improvement in the segmentation accuracy. Moreover, we have achieved multi-fold computational gain. We believe the proposed work, when supported by appropriate post-processing, can be used to develop crowd monitoring applications.
An End-to-End Attention-Based Neural Model for Complementary Clothing Matching

In modern society, people tend to prefer fashionable and decent outfits that can meet more than basic physiological needs. In fact, a proper outfit usually relies on good matching among complementary fashion items (e.g., the top, bottom, and shoes) that compose it, which thus propels us to investigate the automatic complementary clothing matching scheme. However, this is non-trivial due to the following challenges. First, the main challenge lies in how to accurately model the compatibility between complementary fashion items (e.g., the top and bottom) that come from the heterogeneous spaces with multi-modalities (e.g., the visual modality and textual modality). Second, since different features (e.g., the color, style, and pattern) of fashion items may contribute differently to compatibility modeling, how to encode the confidence of different pairwise features presents a tough challenge. Third, how to jointly learn the latent representation of multi-modal data and the compatibility between complementary fashion items contributes to the last challenge. Toward this end, in this work, we present an end-to-end attention-based neural framework for the compatibility modeling, where we introduce a feature-level attention model to adaptively learn the confidence for different pairwise features. Extensive experiments on a public available real-world dataset show the superiority of our model over state-of-the-art methods.
Image Blind Denoising Using a Generative Adversarial Network for LED Chip Visual Localization

The noise in an LED chip image directly affects its visual positioning accuracy and efficiency. The existing denoising method can improve the image visual quality after removing the noise, but the improvement of the recognition rate and the positioning accuracy of the chip is not apparent. To solve this problem, this paper proposes a convolutional neural network combined with generative adversarial networks framework, the (CNN-GAN-GAN)-based blind denoiser, comprising three subnetworks. First, a noise extraction subnetwork with the proposed adaptive residual dense block extracts noise information from noisy images. Then, the noise modeling subnetwork with a dual-discriminator learns the noise distribution and constructs a paired training dataset. Finally, the blind denoising subnetwork with a hybrid loss function is trained for denoising. Contrast experiments show the proposed method can effectively suppress image noise and artifacts and meet the requirements of LED chip visual positioning accuracy while ensuring chip recognition rate.
A Novel Approach to Blockchain-Based Digital Identity System

In today’s life, the demand for privacy and transparency is everywhere. Blockchain is going to lead the modern world with its revolutionary technology because of its security, transparency altogether. Identity is an important aspect in our daily life as a citizen and digital identity will play an important role to make our life easy and hassle free. The use of blockchain in digital identity is important to keep it secure and undoubtedly prevents the violation of privacy. However, there is lack of works that includes the most vital bio-metric information of an individual for creating a blockchain-based digital identity. In this paper, we have proposed a system on blockchain-based digital identity for individuals using bio-information. We have implemented our proposed digital identity system using ethereum smart contract. The system has fulfilled all the criteria of an identity system. Our results show that an intruder cannot access the personal data of a citizen; any unauthorized access attempt is denied instantly, thereby ensuring the privacy of private citizen data. All of the functionalities and the security issues have been tested. Our proposed blockchain-based digital identity system can be useful for the government of a country to provide its citizen a highly secured digital identity. It will make the life of all citizens hassle free without requiring them to carry any paper document.
Real-Time Data Streaming Algorithms and Processing Technologies: A Survey

With the ever-increasing generation of data, there exists a plethora of real-time streaming algorithms proposed in the literature. Enterprises are moving from massive data to quick data; therefore, they will need to use and maybe extend the latest innovative technologies for real-time stream processing. To serve this purpose the paper surveys and classifies the existing streaming data algorithms and techniques. It presents a categorization of existing recent data streaming algorithms that solve the well-known concept drift research problem. In addition, it lists the various datasets used for the purpose of validation. Finally, it highlights the remaining open research inquiries in this field.
ConceptGraph: A Formal Model for Interpretation and Reasoning During Visual Analysis

Abstract In order to discuss the kinds of reasoning a visualization supports and the conclusions that can be drawn within the analysis context, a theoretical framework is needed that enables a formal treatment of the reasoning process. Such a model needs to encompass three stages of the visualization pipeline: encoding, decoding and interpretation. The encoding details how data are transformed into a visualization and what can be seen in the visualization. The decoding explains how humans construct graphical contexts inside the depicted visualization and how they interpret them assigning meaning to displayed structures according to a formal reasoning strategy. In the presented model, we adapt and combine theories for the different steps into a unified formal framework such that the analysis process is modelled as an assignment of meaning to displayed structures according to a formal reasoning strategy. Additionally, we propose the ConceptGraph, a combined graph‐based representation of the finite‐state transducers resulting from the three stages, that can be used to formalize and understand the reasoning process. We apply the new model to several visualization types and investigate reasoning strategies for various tasks.
A Design of an Anti-Phishing Training System Collaborated with Multiple Organizations

Phishing is a dangerous threat to organizations. A sender of a phishing email pretends to be a trusted person to steal valuable information, including personal identity data and credentials. If a targeted organization is sent a large number of attack emails, many members will be stolen valuable information. As a result, there are cases where the damage spreads by attack techniques in which targeted attack emails are sent in chaining from the account to other organizations. It is difficult to defend from such an attack by a single organization alone. In this paper, we design a system that can share information about phishing attack emails quickly and perform an anti-phishing training between multiple organizations. This system semi-automatically detoxifies and anonymizes attack emails received by an organization and shares it with multiple organizations. Each organization becomes possible to perform semiautomatic and continuous anti-phishing training using current attacking information.
Real-Time Optimization of Dynamic Speed Scaling for Distributed Data Centers

This paper proposes a new distributed real-time optimization for MapReduce-style framework in emerging cloud platforms supporting dynamic speed scaling functions. Distinctively different from the existing MapReduce parallelism strategy with fixed specific data chuck sizes, the new approach is able to dynamically dispatch input data of adequate sizes and synthesize interim processing results according to applications and the state of data centers (DCs). The key idea is to decouple the optimizations of data dispatching, processing, and result aggregation without loss of optimality, by employing stochastic optimization techniques. Another important aspect is that we optimize the subproblem of data processing to leverage the energyand speed-configurability of DCs, by optimally deciding the number of servers to be activated at every DC and the CPU speeds of the activated servers. Evident from extensive simulations, the proposed approach is able to increase the throughput-cost ratio by up to 94.3%, as compared to existing initiatives, and substantially improve the throughput in the case of high-rate data streams.
A Cost-Sensitive Approach to Enhance the use of ML Classifiers in Software Testing Efforts

The use of Machine Learning (ML) classifiers to predict defective software modules are useful to help on planning software testing activities. Most of those studies use the accuracy as the main metric to evaluate the quality of the ML classifier. However, when unbalanced datasets are used to train and test the classifier, the ML model becomes biased. Biased ML models hide their real accuracy. In this context, this study proposes an approach to enhance the use of ML classifiers for predicting defective software modules even with unbalanced datasets. The results indicate: (1) a significant reduction on the number of false negatives; (2) a considerable gain on the efficacy of the software testing; (3) an increase of the number of modules correctly indicated as defective; however, there were also (4) an increase of the scope of the test suggested by the model; (5) a reduction of the software testing efficiency; (6) an increase of the number of the false positives; and (7) reduction of the overall accuracy. Therefore, the proposed approach imposes a trade-off to be considered when planning the software testing activities. Finally, this study also proposes an approach to help managers to deal with those trade-offs considering the resource constraints.