Reviewerly/exHarmony huggingface dataset:
Structured Pruning of Convolutional Neural Networks via L1 Regularization

Deep learning architecture has achieved amazing success in many areas with the recent advancements in convolutional neural networks (CNNs).However, real-time applications of CNNs are seriously hindered by the significant storage and computational costs.Structured pruning is a promising method to compress and accelerate CNNs and does not need special hardware or software for an auxiliary calculation.Here a simple strategy of structured pruning approach is proposed to crop unimportant filters or neurons automatically during the training stage.The proposed method introduces a mask for all filters or neurons to evaluate their importance.Thus the filters or neurons with zero mask are removed.To achieve this, the proposed method adopted L1 regularization to zero filters or neurons of CNNs.Experiments were conducted to assess the validity of this technique.The experiments showed that the proposed approach could crop 90.4%, 95.6% and 34.04% parameters on LeNet-5, VGG-16, and ResNet-32respectively, with a negligible loss of accuracy.
Secure TLBs

This paper focuses on a new attack vector in modern processors: the timing-based side and covert channel attacks due to the Translation Look-aside Buffers (TLBs). This paper first presents a novel three-step modeling approach that is used to exhaustively enumerate all possible TLB timing-based vulnerabilities. Building on the three-step model, this paper then shows how to automatically generate micro security benchmarks that test for the TLB vulnerabilities. After showing the insecurity of standard TLBs, two new secure TLB designs are presented: a Static-Partition (SP) TLB and a Random-Fill (RF) TLB. The new secure TLBs are evaluated using the Rocket Core implementation of the RISC-V processor architecture enhanced with the two new designs. The three-step model and the security benchmarks are used to analyze the security of the new designs in simulation. Based on the analysis, the proposed secure TLBs can defend not only against the previously publicized attacks but also against other new timing-based attacks in TLBs found using the new three-step model. The performance overhead is evaluated on an FPGA-based setup, and, for example, shows that the RF TLB has less than 10% overhead while defending all the attacks.
A multi-scheme semi-supervised regression approach

The production of vast amounts of data has increased the necessity of applying Machine Learning (ML) and Pattern Recognition (PR) methods that could perform accurate predictive performance without demanding much human effort for collecting and preparing the necessary data. Keeping in mind that annotating instances is one of the most time-consuming procedures during the learning phase of supervised approaches, the role of Semi-supervised Learning (SSL) schemes, which exploit both labeled and unlabeled data, is totally upgraded considering especially the real-word scenarios. The flexibility that is offered through such schemes about combining various learners for mining useful information through unlabeled instances allows the production of several variants of these schemes. Thus, the construction of generic approaches that could achieve robust learning behavior over problems that stem from different scientific fields is the target of current research. Our contribution through this work is the proposal of a Multi-scheme Semi-supervised regression approach (MSSRA) that examines some well-defined conditions about the outputs of each contained learner and provides its decisions to a meta-level learner to produce the final predictions. The results over twenty-five well-known datasets prove the better generalization behavior of the proposed algorithm against the supervised version of the meta-level learner and two state-of-the-art semi-supervised regression (SSR) algorithms.
Quannet: Joint Image Compression and Classification Over Channels with Limited Bandwidth

The performance of cloud based image classification depends critically on its allocated bandwidth. Traditional data compression methods can negatively impact classification accuracy under limited bandwidth. We investigate the design of bandwidth efficient quantization for image encoding and compression with minimum classification accuracy loss. This work develops a simple neural network framework for joint quantization and classification. The proposed 'QuanNet' can optimize the quantization intervals of JPEG2000 encoder to minimize the classification loss. We show that our quantizer optimization can achieve significant accuracy improvement for a given channel bandwidth. Similarly, significant bandwidth can be saved to achieve a desired accuracy for cloud based image classification.
Exploring the impact of blockchain on digitized Supply Chain flows: A literature review

Blockchain innovations are having a profound effect on the global financial industry, yet little is known about how the emerging technology can potentially disrupt other industries. The supply chain industry represents a prominent and very promising application scenario for blockchain to enable transparency among untrusted stakeholders as well as increased efficiency and traceability throughout production and distribution processes, however there is a lack of empirical research available in that field. The primary purpose of this research is to address that research gap by exploring the application of blockchain technologies in supply chains. First, this research examines recent literature to identify current inefficiencies in existing supply chain information technologies. Secondly, key features of blockchain are identified to provide a clear understanding of the technology. Thirdly, current applications are documented and categorized to identify where blockchain applications can have the most impact on the three flows of the supply chain. The analysis shows most of the process areas in supply chain that blockchain is expected to improve are informational, which given the large amount of interdependencies between flows, has a positive impact on the subsequent financial and material flows. Finally, recommendations for future research are given to address further research questions.
A two-stage minimax concave penalty based method in pruned AdaBoost ensemble

AdaBoost is a highly effective ensemble learning method that combines several weak learners to produce a strong committee with higher accuracy. However, similar to other ensemble methods, AdaBoost uses a large number of base learners to produce the final outcome while addressing high-dimensional data. Thus, it poses a critical challenge in the form of high memory-space consumption. Feature selection methods can significantly reduce dimensionality in regression and have been established to be applicable in ensemble pruning. By pruning the ensemble, it is possible to generate a simpler ensemble with fewer base learners but a higher accuracy. In this article, we propose the minimax concave penalty (MCP) function to prune an AdaBoost ensemble to simplify the model and improve its accuracy simultaneously. The MCP penalty function is compared with LASSO and SCAD in terms of performance in pruning the ensemble. Experiments performed on real datasets demonstrate that MCP-pruning outperforms the other two methods. It can reduce the ensemble size effectively, and generate marginally more accurate predictions than the unpruned AdaBoost model.
Single image super-resolution reconstruction based on multi-scale feature mapping adversarial network

Single image super-resolution (SISR) aims to reconstruct a high-resolution image from a degraded low-resolution image. In recent years, the super-resolution methods based on convolutional neural network (CNN) have achieved promising performance on SISR task, indicating that CNN is a viable approach to image super-resolution reconstruction. The one limitation of the current SISR methods is that many methods use the pixel-wise loss. It is well known that the pixel-wise loss cannot well recover high-frequency details even if the high peak signal-to-noise ratio (PSNR) can be obtained. Some other methods purely focus on restoring more details, which resulted in poor PSNR score and high-frequency noise. In this paper, we proposed a multi-component loss function based on pixel-wise loss, perceptual loss and adversarial loss for a multi-scale feature mapping generator network for SISR image reconstruction model. We evaluated our method on commonly used benchmarks and compared it with other SISR methods. The results showed that our method could achieve the better balance between the high-frequency detail and stable spatial structure generation.
Hypothesis Testing and Statistically-sound Pattern Mining

The availability of massive datasets has highlighted the need of computationally efficient and statistically-sound methods to extracts patterns while providing rigorous guarantees on the quality of the results, in particular with respect to false discoveries. In this tutorial we survey recent methods that properly combine computational and statistical considerations to efficiently mine statistically reliable patterns from large datasets. We start by introducing the fundamental concepts in statistical hypothesis testing, including conditional and unconditional tests, which may not be familiar to everyone in the data mining community. We then explain how the computational and statistical challenges in pattern mining have been tackled in different ways. Finally, we describe the application of these methods in areas such as market basket analysis, subgraph mining, social networks analysis, and cancer genomics.
Performance Analysis of SMOTE-based Oversampling Techniques When Dealing with Data Imbalance

Building classification models on imbalanced data proves to be a challenging task despite the multitude of available classifiers. The classifier bias towards the majority class can be ameliorated through various manners and with varying degrees of success. Oversampling minority or undersampling majority instances are prominent amongst these due to both their simplicity and effectiveness. Probably the most popular approach to oversampling is the well-known SMOTE algorithm, based on which numerous enhancement attempts were made. This paper aims to compare the performance of these, more complex, oversampling techniques with regard to the original on a wide array of problems. Additionally, it attempts to give insight into the behavior of different interpretations of the original algorithm apparent in the literature. In that regard, some interesting findings were made.
Permutations with fixed pattern densities

We study scaling limits of random permutations (“permutons”) constrained by having fixed densities of a finite number of patterns. We show that the limit shapes are determined by maximizing entropy over permutons with those constraints. In particular, we compute (exactly or numerically) the limit shapes with fixed 12 density, with fixed 12 and 123 densities, with fixed 12 density and the sum of 123 and 213 densities, and with fixed 123 and 321 densities. In the last case we explore a particular phase transition. To obtain our results, we also provide a description of permutons using a dynamic construction.
Current Challenges of Agile Hardware Development: What are Still the Pain Points Nowadays?

Abstract Originally developed for the software industry, agile development is being applied in the hardware field nowadays as well due to its benefits when having to deal with volatile, uncertain, complex and ambiguous (VUCA) conditions. However, certain complications arise when applying the concept of agility in the hardware. In this publication, based on the challenges identified by Ovesen in 2012, the current challenges of agile development for hardware are gathered using an embedded design approach, ensuring its actuality based on the latest surveys and empirical data. The current state regarding the challenges is displayed and its interrelations as well as their advancements compared to seven years ago is discussed. Moreover, an attempt to explain the difficulties of applying agile development is given by a hardware-related complexity model.
Searching for Extremist Content Online Using the Dark Crawler and Sentiment Analysis

Purpose – This chapter examines how sentiment analysis and web-crawling technology can be used to conduct large-scale data analyses of extremist content online.Methods/approach – The authors describe a customized web-crawler that was developed for the purpose of collecting, classifying, and interpreting extremist content online and on a large scale, followed by an overview of a relatively novel machine learning tool, sentiment analysis, which has sparked the interest of some researchers in the field of terrorism and extremism studies. The authors conclude with a discussion of what they believe is the future applicability of sentiment analysis within the online political violence research domain.Findings – In order to gain a broader understanding of online extremism, or to improve the means by which researchers and practitioners "search for a needle in a haystack," the authors recommend that social scientists continue to collaborate with computer scientists, combining sentiment analysis software with other classification tools and research methods, as well as validate sentiment analysis programs and adapt sentiment analysis software to new and evolving radical online spaces.Originality/value – This chapter provides researchers and practitioners who are faced with new challenges in detecting extremist content online with insights regarding the applicability of a specific set of machine learning techniques and research methods to conduct large-scale data analyses in the field of terrorism and extremism studies.
Behavior Analysis for Electronic Commerce Trading Systems: A Survey

With the rapid development of the Internet and the continuous progress of computer science and technology, Internet-based online trading has developed rapidly and improved the convenience of everyday life. Increasingly, people conduct business activities through online transactions and payment methods, and the development prospects for online transactions are quite broad. In addition, e-commerce trading systems have attracted considerable attention. Many studies have been conducted to analyze the behaviors of these systems. This paper focuses on analyzing both system behavior and user behavior in e-commerce trading systems. To reflect the characteristics of online trading systems, system behavior reflects the strength of the system, while user behavior reflects various user actions. Consistency, which is a basis for determining whether two objects are similar, provides a reference for designers and modelers. This paper also focuses on analyzing the consistency of system behavior, especially regarding the measurement and diagnosis of e-commerce trading systems. Then, we study the existing user behavior analysis methods for e-commerce trading systems. We divide user behaviors into four groups: web browsing behavior, keystroke behavior, network transaction behavior, and mobile terminal behavior. The performance and correlation analysis techniques of these methods are discussed and compared in detail. Finally, we provide ideas for future research involving behavior analysis for online trading systems.
Medical Image Analysis With Deep Neural Networks

Deep learning is an essential method of machine learning. Deep learning is rapidly suitable for the most sophisticated stage of a technology, prominent to enriched performance in numerous medical applications. The latest growth in machine learning, specifically with respect to deep learning, aids in recognition, classification, and computation of patterns in medical images. The main aim of these improvements is the ability to derive feature representation from learned data rather than designing those features by hand from domain-specific knowledge. In deep learning, the bottom-level network represents a low-level feature representation while the top-level network represents the output feature information. The computation of a deep learning network is faster with cheap hardware. In this chapter, we present the essentials of deep learning methods with convolutional neural networks and analyze their achievements in medical image analysis, such as in deep feature representation, detection, segmentation, classification, and prediction. Finally, we conclude by a discussion of research challenges and indicate future directions for further enhancements.
Auto-Zooming CNN-Based Framework for Real-Time Pedestrian Detection in Outdoor Surveillance Videos

One of the challenges faced by surveillance video analysis is to detect objects from the frames. For outdoor surveillance, detection of small object like pedestrian is of particular interest. This paper proposes a fast, lightweight, and auto-zooming-based framework for small pedestrian detection. An attentive virtual auto-zooming scheme is proposed to adaptively zoom-in the input frame by splitting it into non-overlapped tiles and pay attention to the only important tiles. Without sacrificing detection performance, we have obtained a fully convolutional pedestrian detection model which can be run on low computational resources. It has been trained on an outdoor surveillance dataset and evaluated on two specially prepared testing sets of small (far) pedestrians in outdoor surveillance. We have compared our framework performance with different single-step customized pedestrian detectors as well as the two-step detector faster R-CNN. The results validate the efficiency of our framework.
Computationally Efficient Autonomous Overtaking on Highways

This paper studies the problem of optimal overtaking of a slow-moving leading vehicle in the presence of oncoming and/or adjacent vehicles with varying but known longitudinal speeds. A computationally efficient modeling approach is introduced, in which the overtaking problem is formulated by sampling in relative distance to the leading vehicle, replacing velocity state with its inverse and utilizing a nonlinear change of control variables. These three steps achieve a computationally efficient nonlinear control problem that can be solved using the sequential quadratic programming. Measures have been taken to ensure the feasibility of the nonlinear problem, even when the sequential quadratic programming iterates are stopped prematurely. A case study is presented, where this new formulation is compared with the previously published formulations in terms of solution quality and computation time.
Towards Better Uncertainty Sampling: Active Learning with Multiple Views for Deep Convolutional Neural Network

Convolutional neural network (CNN) has been successfully applied to many fields, such as image classification and object detection. It relies on huge amount of data. However, labelling a large amount of data is expensive. Active learning is one of the approaches to alleviate the labelling effort. We propose a new active learning approach for CNN. Different from existing active learning algorithms for CNN, first, the active query strategy is measured from multiple views, not only the last output of CNN; second, multiple views are obtained from multiple hidden layers in CNN, not from other related data or models. We evaluate our approach on three widely used datasets: Fashion-MNIST, SVHN and CIFAR-10. Experimental results show that the proposed method outperforms baseline methods in image classification.
The Advantages of Blockchain Technology in Commercial Bank Operation and Management

Blockchain technology as a new database technology, is gradually applied to many fields in society. As every country in the world pays more attentions to the development of the financial industry, blockchain technology becomes more widespread in the businesses of commercial banks. This paper discusses the advantages of blockchain technology for commercial banks from the following aspects. At first, the development of blockchain technology and the theory of blockchain technology are introduced. Next, the advantages of blockchain technology are analyzed in bill operation, cross-border payment operation and asset securitization business of commercial bank. Finally, the conclusions show that blockchain technology can decrease transaction cost for both sides and increase operating efficiency of commercial bank in operation and management.
Impact of the conceptual model's representation format on identifying and understanding user stories

Eliciting user stories is a major challenge for agile development approaches. Conceptual models are used to support the identification of user stories and increase their understanding. In many companies, existing model documentation stored as either use cases or BPMN models is available. However, these two types of business process models might not be equally effective for elicitation tasks due to their formats. We address the effectiveness of different elicitation tasks when supported either with visual or textual conceptual model. Since the agile literature shows little attention to reusing existing BPMN documentation, we propose several hypotheses to compare it to the use of textual use case models. We conducted an experiment to compare the effectiveness of the two business process formats: textual use cases and visual BPMN models. We studied their effects on three elicitation tasks: identifying user stories and understanding their execution-order and integration dependencies. The subjects better understood execution-order dependencies when visual input in the form of BPMN models was provided. The performance of the other two tasks showed no statistical differences. We addressed an important problem of user story elicitation: which informationally equivalent model (visual BPMN or textual use case) is more effective when identifying and understanding user stories.
Texture CNN for Histopathological Image Classification

Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variability. The advances in computing have brought this type of system closer to reality. However, datasets of Histopathological Images (HI) from biopsies are quite small and unbalanced what makes difficult to use modern machine learning techniques such as deep learning. In this paper we propose a compact architecture based on texture filters that has fewer parameters than traditional deep models but is able to capture the difference between malignant and benign tissues with relative accuracy. The experimental results on the BreakHis dataset have show that the proposed texture CNN achieves almost 90% of accuracy for classifying benign and malignant tissues.