Reviewerly/exHarmony huggingface dataset:
A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization

Adversarial attack techniques in deep learning have been studied extensively due to its stealthiness to human eyes and potentially dangerous consequences when applied to real-life applications. However, current attack methods in black-box settings mainly employ a large number of queries for crafting their adversarial examples, hence making them very likely to be detected and responded by the target system (e.g., artificial intelligence (AI) service provider) due to its high traffic volume. A recent proposal able to address the large query problem utilizes a gradient-free approach based on Particle Swarm Optimization (PSO) algorithm. Unfortunately, this original approach tends to have a low attack success rate, possibly due to the model’s difficulty of escaping local optima. This obstacle can be overcome by employing a multi-group approach for PSO algorithm, by which the PSO particles can be redistributed, preventing them from being trapped in local optima. In this paper, we present a black-box adversarial attack which can significantly increase the success rate of PSO-based attack while maintaining a low number of query by launching the attack in a distributed manner. Attacks are executed from multiple nodes, disseminating queries among the nodes, hence reducing the possibility of being recognized by the target system while also increasing scalability. Furthermore, we utilize Multi-Group PSO with Random Redistribution (MGRR-PSO) for perturbation generation, performing better than the original approach against local optima, thus achieving a higher success rate. Additionally, we propose to efficiently remove excessive perturbation (i.e, perturbation pruning) by utilizing again the MGRR-PSO rather than a standard iterative method as used in the original approach. We perform five different experiments: comparing our attack’s performance with existing algorithms, testing in high-dimensional space in ImageNet dataset, examining our hyperparameters (i.e., particle size, number of clients, search boundary), and testing on real digital attack to Google Cloud Vision. Our attack proves to obtain a 100% success rate on MNIST and CIFAR-10 datasets and able to successfully fool Google Cloud Vision as a proof of the real digital attack by maintaining a lower query and wide applicability.
Brain Tissue Segmentation using Patch-wise M-net Convolutional Neural Network

The anatomical structures of the brain can be visualized and changes in the brain and delineating pathological regions are analyzed by automatic segmentation of brain tissues into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from magnetic resonance images (MRI). In this paper, we propose a patch-wise M-net architecture for the automatic segmentation of brain structures in MRI. In the proposed method, the limitations of conventional approaches can be overcome by the patch-wise M-net with more retention of local information. In our method, the slices from an MRI scan are divided into non-overlapping patches and then the nonoverlapping patches with their corresponding patches of ground truth are fed to the M-net model to train the network. It is shown that the segmentation performance has been improved with the proposed patch-wise M-net architecture as compared to state-of-the-art methods.
Breast cancer diagnosis and prediction model based on improved PSO-SVM based on gray relational analysis

The following topics are dealt with: learning (artificial intelligence); feature extraction; convolutional neural nets; power engineering computing; particle swarm optimisation; health care; Internet; neural nets; social networking (online); pattern clustering.
Analysis of Factors That Affect The Quality of E-Government Services: A Case Study in Ombudsman of the Republic of Indonesia

The Ombudsman of the Republic of Indonesia implements E-Government, one of which is through the government website. The website requires periodic evaluation to improve the quality of government services to the public. However, the Ombudsman of the Republic of Indonesia has not yet evaluated the service quality of the E-Government website based on user perceptions. The purpose of this study is to determine what factors affect the quality of the Ombudsman website service quality of the Republic of Indonesia to be used as further improvements. The sample used is 250 respondents who have used the Ombudsman website of the Republic of Indonesia. Sampling was done by using purposive sampling and accidental sampling technique. The method used is e-govqual and adds a dimension of perceived effectiveness. Data processing using SmartPLS version 3.2.8 with the PLS-SEM approach. The results of this study are the efficiency, reliability, trust and support of citizens have a significant effect on the quality of E-Government services. There is a relationship between the quality of E-Government services and perceived effectiveness. The results of this research can be used according to the needs of the relevant agencies.
Data Mining Approach for Determining Student Attention Pattern

This Research Full Paper presents the approach of traditional engineering analysis techniques on education. Data mining techniques have been successfully employed to extract hidden information from large data sets within various contexts. We hypothesized that data mining techniques can similarly be applied to large educational data sets to extract and analyze patterns and create insights. Specifically, we examined the degree to which standard data mining techniques can distinguish between different student attention patterns in large lectures in which personal computers were actively used. Our data set consists of electronically captured student attention data (on-task, off-task) that was recorded at 20 second intervals throughout each course lecture over one semester. With Institutional Review Board (IRB) approval, methods involved capturing student data via a backend monitoring system to reduce student awareness of monitoring and reduce false behavior changes during data collection periods. The data were originally captured in the form of images (screenshots), and image processing techniques were applied to extract student attention patterns in the form of zero (off-task) and one (on-task). We conducted descriptive statistical analysis to add other features such as characterization information (e.g., total logged in attention, average class period attention to the recorded data sets). For the data mining analysis, we used three different supervised machine learning classification algorithms: Support Vector Machine (SVM), Decision Tree, and K-Nearest Neighbors (KNN) for classifying the students' dataset. We classified the students into one of four different classes based on their attention pattern in the lecture class. Before applying each classification algorithm, feature extraction was performed. For this purpose, we used Haar wavelets, Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA) dimensionality-reduction techniques before applying the classification algorithm. Their performance is compared. Our results indicate high classification accuracies can be obtained using these dimensional reduction algorithms followed by classification algorithms. Our result highlights the importance of applying traditional engineering analysis techniques to educational data in order to provide engineering education insights.
Label group diffusion for image and image pair segmentation

Diffusion technique is powerful for semi-supervised image segmentation since the geometry of the data manifold can be captured by the affinity propagation. Conventional diffusion methods focus on single label, which however ignore interactions among labels. This workstudies a generalized diffusion framework that considers label group for diffusion (LGD). The proposed framework can effectively capture the interactions among image elements via tensor product graph (TPG). A multivariate affinity framework is proposed to learn on higher-order TPG. Label pair diffusion algorithm is naturally derivedfrom the framework by considering second-order affinity (LG(2)D). We theoretically show that conventional label diffusion is the simplest case of the proposed framework (LG(1)D). Extensive experiments on image segmentation and image pair co-segmentation datasets demonstrate the superior performance of the proposed framework.
Domain-aware Stacked AutoEncoders for zero-shot learning

Zero-shot learning (ZSL), which focuses on transferring the knowledge from the seen (source) classes to unseen (target) ones, is getting more and more attention in the computer vision community. However, there often has a large domain gap between the source and target classes, resulting in the projection domain shift problem. To this end, we propose a novel model, named Domain-aware Stacked AutoEncoders (DaSAE), that consists of two interactive stacked auto-encoders to learn the domain-aware projections for adapting source and target domains respectively. In each of them, the first-layer encoder aims to project a visual feature vector into the semantic space, and the second-layer encoder connects the semantic description of a sample with its label directly. Meanwhile, the two-layer decoders seek to reconstruct the visual representation from the label information and semantic description successively. Moreover, the manifold regularization that explores the manifold structure residing in the target data is integrated to the basic DaAE, which further improves the generalization ability of our model. Extensive experiments on the benchmark datasets clearly demonstrate that our DaSAE outperforms the state-of-the-art alternatives by the significant margins.
Classification of Text regions in a Document Image by Analyzing the properties of Connected Components

Document layout analysis is a mandatory step in order to develop an effective and complete document image processing system. In this step, an input document image is segmented into different regions. These regions are then classified as text or non-text. The non-text regions are further classified into different sub-classes like table, image, separator, graphic, chart etc., whereas text regions are classified as title, paragraph, header, footer, caption, drop-capital etc. In this paper, a connected component analysis based method is presented to classify a particular text region as title, heading, paragraph, drop-capital, header or footer. In doing so, the positional and size-based information of a region along with its alignment property is applied. To showcase the effectiveness of this method, the output of the segmentation system BINYAS is provided both with and without the present text region classification module for some sample document images taken from RDCL 2017 dataset.
A Decision Support System for Optimal Selection of Enterprise Information Security Preventative Actions

Types and complexity of information security related vulnerabilities are growing rapidly and present numerous challenges to the enterprises. One of the key challenges is to identify the optimal set of precautions with limited budget. Despite the fact that majority of enterprises have a budget constraint for installing and maintaining the protection systems, the majority of the previous work only focus on prioritization of security targets and do not consider the preventative actions and budget constraints. This article presents a decision support system (DSS) based on analytical hierarchical process and mixed integer programming techniques for optimal selection of enterprise information security preventative actions. The proposed approach enables maximizing the amount of risk prevented for a fixed amount of budget by identifying the optimal set of precautions. The new DSS also assists enterprise decision-makers in determining the minimum enterprise information security budget for a given level of risk. The main contribution of the paper is that it provides a risk management method to identify a multi-level threat model and the corresponding optimal combination of preventative actions for an enterprise while considering the budget constraints. The treemap information visualization technique is also integrated into the proposed method to improve information security related management decisions.
The ethics of zero-day exploits---

Are U.S. government employees behaving ethically when they stockpile software vulnerabilities?
FaaS2F: A Framework for Defining Execution-SLA in Serverless Computing

The emergence of serverless computing has brought significant advancements to the delivery of computing resources to cloud users. With the abstraction of infrastructure, platform, and execution environments, the administration overhead of these layers is shifted to the cloud provider. Thus, cloud users can focus on the application layer while relying on the cloud provider to provision and operate the underlying layers. Furthermore, desirable features such as autoscaling and high-availability are implemented by the cloud provider and adopted by the user's code at no extra overhead. Despite such advancements, as applications transition from monolithic stand-alone deployments to the ephemeral and stateless microservice model of serverless computing, significant challenges must be overcome. These challenges pertain to the uniqueness of the conceptual and implementation models of serverless computing. In this paper, we investigate the modeling of the Service Level Agreement (SLA) of serverless functions' executions. We highlight how serverless SLA fundamentally differs from earlier cloud delivery models. We then propose an approach to define SLA for serverless functions in terms of resource utilization fingerprints for functions' executions, and a method to assess if executions adhere to that SLA. We conclude by presenting the empirical validation of our proposed approach, which was able to detect Execution-SLA violations with accuracy exceeding 76% and up to 100%.
Semantics Through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation

Semantic segmentation is a crucial task for robot navigation and safety. However, current supervised methods require a large amount of pixelwise annotations to yield accurate results. Labeling is a tedious and time consuming process that has hampered progress in low altitude UAV applications. This paper makes an important step towards automatic annotation by introducing SegProp, a novel iterative flow-based method, with a direct connection to spectral clustering in space and time, to propagate the semantic labels to frames that lack human annotations. The labels are further used in semi-supervised learning scenarios. Motivated by the lack of a large video aerial dataset, we also introduce Ruralscapes, a new dataset with high resolution (4K) images and manually-annotated dense labels every 50 frames - the largest of its kind, to the best of our knowledge. Our novel SegProp automatically annotates the remaining unlabeled 98% of frames with an accuracy exceeding \(90\%\) (F-measure), significantly outperforming other state-of-the-art label propagation methods. Moreover, when integrating other methods as modules inside SegProp’s iterative label propagation loop, we achieve a significant boost over the baseline labels. Finally, we test SegProp in a full semi-supervised setting: we train several state-of-the-art deep neural networks on the SegProp-automatically-labeled training frames and test them on completely novel videos. We convincingly demonstrate, every time, a significant improvement over the supervised scenario.
The point regular automorphism groups of the Payne derived quadrangle of W(q)

In this paper, we completely determine the point regular automorphism groups of the Payne derived quadrangle of the symplectic quadrangle $W(q)$, $q$ odd. As a corollary, we show that the finite groups that act regularly on the points of a finite generalized quadrangle can have unbounded nilpotency class.
Automatic Recognition of Engineering Drawings and Maps

This chapter discusses the techniques related to the recognition of engineering drawings and maps. The main reason for the lack of success is that the algorithms needed to carry out automatic recognition of complex drawings tend to be unwieldy. The category, automatic recognition of maps, is the most rewarding, especially for urban planning and for gas, water, electric, and telephone utility management. Automatic digitizers based on these techniques are also presented, together with the state of the art in the application of drawing-recognition techniques. Grid locking is one such postprocessing technique and are discussed. To effectively use the design data contained in such drawings, automatic recognition of hand-drawn plant diagrams is considered as being an initial data input for a CAD system.
An Energy-Efficient Silicon Photonic-Assisted Deep Learning Accelerator for Big Data

Deep learning has become the most mainstream technology in artificial intelligence (AI) because it can be comparable to human performance in complex tasks. However, in the era of big data, the ever-increasing data volume and model scale makes deep learning require mighty computing power and acceptable energy costs. For electrical chips, including most deep learning accelerators, transistor performance limitations make it challenging to meet computing’s energy efficiency requirements. Silicon photonic devices are expected to replace transistors and become the mainstream components in computing architecture due to their advantages, such as low energy consumption, large bandwidth, and high speed. Therefore, we propose a silicon photonic-assisted deep learning accelerator for big data. The accelerator uses microring resonators (MRs) to form a photonic multiplication array. It combines photonic-specific wavelength division multiplexing (WDM) technology to achieve multiple parallel calculations of input feature maps and convolution kernels at the speed of light, providing the promise of energy efficiency and calculation speed improvement. The proposed accelerator achieves at least a 75x improvement in computational efficiency compared to the traditional electrical design.
Optical Flow guided Motion Template for Hand Gesture Recognition

Gesture representation specially hand gesture has a special role in the computer and human interaction community. Model-based and appearance-based methods are two primary techniques for hand gesture representation. Apart from these two, space-time features and motion-based approaches have gained quite impressive performance in various applications of action and gesture recognition. In space-time features, actions/gestures are considered as local spatiotemporal neighbourhood. But most space-time features are computationally expensive. Motion-based approaches mainly constitute optical flow and motion templates. Motion estimation of the image pixels is the key factor in optical flow, whereas, in motion-templates, video-wide temporal evaluation and their representations are widely used for action/gesture recognition. Both these methods have their own advantages and accordingly applied in the analysis of motion and related applications. In this paper, we tried to combine both and proposed a new method to get the advantages of individual methods in representing the temporal templates of a video by fusing the video dynamics into a single image. The main benefits of the technique are basically its simplicity, ease of implementation, competitive performance and efficiency.
Comparison of Memory Consumptive Against the Use of Various Image Formats for App Onboarding Animation Assets on Android with Lottie JSON

The usage of images or visuals to deliver information on digital products especially on mobile platforms has been widely used. Besides, it being able to attract users, the use of visual images and animated images can increase the brand, increase the trust ratio and strengthen user retention from the user. Various applications made by giant startup companies such as Google, Facebook, Twitter, and others, use visual or animation to deliver their product knowledge with “onboarding screen” or also called “walkthrough animation”. Animation is also used in some interactions to application user experience. For example, “love fireworks animation” when we do the action with like button on Twitter application. Lottie is the latest framework that was created by AirBNB company, which is an open source framework that is used to parse or convert from motion (motion graphic) vector graphics to output in JSON (JavaScript Notation Objects) file type. In its use, the Lottie framework is combined with the Bodymovin plugin that must be installed on the Adobe After Effect application. In this case, researchers want to compare the device resources usage by CPU, RAM, GPU and other performance parameters on applications that use and consumed Lottie assets in their onboarding between applications that used other motion graphic assets (APNG and GIF) on android applications. Also, researcher want to give suggest for developer, especially android mobile developer by the quantitative comparison result of type asset that use for improve their apps UI/UX. For the goal is make powerfull application interface and application experience with also inclusive powerful performance for every smartphone.
Comparison and Combination of Activation Functions in Broad Learning System

Activation function is a crucial component in artificial neural networks for its capability of converting linear function of input to complex nonlinear expression. It also plays an important role generating enhancement nodes in broad learning system(BLS). In this paper, we perform the comparison of 20 popular activation functions on different datasets in classification and regression. Among all selected activation functions, sigmoid leads to faster training process and greater approximation capability than others in general tasks. Meanwhile, the statistical analysis demonstrates that the type of activation function does not affect the performance of BLS too much. Afterwards, we assemble some best-performing activation functions to form a combination within convex restriction, which achieves better performance than corresponding base activation functions in standard BLS.
Using bundling to visualize multivariate urban mobility structure patterns in the São Paulo Metropolitan Area

Abstract Internet-based technologies such as IoT, GPS-based systems, and cellular networks enable the collection of geolocated mobility data of millions of people in large metropolitan areas. In addition, large, public datasets are made available on the Internet by open government programs, providing ways for citizens, NGOs, scientists, and public managers to perform a multitude of data analysis with the goal of better understanding the city dynamics to provide means for evidence-based public policymaking. However, it is challenging to visualize huge amounts of data from mobility datasets. Plotting raw trajectories on a map often causes data occlusion, impairing the visual analysis. Displaying the multiple attributes that these trajectories come with is an even larger challenge. One approach to solve this problem is trail bundling, which groups motion trails that are spatially close in a simplified representation. In this paper, we augment a recent bundling technique to support multi-attribute trail datasets for the visual analysis of urban mobility. Our case study is based on the travel survey from the São Paulo Metropolitan Area, which is one of the most intense traffic areas in the world. The results show that bundling helps the identification and analysis of various mobility patterns for different data attributes, such as peak hours, social strata, and transportation modes.