Reviewerly/exHarmony huggingface dataset:
HAGN: Hierarchical Attention Guided Network for Crowd Counting

In recent years, deep learning based crowd counting networks have achieved significant progress.However, most of them generate rough crowd density maps due to low-resolution features used for estimating crowd distribution, which affects the performance of crowd counting.To solve this problem, in this paper, we propose a Hierarchical Attention Guided Network (HAGN) for crowd counting.We apply the first 13 layers of VGG-16 to extract base features.Then, the extracted features are processed by the Hierarchical Attention Mechanism (HAM), which guided the extracted features to enlarge step by step via our proposed attention guided branch.Finally, the outputs of HAM are fed to 1 × 1 convolutional layer for final crowd density estimation.Experiments are performed on ShanghaiTech and UCF-QNRF datasets, and our HAGN achieves promising performance compared with the other state-of-the-art methods on crowd counting and crowd localization, respectively.
Discovering Underlying Plans Based on Shallow Models

Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or action models in hand. Previous approaches either discover plans by maximally “matching” observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing action models to best explain the observed actions, assuming that complete action models are available. In real-world applications, however, target plans are often not from plan libraries, and complete action models are often not available, since building complete sets of plans and complete action models are often difficult or expensive. In this article, we view plan libraries as corpora and learn vector representations of actions using the corpora; we then discover target plans based on the vector representations. Specifically, we propose two approaches, DUP and RNNPlanner, to discover target plans based on vector representations of actions. DUP explores the EM-style (Expectation Maximization) framework to capture local contexts of actions and discover target plans by optimizing the probability of target plans, while RNNPlanner aims to leverage long-short term contexts of actions based on RNNs (Recurrent Neural Networks) framework to help recognize target plans. In the experiments, we empirically show that our approaches are capable of discovering underlying plans that are not from plan libraries without requiring action models provided. We demonstrate the effectiveness of our approaches by comparing its performance to traditional plan recognition approaches in three planning domains. We also compare DUP and RNNPlanner to see their advantages and disadvantages.
Optimized Feature Integration and Minimized Search Space in Content Based Image Retrieval

The semantic gap between the user request and retrieval result is an important but unsolved problem in the content-based image retrieval (CBIR) systems. This paper introduces a new multi-level structure in a CBIR system to bridge the semantic gap using the combination of low-level visual contents of an image. The initial stage of the proposed system depends on the statistical information of the color images which gives the most prominent images for the further level of the process. In the next step, low-level features such as color and texture details are extracted using dominant color descriptor (DCD) and radial mean local binary pattern over the query and selected images. Subsequently, Particle Swarm Optimization (PSO) is applied over both the color and texture similarity measure between the query and selected images. Finally, this multi-level system is experimented on OT-scene and Corel-10k databases to assess the performance and it gives 78.43% and 52.34% average precision rate.
Recognizing Induced Emotions With Only One Feature: A Novel Color Histogram-Based System

Emotions can be evoked in humans by images. Previous reports on Recognition of Emotions induced by Visual Content of images (REVC) mainly focused on numerous features to improve recognition performance. To devise a more robust REVC system, this paper examines the performance of a wide range of classifiers using color histogram as a single feature. Different numbers of color histogram bins in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces are considered in the examination and the overall classification performance is compared across the bin sizes. This investigation shows that features are not the only important factors affecting the performance of REVC systems, but also the type of classifiers and their parameters. This study shows that the HSV color space is better suited than the RGB color space for REVC systems. This paper proposes a new optimization algorithm called Optimizing Parameters of Ensemble RUSboosted Tree (OPERT) to boost the performance of the REVC system. Furthermore, a novel REVC system called Color histogram with Optimized RUSboosted Tree (CORT) is introduced. It is shown that our method is simpler, faster, and more efficient than the state-of-the-art, while providing comparable recognition performance. The robustness of the CORT system is validated over three different image datasets.
Nanoparticles in groundwater of the Qujia deposit, eastern China: Prospecting significance for deep-seated ore resources

The characterization of nanoparticles in groundwater is recognized as an important tool that can be applied to mineral exploration. In this paper, we investigate the concealed orebodies of the Qujia gold deposit in the Jiaojia orefield of eastern China by analyzing ore-related nanoparticles in groundwater using transmission electron microscopy. The nanoparticles contain variable amounts of ore-related elements (e.g., Ag, Cu, Zn, La, Ce, Pb, Pt, and S). Some of these elements are rarely found in natural settings unrelated to orebodies or mineral extraction, but are strongly linked to concealed orebodies in the study area. These nanoparticles, which are diverse in their shapes, aggregated features, and degrees of crystallinity, can be grouped in terms of composition as: (i) native metal nanoparticles, (ii) metal-based nanoparticles, and (iii) carrier nanoparticles associated with trace elements. We propose that these three main types of nanoparticles enable the efficient transport of ore-forming elements in groundwater. The results of this study provide insights into the migratory behaviour of ore-related nanoparticles in groundwater, and our methods could be applied for detecting deep-seated mineral deposits.
Blockchain

Blockchain, having proved its commercial applications, is receiving increasing interest in the context of ODL system, which has flourished with the support of ICTs. ODL system cannot remain insulated from the associated potential risks of ignoring it and opportunities of embracing it. ODL institutions tend to gain competitive advantage by enhancing capabilities of combining resources. Intangible resources such as technological innovations are more strategically significant than tangible resources. ODL system is yet to taste the benefits of using blockchain. ODL institutions might use it as a resource to fill the gaps between needs, priorities, models, and practices for effective decision making anticipating future trends. The issue of considering it as a resource of competitive advantage for ODL system faces inadequacy of relevant literature. This chapter is an exploratory study considering blockchain as a resource of competitive advantage for ODL system. The significance of the chapter will be for the professionals, policymakers, researchers, governments, and regulators.
Predictive Models with Resampling: A Comparative Study of Machine Learning Algorithms and their Performances on Handling Imbalanced Datasets

Class imbalance is a problem of crucial challenge in many real-world machine learning applications. Traditional machine learning algorithms are likely to produce good accuracy scores on such datasets due to an obvious bias towards the majority class. Thus, accuracy as a measure of performance for algorithms working on imbalanced data is not very clearly defined since the classifier has poor predictive accuracy over the minority class. While previous work has used several resampling techniques to aid in improving the predictive accuracy of the minority class, in this study, we explore and compare the effectiveness of the Synthetic Minority Oversampling and Random Oversampling techniques over multiple learning algorithms and resampling ratios for eight different performance measures against two datasets from diverse domains such as medicine and engineering. The results of this study show that the effectiveness of these resampling techniques is a multivariate function relative to both the learning algorithms and the resampling ratios, as well as the coherent characteristics of datasets. The choice of performance measures to evaluate models built using these resampling techniques also vary, thus giving us more relevant information useful for future research and applications.
Laplacian Coordinates: Theory and Methods for Seeded Image Segmentation

Seeded segmentation methods have gained a lot of attention due to their good performance in fragmenting complex images, easy usability and synergism with graph-based representations. These methods usually rely on sophisticated computational tools whose performance strongly depends on how good the training data reflect a sought image pattern. Moreover, poor adherence to the image contours, lack of unique solution, and high computational cost are other common issues present in most seeded segmentation methods. In this work we introduce Laplacian Coordinates, a quadratic energy minimization framework that tackles the issues above in an effective and mathematically sound manner. The proposed formulation builds upon graph Laplacian operators, quadratic energy functions, and fast minimization schemes to produce highly accurate segmentations. Moreover, the presented energy functions are not prone to local minima, i.e., the solution is guaranteed to be globally optimal, a trait not present in most image segmentation methods. Another key property is that the minimization procedure leads to a constrained sparse linear system of equations, enabling the segmentation of high-resolution images at interactive rates. The effectiveness of Laplacian Coordinates is attested by a comprehensive set of comparisons involving nine state-of-the-art methods and several benchmarks extensively used in the image segmentation literature.
Image interpolation with adaptive <i>k</i> ‐nearest neighbours search and random non‐linear regression

Learning-based image interpolation methods have been proved to be effective in image interpolation. In this study, the authors propose an accurate image interpolation with adaptive k-nearest neighbour searching and non-linear regression. The proposed method aims to find k-nearest neighbours of the input image patch and use them to learn the non-linear mapping between low-resolution and high-resolution image patches. To be specific, they first divide the training image patches into many subspaces, then they utilise an adaptive robust and precise k nearest neighbour searching scheme with proposed normalised Gaussian similarity to find the k nearest neighbours in the matched subspace. The selected k image patch pairs are then used to learn the non-linear regression model through an extreme learning machine. Furthermore, the proposed interpolation method is a cascade framework that consists of two stages. Stage 2 takes the results of Stage 1 as input to further improve the performance. Extensive experimental results on commonly used test images and image datasets indicate that their proposed algorithm obtains competitive performance against the state-of-the-art methods both in terms of objective evaluation values and the subjective effect of reconstructed images.
Machine level classification using support vector machine

In the current era, content based image retrieval based on pattern recognition and classification using machine learning paradigm is an innovative way. In order to retrieve high resolution security for satellite images Support Vector Machine (SVM) a machine learning paradigm is helpful for learning process and for pattern recognition and classification; ensemble methods give better machine learning results. In this paper, SVM based on random subspace and boosting ensemble learning is proposed for very high resolution security for satellite image retrieval and secure from the Mobile Ad-hoc Network (MANET). To taught SVM ensemble model is use to recognize the images that almost all similar informative for dynamic learning. Bias-weighting systems implement to direct the ensemble model to pay a lot of attention on the positive illustration than the negative ones. The UC Merced security for satellite image from dataset is used for experimental work. Accuracy and error rate are found to be precise. The tentative effects illustrate that the proposed model derived enhanced retrieval accurateness at the optimum level as well as significantly more effective and secure data from the MANET. The comparisons for the existing approaches are evaluated by using precision and recall measurements.
Blockchain and IoT for Delivery Assurance on Supply Chain (BIDAS)

Blockchain technology introduced immutable distributed ledgers to the technology landscape. With the current popularity and success of the blockchain technology, researchers are looking for further implementation opportunities for the tamper-free ledgers. The supply chain industry has been a beneficiary of blockchain technology with its rich set of participants. From financing all the way to tracking containers, there are opportunities in the supply chain industry to utilize distributed ledger technologies. Furthermore, Internet of Things (IoT) and wireless enabled devices add value by providing services based on their sensor capabilities. Our study is on the synergy of blockchain technology and IoT to provide quality data to supply chains. We believe that the next generation of IoT services would only be possible with the democratic autonomy of devices in an environment where privacy, trust, transparency, and security are provided. With blockchain and IoT, there is a potential in rearchitecting supply chain systems. With the addition of IoT capabilities, there are opportunities to create better business models.In this paper, we focus on delivery assurance in the supply chain industry, and we propose a novel blockchain-based transparent delivery framework for creating solutions that record and share data on the interaction of business participants. This framework helps create solutions that include handover and monitoring aspects of the delivery businesses and adds several benefits that come with the blockchain technology.
Spam Mail Filtering Using Data Mining Approach

This chapter presents an overview of spam email as a serious problem in our internet world and creates a spam filter that reduces the previous weaknesses and provides better identification accuracy with less complexity. Since J48 decision tree is a widely used classification technique due to its simple structure, higher classification accuracy, and lower time complexity, it is used as a spam mail classifier here. Now, with lower complexity, it becomes difficult to get higher accuracy in the case of large number of records. In order to overcome this problem, particle swarm optimization is used here to optimize the spam base dataset, thus optimizing the decision tree model as well as reducing the time complexity. Once the records have been standardized, the decision tree is again used to check the accuracy of the classification. The chapter presents a study on various spam-related issues, various filters used, related work, and potential spam-filtering scope.
VulHunter: An Automated Vulnerability Detection System Based on Deep Learning and Bytecode

The automatic detection of software vulnerability is undoubtedly an important research problem. However, existing solutions heavily rely on human experts to extract features and many security vulnerabilities may be missed (i.e., high false negative rate). In this paper, we propose a deep learning and bytecode based vulnerability detection system called Vulnerability Hunter (VulHunter) to relieve human experts from the tedious and subjective task of manually defining features. To the best of knowledge, we are the first to leverage bytecode features to represent vulnerabilities. VulHunter uses the bytecode, which is the intermediate representation output by the source code, as input to the neural networks and then calculate the similarity between the target program and vulnerability templates to determine whether it is vulnerable. We detect SQL injection and Cross Site Scripting (XSS) vulnerabilities in PHP software to evaluate the effectiveness of VulHunter. Experimental results show that VulHunter achieves more than 88% (SQL injection) and 95% (XSS) F1-measure when detecting a single type of vulnerability, as well as more than 90% F1-measure when detecting mixed types of vulnerabilities. In addition, VulHunter has lower false positive rate (FPR) and false negative rate (FNR) than existing approaches or tools. In practice, we apply VulHunter to three real PHP software (SEACMS, ZZCMS and CMS Made Simple) and detect five vulnerabilities in which three have not been disclosed before.
Semi-supervised Learning over Streaming Data using MOA

Machine learning algorithms for data streams usually suppose that all data examples available for learning are strictly labeled. Unfortunately, in real-world scenarios, data examples are not always labeled. Semi-supervised learning is a challenging task to learn using labeled and unlabeled data at the same time. It is especially relevant in the context of data streams, where the data is generated in real-time, and the labels may be missing due to various factors (e.g., network delay, errors during the communication between sensors, expensive labeling process, and others). In this paper, we present two novel approaches to handle missing labels for classification learning in data streams, namely cluster-and-label and self-training. We discuss the strengths and weaknesses of each solution to establish a baseline to evaluate semi-supervised learning techniques in data streams. These methods are implemented inside the MOA (Massive Online Analysis) open-source software as an internal benchmark component, to help researchers to run experimental comparisons on semi-supervised learning on data streams easily.
Modulation classification in the presence of adjacent channel interference using convolutional neural networks

Summary This paper investigates a vital issue in wireless communication systems, which is the modulation classification. A proposed framework for modulation classification based on deep learning (DL) is presented in the presence of adjacent channel interference (ACI). This framework begins with the generation of constellation diagrams from the received data. These constellation diagrams are fed to convolutional neural networks (CNNs) for modulation classification. The objective of this process is to eliminate the manual feature extraction from the received data and make feature extraction process as a built‐in step with CNNs. Three types of CNNs are considered in this paper and compared for this objective. These types are AlexNet, VGG‐16, and VGG‐19. The proposed classifier is applied on Rayliegh and Rician fading channels.
MindTheStep-AsyncPSGD: Adaptive Asynchronous Parallel Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) is very useful in optimization problems with high-dimensional non-convex target functions, and hence constitutes an important component of several Machine Learning and Data Analytics methods. Recently there have been significant works on understanding the parallelism inherent to SGD, and its convergence properties. Asynchronous, parallel SGD (AsyncPSGD) has received particular attention, due to observed performance benefits. On the other hand, asynchrony implies inherent challenges in understanding the execution of the algorithm and its convergence, stemming from the fact that the contribution of a thread might be based on an old (stale) view of the state. In this work we aim to deepen the understanding of AsyncPSGD in order to increase the statistical efficiency in the presence of stale gradients. We propose new models for capturing the nature of the staleness distribution in a practical setting. Using the proposed models, we derive a staleness-adaptive SGD framework, MindTheStep-AsyncPSGD, for adapting the step size in an online-fashion, which provably reduces the negative impact of asynchrony. Moreover, we provide general convergence time bounds for a wide class of staleness-adaptive step size strategies for convex target functions. We also provide a detailed empirical study, showing how our approach implies faster convergence for deep learning applications.
Detecting Web Spam in Webgraphs with Predictive Model Analysis

Web spam is a serious threat for both end-users and search engines (w.r.t., query cost). Webgraphs can be exploited in detecting spam. In the past, several graph mining techniques were applied to measure metrics for pages and hyperlinks. In this paper, we justify the importance of webgraph to distinguish spam websites from non-spam ones based on several graph metrics computed for a labelled dataset (WEBSPAM-UK2007) and justify our model by testing on uk-2014 dataset, the most recently available dataset on the same (uk) domain. WEBSPAM-UK2007 dataset includes 0.1 million different hosts and four kinds of feature sets: Obvious, Link, Transformed Link and Content. We use five prominent machine learning (ML) techniques (i.e., Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naïve Bayes and Random Forest) to build a ML-based classifier. To evaluate the performance of our classifier, we compute accuracy and F-1 score and perform 10-fold cross validation. We also compare graph based features with content based textual features and find that graph properties are similar or better than text properties. We achieve above 99% training accuracy for most of our machine learning models. We test our model with uk-2014 dataset with 4.7 million hosts for the graph-based feature sets and achieve accuracy in between 90-94% for most of the models. To the best of our knowledge, prior works on web spam detection with WEBSPAM-UK2007 dataset did not use different test dataset for their models. Our model classifier is capable of detecting web spam for any input webgraph based on its graph metrics features.
Postdigital cross border reflections on critical utopia

Critical pedagogy is in crisis. To address this crisis, this paper reinvents Paulo Freire's concept of utopia in and for our age of the Anthropocene. Understood as a system, postdigital critical utopia provides us with normative foundations and returns agency from invisible data and algorithms to human beings. Understood as a process, postdigital critical utopia unmasks the myth of neutrality and adds an important element of myth, religion, ritual, and faith. Understood as an orientation, postdigital critical utopia needs to be balanced by dystopia, integrate environmental considerations, and act with a combination of epistemical curiosity and hope. Theoretical and practical attempts at introducing advanced technology to reimagine new utopias now take place in media theory, hacking, activism, and small pockets of the academia. To transcend its own crisis and remain relevant, contemporary critical pedagogy movement must urgently join these attempts. However, catching up with technological development is only the tip of a much larger iceberg. In order to take the lead in processes of modernization, critical pedagogy movement needs to actively develop utopian visions and techno-administrative systems which may support these visions.