Reviewerly/exHarmony huggingface dataset:
Social network based filtering of unsolicited messages from e-mails

Nowadays Electronic communication is an important medium and an inevitable way for official communication. So, the email classification into spam or ham gains a lot of importance. Commonly used approaches are text-based or collaborative methods for spam detection. However, not only choosing the rig ht classifier is very difficult but, handling poison attacks and impersonation attacks are also very important. The proposed model considers a powerful spam filtering technique which includes both social network and email factors in addition to the email data analysis for spam classification. The incoming emails are subjected to header parsing for finding the trust and reputation of senders with respect to the receivers and keyword parsing is applied to find the topic of interest using LDA with Gibbs Sampling method. Optical Character Recognition (OCR) method is applied to find the image spam e-mails. Degree and strength of the connection between the users from the social networks are also considered along with the email data factors for better message classification. Logistic Regression is used to combine all the independent input features to get an effective result. The experimental results and comparisons with the existing models vividly show the significant performance of the proposed classifier.
Gesture Elicitation and Usability Testing for an Armband Interacting with Netflix and Spotify

Controlling home entertainment devices, like music and video, via an armband could free the user from using remote controls, but assessing their overall usability with mid-air and micro-gestures still represents an open research question today. For this purpose, this paper reports on results gained by jointly conducting and comparing two studies involving participants using a Thalmic Myo armband to control a NetFlix SmartTV and Spotify: (1) a gesture elicitation study to explore a richer set of user-defined gestures, to measure their effectiveness and the user subjective satisfaction of gesture interaction; (2) a System Usability Scale (SUS) to assess the overall usability of this setup and the subjective satisfaction for user-defined gestures.
RANDOM NEURAL NETWORK METHODS AND DEEP LEARNING

The random neural network (RNN) is a mathematical model for an “integrate and fire” spiking network that closely resembles the stochastic behavior of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning. Recently, the properties of the RNN for DL have been investigated, in order to combine their power. Recent results demonstrate that the gap between RNNs and DL can be bridged and the DL tools based on the RNN are faster and can potentially be used with less energy expenditure than existing methods.
Tiered Deep Similarity Search for Fashion

How similar are two fashion clothing? Fashion apparels demonstrate diverse visual concepts with their designs, styles and brands. Hence, there exist a hierarchy of similarities between fashion clothing, ranging from exact instance or brand to similar attributes, styles. An effective search method, thus, should be able to represent the tiers of similarities. In this paper, we present a deep learning based fashion search framework for learning the tiers of similarity. We propose a new attribute-guided metric learning (AGML) with multitask CNNs that jointly learns fashion attributes and image embeddings while taking category and brand information into account. The two tasks in the framework are linked with a guiding signal. The guiding signal, first, helps in mining informative training samples. Secondly, it helps in treating training samples by their importance to capture the tiers of similarity. We conduct experiments in a new BrandFashion dataset which is richly annotated at different granularities. Experimental results demonstrate that the proposed method is very effective in capturing a tiered similarity search space and outperforms the state-of-the-art fashion search methods.
Obtaining reasonable assurance on cyber resilience

Purpose The purpose of this paper is to highlight the potential of cyber-testing techniques in assessing the effectiveness of cyber-security controls and obtaining audit evidence. Design/methodology/approach The paper starts with an identification of the applicable cyber-testing techniques and evaluates their applicability to generally accepted assurance schemes and cyber-security guidelines. Findings Cyber-testing techniques are providing insight in the effectiveness of the actual implementation of cyber-security controls, which may significantly deviate from the conceptual designs of these controls. Furthermore, cyber-testing techniques could provide concise input for cyber-risk management and improvement recommendations. Originality/value The presented cyber-testing techniques could complement traditional process-oriented assurance techniques with specialized technical analyses of real-world implementations that focus on the adversaries’ viewpoint.
Software structure characteristic measurement method based on weighted network

The function and quality of software is closely linked to its structure. One of the ways to study the structural features of software is the idea of applying complex networks. Software is abstracted into various network models for the purpose of study. Eigenvalues and additional parameters are then extracted from the model. Analyzing the characteristics and performance of the software by measuring the structural characteristics of the software and implementing a basic evaluation of the reliability and security of software. Due to the limitations of traditional measurement methods and most of the current measurement methods are software-based unprivileged networks, the unprivileged networks neglect the differences in the connection relationships in the software and their important attributes (coupling, aggregation). This paper will construct a directed weighted network model of software, and use this network model as a basis to redefine the calculation method of node connectivity, improve the calculation method of the node's degree of ripple, and then obtain the system's average degree of ripple as a measurement index. After experimental verification, the proposed method can more effectively quantify the structure of the software orderly and complex and make the evaluation of software features more accurate.
Deep Representation Learning for Metadata Verification

Verifying the authenticity of a given image is an emerging topic in media forensics research. Many current works focus on content manipulation detection, which aims to detect possible alteration in the image content. However, tampering might not only occur in the image content itself, but also in the metadata associated with the image, such as timestamp, geo-tag, and captions. We address metadata verification, aiming to verify the authenticity of the metadata associated with the image, using a deep representation learning approach. We propose a deep neural network called Attentive Bilinear Convolutional Neural Networks (AB-CNN) that learns appropriate representation for metadata verification. AB-CNN address several common challenges in verifying a specific type of metadata - event (i.e. time and places), including lack of training data, fine-grained differences between distinct events, and diverse visual content within the same event. Experimental results on three different datasets show that the proposed model can provide a substantial improvement over the baseline method.
Application of Cloud Computing in E-learning: A Basic Architecture of Cloud-Based E-learning Systems for Higher Education

Today, there is a growing trend regarding the use of e-learning approach in higher education. However, e-learning systems require huge investments in infrastructure and hardware and software resources of which many academics institutions cannot afford. The coming of cloud computing has brought new development ideas to overcome this problem and can be adopted to cut-down such investments. The purpose of this study is to put forward an overview on what is the current state of the application of cloud computing in e-learning in higher education context, where the use of computers is increasingly intensive. The paper introduces e-learning and cloud computing concepts, by analyzing e-learning systems challenges and trends, the convenience of cloud computing for e-learning and the key benefits of e-learning on the cloud. The study shows that cloud computing has tremendous effects on e-learning modes and it is the core technology of the next generation of e-learning in higher education. Cloud-based e-learning systems are emerging as an attractive method for providing flexible and scalable e-learning services, that can be accessed anytime, anywhere and from any device. The paper exposes also some applications’ solutions using cloud computing in e-learning for higher education, by presenting the most common architecture that have been adopted. Finally, the paper discusses issues related to the implementation of cloud-based e-learning systems for higher education and presents some potential ways to overcome them.
Sparse classification using Group Matching Pursuit

Group structure exists in supervised learning problems inherently. For example, in the training data of a classification problem, samples from the same class will have similar representations. These samples from the same class will form a group. Group sparse features, such as Group Lasso, can reserve this group information and improve classification accuracy by reducing noise using sparse constraints. However, Group Lasso is a sparse approximation, and its performance and solution highly depend on the choice of group sparse penalty weight. In this paper, we propose a Group Matching Pursuit (GMP) based sparse classification approach. Group Matching Pursuit features are sparse features where coefficients of exact K groups are non-zeros. Compared to Group Lasso, 1. Group Matching Pursuit based classification is stable and has no penalty parameters to tune; 2. Group Matching Pursuit features have large Signal-to-Noise Ratio (SNR) and are class discriminant; 3. Group Matching Pursuit features are also robust for occluded images. Extensive experiments on 5 data sets and different levels of occlusion show that the proposed Group Matching Pursuit can improve Group Lasso classification accuracy up to 27.2% in some situation.
Compact Deep Aggregation for Set Retrieval

The objective of this work is to learn a compact embedding of a set of descriptors that is suitable for efficient retrieval and ranking, whilst maintaining discriminability of the individual descriptors. We focus on a specific example of this general problem – that of retrieving images containing multiple faces from a large scale dataset of images. Here the set consists of the face descriptors in each image, and given a query for multiple identities, the goal is then to retrieve, in order, images which contain all the identities, all but one, etc. To this end, we make the following contributions: first, we propose a CNN architecture – SetNet – to achieve the objective: it learns face descriptors and their aggregation over a set to produce a compact fixed length descriptor designed for set retrieval, and the score of an image is a count of the number of identities that match the query; second, we show that this compact descriptor has minimal loss of discriminability up to two faces per image, and degrades slowly after that – far exceeding a number of baselines; third, we explore the speed vs. retrieval quality trade-off for set retrieval using this compact descriptor; and, finally, we collect and annotate a large dataset of images containing various number of celebrities, which we use for evaluation and will be publicly released.
Multikernel Activation Functions: Formulation and a Case Study

The design of activation functions is a growing research area in the field of neural networks. In particular, instead of using fixed point-wise functions (e.g., the rectified linear unit), several authors have proposed ways of learning these functions directly from the data in a non-parametric fashion. In this paper we focus on the kernel activation function (KAF), a recently proposed framework wherein each function is modeled as a one-dimensional kernel model, whose weights are adapted through standard backpropagation-based optimization. One drawback of KAFs is the need to select a single kernel function and its eventual hyper-parameters. To partially overcome this problem, we motivate an extension of the KAF model, in which multiple kernels are linearly combined at every neuron, inspired by the literature on multiple kernel learning. We provide an application of the resulting multi-KAF on a realistic use case, specifically handwritten Latin OCR, on a large dataset collected in the context of the ‘In Codice Ratio’ project. Results show that multi-KAFs can improve the accuracy of the convolutional networks previously developed for the task, with faster convergence, even with a smaller number of overall parameters.
Unsupervised Learning of Human Pose Distance Metric via Sparsity Locality Preserving Projections

Human poses admit complicated articulations and multigranular similarity. Previous works on learning human pose metric utilize sparse models, which concentrate large weights on highly similar poses and fail to depict an overall structure of poses with multigranular similarity. Moreover, previous works require a large number of similar/dissimilar annotated pairwise poses, which is an tedious task and remains inaccurate due to different subjective judgments of experts. Motivated by graph-based neighbor assignment techniques, we propose an unsupervised model called sparsity locality preserving projection with adaptive neighbors (SLPPAN), for learning human pose distance metric. By using a property of the graph Laplacian, SLPPAN introduces a fixed-rank constraint to enforce an adaptive graph structure of poses and learns the neighbor assignment, the similarity measurement, and pose metric simultaneously. Experiments on pose retrieval of the CMU Mocap database demonstrate that SLPPAN outperforms traditional pose metric learning methods by capturing viewpoint variations of human poses. Experiments on keyframe extraction of the MSRAction3D database demonstrate that SLPPAN outperforms current methods by precisely detecting important frames of action sequences.
Alzheimer Detection Based on Multi-Agent Systems: An Intelligent Image Processing Environment

Nowadays, robust image processing and intelligent systems have gained much popularity and importance in several fields o studies; Consequently, the use of Multi-agent systems (MAS) has been adopted as a strength paradigm for analyzing images. Since, medical image segmentation faces multiple obstacles, the use of MAS has proved precious benefits to accomplish many tasks such as quantification of tissue volumes, medical diagnosis, anatomical structure studies, treatment planning, etc. Currently, diagnosis of Alzheimer Disease (AD) can be made by different methods, neuroimaging assessments are the most used one. Meanwhile, Magnetic Resonance Imaging (MRI) offers well-defined measurement of brain structures, it has been considered as one of the best neuroimaging examination for AD. For this reason, MAS adopt the decentralization of knowledge and behavior in order to provide a powerful resolution of segmentation issues. We briefly describe a framework for Agent Based modeling (ABM) which is designed to 3D image processing especially Alzheimer MRI analysis, and highlights its important characteristics: agent behavior, perception, interactions, cooperation, and negotiation.
Securing Infrastructure-as-a-Service Public Clouds Using Security Onion

The shift to Cloud computing has brought with it its specific security challenges concerning the loss of control, trust and multi-tenancy especially in Infrastructure-as-a-Service (IaaS) Cloud model. This article focuses on the design and development of an intrusion detection system (IDS) that can handle security challenges in IaaS Cloud model using an open source IDS. We have implemented a proof-of-concept prototype on the most deployed hypervisor—VMware ESXi—and performed various real-world cyber-attacks, such as port scanning and denial of service (DoS) attacks to validate the practicality and effectiveness of our proposed IDS architecture. Based on our experimental results we found that our Security Onion-based IDS can provide the required protection in a reasonable and effective manner.
Modified iterative guided texture filtering algorithm

Structure-texture decomposition smoothing has been extensively studied due to its wide range of applications in computational photography and image processing. In this paper, we propose a new structure-texture decomposition algorithm which is based on two fundamental ideas: (1) guidance image and (2) iterative smoothing. The guidance image is generated by mitigating high-frequency oscillatory components in the original image. The result is then incorporated in a new generic iterative framework which makes use of well-known guided edge-reserving filters such as bilateral filter (BF), guided filter (GF), domain transform filter (DTF), and the extended Bayesian model averaging filter (BMA) called guided Bayesian model averaging filter (GBMA) to achieve texture smoothing. We have presented a detailed study of the proposed algorithm including: guidance image generation, an evaluation of the guided edge-preserving filters which are incorporated in the proposed iterative framework, the number of iterations for the proposed iterative structure, and the selection of guided edge-preserving filter. We demonstrate that the proposed method is a flexible and effective tool for a wide range of image editing applications including: image abstraction, color pencil drawing, content-aware image resizing, and texture editing. In particular, the proposed approach has the best performance in structure-texture decomposition for an image with low-contrast features.
A New Method to Compute Ratio of Secure Summations and Its Application in Privacy Preserving Distributed Data Mining

Computing the ratio of secure summations (RSS) is one of the most important tools for privacy preserving distributed data mining. It refers to such a problem; given the n parties and their respective secret values (x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ,y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ), where i=1, ..., n , how can they get the value of Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> / Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> without disclosing (x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ,y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ), and Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> and Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> In this paper, we propose a new method to solve this problem. Compared with the existing methods, our method is not only secure under the assumption of semi-honest but also can resist collusion attacks-even when all but one party are corrupt. At the same time, the computational complexity and communication complexity of our method are both O(n) . Therefore, it can meet the needs of the practical application in terms of both security and efficiency. In addition to the theoretical guarantee, the practical usability of our method is also verified through experiments by constructing Naive Bayes classifiers in a privacy-preserving distributed environment.
Evolutionary Computation Algorithms for Detecting Known and Unknown Attacks

Threats against the internet and computer networks are becoming more sophisticated, with attackers using new attacks or modifying existing ones. Security teams have major difficulties in dealing with large numbers of continuously evolving threats. Various artificial intelligence algorithms have been deployed to analyse such threats. In this paper, we explore the use of Evolutionary Computation (EC) techniques to construct behavioural rules for characterising activities observed in a system. The EC framework evolves human readable solutions that provide an explanation of the logic behind its evolved decisions, offering a significant advantage over existing paradigms. We examine the potential application of these algorithms to detect known and unknown attacks. The experiments were conducted on modern datasets.
Interoperability Frameworks for Networked Information Systems: A Comparative Analysis and Discussion

Interoperability frameworks provide specifications for different aspects of interoperability, for communicating and sharing information. Ten prominent industry-neutral interoperability frameworks are analyzed in this paper, distinguishing between operational interoperability frameworks and conceptual interoperability frameworks. To support this analysis, 16 criteria were defined, which represent the basis of a comparison framework. The operational interoperability frameworks analyzed have similarities and differences, and complement each other in some aspects (e.g. messaging service). The differences refer to the manner they handle (or not) different interoperability details relevant for performing e-business, e.g. only ebXML provides guidelines for negotiation and setting-up a collaboration agreement prior to conducting e-business. The five conceptual interoperability frameworks were analyzed based on specific structural elements, as they tackle differently the notion of interoperability, i.e. targeting types of integration, interoperability barriers, levels of interoperability. Despite the advances of interoperability frameworks, full interoperability is not yet achieved. The analysis performed allowed to conclude that although interoperability frameworks represent a good direction towards seamless interoperability in a networked environment, a big challenge is the harmonization of different aspects of the interoperability frameworks towards attaining full interoperability in complex cross-sectorial e-business scenarios, which can be addressed by joint actions of the scientific community and practitioners. Finally, this analysis yields a set of directions for future research work.