Reviewerly/exHarmony huggingface dataset:
Social media content moderation: six opportunities for feminist intervention

Social media content moderation—making and enforcing rules about the content that can (and cannot) be posted to a social media platform—is anything but neutral. While this form of governance has lo...
The new Convolutional Neural Network (CNN) local feature extractor for automated badminton action recognition on vision based data

Abstract Automated action recognition is useful for improving the performance of the athletes through notational analysis. The notational analysis is usually used by the coach or notational analyst to study the movement patterns, strategy and tactics. Therefore, action recognition is the main key before further analysis can be done. This paper focused on developing an automated badminton action recognition using vision based dataset. 1496 badminton match image frames of 5 actions were studied – smash, clear, drop, net shot and lift. At first, the dataset was classified into 0.8:0.2 for training and testing the classification task by machine learning. Secondly, features of the training dataset were extracted using the Alexnet Convolutional Neural Network (CNN) model. In extracting the features, we introduced the new local feature extractor technique that extracts features at the fc8 layer. After collecting all the features at the fc8 layer, features were being classified by using machine learning classifier which is linear Support Vector Machine (SVM). The experiment was repeated using a normal global feature extractor technique. Lastly, both of the new local and global feature extractor techniques were repeated using GoogleNet CNN model to compare the performance between AlexNet and GoogleNet model. The results show that the new local feature extractor using AlexNet CNN model has the best performance accuracy which is 82.0%.
Distilling Knowledge from Refinement in Multiple Instance Detection Networks

Weakly supervised object detection (WSOD) aims to tackle the object detection problem using only labeled image categories as supervision. A common approach used in WSOD to deal with the lack of localization information is Multiple Instance Learning, and in recent years methods started adopting Multiple Instance Detection Networks (MIDN), which allows training in an end-to-end fashion. In general, these methods work by selecting the best instance from a pool of candidates and then aggregating other instances based on similarity. In this work, we claim that carefully selecting the aggregation criteria can considerably improve the accuracy of the learned detector. We start by proposing an additional refinement step to an existing approach (OICR), which we call refinement knowledge distillation. Then, we present an adaptive supervision aggregation function that dynamically changes the aggregation criteria for selecting boxes related to one of the ground-truth classes, background, or even ignored during the generation of each refinement module supervision. Experiments in Pascal VOC 2007 demonstrate that our Knowledge Distillation and smooth aggregation function significantly improves the performance of OICR in the weakly supervised object detection and weakly supervised object localization tasks. These improvements make the Boosted-OICR competitive again versus other state-of-the-art approaches.
Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation

Knowledge distillation (KD) is a new method for transferring knowledge of a structure under training to another one. The conventional application of KD is in the form of learning a small model (named as a student) by soft labels produced by a complex model (named as a teacher). Due to the novel idea introduced in KD, recently, its notion is used in different methods such as compression and processes that are going to enhance the model accuracy. Although different techniques are proposed in the area of KD, there is a lack of a model to generalize KD techniques. In this paper, various studies in the scope of KD are investigated and analyzed to build a general model for KD. All the methods and techniques in KD can be summarized through the proposed model. By utilizing the proposed model, different methods in KD are better investigated and explored. The advantages and disadvantages of different approaches in KD can be better understood and developing a new strategy for KD can be possible. Using the proposed model, different KD methods are represented in an abstract view.
Towards Automatic Skeleton Extraction With Skeleton Grafting

This article introduces a novel approach to generate visually promising skeletons automatically without any manual tuning. In practice, it is challenging to extract promising skeletons directly using existing approaches. This is because they either cannot fully preserve shape features, or require manual intervention, such as boundary smoothing and skeleton pruning, to justify the eye-level view assumption. We propose an approach here that generates backbone and dense skeletons by shape input, and then extends the backbone branches via skeleton grafting from the dense skeleton to ensure a well-integrated output. Based on our evaluation, the generated skeletons best depict the shapes at levels that are similar to human perception. To evaluate and fully express the properties of the extracted skeletons, we introduce two potential functions within the high-order matching protocol to improve the accuracy of skeleton-based matching. These two functions fuse the similarities between skeleton graphs and geometrical relations characterized by multiple skeleton endpoints. Experiments on three high-order matching protocols show that the proposed potential functions can effectively reduce the number of incorrect matches.
Similarity and diversity induced paired projection for cross-modal retrieval

The heterogeneous gap among cross modalities is a critical problem in many applications (e.g., retrieval). Considering that the main purpose of cross-modal learning is to learn a common representation while there also exist specific components across different modalities, a similarity and diversity induced paired projection (SDPP) method is proposed in this paper. SDPP not only extracts the correlation in a common subspace, but also removes the view-specific information which does not contribute to our task. In order to model the specific components, the Hilbert Schmidt Independence Criterion (HSIC) is introduced as a co-regularization to explicitly enforce the diversity. Additionally, different from some existing subspace learning methods which are time consuming in the testing phase, a paired projection strategy is exploited, being capable of obtaining the similar information in a simple but effective way. To optimize the presented approach, an efficient algorithm is designed to update different variables alternatively. Finally, we apply our strategy to the cross-modal retrieval, and experimental results on several real-world datasets substantiate the effectiveness and superiority of our model compared with other state-of-the-art methods.
Effective Context-Aware Recommendations Based on Context Weighting Using Genetic Algorithm and Alleviating Data Sparsity

Context-aware collaborative filtering (CACF) is an effective approach for adapting recommendations under users' specific contextual situations and aims to improve predictive accuracy for Context-aware recommender systems (CARSs). Incorporating context in recommender systems (RSs) considering the equal importance to all contextual dimensions is not appropriate for seeking an intelligent and useful recommendation. In this paper, we propose a Real-coded Genetic Algorithm (RCGA) based CARS framework that exploits contextual pre-filtering and contextual modeling paradigms into CACF with appropriate context feature weights for enhancing accuracy as well as the diversity of the recommendation list. Further to alleviate the data sparsity, an effective missing value prediction (EMVP) algorithm is applied into proposed framework. The accuracy based on RCGA is compared with other two schemes: Support Vector Machine (SVM) and Particle Swarm Optimization (PSO), and RCGA has shown better results. Experimental results based on real-world datasets have clearly established the effectiveness of our proposed CARS schemes.
Understanding and recommending security requirements from problem domain ontology: A cognitive three-layered approach

Socio-technical systems (STS) are inherently complex due to the heterogeneity of its intertwined components. Therefore, ensuring STS security continues to pose significant challenges. Persistent security issues in STS are extremely critical to address as threats to security can affect entire enterprises, resulting in significant recovery costs. A profound understanding of the problems across multiple dimensions of STS is the key in addressing such security issues. However, we lack a systematic acquisition of the scattered knowledge related to design, development, and execution of STS. In this work, we methodologically analyze security issues from a requirements engineering perspective. We propose a cognitive three-layered framework integrating various modeling methodologies and knowledge sources related to security. This framework helps in understanding essential components of security and making recommendations of security requirements regarding threat analyses and risk assessments using Problem Domain Ontology (PDO) knowledge base. We also provide tool support for our framework. With the goal-oriented security reference model, we demonstrate how security requirements are recommended based on PDO, with the help of the tool. The organized acquisition of knowledge from SME groups and the domain working group provides rich context of security requirements, and also enhances the re-usability of the knowledge set.
Deep Learning Based Advanced Spatio-Temporal Extraction Model in Medical Sports Rehabilitation for Motion Analysis and Data Processing

Presently, A wide range of unlabeled and minimal style data significantly decreases the current motion sequence's reuse ability. An important method of data reuse has a successful classification and fragment separation, which has been discussed in this research. This paper focuses on these particular problems and the tremendous progress of deep learning in design and symbolic fields. A Limited Boltzmann Model (LBM) theory is based on the Advanced Spatio-Temporal Extraction Model (ASTEM), which has been used for analyzing the physiological motion of human skeletons. There are primarily three aspects to the results of the study. (1) For constructing a semi-combination model, the stack factor decomposition is used as a spatiotemporal model function and LBM discrimination. (2), Optimized algorithm used to create the three-channel generative LBM model using the weight decomposition idea and then extract the time and space-based abstract properties of the original motion series. (3) The unsupervised related model of frame detection is built using the perception of human interaction through 3D convolution LBM. A significant research direction of the medical analysis and extraction of sports data is used appropriately to interpret and gain valuable information and knowledge from motion analyses. Experimental outcomes show that this technique offers technical assistance and guidance for implementing a real cloud-based fusion system.
MultiComposite Nonconvex Optimization for Training Deep Neural Networks

We present in this paper a novel deterministic algorithmic framework that enables the computation of a directional stationary solution of the empirical deep neural network training problem formulated as a multicomposite optimization problem with coupled nonconvexity and nondifferentiability. This is the first time to our knowledge that such a sharp kind of stationary solution is provably computable for a nonsmooth deep neural network. Allowing for arbitrary finite numbers of input samples and training layers, an arbitrary number of neurons within each layer, and arbitrary piecewise activation functions, the proposed approach combines the methods of exact penalization, majorization-minimization, gradient projection with enhancements, and the dual semismooth Newton method, each for a particular purpose in an overall computational scheme. While a routine implementation of the semismooth Newton method would be computationally expensive, we show that careful linear algebraic implementation helps to greatly reduce the computational and storage costs for problems of arbitrary dimensions. Contrary to existing stochastic approaches which provide at best very weak guarantees on the computed solutions obtained in practical implementation, our rigorous deterministic treatment provides guarantee of the stationarity properties of the computed solutions with reference to the optimization problems being solved. Numerical results from a MATLAB implementation demonstrate the effectiveness of the framework for solving reasonably sized networks with a modest number of training samples (in the low thousands).
QHSE: An efficient privacy-preserving scheme for blockchain-based transactions

As bitcoin has drawn a lot of attention, people have developed various cryptocurrencies based on blockchain framework. The decentralization feature of blockchain makes the transaction information public in the cryptocurrency. It is possible to expose the user's privacy. Therefore, it is necessary to design an efficient and secure scheme to hide transaction information and guarantee that the transaction is carried out normally. In this paper, we propose a quasi-homomorphic symmetric encryption (QHSE) scheme to conceal the transaction amounts in cryptocurrency based on blockchain. For each transaction, transaction participants employ Diffie–Hellman key exchange protocol to generate the specific transaction key as the symmetric key of QHSE. That is, each transaction has a distinct transaction key. Even if the transaction key of one transaction is leaked, other transactions will not reveal any private information, which guarantees the atomicity and stability of the transactions. To achieve the interoperability of different symmetric key, we design a KeyUp algorithm which enables the calculations between different ciphertexts. Combined with KeyUp, we also construct several operation algorithms (Multiplication, Addition, Comparison), so that transactions can be performed and verified normally in the ciphertext state. We prove that the proposed QHSE will not leak any private information to unauthorized parties, and demonstrate the utility and efficiency of QHSE from theoretical and experimental aspects.
ProxSim: GPU-based Simulation Framework for Cross-Layer Approximate DNN Optimization

Through cross-layer approximation of Deep Neural Networks (DNN) significant improvements in hardware resources utilization for DNN applications can be achieved. This comes at the cost of accuracy degradation, which can be compensated through different optimization methods. However, DNN optimization is highly time-consuming in existing simulation frameworks for cross-layer DNN approximation, as they are usually implemented for CPU usage only. Specially for large-scale image processing tasks, the need of a more efficient simulation framework is evident. In this paper we present ProxSim, a specialized, GPU-accelerated simulation framework for approximate hardware, based on Tensorflow, which supports approximate DNN inference and retraining. Additionally, we propose a novel hardware-aware regularization technique for approximate DNN optimization. By using ProxSim, we report up to 11× savings in execution time, compared to a multi-thread CPU-based framework, and an accuracy recovery of up to 30% for three case studies of image classification with MNIST, CIFAR-10 and ImageNet.
MGCM: Multi-modal generative compatibility modeling for clothing matching

With the recent prevalence of online fashion-oriented communities and advances in multimedia processing, increasing research interests have been paid to the fashion compatibility modeling, where the compatibility between complementary fashion items (e.g., a top and a bottom) can be assessed automatically. Existing fashion compatibility modeling techniques mainly focus on measuring the compatible preference between fashion items with Deep Neural Networks (DNN), but overlook the generative compatibility modeling. Differently, in this paper, we explore the potential of the Generative Adversarial Network (GAN) in fashion compatibility modeling and thus propose a Multi-modal Generative Compatibility Modeling (MGCM) scheme. In particular, we introduce a multi-modal enhanced compatible template generation network, regularized by the pixel-wise consistency and template compatibility, to sketch a compatible template as the auxiliary link between fashion items. Accordingly, MGCM is able to measure the compatibility between complementary fashion items comprehensively from both item-item and item-template perspectives. Experimental results on two real-world datasets demonstrate the superiority of the proposed scheme over state-of-the-art methods.
Role of cloud computing in global accounting information systems

Purpose The purpose of this study is to underpin the impact of cloud computing in global accounting information systems (AIS). Moreover, it investigates the key aspects that ought to be considered by the organization before choosing to pick the correct accounting system. Design/methodology/approach The study looks at and depends on narrative investigation of previous studies. In the examination talked about the principle issues with respect to the utilization of cloud and database the executives in the AIS through developing research model. Findings The focal point of the paper is the impact of cloud computing worldview on the business area. This paper highlights different facts of cloud accounting, published research papers and the benefits and possible risks determined by the implementation of cloud services, mostly in relation to the accounting department. Originality/value Considering the numerous ramifications of cloud advancements on the present business process, there is a need for an examination of how these innovations will be used in AIS to improve precision, benefits and risks. At the same time, there is need to investigate the determinant elements of actualizing cloud advances in the AIS. More importantly, to the author’s knowledge, this is the first study that focuses on the number of published research works to show the importance of cloud computing in accounting and information systems.
Evolutionary-based image encryption using biomolecules operators and non-coupled map lattice

Information security is one of the important defensive issues in coping with attacks to valuable information of organizations. Image encryption is one of the passive defense tools to provide the information and communication security. In this paper, a chaotic evolutionary biomolecules operators model (CEBOM) was presented for image encryption. The proposed model consisted of three steps. In the first step, a secret key was considered for the initial value of the non-coupled map lattice function using the SHA (Secure Hash Algorithm)-256 algorithm. In the next step (permutation), the optimization process was performed using the tabu search algorithm (TSA) operators and the chaos function for the image with the least correlation, then the operators of the DNA sequence and chaos function were applied to the diffusion operation. The results were indicative of the superiority of the proposed model over other models. In addition, the proposed model was highly robust to common attacks, with the entropy value obtained as 7.9994.
FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks

With a constant improvement in the network architectures and training methodologies, Neural Networks (NNs) are increasingly being deployed in real-world Machine Learning systems. However, despite their impressive performance on "known inputs", these NNs can fail absurdly on the "unseen inputs", especially if these real-time inputs deviate from the training dataset distributions, or contain certain types of input noise. This indicates the low noise tolerance of NNs, which is a major reason for the recent increase of adversarial attacks. This is a serious concern, particularly for safety-critical applications, where inaccurate results lead to dire consequences. We propose a novel methodology that leverages model checking for the Formal Analysis of Neural Network (FANNet) under different input noise ranges. Our methodology allows us to rigorously analyze the noise tolerance of NNs, their input node sensitivity, and the effects of training bias on their performance, e.g., in terms of classification accuracy. For evaluation, we use a feed-forward fully-connected NN architecture trained for the Leukemia classification. Our experimental results show ±11% noise tolerance for the given trained network, identify the most sensitive input nodes, and confirm the biasness of the available training dataset.
A modified bond energy algorithm with fuzzy merging and its application to Arabic text document clustering

Conventional textual documents clustering algorithms suffer from several shortcomings, such as the slow convergence of the immense high-dimensional data, the sensitivity to the initial value, and the understandability of the description of the resulted clusters. Although many clustering algorithms have been developed for English and other languages, very few have tackled the problem of clustering the under-resourced Arabic language. In this work, we propose a modified version of the Bond Energy Algorithm (BEA) combined with a fuzzy merging technique to solve the problem of Arabic text document clustering. The proposed algorithm, Clustering Arabic Documents based on Bond Energy, hereafter named CADBE, attempts to identify and display natural variable clusters within huge sized data. CADBE has three steps to cluster Arabic documents: the first step instantiates a cluster affinity matrix using the BEA, the second step uses a new and novel method to partition the cluster matrix automatically into small coherent clusters, and the last step uses a fuzzy merging technique to merge similar clusters based on the associations and interrelations between the resulted clusters. Experimental results showed that the proposed algorithm effectively outperformed the conventional clustering algorithms such as Expectation–Maximization (EM), Single Linkage, and UPGMA in terms of clustering purity and entropy. It also outperformed k-means, k-means++, spherical k-means, and CoclusMod in most test cases. However, there are several merits of CADBE. First, unlike the traditional clustering algorithms, it does not require to specify the number of clusters. In addition, it produces clusters with distinct boundaries, which makes its results more objective, and finally it is deterministic, such that it is insensitive to the order in which documents are presented to the algorithm.