Reviewerly/exHarmony huggingface dataset:
Sparsity-Based Naive Bayes Approach for Anomaly Detection in Real Surveillance Videos

Anomaly detection in video surveillance data is a challenging tasks due to the dynamic environment of the surveillance area. Multiple solutions have been proposed to tackle this problem ranging from using handcrafted features to end-to-end deep leaning methods. This paper proposes a model-based approach to anomaly detection for surveillance video data. This solution is based on sparsity estimation of pre-trained deep features. We aim to harvest the sparsity information of C3D deep features and use it as an additional information to differentiate the normal and anomalous event in a video. The proposed approach uses the naïve Bayes model within multiple instance learning framework. Experimental results shows that using sparsity information of C3D deep features could improve naive Bayes approach and enhance the accuracy of the decision boundary.
Maintaining Data Integrity in Fog Computing Based Critical Infrastructure Systems

The evolution of the utilization of technologies in nearly all aspects of life has produced an enormous amount of data essential in a smart city. Therefore, maximizing the benefits of technologies such as cloud computing, fog computing, and the Internet of things is important to manage and manipulate data in smart cities. However, certain types of data are sensitive and risky and may be infiltrated by malicious attacks. As a result, such data may be corrupted, thereby causing concern. The damage inflicted by an attacker on a set of data can spread through an entire database. Valid transactions that have read corrupted data can update other data items based on the values read. In this study, we introduce a unique model that uses fog computing in smart cities to manage utility service companies and consumer data. We also propose a novel technique to assess damage to data caused by an attack. Thus, original data can be recovered, and a database can be returned to its consistent state as no attacking has occurred.
When CNNs meet random RNNs: Towards multi-level analysis for RGB-D object and scene recognition

Recognizing objects and scenes are two challenging but essential tasks in image understanding. In particular, the use of RGB-D sensors in handling these tasks has emerged as an important area of focus for better visual understanding. Meanwhile, deep neural networks, specifically convolutional neural networks (CNNs), have become widespread and have been applied to many visual tasks by replacing hand-crafted features with effective deep features. However, it is an open problem how to exploit deep features from a multi-layer CNN model effectively. In this paper, we propose a novel two-stage framework that extracts discriminative feature representations from multi-modal RGB-D images for object and scene recognition tasks. In the first stage, a pretrained CNN model has been employed as a backbone to extract visual features at multiple levels. The second stage maps these features into high level representations with a fully randomized structure of recursive neural networks (RNNs) efficiently. To cope with the high dimensionality of CNN activations, a random weighted pooling scheme has been proposed by extending the idea of randomness in RNNs. Multi-modal fusion has been performed through a soft voting approach by computing weights based on individual recognition confidences (i.e. SVM scores) of RGB and depth streams separately. This produces consistent class label estimation in final RGB-D classification performance. Extensive experiments verify that fully randomized structure in RNN stage encodes CNN activations to discriminative solid features successfully. Comparative experimental results on the popular Washington RGB-D Object and SUN RGB-D Scene datasets show that the proposed approach achieves superior or on-par performance compared to state-of-the-art methods both in object and scene recognition tasks. Code is available at https://github.com/acaglayan/CNN_randRNN.
Deep Learning and Channel Estimation

Deep Learning has rendered overwhelming potential to support the rising interest of high dependability and maximum capacity wireless communication systems. Channel estimation is a crucial step in a wireless communication system. An increase in the number of channel coefficients can make channel estimation fairly complex. After reading this paper, one can realize Deep Learning's ability in revolutionizing channel estimation for next-generation wireless communication systems. After the inception of Deep Learning in communication system, Channel estimation can be divided into two parts: Implicit and Explicit Channel estimation. A comprehensive view of various ongoing research in the Deep Learning aided channel estimation is provided along with a brief explanation of the deep neural network model utilized. Amidst all the advantages of Deep Learning, the paper discusses existing open challenges needed to make Deep Learning a practical reality.
Customer-oriented ranking of cyber threat intelligence service providers

Cyber Threat Intelligence (CTI) is revolutionizing the cybersecurity ecosystem by enabling organizations to share threat data and allow for proactive defense against sophisticated intrusion attempts. Several cybersecurity vendors are offering a variety of CTI services and capabilities to their customers. The wide variety in the CTI service offerings by different providers makes it difficult for the security experts to decide which service provider is most suitable according to their security program requirements. Current approaches for ranking CTI service providers are based on a limited set of Key Performance Indicators (KPI) and do not consider the customer's security program relevance and requirements. In this work, an extensive set of KPI to rank CTI service providers is defined. This paper proposes a framework, which is a first of its kind that assigns weights to KPI based on the customer's requirements and ranks CTI service providers. This framework significantly helps the security program leaders in decision making for CTI service provider selection. It is designed to improve the quality of service offerings by promoting healthy competition among CTI service providers.
Research on distributed blockchain‐based privacy‐preserving and data security framework in IoT

With the rapid development of the Internet of things (IoT), it has brought great convenience for people's life. However, the security and privacy of IoT still face a major challenge. To remedy these issues, in this study, the authors first introduce the three-tier architecture of IoT and analyse the corresponding security problems of each layer, then they discussed the compatibility between IoT and blockchain. Secondly, they propose a new, distributed blockchain-based security architecture of IoT, which rely on gateway nodes of perception layer to secure data storage and sharing, and use middleware servers to analyse and process data. Finally, they adopt game theory to model and analyse their designed scheme. The results demonstrate that their scheme is a safe and deployable framework for IoT data security and privacy.
Quantum gradient algorithm for general polynomials

Gradient-based algorithms, popular strategies to optimization problems, are essential for many modern machine-learning techniques. Theoretically, extreme points of certain cost functions can be found iteratively along the directions of the gradient. The time required to calculating the gradient of $d$-dimensional problems is at a level of $\mathcal{O}(poly(d))$, which could be boosted by quantum techniques, benefiting the high-dimensional data processing, especially the modern machine-learning engineering with the number of optimized parameters being in billions. Here, we propose a quantum gradient algorithm for optimizing general polynomials with the dressed amplitude encoding, aiming at solving fast-convergence polynomials problems within both time and memory consumption in $\mathcal{O}(poly (\log{d}))$. Furthermore, numerical simulations are carried out to inspect the performance of this protocol by considering the noises or perturbations from initialization, operation and truncation. For the potential values in high-dimension optimizations, this quantum gradient algorithm is supposed to facilitate the polynomial-optimizations, being a subroutine for future practical quantum computer.
A Study of Cyberbullying Detection Using Machine Learning Techniques

Cyberbullying disturbs harassment online, with alarming implications. It exists in different ways, and is in textual format in most social networks. There is no question that over 1.96 billion of them would have an inescapable social operation. However, the developing decade presents genuine difficulties and the online-conduct of clients have been put to address. Expanding instances of provocation and harassing alongside instances of casualty has been a difficult issue. Programmed discovery of such episodes requires smart frameworks. A large portion of the current studies have been moving towards this issue with standard machine learning models and most of the models produced in these studies are scalable at one time into a solitary social network. Deep learning based models have discovered ways in the identification of digital harassing occurrences, asserting that they can beat the restrictions of the ordinary models, and improve the discovery execution. However, numerous old-school models are accessible to control the incident, the need to successfully order the tormenting is as yet weak. To successfully screen the harassing in the virtual space and to stop the savage outcome with the execution of Machine learning and Language preparing. A system is proposed to give a double characterization of cyberbullying. Our technique utilizes an inventive idea of CNN for content examination anyway the current strategies utilize a guileless way to deal with furnish the arrangement with less precision. A current dataset is utilized for experimentation and our system is proposed with other existing methods and is found to give better precision and grouping.
Comparative Study on Different Approaches in Keyword Extraction

Since there is an increasing number of research documents published every year, the documents available on the Internet will also be increasing rapidly. This poses the need to categorize the available research articles into their respective domain to ease the search process and find their research documents under the specific domain. This classification is a tiresome and prolonged process, which can be avoided by using keywords and keyphrases. Keywords or keyphrases provides a summary or information described in a research document. The domain of a research paper can be determined based on extracted keywords and keyphrases. It is monotonous to manually extract keywords and key phrases [4]. Automatic extraction of keyword techniques helps to overcome this challenging task. The classification of these research papers can be achieved more efficiently by using the keywords applicable to a particular domain. This paper aims to compare key extraction algorithms such as TextRank, PositionRank, keyphrase extraction algorithm (KEA) and Multi-purpose automatic topic indexing (MAUI).
Toward Personalized Scaffolding and Fading of Motivational Support in L2 Learner–Dialogue Agent Interactions: An Exploratory Study

This article proposes a computer-based approach to effectively enhance second language learners' willingness to communicate in the target language. To do so, we implemented a conversational agent embedding a dialogue management model based on two conversational strategies (i.e., communication strategies and affective backchannels), serving as scaffolds for enhancing learners' willingness to communicate in the target language. Here, we report on differences observed among second language learners' preferences for both conversational strategies according to their initial level of willingness to communicate and on variations of their willingness with respect to such differences. Although we found that most students generally preferred a combination of both strategies, learners' preferences and the effects of the support provided by these strategies varied according to their level of willingness to communicate. Learners with lower willingness to communicate tended to prefer affective backchannels, whereas those with higher willingness to communicate seemed to favor communication strategies. These results were consistent with post-test results, which showed that learners' expected willingness to communicate tended to be higher after interacting with systems embedding their preferred strategies. In sum, these results are preliminary evidence of the meaningfulness of accounting for such learners' preferences in adaptively using and fading the strategies employed by conversational agents to motivate second language learners to communicate in the target language.
DILS: Constrained clustering through dual iterative local search

Clustering has always been a powerful tool in knowledge discovery. Traditionally unsupervised, it has received renewed attention recently as it has shown to produce better results when provided with new types of information, thus leading to a new kind of semi-supervised learning: constrained clustering. This technique is a generalization of traditional clustering that considers additional information encoded by constraints. Constraints can be given in the form of instance-level must-link and cannot-link constraints, which is the focus of this paper. We propose a new metaheuristic algorithm, the Dual Iterative Local Search, and prove its ability to produce quality results for the constrained clustering problem. We compare the results obtained by this proposal to those obtained by the state-of-the-art algorithms on 25 datasets with incremental levels of constraint-based information, supporting our conclusions with the aid of Bayesian statistical tests.
The Linguistic Analysis of Scene Semantics: LASS

In this paper, we define a new method for analyzing object-scene contextual relationships using computational linguistics: Linguistic Analysis of Scene Semantics, or LASS. LASS uses linguistic semantic similarity relationships between scene object and context labels embedded in a vector-space language model: Facebook Research's fastText. Importantly, the use of fastText permits semantic similarity score calculation between any set of strings and thus elements of any set of image data for which labels are available. Scene semantic similarity scores are then embedded in object segmentation mask locations in the image, creating a semantic similarity map. LASS can also be fully automated by generating context and object labels, as well as object segmentation masks, using deep learning. We compare semantic similarity maps between human- and neural network-generated annotations on a corpus of images taken from the LabelMe database. Semantic similarity maps produced by the fully automated LASS have a number of desirable properties, while maintaining a high degree of spatial and semantic similarity to them. Finally, we use LASS to evaluate the distribution of semantically consistent scene elements in space. Both show relatively uniform distributions of semantic relatedness to scene context, suggesting that contextually appropriate objects are likely to be found in all image regions. Taken together, these results suggest that LASS is accurate, automatic, flexible, and useful in a number of research contexts such as scene grammar and novelty detection.
Communication Protocol Classification Based on LSTM and DBN

In the battlefield, we often don't know the parameters about enemy wireless communication system. Therefore, we need to use electronic reconnaissance equipment to search, intercept, identify and analyze enemy wireless communication signal. However, the exciting electronic reconnaissance methods can only detect signal layer parameters such as signal carrier frequency and bandwidth, and cannot obtain more information. In order to improve the reconnaissance ability, we propose a novel communication protocol classification algorithm based on long short-term memory (LSTM) and deep belief network (DBN). We first introduce the DBN, then simulates communication protocol classification method based on DBN. In order to improve the performance, we optimize the method. We uses the LSTM to pre-process the data to extract the feature firstly. Then we make the feature as the input of DBN to classify the communication protocol. Finally we make simulation to verify the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm has very good performance to classify the communication protocol.
Salient instance segmentation via subitizing and clustering

The goal of salient region detection is to identify the regions of an image that attract the most attention. Many methods have achieved state-of-the-art performance levels on this task. Recently, salient instance segmentation has become an even more challenging task than traditional salient region detection; however, few of the existing methods have concentrated on this underexplored problem. Unlike the existing methods, which usually employ object proposals to roughly count and locate object instances, our method applies salient objects subitizing to predict an accurate number of instances for salient instance segmentation. In this paper, we propose a multitask densely connected neural network (MDNN) to segment salient instances in an image. In contrast to existing approaches, our framework is proposal-free and category-independent. The MDNN contains two parallel branches: the first is a densely connected subitizing network (DSN) used for subitizing prediction; the second is a densely connected fully convolutional network (DFCN) used for salient region detection. The MDNN generates both saliency maps and salient object subitizing. Then, an adaptive deep feature-based spectral clustering operation segments the salient regions into instances based on the subitizing and saliency maps. The experimental results on salient instance segmentation datasets demonstrate the competitive performance of our framework. Its AP reaches 57.32%, which surpasses the state-of-the-art methods by about 5%.
Nonlinear Equalization for TDMR Channels Using Neural Networks

This paper presents new structure and adaptation criterion for equalization of two-dimensional magnetic recording channels, as opposed to typical linear equalizer with minimum mean square error (MMSE) as adaptation criterion. To compensate for the nonlinear channel noise, we propose a neural network based nonlinear equalizer and show it outperforms linear equalizer under the same criterion. To achieve minimum bit error rate (BER) at the detector output, we propose to adapt the equalizer with cross entropy between the true probability of the bit and detector's estimate of it. We show minimizing the cross entropy enables maximum likelihood adaptation, and results in lower detector BER than the MSE criterion. Several variations of nonlinear equalizer structures with cross entropy criterion are investigated. Compared to linear MMSE equalizer, the proposed scheme can provide up to 22.76% detector BER reduction with only 6× increase in complexity.
MaxiMin Active Learning in Overparameterized Model Classes

Generating labeled training datasets has become a major bottleneck in Machine Learning (ML) pipelines. Active ML aims to address this issue by designing learning algorithms that automatically and adaptively select the most informative examples for labeling so that human time is not wasted labeling irrelevant, redundant, or trivial examples. This paper proposes a new approach to active ML with nonparametric or overparameterized models such as kernel methods and neural networks. In the context of binary classification, the new approach is shown to possess a variety of desirable properties that allow active learning algorithms to automatically and efficiently identify decision boundaries and data clusters.
Data-Free Adversarial Perturbations for Practical Black-Box Attack

Neural networks are vulnerable to adversarial examples, which are malicious inputs crafted to fool pre-trained models. Adversarial examples often exhibit black-box attacking transferability, which allows that adversarial examples crafted for one model can fool another model. However, existing black-box attack methods require samples from the training data distribution to improve the transferability of adversarial examples across different models. Because of the data dependence, fooling ability of adversarial perturbations is only applicable when training data are accessible. In this paper, we present a data-free method for crafting adversarial perturbations that can fool a target model without any knowledge about the training data distribution. In the practical setting of black-box attack scenario where attackers do not have access to target models and training data, our method achieves high fooling rates on target models and outperforms other universal adversarial perturbation methods. Our method empirically shows that current deep learning models are still at a risk even when the attackers do not have access to training data.
A Survey and Taxonomy on Energy-Aware Data Management Strategies in Cloud Environment

During the past ten years, the energy consumption problem in cloud-related environments has attracted substantial attention in research and industrial communities. Researchers have conducted many surveys on energy efficiency issues from different perspectives. All of the surveys can be classified into five categories: surveys on the energy efficiency of the whole cloud related system, surveys on the energy efficiency of a certain level or component of the cloud, surveys on all of the energy efficient strategies, surveys on a certain energy efficiency techniques, and other energy efficiency related surveys. However, to the best of our knowledge, surveys on energy-aware data management strategies in cloud-related environment are absent. In this paper, we conduct a comprehensive survey on energy saving-aware data management strategies in cloud-related environments, such as data classification, data placement and data replication strategies. Compared to current existing reviews on energy efficiency in cloud-related environments, we firstly conduct the survey on the energy consumption problem from the data management perspective. Furthermore, we classify the energy-aware data management strategies from different perspectives. This survey and the taxonomy of the energy-aware data management strategies demonstrate the potential for reducing the energy consumption at the data management level of a cloud storage system, which will compress more space for energy reduction and finally achieve energy proportionality. Moreover, this survey and taxonomy on the energy efficiency issue from the data management perspective is an important supplement to current existing surveys on energy efficiency in cloud-related environments.