Reviewerly/exHarmony huggingface dataset:
BitProb: Probabilistic Bit Signatures for Accurate Application Identification

Network traffic classification finds its applications in a variety of network management tasks such as quality of service, security monitoring, traffic engineering, etc. Deep Packet Inspection is one of the methods to identify applications. With the number of proprietary protocols on the rise and network protocols using bit level information for encoding, recently it has been shown that bit level signatures are effective for identifying applications. In this paper, we propose BitProb which generates probabilistic bit signatures for traffic classification. It uses the probability of a bit at a particular position being either 0 or 1 and generates a space efficient signature represented as a state transition machine. Subsequently, it uses the overall probability of an n bit binary string extracted from a network flow to identify which application generated the flow. We experiment with three datasets covering twenty protocols (text, binary and proprietary) and show that BitProb classifies network flows with high accuracy and has a minimum number of misclassifications.
Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks

With the development of vehicular network, location-based services (LBSs) provide increasing diversified services for drivers and passengers. When users enjoy the services, users' location needs to be constantly updated to service providers, which causes the location information to be speculated and attacked by attackers. However, existing schemes don't provide differentiated protection for users' different locations, which may lead to the leakage of location information. Therefore, we propose a location privacy protection method to satisfy users' personalized privacy needs with reasonable protection of their privacy. Firstly, we define a normalized decision matrix to describe the efficiency and privacy effects of a route, and establish a multi-attribute utility function to quantify the utility of different routes for route selection. Then, according to users' personalized privacy protection need, we allocate the privacy budget for each query location on the selected route based on the distance between it and his nearest sensitive location. Experimental results demonstrate that compared to existing methods, our scheme can meet the user's service requirements and achieve better service quality under the conditions of reasonable protection of their privacy.
Varying‐parameter finite‐time zeroing neural network for solving linear algebraic systems

A new recurrent neural network is presented for solving linear algebraic systems with finite-time convergence. The proposed model includes an exponential term in the Zhang neural network dynamical system, which leads to a faster convergence of the error-monitoring function in comparison to previous methods. Theoretical analysis, as well as simulation results, validate the efficacy of the proposed model.
Hourly-averaged solar plus wind power generation for Germany 2016: Long-term prediction, short-term forecasting, data mining and outlier analysis

Nationwide, hourly-averaged solar plus wind power generation (MW) data compiled for Germany for year 2016 is evaluated with ten influencing variables. Those variables cover, on an hourly basis, weather and ground-surface conditions and electricity prices. The transparent open box (TOB) algorithm accurately predicts and forecasts power generation (MW) for this dataset (prediction RMSE = 1175 MW and R2 = 0.9804; hour ahead forecast RMSE = 1632 MW and R2 = 0.9609) and meaningfully data mines the prediction outliers. Some 1.5 % of the data records display significant prediction errors. These records are mined to reveal that many of them form trends on a few specific days displaying unusual and rapidly changing weather conditions. Derivatives of ground level solar radiation, wind velocity and air pressure can meaningfully distinguish such unusual conditions and can be used to filter the dataset to further improve prediction accuracy. Derivatives and ratios of variables are also exploited to focus and modify feature selection for TOB analysis on approximately 10 % of the dataset (900 data records) responsible for the least accurate predictions. This more focused feature selection improves prediction accuracy for these more difficult to predict data records (RMSE improves from 3544 to 2630 MW; R2 from 0.8027 to 0.8938).
QoE Assessment of Mobile Multiparty Audiovisual Telemeetings

The expectations of modern mobile users are increasingly moving towards being able to access demanding services regardless of context or system influence factors, such as network conditions, service topology, and device processing capabilities. Multiparty audiovisual telemeetings are an example of a real-time, delay sensitive, and heavy load service, demanding to run on smartphones that are limited in display size, processing power, and battery capacity. In this paper, we first provide an overview of multiparty audiovisual calls established via mobile devices and key aspects influencing Quality of Experience (QoE). We then report on the results of five user studies conducted over the course of the past 4 years, focused on investigating the impact of video quality in terms of different video encoding parameter configurations (namely bitrate, frame rate, and resolution) on subjective QoE scores for WebRTC-based video calls. We identify lower and upper bounds on video configuration parameters when used in the context of three-party calls. Results have shown that in certain cases it is better to provide constant lower objective video quality than to switch between higher and lower qualities, since participants start to perceive impairments. Finally, we investigate the relationship between objectively measured video quality impairments (blurriness and blockiness) and subjective user scores. Obtained results indicate that the Birnbaum-Saunders distribution for blockiness and the Burr and Gamma distributions for blurriness provide good fits for quality ratings. Gathered results aim to provide input for deriving QoE-aware service adaptation strategies, enabling increased resource allocation efficiency while maintaining acceptable end-user QoE.
Deductions from a Sub-Saharan African Bank’s Tweets: A sentiment analysis approach

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game States Recognition

Automatic video production of sports aims at producing an aesthetic broadcast of sporting events. We present a new video system able to automatically produce a smooth and pleasant broadcast of Basketball games using a single fixed 4K camera. The system automatically detects and localizes players, ball and referees, to recognize main action coordinates and game states yielding to a professional cameraman-like production of the basketball event. We also release a fully annotated dataset consisting of single 4K camera and twelve-camera videos of basketball games.
Deep Edu: A Deep Neural Collaborative Filtering for Educational Services Recommendation

In the modern world, people face an explosion of information and difficulty to find the right choice of their interest.Nowadays, people show interest in online shopping to meet their demands increasingly.For researchers and students, finding and buying the desired books from online shops is very tedious work.Recently Recommender System is an excellent tool to deal with such problems, but the Recommender System is suffering from multiple problems such as data sparsity, cold-start, and inaccuracy.To address these problems, we propose Deep Edu a novel Deep Neural Collaborative Filtering for educational services recommendation.A Deep Edu architecture consists of three parts of a Deep Neural Network model (such as input layer, a multilayered perceptron, and an output layer).The Deep Edu provides the following contributions: first, the users' identifier and books identifier features are mapped into N-dimensional dense embedding vectors, second, the Multi-Layer-Perceptron (MLP) takes the N-dimensional and non-linear features.To increase the performance of Deep Edu in all metrics, we proposed the advance Loss function.Equipped with the following, Deep Edu not only capable of learning the N-dimensional and non-linear interactions between users' identifier and books identifier, but moreover, it also considerably mitigates the cold-start, data sparsity, and inaccuracy problem.Over significant experiments performed on real-world good books dataset, the results show that Deep Edu's recommendation performance obviously outperforms existing Educational services recommendation methods.
Hand Written Digit Recognition using Machine Learning

Hand-written character and digit recognition have been one of the most exigent and engrossing field of pattern recognition and image processing. The main aim of this paper is to demonstrate and represent the work which is related to hand-written digit recognition. The hand-written digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of hand-written numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. SVM is applied for recognition and classification, while Convex hull algorithm is applied for feature extraction.
Dual Sequential Network for Temporal Sets Prediction

Many sequential behaviors such as purchasing items from time to time, selecting courses in different terms, collecting event logs periodically could be formalized as sequential sets of actions or elements, namely temporal sets. Predicting the subsequent set according to historical sequence of sets could help us make better producing, scheduling, or operating decisions. However, most of the existing methods were designed for predicting time series or temporal events, which could not be directly used for temporal sets prediction due to the difficulties of multi-level representations of items and sets, complex temporal dependencies of sets, and evolving dynamics of sequential behaviors. To address these issues, this paper provides a novel sets prediction method, called DSNTSP (Dual Sequential Network for Temporal Sets Prediction). Our model first learns both item-level representations and set-level representations of set sequences separately based on a transformer framework. Then, a co-transformer module is proposed to capture the multiple temporal dependencies of items and sets. Last, a gated neural module is designed to predict the subsequent set by fusing all the multi-level correlations and multiple temporal dependencies of items and sets. The experimental results on real-world data sets show that our methods lead to significant and consistent improvements as compared to other methods.
Domain-Adaptive Neural Automated Essay Scoring

Automated essay scoring (AES) is a promising, yet challenging task. Current state-of-the-art AES models ignore the domain difference and cannot effectively leverage data from different domains. In this paper, we propose a domain-adaptive framework to improve the domain adaptability of AES models. We design two domain-independent self-supervised tasks and jointly train them with the AES task simultaneously. The self-supervised tasks enable the model to capture the shared knowledge across different domains and act as the regularization to induce a shared feature space. We further propose to enhance the model's robustness to domain variation via a novel domain adversarial training technique. The main idea of the proposed domain adversarial training is to train the model with small well-designed perturbations to make the model robust to domain variation. We obtain the perturbation via a variation of the Fast Gradient Sign Method (FGSM). Our approach achieves new state-of-the-art performance in both in-domain and cross-domain experiments on the ASAP dataset. We also show that the proposed domain adaptation framework is architecture-free and can be successfully applied to different models.
Machine learning method for cosmetic product recognition: a visual searching approach

Abstract A cosmetic product recognition system is proposed in this paper. For this recognition system, we have proposed a cosmetic product database that contains image samples of forty different cosmetic items. The purpose of this recognition system is to recognize Cosmetic products with there types, brands and retailers such that to analyze a customer experience what kind of products and brands they need. This system has various applications in such as brand recognition, product recognition and also the availability of the products to the vendors. The implementation of the proposed system is divided into three components: preprocessing, feature extraction and classification. During preprocessing we have scaled and transformed the color images into gray-scaled images to speed up the process. During feature extraction, several different feature representation schemes: transformed, structural and statistical texture analysis approaches have been employed and investigated by employing the global and local feature representation schemes. Various machine learning supervised classification methods such as Logistic Regression, Linear Support Vector Machine, Adaptive k-Nearest Neighbor, Artificial Neural Network and Decision Tree classifiers have been employed to perform the classification tasks. Apart from this, we have also performed some data analytic tasks for Brand Recognition as well as Retailer Recognition and for these experimentation, we have employed some datasets from the ‘Kaggle’ website and have obtained the performance due to the above-mentioned classifiers. Finally, the performance of the cosmetic product recognition system, Brand Recognition and Retailer Recognition have been aggregated for the customer decision process in the form of the state-of-the-art for the proposed system.
MultiDIAL: Domain Alignment Layers for (Multisource) Unsupervised Domain Adaptation

One of the main challenges for developing visual recognition systems working in the wild is to devise computational models immune from the domain shift problem, i.e., accurate when test data are drawn from a (slightly) different data distribution than training samples. In the last decade, several research efforts have been devoted to devise algorithmic solutions for this issue. Recent attempts to mitigate domain shift have resulted into deep learning models for domain adaptation which learn domain-invariant representations by introducing appropriate loss terms, by casting the problem within an adversarial learning framework or by embedding into deep network specific domain normalization layers. This paper describes a novel approach for unsupervised domain adaptation. Similarly to previous works we propose to align the learned representations by embedding them into appropriate network feature normalization layers. Opposite to previous works, our Domain Alignment Layers are designed not only to match the source and target feature distributions but also to automatically learn the degree of feature alignment required at different levels of the deep network. Differently from most previous deep domain adaptation methods, our approach is able to operate in a multi-source setting. Thorough experiments on four publicly available benchmarks confirm the effectiveness of our approach.
DriverMHG: A Multi-Modal Dataset for Dynamic Recognition of Driver Micro Hand Gestures and a Real-Time Recognition Framework

The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. However, real-time recognition of dynamic micro hand gestures from video streams is challenging for in-vehicle scenarios since (i) the gestures should be performed naturally without distracting the driver, (ii) micro hand gestures occur within very short time intervals at spatially constrained areas, (iii) the performed gesture should be recognized only once, and (iv) the entire architecture should be designed lightweight as it will be deployed to an embedded system. In this work, we propose an HCI system for dynamic recognition of driver micro hand gestures, which can have a crucial impact in automotive sector especially for safety related issues. For this purpose, we initially collected a dataset named Driver Micro Hand Gestures (DriverMHG), which consists of RGB, depth and infrared modalities. The challenges for dynamic recognition of micro hand gestures have been addressed by proposing a lightweight convolutional neural network (CNN) based architecture which operates online efficiently with a sliding window approach. For the CNN model, several 3-dimensional resource efficient networks are applied and their performances are analyzed. Online recognition of gestures has been performed with 3D-MobileNetV2, which provided the best offline accuracy among the applied networks with similar computational complexities. The final architecture is deployed on a driver simulator operating in real-time. We make DriverMHG dataset and our source code publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Representation learning via serial robust autoencoder for domain adaptation

Domain adaptation aims to apply knowledge obtained from a labeled source domain to an unseen target domain from a different distribution. Recently, domain adaptation approaches based on autoencoder have achieved promising performances. However, almost of these approaches ignore the potential relationships of intra-domain features, which can be used to further reduce the distribution discrepancy between source and target domains. Furthermore, almost of them depend on the single autoencoder model, which brings the challenge of extracting multiple characteristics of data. To address these issues, in this paper, we propose a new representation learning method based on serial robust autoencoder for domain adaptation, named SERA. SERA first enriches intra-domain knowledge by mining the potential relationships of features in the source domain and target domain, respectively. Then, SERA learns domain invariant representations by serially connecting two new proposed autoencoder models, including marginalized denoising autoencoder via adaptation regularization (AMDA) and robust autoencoder via graph regularization (GRA). Extensive experiments on four public datasets demonstrate the effectiveness of the proposed method.
An Insight into Pixel Value Ordering Prediction Based Prediction-error Expansion

As the core of prediction-error expansion technique, prediction method has a fundamental impact on performance of reversible data hiding. Pixel value ordering (PVO) prediction has been extensively investigated for its high accuracy. However, the correlation of pixels within block has not been fully exploited yet. In this paper, a novel prediction method is proposed by developing PVO prediction in the aspects of spatial correlation and correlated pixel pair. The key to PVO embedding is invariant pixel value order such that the predicted pixel can be identified by value. Instead of predicting and enlarging the largest pixel, we propose to predict and reduce the second largest one and even all others. As the largest pixel which serves as predicted value is maintained after embedding, the numerous predicted pixels can be identified and thus reversibility is guaranteed. IPVO prediction which location-dependently determines the predicted pixel is also developed. For further optimization, multi-pass IPVO embedding is extended from single-layered to double-layered such that full-enclosing pixels can be used to estimate pixel distribution and determine the optimal mode of defining spatial location. Finally, an enhanced pairwise PEE is incorporated with multi-pass IPVO for performance enhancement. Experimental results show that the proposed scheme not only outperforms PVO embedding and its miscellaneous extensions, but also achieves significant superiority in fidelity over a series of state-of-the-art schemes.
Ontologies for finding journalistic angles

Abstract Journalism relies more and more on information and communication technology (ICT). ICT-based journalistic knowledge platforms continuously harvest potentially news-relevant information from the Internet and make it useful for journalists. Because information about the same event is available from different sources and formats vary widely, knowledge graphs are emerging as a preferred technology for integrating, enriching, and preparing information for journalistic use. The paper explores how journalistic knowledge graphs can be augmented with support for news angles , which can help journalists to detect newsworthy events and make them interesting for the intended audience. We argue that finding newsworthy angles on news-related information is an important example of a topical problem in information science: that of detecting interesting events and situations in big data sets and presenting those events and situations in interesting ways.
A Survey on Performance Analysis of Block Cipher Algorithms

In today's scenario, Security is the most vital form of features in the network and internet applications. Network and Internet applications are increasing quickly, so the importance of the data swapped over the internet are also growing. Hence to provide the required protection against the data attacks is one of the significant solutions in the security related applications. Block cipher symmetric key cryptography plays a significant part in data security systems. This paper offers a comparison among the commonly used symmetric key cryptography algorithms: DES (Data Encryption standard), Blowfish, TDES (Triple Data Encryption Standard), PRESENT, KLEIN. The comparison of algorithm is performed based on Attacks, key size and block size and providing state-of-the-art results in their field.
ILFO: Adversarial Attack on Adaptive Neural Networks

With the increasing number of layers and parameters in neural networks, the energy consumption of neural networks has become a great concern to society, especially to users of handheld or embedded devices. In this paper, we investigate the robustness of neural networks against energy-oriented attacks. Specifically, we propose ILFO (Intermediate Output-Based Loss Function Optimization) attack against a common type of energy-saving neural networks, Adaptive Neural Networks (AdNN). AdNNs save energy consumption by dynamically deactivating part of its model based on the need of the inputs. ILFO leverages intermediate output as a proxy to infer the relation between input and its corresponding energy consumption. ILFO has shown an increase up to 100 % of the FLOPs (floating-point operations per second) reduced by AdNNs with minimum noise added to input images. To our knowledge, this is the first attempt to attack the energy consumption of an AdNN.