Reviewerly/exHarmony huggingface dataset:
Mobile app traffic flow feature extraction and selection for improving classification robustness

In machine-learning based mobile app traffic classification, flow feature distributions can easily drift due to changes in network environments, user habits etc. Unstable features may negatively influence mobile app traffic classification robustness, so to remedy this problem, this paper investigates how to obtain optimal feature sets for improving classification robustness of mobile app traffic. Specifically, we develop a method to search for stable and discriminative features by jointly analyzing mobile app traffic characteristics and assessing the degree of feature drift. Along these lines, we first analyze the in-flow behavior characteristics of traffic flows, so as to extract a potential feature set for mobile app traffic data. Next, we present two new metrics to assess the degree of drift experienced by the flow features from different perspectives and design a composite metric to score these features by considering the degree of drift as a penalty factor of discrimination power. Based on these metrics, we further propose an algorithm to search for optimal features with high discrimination power but low degree of drift. Existing flow features and feature selection algorithms were implemented for our comparison experiments. Our experimental results on real mobile app traffic data demonstrate the effectiveness of our feature set and feature selection algorithm on improving classification robustness.
Identifying cluster centroids from decision graph automatically using a statistical outlier detection method

Cluster centroid identification is a crucial step for many clustering methods. Recently, Rodriguez and Laio have proposed an effective density-based clustering method called Density Peak Clustering (DPC), in which the density value of each data point and the minimum distance from the points with higher density values are used to identify cluster centroids from the decision graph. However, there is still a lack of automatic methods for the identification of cluster centroids from the decision graph. In this work, a novel statistical outlier detection method is designed to identify cluster centroids automatically from the decision graph, so that the number of clusters is also automatically determined. In the proposed method, one-dimensional probability density functions at specific density values in the decision graph are estimated using two-dimensional Gaussian kernel functions. Then the cluster centroids are identified automatically as outliers in the decision graph using expectation values and standard deviations computed at specific density values. Experiments on several synthetic and real-world datasets show the superiority of the proposed method in centroid identification from the datasets with various distributions and dimensionalities. Furthermore, it is also shown that the proposed method can be effectively applied to image segmentation.
Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms

We propose to discriminate the pathological grades directly on digital mammograms instead of pathological images. An end-to-end learning algorithm based on the combined multi-level features is proposed. Low-level features are extracted and selected by supervised LASSO logistic regression. Convolutional Neural Network (CNN) is designed to extract high-level semantic features. These extracted multi-level features are combined to optimize the new CNN end to end to make different parts of the network learn to pay attention to different level of features. Results demonstrate that our proposed algorithm is superior to other CNN models and obtain comparable performance compared with pathological images.
Discrete Social Recommendation

Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, which imposes formidable challenges for efficient and accurate recommendations. A promising way is to simply binarize the latent features (obtained in the training phase) and then compute the relevance score through Hamming distance. However, such a two-stage hashing based learning procedure is not capable of preserving the original data geometry in the real-value space and may result in a severe quantization loss. To address these issues, this work proposes a novel discrete social recommendation (DSR) method which learns binary codes in a unified framework for users and items, considering social information. We further put the balanced and uncorrelated constraints on the objective to ensure the learned binary codes can be informative yet compact, and finally develop an efficient optimization algorithm to estimate the model parameters. Extensive experiments on three real-world datasets demonstrate that DSR runs nearly 5 times faster and consumes only with 1/37 of its real-value competitor’s memory usage at the cost of almost no loss in accuracy.
Structured and Sparse Annotations for Image Emotion Distribution Learning

Label distribution learning methods effectively address the label ambiguity problem and have achieved great success in image emotion analysis. However, these methods ignore structured and sparse information naturally contained in the annotations of emotions. For example, emotions can be grouped and ordered due to their polarities and degrees. Meanwhile, emotions have the character of intensity and are reflected in different levels of sparse annotations. Motivated by these observations, we present a convolutional neural network based framework called Structured and Sparse annotations for image emotion Distribution Learning (SSDL) to tackle two challenges. In order to utilize structured annotations, the Earth Mover’s Distance is employed to calculate the minimal cost required to transform one distribution to another for ordered emotions and emotion groups. Combined with Kullback-Leibler divergence, we design the loss to penalize the mispredictions according to the dissimilarities of same emotions and different emotions simultaneously. Moreover, in order to handle sparse annotations, sparse regularization based on emotional intensity is adopted. Through combined loss and sparse regularization, SSDL could effectively leverage structured and sparse annotations for predicting emotion distribution. Experiment results demonstrate that our proposed SSDL significantly outperforms the state-of-the-art methods.
Multi-Attribute Transfer via Disentangled Representation

Recent studies show significant progress in image-to-image translation task, especially facilitated by Generative Adversarial Networks. They can synthesize highly realistic images and alter the attribute labels for the images. However, these works employ attribute vectors to specify the target domain which diminishes image-level attribute diversity. In this paper, we propose a novel model formulating disentangled representations by projecting images to latent units, grouped feature channels of Convolutional Neural Network, to disassemble the information between different attributes. Thanks to disentangled representation, we can transfer attributes according to the attribute labels and moreover retain the diversity beyond the labels, namely, the styles inside each image. This is achieved by specifying some attributes and swapping the corresponding latent units to “swap” the attributes appearance, or applying channel-wise interpolation to blend different attributes. To verify the motivation of our proposed model, we train and evaluate our model on face dataset CelebA. Furthermore, the evaluation of another facial expression dataset RaFD demonstrates the generalizability of our proposed model.
Dynamic Texture Classification Using Unsupervised 3D Filter Learning and Local Binary Encoding

Local binary descriptors, such as local binary pattern (LBP) and its various variants, have been studied extensively in texture and dynamic texture analysis due to their outstanding characteristics, such as grayscale invariance, low computational complexity and good discriminability. Most existing local binary feature extraction methods extract spatio-temporal features from three orthogonal planes of a spatio-temporal volume by viewing a dynamic texture in 3D space. For a given pixel in a video, only a proportion of its surrounding pixels is incorporated in the local binary feature extraction process. We argue that the ignored pixels contain discriminative information that should be explored. To fully utilize the information conveyed by all the pixels in a local neighborhood, we propose extracting local binary features from the spatio-temporal domain with 3D filters that are learned in an unsupervised manner so that the discriminative features along both the spatial and temporal dimensions are captured simultaneously. The proposed approach consists of three components: 1) 3D filtering; 2) binary hashing; and 3) joint histogramming. Densely sampled 3D blocks of a dynamic texture are first normalized to have zero mean and are then filtered by 3D filters that are learned in advance. To preserve more of the structure information, the filter response vectors are decomposed into two complementary components, namely, the signs and the magnitudes, which are further encoded separately into binary codes. The local mean pixels of the 3D blocks are also converted into binary codes. Finally, three types of binary codes are combined via joint or hybrid histograms for the final feature representation. Extensive experiments are conducted on three commonly used dynamic texture databases: 1) UCLA; 2) DynTex; and 3) YUVL. The proposed method provides comparable results to, and even outperforms, many state-of-the-art methods.
Ask Diana: A Keyword-Based Chatbot System for Water-Related Disaster Management

This research developed a keyword-based chatbot system, Ask Diana, for water-related disaster management. Disaster management has been considered difficult and tedious due to the complex characteristics of disaster-related data. To deal with this problem, this research developed a chatbot system with a water-related disaster database, a user intent mechanism, and an intuitive mobile-device-based user interface. With such a system, users are able to access important data or information they need for decision making by directly asking the proposed chatbot or operating the image-based menus. The system was validated through a usability test and a six-month field test. The results demonstrated that Ask Diana can help related personnel access disaster data intuitively and develop corresponding response strategies efficiently.
Boosting decision stumps for dynamic feature selection on data streams

Feature selection targets the identification of which features of a dataset are relevant to the learning task. It is also widely known and used to improve computation times, reduce computation requirements, and to decrease the impact of the curse of dimensionality and enhancing the generalization rates of classifiers. In data streams, classifiers shall benefit from all the items above, but more importantly, from the fact that the relevant subset of features may drift over time. In this paper, we propose a novel dynamic feature selection method for data streams called Adaptive Boosting for Feature Selection (ABFS). ABFS chains decision stumps and drift detectors, and as a result, identifies which features are relevant to the learning task as the stream progresses with reasonable success. In addition to our proposed algorithm, we bring feature selection-specific metrics from batch learning to streaming scenarios. Next, we evaluate ABFS according to these metrics in both synthetic and real-world scenarios. As a result, ABFS improves the classification rates of different types of learners and eventually enhances computational resources usage.
A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets

In order to solve the problem of non-ideal training sets (i.e., the less-complete or over-complete sets) and implement one-iteration learning, a novel efficient quantum perceptron algorithm based on unitary weights is proposed, where the singular value decomposition of the total weight matrix from the training set is calculated to make the weight matrix to be unitary. The example validation of quantum gates {H, S, T, CNOT, Toffoli, Fredkin} shows that our algorithm can accurately implement arbitrary quantum gates within one iteration. The performance comparison between our algorithm and other quantum perceptron algorithms demonstrates the advantages of our algorithm in terms of applicability, accuracy, and availability. For further validating the applicability of our algorithm, a quantum composite gate which consists of several basic quantum gates is also illustrated.
Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm

Recommender systems are intelligent data mining applications that deal with the issue of information overload significantly.The available literature discusses several methodologies to generate recommendations and proposes different techniques in accordance with users' needs.The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user's an item's context.The biggest challenge for a recommender system is to produce meaningful recommendations by using contextual user-item rating information.A context is a vast term that may consider various aspects; for example, a user's social circle, time, mood, location, weather, company, day type, an item's genre, location, and language.Typically, the rating behavior of users varies under different contexts.From this line of research, we have proposed a new algorithm, namely Kernel Context Recommender System, which is a flexible, fast, and accurate kernel mapping framework that recognizes the importance of context and incorporates the contextual information using kernel trick while making predictions.We have benchmarked our proposed algorithm with pre-and post-filtering approaches as they have been the favorite approaches in the literature to solve the context-aware recommendation problem.Our experiments reveal that considering the contextual information can increase the performance of a system and provide better, relevant, and meaningful results on various evaluation metrics.
Machine Learning in Artificial Intelligence: Towards a Common Understanding

The application of "machine learning" and "artificial intelligence" has become popular within the last decade.Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings.In this work, we aim to clarify the relationship between these terms and, in particular, to specify the contribution of machine learning to artificial intelligence.We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents.Hence, we seek to provide more terminological clarity and a starting point for (interdisciplinary) discussions and future research.
Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification

Breast cancer is an important cause of death in females. Early recognition of this disease with the assistance of mammography reduces the death rate. Deep learning (DL) is an approach being utilized and requested by radiologists to assist in making an accurate diagnosis, and it can help to improve outcome predictions. This paper includes a new approach, applied on the Mini-MIAS dataset of 322 images, involving a pre-processing method and inbuilt feature extraction using K-means clustering for Speed-Up Robust Features (SURF) selection. A new layer is added at the classification level, which carries out a ratio of 70% training to 30% testing of the deep neural network and Multiclass Support Vector Machine (MSVM). The outcome described herein demonstrates that the accuracy rate of the proposed automated DL method using K-means clustering with MSVM is improved as compared with a decision tree model. Experimental results show that the average accuracy (ACC) rates of the three classes, i.e., normal, benign and malignant cancer, using the proposed method, are 95%, 94% and 98%, respectively. The increased sensitivity rate is 3%, specificity is 2%, and Receiver Operating Characteristics (ROC) area is 0.99 using SVM compared to the Multi-Layer Perceptron (MLP) and J48+K-mean clustering WEKA manual approach. A 10-fold cross validation was used, and the obtained results for the Support Vector Machine (SVM), K-nearest neighbour (KNN), linear discriminant analysis (LDA) and Decision Tree were 96.9%, 93.8%, 89.7% and 88.7%, respectively.
A Novel Rank Approximation Method for Mixture Noise Removal of Hyperspectral Images

Mixture noise removal is a fundamental problem in hyperspectral images' (HSIs) processing that holds significant practical importance for subsequent applications. This problem can be recast as an approximation issue of a low-rank matrix. In this paper, a novel smooth rank approximation (SRA) model is proposed to cope with these mixture noises for HSIs. The crux idea is to devise a general smooth function under some assumptions to directly approximate the rank function, which attempts to explore a closer approximation than conventional methods. This new optimization model can be easily solved by the convex analysis tool and can remove the mixture noises of HSIs quickly and effectively. Subsequently, we give a feasible iterative algorithm, and the corresponding convergence analysis is discussed mathematically. Experimental results from the simulated data set as well as real data sets illustrate that the proposed SRA method significantly outperforms the state-of-the-art methods on HSI denoising.
Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning

Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies, or other novel imaging technologies. In addition, image segmentation also provides detailed structural description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation methods. Here, we first use some preprocessing methods such as wavelet denoising to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) on 5 MRI head image datasets. We then realize automatic image segmentation with deep learning by using convolutional neural network. We also introduce parallel computing. Such approaches greatly reduced the processing time compared to manual and semiautomatic segmentation and are of great importance in improving the speed and accuracy as more and more samples are being learned. The segmented data of grey and white matter are counted by computer in volume, which indicates the potential of this segmentation technology in diagnosing cerebral atrophy quantitatively. We demonstrate the great potential of such image processing and deep learning-combined automatic tissue image segmentation in neurology medicine.
A security scheme for intelligent substation communications considering real-time performance

Tampering, forgery and theft of the measurement and control messages in a smart grid could cause one breakdown in the power system. However, no security measures are employed for communications in intelligent substations. Communication services in an intelligent substation have high demands for real-time performance, which must be considered when deploying security measures. This paper studies the security requirements of communication services in intelligent substations, analyzes the security capabilities and shortages of IEC 62351, and proposes a novel security scheme for intelligent substation communications. This security scheme covers internal and telecontrol communications, in which the real-time performance of each security measure is considered. In this scheme, certificateless public key cryptography (CLPKC) is used to avoid the latency of certificate exchange in certificate-based cryptosystem and the problem of key escrow in identity-based cryptosystem; the security measures of generic object-oriented substation event, sampled measure value and manufacturing message specification in IEC 62351 are improved to meet the real-time requirements of the messages as well as to provide new security features to resist repudiation and replay attacks; and the security at transport layer is modified to fit CLPKC, which implements mutual authentication by exchanging signatures. Furthermore, a deployment of CLPKC in an intelligent substation is presented. We also evaluate the security properties of the scheme and analyze the end-to-end delays of secured services by combining theoretical calculation and simulation in this paper. The results indicate that the proposed scheme meets the requirements of security and real-time performance of communications in intelligent substations.
Energy-Efficient Coverage Path Planning for General Terrain Surfaces

This letter tackles the problem of energy-efficient coverage path planning for exploring general surfaces by an autonomous vehicle. Efficient algorithms are developed to generate paths on freeform 3-D surfaces according to a special design pattern as height extremity aware Fermat spiral for this purpose. By using the exact boundary-sourced geodesic distances, the method for generating Fermat spiral paths is first introduced to cover a general surface. Then, heuristics for energy efficiency are incorporated to add peak points of a height field as sources for geodesic computation. The paths generated by our method can significantly reduce the cost caused by gravity. Physical experiments have been taken on different terrain surfaces to demonstrate the effectiveness of our approach.