Reviewerly/exHarmony huggingface dataset:
Predictive-Trend-Aware Composition of Web Services With Time-Varying Quality-of-Service

Service composition is a technology capable of combing a collection of existing services where many smaller services are coordinated together to form a larger one. Functionally similar services can often show different quality-of-service (QoS) properties. For a specific service composition request, how to choose from a bag of suitable services that fulfill the required functions under given quality-of-service constraints is widely believed to be a great challenge. The traditional approach usually tackles this problem by assuming fixed, bounded, or statistic QoS and views the decision-making of service composition as a static process. Instead, we address this problem by considering time-varying and fluctuating QoS and presenting a predictive-trend-aware service composition method by using a time series prediction model and genetic algorithms. We conduct extensive case studies based on multiple randomly-generated service templates with varying process configurations and show that our method outperforms existing ones.
Weakly Supervised Disentanglement by Pairwise Similarities

Recently, researches related to unsupervised disentanglement learning with deep generative models have gained substantial popularity. However, without introducing supervision, there is no guarantee that the factors of interest can be successfully recovered (Locatello et al. 2018). Motivated by a real-world problem, we propose a setting where the user introduces weak supervision by providing similarities between instances based on a factor to be disentangled. The similarity is provided as either a binary (yes/no) or real-valued label describing whether a pair of instances are similar or not. We propose a new method for weakly supervised disentanglement of latent variables within the framework of Variational Autoencoder. Experimental results demonstrate that utilizing weak supervision improves the performance of the disentanglement method substantially.
Towards Low-Cost Sign Language Gesture Recognition Leveraging Wearables

Different from traditional gestures, sign language gestures involve a lot of finger-level gestures without wrist or arm movements. They are hard to detect using existing motion sensors-based approaches. We introduce the first low-cost sign language gesture recognition system that can differentiate fine-grained finger movements using the Photoplethysmography (PPG) and motion sensors in commodity wearables. By leveraging the motion artifacts in PPG, our system can accurately recognize sign language gestures when there are large body movements, which cannot be handled by the traditional motion sensor-based approaches. We further explore the feasibility of using both PPG and motion sensors in wearables to improve the sign language gesture recognition accuracy when there are limited body movements. We develop a gradient boost tree (GBT) model and deep neural network-based model (i.e., ResNet) for classification. The transfer learning technique is applied to ResNet-based model to reduce the training effort. We develop a prototype using low-cost PPG and motions sensors and conduct extensive experiments and collect over 7000 gestures from 10 adults in the static and body-motion scenarios. Results demonstrate that our system can differentiate nine finger-level gestures from the American Sign Language with an average recognition accuracy over 98 percent.
Multi-View Multiple Clusterings Using Deep Matrix Factorization

Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can only output a single clustering of the data. Due to their multiplicity, multi-view data, can have different groupings that are reasonable and interesting from different perspectives. However, how to find multiple, meaningful, and diverse clustering results from multi-view data is still a rarely studied and challenging topic in multi-view clustering and multiple clusterings. In this paper, we introduce a deep matrix factorization based solution (DMClusts) to discover multiple clusterings. DMClusts gradually factorizes multi-view data matrices into representational subspaces layer-by-layer and generates one clustering in each layer. To enforce the diversity between generated clusterings, it minimizes a new redundancy quantification term derived from the proximity between samples in these subspaces. We further introduce an iterative optimization procedure to simultaneously seek multiple clusterings with quality and diversity. Experimental results on benchmark datasets confirm that DMClusts outperforms state-of-the-art multiple clustering solutions.
Co-GCN for Multi-View Semi-Supervised Learning

In many real-world applications, the data have several disjoint sets of features and each set is called as a view. Researchers have developed many multi-view learning methods in the past decade. In this paper, we bring Graph Convolutional Network (GCN) into multi-view learning and propose a novel multi-view semi-supervised learning method Co-GCN by adaptively exploiting the graph information from the multiple views with combined Laplacians. Experimental results on real-world data sets verify that Co-GCN can achieve better performance compared with state-of-the-art multi-view semi-supervised methods.
Deep Residual Haze Network for Image Dehazing and Deraining

Image dehazing on a hazy image aims to remove the haze and make the image scene clear, which attracts more and more research interests in recent years. Most existing image dehazing methods use a classic atmospheric scattering model and natural image priors to remove the image haze. In this paper, we propose an end-to-end image dehazing model termed as DRHNet (Deep Residual Haze Network), which restores the haze-free image by subtracting the learned negative residual map from the hazy image. Specifically, DRHNet proposes a context-aware feature extraction module to aggregate the contextual information effectively. Furthermore, it proposes a novel nonlinear activation function termed as RPReLU (Reverse Parametric Rectified Linear Unit) to improve its representation ability and to accelerate its convergence. Extensive experiments demonstrate that DRHNet outperforms state-of-the-art methods both quantitatively and qualitatively. In addition, experiments on image deraining task show that DRHNet can also serve for image deraining.
Strongly Constrained Discrete Hashing

Learning to hash is a fundamental technique widely used in large-scale image retrieval. Most existing methods for learning to hash address the involved discrete optimization problem by the continuous relaxation of the binary constraint, which usually lead to large quantization errors and consequently suboptimal binary codes. A few discrete hashing methods have emerged recently. However, they either completely ignore some useful constraints (specifically the balance and decorrelation of hash bits) or just turn those constraints into regularizers that would make the optimization easier but less accurate. In this paper, we propose a novel supervised hashing method named, Strongly Constrained Discrete Hashing (SCDH), which overcomes such limitations. It can learn the binary codes for all examples in the training set, and meanwhile obtain a hash function for unseen samples with the above mentioned constraints preserved. Although the model of SCDH is fairly sophisticated, we are able to find closed-form solutions to all of its optimization subproblems and thus design an efficient algorithm that converges quickly. In addition, we extend SCDH to a kernelized version SCDHK. Our experiments on three large benchmark datasets have demonstrated that not only can SCDH and SCDHK achieve substantially higher MAP scores than state-of-the-art baselines, but they run much faster than those that are also supervised as well.
Machine Learning Model for Smart Contracts Security Analysis

In this paper, we introduce a machine learning predictive model that detects patterns of security vulnerabilities in smart contracts. We adapted two static code analyzers to label more than 1000 smart contracts that were verified and used on the Ethereum platform. Our model predicted a number of major software vulnerabilities with the average accuracy of 95 percent. The model currently supports smart contracts developed in Solidity, however, the approach described in this paper can be applied to other languages and blockchain platforms.
Semantic head enhanced pedestrian detection in a crowd

Pedestrian detection in a crowd is a challenging task because of intra-class occlusion. More prior information is needed for the detector to be robust against it. Human head area is naturally a strong cue because of its stable appearance, visibility and relative location to body. Inspired by it, we adopt an extra branch to conduct semantic head detection in parallel with traditional body branch. Instead of manually labeling the head regions, we use weak annotations inferred directly from body boxes, which are named as 'semantic head'. In this way, the head detection is formulated into using a special part of labeled box to detect the corresponding part of human body, which surprisingly improves the performance and robustness to occlusion. Moreover, the head-body alignment structure is explicitly explored by introducing Alignment Loss, which functions in a self-supervised manner. Based on these, we propose the head-body alignment net (HBAN) in this work, which aims to enhance pedestrian detection by utilizing the human head prior. Comprehensive evaluations are conducted to demonstrate the effectiveness of HBAN on CityPersons dataset. HBAN achieves about 5% increase for heavily occluded pedestrians over baseline and reaches the state-of-the-art performance.
A novel method for lossless image compression and encryption based on LWT, SPIHT and cellular automata

In this paper, a novel method for lossless image encryption based on set partitioning in hierarchical trees and cellular automata. The proposed encryption method embeds the encryption into the compression process, in which a small part of the data is encrypted quickly, while maintaining the good coding characteristics of set partitioning in hierarchical trees (SPIHT). The proposed encryption system adopts three stages of scrambling and diffusion. In each stage of encryption, different chaotic systems are used to generate the plaintext-related key stream to maintain high security and to resist some attacks. Moreover, the channel length of the coded-and-compressed color image is more uncertain, resulting into higher difficulty for attackers to decipher the algorithm. The experimental results indicate that the length of bitstream is compressed to 50% of the original image, showing that our proposed algorithm has higher lossless compression ratio compared with the existing algorithms. Meanwhile, the encryption scheme passes the entropy analysis, sensitivity analysis, lossless recovery test, and SP800-22 test.
Adversarial attacks on fingerprint liveness detection

Abstract Deep neural networks are vulnerable to adversarial samples, posing potential threats to the applications deployed with deep learning models in practical conditions. A typical example is the fingerprint liveness detection module in fingerprint authentication systems. Inspired by great progress of deep learning, deep networks-based fingerprint liveness detection algorithms spring up and dominate the field. Thus, we investigate the feasibility of deceiving state-of-the-art deep networks-based fingerprint liveness detection schemes by leveraging this property in this paper. Extensive evaluations are made with three existing adversarial methods: FGSM, MI-FGSM, and Deepfool. We also proposed an adversarial attack method that enhances the robustness of adversarial fingerprint images to various transformations like rotations and flip. We demonstrate these outstanding schemes are likely to classify fake fingerprints as live fingerprints by adding tiny perturbations, even without internal details of their used model. The experimental results reveal a big loophole and threats for these schemes from a view of security, and enough attention is urgently needed to be paid on anti-adversarial not only in fingerprint liveness detection but also in all deep learning applications.
Personality Classification from Online Text using Machine Learning Approach

Personality refer to the distinctive set of characteristics of a person that effect their habits, behaviour's, attitude and pattern of thoughts.Text available on Social Networking sites provide an opportunity to recognize individual's personality traits automatically.In this proposed work, Machine Learning Technique, XGBoost classifier is used to predict four personality traits based on Myers-Briggs Type Indicator (MBTI) model, namely Introversion-Extroversion(I-E), iNtuition-Sensing(N-S), Feeling- Thinking(F-T) and Judging-Perceiving(J-P) from input text.Publically available benchmark dataset from Kaggle is used in experiments.The skewness of the dataset is the main issue associated with the prior work, which is minimized by applying Re-sampling technique namely random over-sampling, resulting in better performance.For more exploration of the personality from text, pre-processing techniques including tokenization, word stemming, stop words elimination and feature selection using TF IDF are also exploited.This work provides the basis for developing a personality identification system which could assist organization for recruiting and selecting appropriate personnel and to improve their business by knowing the personality and preferences of their customers.The results obtained by all classifiers across all personality traits is good enough, however, the performance of XGBoost classifier is outstanding by achieving more than 99% precision and accuracy for different traits.
Fuzzy-Based Symmetrical Multi-Criteria Decision-Making Procedure for Evaluating the Impact of Harmful Factors of Healthcare Information Security

Growing concern about healthcare information security in the wake of alarmingly rising cyber-attacks is being given symmetrical priority by current researchers and cyber security experts. Intruders are penetrating symmetrical mechanisms of healthcare information security continuously. In the same league, the paper presents an overview on the current situation of healthcare information and presents a layered model of healthcare information management in organizations. The paper also evaluates the various factors that have a key contribution in healthcare information security breaches through a hybrid fuzzy-based symmetrical methodology of AHP-TOPSIS. Furthermore, for assessing the effect of the calculated results, the authors have tested the results on local hospital software of Varanasi. Tested results of the factors are validated through the comparison and sensitivity analysis in this study. Tabulated results of the proposed study propose a symmetrical mechanism as the most conversant technique which can be employed by the experts and researchers for preparing security guidelines and strategies.
Bi-Directional Adaptive A* Algorithm Toward Optimal Path Planning for Large-Scale UAV Under Multi-Constraints

For large-scale Unmanned Aerial Vehicle (UAV), it is a challenging problem to automatically generate an optimal path considering the multi-constraints. In order to improve the efficiency and optimality of conventional A* algorithm, Bi-directional adaptive A* algorithm is proposed in this paper. Regarding to the defect of expansion efficiency for conventional A* algorithm based on multi-direction, directional search strategy is adopted to improve the efficiency of expansion process and guarantee the smooth of the path. Adaptive step strategy and adaptive weight strategy are used to improve the exploration speed and the accuracy of the proposed algorithm. By adopting bi-directional search strategy, the exploration ability of the algorithm can be improved in some complex environment. Finally, rewiring process is introduced to optimize the path length. The simulation for UAV path planning under the multi-constraints conditions is carried out and the simulation results show that Bi-directional adaptive A* algorithm has the superiority in run time and path quality.
High-capacity adaptive steganography based on LSB and Hamming code

The development of the Internet provides the ability to transfer large amounts of data quickly and easily. However, sensitive data is easily intercepted by hackers on the Internet. Image steganography ensures the secure transmission of information. In order to improve the capacity of hidden secret data and provide imperceptible hidden image quality, this paper proposes a hybrid steganography method based on least significant bit (LSB) replacement and Hamming code (HLAH). Information security is also increased by using two different steganographic methods. Since the sharp areas of the image can tolerate more changes than the smooth areas, more secret messages are embedded in the edge regions of the image and a small amount of information are embedded in the smooth regions. Finally, the performance of the proposed HLAH method is evaluated by measuring payload capacity, mean square error (MSE), peak signal-to-noise ratio (PSNR), and histogram analysis. The experimental results show that the proposed HLAH method not only has greater embedding capacity than other existing methods, but also ensures higher image quality.
Learning design and learning analytics in mobile and ubiquitous learning: A systematic review

Abstract Mobile and Ubiquitous Learning (m/u‐learning) are finding an increasing adoption in education. They are often distinguished by hybrid learning environments that encompass elements of formal and informal learning, in activities that happen in distributed settings (indoors and outdoors), across physical and virtual spaces. Despite their purported benefits, these environments imply additional complexity in the design, monitoring and evaluation of learning activities. The research literature on learning design (LD) and learning analytics (LA) has started to deal with these issues. This paper presents a systematic literature review of LD and LA, in m/u‐learning. Apart from providing an overview of the current research in the field, this review elicits elements of common ground between both communities, as shown by the similar learning contexts and complementary research contributions, and based on the research gaps, proposes to: address m/u‐learning beyond higher education settings, reinforce the connection between physical and virtual learning spaces, and more systematically align LD and LA processes.
Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI

Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.
Hong-Ou-Mandel Interference with Imperfect Single Photon Sources

Hong-Ou-Mandel interference is a cornerstone of optical quantum technologies. We explore both theoretically and experimentally how the nature of unwanted multi-photon components of single photon sources affect the interference visibility. We apply our approach to quantum dot single photon sources in order to access the mean wavepacket overlap of the single-photon component - an important metric to understand the limitations of current sources. We find that the impact of multi-photon events has thus far been underestimated, and that the effect of pure dephasing is even milder than previously expected.
Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

Abstract Future wireless communications are becoming increasingly complex with different radio access technologies, transmission backhauls, and network slices, and they play an important role in the emerging edge computing paradigm, which aims to reduce the wireless transmission latency between end-users and edge clouds. Deep learning techniques, which have already demonstrated overwhelming advantages in a wide range of internet of things (IoT) applications, show significant promise for solving such complicated real-world scenarios. Although the convergence of radio access networks and deep learning is still in the preliminary exploration stage, it has already attracted tremendous concern from both academia and industry. To address emerging theoretical and practical issues, ranging from basic concepts to research directions in future wireless networking applications and architectures, this paper mainly reviews the latest research progress and major technological deployment of deep learning in the development of wireless communications. We highlight the intuitions and key technologies of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation and compression sensing, encoding and decoding, and security and privacy. Main challenges, potential opportunities and future trends in incorporating deep learning schemes in wireless communications environments are further illustrated.
Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation

To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog policy, aimed at fostering a more coherent and controllable dialog.To this end, we first construct a conversational graph (CG) from dialog corpora, in which there are vertices to represent "what to say" and "how to say", and edges to represent natural transition between a message (the last utterance in a dialog context) and its response.We then present a novel CG grounded policy learning framework that conducts dialog flow planning by graph traversal, which learns to identify a what-vertex and a how-vertex from the CG at each turn to guide response generation.In this way, we effectively leverage the CG to facilitate policy learning as follows: (1) it enables more effective long-term reward design, (2) it provides high-quality candidate actions, and (3) it gives us more control over the policy.Results on two benchmark corpora demonstrate the effectiveness of this framework.