Reviewerly/exHarmony huggingface dataset:
Illustrating Changes in Time-Series Data With Data Video

Understanding the changes of time-series is a common task in many application domains. Converting time-series data into videos helps an audience with little or no background knowledge gain insights and deep impressions. It essentially integrates data visualizations and animations to present the evolution of data expressively. However, it remains challenging to create this kind of data video. First, it is difficult to efficiently detect important changes and include them in the video sequence. Existing methods require much manual effort to explore the data and find changes. Second, how these changes are emphasized in the videos is also worth studying. A video without emphasis will hinder an audience from noticing those important changes. This article presents an approach that extracts and visualizes important changes of a time-series. Users can explore and modify these changes, and apply visual effects on them. Case studies and user feedback demonstrate the effectiveness and usability of our approach.
Adaptive Semantic Segmentation for Unmanned Surface Vehicle Navigation

The intelligentization of unmanned surface vehicles (USVs) has recently attracted intensive interest. Visual perception of the water scenes is critical for the autonomous navigation of USVs. In this paper, an adaptive semantic segmentation method is proposed to recognize the water scenes. A semantic segmentation network model is designed to classify each pixel of an image into water, land or sky. The segmentation result is refined by the conditional random field (CRF) method. It is further improved accordingly by referring to the superpixel map. A weight map is generated based on the prediction confidence. The network trains itself with the refined pseudo label and the weight map. A set of experiments were designed to evaluate the proposed method. The experimental results show that the proposed method exhibits excellent performance with few-shot learning and is quite adaptable to a new environment, very efficient for limited manual labeled data utilization.
DeepSignCX: Signature Complexity Detection using Recurrent Neural Networks

This paper proposes a novel approach for on-line signature complexity detection based on Recurrent Neural Networks (RNNs). Complexity of handwritten signatures can vary from very simple ones (just a simple flourish) to very complex signatures (including the handwritten full name and complex flourish). Three different complexity levels are proposed: low, medium, and high. Time functions are extracted from the on-line signatures and a system based on RNNs (BLSTM in particular) is trained to classify the three levels of complexity over a ground truth manually labelled database (BiosecurID with 400 subjects). This initial model is used to automatically label a very large database (DeepSignDB) containing over 1500 subjects, which is then used to train the proposed RNN for signature complexity detection. Promising results ca. 85% of accuracy are achieved. This complexity detector could be used as a first stage in a signature verification system in order to train a specific biometric system per signature complexity level and improve the overall system performance.
Adaptive storytelling based on personality and preference modeling

In almost all forms of storytelling, the background and the current state of mind of the audience members predispose them to experience a given story from a uniquely personal perspective. However, traditional story writers usually construct their narratives based on the average preferences of their audience, which does not guarantee satisfying narrative experiences for its members. When a narrative aims at providing pleasurable entertainment, having some information about the preferences of the current user for the narrative's content is vital to create satisfying experiences. This paper explores personality modeling and proposes a novel approach to generate individualized interactive narratives based on the preferences of users, which we model in terms of the Big Five factors. This paper presents and evaluates the proposed method in a web-based interactive storytelling system that explores the Little Red Riding Hood folktale. The results show that the proposed method is capable of correctly recognizing the preferences of users for story events (average accuracy of 91.9%) and positively improve user satisfaction and experience.
A New Face Recognition Method for Intelligent Security

With the advent of the era of artificial intelligence and big data, intelligent security robots not only improve the efficiency of the traditional intelligent security industry but also propose higher requirements for intelligent security. Aiming to solve the problem of long recognition time and high equipment cost of intelligent security robots, we propose a new face recognition method for intelligent security in this paper. We use the Goldstein branching method for phase unwrapping, which can improve the three-dimensional (3D) face reconstruction effect. Subsequently, by using the three-dimensional face recognition method based on face radial curve elastic matching, different weights are assigned to different curve recognition similarity for weighted fusion as the total similarity for recognition. Experiments show that the method has a higher face recognition rate and is robust to attitude, illumination, and noise.
Cross-Modal Zero-Shot Hashing

Hashing has been widely studied for big data retrieval due to its low storage cost and fast query speed. Zero-shot hashing (ZSH) aims to learn a hashing model that is trained using only samples from seen categories, but can generalize well to samples of unseen categories. ZSH generally uses category attributes to seek a semantic embedding space to transfer knowledge from seen categories to unseen ones. As a result, it may perform poorly when labeled data are insufficient. ZSH methods are mainly designed for single-modality data, which prevents their application to the widely spread multi-modal data. On the other hand, existing cross-modal hashing solutions assume that all the modalities share the same category labels, while in practice the labels of different data modalities may be different. To address these issues, we propose a general Cross-modal Zero-shot Hashing (CZHash) solution to effectively leverage unlabeled and labeled multi-modality data with different label spaces. CZHash first quantifies the composite similarity between instances using label and feature information. It then defines an objective function to achieve deep feature learning compatible with the composite similarity preserving, category attribute space learning, and hashing coding function learning. CZHash further introduces an alternative optimization procedure to jointly optimize these learning objectives. Experiments on benchmark multi-modal datasets show that CZHash significantly outperforms related representative hashing approaches both on effectiveness and adaptability.
New group user based privacy preserving cloud auditing protocol

Cloud storage is now a very popular service for many enterprises and their users. However, ensuring the integrity of the outsourced data is a very challenging problem. The concept of provable data possession is very effective for solving this problem. Based on this concept, in this paper we propose a new group-user-based privacy-preserving secure cloud auditing protocol for enterprise users and analyse its security. This protocol is also constructed on a recently proposed lightweight and privacy-preserving secure cloud auditing scheme for group users via a third-party medium. We point out that scheme is not secure and that the authenticators corresponding to the outsourced data blocks can be easily forged, that means the cloud servers can delete all the outsourced data but can still give correct proof for the possession of outsourced data, while our protocol has no such shortcoming. The performance analysis results demonstrate that our proposal is very practical and can have large-scale applications.
Cryptocurrency Price Prediction based on Multiple Market Sentiment

With the rapid development of the Internet, cryptocurrencies have been gaining increasing amounts of attention dramatically.As a digital currency, it is not only used worldwide for online payments, but also traded as an investment tool on the market.Therefore, the ability to predict the price volatility will facilitate future investment and payment decisions.However, there are many uncertainties in the price movement of cryptocurrencies, and the prediction is extremely difficult.To this end, based on the transaction data of three different markets and the number and content of user comments and responses from online forums, this paper constructs a price prediction model of cryptocurrencies using a variety of machine learning and deep learning algorithms.It turns out that the trading price premium rate in different markets will affect the price to be predicted, and adding social media comment features can significantly improve the accuracy of the forecast.This article is conducive to investors who encrypt currencies to make more scientific decisions.
New Kid On The Block! Understanding Blockchain Adoption in the Public Sector

Blockchain has been heralded as a technology that can transform entire sectors, including the public sector where blockchain applications are believed to bring a wide range of benefits.The public sector is lagging behind, however, in its actual adoption of blockchain technology, and our understanding of the factors that explain the slow adoption rate, is lacking.Based on seven case studies of blockchain projects at various adoption stages, this research contributes to our understanding of what factors influence blockchain adoption in the public sector.We use an extended TOE framework that includes an inter-organizational perspective.The findings show that adoption is influenced by the hype around-and resistance toblockchain technology; by top management support, by (perceptions of) the regulatory environment; as well as by trust between blockchain partners, which is both an antecedent as well as a consequence of blockchain adoption.
Fraud Detection of Bulk Cargo Theft in Port Using Bayesian Network Models

The fraud detection of cargo theft has been a serious issue in ports for a long time. Traditional research in detecting theft risk is expert- and survey-based, which is not optimal for proactive prediction. As we move into a pervasive and ubiquitous paradigm, the implications of external environment and system behavior are continuously captured as multi-source data. Therefore, we propose a novel data-driven approach for formulating predictive models for detecting bulk cargo theft in ports. More specifically, we apply various feature-ranking methods and classification algorithms for selecting an effective feature set of relevant risk elements. Then, implicit Bayesian networks are derived with the features to graphically present the relationship with the risk elements of fraud. Thus, various binary classifiers are compared to derive a suitable predictive model, and Bayesian network performs best overall. The resulting Bayesian networks are then comparatively analyzed based on the outcomes of model validation and testing, as well as essential domain knowledge. The experimental results show that predictive models are effective, with both accuracy and recall values greater than 0.8. These predictive models are not only useful for understanding the dependency between relevant risk elements, but also for supporting the strategy optimization of risk management.
Discriminative dictionary learning algorithm based on sample diversity and locality of atoms for face recognition

Dictionary learning is one of the most important algorithms for face recognition. However, many dictionary learning algorithms for face recognition have the problems of small sample and weak discriminability. In this paper, a novel discriminative dictionary learning algorithm based on sample diversity and locality of atoms is proposed to solve the problems. The rational sample diversity is implemented by alternative samples and new error model to alleviate the small sample size problem. Moreover, locality can leads to sparsity and strong discriminability. In this paper, to enhance the dictionary discrimination and to reduce the influence of noise, the graph Laplacian matrix of atoms is used to keep the local information of the data. At the same, the relational theory is presented. A large number of experiments prove that the proposed algorithm can achieve more high performance than some state-of-the-art algorithms.
A Hybrid Multilingual Fuzzy-Based Approach to the Sentiment Analysis Problem Using SentiWordNet

Sentiment Analysis or in particular social network analysis (SNA) is a new research area which is increased explosively. This domain has become a very active research issue in data mining and natural language processing. Sentiment analysis (opinion mining) consists in analyzing and extracting emotions, opinions or attitudes from product’s reviews, movie's reviews, etc., and classify them into classes such as positive, negative and neutral, or extract the degree of importance (polarity). In this paper, we propose a new hybrid approach for classifying tweets into classes based on fuzzy logic and a lexicon based approach using SentiWordnet. Our approach consists in classifying tweets according to three classes: positive, negative or neutral, using SentiWordNet and the fuzzy logic with its three important steps: Fuzzification, Rule Inference/aggregation, and Defuzzification. The dataset of tweets to classify and the result of the classification are stored in the Hadoop Distributed File System (HDFS), and we use the Hadoop MapReduce for the application of our proposal.
BBB-CFI

Code-reuse attack is a concrete threat to computing systems because it can evade conventional security defenses. Control flow integrity (CFI) is proposed to repel this threat. However, former implementations of CFI suffer from two major drawbacks: complex offline processing on programs and high overheads at runtime. Therefore, it is impractical for performance-constrained devices to adopt the technology, leaving them vulnerable to exploitation. In this article, we develop a cross-layer approach named basic-block-boundary-based control flow integrity (BBB-CFI) to minimize the overheads of both offline analysis and runtime checking. Our approach employs basic block information inside the binary code and read-only data to enforce CFI. We identify a key binary-level property called basic block boundary , and based on it we propose the code-inspired method where short code sequences can endorse a control flow transition. Our solution enables quick application launching because it does not require control flow graph construction at the offline stage. We only demand a lightweight analysis on read-only data and a small amount of code of the application. According to the experiments, our approach incurs a negligible 0.11% runtime performance overhead with a minor processor extension, whereas it achieves an order of magnitude speedup in pre-preprocessing compared to a baseline approach. Without control flow analysis or recompilation, BBB-CFI still effectively reduces 90% of the attack surface in terms of gadget numbers. Besides this, we show that the Turing-completeness in the libc is unsustainable. Our approach also demonstrates high applicability to many programs, and it is capable of protecting striped binaries.
The Impact of Deep Learning Techniques on SMS Spam Filtering

Over the past decade, phone calls and bulk SMS have been fashionable.Although many advertisers assume that SMS has died, it is still alive.It is one of the simplest and most cost-effective marketing tools for companies to communicate on a personal level to their customers.The spread of SMS has led to the risk of spam.Most of the previous studies that attempted to detect spam were based on manually extracted features using classical machine learning classifiers.This paper explores the impact of applying various deep learning techniques on SMS spam filtering; by comparing the results of seven different deep neural network architectures and six classifiers for classical machine learning.Proposed methodologies are based on the automatic extraction of the required features.On a benchmark data set consisting of 5574 records, a fabulous accuracy of 99.26% has been resulted using Random Multimodel Deep Learning (RMDL) architecture.
Wart Treatment Decision Support Using Support Vector Machine

Warts are noncancerous benign tumors caused by the Human Papilloma Virus (HPV).The success rates of cryotherapy and immunotherapy, two common treatment methods for cutaneous warts, are 44% and 72%, respectively.The treatment methods, therefore, fail to cure a significant percentage of the patients.This study aims to develop a reliable machine learning model to accurately predict the success of immunotherapy and cryotherapy for individual patients based on their demographic and clinical characteristics.We employed support vector machine (SVM) classifier utilizing a dataset of 180 patients who were suffering from various types of warts and received treatment either by immunotherapy or cryotherapy.To balance the minority class, we utilized three different oversampling methodssynthetic minority oversampling technique (SMOTE), borderline-SMOTE, and adaptive synthetic (ADASYN) sampling.F-score along with sequential backward selection (SBS) algorithm were utilized to extract the best set of features.For the immunotherapy treatment method, SVM with radial basis function (RBF) kernel obtained an overall classification accuracy of 94.6% (sensitivity = 96.0%,specificity = 89.5%),and for the cryotherapy treatment method, SVM with polynomial kernel obtained an overall classification accuracy of 95.9% (sensitivity = 94.3%, specificity = 97.4%).The obtained results are competitive and comparable with the congeneric research works available in the literature, especially for the immunotherapy treatment method, we obtained 4.6% higher accuracy compared to the existing works.The developed methodology could potentially assist the dermatologists as a decision support tool by predicting the success of every unique patient before starting the treatment process.
Auxiliary graph based routing, wavelength, and time-slot assignment in metro quantum optical networks with a novel node structure

Nowadays, critical sectors in government, finance, and military are facing increasingly high security challenges. However, traditional public-key crypto-systems based on computational complexity are likely to suffer from upgrade computational power. Quantum key distribution (QKD) is a promising technology to effectively address the challenge by providing secret keys due to the laws of quantum physics. Limited by the transmission distance of quantum communications, remote parties have to share secret keys by exchanging keys through the trusted relay nodes hop by hop. However, if relaying hop by hop is still used in metro quantum-optical networks (MQON), a large amount of key resources will be wasted since the distance between any two nodes is short. Therefore, the problem of how to distribute quantum keys with lower waste of key resources over MQON is urgent. In order to solve this problem, we design a novel quantum node structure that is able to bypass itself. Also, by extending the connectivity graph, auxiliary graphs are constructed to describe the adjacency of quantum nodes in different levels influenced by the physical distance. Based on the novel node, two routing, wavelength and time-slot assignment algorithms are proposed, in which some middle nodes can be bypassed to reduce the resource consumption as long as the distance between the two parties meets the requirement of quantum key distribution. Simulations have been conducted to verify the performance of the proposed algorithms in terms of blocking probability, resource utilization, number of bypassed nodes, and security rate per service. Numerical results illustrate that our algorithms perform better on resource utilization than a traditional scheme without bypass. Furthermore, a tradeoff between the keys saved and blocking probability is analyzed and discussed in our paper.
Unsupervised Image-Generation Enhanced Adaptation for Object Detection in Thermal Images

Object detection in thermal images is an important computer vision task and has many applications such as unmanned vehicles, robotics, surveillance, and night vision. Deep learning-based detectors have achieved major progress, which usually need large amount of labelled training data. However, labelled data for object detection in thermal images is scarce and expensive to collect. How to take advantage of the large number labelled visible images and adapt them into thermal image domain is expected to solve. This paper proposes an unsupervised image-generation enhanced adaptation method for object detection in thermal images. To reduce the gap between visible domain and thermal domain, the proposed method manages to generate simulated fake thermal images that are similar to the target images and preserves the annotation information of the visible source domain. The image generation includes a CycleGAN-based image-to-image translation and an intensity inversion transformation. Generated fake thermal images are used as renewed source domain, and then the off-the-shelf domain adaptive faster RCNN is utilized to reduce the gap between the generated intermediate domain and the thermal target domain. Experiments demonstrate the effectiveness and superiority of the proposed method.
Proximal Policy Optimization with Mixed Distributed Training

Instability and slowness are two main problems in deep reinforcement learning. Even if proximal policy optimization (PPO) is the state of the art, it still suffers from these two problems. We introduce an improved algorithm based on proximal policy optimization, mixed distributed proximal policy optimization (MDPPO), and show that it can accelerate and stabilize the training process. In our algorithm, multiple different policies train simultaneously and each of them controls several identical agents that interact with environments. Actions are sampled by each policy separately as usual, but the trajectories for the training process are collected from all agents, instead of only one policy. We find that if we choose some auxiliary trajectories elaborately to train policies, the algorithm will be more stable and quicker to converge especially in the environments with sparse rewards.