Reviewerly/exHarmony huggingface dataset:
Privacy Preserving of Shared Data in Deep Learning

In the age of Big Data the need of developing machine learning algorithms has increased. Such algorithms are used to extract valuable information needed for different types of sectors; health, education, financial ...etc. In many cases data has to be shared among several parties to guarantee better accuracy of the results of such algorithms. In these cases privacy of data would be questionable. In this paper a survey has been conducted on research that focused on Privacy Preserving techniques when applying deep learning algorithms on distributed or shared data.
Quick artificial bee colony algorithm with symbiotic search strategy for global optimization

Artificial bee colony (ABC) is a metaheuristic algorithm (MHA) which has received extensive attention. However, it often suffers from unsatisfactory exploitation. On the other hand, symbiosis organisms search (SOS) is a MHA which has good exploitation ability due to its mutualism search operator. In this paper, we employ the mutualism search operator to enhance the ABC’s exploitation ability, and thus propose a hybrid quick ABC algorithm with symbiotic search strategy (qABCSS). The performance of qABCSS is comprehensively compared with well-established SOS, ABC and other metaheuristic algorithms using the CEC2014 function. Experimental results demonstrate the superiority of qABCSS in terms of solution accuracy and convergence, indicating the effectiveness of the hybridization.
Event Detection in Noisy Streaming Data with Combination of Corroborative and Probabilistic Sources

Global physical event detection has traditionally relied on dense coverage of physical sensors around the world; while this is an expensive undertaking, there have not been alternatives until recently. The ubiquity of social networks and human sensors in the field provides a tremendous amount of real-time, live data about true physical events from around the world. However, while such human sensor data have been exploited for retrospective large-scale event detection such as hurricanes or earthquakes, there has been limited to no success in exploiting this rich resource for general physical event detection. Prior implementation approaches have suffered from the concept drift phenomenon, where real-world data exhibits continuous, unknown, and unbounded changes in its data distribution, making static machine learning models ineffective in the long term. We propose and implement an end-to-end collaborative drift adaptive system that integrates corroborative and probabilistic sources to deliver real-time predictions. Furthermore, our system is adaptive to concept drift and performs automated continuous learning to maintain high performance. We demonstrate our approach in a real-time demo available online for landslide disaster detection, with extensibility to other real-world physical events such as flooding, wildfires, hurricanes, and earthquakes.
METAPHOR - A Multiagent Architecture using IoT and Classification Algorithms for Referral Postoperative Patients

Diagnosing patients in a post-operative situation, more accurate and timely, is one of the health care challenges where computer science should help. According to studies conducted in some countries, patients are concerned about to stay longer time in the hospital after surgery, mainly for financial reasons. Reduction on hospital stay also reduces the risks of hospital-acquired infections. In this context, we tried to verify the impact of Internet of Things (IoT) devices and inference techniques in order to improve the diagnosis procedure. This paper presents METAPHOR (MultiagEnT Architecture POstoperative Referral) as an evolution of a previous proposed model applying machine learning classifier algorithms in order to indicate the best referral for postoperative patients based on on-line medical information. The best prediction classification algorithm reached an accuracy of almost 90% in the evaluated cases, evidencing the possibility of IoT medical devices can be used to improve post-operative patients management. To confirm the results, the Precision, Recall and F1 Score of each estimator where analyzed as well.
Towards the Development of Framework for Semantic Web Interface

This research work will focus exclusively on the evaluation of the ‘Semantic Web interface’ and to identify the gap in these days’ research approaches, to propose a diverse way out to advance the ‘semantic web interface’. Semantic Interface, refers to user’s language that is been called as the product of interactions by the web and semantic interpretation. The interaction abounds in natural language and it might be establishing the subsystems of grammar. In this paper, main focused is to examine models and identify the dimension, wherever research is lacking. As described, the semantic interfaces worked mainly on the user’s input and linked that input with the data stored in the back-end and obtained the most accurate and demanding results. At the initial level, in this paper, described the approaches carried out so far in the field of the semantic interface. Subsequently the evaluation, so that will be able to identify how much it developed up to this moment, how much development prerequisite and how much development required. Then, define the entire missing domain. Our motivation for this work is based on the advantages offered by the semantic web.
A Robust Algorithm for Long-term Object Tracking Based on PTAV

Long-term object tracking is one of the most challenging problems in computer vision due to various factors such as deformation, abrupt motion, heavy occlusion and out-of-view. In this paper, we propose a tracking method based on Parallel Tracking and Verifying (PTAV). Firstly, we replace the fDSST tracker with a better performance tracker ECO-HC. Then we add self-test mechanism for the tracker, which include backtracking check using forward-backward overlap rate and multi-peak detection mechanism. At last, we modify the parallel framework between the tracker and the verifier in the PTAV, so that the algorithm can get timey feedback about the abnormal information. We perform experiments on the benchmark OTB-2015. Results show that our method has better accuracy and robustness in case of occlusion, out-of-view and other interference factors.
Stability Analysis of DESA Optimization Algorithm

This paper investigates the dynamics of the hybrid evolutionary optimization algorithm, Differential Evolution-Simulated Annealing (DESA) algorithm with the binomial crossover and SA-like selection operators. A detailed mathematical framework of the operators of the DESA/rand/1/bin algorithm is provided to characterize the behavior of the DESA-population system. In DESA, the SA-like selection operation provides a nonzero probability of accepting a deteriorated solution that decreases with a sufficient number of generations. This paper shows that the system defined by the DESA-population is stable. Moreover, the DESA-population system time constant, learning and momentum rates are dependent on the value of the crossover constant and the probability of accepting deterioration in the quality of the objective function.
The Management Scheme of Embedded Memory Based on Power Grid System

In the modern smart grid system, power network and communication network are integrated, which make the security situation more and more complex. The diversification of network terminal service data makes higher requirements on real-time performance and security. As an important node for data storage and processing, the security of embedded terminal memory management becomes the inevitable requirement of smart grid terminal. Traditional embedded memory management methods have some problems, such as low security and low space efficiency. In this paper, a memory management scheme based on secure compilation options and address space randomization technology is put forward. By comparing with other traditional memory management schemes, it is concluded that the scheme has better timeliness, security, reliability and less space overhead.
Image Super-Resolution Using Residual Convolutional Neural Network

In this paper, a single-image super-resolution using residual convolutional neural network (CNN) is presented. The proposed residual CNN model includes one short-cut and seven convolutional layers with the batch normalizations and ReLU activation functions. The transposed convolutional layer is also employed to eliminate the need of bicubic interpolation in the image preprocessing and to speed up the calculations of network. Several public benchmarks are tested to show that the proposed network architecture has better performance than the plain convolutional networks without using residual connection.
Improved SINS/CNS Integrated Navigation System with Fault Detection Based on Statistical Distance

Celestial navigation system (CNS) is a completely autonomous navigation technology with strong anti-interference ability and high navigation accuracy, which has a great application prospect. Aim at defect of traditional integrated navigation system using attitude angle measurement celestial navigation system and strapdown inertial navigation system (SINS), which can only correct of strapdown inertial navigation system's gyro drift. This paper proposes an improved integrated navigation system that add double-star positioning celestial navigation to traditional SINS/CNS integrated navigation system, meanwhile, introduces the fault detection technology based on statistical distance to solve the problem that inaccuracy of measurement information leads to the integrated navigation system's performance degradation. By analyzing the simulation results, the improved navigation system can inhibit divergence of system and obtain high accurate and stable navigation information.
IDUVis: An Interactive Dashboard for Uncertainty Visualization and Analysis - Vast Challenge 2019 MC1 Award for Supporting Situation Awareness and Anomaly Detection

This paper presents IDUVis, an interactive visualization tool for the analysis of the VAST 2019 Mini-Challenge 1 dataset. Four connected visualizations compose the tool: a timeline for temporal overview tasks, choropleth maps for spatial overview tasks, horizon-histograms for comparing data distributions and uncertainties, and steamgraphs for details on-demand on how distributions change over time. The timeline allows selecting specific time ranges for analysis, while maps and histograms allow selecting locations and categories to visualize their distribution over time with steamgraphs.
An Improved Outlier Detection Algorithm Based on Distance Increment and Decision Graph Score

In recent years, outlier detection technology has played an important role in all aspects of real life. Faced with increasingly complex production datasets, how to find rare outliers quickly and efficiently is a huge challenge. In this article, we introduce an improved LOF algorithm which is based on increment of distance and decision graph score. Firstly, we use distance increment to calculate the local density of samples. Then we use decision graph score to quantify the anomaly degree of samples, which is the ratio of distance to local density. The higher the score, the more likely it is to be an outlier. Experiments show that this method has a good performance on both synthetic datasets and real datasets, and can effectively deal with complex datasets.
Blockchain Technology and Its Uses in the Tourism Industry

The public and private sectors will undergo a significant transformation with the use of blockchain technology, and this potential of blockchain technology will be influential in all areas of life. In addition, blockchain technology can help ease the integration of the Internet of Things, augmented reality, and artificial intelligence applications. The question of how such a technology that is in the process of development will be applied in areas such as taxation, notary operations, and banking, arises simultaneously. Studies related to blockchain technology have mainly been carried out in the fields of finance, logistics, banking, and education. However, there is a paucity of studies on blockchain technology in the tourism industry which has an important role in the global economy. Therefore, the potential of using blockchain technology in the tourism industry is evaluated in this chapter. For this purpose, the features, advantages, and disadvantages of blockchain technology are explained. The potential uses of blockchain technology in the tourism industry are then discussed.
A Facial Expression Recognition Methond Based on Improved HOG Features and Geometric Features

Facial expression recognition plays a very important role in many applications. In order to extract facial expression features better and improve facial expression recognition rate, this paper proposes an expression classification method that combines facial texture features and geometric features. Firstly, an improved HOG operator is proposed to better extract the edge information of the expression texture. Then, using an ensemble of regression trees algorithm to effectively extract facial feature points, and through a large number of experiments, define and select effective expression geometry information. Finally, the expression classification is realized by fusing the improved HOG operator to extract texture feature information and expression geometry information. Experiments show that the proposed algorithm achieves better results than the general texture feature recognition algorithm.
Enhanced Entity Mention Recognition and Disambiguation Technologies for Chinese Knowledge Base Q&amp;A

Entity linking, which usually involves mention recognition and entity disambiguation, is an important task in knowledge base question and answer (KBQA). However, due to the diversity of Chinese grammatical structure, the complexity of Chinese natural language expressions and the lack of contextual information, there are still many challenges in the task of the Chinese KBQA. We discussed two subtasks of the entity linking separately. For the mention recognition part, in order to get the only topic entity mention of the question, we proposed a topic entity mention recognition algorithm based on sequence annotation. The algorithm combines a variety of feature vectors based on word embedding, and uses model BiGRU-CRF model to perform sequence labeling modeling. We also proposed an entity disambiguation algorithm based on a similarity calculation with extended information. The algorithm not only realized the information expansion by crawling the candidate entity for related problems, but also made full use of contextual information by combining lexical level similarity and sentence semantic similarity. In addition, the experimental results show that the proposed entity linking solution possesses huge advantages compared to several baseline systems.
Optimizing Privacy-Preserving Data Mining Model in Multivariate Datasets

The increased importance of data protection for sensitive or private data paved a new research direction towards privacy-preserving in data mining. This article shares work-in-progress research data privacy-preserving research carried out by the authors. This work focuses on i) The study of different k-anonymization algorithms, ii) Classifying sensitive and non-sensitive data, iii) Protection model against differential privacy attacks. Our observations show i) Depending on the size of the dataset, the performance of the k- anonymization algorithms varies for sequential and parallel computations. ii) The sensitive and nonsensitive data can be differentiated through correlation coefficients. iii) Differential privacy application attacks can be avoided by replacing existing Laplacian with other partial differential equations as noise.
Authenticating Phone Users Using a Gait-Based Histogram Approach on Mobile App Sessions

Collectively, user-friendly interfaces, small but impactful sensing technologies, intuitive device designs, and the variety of mobile applications (or apps) have transformed the expectations for cellular phones. Apps are a primary factor in device functionality; they allow users to quickly carry out tasks directly on their device. This paper leverages mobile apps for continuous authentication of mobile device users. We borrow from a gait-based approach by continuously extracting n-bin histograms from numerically encoded app data. Since more active subjects will generate more data, it would be trivial to distinguish between these subjects and others which are not as active. Thus, we divided a dataset of 19 months of app data from 181 subjects into three datasets to determine if minimally active, moderately active, or very active subjects were more challenging to authenticate. Using the absolute distance between two histograms, our approach yielded a worst-case EER of 0.188 and a best-case EER of 0.036 with a worst-case initial training period of 1.06 hours. We also show a positive correlation between user activity level and performance, and template size and performance. Our method is characterized by minimal training samples and a context-independent evaluation, addressing important factors which are known to affect the practicality of continuous authentication systems.
Symbolic interaction: Customer, Lifestyle and Store APP embedded in WeChat

E-commerce has been rapidly thriving into massive B2C and B2B electronic platforms in China recently, like Taobao and Jingdong. This article is to explore the current trend Chinese entrepreneurs attempt to open up a sort of mobile e- commerce based on the concept of the industry chain. This new type of mobile e-commerce is essentially introduced by a new style of the virtual store, embedded in WeChat, called `applets'. It is important to note that WeChat, as the largest Chinese social media with advanced IT infrastructure, has new media's symbolic power to centralize most sources of the social and technology and to integrate most potential customers for its industry partners. My argument is, based on this power, WeChat acts as the specific symbolic form to exercise all possible business models created by its applets. Accordingly, the Chinese entrepreneurs are seeking for greater opportunities to combine B2C and B2B to develop a new integrative business model ushering in a new commercial age.
Checking the Difficulty of Evolutionary-Generated Maps in a N-Body Inspired Mobile Game

This paper presents the design and development of an Android application (using Unreal Engine 4) called GravityVolve. It is a two-dimensions game based on the N-Body problem previously presented by some of the authors. In order to complete a map, the player will have to push the particle from its initial position until it reaches the circumference’s position. Thus, the maps of GravityVolve are made up of a particle, a circumference and a set of planets. These maps are procedurally generated by an evolutionary algorithm, and are assigned a difficulty level (‘Easy’, ‘Medium’, ‘Hard’). When a player completes a map, he/she will have access to a selection system where he/she will have to choose the level of difficulty he/she considers appropriate. So, the objectives of this study are two: first, to gather a considerable amount of votes from players with respect to their perception about the difficulty of every map; and two, to compare both, the user’s difficulty feeling and the difficulty given by the algorithm in order to check their correlation and reach some conclusions regarding the quality of the proposed method.
Spark-Based Machine Learning Pipeline Construction Method

As the amount of data captured in the industry continues to increase, and the accuracy requirements for machine learning algorithms continue to increase, stand-alone computing is far from meeting the needs of machine learning for computing speed and storage capacity. At the same time, distributed machine learning is facing the situation of high learning cost and inadequate intelligence in building models. In this paper, a pipeline method for constructing data processing, feature engineering, model training, evaluation, and prediction is proposed. It uses concurrent means to speed up data processing. And we use the method to implement a Spark-based machine learning visualization system. It reduces the threshold of distributed machine learning and verifies the feasibility of the method.
Bio-Medical Image Enhancement Using Adaptive Multi-Resolution Technique

The enhancement, resolution, and accuracy are the basic requirements of digital image processing. But in the field of medical, the local details of the images are necessary. In this contest, the Contrast Limited Adaptive Histogram Equalization (CLAHE) is an effective approach, which is used for enhancing the local feature. The downside of CLAHE is the contrast overstretching and noise enhancement. To overcome this problem the low frequency and high-frequency details of an image were considered separately. This paper proposes a new biomedical image enhancement technique based on Bi-dimensional Empirical Mode Decomposition (BEMD) with CLAHE and Sobel Gradient. Where the image is decomposed using BEMD to separate the edge and detail information. The edge information is enhanced using CLAHE and the rest coefficients remain unchanged. Experiments are conducted on numerous brain, retina and tissue images to compare the performance of the proposed algorithm with the state of art approaches. The comparison is made quantitatively in terms of entropy ratio (ER), absolute mean brightness error (AMBE), the peak of signal to noise ratio (PSNR), structural similarity index (SSIM), and image quality index (IQI); qualitatively through visual comparisons. The results indicate a significant improvement especially in terms of structural and edge preservation.
Multi-source Test Data Fusion and Evaluation Based on Improved p-Bayesian Method

An improved multi-source data fusion evaluation method based on p-Bayes method was proposed in the paper. In view of the deficiency of constructing the inheritance factor subjectively, this paper proposed to calculate the inheritance factor based on the mean square error(MSE).Combined with experience and inheritance factors, test data fusion from different sources such as multi-stage, different regions, modified products and similar products was realized, which effectively improved the evaluation accuracy of success or failure type indicators under small sample flying test, and to reduce the test risk.