Reviewerly/exHarmony huggingface dataset:
Chinese Sentence Pattern Feature Extraction Based on Massive Data Analysis

In the era of Data Technology, the data is characterized by huge scale, modal diversity, and rapid growth. The worth of corpus related to Chinese is also increased by multiplication correspondingly. Based on one of the Chinese language processing systems called the Language Technology Platform (LTP), using the Data Mining and the Machine Learning to extract and apply Chinese sentence features is a new perspective and entry point in the field of Chinese information processing. In this paper, the dependency grammar is selected for sentence pattern analysis, and the text representation model consisting of sequences and vectors is established. A specialized “Chinese Sentence Pattern Retrieve Library” including 1,032,480 sentences and 92,451 kinds of sentence patterns is built to provide a sentence pattern database service for more special sentence patterns studies. On the basis of this database, relevant statistics and preliminary analysis are made on the sentence patterns of different genres articles. It is found that there are about 2,000 core sentence patterns in Chinese and commonly used sentence patterns are relatively concentrated, with the frequency of the 10 sentence patterns with a higher frequency accounting for 50%. The proportion of some sentence patterns used in certain articles is much higher or lower than that in other articles. These researches achievements provide the basis for the establishment of the feature vectors of the sentence pattern in the article and offers a basis for feature extraction and application of articles in the later period.
A Behavioural Study of the Crossover Operator in Diploid Genetic Algorithms

The article presents an analysis of seven crossover operators for continuous spaces applied for Diploid Genetic algorithms (DGA). Unlike the classical ones, called in genetic “haploid” in which an individual is synonym with a chromosome, the individuals of DGA carry two chromosomes, which brings in intrinsic diversity in the population. The impact of the recombination operators is analyzed and compared, turning out that BLX operators yields the best results and uniform and arithmetic crossover the worst. With respect to specificity, the uniform crossover and two point crossover have the lowest standard deviation of the results .
Detection of XSS Vulnerabilities of Web Application Using Security Testing Approaches

The security testing of web applications is a method which ascertains that a particular web application defends the web data and retains the functionality as anticipated. Any loopholes or improper validation or lack of sanitization in web application causes major security flaws like cross-site scripting (XSS), SQLi, etc., and the intruder avails the advantage of it which results in deformation of Web sites, disclosure of sensitive data, hijacking of cookies or session, etc. So to overcome these issues, research practitioners have suggested several security testing approaches for detection and removal of vulnerabilities in web application. In this research article, we provide several XSS vulnerability detection approaches using static analysis and evolutionary genetic algorithm (GA) but no study has ever revealed the false-negative results of source code. So, we plan to fill this research gap by integrating the approach of static taint analysis with GA. Static taint analysis can report candidate false negatives, and genetic algorithm will generate the test cases for exposing the actual vulnerabilities.
Hand Segmentation In Complex Background Using UNet

Hand tracking and segmentation are the essential strides for any hand motion acknowledgment framework. Hand segmentation utilizing skin shading models shows terrible output in complex foundation. Because of the comparable shades of skin also, non-uniform enlightenment exist. Most recent accomplishments in deep learning made additional opportunities in understanding the assignment of object recognition and segmentation. This paper compares the different deep learning models for hand segmentation in complex background with UNet. UNet was first applied in bio medical images. Here it's the first time applying in hand segmentation. The reason why UNet is used for segmentation is that, it is able to localize and distinguish borders and there by doing classification on every pixel, so the input and output share the same size. The networks were trained with two datasets Egohands and GTEA which consists of complex background. These models acknowledge hand pictures as info and produce the segmentation map at yield. The outcomes for all models are introduced. These results are compared for analyzing a good model for hand segmentation. Finally the result showed UNet obtain better comprehensive performance than other networks. It achieved an accuracy of 98% in egohands dataset and 90% in GTEA dataset.
VideoCutTool - Online Video Editor Tool for Wikimedia Commons

The image CropTool allows users to crop images present on Wikimedia Commons without leaving the Wikimedia family of sites within a web environment. We implemented the same workflow for videos with the VideoCutTool. Previously, a video on Wikimedia Commons had to be downloaded, changed, and later re-uploaded to Commons. The previous process took a significant amount of time and requires the use of outside software, while the VideoCutTool allows authorized users to work up-to 10x faster. This paper talks about the features of VideoCutTool and its implementation. The tool is currently live on https://tools.wmflabs.org/video-cut-tool/ and published under the Creative Commons Attribution-ShareAlike 4.0 licence.
An Augmented Reality Tour Creator for Museums with Dynamic Asset Collections

In this contribution, we present the architecture and implementation of an Augmented Reality (AR) system that enables museum professionals to create and edit AR tours inside their exhibitions without programming knowledge. Museum professionals can use content created during their regular digitization projects and transform them into rich AR content. To create and edit AR tours a web-based frontend called the Ambient Learning Spaces (ALS) Portal has been developed. The media and metadata are stored in a semantic database inside the Network Environment for Multimedia Objects (NEMO) backend and middleware system. To present the tours to the visitors, the platform-independent mobile AR app InfoGrid has been implemented. The content of AR tours can consist of text, audio, video, and 3D objects. Additionally, we added the option to use the new dynamic overlay type Asset Collection as an AR element. This allows the museum staff to dynamically add animated interactive 2D/3D objects into the AR tour at runtime. The system has been applied and evaluated in the context of a natural history museum.
Cross-Domain Workloads Performance Prediction via Runtime Metrics Transferring

Modern cloud vendors provide thousands of types of virtual machines, presenting the user a bewildering choice. Many previous works try to train accurate models for a workload to predict its performance on a given configuration, which could be applicable to a concrete class of workloads (e.g., Spark-SVM jobs). But it is difficult to build a universal prediction model for all the workloads, mostly because different workloads have different runtime metrics distribution. We argue that if the runtime metrics data could be transferred before being fed to the prediction model, the knowledge learned in one workload may be reused to other workloads. In this paper, we synthetically investigate the similarity and difference between a set of workloads and the possibility to transfer between them. We use an LSTM auto-encoder to encode the temporal runtime metrics into a fixed-size vector, which is fed into a linear regression model. In our experiments, the metrics transferring approach could help reduce the prediction error of the linear regression model.
Spectral Averagely-dense Clustering Based on Dynamic Shared Nearest Neighbors

Spectral averagely-dense clustering is a clustering algorithm based on density, but it has the problem of being sensitive to the parameter ε. Aiming at the above problems, a spectral averagely-dense clustering based on dynamic shared nearest neighbors is put forward. Firstly, a similarity measures is constructed by combining self-tunning distance and shared nearest neighbors. Self-tunning distance can handle clusters of different density, and shared nearest neighbors can draw closer to the data in the same cluster and alienate the data in different clusters. Secondly, based on the sample distribution function, a method capable of self-adaptively determining the k-value of the shared nearest neighbors is proposed without setting the parameter k. Finally, the constructed similarity measure is used as the similarity measure of the fully connected graph. The ε-neighberhood graph of spectral averagely-dense clustering is replaced with the fully connected graph, which avoid setting the parameter ε. Through the experiments on artificial datasets and UCI datasets, the proposed algorithm is compared with the spectral averagelydense clustering and the standard spectral clustering. The experimental results show that the proposed algorithm not only avoids the problem of difficult selection of ε-neighberhood graph parameters, but also has better performance on the datasets.
Performance Evaluation of Association Rule based Classification in Cancer Diagnosis

Cancer is a worse disease that affects Human and a main cause for the high mortality rate. Though techniques and methods have been developed to cure cancer, there is no prior effective identification technique. This system is developed using three different cancer datasets that carry the details of the factors affecting the tumour. The extended Apriori algorithm used in this system would give effective pruned rules for analysing the best association item sets from the dataset to predict cancer.
A New Interval Preference Model and Corresponding Fuzzy Similarity Measure for Collaborative Filtering

Collaborative filtering as one of most popular recommendation engines generates high-quality candidate item sets for users, which reduces their heavy burden on selecting desired products or services in an ocean of information. The basic idea of collaborative filtering is to extract preferences of users according to their historical behaviors, such as ratings. Rating information is able to easily collected and reflects the opinions of users on different items to some extend. However, previous publications indicate that different users have different rating tendencies, thus the same ratings may represent different preferences. To address the issue, we extend the previous preference model and present a new interval preference model. Based on the model, we devised the corresponding fuzzy similarity measure for collaborative filtering. The experimental results indicate that the proposed method performs well on Movielens dataset and has potential application value.
Efficient Traffic Density Estimation Using Convolutional Neural Network

Measuring traffic density is a problem which has been extensively worked upon by the Computer Vision community. Various solutions have been proposed over the years which have surely attributed to the deepening of the field of image analysis but the most significant improvements have been made in recent years after the huge advancements in the area of Deep neural networks. In this paper, an efficient convolutional neural network has been proposed to estimate the traffic density. For the same, a new dataset of labeled images are generated from openly available traffic video footage. The method is compared with state-of-the-art method in terms of accuracy and loss. The results show the significant improvement in the traffic density estimation.
A Quantitative Measurement of Hand Scaling Motion for Dental Hygienist Training

In dental hygienist education, many skills are taught that cannot be acquired without repeated training. To make this training more efficient, we need to measure the students' skills and show correction points in real-time. In this research, we focus on hand scaling work, which is one of the most important tasks of dental hygienists. We developed a measurement system to measure both the motion and force exerted during hand scaling work. This measured data can be used to quantitatively evaluate students' skills. In the experiment, we measured the hand scaling motion of several participants with different levels of job experience, including dental hygienist teachers, dental hygienists, and dental hygienist students. We showed that it is possible to extract from the measured results a quantitative index for discriminating different individual skills.
Heterogeneous Resource Provisioning for Workflow-Based Applications Using AHP in Cloud Computing

In cloud computing domain, resource allocation and scheduling is one of the important criteria for applications. Workflow-based applications are in need of large computing power and big storage capacity. Scientific applications also require dynamic scheduling and best possible resource from available cloud resource pool. Different scientists have different requirements in order to complete the workflow scheduling task within stipulated time. They have different criteria’s for resource selection such as time, cost, memory, availability, and reliability. Selection is based on the user’s criteria, and it is achieved by the AHP process. Qualitative and quantitative conditions are used to find alternative solutions, and subjective decisions are changed as objective judgments. Workflow scheduling is done after the mapping task on the available resources for its execution. The paper aims to find out the affecting quality of service parameters of the resources on the workflow-based application. As per the users’ requirement, ranking is given and priorities for QoS attributes are obtained.
Hierarchical Scheduling in on-demand GPU-as-a-Service Systems

Deep learning (DL) methods have recently gained popularity. Training this class of models is, however, computing-intensive, and frequently GPUs are used to boost performance. Although the costs of GPU-based systems are gradually reducing due to the high demand, they are still prohibitive: in public clouds, GPU-powered virtual machines (VMs) time unit price is 5-8x higher than CPU-only VMs. While the cloud remains the most cost-effective and flexible deployment, operation costs can be reduced, in large settings, by rightsizing and sharing resources among multiple processes. This work addresses the online joint capacity planning and job scheduling with due dates problem and proposes alternative matheuristic solution methods. Our objective is to optimize operation costs by: i) rightsizing the VM capacities at each node, ii) partitioning the set of GPUs among multiple concurrent jobs on the same VM, and iii) determining a due-date-aware job schedule. The effectiveness of the proposed hierarchical approach, coupled with an appropriate Mixed Integer Linear Programming formulation, is validated against first-principle methods by relying on simulation. The experiments prove that the efficiency of GPU-based systems evaluated in terms of costs can be improved by 50-70%. Finally, scalability analyses show that the proposed approach enables to solve problem instances with up to 100 nodes in less than one minute on average, making it suitable for practical scenarios.
Neighbourhood-Inspired Grey Wolf Optimizer

Over the past few decades, swarm intelligence (SI)-based algorithms are performing well in the optimization field. Grey wolf optimizer (GWO) is a new addition for the class of SI-based algorithms. It mimes the social behaviour of grey wolves. In GWO, a solution is updated by incorporating the information from the first fittest, second fittest and third fittest solution of the search space. Half of the generations in GWO are dedicated to exploration and the rest half are used for exploitation. To enhance the exploration capability of GWO, this article proposes a modified variant of GWO. In the proposed method, the searching process is guided by an arbitrarily selected solution to the search space. The proposed strategy is termed as neighbourhood-inspired grey wolf optimizer (NIGWO). To validate the performance of NIGWO, 16 benchmark functions are considered and the results obtained are compared with the basic version of GWO, particle swarm optimization (PSO), power law-based local search in spider monkey optimization (PLSMO) and differential evolution (DE) algorithm. The obtained results validate the proposed NIGWO approach.
Target Tracking using Radar and Image IoT Nodes

The availability of multiple sensing and edge processing capabilities by sensing nodes, that may include radar, image, and acoustic, enables the collection of rich information to improve performance in target tracking. However, the tracking system, that includes a central data fusion center and IoT nodes, needs to extract information from heterogeneous data under constraints of IoT power, processing, and communication rates. This work presents a method for target tracking using image and radar data. The method is able to fuse heterogeneous data and direct agile edge processing of data on board of the sensing IoT nodes. Data fusion and agile edge processing improves tracking performance while reducing processing and communication rates. Simulation-based results, using synthetic radar data and real image data, demonstrate an improved tracking performance when using heterogeneous data versus using a single type of data.
Securing Cryptoasset Insurance Services with Multisignatures

There is a number of threats related to cryptoassets processed by exchange services: the threats range from unintentional data leaks, through fraudulent cryptocurrency exchanges and poorly implemented protocols, to broken private keys. In this paper an information system reducing the risks related to cryptoasset loss is proposed. Specific business model as well as technological countermeasures are applied. The novelty of the presented solution is in the transfer of the security risk to the insurer without transferring full control over the cryptoassets by taking advantage of multisignature and timelock technologies. The architecture for cryptoasset insurance system has been described along with a set of corresponding communication protocols, which are followed by a short discussion. The description of the solution is preceded by a risk analysis related to cryptoasset security and countermeasures analysis related to those risks.
An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

Case-based reasoning has considerable potential to model decision support systems for smart agriculture, assisting farmers in managing farming operations. However, with the explosive amount of sensing data, these systems may achieve poor performance in knowledge management like case retrieval and case base maintenance. Typical approaches of case retrieval have to traverse all past cases for matching similar ones, leading to low efficiency. Thus, a new case retrieval algorithm for agricultural case-based reasoning systems is proposed in this paper. At the initial stage, an association table is constructed, containing the relationships between all past cases. Afterwards, attributes of a new case are compared with an entry case. According to the similarity measurement, associated similar or dissimilar cases are then compared preferentially, instead of traversing the whole case base. The association of the new case is generated through case retrieval and added in the association table at the step of case retention. The association table is also updated when a closer relationship is detected. The experiment result demonstrates that our proposal enables rapid case retrieval with promising accuracy by comparing a fewer number of past cases. Thus, the retrieval efficiency of our proposal outperforms typical approaches.
Application of Data Mining Combined Visualization Technology in Visual Communication

Visual data mining uses data or knowledge visualization technology to discover hidden and useful knowledge from large data sets. It integrates two disciplines, data mining and data visualization, and integrates with computer graphics, multimedia systems, human-machine interface and other disciplines. Closely related, the purpose is to explain and evaluate the data to be mined and the results of the mining task in an intuitive and effective way. This thesis combines visualization technology and data mining technology to design a visual communication webpage design method based on multi-source data integration. The Deep Web crawler is used to retrieve the visual communication website. It uses crawling methods to filter URLs and crawl useful pages. The experimental results show that the intelligent power distribution management webpage designed by this method runs well, has high visualization performance and information mining quality, and has a good visual communication effect.
Aggregation of Fuzzy Equivalences in Data Exploration by kNN Classifier

The article deals with notions of fuzzy equivalences connectives, dependencies between them, and their applicability to real-life problem as engineering efficient classifiers. It is a fundamental problem in supervised learning - part of artificial intelligence. In the contribution the problem of preservation of their properties in the aggregation process is considered. Moreover, suitability of some fuzzy equivalences and aggregations are proved in the task of data classification by a k-nearest neighbour algorithm.
A Multi-Agent Approach to Segment Arabic Handwritten Text Lines

In text line segmentation, there are three classes of methods: either by sorting physical units such as pixels or connected components (CC) constituting a line or by searching for the baseline of each word and grouping together those who participate in the same line. The third class analyzes the separation locations between the lines. After an overview of lines segmentation approaches, the authors introduced a new method emphasizing its simplicity, speed, and originality. The proposed approach detects the starting components of the lines in the first step. In the second step, it defines a number of agents that start the segmentation process from their starting points between the starting components of lines. Each agent aims to reach the left edge of the document through the correct path. The algorithm used by the agents is based on the morphological process, characteristics of the Arabic manuscript and a communication system. The experimental results on an Arabic dataset show that this approach is an effective solution for the segmentation of lines from different Arabic manuscripts.
ConversIAmo: Improving Italian Question Answering Exploiting IBM Watson Services

Chatbots, conversational interfaces and NLP have achieved considerable improvements and are spreading more and more in everyday applications. Solutions on the market allow their implementation easily in different languages, but the proposals for the Italian language are not so effective as the English ones. This paper introduces ConversIAmo, the prototype of a conversational agent which implements a question answering system in Italian on a closed domain concerning artificial intelligence, taking the answers from online articles. This system integrates IBM services (Watson Assistant, Discovery and Natural Language Understanding) with functions developed within ConversIAmo and Tint, an open-source tool for the analysis of the Italian language. Our QA pipeline turned out to give better results than those obtained from using Watson Discovery service on its own, as for precision, F1-score and correct answer ranking (on average +12%, +21% and +20% respectively). Our main contribution is to address the need for an effective but easy-to-apply method aimed to improve performances of IBM Watson services for the Italian language. In addition, the AI domain is a new one for an Italian conversational agent.