Reviewerly/exHarmony huggingface dataset:
Detecting Logical Couplings at Model Level by Analyzing EMF Model repository

In Model-driven engineering (MDE), models constitute the basis source of information to specify and automatically generate an executable system. The evolution history analysis of software system models helps us to discover interesting logical couplings between its artifacts; which is not possible with the analysis of a single version of the model. The detection of such couplings earlier in the software design improves the model (software) in a way to increase its modularity. In this paper, we propose an approach for the analysis of a version repository for EMF models (CDO Model Repository) in order to detect logical couplings between the software model artifacts, using the Apriori algorithm. Such logical couplings help us to understand the model evolution, to predict future changes on the model, to identify potential faults, and consequently to produce automatically a coherent and modular software source code.
Merged Multi-CNN with Parameter Reduction for Face Attribute Estimation

This paper proposes a face attribute estimation method using Merged Multi-CNN (MM-CNN). The proposed method merges single-task CNNs into one CNN by adding merging points and reduces the number of parameters by removing the fully-connected layers. We also propose a new idea of reducing parameters of CNN called Convolutionalization for Parameter Reduction (CPR), which estimates attributes using only convolution layers, in other words, does not need any fully-connected layers to estimate attributes from extracted features. Through a set of experiments using the Celeb A and LFW-a datasets, we demonstrated that MM- CNN with CPR exhibits higher efficiency of face attribute estimation than conventional methods.
Accountable Communication in Ubiquitous Computing

This is a cognitive computing era with Internet of things (IoT) revolution, and ubiquitous computing concept is going to come alive soon. Today, we depend on our smart devices almost for every reason. Most of the gadgets designed to habituate private information sharing mechanisms to fellow users via social media. However, dependency and security are two sides of the coin. With reference to existing literature, various means of security recommendations in server/hosting device/cloud point view like private set insertion (PSI) protocol, private information retrieval (PIR) protocol, etc. but inadequate details present on how to control the sharing at end-user’s end. The research is motivated by practical usage of certain gadgets, while they started sharing the statistical reports in social media, along with important private information, where we could not control the flow even after the uninstallation/disabling the usage of the app. Thus, the purpose of study is to portray that privacy matters are not seriously considered by citizens as gadgets give fun and dynamic adoptability for distinct services. Many a times, the information is being shared is irrespective of the actual needs of what to share. Thus, the research tried to provide a client-level ergonomic solution for privacy control. Our contribution towards this research is to build a simple user interface (UI)-based accountable information sharing method with the help of multimedia elements (text, images, audio and video) and integration of notifications in regional language by using Unicode to interact with all types of end-users in order to build an acceptable pervasive (ubiquitous) computing systems with reliability to serve all kinds of business or domestic deployable purposes with minimized social security anomalies.
Blended Learning Approach in English Language Teaching – Its Benefits, Challenges, and Perspectives

At present, blended learning (BL) is commonly used in majority of the institutions of higher learning since it appears to have a positive impact on student learning outcomes and brings a number of benefits for the whole educational process. This is also true for English language teaching (ELT). In ELT, the BL approach offers more opportunities for exposure, discovery, and use of target language. In addition, the BL approach is especially suitable for distant students, who due to their work commitment cannot be involved in full-time English language study. However, recently there has been a shift from the online courses used as counterparts to traditional instruction in the BL approach towards the use of mobile applications. The findings show that such a BL approach (i.e., a combination of mobile learning via mobile applications and traditional instruction) is particularly effective in vocabulary learning. However, such an approach demands even more rigorous teaching methods and strategies, as well as a more elaborate and meaningful context within which learning can take place. Therefore, future research should focus on the exploration of effectiveness of this new BL approach.
Few-Shot Learning for Crossing-Sentence Relation Classification

There is heavy dependence on the large amount of annotated data in most existing methods of relation classification, which is a serious problem. Besides, we cannot learn by leveraging past learned knowledge in most situation, which means it can only train from scratch to learn new tasks. Motivated from humans’ ability of learning effectively from few samples and learning quickly by utilizing learned knowledge, we use both meta network based on co-reference resolution and prototypical network based on co-reference resolution to resolve the problem of few-shot relation classification for crossing-sentence task. Both of the two network aim to learn a transferrable deep distance metric to recognize new relation categories given very few labelled samples. Instead of single sentence, paragraphs containing multi-sentence is a major concern in the experiment. The results demonstrate that our approach performs well and achieves high precision.
EsiNet: Enhanced Network Representation via Further Learning the Semantic Information of Edges

Network representation learning (NRL) is a crucial method to learn low-dimensional vertex representations to capture network information. However, conventional NRL models only regard each edge as a binary or continuous value while neglecting the rich semantic information on edges. To enhance network representation for Social Relation Extraction (SRE) task, we present a novel deep neural network based model, EsiNet, by learning the structure and semantic information of edges simultaneously. Compared with previous work, EsiNet focuses on further learning the interactions between vertices and capturing the correlations between labels. By jointly optimizing the objective function of these two components, EsiNet can preserve both the semantic and structural information of edges. Extensive experiments on several public datasets demonstrate that EsiNet outperforms other baselines significantly, by around 3% to 5% on hits@10 absolutely.
Robust Multi-Label Learning with Corrupted Features and Incomplete Labels

Weakly-supervised multi-label learning has attracted wide attention recently. Most existing methods deal with such problem with incomplete labels and the feature information is ideal. However, in many scenarios, the acquired features may be corrupted due to the influence of occlusion, illumination and low-resolution, and the robustness of learning methods may be reduced. To overcome the above shortcoming, we propose a novel weakly-supervised multi-label learning algorithm, where a linear self-recovery model is adopted to reconstruct observed label information. Specifically, we first decompose the acquired feature matrix into an ideal feature matrix and an outlier matrix. To adequately utilize the visual information among instances, we introduce the graph Laplacian regularization. In addition, a linear self-recovery model is adopted to reconstruct the observed label matrix. Finally, the desired model is trained on ideal feature matrix and refined label matrix. Extensive experimental results prove the robustness of the proposed framework.
Quality and Energy Aware Services Selection for IOT

In recent years, Internet of Things (IoT) has received massive attention from research and industry. Integrating and composing smart objects functionalities or their services is required to create and promote more complex IoT applications with advanced featuresIn a large scale IOT environment, but the composition of thousands of entities becomes a crucial issue. In this paper, an EQSA algorithm is proposed for service composition. The proposed selection approach consists in the quality of the services in view of the satisfaction of the user through the use of a lexicographical optimization technique. In order to increase the energy conservation without decreasing the user's preferences, a most suitable service is selected among the pre candidate service ones are then Selected using the concept of relative dominance of services in the sense of Pareto. The relative dominance concept mainly depends on the energy profile and QoS attributes and users preferences.
Online Updating Of SVD For Dictionary Learning

Due to the immense growth of information on web,it has already became a primary source of information for each and everyone in this world. As the number of users increase, the organizers update the data, and this will be readily available on the web. Many years ago, a lot of algorithms were developed in order to make retrieval easier and convenient. That is what we call generating big data, which is developed by various applications on different elds such as medical, E-Learning and networking. Therefore it is necessary that we need a better algorithms for faster performance and are cost effective computationally; hence produces more faster output. SVD on sparse representing is such solution in big data analysis. Main aim is iterative SVD update on data representation in dictionary learning. It is well known for data representation in widely applied areas of image processing,compressed sensing,etc. It has two phases i.e. sparse coding and dictionary update. Dictionary learning algorithms can be used along with pursuit algorithms like Singular Value Decomposition (SVD) in dictionary update phase and Orthogonal Matching Pursuit algorithms in sparse representation. This paper presents well implemented an iterative error update phase using Batch OMP algorithm and SVD in dictionary learning, nally we get the best updated dictionary. Our online SVD update method shows that it takes less time than the normal SVD update in dictionary learning.
Exploration on Path Tracking Method of Special

Special underwater robots play an important role in military strategy and marine energy exploitation. Aiming at the complex underwater environment and the influence of uncertain factors, the research on path tracking technology of special underwater robots have great difficulties and challenges. The traditional active disturbance rejection control (ADRC) method is particularly suitable for the uncertain state. But the parameters of the ADRC are not well optimized, it is difficult to obtain the ideal tracking control effect when it performs path tracking. This paper focuses on the optimization characteristics of the enhanced fireworks algorithm (EFWA) and applies it to the control parameter optimization of ADRC. Taking the special underwater robot fish as the research object, the error model of path tracking is established, and its parameters of the traditional ADRC controller are optimized by the enhanced fireworks algorithm in this paper. So, its forward and steer controller of robot fish is controlled in this paper. The results of simulation show that special underwater robots based on the novel EFWA-ADRC has strong robustness and high path tracking accuracy in the path tracking process.
Meta-Learning for Recommendation System with the Multi-Tasking Learning Setting

In the recommendation system, the number of users is often very large, but each user's interaction behavior is very limited. For a specific user, how to fully utilize the less interactive data to train a personalized recommendation model is a very challenging problem. In this paper, we introduce the framework of multi-tasking learning into the recommendation system and regard the recommendation of different users as different tasks with certain commonalities. To solve this multi-tasking learning problem, we apply the task-agnostic meta-learning(TAML) in the model. The experimental results on the public datasets MovieLens illustrate those recommendation models trained in the multi-tasking learning setting by the meta-learning in this paper outperform the most deep recommendation models and get the best performance.
Double Digital Watermarking using Discrete Hartley Transform

The use of internet media as the media of exchanging information, especially photography has become very common in modern times. Internet media has possibilities that can threaten data security such as information theft, plagiarism and tapping from competitors, and many business people who have not aware of these possibilities. Digital Watermarking is one way to secure data by inserting special characters so that the authenticity of the data can be known. Discrete Hartley Transform is an algorithm that has a good level of speed. We proposed to use Discrete Hartley Transform (DHT) with different subblock for embedding an image using double watermarking for better quality. We analyze subblocks and compare the initial image with the image that has watermarked. The Result is 2x2 subblock will produce a better-watermarked image with the NPCR = 0.694, UACI = 0.00534, PSNR = 62.96. and MSE = 0.032.
Efficient Identity-Based Encryption from LWR

The Learning with Rounding (LWR) problem is a deterministic variant of the classical Learning with Errors (LWE) problem, for which sampling an instance does not involve discrete Gaussian sampling. We propose the first probabilistic Identity-Based Encryption (IBE) from the LWR problem which is secure in the standard model. The encryption of our IBE scheme does not require discrete Gaussian sampling as it is based on the LWR problem, and hence it is simpler and faster than that of LWE-based IBEs such as ABB scheme. We also present an efficient instantiation employing algebraic ring structure and MP12 trapdoor sampling algorithms with an implementation result. With our proposed parameter sets, the ciphertext sizes can be reduced in a large extent compared to the ABB scheme with the same security level.
Recurrent Convolutional Attention Neural Model for Sentiment Classification of short text

Nowadays neural attention model doing good in many tasks of natural language processing (NLP). More specifically attention mechanism has been used widely with convolutional neural network (CNN) and recurrent neural network (RNN) for many task of NLP. But the sentiment classification of short text is a challenging task because it contains limited contextual information. Thus, we proposed a new recurrent convolutional attention neural model for sentiment classification of the short text by using attention mechanism with RCNN (recurrent convolutional neural network). In proposed model attention score is calculated by averaging hidden units (feature maps) generated from LSTM (long short-term memory). Then we combined this attention score with recurrent convolution-based encoded text features to get final sentence representation. Here attention will focus on important text features and recurrent convolution makes full use of limited contextual information by processing sentence representation through different windows sizes with specialized recurrent convolution operation. Validation of the proposed model is done through experimentation with three benchmark datasets i.e. MR, SSTl, and SST2. Achieved results exhibit that our model performs better than many existing baselines works on all three datasets.
Automatically Labeling Software Components with Concept Mining

In the software development life cycle, software maintenance phase needs much time and effort of programmer analysts in maintaining the software. With the proliferation of software applications in the agile development environment, it is highly challenging and vital to cope up with software evolution and maintenance. Software maintenance could be carried out using static and dynamic analysis of source code. Recently, text mining techniques are widely used in static analysis of source code. In this work, a tool is designed to automatically label the components with the classes referred by them using text mining and formal concept analysis. This tool can be deployed in the software engineering tasks like architecture recovery and change impact analysis.
Reconstruction to Power Quality Disturbance signal based on Iterative-Reweighed-Least-Squares Algorithm

Reconstruction search algorithm is one of the key problems of compressed sensing. The iterative reweighted least square (IRLS) algorithm is used to reconstruct the power quality disturbance signal. By Relaxing the discontinuity function into a smooth or continuous function, the P0 problem is turned into standard linear algebra and Lagrangian multiplier method. The simulation results show that the IRLS algorithm has similar SNR performance with the widely used Orthogonal Matching Pursuit algorithm.
Remote Control of Excavator with Hand Pose

The excavator is important and widely-used. However, the traditional operation method is non-intuitive, with a fixed seat and restricted visual angle, bringing potential danger [1]. To improve this, this paper proposes a device named 3D mouse’ and a hand pose-based operation method as well as the system implementation utilizing it. The 3D mouse’ makes controlling a bucket in a similar way to using a computer mouse, both of which use hand pose. To implement the system, this paper covers algorithms of hand pose estimation based on human arm kinematics model, and a workspace mapping strategy to find the corresponding target bucket pose based on two reference points. Besides, an interpolation method and a closed-loop control architecture are designed, and forward and inverse kinematics of the excavator are discussed. Finally, the efficiency of this system was evaluated through filed tests.
Agent-based Self-Adaptive System for Cloud

There is a need to analyze the appropriate assets/resource requirements and identify the reason for Quality-of-Service (QoS) degradation (if any), and ensure better assets/resource allocation decisions in Cloud computing scenario. In this work, agents are used to achieve the same through the condition of assets/resource at run-time and about expectations by clients. We have developed an agent application learning system for decision making including readily available globalized and localized information, and client assets/resource requirements etc. Results demonstrate that the proposed system can enhance assets/resource utility, while fulfilling the QoS requested by clients.
Group Method Data Handling Neural Network for CT Abdomen Image Segmentation based on First Order Statistics and Local Binary Pattern

This work proposes Group method Data Handling (GMDH) neural network for the segmentation of liver and liver tumor on abdomen CT images. The structure of the GMDH neural network is automatically structured using heuristic self-organization. Prior to segmentation, Nonlinear Tensor Diffusion (NLTD) filter was used for the preprocessing of input images. Feature extraction was performed by first order statistics and local binary pattern. The parameters of neural network like the number of useful input variables, the number of neurons in each layer and the selection of optimum neural network architecture are determined by using the error criterion derived from AIC (Akaike’s Information Criterion). The performance of the GMDH algorithm was evaluated by success and error rates, similarity measures and the results outperform the back propagation neural network algorithm. The algorithms are developed in Matlab 2013a and tested on real time abdomen CT datasets. The satisfactory results were obtained by GMDH algorithm and are useful for computer aided diagnosis of liver cancer.
Performance of SVM in Classifying the Quartile of Computer Science Journals

Classification is a systematic grouping of objects or objects or certain patterns based on the similarity of characteristics. One method that can be used in classifying data is Support Vector Machine. Support Vector Machine is a technique for finding hyperlines that separate two sets from two different classes. The advantage of this method is that computing is fast in determining distance using support vector. In this study, the Support Vector Machine method was implemented to classify the journal quartile on ScimagoJR. Classification is carried out with 1441 data sets. The classification results show an accuracy of 61.09%.
Unique Group Key Establishment Scheme for Internet of Thing

This paper is part of a research which the authors are carrying out for designing a secured transmission for IoT (Internet of Things). In this scenario group key plays a major role as vehicles would be grouped according to their size. A unique group key establishment system has been proposed where High Definition images(HDRI) are taken as a group key, the images are first watermarked using DWT and then encrypted and are assigned to each group. To access a device(source) of a group the key stored for the source is transmitted to the target trying to establish a connection. The target would be having the decryption algorithm where the image would be decrypted and the watermarked image is extracted and matched with the stored image, once a match is found the connection would be established between the source and the target but to access the device of the source target would again have to establish a simple device key authentication after which a complete connectivity would be possible. The proposed system is unique as there is no decryption key and uses images for authentication. It is the future of a secured IoT and networking as a whole. To check the robustness of the system various attacks including Man in the middle have been tried and quality of the extracted watermarked images has also been checked and it has been observed that the quality remains good even after transmitting the images in a linear form over the network.
File-Sharing and the Darknet

Today the darknet is a term most often is used for privacy networks like TOR. Around the year 2000 at the high of the mp3 file sharing debate between internet activists and copyright holders, it was a term commonly used for file sharing networks allowing the transfer of digital files, especially music and movies. These former darknets were subject of multiple investigations with the aim to identify and sue users who traded copyrighted martial via these networks. Therefore, a technical development started trying to achieve privacy for file sharing users. In this article, the authors show how file sharing networks evolved into the privacy networks we know today and which impact the privacy mechanisms have on file sharing.