Reviewerly/exHarmony huggingface dataset:
DR-PRE: dual receiver proxy re-encryption scheme

In this paper, we coin the notion of dual receiver proxy re-encryption. In dual receiver proxy re-encryption, the delegator's ciphertext is re-encrypted using a single re-encryption key such that it can be decrypted by two-independent delegatees. To realize the dual receiver re-encryption, the traditional proxy re-encryption scheme requires two re-encryption keys to be generated, one for each delegatee and re-encryption should be done independently producing independent ciphertexts for each delegatee. This approach is computationally inefficient and requires larger bandwidth for ciphertext transmission. In the proposed dual receiver proxy re-encryption scheme based on bilinear mapping, only a single re-encryption key, only one re-encryption operation and only one ciphertext enables two delegatees to decrypt the common ciphertext independently. We prove the IND-CPA security of the proposed dual receiver proxy re-encryption scheme based on the DDH assumption. The proposed dual receiver proxy re-encryption scheme can be used in applications where a common ciphertext needs to be re-encrypted for two delegatees.
Predicting Solar Performance Ratio Based on Encoder-Decoder Neural Network Model

Fault diagnosis of photovoltaic (PV) arrays is an important task for improving the reliability and safety of the overall photovoltaic system. In the traditional fault diagnosis, most of the past researches focus on the I-V curve to determine the PV arrays state. However, most inverters as smart meters cannot obtain data in high frequency and high volume in order to calculate the I-V curve, and the information usually cannot be retrieved conveniently. In this paper, we evaluate various Recurrent Neural Network Models' from the recent advances in the deep learning field and their abilities in predicting PV arrays power generation. The results show the Encoder-Decoder model using Convolutional Neural Network (CNN) as the decoder and Temporal Convolutional Network (TCN) are more effective in predicting the PV arrays solar output production and can be used as the foundation for fault diagnosis and prediction.
Cognitive Computing Cybersecurity: Social Network Analysis

The structure and basic principles of technology for increasing the probability of identifying subjects of information processes of open Internet resources based on ontology methods are considered. Based on this ontology the knowledge base intended for creation of the program systems supporting ensuring information security has been realized. The first step in building the domain theory is a formal description of the domain ontology, i.e. the meaning of all used terms specific to the given subject area. Cognitive ontology's of subject areas are a tool necessary to achieve high pertinence of information retrieval and are needed to describe key concepts of a given subject area. The developed ontological knowledge base has been used when developing the software complex intended for identification of the user of social networks when ensuring information security, monitoring and preventing threats. This article is next in a series of articles by the authors in which they continue to monitor and analyze the current state and new tendencies in the field of information security and safety of information.
A New Two-Stage Image Retrieval Algorithm with Convolutional Neural Network

With the rapid growth of available image resources in various areas, effective methods to search image are becoming increasingly important. In this paper we propose an effective two-stage image retrieval algorithm with Convolutional Neural Network (CNN) for image retrieval. Our idea is to modify existing neural network and fine-tune it. Firstly, we use the softmax classifier to classify the images. Then we search further in more detail under the corresponding categories of images. The experimental results show that the image matching speed of our method outperforms several state-of-the-art image retrieval algorithms on the CIFAR-10 and ImageNet datasets, which can speed up the retrieval of similar features, and improve the query efficiency, as well as be applied in real time. Corresponding Author: Yuhua Li, [email protected]
Discovering Hate Sentiment within Twitter Data through Aspect-Based Sentiment Analysis

Abstract Aspect-based sentiment analysis is a vital issue in fine-grained sentiment evaluation, which intends to provide an automatic prediction of the sentiment polarity, given a particular aspect in its context. This paper presents an aspect-based sentiment analysis to find hate sentiment inside twitter data. Word embeddings have had prevalent utilisation in Natural Language Processing (NLP) applications because their vector representations have the ability to capture useful linguistic relationships and semantic properties between words with the help of deep neural networks. Word embeddings have often been used in machine learning models as feature input, which allows for the contextualisation of raw text data in machine learning techniques. The model has the ability to represent the relationship between the word embedding features and the aspects as feature representation within the suggested model. To assess the efficacy of the proposed method, extensive experiments were performed on the dataset of the researcher, as well as on widely utilised datasets. It was demonstrated by the experimental results that the proposed method was able to obtain impressive results among the three datasets.
A fuzzy‐clustering‐based hierarchical i‐vector/probabilistic linear discriminant analysis system for text‐dependent speaker verification

Abstract In the i‐vector/probabilistic linear discriminant analysis (PLDA) technique, the PLDA backend classifier is modelled on i‐vectors. PLDA defines an i‐vector subspace that compensates the unwanted variability and helps to discriminate among speaker‐phrase pairs. The channel or session variability manifested in i‐vectors are known to be nonlinear in nature. PLDA training, however, assumes the variability to be linearly separable, thereby causing loss of important discriminating information. Besides, the i‐vector estimation, itself, is known to be poor in case of short utterances. This paper attempts to address these issues using a simple hierarchy‐based system. A modified fuzzy‐clustering technique is employed to divide the feature space into more characteristic feature subspaces using vocal source features. Thereafter, a separate i‐vector/PLDA model is trained for each of the subspaces. The sparser alignment owing to subspace‐specific universal background model and the relatively reduced dimensions of variability in individual subspaces help to train more effective i‐vector/PLDA models. Also, vocal source features are complementary to mel frequency cepstral coefficients, which are transformed into i‐vectors using mixture model technique. As a consequence, vocal source features and i‐vectors tend to have complementary information. Thus using vocal source features for classification in a hierarchy tree may help to differentiate some of the speaker‐phrase classes, which otherwise are not easily discriminable based on i‐vectors. The proposed technique has been validated on Part 1 of RSR2015 database, and it shows a relative equal error rate reduction of up to 37.41% with respect to the baseline i‐vector/PLDA system.
Collection management of electronic resources in engineering college libraries of Aligarh, India: a study

Purpose The purpose of this paper is to analyze the e-resource collection development practices of the engineering college libraries of Aligarh. The research includes budgeting, collection development policy, collection evaluation, sources of funds, modes of procurement, pricing models and other aspects related to collection development activity. Design/methodology/approach The descriptive method was used for conducting the study, in which a well-structured questionnaire was administered followed by interview of the librarians of six engineering college libraries under study. Findings It was found that these colleges are much more interested in focusing on building a strong e-resource collection in their libraries. During the past few years, the budget has also been increased in majority of libraries for the acquisition of e-resources. The study found that the major factors affecting the selection of electronic resources (e-resources) in these colleges are quality, subject coverage, license agreement and vendor support. It was also revealed that majority of libraries lack proper collection development policy, especially for e-resources. The study suggested that these libraries should build their collections keeping in mind the different areas of specializations of engineering studies and the contemporary changes in the field. Research limitations/implications The paper is restricted exclusively to the study of collection development process of e-resources and the librarians of six major engineering colleges of Aligarh as respondents. Practical implications This study has great importance for the librarians of the similar engineering colleges in India. The findings and suggestions of the study can help not only in understanding the engineering college librarianship and its current trends but can also help library professionals who are facing similar challenges in their libraries. Originality/value The present study is about the e-resource collection development practices followed in engineering colleges of Aligarh, which can serve as a pedestal for future studies in other academic and special libraries in India and elsewhere.
A Comparative Study on Various Search Techniques for Gaming Applications

The main objective of this research work is to deduce best among the existing search techniques. The various methods or the algorithms that we are going to discuss here are breadth-first search, depth-first search, and A* algorithm. Comparing various search techniques that are used in artificial intelligence problems for gaming applications.
Robust, Compliant Assembly with Elastic Parts and Model Uncertainty

In this paper, we present an approach to generate robot motions for robust parts assembly. The computation of motions for parts assembly usually requires an exact model of all relevant objects. Generating detailed object models, including friction and dynamics, is often complex and time-consuming, especially in the context of elastic parts. In addition, executing motions on real hardware will usually introduce further uncertainty. For this reason, we propose an approach that is inherently robust against model parameter uncertainties and unknown characteristics of elastic parts. Our planner explicitly takes into account the internal states of articulated objects, as well as uncertain model parameters, by constructing a search tree in the belief-parameter-space. It yields successful assembly motions from coarse object models and thus eliminates the need for detailed parameter tuning. We evaluated our approach with respect to four assembly tasks. Extensive simulations show that our planner significantly increases the success-rate compared to previous approaches. Numerous experiments on a real robot confirm the simulated results.
Visual Analytics in Mobility, Transportation and Logistics

Mobility, transportation and logistics are more and more influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behavior. These indicators will lead to massive changes in our daily live with regards to mobility, transportation and logistics. New technologies will lead to a different mobility behavior with new constraints. These changes in mobility behavior and logistics require analytical systems to forecast the required information and probably appearing changes. These systems have to consider different perspectives and employ multiple indicators. Visual Analytics provides both, the analytical approaches by including machine learning approaches and interactive visualizations to enable such analytical tasks. In this paper the main indicators for Visual Analytics in the domain of mobility transportation and logistics are discussed and followed by exemplary case studies to illustrate the advantages of such systems. The examples are aimed to demonstrate the benefits of Visual Analytics in mobility.
Intelligent Battlefield Situation Comprehension Method Based On Deep Learning in Wargame

Battlefield situation comprehension is a fundamental part of the operational command decision-making process. In our current information age, the depth and complexity of war have increased exponentially. It is complicated to analyze the situation through human knowledge and experience alone. The development of artificial intelligence technologies such as deep learning provides new technical support for situation comprehension. Firstly, this paper explores the general process of the commander's situation comprehension and analyzes the problems faced by the intelligent situation comprehension: i.e., the sample set is scarce, and the comprehension is very complicated. Secondly, this paper proposes the overall framework to understand the tactical situation intelligently using a Wargame. In the Wargame, the battlefield situation can be deduced, the military operational rules and empirical knowledge is used to generate the desired results, then using the CNN neural network to do training and reduce the gap between the ideal result and training result. Finally, the application value of this method in the auxiliary decision-making of commanders is pointed out.
Learning the Scope of Applicability for Task Planning Knowledge in Experience-Based Planning Domains

Experience-based planning domains (EBPDs) have been proposed to improve problem solving by learning from experience. They rely on acquiring and using task knowledge, i.e., activity schemata, for generating solutions to problem instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we extend our previous work to generate a set of conditions that determine the scope of applicability of an activity schema. The inferred scope is an abstract representation of a potentially unbounded set of problems, in the form of a 3-valued logical structure, which is used to test the applicability of the respective activity schema for solving different task problems. We validate this work on two classical planning domains and a simulated PR2 in Gazebo.
A Framework for Automatically Generating IoT Security Quizzes in 360VR Images/Videos Based on Linked Data

Internet of Things (IoT) technology has greatly developed in the past ten years. Many smart buildings and smart homes are constructed and used. Because of the popularity of smart buildings and smart homes, the security problem becomes more and more important. How to effectively enhance people’s IoT security knowledge is discussed frequently. In the past, we have already developed a framework that can automatically generate the IoT quizzes based on Linked Data in a virtual 3D environment. In this paper, we discuss how to extend it to support the quiz generation using the images and/or videos captured by a 360-degree camera. In this paper, we will apply the object recognition technology to automatically find out the IoT devices from these images and videos. According to the recognizing result, our framework will generate related questions for test or train learners.
Research on Data Mining and Investment Recommendation of Individual Users Based on Financial Time Series Analysis

With the continuous development of financial information technology, traditional data mining technology cannot effectively deal with large-scale user data sets, nor is it suitable to actively discover various potential rules from a large number of data and predict future trends. Time series are the specific values of statistical indicators on different time scales. Data sequences arranged in chronological order exist in our lives and scientific research. Financial time series is a special kind of time series, which has the commonness of time series, chaos, non-stationary and non-linear characteristics. Financial time series analysis judges the future trend of change through the analysis of historical time series. Through in-depth analysis of massive financial data, mining its potential valuable information, it can be used for individual or financial institutions in various financial activities, such as investment decision-making, market forecasting, risk management, customer requirement analysis provides scientific evidence.
Designing and Developing High-Confidence Petroleum Extraction Cyber-Physical System Using the Model-Driven Architecture

In modern petroleum extraction industry, Internet of Things (IoT) technology is widely used, and various of sensors constitute a typical cyber-physical systems (CPS). There are more requirements of interoperability and interaction between well site device systems. In such industrial critical domain, high-confidence is also important. This paper presents the requirements and running environment of developing high-confidence CPS. To satisfy the requirements of interoperability and data exchange among heterogeneous well site systems, a foundation framework with well defined interfaces and quality of service constraints was established. Based on this framework, designing high-confidence petroleum extraction CPS using Model-Driven Architecture approach is proposed, the long-lived models produced in the process that can be applied to any implementation technologies and platforms through model transformation which make the systems more portable. A case study shows the detail of the development process and using communication bridges to build relationships between models. At last some important problems which need to be paid more attention in model transformation process are discussed.
Intelligent Optimization Analysis of Target Tracking Based on Computer Vision

In view of the problems exposed by particle filtering in solving computer vision target tracking, this paper introduces a solution combining ant colony algorithm and Mean Shift. On the one hand, with the help of the ant colony algorithm's spontaneous artificial intelligence search ability, the problem of particle depletion is improved, and the selection of the local optimal position of the target tracking is optimized during the target tracking observation model of particle filtering. On the other hand, the Mean Shift target tracking algorithm is introduced, and the gradient tracking function is used to optimize the target tracking accuracy; in the target state transition model of particle filtering, the global convergence response time of the target tracking is compressed. Simulation experiments show that the improved particle filtering method by ant colony algorithm +Mean Shift not only improves the timeliness and accuracy of target tracking, but also enhances the artificial intelligence attribute of target tracking.
Identity-Based Multi-replicas Data Public Audit Scheme

When users outsource the data to the remote cloud servers, they lose physical control of data while gaining storage convenience and cannot ensure the integrity of cloud data. Therefore, a series of auditing schemes were proposed to solve the problem of cloud data integrity verification. However, the existing public auditing scheme mainly focuses on a single replica, and the dynamic update of data requires a large computational overhead. Aiming at these problems, a new identity-based multi-replicas public auditing scheme is proposed by using homomorphic authentication labels and multi-branch path tree. The analysis results show that the scheme can effectively audit the integrity of cloud data. Compared with similar schemes, it has higher computational efficiency.
Saving and Loan Transaction System in Cooperative Using Blockchain

In savings and loan cooperatives there are many problems of embezzlement of funds carried out by cooperative officers. The embezzlement of funds occurs because of the non-transparency of transactions that occur in savings and loan cooperatives. This transparency problem can be solved using blockchain-based recording technology. With blockchain, cooperative members can contribute as peer to protecting existing data.
Training Convolutional Autoencoders with Metric Learning

We propose a new Training method that enables an autoencoder to extract more useful features for retrieval or classification tasks with limited-size datasets. Some targets in document analysis and recognition (DAR) including signature verification, historical document analysis, and scene text recognition, involve a common problem in which the size of the dataset available for training is small against the intra-class variety of the target appearance. Recently, several approaches, such as variational autoencoders and deep metric learning, have been proposed to obtain a feature representation that is suitable for the tasks. However, these methods sometimes cause an overfitting problem in which the accuracy of the test data is relatively low, while the performance for the training dataset is quite high. Our proposed method obtains feature representations for such tasks in DAR using convolutional autoencoders with metric learning. The accuracy is evaluated on an image-based retrieval of ancient Japanese signatures.
Look Further to Recognize Better: Learning Shared Topics and Category-Specific Dictionaries for Open-Ended 3D Object Recognition

Service robots are expected to operate effectively in human-centric environments for long periods of time. In such realistic scenarios, fine-grained object categorization is as important as basic-level object categorization. We tackle this problem by proposing an open-ended object recognition approach which concurrently learns both the object categories and the local features for encoding objects. In this work, each object is represented using a set of general latent visual topics and category-specific dictionaries. The general topics encode the common patterns of all categories, while the category-specific dictionary describes the content of each category in details. The proposed approach discovers both sets of general and specific representations in an unsupervised fashion and updates them incrementally using new object views. Experimental results show that our approach yields significant improvements over the previous state-of-the-art approaches concerning scalability and object classification performance. Moreover, our approach demonstrates the capability of learning from very few training examples in a real-world setting. Regarding computation time, the best result was obtained with a Bag-of-Words method followed by a variant of the Latent Dirichlet Allocation approach.
City Traffic Analysis Based on Facebook Data

Traffic congestion is a major problem throughout the world, especially in the city of Jakarta. Until now the problem of congestion has not been resolved properly because the number of vehicles is always increase and the number of roads is inadequate, drivers are not getting complete and updated information about the traffic around them, other than that the parties are also less responsive in handling congestion. Therefore, this study was made to analyze and determine which road segments need special attention, help traffic management, make it easier for users to find out the latest traffic information, and make relevant parties able to take quick response actions. This paper is using social media data, social network such as Facebook is able to provide a real-time data. this data can be processed to gain a powerful insight. However, processing social media data have challenge due to the raw text format, therefore the text processing needs to be done in order to make the data readable. In Jakarta, there is an official account which post a traffic condition in Jakarta. In this paper we are getting the post data from this account, do text processing on it and make a reports and dashboard to provide any user insight regarding the traffic data in Jakarta.