Reviewerly/exHarmony huggingface dataset:
On the influence of using fuzzy extensions in linguistic fuzzy rule-based regression systems

Fuzzy Rule-Based Systems, FRBSs, are powerful tools to address regression problems. They can model the relationship between inputs and outputs by linguistic concepts. However, those FRBSs which are based on the conventional Type-1 fuzzy sets may not be able to handle some difficulties of real-world applications. In such situations, using novel representations of fuzzy sets seems like a good idea. Different extensions of fuzzy sets usually help to provide more precise models in the real-world problems. In this study, the influence of using fuzzy extensions in improving the efficiency of linguistic fuzzy rule-based regression models is investigated. For this purpose, a conventional Type-1 Mamdani FRBS is adapted to the three extensions of fuzzy sets, namely Interval Type-2, Intuitionistic, and Interval Type-2 Intuitionistic fuzzy sets. A two-pass method is proposed to define membership (non-membership) functions of these fuzzy sets; this method is based on the 3-tuples representation of the standard Type-1 membership functions. Wang and Mendel’s rule learning method is adapted to extract fuzzy rules from regression data. In order to tune the membership functions up to different extents, three evolutionary extensions are also presented for each type of the proposed FRBSs. Individual, internal, and external comparisons of the proposed FRBSs were done using 22 real-world regression datasets and statistical tests. Experimental results confirm that all the three proposed FRBSs outperform the classical Type-1 framework; furthermore, the Interval Type-2 Intuitionistic FRBS is the superior system so that an appropriate tuning of its parameters makes it the most accurate model.
Secure Data Group Sharing and Conditional Dissemination with Multi-Owner in Cloud Computing

With the rapid development of cloud services, huge volume of data is shared via cloud computing. Although cryptographic techniques have been utilized to provide data confidentiality in cloud computing, current mechanisms cannot enforce privacy concerns over ciphertext associated with multiple owners, which makes co-owners unable to appropriately control whether data disseminators can actually disseminate their data. In this paper, we propose a secure data group sharing and conditional dissemination scheme with multi-owner in cloud computing, in which data owner can share private data with a group of users via the cloud in a secure way, and data disseminator can disseminate the data to a new group of users if the attributes satisfy the access policies in the ciphertext. We further present a multiparty access control mechanism over the disseminated ciphertext, in which the data co-owners can append new access policies to the ciphertext due to their privacy preferences. Moreover, three policy aggregation strategies, including full permit, owner priority and majority permit, are provided to solve the privacy conflicts problem caused by different access policies. The security analysis and experimental results show our scheme is practical and efficient for secure data sharing with multi-owner in cloud computing.
AI Based Student Bot for Academic Information System using Machine Learning

In General all the institutions like colleges sends their notes and information to students individually. Sometimes the student can�t access it quickly and repetition of data also increased. The realm of this work is to create a Chatbot for the college purpose. Our work reduces the human work to send every details and notes to all departments by email or some other medium. In this work, academic information's /details feed it to the database which will be available for the long time period. The academic information consists of information about placements details, exam time tables, semester notes and upcoming events. A Chatbot is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. The chat bot stores the data by key words and when the user entered data is matched with the key it reply the assigned data for it. The Chatbot is created by using python language and Natural language processing. This project make use of the MySQL database to store the information. With the help of natural language processing the bot AI understand the message sent by the user and reply with the matched key value. In this Chatbot the user first need to login by their college roll number and Department. When the valid person asks about the particular information by text the information gets retrieved from the updated database that related to their department. Through this chat box the student can easily access whenever they want and the data need not to be update more than once.
Word Embedding Method of SMS Messages for Spam Message Filtering

SVM has been one of the most popular machine learning method for the binary classification such as sentiment analysis and spam message filtering. We explored a word embedding method for the construction of a feature vector and the deep learning method for the binary classification. CBOW is used as a word embedding technique and feedforward neural network is applied to classify SMS messages into ham or spam. The accuracy of the two classification methods of SVM and neural network are compared for the binary classification. The experimental result shows that the accuracy of deep learning method is better than the conventional machine learning method of SVM-light in the binary classification.
Shortening the Cover for Fast JPEG Steganography

Recently, the most effective steganographic schemes for JPEG images are based on minimal distortion model with Syndrome-Trellis Codes (STCs) as the coding method. However, the execution time of STCs will be severely long for message embedding to the cover object of large size, which cannot meet the demand for real-time communication in a real-world application. According to the time complexity O(2hn), it is suggested in the STCs to accelerate the embedding process by decreasing the constraint height h. However, smaller h corresponds to lower steganographic security. In this paper, we investigate the possibility of shortening the cover (reducing the length n) for speeding up the execution of STCs without weakening the steganographic security. After introducing some properties of cover selection with proofs, we propose several algorithms designed for JPEG images to construct a preferable shortened cover containing DCT coefficients of smaller costs as much as possible. The experimental results display the superiority of the proposed algorithm on the speed profit and the security when compared with the method of decreasing h. With confidence, a JPEG image of arbitrary quality factor can be safely shortened to 1/4 of the original, and correspondingly the execution of STCs can be four times faster.
Multi-Cue-Based Circle Detection and Its Application to Robust Extrinsic Calibration of RGB-D Cameras

RGB-Depth (RGB-D) cameras are widely used in computer vision and robotics applications such as 3D modeling and human⁻computer interaction. To capture 3D information of an object from different viewpoints simultaneously, we need to use multiple RGB-D cameras. To minimize costs, the cameras are often sparsely distributed without shared scene features. Due to the advantage of being visible from different viewpoints, spherical objects have been used for extrinsic calibration of widely-separated cameras. Assuming that the projected shape of the spherical object is circular, this paper presents a multi-cue-based method for detecting circular regions in a single color image. Experimental comparisons with existing methods show that our proposed method accurately detects spherical objects with cluttered backgrounds under different illumination conditions. The circle detection method is then applied to extrinsic calibration of multiple RGB-D cameras, for which we propose to use robust cost functions to reduce errors due to misdetected sphere centers. Through experiments, we show that the proposed method provides accurate calibration results in the presence of outliers and performs better than a least-squares-based method.
Multi-kernel sparse subspace clustering on the Riemannian manifold of symmetric positive definite matrices

Recently, clustering in the Riemannian manifolds has received a great interest. The main specificity of this kind of spaces is their ability to detect nonlinear forms in the real world data groups. Accordingly, numerous Euclidean clustering algorithms have been emerged to the Riemannian framework. One of them is the Sparse Subspace Clustering (SSC) method. Within this context, an interesting method named Kernel Sparse Subspace Clustering in the Riemannian manifold (KSSCR) has been proposed. In order to improve the clustering performance of the SSC method, KSSCR is based on the idea of data projection into a Reproducing Kernel Hilbert Space (RKHS) through a Riemannian kernel. This modification in terms of projection space has yielded to significant clustering results compared to the original SSC. This paper proposed a Multi-Kernel SSCR (MKSSCR) method. The idea is to create a linear combination of a set of Riemannian kernels. In fact, by using a single kernel, the faraway points will be neglected. Thus, the key motivation of this work is to use a mixture of kernels. The purpose here is to emphasis the closest data points without ignoring the faraway ones in the process of selecting neighbors for each data point. Several experiments carried out on varied face clustering data sets demonstrate the clustering accuracy improvement by the proposed method compared to other state-of-the-art methods.
Time-Dependent Survival Neural Network for Remaining Useful Life Prediction

Remaining useful life (RUL) prediction has been a topic of practical interest in many fields involving preventive intervention, including manufacturing, medicine and healthcare. While most of the conventional approaches suffer from censored failures arising and statistically circumscribed assumptions, few attempts have been made to predict RUL by developing a survival learning machine that explores the underlying relationship between time-varying prognostic variables and failure-free survival probability. This requires a purely data-driven prediction approach, devoid of any a survival model and all statistical assumptions. To this end, we propose a time-dependent survival neural network that additively estimates a latent failure risk and performs multiple binary classifications to generate prognostics of RUL-specific probability. We train the neural network by a new survival learning criterion that minimizes the censoring Kullback-Leibler divergence and guarantees monotonicity of the resulting probability. Experiments on four datasets demonstrate the great promise of our approach in real applications.
Founding Secure Computation on Blockchains

We study the foundations of secure computation in the blockchain-hybrid model, where a blockchain – modeled as a global functionality – is available as an Oracle to all the participants of a cryptographic protocol. We demonstrate both destructive and constructive applications of blockchains: We develop a suite of techniques for constructing secure protocols in the blockchain-hybrid model that we hope will find applications to future research in this area.
Prayer-Bots and Religious Worship on Twitter: A Call for a Wider Research Agenda

The automation of online social life is an urgent issue for researchers and the public alike. However, one of the most significant uses of such technologies seems to have gone largely unnoticed by the research community: religion. Focusing on Islamic Prayer Apps, which automatically post prayers from its users’ accounts, we show that even one such service is already responsible for millions of tweets daily, constituting a significant portion of Arabic-language Twitter traffic. We argue that the fact that a phenomenon of these proportions has gone unnoticed by researchers reveals an opportunity to broaden the scope of the current research agenda on online automation.
Cross-object information security: A study on new generation encryption

Research in the Internet of Things (IoT) is increasing the studies in security and privacy aspects. IoT applications is expected to have difficulties in security and privacy issues in the near future. Speed improvement of quantum computers threatens the crypto algorithms used. Strong Mathematics encryption algorithms in today's World can cause weaknesses later. They can be solved by the computers which have large enough transaction volume in the labs. Changes in technology require the change in approaches to security solutions. Smart objects can be managed over the network, including critical applications. The basis of security on data lies in the science of cryptography. Privacy and fidelity of data collected over IoT and its accurate transmission are very important. This study provides an approach on reliability of next-generation encryption algorithm in the context of objects' relativity.
Autonomous Resource Request Transaction Framework Based on Blockchain in Social Network

Currently, blockchain technology has been widely researched and is being applied to many other fields, such as financial transactions, E-government, logistics, and supply-chain system.It can be used to store and maintain transaction data through the de-centralized model.In this paper, we propose an autonomous resource request transaction framework based on blockchain in a social network, in which all kinds of resources in the social community can be traded through blockchain technology.When a user needs to acquire some resources from a community, the user may make a transaction with the members from the community through blockchain technology while the members autonomously negotiate each other to reach an agreement.The proposed framework provides an incentive mechanism to encourage community members to disseminate the resources through a smart contract.
Towards Behavioural Cloning for Autonomous Driving

This paper proposes an off-policy imitation learning methodology for autonomous driving using a doubly-deep recurrent convolutional architecture that learns compositional representations in both space and time domains. The architecture has been referred to as NAVNet (Navigation Network) and is end-to-end trainable. The recurrent long-term models are directly connected with the visual convolutional models. The models can be trained together to learn both temporal dynamics as well as the convolutional perceptual representations. The approach is non-data driven in nature and the system learns a regression-based mapping function between input images and steering angle. Results presented in this research indicate distinct advantages of the proposed LRCN model over the stateof-the-art deep learning techniques for autonomous navigation.
Satellite cloud image classification for cyclone prediction using Dichotomous Logistic Regression Based Fuzzy Hypergraph model

Satellite cloud image classification is a significant step in weather forecasting and climate analysis. Image classification has drawn lots of attention for several decades by remote sensing communities to mitigate the damage caused by cyclones. Classical deep convolutional neural network is being used for detecting the intensity of tropical cyclones. However, it is difficult to improve cyclone prediction accuracy with less time complexity. Dichotomous Logistic Regression Based fuzzy Hypergraph (DLR-FH) model has been developed for improving the classification accuracy of the satellite cyclone image with minimum time. Satellite cyclone images are taken from the cyclone dataset. DLR-FH model consists of three processing steps namely preprocessing, feature extraction and image classification. Preprocessing is carried out using lee filter to remove the noise artifacts from cyclone images. Cyclone feature extraction is then carried out using independent component analysis for extracting the necessary features from the image by estimating the separation matrix. After extracting the cyclone features, dichotomous logistic regression based hypergraph model is used for measuring the relationship between independent variables (extracted feature values) and dependent variables (outcomes) to classify the images. Fuzzy membership function is then applied to estimate the intensity of the cyclone. Cyclone prediction is thus carried out with minimum time and high accuracy. Experimental evaluation of proposed DLR-FH model is carried out using cyclone images with different factors such as peak signal to noise ratio, classification accuracy, classification time and false positive rate. We have considered sufficient number of cyclone images taken from the image dataset.
Software Techniques to Reduce Cybersickness Among Users of Immersive Virtual Reality Environments

The virtual worlds are photorealistic, the users have difficulty distinguishing the reality and virtuality. The high degree of realism of artificial virtual worlds results in deep immersion in virtual environments. The users immerse themselves in the virtual world. The immersion may, however, provoke different undesirable side-effects. The symptoms of motion sickness occurring mainly in transport means (buses, planes, ships, etc.) are also observed among users of virtual worlds where the users usually view moving scenes while they remain physically stationary. In the case of virtual reality it is called cybersickness. The paper presents software techniques to reduce cybersickness among the users of immersive virtual reality environments and analyses the results of experiments showing their efficiency and their impact on the users of virtual worlds.
Learning with imprecise probabilities as model selection and averaging

This paper presents a general framework for learning with imprecise probabilities, consisting of a hierarchical approach with two sets of parameters. In the top set we have imprecise information, and conditioned on this set we have precise Bayesian information about the other set of parameters. Given a set of observations, the information about both sets of parameters is updated by conditioning, and a model selection method is applied to compute a reduced top set. This model selection method is based on decisions with imprecise probabilities. It will be shown that many existing approaches can be fitted in this general procedure, and a theoretical justification will be provided. Finally, the method will be applied to the problem of learning credal networks.
Lithic or metal tools: Techno-traceological and 3D analysis of rock art

Techno-traceological studies in rock art are dedicated to reveal the properties of gestures and tools used for making petroglyphs. The purpose of this kind of research is to identify technological tendencies in figurative traditions, for that we have to analyze a great number of figures. Determination of the tools demands large efforts for manual classification, but today the procedure is much simpler thanks to 3D visualization. We aim at supporting techno-traceological studies of petroglyphs by 3D shape classification algorithms. We use 3D segmentation approach to determine the shapes of tool marks and classification algorithms to reveal the difference (for lithic/metal tool traces). We applied the approach to experimental and archaeological test on 3D models of petroglyphs. As a perspective in developing a complete analytical tool we think of creating a more accurate classification.
Learning from Privacy Preserved Encrypted Data on Cloud Through Supervised and Unsupervised Machine Learning

With the advent of new technologies and the ever increasing use of Cloud in nearly every sphere of our day to day life, the data owner using cloud is still less confident. This lack of trust is obvious as the data owner entrusts the data with a third party which stores, manages and processes the data. Whatever be the level of security, there is always some loop hole for the misusers. Various machine learning techniques have been in use to learn from the data available for analysis and to use the results accordingly for benefits. Homomorphic secure multi-party computation (SMC) or homomorphic encryption (HE) encryption schemes have been one means to securely process the data on cloud while preserving the privacy of the data. In this work we have tried to investigate both supervised and unsupervised machine learning capability through neural networks over encrypted data from a semantically secure cryptosystem based on Homomorphic properties. This work will provide a base for the machine learning performance over the data on cloud whose privacy is claimed to be preserved using Homomorphic encryption. The findings are supported with experimental results.
The Effect of Scientific Collaboration on CSCW Research: A Scientometric Study

The structure and evolution of a scientific research community can be quantitatively assessed taking into account the interactions between scientific agents dispersed geographically. In the recent years, CSCW has stabilized as a cross-disciplinary field suffering significant changes in its core structure, and there is limited understanding about the factors influencing the nature and progress of collaborative computing research. In this paper, we measure the correlation between a set of features related to the influence of collaboration types on the number of citations as well as the geographical distribution of the accumulated contribution to the CSCW literature. Overall, our work can represent a starting point to demonstrate how the study of scientific collaboration can partly explain the variations in the number of citations, frequency of papers, and topics addressed.
A fast numerical solver for local barycentric coordinates

The local barycentric coordinates (LBC), proposed in Zhang et al. (2014), demonstrate good locality and can be used for local control on function value interpolation and shape deformation. However, it has no closed-form expression and must be computed by solving an optimization problem, which can be time-consuming especially for high-resolution models. In this paper, we propose a new technique to compute LBC efficiently. The new solver is developed based on two key insights. First, we prove that the non-negativity constraints in the original LBC formulation is not necessary, and can be removed without affecting the solution of the optimization problem. Furthermore, the removal of this constraint allows us to reformulate the computation of LBC as a convex constrained optimization for its gradients, followed by a fast integration to recover the coordinate values. The reformulated gradient optimization problem can be solved using ADMM, where each step is trivially parallelizable and does not involve global linear system solving, making it much more scalable and efficient than the original LBC solver. Numerical experiments verify the effectiveness of our technique on a large variety of models.
General Distributed Hash Learning on Image Descriptors for $k$-Nearest Neighbor Search

Hashing methods have attracted much attention due to their superior time and storage properties for image retrieval. To learn similarity-preserving hash function, most existing methods are designed for the centralized setting. However, the current data storage systems are distributed to increase scalability. Obviously, it is infeasible to aggregate all the data into a fusion center because of the prohibitively expensive communication and computation overhead. Motivated by this, some methods are proposed to achieve hashing for distributed data. However, these methods mostly focus on extending one specific hashing to a distributed model without considering the generality. In this letter, we propose a novel general distributed hash learning model, which can be viewed as an effective distributed model of most hashing methods. The proposed model can achieve up to 15.2% accuracy gains over state-of-the-art distributed hashing methods, while the communication cost is independent on the data size.