Reviewerly/exHarmony huggingface dataset:
Lightweight Searchable Encryption Scheme Based on Certificateless Cryptosystem

Searchable encryption technology can guarantee the confidentiality of cloud data and the searchability of ciphertext data, which has a very broad application prospect in cloud storage environments. However, most existing searchable encryption schemes have problems, such as excessive computational overhead and low security. In order to solve these problems, a lightweight searchable encryption scheme based on certificateless cryptosystem is proposed. The user's final private key consists of partial private key and secret value, which effectively solves the certificate management problem of the traditional cryptosystem and the key escrow problem of identity-based cryptosystem. At the same time, the introduction of third-party manager has significantly reduced the burden in the cloud server and achieved lightweight multi-user ciphertext retrieval. In addition, the data owner stores the file index in the third-party manager, while the file ciphertext is stored in the cloud server. This ensures that the file index is not known by the cloud server. The analysis results show that the scheme satisfies trapdoor indistinguishability and can resist keyword guessing attacks. Compared with similar certificateless encryption schemes, it has higher computational performance in key generation, keyword encryption, trapdoor generation and keyword search.
Impact Analysis of Additional Input Parameters on Neural Network Cryptocurrency Price Prediction

Neural network is the universal approximator, but its precision highly depends on sufficient set of inputs. Cryptocurrencies have great volatility, due to absence of fundamentals to back up their price. In this paper we analyze approaches to obtain additional parameters for neural networks and explore their impact on its prediction accuracy. This study indicates significant improvement of neural network predictions due to inclusion of wider selection of relevant data points.
3-Party Adversarial Cryptography

The domain of Artificial Intelligence (AI) has seen an outstanding growth during the last two decades. It has proven its efficiency in handling complex domains including speech recognition, image recognition and many more. One interesting and evolving branch that was put forward years ago but have seen a good growth only during the past few years is encryption using AI. After Google announced that it has succeeded teaching neural networks encryption in the presence of Eavesdroppers, research in this particular area has seen a rapid spread of interest among different researchers all over the world to develop new Neural Networks capable of operating different cryptographic tasks. In this paper, we take initial steps to achieve secure communication among more than two parties using neural network based encryption. We forward the idea of two party symmetric encryption scheme of Google to a multi party Encryption scheme. In this paper we will focus on a 3-Party case.
Fuzzy topsis optimization on expert systems for core competency detection and Islamic religious education student learning achievement at Perguruan Tinggi Keagamaan Islam Negeri (PTKIN)

Abstract The research aims to assess the development and implementation of Fuzzy Topsis optimization on core competency detection expert systems and student learning achievement majoring in Islamic religious education at PTKIN. In the learning process of KKNI based curriculum designed to detect the level of development of core competencies and achievements Study students at the faculty or PAI study Program at PTKIN. The main product development of this research is expert system application With the Fuzzy Topsis method of measuring the indicator Development of Core Competencies (Hard skills and soft skills) and Learning factors that have an effect on student learning achievements. The results of this expert system can help students and lecturers independently to detect how the level of development student core complications, detecting internal learning factors and external influences and measures how they influence to student learning achievements. Optimizing Results of Fuzzy TOPSIS shows optimal results better.
Path Planning for Surgery Robot with Bidirectional Continuous Tree Search and Neural Network

Solving a thorny issue of real-time path planning for surgery robot in uncertain environments, a novel algorithm named bidirectional continuous tree search (BCTS) is proposed. Most partially observable markov decision process (POMDP) planners address challenges of unknown environments with discrete states, observations and actions, which are fail to automate the operative procedure. However, the BCTS method addresses the issue by handling POMDPs in continuous state, observation and action spaces. The proposed approach has a bidirectional search structure with the intent of greatly improving the calculation efficiency. Meanwhile, Bayesian optimization (BO) algorithm is considered to dynamically sample promising actions while we construct a belief tree. In view of the speed of BO process, the upper and lower bounds of the optimal action values given by fast informed bound (FIB) and point-based value iteration (PBVI) limit the search scope, so we can improve the speed of BO. In addition, we apply an optimal path planning generator, radial basis function neural network (RBFNN), to obtain a smoother trajectory. Finally, simulation of glaucoma surgery has been carried out to explore the best surgical approach. The results show that the introduced structure can effectively guide the surgery robot to perform surgical procedures and receive a real-time as well as smooth path.
Predicting Forward &amp; Backward Facial Depth Maps From a Single RGB Image For Mobile 3d AR Application

Cheap and fast 3D asset creation to enable AR/VR applications is a fast growing domain. This paper addresses a significant problem of reconstructing complete 3D information of a face in near real-time speed on a mobile phone. We propose a novel deep learning based solution to predict robust depth maps of a face, one forward facing and the other backward facing, from a single image from the wild. A critical contribution is that the proposed network is capable of learning the depths of the occluded part of the face too. This is achieved by training a fully convolutional neural network to learn the dual (forward and backward) depth maps, with a common encoder and two separate decoders. The 300W-LP, a cloud point dataset, is used to compute the required dual depth maps from the training data. The code and results will be made available at project page.
Comparison of COSYSMO Model with Different Software Cost Estimation Techniques

Cost Estimation of any software is process of evaluating the estimating cost and effort which is must necessary to build any software system or project for increasing the output. The basic input is the cost drivers set and the size of code and the output is the effort which is calculated in terms of Person-Months (PM's). To estimate cost for software is the main portions of any project type and right estimation of cost for the software will help in completing project in of time and budget. Proper cost estimation of a software project is highly needed to avoid any risk failure. So, there is a strong need to calculate the project's cost annually and compare it to the other techniques. This paper deals with the analysis and comparison of COCOMO model of Boehm's and COSYSMO model of Valerdi's through the help of formulae's and an online tool present at the CSSE website. For this we have used the COCOMO dataset and it has been observed that COSYSMO model works better than the COCOMO model in every aspect.
Concept extraction for structured text using entropy weight method

With the rapid development of computer and communication, data mining and related fields are facing huge challenges. In the research of data mining and text analysis, how to extract concepts more objectively and reasonably is a key issue. Existing approaches seem to lack the intensive analysis of structural nature of structured text, which will affect the effectiveness and accuracy of text analysis. This paper proposes a novel approach of structured text analysis and concept extraction using entropy weight method. It quantitatively assesses the contribution of each module in evaluating the weight of concept. Based on the position of the concept in the text, concepts can be mined more effectively. Furthermore, the method is applied to the field of academic text analysis to verify its effectiveness and advantages over traditional methods. And the experimental results also prove that the method can provide solid theoretical and technical support for personalized academic information services
A change-point detection and clustering method in the recurrent-event context

Change-point detection in the context of recurrent-event is a valuable analysis tool for the identification of the intensity rate changes. It has been an interesting topic in many fields, such as medical studies, travel safety analysis, etc. If subgroups exist, clustering can be incorporated into the change-point detection to improve the quality of the results. This paper develops a new algorithm named Recurrent-K-means to detect the change-points of the intensity rates and identify clusters of objects with recurrent events. It also proposes a test-based method to perform a heuristic search in determining the number of underlying clusters. In this study, the objects are assumed to fall in several clusters while the objects in the same cluster share identical change-points. The event count for an object is assumed to be a non-homogeneous Poisson process with a piecewise-constant intensity function. The methodology estimates the change-point as well as the intensity rates before and after the change-point for each cluster. The methodology establishes a clustering analysis based on K-means algorithm but enhances the procedure to be model based. The simulation study shows that the methodology performs well in parameter estimation and determination of the number of clusters in different scenarios. The methodology is applied to the UK coal mining disaster data to show its possible role in shaping government regulations and improving coal industry safety.
Small Models for Big Data

Clinical Pharmacology & TherapeuticsVolume 107, Issue 4 p. 710-711 Perspective Small Models for Big Data Hitesh B. Mistry, Corresponding Author Hitesh B. Mistry hitesh.mistry@manchester.ac.uk Division of Pharmacy, University of Manchester, Manchester, UK Correspondence: Hitesh B. Mistry (hitesh.mistry@manchester.ac.uk)Search for more papers by this authorDavid Orrell, David Orrell Systems Forecasting, Toronto, Ontario, CanadaSearch for more papers by this author Hitesh B. Mistry, Corresponding Author Hitesh B. Mistry hitesh.mistry@manchester.ac.uk Division of Pharmacy, University of Manchester, Manchester, UK Correspondence: Hitesh B. Mistry (hitesh.mistry@manchester.ac.uk)Search for more papers by this authorDavid Orrell, David Orrell Systems Forecasting, Toronto, Ontario, CanadaSearch for more papers by this author First published: 29 January 2020 https://doi.org/10.1002/cpt.1770Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Conflict of interest All authors declared no competing interests for this work. Citing Literature Volume107, Issue4Data ScienceApril 2020Pages 710-711 RelatedInformation
A Study of Action Recognition Using Pose Data Toward Distributed Processing Over Edge and Cloud

With the development of cameras and sensors, and the spread of cloud computing, life logs can be acquired and stored in general households for various services using the logs. However, it is difficult to analyze moving images acquired by a home sensor in real time using machine learning because the data size and the computational complexity are large. New computing paradigm called edge computing or fog computing, which enables distributed computing over edge and cloud, has the possibility to address this issue. The feature vectors are extracted from moving images by preprocessing on the sensor side and the only small feature vectors are sent to the cloud and used for learning. But, it is not clear how accurately we can recognize actions using only the feature vectors in the learning and inferring. We investigate the accuracies of action recognition with various machine learning methods using feature vector information obtained from moving images. We use the pose estimation library OpenPose for detection of the feature vectors and recognize actions using logistic regression, random forest, support vector machine, and neural network (NN) models, general NN and LSTM, as machine learning methods. The experimental results show that it is possible to recognize an action with 80% accuracy or higher when using random forest and neural network models. We also discuss a method to further improve the accuracy based on the experimental results.
An Efficient Collaborative Recommender System for Removing Sparsity Problem

Recommender Systems is a special type of information filtering system which has become important in the information overloaded and strategic decision making environment. Recommender System is used to produce meaningful suggestions about new items for particular consumers. These recommendations may be based on the user profile or item ratings, facilitate the users to make decisions in multiple contexts, such as what items to buy, what online news to read or what music to listen. Recommender Systems helps their founders to increase profits by recommending items and attracting new consumers. Collaborative filtering technique recommends items basis of conclusion of opinions about various products by users of similar profile to the active user. This technique requires user-items-ratings matrix. Although this is the most mature and commonly implemented technique, it faces major problem of Data Sparsity problem. Sparsity Problem occurs as a result of lack of enough information when only a few of the total number of items are rated by the users. This produces a sparse user item matrix leads to weak recommendations. This paper presents a recommender system using collaborative filtering implemented with RapidMiner tool. The proposed recommendation system is designed with users’ similarity calculated by Sequence and Set Similarity Measure (S3M) with utilizing similarity upper approximation and a Singular Value Decomposition (SVD) model based technique used for recommending ratings for removing sparsity.
Digital Twin for Tuning of Server Fan Controllers

Cooling of IT equipment consumes a large proportion of a modern data centre's energy budget and is therefore an important target for optimal control. This study analyses a scaled down system of six servers with cooling fans by implementing a minimal data driven time-series model in TensorFlow/Keras, a modern software package popular for deep learning. The model is inspired by the physical laws of heat exchange, but with all parameters obtained by optimisation. It is encoded as a customised Recurrent Neural Network and exposed to the time-series data via n-step Prediction Error Minimisation (PEM). The thus obtained Digital Twin of the physical system is then used directly to construct a Model Predictive Control (MPC) type regulator that executes in real time. The MPC is then compared in simulation with a self-tuning PID controller that adjust its parameters on-line by gradient descent.
Smartphone Zombie Detection From LiDAR Point Cloud for Mobile Robot Safety

Awareness of surrounding and prediction of dangerous situations is essential for autonomous mobile robots, especially during navigation in a human-populated environment. To cope with safety issues, state-of-the-art works have focused on pedestrian detection, tracking, and trajectory prediction. However, only a few studies have been conducted on recognizing some specific types of dangerous behaviors exhibited by pedestrians. Here, we propose a tracking enhanced detection method to recognize people using their smartphones while walking, referred to as smartphone zombie. Features used for pedestrian detection usually involve the rotation variance problem, and in this paper, the drawback is handled by employing motion information from multi-object tracking. The proposed solution has been validated through experiments performed on a newly collected dataset. Results showed that our detector can learn a distinct pattern of the appearance of smartphone zombies. Thus, it can successfully detect them outperforming the existed detection method.
Active Inverse Model Learning with Error and Reachable Set Estimates

In this work, we propose a framework to learn an inverse model of redundant systems. We address three problems. By formalizing what it actually means to learn an inverse model, we derive a method where the inverse model, represented as a neural network, is learned by minimizing an upper bound on the real performance error, which is provided by a forward model (kernel regression or Gaussian process) learned on the currently available data. Most machine learning methods focus on learning the mapping of the function. For inverse models, it is, however, crucial to know the reachable set of the true forward model, since this becomes the domain of the inverse. Therefore, we secondly propose a method to estimate the reachable set of the system. Finally, we develop an active exploration strategy that is based on maximizing a lower bound on the true fill-distance to efficiently generate the data in the high dimensional input space. A key feature of our method is that the resulting learned inverse model provides error bounds on its performance.From an application point of view, this work is motivated by learning to control musculoskeletal systems. In the experiments, we show for both a simulated model of a human arm with six muscles and a real muscle-driven robot that the proposed method is able to learn the reachable set of these systems as well as a policy that enables to accurately control the position.
Document Domain Adaptation with Generative Adversarial Networks

Despite the digitization of communication flows, information is still commonly exchanged via printed documents. Since manual information extraction becomes inefficient and prone to errors, automatic document processing (ADP) tools have been proposed. Following the current trends in machine learning, many of these tools are based on deep learning methods which require a representative set of training documents to achieve good performance for a target domain. In practice, training documents are often scarce and limited to certain domains which makes it difficult to train a model that generalises well to varying domains. This paper analyses the influence of domain shifts on the performance of document analysis tasks. It further explores the improvements that can be achieved with visual domain adaptation using generative adversarial networks (GANs). The results show that the impact of the domain shift on the performance is not only depending on the difference between the domains but also on the analysis task itself. While some tasks such as document binarization are noticeably affected by the domain shift, other tasks like page classification are less sensitive. It is also shown that the use of mapped training data obtained from a GAN, which translates between the source and target domain, can improve the performance considerably.
Automatic Annotation for Semantic Segmentation in Indoor Scenes

Domestic robots could eventually transform our lives, but safely operating in home environments requires a rich understanding of indoor scenes. Learning-based techniques for scene segmentation require large-scale, pixel-level annotations, which are laborious and expensive to collect. We propose an automatic method for pixel-wise semantic annotation of video sequences, that gathers cues from object detectors and indoor 3D room-layout estimation and then annotates all the image pixels in an energy minimization framework. Extensive experiments on a publicly available video dataset (SUN3D) evaluate the approach and demonstrate its effectiveness.
Quantum-Enhanced Microwave Backscattering Communications

The recent advances in the field of microwave superconducting circuits open the way for a multitude of engineering applications that revolutionize the field of classical communications. To exploit this new technology, we propose a novel microwave quantum-enhanced backscattering system based on the laws of quantum physics. Both the transmitter and the receiver are quantum mechanical in nature and are accommodated at the infrastructure side, while the backscattering device is classical. The advocated system breaks the performance barrier of the classical backscattering systems and approaches the ultimate attainable receiver sensitivity. Finally, our quantum solution outperforms the classical solutions in terms of its level of security.
Contrast Enhancement and Image Completion: A CNN Based Model to Restore Ill Exposed Images

Digital cameras work through transforming the scene's radiance into an electrical charge. Optical arrangement, sensors, and embedded electronics often limit the accuracy of the representation. Scenes with a dynamic range above the capability of the camera or poor lighting are challenging conditions, which usually result in low contrast images. Soft clipping is usually compensated by transforming the power and shifting the image's histogram. However, under extreme conditions, ill exposure results in severe clipping that requires interpolation and painting. We introduce a model of convolutional neural network to perform signal reconstruction and interpolation. It is designed to be used on sRGB images. The results are evaluated using several metrics of image quality that indicate that the proposed network can improve images that are damaged by different conditions of exposure. In addition, our method offers a substantial gain over state-of-the-art methods.
Monocular Outdoor Semantic Mapping with a Multi-task Network

In many robotic applications, especially for the autonomous driving, understanding the semantic information and the geometric structure of surroundings are both essential. Semantic 3D maps, as a carrier of the environmental knowledge, are then intensively studied for their abilities and applications. However, it is still challenging to produce a dense outdoor semantic map from a monocular image stream. Motivated by this target, in this paper, we propose a method for large-scale 3D reconstruction from consecutive monocular images. First, with the correlation of underlying information between depth and semantic prediction, a novel multi-task Convolutional Neural Network (CNN) is designed for joint prediction. Given a single image, the network learns low-level information with a shared encoder and separately predicts with decoders containing additional Atrous Spatial Pyramid Pooling (ASPP) layers and the residual connection which merits disparities and semantic mutually. To overcome the inconsistency of monocular depth prediction for reconstruction, post-processing steps with the superpixelization and the effective 3D representation approach are obtained to give the final semantic map. Experiments are compared with other methods on both semantic labeling and depth prediction. We also qualitatively demonstrate the map reconstructed from large-scale, difficult monocular image sequences to prove the effectiveness and superiority.