Reviewerly/exHarmony huggingface dataset:
Simulation study using building-design energy analysis to estimate energy consumption of refrigerated container

Refrigerated containers account for half of the total electricity consumption by storage yards and that this is expected to increase continuously each year. A refrigerated container is a special cargo container equipped with an integral refrigeration unit. The amount of power consumption of the refrigerated container will change depending on many external variables. Environmental factors mainly solar radiation received on the container walls caused surface temperatures increase will then affect the power consumption. This paper provides a simulation study on the estimation of energy consumption of refrigerated container. The simulation model performed on the building-design energy analysis used Integrated Environmental Solution (IES) software packages. The geometry of simulation model considers the actual dimension of refrigerated container consists of insulation walls in the three-dimensional analysis. Physical properties of the insulation walls and environmental factors used weather data are applied to the simulation model as parameter inputs. Estimation of energy consumption of the model based on the calculation of cooling load from the object considers the thermal effect from the sun energy. The measurement data from the experimentation was conducted by Shinoda and Budiyanto (2016) used as validation of simulation model. The comparison of energy consumption from simulation and measurement shows in shows in good agreement.
Neural embedding-based indices for semantic search

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.
Matrix-based Bayesian Network for efficient memory storage and flexible inference

For real-world civil infrastructure systems that consist of a large number of functionally and statistically dependent components, such as transportation systems or water distribution networks, the Bayesian Network (BN) can be a powerful tool for probabilistic inference. In a BN, the statistical relationship between multiple random variables (r.v.’s) is modeled through a directed acyclic graph. The complexity of inference in the BN depends not only on the number of r.v.’s, but also the graphical structure. As a consequence, the application of standard BN techniques may become infeasible even with a moderate number of r.v.’s as the size of an event set exponentially increases with the number of r.v.’s. Moreover, when the exhaustive set that is required for full quantification of a discrete BN node becomes intractably large, only approximate inference algorithms are feasible, which do not require the full (explicit) description of all BN nodes. We address both issues in discrete BNs by proposing a matrix-based Bayesian Network (MBN) that facilitates efficient modeling of joint probability mass functions and flexible inference. The MBN is developed for exact as well as approximate BN inference. The efficiency and applicability of the MBN are demonstrated by numerical examples. The supporting source code and data are available for download at https://github.com/jieunbyun/GitHub-MBN-code.
Vehicle to Vehicle (V2V) communication for Collision avoidance for Multi-copters flying in UTM –TCL4

NASA’s UAS Traffic management (UTM) research initiative is aimed at identifying requirements for safe autonomous operations of UAS operating in dense urban environments. For complete autonomous operations vehicle to vehicle (V2V) communications has been identified as an essential tool. In this paper we simulate a complete urban operations in an high fidelity simulation environment. We design a V2V communication protocol and all the vehicles participating communicate over this system. We show how V2V communication can be used for finding feasible, collision-free paths for multi agent systems. Different collision avoidance schemes are explored and an end to end simulation study shows the use of V2V communication for UTM TCL4 deployment.
Weighted Extreme Sparse Classifier and Local Derivative Pattern for 3D Face Recognition

A novel weighted hybrid classifier and a high-order, local normal derivative pattern descriptor are proposed for 3D face recognition. The local derivative pattern (LDP) captures the detailed information based on the local derivative variation in different directions. The LDP is computed on three normal maps in x-, y-, and z-directions and on different scales. The surface normal captures the orientation of a surface at each point of 3D data. More informative local shape information is extracted using the surface normal, as compared to depth. The nth-order LDP on the surface normal is proposed to encode the more detailed features from the (n-1)th-order's local derivative direction variations. An extreme learning machine (ELM)-based autoencoder, using a multilayer network structure, is employed to select more discriminant features and to provide a faster training speed. A weighted hybrid framework is proposed to handle facial challenges using a combination of the ELM and the sparse representation classifier (SRC). The advantage of speed for the ELM and the accuracy for the SRC in a weighted scheme is used to enhance the performance of the recognition system. Experimental results regarding four famous 3D face databases illustrate the generalization and effectiveness of the proposed method in terms of both computational cost and recognition accuracy.
Obstacle Avoidance of Two-Wheel Differential Robots Considering the Uncertainty of Robot Motion on the Basis of Encoder Odometry Information

It is important to overcome different types of uncertainties for the safe and reliable navigation of mobile robots. Uncertainty sources can be categorized into recognition, motion, and environmental sources. Although several challenges of recognition uncertainty have been addressed, little attention has been paid to motion uncertainty. This study shows how the uncertainties of robot motions can be quantitatively modeled through experiments. Although the practical motion uncertainties are affected by various factors, this research focuses on the velocity control performance of wheels obtained by encoder sensors. Experimental results show that the velocity control errors of practical robots are not negligible. This paper proposes a new motion control scheme toward reliable obstacle avoidance by reflecting the experimental motion uncertainties. The presented experimental results clearly show that the consideration of the motion uncertainty is essential for successful collision avoidance. The presented simulation results show that a robot cannot move through narrow passages owing to a risk of collision when the uncertainty of motion is high. This research shows that the proposed method accurately reflects the motion uncertainty and balances the collision safety with the navigation efficiency of the robot.
TransCut2: Transparent Object Segmentation From a Light-Field Image

Transparent object segmentation can be very useful in computer vision applications. However, because the transparent objects borrow texture from their background and have a similar appearance to their surroundings, they are not handled well by regular image segmentation methods. In this paper, we propose a method that overcomes these problems using the consistency and distortion properties in a light-field image. The light-field linearity is used to estimate the likelihood of a pixel belonging to the transparent object or Lambertian background, and the occlusion detector is used to find the occlusion boundary. Graph-cut optimization is applied for the pixel labeling problem. We acquire light field datasets from both camera array and lenslet camera for the transparent object, and use these datasets to evaluate our method. The results demonstrate that the proposed method successfully segments transparent objects from the background under various conditions.
User stateless privacy-preserving TPA auditing scheme for cloud storage

Abstract In recent years, researchers have proposed some privacy-preserving public auditing schemes to remotely check the integrity of the outsourced data in cloud storage based on the homomorphic authenticators, random blocks sampling and random masking techniques. However, users need to maintain a table related to the block index in almost all of these schemes. It is difficult for users to maintain the table, especially when the outsourced data is frequently updated. To deal with it, a user stateless privacy-preserving public auditing scheme is put forward on rank-based authenticated skip list, which can support dynamical operations completely and makes it unnecessary for users to maintain the relevant table. When the server receives the data originally stored by the user or the user performs dynamic operations on its outsourced data, the server provide users with a time-stamped receipt signed by the both parties. The receipt can not only protect the cloud server from being extorted by the dishonest user, but also ensure that the cloud server update the user's data as required. A formal security proof for data integrity guarantee and an analysis for privacy-preserving property are made in the auditing phase to show the correctness of the scheme. The performance analysis indicates that our interactive audit protocol is with high efficiency.
Profiled Power Analysis Attacks Using Convolutional Neural Networks with Domain Knowledge

Evaluation of cryptographic implementations against profiled side-channel attacks plays a fundamental role in security testing nowadays. Recently, deep neural networks and especially Convolutional Neural Networks have been introduced as a new tool for that purpose. Although having several practical advantages over common Gaussian templates such as intrinsic feature extraction, the deep-learning-based profiling techniques proposed in literature still require a suitable leakage model for the implementation under test. Since this is a crucial task, we are introducing domain knowledge to exploit the full power of approximating very complex functions with neural networks. By doing so, we are able to attack the secret key directly without any assumption about the leakage behavior. Our experiments confirmed that our method is much more efficient than state-of-the-art profiling approaches when targeting an unprotected hardware and a protected software implementation of the AES.
Automation in Systematic, Scoping and Rapid Reviews by an NLP Toolkit: A Case Study in Enhanced Living Environments

With the increasing number of scientific publications, the analysis of the trends and the state-of-the-art in a certain scientific field is becoming very time-consuming and tedious task. In response to urgent needs of information, for which the existing systematic review model does not well, several other review types have emerged, namely the rapid review and scoping reviews. In this paper, we propose an NLP powered tool that automates most of the review process by automatic analysis of articles indexed in the IEEE Xplore, PubMed, and Springer digital libraries. We demonstrate the applicability of the toolkit by analyzing articles related to Enhanced Living Environments and Ambient Assisted Living, in accordance with the PRISMA surveying methodology. The relevant articles were processed by the NLP toolkit to identify articles that contain up to 20 properties clustered into 4 logical groups. The analysis showed increasing attention from the scientific communities towards Enhanced and Assisted living environments over the last 10 years and showed several trends in the specific research topics that fall into this scope. The case study demonstrates that the NLP toolkit can ease and speed up the review process and show valuable insights from the surveyed articles even without manually reading of most of the articles. Moreover, it pinpoints the most relevant articles which contain more properties and therefore, significantly reduces the manual work, while also generating informative tables, charts and graphs.
Mining Hard Augmented Samples for Robust Facial Landmark Localization With CNNs

Effective data augmentation is crucial for facial landmark localization with convolutional neural networks (CNNs). In this letter, we investigate different data augmentation techniques that can be used to generate sufficient data for training CNN-based facial landmark localization systems. To the best of our knowledge, this is the first study that provides a systematic analysis of different data augmentation techniques in the area. In addition, an online hard augmented example mining (HAEM) strategy is advocated for further performance boosting. We examine the effectiveness of those techniques using a regression-based CNN architecture. The experimental results obtained on the AFLW and COFW datasets demonstrate the importance of data augmentation and the effectiveness of HAEM. The performance achieved using these techniques is superior to the state-of-the-art algorithms.
Cybersecurity economics – balancing operational security spending

Purpose The purpose of this paper is to demonstrate how to find the optimal investment level in protecting an organisation’s assets. Design/methodology/approach This study integrates a case study of an international financial organisation with various methods and theories in security economics and mathematics, such as value-at-risk (VaR), Monte Carlo simulation, exponential and Poisson probability distributions. Thereby it combines theory and empirical findings to establish a new approach to determining optimal security investment levels. Findings The results indicate that optimal security investment levels can be found through computer simulation with historical incident data to find VaR. By combining various scenarios, the convex graph of the risk cost function has been plotted, where the minimum of the graph represents the optimal invest level for an asset. Research limitations/implications The limitations of the research include a modest number of loss observations from one case study, and the use of normal probability distribution. The approach has limitations where there are no historical data available or the data has zero losses. These areas should undergo further research including larger data set of losses and exploring other probability distributions. Practical implications The results can be used by leading business practitioners to assist them with decision making on investment to the increased protection of an asset. Originality/value The originality of this research is in its new way of combining theories with historical data to create methods to measure theoretical and empirical strength of a control (or set of controls) and translating it to loss probabilities and loss sizes.
A New Method for Generating Short-Term Power Forecasting Based on Artificial Neural Networks and Optimization Methods for Solar Photovoltaic Power Plants

In recent times, solar PV power plants have been used worldwide due to their high solar energy potential. Although the PV power plants are highly preferred, the main disadvantage of the system is that the output power characteristics of the system are unstable. As PV power plant system is connected to the grid side, unbalanced power flow effects all systems controls. In addition, the load capacitys is not exactly known. For this reason, it has become an important issue to be known correctly in PV output power and their time-dependent changes. The main aim of this work is to eliminate power plant instability due to the output power imbalance. For the short-term, power prediction is estimated by real-time data of 1 MW PV power plant in use. Estimation power data are compared with real-time data and precision of the proposed method is demonstrated. In the first phase, traditional artificial intelligence algorithms are used. Then, these algorithms are trained with swarm based optimization methods and the performance analyses are presented in detail. Among all the algorithms used, the algorithm with the lowest error is determined. Thus, this study provides useful information and techniques to help researchers who are interested in planning and modeling PV power plants.
Text representation and classification based on bi-gram alphabet

In text classification, texts have to be transformed into numeric representations suitable for the learning algorithms. A main problem with the commonly used bag of words method is the high dimensions of vector space, as well as the need for language-dependent tools. In the present study, text classification is performed based on a novel bi-gram alphabet approach to construct feature terms. The proposed approach has two main contributions to text classification area. First, we have demonstrated the possibility of using constant feature terms that are based on the standard alphabet without the need for the documents vocabularies; this definitely helps in reducing the dimensions of the vector space for large corpus. Second, it does not require natural language processing tools. The current work has proved the ability to classify collections of Arabic or English text documents successfully. It showed approximately 80% savings in vector space and 2% performance improvement compared to the best recorded results on Arabic dataset Aljazeera News.
Probabilistic moving least squares with spatial constraints for nonlinear color transfer between images

The color of a scene may vary from image to image because the photographs are taken at different times, with different cameras, and under different camera settings. To align the color of a scene between images, we introduce a novel color transfer framework based on a scattered point interpolation scheme. Compared to the conventional color transformation methods that use a parametric mapping or color distribution matching, we solve for a full nonlinear and nonparametric color mapping in the 3D RGB color space by employing the moving least squares framework. We further strengthen the transfer with a probabilistic modeling of the color transfer in the 3D color space as well as spatial constraints to deal with mis-alignments, noise, and spatially varying illumination. Experiments show the effectiveness of our method over previous color transfer methods both quantitatively and qualitatively. In addition, our framework can be applied for various instances of color transfer such as transferring color between different camera models, camera settings, and illumination conditions, as well as for video color transfers.
Single document keyword extraction via quantifying higher-order structural features of word co-occurrence graph

Keyword extraction is a building block of information retrieval. Many techniques have been developed to tackle this problem. However, most of the existing methods suffer from high computational complexity or large corpus dependency, which limits the practical applications. Indeed, given a single document, new visions and strategies are needed for keyword extraction to face the challenges. In this work, we proposed a new graph-based measure for keyword extraction, by leveraging higher-order structural features (e.g. motifs) of word co-occurrence graph. The experiments on real datasets shows superior performance of the proposed method, compared to TF-IDF and PageRank based methods.
A Novel Algorithm for Breast Lesion Detection Using Textons and Local Configuration Pattern Features With Ultrasound Imagery

Breast cancer is the most commonly occurring cancer in women worldwide. While mammography remains the gold standard in breast cancer screening, ultrasound is an important imaging modality for both screening and cancer diagnosis. This paper presents a novel method for the detection of breast lesions in ultrasound images using texton filter banks, local configuration pattern features, and classification, without employing any segmentation technique. The developed method was able to accurately detect and classify breast lesions and achieved an accuracy, sensitivity, specificity, and positive predictive value of 96.1%, 96.5%, 95.3%, and 97.9%, respectively. The paradigm that we describe may, therefore, be useful as an effective tool to detect breast nodules during screening and in whole breast imaging, enabling clinicians to focus on images where a lesion is already known to be present. The developed method may also serve as a component for automatic breast nodule detection, and, when found, for the subsequent classification between lesion type benign versus malignant.
Semi-supervised Multi-view Individual and Sharable Feature Learning for Webpage Classification

Semi-supervised multi-view feature learning (SMFL) is a feasible solution for webpage classification. However, how to fully extract the complementarity and correlation information effectively under semi-supervised setting has not been well studied. In this paper, we propose a semi-supervised multi-view individual and sharable feature learning (SMISFL) approach, which jointly learns multiple view-individual transformations and one sharable transformation to explore the view-specific property for each view and the common property across views. We design a semi-supervised multi-view similarity preserving term, which fully utilizes the label information of labeled samples and similarity information of unlabeled samples from both intra-view and inter-view aspects. To promote learning of diversity, we impose a constraint on view-individual transformation to make the learned view-specific features to be statistically uncorrelated. Furthermore, we train a linear classifier, such that view-specific and shared features can be effectively combined for classification. Experiments on widely used webpage datasets demonstrate that SMISFL can significantly outperform state-of-the-art SMFL and webpage classification methods.