Reviewerly/exHarmony huggingface dataset:
A Remote Device Authentication Scheme for Secure Communication in Cloud Based IOT

The evolution of IOT that began with computers has now developed into an ecosystem. This IOT ecosystem consists of a large number of different types of devices embedded with sensors and technology, which makes them smart enough to interact within themselves. With IOT omnipresent, the challenge would be how to ensure only authorized access to the resulting smart system which in turn will ensure the security of the data gathered from these devices. In this paper, a very lightweight mutual authentication protocol based on Diffie Hellman for IOT enabled smart systems has been proposed. The proposed scheme possesses all the required security attributes and these attributes are validated by the widely accepted BAN logic.A thorough security analysis has been done to show that the proposed scheme is secure against different security attacks. The performance of the same has also been evaluated and compared with a number of existing methods. Performance analysis also demonstrates the effectivity of our proposed protocol.
Dynamics of a dissipative two-qubit system interacting non-linearly with a generalized field: Entanglement and mixedness

In this paper, two identical qubits nonlinearly interacting with a generalized coherent cavity field is explored. The dipole–dipole interaction between the two qubits is considered. Analytical solution of the master equation is derived. Mixedness and entanglement are analyzed through the study of entropies and negativity in all bipartitions of the dissipative system. We show that these measures are very sensitive not only to the dissipation rate, but also to the dipole–dipole parameter. Moreover, we notice that the system presents entanglement sudden birth and death.
Problems of accurate localization objects in imagers

The grayscale image can be represent as vector gradient field. The analysis of vector fields form with the help of set of single-length vector templates made it possible to indicate in the image such objects as vortices, Traffic Signs (TS), Facial Features (FF), and so on. The proposed method of indicating local objects using gradient fields turned out to be highly precision and noise-immunity. The presented advantages of the proposed method recommend it as a promising tool in targets designation problems.
Towards Automatically Extracting Contextual Valence Shifters in Reviews of Saudi Universities

Text sentiment orientations are affected by valence shifters. Therefore, it must be taken into account when a sentiment analysis system is developed. This paper presents an overview about existence of contextual valence shifters, such as intensifiers, negators, and connectors, in Saudi university reviews in order to seek a method for automatically extracting frequent shifting patterns. For achieving this study, a dataset containing 85,658 tweets written in Saudi dialectal Arabic language about Saudi universities was collected from the Twitter platform. It was found that many types of valence shifters exist in the dataset. As a result, for achieving accurate sentiment analysis the sentiment or polarity shifters needs to be first addressed.
A comprehensive study on robustness of HOG and LBP towards image distortions

Abstract Autonomous driving has been attracting increasing attention in modern society. Lots of work have been conducted in this field focused on vehicle detection. Existing vehicle detection algorithms are developed based on the assumption that data come with good quality. However, the physical world is never free of distortions. In this paper, a comprehensive review study of vehicle detection feature extractors is conducted to test and compare their robustness towards image distortions in practical applications. The two tested mainstream feature extraction methods are Histogram of oriented gradient (HOG) and Local Binary Pattern (LBP). The specific tested distortion type includes Gaussian noise, Salt and Pepper noise, Gaussian blur, change of color saturation, change of contrast and change of brightness. The review test result shows that: 1). Gaussian noise has negative effects on both HOG and LBP; 2). A certain level of Gaussian Blur and decreasing contrast can do help to increase detection accuracy for LBP, HOG is not affected by them; 3). Color Saturation change, increasing contrast and brightness change will not affect the performance of both feature extraction methods too much when the intensity of these distortions stays in reasonable range; 4). The effect of Salt and Pepper noise is still uncovered based on our experiments.
Re‐identification in the Absence of Common Variables for Matching

Summary A basic concern in statistical disclosure limitation is the re‐identification of individuals in anonymised microdata. Linking against a second dataset that contains identifying information can result in a breach of confidentiality. Almost all linkage approaches are based on comparing the values of variables that are common to both datasets. It is tempting to think that if datasets contain no common variables, then there can be no risk of re‐identification. However, linkage has been attempted between such datasets via the extraction of structural information using ordered weighted averaging (OWA) operators. Although this approach has been shown to perform better than randomly pairing records, it is debatable whether it demonstrates a practically significant disclosure risk. This paper reviews some of the main aspects of statistical disclosure limitation. It then goes on to show that a relatively simple, supervised Bayesian approach can consistently outperform OWA linkage. Furthermore, the Bayesian approach demonstrates a significant risk of re‐identification for the types of data considered in the OWA record linkage literature.
Participatory Design with Children in Designing a Smartphone Holder

Participatory design is a method involving stakeholders in the design process, from the beginning until the end of the process. The heart of this method is the participants. Participatory design is a method developed in Scandinavian traditional with the core in workshop and design sessions. This paper presents the adaption of participatory design method in designing a smartphone holder, with children as the main participants. An ophthalmologist and a product designer are also involved as informants.
Target Identification from Coded Diffraction Patterns via Template Matching

Traditional target detection techniques have been developed using measurements from the object acquired by optical systems that are only able to measure its intensity, losing its optical phase information. The optical phase, for instance, allows describing the shape and depth of an object. This work proposes a target identification methodology that operates over measurements acquired through an optical system that collects coded diffraction patterns (CDP). In contrast to traditional detection techniques, the proposed methodology is able to incorporate the optical phase information of an object as a discriminant in the target detection task. The proposed methodology consists of two steps: first, an estimation of the scene from the acquired CDP is accomplished, second, a scanning procedure with a reference pattern over the estimated scene is performed. Numerical results show that the phase information can be used as an identification discriminant for target detection. Also, simulations demonstrate that the proposed methodology is able to identify a target under highly noisy scenarios using one single snapshot with a success rate up of 84%. Furthermore, it is worth to mention that to the best of our knowledge this is the first methodology that uses the optical phase of an object as a target identification discriminant.
Technology supporting health services for rural areas based on image processing

Abstract Limited health facilities, both medical equipment and health workers in developing countries and other rural areas in the world, bring significant problems for public health services. In order to make a correct diagnosis, it is necessary to support accurate data from health equipment that should be available at the health care centre. On the other hand, the development of technology, especially in the field of digital image processing is quite rapid. This paper discusses the application of digital image processing in solving problems concerning the limitations of health care facilities. In this paper, we will discuss the application of digital image processing in cataract screening, image quality improvement of ultrasonography, identification of leukocyte cells, and identification of ventricular areas and the hippocampus of magnetic resonance imaging images. The results show that the implementation of digital image processing techniques can help overcome some of the problems that arise due to the limitations of these health facilities.
Research on UAV Image Registration Based on Sift Algorithm Acceleration

Considering the low real-time performance and the large amount of false matches exist in the feature matching stage of traditional Scale Invariant Feature Transform(SIFT) algorithm in unmanned aerial vehicle (UAV) remote sensing image registration. In this paper, a series of optimization methods for traditional SIFT algorithms are proposed, including SIFT execution process optimization, changing the parameters of scale space construction, Simplified method for judging the construction area of feature descriptor and construction of bidirectional matching filters. Experiments on UAV remote sensing images show that the optimization method can significantly improve the matching efficiency compared with traditional methods, and the comprehensive acceleration ratio is about 35% to 40%, which proves the effectiveness of the acceleration method.
Unmanned Aerial Vehicles Path Planning Based on Deep Reinforcement Learning

Obstacle avoidance and path planning of unmanned aerial vehicles (UAVs) is an essential and challenging task, especially in the unknown environment with dynamic obstacles. To address this problem, a method of UAV path planning based on Deep Q-Learning is proposed. The experience replay mechanism is introduced in the deep reinforcement learning (DRL) process, and a value network is established to calculate the optimal value for the action of the UAV. The optimal flight policy of the UAV is determined through the \(\epsilon \)-greed algorithm. The experimental results show that the UAV with well-trained model can avoid the obstacles in motion perfectly, and the cruise time is reduced by half compared with the untrained UAV.
Inhomogeneous Template Adaptation of Temporal Quasi-Periodic Three-Dimensional Signals

We propose a novel framework for performing inhomogeneous template adaptation on temporal three-dimensional data. Templates of interest are first globally translated, scaled and rotated using statistical methods in the form of Procrustes analysis. In this globally adapted form, the data is parameterized to a minimum bounding rectangular prism using a parameterization method known as free-form deformation. The parameterized model is then reduced to a linear least squares problem, which subsequently allows for a robust local template adaptation scheme utilizing a non-parametric formulation. To account for complex noise behaviour encountered in quasi-periodic signals, we ensue to complete the framework with an adaptive noise model. We demonstrate the power of the proposed framework using three applications: firstly, we quantify the ability of the proposed framework to track geometric features of heart disease on a synthetic dataset containing three-dimensional electrocardiogram signals. Next we track these features on a publicly available real dataset (PTB database) to demonstrate its potential in heart disease diagnostics. Lastly, we apply the proposed framework to map three-dimensional velocity profiles using the publicly available Complex Upper-Limb Movements database.
Key Phrase Extraction System for Agricultural Documents

Keywords play a vital role in extracting relevant and semantically related documents from a huge collection of various documents. Keywords represent the main topics covered in the document. But manual keyword extraction is a tedious and time-consuming process. Thus, there is a need for an automated keyword extraction system for easier extraction of relevant documents. In this paper, the focus is on agriculture-related documents. Agrovoc, an agriculture-based vocabulary that contains more than 35,000 concepts is used for extracting relevant keywords from the document. The proposed system extracts the relevant keywords from agricultural documents which are further used to extract relevant documents. Also, with the increasing number of documents on the Internet, the need for efficient storage for keywords with their corresponding documents is necessary. In the proposed system, a trie-based inverted index has been used for efficient storage and retrieval of keywords and the related documents.
Semantic Multi-Keyword Search over Encrypted Cloud Data with Privacy Preservation

Cloud storage provides the great convenience for people to access their data at anytime from any place. Since cloud storage is usually run by the third-party service provider, keyword search over cloud data with privacy protection is of great importance. Many studies in the literature have proposed keyword search scheme for document search, but, in most schemes, the query keywords must exactly match those in the document indexes. However, it is impractical to restrict query keywords provided by the user when performing the search. This paper proposes the scheme for semantic multi-keyword search over encrypted cloud data. Users are able to select query keywords on their own choice. In addition, the query privacy of the user and the security of the documents are protected simultaneously through encrypted document search to prevent snooping from the cloud service provider. Experiments are conducted using a dataset of massive real world papers. The results show that the proposed scheme can effectively perform the semantic multi-keyword search over encrypted cloud data with great efficiency.
Soil gases, pathfinders for exploration of buried sulphide deposits: insights from laboratory experiments

SummarySummaryThe future of mineral resources in Australia relies on the discovery of deposits under sedimentary cover. Traditional surface geochemistry techniques are of limited use in this context, and alternative exploration tools such as the detection of soil gases are gaining increasing interest. Previous studies have highlighted the potential of soil gases, such as sulphur gases and soil gas hydrocarbons, for locating buried mineralisation. Here, we performed laboratory weathering experiments of sulphides under sterile and non-sterile conditions to gain insights into the origin of these gases.The experiments revealed that hydrocarbon gases could not be detected, suggesting they commonly originate from microbial ecosystems in the cover and/or in the soil. In addition, equilibrium thermodynamic predictions indicate a larger range of sulphur gases than detected, which suggests the experimental system did not reach thermal equilibrium. Our results also reveal that CS2 is the most abundant gas produced, and could be of particular interest as a pathfinder for mineral exploration through cover.soil gashydrocarbon gasessulphur gasescovermineral exploration
FUROSHIKI: Augmented Reality Media That Conveys Japanese Traditional Culture

Furoshiki is a traditional culture of Japan which has prevailed in its everyday life and is widely used even today. In this paper, we propose a system through which users can be, while getting interested, exposed including to Japanese wrapping culture and attitude of dealing with things with care by augmenting an act to “wrap” with a piece of Japanese traditional furoshiki using the image-recognition technique and the motion-image projection technique.
Streamline Density Peak Clustering for Practical Adoptions

Since Density Peak Clustering (DPC) algorithm was proposed in 2014, it has drawn lots of interest in various domains. As a clustering method, DPC features superior generality, robustness, flexibility and simplicity. There are however two main roadblocks for its practical adoptions, both centered around the selection of cutoff distance, the single critical hyperparameter of DPC. This work proposes an improved algorithm named Streamlined Density Peak Clustering (SDPC). SDPC speeds up DPC executions on a sequence of cutoff distances by 2.2-8.8X while at the same time reducing memory usage by a magnitude. As an algorithm preserving the original semantic of DPC, SDPC offers an efficient and scalable drop-in replacement of DPC for data clustering.
Domain Identification for Commercial Intention-holding Posts on Twitter

Today, more people use social networking platforms to convey their desires and recent needs. Actually, there are numerous daily posts carrying commercial intention. The detection of these kinds of user intention would be quite valuable, especially for the platform itself. Firstly, it could help the platform provide precise and instant recommendations to users for its own business interests. Secondly, intention mining works may help link users' needs by detecting potential buyers and sellers and their specific intentions which can benefit users by optimizing the resources in their hand and increase functional richness. The whole intention mining process generally includes three main stages: user commercial intention filtering, intention domain identification and specific intention words extraction. In this work, the first stage was simplified using keywords-based automatic filter followed by a manual screening. The main focus of this paper is the second stage, assigning the intention-holding posts into their own single domain. Three machine learning models and two deep learning models were proposed to solve this text classification problem. The proposed methods have been evaluated on a dataset containing 5500 real-time intention-holding tweets collected from Twitter. In general, the experimental results showed impressive performance with the highest classification accuracy of 96% achieved by Long short-term memory.
Image Segmentation and Geometric Feature Based Approach for Fast Video Summarization of Surveillance Videos

In this paper, we propose a geometric feature and frame segmentation based approach for video summarization. Video summarization aims to generate a summarized video with all the salient activities of the input video. We propose to retain the salient frames towards generation of video summary. We detect saliency in foreground and background of the image separately. We propose to model the image as MRF (Markov Random Field) and use MAP (Maximum a-posteriori) as final solution to segment the image into foreground and background. The salient frame is defined by the variation in feature descriptors using the geometric features. We propose to combine the probabilities of foreground and background segments being salient using DSCR (Dempster Shafer Combination Rule). We consider the summarized video as a combination of salient frames for a user defined time. We demonstrate the results using several videos in BL-7F dataset and compare the same with state of art techniques using retention ratio and condensation ratio as quality parameters.
Research of Query Verification Algorithm on Body Sensing Data in Cloud Computing Environment

Cloud computing enables users with limited resources to accomplish complex computation. Meanwhile, the massive human physiological data generated by Wireless Body Area Network (WBAN) can be benefited by it through being stored in cloud servers. However, cloud storage of human physiological data is challenged by some issues, while one of them is soundness verification. Although local WBAN user is not powerful enough to compute the result on whole data repository, he may have concern on the correctness of the results from cloud which he pays for. To address this problem, this paper proposes a DMB+ tree data structure, and designs a query and verification algorithm for human physiological data in cloud computing environment based on DMB+ tree. By reducing the number of layers returned from the query tree, the algorithm reduces the height of the tree when verifying, thus improving the efficiency of query validation. The experimental results show that the performances in time and network traffic of DMB+ query verification are superior to Merkle tree and digital signature algorithm.
Dynamic Image Networks for Human Fall Detection in 360-degree Videos

Detection of falls of elderly people is a trivial yet an immediate problem due to the growing age of the population. This demands the need for autonomous self care systems for providing a quick assistance. The three basic approaches used for fall detection include non-invasive vision based devices, ambient based devices and wearable devices. The paper tries to improve upon the state-of-art of accuracy to 98% using vision based system. This was achieved through transfer learning by extending the idea of action recognition using dynamic images which is a standard RGB image containing the appearance and dynamics of a whole video sequence. Such information is vital in dealing with applications like human action recognition. Since we are also looking for a cheap and scalable solution, the use of a 360$$^\circ $$ camera seems reasonable and reliable. The top view provided by this camera gives a better perspective than any other alternatives by giving an un-obstructive view of the subjects.
Overbooking Lambda Functions in the Cloud

We consider the problem of scheduling serverless-computing instances such as Amazon Lambda functions. Instead of a quota per tenant/customer, we assume demand for Lambda functions is modulated by token-bucket mechanisms per tenant. Such quotas are due to, e.g., limited resources (as in a fog/edge-cloud context) or to prevent excessive unauthorized invocation of numerous instances by malware. Based on an upper bound on the stationary number of active "Lambda servers" considering the execution-time distribution of Lambda functions, we describe an approach that the cloud could use to overbook Lambda functions for improved utilization of IT resources. An earlier bound for a single service tier is extended to multiple service tiers.
Target Detection Based on Statistical Saliency Analysis and Geodesic Active Contour Model for Sar Imagery

Saliency analysis is a hot topic in target detection for synthetic aperture radar (SAR) image. In this paper, we come up with a novel target detection model based on statistical saliency analysis and saliency-oriented geodesic active contour model for SAR imagery. Firstly, the contrast and homogeneity features are extracted from the gray-level co-occurrence matrix to make up the texture saliency map. Then we obtain the prior graph and likelihood map using superpixels segmentation and Otsu, respectively, which comprise the Bayesian saliency map. The two saliency maps subsequently merge into the final statistical saliency map. Finally, we present a saliency-oriented geodesic active contour model, in which a saliency orientation map is embedded into the level set-based energy functional. Based on the final saliency map, the saliency-oriented geodesic active contour model can acquire the specified and accurate target contour. In qualitative and quantitative experiments, the comprehensive performance of the proposed method outperforms the competing models in maintaining complete targets and accurate boundaries.