Reviewerly/exHarmony huggingface dataset:
A Brief Understanding of IOT Health Care Service Model Over Remotely Cloud Connected Environment

In this paper, we are going to focus a cloud-based internet of things (IOT) platform-specific model in healthcare sector. Health care sector in remote area is highly dispersed and the citizens are not quite competent enough to avail all the benefit provided by Government to them considering this issue here we have integrated all vital service need for making the model further implementation. The data storage of this cloud-based model can implement azure service for its service delivery to end-user. Healthcare industry generates a massive amount of data to perform and process the services of the citizen. Subsequently, it puts pressure on internet infrastructure so the revolutionary solution emerges to minimize the pressure by using IoT-based cloud infrastructure. It is a structure in which the traditional infrastructure of healthcare industry metamorphoses into IoT-Cloud infrastructure. It is further providing scalability in the delivery of healthcare services. It migrates load to distributed cloud data store via a dedicated link. Both cloud computing and IoT enables as a better collaboration for every industry. The proposed model in this paper allow user to have on-demand access to cloud service. IoT captures citizen detail through sensor. This is an innovative approach that is modifying the interaction value chain between citizen, hospital, and doctor. IoT-cloud ecosystem is a dynamic fine-grained access control scheme
Travel Attractions Recommendation based on Max-negative the Gated Recurrent Unit trajectory mining Representation

Abstract Although the traditional recommendation algorithm has achieved good results in the field of travel recommendation, due to the lack of data, the cold start and data sparseness problems and the neglect of the semantic problems hidden in the travel track, the low recommendation accuracy remains unresolved. Recently, the RNN model performed very well in recommending system sequence learning. We use RNN to model the travel sequence in the travel recommendation. In the pairwise, we can achieve a more accurate recommendation by effectively processing the negative samples and then training to generate a smaller loss function. Our method (Max-GRU) is optimized by adding additional negative sample and finding Max-negative on the Gated Recurrent Unit trajectory mining Representation Model. On the Shanghai tourism data and Guilin tourism data, both MRR@10 and RECALL@10 have been significantly improved compared to the use of the RNN model and baselines.
Cramér-Rao Lower Bound of Target Localization Method Based on TOA Measurements

The Cramér-Rao bound of target localization method based on time-of-arrival measurements is analyzed. For the localization error analysis, the CRLB is derived under the assumptions that the measurement errors are independent and characterized by zero-mean Gaussian distributed process with identical variance, which is not satisfied in the situation of localizing noncooperative targets. Cramér-Rao bound is deduced by using the fact that the variances of TOA measurements of different sensors are affected by the signal-to-noise ratio of the echo signal and are different from each other. Simulations of Monte Carlo experiments are carried out so as to verify the analytical results.
Steganography over Redundant Residue Number System Codes

In this paper we define steganography over Redundant Residue Number System (RRNS) Codes. We describe distortion-less RRNS based steganographic schemes, analyse their corresponding embedding capacities, discuss their linearity and compare them with well known steganographic protocols. Specifically, we take advantage of the redundancy and correction capacity of these codes to hide secret information in such way that the number of the altered residues does not exceed half the redundancy of the code.
A Low-Complex Frame Rate Up-Conversion with Edge-Preserved Filtering

The improvement of resolution of digital video requires a continuous increase of computation invested into Frame Rate Up-Conversion (FRUC). In this paper, we combine the advantages of Edge-Preserved Filtering (EPF) and Bidirectional Motion Estimation (BME) in an attempt to reduce the computational complexity. The inaccuracy of BME results from the existing similar structures in the texture regions, which can be avoided by using EPF to remove the texture details of video frames. EPF filters out by the high-frequency components, so each video frame can be subsampled before BME, at the same time, with the least accuracy degradation. EPF also preserves the edges, which prevents the deformation of object in the process of subsampling. Besides, we use predictive search to reduce the redundant search points according to the local smoothness of Motion Vector Field (MVF) to speed up BME. The experimental results show that the proposed FRUC algorithm brings good objective and subjective qualities of the interpolated frames with a low computational complexity.
Efficient Ranking Framework for Information Retrieval Using Similarity Measure

The information on the web is increasing day by day and to manage such vast amount of information is really a difficult task. The user finds it really hard to capture the desired information as per their need and maximum amount of time is spent in framing proper query and filtering the resultant web pages. The search engine plays a major role in filtering the information and ranking the desired result. The quest for accurate information is still a dream and in this regard this paper presents an approach that tries to optimize the ranking algorithm by employing document clustering and similarity measures. In this paper we present an outline of different ranking algorithms and proposed an approach where PageRank algorithm is optimized by using document clustering. It also employs content mining along with structural mining that help to reduce the computational complexity of the algorithm and thereby diminish the time in performing the ranking of the web pages.
A study on Target Chemical Substances for Environmental Contamination Management in Disasters and Accidents

Domestic and overseas efforts on response to accidents and disasters with release of chemical substances were investigated, mainly from the viewpoint of exposure level. To prioritize chemical substances that should be managed in emergency in Japan, acute exposure levels used in Europe and the United States were combined with available information on the amount of chemical substances in Japan. Regarding the amount information, two groups of substances, industrial use substances (substances related to the Chemical Substitution Law and PRTR law) and pesticides, were targeted. Priority substance list was prepared by linking with the toxic judgment value. In preparing the lists, some substances were excluded by individual judgment due to existence of existing management system, concern of risk to the public, etc. Therefore, some substances which is not focused under the existing environmental regulations were listed as high priority substances.
Versatile Video Coding of 360-Degree Video using Frame-Based FoV and Visual Attention

High quality omnidirectional video requires ultra high resolution formats encoded with very high bit rates to guarantee acceptable QoE in video delivery services. Since in general the full FoV, i.e., 360°, is not required at once by users, rather than agnostic encoding of the whole 360° video, this work proposes a flexible coding approach where the full FoV is mapped into video frames and efficiently encoded using intra-FoV prediction. By avoiding inter-FoV prediction, the proposed approach enables independent decoding of one or more FoVs extracted from a single compressed stream containing the full FoV video. To achieve improved quality in those FoVs which attract more visual attention, non-uniform coding is proposed for the new Versatile Video Coding standard (VVC), using perceptually-driven quantisation for each FoV. This strategy, makes use of visual attention maps to decrease the overall bit rate without compromising the quality of the most relevant regions. The simulation results show that the proposed coding mechanism achieves consistent quality gains in the relevant FoV without significant losses in the remaining ones. In comparison with the reference VVC, the proposed method is able to achieve average quality gains up to 1.56 dB and to efficiently adapt the coding parameters to the visual attention information.
Domain Reduction Techniques for Sequential Fuzzy Indexing Tables – A Case Study

In recent years, single-board computers have been gaining popularity because they make it possible to use low cost, low energy consumption devices to solve complex tasks that would be much harder to solve with microcontrollers. However, these devices have much more limited capabilities both computation and memory-wise compared to traditional computers, which in turn limits the options available to use them for classification problems. One of the available options is using Lookup Table-based classifiers that require minimal computation, although in return they require more memory space. Sequential Fuzzy Indexing Tables are improved versions of Lookup Tables that require less memory, but for large problem spaces their storage cost is still very high. This is due to the size of its structure, which can be reduced with suitable domain conversion techniques. In this paper, multiple options are investigated, analyzed and compared in order to solve this problem.
A comparison of methods for link sign prediction with signed network embeddings

In many real-world networks, it is important to explicitly differentiate between positive and negative links, thus considering the observed networks as signed. To derive useful features, just as in the case of unsigned networks, representation learning can be used to learn meaningful representations of a network that characterize its underlying topology. Several methods for learning representations on signed networks have already been proposed but have not been systematically benchmarked together before. Hence, in this paper, we bridge this literature gap providing a quantitative and qualitative benchmark of the four most prominent representation learning methods for signed networks. Results on three different datasets for link sign prediction showcase the superiority of the StEM method over its competitors both from a predictive performance and runtime perspective.
Online visual tracking via cross‐similarity‐based siamese network

Summary Among deep‐learning‐based trackers, the siamese‐based method inspires many researchers due to its effectiveness and simplicity. However, the traditional siamese tracker has not achieved satisfactory performance due to the limited representation ability and the lack of appropriate model update strategy. To cover the shortage of siamese models, we proposed a cross‐similarity‐based siamese network with three contributions. First, we introduce a novel cross similarity module into the SiameseFC framework, which could improve the matching ability of fully convolutional networks during the tracking process. Second, we propose a novel attention weighting layer to emphasize various contributions of matching scores in different positions. This adaptive attention weighting scheme makes our tracker well adapt to the appearance change caused by pose variation, partial occlusion, and so on. Third, we develop a simple yet effective model update strategy, which exploits an independent classification model to invoke the model fine‐tuning process. Experimental results on the standard tracking benchmark show that our tracker performs much better than the baseline SiameseFC method and also achieves promising results in comparisons to other state‐of‐the‐art algorithms.
Accurate All-Round 3D Measurement Using Trinocular Spherical Stereo via Weighted Reprojection Error Minimization

Comparing to perspective cameras, the all-round 3D measurement of the environment can be done by spherical cameras in a more efficient way. However, the measurement using binocular spherical stereo has two singularity points at the epipoles of each spherical camera, where the measurement result gets extremely sensitive to the error when getting close to the epipoles and along the epipolar directions. This affects the accuracy of 3D reconstruction along with the epipolar directions. A three-way measurement method using three spherical cameras with trinocular spherical stereo setup is proposed in this paper to achieve accurate all-round 3D measurement. The improved accuracy of 3D measurement by the implementation of weighted reprojection error optimization was verified in experiments.
Ride the Lightning: Turning Bitcoin into Money

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A flexible method for converting non-relational text data to relational data for data acquisition of equipment operation

Whether in the field of manufacturing, scientific research or other aspects, some structured non-relational data generated by equipment operation is stored in the form of text files, these non-relational text data forms are scattered, data formats are diversified, and the lack of overall storage framework structure makes it difficult to read data, manage and reuse data. To solve this problem, this paper presents a flexible method to convert non-relational text data to relational data for equipment operation data collection. This method establishes a mathematical model of row and column retrieval based on string group, defines flexible conversion template, develops and designs a flexible conversion software of non-relational data to relational data, and realizes the recognition and conversion of TXT file content timing increment. The results show that the relational data obtained by this conversion method has obvious structure and regularity, which can reduce the complexity of the data, so as to achieve better equipment interconnection and monitoring in the future.
VLSI Architecture of DNN neuron for face recognition

Facial recognition finds wide application in various fields. Currently most facial recognition algorithms are implemented using software. Implementing in hardware allows real-time processing. This paper describing a method on devising an efficient neuron for CNN, RNN to run on FPGA’s with minimal space and hardware resource dependencies. Hardware implementation is achieved through Zynq-7000 development board.
OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

Due to the complex and variable environments of mobile communication, the mobile multiuser networks become a hot topic. To process active complex event in mobile multiuser networks, it is important to predict the system performance. In this work, the authors consider the multiuser networks which utilizes transmit antenna selection (TAS). We derive novel closed-form expressions for the outage probability (OP) in terms of the Meijer's G-function. Then, a extreme learning machine (ELM)-based OP performance prediction algorithm is proposed. We use the theoretical results to generate training data. We test back-propagation (BP) neural network, locally weighted linear regression (LWLR), wavelet neural network, ELM, and support vector machine (SVM) methods. Compared with wavelet neural network, SVM, BP neural network, and LWLR methods, the Monte-Carlo results shows that the proposed prediction algorithm can consistently achieve higher OP performance prediction results.
Automated Analyzing System for Recognizing the Elemental Processes Based on the Labeled LDA

In this paper, we described an automated analyzing method for the elemental processes. This method predicted the elemental processes from the sensor data by using labeled latent Dirichlet allocation (L-LDA) that is supervised topic model. The L-LDA studies automatically characteristic motion. We do not need to define characteristic motion by applying the L-LDA to motion analysis. The sensor data are motion sensors of both hands and a pressure sensor of working space. Numerical data obtained from the sensors were converted into word data by the threshold process using statistically determined thresholds. The automated analysis by the L-LDA was conducted by using the word data. We confirmed that recall by the method was over 86.9% by the evaluation experiment.
Human Detection from RGB Depth Image using Active contour and Grow-cut Segmentation

Computer vision based human detection systems are gaining much significance in modern security and surveillance systems. Recently, advanced camera sensors like Microsoft Kinect came to the scene and such devices are capable of detecting both color and depth details from a captured scene. This article proposes an intelligent human segmentation system by analyzing both color and depth details retrieved by an RGB-Depth camera. Here the depth data is analyzed first, to create an initial segmentation of human factors from the scene. Since depth analysis have restrictions in identifying accurate edge details, post processing steps are used for refurbishing the missed human portions in the frame. The hair regions are first restored first using Chan-vase active contour detection and the subset of foreground pixels thus obtained after this coarse level segmentation are used as seeds to segment the rest of the image frame using Grow-cut segmentation. An iterative cellular automation is employed here to classify the foreground human region. This final segmentation mask thus generated after Grow-cut segmentation can be used for segmenting human region from the original image frame. Experimental results show that the segmentation is helpful for analyzing the human factors in each frame and can be used for further human tracking or activity recognition.
Deanonymizing Tor in a Stealthy Way

The Tor network is one of the largest anonymity networks and has attracted millions of users worldwide. Since deployed in October 2002, many forms of attacks against Tor had been proposed, aiming to deanonymize the network. The protocol-level attacks, which can deanonymize users by manipulating a cell, are effective to compromise the anonymity of Tor by controlling the entry node and the exit node in a circuit. However, due to the absence of stealthiness, it may be noticed by the victim since the connection will be released. To address this issue, we present two types of attacks. The Type-I is based on protocol-level strategy but attempts to keep the connections alive by fixing damaged cells in the network, thereby making it much stealthier in some cases and it is quite difficult to be defended. In the Type-II attack, the entry node sends a signal to the accomplice exit node via a special type of outbound cells to prevent the connection from being closed, thus the attack can keep stealthy in more general situations. We also propose some countermeasures to keep the network away from the Type- II attack. An evaluation of the two types of attacks has been performed. And the results showed that both the two types of attacks are effective and pose serious threats to the Tor network.
Depth estimation of a single RGB image with semi-supervised two-stage regression

Obtaining accurate depth estimation at low computational cost is a major problem in the field of computer vision. To tackle this problem, we propose a framework that integrates different neural networks, for predicting the corresponding depth from a single RGB image and sparse depth samples. This method combines two different types of deep learning frameworks with the best performance, including the improved Residual Neural Network and conditional generation adversarial network (cGAN). It has been proved that the improved ResNet has strong depth prediction capability, but the depth map is still incomplete in detail. We improve the existing cGAN model to enhance ResNet-based depth prediction. Experiments compared with stage-of-the-art are performed on publicly available data sets. And the results demonstrate that the proposed two-stage deep regression model is superior to other existing methods of the same type.
Human or Machine? A Study of Anthropomorphism Through an Affordance Lens

Anthropomorphism—the tendency of humans to apply human-like attributes to non-human objects—has received growing attention by scholars across multiple disciplines. With increasing popularity of service and personal robotics and conversational agents, scholars of information systems have begun to shed light on some of the technology features and processes related to anthropomorphism. This study applies a socio-technical approach using affordance theory to examine the relationship between technology and anthropomorphic perceptions among users. Evidence is gathered from an empirical study involving the introduction of interactive voice response (IVR) with savings clients of a savings and loans company in Ghana. The findings highlight four main ways that the IVR technology exhibited human-like qualities within the user-technology interaction (as perceived by users). This paper illustrates how a study on the relationship between technology and anthropomorphism might be conducted through an affordance perspective. It also offers implications for technology development.
Pipeline Direction Extraction Algorithm Using Level Set Method

Autonomous underwater vehicles (AUVs), unmanned underwater vehicles, provide lower operation costs in underwater pipeline inspection than remotely operated vehicles (ROVs), since there is no need for a human operator. However, the navigation algorithm is an important part of the successful deployment of AUVs. In this work, we propose an algorithm that extracts the direction of a pipeline based on the level set method (LSM). This direction shall be used for the automatic navigation of an AUV. The low light and turbid water prohibit the use of an optical camera. Instead, a forward-looking sonar (FLS) was selected to provide input sequence images. Due to speckle noise, the traditional image segmentation algorithm cannot be used. Thus, the LSM is applied to represent the direction of pipelines. Lastly, the post-processing which estimates the direction is used to increase the accuracy. From experiments, our algorithm can extract the pipeline direction with accuracies of as high as 94.33%.
Passivo e aspetto verbale in resiano, russo e sloveno

Resian is a micro-language of Slovene origin spoken in the north-eastern Italian region of Friuli, specifically in the province of Udine. It has been in a situation of absolute linguistic contact with Romance varieties (Friulian and Italian) for centuries. This paper describes the forms and functions of the passive voice in Resian, taking into account how it interplays with the verbal categories of Tense and Aspect. Analysis is drawn through comparison with other Slavic languages and takes into account the possible role of language contact in the specific situation of Resian.
Teaching a Robot how to Spatially Arrange Objects: Representation and Recognition Issues

This paper introduces a technique to teach robots how to represent and qualitatively interpret perceived scenes in tabletop scenarios. To this aim, we envisage a 3-step humanrobot interaction process, in which (i) a human shows a scene to a robot, (ii) the robot memories a symbolic scene representation (in terms of objects and their spatial arrangement), and (iii) the human can revise such a representation, if necessary, by further interacting with the robot; here, we focus on steps i and ii. Scene classification occurs at a symbolic level, using ontology-based instance checking and subsumption algorithms. Experiments showcase the main properties of the approach, i.e., detecting whether a new scene belongs to a scene class already represented by the robot, or otherwise creating a new representation with a one shot learning approach, and correlating scenes from a qualitative standpoint to detect similarities and differences in order to build a scene hierarchy.