Reviewerly/exHarmony huggingface dataset:
Recognition of group patterns in geological maps by building similarity networks

The recognition of structures is fundamental to map generalization, furnishing structural information that assists in choosing and parameterizing generalization operators. We specifically focus on the process of recognizing groups of small polygons in geological maps as a prerequisite to subsequent aggregation or typification operators. Proximity between polygons represents an essential criterion in identifying neighboring map objects. Here, network-based analysis is used for effective definition and refinement of candidate group members, applying criteria such as the distance between polygons, polygon size, shape, orientation, and feature attributes such as rock type. Starting off from the Delaunay triangulation of the polygon centroids, the global and local long edges, which initially define the network, are removed. The modified network is loaded with additional criteria, and edges are kept or removed based on the local similarities of the polygons they connect. This approach leads to more homogeneous, meaningful groups of polygon features in geological maps.
Counterfactual Depth from a Single RGB Image

We describe a method that predicts, from a single RGB image, a depth map that describes the scene when a masked object is removed - we call this "counterfactual depth" that models hidden scene geometry together with the observations. Our method works for the same reason that scene completion works: the spatial structure of objects is simple. But we offer a much higher resolution representation of space than current scene completion methods, as we operate at pixel-level precision and do not rely on a voxel representation. Furthermore, we do not require RGBD inputs. Our method uses a standard encoder-decoder architecture, and with a decoder modified to accept an object mask. We describe a small evaluation dataset that we have collected, which allows inference about what factors affect reconstruction most strongly. Using this dataset, we show that our depth predictions for masked objects are better than other baselines.
Analysis of Masking Techniques to Find out Security and other Efficiency Issues in Healthcare Domain

Increased use of modern advanced electronic devices rapidly increased the data collection rate, most of the advanced healthcare industries today are using updated healthcare facilities with the advanced healthcare technologies to collect and process the data. Healthcare data generated by the most of the industries are in the digital format. Provisioning protection and security to the PHI is the major concern but it is very difficult to safeguard the generated data from unauthorized users or breaches. There are many advanced techniques are in use today to protect the individuals sensitive data. Data masking approach is the advanced technique that enables security provisioning of personnel health records. This paper presented the in-depth study on current healthcare security techniques and summarized the gaps in security provisioning. Conclusion part of the paper highlights the some of the acts and policies adopted by the countries to safeguard the citizens' healthcare data.
Stealth Design of the Quantum Radar Scattering Based on the Two Typical 2D Targets

Quantum radar is a sort of cutting-edge technology with massive potential applications. Further, quantum radar scattering has drawn more attention from many researchers. However, there is little work has been done about the stealth design of quantum radar targets (QRT). In this study, we propose a kind of case of typical stealth design of QRT based on two plates with the given distance between them. Our 3D simulation shows that this kind of design can bring a distinct stealth effect.
Extortion or Expansion? An Investigation into the Costs and Consequences of ICANN’s gTLD Experiments

Since October 2013, the Internet Corporation of Assigned Names and Numbers (ICANN) has introduced over 1K new generic top-level domains (gTLDs) with the intention of enhancing innovation, competition, and consumer choice. While there have been several positive outcomes from this expansion, there have also been many unintended consequences. In this paper we focus on one such consequence: the gTLD expansion has provided new opportunities for malicious actors to leverage the trust placed by consumers in trusted brands by way of typosquatting. We describe gTLDtm (The gTLD typosquatting monitor) – an open source framework which conducts longitudinal Internet-scale measurements to identify when popular domains are victims of typosquatting, which parties are responsible for facilitating typosquatting, and the costs associated with preventing typosquatting. Our analysis of the generated data shows that ICANN’s expansion introduces several causes for concern. First, the sheer number of typosquatted domains has increased by several orders of magnitude since the introduction of the new gTLDs. Second, these domains are currently being incentivized and monetarily supported by the online advertiser and tracker ecosystem whose policies they clearly violate. Third, mass registrars are currently seeking to profit from the inability of brands to protect themselves from typosquatting (due to the prohibitively high cost of doing so). Taken as a whole, our work presents tools and analysis to help protect the public and brands from typosquatters.
Reversible Steganography Scheme Based on Position-Recording in DCT Coefficients

The basic requirements of the information hiding system are good visual quality, high hiding performance and high steganography security. Reversible steganography can reconstruct an original image without loss from the stego image after extracting the hidden message. The traditional methods modify the value of the image to embed the message. The differences between the proposed method and existing algorithms is that: this paper proposes an algorithm based on non-zero number locations. A block selection strategy based on the number of zero coefficients in each 8 × 8 block is proposed, which can be utilized to select DCT coefficients for data hiding in JPEG images. We first arrange the bottom right corner of each 8×8 DCT block into a one-dimensional vector. Then the secret message is recorded in a non-zero number position within the vector. Finally, the original data and the secret message are synthesized into reversible data, which is embedded into the stego image. It can be used to recover the original carrier DCT coefficients. Experimental results show that the PSNR value of the proposed method is higher than the traditional algorithm. The algorithm in this paper has high security and concealment.
Augmented Reality Rendered for IoT applications

With the growing number of Internet of Things (IoT) devices, there's an increase in the number of applications required to interact with them. To overcome the challenge of handling many applications and to enhance the user experience, the authors have demonstrated a unique approach using Augmented Reality (AR) and Image processing to control devices. The work is carried out in two phases. In the first phase, AR application for mobile devices has been implemented, where the user can interact with the augmentation and thus the changes made in AR environment are reflected in the physical world. In the second phase, low cost, portable smart glass has been designed with features to control a device by just having its context. It is a custom hardware and can be used as a plug and play device.
Blockchain Research Beyond Cryptocurrencies

One of the key requirements of a properly functioning digital society is the implementation of a decentralized identity management scheme through which the identity of every citizen can be uniquely determined. Specifically, we believe what is required is for a PKI to be deployed at a global scale. A PKI consists of a collection of digital certificates that cryptographically bind an individual's identity to their public key. In this article, we present an overview of our recent work in the areas of identity management and privacy, with specific emphasis on the deployment of a blockchain backed PKI. Once a PKI is in place, we are in a position to perform a number of common network tasks much more securely and efficiently than we can do currently.
Maximum Payload for Digital Image Steganography Obtained by Mixed Edge Detection Mechanism

The spatial domain embedding methodology for image steganography is the least significant bit (LSB) substitution technology. It urges that information may be concealed in the LSB of the cover image and our human visual system (HVS) cannot notice the confidential message inside it. LSB is a stegonagraphy technique widely used both in the spatial and frequency domain. In this article, together with the use of the LSB replacement as a crucial method, the edge detection methodology is used to achieve maximum payload, since the edges are abrupt changes in boundary areas of an image. A mixed edge detection mechanism is implemented to accomplish high payload steganography in the proposed scheme. The scheme then used an embedding algorithm to store a large volume of data in the cover image, so that, a high payload is attained. Simulation outcome manifest that steganography by mixed edge detection mechanism achieved a desirable and better signal-to-noise ratio, in comparison with other classical steganographic models, for equal number of bits per pixel in the stegoimage.
Predictors of Readmissions and Length of Stay for Diabetes Related Patients

Unplanned readmissions and long stays are becoming a major concern of hospitals and healthcare providers as an indicator for quality of service. Predicting readmissions and long hospital stays in early stages allows extensive attention to patients identified with higher risks which leverages the community's healthcare and saves healthcare expenses and resources. This paper aims to investigate the predictive factors and develop a robust risk prediction framework, by combining feature engineering and machine learning algorithms. An actual data set has been used with various levels of routinely collected data that includes demographics, admission information, diagnosis, medications, tests, and service utilization information. The accuracy of our model using the readmission prediction achieved 94.8% with random forests. The support vector machine (SVM) gives the highest area under curve (AUC) statistic (i.e. 0.97) in readmission prediction, and it stands out as an efficient algorithm in predicting length of stay with prediction accuracy of 78.5%. Finally, analysis of features can be used to determine at-risk patients and target the delivery of early resource-intensive interventions.
Service Layer Security Architecture for IOT Using Biometric Authentication and Cryptography Technique

Data security and authentication mechanism is a very challenging job for smart devices. And more ever IOT is suffering with login and verification process. Here in our paper, we have focused on human characteristics base security system which cannot be pinched easily such as iris, thumb, palm, DNA and voice base authentication system. Using biometrics authentication theory, we have presented that how biometric systems are the boundless computational resources and prospective of flexibility, reliability and cost reduction along with high security performances resources. To maintain the security of biometric traits over the Internet channel, end user can apply cryptography algorithm such as Elgamal, MAC Omura, Cramer Shoup, RSA. As a final point, this paper is contributed for evidencing the strength of integrating the biometrics authentication system with cryptography techniques and its application on Internet base applications. In order to develop strong security, we have proposed integrated approach of three mechanism using biometrics, OTP and cryptography. The work is validated for biometrics through AVISPA (SPAN) security tool which is worldwide acceptable for approving the security architecture.
Relative Distribution Entropy Loss Function in CNN Image Retrieval

Convolutional neural networks (CNN) is the most mainstream solution in the field of image retrieval. Deep metric learning is introduced into the field of image retrieval, focusing on the construction of pair-based loss function. However, most pair-based loss functions of metric learning merely take common vector similarity (such as Euclidean distance) of the final image descriptors into consideration, while neglecting other distribution characters of these descriptors. In this work, we propose relative distribution entropy (RDE) to describe the internal distribution attributes of image descriptors. We combine relative distribution entropy with the Euclidean distance to obtain the relative distribution entropy weighted distance (RDE-distance). Moreover, the RDE-distance is fused with the contrastive loss and triplet loss to build the relative distributed entropy loss functions. The experimental results demonstrate that our method attains the state-of-the-art performance on most image retrieval benchmarks.
Study and Analysis of Modified Mean Shift Method and Kalman Filter for Moving Object Detection and Tracking

In the scenario of Vehicle Tracking is the process of locating a moving object or multiple entities over time using a camera. It can be a time-consuming process due to the amount of data that is contained in video. Capture objects in video sequence of surveillance camera is nowadays a demanding application to improve tracking performance. The advanced technology makes video acquisition devices better and less costly, thereby increasing the number of applications that can effectively utilize digital video. Compared to still images, video sequences provide more information about how objects change over time. Thus, in this review, we discussed the several techniques attempted to site the available technique and various merits and demerits of it. In the present work we are detecting the moving object done by using simple background subtraction. For tracking of single moving object has been done using modified mean shift method and Kalman filter. Further result of both algorithms is compared on basis on time and accuracy.
SCA-Pitaya

Athough side-channel attacks appeared more than two decades ago, they remain very little discussed by security professionals outside the academia or very specific sectors (e.g., smartcard industry, governments). However, with the increasing generalisation of Internet of Things systems, they are a threat that can no longer be ignored by the operational world. This work aims to demonstrate that side-channel attacks can be practically achieved by an attacker, with reasonable means, effort, knowledge, and time. For this purpose, the contribution of this work is twofold. First, it is shown how a side-channel attack setup exploiting power leakages through electro-magnetic radiations, and making use of general-purpose and affordable equipment, can be built. The acquisition of attack power traces is made thanks to a Red Pitaya STEMlab platform coupled with a home-built radio front-end. Second, it is shown how an attack can be conducted against targets that are representative of Internet of Things devices: 8-bit and 32-bit Arduino boards.
Proximal Recursion for the Wonham Filter

This paper contributes to the emerging viewpoint that governing equations for dynamic state estimation, conditioned on the history of noisy measurements, can be viewed as gradient flow on the manifold of joint probability density functions with respect to suitable metrics. Herein, we focus on the Wonham filter where the prior dynamics is given by a continuous time Markov chain on a finite state space; the measurement model includes noisy observation of the (possibly nonlinear function of) state. We establish that the posterior flow given by the Wonham filter can be viewed as the small time-step limit of proximal recursions of certain functionals on the probability simplex. The results of this paper extend our earlier work where similar proximal recursions were derived for the Kalman-Bucy filter.
Artificial Neural Network Trained by Plant Genetic-Inspired Optimizer

As a great computational intelligence technique, artificial neural networks (ANNs) have intensively attracted the interest of researchers of artificial intelligence. Due to the easy implementation of ANN, vast types of structures and associated rules, their successful application can be seen in real-life and industrial problems. From a wide variety of ANN such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, and spiking ANN, we describe a multi-layer perceptron ANN with the focus on designing AI-based condition monitoring system. This chapter presents the neuroevolution-based monitoring system to detect the oil filter condition in agricultural (Ag) machines using meta-heuristic METO algorithm. Evolutionary learning algorithm finds the optimal weights of ANN along with the behavior of each neuron.
Quantum Information Encoding from Stabilizing Dynamics

In order to ensure reliable quantum information processing in realistic noisy devices, it is necessary to resort to suitable error correction and avoidance strategies. A key aspect of these techniques is how the relevant information is logically mapped (encoded) in the noise-protected codewords. Here, we study how to engineer Markov dynamics that transfers the desired information from an "upload" subsystem to the target error-correcting or noiseless quantum code, effectively acting as a dissipative quantum encoder. Dissipative encoders offer advantages with respect to standard unitary encoding protocols based on quantum circuits, as they can associate the target states to non-trivial basins of attraction, and thus tolerate more general initializations in the upload qubits. In particular, we show that devising continuous-time dissipative encoders requires the target code to be invariant, making this task more delicate as compared to its discrete-time counterpart. Nonetheless, we show how this is always possible for stabilizer quantum error-correcting codes.
Wizard of Oz Interface to Study System Initiative for Conversational Search

We describe a Wizard of Oz (WoZ) system, a WebApp, which we use to study how a conversational search system should take the initiative when engaging with users during collaborative search. This system integrates directly into Slack, a chat-messaging platform where users will collaborate. Through our system, the Wizard plays the role of a conversational search system that can search for information, send relevant web results, and message users. In our research, we study three Wizard conditions: bot\_info, bot\_dialog, and bot\_task, which differ in terms of how the Wizard can intervene in a conversation. The intervention modes follow the mixed-initiative framework by Chu-Carroll and Brown~\citechu1997tracking and provide us a foundation to study system initiative for conversational search. In this paper, we describe our design decisions and technical details on how we implemented the system.
Personalized information recommendation simulation system based on compound recommendation algorithm——A research tool to study the push effect of algorithm

Abstract The benefits and effects of algorithmic push are affecting all walks of life. It has also aroused the interest of media research and the discussion on the technical and moral issues involved in algorithmic push. In recent years, the discussion about “information cocoons” has become more and more intense. How to make effective quantitative calculation for algorithm recommendation is the key to empirical research on this problem. Therefore, this study designed a tool to effectively operate this variable around this problem. Based on the analysis of the main recommendation mechanism of algorithmic information distribution, this paper points out a method of how to measure the accuracy of algorithm recommendation in practical research, and designs a simulation personalized information recommendation system based on composite recommendation algorithm combining two mainstream recommendation mechanisms, collaborative filtering and text analysis.
Detection of AMR double compression using compressed-domain speech features

The adaptive multi-rate (AMR) audio codec is an established standard of speech signal compression, for either transmitting speech signal over mobile networks or storing digital audio on handheld devices. The widespread use of AMR codec and the high availability of tampering software have increased authentication cases in court. AMR double compression detection is a challenging engineering problem and a topic of multimedia forensics. As a general rule, a double compressed AMR file cannot be considered an original file. In this paper, a new method based on support vector machine (SVM) is proposed to classify single and double compressed AMR digital audio. Instead of using the decoded speech waveform, the proposed method uses only compressed-domain speech features. Specific parameters are extracted from encoded AMR files and used to create a set of statistical features. After applying robust scaling to features and selecting the SVM model, recursive feature elimination with correlation bias reduction (RFE-CBR) is used to determine the best number of features to maximize accuracy in SVM classification. The experiments reveal that the proposed algorithm can discriminate single and double compressed AMR speech, outperforming the published methods. The average accuracies using TIMIT database and CARIOCA1 database, the last recorded from landline phone calls, are about 99%. Other experiments, including frame offset attack and noise addition, found that the method is robust and reliable.
Reduced Order Observer for Structure from Motion using Concurrent Learning

In this paper, a concurrent learning based reduced order observer for a perspective dynamical system (PDS) is developed. The PDS is a widely used model for estimating the depth of a feature point from a sequence of camera images. Leveraging the recent advances in concurrent learning for adaptive control, the depth observer is developed for a PDS model where the inverse depth appears as a time varying parameter in the dynamics. Using the data recorded over a sliding time window in the near past, information about the recent depth values is used in a CL term and an observer is developed. A Lyapunov-based stability analysis is carried out to prove the uniformly ultimately bounded (UUB) stability of the observer. Simulations demonstrate the convergence using mean absolute percentage error (MAPE) and root mean squared error (RMSE) metrics in the presence and absence of persistence of excitation (PE).