Reviewerly/exHarmony huggingface dataset:
Bayesian Nonparametric Modeling for Predicting Dynamic Dependencies in Multiple Object Tracking

The paper considers the problem of tracking an unknown and time-varying number of unlabeled moving objects using multiple unordered measurements with unknown association to the objects. The proposed tracking approach integrates Bayesian nonparametric modeling with Markov chain Monte Carlo methods to estimate the parameters of each object when present in the tracking scene. In particular, we adopt the dependent Dirichlet process (DDP) to learn the multiple object state prior by exploiting inherent dynamic dependencies in the state transition using the dynamic clustering property of the DDP. Using the DDP to draw the mixing measures, Dirichlet process mixtures are used to learn and assign each measurement to its associated object identity. The Bayesian posterior to estimate the target trajectories is efficiently implemented using a Gibbs sampler inference scheme. A second tracking approach is proposed that replaces the DDP with the dependent Pitman-Yor process in order to allow for a higher flexibility in clustering. The improved tracking performance of the new approaches is demonstrated by comparison to the generalized labeled multi-Bernoulli filter.
De-identification and Privacy Issues on Bigdata Transformation

As the number of data in various industries and government sectors is growing exponentially, the `7V' concept of big data aims to create a new value by indiscriminately collecting and analyzing information from various fields. At the same time as the ecosystem of the ICT industry arrives, big data utilization is treatened by the privacy attacks such as infringement due to the large amount of data. To manage and sustain the controllable privacy level, there need some recommended de-identification techniques. This paper exploits those de-identification processes and three types of commonly used privacy models. Furthermore, this paper presents use cases which can be adopted those kinds of technologies and future development directions.
Blockchain Technology, Technical Challenges and Countermeasures for Illegal Data Insertion

Blockchain is a decentralized transaction and data management technology.It was developed for the world's first cryptocurrency known as Bitcoin in 2008.The reason behind its popularity was its properties which provide pseudonymity, security, and data integrity without third-party intervention.Initially, most of the researches were focused on the Bitcoin system and its limitation, but later other applications of Blockchain e.g.smart contracts and licensing [1] also got famous.Blockchain technology has the potential to change the way how transactions are conducted in daily life.It is not limited to cryptocurrencies but could be possibly applied in various environments where any forms of transactions are done.This article presents a comprehensive overview of Blockchain technology, its development, applications, security issues, and their countermeasures.In particular, the security towards illegal data insertion and the countermeasures is focused.Our analysis of countermeasures of illegal data insertion can be combined for increased efficiency.After the introduction of the Blockchain and consensus algorithm, some famous Blockchain applications and expected future of Blockchain are deliberated.Then, the technical challenges of Blockchain are discussed, in which the main focus here is on the security and the data insertion in Blockchain.The review of the possible countermeasures to overcome the security issues related to data insertion are elaborated.
What is the best triangulation approach for a structured light system?

It has become customary to calibrate a camera-projector pair in a structured light (SL) system as a stereo-vision setup. The 3D reconstruction is carried out by triangulation from the detected point at the camera sensor and its correspondence at the projector DMD. There are several algebraic formulations to obtain the coordinates of the 3D point, especially in the presence of noise. However, it is not clear what is the best triangulation approach. In this study, we aimed to determine the most suitable triangulation method for SL systems in terms of accuracy and execution time. We assess different strategies in which both coordinates in the projector are known (point-point correspondence) and the case in which only the one coordinate in the DMD is known (pointline correspondence). We also introduce the idea of estimating the second projector coordinate with epipolar constraints. We carried out simulations and experiments to evaluate the differences between the triangulation methods, considering the phase-depth sensitivity of the system. Our results show that under suboptimal phasedepth sensitivity conditions, the triangulation method does influence the overall accuracy. Therefore, the system should be arranged for optimal phase-depth sensitivity so that any triangulation method ensures the same accuracy.
Assessment of Heavy Metals Distribution in Soil Cover of Industry-Urban Area of Krasnoyarsk

The results of analysis of heave metals concentration in soil and subsoil of large urbanized area are presented. The dates of observation from 2014 to 2016 are presented. The 108 soil samples from 12 monitoring areas were analyzed. The distribution of mobile forms of heavy metals in the soil cover within Krasnoyarsk city are considered. The rate of soil pollution was studied. It was discovered soil pollution with Pb, Cu, Ni, Zn, Mn, Cd, Co exceeds maximum acceptable concentration (MAC). Geochemical associations of heavy metals for functional specialization areas were established. The indices of accumulation of heavy metals can be used to the monitoring to urban territories.
Continuous variable quantum teleportation via entangled Gaussian state generated by a linear beam splitter

We theoretically implement the protocol of continuous variable quantum teleportation wherein Alice and Bob share a state built by a linear beam splitter as general two-mode Gaussian entangled state. Using the non-classicality and purity of the two single-mode Gaussian states employed initially at the input of the beam splitter, the unknown state teleportation protocol is explicitly implemented in terms of squeezing and phase space quadratures. We show that fidelity of the teleported state is maximal when the gain factor g is equal to 1 and gradually decreases when g deviates from 1 on either side. We further find that teleportation fidelity decreases when one of the input states of the beam splitter is replaced by the state of thermal photons.
Language Detection of Text Document Image

Language detection is an important feature for developing a Multilingual OCR. Lots of research has been done on Languages such as English, French, Spanish but very limited work has been performed on Indian languages such as Hindi, Tamil etc. due to their complex nature. In this paper we are presenting a method to detect the language of a text document image. The language chosen are such that they are of different scripts. The process of language detection involves segmentation of individual characters from the document and then labelling them to a particular language. A Convolutional Neural Network is trained on characters of different languages. A text document is classified on the basis of what language its individual characters belong to. This is achieved by first segmenting individual characters of a document and then classifying each of them separately.
Introduction to the Special Issue on Advanced Technologies in Assessment: A Science-Practice Concern

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, and diverging publication practices. Finally, we provide recommendations preparing psychologists who want to engage in collaborations with computer scientists. We argue that psychologists should proactively approach computer scientists, learn computer scientific fundamentals, appreciate that research interests are likely to converge, and prepare novice psychologists for a data-oriented scientific future.
Tight and Optimal Reductions for Signatures Based on Average Trapdoor Preimage Sampleable Functions and Applications to Code-Based Signatures

The GPV construction [GPV08] presents a generic construction of signature schemes in the Hash and Sign paradigm and is used in some lattice based signatures. This construction requires a family $$\mathcal {F}$$ of trapdoor preimage sampleable functions (TPSF). In this work we extend this notion to the weaker Average TPSF (ATPSF) and show that the GPV construction also holds for ATPSF in the Random Oracle Model (ROM). We also introduce the problem of finding a Claw with a random function (Claw(RF)) and present a tight security reduction to the Claw(RF) problem. Our reduction is also optimal meaning that an algorithm that solves the Claw(RF) problem breaks the scheme. We extend these results to the quantum setting and prove this same tight and optimal reduction in the QROM. Finally, we apply these results to code-based signatures, notably the Wave signature scheme and prove security for it in the ROM and the QROM, improving and extending the original analysis of [DST19a].
A Benchmark Dataset and Multi-Scale Attention Network for Semantic Traffic Light Detection

Accurate traffic light detection is an important problem for intelligent vehicles. While most existing methods deal with classification of red, green and yellow signals, traffic lights in real world contain a diversity of specific semantic forms, such as red right-turn arrow, green left-turn arrow, and red U turn. In this paper, we propose a multi-scale attention (MSA) network for semantic traffic light detection. The task is to infer the bounding boxes of traffic lights in images and identify their specific semantic categories. Our MSA network combines multi-scale information with attention blocks overcome the `small object' challenge and increase the computation efficiency. Since there was no a proper dataset containing specific semantic traffic lights, we collected a large scale semantic traffic light dataset. Our dataset contains 11 standard categories of specific semantic traffic lights and about 14800 sample images. We test the proposed approach on the new dataset, Bosch Small Traffic Lights Dataset, and LISA Dataset. Experiments show that the proposed method improves the performance of the traffic light detection.
New identity based proxy re-encryption scheme from lattices

Identity-Based Proxy Re-Encryption (IB-PRE) allows a semi-trusted proxy to convert the ciphertext encrypted under Alice's identity into Bob's ciphertext of the same message without leaking plaintext. Lattice-based cryptography enjoys potential resistance to quantum analysis and low computational complexity. A multi-hop and unidirectional IB-PRE from lattices is presented. We split the functions of decryption and ciphertext transformation separately, and design the double private keys mechanism, where two keys are generated for each user, one key is used to decrypt the ciphertext by Pre-Image Sampling technique, and the other is used to generate the re-encryption key by Bonsai Trees technique. The generation of the re-encryption key is non-interactive and collusion resistant. Moreover, its IND-sID-CPA security over the decisional Learning With Errors (LWE) assumption under the standard model is proved. Compared with some previous IB-PRE schemes from Bilinear Pairings, the format of transformed ciphertext in our scheme remains unchanged, furthermore, it can also resist quantum analysis. Compared with some existing IB-PRE schemes from lattices with similar properties, the space of the message in our scheme is a vector of length l and the encryption process remains a lower encryption blowup factor. At last, a proof-of-concept implementation is provided.
A Novel Hypogenetic Chaotic Jerk System: Modeling, Circuit Implementation, and Its Application

When revising the polarity and amplitude information in the feedback, a unique hypogenetic jerk system was obtained which has two controllers to switch the equilibria between stable and unstable. After providing some basic dynamical analysis, an electronic circuit was implemented, and the phase trajectory in the oscilloscope agrees with the numerical simulation. Further exploration shows that this unique chaotic system has superior performance as a random number generator or in voice encryption application.
Reshaping Autonomous Driving for the 6G Era

Autonomous vehicles are self-driving platforms that will transform the whole transportation system into a new form of connected intelligent ecosystem. However, vehicle automation will go through multiple evolution stages that start by partially automated driving where platforms are self-managed while driving decisions are made by humans, to the collaborative driving vehicles where humans intervene is needed during emergencies, and ultimately reaching fully automated vehicles that are completely driven by artificial intelligence (AI). Although fully automated vehicles are anticipated to operate as independent platforms, in-reality they are network-dependent systems that require continuous access to communication channels. The terrain data obtained from vehicle sensors are limited in distance and may not be able to capture the surrounding landscape for safe automated driving.
Gray Code Based Data Hiding in an Image using LSB Embedding Technique

Data concealment is one among the techniques for concealment knowledge the information from data hackers. It's the foremost helpful technique. Gray code technique is additionally used as a result of the security level is high. During this technique a standard image is reborn into black and white color image.To use LSB embedding technique it suggests that an information is encrypted and it's reborn into binary kind.A picture contains the components every pixel to insert the LSB and encrypts the information, and also the image.A sender is to insert the information within the image and sends to the receiver aspect.The receiver decrypts the image, and it's simply read a picture and information.An information is definitely hack by the hacker, so, we to tend to use Separate reversible technique it suggests that an information, and it contains random bits enclosed the information.Therefore, the hacker does not perceive which means of the knowledge.It's terribly high level security.
Depth Augmented Omnidirectional Stereo for 6-DoF VR Photography

We present an end-to-end pipeline that enables head-motion parallax for omnidirectional stereo (ODS) panoramas. Based on an ODS panorama containing a left and right eye view, our method estimates dense horizontal disparity fields between the stereo image pair. From this, we calculate a depth augmented stereo panorama (DASP) by explicitly reconstructing the scene geometry from the viewing circle corresponding to the ODS representation. The generated DASP representation supports motion parallax within the ODS viewing circle. Our approach operates directly on existing ODS panoramas. The experiments indicate the robustness and versatility of our approach on multiple real-world ODS panoramas.
Master-Key KDM-Secure IBE from Pairings

Identity-based encryption (IBE) is a generalization of public-key encryption (PKE) by allowing encryptions to be made to user identities. In this work, we seek to obtain IBE schemes that achieve key-dependent-message (KDM) security with respect to messages that depend on the master secret key. Previous KDM-secure schemes only achieved KDM security in simpler settings, in which messages may only depend on user secret keys. An important motivation behind studying master-KDM security is the application of this notion in obtaining generic constructions of KDM-CCA secure PKE, a primitive notoriously difficult to realize. We give the first IBE that achieves master-KDM security from standard assumptions in pairing groups. Our construction is modular and combines techniques from KDM-secure PKE based from hash-proof systems, together with IBE that admits a tight security proof in the multi-challenge setting, which happens to be unexpectedly relevant in the context of KDM security. In fact, to the best of our knowledge, this is the first setting where techniques developed in the context of realizing tightly secure cryptosystems have led to a new feasibility result. As a byproduct, our KDM-secure IBE, and thus the resulting KDM-CCA-secure PKE both enjoy a tight security reduction, independent of the number of challenge ciphertexts, which was not achieved before.
Anomalies Identification in Images from Security Video Cameras Using Mask R-CNN

In this work we developed a system to identify anomalies in images from video security cameras in an urban environment. Initially people are detected in the images using Mask R-CNN. From the binary mask are extracted characteristics of the people so that the anomalies can be detected. In order to facial recognition we used Facial Landmarks so that the system knows the residents and authorized people avoiding the false anomalies. We considered four anomalies in this work: the act of jumping a wall, standing for a long time in front of the residence, walking thru the sidewalk several times and entering a place without permission.
Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System

This paper provides models for managing and investigating the power flow of a grid-connected solar photovoltaic (PV) system with an energy storage system (ESS) supplying the residential load. This paper presents a combination of models in forecasting solar PV power, forecasting load power, and determining battery capacity of the ESS, to improve the overall quality of the power flow management of a grid-connected solar PV system. Big data tools were used to formulate the solar PV power forecasting model and load power forecasting model, in which real historical solar electricity data of actual solar homes in Australia were used to improve the quality of the forecasting models. In addition, the time-of-use electricity pricing was also considered in managing the power flow, to provide the minimum cost of electricity from the grid to the residential load. The output of this model presents the power flow profiles, including the solar PV power, battery power, grid power, and load power of weekend and weekday in a summer season. The battery state-of-charge of the ESS was also presented. Therefore, this model may help power system engineers to investigate the power flow of each system of a grid-connected solar PV system and help in the management decision for the improvement of the overall quality of the power management of the system.
Competition between whole-word and decomposed representations of English prefixed words

English aspiration is influenced by word structure: in general, a voiceless stop following s is unaspirated (des[t]royed), but it can be aspirated if a prefix-stem boundary intervenes (dis[th]rusts) (Baker et al. 2007). In a production study of 110 words prefixed with dis- or mis-, we show that even in prefixed words, there is variation (dis[k]laimers ∼ dis[kh]laimers), and that aspiration in such words is correlated with word and stem frequency. The more frequent the word, the less likely aspiration, but the more frequent the stem, the more likely aspiration. This contrasting frequency effect is characteristic of the type of competition Hay posits between whole-word lexical access and morphologically decomposed lexical access (Hay 2003): frequent words will tend to be accessed as wholes (and therefore behave as though there is no prefix-stem boundary), but frequent stems will encourage decomposed, prefix + stem access. In order to test whether there is active online competition, as opposed to simply frequency effects that are somehow lexicalized, we also conduct a priming experiment. We find that exposing participants to other prefixed words encourages them to aspirate target words, as compared to when they have been exposed to similar but non-prefixed words. These results provide evidence for active online competition.
Spectral Clustering Algorithm for Cognitive Diagnostic Assessment

In cognitive diagnostic assessment (CDA), clustering analysis is an efficient approach to classify examinees into attribute-homogeneous groups. Many researchers have proposed different methods, such as the nonparametric method with Hamming distance, K-means method, and hierarchical agglomerative cluster analysis, to achieve the classification goal. In this paper, according to their responses, we introduce a spectral clustering algorithm (SCA) to cluster examinees. Simulation studies are used to compare the classification accuracy of the SCA, K-means algorithm, G-DINA model and its related reduced cognitive diagnostic models. A real data analysis is also conducted to evaluate the feasibility of the SCA. Some research directions are discussed in the final section.
Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation.
Unsupervised Learning of Contextual Information in Multiplex Immunofluorescence Tissue Cytometry

New machine learning models designed to capture the histopathology of tissues should account not only for the phenotype and morphology of the cells, but also learn complex spatial relationships between them. To achieve this, we represent the tissue as an interconnected graph, where previously segmented cells become nodes of the graph. Then the relationships between cells are learned and embedded into a low-dimensional vector, using a Graph Neural Network. We name this Representation Learning based strategy NARO (NAtural Representation of biological Objects), a fully-unsupervised method that learns how to optimally encode cell phenotypes, morphologies, and cell-to-cell interactions from histological tissues labeled using multiplex immunohistochemistry. To validate NARO, we first use synthetically generated tissues to show that NARO's generated embeddings can be used to cluster cells in meaningful, distinct anatomical regions without prior knowledge of constituent cell types and interactions. Then we test NARO on real multispectral images of human lung adenocarcinoma tissue samples, to show that the generated embeddings can indeed be used to automatically infer regions with different histopathological characteristics.