Reviewerly/exHarmony huggingface dataset:
A survey on Pose Estimation using Deep Convolutional Neural Networks

Abstract Human pose estimation is a technique, which identifies the human body’s landmarks in images and videos. Human pose estimation can be divided into single person pose estimation and multi-person pose estimation, also an estimated human poses in crowded places as well as in videos. Depends upon the application such as activity recognition, Animation, Sports, Augmented reality, etc., Pose estimation output can be in 2D or 3D coordinate format. 3D pose estimation is estimated considering joint angles in 2D. Some challenges like small and barely visible joints, strong articulations, occlusions, clothing, and lighting changes increase difficulty in estimating pose. Remarkable progress has gained in the field of human pose estimation using Deep learning-based CNN models. In this paper, we compare and summarized various deep learning models for pose estimation of a single person and multi-person.
Foreign Accent Recognition with South Indian Spoken English

The type of non-native speech is majorly influenced by the mother tongue. A study on Mother Tongue Influence (MTI) on a second language was carried out by employing Native Language Identification (NLI) approach. This included extraction of cepstral features, mainly the Mel Frequency Cep-stral Coefficients (MFCCs). Recognition of characteristic English accents of South Indian speakers belonging to the three regional accent groups corresponding to languages: Telugu, Tamil, and Kannada was investigated. A specifically developed corpus containing native speech samples of the speakers from each group, and non-native English speech of their compatriots was used. Experiments in text-independent mode using MFCC based Gaussian Mixture Models-Universal Background Model (GMM-UBM) classifier resulted in accuracies up to 91%, and an EER of 9.1%. Identification using i-vector paradigm produced an accuracy of 94%, with an EER of 6.1%. A general purpose recognition method is proposed for automatic classification of speakers based upon foreign accent.
Statistical foundation of Variational Bayes neural networks

Despite the popularism of Bayesian neural networks (BNNs) in recent years, its use is somewhat limited in complex and big data situations due to the computational cost associated with full posterior evaluations. Variational Bayes (VB) provides a useful alternative to circumvent the computational cost and time complexity associated with the generation of samples from the true posterior using Markov Chain Monte Carlo (MCMC) techniques. The efficacy of the VB methods is well established in machine learning literature. However, its potential broader impact is hindered due to a lack of theoretical validity from a statistical perspective. In this paper, we establish the fundamental result of posterior consistency for the mean-field variational posterior (VP) for a feed-forward artificial neural network model. The paper underlines the conditions needed to guarantee that the VP concentrates around Hellinger neighborhoods of the true density function. Additionally, the role of the scale parameter and its influence on the convergence rates has also been discussed. The paper mainly relies on two results (1) the rate at which the true posterior grows (2) the rate at which the Kullback–Leibler (KL) distance between the posterior and variational posterior grows. The theory provides a guideline for building prior distributions for BNNs along with an assessment of accuracy of the corresponding VB implementation.
Enabling Pulse-Level Programming, Compilation, and Execution in XACC

Noisy gate-model quantum processing units (QPUs) are currently available from vendors over the cloud, and digital quantum programming approaches exist to run low-depth circuits on physical hardware. These digital representations are ultimately lowered to pulse-level instructions by vendor quantum control systems to affect unitary evolution representative of the submitted digital circuit. Vendors are beginning to open this pulse-level control system to the public via specified interfaces. Robust programming methodologies, software frameworks, and backend simulation technologies for this analog model of quantum computation will prove critical to advancing pulse-level control research and development. Prototypical use cases for this include error mitigation, optimal pulse control, and physics-inspired pulse construction. Here we present an extension to the XACC quantum-classical software framework that enables pulse-level programming for superconducting, gate-model quantum computers, and a novel, general, and extensible pulse-level simulation backend for XACC that scales on classical compute clusters via MPI. Our work enables custom backend Hamiltonian definitions and gate-level compilation to available pulses with a focus on performance and scalability. We end with a demonstration of this capability, and show how to use XACC for pertinent pulse-level programming tasks.
Data Mining Approach to Predict Air Pollution in Makassar

Air pollution level in Makassar has increased based on data from 2018 to 2019. There were 646 data obtained from the Ministry of Environment and Forestry data archive through the official site, there as five variables in data training, particulate matter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3). Lack of information on air pollution causes the people become unaware on their personal health. There is an effective analysis method for exploring data. This research used knowledge discovery technique in databases in data mining to facilitate decision making. Finally, continuing from the results of previous studies where the prediction of air pollution levels used Naïve Bayes algorithm, this research predicts the level of air pollution using the K-Nearest Neighbor Algorithm to classification data testing and data training with an accuracy rate of 96%, a precision value of 97% and also a recall value of 100%.
Finding Suitable Membership Functions for Mining Fuzzy Association Rules in Web Data Using Learning Automata

Transactions in web data are huge amounts of data, often consisting of fuzzy and quantitative values. Mining fuzzy association rules can help discover interesting relationships between web data. The quality of these rules depends on membership functions, and thus, it is essential to find the suitable number and position of membership functions. The time spent by users on each web page, which shows their level of interest in those web pages, can be considered as a trapezoidal membership function (TMF). In this paper, the optimization problem was finding the appropriate number and position of TMFs for each web page. To solve this optimization problem, a learning automata-based algorithm was proposed to optimize the number and position of TMFs (LA-ONPTMF). Experiments conducted on two real datasets confirmed that the proposed algorithm enhances the efficiency of mining fuzzy association rules by extracting the optimized TMFs.
Optimization-Time Analysis for Cybersecurity

A mathematical framework to reason about time resilience in cybersecurity is here introduced. We first consider an attacker who is able to mount several multi-stage attacks on the organization: the defender’s objective is to select an optimal portfolio of security controls, within a given budget, to withstand the highest number of attacks. The mathematical model is a Markov chain with an initial state called the safe state, intermediate states for all possible attacks (each attack state denoting a probabilistic attack graph), and a sink state denoting a successful attack. The overall defence problem is formulated as a bi-level multi-objective optimization, i.e., the defender selects an optimal portfolio of security controls to mitigate an optimal attacker. In order to determine the probability of success of an attack two cases will be considered: (a) the expected probability of success and (b) the highest probability of success. We refer to these two cases as expected-time analysis and worst-case time analysis, respectively. To solve precisely these bi-level optimizations strong duality and Mixed Integer Linear Programming are used. We then extend the framework to investigate resilience in terms of the total duration of the attacks; variations of the previous optimizations are presented to this purpose. Finally numerical evaluations are provided to compare the results obtained from the expected-time analysis and the worst-case time analysis.
Behavioral Responses to Phone Snubbing

This manuscript reports an experiment in which undergraduate confederates interacted with naïve participants. At 5-min into a 7-min interaction, the confederate manipulated their cell phone: (1) phone rings, confederate ignores; (2) phone rings, confederate picks up; or (3) confederate proactively checks cell phone. Both the participants and the confederates were video recorded for the entire 7 min. In addition, participants filled out a questionnaire at the end of the interaction. The videos were coded and analyzed to determine what behavioral reactions, if any, were related to responses on the questionnaire. The data indicated that participants who looked away from the confederate after phone manipulation had significantly more negative perceptions of the confederate than those who emitted other behaviors.
Lossless Dimension Expanders Via Linearized Polynomials and Subspace Designs

For a vector space $${\mathbb{F}^n}$$ over a field $\mathbb{F}$ , an (η, β)-dimension expander of degree d is a collection of d linear maps $${\Gamma _j}:{\mathbb{F}^n} \rightarrow {\mathbb{F}^n}$$ such that for every subspace U of $${\mathbb{F}^n}$$ of dimension at most ηn, the image of U under all the maps, ∑ =1 Γj(U), has dimension at least α dim(U). Over a finite field, a random collection of d = O(1) maps Γj offers excellent “lossless” expansion whp: β≈d for η ≥ Ω(1/d). When it comes to a family of explicit constructions (for growing n), however, achieving even modest expansion factor β = 1+ ε with constant degree is a non-trivial goal. We present an explicit construction of dimension expanders over finite fields based on linearized polynomials and subspace designs, drawing inspiration from recent progress on list decoding in the rank metric. Our approach yields the following: Previously, an approach reducing to monotone expanders (a form of vertex expansion that is highly non-trivial to establish) gave (Ω(1), 1 + Ω(1))-dimension expanders of constant degree over all fields. An approach based on “rank condensing via subspace designs” led to dimension expanders with $$\beta \mathbin{\lower.3ex\hbox{$\buildrel>\over {\smash{\scriptstyle\sim}\vphantom{_x}}$}} \sqrt d $$ over large finite fields. Ours is the first construction to achieve lossless dimension expansion, or even expansion proportional to the degree.
MFRC522 RFID Technology Implementation for Conventional Merchant with Cashless Payment System

Abstract This article discusses how technological innovation develops to support conventional merchants by implementing RFID for a fast and easy payment solution. This payment uses a contactless smart card as a medium for electronic money or e-Money. This e-Money card can be obtained through a cashier available at merchants and e-Money balance can also be topped up by registering the card first. This cashless machine uses Arduino UNO as a microprocessor, MFRC522, Micro SD Module and 16x02 LCD Display. This cashless machine does not require internet to access the database and can also be used immediately without problems. This cashless machine can work within a range of 3cm for e-Money card detection with the fastest response time of 0.1s and takes a long time to process a balance of 1.4s. This machine is equipped with a menu variant selection at merchants using a serial monitor display integrated on the Arduino UNO.
Parallel Realization of the Problem of Heat and Moisture Transfer in Fractal-structure Materials

This paper was analyzes the parallel solutions for the research of heat and moisture transfer in fractal-structure materials on multicore computers. A mathematical model is presented for the numerical solution of the system of partial differential equations. This model describes the process of heat and moisture transfer in fractal-structure materials. Software for the research of this process has been implemented. The Producer/Consumer parallel programming pattern was used to design the software. We used the Threads and algorithmic features of TPL from Framework .Net 4.0. This made it possible to implement a parallel algorithm based on the predictor-corrector method. The developed software was tested on multi-core computer systems with CPUs of different power. The obtained acceleration graphs and algorithm efficiency are analyzed.
The distortion elimination of an optical microscope based on optimized windowed Fourier transform

An image distortion elimination method based on optimized windowed Fourier transform processing is proposed in this paper. Simulation testing was performed beforehand to find the optimal window size and grating pitch (in pixels), which are crucial for the distortion calibration accuracy. With the optimized parameters, gratings with different frequencies were fabricated by photolithography as standard plates to calibrate the aberration of the optical microscope. The influence induced by the misalignment of the grating lines and the viewing field axis on the distortion calibration was addressed and eliminated by the proposed method in this paper. With the proposed method, the image distortion of the optical microscope under various magnifications was calibrated successfully.
An Intelligent and Hybrid Method of Combining Spatial and Frequency Representation for Digital Image Steganography

Data security becomes important concern in this internet world. Lots of internet users are losing their conceal data when they are communicating it through the internet. So, there is a demand to adopt secured communication technique to ensure the safety of users’ data. One such technique is steganography. Steganography is the practice of embedding conceal information into another skin data which is not easy to recognize the conceal information by unintended person. The conceal and skin data may be of any format. In this proposed method, conceal image is hided into a skin image by using hybrid method where steganography is performed by using both spatial as well as frequency representation as a hybrid method. By using the proposed method, the secureness of the conceal image is assured by three processes whereas in conventional method, only one process is used. Initially, in spatial representation, binary values of conceal image pixels are hidden by mapping suitable locations in the skin image pixels. Then, the identified suitable locations are extracted as coefficients which converted to gray codes. Finally, the gray codes are embedded into the skin image in its frequency representation which is transformed using Haar DWT. The resultant stego image is communicated to the recipient side where the conceal image is recovered back from stego image by inverse DWT and mapping coefficients. The experimental results show better performance parameters like entropy, cross correlation, MSE, PSNR and SSIM values compared to that of conventional LSB method.
Fair payments for privacy-preserving aggregation of mobile crowdsensing data

With the advent of mobile crowdsensing, the mobile devices equipped with a variety of sensors (such as accelerometer, gyroscope, microphone etc.) are used to collect sensory data. A data aggregator processes the collected sensor data to deliver various services such as traffic management, health care and environmental monitoring. To ensure the privacy of the data, privacy-preserving aggregation (PPA) has attracted much attention since it can find aggregated statistics on the encrypted data. In this paper, we extend the existing PPA schemes in two directions: (1) Aggregator unforgeability – The aggregator performs the aggregation operation correctly. Although there are some schemes which consider aggregator unforgeability, they rely on cryptographic techniques. (2) Fair payments – The data owners receive the payments for their data contribution if and only if the aggregator receives the data. Contrary to existing works, we achieve the aggregator unforgeability and fair payments by modeling the aggregator as a smart contract running on a public Blockchain network. We design two PPA schemes FairNaivePPA and FairPPA for secure aggregation of MCS data with fair payments. We show the financial and transactional cost analysis of proposed contracts by implementing them in solidity and running them on Ethereum Blockchain.
Constructing a campus-wide infrastructure for virtual reality

The tools, techniques, and physical infrastructure to conduct VR explorations can be expensive and require specialized facilities and training, creating obstacles for faculty and students to explore potential applications. A gap exists for a trusted environment to develop and support best practices. This study documents lessons learned from the transformation of existing library spaces into VR content creation and exploration spaces, as part of an integrated campus-wide initiative. Results argue for the central role libraries can play in the VR lifecycle. The study presents recommendations for adapting to the rapidly-developing VR marketplace, suggests methodologies for standardizing hardware deployment, training, and application support, and recommends a support structure for academic VR support.
Multi‐level feature fusion network for crowd counting

Crowd counting has become a noteworthy vision task due to the needs of numerous practical applications, but it remains challenging. State-of-the-art methods generally estimate the density map of the crowd image with the high-level semantic features of various deep convolutional networks. However, the absence of low-level spatial information may result in counting errors in the local details of the density map. To this end, a novel framework named Multi-level Feature Fusion Network (MFFN) for single image crowd counting is proposed. The proposed MFFN, which is constructed in an encoder–decoder fashion, incorporates semantic and spatial information for generating high-resolution density maps of input crowd images. Skip connections are developed between the encoder and the decoder so that low-level spatial information and high-level semantic features can be combined by element-wise addition. In addition, a dense dilated convolution block is placed behind the encoder, extracting multi-scale context features to guide feature fusion by a channel attention mechanism. The model is trained by multi-task learning; semantic segmentation supervision is introduced to enhance feature representation. Extensive experiments are conducted on three crowd counting datasets (ShanghaiTech, UCF_CC_50, UCF-QNRF), and the results show that MFFN outperforms state-of-the-art methods. In addition, sufficient ablation studies are performed to verify the effectiveness of each component in our proposed method.
On the Impact of Aging on Power Analysis Attacks Targeting Power-Equalized Cryptographic Circuits

Side-channel analysis attacks exploit the physical characteristics of cryptographic chip implementations to extract their embedded secret keys. In particular, Power Analysis (PA) attacks make use of the dependency of the power consumption on the data being processed by the cryptographic devices. To tackle the vulnerability of cryptographic circuits against PA attack, various countermeasures have been proposed in literature and adapted by industries, among which a branch of hiding schemes opt to equalize the power consumption of the chip regardless of the processed data. Although these countermeasures are supposed to reduce the information leakage of cryptographic chips, they fail to consider the impact of aging occurs during the device lifetime. Due to aging, the specifications of transistors, and in particular their threshold-voltage, deviate from their fabrication-time specification, leading to a change of circuit's delay and power consumption over time. In this paper, we show that the aging-induced impacts result in imbalances in the equalized power consumption achieved by hiding countermeasures. This makes such protected cryptographic chips vulnerable to PA attacks when aged. The experimental results extracted through the aging simulation of the PRESENT cipher protected by Sense Amplifier Based Logic (SABL), one of the well-known hiding countermeasures, show that the achieved protection may not last during the circuit lifetime.
An autonomous loyalty program based on blockchains for IoT solution providers

Data exchange and sharing methods for internet of things are widely studied and implemented, mostly in isolation from business considerations. In this paper, we introduce the concept of an autonomous loyalty program for IoT operators and explore its implementation. The purpose of the concept is to establish connection between business and technology layers by design, and in an autonomous fashion. The proposed concept utilizes tokenized economy on blockchain where the operators can implement gamification techniques using smart contracts to maximize profit during service offerings and requests. The system model, exchange procedures and implementation of the concept are discussed.
The Effects of Rewards on Autonomous Unmanned Aerial Vehicle (UAV) Operations Using Reinforcement Learning

The effects of rewards on the ability of an autonomous UAV controlled by a Reinforcement Learning agent to accomplish a target localization task were investigated. It was shown that with an increase in the reward obtained by a learning agent upon correct detection, systems would become more risk-tolerant, efficient and have a tendency to locate targets faster with an increase in the sensor sensitivity after systems achieve steady-state performance.
Sign Language Recognition in Virtual Reality

A real-time system for signal language recognition in virtual reality (VR) is presented in this paper. The system makes use of an egocentric view with the Vive HTC VR headset along with a Leap Motion controller. In this demo, a random forest is used to classify the 26 letters of the alphabet, in American Sign Language, from hand-crafted features extracted from the Leap Motion controller. We detail offline classification results showing the expressive power of the features used for recognition.