Reviewerly/exHarmony huggingface dataset:
SIFT Keypoints Data Hiding Scheme with Rotation Invariant

This Data hacking and tampering (attacks) appear to be an alarming concern as the number of internet users grow exponentially. Geometrical and non-geometrical tampering are both possible. Due to its distortion properties, the former makes it difficult to protect the hidden data. In this research, we provide a data hiding strategy that is resistant to rotating geometrical attacks while maintaining perceptual quality. The keypoints of the cover image are identified using a scale-invariant feature transform (SIFT) algorithm, and then data is concealed in an order that is invariant to any degree of rotation of the stego image. To demonstrate the proposed scheme’s resiliency, a brief experimental examination was undertaken with a collection of standard images. The algorithm is not just resistant to rotation but also to other non-geometrical attacks. With a payload capacity of 0.12 bpp, the average PSNR is 69.29 dB.
Space-Time Memory Networks for Multi-person Skeleton Body Part Detection

Deep CNNs have recently led to new standards in all fields of computer vision with specialized architectures for most challenges, including Video Object Segmentation and Pose Tracking. We extend Space-Time Memory Networks for the simultaneous detection of multiple object parts. This enables the detection of human body parts for multiple persons in videos. Results in terms of F1-score are satisfactory (a score of 47.6 with the best configuration evaluated on PoseTrack18 datatset) and encouraging for follow-up work.
Offline Handwritten Text Recognition Using Hybrid CNN–BLSTM Network

Handwriting text recognition, or htr, is a computer capability to receive and understand handwritten data from various sources, like paper journals and graphics, touchpad screen. Identification of handwritten text is a new and complicated field of study that has a vast range of applications in banks and other enterprises. In this work , we use a hybrid convolutional neural network (CNN) and Hybrid Bi-directional Long Short Term Memory (BLSTM) based Recurrent Neural Network design to determine text. We deploy CNN layers to create a variety of activation maps. Activation maps highlight the image's most important characteristics. This feature is then feed to BLSTM layer for encoding and decoding visual feature present in the input image .Our CNN-BLSTM based implementation is experimented with the dataset IAM to obtain the accuracy of handwritten text. We achieve an accuracy of 97.8% in IAM word dataset.
Ultrasound Medical Image Deconvolution Using L2 Regularization Method and Artificial Bee Colony Optimization Algorithm

In this work, we propose a novel method for ultrasound medical image deconvolution with regularization parameter estimation based on l2 regularization approach and artificial bee colony optimization algorithm. We investigate the impact of regularization choice on the deconvolution process and we evaluate the regularization parameter estimation using our proposed approach. The experimental results confirms the efficient performance of the ultrasound medical image deconvolution operation using our proposed method in comparison with other experimental state-of-the-art deconvolution methods
Efficient Revocation of Capability Tokens

In this paper we propose L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cap, an approach to efficiently store and distribute revocation data of LCap capability tokens. By using a probabilistic data structure, L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cap is able to operate with a small and fixed size memory, even when no upper bound for the number of revoked capability tokens can be given. The problem of a high false positive rate of a saturated Bloom filter is mitigated by guaranteeing that renewed capability tokens are correctly detected as valid until at least the next revocation data update. This ensures normal quality of service operation can be resumed swiftly after a mass revocation event.We have shown that the computational overhead of L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cap and its impact on response time compared to plain LCap are negligible. The use L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cap is shown to be safe in regard to the attacker model analyzed in this paper. L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cap allows the use of Capability Based Access Control (CBAC) for Class 0 IoT devices with only sporadic internet connectivity, while issuing capability tokens with a validity that is longer than the use of stale tokens can be tolerated.
Simulation Petri Net Model in the Tasks of Operational Control of Discrete Production

The presented article describes the approach to solving the task of discrete production control based on the reference imitation model. A discrete production system, represented by typical technological equipment, was chosen as the object of research. The simulation model uses the mathematical apparatus of temporary Petri nets. A method for automated synthesis of ready-to-use production models has been formed and tested. The method is based on the idea of synthesizing a production process model from models of typical technological processes. In the practice of applying Petri nets, the complexity of developing, subsequent interpretation of models, and, consequently, making changes are factors that significantly hinder their practical use. A new way of influencing the criterion is proposed by setting different incidence ratios in the Petri net. In the structure of the incidence matrix, the invariable and variable parts are distinguished. A method for specifying the structure of the variable part through the vector of parameters is described, which made it possible to use a metaheuristic algorithm for finding its best structure. The problem of optimal production planning defined for the approach described above is formulated. The bioinspired algorithm of jumping frogs is adapted to the search for the best network structure for a given optimality criterion. Changes in the specified algorithm made it possible to reduce the number of search steps, as well as work with discrete type parameters. In the process of solving, the most popular optimality criterion was used. The obtained theoretical results are within the framework of the optimization-simulation approach and are its logical development. The developed approach to solving the problem of optimal production control develops the theory of Petri nets, makes it more suitable for modeling complex systems with a branched structure and a large number of interconnections. On the basis of the developed theoretical provisions, a test example is presented that characterizes the effect of their application. Recommendations for the practical use of the proposed approach in the sense of minimizing the time for making managerial decisions with the required accuracy are determined.
Streamline Seeding Strategy Based on Quantitative Evaluation

Seeding strategy can provide deep insight into complex flow data and plays a crucial role in generating comprehensible streamlines. For lack of quantitative evaluation in streamline distribution methods, we propose an evaluation method based on information theory, which quantifies the information difference between the original and current vector field with distribution of streamlines. Furtherly, we utilize the evaluation method to guide the seeding strategy, displaying the critical information with a specified number of streamlines. And we consider both direction and magnitude of vector to effectively exhibit the detailed content in the flow field. Quantitative and qualitative comparisons are also carried out with an existing classical approach, indicating that our seeding strategy can succinctly express more effective flow field information.
CS-GAN: Centrosymmetric Generative Adversarial Network for Cross-Media Retrieval

Cross-Media retrieval has become necessary to satisfy the urgent needs of multimedia economic and cultural value. The heterogeneous gap makes it difficult to directly retrieve media of different modalities. Existing common space-based Cross-Media retrieval methods usually focus on increasing the inter-class distance while paying little attention to decreasing the intra- class distance, they neglect to correlate the mapping process from original space to common space, which limits the narrowing of intra-class distance. To address this problem, we propose Centrosymmetric Generative Adversarial Network (CS-GAN) for Cross-Media retrieval, which consists of two generative models and our proposed Heterogeneous Input Discriminative Model (HIDM), forming a centrosymmetric structure. Our proposed HIDM conjoins the mapping processes of each media type by dimensionality reduction and knowledge transfer, which ensures the unification of internal processing and well retains the underlying manifold structure information. Besides, it discriminates the distribution of common representations for both image and text at the same time, which can interrelate the generative mapping, preserve the distribution of the original features and narrow the intra-class distances of common embeddings. The experimental results on PKU XMediaNet and Pascal Sentences show the effectiveness of our proposed CS-GAN.
AI-based Solutions for ADAS

In today's time, the Advanced Driver Assistance System (ADAS) has become indispensable to top car manufacturers. With the constant evolution of vehicle safety systems, automobile manufacturers and consumers can envision a world where vehicle collisions are a thing of the past. Object detection is a crucial part of ADAS for pedestrian and vehicle detection, and for enabling various functions like automatic emergency braking, blind spot monitoring and so on. This paper aims to develop a You Only Look Once version 4 (YOLOv4)-based object detection model. This model is trained on our custom dataset which has ten object classes that one would find on Indian roads.
AnomalyByClick: An Interactive Visualization Tool for Monitoring Activities of Daily Living and Anomaly Annotation

We present AnomalyByClick, an interactive visualization system that allows the monitoring and analysis of anomalies in the behavior of elderly patients during daily activities in a living environment.
Weather Forecasting Using Artificial Neural Network for Rice Farming in Delanggu Village

Abstract Extreme climate change results in unpredictable weather patterns. This problem brings an impact particularly for rice farmers. Weather prediction system can help farmers to make the right decisions before weather changes occur. However, the coverage of the current weather station system is too wide, resulting in low accuracy and effectiveness of weather forecasts. Therefore, a local weather prediction system based on the Internet of Things (IoT) has been developed. With the help of machine learning (Artificial Neural Network algorithm), this system can predict the weather in the next three days. This will help farmers in making the right decisions in dealing with their rice field problems. The ANN model with 40 training cycles and 7 hidden layers is the best combination in producing accurate weather predictions.
Looking Confused? – Introducing a VR Game Design for Arousing Confusion Among Players

This paper describes a virtual reality prototype’s game and level design that should serve as stimulus material to arouse confusion among players. In doing so, we will use the stimulus to create a system that analyzes the players’ gaze behavior and determine whether and when the player is confused or already frustrated. Consequently, this information can then be used by game and level designers to give the player appropriate assistance and guidance during gameplay. To reach our goal of creating a design that arouses confusion, we used guidelines for high-quality level design and did the exact opposite. We see the PLEY Workshop as a forum to identify and discuss the potentials and risks of the stimulus’ design and level structure. In general, the paper should provide insights into our design decisions for researchers interested in investigating gaze behavior in games.
Downhole density estimation using multielement geochemistry and machine learning

Machine learning (ML) was used to estimate bulk density from multielement geochemistry. At the Wheeler River site, host to the Phoenix and Gryphon uranium deposits, multielement geochemistry data were acquired for 829 exploration holes. Of those holes, 41 were logged with a downhole dual-spaced density probe during several mobilizations between 2009 and 2019. Density measurements were collected to provide constraints for inversions of airborne gravity data. To improve the density model's spatial resolution, ML models were trained to estimate bulk density from collocated multielement geochemistry data. Two geochemical laboratory methods were used (251 holes for the old method and 578 holes for the new method); therefore, two separate models were trained. Leave-one-hole-out cross-validation mean absolute error (MAE) results from the old and new geochemistry models showed similar scores of 0.027 g/cm 3 and 0.025 g/cm 3 , respectively. Eight test holes were removed from the training data and used for final evaluation once the model was trained. Test hole results showed MAE scores of 0.026 g/cm 3 for the old geochemistry model and 0.043 g/cm 3 for the new geochemistry model. A unique aspect of this data set was the presence of repeat logs for multiple boreholes over a decade-long logging campaign. This provided the opportunity to assess the measurement uncertainty across time, density probes, operators, and boreholes conditions. The process of estimating downhole density from multielement geochemistry data could be used for many exploration projects to help generate better starting density models for use in geophysical inversions and other applications.
Evolutionary Standard Cell Synthesis of Unconventional Designs

Conventional synthesis algorithms transform the behavioral RTL design to a standard cell mapped gate level netlist, with support to customize optimization effort of few operators. HDL description standards and current synthesis methods lack support to generate netlist of custom functions for quick validation and characterization of the design. Additionally, synthesis does not cater directly to various mathematical functions, design efforts towards approximating the desired function is needed. Hence a synthesis method for realizing circuits applicable to not only arithmetic but also to non-linear functions will be highly valuable and appreciated among the VLSI design community. This work employs Cartesian Genetic Programming (CGP) algorithm, an evolutionary design methodology suitable to synthesize digital circuits. CGP benefits in accelerating the design process and offers the ease to realize complex functions with little to no design effort. Activation functions are difficult to realize as combinational circuits using traditional design methods, this work validates the synthesis results for 6 non-linear activation functions using both classical and standard cell synthesis oriented CGP. The ability to incorporate such unconventional designs to the traditional synthesis flow will be instrumental for implementing accelerators in hardware space, and eventually for efficient design of heterogeneous SoC systems.
Secure retrieval method of network space data based on block chain technology

In order to solve the shortcomings of traditional methods in storage capacity, fault tolerance and time consuming, a secure data retrieval method based on block chain technology was proposed. Initialize the blockchain system, encrypt the cyberspace data through the public key, and upload the encrypted data by connecting the public key of the data aggregator. Complete the blockchain data consensus and verify the encrypted data through the aggregator workload calculation and cyberspace data verification. The blockchain data storage structure is constructed by combining the on-chain index table and the off-chain database. Decrypt the data retrieval instruction with the key, and then transfer the required data into the block chain data storage structure to achieve secure data retrieval in network space. Experimental results show that the proposed method has high storage capacity and fault tolerance, and the maximum running time is only 1.43 s.
Dual Attitude-GNSS/INS Unscented Kalman Filter for Low Cost Integrated Navigation Systems

One of the most difficult issues in highdynamic settings for moving vehicles is estimating the navigation parameters accurately. This paper proposes a dual unscented Kalman filter (DUKF) based on inertial and orientation estimates. DUKF is made up of two distinct filters: one for orientation estimation with an adaptive external acceleration compensation model and the other for velocity and location estimation. When compared to previous model-based Kalman filters, the proposed DUKF is unique in that the implementation of the covariance error models is integrated into the propagation model equations without state-augmentation. This results in a reduction in the matrix-state dimension, which reduces computing time when compared to the analogous state-of-the-art Kalman filter architectures. The suggested technique is tested experimentally in a variety of environmental dynamics circumstances utilizing a low-cost inertial sensor. When compared to the standard Kalman filter with state augmentation, the results analysis indicates superior navigation performance and computation effectiveness.
Minimizing the Cost of Spatiotemporal Searches Based on Reinforcement Learning with Probabilistic States

Portraying the trajectories of certain vehicles effectively is of great significance for urban public safety. Specially, we aim to determine the location of a vehicle at a specific past moment. In some situations, the waypoints of the vehicle’s trajectory are not directly available, but the vehicle’s image may be contained in massive camera video records. Since these records are only indexed by location and moment, rather than by contents such as license plate numbers, finding the vehicle from these records is a time-consuming task. To minimize the cost of spatiotemporal search (a spatiotemporal search means the effort to check whether the vehicle appears at a specified location at a specified moment), this paper proposes a reinforcement learning algorithm called Quasi-Dynamic Programming (QDP), which is an improved Q-learning. QDP selects the searching moment iteratively based on known past locations, considering both the cost efficiency of the current action and its potential impact on subsequent actions. Unlike traditional Q-learning, QDP has probabilistic states during training. To address the problem of probabilistic states, we make the following contributions: 1) replaces the next state by multiple states of a probability distribution; 2) estimates the expected cost of subsequent actions to calculate the value function; 3) creates a state and an action randomly in each loop to train the value function progressively. Finally, experiments are conducted using real-world vehicle trajectories, and the results show that the proposed QDP is superior to the previous greedy-based algorithms and other baselines.
COMPARING FUEL-OPTIMAL AND SHORTEST PATHS WITH OBSTACLE AVOIDANCE

This paper presents a comparison of fuel-optimal and shortest paths of an unmanned combat aerial vehicle (UCAV) with obstacle avoidance. A nonlinear constrained optimization algorithm is applied to obtain the optimal paths. An initial value problem (IVP) and an inverse-dynamics approach are used separately to determine optimal paths for various scenarios and in order to reduce computation time. While inputs of the optimization algorithm are discrete control variables in the IVP method, discrete state variables are used as inputs in the inverse-dynamics method. The minimized path segments of the geometrical model provide an initial estimation of the heading angle for the aircraft flight mechanics model. The number of variables used by the optimization algorithm has a direct effect upon the optimal accuracy; however, the computation time is inversely proportional to the number of the variables. Simulation results demonstrate that the proposed IVP method effectively converges to optimal solutions.
Hybrid Approach for Image Enhancement and Corner Detection Framework Using Contrast Enhancement Technique and Edge Preserving Filter

Image processing is the new edge of computing. Images are very useful for variety of purposes or to solve real life problems. Image enhancement is the key factor of image processing because it effects the outcome of end result. Here we propose the hybrid technique for image enhancement framework. This hybrid technique combination of contrast enhancement technique and edge preserving filters. Contrast enhancement enhanced the image which is acquisition by the variety of cameras and edge preserving filters locate the boundaries or corner which is present in the image. This hybrid approach applied to some medical images, it also helpful for any other types of images as well. Experimental result calculated in MATLAB 2020.
Super Resolution for Magnetic Resonance Images Using Self-Super Resolution Technique

Recently, the need for MRI images in clinical applications has skyrocketed. While this is very desired, a trade-off will exist between acquisition speed, resolution, and noise. By and large, MRI pictures have a higher in-plane resolution than through-plane images. It is impossible to get high frequency information from these pictures through through-plane and it cannot be derived by interpolation either. To address this issue, the super resolution (SR) approach was created to improve spatial resolution. SR approaches learn the transition from lowresolution (LR) to high-resolution (HR) pictures using a training set. HR atlas pictures are not readily accessible for MRI for a variety of reasons. In this article, we offer a self-SR method that is not reliant on an external training data. However, it is possible to bypass this limitation using HR pictures, which are dependant upon obtaining LR images. To create training sets, several forms of distorted input pictures are employed. To estimate the HR picture, the trained set is applied to the input image. When compared to existing self-super-resolution (SSR) approaches, the suggested result improves through-plane resolution significantly.