Reviewerly/exHarmony huggingface dataset:
Secure audio signal transmission based on color image watermarking

Abstract This paper presents a new color image watermarking system with encrypted audio signals using singular value decomposition (SVD), Arnold, and chaotic techniques for secure audio transmission over open channels. This system is based on embedding encrypted audio watermarks after transforming them into a 2‐D format in the singular values of the color cover image components. Three different audio watermarks are embedded in the three color image components after separating the cover image into its red, green and blue components. The three resulting watermarked components are merged again to produce the final color watermarked image. To maintain the audio signal quality, we estimate some audio signal quality metrics after converting the audio signal into a 1‐D format. The transformation from the 1‐D to 2‐D formats is performed through lexicographic ordering. Simulation results demonstrate that the proposed system preserves the high quality of the RGB cover image. In addition, the audio watermark extraction and decryption are possible, even in the presence of attacks. Furthermore, the results show an enhancement in the watermark detectability in the presence of severe attacks, such as rotation, blurring, cropping, histogram equalization, and noise addition to the watermarked images.
Method of Assessing the Impact of External Factors on Geopositioning System Operation Using Android GPS API

The main methods of analysis the accuracy of the geolocation system were studied. The aspects of technology for obtaining geographical coordinates using Google geolocation system (GPS) were considered. The best method of obtaining data from satellites was searched. Today the geolocation system has become widely used in many industries, such as military, aviation, maritime, navigation of ordinary users. Also, a significant spread of this technology is noticed in many geographical areas, such as cartography, geodesy and is an area of interests of many scientists. A mobile app based on Android platform for conducting the experiments to determine the accuracy of geolocation services in the form of Web Quests was developed.
A Hybrid Fruit Fly ELM Framework for Stock Index Price Movement Prediction

Predicting the upward or downward direction of financial time-series data is very challenging due to complex factors involved in the data such as volatility, non-linearity, and sudden variations. This task requires a reliable and robust prediction model which can not only predict the direction accurately but also be faster than traditional algorithms. Extreme learning machine (ELM) is among the leading algorithms for such task at hand due to its extremely fast training and superior adaptation to new data. This study proposes a hybrid framework integrating fruit fly optimization with extreme learning machine (FFO-ELM) to predict the forthcoming movements of stock indices. Additionally, the performance of the advocated FFO-ELM framework is compared with differential evolution-based ELM (DE-ELM), particle swarm optimization-based ELM (PSO-ELM) and singleton ELM. The empirical analysis has been done utilizing BSE SENSEX and NIFTY 50 stock indices and the outcomes indicated that the FFO-ELM model performed comparatively better in terms of accuracy, fl-score, and g-mean in contrast with other inspected models.
Camera Parameters Aware Motion Segmentation Network with Compensated Optical Flow

Learning to distinguish independent moving objects from the observed optical flow with a moving camera remains challenging. In this work, we first present a novel camera pose compensation (CPC) scheme. With the help of ingenious geometric analysis, it breaks the observed optical flow into patterns that are easier to interpret for the motion segmentation network. Secondly, we further refine such compensation with a camera parameter aware (CPA) module to account for poses’ errors in the CPC processing and enhance the entire network’s tolerance to noises. Additionally, an MMPNet is developed to intensify the identification ability of overall motion patterns. It reaches a larger receptive field with a bottom-up information transmission structure and integrates motion information at different granularities. We demonstrate the benefits of our framework on FlyingThings3D and Monkaa datasets. Without the complement of semantic information, our approach outperforms the top methods for moving objects segmentation.
University Philosophy in the Context of the Philosophy of Science

The purpose of the research is to analyse university philosophy as a complex multidimensional phenomenon in the context of the philosophy of science. Novelty of the research lies in the understanding of university philosophy as a form of organisation of scientific activities and translation of scientific knowledge. As a result, the researcher has identified the place that university philosophy occupies in the system of philosophical science and substantiated its characteristics from the standpoint of the philosophy of science on the basis of the Western European and Russian scientific tradition.
Hybrid Sensor Based Path-Planning for Autonomous Vehicle

An Autonomous Vehicle (AV) is an intelligent cognitive engineering system that processes streams of observations from different onboard sources, like cameras, ultrasonic sensors, GPS units, radars, lidar's, or inertial sensors, to guide itself without human intervention. The application and research of AV on multimodal disciplines are gaining attention both to academia and automotive companies. In this paper, we use a GPS signal to find the destination so that AV can automatically detect the route to reach the destination. With the help of different sensors, it will detect an obstacle towards its route. If it finds any obstacles in front of the sensor, the vehicle will detect them and will avoid the obstacle. With the Arduino and Raspberry pi command, it will find the other shortest route towards its destination. Simultaneous Localization and Mapping (SLAM) are some of the possible algorithms to find the destination successfully. The goal of this research work is to design a Self-driving AV prototype. The design will target implementing an optimized path planning algorithm to reach the destination point.
Integration of Path Optimization and Obstacle avoidance for Autonomous Precision Immobilization Technique Maneuver

The PIT (Precision Immobilization Technique) maneuver is a pursuit tactic used by law-enforcement officials to handle dangerous vehicle pursuit situations, wherein a pursuing car can force a fleeing car to turn sideways abruptly, causing the driver to lose control and stop. Such maneuvers have pitfalls, in that they require sufficient skill on the part of the driver to be carried out. Furthermore, given the utilization of electronic stabilizing control systems in modern vehicles&#x0027; manual PIT maneuvers require more precision to be carried out. However, the previous decades have also undergone significant changes, in particular the development of autonomous driving technologies, which are now available in commercial vehicles. Autonomous PIT maneuvers could be the next step in handling dangerous vehicle pursuits of fugitives. But for autonomous execution of such maneuvers, accurate path planning and obstacle avoidance must be carried out first. This work presents the implementation of path optimization technique along with obstacle avoidance in simulation environment as well as in physical environment for execution of autonomous PIT maneuvering. Path optimization ensures that the bullet vehicle arrives at the specific point where the PIT maneuver is carried out. An important path optimization method has been utilized to accomplish the goal. Furthermore, obstacle avoidance is integrated with path optimization to avoid mid-way stationary obstacles, thereby adding a more realistic scenario for the same. This paper makes a simplifying assumption that the target vehicle is stationary or moving at a constant velocity, which can be extended by future work to a more realistic scenario involving accelerated target vehicle.
Glue Semantics

Glue Semantics (Glue) is a general framework for semantic composition and the syntax–semantics interface. The framework grew out of an interdisciplinary collaboration at the intersection of formal linguistics, formal logic, and computer science. Glue assumes a separate level of syntax; this can be any syntactic framework in which syntactic structures have heads. Glue uses a fragment of linear logic for semantic composition. General linear logic terms in Glue meaning constructors are instantiated relative to a syntactic parse. The separation of the logic of composition from structural syntax distinguishes Glue from other theories of semantic composition and the syntax–semantics interface. It allows Glue to capture semantic ambiguity, such as quantifier scope ambiguity, without necessarily positing an ambiguity in the syntactic structure. Glue is introduced here in relation to four of its key properties, which are used as organizing themes: resource-sensitive composition, flexible composition, autonomy of syntax, and syntax/semantics non-isomorphism.
SSD Target Detection Algorithm Based on Multi-Scale Fusion and Attention

Aiming at the problems of weak effective information in feature maps and high miss-detection rate of difficult targets when traditional SSD target detection algorithms perform target detection, we propose an improved SSD target detection algorithm. First, add a CBAM module after each feature layer of the SSD. CBAM is a hybrid module that combines spatial attention and channel attention. This module strengthens the network's ability to discriminate targets and backgrounds, improves the expression of effective feature weights, and suppresses interference from irrelevant information; then, adopt the idea of FPN to construct a feature fusion module, which effectively integrates feature layers of different scales, thereby improving the network's ability to detect difficult targets. Verifying the method proposed in this paper on the PASCAL VOC data set fully proves that the improved network performance has been greatly improved.
The modal Θ-valent extensions of BCH codes

In this paper, the purpose is to define a modal Θ-valent extension of a BCH code over the finite modal Θ-valent field and to propose a decoding process of this new class of codes on the modal Θ-valent view.
Risk Mapping against Cyber Attack Trend in the Perspective of National Defence and Military Sector in Indonesia

The European Union Agency for Cybersecurity study shows that there are 15 (fifteen) types of cyber-attacks that will emerge in the next five years. This trend is obtained through an in-depth study of the trend of recent phenomena. The purpose of this study is to try to detect which attacks need attention by the military and state defence sectors in Indonesia. To detect it, a risk analysis method is used in combination with prioritization based on weights. The data was obtained through the involvement of several key experts in the field of cyber defence and security. The results of the study show that eight of the fifteen defined threat trends need special attention by the government and cyber security defence practitioners in Indonesia.
Election Security in the Cloud: A CTF Activity to Teach Cloud and Web Security

In this innovative practice work in progress (WIP) paper, we present a novel capture the flag (CTF) activity to teach students about the potential pitfalls and consequences of cloud misconfiguration. While cloud computing has proved an attractive option in terms of pricing, availability, and scalability, potential cloud consumers must equally weigh the security concerns of a cloud environment. The real-world consequences of misconfigurations are self-evident; cloud consuming companies that suffer a misconfiguration-related breach lose data, time, money, and trust from their customers. However, breaches due to misconfiguration are common, and this prevalence starts with inadequate education. Existing resources in cloud computing courses do not provide sufficient urgency, depth, or engagement when covering cloud security. Consequently, we created a CTF activity that has students pose as malicious actors who seek to compromise an election application running on a cloud environment. We believe that students who complete our CTF activity will have a deeper understanding of the potential pitfalls and consequences of cloud misconfiguration and a better understanding of how to protect against such issues in their own applications, and we are currently evaluating the extent to which our CTF activity achieves these goals.
Control of an Acrobot system using reinforcement learning with probabilistic policy search

Reinforcement learning with probabilistic policy search method is used in this paper for controlling an Acrobot system. Reinforcement learning with probabilistic policy search is a technique that is data-efficient and based on a model. Model bias is one of the main reasons for not using methods which are based on the model to learn from scratch. The model bias is not a severe problem in reinforcement learning with probabilistic policy search as it uses the Gaussian process which considers model uncertainty. Reinforcement learning with probabilistic policy search has the ability to give the best results even when very less data is available. The state of the art approximate inference is used for policy evaluation and for policy improvement. Policy gradients are calculated analytically.
Overview on visual attention and computational saliency

In the last decades, saliency detection was extensively studied. The number of computational models that help to detect salient regions in still images is increasing, whereas, detecting salient regions in videos is in its early stages. In this paper we present an overview on the visual attention field. We explain how salient object is selected and how this selection process is performed.
Improving Upon Photographic Steganography

Extensive research encompassing steganography has been carried out in the past couple of decades and has spurred numerous studies. Research regarding steganography and steganalysis has recently shifted, and impressive progress has been carried out with the aid of the emerging popularity of deep convolutional neural networks. This paper addresses a new branch of steganography, namely photographic steganography, which intends to approach hidden message recovery from a steganographic image captured on a consumer-grade device. Challenges faced by this technique require the modeling of image perturbations to facilitate accurate message retrieval, and several papers have thus far managed to obtain impressive results in various image capturing conditions and in bit recovery error rates. However, several limitations persist, such as short hidden message lengths and restrictions imposed upon encoded images. This paper addresses the shortcomings, and novel techniques to improve the current state-of-the-art will be proposed to offer a more complete, accessible, and performant solution.
Monster Pets

Drac's loveable, monster-sized puppy, Tinkles, has more energy than ever and just wants to play ball! Unfortunately, Drac is too busy juggling his hotel duties, so he is determined to find a monster pet companion for his huge furry friend. After a series of mismatches, Drac's plan is upended when Tinkles chooses a very unlikely companion!
SAdvGAN : Multiple Information Fusion For Adversary Generation

There are many methods for generating adversarial samples. However, most methods do not achieve considerable performances both in the attack accuracy and image realism. In this paper, we generate adversarial samples based on the improved generative adversarial network SAdvGAN. For generator, multidimensional feature fusion is adopted to generate high-quality noises. For discriminator, self-attention is introduced to encourage model to focus on features which benefit the discrimination process. After training with Relativistic average GANs(RaGANs), it reduces the probability of real samples being identified as true. Compared with the current mainstream attack strategies, adversarial samples generated by SAdvGAN perform well in terms of perceived similarity. Concretely, the success rates of SAdvGAN in the semi-white box setting on MNIST and CIFAR10 is higher than that of AdvGAN by 9.68% and 6.1% respectively.
Datacenter Routing

The preceding chapters of this book focused on the NSX-T data plane, including the communication and configuration of Host Transport Nodes and Edge Nodes. Readers should now be familiar with NSX-T data plane constructs, their functionality, and how they operate.
Sentiment Polarity Detection of Book Reviews in Bangla Using Deep Neural Network

Sentiment analysis is a Natural Language Processing (NLP) task that extracts people’s feelings or opinions from words or documents. Sentiment analysis has already found acceptance in rich languages such as English. Unfortunately, research in this topic is very limited for Bangla. The majority of the researchers employed standard machine learning techniques with unimpressive results. Some researchers used deep learning techniques, but their accuracy was sub-par. Most of them ran into problems due to a lack of sufficient Bangla datasets. So, our main objective was improving sentiment polarity detection model as well as it’s accuracy. We proposed a deep learning based method for detecting sentiment polarity in the Bangla Book Review in this research. Our model is a hybrid architecture composed of recurrent and convolutional layers. The model’s dataset was correctly pre-processed by using tag removal, word token generation, POS tagging, punctuation removal and tokenization. It improved the model’s effectiveness and performance. This algorithm achieved a greater accuracy of 92.54% whereas the precision is 93.07%, recall is 92.16% and F1 score is 92.61%.
A Machine Learning Approach to Predict Disease for Accurate Diagnosis

Nowadays, people are suffering from a variety of diseases. One of the reasons for people suffering extensively from these diseases is the late recognition of diseases by medical professionals. Early disease detection provides the doctor with additional benefits to the medical practices for a patient’s treatment. Early detection depends on the symptoms the patients show. This is more of a prognostication which can be done by learning and understanding the patterns for any disease to occur based on the symptoms. These patterns are obtained by analysing the medical records of all patients from the database of a healthcare organisation. This research focuses on predicting diseases by using symptoms of patients using a machine learning approach. It focuses on all diseases, for primary investigation of patients’ treatment. We have used the k-NN algorithm for classification which has given a great result in determining the disease. This system acts as an advisor to clinical decisions.
Algorithm for EEG Electrodes Labeling

This paper presents a method to identify and label each of 128 electroencephalographic (EEG) electrodes placed on the patient’s skin using three-dimensional position of each one, which are obtained from the Stryker© Neuronavigator. A recursive algorithm optimizes a geometric transformation that approximates the location of the electrodes of the patient with a template. The main aim is to label each electrode through an automatic process which is currently made by hand. This project is framed within a global context which purpose is to obtain the Current Source Density Map (CDM) in patients who suffer Refractory Epilepsy. In CDM the anatomic images and the position of the electrodes are combined to correlate EEG registers and anatomic structures.
Implementation of foveated self-similarity filter for GPU

In this paper we present an implementation of foveated self-similarity filter for GPU. This variation of self-similarity filter incorporates human vision properties for measuring the difference between image patches. The filter was implemented on NVIDIA CUDA 11 and OpenMP. The performance comparison between CPU and GPU implementations for different image sizes is presented. Results show, that NVIDIA GeForce RTX 3080 is 23 times faster than AMD Ryzen 3700X in case of 4096x4096 image processing (4.6 s and 105.8 s respectively).
Development of an ANN model for forecasting time series of cooling performances in CSP plants - Solar tower power plant (Gemasolar) as a case study-

Unlike conventional thermal power plants which are generally erected in the coastal regions near water bodies, where water resource availability is not a critical factor, concentrating solar power (CSP) plants are usually installed in high sunny zones, where water resources are hard to find and/or to employ. This from a side, on the other side, the hourly simulations of cooling performances in CSP plants, including power required for cooling and water consumption, are very complex and require many calculations, and time consuming. Therefore, the aim of this paper is to develop an artificial neural network (ANN) model to estimate the hourly cooling performances of a solar thermal power plant based on its hourly generated power, ambient temperature and wind speed. In this regard, the commercial power plant Gemasolar with solar tower technology has been chosen to perform the study. The obtained results of the statistical analysis show that ANN can be used as a good option to forecast these two parameters in a large-scale solar tower power plant without passing through detailed modelling. However, it should be pointed out that this model can be only used for plants that are simulated in locations with similar meteorological conditions and solar resources as the site where this study has been performed.
The Research on High Precision Digital Oscilloscope High Performance Trigging System

This paper focuses on mechanism of the high-performance trigger of the high-precision digital oscilloscope, the trigger classification and realization of the trigger control system. High speed data stream is obtained after high-precision ADC sampling at the front end, which is transmitted to the FPGA asynchronous FIFO to receive at a reduced speed, and the data is divided into two signals, one is sent to the trigger control system as the source signal, and the other is sent to the synchronous FIFO as the data storage. The trigger control system generates an enable signal according to the original signal and the set trigger mode, and the enable signal controls the storage of data. The function of the trigger control system is mainly to accurately locate the trigger point of the stored data and store the data in an orderly manner according to the trigger point.
Performance Analysis of Storage Media Cluster Using Ceph Platform

Growing data triggers better performance on storage systems. The type of storage and the storage system used also affect storage performance. This study aims to analyze the comparison of the performance of block storage and object storage in running a virtual environment using the Ceph storage system. Performance measurement was carried out with several tests including Input-Output per Second and Throughput. The test was carried out using 2 client nodes with an rbd bench. Based on the measurement results, the Write-type on block storage had a value of 81.02 %, while the result of the Puts-type on object storage had a value of 41.02%. This fact showed that the Write-type of the storage process in block storage was better than the Puts-type in object storage. The result of the Read-type on block storage had a value of 31.59%, while the result of the Gets-type measurement on object storage had a value of 89.71 %. This showed that Puts-type process in object storage was better than Read-type in block storage. The conditions on the Ceph server are still in good condition.