Reviewerly/exHarmony huggingface dataset:
Real time eye gaze tracking for human machine interaction in the cockpit

The Aeronautics industry has pioneered safety from digital checklists to moving maps that improve pilot situational awareness and support safe ground movements. Today, pilots deal with increasingly complex cockpit environments and air traffic densification. Here we present an intelligent vision system, which allows real-time human-machine interaction in the cockpits to reduce pilot’s workload. The challenges for such a vision system include extreme change in background light intensity, large field-of-view and variable working distances. Adapted hardware, use of state-of-the-art computer vision techniques and machine learning algorithms in eye gaze detection allow a smooth, and accurate real-time feedback system. The current system has been over-specified to explore the optimized solutions for different use-cases. The algorithmic pipeline for eye gaze tracking was developed and iteratively optimized to obtain the speed and accuracy required for the aviation use cases. The pipeline, which is a combination of data-driven and analytics approaches, runs in real time at 60 fps with a latency of about 32ms. The eye gaze estimation error was evaluated in terms of the point of regard distance error with respect to the 3D point location. An average error of less than 1.1cm was achieved over 28 gaze points representing the cockpit instruments placed at about 80-110cm from the participants’ eyes. The angular gaze deviation goes down to less than 1° for the panels towards which an accurate eye gaze was required according to the use cases.
The Effect of Online Learning, Use of Social Media, Learning Motivation on Student Achievement of Citra Negara Vocational High School

This study aims to see the effect of online learning, use of social media, student motivation on student learning achievement. This study uses quantitative methods with a research sample of 160 people. The results of this study indicate that the influence of online learning gives positive results on student achievement at SMK Citra Negara, the use of social media gives positive results on student achievement at SMK Citra Negara, and student motivation gives negative results on student achievement at SMK Citra Negara.
Technical and economic feasibility of a fully solar-powered Airport in Nepal

Potential of limiting greenhouse gases (GHG) production through the aviation industry is high with the industry being one of the major GHG emitters. The use of clean energy sources is the only way to limit the carbon footprint from the airport. So, solar PV installation systems for the green energy airport is an interesting new application for the stakeholder working in the field of solar technology. In this regard, this research focuses on developing a domestic airport in Nepal as a fully solar-powered airport by identifying ideal site for the installation of a solar PV system. In this paper, feasible glare site of Chandragadi Airport was chosen and suggested for Solar PV installation without compromising the compatibility of the airport concurrence, environment, and the electrical infrastructures. PVsyst V6.8.7 configuration analysis tool was chosen for the analysis. The result of the studyshows that a 213 MWh/year energy is adequate to fully operate an airport on solar energy in respect to the current demand load. It has been identified that 134KWh solar plant is sufficient to run the airport fully on solar energy. These findings assure and increase the level of airport authorities and stakeholder's confidence to approach towards establishing first green airport of Nepal.
Visual Normalization of Handwritten Chinese Characters Based on Generative Adversarial Networks

Compared with printed Chinese characters, handwritten Chinese characters are affected by writing coherence and anthropic factors, resulting in differences in stroke deformation, displacement, tilt, length, and thickness. This consequently affects the recognition of handwritten Chinese characters. A method based on generative adversarial networks (GANs) of Chinese character handwriting-to-printing font conversion is proposed herein, where Chinese handwriting-to-printing font conversion is represented as a font style conversion and regarded as the optimal state of normalization of handwritten Chinese characters. First, an encoder–decoder is integrated into a generative adversarial network, and a symmetric network extracts handwritten font multi-scale information. Subsequently, U-Net with skip connection is used to extract deep features and reduce the large amount of low-level information shared between the input and output. Furthermore, the skip connection reduces information loss during down-sampling. Finally, the integration loss measures the differences in the character structure and font style between the generated Chinese character image and the target font image by combining font style, encoding consistency, and [Formula: see text] losses. An experiment to convert Chinese character handwriting to multiple printing fonts is performed on the CASIA and CASIA-HWDB1.1 datasets, in which image pixel difference and character recognition accuracy are used as evaluation metrics. The obtained results show a more normalized visual writing style and contribute to recognition improvement, thereby verifying the effectiveness of this method.
Implementation of an Efficient, Portable and Platform-Agnostic Cryptocurrency Mining Algorithm for Internet of Things Devices

Recently, there has been a remarkable amount of research being done in both, the fields of Blockchain and Internet of Things (IoT). Blockchain technology synergises well with IoT, solving key problems such as privacy, concerns with interoperability and security. However, the consensus mechanisms that allows trustless parties to maintain an agreement, the same algorithms that underpins cryptocurrency mining, are usually extremely computationally expensive, making implementation on low-power IoT devices difficult. More importantly, mining requires downloading and synchronizing hundred of gigabytes worth of blocks which is far beyond the capabilities of most IoT devices. In this paper, we present an efficient, portable and platform-agnostic cryptocurrency mining algorithm using the Stratum protocol to avoid downloading the entire blockchain. We implement the algorithm in four different platforms- PC, ESP32, an emulator and an old PlayStation Portable (PSP) to demonstrate that it is indeed possible for any device to mine cryptocurrencies with no assumptions except the ability to connect to the internet. To make sure of ease of portability on any platform and for reproducibility of the reported results we make the implementation publicly available with detailed instructions at: https://anonymous.4open.science/r/cryptominer.
Quantum secure privacy preserving technique to obtain the intersection of two datasets for contact tracing

Contact tracing has emerged as a powerful and effective measure to curb the spread of contagious diseases. It is a robust tool, but on the downside, it possesses a risk of privacy violations as contact tracing requires gathering a lot of personal information. So there is a need for a cryptographic primitive that obfuscate the personal data of the user. Taking everything into account, private set intersection seems to be the natural choice to address the problem. Nearly all of the existing PSI protocols are relying on the number theoretic assumption based hard problems. However, these problems are not secure in quantum domain. As a consequence, it becomes essential to designing PSI that can resist quantum attack and provide long-term security. One may apply quantum cryptography to develop such PSI protocol. This paper deals with the design of PSI using quantum cryptography (QC), where the security depends on the principles of basic quantum mechanics. Our scheme achieves long-term security and remains secure against quantum attacks due to the use of QC. As opposed to the existing quantum PSI protocols, the communication and computation costs of our scheme are independent of the size of universal set. Particularly, our proposed protocol attains optimal communication cost in the context of quantum PSI. In addition, our designed protocol needs smaller amount of measurements. Moreover, we require only single photon quantum resources and simple single-particle projective measurements, unlike most of the existing quantum PSI protocols.
An Autonomous System for Efficient Control of PTZ Cameras

This article addresses the research problem of how to autonomously control Pan/Tilt/Zoom (PTZ) cameras in a manner that seeks to optimize the face recognition accuracy or the overall threat detection and proposes an overall system. The article presents two alternative schemes for camera scheduling: Grid-Based Grouping (GBG) and Elevator-Based Planning (EBP). The camera control works with realistic 3D environments and considers many factors, including the direction of the subject’s movement and its location, distances from the cameras, occlusion, overall recognition probability so far, and the expected time to leave the site, as well as the movements of cameras and their capabilities and limitations. In addition, the article utilizes clustering to group subjects, thereby enabling the system to focus on the areas that are more densely populated. Moreover, it proposes a dynamic mechanism for controlling the pre-recording time spent on running the solution. Furthermore, it develops a parallel algorithm, allowing the most time-consuming phases to be parallelized, and thus run efficiently by the centralized parallel processing subsystem. We analyze through simulation the effectiveness of the overall solution, including the clustering approach, scheduling alternatives, dynamic mechanism, and parallel implementation in terms of overall recognition probability and the running time of the solution, considering the impacts of numerous parameters.
Epistemic Modals in Hypothetical Reasoning

Abstract Data involving epistemic modals suggest that some classically valid argument forms, such as reductio , are invalid in natural language reasoning as they lead to modal collapses. We adduce further data showing that the classical argument forms governing the existential quantifier are similarly defective, as they lead to a de re–de dicto collapse. We observe a similar problem for disjunction. But if the classical argument forms for negation, disjunction and existential quantification are invalid, what are the correct forms that govern the use of these items? Our diagnosis is that epistemic modals interfere with hypothetical reasoning. We present a modal first-order logic and model theory that characterizes hypothetical reasoning with epistemic modals in a principled manner. One upshot is a sound and complete natural deduction system for reasoning with epistemic modals in first-order logic.
Side-Channel based Disassembler for AVR Micro-Controllers using Convolutional Neural Networks

Reverse engineering using Side Channel Attacks (SCA) have been known as a serious menace against embedded devices. The attacker could employ side channel data to retrieve some sensitive information from the device, security analysis, existence of a library in the device or execution of a special stream of codes. Side channel data could be gathered from the power consumption or electromagnetic radiations by the device. In this paper, we propose a disassembler to extract the instructions of the device under attack. A deep convolutional neural network is employed to make templates of the target to use it for real-time scenarios. Short Time Fourier Transform (STFT), and Mel-Frequency Cepstrum Coefficients (MFCC) are utilized as feature extractors. The proposed method consists of two different parts: 1) Hierarchical scenario and 2) Sole model. Atmel 8-bit AVR micro-controller is employed as the target device under attack. Our results indicate that, even with an experimental and low cost setup a vast number of instructions are detectable. The proposed method reaches 98.21% accuracy on the real code, outperforms state-of-the-art methods on the proposed dataset.
Blockchain Powered Energy Monitoring System

With growing energy demand and advancement in technologies, it is essential to ensure security to the energy usage data and transparency in the transactions while optimizing costs and improving the efficiency of the processes. For this purpose, we have presented one of the emerging technologies called Blockchain technology, which is a highly immutable and cryptographically secured distributed ledger system, using which important transaction data, energy records and contracts are digitally maintained in real-time across a peer-to-peer network. In the current work, a blockchain-based prototype is proposed by integrating AMI and Ethereum smart contract to come up with a peer-to-peer monitoring system that will ensure transparency in energy usage and automation of the billing process. By this work, we aim to provide solutions to the major issues associated with the existing infrastructure of energy monitoring and billing systems like the manual intervention of billing process, overdue bill payment, illegal tapping of power lines and unethical selling of units by the electricity board.
Mitigating Overflow of Object Detection Tasks Based on Masking Semantic Difference Region of Vision Snapshot for High Efficiency

Object recognition functions are essential to properly perform safety and autonomous driving functions. However, sophisticated object recognition work requires extensive computation. It is difficult to handle a large amount of computation on the lightweight embedded boards currently used in vehicles. In this paper, we propose a method using machine learning and deep learning for lightweight object recognition algorithm in lightweight embedded boards. We created an algorithm suitable for lightweight embedded boards by appropriately using deep neural network architecture that requires small computational volumes but provides low accuracy, as well as deep-learning algorithms that require large computational volumes but provide high accuracy. After determining the area using a deep neural network architecture algorithm with a relatively small amount of computation, we improved the accuracy by using a more accurate deep learning algorithm. We used OpenCV to process input images in Python, and we processed image by using efficient neural network (ENet) and You Only Look Once (YOLO). By executing this algorithm, we can realize more accurate and lightweighted object recognition.
Voice spoofing detection with raw waveform based on Dual Path Res2net

The natural-sounding speech produced by recent text-to-speech and voice conversion techniques pose serious threats to automatic speaker verification systems. The majority of existing spoofing detection countermeasures perform well when the nature of the attacks is known during training. However, their performance in realistic applications degrades in dealing with unseen types of attacks. To address this concern, we propose a novel method for spoof detection, namely Dual Path Res2Net (DP-Res2Net) to improve the robustness to unknown attacks. As to the feature engineering, we employ the time domain features rather than the commonly-used frequency domain ones. We directly input the time domain features of 80,000 sampling points into the network. The input features are further processed by shallow feature learning module, interactive feature learning module, deep feature learning module as well as the discriminator network. The dual-path residual-like block exploit the dependence between successive pieces of audios with large receptive fields. Furthermore, the proposed DP-Res2Net significantly improves the model’s generalizability to unseen spoofing attacks. We evaluate the performance of the proposed method over public-available ASVspoof 2019 logic access evaluation set, and the results demonstrate that it outperforms state-of-the-art audio spoof detection models.
Performance Analysis of IoT Based Solar Operated Smart Water Management System for Irrigation Field

The economy of Bangladesh is mostly dependent upon agriculture that requires an innovative and reliable system for irrigation. The limitations of conventional systems can be reformed by employing smart irrigation systems which will be IoT (Internet of Things) based and automated. This paper offers the idea of smart irrigation and the following research conveys the idea. The process of smart irrigation is done by the help of soil moisture sensors placed adjacent to the roots of plants since plants need sufficient water to their roots, not the surface of lands yet the conventional system checks the surface water level of lands and makes decisions on it. Thus, no sooner does the water reach the roots of plants than an extra amount of water is supplied on the lands as wastage. The proposed system is totally IoT based and solar panels are employed in it for pollution free energy supply. In order to produce 1kg of rice, water savings in conventional irrigation is around 35% however this proposed smart irrigation system can increase the water savings rate to 64%.
A Study on the improvement of chinese automatic speech recognition accuracy using a lexicon

In this paper, in order to improve the error rate that occurs when Automatic Speech Recognition (ASR), one of the technologies widely applied in the field of speech recognition, is applied to Chinese, the study results on how to improve the Chinese recognition error rate by proposing a model to be added are described. As a result of testing by applying the xlsr-53 data set based on the Wav2vec2.0 model, it was confirmed that the Chinese recognition rate was improved.
Generative adversarial networks

Generative Adversarial Networks has shown impressive achievement in computer graphics applications, and it's now widely used worldwide. GAN is composed of generator and discriminator, which are crucial compositions of GAN. GAN can generate 3D models, graphics that are required in animated movies or video game characters. For example, a generative model could simply generate a new picture that looks like a specific type of animal. In the meantime, the discriminative model could distinguish the picture from a human being or an animal. GAN variants consist of progressive GAN as well as conditional GAN. Since GAN can be applied in so many different areas, in this essay, we are going to talk about how GAN is applied in image and computer vision. More specifically, face synthesis, image to image translation, and super resolution. Conclusively, GAN has a significant contribution to various areas, and it boosts the advancement in the domain of computer graphics.
Blockchain-Based Access Control for the Internet of Things: A Survey

Recently, the IoT environment is becoming more fertile for malicious users to break the security and privacy of users. Access control becomes a critical requirement to protect against illegal access that usually leads to sensitive data leakage. Unfortunately, the traditional access control mechanisms are centralized in terms of design and management, thus are not suitable for decentralized IoT systems. To deal with the distributed IoT environment, blockchain is considered as a promising distributed data management technology to different non-crypto currency areas, including IoT. Although blockchain can optimize some IoT requirements such as dynamicity, security, decentralization, and anonymity, it has some issues such as complex consensus algorithms, overhead, and scalability. This paper surveys and compares the most recent researches in the blockchain-based access control for IoT and suggests a set of requirements that should be considered while applying blockchain to control access in the IoT environment.
Optimum Group Pixel Matching Strategies for Image Steganography

LSB matching techniques are widely applied in the field of image steganography. In such algorithms, pixel values of each group must be changed in a way that a predefined function of the pixel group matches the secret digit. The notational system of the secret digits can be every desired number, as well as the size of the pixel groups. In order to preserve the quality of the stego image, it is desired to limit the changes in the pixel groups as much as possible. Therefore, optimum strategies must be found to match the function of the pixel group to the secret digit with the least possible imposed distortion in terms of mean square error. Having been recently found for pixel pairs, such strategies are found for the larger pixel groups by the proposed method in this paper. Among all the strategies providing the similar minimum MSE value, the one is chosen that helps to preserve the histogram of the original image. Optimum strategies found for all notational systems and pixel group sizes make the algorithm flexible for various application with different payloads, while it improves the similar techniques in terms of both MSE reduction and histogram preservation.
Goals and Solutions of Data Allocation in Data Center

Nowadays, data centers have become important resources to store a huge amount of data and to offer data access service. In a data center, data are stored in servers, which also perform computing tasks. Usually, initial data need to be transferred to the data center before it starts the service. This paper investigates how to allocate data from external data sources to servers in a data center, particularly exploring the impact of partitioning data. Three goals for data allocation are presented, i.e., minimizing the number of active servers, minimizing the average number of partitions per data, and balancing servers' workload. Different solutions for these goals are proposed to show the tradeoff. Performance evaluation demonstrates that the goals can be achieved well by the solutions respectively.
Sarcasm Detection Using Deep Learning

In social media, people use positive words or give more stress on positive words to express their negative opinion which is known as sarcasm. This on purpose ambiguity makes sarcasm detection is a challenging and popular task in natural language processing. In this work, we have used a CNN-GRU based model with word embedding to find the occurring of positive words. In evaluation metric we have used precision and recall to measure the accuracy of the CNN-GRU based model. We achieved 72.58% precision and 83.06% recall. The proposed system is compared with some existing system and the system gives encouraging results.
Construction of English Aided Translation Learning System Based on Decision Tree Classification Algorithm

English-assisted translation is one of the basic subjects for students to learn. Teachers are influenced by traditional teaching concepts in the process of English-assisted translation learning system. Using a single English-assisted translation learning system to guide students in English translation can not really expand students’ logical thinking consciousness and stimulate students’ enthusiasm for English translation. English-assisted translation learning system is never perfect. Bilingual sentence pairs are often wrongly arranged sentence by sentence, or due to human error, these sentences can not translate each other well. English translation-assisted learning system urgently needs an algorithm to optimize it. The decision tree classification algorithm is helpful for students to construct knowledge in English assisted translation. Through the decision tree classification algorithm, this paper can understand the relationship between the indicators of the construction of English assisted translation learning system, so as to guide students’ English assisted translation, so as to improve the construction of students’ English assisted translation learning system.