Reviewerly/exHarmony huggingface dataset:
Flight anomaly detection model based on QAR data autoencoder and DBscan algorithm

Based on the characteristics of QAR as big data, this paper tries to use wavelet transform and autoencoder to pre-process the QAR data, extract the effective features of the data, and further clustering algorithm to identify abnormal QAR data. More than 1,000 sets of QAR data of an airline's Boeing737-800 model were collected, and the feasibility of the method was verified by using real flight data. The results show that this method can detect anomalies in flight data during the landing phase, and the recognition effect is better than directly inputting QAR data for anomaly detection, and can be used as a supplement to traditional anomaly detection methods.
Development of a Bot using Sentiment Dictionary and Machine Learning for Proactive Detection of Slanderous Japanese Posts on Social Media

Slanderous posts and other forms of abusive online behavior have become prevalent in recent times. We emphasize that detecting and deleting slanderous messages after they have been posted on social media is not enough as the damage may have already been inflicted on the victims in the meantime. Of course, there are deliberate slanderers on the Internet, but many people run the risk of becoming unintentional slanderers for having no or inadequate knowledge about what may constitute an abusive online behavior. For such uninitiated users, a social media tool for checking whether a to-be-posted message can be deemed potentially slanderous would be useful. Therefore, in this paper, we present the design and implementation of a social media bot for proactively detecting slanderous post written in Japanese. Our bot combines the use of sentiment dictionaries and state-of-the-art machine learning techniques. A preliminary evaluation of the bot showed promising results.
Research on the computer intelligent recognition of electric appliance by Yolo V5 algorithm

Abstract In the process of power operation, the correct identification of tools can lay a foundation for the detection of violations in power operation. In order to realize the recognition of power instruments, based on the current Yolo V5 algorithm, a detection algorithm for power instruments is proposed by improving Yolo V5 algorithm. Firstly, the model of Yolo V5 algorithm is introduced. Then the establishment of the power tool database and the process of model training are analysed. Finally, the test results are analysed and evaluated. The models generated after training were accelerated by TensorRT and then deployed on Jetson Xavier NX.
Large-scale tasks as a means to prepare schoolchildren for the basic state examination in informatics

The article considers the problem of the lack of task variety in the existing task bases for preparing schoolchildren for the Basic State Exam in informatics. The need to use tasks in the training process with the similar texts as in the Basic State Exam tasks, but excluding the possibility of applying a template, is substantiated. The authors introduce the concept of a large-scale task, which is a sequence of interconnected tasks. It is proposed to use an approach based on the analysis of the structure of the conditions of the tasks presented in the demo versions of test materials. The large-scale task is based on a variation of conditions and requirements of the tasks for multi-component tasks and for two-component tasks it is based on the selection of a sequence of subtasks. The article describes examples of the process of developing large-scale tasks aimed to prepare for the Basic State Exam in informatics.
Energy-Efficient Interactive 360° Video Streaming with Real-Time Gaze Tracking on Mobile Devices

360° videos are becoming one of the major media in recent years, providing immersive experience for viewers with more interactions compared to traditional videos. Most of today’s implementations rely on bulky Head-Mounted Displays (HMDs) or require touch screen operations for interactive display, which are not only expensive but also inconvenient for viewers. In this paper, we demonstrate that interactive 360° video streaming can be done with hints from gaze movement detected by the front camera of today’s mobile devices (e.g., a smartphone). We design a lightweight real-time gaze point tracking method for this purpose. Using only the front camera, our solution detects the users’ faces by a lightweight Haar-like cascaded classifier, measures the user’s face-to-screen distance and sight angle, and then derives the location of the user’s gaze point following a customized triangularity model. We integrate it with streaming module and apply a dynamic margin adaption algorithm to minimize the overall energy consumption for battery-constrained mobile devices. Our experiments on state-of-the-art smartphones show the feasibility of our solution and its energy efficiency toward cost-effective real-time 360° video streaming.
Design and Evaluation Study of Visual Analytics Decision Support Tools in Air Traffic Control

Abstract Operators in air traffic control facing time‐ and safety‐critical situations call for efficient, reliable and robust real‐time processing and interpretation of complex data. Automation support tools aid controllers in these processes to prevent separation losses between aircraft. Issues of current support tools include limited ‘what‐if’ and ‘what‐else’ probe functionalities in relation to vertical solutions. This work presents the design and evaluation of two visual analytics interfaces that promote contextual awareness and support ‘what‐if’ and ‘what‐else’ probes in the spatio‐temporal domain aiming to improve information integration and support controllers in prioritising conflict resolution. Both interfaces visualize vertical solution spaces against a time‐altitude graph. The main contributions of this paper are: (a) the presentation of two interfaces for supporting conflict solving; (b) the novel representation of how vertical information and aircraft rate of climb and descent affect conflicts and (c) an evaluation and comparison of the interfaces with a traditional air traffic control support system. The evaluation study was performed with domain experts to compare the effects of visualization concepts on operator engagement in processing solutions suggested by the tools. Results show that the visualizations support operators' ability to understand and resolve conflicts. Based on the results, general design guidelines for time‐critical domains are proposed.
Implementation of Speed-Efficient Key-Scheduling Process of AES for Secure Storage and Transmission of Data

Nowadays, a large number of digital data are transmitted worldwide using wireless communications. Therefore, data security is a significant task in communication to prevent cybercrimes and avoid information loss. The Advanced Encryption Standard (AES) is a highly efficient secure mechanism that outperforms other symmetric key cryptographic algorithms using message secrecy. However, AES is efficient in terms of software and hardware implementation, and numerous modifications are done in the conventional AES architecture to improve the performance. This research article proposes a significant modification to the AES architecture's key expansion section to increase the speed of producing subkeys. The fork-join model of key expansion (FJMKE) architecture is developed to improve the speed of the subkey generation process, whereas the hardware resources of AES are minimized by avoiding the frequent computation of secret keys. The AES-FJMKE architecture generates all of the required subkeys in less than half the time required by the conventional architecture. The proposed AES-FJMKE architecture is designed and simulated using the Xilinx ISE 5.1 software. The Field Programmable Gate Arrays (FPGAs) behaviour of the AES-FJMKE architecture is analysed by means of performance count for hardware resources, delay, and operating frequency. The existing AES architectures such as typical AES, AES-PNSG, AES-AT, AES-BE, ISAES, AES-RS, and AES-MPPRM are used to evaluate the efficiency of AES-FJMKE. The AES-FJMKE implemented using Spartan 6 FPGA used fewer slices (i.e., 76) than the AES-RS.
News Articles Classification for Electronic Information and Transaction Law in Indonesia using Support Vector Machine

Currently, in Indonesia there are many cases related to the Electronic Information and Transaction (ITE) Law. The ITE Law is a law that regulates violations in the digital world and the internet. Most of the people in Indonesia do not have the correct understanding of ITE law, and even often misinterpret the existing verses. Meanwhile, the media in Indonesia often reports about violations of the ITE Law in their printing and digital media. However, due to the lack of good socialization, people's understanding does not increase, which also effect the increasing ITE cases occurred. To see the violations of these cases in more depth, we should see the official crime document provided by law enforcement elements. Because it is necessary to provide early education to the public, both adults and adolescents, regarding the violations of ITE Law, it is important to build a system that is able to classify news articles according to the violations committed automatically and quickly. Grouping news articles can use one of the classification algorithms, namely Support Vector Machine (SVM). This study utilizes SVM in classifying 5 categories related to violations of the ITE Law in Indonesia. This research produces a classification system with RBF kernel with an accuracy of 86.27%.
FINN: Fingerprinting Network Flows using Neural Networks

Traffic analysis is essential to network security by enabling the correlation of encrypted network flows; in particular, traffic analysis has been used to detect stepping stone attackers and de-anonymize anonymous connections. A modern type of traffic analysis is flow fingerprinting, which works by slightly perturbing network flows to embed secret information into the flows that later can be used for traffic analysis. It is shown that flow fingerprinting enables the use of traffic analysis in a wide range of applications. In this paper, we introduce an effective flow fingerprinting technique by leveraging neural networks. Specifically, our system uses a fully connected network to generate slight perturbations that are then added to the live flows to fingerprint them. We show that our fingerprinting system offers reliable performance in the different network settings, outperforming the state-of-the-art. We also enforce an invisibility constraint in generating our flow fingerprints and use GAN to generate fingerprinting delays with Laplacian distribution to make it similar to natural network jitter. Therefore, we show that our fingerprinted flows are highly indistinguishable from benign network flows.
Face Recognition with Mask Using MTCNN and FaceNet

Face recognition is a method of identifying or verifying the identity of an individual using their face but what if this recognition method could be extended further to suit the needs of the current scenario. Given this COVID pandemic, this paper fits best by recognizing the people wearing masks. The research has been done by creating our own dataset using images from our friends and relatives followed by doing image augmentation by performing operations like rotating by some angle, changing brightness and contrast, zooming in and out, etc. Then, face with the mask is extracted from the given image with the help of MTCNN to get a bounding box, width, and the height of the face, and then, segmentation has been done by reducing the height by a factor of 2. FaceNet pretrained model has been used to represent the faces on a 128-dimensional unit hyper-sphere and get the embeddings for further classification. Many different algorithms like linear Discriminant analysis, SVM, ridge classifier, K-neighbors classifier, logistic regression, Naive Bayes, XGBoost, Ada Boost, random forest classifier, and decision tree classifier have been used for experimentation. After testing this, good accuracy was obtained as can be seen in the result section of this paper. The scope of this paper is quite vast as it covers many practical applications in real-scenario like detecting the presence of a particular person from an image or even from video by capturing faces frame by frame.
Online Monitoring of Object Detection Performance During Deployment

During deployment, an object detector is expected to operate at a similar performance level reported on its testing dataset. However, when deployed onboard mobile robots that operate under varying and complex environmental conditions, the detector’s performance can fluctuate and occasionally degrade severely without warning. Undetected, this can lead the robot to take unsafe and risky actions based on low-quality and unreliable object detections. We address this problem and introduce a cascaded neural network that monitors the performance of the object detector by predicting the quality of its mean average precision (mAP) on a sliding window of the input frames. The proposed cascaded network exploits the internal features from the deep neural network of the object detector. We evaluate our proposed approach using different combinations of autonomous driving datasets and object detectors.
An Optimization Scheme for Consortium Blockchain in Airport Collaborative Decision Making

Consortium blockchain-based A-CDM (Airport Collaborative Decision Making) was a decentralized technology solution proposed by some researchers in recent years to solve robustness, privacy, and security issues in the traditional centralized architecture. The key idea of blockchain-based A-CDM is to use blockchain technology to store the flight operation data in a distributed, consistent, and tamper-proof ledger, further improve the capability of flight management and reduce the trust barriers among the multi-stakeholders such as airport, air traffic management bureau, airlines, and ground service agency, etc. The RAFT consensus was adopted in many blockchain-based data sharing solutions to meet the need for efficiency in data transmission. However, the RAFT algorithm can only be used in a non-byzantine network because security requirements such as preventing malicious nodes are not fully considered. It will bring an immeasurable impact on flight operation when the leader node of RAFT-based A-CDM was caused by malicious control. In response to the above problems, this paper proposes an optimization scheme to improve the security performance of the consortium blockchain in A-CDM.
Tuxedo: Maximizing Smart Contract Computation in PoW Blockchains

Proof-of-Work (PoW) based blockchains typically allocate only a tiny fraction (e.g., less than 1% for Ethereum) of the average interarrival time (I) between blocks for validating smart contracts present in transactions. In such systems, block validation and PoW mining are typically performed sequentially, the former by CPUs and the latter by ASICs. A trivial increase in validation time (τ) introduces the popularly known Verifier's Dilemma, and as we demonstrate, causes more forking and hurts fairness. Large τ also reduces the tolerance for safety against a Byzantine adversary. Solutions that offload validation to a set of non-chain nodes (a.k.a. off-chain approaches) suffer from trust and performance issues that are non-trivial to resolve. In this paper, we present Tuxedo, the first on-chain protocol to theoretically scale τ/I ≈1 in PoW blockchains. The key innovation in Tuxedo is to perform CPU-based block processing in parallel to ASIC mining. We achieve this by allowing miners to delay validation of transactions in a block by up to ζ blocks, where ζ is a system parameter. We perform security analysis of Tuxedo considering all possible adversarial strategies in a synchronous network with maximum end-to-end delay Δ and demonstrate that Tuxedo achieves security equivalent to known results for longest chain PoW Nakamoto consensus. Our prototype implementation of Tuxedo atop Ethereum demonstrates that it can scale τ without suffering the harmful effects of naive scaling up of τ/I in existing blockchains
Factors Influencing Customers' Trust in E-Commerce during COVID-19 Pandemic

Trust is considered one of the most important factors that affects e-commerce success. Due to significant role presented by trust, many studies have been conducted to understand the trust variable and its effect e-commerce platforms. Researchers have suggested several factors that may influence e-commerce trust. The Pandemic scenario currently faced by the world has led to many changes in consumers' perceptions towards e-commerce on a large scale. This study reviews more in-depth the customers' trust factors on e-commerce during the COVID-19 period. Hence, in this paper presents a review conducted on customers' trust factors for e-commerce from 2007 to 2019 includes information quality, customer awareness, perceived privacy, perceived security risk, and user interface quality factors. This review reveals influence of this factors on customer trust in using e-commerce during COVID-19 situation.
Single-shot three-input phase retrieval for quantitative back focal plane measurement

This paper presents quantitative measurements facilitated with a new optical system that implements a single-shot three-input phase retrieval algorithm. The new system allows simultaneous acquisition of three distinct input patterns, thus eliminating the requirement for mechanical movement and reducing any registration errors and microphonics. We demonstrate the application of the system for measurement and separation of two distinct attenuation measurements of surface waves, namely, absorption and coupling loss. This is achieved by retrieving the phase in the back focal plane and performing a series of virtual optics computations. This overcomes the need to use a complicated series of hardware manipulations with a spatial light modulator. This gives a far more accurate and faster measurement with a simpler optical system. We also demonstrate that phase measurements allow us to implement different measurement methods to acquire the excitation angle for surface plasmons. Depending on the noise statistics different methods have superior performance, so the best method under particular conditions can be selected. Since the measurements are only weakly correlated, they may also be combined for improved noise performance. The results presented here offer a template for a wider class of measurements in the back focal plane including ellipsometry.
Prediction of Bitcoin, Litecoin and Ethereum trends using State-of-Art Algorithms

Bitcoin, Etherium, and Litecoin are among the most extensive market capitalized cryptocurrencies in the present era. With the increased popularity, there is also an increased proclivity of investors towards investing in cryptocurrencies. To gain maximum profits and avoid risks, one needs to analyze the trends and history of the cryptocurrency diligently. This paper put forth various machine learning algorithms to scrutinize cryptocurrencies such as Bitcoin, Etherium, and Litecoin based on multiple trading factors such as open price, close price, volume, market price, history, etc. We have performed various state-of-the-art machine learning to predict the future market value of the cryptocurrencies and derived the performance analysis of the same.
A Hybrid Watermarking Scheme Based on Arnold Cat Map Against Lossy JPEG Compression

Watermarking is a data hiding method for multimedia elements such as image, video, audio, and software. Several works have been conducted in the spatial frequency domains for cover images. An essential demand in the watermarking algorithm is to be resistant to common attacks. In this work, the authors investigate the robustness and reliability of the transform domain watermarking against the Lossy JPEG compression, which is widely used in digital imaging applications and hardware. In addition, we propose a transform domain digital watermarking algorithm that resists the JPEG compression in low frequencies. The Discrete Fourier transform (DFT) and the Singular value decomposition (SVD) are selected as the embedding domain where the binary logo watermark is iteratively scrambled using the Arnold Cat Map (ACM) before embedding for robustness. Two main metrics are used for evaluation the scheme; Peak signal to noise ratio (PSNR) and Normalized Correlation (NC). Experimental results show very promising results with a PSNR value above 45 dB while the NC value remains above 0.9 even after high compression with a Quality Factor of only 1%.
Goal-Oriented Machine Learning-Based Component Development Process

Along with the rising usage of machine learning-based (ML-based) solution, a significant amount of tools to train them has been proposed. However, there is little attention on aligning those tools with the overall goals of the development itself. We proposed a goal-oriented approach to align and rationalize the utilization of those tools with higher-level development goals. We demonstrated the proposed technique’s capabilities through a case study to show the potential of our proposed method.
Using VR in Education: The Current Situation in the UK

Covid-19 has triggered a wave of online teaching and learning that will permanently reshape the future of our education system. Among the various technologies being invented and reinvented, virtual reality is of particular interest as it has many features that make it desirable for distance learning such as bringing a simulated environment close to the learners. In the pedagogical literature, virtual reality has proven to be a useful and engaging tool in classrooms. However, on a large scale, it remains unclear what services and resources are available to educators, and what implications it has on the education system. The current paper, using the UK as an example, reveals a snapshot of how VR is being used in the education system, the VR resources available to the educators including services, hardware and contents that can be used for educational purposes. It discusses the implications this has, also highlights a few associated challenges and opportunities.
A Deep Learning Approach for Negation Detection from Product Reviews written in Spanish

Online product reviews are becoming common and are being used more frequently by consumers to choose the most competitive products. Negation detection is a crucial task for information extraction from product review texts because negation can change the meaning of opinions given by consumers about products or services. Although several approaches have been proposed for negation detection in product reviews, research efforts have concentrated mainly on English. This paper describes a transformer-based approach for detecting negation in product reviews written in Spanish. This approach takes advantage of transfer learning techniques and uses a BERT-based model to perform negation detection. Performed tests using the SFU corpus for Spanish, showed an F1 score of 95.4% in the cue detection task and 91.5% in the scope resolution task. Our finding suggests that our BERT-based approach is feasible to perform negation detection in Spanish.
Producing Multiple Qubits via Spontaneous Parametric Down-Conversion

We present a simple approach to produce multiple correlated photon pairs through spontaneous parametric down-conversion. We develop Gaussian masks to subdivide the pump beam before passing it through a nonlinear medium. In this way, we are able to observe simultaneous separate down-converted emission cones with spatial overlap. The technique we employ could be used to greatly increase the dimensionality of entangled photonic systems generated from spontaneous parametric down-conversion, affording greater scalability to optical quantum computing than previously explored.