Reviewerly/exHarmony huggingface dataset:
Quantum Ecosystem Development Using Advanced Cloud Services

The four fundamental quantum phenomena of superposition, entanglement and tunneling are envisioned to give quantum computing the ability to solve complex problems in very low-resolution times as compared with classical computers. Quantum computing relies on various types of processors, frameworks, algorithms, and simulators which can be accessed using Cloud services. Quantum computers provide an incredible processing power to the users. Having these quantum capabilities as services over cloud to access through internet is called Cloud-based quantum computing. Many tech giants have connected their quantum computers to the cloud platform and provided access to internet users from various sectors to be able to build and run simple programs on them. Industry-leading companies have started capitalizing on the capabilities of quantum technologies. It is exciting to see potentially transformative applications in the quantum algorithm space. Quantum computing software projects have significantly increased in areas where complex problems could be solved in very low-resolution times. From computing to communications, signal processing, navigation systems, quantum solutions offer unprecedented level of performance, privacy, security, and speed. The existing single layer architecture of providing Quantum as a service is still very basic and makes it complicated for users to have a choice over the kind of applications they would like to implement. This is because the services are not distinct and relevant to the needs of the users. This paper attempts to study IBM’s existing Quantum cloud platform and provides a multilayered enhanced approach with advanced quantum cloud services. This paper presents a new approach to develop a sophisticated quantum ecosystem to enhance the user base and quantum computing applications.
Unsupervised Face Recognition Algorithm based on Fast Density Clustering Algorithm

Most classic face recognition classification algorithms need to extract enough face images with class label information as training samples. However in most practical applications, face recognition based on supervised methods are incapable to deal with images without any label information. A novel unsupervised face recognition algorithm based on fast density clustering algorithm is proposed in this paper, which doesn't need sample images with class label information. Without any labelled images as examples, the designed method still get higher recognition rate compared with the same classifiers with labelled training sample. The main contributions of this paper include three aspects. Firstly, aiming at most current clustering algorithm has challenges as low clustering purity, parameter sensibility and cluster center manual determination, a fast density clustering algorithm (FDCA) with automatic cluster center determination (ACC) is proposed. Secondly, based on ACC-FDCA, an unsupervised face image recognition algorithm is designed. SSIM, CW-SSIM and PSNR are adopted to calculate face image similarity matrix. Finally, an online unsupervised face video recognition platform is developed based on brought up ACC-FDCA face recognition algorithm. Real life videos are recorded and recognized to testify the high performance of brought up method. We can conclude that classifiers using FDCA to get image samples label information for training could achieve higher recognition rate compared with the same classifiers trained with labelled image samples.
Arabic Question Answering System for Information Retrieval on Large-scale Image Objects

The image objects retrieval modeling focused on discovering the images contained within each document based on a specific “object” statistic. In this study, we introduce the potential of classifying images into 17 classes to extract objects using Deep Neural Networks such as Convolutional Neural Networks (CNN), Visual Geometry Group-16 (VGG-16), and Sequential Minimal Optimization (SMO) algorithms. Moreover, we show the objects extraction process from the text (user’s queries) in Arabic language using NLP algorithms. Building the proposed model started by collecting 2649 images from multiple sources that were entered into three image classifiers to extract objects: CNN, VGG-16, and SMO. Afterward, building an index for image URLs, images objects, and classes. The retrieved against our index was tested using user’s queries that will go through multiple NLP approaches to extract objects to retrieve the required images. The most frequent object names types were Nouns (NNs), Noun Phrases (NPs), and a combination of NN and NPs within 110 queries as a total. The retrieving process will start by testing queries against the Image Index. For this query level, the retrieved images information was evaluated in Mean Average Precision (MAP) with multiple retrieved values for retrieving images: 1,5,10,100,1000 as the Recall and F1 were calculated for the top 1000 retrieved images. Our proposed system obtained the best results when searching with expanded queries in terms of MAP where the retrieval from Image Index scored an 88% MAP for retrieving 1000 images for object types as NNs and NPs.
Geophysical Bias Correction of Trace Green House Gas Satellite Retrievals Using Explainable Machine Learning Methods

OCO-2, launched in 2014, uses reflected solar spectra and other retrieved geophysical variables to estimate ("retrieve") the column averaged dry air mole fraction of CO2, termed XCO2.A critical issue in satellite estimates of trace greenhouse gasses and remote sensing at large is the error distribution of an estimated target variable which arises from instrument artifacts as well as the under-determined nature of the retrieval of the quantities of interest.A large portion of the error is often incurred during inference from measurement of retrieved physical variables.These residual errors are typically corrected using ground truth observations of the target variable or some other truth proxy.Previous studies used multilinear regression to model the error distribution with a few covariates from the retrieved state vector, sometimes termed "features."This presentation will
Gesture Recognition for Human-computer Interaction Based on CNN Model

Gesture recognition plays an important role in analyzing human-computer interaction. The existing gesture recognition methods mainly include hand postural morphological analysis and sensor data modeling. The former method can effectively identify hand gestures with large morphological differences in human-computer interaction, the latter use sensor devices to collect hand data for modeling. This paper proposes a gesture recognition method based on CNN visual model in human-computer interaction. The method does not require sensor to collect data, and can identify similar gestures in interaction process. The CNN model is constructed based on the VGG16 model, which extracts the system feedback sequence frames for each gesture action, constructs the frame sequence feature vector through the convolution layer of the CNN model, and finally predicts the probabilities of various types of gesture actions through the feature vector.
Partial Facial Identification using Transfer Learning Technique

In today’s world, people go outside wearing a face mask, so face detection and face recognition models need to take this into account. Facial recognition has been researched widely with various algorithms. Since the coronavirus disease of 2019 (COVID-19) outbreak has spread across Thailand, our use of face recognition models has reminded people to wear a face mask. This is because when people go outside, they are likely to be exposed to facial image detection and classification methods which are used for authentication and authorization. In this paper, we use transfer learning such as YOLOv3 and training with public datasets and donation datasets. Our models can recognize faces with a 98.7% accuracy rate and identify faces including those with face masks-with a 92.7% accuracy rate.
Near space hypersonic vehicle target tracking adaptive non-zero mean model

As a typical maneuver model, the second-order Markov model, which is based on acceleration periodic autocorrelation, is an effective way used for tracking near space hypersonic vehicle(NSHV) target tracking.It is found that, however, the model responds slowly to the target maneuvering and has weak maneuverability.To solve this problem, the adaptive non-zero mean damped oscillation model (ANM-DO), which based on the idea of mean compensation, is proposed.Then the difference of the mean compensation method between the first order Markov model and the two order Markov model is analyzed, and the physical essence of adaptive nonzero mean is discussed from time domain and frequency domain.Furthermore, to further investigate the performance of the ANM-DO model, we deduced the systematic dynamic errors of ANM-DO taking Kalman Filter (KF) as filtering algorithm.On this basis, the superior performance of ANM-DO model is verified in terms of maneuverability.Finally, simulation experiments in different scenarios show that the ANM-DO model shows lower filtering errors tracking near space hypersonic jump gliding targets, and verified the adaptability of the model proposed in this paper.
A Video Analytic System for Rail Crossing Point Protection

With the rise of AI deep learning, video surveillance based on deep neural networks can provide real-time detection and tracking of vehicles and pedestrians. We present a video analytic system for monitoring railway crossing and providing security protection for rail intersections. Our system can automatically determine the rail-crossing gate status via visual detection and analyze traffic by detecting and tracking passing vehicles, thus to oversee a set of rail-transportation related safety events. Assuming a fixed camera view, each gate RoI can be manually annotated once for each site during system setup, and then gate status can be automatically detected afterwards. Vehicles are detected using YOLOv4 and multi-target tracking is performed using DeepSORT. Safety-related events including trespassing are continuously monitored using rule-based triggering. Experimental evaluation is performed on a Youtube rail crossing dataset as well as a private dataset. On the private dataset of 76 total minutes from 38 videos, our system can successfully detect all 56 events out of 58 annotated events. On the public dataset of 14.21 hrs of videos, it detects 58 out of 62 events.
A new class of neural networks for NCPs using smooth perturbations of the natural residual function

We present a new class of neural networks for solving nonlinear complementarity problems (NCPs) based on some family of real-valued functions (denoted by ℱ) that can be used to construct smooth perturbations of the level curve defined by ΦNR(x,y)=0, where ΦNR is the natural residual function (also called the "min" function). We introduce two important subclasses of ℱ, which deserve particular attention because of their significantly different theoretical and numerical properties. One of these subfamilies yields a smoothing function for ΦNR, while the other subfamily only yields a smoothing curve for ΦNR(x,y)=0. We also propose a simple framework for generating functions from these subclasses. Using the smoothing approach, we build two types of neural networks and provide sufficient conditions to guarantee asymptotic and exponential stability of equilibrium solutions. Finally, we present extensive numerical experiments to validate the theoretical results and to illustrate the difference in numerical performance of functions from the two subclasses. Numerical comparisons with existing neural networks for NCPs are also demonstrated.
Software Implementation of Neural Recurrent Model to Predict Remaining Useful Life of Data Storage Devices

This article explores the problem of predicting the remaining useful life, which often arises when working with disk drives. The approaches to effectively solving this problem using recurrent neural networks, in particular, SimpleRNN, GRU (Gated Recurrent Unit), and LSTM (Long Short-Term Memory) are considered. At the same time, for the development of predicting models, the dataset of the BackBlaze service is used, which is publicly available. The data are presented as multidimensional time series, which were formed according to the readings of SMART (Self-Monitoring, Analysis and Reporting Technology) sensors of the data accumulators. Approaches to improving predicting accuracy are considered. The software implementation of the predicting models was performed in Python 3.8. The models were trained over 20 epochs. The results of predicting the remaining service life of disk drives from the BackBlaze database, as well as graphical dependences of the loss function and comparative tables with neural networks used in the study are presented.
A Transaction Transmission Model for Blockchain Channels

Transactions in blockchain networks can be transmitted through channels. Existing methods for channel transaction transmission depend on transaction routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds (also called balance) or low transmission rate. To increase transmission success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game theory. The model considers the channel balance, states, and nodes’ channel preferences. Steady-state transmission rates of selected channels are analyzed. The ideal solution for the model is found.
Convolutional Neural Networks for Multi-fidelity Data Aggregation

Multi-fidelity data are common in almost every engineering and science discipline, which can be from simulation, experiments, and a hybrid form. High fidelity data usually associate with higher accuracies and expenses (e.g., high resolution experimental testing or finer scale simulation), while low-fidelity data are exactly opposite in terms of the accuracy and cost. Multi-fidelity data aggregation (MDA) in this study refers to the process of combining of two or multiple sources of different fidelity data to have a high accuracy and low computational cost. MDA has a wide range of application in engineering and science, such as multiscale simulation, multi-resolution imaging, and hybrid simulation-testing. This paper presents a novel framework named Multi-fidelity Data Aggregation using Convolutional Neural Networks (MDA-CNN) for multi-fidelity modeling. The MDA-CNN architecture has three components: multi-fidelity data compiling, multi-fidelity perceptive field and convolution, and deep neural network for mapping. This framework captures and utilizes implicit relationships between any high-fidelity datum and all available low-fidelity data using a defined local perceptive field and convolution. Most existing strategies relies on the collocation method and interpolation, which focuses on the single point relationship. The proposed method has several unique benefits. First, the proposed framework treats the multi-fidelity data as image data and processes them using CNN, which is very scalable to high dimensional data with more than two fidelities. Second, the flexibility of nonlinear mapping in neural network facilitates the multi-fidelity aggregation and does not need to assume specific relationships among multiple fidelities. Third, the proposed framework does not assume multi-fidelity data are at the same order or from the same physical mechanisms (e.g., assumption is needed for some error estimation-based multi-fidelity model). Thus, the proposed method can handle data aggregation from multiple sources across different scales, such as different order derivatives and other correlated phenomenon data in a single framework. The proposed MDA-CNN is validated using extensive numerical examples and experimental data with multi-source and multi-fidelity data. Conclusions and future work are presented based on the observations in the current study.
“SmartEval”—Evaluation System for Descriptive Answers in Examinations Using Natural Language Processing and Artificial Neural Networks

With the progress in technology, came the era of exercising it to reduce the effort of manpower in numerous areas. It’s safe to assume that the education industry has reaped a rather large benefit from it. Say it be through online classes, online quizzes, or plethora of online tutorials just a click away. Amongst the handful of branches that the industry is yet to adapt to is online descriptive answer evaluation. Most of the educational institutes still rely on faculties to go through and grade answers manually. Computer-assisted assessment models exist which mainly a keyword checks between the student’s answer and teacher answer key. The rapid insurgence in technology results in reducing the effort of manpower in many of the areas. With the help of technologies such as internet and personal computers, it’s rather safe to say that the educational environments are much more welcoming than ever before. When it comes to developing systems that helps in automating the process of evaluating and grading an answer, it will reduce a lot of human time that's usually wasted on doing a repetitive job. But the concept of using a computer to evaluate answers isn’t totally new. Starting at now, the goal type questions can be evaluated through a mechanized specialist. The fundamental test with a distinct answer lies in perceiving the natural language answers and separating the exact importance to fittingly assess the information got by the understudy.
An innovative tripternion algebra for designing NTRU - like cryptosystem with high security

Due to the dependence on computers and the internet in many areas as a result of what it provides in terms of storing and transmitting information with high speed and accuracy, but there are many obstacles appeared when using a lot of computers, and the biggest of these obstacles and problems is the protection and securing of information that is stored in the computer and sent over the internet has become a necessity Urgency to the encryption system in order to maintain the security of the information and hide it so that even if it is reached, you ensure that they cannot understand it. NTRU is a good way to maintain information security characterized by its great resistance to attacks. In this paper we define a new multidimensional algebra called tripternion algebra and created a new algebraic structure to design analogue of NTRU public key cryptosystem which called NTRTRN with high security.
EA-MLM: Error-aware Masked Language Modeling for Grammatical Error Correction

In recent years, BERT has been used in the task of grammatical error correction (GEC) and achieved good performance. However, few previous studies have investigated the incorporation of real grammatical errors into BERT for the GEC task. We argue that the distribution of GEC data (containing several types of errors) is different from the distribution of BERT pre-training data (usually error-free). To fill this gap, in this paper, we extend masked language modeling and propose a novel error-aware masked language modeling strategy (EA-MLM) to fine-tune BERT so that the representation distribution of the pre-trained BERT is better adapted to the GEC task. We conduct extensive experiments on public datasets and the experimental results demonstrate the effectiveness of our proposed method.
Differential Paradox: How an SBox Plays Against Differential Fault Analysis

Differential Fault Analysis (DFA) is a well-known cryptanalytic method that has been successfully applied to many block ciphers based on Substitution Permutation Network (SPN). In this work we seek the answer: How exactly DFA works and how can we possibly build a cipher level protection against it. Our study shows that SBoxes play a crucial role for DFA to succeed. Interestingly, SBoxes that are better against DFA are proved to be worse against differential cryptanalysis, and vice versa.
Towards Cybersecurity Risk Management Investment: A Proposed Encouragement Factors Framework for SMEs

Disregarding cybersecurity risk management by SME decision-makers became a phenomenon that needs to be studied. Specifically, the lack of cybersecurity risk management investments, which will lead to further risks. The Covid-19 pandemic has proven how the seriousness of cybersecurity threats could increase dramatically in a short period. The authors attempt to detect the role of SMEs' decision-makers in addressing cybersecurity risks nowadays and to suggest avenues for further studies. This paper aims to find out the encouragement factors and how they positively impact cybersecurity risk management investment in SMEs based on conducting several interviews with decision-makers in the sector. The results reveal that eight factors are playing a major role in encouraging SMEs' decision-makers to invest in cybersecurity risk management as a strategic priority. These encouragement factors have been categorised as follows technological context, organizational context, and environmental context. Importantly, a proposed framework of these factors has been discussed widely in order to illustrate expected returns of this investment on the SMEs' sector in the long term. Meaningfully, this research contributes to the financial cybersecurity risk management literature with new insight into the current cybersecurity risk management investment situation in SMEs.
Frigate: a fast, in-memory tool for counting and querying k-mers

K-mer counting is an important step in many bioinformatics applications including genome assembly, sequence error correction, and sequence alignment. As the advancements in next generation sequencing technologies have resulted in tremendous growth of genomic data, it is inevitable for k-mer counters to be faster and more efficient. We present Frigate, a fast and efficient tool capable of counting and querying k-mers. Its in-memory design utilizes multithreaded, lock-free data structures to improve performance. Frigate was developed with the emphasis on values of k less than 20, aiming to maximize performance by employing different algorithms for different ranges of k values. The results show that Frigate achieves comparable or up to 2-3x speedup compared to the state-of-the-art k-mer counters, especially for large datasets.
Cybersecurity Taxonomy: research and knowledge areas

In this paper, we proposed a taxonomy to relate areas of knowledge with cybersecurity research. The structure of the taxonomy integrates various areas of knowledge applied in research, congresses, certifications, tools and work roles defined in the ACM curriculum. Through the proposed taxonomy, students in science and engineering can find lines of research and knowledge generated in the field of cybersecurity. In this way, the generation of academic projects and research related to cybersecurity is promoted. The proposal aims to reduce the gap in the search for bibliographic sources by allowing the addition in academic curricula in engineering and science careers. This contributes to the development of new work roles and research methodologies generating knowledge through the cases of cyber-attack suffered by organizations. The basis of the proposed taxonomy is based on the integration of the ACM curriculum with the NICE framework and the topics most relevant to the understanding of cybersecurity. The proposal was validated based on the judgment of experts in higher education resulting in a 54% improvement in the level of knowledge in cybersecurity lines generated by the proposed taxonomy.