Reviewerly/exHarmony huggingface dataset:
A Meta-Learning-Based Approach for Hand Gesture Recognition Using FMCW Radar

In recent years, the frequency modulated continuous wave (FMCW) radar have been widely applied for hand gesture detection and recognition. In this paper, we propose a Meta-Learning-based multi-branch network with Range-Doppler-Angle multi-dimensional parameters (ML-RDA-Net) for FMCW radar based hand gesture recognition. Furthermore, we construct the Range-Frame-Map, Doppler-Frame-Map, and Angle-Frame-Map datasets for the proposed model. Finally, we carry out extensive experiments to evaluate the performance of the proposed scheme. The experimental results show that the proposed model with multidimensional parameter dataset can achieve a 3%-7% accuracy improvement with much fewer samples comparing with some existing methods.
Monomials as Set Term Clusters in Terminologies

The present paper tightly links to the earlier introduced idea of monomials and polynomials as substitutes of the term in linguistics when applicable to languages for specific purposes, or terminologies.The classification by Oksana Chaika (2019) of monomials and polynomials stresses out the similarities between terms and interrelation of the terms in an algebraic expression (its terms/components and operations) in mathematics and terms / components of the term and interrelation of the terms in the term, which is a terminological set expression in a language for specific purpose.Under the research, monomials are seen as set term clusters that form a specific term in a relevant terminology of the relevant language.Monomials are differentiated for their structure and syntagmatic relations inter alia.The underlying focus is on the number of terms in the termmonomial, which causes taxonomy of such monomials in relevant terminologies and languages.Where a monomial consists of one term only, it is referred to as a mono-term monomial.Where it is a two-term, three-term or multi-term, the monomials are referred to as a bi-term or multi-term, respectively.Bi-term monomials and multi-term monomials should not be confused with a binomial or a polynomial.The syntactic relation of coordination is of critical value for polynomials in general and binomials, in particular.
Classifying aggravation status of COVID-19 event from short-text using CNN

COVID-19 pandemic is a new precedent that has changed many aspects of human life.With the uncertainty of vaccine availability, stakeholders are required to track the dynamics of COVID-19 events to prepare the necessary response.One sub-task in tracking the dynamics of an event is to identify the aggravation status of the event (i.e., whether an event is worsening or getting better).We experimented with convolutional neural network (CNN) models to classify the status of COVID-19 aggravation status from a short text.CNN without one hot encoding prevailed.Furthermore, we conduct tuning to achieve better performance of CNN.The highest performance was achieved by tuning some of the configuration parameters.As the final result, the model performed at best (accuracy = 87.585%and F1-score = 76%) when using 80 nodes, SGD optimizer, lr = 0.1, and momentum = 0.9.
Neural Network Machine Translation Method Based on Unsupervised Domain Adaptation

Relying on large-scale parallel corpora, neural machine translation has achieved great success in certain language pairs. However, the acquisition of high-quality parallel corpus is one of the main difficulties in machine translation research. In order to solve this problem, this paper proposes unsupervised domain adaptive neural network machine translation. This method can be trained using only two unrelated monolingual corpora and obtain a good translation result. This article first measures the matching degree of translation rules by adding relevant subject information to the translation rules and dynamically calculating the similarity between each translation rule and the document to be translated during the decoding process. Secondly, through the joint training of multiple training tasks, the source language can learn useful semantic and structural information from the monolingual corpus of a third language that is not parallel to the current two languages during the process of translation into the target language. Experimental results show that better results can be obtained than traditional statistical machine translation.
Optimization Space Exploration of Hardware Design for CRYSTALS-KYBER

Public key cryptography is important in the global communication digital infrastructure. However, the emergence of quantum computer and Shor algorithm has greatly threatened the security of public key cryptography. The CRYSTALS-KYBER, as a lattice-based KEM algorithm, passed three rounds of a global solicitation for post-quantum cryptography algorithms held by the National Institute of Standards and Technology (NIST). This paper explores the implementation and optimization space of hardware design according to CRYSTALS-KYBER algorithm. We analyze its software code and try different strategies to optimize the hardware implementation, and conduct comparative analysis in terms of area and speed. The experimental results show that the performance can be greatly improved by moderately optimizing the loops. In comparison with optimal results of the work [12], our optimizations improve the performance by up to 74.6% for encapsulation algorithm and 54.4% for decapsulation algorithm.
Machine Learning with Synthetic Data – a New Way to Learn and Classify the Pictorial Augmented Reality Markers in Real-Time

The idea of Augmented Reality (AR) appeared in the early 60s, which recently received a large amount of public attention. AR allows us to work, learn, play, and connect with the world around us both virtually and physically in real-time. However, picking the AR marker to match the users' needs is one of the most challenging tasks due to different marker encryption/decryption methods and essential requirements. Barcode AR cards are fast and efficient, but they do not contain much visual information; pictorial coloured AR card, on the other hand, is slow and not reliable. This paper proposes a solution to obtain detectable arbitrary pictorial/colour AR cards in real-time by applying the benefit of machine learning and the power of synthetic data generation techniques. This technique solves the issue of labour-intensive tasks of manual annotations when building a massive training dataset of deep-learning. Thus, with a small number of input of the AR-enhanced target figures (as few as one for each coloured card), the synthetic data generated process will produce a deep-learning trainable dataset using computer-graphic rendering techniques (ten of thousands from just one image). Second, the generated dataset is then trained with a chosen object recognition convolutional neural network, acting as the AR marker tracking functionality. Our proposed idea works effectively well without modifying the original contents (of the chosen AR card). The benefits of using synthetic data generated techniques help us to improve the AR marker recognition accuracy and reduce the marker registration time. The trained model is capable of processing video sequences at approximately 25 frames per second without GPU Acceleration, which is suitable for AR experience on the mobile/web platform. We believed that it could be a promising low-cost AR approach in many areas, such as education and gaming.
API Security Risk Assessment Based on Dynamic ML Models

Adding machine learning (ML) and artificial intelligence (AI) logic models to authentication is an inevitable process. In this work, we show that the combination of qualitative and quantitative verification over model created on training data may significantly reduce false access probability, even if the user's credentials ID and password are compromised. We propose three layers of authentication based on user ID and password, silent signals, and biometrical data. The system uses supervised ML to determine the user's risk level. Basic model and associate implementation performance shows that we can, with high probability, identify an intruder based on silent signals, historical data, and behavioural biometrics. The system is compositional, so further improvement by introducing more silent signals and behavioural analytics can, theoretically, eliminate false acceptance. Whenever the risk level is higher than some threshold, an additional verification is required. The threshold may increase over time and in that case, the probability of additional verification of a legitimate user decreases.
Lightweight Encryption Algorithm Implementation for Internet of Thing Application

Internet of Things (IoT) application utilizes devices with limited hardware resources such as the wireless sensor network application and devices with sufficient hardware resources such as satellite applications. The main challenge is to design a lightweight encryption algorithm to be implementable on devices with limited hardware resources in IoT applications. This work proposes a lightweight encryption algorithm with the provision of protection against the side-channel and nonce misuse attack. The lightweight encryption algorithm utilizes an AES like rounds architecture with reduced rounds to reduce area consumption and high-throughput provision. Its implementation of software and hardware validates the lightweight encryption algorithm. The lightweight encryption algorithm provides similar avalanche effects, as shown by the Advanced Encryption Standard (AES) algorithm. The lightweight encryption algorithm utilizes sub-algorithms for the randomization of Initialization Vector (IV) and the generation of randomizing keys for every ciphertext. The encryption algorithm is compared with the recent implementations for resource comparison and security analysis.
Detection of evolving Lagrangian coherent structures: A multiple object tracking approach

During the course of a complex flow, many coherent structures may be created and destroyed. Here, tools from object tracking in video analysis are used to dynamically track these transient coherent structures, allowing for coherence timescales to be considered independently of the overall flow timescale and revealing the inner dynamics of the structures themselves.
Poster: A Novel Approach for POS Tagging of Pashto Language

Pashto is a language that belongs to the Indo-European family, mostly spoken in South Asian countries, especially in Pakistan and Afghanistan. To build software that enables us to translate Pashto sentences into various languages and building Natural Language Understand (NLU) applications to make interactive Pashto software requires a well-defined corpus and Parts of Speech (POS) tagging approach. Therefore, a well-defined corpus is developed by scraping data from different websites. We have prepared dataset according to the guidelines written for Persian and Arabic languages, as these languages are somehow similar to these languages. Training of POS tagging using BiLSTM with GloVe embedding shows the effectiveness of our proposed approach and achieved 97% accuracy.
Imbalanced Encrypted Traffic Classification Scheme Using Random Forest

Encrypted traffic classification techniques can identify different types of traffic for network security. The existing schemes seldom consider the imbalanced distribution of the traffic. In this paper, we propose an encrypted traffic classification scheme using random forest for imbalanced learning. Firstly, the weighted information gain is used to select the features which are beneficial to the minority class to filter redundant features. Then the hybrid sampling method is used to balance the number of the majority class and the minority class. Finally, the random forest is constructed for encrypted traffic classification. Experimental results show that the feature selection method can filter the redundant features of the traffic and the hybrid sampling method can effectively improve the classification ability. Compared with K-nearest neighbor and C4.5 decision tree algorithm, the proposed scheme can classify imbalanced encrypted traffic more effectively.
A Multi-Scaled Method for Parallel Bayesian Optimization in Deep Predictive Analytics

This paper proposes a new roll-out scaling method for parallel Bayesian optimization and discusses how the proposed multi-scaled optimization guarantees a better convergence speed with outperformed accuracy than the conventional parallel search algorithms. Experiment results demonstrate that an entire search space can be efficiently reduced to more feasible subdomains. The performance of parallel Bayesian search can be further accelerated based on the interchangeable local evidence by properly adjusting three quantitative aspects in terms of space factorization, search direction, and architecture scaling.
Computing Admissible Temporal SLAs for Web Service Compositions

Web service compositions establish Temporal Service Level Agreements to have guarantees of temporal correctness. Temporal SLAs can be realized through the exchange of temporal parameters between service providers and clients. However, so far, it was not possible to compute a priori restrictions on the values for these parameters, such that the composition is dynamically controllable, i.e. free from time failures under any foreseeable circumstance. Here we provide a sound and complete method to compute restrictions for temporal parameters in a Web service composition with guarantees of dynamic controllability.
User Study on Virtual Reality for Design Reviews in Architecture

Virtual reality is a candidate to become the preferred interface for architectural design review, but the effectiveness and usability of such systems is still an issue. We put together a multidisciplinary team to implement a test methodology and system to compare VR with 2D interaction, with a coherent test platform using Rhinoceros as industry-standard CAD software. A direct and valid comparison of the two setups is made possible by using the same software for both conditions. We designed and modeled three similar CAD models of a 2 two-story villa (1 for the training and 2 for the test) and we implanted 13 artificial errors, simulating common CAD issues. Users were asked to find the errors in a 10 minutes fixed-time session for each setup respectively. We completed our test with 10 students from the design and architecture faculty, with proven experience of the 2D version of the CAD. We did not find any significant differences between the two modalities in cognitive workload, but the user preference was clearly towards VR. The presented work may provide interesting insights for future human-centered studies and to improve future VR architectural applications.
Diagnosis of fever symptoms using naive bayes algorithm

Dengue Hemorrhagic Fever (DHF) and Typhus Fever (TF) are diseases that have similar symptoms. Fever in DHF is caused by the bite of the Aedes Aegypti mosquito, whereas fever in TF is caused by the bacterium Salmonella Typhi. The similarity of symptoms in these two diseases often leads to misdiagnosis of the patient, which can cause the patient's condition to worsen due to incorrect handling. To overcome this problem, we need a method to diagnose the symptoms of fever in both diseases. In data mining, the diagnosis of the disease can be done by classification techniques. The classification process for diagnosing fever symptoms is using the Naïve Bayes algorithm. Algorithm testing is done using k-fold cross-validation, with k equal to 10. The evaluation of the algorithm is measured by calculating the value of accuracy, precision, and recall from prediction results. The results showed that the average accuracy rate was 94%, precision was 90%, and recall was 92%. This shows that the Naïve Bayes algorithm has good performance in diagnosing fever in patients.
Energy Efficient Path Planning for Indoor Wheeled Mobile Robots

There has been an increased interest in the optimal robot path planning study. However, the optimal path found by the traditional path planning methods only considers distance and time constraints. Thus, it is not necessarily the most energy efficient path. In order to make robots perform more tasks under insufficient energy supply efficiently, reducing energy consumption has become essential in the path planning. In this paper, we proposed a novel path smoothing method that overcomes the deficiencies (e.g., some redundant inflection points in path.) of the traditional A* algorithm. We designed an energy-based adjacency matrix to represent the environmental information in grid map. Then we added a new energy-related criterion based on an adjacency matrix to the cost function of the A* algorithm for path planning. The simulation results demonstrated that the proposed method is more energy efficient than the existing A* algorithm. Moreover, the method realizes a good trade-off between preserving energy and not extending too much the path length. With such advantages, the proposed methods can help indoor wheeled mobile robots or automated guided vehicles (AGVs) to complete path planning tasks with limited power.
Research on economic system based on fuzzy set comprehensive evaluation model

The development of the economic system is affected by many factors, and the stability of the traditional economic analysis model is difficult to maintain. In order to explore the efficient and stable economic system evaluation and analysis model, based on machine learning ideas, this study uses rough set algorithm as the basic algorithm, and applies the related methods of rough set and catastrophe model theory to the evaluation of ecological economic development level. Moreover, this study reduces the redundant index of the index system and calculates the importance of the index after reduction. Based on the catastrophe set model, this study uses MATLAB software programming to comprehensively quantify the ecological economy, and finally divides the ecological economic grade. In addition, this study combines rough set theory with fuzzy mathematics, and initially establishes a two-branch fuzzy evaluation model. Finally, this study combines the actual situation to use the established model to evaluate the regional eco-economic system. The research results show that the method proposed in this paper has a certain effect, which can provide a reference for subsequent related research.
CPSPNet: Crowd Counting via Semantic Segmentation Framework

Crowd counting, i.e., estimation number of the pedestrian in crowd images, is emerging as an essential research problem with the public security applications. The density-based method of crowd counting still has some challenges, such as lack of perspective information in density map and background noise. Current models often misjudge background noise as a person and the ground truth density map widely used now is not so accurate. In this paper, we present a novel approach to help generate a higher quality density map. On the one hand, we eliminate the apparent mistakes in the density map with the help of a semantic segmentation model, which provides more information about fine-granted negative samples. On the other hand, we modify the density map to make sure it maintains a natural attribute. The experimental results prove the effectiveness of our method for crowd counting models, especially in uneven distribution monitoring scenario.
Intelligent Radar Software Defect Prediction Approach and Its Application

Radar software defects are not used and applied effectively and sufficiently in the testing process. As a result, defects often occur repeatedly, which causes safety hazards in software operation. To resolve this problem, this paper proposed a novel intelligent defect prediction approach for radar software by using Naïve Bayesian to classify defect data and predict defects according to radar software requirements. We apply the proposed approach on the typical radar software. The experiment results show that the defect prediction precision rate of the proposed defect prediction approach is 75%, and the prediction recall rate is 70%, approx. The experiment results are better compared to the defect prediction methods without Naïve Bayesian and defect classification. Therefore, the proposed defect prediction approach can be applied on radar software effectively and applicably to improve the effectiveness of radar software testing and provide the positive feedback to the radar software design process significantly.
A Neural-Assessment System Based on Emirates (QFE)

In order to strengthen the teaching and learning phase it is assumed that the assessment of course results dependent upon student grades is important. Our analysis methods and workflows leverage the benefits of AI, for example the capacity to evaluate vast data sets and detect correlations more accurately than humans would use artificial intelligence technologies to help classify large data. It would be used to assess the course learning results based on QF-Emirates (Qualifiers Frame of the United Arab Emirates) criteria with actual data and use it to recommend teaching and learning interventions. We investigate and validate the right neural networks architecture that produces full performance. To that end, a modern algorithm has been improvised. Application to a database to store data and provide data regarding the review of course learning results would be deployed for our suggested recommendation framework. The suggestion method is evaluated and findings are promising as a machine-learning framework. Our neural network based system was able to generate solutions for new cases and provide support in the assessment of courses learning outcomes.
The Utilization of Augmented Reality Technologies within the Engineering Curricula – Opportunities and Challenges

Augmented Reality (AR) is technology that combines videos, photos, texts, and location data of the real world with computer-generated data. Recently, an increased interest from both academia and industry has grown to study and deploy AR technology. Current literature shows that AR has been successfully integrated into many fields of the industry, like navigation, tourism, engineering, entertainment, and automotive. However, its deployment in education and specifically in high education seems to be limited as more research is required to find ways to overcome the associated implementation barriers. This paper aims to explore the potential use of AR in the engineering education sector by highlighting its importance and evaluating the factors that would enable augmented reality application and identifying the associated challenges accordingly. To achieve this, several previous research papers on the potential use of AR applications within the engineering discipline, including the challenges and barriers facing their integration in this discipline were reviewed. This paper, therefore, reports on the findings of this study, providing key foundations towards establishing better-designed AR systems and applications that will increase the acceptance and implementation potential of AR technology in the education industry.
A survey on Arabic Image Captioning Systems Using Deep Learning Models

This paper describes a literature survey for the deep leaning approaches used in image captioning. Approaches will be discussed based on four main categories: model architecture, attention mechanism, image model and language model. Most of the current research focuses on generating captions in English language, leaving a gap in research for other languages, especially for Arabic language. Therefore, we will highlight the available research and approaches used to generate captions in Arabic. We will discuss the used datasets, translation approach, evaluation metrics and the results for each method. We conclude the survey by proposing some possible future directions for Arabic image captioning.