Reviewerly/exHarmony huggingface dataset:
FWSResNet: An Edge Device Fingerprinting Framework Based on Scattering and Convolutional Networks

Lightweight device authentication is a critical aspect in edge computing to guarantee the rightness of edge device identities and services. Radio frequency fingerprinting (RFF) is such an enabling technology able to provide robust and affordable security by employing the unique device and channel features which are usually extracted via machine learning (ML) method. The challenge is how to realize strong interpretability and support sufficient generalization ability under non-stationary channel characteristics. To solve this, we in this paper propose a novel hybrid network named FWSResNet which exploits fractional wavelet scattering transform and residual neural network to deal with the device and channel features subtlety. In particular, the proposed FWSResNet uses the scattering network based on fractional domain wavelet transform to extract the low and high-frequency features of the input signal through multiscale fractional wavelet. We find that this design can be robust to non-stationary signals and can extract the key features of noise signals. We also present a comprehensive theoretical analysis of the performance of FWSResNet under non-stationary signal distortion. Finally, we evaluate the hybrid network under largescale long term evolution (LTE) data in the practical application scenario and show that our proposed FWSResNet can achieve 93% recognition accuracy rate with only 280 training samples per device, and achieve up to 99.5% when training samples increase to 4200 per device.
FIND: Mall Navigation Using Augmented Reality

The pandemic has hit all countries in the world, including Indonesia. It affected many sectors, such as malls, which have become deserted. Meanwhile, the visitors often experience the problem of finding a location inside the mall because the mall building is wide and complex. For solving these problems, a mall navigation application needed. This study presents the augmented reality-based mall navigation using AR Core that not only guide visitors to the exhibition location but also offer them having experience to enjoy the virtual objects in the mall. This AR application is built as an innovation to attract the visitor to come and shop to this mall while they are enjoying using this AR application. To analyze if this application may leverage the number of visitors, this application stores the number of visitors into database.
An Improved 3D Human Pose Estimation Model Based on Temporal Convolution with Gaussian Error Linear Units

With the advancement of image sensing technology, estimating 3D human poses from monocular video has become a hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequent action analysis and understanding. It has a wide range of applications, such as intel-ligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methods for 3D human pose estimation in monocular video employ temporal convolutional network to extract inter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationship extractions. In this paper, we decompose the 3D joint location regression into bone direction and bone length, we propose a temporal convolutional network incorporating Gaussian error linear units (TCG) to solve bone direction. It enables more inter-frame features to be captured, allowing the feature relationships between data to be fully utilized. And we use kinematic structural information to sovle bone lenght which enhance the use of intra-frame joint features. The proposed method has extensively experimented on the public benchmark dataset Human3.6M. The quantitative and qualitative evaluation results show that the proposed method can achieve more accurate 3D human pose estimations.
Vehicle Recognition Combining ResNeXt Network and Random Erasing

Random erasing is a commonly used data augmentation method. Vehicle recognition now uses deep neural networks as training models. Deep neural networks are getting deeper and deeper, with more and more parameters, and more and more complex models. Random erasing approach is used to preprocess the vehicle identification data set. Then, the expanded data set is used as the training data set of the model recognition model ResNeXt network. Experiments verify that random erasing and preprocessing of the data set can reduce the loss of the model recognition network model and improve the robustness of the model.
Side-Channel Masking with Common Shares

To counter side-channel attacks, a masking scheme randomly encodes keydependent variables into several shares, and transforms operations into the masked correspondence (called gadget) operating on shares. This provably achieves the de facto standard notion of probing security.We continue the long line of works seeking to reduce the overhead of masking. Our main contribution is a new masking scheme over finite fields in which shares of different variables have a part in common. This enables the reuse of randomness / variables across different gadgets, and reduces the total cost of masked implementation. For security order d and circuit size l, the randomness requirement and computational complexity of our scheme are Õ(d2) and Õ(ld2) respectively, strictly improving upon the state-of-the-art Õ(d2) and Õ(ld3) of Coron et al. at Eurocrypt 2020.A notable feature of our scheme is that it enables a new paradigm in which many intermediates can be precomputed before executing the masked function. The precomputation consumes Õ(ld2) and produces Õ(ld) variables to be stored in RAM. The cost of subsequent (online) computation is reduced to Õ(ld), effectively speeding up e.g., challenge-response authentication protocols. We showcase our method on the AES on ARM Cortex M architecture and perform a T-test evaluation. Our results show a speed-up during the online phase compared with state-of-the-art implementations, at the cost of acceptable RAM consumption and precomputation time.To prove security for our scheme, we propose a new security notion intrinsically supporting randomness / variables reusing across gadgets, and bridging the security of parallel compositions of gadgets to general compositions, which may be of independent interest.
Active Learning Query Strategies for Linear Regression Based on Efficient Global Optimization

Active learning, a subfield of machine learning, can train a good model by selecting a minimum number of labeled samples. In many machine learning scenarios, needed information (such as the best value in unlabeled datasets) is acquired by prediction. When there is too little data in the training model, the prediction accuracy would obviously affect the accuracy of the results. To establish a high-performance regression model for a small dataset while accelerating the search for the best sample, a new active learning query strategy, EGO-ALR, that combines efficient global optimization (EGO) and active learning for regression (ALR) was proposed. It was found that the performance of EGO-ALR was significantly better than the original ALR query strategies in terms of the root mean square error (RMSE), correlation coefficient (CC), and opportunity cost (Oppo Cost). Specifically, EGO-ALR increased the Oppo Cost by an average of 25.27% when the RMSE and CC values were not more than 1.07% different from the original ALR. This study validated the efficiency and robustness of EGO-ALR approaches using 19 datasets from various domains and three distinct linear regression models (Ridge regression, Lasso, and Elastic network).
Tabular strategies for metadata in ecology, evolution, and the environmental sciences

Abstract Data support knowledge development and theory advances in ecology and evolution. We are increasingly reusing data within our teams and projects and through the global, openly archived datasets of others. Metadata can be challenging to write and interpret, but it is always crucial for reuse. The value metadata cannot be overstated—even as a relatively independent research object because it describes the work that has been done in a structured format. We advance a new perspective and classify methods for metadata curation and development with tables. Tables with templates can be effectively used to capture all components of an experiment or project in a single, easy‐to‐read file familiar to most scientists. If coupled with the R programming language, metadata from tables can then be rapidly and reproducibly converted to publication formats including extensible markup language files suitable for data repositories. Tables can also be used to summarize existing metadata and store metadata across many datasets. A case study is provided and the added benefits of tables for metadata, a priori, are developed to ensure a more streamlined publishing process for many data repositories used in ecology, evolution, and the environmental sciences. In ecology and evolution, researchers are often highly tabular thinkers from experimental data collection in the lab and/or field, and representations of metadata as a table will provide novel research and reuse insights.
A Recommended Approach to Classical Literature and Art Exhibition Activities Oriented towards Interactive Modelling

Chinese classical literature and art exhibition activities have a special role to play in improving the humanities of students. Introducing elements of classical literature into cultural activities can effectively complement the current humanities education in universities. To improve the efficiency and intelligence of classical literature and art exhibition activities, this paper proposes an interactive modelling-oriented method for recommending classical literature and art exhibition activities in response to the characteristics of existing art exhibition event management systems (AEEMS) in supporting process modelling and the interactive modelling needs of audiences. By using exhibition event segments as the recommended reference model, the process includes steps such as data preprocessing and calculating the matching degree and can recommend reasonable classical literature art exhibition events based on the art exhibition model under construction and the modelling needs of the audience. Experimental data are obtained by using a questionnaire survey method, and the experimental results show that it outperforms existing methods in terms of evaluation indicators such as accuracy, recall, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> , indicating the effectiveness of the proposed method.
Hardware Flexible Systolic Architecture for Convolution Accelerator in Convolutional Neural Networks

A flexible systolic architecture to achieve hardware acceleration in the convolution operation for convolutional neural networks (CNNs) is described in this paper. The main architecture feature is the capability to be adapted to perform the convolution from different main hyperparameters typically required in CNN stages, like input feature map and filter sizes. The proposed architecture is applied in processing the convolution stages for different pretrained CNNs to classify images size of 28x28 pixels from MNIST database with filter sizes of 3x3, 5x5, 7x7, 9x9 and 11x11. The proposed architecture goal is to reduce the processing data time without depending on a single CNN thanks to its adaptability, scalability, data reuse and the use of different data processing parallelisms. The reported architecture was described in VHDL taking as reference a FPGA Cyclone V SE 5CSXFC6D6F31C6N and the results shown were obtained by simulation.
A Joint Multiple Hypothesis Tracking and Particle Filter Approach for Aerial Data Fusion

The use of Unmanned Aerial Vehicles (UAV) has increased in recent years. Increased density of air traffic as well as the autonomy of the vehicles involved, demand robust safety of traffic operations in terms of dependable decision making for flight operations. Since future traffic management services (U-space) will focus on registration, identification, approval to fly, etc., and cooperative traffic avoidance such as FLARM requires that other parties be equipped as well, future UAVs should be able to robustly detect uncooperative parties and avoid mid-air collisions in airspace. To ensure the highest robustness and to increase sensitivity and accuracy, a combination of several sensors systems by multi-sensor data fusion techniques is highly recommended. This paper formulates a novel multi sensor data fusion algorithm, that is a joint approach of Multiple Hypothesis Tracking algorithm and Particle Filtering. The union of these two algorithms combines the strength of the Multiple Hypothesis Tracking for data association with the robustness of the Particle Filter to estimate the position of the tracked objects. This joint approach has been validated with the use of simulated data.
A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic

Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets.
A Methodological Study of SMS Spam Classification Using Machine Learning Algorithms

Despite the increasing growth of protocol-predicated messaging systems, SMS remains a debatable communication medium in contemporary societies. For its representation, several firms regard SMS to be preferable to emails. Spammers have used SMS to attract the attention of mobile phone users. By delivering SMS to the link or personally contacting the target, the attacker can steal personal data. Therefore, developing a system is mandatory to detect spam. In the present study, with the help of the machine learning, the accuracy of six different models namely Random Forest, Gradient Boosting, Extra Trees, Logistic Regression, Support Vector Machine, and Multinomial Naive Bayes were evaluated and compared using various pre-processing methods that include lemmatization and stemming, subsequently, TF-IDF extraction method was utilized to conclude better pre-processing method and classifier based on ROC AUC curve, accuracy score, f1 score, and balanced accuracy score.
Research on Risk Prediction Model Construction and Learning Strategy of Distance Learning

With the popularization of distance education, the monitoring and improvement of distance education quality has become the focus. There are many distance learners, but only a few teachers or their teaching team. The teacher-student ratio is very low. Coupled with the teaching method of time-space isolation, it is more and more important to study how to reduce the risk of failing in learning, stimulate learning motivation and help teachers realize personalized teaching in the case of time-space separation.
Advanced Generative Adversarial Network for Image Superresolution

The superresolution (SR) method based on generative adversarial networks (GANs) cannot adequately capture enough diversity from training data, resulting in misalignment between input low resolution (LR) images and output high resolution (HR) images. GAN training has difficulty converging. Based on this, an advanced GAN-based image SR reconstruction method is presented. First, the dense connection residual block and attention mechanism are integrated into the GAN generator to improve high-frequency feature extraction. Meanwhile, an added discriminator is added into the GAN discriminant network, which forms a dual discriminator to ensure that the process of training is stable. Second, the more robust Charbonnier loss is used instead of the mean square error (MSE) loss to compare similarities between the obtained image and actual image, and the total variation (TV) loss is employed to smooth the training results. Finally, the experimental results indicate that global structures can be better reconstructed using the method of this paper and texture details of images compared with other SOTA methods. The peak signal-to-noise ratio (PSNR) values by the method of this paper are improved by an average of 2.24 dB, and the structural similarity index measure (SSIM) values are improved by an average of 0.07.
Mixture of Experts for Facial Forgery Detection

With the rapid development of artificial intelligence, facial manipulation technology has also evolved remarkably. This has led to the problem of malicious misuse of technology in a society and also developed the research topic of facial forgery detection. In practice, substantial videos are processed by unknown compression and low-quality methods, which makes the detection of facial forgery extremely challenging. We find that deep neural networks trained for specific forged features have better robustness in the presence of unknown compression and low quality. For this reason, we propose an approach to improve facial forgery detection by implementing a divide-and-conquer idea based on the mixture of experts (MoE) framework. (1)&#xA0;Based on the characteristics of the forgery method itself, the facial forgery detection problem is divided into face-swapping detection and expression-swapping detection. (2)&#xA0;Based on the special similarities and differences between expert models, we design an effective lightweight combination method. (3)&#xA0;We propose the focal loss with adaptive decay (FAD) loss function, which effectively alleviates the problem of over-focusing on noisy samples of the focal loss function and further improves the similarity and difference between expert models. Numerous experiments have demonstrated the effectiveness and superiority of our framework on FaceForensics++ (FF++) datasets and benchmarks.
PENERAPAN ALGORITMA BRUTE FORCE PADA MENU SEARCH WEBSITE "CALONKU" DALAM RANGKA PEMILU BERBASIS WEB

General elections are a means of people's involvement in the government process. Democratic countries, including Indonesia, conduct general elections. General election does not always go smoothly, at times tinged with some constraints. The lack of information has prompted a platform to contain the complete information that voters need. Website "Calonku" was created to meet the shortage of such information. In this study, the website "Calonku" implements the algorithm in its search feature, brute force deemed appropriate for solving problems using a straightforward approach. This research discusses the brute force algorithm's appropriate level and effectiveness on the "Calonku" system. Testing the brute force algorithm's performance is by calculating the recall, precision, and accuracy. This test calculates the similarity of each document to the given query. Similarity calculation will produce a weight on each paper that determines how relevant it is to the question. In this study, the average Accuracy value was 0.76 Recall of 0.85, and an average Precision of 0.83.
Intra-Hour Pv Power Forecasting Based on Sky Imagery

This paper introduces an enhanced version of the PV2-state model, for intra-hour PV power forecasting. The model operates as follows: if it is expected that the Sun will shine at the forecast time, the PV power is estimated with a real-time adjusted clear-sky model; otherwise, the estimated PV power under clear sky is real-time corrected according to the cloud transmittance. The relative position of the Sun and clouds is measured by the sunshine number SSN, a binary quantifier stating whether the Sun is shining (SSN = 1) or not (SSN = 0). A physics-based approach to intra-hour forecasting, processing cloud field information from an all-sky imager, is proposed to predict SSN. The model performance with the new procedure is investigated from three perspectives: forecast accuracy, forecast precision and the response to the variability in the state-of-the-sky. The study was conducted with high-quality 1-minute data collected from a micro-PV plant. In combination with the proposed procedure for SSN forecasting, PV2-state proves a notable advance in performance when operated in harsh conditions (high variability in the state-of-the-sky and/or longer forecast horizon), in which the performance of models is, generally, low.
Good Governance And Economic Growth In Africa

African countries face with the problem of good governance and steady economic growth for over period of time. Therefore, the paper aims to examine the good governance and economic growth for the 50 African countries for the period of 2002 to 2020 using the System GMM approach. The data were sourced from the World Development Indicators and World Governance Indicators (2020). The Empirical findings indicated that all the good governance indicators are positive and statistically significant in influencing the level of economic growth in the region, except the government effectiveness which is positive but statistically insignificant. Based on the findings, African countries should provide extensive policies that promote good governance in the region as the region is the backward in terms of good governance compare with other regions in the world. More so, governments in the region should strengthen their political will through transparency and accountability in all aspect of government activities which will reduce the level of political unrest and promote regulatory quality in the region and propel growth.
Scope of Augmented Reality in Education

Abstract: Augmented Reality (AR) can be defined as a technology in which virtual objects are mixed with the real-world. Users of AR can interact with virtual 3D models just as they do in real life. Augmented Reality (AR) applications nowadays used in many sectors such as Architecture, Science, Military etc. But our focus for this study is Education sector. As in AR technology virtual objects is combined with real objects this can be used by teachers and educational institutions to explain different topics to students easily. This also increase interest of students as AR provides very immersive and enjoyable experience for the students. There are many applications out there that can help students in learning many complex topics easily as AR can be used to display many 3D models that helps students in learning. When students interact with virtual objects they can learn more easily and in a fun way. Also, for teachers AR will help them in explaining concepts to students as they have good reference to explain. In the light of all these, this study focuses on the positive impact that AR can make in education system. In this study we will dive deep in how actually AR must be implemented in education so that students and teachers can get benefited from this revolutionary technology. After that we will be discussing about different SDKs that can be used for developing AR based applications and finally, we will be focusing on which apps are available nowadays that uses AR to promote learning. Keywords: Augmented Reality (AR), Software Development Kit (SDK), Virtual Reality (VR)