Reviewerly/exHarmony huggingface dataset:
Actual features of the assistive technologies market

The article focuses on the actual features of the assistive technologies market. The author distinguishes two market segments: traditional technologies and the latest assistive ones which are based on modern scientific and technological achievements and are frequently invasive for humans. The author analyzes the dynamics of patent activity connected with the development of assistive technologies. She identifies the main trends over the period under study (2010-2019). The analysis of the geographical structure of patent activities revealed the undisputed leadership of the United States as the most attractive market for assistive technologies holders. Yet, the author pinpoints the growing role of China and the possibility of the leaders’ displacement for some groups of technologies in the near future.
Hourglass Dehazing Network Based on Multi-scale Parallel Fusion

As a common weather phenomenon, haze currently has some negative effects on advanced computer vision tasks. Image dehazing, as a significant technique, plays a crucial role in deploying downstream vision tasks. In recent years, CNN-based end-to-end models have been proved to have great advantages over traditional image enhancement-based methods in removing image haze. However, some previous works failed to effectively utilize multi-scale features and perform reasonable feature fusion, resulting in the model losing the ability to restore details and colors. In this work, we adopt hourglass network which is a mature framework to better utilize the bottom-up and top-down symmetric topology structure for feature fusion and representation, so as to better recover the details and color information of haze-free images. At the same time, for the phenomenon of relatively indirect feature fusion in hourglass network, we propose a multi-scale parallel feature fusion module to perform more direct and effective feature fusion, thereby promoting the flow of features within different scales. What’s more, for the sake of strengthening the recovery power of the network for high-frequency information, we raise a spatial-frequency domain channel attention mechanism, so as to more effectively utilize spatial and frequency information for feature representation and enhancement. Comprehensive experiments on the public RESIDE dataset confirm the effectiveness of our proposed network that can satisfactorily reconstruct more elaborate and realistic colors.
Model Compression for Non-Sequential and Sequential Visual Pattern Recognition Networks ― A Hybrid Approach

Visual Pattern Recognition Networks (VPRNs) delivers remarkable performance using Deep Neural Networks (DNN). Various DNN architectures are available such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and its variants. Continuous emergence of novel architectures, availability of big data and enormous computing powers keeps this area flourishing. However, such DNN based VPRN models are plunged with enormous computational complexities, intense memory requirements, and high power consumptions which restricts its use in edgeAI environments. For instance, a typical pre-trained VGG-16 model needs 500MB memory, has over 138 million parameters and involves 15.5 billion Floating Point Operations (FLOPs). Such overheads demand for compression of VPRN models while retaining its performance. In this regard, various compression methods have been reported in literature. This paper presents experimentation on hybrid compression method which combines pruning and quantization to achieve higher compression ratio for nonsequential as well as sequential DNN based VPRNs.
Enhancement of Liver Ultrasound Images by Guided Image Filtering Technique

The liver ultrasound images suffer from low contrast and resolution, that results in deterioration of the quality of such kind of images as well as having less clarity of fine details i.e. edges or boundaries within the image as well. Therefore, visual interpretation of liver US images and its analysis for disease diagnosis becomes a challenging task for medical examiners or sonographers. So there is need of enhancement of these images so as to figure out the significant details of the image accurately. In this paper, an attempt is made for enhancing the liver US images by utilizing guided image filter (GIF) technique, in order to obtain images with suppressed noise & more highlighted fine detail that will help in liver ultrasound image analysis for any abnormality identification associated to it. The result outcome from the experimental analysis reveals that better enhancement of liver ultrasound images is achieved after applying weighted guided image filter (WGIF) technique. It is justified by the obtained image quality metrics, for GIF-technique PSNR value is (32.575-35.007) and SSIM value is (0.8269-0.8785), whereas for WGIF-technique, the range of PSNR value is (49.056-51.250) and SSIM value is (0.993-0.995) respectively.
Research on Arbitrary Shape Tibetan Text Detection with Graph Network

Text detection is an important field in computer vision. The research on Chinese and English text detection has achieved excellent results. But Tibetan text detection still needs further development. We use a relational reasoning graph network to improve the detection of arbitrary-shaped Tibetan text. The method is divided into two parts, text proposal part based on Convolutional Neural Network (CNN), and relational reasoning based on Graph Convolutional Network (GCN). Experiments on Tibetan text image datasets demonstrate the excellent performance.
Low-cost Embedded System Framework For Industrial IoT Solutions

These days, with the rapid growth in the internet of Things (IoT) technology, embedded systems incorporate IoT devices, making computation truly pervasive. In almost every industrial applications, continuous monitoring / tracking of the real time system is required in order to maintain track of each operation. In this work, we present a low cost embedded system framework to implement industrial IoT. The concept is implemented and demonstrated successfully for the automation of industrial sewing process. We have implemented IoT solution on Wemos Lolin32 which is ESP32 based 32-bit development board. The application demands the parallel execution of various tasks, so we have proposed multi-core programming in which an application demands parameter sensing, counting and data communication with the server, all these operations are per-formed parallely. This enables, the real time counting and data communication for effective implementation of IoT framework. The proposed framework is very cost effective compared to the existing state of the art available for the similar operations. The proposed framework is scalable and can be implemented for various other industrial applications with data flow.
Real-Time EAR Based Drowsiness Detection Model for Driver Assistant System

Driver exhaustion can be an essential factor in an excessive number of vehicle accidents. Detection technology or avoiding drowsiness at the wheel may be a significant challenge in collision avoidance systems. This research aims to develop a model of a drowsiness detection system that can track whether the driver's eyes are open or closed in real-time. It is thought that early identification of signs of driver exhaustion, such as watching the eyes, is often enough to avoid a road accident. The detection of fatigue necessitates a series of eye photographs to observe the eye movements and blink patterns. The proposed system is based on eye localization, which entails scanning the captured image to determine the eyes' location using the facial landmarks algorithm with the help of Dlib and OpenCV. It determines whether they are open or closed to detect drowsiness, adding a feature for the driver assistant system to avoid collisions.
Operator Motion Comparison with Standard using Computer Vision and Deep Learning

The new recruits in garment production lines are manually trained via instructor-led training. Slight deviations in operator motion compared to Standard motion defined in Garment Sewing Data (GSD), impact operator efficiency, and global output. Hence, operator motion represents the key to running at superior efficiency. Trainees are supervised by trainers for 4–6 weeks on training lines and then transferred to production lines if qualified. It is challenging to detect efficiency loss due to non-GSD micro motions performed knowingly or unknowingly. With advancements in vision systems and Machine Learning or Artificial Intelligence techniques, there's an opportunity to compare operator micro-motions captured using a vision system with micro-motions specified in GSD to find motion variations and thus decrease the time needed for training.
MSA-VNet: Multi-scale Attention-based V-Net for DCE-MRI Lesion Segmentation

Dynamic Contrast Enhancement MRI (DCE-MRI) has become an essential tool for detecting breast cancer in recent years. However, the shape and size of lesions vary widely, and the boundary of lesions is blurry. This work proposes a multi-scale attention-based V-Net (MSA-VNet) for DCE-MRI lesion segmentation, to address these issues. MSA-VNet, based on V-Net, initially employs the 3D multi-receptive-field feature extraction module, which includes multi-branch residual structure, atrous convolutions, and instance normalization layers. Second, to replace the long-range skip connection structure in V-Net, an attention-based long-range skip module is proposed. Finally, the Focal Toversky loss function is introduced in MSA-VNet to enable the model to focus on tiny lesions. The experiments on the breast cancer DCE-MRI dataset show that, the proposed method outperforms the state-of-the-art methods.
Feasibility Study on Browser-Based Federated Machine Learning (FML) Architecture for Medical Application

Traditional approaches to Artificial Intelligence (AI) based medical image classification requires huge amounts of data sets to be stored in a centralized server for analysis and training. In medical applications, data privacy and ownership may pose a challenge. In addition, costs incurred by data transfer and cloud server may pose a challenge to implementing a large dataset. This work studies the feasibility of a decentralized, browser-based Artificial Intelligence (AI) federated machine learning (FML) architecture. The proposed work studies the feasibility of bringing training and inference to the browser, hence removing the need to transfer raw data to a centralized server. If feasible, the system allows practitioners to compress and upload their pre-trained model to the server instead of raw data. This allows medical practitioners to update the model without the need to reveal their raw data. A sandbox system was implemented by applying transfer learning on MobileNet V3 and was tested with chest X-ray image datasets from COVID-19, viral pneumonia, and normal patients to simulate medical usage environment. The training speed, model performance and inference speed were tested on a PC browser and mobile phone with various levels of network throttling and image degradation.
JETAM: A Joint Embedding Method for Research Paper Mutil-Label Classification

With the increase of published research papers, an information explosion problem is formed in the process of paper retrieval, and to overcome the retrieval difficulties, papers can be classified by tagging them. In this paper, we propose a joint embedding method for paper multi-label classification. For the title text, this paper establishes the title embedding model (TEM), based on Transformer. On the other hand, considering the long text feature of abstracts, this paper combines Glove(word vector pre-training model) and GRU network to extract deep features and build an abstract embedding model (AEM), and finally, the joint embedding representation of research paper is obtained by jointly modeling the title embedding and abstract embedding based on the two-tower structure. In addition, in the paper’s multi-label classification task, the proposed method is compared with several baseline models on the CiteULike dataset, and the proposed method outperforms them all.
Mining for Framework Instantiation Pattern Interplays

Software frameworks define generic application blueprints which can be instantiated into an application through application-specific instantiation actions such as overriding a method or providing an object that implements an interface. In case the framework's documentation falls short, developers may use other instantiations of the same framework as a guide to the required instantiation actions. In this paper, we propose an automated approach to mining framework instantiation patterns from existing open-source instantiations. The approach leverages a graph-based representation to capture the common ways of implementing instantiation actions as well as their interplays, so called instantiation interplays. As a case study, we mined for patterns in a set of 2,028 Java projects that instantiate four of the most popular Java frameworks. We also classify the extracted interplays according to the different contexts in which they occur. We found that our approach discovers relevant practices and interplays that are not covered by previous approaches. Our results will allow developers to have a better understanding of the frameworks they instantiate.
Position Transformer for Image Retrieval

In recent years, the methods of combining global and local features have shown great potential in image retrieval task, the most recent and impressive work focus on feature fusion of two features. In this paper, based on the method mentioned above, we proposed a new feature fusion module, named Position Transformer, which takes advantage of the self-attention mechanism and uses its relation matrix of the final layer as relation scores between the features, and thus conducts an effective and efficient feature split. The precise feature split helps later feature fusion which handles the problem of information loss caused by roughly joining features from multiple spatial dimensions. Moreover, we introduce model fusion and contrastive loss to help train a robust model, ablation experiment has proved the rationality of these designs.
Recognition of Dynamic Hand Gesture using Hidden Markov Model

A simple methodology for Malaysian Sign Language recognition using image processing is developed. Frame difference, thresholding of the frame difference and edge detection are used for the preprocessing stage. For the segmentation part, HSV color detection is used to detect the skin color. Combination of threshold of the frame difference, edge detection and HSV color detection are used to segment out the hand region. Feature extraction is used to track the hand gesture path. Centroid of the hand motion and 16-directional codewords are used. For classification, Hidden Markov Models (HMM) is used to recognize the hand gesture. A total of 100 videos are used for training and testing purpose. Overall, there are 14 gestures of the Malaysia Sign Language that were used in the recognition. The percentage of recognition for the testing set is 92.86%.
On the Usage of Programming Languages in the iOS Ecosystem

This paper studies how developers use different programming languages in the iOS ecosystem by examining 161,883 releases of 25,231 third-party libraries spanning 11 years available through CocoaPods, a popular iOS dependency manager. Our empirical study shows that since its release, Swift has been widely adopted, but most libraries, even recent ones, still use Objective-C as their primary programming language. Looking at a small set of 38 open-source iOS apps, instead, we observe that apps are instead predominantly written in Swift by now. We also observe significant C usage across both libraries and apps. Our results suggest that analysis tools for iOS apps should not only support Swift, but also Objective-C and C code.
LSTM and K-means Algorithm for Irradiation and Temperature Clustering to Forecast Power Output of a PV Array Installed in Benin

In this paper, we present a photovoltaic power forecasting technique based on the clustering over a large area (Benin territory), of parameters influencing the output power of a photovoltaic generator such as irradiation and temperature. We associated a time series clustering model (k-means) and a time series prediction model (LSTM). To perform the clustering, we identified a total of 287 points over the whole Beninese territory. For each point, irradiation and temperature data were downloaded from the NASA database from January 1st 2015 to December 31st 2021. From these 287 points, we arrived at 18 clusters for irradiation and 39 for temperature with a Silhouette score of at least 0.99 between elements of the same cluster. Then, using the LSTM model, we predicted irradiance and temperature for a 24-hour period with RMSE 3.66 W/$\text{m}^{2}$ and 1. 11°C and regression coefficients R, 0.97 and 0.95 for irradiance and temperature respectively.
Statistical Learning based Treatment Effect calculation for Spurious Classifiers

The predictions of text classifiers are often driven by spurious correlations - e.g., the term Spielberg may correlate with positive movie reviews, but the term itself does not convey a positive meaning. In this paper, we proposed alternative methods to distinguish spurious and genuine correlation in text classifiers based on previous work. We use logistic regression and MLP to replace matching in training word classifier and get the ITE for each method. Also, we aim to show the role of ITE in causal inference tasks. Experiments on two datasets (IMDB reviews and Kindle reviews) suggest that logistic regression and MLP perform better for feature selection on larger datasets, and the use of ITE can improve the accuracy for identifying spurious correlation.
Protecting Author Royalty of Digital Assets Using Blockchain and Elliptic Curve Cryptography

Digital assets are one of the assets that have become an option in the internet era. With the internet’s adoption, many creators create digital assets and sell them via the internet. A proper sales mechanism is needed to tackle this phenomenon. Lack of transparency on sales became a consideration for a creator who wanted to sell an asset in this medium. Most publishers do not honestly publish the number of sales to a creator because all the data is stored privately in the publisher server and can only be accessed by the publisher. This condition can lead to unfair distribution of royalties for the creator. To overcome the centralized system’s weakness, researchers proposed using smart contracts in the public blockchain. In this case, the data link of the digital assets file is kept as plain text in the smart contract, such that the user can directly obtain or copy the digital assets from the smart contract. To overcome this problem, this study uses the Ethereum smart contract for became an intermediate, IPFS for distributing the file, ERC-721 as Token standard ownership on the Ethereum blockchain, and ECC to secure the data link of the digital assets file on the smart contract. Based on the discussion, it is proven that the proposed method can keep the link of the digital assets file secured such that the author can maintain his intellectual property.
A Lightweight Multifactor Authentication Scheme for Wireless Sensor Networks in the Internet of Things

Internet of Things (IoT) has become an information bridge between societies. Wireless sensor networks (WSNs) are one of the emergent technologies that work as the main force in IoT. Applications based on WSN include environment monitoring, smart healthcare, user legitimacy authentication, and data security. Recently, many multifactor user authentication schemes for WSNs have been proposed using smart cards, passwords, as well as biometric features. Unfortunately, these schemes are shown to be susceptible towards several attacks and these includes password guessing attack, impersonation attack, and Man-in-the-middle (MITM) attack due to non-uniform security evaluation criteria. In this paper, we propose a lightweight multifactor authentication scheme using only hash function of the timestamp (TS) and One Time Password (OTP). Furthermore, public key and private key is incorporated to secure the communication channel. The security analysis shows that the proposed scheme satisfies all the security requirement and insusceptible towards some well-known attack (password guessing attack, impersonation attack and MITM).
Static Alphabet American Sign Language Recognition using Convolutional Neural Networks

Sign language recognition is one of the vital research problems in computer vision systems. Sign language is used as a natural mode of communication by speech and hearing-impaired people in their daily life activities. This paper proposes a useful method to recognize static gestures with the application of Deep Learning. The pre-trained deep learning models trained on the ImageNet data and transfer learning technique are used to recognize 24 static alphabets of the American Sign Language (ASL) dataset. The publicly available benchmark American Fingerspelling dataset is used for the training of models. The paper presents the comparison of Convolutional Neural Network (CNN) models like VGG16, VGG19 and MobileNet on the same dataset and achieved average validation accuracy of 94.9% using the MobileNet model that gave good predictions using cross-validation. Also, the model has performed better than previous studies on the same dataset. The evaluation of the model performance is presented in the confusion matrix for the best trial fold that gives the prediction results of all 24 alphabets as recognition accuracy in percentage form.
Face Recognition to Determine Visitor Attraction Using Residual Deep Neural Network

The development of social media in the form of websites and android applications should be appreciated. The background of this research is the stage to develop the software needed to know the age range. The objective thing to observe is through the visitor’s face on the web or application. The purpose is to avoid false information and get the proper process flow. The Technology used is deep learning for facial image recognition. The methods use Convolutional Neural Network with residuals because the advantage is using multi-branch layers but having a stable training process. The use of augmentation is needed to increase the variety of facial recognition image positions and to overcome unbalanced classes. The dataset used consists of seven categories, namely Children, Youth, Early Workers, Middle Ages, Pre-Retirement, Retirement, and Old People, which Bappenas regulate. Each folder contains about 1157 images with a total data of 8,105 images. The training process results can obtain a model with an average accuracy of 99.08% and a computational training time of about three hundred minutes. The model’s accuracy is 99.08%, with MSE 0.0037, RMSE 0.0610, and MAE 0.0037. Testing time is about 2 seconds.