Reviewerly/exHarmony huggingface dataset:
GSAS: Enhancing efficiency of human activity recognition using GRU based Sub-activity stitching

Recognition of human activities requires the design of highly efficient models for key-frame selection, image segmentation, feature extraction and selection, sub-activity classification, and sub-activity stitching. Due to the advent of effective frame selection and segmentation methods, most of the research in human activity recognition (HAR) is focused on feature extraction and its post-processive classification. Most of these models work on the principle of direct activity recognition, wherein the entire video sequence is scanned, and the final activity is recognized from it. In this approach has some lacunas, which include, limited accuracy due to classification of a large number of frames; limited scalability due to large delays for training and evaluation, and limited applicability due to the requirement of large datasets for model training and validation. To remove these drawbacks, this text proposes the design of a novel gated recurrent unit (GRU) based sub-activity stitching model (GSAS). The proposed model works in 2 phases, which include, sub-activity recognition using GRU, followed by sub-activity stitching using a 1D convolutional neural network (CNN). The former converts input frames into numerical sub-activity instances, while the latter processes these instances and converts them into final activity types. Due to the use of this 2-layered architecture, the proposed model can achieve a precision of 89%, a recall of 85%, and an accuracy of 90% which is higher than most of the recently proposed HAR models. The evaluation was done on untrimmed datasets, which makes the proposed model applicable to real-time video sequences. This text also recommends some transfer-learning approaches that can be applied to the proposed model to further improve its performance in terms of scalability and accuracy.
Different Data Enhancement Methods applied on Imbalanced Data

Imbalanced data distribution caused over-centered performance when doing humorousness rating. The majority of data focuses on a specific part instead of varies at an appropriate range. This paper aims at implementing an ideal combination of different methods to improve the final result of the deep learning model. We tried various methods to smooth the skewed distribution. The results show that the best collaboration of methods is kernel density estimation, synonym substituting, sentence paraphrasing, under-sampling to develop the model performance. After testing more than five times, it could be seen that the model performs a little better than the model designed previously, no matter from which aspects. Although the validation loss and training loss was controlled at an acceptable error, we still encountered some dreadful challenges like missing predictions of the actual data, the severely skewed distribution of data predictions.
The Case for Phase-Aware Scheduling of Parallelizable Jobs

Parallelizable workloads are ubiquitous and appear across a diverse array of modern computer systems. Data centers, supercomputers, machine learning clusters, distributed computing frameworks, and databases all process jobs designed to be parallelized across many servers or cores. Unlike the jobs in more classical models, such as the M/G/k queue, that each run on a single server, parallelizable jobs are capable of running on multiple servers simultaneously. When a job is parallelized across additional servers or cores, the job receives a speedup and can be completed more quickly.
Self-supervised depth estimation with uncertainty-weight joint loss function based on laparoscopic videos

In this paper, we propose a self-supervised depth estimation with uncertainty-weight joint loss function based on laparoscopic videos. Although self-supervised learning has achieved impressive performance in depth estimation using pose estimation as an auxiliary task, it still shows undesired results for the pose estimation. Different from streetscape datasets, the laparoscope motion is limited by the minimally invasive surgery settings. It is challenging to estimate laparoscopes’ poses with complex rotations from RGB images. To address this issue, we propose an improved self-supervised depth estimation method with relative pose loss for laparoscopic videos. Furthermore, we adopt homoscedastic uncertainty to weigh our loss function to balance each subtask. In addition to the evaluation for known datasets, we also tested the generalization ability of our proposed method using known datasets and unseen datasets. The experimental results showed that our proposed method outperforms baseline for depth estimation and pose estimation on known datasets and had competitive results on unseen datasets. For depth estimation, the proposed method had about 10.5% improvement on RMSE evaluation compared to the baseline.
Research on fake online review recognition method based on feature engineering

In the context of the explosive growth of online reviews in e-commerce, some merchants began to hire consumers to make fake purchases to increase sales, which caused the difficulty in identifying fake reviews. Therefore, from the perspective of consumers’ shopping behavior, a feature recognition method based on comment text, reviewer attribute and reviewer’s shopping behavior (referred to as TAB) is proposed to solve the problem of fake reviews caused by fake shopping behavior. Compared with BOW, TF-IDF, N-gram, FSP and other traditional feature extraction methods, experiments were carried out on Naive Bayes, SVM, Logistic Regression, Random Forest, CNN and other classification models. By changing the data dimension, the stability of each false comment recognition model was analyzed. The experimental results show that the TAB feature recognition method has better classification performance on logistic regression and support vector machine models, and the model is relatively stable, and the classification effect is not easily affected by dimension change.
Synthetic data generation for watermark extraction from 3D printed objects

The use of computer generated images to train Deep Neural Networks is a viable alternative to real images when the latter are scarce or expensive. In this paper, we study the effectiveness of various rendering techniques in the generation of training data for the task of watermark retrieval from 3D printed objects. We created eight training sets, each one with a different lighting environment, and tested them on three different network architectures, ResNet, U-Net and a combined architecture developed by us. The test set consisted of photos of 3D printed objects produced from the same CAD models used to generate the training set. Our results show that the effect of the lighting environment is comparable in significance to that of the network architecture. We also show that both light probes, and modelled lighting environments, can give good results. In the case of light probes, we identified as two significant factors affecting performance the similarity between the light probe and the test environment, as well as the light probe’s resolution.
A Context-Aware Automatic Smartphone Reconfiguration

New technologies emerge every day to improve people&#x0027;s well-being. However, as people&#x0027;s daily lives become more and more intense, some small details may go unnoticed. One of these issues is related to the suitable configuring smartphones in specific contexts. Failure in these settings can result in embarrassing situations. Examples of contexts might be at a meeting, at the cinema, or in a noisy place. This paper details a method to collect user profile, to notify the user and change these settings according to each user&#x0027;s profile and context using the computing power of smartphones. Examples of automatic adjustments are screen brightness, media volume and ring mode. The experimental results of a controlled experiment indicate that the system is promising because it was able to detect the context and make the necessary adjustments, even before the user&#x0027;s schedule activities.
Real-Time Tracking of Object Melting Based on Enhanced DeepLab<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mi>v</mi><mn>3</mn><mo>+</mo></math>Network

In order to reveal the dissolution behavior of iron tailings in blast furnace slag, the main component of iron tailings, SiO2, was used for research. Aiming at the problem of information loss and inaccurate extraction of tracking molten SiO2 particles in high temperature, a method based on the improved DeepLab v3+ network was proposed to track, segment, and extract small object particles in real time. First, by improving the decoding layer of the DeepLab v3+ network, construct dense ASPP (atrous spatial pyramid pooling) modules with different dilation rates to optimize feature extraction, increase the shallow convolution of the backbone network, and merge it into the upper convolution decoding part to increase detailed capture. Secondly, integrate the lightweight network MobileNet v3 to reduce network parameters, further speed up image detection, and reduce the memory usage to achieve real-time image segmentation and adapt to low-level configuration hardware. Finally, improve the expression of the loss function for the binary classification model of small object in this paper, combining the advantages of the Dice Loss binary classification segmentation and the Focal Loss balance of positive and negative samples, solving the problem of unbalanced dataset caused by the small proportion of positive samples. Experimental results show that MIoU (mean intersection over union) of the proposed model for small object segmentation is 6% higher than that of the original model, the overall MIoU is increased by 3%, and the execution time and memory consumption are only half of the original model, which can be well applied to real-time tracking and segmentation of small particles.
Underwater image stitching method based on optimal seam and multi-resolution fusion algorithms

Underwater high-resolution optical imaging is widely used in marine resource development, ecological monitoring, underwater archaeology, underwater search and rescue, and marine scientific research. However, small field of view (FOV) in optical imaging is ineffective for large scene mapping. Underwater image stitching aims to generate a highresolution panoramic image containing more information from a series of small-FOV and high-resolution images. This paper proposes an underwater optical image stitching method, including image alignment algorithm, optimal seam algorithm, Hue-Saturation-Value correction algorithm based on histogram matching, and multi-resolution fusion algorithm. The experimental results show that the proposed algorithms are effective for targets in different environments, and the panorama can be produced without artifacts or visible seams.
A storage-efficient ensemble classification using filter sharing on binarized convolutional neural networks

This paper proposes a storage-efficient ensemble classification to overcome the low inference accuracy of binary neural networks (BNNs). When external power is enough in a dynamic powered system, classification results can be enhanced by aggregating outputs of multiple BNN classifiers. However, memory requirements for storing multiple classifiers are a significant burden in the lightweight system. The proposed scheme shares the filters from a trained convolutional neural network (CNN) model to reduce storage requirements in the binarized CNNs instead of adopting the fully independent classifier. While several filters are shared, the proposed method only trains unfrozen learnable parameters in the retraining step. We compare and analyze the performances of the proposed ensemble-based systems depending on various ensemble types and BNN structures on CIFAR datasets. Our experiments conclude that the proposed method using the filter sharing can be scalable with the number of classifiers and effective in enhancing classification accuracy. With binarized ResNet-20 and ReActNet-10 on the CIFAR-100 dataset, the proposed scheme can achieve 56.74% and 70.29% Top-1 accuracies with 10 BNN classifiers, which enhances performance by 7.6% and 3.6% compared with that using a single BNN classifier.
Multiple Linear Regression Model of Environmental Variables, Predictors of Global Solar Radiation in the Area of East Lima, Peru

Abstract Multiple regression models are very relevant to predict values using predictor variables. The objective of this study was to predict the global solar radiation in the year 2019 in the area of East Lima, Peru. Three continuous quantitative predictor variables were analyzed: temperature, humidity, wind speed and the response variable was global solar radiation, resulting in a model with excellent significance p&lt;0.001 that shows the prediction is effective. The multiple linear regression method was used, finding an average global radiation of 175 W/m2 and predictor variables with average temperature of 19.2 °C, humidity 23.9% and wind speed 1.77 m/s, with the highest temperature in summer recorded at 24.6°C, the highest humidity of 51.2% in autumn, the highest wind speed in summer at 2.63 m/s and the highest maximum global solar radiation in spring with 183 W/m 2 .
Higher-order Nonclassicality in Superposition of Three-mode Photon-added Trio Coherent State

In this paper, we study some higher-order nonclassical properties of the superposition of three-mode photon-added trio coherent state such as antibunching, squeezing, and entanglement. The results show that in the case with fixed the higher-order, when increasing the numbers of added photons, the manifestation of the higher-order three-mode sum squeezing is more obvious, but the degrees of higher-order antibunching, and higher-order three-mode entanglement are more reduced. Besides, with fixed the numbers of photon addition to the superposition of three-mode photon-added trio coherent state, the higher-order nonclassical properties as antibunching and entanglement are more pronounced, but three-mode sum squeezing is more decreased when increasing the number of the higher-order.&#x0D;
POST COVID-19: Digital Epistemology and Flexible Education in Digital Era

This essay presents a strategy on school innovations supported by ICTs, MOOCs, and the internet in pedagogies as well as in learning modalities during and after COVID-19. A review of the literature on educational systems and innovation in educational quality and epistemology of digital education is included. Therefore, the problem is to flexibly rethink the internal processes of the school, but also to develop technological skills for both teachers and students in the transition from the traditional epistemological model to the digital one.
Household Behavior Observation System Utilizing Mesh Sensor Network Consisting of Smart Taps

In recent years, the devices focusing on the smart home have rapidly been spreading and have been expected to be applied to the services that support daily life of the residents such as the observation of daily activity of the elderly people living alone and controlling the energy efficiency. To realize the smart home, the system that can recognize daily activities of the residents with high accuracy and low cost is indispensable. The existing studies have proposed the recognition methods using various sensors such as cameras and ultrasonic sensors for estimating the living behavior. However, the existing methods have problems in terms of the privacy and installation cost. Therefore, in this study, we propose and develop a new behavior observation system that estimates daily activities of the residents by placing smart taps supporting the wireless communication on various places at home. The smart tap has a function of measuring and analyzing the power consumption of the connected devices and the received signal strength of radio waves between the smart taps. In addition, the system constructs a mesh network of the smart taps using the Thread, a type of short-range wireless communication, and the location and behavior of the residents are estimated by collecting and analyzing the measurement data. By analyzing only the measurement results of the power consumption of appliances and the received signal strength of the radio wave, the proposed system achieves accurate behavioral estimation with no privacy issues and low installation cost.
How Much a Model be Trained by Passive Learning Before Active Learning?

Most pool-based active learning studies have focused on query strategy for active learning.In this paper, via empirical analysis on the effect of passive learning before starting active learning, we reveal that the amount of data acquired by passive learning significantly affects the performance of active learning algorithms.In addition, we confirm that the best amount of data that should be acquired by passive learning depends on the given settings: network complexity, query strategy, and datasets.Inspired by these observations, we propose a method to automatically determine the starting point of active learning for the given settings.To this end, we suggest entropy of sample-uncertainty to measure the training degree of a target model and develop three empirical formulas to determine an appropriate entropy of sample-uncertainty that should be obtained by passive learning before starting active learning.The effectiveness of the proposed method is validated by extensive experiments on popular image classification benchmarks and query strategies.
Cloud-Based Model for Detecting Skin Cancer

Medicine is a crucial sector in human society and as generations go by, there is the constant strive to improve on healthcare to make diagnoses, cures, treatment, etc. better and easier. With the rise of machine learning and artificial intelligence in several markets, there is the obligatory attempt to use it in healthcare as well, wherein successful use cases have been developed; there have been multiple papers on using deep learning in medicine, and deep learning has shown immense potential in classifying medical images for detecting various diseases. This project aims to use deep learning to train a model to classify images of skin lesions as malignant or benign, and implement it in a marketable manner with a user sided interface. Cloud infrastructures have proven itself to be the go-to solution for hosting online services, because of its business efficiencies, cost savings, data security, interoperability and the redundancy of on-premise hardware. The focus on optimizing the current problem statement of detecting skin cancer is assisted by deploying the neural network model on the cloud service Amazon Web Services (AWS), to outsource data-intensive computing. To complete the planned execution, an Android mobile application was proposed so that the general public can make use of the neural network in a simple and easy-to-use manner.
Fine-grained remote sensing target detection with oriented bounding box based on improved Yolov5

Deep learning has achieved great success in the field of general target detection. However, remote sensing target detection have different characteristics from general image target detection in many aspects. Due to the high orbit height (usually over 500 km), the target usually only occupies a few pixels, which makes it difficult to detect. In addition, targets such as ships, trucks, etc. have a large aspect ratio, so traditional anchor designs are difficult to cover completely, resulting in failure of target detection because the intersection ratio of IOU is less than the typical value of 0.5. In addition, such targets are usually densely arranged when they are docked. The random imaging angle of satellite causes the traditional horizontal bounding box detection algorithm to be unable to accurately locate the target position. As a result, the deep learning training process is mixed with background information and cannot effectively learn the target features. Therefore, the oriented bounding box is used for labeling and training the remote sensing image. As a single-stage target detection algorithm and after several versions of the update, the detection accuracy and detection speed of the Yolo series algorithm have been greatly improved compared with the previous version. Based on the latest version of the Yolov5, the algorithm is modified to make it possible to support rotating target detection. Through the Gao-Fen competition of fine-grained classification of targets such as airplanes, cars, and ships (such as Boeing737, Boeing747, Boeing777, Boeing787, etc.), the average classification mAP value reaches 22.9235. For comparison, the circumscribed rectangle of the oriented bounding box is taken as the horizontal box which is used for another object detection called YOLOX training process, and the horizontal box target detection mAP value is 9.4679. The average classification mAP value of the modified Yolov5 algorithm is 13.4556 higher than the YOLOX-based horizontal box target detection mAP value. The rotating box training data is trained with another open source code called RotationDetection provided by Yangxue, The average classification mAP value obtained is 21.0232, which is lower than that of the modified Yolov5 algorithm by 1.9003. Therefore, the modified Yolov5 algorithm and the oriented bounding box have certain advantages, which can meet the needs of fine-grained rotating target detection in remote sensing images.
A comparative study of cluster-based Big Data Cube implementations

Research on Data Cubes scalability is extensive, yet sparse. Scalable design patterns for Data Cube implementations are a trend as the technology shifts from centralized and fully materialized models to distributed and partially materialized ones. The implementations explore cheaper and upgraded hardware in clusters of computer nodes. It is a common understanding that the parallel and distributed hardware enables to handle large amounts of multidimensional data for online analytical processing, up to billions of tuples or more, with increased performance and fault tolerance. However, the number of research works and their heterogeneity may overwhelm new initiatives in this field, as there is little discussion regarding the state-of-the-art and ways for improvement. Moreover, the baseline for comparison in most works is often too limited and requires that the reader crosscheck the information among many articles to identify possible gaps. In order to help identifying these gaps, we analyzed the works on Data Cube scalability and elaborated a comparative study that provides directions for new research on the parallel and distributed implementations of data cubes. We identified some features for comparison that include cube function, implementation technology, cube storage type, and various experiments information. We expect that the features and comparisons help researchers to identify research gaps and pave ways for future works on the field.
Outlier detection in Wireless Sensor Network for Health Care: A statistical approach

A wireless sensor network in the healthcare system collects data from remotely observed patients using contemporary medical sensors and a reliable network. The sensors used to generate a large amount of data. Two types of unusual events that may occur during data generation are anomaly data from malfunctioning sensors that causes a false alarm or data from a reliable sensor that reports abnormal values that causes a medical emergency in the patient&#x0027;s health condition. To improve remote patient monitoring systems and provide better healthcare, researchers must find out how to distinguish between a genuine medical emergency and a false alarm. This study recommends utilizing Spearman&#x0027;s rank correlation and simple linear regression to detect outliers in medical wireless sensor networks. The outliers are identified using regression with high detection accuracy and a low false alarm rate.