Reviewerly/exHarmony huggingface dataset:
The Effect of Climate Change on Rain Characteristics in the Mepanga Watershed

Abstract Climate change has brought changes to the characteristics of the rain, wherein the rainy season duration is short; however, the dry season is getting longer. This study aims to detect the climate change presence or absence, identify the relationship of climate change to the nature of rainfall, and the relationship of climate change to rainy, humid and dry months. This research was conducted in the Mepanga Watershed, Parigi Moutong Regency, Central Sulawesi, Indonesia. The Makesens method is used for climate change detection. The rainfall characteristics use standard deviation statistical methods; furthermore, the Oldman method is used for dry, humid, and wet months. The data used is rainfall data for 30 years (1990-2019) from Kayu Agung Station. The results show that there has been a climate change in the Mepanga watershed. Marked by a Z ≠ zero value, where there is a positive trend (Z&gt; 0) and a negative trend (Z &lt;0). The increase in rainfall and conversely indicates the positive trend; otherwise, a decrease in rainfall indicates a negative trend. There is an effect of climate change on the nature of rain, wet, humid and dry months but not significant.
Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering

The progress of a company is influenced by the excellent performance of its employee. The recruitment process should be done in a correct procedure so that it would not have the potential to harm the company. The improved use of social media can be an aspect to be applied in a recruitment process. LinkedIn is a social media platform that has many users which focuses on the career development aspect. Profile photos are commonly used in social media. In physiognomy, a personality analysis can be carried out based on his/her outward appearance. The profile photo can be an aspect of personality analysis with this knowledge. This research aimed to predict the face shape based on LinkedIn profile photos. A Cascade classifier algorithm with a haar-like feature was used to detect the face area. Dlib library was used to detect face landmarks. K-Means algorithm was used to differentiate the border of hair and facial skin. Indicators of the face shape calculation are the value of face angle, which is the arctangent of the face landmarks matrix; similarity value from the standard deviation calculation between horizontal line 1, 2, and 3; and diameter value which resulted from the standard deviation calculation between horizontal line 2 and vertical line 4. We provide output as face shape from the LinkedIn profile photos. Based on ten profile photo samples, only two predictions were incorrect.
A Rule-Based Approach for Marathi Part-of-Speech Tagging

This paper focused on developing the POS-tagger for Marathi. It is one of the very popular Indian languages spoken by the Marathi people. It has its semantic richness and standard in the literature and culture of Maharashtra. We deploy a technique to find Marathi words for their type, such as noun, verb or adjective, and so on. This task is carried out manually and marked in a corpus consisting of words already tagged with their corresponding part-of-speech. This system uses a rule-based approach based on the Marathi transformational grammar. It is important for preprocessing and developing NLP applications. In the absence or less information available related to other phrases and the possible existence of lexical or syntactic mistakes in the training corpus, our proposed system identifies a correct tag and finds its impact on their performance to verify usability for NLP applications. The overall accuracy of the system is 97.56%.
User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education, and entertainment. However, when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes.
Improved Pedestrian Detection Algorithm of Yolov4 Network Structure

When the YOLOV4 network detects pedestrians alone, the small target pedestrians will be missed, resulting in the reduction of P (Precision) and AP (Average Precision) values. This paper improves the YOLOV4 network structure. In order to improve the feature extraction capability of the network for small targets, a shallower feature layer is added to the original three output feature layers of the YOLOV4 backbone network to build PANet (Path Aggregation Network) together. And two SPP (Spatial Pyramid Pooling) structures are added to expand the receptive field. The channel attention mechanism module is added and some convolutional layers of the original network are deleted. Finally, transfer learning is used to make the detection effect better. The P value of the pedestrian on the PASCAL VOC data set increased from 84.43% to 91.37%, and the AP value increased from 74.78% to 87.39%, and the P value on the commonly used pedestrian detection data set INRIA (INRIA Person Dataset) increased from 93.20% increased to 98.02%, AP value increased from 91.08% to 94.02%. Experimental results show that the network has a better effect on pedestrian detection, and the accuracy and average precision are improved.
Improved Extreme Learning Machine Based on Deep Learning and Its Application in Handwritten Digits Recognition

Traditional extreme learning machine (ELM) requires a large number of hidden layer neurons in its applications, and the ability to process high-dimensional big data samples is weak. In response to the above problems, this paper proposes an improved extreme learning machine algorithm based on deep learning. This algorithm combines the double pseudo-inverse extreme learning machine (DPELM) algorithm, which has high classification accuracy and simple network structure, with the denoising autoencoder (DAE) which can extract more essential data features. Among them, DAE is used to extract the features of the data that needs to be recognized, and the DPELM mainly plays as a classifier to quickly classify and recognize the extracted features. Experimental results show that in the recognition of handwritten digits, the double pseudo-inverse extreme learning machine based on denoising autoencoder (DAE-DPELM) algorithm needs only a small number of hidden layer neurons. In addition, compared with the traditional ELM algorithm and DAE-ELM algorithm, DAE-DPELM algorithm has a higher classification accuracy.
Kaoline Mapping Using ASTER Satellite Imagery: The Case Study of Kefalos Peninsula, Kos Island

The present work aims to map kaolin occurrences on the Kefalos peninsula, SW Kos Island, Greece, through the elaboration of ASTER satellite imagery. The island of Kos is located on the eastern edge of the South Aegean Active Volcanic Arc (SAAVA) and is characterised by its complex geologic structure. During Plio-Pleistocene, the voluminous eruption of the Kos Plateau Tuff was recorded on Kefalos; the largest quaternary eruption in the Mediterranean. Kaolin is the product of hydrothermal alteration of the Pliocene volcanic rocks with rhyolitic composition. Our study emphasises the usefulness of satellite imagery combined with the Mixture Tuned Matched Filtering (MTMF) technique to detect occurrences of industrial minerals, kaolin-group minerals in this case, either in terms of raw mineral exploitation or by mapping hydrothermal alteration.
Improved Intelligent Partitioning Scheme for Audio Data Hiding

Reversible data hiding method can be implemented not only for images but also on other media such as audio. Data dimension changes are required to make an image-based data hiding method works in the audio domain and vice versa. For instance, 16-bit audio must be converted into an 8-bit image. An intelligent partitioning (IP) method was utilized to split the audio signals into segments of partitions. It modifies the size of the expansion media in audio data; thus, it applies to the image data hiding scheme. In this experiment, the image data hiding scheme is supported by an improved IP-based method. Two IP methods are proposed, aiming to maintain audio quality after embedding. The experimental results find that the second proposed IP method produces higher capacity values, and the first proposed IP method generates better quality results. This study also discovers that audio genres influence the performance of the proposed methods.
Are attention, larger receptive filed and multi task loss good for crowd counting?

In this paper, we transfer some techniques used in computer vision to crowd counting and verify whether they are valid. These techniques are attention mechanism, using larger receptive filed and combination of two different ways ——MSE loss and Bayesian loss. Experiments show that channel attention and spatial attention is not helpful for crowd counting; using larger receptive filed can improve model performance and context-aware method is better than SPP block; combination of two different ways is also not helpful for crowd counting.
Advanced Discrete Generalized-Neurodynamic Model Applied to Solve Discrete Time-Variant Augmented Sylvester Equation with Perturbation Suppression

In this paper, an advanced discrete generalized-neurodynamic (A-DGND) model is proposed to solve discrete time-variant augmented Sylvester equation (DTV-ASME) with perturbation suppression. Firstly, we present the discrete time-variant augmented Sylvester matrix equation that can be transformed into a simple matrix-vector problem. Secondly, in the continuous-time environment, for solving the continuous time-variant augmented Sylvester matrix equation (CTV-ASME), an advanced continuous generalized-neurodynamic (A-CGND) model is obtained. Then, based on the four-step discretization formula, an A-DGND model is proposed by discretizing the A-CGND model for solving DTV-ASME with perturbation suppression. Finally, according to the numerical experiment results, the effectiveness and robustness of A-DGND model for solving DTVASME are verified.
Segment-based ensemble learning model for stock price forecasting

Stock price forecasting is sometimes affected by factors such as timeliness and nonlinearity. Therefore, using traditional forecasting methods to model the entire dataset often results in problems such as low forecasting accuracy. These problems are collectively referred to as conceptual drift problems when the stock data does not match the degree of change in the previous period due to intra-flrm changes. In this paper, we propose a segmentation modeling and integration learning approach to segment the data set in temporal order and generate subsets of data with the same distribution characteristics, and then integrate them using an integration learning algorithm such as Adaboost to generate several models. The results show that such concept drift problems can be effectively solved by using segmentation modeling and integration learning methods.
Learning State-Dependent Sensor Measurement Models with Limited Sensor Measurements

We present a two-stage transfer learning method for training state-dependent sensor measurement models (SDSMMs) with limited sensor data. This method can alleviate collecting sizeable sensor and ground truth data to learn accurate sensor models, especially when we must learn many sensor models (for example, a fleet of autonomous cars, drones, or warehouse robots). In the first stage, we use prior knowledge of the sensor (such as a physical model) to generate a sizeable artificial dataset. Then the artificial dataset is used to pre-train an SDSMM. The second stage fine-tunes the pre-trained SDSMM using a "small" number of data collected by our target real sensor. To our knowledge, we are the first to learn measurement distributions using data generated from a physical model and data from a real sensor. We evaluated our proposed method using the Extended Kalman Particle Filter and a real-world localization dataset collected by several robots. Compared to the prior method, the proposed method achieved comparable performance with as little as ~19% of the real training data.
Design of enterprise management system based on cluster algorithm

At present, the supplier management of small and medium-sized enterprises mostly lacks detailed classification and rigorous evaluation. In the face of this phenomenon, on the basis of understanding the specific work of supplier management, this paper proposes the K-means algorithm with adaptive attribute optimization as the core by rectifying the traditional K-means algorithm. By integrating the design and analysis of the actual enterprise management system, it can be seen that the improved K-means algorithm has a higher accuracy rate than the previous application.
Design and Analysis of AEGIS Command and Control System Based on C2BMC Architecture

In the Gulf War, the U.S. military achieved a crushing victory over the Iraqi military with a highly informationized army. This war exposed the traditional command and control system's major defects in processing and utilizing battlefield information. In modern warfare, a more information-capable command and control system is required to adapt the extremely dynamic and complex battlefield environment. To meet this demand, the C2BMC system emerges. In this paper, according to the BMDS configuration, a possible structure of the AEGIS command and control system based on C2BMC architecture is designed. Besides, we model the system under some simplifications which have little impact on the main features of this system. Finally, in accordance with the FTM-44 interception test, a comparative simulation is carried out. The simulation results validate the rationality of the designed system.
Leveraging Partial Model Extractions using Uncertainty Quantification

Companies deploy deep learning models in the cloud and offer black-box access to them as a pay as you go service. It has been shown that with enough queries those models can be extracted. This paper presents a new cloning scheme using uncertainty quantification, enabling the adversary to leverage partial model extractions. First, a relatively small number of queries is spent to extract part of the target’s model. Second, for every query directed at the adversary, the uncertainty of the output of the extracted model is computed; when below a given threshold, the adversary will return the output. Otherwise, the query is delegated to the target’s model and its output returned. In this way the adversary is able to monetize knowledge that has successfully been extracted. We propose methods to determine thresholds such that the accuracy of the new scheme is close to the target network’s accuracy. The new scheme has been implemented, and experiments were conducted on the Caltech-256 and indoor datasets using multiple uncertainty quantification methods. The results show that the rate of delegation decreases logarithmically with the initial number of queries spent on extraction. Compared to conventional cloning techniques, the main advantages of the new scheme are that the total costs in terms of queries to the target model can be lower while achieving the same accuracy, and that the accuracy of the new scheme can be arbitrarily close to the target model’s accuracy by selecting a suitable value of the threshold.
High Dimensional Data Completion using Transform induced t-SVD

High-Dimensional Data completion algorithms provide the solution to most of the machine learning and color image processing applications. It is a process of recovering of corrupted data from uncorrupted data set. The recovery issues are addressed by minimizing the rank of tensor-based algorithms, which is an extension to matrix completion. It acquired the attention of researchers, to work towards the low rank tensor completion. The low rank tensor completion algorithms are introduced by the researchers to retrieve the incomplete visual data present in highly corrupted high- dimensional data. A transform based low rank tensor completion can produce more accurate results. Here an approach, Lifting Wavelet Transform (LWT) induced tensor singular value decomposition (t-SVD) based regularizer is introduced to recover the more than 80% corrupted tensors from un-corrupted tensors. The LWT features like in-place implementation and custom design filters, are leading to use. The optimization method, Accelerated Proximal Gradient Line (APGL) is used. The recovered results are assessed with various measures like Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Natural Image Quality Evaluator (NIQE) and Perceptual Image Quality Evaluator (PIQE). The proposed approach has better results than state-of-the-art methods.
Improving Image Matching Accuracy with Keypoint Features and Feature Fusion

Image matching is a frequently performed task in computer vision with numerous applications. Keypoint features such as SIFT, SURF, and ORB have been widely used in image matching and provides robustness against up to a significant degree of scaling, rotation, and blurring. The proportion of keypoints matched between two images can be used as a metric for how much the two images are related to each other. The image matching problem can be viewed as a classification problem in which the output is one of two classes, matching or non-matching. Ensemble methods, such as boosting and bagging, combine multiple child classifiers to achieve better accuracy than any of those classifiers alone. In this study, a method combining the keypoint features SIFT, SURF, and ORB with Adaptive Boosting is proposed to try to achieve a higher accuracy. Bag of Visual Words is applied to limit the computational cost of matching SIFT and SURF features. Results show that the ensemble model outperforms each keypoint feature alone. In the future, more image keypoint features can be added and different ensemble methods experimented with to try to further improve the model&#x0027;s performance.
Smooth and Efficient Scanning Algorithm for Mobile Robot using Double-Scroll Chua Attractor and Artificial Potential Field

Building a robot that is perform a thorough area scan can be done by applying chaotic motion. To be able to build a robot that can avoid an obstacle, it is necessary to add an obstacle avoidance algorithm. In this study, two algorithms were combined: Double-Scroll Chua Attractor and Artificial Potential Field (APF). Double-Scroll Chua Attractor was used to create the chaotic motion for mobile robot path planning. Meanwhile, the APF algorithm was used for obstacle avoidance. To get the desired result, it is necessary to constrains parameter, i.e.: initial condition, number of iteration, and speed. The obstacle used in this study was a circular obstacle with a radius of 0.001 meter. It was placed in various coordinates and the path planning of the mobile robot was able to avoid it well. The simulation results of this study show that the robot is capable of scanning the area while avoiding obstacles in certain coordinates.
Computer Vision Based Vehicle Intension Finding by Understanding Driver Hand Signal

Over a decade the increase in usage of vehicles and need for safety of people has greatly influenced the development of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD). The main purpose behind these systems is to enhance the ability of vehicle and its driver to react to various conditions on road. The ADAS provides high safety to vehicle drivers by warning about the external environment and possible threats. Similar to Vulnerable Road Users (VRUs) like pedestrian, cyclist, motorbike riders etc. if it is possible to anticipate the hand signals of the vehicle driver then the number of accidents can be controlled and its severity can be reduced drastically. This paper introduces a Computer Vision (CV) based solution to address this issue. A Convolutional Neural Network (CNN) architecture based automatic system is introduced that can help the ego-vehicle to recognize the hand signals of a vehicle driver and take the necessary actions in advance to prevent the road accidents. The proposed algorithm can recognize left, right and stop signals indicated by the vehicle driver that has never been addressed before. This method uses Regional Multi-Person Pose estimation (RMPE) - Alphapose [1] framework for human pose estimation and vehicle arm signal recognition algorithm is built on this framework. The testing of proposed work is done on self-captured dataset. The proposed work provides accurate results to recognize hand signals of the vehicle driver and the accuracy is 92.87&#x0025; at 10 fps.
A Detection Network United Local Feature Points and Components for Fine-Grained Image Classification

In recent years, fine-grained image classification has been a new research field in computer vision due to the characteristics of significant intra-class differences and minor inter-class differences in fine-grained image classification tasks. Traditional image classification algorithms are still challenging to obtain good classification results despite relying on manual annotation. Since minor local differences can only distinguish the subcategories, accurate detection of local details is the key to improving fine-grained classification accuracy. Therefore, this paper proposes a joint detection network model of local feature points and components for fine-grained image classification to effectively predict and extract local feature positions. Experiments verify the effectiveness of the proposed method on the public data set CalTech-UCSD Birds (CUB 200-2011) for fine-granularity classification tasks.
argodata: An R interface to oceanographic data from the International Argo Program

This paper describes argodata, an R package that makes it easier to work with data acquired in the International Argo Program, which provides over two decades of oceanographic measurements from around the world.Although Argo data are publicly available in NetCDF format and several software packages are available to assist in locating and downloading relevant Argo data, the multidimensional arrays used can be difficult to understand for non-oceanographers, particulary for the expanding arrays of biogeochemical variables measured by Argo floats.Given the increasing use of Argo data in other disciplines, we built a minimal interface to the data set that uses the data frame as the primary data structure.This approach allows users to leverage the rich ecosystem of R packages that manipulate data frames (e.g., the tidyverse) and associated instructional resources.