Reviewerly/exHarmony huggingface dataset:
How Weather Affects over Time the Repeatability of Spectral Indices Used for Geological Remote Sensing

Geologic remote sensing studies often targets surface cover that is supposed to be invariant or only changing on a geological timescale. In terms of surface material characteristics, this holds for rocks and minerals, but only to a lesser degree for soils (including alluvium, colluvium, regolith or weathered outcrop) and not for vegetation cover, for example. A view unobstructed by clouds, vegetation or fire scars is essential for a persistent observation of surface mineralogy. Sensors with a continuous multi-temporal operation (e.g., Landsat 8 OLI and Sentinel-2 MSI) can provide the data volume needed to come to an optimal seasonal acquisition and the application of data fusion approaches to create an unobstructed view. However, the acquisition environment always changes over time, driven by seasonal changes, illumination changes and the weather. Consequently, the creation of an unobstructed view does not necessarily lead to a repeatable measurement. In this paper, we evaluate the influence of weather and resulting soil moisture conditions over a 3-year period, with alternating dry and wet periods, on the variance of several “geological” spectral indices in a semi-arid area. Sentinel-2 MSI data are chosen to calculate band ratios for green vegetation, ferric and ferrous iron oxide mineralogy and hydroxyl bearing alteration (clay) mineralogy. The data were used “as provided”, meaning that the performance of the atmospheric correction and geometric accuracy is not changed. The results are shown as time-series for selected areas that include solid rock, beach sand, bare soil and natural vegetation surfaces. Results show that spectral index values vary not only between dry and wet periods, but also within dry periods longer than 45 days, as a result of changing soil moisture conditions long after a last rain event has passed. In terms of repeatability of measurements, an overall low soil-moisture level is more important for long-term stability of spectral index values than the occurrence of minor rain events. In terms of creating an unobstructed view, we found that thresholds for NDVI should not be higher than 0.1 when masking vegetation in geological remote sensing, which is lower than what usually is indicated in literature. In conclusion, multi-temporal data are not only important to study dynamic Earth processes, but also to improve mapping of surfaces that are seemingly invariant. As this work is based on a few selected pixels, the obtained results should be considered only indicative and not as a numerical truth. We conclude that multi-temporal data can be used to create an unobstructed view, but also to select the data that give the most repeatability of measurements. Images selection should not be based on a certain number of days without rain in the days preceding data acquisition but aim for the lowest soil moisture conditions. Consequently, weather data should be incorporated to come to an optimal selection of remote sensing imagery, and also when analyzing multi-temporal data.
A multi‐agent K‐means with case‐based reasoning for an automated quality assessment of software requirement specification

Abstract Automating the quality assessment of Software Requirement Specification poses major challenges related to the need for advanced algorithms to extract the SRS quality features, interpret the context of the features, formulate accurate assessment metrics, and document the shortcomings as well as possible improvements. In the existing methods, such as Reconstructed Automated Requirement Measurement, and Rendex, some major processes are still handled offline by humans (semi‐automated) or encompass automating the measurement of a few quality attributes due to the mentioned challenges. This paper addressed this gap and proposed an Automated Quality Assessment of SRS (AQA‐SRS) framework to assess the SRS documents by automatically extracting features related to 11 quality attributes through a deep analysis of the SRS textual content. Also, it constructs a flexible platform that is able to minimize the human expert’s role in the SRS assessment. The AQA‐SRS framework integrates Natural Language Processing, K‐means, Multi‐agent, and Case‐Based Reasoning. The AQA‐SRS framework is evaluated by processing two standard SRS datasets and comparing the results with state‐of‐the‐art methods and analysis by software engineering experts. The results show that the AQA‐SRS framework effectively assesses the tested SRS documents and achieves a 78% total agreement with the tested methods and software engineering experts.
The role of foreign direct investment in structural change in Ethiopia

The paper examines the effect of foreign direct investment (FDI) on the structural changes in Ethiopia using an ARDL model. The authors found both FDI and domestic investment to have a positive effect while trade openness and inflation to have a negative effect on structural change. However, government expenditure does not seem to have a significant effect. The study concludes that FDI is an important tool for Ethiopia to achieve its transformation agenda generally and to bring about structural changes in particular. To this end, the government needs to attract sufficient FDI and ensure that it is used efficiently by improving absorption capacity and enabling domestic firms to make links with foreign investors.
Simulation Method for Blockchain Systems with a Public Chain

The potential security problems of blockchain technology are constantly restricting the development process of related industrial applications. The cost of deploying a blockchain system in a real environment to conduct research on security issues is relatively high, and the related security analysis and verification are also destructive and irreproducible. Therefore, based on the idea of layered design, this paper proposes a blockchain system simulation platform. The blockchain system is divided into four layers in the simulation platform: the consensus layer, network layer, contract layer, and storage layer. In the consensus layer, the problem of computing resource waste is solved. In the network layer, a peer-to-peer network topology simulation is implemented. In the storage layer, the problem of redundant storage is solved. In the contract layer, the contract replay speed is accelerated. Finally, a prototype of an efficient blockchain simulation system is implemented based on the above methods.
A Dynamic Obstacle Avoidance Method for AGV Based on Improved Speed Barriers

The complexity and difficulty of dynamic obstacle avoidance for AGVs are increased by the uncertainty in a dynamic environment. The adaptive speed obstacle method allows the size of the collision cone to be dynamically changed to solve this problem, but this method may cause the AGV to turn too much when it is close to obstacles, as the collision cone expands too fast, which may lead to unstable operations or even collision. In order to address these problems, we propose an improved speed obstacle algorithm. The proposed algorithm uses Kalman filtering to estimate the positions of dynamic obstacles and adopts the idea of forward simulation to build a speed obstacle buffer according to the estimated positions of obstacles, such that the AGV can use the predicted positions of obstacles in the next moment, instead of the current positions, to build a speed obstacle model. Finally, an objective function that balances efficiency and safety was established to score all the candidate speeds, such that the highest-rated speed could be selected as the candidate speed for the next moment.
CNN-Based Fluid Motion Estimation Using Correlation Coefficient and Multiscale Cost Volume

Motion estimation for complex fluid flows via their image sequences is a challenging issue in computer vision. It plays a significant role in scientific research and engineering applications related to meteorology, oceanography, and fluid mechanics. In this paper, we introduce a novel convolutional neural network (CNN)-based motion estimator for complex fluid flows using multiscale cost volume. It uses correlation coefficients as the matching costs, which can improve the accuracy of motion estimation by enhancing the discrimination of the feature matching and overcoming the feature distortions caused by the changes of fluid shapes and illuminations. Specifically, it first generates sparse seeds by a feature extraction network. A correlation pyramid is then constructed for all pairs of sparse seeds, and the predicted matches are iteratively updated through a recurrent neural network, which lookups a multi-scale cost volume from a correlation pyramid via a multi-scale search scheme. Then it uses the searched multi-scale cost volume, the current matches, and the context features as the input features to correlate the predicted matches. Since the multi-scale cost volume contains motion information for both large and small displacements, it can recover small-scale motion structures. However, the predicted matches are sparse, so the final flow field is computed by performing a CNN-based interpolation for these sparse matches. The experimental results show that our method significantly outperforms the current motion estimators in capturing different motion patterns in complex fluid flows, especially in recovering some small-scale vortices. It also achieves state-of-the-art evaluation results on the public fluid datasets and successfully captures the storms in Jupiter’s White Ovals from the remote sensing images.
Applying an Intelligent Approach to Environmental Sustainability Innovation in Complex Scenes

Environmental protection is still a key issue that cannot be ignored at this stage of social development. With the development of artificial intelligence, various technologies increasingly tend to be widely used in the field of environmental protection, such as searching the wilderness through an unmanned aerial vehicle (UAV) and cleaning garbage by robots. Traditional object detection algorithms for this scenario suffer from low accuracy and high computational cost. Therefore, this paper proposes an algorithm applied to automatic garbage detection and instance segmentation in complex scenes. First, we construct sample-fused feature pyramid networks (SF-FPN) to achieve multi-scale feature sampling on multiple levels, to enhance the semantic representation of features. Second, adding the mask branch based on conditional convolution, introducing the idea of instance-filters to automatically generate the filter parameters of the Fully Convolutional Networks (FCN), to realize the instance-level pixel classification. Moreover, the Atrous Spatial Pyramid Pooling (ASPP) module is introduced to encode the feature information in a dense way to assist the generation of MASK. Finally, the object is detected and the instance is segmented by a two-branch structure. In addition, we also perform data augmentation on the original dataset to prevent model overfitting. The proposed algorithm reaches 82.7 and 72.4 according to the mAP index of detection and instance segmentation while using the public TACO dataset.
Unsupervised Monocular Visual Odometry for Fast-Moving Scenes Based on Optical Flow Network with Feature Point Matching Constraint

Robust and accurate visual feature tracking is essential for good pose estimation in visual odometry. However, in fast-moving scenes, feature point extraction and matching are unstable because of blurred images and large image disparity. In this paper, we propose an unsupervised monocular visual odometry framework based on a fusion of features extracted from two sources, that is, the optical flow network and the traditional point feature extractor. In the training process, point features are generated for scene images and the outliers of matched point pairs are filtered by FlannMatch. Meanwhile, the optical flow network constrained by the principle of forward-backward flow consistency is used to select another group of corresponding point pairs. The Euclidean distance between the matching points found by FlannMatch and the corresponding point pairs by the flow network is added to the loss function of the flow network. Compared with SURF, the trained flow network shows more robust performance in complicated fast-motion scenarios. Furthermore, we propose the AvgFlow estimation module, which selects one group of the matched point pairs generated by the two methods according to the scene motion. The camera pose is then recovered by Perspective-n-Point (PnP) or the epipolar geometry. Experiments conducted on the KITTI Odometry dataset verify the effectiveness of the trajectory estimation of our approach, especially in fast-moving scenarios.
Power control management system model using wireless sensor network

Several researchers would study the development of controlling and centralized management approaches in response to improvements in wireless communication, microsystems, microcontrollers, deeply integrated computers, deeply integrated devices, and the expanding demand for more productive controlled electric equipment. The objective is to develop a computerized device that employs Wireless Sensor Networks (WSN) and Dynamic Power Management (DPM) to supervise and troubleshoot the electrical system. The Intelligent Sensor Modules (ISM) and isolated data automation systems, that are in charge of Signal Detection, Computing, and Transmission, have been deployed on two network architectures (SDCT). The data collection devices must be accepted by the Remote Servers (RS) design, and the monitoring inverter serves as the wired connection's gateway. A DPM approach is used by the sensor networks to extend the WSN's lifespan. By simply extending the sleep duration, which could be modified according to the application operating on the sensor network, the life of the node can be easily increased.
Understanding the Behavior of Gas Sensors Using Explainable AI

Exposure to pollutants like ozone and nitrogen dioxide gas can cause serious health issues and harm the environment. Therefore, the interest in air quality and its impact on health and well-being has been steadily increasing over the years, making low-cost gas sensing devices combined with artificial intelligence (AI) increasingly popular due to their flexibility and small form factor. While AI provides state-of-the-art performance, it makes the system less transparent and more difficult to trust its decisions. With the aid of three different approaches, this paper seeks to understand and explain the predictions made by complex models for gas sensors. The use of such techniques can increase our confidence in the AI systems embedded in our products in terms of fairness, or impartiality, and robustness, or reliability. This also improves our understanding of sensor behavior and provides a more robust explanation for algorithmic choices.
Effect of word embedding vector dimensionality on sentiment analysis through short and long texts

&lt;span lang="EN-US"&gt;Word embedding has become the most popular method of lexical description in a given context in the natural language processing domain, especially through the word to vector (Word2Vec) and global vectors (GloVe) implementations. Since GloVe is a pre-trained model that provides access to word mapping vectors on many dimensionalities, a large number of applications rely on its prowess, especially in the field of sentiment analysis. However, in the literature, we found that in many cases, GloVe is implemented with arbitrary dimensionalities (often 300d) regardless of the length of the text to be analyzed. In this work, we conducted a study that identifies the effect of the dimensionality of word embedding mapping vectors on short and long texts in a sentiment analysis context. The results suggest that as the dimensionality of the vectors increases, the performance metrics of the model also increase for long texts. In contrast, for short texts, we recorded a threshold at which dimensionality does not matter.&lt;/span&gt;
A Sparse CNN Accelerator for Eliminating Redundant Computations in Intra- and Inter-Convolutional/Pooling Layers

Neural network pruning, which can be divided into unstructured pruning and structured pruning strategies, has been proven to be an efficient method to substantially reduce the number of computations of convolutional neural networks (CNNs). However, it remains difficult to combine the advantages of these two pruning strategies. This article proposes a high-performance accelerator for unstructured sparse CNNs. First, a convolution-based filter selection and clustering method (FSCM) is proposed to reorder unstructured sparse filters into uniform-size dense filters, eliminating redundant computations in the convolutional layers while maintaining a regular structure. In addition, a convolution and pooling calculation method (CPCM) is presented to reduce interlayers’ computational redundancy. Third, a hardware accelerator with high digital signal processing (DSP) efficiency is designed to take advantage of FSCM and CPCM. The proposed accelerator is implemented on a Xilinx XCVU9P platform at 300 MHz. With different CNN configurations, the performance and DSP efficiency for LeNet, AlexNet, and VGG16 are 1490.53 GOPS and 3.882, 913.85 GOPS and 1.785, and 862.16 GOPS and 1.68, respectively. Compared to previous field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) designs, the accelerator achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15.43\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.73\times $ </tex-math></inline-formula> speedup in terms of performance and DSP efficiency.
A Hybrid CNN-LSTM Architecture for High Accurate Edge-Assisted Bandwidth Prediction

In this letter, we propose an edge-assisted bandwidth prediction scheme based on a hybird convolutional neural network (CNN) and long short-term memory (LSTM) architecture. To accurately predict available downlink bandwidth with limited and regulated monitoring information, we decouple feature extraction and sequence learning to capture the feature vector by CNN and resolve the historical feature collection issue by LSTM, respectively. A two-stage training strategy is further adopted to improve the prediction accuracy and generalization ability. In addition, we deploy and evaluate the proposed bandwidth prediction scheme in our prototype system. The test results show that our proposed scheme improves the mean absolute error (MAE) and root mean square error (RMSE) by 81.4% and 82.0%, respectively, compared with the LSTM only prediction scheme, and consumes less than 3 ms running time, which is sufficient for many real-time video transmission applications.
Software Effort Estimation using Machine learning Algorithm

Software project development requires a plan with accurate estimation of time, cost, scope resource, manpower, and others that are needed for the development of a project. However accurate effort estimation is the main challenge in software development. Since lots of a project failed due to poor effort estimation. The main objective of this paper is to find out better effort prediction algorithm that outperforms by computing three different machine learning algorithm. The algorithms that we have used for effort estimation are linear regression, Support vector machine, multi layer perception with SEERA (Software engineering in Sudan) data set. In-addition, we have discussed to what extent the predicting model estimate the software development effort by using mean square error (MSE), Mean absolute error(MAE), and R-square evaluation metrics. The experiment for all the selected machine learning algorithm applied using anaconda IDE in Jupyter lab environment. As a result R-square of linear regression scores 0.95 and MLP scores 0.83 where as SVR scores −0.04. Therefore we can conclude that liner regression model preforms better effort prediction than MLP and SVR model.
Hand Gesture Recognition for Controlling Game Objects Using Two-Stream Faster Region Convolutional Neural Networks Methods

Hand gesture recognition is part of Human-Computer Interaction as a place for the user interface to be presented. Hand gestures have symbols that can be interpreted as commands or messages and can be used to control objects in games. The Faster R-CNN method can classify hand patterns based on specific regions but does not contain hand movement information for each frame in the video data. The data segmentation process with lighting, position, and configuration of hand gesture classification also significantly affects accuracy in hand gesture recognition. The purpose of this research is to separate the background of the hand gesture object and to be able to control the game object through the method applied. This research has built a hand gesture recognition system using the Two-Stream Faster R-CNN method in dividing spatial and temporal video data information by region to produce good accuracy. Data obtained from the camera by video processing using dense optical flow and frame selection, resulting in a sequence of spatial and temporal images. The resulting output can directly classify five hand gesture classes and control game objects. The results of this study indicate that the proposed method has the highest accuracy on spatial data at step 7500 with mAP values of 92.51% and temporal data at step 6500 with mAP values of 90.46%. IoU testing also affects the detection results with a minimum standard of 0.7 to 0.9 on the IoU coefficient value to avoid detection errors.
Implementation of Convolutional Neural Network (CNN) in Android-Based Acne Detection Applications

Acne is a skin disorder caused by the overproduction of oil glands that causes infection and inflammation of the skin and is one of the most common conditions in the world. The Convolutional Neural Network (CNN) approach method of convolution operation is used to detect objects in the assessment of acne and the correct handling solutions to overcome them in the form of an android-based application. Data processing is carried out in several stages. The initial phase of data processing use LabeIImg software to label the image object, producing a file with an XML extension. The Efficientdet-lite model is a model that has been optimized to run on mobile devices with the Efficientdet-lite0 model architecture in the modeling process that has been trained using the hyperparameter model, which has a detection loss of 0.056 and produces an Average Precision value of 0.1256. The androidbased acne detection application was successfully created using the CNN method and the Efficientdet-lite model, the application work on smartphones with a minimum version of nougat 7.1. The results of the Average Precision values from the labeling of seven classes are 0.0627, 0.0027, 0.1575, 0.0217, 0.0038, 0.01224, and 0.014, so it can be concluded that the average precision value in each category is quite good but must be developed to achieve more accurate detection performance.