Reviewerly/exHarmony huggingface dataset:
Ball-Catching System Using Image Processing and an Omni-Directional Wheeled Mobile Robot

The ball-catching system examined in this research, which was composed of an omni-directional wheeled mobile robot and an image processing system that included a dynamic stereo vision camera and a static camera, was used to capture a thrown ball. The thrown ball was tracked by the dynamic stereo vision camera, and the omni-directional wheeled mobile robot was navigated through the static camera. A Kalman filter with deep learning was used to decrease the visual measurement noises and to estimate the ball’s position and velocity. The ball’s future trajectory and landing point was predicted by estimating its position and velocity. Feedback linearization was used to linearize the omni-directional wheeled mobile robot model and was then combined with a proportional-integral-derivative (PID) controller. The visual tracking algorithm was initially simulated numerically, and then the performance of the designed system was verified experimentally. We verified that the designed system was able to precisely catch a thrown ball.
Barium Titanate Semiconductor Band Gap Characterization through Gravitationally Optimized Support Vector Regression and Extreme Learning Machine Computational Methods

Barium titanate (BaTiO3) is a class of ceramic multifunctional materials with unique thermal stability, prominent piezoelectricity constant, excellent dielectric constant, environmental friendliness, and excellent photocatalytic activities. These features have rendered barium titanate indispensable in many areas of applications such as electromechanical devices, thermistors, multilayer capacitors, and electrooptical devices. The photocatalytic activity of barium titanate semiconductor is hindered by its large band gap and high rate of charge recombination. Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. This present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine- (ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. The developed gravitationally optimized SVR (GSVR) is characterized with a low value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of 0.036 ev, 1.145 ev, and 0.122 ev, respectively. The developed GSVR model outperforms ELM-Sine and ELM-Sig models using various performance evaluators. The developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. The demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources.
Reinforcement Learning-Based Autonomous Navigation and Obstacle Avoidance for USVs under Partially Observable Conditions

Unmanned surface vehicles (USVs) have been widely used in research and exploration, patrol, and defense. Autonomous navigation and obstacle avoidance, as the essential technology of USVs, are the key conditions for successful mission execution. However, fine modeling of conventional algorithms cannot meet the real-time precise behavior control strategy of USVs in complex environments, which poses a great challenge to autonomous control policy. In this paper, a deep reinforcement learning-based UANOA (USVs autonomous navigation and obstacle avoidance) method is proposed. The UANOA achieves the autonomous navigation task of USVs by real-time sensing of partially complex ocean information around and real-time output of rudder angle control commands of USVs. In our work, we employ a double Q-network to achieve end-to-end control from raw sensor input to output of discrete rudder action, and design a set of reward functions that can be adapted to USV navigation and obstacle avoidance. To alleviate the decision bias caused by partial observable of USVs, we use the long short-term memory (LSTM) networks to enhance the ability to remember the ocean environment of USVs. Experiments demonstrate that UANOA ensures a USV arrives at the target points with optimal path planning in complex ocean environments without any collisions occurring, and UANOA outperforms deep Q-network (DQN) and random control policy in convergence speed, sailing distance, rudder angle steering consumption, and other performance measurements.
Rotation-aware representation learning for remote sensing image retrieval

The rising number and size of remote sensing (RS) image archives makes content-based RS image retrieval (CBRSIR) more important. Convolutional neural networks (CNNs) offer good CBRSIR performance, but the features they extract are not rotation-invariant. This is problematic as objects in RS images appear in arbitrary rotation angles. We develop and investigate two new rotation-aware CNN-based CBRSIR methods: 1) In the Feature Map Transformation Based Rotation-Aware Network (FMT-RAN), the last pooling layer is rotated in four different angles during training. Its outputs are passed through the same fully connected-, coding-, and classification layer, and the resulting losses are added. 2) The Spatial Transformer-based Rotation-Aware Network (ST-RAN) contains a spatial transformer network (STN) and a rotation aware network (RAN). For training, the original and a randomly rotated version of an image are fed into the ST-RAN. The STN generates a transformed version of the original to match the rotated image. The RAN extracts the features of all three images. We apply two-stage training, which first optimizes the STN and then the RAN. Both of our methods are efficient in terms of retrieval accuracy and time, but ST-RAN has the overall best performance. It outperforms the state-of-the-art CBRSIR methods.
Cloud Computing Deployment Models: A Comparative Study

Cloud computing has become the embraced in the computer world.Cloud implementation is the process of creating a virtual computing environment.Deployment in the cloud provides organizations with flexible and scalable virtual computing resources.A cloud deployment model is the type of architecture in which a cloud system is deployed.These models differ in terms of administration, ownership, access control, and security protocols.This paper describes the different types of cloud computing service models and deployment models; it also gives us a comparative study of various clouds using many factors.The comparison is simply based on various factors such as reliability, cost, data control, workload, performance, and many other cloud parameters.
A Region-Based Efficient Network for Accurate Object Detection

Object detection in images is an important task in image processing and computer vision. Many approaches are available for object detection. For example, there are numerous algorithms for object positioning and classification in images. However, the current methods perform poorly and lack experimental verification. Thus, it is a fascinating and challenging issue to position and classify image objects. Drawing on the recent advances in image object detection, this paper develops a region-baed efficient network for accurate object detection in images. To improve the overall detection performance, image object detection was treated as a twofold problem, involving object proposal generation and object classification. First, a framework was designed to generate high-quality, class-independent, accurate proposals. Then, these proposals, together with their input images, were imported to our network to learn convolutional features. To boost detection efficiency, the number of proposals was reduced by a network refinement module, leaving only a few eligible candidate proposals. After that, the refined candidate proposals were loaded into the detection module to classify the objects. The proposed model was tested on the test set of the famous PASCAL Visual Object Classes Challenge 2007 (VOC2007). The results clearly demonstrate that our model achieved robust overall detection efficiency over existing approaches using fewer or more proposals, in terms of recall, mean average best overlap (MABO), and mean average precision (mAP).
Generic Reversible Visible Watermarking via Regularized Graph Fourier Transform Coding

Reversible visible watermarking (RVW) is an active copyright protection mechanism. It not only transparently superimposes copyright patterns on specific positions of digital images or video frames to declare the copyright ownership information, but also completely erases the visible watermark image and thus enables restoring the original host image without any distortion. However, existing RVW algorithms mostly construct the reversible mapping mechanism for a specific visible watermarking scheme, which is not versatile. Hence, we propose a generic RVW framework to accommodate various visible watermarking schemes. In particular, we obtain a reconstruction data packet-the compressed difference image between the watermarked image and the original host image, which is embedded into the watermarked image via any conventional reversible data hiding method to facilitate the blind recovery of the host image. The key is to achieve compact compression of the difference image for efficient embedding of the reconstruction data packet. To this end, we propose regularized Graph Fourier Transform (GFT) coding, where the difference image is smoothed via the graph Laplacian regularizer for more efficient compression and then encoded by multi-resolution GFTs in an approximately optimal manner. Experimental results show that the proposed framework has much better versatility than state-of-the-art methods. Due to the small amount of auxiliary information to be embedded, the visual quality of the watermarked image is also higher.
Improved Twofish Algorithm: A Digital Image Enciphering Application

With the growth of networks, the Internet of Things (IoT) and new cyber attacks pose threats to privacy and security, secure communication is therefore becoming one of the most crucial concerns. For this purpose, symmetric algorithms namel; The Rijndael algorithm, the Serpent algorithm, and the TWOFISH algorithm pay equal attention. In this paper, the TWOFISH algorithm's mathematical complexity is improved by using substitution boxes (S-boxes) drawn from a multiplicative group of units of chain ring Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=0</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . As these S-boxes have the property of having copious generators, they, therefore, produce a rich algebraic complexity. Moreover, the time complexity of the proposed work is modified by processing the 64-bit block throughout the process and reducing the number of subkeys. To measure the strength of the proposed algorithm, various standard color digital images, with a size of 256×256, are encrypted and tested. The computation speed of the encryption is compared to the standard TWOFISH algorithm's speed and found that the newly designed algorithm is quite fast. For security analysis and quality assessment, various statistical tests are performed on the standard encrypted images. The results recommend that the proposed algorithm is a strong candidate for digital image encryption.
Study of the Ways and Methods of Searching Water Leaks in Water Supply Networks of the Settlements of Ukraine

The problem of search of accidental and unauthorized water leaks in water supply networks is considered.The analysis of the current state of water supply sector in Ukraine based on information data of the Ministry of Regional Development, Construction and Housing Communal Services of Ukraine and data of the State Statistics Service of Ukraine is conducted.Existing methods of finding and determining places of water leakage in water supply networks are considered.The findings of the comparative analysis of the practical capabilities of search devices are reported.The primary causes and rates of water losses of the settlements of Ukraine have been determined.The results of the leakage search using device APGK-015/2, the calculations of the volume of water losses and the total cost of the water saved during the study are represented.
ArSL-CNN a convolutional neural network for Arabic sign language gesture recognition

&lt;p class="IJASEITAbtract"&gt;Sign language (SL) is a visual language means of communication for people who are Deaf or have hearing impairments. In Arabic-speaking countries, there are many Arabic sign languages (ArSL) and these use the same alphabets. This study proposes ArSL-CNN, a deep learning model that is based on a convolutional neural network (CNN) for translating Arabic SL (ArSL). Experiments were performed using a large ArSL dataset (ArSL2018) that contains 54049 images of 32 sign language gestures, collected from forty participants. The results of the first experiments with the ArSL-CNN model returned a train and test accuracy of 98.80% and 96.59%, respectively. The results also revealed the impact of imbalanced data on model accuracy. For the second set of experiments, various re-sampling methods were applied to the dataset. Results revealed that applying the synthetic minority oversampling technique (SMOTE) improved the overall test accuracy from 96.59% to 97.29%, yielding a statistically signicant improvement in test accuracy (p=0.016, α&amp;lt;0=05). The proposed ArSL-CNN model can be trained on a variety of Arabic sign languages and reduce the communication barriers encountered by Deaf communities in Arabic-speaking countries.&lt;/p&gt;
Multi-Frequency RF Sensor Fusion for Word-Level Fluent ASL Recognition

Deaf spaces are unique indoor environments designed to optimize visual communication and Deaf cultural expression. However, much of the technological research geared towards the deaf involve use of video or wearables for American sign language (ASL) translation, with little consideration for Deaf perspective on privacy and usability of the technology. In contrast to video, RF sensors offer the avenue for ambient ASL recognition while also preserving privacy for Deaf signers. Methods: This paper investigates the RF transmit waveform parameters required for effective measurement of ASL signs and their effect on word-level classification accuracy attained with transfer learning and convolutional autoencoders (CAE). A multi-frequency fusion network is proposed to exploit data from all sensors in an RF sensor network and improve the recognition accuracy of fluent ASL signing. Results: For fluent signers, CAEs yield a 20-sign classification accuracy of %76 at 77 GHz and %73 at 24 GHz, while at X-band (10 Ghz) accuracy drops to 67%. For hearing imitation signers, signs are more separable, resulting in a 96% accuracy with CAEs. Further, fluent ASL recognition accuracy is significantly increased with use of the multi-frequency fusion network, which boosts the 20-sign fluent ASL recognition accuracy to 95%, surpassing conventional feature level fusion by 12%. Implications: Signing involves finer spatiotemporal dynamics than typical hand gestures, and thus requires interrogation with a transmit waveform that has a rapid succession of pulses and high bandwidth. Millimeter wave RF frequencies also yield greater accuracy due to the increased Doppler spread of the radar backscatter. Comparative analysis of articulation dynamics also shows that imitation signing is not representative of fluent signing, and not effective in pre-training networks for fluent ASL classification. Deep neural networks employing multi-frequency fusion capture both shared, as well as sensor-specific features and thus offer significant performance gains in comparison to using a single sensor or feature-level fusion.
Exploring 3D Human Action Recognition Using STACOG on Multi-View Depth Motion Maps Sequences

This paper proposes an action recognition framework for depth map sequences using the 3D Space-Time Auto-Correlation of Gradients (STACOG) algorithm. First, each depth map sequence is split into two sets of sub-sequences of two different frame lengths individually. Second, a number of Depth Motion Maps (DMMs) sequences from every set are generated and are fed into STACOG to find an auto-correlation feature vector. For two distinct sets of sub-sequences, two auto-correlation feature vectors are obtained and applied gradually to L2-regularized Collaborative Representation Classifier (L2-CRC) for computing a pair of sets of residual values. Next, the Logarithmic Opinion Pool (LOGP) rule is used to combine the two different outcomes of L2-CRC and to allocate an action label of the depth map sequence. Finally, our proposed framework is evaluated on three benchmark datasets named MSR-action 3D dataset, DHA dataset, and UTD-MHAD dataset. We compare the experimental results of our proposed framework with state-of-the-art approaches to prove the effectiveness of the proposed framework. The computational efficiency of the framework is also analyzed for all the datasets to check whether it is suitable for real-time operation or not.
A Comprehensive Survey on Computational Aesthetic Evaluation of Visual Art Images: Metrics and Challenges

Computational image aesthetic evaluation is a computable human aesthetic perception and judgment realized by machines, which has a significant impact on a variety of applications such as image advanced search and promotional exhibition of painting arts.Various approaches have been proposed in copious literature trying to solve this challenging problem.However, there have been few attempts in reviewing works from different types of visual arts, due to their significant differences in visual features and aesthetic principles.In this survey, we present a comprehensive listing of the reviewed works on aesthetic assessment of photographs and paintings, mainly highlighting the contributions and innovations of the existing approaches.We firstly introduce aesthetic assessment benchmark datasets in different categories.Then, conventional aesthetic evaluation approaches based on handcrafted features are reviewed.Besides, we systematically evaluate recent deep learning techniques that are useful for developing robust models for aesthetic prediction tasks in scoring, distribution, attribute, and description.Moreover, the possibility of aesthetic-aware color enhancement, recomposition of photo images, and automatic generation of aesthetic-guided art paintings through computational approaches are summarized.Finally, challenges and potential future directions for this field are discussed.We hope that our survey could serve as a comprehensive reference source for future research on computational aesthetics in visual media.
AFF‐Dehazing: Attention‐based feature fusion network for low‐light image Dehazing

Abstract Images captured in haze conditions, especially at nighttime with low light, often suffer from degraded visibility, contrasts, and vividness, which makes it difficult to carry out the following vision tasks. In this article, we propose an attention‐based feature fusion network (AFF‐Dehazing) for low‐light image dehazing. Our method decomposes the low‐light image dehazing into two task‐independent streams containing four modules: image dehazing module, low‐light feature extractor module, feature fusion module, and image restoration module. The basic block of these modules is the proposed attention‐based residual dense block. Since the dual‐branch are used, AFF‐Dehazing can avoid learning the mixed degradation all‐in‐one and enhance the details of low‐light haze images. Extensive experiments show that our method surpasses previous state‐of‐the‐art image dehazing methods and low‐light enhancement methods by a very large margin both quantitatively and qualitatively.
Correction to: Big Data Processing and Analysis in Internet of Vehicles: Architecture, Taxonomy, and Open Research Challenges

A correction to this paper has been published: https://doi.org/10.1007/s11831-021-09607-5
Should we find another model?: Improving Neural Machine Translation Performance with ONE-Piece Tokenization Method without Model Modification

Most of the recent natural language processing (NLP) studies are based on the pretrainfinetuning approach (PFA).However for small and medium-sized industries with insufficient hardware, there are many limitations in servicing latest PFA based NLP application software, due to slow speed and insufficient memory.Since these approaches generally require large amounts of data, it is much more difficult to service with PFA especially for low-resource languages.We propose a new tokenization method, ONE-Piece, to address this limitation.ONE-Piece combines morphologically-aware subword tokenization and vocabulary communicating method, which has not been carefully considered before.Our proposed method can also be utilized without modifying the model structure.We experiment by applying ONE-Piece to Korean, a morphologically-rich and low-resource language.We revealed that ONE-Piece with vanilla transformer model can achieve comparable performance to the current Korean-English machine translation state-of-the-art model.
A Novel Hybrid Deep Learning Model for Activity Detection Using Wide-Angle Low-Resolution Infrared Array Sensor

In this paper, we propose a deep learning-based technique for activity detection that uses wide-angle low-resolution infrared (IR) array sensors. Alongside with the main challenge which is how to further improve the performance of IR array sensor-based methods for activity detection, throughout this work, we address the following challenges: we employ a wide-angle infrared array sensor with peripheral vision in comparison to a standard IR array sensor. This makes activities at different positions have different patterns of temperature distribution, making it challenging to learn these different patterns. In addition, unlike previous works, our goal is to perform the activity detection using the least possible amount of information. While the conventional works use a time window equal to 10 seconds where a single event occurs, we aim to identify the activity using a time window of less than 1 second. Nevertheless, we aim to improve over the accuracy obtained in previous work by employing deep learning, while keeping the approach light for it to run on devices with low computational power. Therefore, we use a hybrid deep learning model well suited for the classification of distorted images because the neural network learns the features automatically. In our work, we use two IR sensors ( 32×24 pixels), one placed on the wall and one on the ceiling. We collect data simultaneously from both the IR sensors and apply hybrid deep learning classification techniques to classify various activities including “walking”, “standing”, “sitting”, “lying”, “falling”, and the transition between the activities which is referred to as “action change”. This is done in two steps. In the first step, we classify ceiling data and wall data separately as well as the combination of both (ceiling and wall) using a Convolutional Neural Network (CNN). In the second step, the output of the CNN is fed to a Long Short Term Memory (LSTM) with a window size equal to 5 frames to classify the sequence of activities. Through experiments, we show that the classification accuracy of the ceiling data, wall data, and combined data with the LSTM reach 0.96, 0.95, and 0.97, respectively.