Reviewerly/exHarmony huggingface dataset:
IntereStyle: Encoding an Interest Region for Robust StyleGAN Inversion

Recently, manipulation of real-world images has been highly elaborated along with the development of Generative Adversarial Networks (GANs) and corresponding encoders, which embed real-world images into the latent space. However, designing encoders of GAN still remains a challenging task due to the trade-off between distortion and perception. In this paper, we point out that the existing encoders try to lower the distortion not only on the interest region, e.g., human facial region but also on the uninterest region, e.g., background patterns and obstacles. However, most uninterest regions in real-world images are located at out-of-distribution (OOD), which are infeasible to be ideally reconstructed by generative models. Moreover, we empirically find that the uninterest region overlapped with the interest region can mangle the original feature of the interest region, e.g., a microphone overlapped with a facial region is inverted into the white beard. As a result, lowering the distortion of the whole image while maintaining the perceptual quality is very challenging. To overcome this trade-off, we propose a simple yet effective encoder training scheme, coined IntereStyle, which facilitates encoding by focusing on the interest region. IntereStyle steers the encoder to disentangle the encodings of the interest and uninterest regions. To this end, we filter the information of the uninterest region iteratively to regulate the negative impact of the uninterest region. We demonstrate that IntereStyle achieves both lower distortion and higher perceptual quality compared to the existing state-of-the-art encoders. Especially, our model robustly conserves features of the original images, which shows the robust image editing and style mixing results. We will release our code with the pre-trained model after the review.
Contextual Text Block Detection Towards Scene Text Understanding

Most existing scene text detectors focus on detecting characters or words that only capture partial text messages due to missing contextual information. For a better understanding of text in scenes, it is more desired to detect contextual text blocks (CTBs) which consist of one or multiple integral text units (e.g., characters, words, or phrases) in natural reading order and transmit certain complete text messages. This paper presents contextual text detection, a new setup that detects CTBs for better understanding of texts in scenes. We formulate the new setup by a dual detection task which first detects integral text units and then groups them into a CTB. To this end, we design a novel scene text clustering technique that treats integral text units as tokens and groups them (belonging to the same CTB) into an ordered token sequence. In addition, we create two datasets SCUT-CTW-Context and ReCTS-Context to facilitate future research, where each CTB is well annotated by an ordered sequence of integral text units. Further, we introduce three metrics that measure contextual text detection in local accuracy, continuity, and global accuracy. Extensive experiments show that our method accurately detects CTBs which effectively facilitates downstream tasks such as text classification and translation. The project is available at https://sg-vilab.github.io/publication/xue2022contextual/ .
Research on the necessity and strategy of cultivating physical culture literacy of teachers' groups in Henan Province

The necessity and strategy of cultivating the cultural literacy of teachers' groups in Henan Province were studied by means of investigation and literature.It is believed that the macro guidance of education policy and the practical needs of student development determine the necessity of cultivating the cultural literacy of teachers.This paper puts forward the cultivation strategy of the physical culture literacy of teachers' groups from the three aspects of material foundation, spiritual orientation and institutional construction.
An adaptive environmental sampling path planning technique for autonomous underwater vehicles based on ant colony optimization algorithm

Path planning, navigation, and localization are still challenging for Autonomous Underwater Vehicles (AUVs). This paper proposes an adaptive environmental sampling technique for autonomous underwater vehicles based on an enhanced ant colony optimization algorithm to achieve fast and efficient path planning for AUVs operating in a known or partially known undersea environment. Three strategies for improving the conventional ant colony algorithm are presented. To begin, the state transition rule is used to control the degree of new paths exploration so that the ant's search activities are concentrated in the spatial vicinity of the optimal solution. Second, the global update rule is only applied to the optimal ant path to raise the pheromone difference between the optimal and worst ant paths. Finally, the local pheromone update rule forces the ant to update the path locally while constructing it and then globally after each cycle. Simulations are used to verify each strategy. Then, it is compared to the A* algorithm and the classic ant colony algorithm. The results show that the adaptive environmental sampling for autonomous underwater vehicles based on our proposed ant colony optimization algorithm is optimal and efficient.
Context-Aware Streaming Perception in Dynamic Environments

Efficient vision works maximize accuracy under a latency budget. These works evaluate accuracy offline, one image at a time. However, real-time vision applications like autonomous driving operate in streaming settings, where ground truth changes between inference start and finish. This results in a significant accuracy drop. Therefore, a recent work proposed to maximize accuracy in streaming settings on average. In this paper, we propose to maximize streaming accuracy for every environment context. We posit that scenario difficulty influences the initial (offline) accuracy difference, while obstacle displacement in the scene affects the subsequent accuracy degradation. Our method, Octopus, uses these scenario properties to select configurations that maximize streaming accuracy at test time. Our method improves tracking performance (S-MOTA) by $$7.4\%$$ over the conventional static approach. Further, performance improvement using our method comes in addition to, and not instead of, advances in offline accuracy.
The Effect of Spell-Checker Features on Spelling Competence among EFL Learners: An Empirical Study

Writing is an essential skill in any languages.There are several sub-skills involved in writing, the most important one is spelling.Spelling is regarded as one of the most challenging aspects of writing as it requires learners to acquire a range of detailed knowledge including writing system, orthography, and punctuation.With the wide spread of technology use in almost all aspects of our lives, learning and teaching processes are not exceptional from this storm that ultimately affects the way language learning and teaching are maintained.In this line, the current study intended to examine the effects of using technological tools that are featured with auto-spell checker on the spelling abilities of learners.To do so, a quasi-experimental method was carried out to address the issue under investigation.The collected data were analyzed using SPSS and GraphPad Prism.The findings exhibited the influence of such technological tools on spelling competence of the learners in which those who heavily depend on such technological tools face more challenges pertaining spelling in their writing tasks.
Equilibrium Optimizer and Henry Gas Solubility Optimization Algorithms for Feature Selection: Comparison Study

One of the most critical processes is feature selection, which eliminates features that may decrease classification performance and increase computational time. In this paper, we introduce and provide a comparison study between two algorithms, which are Equilibrium Optimizer (EO) and Henry Gas Solubility Optimization (HGSO) for Feature Selection (FS). The function objective of both algorithms are based on two main objectives, such as Error Rate (ER) and feature Reduction Rates (RR). In this comparative study, three classifiers (Naive Bayes NB, k-Nearest Neighbor KNN, and Random Forest RF) have been employed. The evaluation of the work was conducted on ten datasets, including Iris, Lung Cancer, Spambase, and Musk. The two algorithms show higher performances according to the accuracy and number of features, especially HGSOFS, which in turn shows its effectiveness and provides good results in the two tasks of FS when we compare it to the PSOFS (Particle Swarm Optimization for Feature Selection) and FAFS (Fire Fly for Feature Selection).
Three-dimensional Path Planning for Unmanned Aerial Vehicle (UAV) Based on Improved Mayfly Algorithm

A three-dimensional (3D) path planning method based on the improved mayfly algorithm (IMA) is proposed in this paper for the unmanned aerial vehicle (UAV) path planning problem under the condition of diverse static features and obstacle threats. Firstly, the 3D flight area environment model with obstacles is built. Then, the path planning method is developed, which can increase the global search capability by keeping population diversity with the improved Tent chaotic map, and balance the global and local searching capability through incorporating the dynamic adaptive inertia weight into the algorithm. In addition, Gaussian mutation strategy is used to increase the solution accuracy and the ability of the algorithm jumping out from the local optimum. Finally, the optimal collision-free flight path is obtained by smoothing the planned path using the cubic B-spline curve. Results show that the developed algorithm can plan a smooth flight path, and avoid obstacle threats.
Increased Efficiency of the Artificial Bee Colony Algorithm Using the Pheromone Technique

Artificial Bee Colony (ABC) is a powerful metaheuristic algorithm inspired by the behavior of a honey bee swarm. ABC suffers from poor exploitation and, in some cases, poor exploration. Ant Colony Optimization (ACO) is another metaheuristic algorithm that uses pheromones as a guide for an ant to find its way. This study used a pheromone technique from ACO on ABC to enhance its exploration and exploitation. The performance of the proposed method was verified through twenty instances from TSPLIB. The results were compared with the original ABC method and showed that the proposed method leverages the performance of ABC.
ER-Caps: ELU Residual Capsule Network for Complex Images Classification

Capsule Network has proved its performance against image deformation, through its ability to preserve space relations among features.However, the Capsule Network underperforms at classifying complex images datasets.Through an in-depth examination of Residual Neural Network, we present a novel Capsule Network named ER-caps to improve the model performance toward complex images classification and reconstruction.The architecture of ERcaps uses customized residual blocks to enhance feature extraction without using any pooling methods to conserve spatial information among features and preserve the Capsule Network equivariance.Our model adopts the RELU and the ELU activation function to ensure fast convergence and uses an advanced decoder to allow better reconstruction for complex images.The experiments show that the suggested model outperforms the baseline Capsule Network with dynamic routing on a variety of benchmark datasets CIFAR-10, CIFAR-100, and SVHN by approximately: 17 %, 9 %, and 4 % respectively.Moreover, ER-caps reduces the number of training parameters.
Securing Data Transmission and Privacy Preserving Using Fully Homomorphic Encryption

The Cloud computing is a repository that stores massive amounts of data that is accessible online.There are many security and data privacy concerns threatening cloud computing services, especially through key sharing.So, there is an important need to improve security algorithms.An electrocardiogram (ECG) measures heart performance and detects arrhythmias.Nowadays, they are used for identification because they vary from one person to another.Therefore, securing an ECG signal is important.Homomorphic encryption (HE) is a security solution that processes encrypted data without decryption.HE supplies the same raw data results.In this paper, to enhance security and maintain data privacy while getting rid of sharing key restrictions, a new fully homomorphic encryption algorithm with an advanced encryption standard (FHEAES) is proposed to encode the ECG signals.This is accomplished by proposing the evaluation process to be appended to an AES algorithm as an additional security layer, which performs a variety of mathematical operations homomorphically on the encrypted data without decrypting it, instead of the original data.The evaluation process is proposed to be a linear polynomial function with variable values to enhance security.A Pan and Tompkins algorithm is used to process ECG signals.Then, the FHEAES algorithm was used for encoding the ECG signal.At last, ECG signals were classified for further diagnosis.According to the findings, FHEAES will take 1.078950 × 1033 years to break or hack.This is due to the using of the proposed three encryption keys to make it harder to break.Also, the FHEAES algorithm has 95.8% accuracy in classification and 100% accuracy in decryption (recovering the original signal).When compared to other algorithms, it is nearly 6 times and 3 times faster during encryption and decryption, respectively.This is because it is proposed to be based on computationally lightweight matrix operations, making it perfect to address the noise that arises from any FHE scheme.Thus, FHEAES provides superior security and privacy while computing and is solid against attacks.
Lightweight Super-Resolution with Self-Calibrated Convolution for Panoramic Videos

Panoramic videos are shot by an omnidirectional camera or a collection of cameras, and can display a view in every direction. They can provide viewers with an immersive feeling. The study of super-resolution of panoramic videos has attracted much attention, and many methods have been proposed, especially deep learning-based methods. However, due to complex architectures of all the methods, they always result in a large number of hyperparameters. To address this issue, we propose the first lightweight super-resolution method with self-calibrated convolution for panoramic videos. A new deformable convolution module is designed first, with self-calibration convolution, which can learn more accurate offset and enhance feature alignment. Moreover, we present a new residual dense block for feature reconstruction, which can significantly reduce the parameters while maintaining performance. The performance of the proposed method is compared to those of the state-of-the-art methods, and is verified on the MiG panoramic video dataset.
Non-Markovianity of an atom in a semi-infinite rectangular waveguide

We investigate the non-Markovianity (NM) of a waveguide QED with a two-level atom as the system and a semi-infinite rectangular waveguide as the environment, where the transverse magnetic (TM mn ) modes define the quantum channels of guided photons. The perfect mirror imposed by the finite end exerts a retarded feedback mechanism to allow for information backflow, which leads to NM dynamics. For the energy separation of the atom far away from the cutoff frequencies of transverse modes, the delay differential equations are obtained with single-excitation initial in the atom. Our attention is focused on the effects of multiple quantum channels involved in guiding photons on the degree of non-Markovian behavior. An asymptotic value of the non-Markovianity <?CDATA ${{\mathscr{N}}}_{1}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi mathvariant="script">N</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> </mml:math> can be found as the atom–mirror distance is large enough, however, the asymptotic value of <?CDATA ${{\mathscr{N}}}_{2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi mathvariant="script">N</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> of the atom interacting with the effective double-modes is lower than that of the atom interacting with the effective single-mode. We also show that <?CDATA ${{\mathscr{N}}}_{1}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi mathvariant="script">N</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> </mml:math> is a constant, and the analytical expression for <?CDATA ${{\mathscr{N}}}_{2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi mathvariant="script">N</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> is related to the parameters associated with the modes, which is related to the interference of the two modes.
Entropic lens on stabilizer states

The $n$-qubit stabilizer states are those left invariant by a ${2}^{n}$-element subset of the Pauli group. The Clifford group is the group of unitaries which take stabilizer states to stabilizer states; a physically motivated generating set, the Hadamard, phase, and controlled-not (cnot) gates which comprise the Clifford gates, impose a graph structure on the set of stabilizers. We explicitly construct these structures, the ``reachability graphs,'' at $n\ensuremath{\le}5$. When we consider only a subset of the Clifford gates, the reachability graphs separate into multiple, often complicated, connected components. Seeking an understanding of the entropic structure of the stabilizer states, which is ultimately built up by cnot gate applications on two qubits, we are motivated to consider the restricted subgraphs built from the Hadamard and cnot gates acting on only two of the $n$ qubits. We show how the two subgraphs already present at two qubits are embedded into more complicated subgraphs at three and four qubits. We argue that no additional types of subgraph appear beyond four qubits, but that the entropic structures within the subgraphs can grow progressively more complicated as the qubit number increases. Starting at four qubits, some of the stabilizer states have entropy vectors which are not allowed by holographic entropy inequalities. We comment on the nature of the transition between holographic and nonholographic states within the stabilizer reachability graphs.
Odgovornost za nedostatke kod ugovora o kupoprodaji stvari s digitalnim elementima

This paper presents the issue of liability for material defects in relation to things with digital elements. Croatian law was recently amended in this field as a consequence of implementation of the Directive (EU) 2019/771, and this paper compares the provisions of EU law with the Croatian law and the legal systems of some other EU member states. The focus of the paper is liability for material defects of things with digital elements, and consequently, the notion of things with digital elements is analyzed along with provisions on defects of such things, updates of things with digital elements, and defects in contracts for the continuous supply of digital content and digital services.
Human Pose Estimation for Boxing Based on Model Transfer Learning

The boxing as a competitive sport is now walking into Chinese campus and daily fitness exercises. The rapid growth of boxing sports has resulted in a coaching shortage. The pose of boxing can be estimated by using human pose estimation technology in artificial intelligence, to teach interns and relieve the coach shortage. However, the large number of training datasets and the lack of consistency of camera views in training limit the technology's practical application. Therefore, the model transfer methodology was implemented to address these limitations, three basic models of OpenPose (OP), High Resolution (HR), and stacked Hourglass (HG) networks were employed for model transfer learning and analyzing of boxing application. The results show the model transfer can efficiently improve the average accuracy of pose key points from 1% to 19%, model transfer can practically help address the issue of dependence on dataset collection. The HR network achieves the best performance among these three networks, but the HG network still gets better estimation on some key points.
A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute

Deaf and mute people are an integral part of society, and it is particularly important to provide them with a platform to be able to communicate without the need for any training or learning. These people rely on sign language, but for effective communication, it is expected that others can understand sign language. Learning sign language is a challenge for those with no impairment. Another challenge is to have a system in which hand gestures of different languages are supported. In this manuscript, a system is presented that provides communication between deaf and mute (DnM) and non-deaf and mute (NDnM). The hand gestures of DnM people are acquired and processed using deep learning, and multiple language support is achieved using supervised machine learning. The NDnM people are provided with an audio interface where the hand gestures are converted into speech and generated through the sound card interface of the computer. Speech from NDnM people is acquired using microphone input and converted into text. The system is easy to use and low cost. The system is modular and can be enhanced by adding data to support more languages in the future. A supervised machine learning dataset is defined and created that provides automated multi-language communication between the DnM and NDnM people. It is expected that this system will support DnM people in communicating effectively with others and restoring a feeling of normalcy in their daily lives. The hand gesture detection accuracy of the system is more than 90% for most, while for certain scenarios, this is between 80% and 90% due to variations in hand gestures between DnM people. The system is validated and evaluated using a series of experiments.
Cyclomatic Complexity Analysis for Smart Contract Using Control Flow Graph

Smart Contracts, which are embedded in block-chains, allow for the automatic fulfillment of contractual obligations without the need for a reliable third party. Due to this, companies can save administration and service costs, and improve their processes which in turn improve efficiency and reduce risks. EthIR framework is one of the most precise instruments available, with a high success rate. Smart-Contracts need to ensure that they have a minimal number of flaws and vulnerabilities is critical. In this work, we present a Control Flow Graph to apply Cyclomatic Complexity for analyzing smart contracts. Our approach uses EtiIR framework, for creating a CFG from an Etherium Virtual Machine smart contract.