Reviewerly/exHarmony huggingface dataset:
A multi-level method noise based image denoising using convolution neural network

Abstract Gaussian noise has been the bane of any and every denoising process under the sun. Being a very corrosive noise with huge disruptive potential, this has received much attention form the image restoration community. Building on the premise, a novel framework is proposed to leverage multi-level image denoising that iteratively removes gaussian noise while recovering details lost during processing. This framework uses existing deep learning based CNN systems whilst enhancing the same by the addition of method denoising to the process. This framework is habile in competing with state-of-the-art technologies and outperforming them in some cases.
A Parallel and Scalable Framework for Insider Threat Detection

In this article, we propose an innovative method for the detection of insider threats. This method is based on a unite and conquer approach used to combine ensemble learning techniques, which have the particularity of being intrinsically parallel. Furthermore, it showcases multi-level parallelism properties, offers fault tolerance, and is suitable for heterogeneous architectures. To highlight our approach's efficacy, we present a use case of insider threat detection on a parallel platform. This experiment's results showed the benefits of this method relative to its improvement of classification AUC-score and its scalability.
Evolutionary Planning in Latent Space

Planning is a powerful approach to reinforcement learning with several desirable properties such as sampling efficiency. However, it requires a world model, which is not readily available in many real-life problems. In this paper, we propose to learn a world model that enables Evolutionary Planning in Latent Space (EPLS). We use a Variational Auto Encoder (VAE) to learn a compressed latent representation of individual observations and extend a Mixture Density Recurrent Neural Network (MDRNN) to learn a stochastic, multi-modal forward model of the world used for planning. We use the Random Mutation Hill Climbing (RMHC) algorithm to find a sequence of actions that maximize expected reward in this learned model of the world. We demonstrate how to build a world model by bootstrapping it with rollouts from a random policy and iteratively refining it with rollouts from an increasingly accurate planning policy using the learned world model. After few iterations, our planning agents exceed standard model-free reinforcement learning approaches, which demonstrates the viability of our approach. Code to reproduce the experiments is available at https://github.com/two2tee/WorldModelPlanning and videos at https://youtu.be/3M39QgeF27U .
Pupil Reaction Model Using a Neural Network for Brightness Change

Pupil reaction models for brightness change are developed in order to introduce emotional pupillary changes into the evaluation of a video. The models are designed with experimental pupil reactions to temporal changes in brightness using a linear model and a layered neural network model. Their performance in reproducing the training data, and the possibility of applying these models to short video clips are evaluated.
On the use of higher-density wavelet packet neighboring coefficient to improve SNR for signal denoising

Wavelet denoising is an effective method to eliminate heavy background noise and has been widely utilized to extract useful characteristic signal information. However, the traditional higher-density wavelet transform has one scale function and two wavelet functions overlapping in frequency domain, which causes detailed signal extraction problems. In this paper, a novel method called higher-density wavelet packet neighboring coefficient denoising algorithm is proposed to extract the detailed signal information located in the middle and high frequency bands furtherly. Firstly, higher-density wavelet packet transform is adopted to achieve more sophisticated division of the target signal frequency band. Secondly, neighboring coefficient denoising method is implemented to extract more characteristic signals since they tend to be concentrated in adjacent coefficients. Finally, the proposed algorithm is simulated and verified by periodic impact signals coupled with random noise. It is demonstrated that the proposed method can improve signal-to-noise ratio greatly and extract more characteristic signals.
Innovation in Earthquake Early Warning System: A Case Study of EQ Guard

Global frequency of disasters is following an upward trend. While the frequency of significant earthquake linked disasters is much less compared to hydrometeorological disasters, the causalities attributed to earthquakes considerably surpass those attributed to any other disasters. This makes earthquakes a low-frequency but high-impact hazard. Further, unlike other natural hazard warning mechanism, earthquake warning has a window of a few seconds to a minute, hence catching us mostly unaware and leading to more injuries and causalities. With the advancement in the field of earthquake research, and science and technology, many countries including Japan, Turkey, Mexico, and USA boast of having an earthquake warning system. Although it is not possible to reliably predict earthquakes, but the existing technologies create a window for decisive action by analyzing the P-wave, which provides advance information of the estimated seismic intensities and arrival time of strong tremors prior to their actual arrival. Ranging from a few seconds to a minute in hand, such a warning allows to take necessary safety measures including individual safety, evacuation, shutting down of critical/hazardous processes to mitigate impacts, stopping elevators, trains, initiating response, etc. This study discusses the innovation and advancement in earthquake early warning (EEW) systems in Japan. It explores the functioning of the existing product, namely, Earthquake Guard (EQG-III) developed by Challenge Co. Ltd., Japan which is being used for earthquake early warning.
Improved K-means Based on Density Parameters and Normalized Distance

The existing K-means has problems such as random selection of initial cluster centers, sensitivity to outliers, and inability to unify the data size. In this paper we describe a new improved K-means based on density parameters and normalized distance (K-DPND) we developed that addresses these problems. In the stage of selecting the initial cluster centers, K-DPND constructs a density parameter set according to the distance matrix and the average distance of the data set, selects the largest density parameter point as the cluster center, sets the density parameter of the point whose distance from this cluster center is less than the average distance to 0. Then loops until k initial cluster centers are found. In the clustering stage, the normalized distance is used to replace the Euclidean distance to calculate to determine the cluster to which each point belongs, and the median is used to replace the mean to calculate the new cluster center. Finally, compared with K-means, K-mediods, IK-DM and KICIC, K-DPND has good clustering results in most cases.
Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning

Transductive zero-shot learning is designed to recognize unseen categories by aligning both visual and semantic information in a joint embedding space. Four types of domain biases exist in Transductive ZSL, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual bias</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantic bias</i> in two domains, and two <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual-semantic biases</i> exist in the seen and unseen domains. However, the existing work has only focused on specific components of these topics, leading to severe semantic ambiguity during knowledge transfer. To solve this problem, we propose a novel attribute-induced bias eliminating (AIBE) module for Transductive ZSL. Specifically, for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual bias</i> between the two domains, the mean-teacher module is first used to bridge the visual representation discrepancy between the two domains using unsupervised learning and unlabeled images. Then, an attentional graph attribute embedding process is proposed to reduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantic bias</i> between seen and unseen categories using a graph operation to describe the semantic relationship between categories. To reduce semantic-visual bias in the seen domain, we align the visual center of each category with the corresponding semantic attributes instead of with the individual visual data point, which preserves the semantic relationship in the embedding space. Finally, for the semantic-visual bias in the unseen domain, an unseen semantic alignment constraint is designed to align visual and semantic space using an unsupervised process. The evaluations on several benchmarks demonstrate the effectiveness of the proposed method, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> 82.8%/75.5%, 97.1%/82.5%, and 73.2%/52.1% for Conventional/Generalized ZSL settings for CUB, AwA2, and SUN datasets, respectively.
Performing Weakly Supervised Retail Instance Segmentation via Region Normalization

Instance segmentation can clearly distinguish object instances from the pixel level, which is the foundation for applications in the retail industry. Supervised instance segmentation methods require pixel-level annotations for learning accurate object patterns, which is expensive and labor-intensive to obtain. In the case of retail industry, objects of interest vary frequently and only category-level labels are available, which motivates us to study the weakly supervised instance segmentation algorithm for the retail industry. Although weakly supervised instance segmentation algorithms have been extensively studied on standard benchmarks, e.g., VOC and COCO, directly employing these algorithms to the retail industry usually generates insufficient segmentation accuracy. We found a fundamental reason lies in the specific characteristics of the retail industry. Specifically, one challenge for the traditional benchmark dataset is dramatic appearance variations, but the retail industry only tackles limited object categories and each object possesses fixed appearances. To thoroughly explore characteristics of the retail industry, we propose the region normalization mechanism to thoroughly explore characteristics of the retail industry. It divides an image into regions and requires pixels from the same region to possess consistent features. The region normalization mechanism is a novel attempt to perform normalization within irregular regions, distinct from the existing normalization mechanism. Besides, as region division can influence the effectiveness of the region normalization operation, we explore feature information to dynamically divide an image into regions, under constraints that pixels belonging to the same region should show similar features and keep spatially adjacent. The proposed region normalization mechanism and region division strategy endow a simple but effective solution to weakly supervised instance segmentation for the retail industry. On a large-scale benchmark dataset MVTec D2S, the proposed method performs favorably over existing well-performed methods, and the effectiveness of the region normalization mechanism is demonstrated as well.
A survey on Face Recognition Technique

Face recognition has attracted tremendous notice since it has numerous applications in computer vision, communication and automatic control systems. Face recognition frameworks either perform face verification or face identification. Face Detection is the first and most fundamental step in face recognition, and its essential point is to decide if there is a face in an image or not. Be that as it may, automatic face discovery is a mind boggling issue in image and video processing. Anticipating facial features is another major task in facial analysis and is an important step in face identification. Both these tasks are challenging because faces differ in size, shape, color, due to messy background, different face orientation and large fluctuations of the features in different images etc. The main objective of this paper is to provide an overview of some of the popular face recognition approaches and techniques and to compare them.
A Framework for Authorial Clustering of Shorter Texts in Latent Semantic Spaces

Authorial clustering involves the grouping of documents written by the same author or team of authors without any prior positive examples of an author’s writing style or thematic preferences. For authorial clustering on shorter texts (paragraph-length texts that are typically shorter than conventional documents), the document representation is particularly important. We propose a high-level framework which utilizes a compact data representation in a latent feature space derived with non-parametric topic modeling. Authorial clusters are identified thereafter in two scenarios: (a) fully unsupervised and (b) semi-supervised where a small number of shorter texts are known to belong to the same author (must-link constraints) or not (cannot-link constraints).We report on experiments with 120 collections in three languages and two genres and show that the topic-based latent feature space provides a promising level of performance while reducing the dimensionality by a factor of 1500 compared to state-of-the-art. We also demonstrate that little knowledge on constraints in authorial clusters memberships leads to auspicious improvements in front of this difficult task.
USING INFORMATION AND COMMUNICATION TECHNOLOGIES FOR THE FORMATION OF LINGUODIDACTIC COMPETENCE OF FUTURE FOREIGN LANGUAGE TEACHERS

The article explores the use of information and communication technologies for the development of future foreign language teachers’ linguodidactic competence. On the grounds of the analysis of the scientific and pedagogical literature the authors have proved the topicality and importance of applying information and communication technologies in future foreign language teachers’ training. The empirical methods (questionnaires, observation, surveys) were used to determine the current situation with the application of information and communication technologies in the process of future foreign language teachers’ professional training. The essence of the key notions of the research has been specified: “information and communication technologies”, “professional and pedagogical competence”, “future foreign language teachers’ linguodidactic competence”. The experience of applying information and communication technologies in the process of training foreign language teachers has been presented, as the abovementioned technologies have been proved to be an important means for the formation of future foreign language teachers’ linguodidactic competence. It has been determined that educational information and communication technologies (multimedia presentations, cloud services, blog, video scribing, e-portfolio, web-quests, etc.) provide unique didactic possibilities to form future foreign language teachers’ linguodidactic competence, namely: create conditions for working with foreign language business documents, modeling virtual environment for foreign language business communication, obtaining linguistic and extralinguistic information, contribute to all types of speech activity (reading, speaking, listening, writing), presenting and looking for data on languages and countries, etc. Particular attention has been paid to such kind of work with information and communication technologies as creating personal e-portfolios, which demonstrate creativity, innovativeness and professional competence of the modern teacher and develop future teachers’ self-presentation and self-reflection skills. There has been determined and experimentally proved the effectiveness of using information and communication technologies in the formation of future foreign language teachers’ linguodidactic competence.
Generating Adversarial Patches Using Data-Driven MultiD-WGAN

In recent years, machine learning algorithms and training data are faced many security threats, which affect the security of practical applications based on machine learning. At present, generating adversarial patches based on Generative Adversarial Nets (GANs) has been an emerging study. However, existing attack strategies are still far from producing local adversarial patches with strong attack power, ignoring the attacked network's perceived sensitivity to the adversarial patches. This paper studies the security threat of adversarial patches to classifiers; adding an adversarial patch to the data can mislead the classifier into incorrect results. Considering the attention to aggression and reality, we propose the data-driven MultiD-WGAN, which can simultaneously enhance adversarial patches' attack power and authenticity through multi-discriminators. The experiments confirm that our datadriven MultiD-WGAN dramatically reduces the recall of seven classifiers attacked on four datasets. The attack of data-driven MultiD-WGAN on 25/28 groups of experiments leads to a decreased recall rate, which is better than the conventional GANs. Finally, we have proved a positive correlation between attack intensity and attack ability, both theoretically and experimentally.
Study of Parameters in the Genetic Algorithm for the Attack on Block Ciphers

In recent years, the use of Genetic Algorithms (GAs) in symmetric cryptography, in particular in the cryptanalysis of block ciphers, has increased. In this work, the study of certain parameters that intervene in GAs was carried out, such as the time it takes to execute a certain number of iterations, so that a number of generations to be carried out in an available time can be estimated. Accordingly, the size of the set of individuals that constitute admissible solutions for GAs can be chosen. On the other hand, several fitness functions were introduced, and which ones led to better results was analyzed. The experiments were performed with the block ciphers AES(t), for t∈{3,4,7}.
Fast Barcode Detection Method Based on ThinYOLOv4

Barcode detection is a key step before decoding so that achieving a fast and accurate detection algorithm is of significant importance. In the present study, we propose to guide the pruning of channels and shortcut layers in YOLOv4 through sparse training to obtain the compressed model ThinYOLOv4 for barcode detection. Then a binary classification network is established to remove the prediction boxes that do not contain a barcode, thereby obtaining a fast and accurate barcode detection model. In order to evaluate the performance of the proposed method, a barcode dataset consisting of 16,545 images is provided. This dataset contains common types of barcodes in the market and covers different practical scenarios. Furthermore, interference factors such as blur, low-contrast are considered in the dataset purposefully. Obtained results show that the proposed method achieves a recall rate of 93.8% on the provided dataset, Meanwhile, parameters of YOLOv4 are reduced from 63,943,071 to 400,649, and the model size is reduced from 250,037 KB to 1,587 KB, while the corresponding detection speed is increased to 260% of YOLOv4. When the experiment is performed on the 1050Ti GPU, a detection speed of 23.308 ms/image is achieved.
Design of Healthcare Information Sharing Methods Using Range-Based Information Disclosure Incentives

The means by which healthcare information is collected and utilized are progressing. Collecting and analyzing such information on a large scale can result in the extraction of useful information and further service development. However, the provision of healthcare information has privacy considerations because healthcare information contains personal information. Therefore, in this paper, we propose a healthcare information sharing method that promotes the sharing of useful healthcare information with ample privacy safeguards using auto-negotiation considerations and user/system intentions. In addition, we evaluate the effectiveness of the proposed method in simulation experiments.
An exploratory study on the identification and evaluation of bad smell agglomerations

Systems must evolve to cope with new stakeholders requirements, or to fix existing problems. These changes are complex due to several factors, including the need of understanding the source code, activity that is impaired by the presence of bad smells. Bad smell is a symptom of bad decisions about the system design or code. When two or more bad smells occur in the same snippet of code, they form an agglomeration. Hence, developers need to put more effort to perform their development and maintenance tasks. However, few studies in the literature evaluate how such agglomerations may impact development activities. We aim at exploring agglomerations focusing on evaluating how they are spread in the code, and how they impact on the metrics of software modularity. In this work, we evaluate agglomerations composed of four kinds of bad smells: Large Class, Long Method, Feature Envy and Refused Bequest. Our results are achieved through the use of association rules and effect size measurements. We have found that classes with two or more smells are frequent in the source code, even when the smells present in the class are of the same type. We also found that agglomerations are highly spread in the source code, even when the size of the systems are taken into account, and they have a significant effect on most modularity metrics.
High-Intensity Interval Training Exercise Recognition using Smartwatch

The use of a smartwatches to enable human activity recognition has brought forth immersive applications. This paper presents an end-to-end approach using deep learning to recognise physical exercises from a commercially available smartwatch. The exercises are recognised based on two different settings namely; constrained and unconstrained workouts in the form of High-Intensity Interval Training. The model reported a 97.35% accuracy for constrained exercise recognition, and a 82.29% accuracy for unconstrained exercise recognition. This method is capable of recognising 18 High-Intensity Interval Training exercises. The model was deployed to Google Cloud Platform to recognise exercises in real-time settings. The method will be further expanded to operate as a real-time “Fitness Coach”, which could automatically suggest optimal workout plans for users and monitor their health conditions during workout sessions.
Learning with Multiplicative Perturbations

Adversarial Training (AT) and Virtual Adversarial Training (VAT) are the regularization techniques that train Deep Neural Networks (DNNs) with adversarial examples generated by adding small but worst-case perturbations to input examples. In this paper, we propose xAT and xVAT, new adversarial training algorithms that generate multiplicative perturbations to input examples for robust training of DNNs. Such perturbations are much more perceptible and interpretable than their additive counterparts exploited by AT and VAT. Furthermore, the multiplicative perturbations can be generated transductively or inductively, while the standard AT and VAT only support a transductive implementation. We conduct a series of experiments that analyze the behavior of the multiplicative perturbations and demonstrate that xAT and xVAT match or outperform state-of-the-art classification accuracies across multiple established benchmarks while being about 30% faster than their additive counterparts. Our source code can be found at https://github.com/sndnyang/xvat.