Reviewerly/exHarmony huggingface dataset:
Data-dependent metric filtering

Filtering is a fundamental strategy of metric similarity indexes to minimise the number of computed distances. Given a triplet of objects for which distances of two pairs are known, the lower and upper bounds on the third distance can be determined using the triangle inequality property. Obviously, tightness of the bounds is crucial for efficiency reasons — the more precise the estimation, the more distance computations can be avoided, and the more efficient the search is. We show that it is not necessary to consider arbitrary angles in triangles formed by pairwise distances of three objects, as specific range of possible angles is data dependent. When considering realistic ranges of angles, the bounds on distances can be much more tight and filtering much more effective. We formalise the problem of the data dependent estimation of bounds on distances and deeply analyse limited angles in triangles of distances. We justify the potential of the data dependent metric filtering both, analytically and experimentally, executing many distance estimations on several real-life datasets.
İnternet Sayfası Verileri Kullanarak Kimlik Avı Web Sitelerinin Makine Öğrenme Tekniğiyle Tespiti

Attackers are designing fake websites to collect sensitive data such as credit card, email, social media account information of their victims.These attacks keep getting more sophisticated day by day.This leads to highly convincing website designs that can easily trick users and steal their information.In order to prevent this increasingly dangerous problem from spreading, systems with machine learning capabilities have been developed to predict if a web page or web site is created exclusively for phishing or not.In this study, using the determined 6 features of the selected sample website address 12.275.It is tried to determine whether this website address is prepared for phishing purposes or not by using a random forest algorithm.The selected data set in this study have been collected from open-source datas which is published on the official website of the Computer Emergency Response Team of Turkey.The data set was created by categorizing and labeling internet urls and domain names according to 6 determined features.Tags (Phishing=1, Suspect=0, Legitimate=-1) were determined using scripts and tabulation programs developed on python programming language.As a conclusion of the study, used method has shown 95 % success performance.
Refining and Selecting Pseudo Ground Truth for Weakly-Supervised Object Detection

Weakly supervised object detection (WSOD) is an important issue in vision tasks. Unlike fully supervised learning, weakly supervised learning uses only image-level labels without bounding boxes. Training with only image-level labels makes it difficult to train deep networks based detectors in a weakly supervised manner. This paper proposes methods which are denoted as refining pseudo ground truth (RPG) and selecting pseudo ground truth (SPG), respectively. Pseudo ground truth for 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> instance classifier refinement network is refined to make better bounding boxes with RPG and select good bounding boxes with SPG. The proposed methods obtain 55.75% as a mean average precision (mAP) on VOC 2007 that outperforms the previous state-of-the-art.
An improved target detection model based on YOLOV3 framework

YOLOv3 is one of the target detection methods based on DNN, which has good performance in terms of speed and accuracy. However, with the gradual increase in application scenarios and the increasing demand for detection, the missed detection problem of YOLOv3 is deeply criticized. To further improve detection performance, we propose an improved model YOLOv3-A based on YOLOv3. Firstly, YOLOv3-A introduces channel and space two-dimensional attention modules in the feature extraction backbone network to achieve feature focusing on different dimensions, thereby reducing the missed detection rate. Then, in order to avoid network degradation, YOLOv3-A uses a residual module to replace the multi-layer convolutional layer at the rear of the feature extraction backbone network. Finally, YOLOv3-A introduces two different loss functions for positioning and confidence, respectively, using GIoU loss for bounding box positioning and Focal loss for confidence to improve model accuracy. On the PASCAL VOC data set, the mAP of YOLOv3-A is 59.13%, and the mAP of YOLOv3 is 50.92%.
3D Reconstruction from Micro-CT Slices for Non-Destructive Viewing inside a Fossil

Fossils undergo atypical taphonomic and diagenetic deformations while they are being formed. The internal structure of such deformities requires deep and thorough paleontological investigation, and hence demands a reliable digital reconstruction. However, non-destructive study of fossils using conventional medical imaging fails to preserve the internal tissue structure and suffers from slow computation. This has motivated us to pick up this maiden problem and come up with an efficient technique that can digitally reconstruct a fossil from a sequence of 2D micro computerized tomographic scan (micro-CT) slices. The proposed technique is marked with a couple of novel concepts. First, the components on each slice are represented by their encoded boundaries, which are smoothed to get rid of their jagged nature. For this, the notion of majority code is introduced, followed by bilinear interpolation to make up the inter-slice gaps. Second, for various tasks such as intersection, cutting, joining, and the like, we consider the thinnest digital plane. The reconstructed volume being a well-defined set of voxels, admits quick operations with such planes, thereby expediting the procedure invoked by the user. Furthermore, as the surface of the reconstructed volume is well-formed, the marching cube method can easily be used for 3D mesh creation and for visualization. The reconstruction algorithm runs in a time linear in the number of slices and is found to have 98% accuracy in reconstruction with preservation of both bone and tissue structures, as validated by professional paleontologists.
Redundancy of Linear Codes based on Finite Rings

For linear codes based on finite fields, the Gilbert-Varshamov bound is a benchmark in measuring the efficiency in terms of the trade-off between overhead and error correction capability. In this paper we study redundancy of linear codes based on finite rings. We use the probabilistic method to obtain Gilbert-Varshamov type redundancy bounds for linear codes based on arbitrary finite rings and with a given relative distance.
INVARIANCE CRITERIA AS META-CONSTRAINTS

Abstract Invariance criteria are widely accepted as a means to demarcate the logical vocabulary of a language. In previous work, I proposed a framework of “semantic constraints” for model-theoretic consequence which does not rely on a strict distinction between logical and nonlogical terms, but rather on a range of constraints on models restricting the interpretations of terms in the language in different ways. In this paper I show how invariance criteria can be generalized so as to apply to semantic constraints on models. Some obviously unpalatable semantic constraints turn out to be invariant under isomorphisms. I shall connect the discussion to known counter-examples to invariance criteria for logical terms, and so the generalization will also shed light on the current existing debate on logicality. I analyse the failure of invariance to fulfil its role as a criterion for logicality, and argue that invariance conditions should best be thought of as merely methodological meta-constraints restricting the ways the model-theoretic apparatus should be used.
Mammography Data Augmentation Using ACGAN

As high morbidity and mortality of breast cancer, early diagnosis plays a key role in improving survival rate. So it is significant to develop breast cancer detection techniques. Nowadays, Convolutional Neural Networks (CNN) have been proven to detect breast cancer well, but face two challenges: lack of large labelled datasets and unbalanced distribution of incident categories. Therefore, we propose BreastGAN for further data augmentation via using an Auxiliary Classifier Generative Adversarial Network (ACGAN), in order to generate labeled images. Integrating self-attention and spectral normalization components in the generator and discriminator respectively, BreastGAN can generate higher resolution images and more accurate labels. Additionally, label smoothing can further improve the accuracy of labels by increasing the distance between different labels and reduce the distance within same labels. In our experiments, BreastGAN is also evaluated by the accuracy of the trained classification model with new synthesized datasets, not limited to the generated images quality themselves. The results show that our proposed model achieves up to higher IS (56.54) and lower FID (21.25) of image quality, 4.9% higher accuracy of classification model over latest ACGAN.
Improved SVM-AdaBoost Stacking Algorithm with ResNet18

Image classification with small amount of data has become a key issue in computer vision. In this paper, a novel stacking architecture based on support vector machine (SVM) and adaptive boosting (AdaBoost) is proposed. The 18-layer ResNet model is used to extract features, acting as the input of the stacking algorithm. Then, the SVM classifiers in the first layer produce the basic predictions, based on which, the AdaBoost classifier in the second layer decides the final class of image. Experimental results on CIFAR-10 dataset demonstrate that the SVM-AdaBoost stacking algorithm outperforms the existing competitive benchmark algorithms with single SVM or AdaBoost.
Smart Camera for monkeys: A Novel IoT approach for detection and controlling the monkeys using YOLOv3

Nowadays, there are several instances in temples where the presence of additional monkeys irritates visitors. Not only that, but there are several times in which individuals in the villages will be harassed by money-related operations. Since tracking and splitting monkeys is a challenging task, the proposed system incorporates a camera with a built-in laser gun. When a monkey or a gathering of monkeys is discovered, the laser gun will accurately shoot the monkeys by making them unconscious for a specific amount of time. This idea will be implemented through the Internet of Things, which will identify specific entities and then notify a laser gun that is filled with unconscious injections to accurately fire such targets. The benefits of this technology include the ability to readily avoid noise and disturbance from such monkeys, as well as the ability to accurately implement computation intelligence (given by a machine learning algorithm). The modules utilized in this are a movement sensor for identifying the specially taught things in dynamic photos, a communication module that alerts to fire specific objects with accuracy, and a history module that sends the monkeys' personal information over time to the concerned center. The daily report will be delivered to the appropriate centre along with a video of monkeys being attacked with laser injections in order to render them unconscious. It could be extended in the future to detect many objects at once by using YOLOv3 or another advanced algorithm
Image Quality Improvement of Surveillance Camera Images by Noise Removal Using Learning Method

In this paper, we propose an image denoising method that combines the Total Variation (TV) regularization method with a learning method of Denoising Convolutional Neural Network (DnCNN). In this method, a texture component loss, which is one of the problems of learning methods, is addressed by performing image decompositions using the TV regularization method to denoise the image without losing the texture components. As a result, the subjective evaluation was improved over the conventional method in terms of denoising performance and texture preservation.
An Efficient Parallel Implementation of Whale Colony Optimization Algorithm on GPU

Whale colony optimization is a Swarm Intelligence (SI) Optimization algorithms derived from the hunting behavior of Humpback whales. The algorithm like other SI algorithms suffers from scaling problem when applied to large population. The problem can be overcome by efficiently distributing computationally expensive task on GPU and serial implementations on CPU. The paper proposes an efficient implementation of WCO on GPU/distributed platforms. The paper explores different strategies or parallel implementation on GPU and task balancing between CPU and GPU. The speed up/ other parameters are compared on standard dataset and result obtained provide a significant speed up compared to serial implementation on CPU
Avatars as Bodiless Characters

In this chapter, the author introduces the reader to the importance of virtual reality in human life, avatars, and communicating with digital characters and demonstrates the pervasiveness of technology's penetration into our lives, not only physically, cognitively, and emotionally, but also environmentally. As the created interpreters and representatives of scientific work as the substance subject of scientific history, avatars participate, along with robots, cyborgs, and artificial intelligence, in the desubjectivization, biological denaturalization, and despiritualization of man and death of biological life. The ‘cyborgization' of humans in virtual space extends the landscape of the discussion on cyborgoethics.
When Wikipedia met Tor: trials of legitimacy at a key moment in internet history

In 2005, the paths of Wikipedia and The Tor Project crossed publicly and ferociously. Tired of trolls and vandals, Wikipedia decided to block all Tor users from editing encyclopaedia articles. The Tor Project protested, arguing that such a block was not only ineffective, but constituted a form of censorship. This conflict came at a time when both projects were fighting to establish, maintain, and expand their perceived legitimacy. Using a threefold definition of “legitimacy,” this essay explores the Wikipedia/Tor conflict as a legitimacy conflict. We argue that Wikipedia was heavily invested in a conception of legitimacy as authenticity, focusing on who should be counted as a Wikipedian and who should be excluded. In contrast, the Tor Project was more concerned with Weberian legitimacy, challenging states’ claims to the monopoly of violent power. However, both projects shared an interest in acquiring resources and respect, or legitimacy as propriety. To explain this conflict, we draw on an archive of primary source emails and historical documents focusing on the early days (2001–2005) of both projects.
Analysis of the possibility for seismic early warning in the Balkan Peninsula

This study analyses the possibility for seismic early warning (EW) in the Balkan Peninsula. A number of characteristics of seismic record were evaluated for EW utility. Some tests checked the possibility to locate events reliably by Golitsyn’s method, using one seismic station (SS). The distance and relevant travel time from some crustal faults to the nearest SS and big towns were appraised. EW procedures for most of the seismic zones in the Balkan Peninsula are not reliable, excepting the Vrancea zone, because of the small density of the SS, crustal depth of the earthquakes and fault crowdedness of the region.
Intelligent real-time control system through socket communication using deep learning-based de-hazing and object detection in an embedded board environment

This paper is a paper on the development of an intelligent real-time marine signal light that applies Socket communication after sea fog remove and object detection using artificial intelligence and computer vision through camera video in an embedded board environment. The embedded board used NVIDIA Jetson Nano board, and for sea fog remove, deep learning-based FFA-Net with excellent sea fog and fog removal performance among current technologies was used. For human detection, YOLOv4 based on light-weight deep learning with excellent detection performance among current technologies was applied for the purpose of research. For learning and testing, customized data was built through RESIDE dataset, MSCOCO dataset and data labeling, and socket communication was used for information delivery.
Reinforcement Learning that Emerges Teamwork in Heterogeneous Multi-Agent Environments

This paper approaches problem-solving in multi-agent environments using deep reinforcement learning. In this paper, we solve a cooperative air rescue task by a fixed-wing aircraft and a helicopter as a problem in multi-agent environments. They have different abilities about speed and expected tasks. Therefore, the purpose of this research is to emerge teamwork that takes advantage of different abilities in multi-agent environments. This paper proposes a method for agents to learn to communicate. We compare among the "Proposed model," "No comm," "MADDPG" using an air rescue task. From two experiments, we confirm the accomplishment of the cooperative task by the proposed method and the effectiveness of the proposed method.
Specific study of biblical parables in a pedagogical university

The author of the article focuses on the analysis of specific methods and techniques for identifying the universal and unique cultural meanings of biblical parables in the study of the disciplines of the humanities cycle in a pedagogical university. The purpose of the article is to develop techniques for working with parables for practical classes in the cultural cycle at the Pedagogical University. Research methodology and methods. The work is based on the use of the methodology of the sociocultural and activity-based approach to the psychological development of the individual, as well as systemic and holistic approaches to education. When decoding the meanings of parables as texts of culture, it is proposed to use axiological, semiotic, cognitive and systemic approaches. The systemic culturological approach allows you to create conditions for the development of aesthetic taste, artistic thinking. Research results. As the scientific literature and the practice of teaching culturological disciplines at the Pedagogical University show, working with parables teaches us to personally and emotionally perceive the text of culture, to adequately assess the relationship in the system man-man and man-world, to recode the content from one semiotic system to another. Through the interpretation of the text, the individual creative abilities of students are developed, a steady interest in modern art practices is formed. Interpretation of the categories of culture, embodied in human and natural images of the transcendental, time, space, make it possible to essentially comprehend the texts of culture containing the plots of biblical parables. Conclusion. The consistent identification of the specifics of the study of parables in the context of the spiritual quest of a certain historical period contributes to the emergence of interest and the formation of a respectful attitude towards the cultural heritage, the values of world culture. Cultural practices of working with cultural texts contribute to an adequate understanding of the place of national culture in the world artistic process and its creative enhancement.
A dense state search method in edge computing environment

In view of the common edge computing-based cloud-side collaborative environment summary existing search key and authentication key sharing caused by data information leakage, this paper proposes a cryptographic search based on public key searchable encryption in an edge computing environment method, this article uses the public key to search for the characteristics of the encryption algorithm, and allows users to manage the corresponding private key. In the process of retrieval and execution, the security of the system can be effectively ensured through the secret trapdoor. Through the comparison of theoretical algorithms, the searchable encryption scheme in the edge computing environment proposed in this paper can effectively reduce the computing overhead on the user side, and complete the over-complex computing process on the edge server or the central server, which can improve the overall efficiency of encrypted search.
Curriculum Learning-based Object Transportation using Region Partitioning

This paper presents a deep reinforcement learning (DRL)-based object transportation technique using a region-partitioning curriculum. Previous studies on object transportation using DRL algorithms have suffered a sparse reward problem where a robot cannot gain success experiences frequently due to random actions at the learning stage. To solve the sparse reward problem, we partition pose-initialization regions based on the distance between an object and goal, then a robot gradually extends the partitioned regions as training episodes increase. The robot has more success opportunities using this method, and thus, it can learn effective object transportation methods quickly. We demonstrate simulations to verify the proposed method.
Micro-expression Recognition Based on Multi-Scale Attention Fusion

In the field of micro-expression recognition (MER), facial feature extraction is an important factor that affects the accuracy of MER. Most of the existing facial MER technologies use traditional feature extraction methods for classification. In view of the lack of effective fusion of global and local features in traditional feature learning methods, and the inability to learn the action information of key areas of the face, we propose a Multi-Scale Attention Fusion (MSAF) method. Specifically, we use attention gates to select the most informative image regions at different scales to generate category predictions. This can better obtain the image information of the detailed part of the datasets. Furthermore, we adopt a skip connection before fusion to ensure the effective transmission of image detail information. Convolutional Long Short-term Memory (ConvLSTM) is used to extract the fused temporal features to construct more advanced local and global temporal change fusion features. The experiments in the SMIC, CASME, and CASME-II datasets show that our method is effective, especially in the CASME dataset.
Queueing Model of Hysteretic Congestion Control for Cloud Wireless Sensor Networks

This paper considers a Hysteretic congestion control model to mitigate congestion problems on WSN gateway. We model the system as a queueing system with two thresholds working under three modes: normal mode, reduced load mode and deny mode. We consider average number of requests in the system and blocking probability as the performance metrics and compare the acquired results against the results for IRED algorithm.
Exploring Techniques for Photo-realistic Image Generation from 3D Models - A Deep Learning Approach

The product companies are going towards Digital Transformation for digital customer experience. This is effective for marketing and sales specially during the pandemic. For the product visualization, it is necessary to create multiple 2D views of a product which is a cumbersome and time-consuming process especially when there are copious number of products. Traditional rendering techniques and image augmentation for dataset generation either take quite a lot of time or do not yield photo-realistic images. The recent progress in generative models and 3D deep learning provide a promising avenue in this regard. This paper explores different techniques for rendering photo-realistic 2D images from 3D CAD models. This study compares the performance of three different models pix2pix, Pytorch3D and SynSin based on three different approaches namely image-to-image translation, mesh rendering, and view synthesis respectively to generate photo-realistic images. The models are tested using the dataset provided by Schneider Electric. Results demonstrate that Pytorch3D is the better model to generate photo-realistic images. These approaches can emerge as the initial steps for digital twin technology.