Reviewerly/exHarmony huggingface dataset:
ЭКСТЕРНАЛИЗМ И ИНТЕРНАЛИЗМ В ФИЛОСОФИИ ТЕХНОЛОГИИ: СЕМАНТИКА ВОЗМОЖНЫХ МИРОВ КАК ПОДХОД К ПОНИМАНИЮ ТЕХНОЛОГИЧЕСКОГО РАЗВИТИЯ (Externalism and Internalism in the Philosophy of Technology: Semantics of Possible Worlds as An Approach to Understanding Technological Development)

The article deals with the problem of interaction of externalist and internalist points of view in relation to modern technological development. These two approaches in the world literature today coexist and are characterized by a deep divergence of views. A method of reconciling these points of view is proposed, associated with the well-known concept of possible worlds, which arose in line with modern logic (p. Kripke et al.). This approach allows us to comprehend the rapid changes in technological development, without reducing them to the consequences of the impact of external determinants of technical progress. It helps to clarify the internal parameters of technological development, which play a huge role in modern engineering and technical development processes.
Compressed Channel Attention Mechanism for 3D Medical Image Segmentation of Liver

More and more Convolutional Neural Networks (CNNs) are used in computer vision, especially in the field of medical images. Since most of the medical data in clinical practice are three-dimensional, which is mainly obtained from imaging techniques such as MRI and CT, the use of the previous two-dimensional neural network becomes untimely. In this work, an end-to-end trained 3D image segmentation network combined with the Compressed Channel Attention Module (CCAM) is proposed to learn to predict the segmentation of the entire liver at one time. We embed the CCAM in the network structure of up-sampling and down-sampling. First, we divide the feature map obtained by down-sampling into two parts. The first part performs global average pooling and maximum pooling and then splicing, and the other part performs 1 × 1 convolution on the feature map. Then the two parts are merged, and finally spliced with the up-sampled feature map to realize the use of down-sampling features to monitor the up-sampled features, focusing on specific livers, and suppressing irrelevant areas in the input image. Experiments have been carried on the LITS dataset. Compared with the existing segmentation methods, the proposed method has better segmentation performance in both subjective and objective evaluation.
Book Review

In recent years, wearable and mobile technologies have been applied in different domains such as medical, personal safety, sports, and lifestyle computing (Cheng &amp; Mitomo, 2017). Wearable technologies may help users bring their experiences, engagements, and interactions from one context to another (Ching, Stewart, Hagood &amp; Rashedi, 2016). These kinds of devices can be worn by people and they make it possible to enhance our lives. It is believed that wearable technology may also have a great impact on education because it could be adopted to foster educational experiences, inspire intrinsic motivation, and bring one close to youth culture and usage behavior (Larabi Marie-Sainte et al, 2016). However, there are few publications introducing wearable and mobile technologies within the framework of teaching, training, and learning.
An Image Watermarking Framework based on Saliency and Phase Congruency using LSB Matching Technique

The outcomes from Perceptual watermarking attain full benefits of Human Visual System (HVS). Saliency and Phase Congruency (PC) use these benefits by categorizing various perceptual regions of an image. Saliency gives the low perceptual region of an image where Phase Congruency identifies the corner points of an image. Hiding information at this low perceptual region of an image gives better imperceptibility. In this proposed method, bit depth images are generated from Saliency and Phase Congruency which are used to create hiding capacity map for inserting watermark data. Low perceptually visible pixels of Hiding Capacity Map are used for better hiding locations. Watermark information are inserted into the cover image by the help of a LSB matching technique where insertion and extraction are carried out using pair of pixels. The experimental result ensures better Imperceptibility and Robustness with superior data hiding Capacity.
Affine Non-negative Hybrid Collaborative Representation Based Classification

Representation based classification methods have achieved essential results in image classification recent in recent years. Primarily, the proposed affine non-negative representation based classification (ANCR) model has achieved satisfactory results, but ANCR does not consider the specific class representation for a test sample. We offer an affine non-negative hybrid collaborative representation based classification (ANHC) algorithm for image classification. Specifically, the ANHC method combines the traditional representation with specific class of collaborative representation to represent test samples. Then, we introduce affine transformation to use the image features of the affine subspace to obtain better classification performance. Various experiments on face recognition data and handwriting recognition datasets show that the ANHC method is superior to several traditional methods in classification accuracy.
A Novel Approach to Recommend Products in E-Commerce

Electronic commerce (e-commerce) refers to the purchasing and selling of products via the web. E-commerce platforms have been used by people around the globe in some form or another because everything can be purchased online with just a few mouse clicks. Since, there is a huge amount of data on every e-commerce site; a consumer may struggle to identify the product they require. In this scenario, the Recommendation System is used. A product's recommendation might be based on a variety of variables, including past search or purchase history, user reviews, and the most popular product. We have several Machine Learning-based approaches for these recommendations. We shall implement Collaborative Filtering and K-Means clustering in this work. We utilized Jupyter Notebook to develop the recommendation system, and the Amazon-ratings dataset from Kaggle was used. We will also examine numerous other recommendation techniques in this paper.
Sentiment Analysis High_School' Feedback for Predicting Career Possibilities

For many years, the issue of admissions counseling has been of interest to many people, especially students and parents. Choosing the right career, following your abilities and skills is extremely necessary. However, the traditional form of admissions counseling makes students feel bored and uninterested. In addition, understanding students' feelings and feedback is an essential issue when directing aspirations to universities or colleges. Knowing this, the study offers a solution to help high school students understand their abilities and abilities to choose the right career and knack that they love. The student feedback dataset has been collected by the author since 2015. Next, manually removing all junk, duplicate, or invalid data, the author has more than 52,000 raw data. The author then used three algorithms, Naïve Bayes, SVM, and Entropy to build a model for analyzing feedback on students' feelings based on classification algorithms. The results proved that the Maximum Entropy algorithm was better than Naïve Bayes and that vectors supported with a score of 78%. With such accuracy, the author found that the data and results of the study could be useful data sources for the community to analyze feedback on future feelings.
Automatic Detection and Risk Assessment of Session Management Vulnerabilities in Web Applications

The session management mechanism is a source of several threats to the security of web applications. While lots of web-based software vulnerabilities are due to weaknesses in session management design and implementation, existing methods and tools still have considerable limitations to fully detect those vulnerabilities. In this paper, a black-box method is presented to detect session management vulnerabilities by analyzing browser-server traffic. We have identified some features in the traffic related to the security of session management. The features are either dependent on or independent of languages and programming frameworks. They are the leaves of our constructed attack tree upon which we assess the total risk of session vulnerabilities for a web application. Our simple yet effective idea results in more accurate detection of session vulnerabilities compared to well-known vulnerability scanners. Our experimental evaluations on several case studies confirm the effectiveness of our approach in terms of its vulnerability detection as well as in its risk assessment.
From more to less ‘Civil’ borderline discourses in mainstream media and government

Abstract Between the civil and uncivil lie ‘borderline’ discourses where speech that appears civil is laden with uncivil ideas, norms and discourses that normalise anti-pluralist, nativist and exclusionary views. Such discourses are found in videos and websites of far-right groups and in some mainstream media. Here, we argue that Turkey’s government and mainstream media use similar discursive strategies. We examine both speeches and media representations of these that represent capital punishment since Recep Tayyip Erdoğan’s 2002 rise to politics. Analysis leans on Multimodal Critical Discourse Studies to expose how Erdoğan, who transforms from a cautious reformist prime minister into an authoritarian-populist president, has always articulated uncivil ideas cloaked in civility, with uncivility increasing over time. Analysis of lexica and imagery in associated news stories reveal how media normalise such discourses. As such, these borderline discursive acts contribute to a decline in civility in a deeply polarised society.
Application of vehicle detection with YOLOv3 in road surveillance video

Vehicle detection from traffic monitoring video, which lays the foundation to serials consequential operations such as vehicle counting and accident detection, is an essential part of traffic monitoring system. Traditional target detection methods always have some kind of drawbacks more or less, while target detection based on deep learning has the benefits of more abundant target feature extraction, thus much higher detection accuracy can be achieved, of which YOLO v3 is a typical representative. In this paper, the model structure and principles of YOLOv3 is analyzed in depth firstly, and then its application to vehicle detection in road surveillance video is carried out with the discussion of some existing problems.
The Influence of the Teaching Learning Process, Learning Motivation, and Campus Learning Environment on Students' Learning Achievement in Economics and Administration

This study aims to determine the effect of the teaching and learning process, learning motivation, and campus learning environment on student achievement in Economics and Administration in the 2019 academic year, Faculty of Economics, State University of Jakarta. This research is a quantitative research with quantitative descriptive method. The population in this study were Economics and Administration students in the 2019 academic year as many as 345 people with a sample of 150 student respondents. The data instrument used is a questionnaire with a Likert scale. Hypothesis testing using a partial test and simultaneous test using the SPSS (Statistical Package for Social Sciences) version 25. The results of the study have a positive and significant influence on the teaching and learning process, learning motivation.
Supporting End User Comprehension in Intelligent Decision Support Systems Using Data Visualisation Techniques

This project explores the use of data visualisation techniques to render the outcome of an inference process using graphics, symbolic and schemas in stylised pictograms that supports understanding. The example demonstrated in this paper is a continuation of CRISIS-Expert, an intelligent decision support system that accepts multiple input of various data types and then generate an outcome which is further rendered into symbols that within reason is, understandable by the end user.
A Scalable Quantum Key Distribution Network Testbed Using Parallel Discrete-Event Simulation

Quantum key distribution (QKD) has been promoted as a means for secure communications. Although QKD has been widely implemented in many urban fiber networks, the large-scale deployment of QKD remains challenging. Today, researchers extensively conduct simulation-based evaluations for their designs and applications of large-scale QKD networks for cost efficiency. However, the existing discrete-event simulators offer models for QKD hardware and protocols based on sequential event execution, which limits the scale of the experiments. In this work, we explore parallel simulation of QKD networks to address this issue. Our contributions lay in the exploration of QKD network characteristics to be leveraged for parallel simulation as well as the development of a parallel simulation framework for QKD networks. We also investigate three techniques to improve the simulation performance including (1) a ladder queue based event list, (2) memoization for computationally intensive quantum state transformation information, and (3) optimization of the network partition scheme for workload balance. The experimental results show that our parallel simulator is 10 times faster than a sequential simulator when simulating a 128-node QKD network. Our linear-regression-based network partition scheme can further accelerate the simulation experiments up to two times over using a randomized network partition scheme.
Face Image Scanning for Differentiation of Child/Adult images using a CNN-Based Model

Differentiating child and adult images is a crucial requirement for many applications. This paper proposes an approach using a Convolutional Neural Network (CNN) model to distinguish between children&#x0027;s images from adults. In contrast to predetermined face landmarks, the suggested approach learns complex face features and achieves 85&#x0025; accuracy regardless of variation in age, gender, race, or ethnicity. The approach could be used to leverage the performance of digital image forensic, security control and, surveillance monitoring, and robotics.
A Survey on the Security Vulnerabilities of IoT Smart Home Application

The Internet of Things (IoT) is a revolutionary technology connecting devices to the Internet. There are many applications of the IoT, but security remains the biggest challenge for designers and organizations. The review of three layers of the IoT architecture for their security vulnerabilities is the theme of this paper. The security of the architecture is preferably in the form of frameworks. There are standard security frameworks used to secure the devices and applications from numerous vulnerabilities and attacks. This paper reviews some of the security frameworks with their features. A list of security vulnerabilities in the different layers of IoT architecture is explained. The design and implementation of the security framework are considered the future scope of the article.
When Pairs Meet Triplets: Improving Low-Resource Captioning via Multi-Objective Optimization

Image captioning for low-resource languages has attracted much attention recently. Researchers propose to augment the low-resource caption dataset into (image, rich-resource language, and low-resource language) triplets and develop the dual attention mechanism to exploit the existence of triplets in training to improve the performance. However, datasets in triplet form are usually small due to their high collecting cost. On the other hand, there are already many large-scale datasets, which contain one pair from the triplet, such as caption datasets in the rich-resource language and translation datasets from the rich-resource language to the low-resource language. In this article, we revisit the caption-translation pipeline of the translation-based approach to utilize not only the triplet dataset but also large-scale paired datasets in training. The caption-translation pipeline is composed of two models, one caption model of the rich-resource language and one translation model from the rich-resource language to the low-resource language. Unfortunately, it is not trivial to fully benefit from incorporating both the triplet dataset and paired datasets into the pipeline, due to the gap between the training and testing phases and the instability in the training process. We propose to jointly optimize the two models of the pipeline in an end-to-end manner to bridge the training and testing gap, and introduce two auxiliary training objectives to stabilize the training process. Experimental results show that the proposed method improves significantly over the state-of-the-art methods.
Progressive Image Generation Model Based on Multi-level Features

Aiming at the demand of high-quality appearance product generation, a progressive image generation model based on multi-level features is proposed in this paper. The algorithm divides the image generation task into multiple stages, and each stage aims to generate the features of a specific abstraction level. Start from the high-level features, and then introduce the lower level features step by step with the training. This incremental generation algorithm enables the model to first focus on high-level abstract semantic information and large-scale structural information, and then gradually shift its attention to more and more fine scales without learning all scales at the same time. The design of the algorithm is inspired by the process of drawing the picture by the painter. The painter first outlines an approximate outline in the image area, and then depicts the detailed texture according to the color of the reference image, so that the whole picture looks more harmonious and consistent. This incremental generation method not only makes the generation process more orderly, but also at each stage in the later order, because the features to be generated have completed the generation of the previous abstract level, the generation task at the current stage is equivalent to being simplified, that is, the incremental generation strategy can use the knowledge learned from the previous subtask to simplify the learning of subsequent subtasks, Therefore, it is easier to train and get better results than the traditional one-step generation algorithm.
Determining Jigsaw Puzzle State from an Image based on Deep Learning

The intriguing nature of jigsaw puzzle has captured the attention of researchers for many years. In this paper, we propose a deep learning model to determine different states of jigsaw puzzle from an image. We represent the task as a classification problem where each state of the puzzle is considered as a class. For this purpose, we have proposed a method to generate a dataset that can efficiently represent the jigsaw puzzle states space. The proposed model has 93% accuracy on the test dataset. In addition, we have shown that when the tiles size changes the model is still able to recognize 83% of the states. Though, genetic algorithms (GA) have been successful in solving larger puzzles, they require hand-crafted sophisticated compatibility scores. The computation and memory requirement to store the piecewise compatibility measure increases with the size of the puzzle. As an application, we have shown that the proposed method can be used as a fitness function of GA based jigsaw puzzle solver without using any compatibility measure.
List-decodability with large radius for Reed-Solomon codes

List-decodability of Reed-Solomon codes has re-ceived a lot of attention, but the best-possible dependence between the parameters is still not well-understood. In this work, we focus on the case where the list-decoding radius is of the form <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r=1-\varepsilon$</tex> for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> tending to zero. Our main result states that there exist Reed-Solomon codes with rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega(\varepsilon)$</tex> which are <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1-\varepsilon, O(1/\varepsilon)$</tex> -list-decodable, meaning that any Hamming ball of radius <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1-\varepsilon$</tex> contains at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(1/\varepsilon)$</tex> codewords. This trade-off between rate and list-decoding radius is best-possible for any code with list size less than exponential in the block length. By achieving this trade-off between rate and list-decoding radius we improve a recent result of Guo, Li, Shangguan, Tamo, and Wootters, and resolve the main motivating question of their work. Moreover, while their result requires the field to be exponentially large in the block length, we only need the field size to be polynomially large (and in fact, almost-linear suffices). We deduce our main result from a more general theorem, in which we prove good list-decodability properties of random puncturings of any given code with very large distance.
Evolutionary Method to Discover Itemsets with Statistically Distinctive Backgrounds

In this paper, we propose a method for discovering combinations of attributes (itemsets) against a background of statistical characteristics without obtaining frequent itemsets. The method consists of a database with numerous attributes and can directly find a combination of attributes that are highly correlated with small populations in two consecutive variables of interest in an incomplete database. It determines locally found patterns in large-scale data, and is expected to be the basis of large-scale data analysis. It uses evolutionary computations characterized by a network structure and a strategy to pool solutions throughout the generations. Moreover, it uses association rules to generalize the analysis method. The class-association rules used for classification are a discovery method of attribute combinations, which are characteristic when the ratio of class attributes is noted. The proposed method can be positioned as an extension to the statistical analysis method of the bivariate. The results of the evaluation experiment show the characteristics and effectiveness of the method.
Exploring the Evolutionary Characteristics of Project Management Approaches at Different Levels of Operations

Project management sets the foundation of software development and is considered a critical area as it has a direct relationship with project success. In recent years, project management has rapidly evolved into a more structured and sophisticated methodology which is vital not only to meet the growing stakeholder expectations and technology improvements but also a mandatory requirement for the software development companies to achieve competitive advantage and maintain business survival. Software project management approaches have evolved from traditional to agile to global with a shift in their inherent characteristics at different levels of operations and this shift poses a challenge for companies in choosing the appropriate approach based on their project needs. Current literature lacks attention in understanding the evolutionary characteristics of the varying project management approaches at the different operational levels. This study investigates the project management approaches at the individual, project, team and company levels to examine how these approaches vary in terms of characteristics in traditional, agile, and global software development contexts. Improved understanding of the evolution of project management approaches and their underlying characteristics will enable project management professionals and software developers to make appropriate decisions to achieve project success.