Reviewerly/exHarmony huggingface dataset:
TP-GAN: Simple Adversarial Network With Additional Player for Dense Depth Image Estimation

We present a simple yet robust monocular depth estimation technique by synthesizing a depth map image from a single RGB input image using the advantage of generative adversarial networks (GAN). We employ an additional sub-model termed refiner to extract local depth features, then combine it with the global scene information from the generator to improve the GAN’s performance compared to the standard GAN architectural scheme. Notably, the generator is the first player to learn to synthesize depth images. The second player, the discriminator, classifies the generated depth. In the meantime, the third player, the refiner, enhances the final reconstructed depth. Complementing the GAN model, we apply a conditional generative network (cGAN) to lead the generator in mapping the input image to the respective depth representation. We further incorporate a structured similarity (SSIM) as our loss function for the generator and refiner in GAN training. Through extensive experiment validation, we confirmed the performance of our strategy on the publicly indoor NYU Depth v2 and KITTI outdoor data. Experiment results on the NYU depth v2 dataset show that our proposed approach achieves the best performance by 96.0% on threshold accuracy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta &lt; 1.25^{2}$ </tex-math></inline-formula> ) and the second-best accuracy on all thresholds on the KITTI dataset. We discovered that our proposed method compares favorably to numerous existing monocular depth estimation strategies and demonstrates a considerable improvement in the accuracy of image depth estimation despite its simple network architecture.
A novel SVMþNDA model for classification with an application to face recognition

&lt;p&gt;Support vector machine (SVM) is a powerful classification methodology, where the support vectors fully describe the decision surface by incorporating local information. On the other hand, nonparametric discriminant analysis (NDA) is an improvement over LDA where the normality assumption is relaxed. NDA also detects the dominant normal directions to the decision plane. This paper introduces a novel SVM þNDA model which can be viewed as an extension to the SVM by incorporating some partially global information, especially, discriminatory information in the normal direction to the decision boundary. This can also be considered as an extension to the NDA where the support vectors improve the choice of k-nearest neighbors on the decision boundary by incorporating local information. Being an extension to both SVM and NDA, it can deal with heteroscedastic and non-normal data. It also avoids the small sample size problem. Moreover, it can be reduced to the classical SVM model, so that existing softwares can be used. A kernel extension of the model, called KSVM þ KNDA is also proposed to deal with nonlinear problems. We have carried an extensive comparison of the SVM þ NDA to the LDA, SVM, heteroscedastic LDA (HLDA), NDA and the combined SVM and LDA on artificial, real and face recognition data sets. Results for KSVM þ KNDA have also been presented. These comparisons demonstrate the advantages and superiority of our proposed model&lt;/p&gt;
Linear sequential filtering based on the debiased measurement matrix of the converted pseudo measurement for radar target tracking

For radar target tracking with nonlinear position and range rate measurements, a linear sequential filtering method based on the debiased measurement matrix of the converted pseudo measurement (DMM-LSF) is proposed. A novel and simple linear measurement matrix is constructed based on the relationship between the pseudo measurement and target's state vector. To estimate the involved unknown parameters in the measurement matrix, the filtering results from the linear position filter are utilized in the sequential linear filter, where a totally linear sequential filtering framework is formed. The measurement matrix estimation error is fully considered, which is incorporated into original converted pseudo measurement error and a new synthetical measurement error is formed. The statistic characteristics of the synthetical measurement error are deduced, resulting a debiased measurement matrix. Simulation results demonstrate the effectiveness of the proposed DMM-LSF algorithm. Compared with existing algorithms, the proposed one can achieve the best tracking performance for non-maneuvering and maneuvering target tracking in different scenes.
Efficient hardware mapping of Boolean substitution boxes based on functional decomposition for RFID and ISM band IoT applications

This paper investigates three substitution box (S-box) designs for lightweight ciphers. The proposed designs involve functional decomposition, which is preferable as compared to the look-up tables or logic gates-based S-box designs due to their advantages, as outlined in this paper. The aim is to reduce the number of literals at the input of the S-box to achieve resource reduction in the overall circuit design of the encryption algorithms. The proposed S-box architectures provide optimized resource mapping for specific target hardware, and high throughput along with lesser energy consumption compared to state-of-the-art designs. The proposed S-box designs are applied to the substitution layers of symmetric lightweight block ciphers with a standard security level. In order to be fair in comparison, the overall cipher designs are targeted for similar FPGA-based implementation as used in existing works, while the architectures of the individual 4-bit S-boxes are evaluated using SAED90 nm standard cell libraries. The performance of the proposed S-box designs is observed to be superior at RFID and ISM frequency ranges which can be deployed in applications constrained to low resources as well as demanding high performance and medium security.
Scenerio-Based Image Generation Using Deep Learning

Regardless of the contents of the image, this model can determine its scenario. It works with open CV, Natural Language Processing, and websites. This system does a dictionary search on the image and displays the required results. Any type of image that the user possesses can be given the appropriate context. The computer's eyesight has made incredible strides in recent years. Using the latest technology, object detection is now simple and produces precise results. On the basis of the photograph, the goal is to create an exact situation. User-provided photographs may be utilised in a variety of online educational technology platforms to visualise and learn as well as for image searches. Resnet-50 algorithm is used and ResNet-50 is a convolution neural network that is 50 layers deep. You can load a pretrained version of the neural network trained on more than a million images from the ImageNet database we first encrypt the picture and the words that are kept in the scenario dictionary before the user searches using the image that he has. It will match the user-provided picture and the scenario dictionary after being encoded. After completing this procedure, it will discover the precise situation depicted in the image.
From Tractatus to Later Writings and Back – New Implications from Wittgenstein’s<i>Nachlass</i>

Abstract As a celebration of the Tractatus 100th anniversary it might be worth revisiting its relation to the later writings. From the former to the latter, David Pears recalls that “everyone is aware of the holistic character of Wittgenstein’s later philosophy, but it is not so well known that it was already beginning to establish itself in the Tractatus ” ( The False Prison , 1987). From the latter to the former, Stephen Hilmy’s ( The Later Wittgenstein , 1987) extensive study of the Nachlass has helped removing classical misconceptions such as Hintikka’s claim that “Wittgenstein in the Philosophical Investigations almost completely gave up the calculus analogy.” Hilmy points out that even in the Investigations one finds the use of the calculus/game paradigm to the understanding of language, such as “in operating with the word” (Part I, §559) and “it plays a different part in the calculus”. Hilmy also quotes from a late (1946) unpublished manuscript (MS 130) “this sentence has use in the calculus of language”, which seems to be compatible with “asking whether and how a proposition can be verified is only a particular way of asking ‘How do you mean?’” Central in this back and forth there is an aspect which seems to deserve attention in the discussion of a semantics for the language of mathematics which might be based on (normalisation of) proofs and/or Hintikka/Lorenzen game-dialogue: the explication of consequences. Such a discussion is substantially supported by the use of the open and searchable The Wittgenstein Archives at the University of Bergen . These findings are framed within the discussion of the meaning of logical constants in the context of natural deduction style rules of inference.
Paving the way toward 800 Gbps quantum-secured optical channel deployment in mission-critical environments

This article describes experimental research studies conducted towards understanding the implementation aspects of high-capacity quantum-secured optical channels in mission-critical metro-scale operational environments using Quantum Key Distribution (QKD) technology. To the best of our knowledge, this is the first time that an 800 Gbps quantum-secured optical channel -- along with several other Dense Wavelength Division Multiplexed (DWDM) channels on the C-band and multiplexed with the QKD channel on the O-band -- was established at distances up to 100 km, with secret key-rates relevant for practical industry use cases. In addition, during the course of these trials, transporting a blockchain application over this established channel was utilized as a demonstration of securing a financial transaction in transit over a quantum-secured optical channel. The findings of this research pave the way towards the deployment of QKD-secured optical channels in high-capacity, metro-scale, mission-critical operational environments, such as Inter-Data Center Interconnects.
Bayesian estimation for Bell state rotations

This paper explores the effect of three-dimensional rotations on two-qubit Bell states and proposes a Bayesian method for the estimation of the parameters of the rotation. We use a particle filter to estimate the parameters of the rotation from a sequence of Bell state measurements, and we demonstrate that the resultant improvement over the optimal single qubit case approaches the 2 factor that is consistent with the Heisenberg limit. We also demonstrate how the accuracy of the estimation method is a function of the purity of mixed states.
Convergence analysis of flow direction algorithm in continuous search space and its improvement

The flow direction algorithm (FDA) is a new physics-based meta-heuristic optimization algorithm that is being used successfully in a variety of applications. However, FDA lacks theoretically rigorous convergency analysis and suffers from several drawbacks, such as premature convergence, lack of population diversity, and imbalance between exploitation and exploration. In this paper, the supermartingale convergence theorem is used to analyse the global convergence of FDA in a continuous search space. We first demonstrate that FDA's global convergence is determined by the accumulation of the minimum probability (Pt∗) that the flow swarm fall into the global optimal region in each iteration. Then, an improved flow direction algorithm, namely, guided flow direction algorithm (GFDA), is proposed to increase the minimum probability by making full use of the neighbourhood information. Comprehensive experimental studies were conducted to test and validate the proposed GFDA. Twenty competitive meta-heuristic optimization algorithms, twenty-three classical benchmark functions, ten recently single objective bound constrained numerical optimization problems(CEC2020), and four constrained engineering problems were used. Finally, the experimental results and statistical tests (Friedman test and Wilcoxon test) demonstrate the superiority of GFDA compared to other algorithms.
SDBGPChain: A decentralized low complexity framework to detect and prevent the BGPattacks using SDN with smart contract based Dendrimer tree blockchain

The Border Gateway Protocol (BGP) helps to exchange the information over the internet. However, BGP is susceptible to malicious attacks and misconfigurations meanwhile BGP does not focus on providing security during routing information advertisement. The secure BGP environment provides constant routing services on the internet, but existing works does not provide an optimal solution for BGP router security. To overcome these issues, we proposed decentralized low complexity framework for providing security to BGP attacks using deep learning and blockchain technologies. The proposed SDBGPChain (SDN based BGP using dendrimer tree blockchain) model whereas the SDN provides network management, flexibility and scalability for BGP environment, the proposed work includes three processes such as entity authentication, low complexity BGP attack detection and prevention, and deep learning-based route update validation. Initially, users and BGP routers are authenticated using Enhanced Blowfish and Elliptical Curve Cryptography (EB-ECC) which generated the unique key using EB algorithm and the unique is encrypted by enhanced ECC for improving security. Every Autonomous System (AS) is constructed as a graph structure using Directed Acyclic Graph (DAG) which improves the scalability and interoperability of the network. Secondly, we perform BGP attack detection using Dual Agent based Twin Actor Twin Delayed Deep Deterministic Policy Gradient (DA-T2TD3) algorithm by performing both proactive and reactive methods. Finally, we perform route update validation by Gated Recurrent Unit (GRU) by considering update message tuples which validate the updates by identifying misconfigurations and errors in the updated messages. Here, updated messages are passed to the blockchain for validation using the DiemBFT consensus algorithm which improves high security. The simulation of the proposed SDBGPChain model is conducted by OMNET++ simulation tool, and the performance of this work is evaluated based on various performance metrics such as Packet drop ratio (0.22), Throughput (39.8 bits/sec), Block validation time (20.2 s), Detection accuracy ((no. of attacks 92.5%) and (no of anomalies 93%)), and Network traffic (27.6%) which proved that the proposed work achieved superior performance compared to state-of-the-art works.
A mixed method study of DevOps challenges

DevOps practices combine software development and IT (Information Technology) operations. The continuous needs for rapid but quality software development requires the adoption of high-quality DevOps tools. There is a growing number of DevOps related posts in popular online developer forum Stack Overflow (SO). While previous research analyzed SO posts related to build/release engineering, we are aware of no research that specifically focused on DevOps related discussions. This paper aims to learn the challenges developers face while using the currently available DevOps tools and techniques along with the organizational challenges in DevOps practices. We conduct an empirical study by applying topic modeling on 174K SO posts that contain DevOps discussions. We then validate and extend the empirical study findings with a survey of 21 professional DevOps practitioners. We find that: (1) There are 23 DevOps topics grouped into four categories: Cloud & CI/CD Tools, Infrastructure as Code, Container & Orchestration, and Quality Assurance. (2) The topic category ‘Cloud & CI/CD Tools’ contains the highest number of topics (10) which cover 48.6% of all questions in our dataset, followed by the category Infrastructure as Code (28.9%). (3) The file management is the most popular topic followed by Jenkins Pipeline, while infrastructural Exception Handling and Jenkins Distributed Architecture are the most difficult topics (with least accepted answers). (4) In the survey, developers mention that it requires hands-on experience before current DevOps tools can be considered easy. They raised the needs for better documentation and learning resources to learn the rapidly changing DevOps tools and techniques. Practitioners also emphasized on the formal training approach by the organizations for DevOps skill development. Architects and managers can use the findings of this research to adopt appropriate DevOps technologies, and organizations can design tool or process specific DevOps training programs.
Globally Harmonized System Label detection using Color Segmentation

This paper presents an approach for color detection and segmentation based on Image Processing Process to retrieve candidate regions of GHS Sign. For color classification, where the dimension of each vector can be extended by a group of neighboring pixels. The experimental results are highly accurate and robust for our testing database, where samples are recorded on various states of environment. But in the meantime, it was found that the error processing. It is an image that looks asymmetrical image. It is impossible to detect the symbol GHS.
Matching Thai Profession Descriptions from Different Sources Using English as Interlingual Language

This paper proposes a method to improve text matching for occupation descriptions from different standards in Thailand. To solve ambiguity from technical compound words from standards, we apply machine translation, and similarity calculation for matching occupation and profession from sources that focused on different aspects. Thus, despite mentioning on same occupation, the descriptions from different standards share very little terms for matching the job across standards. By translating terms from Thai to English, we solve the issue of synonym technical terms varily used in different sources to improve similarity scores. Furthermore, tree structure of the standards is considered to assist on limiting search space. From experiments, the results of the baseline is very low, and the proposed method averagely exceed the result from the baseline for more than 6 percents for top-3 matching and 5 percents for top-5 matching in terms of accuracy. The use of both Thai and English translated job descriptions for matching using similar terms can noticeably increase a capability of matching jobs that cannot be found using only original Thai job description by adding more features of English-translated terms.
Pillow for Detecting Snoring with Embedded Techniques for Elderly People with Snoring Problems

This research aimed to design, produce, and assess a snoring detection neck pillow for elderly individuals with snoring symptoms. The research process began with finding information on snoring. The data obtained were used to design a snore detection neck pillow in the form of a U-shaped neck pillow. The functional principle was that when the microphone module detects the snoring sound, it activates a vibrating motor to alert the snorer to awake or change sleeping positions and stop snoring. There was a program to control the operation. The results of the snoring detection neck pillow test with a sample size of 20 people showed an average efficiency of 80%.
Missing Values Imputation Framework for Mixed Datasets

A missing value is a critical problem in data mining. The quality of the dataset is important in the mining process. This problem solves by the imputation method. In 2019 Chungnoy et al. proposed bees-based imputation using nearest neighbor for heuristic function [7]. This method shows outperform in imputation task compare to other methods. However, this method can't impute a numerical data type. There is room to be improving. In this work, we propose a hybrid bees-based imputation method for a mixed datatype. The method is applied to mean and estimation mode for numerical and categorical data. Form evaluation, the hybrid bee imputation successfully imputes missing values in mixed data types for all missing percentages. In comparison to other approaches, the average accuracy has improved by 8.57%, which is the biggest improvement in 15% missing percentage. The overall average accuracy of the predictive models from the proposed method is 81.10%
The application of data mining techniques for predicting education to new undergraduate students at Chiang Mai Rajabhat University

This study aimed to predict the number of new students enrolled in bachelor's degree programs using data mining techniques. The population and sample consisted of 31,918 applicants for undergraduate studies at Chiang Mai Rajabhat University only for the regular semester in the quota applied and screened for the year 2014-2019. There were two components in the tool used in this study: hardware and software. that uses RapidMiner Studio 9 to analyze data with data mining. The association rules technique of the Apriori model was used to identify the association rules of recruitment behavior in each field of applicants in each group. both general and quota The results of the analysis evaluated the support-based correlation rule model. confidence and an increase from a total of 340 correlation rules considering support = 0.01, confidence = 95%, and a significant addition of more than 1 correlation rule 89.
Prediction and Efficiency of Stroke Disease using data mining technique

This research applies data mining techniques to compare the appropriate models. The predictions and efficiency of Stroke Disease. It was found that the significant factors influencing stroke disease included 10 factors consisting of performance focus on Gender, age, hypertension, heart disease, ever married, work type, residence type, avg glucose level, BMI, and Smoking Status. The model was used to compare 3 techniques: Decision Tree, Naïve Bayes, and K-Nearest Neighbors. The results showed that the K-Nearest Neighbors technique was the most suitable for predicting Stroke disease. By measuring the performance of the model with an Accuracy of 97.76%. Decision Tree performance with an accuracy of 97.09%. and Naïve Bays performance with an accuracy of 93.60%.
Video-Based Online Interactive System Using CNN Segmentation Method

In recent years, the COVID-19 has made it difficult for people to interact with each other face-to-face, but various kinds of social interactions are still needed. Therefore, we have developed an online interactive system based on the image processing method, that allows people in different places to merge the human region of two images onto the same image in real-time. The system can be used in a variety of situations to extend its interactive applications. The system is mainly based on the task of Human Segmentation in the CNN (convolution Neural Network) method. Then the images from different locations are transmitted to the computing server through the Internet. In our design, the system ensures that the CNN method can run in real-time, allowing both side users can see the integrated image to reach 30 FPS when the network is running smoothly.