Reviewerly/exHarmony huggingface dataset:
Using ChatOps to Achieve Continuous Certification of Cloud Services

Continuous service certification (CSC) recently emerged as a promising means to provide ongoing assurances and disrupt pertinent certification approaches.CSC involves the consistent gathering and assessing of certification-relevant data by certification authorities about service operation to validate ongoing adherence to certification criteria.While research on CSC is increasing, practitioners still struggle in transferring researchers' suggestions and guidelines into practice.This study provides a tentative design and a prototype of a monitoring-based service certification (MSC) system based on the novel ChatOps approach.Iterative evaluations support our propositions that ChatOps' three key elements, a chat platform, chatbots, and third-party integrations, support the achievement of CSC.We contribute to research and practice by proving the technical feasibility of an MSC system, guiding future research and practitioners on achieving monitoring-based CSC, and validate the applicability and usefulness of extant guidelines on monitoring-based CSC proposed by prior research.
Application Analysis of Image Enhancement Method in Deep Learning Image Recognition Scene

Application analysis of the image enhancement method in deep learning image recognition scene is conducted in this paper. Generally speaking, scene recognition of natural scenes is relatively difficult due to the more complex and diverse environment. It is usually done through two steps: text detection and text recognition. To enhance the traditional methods, this paper integrates the deep learning models to construct the core efficient framework for dealing with the complex data. The text method uses a sequence recognition network based on a two-way decoder based on adjacent attention weights to recognize text images and predict the output. For the further analysis, the core systematic modelling is demonstrated. The proposed model is tested on the public data sets as a reference. The experimental verification has shown the result that the proposed model is efficient.
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness

To ensure trust in AI models, it is becoming increasingly apparent that evaluation of models must be extended beyond traditional performance metrics, like accuracy, to other dimensions, such as fairness, explainability, adversarial robustness, and distribution shift. We describe an empirical study to evaluate multiple model types on various metrics along these dimensions on several datasets. Our results show that no particular model type performs well on all dimensions, and demonstrate the kinds of trade-offs involved in selecting models evaluated along multiple dimensions.
Dr Jekyll and Mr Hyde: the Strange Case of Off-Policy Policy Updates

The policy gradient theorem states that the policy should only be updated in states that are visited by the current policy, which leads to insufficient planning in the off-policy states, and thus to convergence to suboptimal policies. We tackle this planning issue by extending the policy gradient theory to policy updates with respect to any state density. Under these generalized policy updates, we show convergence to optimality under a necessary and sufficient condition on the updates' state densities, and thereby solve the aforementioned planning issue. We also prove asymptotic convergence rates that significantly improve those in the policy gradient literature. To implement the principles prescribed by our theory, we propose an agent, Dr Jekyll &amp; Mr Hyde (JH), with a double personality: Dr Jekyll purely exploits while Mr Hyde purely explores. JH's independent policies allow to record two separate replay buffers: one on-policy (Dr Jekyll's) and one off-policy (Mr Hyde's), and therefore to update JH's models with a mixture of on-policy and off-policy updates. More than an algorithm, JH defines principles for actor-critic algorithms to satisfy the requirements we identify in our analysis. We extensively test on finite MDPs where JH demonstrates a superior ability to recover from converging to a suboptimal policy without impairing its speed of convergence. We also implement a deep version of the algorithm and test it on a simple problem where it shows promising results.
SUFFICIENT CONDITION FOR COINCIDENCE OF THE LS AND AITKEN ESTIMATIONS OF PARAMETER OF QUADRATIC REGRESSION IN CASE HETEROSCEDASTIC DEVIATIONS

In the paper in case heteroscedastic independent deviations a regression model whose function has the form $f(x) = ax^2+bx+c$, where $a$, $b$ and $c$ are unknown parameters, is studied. Approximate values (observations) of functions $f(x)$ are registered at equidistant points of a line segment. The theorem which is proved at the paper gives a sufficient condition on the variance of the deviations at which the Aitken estimation of parameter $a$ coincides with its estimation of the LS in the case of odd number of observation points and bisymmetric covariance matrix. Under this condition, the Aitken and LS estimations of $b$ and $c$ will not coincide. The proof of the theorem consists of the following steps. First, the original system of polynomials is simplified: we get the system polynomials of the second degree. The variables of both systems are unknown variances of deviations, each of the solutions of the original system gives a set variances of deviations at which the estimations of Aitken and LS parameter a coincide. In the next step the solving of the original system polynomials is reduced to solving an equation with three unknowns, and all other unknowns are expressed in some way through these three. At last it is proved that there are positive unequal values of these three unknowns, which will be the solution of the obtained equation. And all other unknowns when substituting in their expression these values will be positive.
Politeness Strategies in the Criminal Trial

Politeness is one of the aspects in pragmatic studies that is still relevant to this day.In court trials, whether involving criminal or civil case trials, the speakers is bound by the court ethics.The legal mechanism includes legislation to guide and promote fairness involving the Evidence Act 1950, Criminal Procedure Code and Advocate (Practice and Etiquette) Rules 1988.It is crucial to utilize positive politeness to ensure justice in criminal trial.The current development raises concern on politeness strategies from the highest hierarchy to the lowest hierarchy in a criminal court trial.This study adopted a qualitative methodology applying the case content analysis approach.Therefore, this paper aimed to study the politeness strategies during the cross-examination in court trials based on the excerpt from the summary of a selected case.This study found that the politeness strategies in criminal trials is crucial in upholding fairness in judicial decisions.
Fx Event Organization

Agriculture is said to be the backbone of the economy. Farmers toil hard with different kinds of crops to make good and healthy food for the country. There are more existing systems but uses outdated machine-learning techniques based on RNN( Recurrent neural network) which makes the process slower and more time-consuming. Here We are proposing a new CNN(Convolutional neural network ) based system which is fast and gives accurate results within seconds. CNN is power-efficient and is more suitable for real-time implementation. In this project, we use CNN algorithms which is very much better than the RNN algorithms used in the existing system.More parameters will be taken for the consideration of prediction in the proposed system. And we use Random Forest Regression, Multiple Linear Regression
SEGMENTASI CITRA JALAN RAYA MENGGUNAKAN AUTOENCODER CONVOLUTIONAL NEURAL NETWORK BERBASIS AGGREGATED RESIDUAL TRANSFORMATIONS

Teknologi autonomous car menjadi bidang baru dalam penelitian kecerdasan buatan. Salah satunya adalah path planning dengan menentukan jalan yang bisa dilalui. Tugas segmentasi dengan Convolutional Neural Network (CNN) lebih efektif terhadap data yang belum diketahui, namun banyaknya parameter dan kompleksnya membuat waktu komputasi lebih lama dan kurang sesuai untuk inference. Hal ini dikarenakan kebanyakan CNN membangun layer secara mendalam atau menumpuk. CNN dengan layer yang bersifat melebar dalam ILSVRC 2016 ResNeXt dapat digunakan sebagai solusi baru. Fungsi ResNeXt untuk klasifikasi gambar dapat dirubah untuk mengklasifikasi tiap pixel citra. Adapun citra yang digunakan sebagai masukkan dapat divariasi dari pemotongan secara acak dan blurring secara acak. Kumpulan prediksi ini kemudian perlu direkonstruksi ulang, operasi upscaling citra dapat mengubah resolusi kecil menjadi besar dan dapat ditumpuk menjadi beberapa layer. Hasilnya adalah binary map prediksi kategori tiap pixel. Dalam penelitian ini, digunakan model autoencoder dengan memodifikasi ResNeXt menjadi encoder dan digabungkan dengan sekumpulan layer operasi upscaling (UpscaleBlock) sebagai decoder untuk mensegmentasi citra jalan raya. Data latih dan uji menggunakan KITTI Road Evaluation 2013 dan mencapai performa F-max 89.28% pada kategori UM, 91.70% pada kategori UMM, 87.02% pada kategori UU, dan 89.83% pada kategori Urban dengan waktu komputasi 0.1 detik pada GPU. Autonomous car technology became a new field for articifal intelligence research. One of the studied problems is path planning to determine which lane for vehicle. Segmentation task with Convolutional Neural Network (CNN) proved to be more effective against unknown data, but there is some drawback. CNN creates a lot and complex parameters which makes computation longer and doesnâ€™t fit very well in inference circumstances. Those happened because CNN build with deeply stacked layers. CNN build with parallel stacked network like ResNeXt in ILSVRC 2016 can be used as a new approach to road segmentation. We converted ResNeXt to classify each pixel instead image. For variation, input images are randomly cropped and blurred. All predicted pixels then require reconstruction, upscaling operation can alter small image reconstruction and grouped. Final result is a binary map that contains pixel according to their predicted category. In this study, ResNeXt network for image classification was modified as encoder with group of upscaling process (UpscaleBlock) as decoder for road segmentation task. Training images and testing came from KITTI Road Evaluation 2013. Proposed architecture achieved F-max 89.28% on UM, 91.70% on UMM, 87.02% on UU, and 89.93% on Urban category with computation speed of 0.1s on GPU.
FINE Samples for Learning with Noisy Labels

Modern deep neural networks (DNNs) become frail when the datasets contain noisy (incorrect) class labels. Robust techniques in the presence of noisy labels can be categorized into two folds: developing noise-robust functions or using noise-cleansing methods by detecting the noisy data. Recently, noise-cleansing methods have been considered as the most competitive noisy-label learning algorithms. Despite their success, their noisy label detectors are often based on heuristics more than a theory, requiring a robust classifier to predict the noisy data with loss values. In this paper, we propose a novel detector for filtering label noise. Unlike most existing methods, we focus on each data's latent representation dynamics and measure the alignment between the latent distribution and each representation using the eigendecomposition of the data gram matrix. Our framework, coined as filtering noisy instances via their eigenvectors (FINE), provides a robust detector with derivative-free simple methods having theoretical guarantees. Under our framework, we propose three applications of the FINE: sample-selection approach, semi-supervised learning approach, and collaboration with noise-robust loss functions. Experimental results show that the proposed methods consistently outperform corresponding baselines for all three applications on various benchmark datasets.
An extended Krylov subspace method for decoding edge-based compressed images by homogeneous diffusion

The heat equation is often used in order to inpaint dropped data in inpainting-based lossy compression schemes. We propose an alternative way to numerically solve the heat equation by an extended Krylov subspace method. The method is very efficient with respect to the direct computation of the solution of the heat equation at large times. And this is exactly what is needed for decoding edge-compressed pictures by homogeneous diffusion.
A Case Study on Library Automation Process in Anurag Group of Institutions

The case study emphasis on the automation process in the Anurag Group of Institutions, Ghatkesar Telangana. Library automation is a major task for engineering college libraries, with the introduction of advance technologies in field of library and information science, major automation areas and outlines the various factors needed to be considered by the librarians while doing automation work for their respective libraries. Now day’s modern libraries are equipped with traditional resources like books magazines reports etc. with the advent of information technology they are also exaggerated with digital resources like e-journals, online research publications and web resources. The study mainly focuses on the use of automation process for Engineering College libraries in Telangana region. The Information Communication Technology has brought rapid changes in the performance of libraries and its services. It helps libraries in creating database of their own collections and creation them accessible for easy access to users within and outside the libraries through various networks. We have been using library management software in central library; successfully completed bar coding work for 45000 volumes of books, and have successfully completed library automation work. Digitization of libraries reduces the unending work and preserves time and brings precision and speed & keeps material safe. It increases the efficiency in technical processing of library materials and amends the efficiency of library administration and management processes. The AGI central library gives much importance to the library automation services integrated with a mobile application for an effective library services is the need of hour. The integrated approach of the mobile application will ease the process to the needy user in a short time and cost effective way to get the right information in right time at right place. The objective of the case study is understand and implement the integrated library automation process along with a user friendly mobile application for ease of information to the faculty, researchers and student.
Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation

We present MetaUVFS as the first Unsupervised Meta-learning algorithm for Video Few-Shot action recognition. MetaUVFS leverages over 550K unlabeled videos to train a two-stream 2D and 3D CNN architecture via contrastive learning to capture the appearance-specific spatial and action-specific spatio-temporal video features respectively. MetaUVFS comprises a novel Action-Appearance Aligned Meta-adaptation (A3M) module that learns to focus on the action-oriented video features in relation to the appearance features via explicit few-shot episodic meta-learning over unsupervised hard-mined episodes. Our action-appearance alignment and explicit few-shot learner conditions the unsupervised training to mimic the downstream few-shot task, enabling MetaUVFS to significantly outperform all unsupervised methods on few-shot benchmarks. Moreover, unlike previous few-shot action recognition methods that are supervised, MetaUVFS needs neither base-class labels nor a supervised pretrained backbone. Thus, we need to train MetaUVFS just once to perform competitively or sometimes even outperform state-of-the-art supervised methods on popular HMDB51, UCF101, and Kinetics100 few-shot datasets.
Research on Radiator Structure Optimization Using Fireworks Algorithm Based on Elite Opposition-Based Learning

Aiming at the shortcomings of the fireworks algorithm (FWA) that it is easy to mature prematurely and has low accuracy and weak global search ability, a novel improved FWA whose solution space search strategy based on Elite Opposition-Based Learning (EOBLFWA) is proposed. The optimization results of 4 sets of standard functions show that EOBLFWA has higher convergence speed and convergence accuracy for numerical optimization. In this paper, the proposed algorithm is used to optimize the structural parameters of radiator. The result shows that the temperature of the heat source on the surface of the optimized radiator is lowered by 4.8 degree compared with the original model, and the calculation results are verified by the ICEPAK software.
Ontology specific visual canvas generation to facilitate sense-making – an algorithmic approach

Visualization is a critical necessity for ontological sense-making. This is very important for both ontologists as well as domain specialists. Domain specialists being, non-computing specialists like lawyers, medical doctors, farmers, agricultural specialists cannot tackle complex visualization representations, which is beyond their expertise. But, the involvement of the domain specialists is very critical for the verification of the ontology increments under construction. The majority of the existing visualization IDEs on ontology construction are not fine-tuned to assist the technical hurdles faced by non-computing domain specialists. This research investigates that aspect and proposed a new algorithm and a  visualization canvas that can resolve several of the critical problems faced by domain specialists. The proposed algorithm and the canvas has been tested for their efficacy on three distinct domains. Test results were quantitatively and qualitatively assessed and the outcomes were satisfactory
Development Of Executive Information Systems Of Cirebon City Government (Case Study: Department Of Communication, Informatics And Statistics)

Purpose: Developing an executive information system to meet the information needs of the Mayor, Deputy Mayor, Regional Secretary and the heads of SKPD within the Cirebon City Government.Design / method / approach: Using the drill down method for solving information on executive information systems and the GRAPPLE system development methodResult: The development of an executive information system in Cirebon city government has assisted the executive, consisting of mayors, deputy mayors and regional secretaries and middle executives consisting of skpd within the Cirebon city government. Cirebon city government executive information system consists of five sectors in the city of Cirebon, namely economy, health, population, education and government. The results of the validation testing are 100% and the average user acceptance testing results are 85.29%.Authenticity / state of the art: Based on previous research, this study has the same characteristics but in the development of executive information systems it has differences in objects and methods of software development.
Machine learning attack on copy detection patterns: are 1x1 patterns cloneable?

Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market. Copy detection patterns (CDP) are considered as such solution in several applications. It is assumed that being printed at the maximum achievable limit of a printing resolution of an industrial printer with the smallest symbol size 1x1 elements, the CDP cannot be copied with sufficient accuracy and thus are unclonable. In this paper, we challenge this hypothesis and consider a copy attack against the CDP based on machine learning. The experimental based on samples produced on two industrial printers demonstrate that simple detection metrics used in the CDP authentication cannot reliably distinguish the original CDP from their fakes. Thus, the paper calls for a need of careful reconsideration of CDP cloneability and search for new authentication techniques and CDP optimization because of the current attack.
Digital Language Learning Environment Influence On Pe Students Professional Development

The article analyzes the process of formation of a digital language educational environment in the framework of personal and professional development of students of physical education university. The authors of the article compare characteristics of the process of forming a professional orientation and development of professional consciousness of students of the 1st and 4th years of the Federal State Budgetary Educational Institution of Higher Education "VLGAFC" in various areas of training, taking into account the results obtained in 2018. In 2021, based on a survey of freshmen on vocational training and a survey to determine the level of professional orientation with an emphasis on digitalization of university language environment, it was revealed that personal and professional development of students significantly affects formation of a digital language educational environment, since motivated learning foreign language information of a professionally oriented nature allows you to get more ideas about future professional activity, to lay the foundation for the formation of a system of values that is consistent with the requirements presented in the Federal State Educational Standards for the level of training of specialists in the field of physical education. The factors formed by digital foreign language environment, influencing development of physical education university students in personal and professional aspects, are determined, a conclusion is made about positive influence of digitalization of process learning a foreign language on formation of stable ideas about future professional activity of students.
Articulatory Features for Multilingual Phone Recognition

This chapter describes the use of predicted multilingual AFs to improve the performance of Multi-PRS. The lattice rescoring method and tandem method of combination for combining the AFs from various AF groups are described. The AF-based Multi-PRSs are developed using predicted AFs and their performance is evaluated. The results are analysed and compared with their corresponding oracle AF-based Multi-PRSs.
Faster Key Generation of Supersingular Isogeny Diffie-Hellman

Supersingular isogeny Diffie-Hellman (SIDH) is attractive for its relatively small public key size, but it is still unsatisfactory due to its efficiency, compared to other post-quantum proposals. In this paper, we focus on the performance of SIDH when the starting curve is E6 : y2 = x3 + 6x2 + x, which is fixed in Round-3 SIKE implementation. Inspired by previous works [1], [2], we present several tricks to accelerate key generation of SIDH and each process of SIKE. Our experimental results show that the performance of this work is at least 6.09% faster than that of the SIKE implementation, and we can further improve the performance when large storage is available.