Reviewerly/exHarmony huggingface dataset:
Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian

We present the shared task on artificial text detection in Russian, which is organized as a part of the Dialogue Evaluation initiative, held in 2022. The shared task dataset includes texts from 14 text generators, i.e., one human writer and 13 text generative models fine-tuned for one or more of the following generation tasks: machine translation, paraphrase generation, text summarization, text simplification. We also consider back-translation and zero-shot generation approaches. The human-written texts are collected from publicly available resources across multiple domains. The shared task consists of two sub-tasks: (i) to determine if a given text is automatically generated or written by a human; (ii) to identify the author of a given text. The first task is framed as a binary classification problem. The second task is a multi-class classification problem. We provide count-based and BERT-based baselines, along with the human evaluation on the first sub-task. A total of 30 and 8 systems have been submitted to the binary and multi-class sub-tasks, correspondingly. Most teams outperform the baselines by a wide margin. We publicly release our codebase, human evaluation results, and other materials in our GitHub repository.
Capability oriented RE for Cybersecurity and Personal Data Protection: Meeting the challenges of SMEs

SMEs face multiple challenges related to cybersecurity and personal data protection. Prominent amongst these challenges is the lack of appropriate guidelines addressing specific SME requirements. This paper proposes a Requirements Engineering (RE) methodology with the aim to bridge this gap by establishing a generic process specifically targeting SMEs needs and capabilities. This paper describes the conceptual foundation of the methodology and reports on the way that the theoretical foundations were applied on an SME offering social care services.
Segmentation Based Approach For Off-line Handwritten Sinhala Word Recognition From Touch Screen Gestures

The traditional way of using pen and paper to take notes is getting over by the touch screen devices. These devices provide more options to the users to enhance their productivity while taking notes. The ability to recognize and validate the words written on the touch screens facilitates further capabilities to the users. Hence, in this paper, we describe a segmentation-based approach combined with an n-gram model for the recognition and validation of the Sinhala words written on touch screens. We compare the results of 6 commonly used machine learning models to find the best performing classifier for recognizing individual characters of words. The classifiers are trained to identify 19 different Sinhala characters. Based on the results, Convolution Neural Network (CNN) based word classifier stands ahead of other classifiers.
An Effective Lightweight Measurement Model to Enable Coal Dust Size Distribution Analysis

This article presents an image segmentation model based on U-Net combined with visual sensing. It is aimed at studying the inherent mechanism of coal dust imagery characteristics and is employed to carry out semantic segmentation of particle images. For the feature maps with more redundant information generated by the conventional method, the parameter quantity of the proposed model can be lessened by Ghost-bottleneck, which substitutes for the original convolution in U-Net. The Ghost-SE bottleneck is introduced in the feature backbone to capture the attention of key feature channels, which is able to improve the learning ability for particle characteristics. Thus, particles with different shapes and sizes are credibly discriminated. Finally, the proposed model is evaluated with the coal dust particle proportion in six dust-producing scenes. The experimental results show that the performance metrics accuracy, recall rate, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> scores are increased to 0.8732, 0.8434, and 0.8580, respectively, and the mean error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R &lt; 75~\mu \text{m}$ </tex-math></inline-formula> is 3.526% compared with the reference standard. This indicates that introducing modules in the backbone is indeed able to speed up the convergence of the network and improve the accuracy of coal dust particle segmentation. It can be used as an effective auxiliary measurement of coal dust detection for coal mine safety video monitoring.
Applying the Algorithm of Fuzzy K-Nearest Neighbor in Every Class to the Diabetes Mellitus Screening Model

Heart disease is the number one killer in the world. Someone who has the potential to experience heart disease is a person with Diabetes Mellitus (DM). Diabetes that is detected early can reduce the risk of heart disease and various other complications. This study aims to analyze the performance of the model that is expected to be an alternative for screening DM by using a machine learning method, namely the Fuzzy K-Nearest Neighbour in Every Class (FKNNC) algorithm. The input in this study was clinical data from Hospital X which consisted of 7 predictor variables and 1 response variable. We used the confusion matrix and the Area Under Curve (AUC) value of the Receiver Operating Characteristic (ROC) curve to maseure the performance of the FKNNC model, with the help of the Python programming language. The results obtained indicate that the FKNNC model is classified as a “good classification” model. This can be seen from the accuracy of the FKNNC model of 86% and F1-score of 81,8%.
Analysis of Feature Extraction and Classification Methods on Histopathological Images for Diagnosing Invasive Ductal Carcinoma

Invasive Ductal Carcinoma (IDC) is one of the most common types of breast cancer, accounting for almost 70-80% of all breast cancer diagnoses. Early detection of breast cancer is an effort to prevent or control the occurrence of breast cancer. The first step in treating breast cancer is a proper examination, typically conducted using biomedical images such as mammograms and histopathology images. In this study, we conducted breast cancer detection using histopathological images in several steps, namely histopathological data input, feature extraction, and classification. As feature extraction is the most important step in the classification process, we combined several feature extraction techniques, including Gray-Level Co-occurrence Matrix (GLCM), Red Green Blue (RGB), Hue Saturation Value (HSV), and Histogram. The feature extraction results were subsequently classified using the backpropagation and Support Vector Machine (SVM) classifiers. We used a public dataset from Kaggle that has 1,080 breast histopathology images consisting of 540 negative IDC and 540 positive IDC. Our experimental results show that combining the aforementioned feature extraction techniques and using a backpropagation classifier obtained the best prediction accuracy of 96.94%. The result implies that IDC type breast cancer based on histopathological images can be detected accurately using suitable methods.
Attentive Cascaded Pyramid Network for Online Video Stabilization

Online video stabilization is important for hand-held camera shooting or remote robots control. Existing methods either need use the whole video to perform offline stabilization and result in long latency, or dismiss the nonuniform motion field in each frame and lead to large distortion. The non-uniform motion includes dynamic foreground motion and non-planar background motion. To better describe the shaky motion field online, we propose a novel attentive and multi-scale regression and refinement framework called ACP-Net. It exploits the idea of modeling camera motion on progressive levels, consisting of a flow-guided quiescent attention (FQA) module and a cascaded pyramid prediction (CPP) module. FQA module takes optical flow as an extra input and generates a soft mask to remedy the disturbance from dynamic foreground objects. Based on the attentive feature, the CPP module utilizes a multi-scale residual pyramid structure to do coarse to fine stabilization. Experimental results on public benchmarks show that our proposed method can achieve state-of-the-art performance both qualitatively and quantitatively, comparing to both online and offline methods.
Iterative Depth-First Search for FOND Planning

Fully Observable Non-Deterministic (FOND) planning models uncertainty through actions with non-deterministic effects. Existing FOND planning algorithms are effective and employ a wide range of techniques. However, most of the existing algorithms are not robust for dealing with both non-determinism and task size. In this paper, we develop a novel iterative depth-first search algorithm that solves FOND planning tasks and produces strong cyclic policies. Our algorithm is explicitly designed for FOND planning, addressing more directly the non-deterministic aspect of FOND planning, and it also exploits the benefits of heuristic functions to make the algorithm more effective during the iterative searching process. We compare our proposed algorithm to well-known FOND planners, and show that it has robust performance over several distinct types of FOND domains considering different metrics.
An Efficient Method for Outlying Aspect Mining Based on Genetic Algorithm

Outlying aspect mining (OAM) aims to identify a feature subspace in which a given query object is dramatically distinctive from the rest data. The identified features can assist the formulation and optimization of decisions. Score-and-search methods are widely used in outlying aspect mining. However, limited by scoring instability and search inefficiency, studies using this strategy are unable to be comprehensive and accurate for mining outlying aspects. In this paper, it proposes a novel OAM method based on genetic algorithm, named OSIER, which can be applied in mining outlying aspects from multi-dimensional spaces. OSIER improves the search efficiency by analyzing the correlations between dimensions. By combining the genetic algorithm with the traditional beam search strategy, OSIER effectively improves the diversity of the searched aspects. As a result, the execution time for candidate outlying aspects search is controlled in an acceptable range. Experiments show that OSIER outperforms the benchmark methods in terms of effectiveness on the OAM task. Besides, OSIER is capable of providing valuable outlying aspect mining results for various types of datasets.
QBRBS: Quantum Block Representation by Basis States

A generalization of the new quantum block representation by basis states (QBRBS) is presented and analyzed. This representation facilitates the manipulation of signals by providing a flexible block size to store signal elements. Furthermore, QBRBS uses quantum superposition and entangled qubits to store signal values on the basis states of qubit registers instead of probability amplitude, enabling accurate information retrieval after finite measurements. A detailed analysis exposed a polynomial growth in qubit complexity and decreased quantum complexity on the presented quantum block format. The geometric, statistical, element-value, element-value block, and block position operations are described. Simulation experiments show the feasibility and capabilities of the block representation to achieve data hiding applications. A quantum steganography scheme exploiting the QBRBS features is presented. Finally, the quantum circuit and simulation verification are performed using Qiskit.
Multi-Attribute Decision Making Problem Using OWA Operator

The development of decision-making processes lies at core of the fuzzy set.Multi-attribute (MADM) is the most prevalent method of decisionmaking is of such situations.A common MADM problem involves analysing a comparison between the collection of alternatives and a selection of decision criteria.This paper provides a central government economic benefit to provinces and municipalities by using MADM situations.
Optimal Mixed Strategies for Cost-Adversarial Planning Games

This paper shows that domain-independent tools from classical planning can be used to model and solve a broad class of game-theoretic problems we call Cost-Adversarial Planning Games (CAPGs). We define CAPGs as 2-player normal-form games specified by a planning task and a finite collection of cost functions. The first player (a planning agent) strives to solve a planning task optimally but has limited knowledge about its action costs. The second player (an adversary agent) controls the actual action costs. Even though CAPGs need not be zero-sum, every CAPG has an associated zero-sum game whose Nash equilibrium provides the optimal randomized strategy for the planning agent in the original CAPG. We show how to find the Nash equilibrium of the associated zero-sum game using a cost-optimal planner via the Double Oracle algorithm. To demonstrate the expressivity of CAPGs, we formalize a patrolling security game and several IPC domains as CAPGs.
ASSESSMENT OF THE ECONOMIC FEASIBILITY OF A COMPLETE TRANSITION TO THE VIRTUAL EXISTENCE OF MODERN PRINT MEDIA

This material is deals with the study of an issue of transformation from the classical to the virtual form of modern media. That study includes the one of two main points – identifying the reasons for the transition to a new format, and assessing the economic side of the issue. The author studied basic reasons for the transition, such as a large audience and low cost of advertising, and compared those parameters in electronic and print media. Changes and transformations in the media are always aimed at realizing key goals – increa-sing the cash receipts and audience size, promoting a brand and creating unique content, maintaining and increasing competitiveness. Accordingly, the assess- ment of economic feasibility is based on an analysis of the possibility of reali-zing the designated goals when the form of existence changes. Open sources and archives of publications (print and electronic) and small business compa- nies were used as a database
Jump-Starting Software Development to Minimize Defects

How much of your time or team's time is spent debugging?
New Refinement Strategies for Cartesian Abstractions

Cartesian counterexample-guided abstraction refinement (CEGAR) yields strong heuristics for optimal classical planning. CEGAR repeatedly finds counterexamples, i.e., abstract plans that fail for the concrete task. Although there are usually many such abstract plans to choose from, the refinement strategy from previous work is to choose an arbitrary optimal one. In this work, we show that an informed refinement strategy is critical in theory and practice. We demonstrate that it is possible to execute all optimal abstract plans in the concrete task simultaneously, and present methods to minimize the time and number of refinement steps until we find a concrete solution. The resulting algorithm solves more tasks than the previous state of the art for Cartesian CEGAR, both during refinement and when used as a heuristic in an A* search.
Ad-Gency: Hospital Patients' Admission Management Information System and Analytics Development on Health Emergency Situation

The problem of getting admitted to the hospital is one of the concerns that patients are experiencing right now; due to circumstances where the hospital cannot accommodate the emergency patient. This study aims to develop a computing solution that could ease patients' pain points about trying to find and get admitted into a hospital during emergencies. While simultaneously helping the healthcare institutions to have a better way of processing their pre-admissions. The SCRUM approach is utilized in this study to guide and efficiently handle the applications' development stage. The study was completed with a survey of potential consumers to obtain feedback. The result shows that the participants had good impressions of the application, saying that it was easy to use, user-friendly, and a great help for finding hospitals that could take care of them.
Physics teaching in high school through the collective systematization of knowledge

This article addresses a research-teaching focused on the pedagogical practices of teachers and the study practices of high school physics students having as its epistemological axis the concept of theory as an expression of practice. Its objective is to bring to reflection the Collective Systematization of Knowledge as an alternative for teaching and studying in this area of knowledge. The data were produced through the speeches and texts prepared by the students in the experience of four moments of the Collective Systematization of Knowledge methodological process, based on pre-established theoretical references with the research participants describing, analyzing, problematizing, discussing, explaining and understanding both their study practices and the pedagogical practices of high school physics teachers. The study shows that the deconstruction and reconstruction of teaching based on transmission-assimilation both in teacher practice and in student study practices can be modified if the Collective Systematization of Knowledge is carried out favoring the critical-reflective appropriation of contents inherent to Physics.
The generic approach to modern optimization

In this paper, we survey some recent applications of the generic approach to selected optimization problems, such as minimization of convex functionals and stochastic feasibility problems.We also overview a few recent developments related to the residuality properties of certain classes of convex functions, the abundance of which is known to be crucial in many optimization algorithms for which the elements of a certain class can be used as good approximates of given convex functions.
The design and implementation of civil aviation meteorological emergency service platform based on 5G

In view of the problem that users cannot obtain meteorological data in time due to the interruption of Internet line in the current civil aviation meteorological external service system. Designing and implementing a civil aviation meteorological emergency service platform based on 5G. The platform is built based on C/S architecture. Firstly, designing and implementing the whole business module of the platform, and then establishing the corresponding protective measures for the security of the platform. Finally, deploying the 5G module between the data upload and the client, meanwhile, realizing the underlying switching logic between the Internet and 5G line. The application result shows that due to the use of 5G technology, The platform can provide users with meteorological emergency service in case of Internet interruption
A Multi-View SAR ATR Optimal Observation Path Planning Method

Multi-view SAR images contain richer target information than single-view, which is beneficial to synthetic aperture radar automatic target recognition (SAR ATR). It is a huge challenge to select the best observation viewpoints and the most suitable flight path for multi-view SAR ATR in an unknown environment. Therefore, we propose a multi-view SAR ATR optimal observation path planning method in this paper. The geometrical and the optimization mathematical models based on the task requirements are constructed, and the convolutional neural networks with two inputs are designed as the base classifier. An autonomous path planning method forms the best observation path planning in the absence of global information of the surroundings. Thus the selection of the optimal viewpoint for multi-view SAR ATR is solved by the path search algorithm. The multi-view SAR images are collected on the solved optimal viewpoints, and the final recognition result is obtained by the base classifiers ensemble. Experimental results based on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown that the proposed method obtains superiority in optimal observation path planning.
Dynamic Graph Pattern Matching in Medical Knowledge Graphs

As the study of graph pattern matching (GPM) attracts more and more scholars, the research of GPM in the medical field begins to emerge, from protein structure analysis to breast cancer classification diagnosis, and GPM is more and more widely adopted. However, in medical knowledge graphs (MKG), when the pattern graph dynamically changes, to obtain the matching results of the changed pattern graph, the traditional method has to carry out global matching instead of local matching, which causes a waste of resources and takes a lot of time. To quickly return the matching results of the dynamic pattern graph in MKGs, in this paper, we introduce a caching mechanism to cache the previous matching results and propose a new longest common path matching (M-LCPM) algorithm based on multithreading. The algorithm only performs partial matching on the changed pattern graph and can quickly obtain the matching result. Considering that the M-LCPM algorithm cannot get matching results when the pattern graph changes completely and there may be missing matching subgraphs when the M-LCPM algorithm performs matching, we optimize the M-LCPM algorithm and propose the M-LCPM-Enhance algorithm. Experimental results on two data sets show that our dynamic algorithm is more efficient than existing algorithms.
3D Object Aided Self-Supervised Monocular Depth Estimation

Monocular depth estimation has been actively studied in fields such as robot vision, autonomous driving, and 3D scene understanding. Given a sequence of color images, unsupervised learning methods based on the framework of Structure-From-Motion (SfM) simultaneously predict depth and camera relative pose. However, dynamically moving objects in the scene violate the static world assumption, resulting in inaccurate depths of dynamic objects. In this work, we propose a new method to address such dynamic object movements through monocular 3D object detection. Specifically, we first detect 3D objects in the images and build the per-pixel correspondence of the dynamic pixels with the detected object pose while leaving the static pixels corresponding to the rigid background to be modeled with camera motion. In this way, the depth of every pixel can be learned via a meaningful geometry model. Besides, objects are detected as cuboids with absolute scale, which is used to eliminate the scale ambiguity problem inherent in monocular vision. Experiments on the KITTI depth dataset show that our method achieves State-of-The-Art performance for depth estimation. Furthermore, joint training of depth, camera motion and object pose also improves monocular 3D object detection performance. To the best of our knowledge, this is the first work that allows a monocular 3D object detection network to be fine-tuned in a self-supervised manner.
A Robot Skill Learning Method with Obstacle Avoidance Ability Based on Dynamic System Approach

The dynamic system method is an effective and relatively mature method for learning robot skills. At present, there are relatively few researches on obstacle avoidance algorithms based on dynamic systems. Considering the possible collision problem when the operator and the manipulator work together, this article proposes an obstacle avoidance algorithm for manipulators based on a dynamic system. The algorithm optimizes the end obstacle avoidance algorithm under the existing dynamic system learning method, by introducing reference point and reference direction, the problem of saddle point and local minimum point existing in obstacle boundary is solved. Finally, the simulation on Robotic System Toolbox and the experiment on Franka Emika Panda robot platform verify the effectiveness of the algorithm in end obstacle avoidance.