Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: Combined charge difficulty evaluation and testees level for intellect appraisal

AI has the potential to seek out patterns in massive information to find out and reveal hidden data or deliver solutions to deliver complicated issues. It is an automatic method. The consolidated charge trouble assessment and lab rats level for insight evaluation are to discover the worker execution. In testing the tests and assignment assessment of various assignment representative's exhibition result is hard to rank the result. Whereby higher specialists surveys and give analysis on laborer work execution, including steps to improve or redirect practices as needed. Higher official announcing execution gives a reason to pay additions and head ways. In this undertaking, higher authority assesses the lower official by relegating errand and lab rats. The higher authority will settle the troubles of testing and positioning issue for examination. Every one of these discoveries are helpful to insight tests. Here the assessment of every representative was determined and see by the Bernoulli's Distribution calculation.
IOT Technology-Based Construction and Management of College Sports Information Platform

In this paper, we mainly discussed the intervention, construction and application of Internet of things (hereinafter referred to as IOT) in college sports information platform. College sports is an integrated organism of multiple components, which includes sports competitions, physical fitness tests, sports teaching and sports club activities. In the running process, how does the organism accurately collect, reasonably use and efficiently manage those related information would provide warrant for the function of the college sports. The development and application of IOT provides us a new perspective to study the college sports. Research methods used in this study include literature review, logic analysis and expert interview. Firstly, we elaborated the significance and implication of the construction of the college sports information platform. Secondly, we constructed the structure of the information platform and introduced the functions of each part. Furthermore, we clarified the IOT technical support for college sports information platform. Finally, the system standard of the information platform and the issues of its information security are proposed to promote and advance the development of the college sports informatization construction and smart management..
A transmission model for motion estimation of instability space targets

Motion estimation is an effective way to rectify the distortion caused by motion existed in captured point clouds of space targets. However, the non-cooperative and motion complexity of instability space targets make it difficult to precisely estimate the motion parameters, especially in the linear measurement system with a single sensor. In order to solve this problem, we present a novel motion representation for motion estimation of instability space targets. The motion representation, called the transmission model, converts a nonlinear system of motion parameters into a polynomial system of orthogonal matrices, which improve the efficiency of motion estimation. Moreover, we exploit a self-constrained form to express the target orthogonal matrices, which makes them be further solved efficiently by transferring the constrained optimization problem into a self-constrained one. Additionally, an effective strategy is devised to solve the polynomial system of orthogonal matrices by progressively increasing the consecutive number of point clouds until the precision is attained. Experimental results have demonstrated that our approach achieves a high estimation accuracy and a good performance of reconstruction within few seconds under a variety of motion states in our research background.
Probabilistic personalised cascade with abstention

Cascade learning with abstention and individualised feature selection is a class of models in high demand in personalised medical applications. The cascade consists of sequential classifiers and rejectors, where the classifiers estimate confidence of prediction, and the rejectors evaluate an expected cost-to-go of features not selected yet. The number of models is exponential in the number of features and, therefore, the challenge is to find efficient heuristics for the NP-hard problem. The state-of-the-art is based on complex deep neural networks. We introduce an efficient and robust approach based on a probabilistic graphical model representing a unified probabilistic classifier that can be applied at any stage of a multi-stage sequential model. As for the rejector, we build it on the probabilistic-based neural network that incorporates the very same probabilistic model to treat unobserved feature values. We illustrate the efficiency of the proposed method on several data sets, and compare our results to the state-of-the-art.
A Study on Priorities for Utilization of AI Recruitment System

Digital transformation not only improves efficiency with automation but also enables new forms of innovation and creativity by replacing analog manual process with a digital process. The transformation in the recruitment process can be seen as automatic selection of applicants using AI. It has yet to be verified whether the recruitment with automation is reliable enough to replace traditional hiring methods. However, as more and more corporations are utilizing AI based recruitment system, research on the priority of the characteristics in the system is needed to maximize the utilization of AI recruitment system. In this research, the research model was designed to find the priority of factors affecting utilization of AI-based recruitment system with variables of TOE framework.
Comparison Multi Transfer Learning Models for Deep Fake Image Recognizer

The advancement of technologies nowadays cause image digitally to become essential as an official document many available applications used for image editing. Those applications have become a threat to detect image authenticity when dealing with an abundance of digital image evidence in cyber court. Hence, many researchers realize the importance of image authentication fields as deep fake is a powerful weapon for spreading misinformation on the digital platform. Deep learning has known to obtain relevant attributes automatically in placing handcrafted features in a deep network against other single-layer networks. The objective of the research is to compare two models from Convolutional Neural Network (CNN), which are VGG19 dan ResNet50 in deep learning with transfer learning for image fake detector. A total of 1500 random images consisting of 450 forgery images from the IEEE Image Forensics Challenge in 2013 were tested on the splicing technique. From this research, we used two transfer learning techniques to identify tamper images from image splicing. Based on model VGG16 and ResNet50 transfer learning, the accuracy achieved about 94.65% and 95.08%, respectively.
Deep Learning Model for Detection of Breast Cancer

Breast cancer is a type of cancer that has highest mortality rate in women in almost all the nations, accounting for 14% of all cancer deaths worldwide. Early and accurate diagnosis can help medical practitioners to take early decisions that can protect a large number of patients from unnecessary medical expenses and can provide them with early treatment. There are some widely accepted techniques or methods that are being used by the clinical experts for the diagnosing of breast cancer. Some of them are biopsy, mammography and Fine Needle Aspirate. The emergence of machine learning algorithms and accessibility to health care data offers more opportunities for comprehensive analysis and identification of diseases non-invasively much accurately and hence can improve treatment and prevention of diseases like breast cancer. The proposed study provides another novel method for the identification of breast cancer using a multilayer perceptron feed forward network. For experiment purpose, we used the breast cancer related data available at UC Irvine machine-learning repository. The model achieved the accuracy of 98.43%.
Classical to Contemporary: An M3-Based Model for Framing Change in Information Architecture

This chapter frames the ongoing epistemological evolution of information architecture as a shift, under the pressure of social and technological change, from the “classical” information architecture of the 1990s and early 2000s to a “contemporary” information architecture. It then establishes a differentiation between the two, modeled in accordance with the conceptualization of innovation in fields of knowledge offered by Van Gigch and Pipino’s Meta-Modeling Methodology.
"Did you buy it already?", Detecting Users Purchase-State From Their Product-Related Questions

In this study we address the problem of identifying the purchase-state of users, based on product-related questions they ask on an eCommerce website. We differentiate between questions asked before buying a product (pre-purchase) and after (post-purchase). At first, we study the ambiguity that exists in purchase-states' definition, and then investigate the linguistic characteristics of the questions in each state. We analyze the discrepancy between the language models of pre- and post-purchase questions, and offer two classification schemes for this task, both outperform human judgments. We additionally show the effectiveness of our classification models in improving real world applications for both consumers and sellers.
Scene Recognition Model in Underground Mines Based on CNN-LSTM and Spatial-Temporal Attention Mechanism

Based on the convolutional neural network (CNN) and long short-term memory neural network (LSTM), combined with data enhancement technology and spatial-temporal attention mechanism, a scene recognition model is established. In order to balance the difference in the amount of data between different samples, data enhancement technology based on video data samples is introduced. Aiming at improving the performance of the model, a spatial-temporal attention mechanism is used to improve the accuracy of scene recognition. The model sample scenes used in this paper include three types: fully mechanized coal face, coal mine roadway and mining machinery. The experimental verification shows that: compared with the traditional convolutional neural network, the accuracy of the CNN-LSTM model is improved by 2.136%, and the CNN-LSTM model with spatial-temporal attention mechanism is improved by 2.921%. The accuracy of the deep CNN-LSTM model with spatial-temporal attention mechanism is about 93.063%.
Implementation of Customized Adaptive Algorithms for Visualization of Stereoscopic Face Mask

Computer vision and Image processing are some of the most important technical aspects in the domain of object detection and reconstruction. These concepts provide solutions not only restricted to the engineering domain but also across the various domains. In this article, the proposed framework mainly deals with the scenario of the generation of face masks in 3D view with the aid of selfie images using computer vision and image processing methodologies. The proposed framework consists of two kinds of algorithms that generate face mak with the aid of single and multiple images respectively. Those adaptive algorithms are discussed along with their implementation. The adaptive algorithm utilized for multiple image input names as MIIO3D and the adaptive algorithm for single image input named as SIIO3D implementation for the construction of an efficient 3D face mask. The input images considered are from real-time aspects. Data visualization and their comparisons made with the aid of Tableau.
Is it possible to disregard obsolete requirements? a family of experiments in software effort estimation

Abstract Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. By conducting a family of six experiments using both students and practitioners as research subjects ( $$N=461$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>461</mml:mn> </mml:mrow> </mml:math> ), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimations due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect.
Research and Development of the Method of Investigating the Possibility of Transformation Relational Databases to NoSQL

Relation database has been a universal solution for any system, allowing manipulating various types of data and creating complex relationship between them for over forty years. RDBMS are not adapted to work with unstructured and semi-structured data and the increase the number of such data has led to the emergence of NoSQL (Not only SQL) database models. The article presents the method of investigating the possibility of transformation relational databases to a configuration from a relational and document-oriented database, based on an assessment of the speed of query execution. The main problems of using relational databases and prerequisites for the transition to NoSQL databases are highlighted.
Automated Service Composition Using AI Planning and Beyond

Automated Service Composition is one of the “grand challenges” in the area of Service-Oriented Computing. Mike Papazoglou was not only one of the first researchers who identified the importance of the problem, but was also one of the first proposers of formulating it as an AI planning problem. Unfortunately, classical planning algorithms were not sufficient and a number of extensions were needed, e.g., to support extended (rich) goal languages to capture the user intentions, to plan under uncertainty caused by the non-deterministic nature of services; issues that where formulated (and, partially addressed) by Mike, being one of his key contributions to the service community.In this chapter, we look at the development of the original vision of automated service composition as AI planning, going from planning with extended (rich) goals, further developing into composition under uncertainty, extending it to other domains (and reformulating the service composition as composition of sensors and actuators), and then showing possible alternative techniques for highly scalable service composition at the expense of the richness of the domain representation.
Label Propagation from Laplacian Score for Semi-Supervised Attribute Selection

Attribute selection is primary for many machine learning applications. Attribute selection in labelled data is not a difficult issue, because of the presence of class labels. The correlation of the features with the class labels guides the feature selection process in supervised learning. It is a difficult issue in unlabelled data because of the absence of the labels. Most of the attribute selection algorithms are proposed for attribute selection in supervised and unsupervised learning. But in real time getting labels for all the data is much harder. So it is required to propose a feature selection technique for partially labelled data. It guides the selection of the most informative features for analysing the data. Since the data is partially labelled, the label information cannot be ignored. A semi supervised attribute selection technique known as Label Propagation based laplacian Score is proposed in this work. It works on the basis of one of the assumptions of Semi - supervised learning i.e cluster assumption. This assumption is utilized to evaluate the affinity between the data. The labels are propagated by using the label propagation technique. The features are ranked based on the laplacian score and the relevant attributes are selected based on this score.
A Modified Circular Version of Playfair Cipher

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Metrics for Measuring Progress and Productivity in Agile Software Development

The constant modifications in modern day technology, ideas and information generate the need to constantly revaluate existing knowledge. Agile software development methods being relatively new and quite in its developmental stages, need the design of correlating metrics is since the traditional metrics may not suffice. This paper focuses on proposing some metrics that can measure the progress and quality of a product being developed using Agile software development methods with the hope that they will be useful for development.
Exploration and Research on the Mixed Teaching Mode of Basic Japanese Course under the Background of Information Technology

Abstract At present, the general trend of information technology development is changing from the typical technology-driven development mode to the combination of application-driven and technology-driven mode. The development of information technology brings both opportunities and challenges to the reform and innovation of foreign language teaching. This paper starts with features and types of information-based foreign language teaching, to explore the mixed teaching mode of basic Japanese course under the background of information technology. Choose a reasonable teaching platform and teaching resources according to the teaching objectives; make a good teaching design for online autonomous learning for each chapter before class; to do well in offline classroom teaching development and classroom network-assisted teaching; and supplement online learning materials effectively for students after-class learning. Pay more attention to process evaluation, multi-subject evaluation and visual data analysis and evaluation based on big data. Pay attention to the role of games in basic Japanese learning, applying information technology to the teaching of basic Japanese courses and exploring the mixed teaching mode of basic Japanese courses can improve teaching effectiveness in all directions and contribute to the overall improvement of teaching quality.
Underwater Image Mosaics for AUV-Mounted Cameras

This paper presents a novel method for automatically selecting the highest quality image sections for an underwater optical mosaic. We have constructed an image quality measure, which is used to determine the optimal seam between two overlapping images. We also present our preprocessing algorithm, which is a novel combination of existing methods. The full mosaic processing chain is demonstrated on images collected with a HUGIN autonomous underwater vehicle (AUV), using a camera system consisting of a high-sensitivity, still-image camera and a synchronized strobe with multiple light emitting diodes (LEDs).
Sequence-Constrained Trajectory Planning and Execution for a Quadrotor UAV with Suspended Payload

The paper presents the design of a trajectory planner and feedback control system to autonomously navigate a quadrotor UAV with a suspended payload through a confined environment consisting of horizontal and vertical tunnels. The trajectory planning task is formulated as an optimal control problem and solved by applying an A⁎ search algorithm. A novel sequence-constrained action space is implemented to encourage the use of input shaping actions, which is an open-loop control technique for reducing vibrations in a response. To execute the planned trajectory, a trajectory regulator is designed to work in conjunction with the trajectory planner. The trajectory regulator uses feedback control to provide disturbance rejection and robustness to parameter uncertainty. The planning and execution is verified in simulation, using a system that is constrained to two dimensions. The trajectory planner successfully plans a collision-free path for the quadrotor with suspended payload through an environment with obstacles, tunnels and vertical chimneys. The regulator successfully controls the quadrotor with suspended payload to follow the planned trajectory through the environment, in the presence of external wind disturbances.
The J-Orthogonal Square-Root Euler-Maruyama-Based Unscented Kalman Filter for Nonlinear Stochastic Systems

This paper addresses the issue of square-rooting in the Unscented Kalman Filtering (UKF) methods. Since their discovery the UKF is considered to be among the most valued state estimation algorithms because of its outstanding performance in numerous real-world applications. However, the main shortcoming of such a technique is the need for the Cholesky decomposition of predicted and filtering covariances derived in all time and measurement update steps. Such a factorization is time-consuming and highly sensitive to round-off and other errors committed in the course of calculation, which can result in losing the covariance’s positivity and, hence, in failing the Cholesky decomposition. The latter problem is usually overcome via square-root filtering implementations, which propagate not the covariance itself but only its square root (Cholesky factor). Unfortunately, negative weights arising in applications of the UKF schemes to large stochastic systems preclude from designing conventional square-root UKF methods. So, we resolve it with a hyperbolic QR factorization used for yielding J-orthogonal square roots. Our novel square-root filter is grounded in the Euler-Maruyama discretization of order 0.5. It is justified theoretically and examined and compared numerically to the conventional (non-square-root) UKF in an aircraft’s coordinated turn scenario with ill-conditioned measurements.
An Accurate and Efficient Large-scale Regression Method through Best Friend Clustering

As the data size in Machine Learning fields grows exponentially, it is inevitable to accelerate the computation by utilizing the ever-growing large number of available cores provided by high-performance computing hardware. However, existing parallel methods for clustering or regression often suffer from problems of low accuracy, slow convergence, and complex hyperparameter-tuning. Furthermore, the parallel efficiency is usually difficult to improve while striking a balance between preserving model properties and partitioning computing workloads on distributed systems. In this paper, we propose a novel and simple data structure capturing the most important information among data samples. It has several advantageous properties supporting a hierarchical clustering strategy that contains well-defined metrics for determining optimal hierarchy, balanced partition for maintaining the clustering property, and efficient parallelization for accelerating computation phases. Then we combine the clustering with regression techniques as a parallel library and utilize a hybrid structure of data and model parallelism to make predictions. Experiments illustrate that our library obtains remarkable performance on convergence, accuracy, and scalability.