Reviewerly/exHarmony huggingface dataset:
Ensemble Based Temporal Weighting and Pareto Ranking (ETP) Model for Effective Root Cause Analysis

Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.Aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses.This paper proposes the Ensemble based temporal weighting and pareto ranking (ETP) model for Root-cause identification.Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model.The obtained aspects are validated and ranked using the proposed aspect weighing scheme.Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making.Experiments were performed with the standard five product benchmark dataset.Performances on all five product reviews indicate the effective performance of the proposed model.Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates.The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%-15% and detection rates at 4%-24% compared to the state-of-the-art models.
AI Enabled Context Sensitive Information Retrieval System

Artificial Intelligence (AI) is slowly seeping into all areas of knowledge and many of the activities we carry out day by day. Most of the repetitive and manual tasks that do not require great capacity can be moderately simple optimized using AI. The machines are getting smarter everyday through AI to deliver improved results quickly and improving the customer experience and learning on the fly. Search engines are a text book application of Natural Language Processing (NLP), a field of AI dedicated to teaching computers to understand human written language in order to find users the information they're looking for. A major limitation of most existing information retrieval systems and models is that there is a gap between the information about the actual user and understanding the search context is largely ignored since the retrieval decision is made solely on the query and document collection. In this paper we are focusing on building an In-house AI based Context Sensitive Information Retrieval System with Keyword Search, Latent Drichlet Allocation (LDA) and Document to Vector (Doc2Vec) Similarity models and Entity Extraction with the available In-house data which helps in bridging the gap between the understandings of user search query by the computer and brings the context sensitive results.
The Analysis of Higher Education Reform Strategy Based on the New Situation of Network

In the process of the continuous development of China's modern education, as an important part of the current education system, the quality of high-level education has a very important impact on the development of students and social economy. With the deepening of education reform, people pay more and more attention to the reform of higher education. In the process of the continuous development of modern information technology, promoting the effective integration of high-level education reform and modern information technology has become an important way for the development of modern higher education. Based on this, this paper deeply explores the reform strategy of Higher Education under the new situation of network, in order to improve the quality of higher education reform, and provide some valuable reference for promoting the further development of modern education.
Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path

The recently discovered Neural Collapse (NC) phenomenon occurs pervasively in today's deep net training paradigm of driving cross-entropy (CE) loss towards zero. During NC, last-layer features collapse to their class-means, both classifiers and class-means collapse to the same Simplex Equiangular Tight Frame, and classifier behavior collapses to the nearest-class-mean decision rule. Recent works demonstrated that deep nets trained with mean squared error (MSE) loss perform comparably to those trained with CE. As a preliminary, we empirically establish that NC emerges in such MSE-trained deep nets as well through experiments on three canonical networks and five benchmark datasets. We provide, in a Google Colab notebook, PyTorch code for reproducing MSE-NC and CE-NC: at https://colab.research.google.com/github/neuralcollapse/neuralcollapse/blob/main/neuralcollapse.ipynb. The analytically-tractable MSE loss offers more mathematical opportunities than the hard-to-analyze CE loss, inspiring us to leverage MSE loss towards the theoretical investigation of NC. We develop three main contributions: (I) We show a new decomposition of the MSE loss into (A) terms directly interpretable through the lens of NC and which assume the last-layer classifier is exactly the least-squares classifier; and (B) a term capturing the deviation from this least-squares classifier. (II) We exhibit experiments on canonical datasets and networks demonstrating that term-(B) is negligible during training. This motivates us to introduce a new theoretical construct: the central path, where the linear classifier stays MSE-optimal for feature activations throughout the dynamics. (III) By studying renormalized gradient flow along the central path, we derive exact dynamics that predict NC.
It is better to Verify: Semi-Supervised Learning with a human in the loop for large-scale NLU models

When a NLU model is updated, new utterances must be annotated to be included for training.However, manual annotation is very costly.We evaluate a semi-supervised learning workflow with a human in the loop in a production environment.The previous NLU model predicts the annotation of the new utterances, a human then reviews the predicted annotation.Only when the NLU prediction is assessed as incorrect the utterance is sent for human annotation.Experimental results show that the proposed workflow boosts the performance of the NLU model while significantly reducing the annotation volume.Specifically, in our setup, we see improvements of up to 14.16% for a recall-based metric and up to 9.57% for a F1score based metric, while reducing the annotation volume by 97% and overall cost by 60% for each iteration.
CLASSIFICATION OF PNEUMONIA USING SUPPORT VECTOR MACHINE

Pneumonia is a type of lung disease caused by bacteria, viruses, fungi, or parasites. One way to find out pneumonia is by x-ray. X-rays will be analyzed to determine whether there is pneumonia or not. This study aims to classify the x-ray results whether there is pneumonia or not on the x-ray results. The classification method used in this study were Support Vector Machine (SVM) and Gray Level Co-Occurrence (GLCM) for the extraction method. There are several stages before classification, namely cropping, resizing, contrast stretching, and thresholding then extracted using GLCM and classified using SVM. The results showed that the best accuracy of 62.66%.
Leveraging Non-uniformity in First-order Non-convex Optimization

Classical global convergence results for first-order methods rely on uniform smoothness and the \L{}ojasiewicz inequality. Motivated by properties of objective functions that arise in machine learning, we propose a non-uniform refinement of these notions, leading to \emph{Non-uniform Smoothness} (NS) and \emph{Non-uniform \L{}ojasiewicz inequality} (N\L{}). The new definitions inspire new geometry-aware first-order methods that are able to converge to global optimality faster than the classical $\Omega(1/t^2)$ lower bounds. To illustrate the power of these geometry-aware methods and their corresponding non-uniform analysis, we consider two important problems in machine learning: policy gradient optimization in reinforcement learning (PG), and generalized linear model training in supervised learning (GLM). For PG, we find that normalizing the gradient ascent method can accelerate convergence to $O(e^{-t})$ while incurring less overhead than existing algorithms. For GLM, we show that geometry-aware normalized gradient descent can also achieve a linear convergence rate, which significantly improves the best known results. We additionally show that the proposed geometry-aware descent methods escape landscape plateaus faster than standard gradient descent. Experimental results are used to illustrate and complement the theoretical findings.
Model Identiﬁcation and Data Analysis

Today, a deluge of information is available in a variety of formats. Industrialplants are equipped with distributed sensors and smart metering; huge datarepositories are preserved in public and private institutions; computer net-works spread bits in any corner of the world at unexpected speed. No doubt,we live in the age of data.ᘐis new scenario in the history of humanity has made it possible to use newparadigms to deal with old problems and, at the same time, has led to challeng-ing questions never addressed before. To reveal the information content hiddenin observations, models have to be constructed and analyzed.ᘐe purpose of this book is to present the ﬁrst principles of model construc-tion from data in a simple form, so as to make the treatment accessible to a wideaudience. As R.E. Kalman (1930–2016) used to say “Let the data speak,” this isprecisely our objective.Our path is organized as follows.We begin by studying signals with stationary characteristics (Chapter 1).After a brief presentation of the basic notions of random variable and randomvector, we come to the deﬁnition of white noise, a peculiar process throughwhich one can construct a fairly general family of models suitable for describ-ing random signals. ᘐen we move on to the realm of frequency domain byintroducing a spectral characterization of data. ᘐe ﬁnal goal of this chapter isto identify a wise representation of a stationary process suitable for developingprediction theory.
The restrictor view, without covert modals

Abstract The view that if -clauses function semantically as restrictors is widely regarded as the only candidate for a fully general account of conditionals. The standard implementation of this view assumes that, where no operator to be restricted is in sight, if -clauses restrict covert epistemic modals. Stipulating such modals, however, lacks independent motivation and leads to wrong empirical predictions. In this paper I provide a theory of conditionals on which if -clauses are uniformly interpreted as restrictors, but no covert modals are postulated. Epistemic if -clauses, like those in bare conditionals, restrict an information state parameter which is used to interpret an expressive layer of the language. I show that this theory yields an attractive account of bare and overtly modalized conditionals and solves various empirical problems for the standard view, while dispensing with its less plausible assumption.
Two-step phase shifting profilometry based on Lissajous ellipse fitting technique

&lt;sec&gt; Phase shifting profilometry (PSP) is an effective technique to reconstruct the three-dimensional shape of object. In general, PSP needs three or more fringe patterns with phase-shifting accurately known to extract the phase distribution of objects. Therefore, the scene and the test objects should remain stationary during capturing the fringe patterns. However, the phase shifts may be unknown in an actual PSP measurement system, especially when measuring the moving object, that is, the phase-shift error may be introduced during the obtaining of the phase-shifting fringe patterns of moving object. In the dynamic measurement scenario, the use of fewer fringe patterns can realize the faster measurement speed and suppress the phase shift error introduced by the moving object. In this paper, a two-step PSP algorithm is proposed based on Lissajous ellipse fitting (LEF). The proposed method uses only two fringe patterns to extract the phase distribution of the object and can suppress the phase shift error caused by the moving object. &lt;/sec&gt;&lt;sec&gt; However, in a practical PSP system, the spatiotemporally varying background intensity and modulation also significantly affect the phase accuracy extracted by LEF, and thus three error-suppressing methods are proposed to reduce the phase error caused by the non-uniform background intensity and modulation. In order to verify the effectiveness of the three error-suppressing methods, we analyze and compare their performances of error suppression under different background intensities and modulations. The advantages of three error-suppressing methods can be summarized as follows. 1) The mean and modulation correction technique has greater advantage than the other two when the background intensity and modulation vary with time. 2) When the background intensity and modulation are relevant to pixel position and the number of fringe patterns, the empirical mode decomposition normalization can more effectively suppress the influence of the non-uniform background intensity and modulation. &lt;/sec&gt;&lt;sec&gt; In experiment, a two-step phase-shifting dynamic measurement based on LEF is conducted. Compared with the traditional PSP which needs at least three fringe patterns, the two-step PSP algorithm successfully extracts the phase with only two fringe patterns and suppresses the phase shift error caused by the motion of the object. Compared with Fourier transform profilometry (FTP), the two-step PSP algorithm can obtain very accurate phase distribution and retain many phase details. &lt;/sec&gt;
Architecture Design of 5G and Virtual Reality-Based Distributed Simulated Training Platform for Ship Pilots

In this paper, virtual reality (VR) and fifth-generation cellular network (5G) technology are applied to the remote training fields. We propose a new distributed simulator platform for ship pilots. Compared to traditional training methods, the application of the “5G + VR” technology can simulate the movements of various ship types and various sea conditions in different pilot training locations. Moreover, comprehensive training and assessment sub-systems can be provided for trainees through the intelligent auxiliary decision-making support and expert evaluation functions. The proposed new distributed simulator platform aims at solving the problems due to large-scale pilotage training difficulty, high-risk experiments and lack of high-quality teaching resources.
Smart Board Live Application: Super Complete Online Learning Platform for Effective Online Learning

Online learning in Indonesia currently has many obstacles. Some of the obstacles are less supportive learning device, participants find it difficult to download various applications to support online learning, and the difficulty of educators when explaining calculation material and formulas online. So, we provide solutions in the form of user interface design in the application that we design, namely Smart Board Live. This paper is made using quantitative descriptive data and literature studies. The design of this application will provide all the features needed to support teaching and learning activities. In this application there are some menu; Live Board, Attendance List, Exam, Collection of Materials, Student Assignment, and Account. The Live Board menu is the main menu in the form of an interactive digital whiteboard that can make interconnect users, brainstorm, write, draw, visualize ideas, record, share documents, and share editing and additions made on the board.
Event Outlier Detection in Continuous Time

Continuous-time event sequences represent discrete events occurring in continuous time. Such sequences arise frequently in real-life. Usually we expect the sequences to follow some regular pattern over time. However, sometimes these patterns may be interrupted by unexpected absence or occurrences of events. Identification of these unexpected cases can be very important as they may point to abnormal situations that need human attention. In this work, we study and develop methods for detecting outliers in continuous-time event sequences, including unexpected absence and unexpected occurrences of events. Since the patterns that event sequences tend to follow may change in different contexts, we develop outlier detection methods based on point processes that can take context information into account. Our methods are based on Bayesian decision theory and hypothesis testing with theoretical guarantees. To test the performance of the methods, we conduct experiments on both synthetic data and real-world clinical data and show the effectiveness of the proposed methods.
The Training of Future Teachers for Innovative Teaching Activities

The conceptual and methodological principles of future technology teachers’ training to the innovative pedagogical activities in terms of the postmodern approach are scientifically argued and developed in the article. It is proved that their training becomes effective if it is carried out according to the defined theoretical and methodological bases of the search, taking into account the main directions of reforming the system of higher pedagogical education in the context of innovative development of Ukraine. The essence and content of readiness of future teachers of technologies for innovative pedagogical activity are revealed, components (motivational, cognitive, activity-technological, and creative-reflexive), criteria, indicators, and levels of its formation are defined. The pedagogical conditions of training of future teachers of technologies for innovative pedagogical activity are substantiated; theoretically developed and experimentally tested component model of the professionally-oriented educational environment of pedagogical free economic zone and structural-functional model of training of future teachers of technologies for innovative pedagogical activity. The effectiveness of the gradual introduction of methods of training future teachers of technology to innovative pedagogical activities through the use of developed methodological support and improvement of content, forms, methods, and means of educational interaction of subjects and objects of educational process environment of pedagogical higher educational establishments was experimentally tested and confirmed using the methods of mathematical statistics.
External Service Sensing (ESS): Research Framework, Challenges and Opportunities

The flourish of web-based services gave birth to the research area \textit{services computing}, a rapidly-expanding academic community since nearly 20 years ago. Consensus has been reached on a set of representative research problems in services computing, such as service selection, service composition, service recommendation, and service quality prediction. An obvious fact is that most services keep constant changes to timely adapt to changes of external business/technical environment and changes of internal development strategies. However, traditional services computing research does not consider such changes sufficiently. Many works regard services as \textit{static} entities; this leads to the situation that some proposed models/algorithms do not work in real world. Sensing various types of service changes is of great significance to the practicability and rationality of services computing research. In this paper, a new research problem \textit{External Service Sensing} (ESS) is defined to cope with various changes in services, and a research framework of ESS is presented to elaborate the scope and boundary of ESS. This framework is composed of four orthogonal dimensions: sensing objects, sensing contents, sensing channels, and sensing techniques. Each concrete ESS problem is defined by combining different values in these dimensions, and existing research work related to service changes can be well adapted to this framework. Real-world case studies demonstrate the soundness of ESS and its framework. Finally, some challenges and opportunities in ESS research are listed for researchers in the services computing community. To the best of our knowledge, this is the first time to systematically define service change-related research as a standard services computing problem, and thus broadening the research scope of services computing.
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages

Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm become the dominant way to learn on graphic data. Models in this paradigm have to spend extra space to look up adjacent nodes with adjacency matrices and extra time to aggregate multiple messages from adjacent nodes. To address this issue, we develop a method called LinkDist that distils self-knowledge from connected node pairs into a Multi-Layer Perceptron (MLP) without the need to aggregate messages. Experiment with 8 real-world datasets shows the MLP derived from LinkDist can predict the label of a node without knowing its adjacencies but achieve comparable accuracy against GNNs in the contexts of semi- and full-supervised node classification. Moreover, LinkDist benefits from its Non-Message Passing paradigm that we can also distil self-knowledge from arbitrarily sampled node pairs in a contrastive way to further boost the performance of LinkDist.
Positive effects of formation of knowledge, skills and skills on the basis of interdisciplinary relations

This article analyzes the natural, scientific, neurophysiological, psychological and pedagogical approaches to the nature of interdisciplinary connections in special education. Interdisciplinary links ensure the establishment of a unified system of teaching, education and work in a special school, which significantly affects the level of generalization, robustness, completeness and differentiation of the formed concepts. This has a positive effect on the acquisition of the concepts that make up the content of the subject. The positive effects of developing knowledge, skills and competencies based on interdisciplinary connections are highlighted.
We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature

Recently, the object detection based on deep learning has proven to be vulnerable to adversarial patch attacks. The attackers holding a specially crafted patch can hide themselves from the state-of-the-art person detectors, e.g., YOLO, even in the physical world. This kind of attack can bring serious security threats, such as escaping from surveillance cameras. In this paper, we deeply explore the detection problems about the adversarial patch attacks to the object detection. First, we identify a leverageable signature of existing adversarial patches from the point of the visualization explanation. A fast signature-based defense method is proposed and demonstrated to be effective. Second, we design an improved patch generation algorithm to reveal the risk that the signature-based way may be bypassed by the techniques emerging in the future. The newly generated adversarial patches can successfully evade the proposed signature-based defense. Finally, we present a novel signature-independent detection method based on the internal content semantics consistency rather than any attack-specific prior knowledge. The fundamental intuition is that the adversarial object can appear locally but disappear globally in an input image. The experiments demonstrate that the signature-independent method can effectively detect the existing and improved attacks. It has also proven to be a general method by detecting unforeseen and even other types of attacks without any attack-specific prior knowledge. The two proposed detection methods can be adopted in different scenarios, and we believe that combining them can offer a comprehensive protection.