Reviewerly/exHarmony huggingface dataset:
Probing for Artifacts: Detecting Imagenet Model Evasions

While deep learning models have made incredible progress across a variety of machine learning tasks, they remain vulnerable to adversarial examples crafted to fool otherwise trustworthy models. Previous work has proposed examining the internal activation of Imagenet models to detect adversarial examples. Our work expands the scale and scope of previous research by simultaneously probing every activation within an Imagenet model using a novel probe block. This probe block model is trained against multiple adversarial algorithms to create a more robust detector. Parameterization of the probe block and adversarial classification networks that utilize probe block output are examined in an ablation experiment with probes of Resnet-50, Inception-v3 and Xception. Considered adversarial classification networks include examples built with Mobilenet-v2 which is shown to be better than a VGG alternative for detecting adversarial artifacts. Results are compared to logistic regression feature squeezing results, which we suggest is an improvement to feature squeezing.
Tomographic Image Deblurring Using Steepest Descent

In the course of image reconstruction, pixel values can scatter in a diverse way (e.g., speckling, diffraction and diffusion). Speckle is a kind of scattering that leads towards blur. Speckled signal values swing from high to low in concerned pixel. Speckle is not a random error. It removed by further processing of image using suitable deconvolution technique. The digitalization of the problem leads towards linear equations of an ill-posed matrix --- Krylov operator such as steepest descent useful tool to handle such situations. The methods discussed in this paper are modified residual norm steepest descent (MRNSD) and conjugate gradient for least-square problems (CGLS). These two techniques variation of steepest descent, hence the iterative algorithm in nature. Like many other iterative algorithms, these two practices suffer from semi-convergence. This paper focus on the deblurring of image reconstructed from the received data in industrial tomography along-with effective way to tackle semi-convergence.
Prevention of the Emergence and Development of College Teachers Emotional Burnout

The article is devoted to the problem of preventing the occurrence and development of college teachers with and without basic pedagogical education, burnout syndrome. The article presents the results of an empirical study, the purpose of which was to identify the degree of expression of burnout syndrome among teachers of secondary vocational education, the disclosure of the relationship between the level of its formation and the needs of teachers in self-education and self-development. The study involved 214 college teachers, whose teaching experience ranged from 3 to 38 years. The author substantiates the relevance of the undertaken research, describes its results, on the basis of which it determines the main directions of prevention of the occurrence and development of burnout syndrome among college teachers in the process of organizing the management of their professional and personal development.
Primary Research on Standardization of Real-Time Interactive e-Learning Platform

In recent years, under the joint promotion of national policies, science and technology and capital, the e-Learning education market has developed rapidly. Among them, real-time interactive e-Learning is gradually recognized by the public for its good interactivity and effective learning scenes. However, real-time interactive e-Learning is still at an early stage of development and lacks standardization and systematization. How to establish and perfect the real-time interactive e-Learning platform standard conforming to the educational law? How to establish a teaching mode suitable for most teachers? This paper expounds the differences between real-time interactive e-learning and other online learning. The characteristics and advantages of the existing real-time interactive e-learning platform are analyzed, and the hardware equipment standards, teaching standards, standardization activities and organization are proposed. As well as standards-based development practices and driving forces for improving existing standards and developing new standards. Optimizing the educational process, cultivating learning habits and improving learning efficiency are the research objectives of this paper.
A New Method of Intelligent Maintenance of Smart Girds via Mean-Shift-Tracking Image Collaborative Displays

A mean-shift-tracking method for fusion image collaborative displays is proposed for maintenance and repair of smart gird via Hololens. It uses weighted mean fusion scheme based on region target detection to make pixel-level dynamic image fusion of video from Hololens vision. The target was tracked via Mean Shift Algorithm. Experiments were carried out with real image sequences to evaluate the Root Mean Square(RMS) error of steady-state. Results show that the target motion tracking method is effective, efficient, and robust in Hololens displays for maintenance and repair of smart grids.
Prediction of Opinion Keywords and Their Sentiment Strength Score Using Latent Space Learning Methods

Most item-shopping websites give people the opportunity to express their thoughts and opinions on items available for purchasing. This information often includes both ratings and text reviews expressing somehow their tastes and can be used to predict their future opinions on items not yet reviewed. Whereas most recommendation systems have focused exclusively on ranking the items based on rating predictions or user-modeling approaches, we propose an adapted recommendation system based on the prediction of opinion keywords assigned to different item characteristics and their sentiment strength scores. This proposal makes use of natural language processing (NLP) tools for analyzing the text reviews and is based on the assumption that there exist common user tastes which can be represented by latent review topics models. This approach has two main advantages: is able to predict interpretable textual keywords and its associated sentiment (positive/negative) which will help to elaborate a more precise recommendation and justify it, and allows the use of different dictionary sizes to balance performance and user opinion interpretability. To prove the feasibility of the adapted recommendation system, we have tested the capabilities of our method to predict the sentiment strength score of item characteristics not previously reviewed. The experimental results have been performed with real datasets and the obtained F1 score ranges from 66% to 77% depending on the dataset used. Moreover, the results show that the method can generalize well and can be applied to combined domain independent datasets.
Handwritten Text Line Segmentation Method by Writing Pheromone Diffusion and Convergence

Text line segmentation in offline handwritten documents remains a challenge because the offline handwritten text lines are often inconsistency curved and skewed. More serious is the space between lines is not enough to distinguish them. In this paper, we propose a novel offline handwritten text line segmentation method by writing pheromone diffusion and convergence. According to the principle of gravity, we apply it to the lines location of the offline handwritten texts, the pheromone diffusion and convergence can learn to generate the pheromone matrix for extracting the key locations and fragments of the text line, that is made robust to deal with various offline handwritten documents with curved and multi-skewed text lines. In experiments on a commonly used database with offline handwritten text images, our method can significantly improve upon state-of-the-art text line segmentation methods.
Two-Stream Framework for Activity Recognition with 2D Human Pose Estimation

Two-Stream framework with spatial information and optical flow information have reached the great performance for action recognition task in video. The optical flow information captures the low-level motion characteristics via a fixed quantity of consecutive video frames, which however contains noise information and is incompetent to characterize different actions with varying posture and duration. Usually ten frames before and after a frame are used as optical flow information, which may be too long or too short to capture the useful motion features for different actions. Moreover, the cost of calculating optical flow information from several consecutive video frames is high. To solve these issues, we propose a novel framework to recognize actions by capturing a high-level motion feature, human pose estimation, instead of the optical flow. Our framework uses 2D human pose estimation as the motion feature, and fuses it with the spatial information using attention mechanisms. We handle extensive experiments on two excellent and challenging datasets of realistic human action, HMDB-51 and UCF-101. The experimental results illustrate that our two-stream framework outperforms state-of-the-art approaches in terms of accuracy.
Library Guides: eBooks: Home

Finding and accessing eBooks available through University of Melbourne subscriptions and online.
Ranking with Genetics

There are many applications for ranking, including page searching, question answering, recommender systems, sentiment analysis, and collaborative filtering, to name a few. In the past several years, machine learning and information retrieval techniques have been used to develop ranking algorithms and several list wise approaches to learning to rank have been developed. We propose a new method, which we call GeneticListMLE++ and GeneticListNet++, which build on the original ListMLE and ListNet algorithms. Our method substantially improves on the original ListMLE and ListNet ranking approaches by incorporating genetic optimization of hyperparameters, a nonlinear neural network ranking model, and a regularization technique.
Healthcare Privacy Approach Using Blockchain Technology

Nowadays, with the advancement in information technology, great progress is seen in the healthcare domain. However, such advancement has also made healthcare data not only much bigger but also much more difficult to handle. Also, the data generated is in different formats and to access such largely scattered data is merely impossible. Health care today suffers from fragmented data, delayed communications, and disparate workflow tools caused by the lack of interoperability. Blockchain technology possesses key properties that can potentially address pressing issues in health care such as incomplete records at the time of care and difficult access to patients’ health information in a secure manner. The proposed solution emphasizes on solving a current problem of storing the largely scattered healthcare information in a decentralized fashion and allowing the access of data by the authorized persons only. This application manages authentication, confidentiality, accountability, and data sharing while handling sensitive health information.
Foreign Linguistic System Construction Based on Computer-aided Technology

Abstract From the perspective of constructivist linguistics, modern information and network technologies are applied to explore the project contents and methods of foreign language practice teaching under the mode of foreign language practical teaching based on the construction of computer-aided technology.
Identification of Multiple-Antenna Emitters Using Power Amplifier Nonlinearity

Power amplifier (PA) nonlinearity typically plays a crucial role in the application of specific emitter identification (SEI), due to its uniqueness at the radio frequency (RF) frontend for particular emitter. For a multiple-antenna emitter with multiple PAs inside, this paper first presents a PA parameter estimator based on a two-stage linear least square (LLS) method in the MIMO multipath communication scenario. Treated as the RF fingerprint, the extracted nonlinear coefficients are then used to classify the individual multipleantenna emitters based on the minimum error probability (MEP) criterion. In addition, we analyze the impacts of mismatch of PA model and rank-deficient condition of the LLS problem in depth. Finally, the proposed approach is validated to be effective through extensive numerical simulations.
The Falsificationist Foundation for Null Hypothesis Significance Testing

There have been many criticisms of null hypothesis significance testing (NHST), including important ones made by Dr. Hung Nguyen. The goal of this chapter is to honor Dr. Nguyen for his important scientific contributions by introducing a new argument against NHST that complements the arguments he already has made. Specifically, NHST aficionados sometimes justify NHST by arguing that it is based on Popper’s falsificationist philosophy of science, which has a history of being very popular among scientists in the 20th century, and much of this popularity continues into the 21st century. In contrast, I argue that NHST is inconsistent, rather than consistent, with Popper. I further argue that there are problems with Popper’s philosophy, at least in the simple form used to justify NHST.
Comparison and Evaluation of Video Retrieval Approaches Using Query Sentences

Following are two mainstream approaches of video retrieval from large-scale video data using query sentences: (1) an approach to find pre-trained concepts such as objects, persons, scenes, and activities corresponding to a query sentence, and (2) an approach to map a query sentence and images/videos into the same feature space and directly search for images/videos that match the query sentence. In this study, we analyze the advantages and disadvantages of these two approaches using a large-scale video database of TRECVID benchmark and confirm whether the fusion of these approaches can improve video retrieval performance.
Toward a general model for reflection recovery and single image enhancement

Images often suffer from low visual quality due to poor imaging conditions such as low light or hazy weather. The haze imaging model is widely used in contrast enhancement in daylight condition with haze, while the retinex model is universal for low-light conditions. Although their forms and applications are different, they can be unified into a more general form through the proposed observation. Based on this model, the authors can estimate the reflection of the scene more accurately in more complex imaging conditions. In this study, the authors propose a simple but effective method for estimating the reflection and enhancing the image contrast based on a general imaging model. To preserve the image details and control contrast, the authors introduce dark boundary and bright boundary to handle the high-light and low-light conditions, and a guided structure-preserving optimization algorithm is proposed to estimate them. After obtaining the dark and bright boundaries, the reflection is calculated and the image is enhanced accordingly. Different from previous approaches, which were designed for specific applications, the proposed method can be used for more diverse imaging conditions. Experiments show that the proposed method can be applied to many poor imaging conditions and maintain good performance.
Performance of Autoregressive Tree Model in Forecasting Cancer Patients

In this paper, our aim is to study the time series data on cancer patients of Punjab, a state of India, by applying the autoregressive tree (ART) model. ART model is an extension of AR model which provides a superior predictive accuracy as compared to AR. In ART model, at first predicted variable and target variable are to be decided on the basis of our own data. And the second step is to form a decision tree for our target variable by transforming the data. This approach of AR model is very much effective for forecasting and a very few research work had been done up to date with ART model. We choose this region for our research purpose as Punjab is considered as a cancer capital of India due to its high increasing rate of cancer-affected patients. In this paper, our goal is to forecast the future trend of cancer patients of Punjab and a case study of cancer incidence is also analyzed and forecast trends of common cancer types among male, female, and both of them. A hypothesis test and calculating our forecast result are also provided in this paper on the basis of mean forecast error and mean square forecast error. The purpose of this study is to aware the people and various organizations regarding the frightened issue of this region and our results could provide appropriate planning regarding eradicate this disease.
A study on support method of consulting service using text mining-Application to real problem-

This study was conducted to produce a supporting method for consulting service companies so that the companies can respond to client demands irrespective of the consultant expertise. Occurrence of future problems in client companies is predicted using text mining with data taken from a consulting company. To do so, correspondence analysis and Data Envelopment Analysis (DEA) discriminant analysis are used. As described herein, the proposed method is improved using Intermediate Distribution Frame (IDF) values and standardization for the application in a real problem. For computer experiments to verify the effectiveness of the proposed method, prediction is made about cancellation problems. The prediction is compared to predictions produced by the consultant.
PID Controller Optimization of Teleoperated 2DOF Robot Manipulator Using Artificial Bee Colony Algorithm

Robotics and artificial intelligence are the basic building blocks for industry revolution 4.0. The intelligent optimization techniques have been recently used to solve complex engineering problems. The robotics is one of the fields that intelligent optimization techniques can be applied in both controller design and motion planning. In this paper, we introduce the application of artificial bee colony for tuning the PID controllers for a teleported 2 Degree-of-Freedom robot manipulator. The presence of the time delay degrades the performance of the system or at worst leads to system instability. The performance of the 2DOF manipulator has been optimized using the nonlinear model with artificial bee colony algorithm (ABCA). The performance of the 2DOF robot using ABCA optimization has been compared with the performance of the robot using particle swarm optimization algorithms and the ABCA showed better performance.
Segmentation of Offline Handwritten Gurmukhi Words Using Projection Features

Segmentation of words into isolated characters is the essential component in handwritten character recognition systems. In this paper, the segmentation of Gurmukhi handwritten words into characters is presented. For this, horizontal and vertical projection features have been used to segment the characters from words. Simple words without upper and lower modifier of Gurmukhi handwritten text having three and four characters are considered in the present work. An overall accuracy of 91.4 % on a dataset of 550 handwritten Gurmukhi words has been achieved in this work.
Summary of Research on Error Prevention Technology of Secondary Safety Measures in Smart Substations

The secondary system of smart substation replaces the cable communication of traditional substation with network communication, which greatly improves the information sharing of substation. At the same time, it also makes the operation of secondary safety measures complex, without "obvious electrical break point", and the reliability of secondary safety measures is difficult to guarantee. Firstly, the concept and basic principles of secondary safety measures in smart substations are discussed, and the reasons for the low reliability of secondary safety measures are analyzed. Secondly, three solutions are summarized, which are preview check, online error prevention and automatic generation of secondary safety measures. Then the difficulties of engineering application are analyzed. Finally, the future development direction of secondary safety measures is prospected.
Comparison of global and local features for author’s identification by using geometrical and zoning methods

Abstract Identification analysis for author’s handwriting image in forensic investigation is still an important research area in this current big data era. Images feature extraction can lead to an issue of high dimensionality of data. The process of feature extraction is the most crucial process in author’s identification. It is important to choose the best method to represent the image. This study compared two feature extraction methods, namely Higher-Order United Moment Invariant (HUMI) and the Edge-based Directional (ED) method that construct the Global and Local Features respectively. The additional process of discretization was implemented before the training and testing phase to represent the generalized features for the classifier models. This process induced a better performance accuracy for both methods where the discretized Local Features achieved 99.95% accuracy rate that slightly outperforms the discretized Global Features with only 99.91%.
Finding Multiple Variables Causal Dispositions In Images

In the big data era, one of the fundamental tasks is to explore and discover the causal relationship between variables from observation data and images, which will play a key role in various future applications. This paper reveals in datasets of images that has been emerged the “multiple variables causal direction” of object categories. To start with, it was using an approach to high-dimensional data observational causality inference, and this research can detecting the causal direction between random variables through build a classifier. What is more, this research can validly differentiate the characteristics of objects and their contexts by use causal direction classifier in the static picture. Last but not least, the research certify that there presence a relationship in the causal direction and images' objects or their contexts, also presence visible trace which uncover objects' causal dispositions.
Automatic detection of body packing in abdominal X-ray images

It is difficult and time-consuming to detect narcotic drugs concealed within human bodies by visual assessment of X-ray images . This study aimed to describe a method for automatic detection of concealed drugs based on abdominal X-ray transmission images. 43 X-ray images from suspects during border inspection are used in this study. Image features were extracted from both the directional fractal dimension and the gray level to form feature vectors. An unbalanced data preprocessing method was implemented before training to minimize the false alarms. A support vector machine model was applied for training and final classification. The proposed method yielded a 97.7% classification accuracy and a 96.1% sensitivity in 43 image cases, which were superior to the method without unbalanced data process resulting in a 95.3% classification accuracy and a 92.3% sensitivity. This fast, computer-based X-ray transmission image processing method can be used in the field inspection for rapid, automatic detection of body packing.
Detail-Preserved Tone Mapping Based on Joint Global and Local Histogram Equalization

This paper presents a tone mapping method to generate a high-dynamic-range (HDR) image, in which intensity values are smoothly allocated to a final low-dynamic-range (LDR) image. First, a global histogram equalization scheme is applied to obtain an image histogram with an equalized redistribution. In addition, the image is divided into blocks, and transformation functions are performed using the cumulative distribution function of these blocks. To resolve the seam artefacts that typically arise in the local tone mapping operation, the two tone-mapped images are blended using a multiscale pyramid-based structure. The experimental results show the validity of the proposed method.