Reviewerly/exHarmony huggingface dataset:
Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization

The fundamental difficulty in person re-identification (ReID) lies in learning the correspondence among individual cameras. It strongly demands costly inter-camera annotations, yet the trained models are not guaranteed to transfer well to previously unseen cameras. These problems significantly limit the application of ReID. This paper rethinks the working mechanism of conventional ReID approaches and puts forward a new solution. With an effective operator named Camera-based Batch Normalization (CBN), we force the image data of all cameras to fall onto the same subspace, so that the distribution gap between any camera pair is largely shrunk. This alignment brings two benefits. First, the trained model enjoys better abilities to generalize across scenarios with unseen cameras as well as transfer across multiple training sets. Second, we can rely on intra-camera annotations, which have been undervalued before due to the lack of cross-camera information, to achieve competitive ReID performance. Experiments on a wide range of ReID tasks demonstrate the effectiveness of our approach. The code is available at https://github.com/automan000/Camera-based-Person-ReID.
Detection of Deceptive Phishing Based on Machine Learning Techniques

Internet-based sources facing security problem because of the most sophisticated phishing attacks on online world. Advanced persistent threat attackers are using targeted emails, phishing websites and social engineering techniques to reach their goals. Deceptive Phishing targets confidential information using social engineering thefts online identity and uses spoofed emails and lure to be forged websites. In this paper we discussed about different classification and filtering methods to protect the cloud. An experimental approach is provided with implementation procedures using machine learning techniques to combat on malicious websites and email spams. We concentrated on different approaches, algorithms, techniques to detect the phishing attacks, and a new model is designed and implemented on the dataset and results are evaluated. The evaluation metrics are implemented on datasets based on different algorithms, and results are tabulated and graphical analysis is done.
Courtice, FC - Biographical entry - Index to the AMA archive

Courtice, FC - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Deep Semantic Matching with Foreground Detection and Cycle-Consistency

Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intra-class variations. In this paper, we address weakly supervised semantic matching based on a deep network where only image pairs without manual keypoint correspondence annotations are provided. To facilitate network training with this weaker form of supervision, we 1) explicitly estimate the foreground regions to suppress the effect of background clutter and 2) develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent. We train the proposed model using the PF-PASCAL dataset and evaluate the performance on the PF-PASCAL, PF-WILLOW, and TSS datasets. Extensive experimental results show that the proposed approach performs favorably against the state-of-the-art methods.
Cross-Spectral Face Hallucination via Disentangling Independent Factors

The cross-sensor gap is one of the challenges that have aroused much research interests in Heterogeneous Face Recognition (HFR). Although recent methods have attempted to fill the gap with deep generative networks, most of them suffer from the inevitable misalignment between different face modalities. Instead of imaging sensors, the misalignment primarily results from facial geometric variations that are independent of the spectrum. Rather than building a monolithic but complex structure, this paper proposes a Pose Aligned Cross-spectral Hallucination (PACH) approach to disentangle the independent factors and deal with them in individual stages. In the first stage, an Unsupervised Face Alignment (UFA) module is designed to align the facial shapes of the near-infrared (NIR) images with those of the visible (VIS) images in a generative way, where UV maps are effectively utilized as the shape guidance. Thus the task of the second stage becomes spectrum translation with aligned paired data. We develop a Texture Prior Synthesis (TPS) module to achieve complexion control and consequently generate more realistic VIS images than existing methods. Experiments on three challenging NIR-VIS datasets verify the effectiveness of our approach in producing visually appealing images and achieving state-of-the-art performance in HFR.
Combining detection and tracking for human pose estimation in videos

We propose a novel top-down approach that tackles the problem of multi-person human pose estimation and tracking in videos. In contrast to existing top-down approaches, our method is not limited by the performance of its person detector and can predict the poses of person instances not localized. It achieves this capability by propagating known person locations forward and backward in time and searching for poses in those regions. Our approach consists of three components: (i) a Clip Tracking Network that performs body joint detection and tracking simultaneously on small video clips; (ii) a Video Tracking Pipeline that merges the fixed-length tracklets produced by the Clip Tracking Network to arbitrary length tracks; and (iii) a Spatial-Temporal Merging procedure that refines the joint locations based on spatial and temporal smoothing terms. Thanks to the precision of our Clip Tracking Network and our merging procedure, our approach produces very accurate joint predictions and can fix common mistakes on hard scenarios like heavily entangled people. Our approach achieves state-of-the-art results on both joint detection and tracking, on both the PoseTrack 2017 and 2018 datasets, and against all top-down and bottom-down approaches.
Science Students’ Acceptance to use LINE Application in Laboratory Subject

This phenomenological study aimed to determine if it could be used for academic purposes.In this study, we examined how undergraduate students accepted LINE for classroom-related activities and explored the factors that might affect their intention to use it.Data were collected from 47 undergraduate students of Suan Sunandha Rajabhat University.Data were collected by using a questionnaire and analyzed to see if relationships existed among factors when LINE was used to organize classroom experiences.The findings showed that students have good experience on LINE using.The perceived ease of LINE using, the attitude towards usage and the intention to use LINE were rated above 4.0 (from 5.0).The study also suggested some kinds of LINE-based learning activities preferred by students.Students preferred to use LINE for reading the story posted by the teacher (89.4%), followed by communicating with teacher and classmates (80.8 %).Moreover, students like to send work (Informal) via line (76.6 %).Expressing emotions by sending stickers is another element that students like (74.5 %).The least popular usage is calling through LINE (12.76 %).
Weighted nonlocal total variation in image processing

In this paper, a novel weighted nonlocal total variation (WNTV) method is proposed. Compared to the classical nonlocal total variation methods, our method modifies the energy functional to introduce a weight to balance between the labeled sets and unlabeled sets. With extensive numerical examples in semi-supervised clustering, image inpainting and image colorization, we demonstrate that WNTV provides an effective and efficient method in many image processing and machine learning problems.
Cole, Percival R - Biographical entry - Index to the AMA archive

Cole, Percival R - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels

Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges. We present a novel approach based on neural networks for depth estimation that combines stereo from dual cameras with stereo from a dual-pixel sensor, which is increasingly common on consumer cameras. Our network uses a novel architecture to fuse these two sources of information and can overcome the above-mentioned limitations of pure binocular stereo matching. Our method provides a dense depth map with sharp edges, which is crucial for computational photography applications like synthetic shallow-depth-of-field or 3D Photos. Additionally, we avoid the inherent ambiguity due to the aperture problem in stereo cameras by designing the stereo baseline to be orthogonal to the dual-pixel baseline. We present experiments and comparisons with state-of-the-art approaches to show that our method offers a substantial improvement over previous works.
Visualization of Methods Changeability Based on VCS Data

Software engineers have a wide variety of tools and techniques that can help them improve the quality of their code, but still, a lot of bugs remain undetected. In this paper we build on the idea that if a particular fragment of code is changed too often, it could be caused by some technical or architectural issues, therefore, this fragment requires additional attention from developers. Most teams nowadays use version control systems to track changes in their code and organize cooperation between developers. We propose to use data from version control systems to track the number of changes for each method in a project for a selected time period and display this information within the IDE's code editor. The paper describes such a tool called Topias built as a plugin for IntelliJ IDEA. Its source code is available at this https URL. A demonstration video can be found at this https URL.
Dialogue with Different Fields Surrounding Agriculture through the Development of Research using Simulation Methods

Simulation is a thought process that foresees the uncertain future and guides for a better future by altering the present behavior and social system on the basis of the information received. Currently, when agriculture and forestry are changing rapidly, and its future is uncertain, research using simulation is beneficial for developing regional agriculture and forestry economics. In Japan, this is particularly relevant when the significance of agriculture and forestry is decreasing. It is a critical mission of the regional agricultural and forestry economic associations to not only address the problems in reality but also engender a drastic paradigm shift and innovation within the region. Therefore, it is essential to integrate knowledge from not only narrow economics but also various academic fields and to urgently publish new research results. The present study introduces a research example using multi-agent simulation and solution of a traveling salesman’s problem using several simulation approaches. Furthermore, it also recommends further studies using simulation in the future by exhibiting many useful experiences as encountered by the author in the process.
The Influence of Trust and Confidence of Claim Successfulness on Individual Attitude in Committing Motor Insurance Fraud

The objective of this paper is to examine the influence of trust and confidence of claim successfulness on individual attitude in committing motor insurance fraud.The sample for this study consisted of teachers from selected schools in Selangor.Questionnaires were used to collect the data.The results showed that trust in the third party has a significant but negative relationship with attitude, while the confidence of claim successfulness has a significant positive relationship with attitude.Confidence of claim successfulness shows stronger impact in determining individual attitude to commit motor insurance fraud.Findings from this research are important for the insurance industry and Bank Negara Malaysia (BNM) to understand customers' attitude in committing motor insurance fraud and identify steps in reducing fraud cases.It is also beneficial for customers who indirectly become victims of increased premium payment due to insurance fraud.
THE POSSIBILITIES OF THE LECTURES WITH PRESENTATIONS USED TO IMPROVE THE QUALITY OF TRAINING OF ENGINEERING STUDENTS

The article contains the aspects used to improve the quality of education for engineering students specialties. There is necessity of using a modern form of lecturing with the use of slides, videos, interactive whiteboards. It is noticed based on the results of the conducted polling of students and professors of the technical University. Such form of lectures are lectures with presentations. There is variety of forms of presentation of lecture material in their development from 60 – 70 years of the twentieth century to the present day considered in the article, the assessment of the advantages and disadvantages of each of them is also given. The features of lectures-presentations in accordance with the originality of different courses and specializations of students (bachelor, specialist, master) for technical University are revealed. Possibilities and advantages of lectures-presentations for students of engineering specialties are estimated.
Shaping Digital Literacy in Knowledge Society

The paper argues that digital literacy includes the ability to search, select and process information in computer and network sources, content creation, and Internet safety skills. Educational approaches, training and learning methods which are parts of educational technologies geared towards shaping digital skills so important in the modern world. The article is based on the survey “Digital Media Literacy in the context of “Knowledge Society”: state and challenges” KII-06-H25/4, team leader V. Milenkova. The main conclusions are that education and technological environment, are crucial in shaping digital skills and more efforts needed to improve the educational methods to create digital competences of learners.
Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques

This is the report for the PRIM project in Telecom Paris. This report is about applications based on spatial-frequency transform and deep learning techniques. In this report, there are two main works. The first work is about the enhanced JPEG compression method based on deep learning. we propose a novel method to highly enhance the JPEG compression by transmitting fewer image data at the sender's end. At the receiver's end, we propose a DC recovery algorithm together with the deep residual learning framework to recover images with high quality. The second work is about adversarial examples defenses based on signal processing. We propose the wavelet extension method to extend image data features, which makes it more difficult to generate adversarial examples. We further adopt wavelet denoising to reduce the influence of the adversarial perturbations. With intensive experiments, we demonstrate that both works are effective in their application scenarios.
Optical Flow in Dense Foggy Scenes using Semi-Supervised Learning

In dense foggy scenes, existing optical flow methods are erroneous. This is due to the degradation caused by dense fog particles that break the optical flow basic assumptions such as brightness and gradient constancy. To address the problem, we introduce a semi-supervised deep learning technique that employs real fog images without optical flow ground-truths in the training process. Our network integrates the domain transformation and optical flow networks in one framework. Initially, given a pair of synthetic fog images, its corresponding clean images and optical flow ground-truths, in one training batch we train our network in a supervised manner. Subsequently, given a pair of real fog images and a pair of clean images that are not corresponding to each other (unpaired), in the next training batch, we train our network in an unsupervised manner. We then alternate the training of synthetic and real data iteratively. We use real data without ground-truths, since to have ground-truths in such conditions is intractable, and also to avoid the overfitting problem of synthetic data training, where the knowledge learned on synthetic data cannot be generalized to real data testing. Together with the network architecture design, we propose a new training strategy that combines supervised synthetic-data training and unsupervised real-data training. Experimental results show that our method is effective and outperforms the state-of-the-art methods in estimating optical flow in dense foggy scenes.
Are They There Yet? Revisiting the Accuracy Level of Computer-Generated Translation

Speed, cost, and quality have been the central issues in the translation industry. While computers can offer fast and economical translation, their ability to produce accurate translation at a publishable level like human translators may do is still questionable. In the present study, the author linguistically evaluated the accuracy of English to Indonesian translation generated by Google Translate. Freely accessible English texts of various types were chosen to heuristically exemplify the units of analysis. The results of analysis lead to the conclusion that although Google’s Neural Machine Translation system is quite an improvement to the conventional phrase-based translation system, highly accurate translation does not seem to be achieved by Google Translate in the near future.
Study on Worldwide Development and Trends of Quantum Technologies Based on Patent Data

Quantum technologies attracted much attention for their disruptive potential in last two decades.The article analyzes the worldwide patent landscape for quantum technologies based on data extracted from Derwent Innovation and Web of Science.The quantum technologies were grouped into three distinct technology areas of quantum computing, quantum communication and quantum sensing, to demonstrate detailed development and trends respectively.It shows that quantum technology is a highly competitive research field, and United States, China and Japan are the most prominent countries, in particular China made a great progress in recent years.United States has a significant advantage in the field of quantum computing, which is the most promising field, meanwhile China has a significant advantage in the field of quantum communication and succeeds in launching a quantum satellite.
Prediction Of Pm10 Concentrations Using Logistic Regression Analysis: Case Study In Jerantut

Particulate matter (PM10) can cause several serious negative health effects to humans when it is present in the environment. Thus, it is important for us to forecast its concentration levels in the environment so that we can reduce the risk of exposure towards particulate matter. Secondary data on the concentration of PM10, sulphur dioxide (SO2), nitrogen dioxide (NO2), ground level ozone (O3), carbon monoxide (CO) along with temperature and relative humidity at Jerantut monitoring stations between 2010 to 2012 obtained from Department of Environment. The main objective of this study is to describe the relationship between PM10 with other gases and weather conditions by using correlation. It also aims to determine the best prediction categories. Furthermore, this research aims to find a model for predicting the concentration of PM10 using logistic regression. PM10 and O3 at Jerantut monitoring station were found to have a strong positive correlation. The best logistic regression model was obtained at Jerantut station in 2010 with an R2 value of 0.565. The best prediction category for Jerantut monitoring stations was shown to be healthy with a correct percentage of more than 85% obtained from the analysis of the overall and annual results between 2010 to 2012.
A Conceptual Framework for META ProBaca Module in Improving Reading Comprehension and Reading Fluency among Dyslexia Pupils

The limited mastery of reading skills will forestall the success of pupils not only in both the classroom and school environment but will also influence the pupil's life.Nonetheless, the ability to master reading skills is an issue for dyslexic pupils.This study designs META ProBaca module based on metacognitive theory, interactive reading model and the standard curriculum of primary education for the pupils of special need.This article examines the literature review on dyslexia, metacognitive, reading interactive, the standard curriculum of primary education for the pupils of special need, reading comprehension and reading fluency.This discussion aspires to configure the conceptual framework of the META ProBaca Module to enhance reading comprehension skills and reading fluency among dyslexic pupils in the Primary Integration Special Education Program.Predicated on the conceptual framework, the study is split into two phases namely the module development phase and module evaluation phase.In the development phase, there are one theory and two models that are adopted Flavell's metacognitive theory (1976), Interactive Reading Model (1977) and The Special National Curriculum Standard for Primary Education Curriculum (KSSRPK) Learning Problem (2017).The Four elements that are implemented in the creation of META ProBaca Module are knowledge, experience, targets and strategies.This is a homogeneous quasi-experimental type of study.The researcher splits the experimental group into two groups that are treatment and control group.The treatment group will be taught using the META ProBaca Module.The result of using the META ProBaca Module is anticipated to assist dyslexic pupils to master the skills of reading comprehension and fluency.
PICTO TEXTUAL GLOSSES IN TEACHING VOCABULARY

The purpose of this research is to know english vocabulary mastery incresed by using picto textual glossesin every meeting in class. The object of this research is 8th grade students in Mutiara 1 junior high school. the main focus in teaching english is to improving students english vocabulary mastery . By improving vocabulary mastery, students will be improving english material easier. The data in this research come from interaction between teacher and students in learning process and it is as students learning activity in the class. the research instruments used test questions.The method of data collecting of the resarch is by using pretest-treatment-postest in the class. Based on pre test and post test, they showed some improvement on process of get new vocabulary from learning with that method. . The result or the conclusion can be got from the resarch by using picto textual glosses method, data from pre test and post test showed that there were some benefits of picto textual glosses method: 1. Develop of students’ memory ability to increasing vocabulary, 2. Having a fun learning situation. The data showed HI is accepted, because Asymp. Sig. (2-tailed) worth 0,000. because the value of 0,000 is smaller; of, &lt;0.05. Keywords: Vocabulary, Vocabulary mastery, Picto textual glosses, SMP Mutiara I, 8th grade.
Development of Web Application in English Subject

An objective of this research paper is to develop of web application in English subject, and to study the students’ satisfaction in using the created web application. The sample of this research was 87 students who studies in Grade 7 at the Bangpakok Wittayakom School, Thailand from purposive sampling method. The research tools included the web application in English subject, and the students’ satisfaction form on the created media. As a result, the average satisfaction score is 4.53 and standard deviation is 0.57 in a good rate.