Reviewerly/exHarmony huggingface dataset:
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.
Detecting Information Relays in Deep Neural Networks

Deep learning of artificial neural networks (ANNs) is creating highly functional processes that are, unfortunately, nearly as hard to interpret as their biological counterparts. Identification of functional modules in natural brains plays an important role in cognitive and neuroscience alike, and can be carried out using a wide range of technologies such as fMRI, EEG/ERP, MEG, or calcium imaging. However, we do not have such robust methods at our disposal when it comes to understanding functional modules in artificial neural networks. Ideally, understanding which parts of an artificial neural network perform what function might help us to address a number of vexing problems in ANN research, such as catastrophic forgetting and overfitting. Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here, we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity: the relay information $I_R$. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. Combined with a greedy search algorithm, relay information can be used to identify computational modules in neural networks. We also show that the functionality of modules correlates with the amount of relay information they carry.
Deep Learning and Computational Physics (Lecture Notes)

These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics. The main objective of these notes is to introduce a student who is familiar with concepts in linear algebra and partial differential equations to select topics in deep learning. These lecture notes exploit the strong connections between deep learning algorithms and the more conventional techniques of computational physics to achieve two goals. First, they use concepts from computational physics to develop an understanding of deep learning algorithms. Not surprisingly, many concepts in deep learning can be connected to similar concepts in computational physics, and one can utilize this connection to better understand these algorithms. Second, several novel deep learning algorithms can be used to solve challenging problems in computational physics. Thus, they offer someone who is interested in modeling a physical phenomena with a complementary set of tools.
Benchmarking the Robustness of LiDAR Semantic Segmentation Models

When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.
Data Valuation Without Training of a Model

Many recent works on understanding deep learning try to quantify how much individual data instances influence the optimization and generalization of a model. Such attempts reveal characteristics and importance of individual instances, which may provide useful information in diagnosing and improving deep learning. However, most of the existing works on data valuation require actual training of a model, which often demands high-computational cost. In this paper, we provide a training-free data valuation score, called complexity-gap score, which is a data-centric score to quantify the influence of individual instances in generalization of two-layer overparameterized neural networks. The proposed score can quantify irregularity of the instances and measure how much each data instance contributes in the total movement of the network parameters during training. We theoretically analyze and empirically demonstrate the effectiveness of the complexity-gap score in finding `irregular or mislabeled' data instances, and also provide applications of the score in analyzing datasets and diagnosing training dynamics. Our code is publicly available at https://github.com/JJchy/CG_score
Correction to: A Systematic Literature Review on Cybersecurity Threats of Virtual Reality (VR) and Augmented Reality (AR)

In an earlier published version of this paper, a part of the acknowledgement was missing. This has now been corrected. The correction chapter and the book has been updated with the change.
Communications: PhotonicsViews 1/2023

PhotonicsViewsVolume 20, Issue 1 p. 12-17 CommunicationsFree Access Communications: PhotonicsViews 1/2023 First published: 03 January 2023 https://doi.org/10.1002/phvs.202370104AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume20, Issue1February/March 2023Pages 12-17 RelatedInformation
Further Improving Weakly-supervised Object Localization via Causal Knowledge Distillation

Previous weakly-supervised object localization (WSOL) methods aim to expand activation map discriminative areas to cover the whole objects, yet neglect two inherent challenges when relying solely on image-level labels. First, the ``entangled context'' issue arises from object-context co-occurrence (\eg, fish and water), making the model inspection hard to distinguish object boundaries clearly. Second, the ``C-L dilemma'' issue results from the information decay caused by the pooling layers, which struggle to retain both the semantic information for precise classification and those essential details for accurate localization, leading to a trade-off in performance. In this paper, we propose a knowledge-guided causal intervention method, dubbed KG-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention, which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the disentangled object feature, we introduce a multi-source knowledge guidance framework to strike a balance between absorbing classification knowledge and localization knowledge during model training. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness of KG-CI-CAM in learning distinct object boundaries amidst confounding contexts and mitigating the dilemma between classification and localization performance.
Renewable Energy Prediction Market Participation Type Photovoltaics Prediction Model Development

For stable operation of the power system, predicting the amount of renewable energy generated can help establish a reasonable power generation plan.Solar photovoltaic power generation cannot be arbitrarily controlled and is directly affected by climatic conditions.Therefore, this study introduces a solar photovoltaic power generation forecasting technique that can reduce forecasting errors and maximize settlement income.This power generation forecasting technique uses easily accessible public meteorological data.Participation in the renewable energy generation prediction market is expected to be activated through this study, and various business models could possibly be developed by lowering the entry barrier for participation in the solar photovoltaic power generation prediction market.
Why Ovals in Eliciting Intervals?

To elicit people’s opinions, we usually ask them to mark their degree of satisfaction on a scale—e.g., from 0 to 5 or from 0 to 10. Often, people are unsure about the exact degree: 7 or 8? To cover such situations, it is desirable to elicit not a single value but an interval of possible values. However, it turns out that most people are not comfortable with marking an interval. Empirically, it turned out that the best way to elicit an interval is to ask them to draw an oval whose intersection with the 0-to-10 line is the desired interval. Surprisingly, this seemingly more complex 2-D task is easier for most people that a seemingly simpler 1-D task of drawing an interval. In this paper, we provide a possible explanation of why eliciting an interval-related oval is more efficient than eliciting the interval itself.
Saliency-Aware Spatio-Temporal Artifact Detection for Compressed Video Quality Assessment

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.
An Inference Method of Influence Parameters for a Physical Calculation Model of Photovoltaic Power Generation

To cope with the fluctuating and irregular production of solar electricity, a renewable energy generation prediction system, which gives incentives when the forecast error is low, was implemented in Korea in October 2021.Most previous studies have focused on predicting solar irradiance, rather than power output.Physical models are used when the research range is broad, but the accuracy of these models is dependent on the extent of known design parameters.Thus, the purpose of this study was to infer unknown parameters and to examine the effect of applying inferred values on physical model performance.According to sensitivity analysis, azimuth, loss factor, tilt, and albedo had high contributions to the photovoltaic power output, in that order, and together, these parameters accounted for 97% of the output variability.The best prediction accuracy was achieved when azimuth and tilt, which have a high level of interaction, were inferred simultaneously, and an inferred loss factor was also used.As a result, the mean absolute error was 0.079 MWh.The findings of this study may be applied to inferring installation information for solar systems located in abandoned mines and idle sites.
Applications of Machine Learning Techniques to Detect Phishing Websites

Phishing attacks are a common way for hackers to obtain sensitive and valuable information from unsuspecting users. These attacks often target critical data such as passwords and financial details. To combat this threat, cybersecurity professionals are constantly searching for reliable and effective techniques for detecting phishing websites. This project investigates the use of machine learning algorithms to identify phishing URLs by extracting and analyzing various features of both legitimate and phishing URLs. The goal is to create a blacklist of known phishing websites that can alert individuals when they browse or access a potentially dangerous site. The project will compare the performance of four machine learning algorithms such as Ensemble Adaboost Classifier, Multi-Layer Perceptron Classifier, Stochastic Gradient Descent classifier, and XGBoost - based on their accuracy, speed, and other factors.
Student-Centered Online Assessment in Foreign Language Classes

In 2020 educational institutions in many countries had to implement online learning due to quarantine restrictions caused by the coronavirus pandemic. The research aims to study the peculiarities of the distance learning technologies used by Ukrainian foreign language teachers for formative and summative assessment and their impact on students. At the end of 2020, a survey about online resources used for creating different types of tasks for foreign language classes in Ukraine was conducted by the authors of the study, and the main characteristics of the most popular online resources were analyzed. According to the survey, to create assessment tasks the majority of Ukrainian teachers use the following platforms: Kahoot, Google Forms, Quizlet, Classtime, Quizizz, Socrative, Quizalize, Gimkit, Blooket, Liveworksheets, and Wizerme. Some of these resources provide a strong element of competition that makes them perfect for formative assessment, while the others better suit summative assessment as they have a clear interface without distracting elements. When designing online tasks for the assessment, teachers should also take into consideration the possibilities online resources provide to reduce their students’ anxiety and stress caused by performing test tasks.
DEPTH MEASUREMENT OF HUMANS USING WEB CAM

Artificial Intelligence has gradually become the mainstream in the detection algorithms because of its powerful feature extraction ability and adaptive ability. However, how to guarantee the accuracy and speed is still a huge challenge in the field of depth measurement. We implement face mesh from media pipe (module) which is developed by google. Media Pipe Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning (ML) to infer the 3D surface geometry, requiring a single camera input without the need for a dedicated depth sensor. Using face mesh, we take points from face and depth is measured. This makes project more accurate and speed. In our project we measure real time 3D depth, using these we make our text flexible with depth measured from webcam. When we move closer to our webcam text size reduces, and when we go far from webcam, text size is enhanced, which is accordance to the convenience to our end users. When we implement this in real life, end users can see the captions comfortably from any place they are watching the screen only. Keywords: OpenCV, Mediapipe , FaceMesh, Windows, Python.
Mnemonic Learning Technique in Review Writing Skills

Students' ability in writing reviews is one of the writing skills objectives as stated in the Standard-Based Curriculum and Assessment Document (DSKP) of the Malay language subject.The skills of writing reviews involve the process of transferring information that is heard, watched, or read in a form of a review.For some students, mastering the skills of writing a review is difficult because they lack of vocabulary, ideas, information transfer skills and paragraph arrangement skills.As a result, the reviews produced by students are too short, do not fulfil the requirements of the title and are unclear in terms of the descriptions of their main and supporting ideas.However, the failure of students in mastering it can be overcome by selecting effective learning methods to stimulate students' thinking skills.To overcome this, the learning method using the mnemonic technique, a process of remembering information more easily, can be implemented.The mnemonic technique implemented in this study is an acronym technique that can help students to quickly produce an outline and write a good review.In addition, the acronym technique used can also improve the skills of finding main ideas and processing supporting ideas of reviews among students.
Utilization of Teaching Language Skills Across the Curriculum for Developing Language Skills to Rich Academic Content in All Subjects

This study is based on the involvement of language skills among the students in academics. Language skills improvement can help individuals manage their communication with others, increasing their confidence level. The objectives have been developed to determine the need for language skill development in the curriculum. High-level negotiations with native languages are managed through academic language improvement. The application of Krashen’s monitor model and Hardlry's theory of language development can help manage the language learning opportunities for students in academics. The use of the secondary research method has helped uncover the importance of using language skills in future development. The qualitative analysis has helped in analyzing the data and finding appropriate results for the study. This study aimed to discover students' creativity in order to maintain language skill development. The inclusion of issues such as lack of interest among the students is affecting the proficiency of the educational system. Moreover, the use of the language skill helps in managing communication, through which the ideas of the students are increased. This aids in the development of critical thinking processes in students in order to improve their skills.
Covid-19 Control: Face Mask Detection Using Deep Learning for Balanced and Unbalanced Dataset

Facemask wearing is becoming a norm in our daily lives to curb the spread of Covid-19. Ensuring facemasks are worn correctly is a topic of concern worldwide. It could go beyond manual human control and enforcement, leading to the spread of this deadly virus and many cases globally. The main aim of wearing a facemask is to curtail the spread of the covid-19 virus, but the biggest concern of most deep learning research is about who is wearing the mask or not, and not who is incorrectly wearing the facemask while the main objective of mask wearing is to prevent the spread of the covid-19 virus. This paper compares three state-of-the- art object detection approaches: Haarcascade, Multi-task Cascaded Convolutional Networks (MTCNN), and You Only Look Once version 4 (YOLOv4) to classify who is wearing a mask, who is not wearing a mask, and most importantly, who is incorrectly wearing the mask in a real-time video stream using FPS as a benchmark to select the best model. Yolov4 got about 40 Frame Per Seconds (FPS), outperforming Haarcascade with 16 and MTCNN with 1.4. YOLOv4 was later used to compare the two datasets using Intersection over Union (IoU) and mean Average Precision (mAP) as a comparative measure; dataset2 (balanced dataset) performed better than dataset1 (unbalanced dataset). Yolov4 model on dataset2 mapped and detected images of masks worn incorrectly with one correct class label rather than giving them two label classes with uncertainty in dataset1, this work shows the advantage of having a balanced dataset for accuracy. This work would help decrease human interference in enforcing the COVID-19 face mask rules and create awareness for people who do not comply with the facemask policy of wearing it correctly. Hence, significantly reducing the spread of COVID-19.
ExploreADV: Towards exploratory attack for Neural Networks

Although deep learning has made remarkable progress in processing various types of data such as images, text and speech, they are known to be susceptible to adversarial perturbations: perturbations specifically designed and added to the input to make the target model produce erroneous output. Most of the existing studies on generating adversarial perturbations attempt to perturb the entire input indiscriminately. In this paper, we propose ExploreADV, a general and flexible adversarial attack system that is capable of modeling regional and imperceptible attacks, allowing users to explore various kinds of adversarial examples as needed. We adapt and combine two existing boundary attack methods, DeepFool and Brendel\&Bethge Attack, and propose a mask-constrained adversarial attack system, which generates minimal adversarial perturbations under the pixel-level constraints, namely ``mask-constraints''. We study different ways of generating such mask-constraints considering the variance and importance of the input features, and show that our adversarial attack system offers users good flexibility to focus on sub-regions of inputs, explore imperceptible perturbations and understand the vulnerability of pixels/regions to adversarial attacks. We demonstrate our system to be effective based on extensive experiments and user study.