Reviewerly/exHarmony huggingface dataset:
Detection of Dangerous Situations using Deep Learning Model with Relational Inference

Crime has become one of the major problems in modern society.Even though visual surveillances through closed-circuit television (CCTV) is extensively used for solving crime, the number of crimes has not decreased.This is because there is insufficient workforce for performing 24-hour surveillance.In addition, CCTV surveillance by humans is not efficient for detecting dangerous situations owing to accuracy issues.In this paper, we propose the autonomous detection of dangerous situations in CCTV scenes using a deep learning model with relational inference.The main feature of the proposed method is that it can simultaneously perform object detection and relational inference to determine the danger of the situations captured by CCTV.This enables us to efficiently classify dangerous situations by inferring the relationshi p between detected objects (i.e., distance and position).Experimental results demonstrate that the proposed method outperforms existing methods in terms of the accuracy of image classification and the false alarm rate even when object detection accuracy is low.
Technology-Enhanced Learning and Teaching in COVID-19 Era: Challenges and Recommendations

Technology-enhanced learning and teaching methods have been in literature and for many years now. Many educational institutes all over the world have been using these methods to deliver their programs and degrees. Nevertheless, some institutes are not very keen on using technology in some disciplines, and deliver their programs in a traditional way for a number of reasons, especially if these have been successful and well-attended (i.e. popular) by students. In the current era, where COVID-19 pandemic has disrupted every corner of our life including higher education, technology has become a critical success factor to reduce the negative impact of this pandemic. Accordingly, it is now no longer an option to opt out from using technology in learning and teaching. This doesn’t just refer to providing (dumping) contents to students digitally, but to facilitate learning and deliver engaging and highly interactive experience to compensate for lack of face-to-face interaction between the students and their teachers and also amongst the students themselves. The use of technology in education due to COVID-19 pandemic, however, has confronted by a number of challenges. In some cases, the focus was shifted to the contents (documents, videos…etc.) rather than interactivity and student engagement. Furthermore, the students were highly overwhelmed with contents in a short period of time, which has caused anxiety, dissatisfaction and performance issues. In this paper, examples of teaching methods based on the use of technology that are employed during the lockdown period are provided. Moreover, a number of subsequent challenges due to current situation are discussed, and recommendations for implementation and best practice are shared. Also a proposal for a flipped delivery model to move forward is provided and discussed. Anecdotal student feedback has shown that the used methods and techniques were really helpful and have boosted student learning and enthusiasm in this difficult time.
Variational Fuzzy Superpixel Segmentation

This article presents a novel variational model based on fuzzy clustering and total variation regularization for superpixel segmentation. Compared with the classical hard-labeled methodologies, our approach gives soft results via the fuzzy membership function, and moreover, the use of total variation provides additional information that can enhance the superpixel regularity, which in turn improves the segmentation performance. To efficiently minimize the energy functional of the proposed model, we adopt an alternating direction method of multipliers with the modified Chambolle’s fast duality projection algorithm. Our algorithm can generate regular and compact superpixels with high segmentation accuracy, satisfactory boundary adherence, and low computational cost. Comparative experimental results with the current state-of-the-art approaches reveal the superior performance of the proposed method.
A Pragmatic Approach to On-device Incremental Learning System with Selective Weight Updates

Incremental learning is drawing attention to widen capabilities of device-AI. Previous works have researched to reduce numerous computations and memory accesses required for the training process of IL, but they could not show a noticeable improvement in the weight gradient computation (WGC) phase. Therefore, we propose a selective weight update technique that searches for critical weights to be updated by applying the IL algorithm that training per-task binary masks. Also, we introduce a novel dataflow for the implementation of selective WGC on typical NPUs with minimum overheads. On average, our system shows a 2.9× speed up and 2.5× energy efficiency in WGC without degrading training quality.
Assessing observability of chaotic systems using Delay Differential Analysis

Observability can determine which recorded variables of a given system are optimal for discriminating its different states. Quantifying observability requires knowledge of the equations governing the dynamics. These equations are often unknown when experimental data are considered. Consequently, we propose an approach for numerically assessing observability using Delay Differential Analysis (DDA). Given a time series, DDA uses a delay differential equation for approximating the measured data. The lower the least squares error between the predicted and recorded data, the higher the observability. We thus rank the variables of several chaotic systems according to their corresponding least square error to assess observability. The performance of our approach is evaluated by comparison with the ranking provided by the symbolic observability coefficients as well as with two other data-based approaches using reservoir computing and singular value decomposition of the reconstructed space. We investigate the robustness of our approach against noise contamination.
Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy

Radioisotope identification (RIID) algorithms for gamma-ray spectroscopy aim to infer what isotopes are present and in what amounts in test items. RIID algorithms either use all energy channels in the analysis region or only energy channels in and near identified peaks. Because many RIID algorithms rely on locating peaks and estimating each peak’s net area, peak location and peak area estimation algorithms continue to be developed for gamma-ray spectroscopy. This paper shows that approximate Bayesian computation (ABC) can be effective for peak location and area estimation. Algorithms to locate peaks can be applied to raw or smoothed data, and among several smoothing options, the iterative bias reduction algorithm (IBR) is recommended; the use of IBR with ABC is shown to potentially reduce uncertainty in peak location estimation. Extracted peak locations and areas can then be used as summary statistics in a new ABC-based RIID. ABC allows for easy experimentation with candidate summary statistics such as goodness-of-fit scores and peak areas that are extracted from relatively high dimensional gamma spectra with photopeaks (1024 or more energy channels) consisting of count rates versus energy for a large number of gamma energies.
Learnable Optimal Sequential Grouping for Video Scene Detection

Video scene detection is the task of dividing videos into temporal semantic chapters. This is an important preliminary step before attempting to analyze heterogeneous video content. Recently, Optimal Sequential Grouping (OSG) was proposed as a powerful unsupervised solution to solve a formulation of the video scene detection problem. In this work, we extend the capabilities of OSG to the learning regime. By giving the capability to both learn from examples and leverage a robust optimization formulation, we can boost performance and enhance the versatility of the technology. We present a comprehensive analysis of incorporating OSG into deep learning neural networks under various configurations. These configurations include learning an embedding in a straight-forward manner, a tailored loss designed to guide the solution of OSG, and an integrated model where the learning is performed through the OSG pipeline. With thorough evaluation and analysis, we assess the benefits and behavior of the various configurations, and show that our learnable OSG approach exhibits desirable behavior and enhanced performance compared to the state of the art.
Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph

Webly supervised learning becomes attractive recently for its efficiency in data expansion without expensive human labeling. However, adopting search queries or hashtags as web labels of images for training brings massive noise that degrades the performance of DNNs. Especially, due to the semantic confusion of query words, the images retrieved by one query may contain tremendous images belonging to other concepts. For example, searching 'tiger cat' on Flickr will return a dominating number of tiger images rather than the cat images. These realistic noisy samples usually have clear visual semantic clusters in the visual space that mislead DNNs from learning accurate semantic labels. To correct real-world noisy labels, expensive human annotations seem indispensable. Fortunately, we find that metadata can provide extra knowledge to discover clean web labels in a labor-free fashion, making it feasible to automatically provide correct semantic guidance among the massive label-noisy web data. In this paper, we propose an automatic label corrector VSGraph-LC based on the visual-semantic graph. VSGraph-LC starts from anchor selection referring to the semantic similarity between metadata and correct label concepts, and then propagates correct labels from anchors on a visual graph using graph neural network (GNN). Experiments on realistic webly supervised learning datasets Webvision-1000 and NUS-81-Web show the effectiveness and robustness of VSGraph-LC. Moreover, VSGraph-LC reveals its advantage on the open-set validation set.
Sensitivity of BLANC to human-scored qualities of text summaries

We explore the sensitivity of a document summary quality estimator, BLANC, to human assessment of qualities for the same summaries. In our human evaluations, we distinguish five summary qualities, defined by how fluent, understandable, informative, compact, and factually correct the summary is. We make the case for optimal BLANC parameters, at which the BLANC sensitivity to almost all of summary qualities is about as good as the sensitivity of a human annotator.
Artificial intelligence driven resiliency with machine learning and deep learning components

With the demand of Power to make a decision with minimum risk based on Knowledge of Information from Data accumulated in Big Data (i.e.Figure1) [1] repository, we need real-time processing of the data coming to us from Omni-direction perspective.These data are centric around the BD and get deposited at the speed of Internet-driven mainly by IoT.Thus, at this stage, we need to understand, what is the Big Data (BD) and why it matters when we are bringing the AI into play toward real-time processing of data with infrastructure around the Big data is a term that describes the large volume of data -both structured and unstructured -that inundates a business on a day-to-day basis.Big data usually has three characteristics.They are volume (the amount of data), velocity (the rate at which the data is received), and variety (types of data).Each of these three characteristics is briefly defined here as:1. Volume: Organizations collect data from a variety of sources, including business transactions, social media, and information from sensor or machine-to-machine data.In the past, storing it would've been a problembut new technologies (such as Hadoop) have eased the burden. Velocity:Data streams in at an unprecedented speed and must be dealt with in a timely manner.RFID tags, sensors, and smart metering are driving the need to deal with torrents of data in near-real-time. Variety:Data comes in all types of formatsfrom structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data, and financial transactions.
Universality, intertwiners and black hole information

The central question in this article is how information does leak out from black holes. Relying on algebraic arguments and the concept of superselection sectors, we propose the existence of certain operators whose correlations extend across the black hole atmosphere and range into the interior. Contained in the full algebra, these black hole intertwiners will not belong to the subalgebra describing semiclassical bulk physics. We study this proposal in the context of operator reconstructions for code spaces containing a large number of microstates. As long as the atmosphere is excluded from a particular subsystem, the global state seen under the action of the associated algebra is maximally mixed and therefore described by a single classical background. Once the relevant correlations are encoded, i.e. if the algebra is sufficiently enlarged, perfect state distinguishability becomes possible. We arrive at this by computing the von Neumann entropy which may explain the result obtained by applying the quantum extremal surface prescription to the mixed state. We then examine these insights in the context of black hole evaporation and argue that information is transferred to the radiation via black hole intertwiners. We derive the Page curve. The mechanism above suggests that black hole information is topologically protected. An infalling observer would experience no drama. This may resolve the unitarity problem without running into any firewall or state puzzle, the latter being evident in generalized entropy computations. We also examine the question of how certain wormhole topologies may be understood given these findings. We argue that their occurrence in gravity replica computations may be related to the maximal correlation between radiation and atmosphere surrounding the old black hole. This may suggest a connection between topology change and near horizon quantum gravitational effects.
A hybrid of CNN and LSTM methods for securing web application against cross-site scripting attack

&lt;span&gt;Cross-site scripting (XSS) is today one of the biggest threatthat could targeting the Web application. Based on study published by the open web applications security project (OWASP), XSS vulnerability has been present among the TOP 10 Web application vulnerabilities.Still,an important security-related issue remains how to effectively protect web applications from XSS attacks.In first part of this paper, a method for detecting XSS attack was proposed by combining &lt;/span&gt;&lt;span lang="EN-GB"&gt;convolutional&lt;/span&gt;&lt;span&gt; neural network (CNN) with long short term memories&lt;strong&gt; (&lt;/strong&gt;LSTM), Initially, pre-processing was applied to XSS Data Set by decoding, generalization and tokanization, and then word2vec was applied to convert words into word vectors in XSS payloads. And then we use the combination CNN with LSTM to train and test word vectors to produce a model that can be used in a web application. Based on the obtaned results, it is observed that the proposed model achevied an excellent result with accuracy of 99.4%.&lt;/span&gt;
Least Squares Relativistic Generative Adversarial Network for Perceptual Super-Resolution Imaging

Currently, deep-learning-based methods have been the most popular super-resolution techniques owing to the improvement of super-resolution performance. However, they are still lack perceptual fine details and thus result in unsatisfying visual quality. This article proposes a novel method for high-quality perceptual super-resolution imaging, named SRLRGAN-SN. It aims to recovery visually plausible images with perceptual texture details by using the least squares relativistic generative adversarial network (GAN). The method applies the spectral normalization on the network with the target of enhancing the performance of GAN for super-resolution task. The least squares relativistic discriminator is designed to drive reconstruction images approximating high-quality perceptual manifold. Besides, a novel perceptual loss assembly is proposed to preserve structural texture details as much as possible. Results of experiment show that our method can not only recovery more visually realistic details, but also outperforms other popular methods regarding to quantitative metrics and perceptual evaluations.
A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem

In the era of big data, the size and complexity of the data are increasing especially for those stored in remote locations, and whose difficulty is further increased by the ongoing rapid accumulation of data scale. Real-world optimization problems present new challenges to traditional intelligent optimization algorithms since the traditional serial optimization algorithm has a high computational cost or even cannot deal with it when faced with large-scale distributed data. Responding to these challenges, a distributed cooperative evolutionary algorithm framework using Spark (SDCEA) is first proposed. The SDCEA can be applied to address the challenge due to insufficient computing resources. Second, a distributed quantum-behaved particle swarm optimization algorithm (SDQPSO) based on the SDCEA is proposed, where the opposition-based learning scheme is incorporated to initialize the population, and a parallel search is conducted on distributed spaces. Finally, the performance of the proposed SDQPSO is tested. In comparison with SPSO, SCLPSO, and SALCPSO, SDQPSO can not only improve the search efficiency but also search for a better optimum with almost the same computational cost for the large-scale distributed optimization problem. In conclusion, the proposed SDQPSO based on the SDCEA framework has high scalability, which can be applied to solve the large-scale optimization problem.
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond

Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense. The majority of LiRPA-based methods focus on simple feed-forward networks and need particular manual derivations and implementations when extended to other architectures. In this paper, we develop an automatic framework to enable perturbation analysis on any neural network structures, by generalizing existing LiRPA algorithms such as CROWN to operate on general computational graphs. The flexibility, differentiability and ease of use of our framework allow us to obtain state-of-the-art results on LiRPA based certified defense on fairly complicated networks like DenseNet, ResNeXt and Transformer that are not supported by prior works. Our framework also enables loss fusion, a technique that significantly reduces the computational complexity of LiRPA for certified defense. For the first time, we demonstrate LiRPA based certified defense on Tiny ImageNet and Downscaled ImageNet where previous approaches cannot scale to due to the relatively large number of classes. Our work also yields an open-source library for the community to apply LiRPA to areas beyond certified defense without much LiRPA expertise, e.g., we create a neural network with a probably flat optimization landscape by applying LiRPA to network parameters. Our opensource library is available at https://github.com/KaidiXu/auto_LiRPA.
Extracting Radiotherapy Treatment Details Using Neural Network-Based Natural Language Processing

Most radiotherapy (RT) treatment details are either directly entered into the EMR as free text, or are converted to free text when transferred from treatment planning systems and are therefore not directly extractable in analyzable units, limiting monitoring and investigation efforts. Despite recent significant advances in natural language processing (NLP), there are currently no robust NLP models for automated RT treatment detail extraction. We developed neural models using contextualized embedding representations for named entity recognition of RT details. Data consisted of 270 clinical descriptions of RT regimens from HemOnc.org, 79 complete breast and colorectal cancer clinician notes, and s from breast, prostate, and lung cancer patients from a state cancer registry. Annotation guidelines for RT details were developed with experts in radiation oncology and NLP and are available at github.com/RTParse/RTAnnot. Data were split into train, development, and test sets. Flair, a neural network pre-trained on 1 billion words to learn a multi-layer Long Short-Term Memory (LSTM) language model with contextual character and word embeddings (Akbik et al., COLING 2018), was used to train entity extractors for RT dose, fraction number, fraction frequency, boost, and treatment site. Best performing models on the development set were used for the final testing. Parameters/hyperparameters: LSTM layers (1-2), hidden size (128-512), dropout (0-0.5), epochs (50), mini-batch size (16-64), learning rate (0.1-0.15). Performance for each model is below. The best result was for RT dose, likely because of its standard, unique reporting. The worst results were for boost, likely due to few entity instances. The F1 score approximated or exceeded the inter-annotator agreement, which serves as the system target, for RT dose, fraction number, and treatment site. Taking advantage of language models pre-trained on non-medical text and using contextual word and character embeddings, neural networks achieved reasonable performance on RT detail extraction despite the small dataset and the highly specialized language. Ongoing efforts include expanding the dataset, and developing models for additional RT entities and for relations between RT details.Abstract 3795; TableEntityIAA* F1InstancesPrecision - P (PPV)Recall – R (sensitivity)F1 (harmonic mean of P and R)Train SetDevelopment SetTest SetRT Dose0.993971291780.960.950.95Fraction No.0.8316355900.860.740.80Fraction Frequency1.005215150.930.930.93Boost1.002316100.700.700.70Treatment Site0.80153591200.970.940.95∗Inter-annotator agreement between 2 human annotators which serves as system target. Open table in a new tab
Research on ecological environment impact assessment based on PSR and cloud theory in Dari county, source of the Yellow River

Abstract In order to reasonably evaluate the ecological environment of Dari County in the source region of the Yellow River, the characteristics and actual conditions of Dari County's ecological environment are taken into account, and the principles of representativeness, scientificity, operability and systematicness of index are followed. An evaluation index system is established for Dari County based on the Pressure-State-Response (PSR) conceptual framework. Combining the Analytic Hierarchy Process (AHP) with the cloud model, an ecological environment evaluation model for Dari County is established, and the ecological environment of Dari County is quantitatively evaluated. The model organically combines the ambiguity and randomness of the uncertainty concept. It not only describes the ambiguity of the evaluation level with a membership function, but also considers the randomness of the membership itself using the concept of superentropy, which is more in line with the actual evaluation object. The results indicate that the ecological situation of Dari County is in a ‘general’ state that should be urgently protected for sustainable development, and land degradation is the most important factor affecting the ecological environment of Dari County.