Reviewerly/exHarmony huggingface dataset:
Incompatibility Clustering as a Defense Against Backdoor Poisoning Attacks

We propose a novel clustering mechanism based on an incompatibility property between subsets of data that emerges during model training. This mechanism partitions the dataset into subsets that generalize only to themselves, i.e., training on one subset does not improve performance on the other subsets. Leveraging the interaction between the dataset and the training process, our clustering mechanism partitions datasets into clusters that are defined by--and therefore meaningful to--the objective of the training process. We apply our clustering mechanism to defend against data poisoning attacks, in which the attacker injects malicious poisoned data into the training dataset to affect the trained model's output. Our evaluation focuses on backdoor attacks against deep neural networks trained to perform image classification using the GTSRB and CIFAR-10 datasets. Our results show that (1) these attacks produce poisoned datasets in which the poisoned and clean data are incompatible and (2) our technique successfully identifies (and removes) the poisoned data. In an end-to-end evaluation, our defense reduces the attack success rate to below 1% on 134 out of 165 scenarios, with only a 2% drop in clean accuracy on CIFAR-10 and a negligible drop in clean accuracy on GTSRB.
Parallel Attention Network with Sequence Matching for Video Grounding

Given a video, video grounding aims to retrieve a temporal moment that semantically corresponds to a language query. In this work, we propose a Parallel Attention Network with Sequence matching (SeqPAN) to address the challenges in this task: multi-modal representation learning, and target moment boundary prediction. We design a self-guided parallel attention module to effectively capture self-modal contexts and cross-modal attentive information between video and text. Inspired by sequence labeling tasks in natural language processing, we split the ground truth moment into begin, inside, and end regions. We then propose a sequence matching strategy to guide start/end boundary predictions using region labels. Experimental results on three datasets show that SeqPAN is superior to state-of-the-art methods. Furthermore, the effectiveness of the self-guided parallel attention module and the sequence matching module is verified.
Efficient Least Squares Monte-Carlo Technique for PFE/EE Calculations

We describe a regression-based method, generally referred to as the Least Squares Monte Carlo (LSMC) method, to speed up exposure calculations of a portfolio. We assume that the portfolio contains several exotic derivatives that are priced using Monte-Carlo on each real world scenario and time step. Such a setting is often referred to as a Monte Carlo over a Monte Carlo or a Nested Monte Carlo method.
Component Based Solutions Under Architecture

Many of today's applications have an, almost tangible, monolithic nature. They are built as 'islands', purporting to be self contained, offering little or nothing in the way of integration with other applications. In the past, being large and self-contained may have eliminated the need to interact with other solutions to some extent. However, in the business environments of today the interaction with other applications becomes paramount. As a result of this, many ad-hoc point-to-point integration solutions have been built between different applications. This has already led to an 'application spaghetti' at many of our customer sites. Many of today's applications are poorly structured, which makes their responsiveness to business change sluggish. The application spaghetti with its plethora of point-to-point interfaces further inhibits the responsiveness to change.
Systematic Review on Social Engineering: Hacking by Manipulating Humans

Despite the availability of advanced security software and hardware mechanisms available, still, there has been a breach in the defense system of an organization or individual. Social engineering mostly targets the weakest link in the security system i.e. “Humans” for gaining access to sensitive information by manipulating human psychology. Social engineering attacks are arduous to defend as such attacks are not easily detected by available security software or hardware. This article surveys recent studies on social engineering attacks with a discussion on the social engineering phases and categorizing the various attacks into two groups. The main aim of this survey is to examine the various social engineering attacks on individuals and countermeasures against social engineering attacks are also discussed.
A Text Detection Structure Based on Attention Relational Network

With the continuous development of OCR technology, the text detection in a wide range of scenarios has gradually become a research hotspot and has very good application value. Compared with OCR applications such as license plate recognition, text recognition in natural scenes with more complex and diverse environmental backgrounds is more difficult. In particular, the presence of a background that is very close to the font in the image background often leads to detection errors. This paper proposes a relational network text detection architecture based on the attention mechanism, which is used to select and weight different granular nodes in the relational network, and also used for the nodes in the fusion of link to further reduce the computational cost. The experimental results show that the proposed framework in this paper can further predict and learn the connection relationships in the relational graph network, and has good accuracy in some indicators.
Leveraging Machine Learning Techniques to Identify Deceptive Decoy Documents Associated With Targeted Email Attacks

Detecting and preventing targeted email attacks is a long-standing challenge in cybersecurity research and practice. A typical targeted email attack capitalizes on a sophisticated email message to persuade a victim to run a specific, seemingly innocuous, action such as opening a link or an attachment and downloading and installing a software program. To successfully perform such an attack without being noticed afterwards, the attached exploit documents (hereafter referred to as decoy documents), must contain content that is highly relevant to the target. An analysis of such decoy documents can provide crucial information for inferring and identifying the targeted or potentially harmed victims. In this paper, we propose an automatic approach that leverages natural language processing and machine learning to identify decoy documents that have a high chance of deceiving the targeted users. The experimental results show that the proposed method provides good prediction accuracy: the best result obtained on a collection of 200 Chinese decoy documents yielded an accuracy of 97.5%, an F-measure of 97.9% and a low FPR of 3.1%. The proposed scheme can be deployed at various access points to fortify the defense against targeted email attacks that threaten various targets.
The D-plus Discriminant and Complexity of Root Clustering

Let $p(x)$ be an integer polynomial with $m\ge 2$ distinct roots $ρ_1,\ldots,ρ_m$ whose multiplicities are $\boldsymbolμ=(μ_1,\ldots,μ_m)$. We define the D-plus discriminant of $p(x)$ to be $D^+(p):= \prod_{1\le i
Comparison Data Mining based on Optimization Algorithms in Receiving Electricity Subsidies

Electricity subsidy is the Indonesian Government program through PT. PLN (Persero) to the underprivileged community. Hopefully this program can ease the burden of society due to the economic impacts that occur. There is a problem in the distribution process that has been happening is not on target, it certainly resulted in a financial loss that has been issued by the Government and people who do not feel government assistance. This research uses a comparison of Data Mining classification method based on optimization algorithm to get the best model. Solutions that are expected to assist the Government in reducing financial losses in the delivery process are occurring as well as providing benefits for the government to properly process the distribution of targets. Research provides results that the C4.5 method with the Genetic Algorithm (GA) optimization is a better level of accuracy than Particle Swarm Optimization (PSO) and Differential Evolutin (DE) with a value of 92.28% accuracy.
Knowledge Distillation With Multi-Objective Divergence Learning

Knowledge distillation has proven to be an effective model compression method that exploits the knowledge from a teacher model for supervising a student model by minimizing the distribution difference between the knowledge and the prediction produced by the student model. In this work, we focus on improving its performance from the perspective of divergence measures. A general form representing a family of divergence measures is introduced and a novel learning paradigm that jointly optimizes multiple measures is proposed by formalizing it as a multi-objective learning problem. Conditioned on Pareto optimality, the weights of different divergences are tuned in an automated way during training. Extensive experiments on multiple datasets show the proposed method can significantly improve the performance of student networks compared to the state-of-the-art methods for knowledge distillation. Codes are available at https://github.com/CML00/MoDiv.
SpeechNet: A Universal Modularized Model for Speech Processing Tasks

There is a wide variety of speech processing tasks ranging from extracting content information from speech signals to generating speech signals. For different tasks, model networks are usually designed and tuned separately. If a universal model can perform multiple speech processing tasks, some tasks might be improved with the related abilities learned from other tasks. The multi-task learning of a wide variety of speech processing tasks with a universal model has not been studied. This paper proposes a universal modularized model, SpeechNet, which treats all speech processing tasks into a speech/text input and speech/text output format. We select five essential speech processing tasks for multi-task learning experiments with SpeechNet. We show that SpeechNet learns all of the above tasks, and we further analyze which tasks can be improved by other tasks. SpeechNet is modularized and flexible for incorporating more modules, tasks, or training approaches in the future. We release the code and experimental settings to facilitate the research of modularized universal models and multi-task learning of speech processing tasks.
Topologically ordered time crystals

We define topological time crystals, a dynamical phase of periodically driven quantum many-body systems capturing the coexistence of topological order with the spontaneous breaking of discrete time-translation symmetry. We show that many-body localization can stabilize this phase against generic perturbations and establish some of its key features and signatures. We link topological and ordinary time crystals through three complementary perspectives: higher-form symmetries, quantum error-correcting codes, and a holographic correspondence. We also propose an experimental realization of a surface-code-based topological time crystal for the Google Sycamore processor.
A Model-Based on Filtration Technique for Speckle Noise Removal from Ultrasound Images

This paper proposes an equation based on a nonlinear filter for speckle noise removal by introducing a region indicator. The use of Gaussian convolution in the proposed region indicator makes the quality of the edges of the image better than other models. The proposed equation also removes noise well due to having a nonlinear filter while preserving important image details such as edges. Experimental results show that the proposed model can handle speckle noise removal quite well.
Review of Research on Biomedical Image Processing Based on Pattern Recognition

Pattern recognition algorithms can discover valuable information from mass data of biomedical images as guide for basic research and clinical application. In recent years, with improvement of the theory and practice of pattern recognition and machine learning, especially the appearance and application of deep learning, the crossing researches among artificial intelligence, pattern recognition, and biomedicine become a hotspot, and achieve many breakthrough successes in related fields. This review introduces briefly the common framework and algorithms of image pattern recognition, summarizes the applications of these algorithms to biomedical image analysis including fluorescence microscopic images, histopathological images, and medical radiological images, and finally analyzes and prospect several potential research directions.
Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions

As the quantity and complexity of information processed by software systems increase, large-scale software systems have an increasing requirement for high-performance distributed computing systems. With the acceleration of the Internet in Web 2.0, Cloud computing as a paradigm to provide dynamic, uncertain and elastic services has shown superiorities to meet the computing needs dynamically. Without an appropriate scheduling approach, extensive Cloud computing may cause high energy consumptions and high cost, in addition that high energy consumption will cause massive carbon dioxide emissions. Moreover, inappropriate scheduling will reduce the service life of physical devices as well as increase response time to users' request. Hence, efficient scheduling of resource or optimal allocation of request, that usually a NP-hard problem, is one of the prominent issues in emerging trends of Cloud computing. Focusing on improving quality of service (QoS), reducing cost and abating contamination, researchers have conducted extensive work on resource scheduling problems of Cloud computing over years. Nevertheless, growing complexity of Cloud computing, that the super-massive distributed system, is limiting the application of scheduling approaches. Machine learning, a utility method to tackle problems in complex scenes, is used to resolve the resource scheduling of Cloud computing as an innovative idea in recent years. Deep reinforcement learning (DRL), a combination of deep learning (DL) and reinforcement learning (RL), is one branch of the machine learning and has a considerable prospect in resource scheduling of Cloud computing. This paper surveys the methods of resource scheduling with focus on DRL-based scheduling approaches in Cloud computing, also reviews the application of DRL as well as discusses challenges and future directions of DRL in scheduling of Cloud computing.
AUDIO CAPTION GENERATION FROM IMAGES USING DEEP LEARNING

Visually impaired individuals face various types of difficulties as they cannot visualize the natural environment.To overcome this problem, the proposed system would automatically generate captions for an input images and convert the generated caption to an audio format so that visually impaired individuals can listen to the generated captions.Captioning is performed using Deep Learning algorithm Convolution Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory.
An Initial Exploration of Machine Learning Techniques to Classify Source Code Comments in Real-time

Providing real-time feedback to novice programmers is critical to their ability to learn to program.Higher enrollment in introductory computer science courses reduces the amount of time for individual student-instructor interaction.Reduced interaction time equates to a reduction in the time for and amount of instructor feedback.Building on our work involving manual classification and analysis of student source code comments, in this full paper we explore how machine learning techniques can be leveraged to provide automated feedback to students with regards to their computational thinking processes.This paper discusses the initial classification of student source code comments using supervised machine learning methods.In this phase of classification, we focus on whether a comment is sufficient or insufficient.The classification process is broken down into three steps: text processing, data exploration, and comment classification using the Multinomial Naïve Bayes Classifier and a Random Forest Classifier.We detail the text processing requirements, including how to prepare the raw student data using natural languages processing techniques such as stop word filtration, tokenization, and lemmatization.We also show how the data preparation process can affect the final classification outcome.Using Multinomial Naïve Bayes we achieved a precision rate of 82%.Using a Random Forest classifier and lemmatization we achieved a classification precision of 90%.We conclude with a description of how the current classification results can be used to provide real-time feedback to students while they are learning to program.Towards our ultimate goal of providing comprehensive real-time feedback to students, we describe future research plans, which include using unsupervised machine learning techniques to move beyond basic binary classification.
Application Of Data Mining Classification For COVID-19 Infected Status Using Algortima Naïve Method

The number of virus infected status known as COVID-19 is increasing in the southern Jakarta area, namely Pondok Labu, West Cilandak, Jagakarsa, Lenteng Agung, Pasar Minggu and Ragunan, it is necessary to classify the data to find out the negative status of COVID-19 virus infection or positive infected with COVID-19. The technique of classifying positive or negative COVID-infected virus status with the naive bayes classification method. To manage the data, software rapid miner 9. 6 is used, the infected status dataset COVID-19 is obtained from the websitejeo. compass. CalculationPrediction shows the classification of the Naive Bayes method obtained a positive prediction that shows a figure of 55.48% and a negative prediction result of 44.52%. From the results of the classification of data that has been obtained can be seen that the largest prediction found in the positive status infected with COVID-19 virus reached 55.48%.
Evaluation of Multimodal Tongue Drive System by People with Tetraplegia for Computer Access

The multimodal Tongue Drive System (mTDS) is an assistive technology for people with tetraplegia that provides an alternative method to interact with a computer by combining tongue control, head gesture, and speech. This multimodality is designed to facilitate the completion of complex computer tasks (e.g. drag-and-drop) that cannot be easily performed by existing uni-modal assistive technologies. Previous studies with able-bodied participants showed promising performance of the mTDS on complex tasks when compared to other input methods such as keyboard and mouse. In this three-session pilot study, the primary objective is to show the feasibility of using mTDS to facilitate human-computer interactions by asking fourteen participants with tetraplegia to complete five computer access tasks with increased level of complexity: maze navigation, center-out tapping, playing bubble shooter and peg solitaire, and sending an email. Speed and accuracy are quantified by key metrics that are found to be generally increasing from the first to third session, indicating the potential existence of a learning phase that could result in improved performance over time.
Distribution-free calibration guarantees for histogram binning without sample splitting

We prove calibration guarantees for the popular histogram binning (also called uniform-mass binning) method of Zadrozny and Elkan [2001]. Histogram binning has displayed strong practical performance, but theoretical guarantees have only been shown for sample split versions that avoid 'double dipping' the data. We demonstrate that the statistical cost of sample splitting is practically significant on a credit default dataset. We then prove calibration guarantees for the original method that double dips the data, using a certain Markov property of order statistics. Based on our results, we make practical recommendations for choosing the number of bins in histogram binning. In our illustrative simulations, we propose a new tool for assessing calibration -- validity plots -- which provide more information than an ECE estimate. Code for this work will be made publicly available at https://github.com/aigen/df-posthoc-calibration.
Not so simple! Mechanisms increase preference for complex explanations

Mechanisms play a central role in how we think about causality, yet not all causal explanations describe mechanisms. Across four experiments, we find that people evaluate explanations differently depending on whether or not they include mechanisms. Despite common wisdom suggesting that explanations ought to be simple (appealing to as few causes as necessary to explain an effect), the previous literature is divided over whether people adhere to this simplicity principle. Our findings suggest that the presence of mechanisms in an explanation is one factor that moderates this preference for simplicity. Without mechanisms, people typically exhibit a preference for simple explanations, consistent with probabilistic accounts. This preference is significantly reduced or even reversed when explanations contain mechanisms, suggesting that mechanisms afford a different way of evaluating explanations. Rather than focusing on probability, complex explanations that contain mechanisms may be preferred because they provide a fuller account of the underlying causal network, promoting a greater sense of understanding.
Towards automatic microseismic cluster localization with DAS

&amp;lt;p&amp;gt;Currently the capability of detecting earthquakes with decreasing magnitudes demands efficient source localization, especially in seismic monitoring. This work is a step towards automatic high-resolution earthquake localization in a seismic monitoring setup that makes use of Distributed Acoustic Sensing (DAS) as its primary measuring technique. With DAS, the dense spatial sampling of the seismic wavefield leads to an improvement of both event detection and localization of earthquakes. The advantage of DAS is easy and cost-effective deployment compared to traditional seismic instruments (especially in boreholes). However, the single-component nature and the large storage requirements of DAS data demand novel methods for efficient analysis of the recorded events.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We apply a new seismic event location method to DAS data, based on a distance geometry problem in biochemistry for protein structure determination (HADES&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt;). From the distances between individual earthquakes and a seismic station, the relative distance between the events can be computed. This approach allows us to first determine the relative location of earthquakes within a seismic cluster, and subsequently position the cluster in its correct absolute location. The technique has already been successfully applied for a single traditional seismometer. The densely spaced channels in DAS measurements accommodate accurate relative distance computation, without the ability to constrain the azimuth of the seismic cluster. Therefore, after finding the relative locations within the cluster, the position and orientation of the cluster with respect to the fiber-optic cable is calculated by minimizing the difference between observed and calculated P- and S-wave first arrival times, using a grid search approach (multi-event location). In this way, the absolute locations of all earthquakes present in the cluster are found efficiently. We first test this DAS-adapted method on synthetics, then we will move towards a real data application.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;sup&amp;gt;1&amp;lt;/sup&amp;gt; HADES: https://github.com/wulwife/HADES&amp;lt;/p&amp;gt;