Reviewerly/exHarmony huggingface dataset:
Forecasting Future Action Sequences With Attention: A New Approach to Weakly Supervised Action Forecasting

Future human action forecasting from partial observations of activities is an important problem in many practical applications such as assistive robotics, video surveillance and security. We present a method to forecast actions for the unseen future of the video using a neural machine translation technique that uses encoder-decoder architecture. The input to this model is the observed RGB video, and the objective is to forecast the correct future symbolic action sequence. Unlike prior methods that make action predictions for some unseen percentage of video one for each frame, we predict the complete action sequence that is required to accomplish the activity. We coin this task action sequence forecasting. To cater for two types of uncertainty in the future predictions, we propose a novel loss function. We show a combination of optimal transport and future uncertainty losses help to improve results. We evaluate our model in three challenging video datasets (Charades, MPII cooking and Breakfast). We extend our action sequence forecasting model to perform weakly supervised action forecasting on two challenging datasets, the Breakfast and the 50Salads. Specifically, we propose a model to predict actions of future unseen frames without using frame level annotations during training. Using Fisher vector features, our supervised model outperforms the state-of-the-art action forecasting model by 0.83% and 7.09% on the Breakfast and the 50Salads datasets respectively. Our weakly supervised model is only 0.6% behind the most recent state-of-the-art supervised model and obtains comparable results to other published fully supervised methods, and sometimes even outperforms them on the Breakfast dataset. Most interestingly, our weakly supervised model outperforms prior models by 1.04% leveraging on proposed weakly supervised architecture, and effective use of attention mechanism and loss functions.
Bengali Handwritten Character Classification Using Transfer Learning on Deep Convolutional Network

Bengali is the sixth most popular spoken language in the world. Computerized detection of handwritten Bengali (Bangla Lekha) character is very difficult due to the diversity and veracity of characters. In this paper, we have proposed a modified state-of-the-art deep learning to tackle the problem of Bengali handwritten character recognition. This method used the lesser number of iterations to train than other comparable methods. The transfer learning on Resnet-50 deep convolutional neural network model is used on pretrained ImageNet dataset. One cycle policy is modified with varying the input image sizes to ensure faster training. Proposed method executed on BanglaLekha-Isolated dataset for evaluation that consists of 84 classes (50 Basic, 10 Numerals and 24 Compound Characters). We have achieved 97.12% accuracy in just 47 epochs. Proposed method gives very good results in terms of epoch and accuracy compare to other recent methods by considering number of classes. Without ensembling, proposed solution achieves state-of-the-art result and shows the effectiveness of ResNet-50 for classification of Bangla HCR.
Predicting Photovoltaic Soiling From Air Quality Measurements

Ambient particulate matter in urban environments is dynamic and heterogeneous; therefore, understanding photovoltaic energy loss due to soiling is challenging. Silicon reference cells were deployed in an urban-industrial area in Colorado colocated with measurements of ambient particulate matter concentrations. Regressing measured soiling ratios against cumulative sums of ambient coarse particulate matter since the last precipitation event and ambient fine particulate matter since the first day of deployment produces a root-mean-square error of approximately 0.013. This model partially addresses the challenge of quantifying the relationships between ambient air quality and photovoltaic soiling.
A Hybrid Fault-Tolerant Scheduling for Deadline-Constrained Tasks in Cloud Systems

Among multiple fault-tolerant strategies, resubmission, and replication are fundamental and widely recognized in distributed computing systems. In recent years, many algorithms based on replication or resubmission have been proposed. However, few of them consider these two techniques together, especially in Cloud systems. In this article, we propose a Hybrid Fault-Tolerant Scheduling Algorithm (HFTSA) for independent tasks with deadlines by integrating the above techniques in virtualized Cloud systems. During the task scheduling process, HFTSA selects fault-tolerant strategies from resubmission and replication for each accepted task based on the characteristics of both task and Cloud resources and then reserves suitable resources. During the task execution process, HFTSA adopts an online adjustment scheme for fault-tolerant strategies of some tasks if necessary while providing an online scheduling scheme for faults. Moreover, an elastic resource provisioning mechanism is designed and incorporated into HFTSA to dynamically adjust the provided resources to improve resource utilization. Experiments on a real cloud platform and a simulated platform are conducted to verify the effectiveness of the proposed HFTSA. The results demonstrate that HFTSA can provide an efficient fault-tolerant scheduling strategy for deadline-constrained tasks with high resource utilization and performs better than corresponding competitors.
Computers do not make art, people do

The continually evolving relationship between artistic technologies and artists.
(One) Failure Is Not an Option: Bootstrapping the Search for Failures in Lattice-Based Encryption Schemes

Lattice-based encryption schemes are often subject to the possibility of decryption failures, in which valid encryptions are decrypted incorrectly. Such failures, in large number, leak information about the secret key, enabling an attack strategy alternative to pure lattice reduction. Extending the "failure boosting" technique of D'Anvers et al. in PKC 2019, we propose an approach that we call "directional failure boosting" that uses previously found "failing ciphertexts" to accelerate the search for new ones. We analyse in detail the case where the lattice is defined over polynomial ring modules quotiented by $$\langle X^{N} + 1 \rangle $$ and demonstrate it on a simple Mod-LWE-based scheme parametrized à la Kyber768/Saber. We show that for a given secret key (single-target setting), the cost of searching for additional failing ciphertexts after one or more have already been found, can be sped up dramatically. We thus demonstrate that, in this single-target model, these schemes should be designed so that it is hard to even obtain one decryption failure. Besides, in a wider security model where there are many target secret keys (multi-target setting), our attack greatly improves over the state of the art.
Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network

Due to the nonlinear distortion caused by radio-frequency (RF) components in the transceiver, detecting transmitted symbols for multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems can be challenging and resource consuming. In this work, we introduce a Deep Echo State Network (DESN) to serve as the symbol detector for 5G communication networks. Our DESN employs memristive synapses as the dynamic reservoir layer to accelerate the learning algorithm and computation. By cascading multiple dynamic reservoir layers in a hierarchical processing structure, our DESN processes received signal in both spatial and temporal domains. The resulting hybrid memristor-CMOS co-design provides the nonlinear computation required by the reservoir layer while significantly reduces the power consumption. From the benchmark on nonlinear system prediction, our DESN exhibits 10.31 X reduction on the prediction error compared to state-of-the-art neural network designs. Moreover, our DESN records a bit error rate (BER) of 5.76 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> on the high-speed transmitted symbol detection task for MIMO-OFDM systems, yielding 47.73% more precise than state-of-the-art techniques in the literate for 5G communication networks.
Blind Quality Assessment for Tone-Mapped Images by Analysis of Gradient and Chromatic Statistics

A tone-mapped image (TMI) obtained from the corresponding high dynamic range (HDR) image induces artifacts and distortion, which might result in the loss of structure information and impaired color. By analyzing the visual characteristics of TMIs, this work proposes a robust blind visual quality evaluation method for TMIs by using gradient and chromatic statistics (VQGC). First, motivated by the perceptual mechanism that the human visual system (HVS) is sensitive to image structure variation, we employ the gradient features to measure structure degradation in TMIs. To predict structure distortion accurately, we compute the gradient magnitude and orientation to measure image structure variation, and the relative gradient magnitude and orientation are also computed to capture microstructure change. Second, the color invariance descriptors are utilized to capture the visual degradation of colorfulness by local binary pattern (LBP) on four chromatic feature maps. Finally, the gradient and chromatic features are combined together as the final quality-aware feature vector, which is applied to assess the perceptual quality of TMIs by support vector regression (SVR). Comparison experiments show that the performance of the proposed method is better than other existing blind quality assessment methods on public databases.
RMDevOps

DevOps is a new software engineering paradigm adopted by various software organizations to develop an environment of continuous deployment and delivery within time. Numerous experts are offering their services to help organizations, how to implement DevOps activities in software organization. Though, still there are various issues for software organizations to adopt DevOps activities. To overcome such issues, there must be an approach that could assist software organizations towards better adoption of DevOps activities. The core objective of this research is to design a Readiness Model for DevOps (RMDevOps) to improve the adoption of DevOps activities in a software organization. Based on existing models in other fields of software engineering, we will develop this model. We have conducted a systematic literature review and empirical study on DevOps, for understanding the impact of the success factors of DevOps in the real world and literature. This study covers the first step of development of RMDevOps model, by identifying the success factors of DevOps and presenting the outcomes in the form of robust framework.
Assessing the Potential of Augmented Reality in Education

Augmented Reality (AR) is a technology that combines three-dimensional virtual objects (3D) into a real three-dimensional environment. The 3-dimensional model is commonly used as a teaching material aid to make students better understand the knowledge provided. AR technology has been applied in various diverse fields, including in education. The purpose of this study is to look at the potential of AR in the world of Education using the scoping review method. Using this method, 10 articles were obtained from the results of a quasi-experiment study. The results showed that AR can improve spatial abilities, problem-solving and student motivation.
Knowledge-Grounded Chatbot Based on Dual Wasserstein Generative Adversarial Networks with Effective Attention Mechanisms

A conversation is based on internal knowledge that the participants already know or external knowledge that they have gained during the conversation. A chatbot that communicates with humans by using its internal and external knowledge is called a knowledge-grounded chatbot. Although previous studies on knowledge-grounded chatbots have achieved reasonable performance, they may still generate unsuitable responses that are not associated with the given knowledge. To address this problem, we propose a knowledge-grounded chatbot model that effectively reflects the dialogue context and given knowledge by using well-designed attention mechanisms. The proposed model uses three kinds of attention: Query-context attention, query-knowledge attention, and context-knowledge attention. In our experiments with the Wizard-of-Wikipedia dataset, the proposed model showed better performances than the state-of-the-art model in a variety of measures.
Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization

The Bayesian method is capable of capturing real-world uncertainties/incompleteness and properly addressing the overfitting issue faced by deep neural networks. In recent years, Bayesian neural networks (BNNs) have drawn tremendous attention to artificial intelligence (AI) researchers and proved to be successful in many applications. However, the required high computation complexity makes BNNs difficult to be deployed in computing systems with a limited power budget. In this article, an efficient BNN inference flow is proposed to reduce the computation cost and then is evaluated using both software and hardware implementations. A feature decomposition and memorization (DM) strategy is utilized to reform the BNN inference flow in a reduced manner. About half of the computations could be eliminated compared with the traditional approach that has been proved by theoretical analysis and software validations. Subsequently, in order to resolve the hardware resource limitations, a memory-friendly computing framework is further deployed to reduce the memory overhead introduced by the DM strategy. Finally, we implement our approach in Verilog and synthesize it with a 45-nm FreePDK technology. Hardware simulation results on multilayer BNNs demonstrate that, when compared with the traditional BNN inference method, it provides an energy consumption reduction of 73% and a 4× speedup at the expense of 14% area overhead.
Fuzzy mutation embedded hybrids of gravitational search and Particle Swarm Optimization methods for engineering design problems

Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) are nature-inspired, swarm-based optimization algorithms respectively. Though they have been widely used for single-objective optimization since their inception, they suffer from premature convergence. Even though the hybrids of GSA and PSO perform much better, the problem remains. Hence, to solve this issue we have proposed a fuzzy mutation model for two hybrid versions of PSO and GSA - Gravitational Particle Swarm (GPS) and PSOGSA. The developed algorithms are called Mutation based GPS (MGPS) and Mutation based PSOGSA (MPSOGSA). The mutation operator is based on a fuzzy model where the probability of mutation has been calculated based on the closeness of particle to population centroid and improvement in the particle value. We have evaluated these two new algorithms on 23 benchmark functions of three categories (unimodal, multi-modal and multi-modal with fixed dimension). The experimental outcome shows that our proposed model outperforms their corresponding ancestors, MGPS outperforms GPS 13 out of 23 times (56.52%) and MPSOGSA outperforms PSOGSA 17 times out of 23 (73.91 %). We have also compared our results against those of recent optimization algorithms such as Sine Cosine Algorithm (SCA), Opposition-Based SCA, and Volleyball Premier League Algorithm (VPL). In addition, we have applied our proposed algorithms on some classic engineering design problems and the outcomes are satisfactory. The related codes of the proposed algorithms can be found in this link: Fuzzy-Mutation-Embedded-Hybrids-of-GSA-and-PSO.
Dynamic Region-biased Rapidly-exploring Random Trees

Current state-of-the-art motion planners rely on samplingbased planning to explore the problem space for a solution. However, sampling valid configurations in narrow or cluttered workspaces remains a challenge. If a valid path for the robot correlates to a path in the workspace, then the planning process can employ a representation of the workspace that captures its salient topological features. Prior approaches have investigated exploiting geometric decompositions of the workspace to bias sampling; while beneficial in some environments, complex narrow passages remain challenging to navigate.In this work, we present Dynamic Region-biased RRT, a novel samplingbased planner that guides the exploration of a Rapidly-exploring Random Tree (RRT) by moving sampling regions along an embedded graph that captures the workspace topology. These sampling regions are dynamically created, manipulated, and destroyed to greedily bias sampling through unexplored passages that lead to the goal. We show that our approach reduces online planning time compared with related methods on a set of maze-like problems.
FormNet: Formatted Learning for Image Restoration

In this paper, we propose a deep CNN to tackle the image restoration problem by learning formatted information. Previous deep learning based methods directly learn the mapping from corrupted images to clean images, and may suffer from the gradient exploding/vanishing problems of deep neural networks. We propose to address the image restoration problem by learning the structured details and recovering the latent clean image together, from the shared information between the corrupted image and the latent image. In addition, instead of learning the pure difference (corruption), we propose to add a residual formatting layer and an adversarial block to format the information to structured one, which allows the network to converge faster and boosts the performance. Furthermore, we propose a cross-level loss net to ensure both pixel-level accuracy and semantic-level visual quality. Evaluations on public datasets show that the proposed method performs favorably against existing approaches quantitatively and qualitatively.
Intelligent Methods for Accurately Detecting Phishing Websites

With increasing technology developments, there is a massive number of websites with varying purposes. But a particular type exists within this large collection, the so-called phishing sites which aim to deceive their users. The main challenge in detecting phishing websites is discovering the techniques that have been used. Where phishers are continually improving their strategies and creating web pages that can protect themselves against many forms of detection methods. Therefore, it is very necessary to develop reliable, active and contemporary methods of phishing detection to combat the adaptive techniques used by phishers. In this paper, different phishing detection approaches are reviewed by classifying them into three main groups. Then, the proposed model is presented in two stages. In the first stage, different machine learning algorithms are applied to validate the chosen dataset and applying features selection methods on it. Thus, the best accuracy was achieved by utilizing only 20 features out of 48 features combined with Random Forest is 98.11%. While in the second stage, the same dataset is applied to various fuzzy logic algorithms. As well the experimental results from the application of Fuzzy logic algorithms were incredible. Where in applying the FURIA algorithm with only five features the accuracy rate was 99.98%. Finally, comparison and discussion of the results between applying machine learning algorithms and fuzzy logic algorithms is done. Where the performance of using fuzzy logic algorithms exceeds the use of machine learning algorithms.
Exploring the Role of Loss Functions in Multiclass Classification

Cross-entropy is the de-facto loss function in modern classification tasks that involve distinguishing hundreds or even thousands of classes.To design better loss functions for new machine learning tasks, it is critical to understand what makes a loss function suitable for a problem.For instance, what makes the cross entropy better than other alternatives such as quadratic loss?In this work, we discuss the role of loss functions in learning tasks with a large number of classes.We hypothesize that different loss functions can have large variability in the difficulty of optimization and that simplicity of training is a key catalyst for better test-time performance.Our intuition draws from the success of over-parameterization in deep learning: As a model has more parameters, it trains faster and achieves higher test accuracy.We argue that, effectively, cross-entropy loss results in a much more over-parameterized problem compared to the quadratic loss, thanks to its emphasis on the correct class (associated with the label).Such over-parameterization drastically simplifies the training process and ends up boosting the test performance.For separable mixture models, we provide a separation result where cross-entropy loss can always achieve small training loss, whereas quadratic loss has diminishing benefit as the number of classes and class correlations increase.Numerical experiments with CIFAR 100 corroborate our results.We show that the accuracy with quadratic loss disproportionately degrades with a growing number of classes; however, encouraging quadratic loss to focus on the correct class results in a drastically improved performance.
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning

Outlier detection is an important task for various data mining applications. Current outlier detection techniques are often manually designed for specific domains, requiring large human efforts of database setup, algorithm selection, and hyper-parameter tuning. To fill this gap, we present PyODDS, an automated end-to-end Python system for Outlier Detection with Database Support, which automatically optimizes an outlier detection pipeline for a new data source at hand. Specifically, we define the search space in the outlier detection pipeline, and produce a search strategy within the given search space. PyODDS enables end-to-end executions based on an Apache Spark backend server and a light-weight database. It also provides unified interfaces and visualizations for users with or without data science or machine learning background. In particular, we demonstrate PyODDS on several real-world datasets, with quantification analysis and visualization results.
Breast cancer pathological image classification based on deep learning

The automatic classification of breast cancer pathological images has important clinical application value. However, to develop the classification algorithm using the artificially extracted image features faces several challenges including the requirement of professional domain knowledge to extract and compute highiquality image features, which are often time-consuming, laborious, and difficult. For overcoming these challenges, this study developed and applied an improved deep convolutional neural network model to perform automatic classification of breast cancer using pathological images. Specifically, in this study, data enhancement and migration learning methods are used to effectively avoid the overfitting problems with deep learning models when they are limited by training image sample size. Experimental results show that a 91% recognition rate or accuracy when applying this improved deep learning model to a publicly available dataset of BreaKHis. Comparing with other previously used models, the new model yields good robustness and generalization.