Reviewerly/exHarmony huggingface dataset:
Breast Cancer Prediction using varying Parameters of Machine Learning Models

Malignancy of tumor has caused major number of deaths among women. Machine learning tools with proper hyper parametric can help in identifying tumors efficiently. This paper presents six supervised machine learning algorithms such as k-Nearest Neighborhood, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine with radial basis function kernel. Deep learning using Adam Gradient Descent Learning was also applied because it combines the benefits of adaptive gradient algorithm and root mean square propagation. A unique hyper parametric change in each model is shown so that it gives better accuracy within the model as well as comparing each model with one other. The result of deep learning as the most accurate with minimum loss. The accuracy achieved by deep learning using Adam Gradient Descent Learning is 98.24%.
Towards Transferable Adversarial Attack Against Deep Face Recognition

Face recognition has achieved great success in the last five years due to the development of deep learning methods. However, deep convolutional neural networks (DCNNs) have been found to be vulnerable to adversarial examples. In particular, the existence of transferable adversarial examples can severely hinder the robustness of DCNNs since this type of attacks can be applied in a fully black-box manner without queries on the target system. In this work, we first investigate the characteristics of transferable adversarial attacks in face recognition by showing the superiority of feature-level methods over label-level methods. Then, to further improve transferability of feature-level adversarial examples, we propose DFANet, a dropout-based method used in convolutional layers, which can increase the diversity of surrogate models and obtain ensemble-like effects. Extensive experiments on state-of-the-art face models with various training databases, loss functions and network architectures show that the proposed method can significantly enhance the transferability of existing attack methods. Finally, by applying DFANet to the LFW database, we generate a new set of adversarial face pairs that can successfully attack four commercial APIs without any queries. This TALFW database is available to facilitate research on the robustness and defense of deep face recognition.
Differential Privacy for Industrial Internet of Things: Opportunities, Applications, and Challenges

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy.
Review of Classification Methods on Unbalanced Data Sets

This paper studies the classification of unbalanced data sets. First, this kind of data sets is briefly introduced, and then the classification methods of unbalanced data sets are analyzed in detail from different perspectives such as data sampling method, algorithm level, feature level, cost-sensitive function, and deep learning. In addition, the data sampling methods are divided into different technologies for introduction: unbalanced data set classification method based on synthetic minority over-sampling technology (SMOTE), support vector machine (SVM) technology, and k-nearest neighbor (KNN) technology, etc. Then, the advantages and disadvantages of these methods are compared. Finally, the evaluation criteria of the unbalanced data set classifier are summarized, and the future work directions are prospected and summarized.
SoK: Shining Light on Shadow Stacks

Control-Flow Hijacking attacks are the dominant attack vector against C/C++ programs. Control-Flow Integrity (CFI) solutions mitigate these attacks on the forward edge, i.e., indirect calls through function pointers and virtual calls. Protecting the backward edge is left to stack canaries, which are easily bypassed through information leaks. Shadow Stacks are a fully precise mechanism for protecting backwards edges, and should be deployed with CFI mitigations. We present a comprehensive analysis of all possible shadow stack mechanisms along three axes: performance, compatibility, and security. For performance comparisons we use SPEC CPU2006, while security and compatibility are qualitatively analyzed. Based on our study, we renew calls for a shadow stack design that leverages a dedicated register, resulting in low performance overhead, and minimal memory overhead, but sacrifices compatibility. We present case studies of our implementation of such a design, Shadesmar, on Phoronix and Apache to demonstrate the feasibility of dedicating a general purpose register to a security monitor on modern architectures, and Shadesmar's deployability. Our comprehensive analysis, including detailed case studies for our novel design, allows compiler designers and practitioners to select the correct shadow stack design for different usage scenarios. Shadow stacks belong to the class of defense mechanisms that require metadata about the program's state to enforce their defense policies. Protecting this metadata for deployed mitigations requires in-process isolation of a segment of the virtual address space. Prior work on defenses in this class has relied on information hiding to protect metadata. We show that stronger guarantees are possible by repurposing two new Intel x86 extensions for memory protection (MPX), and page table control (MPK). Building on our isolation efforts with MPX and MPK, we present the design requirements for a dedicated hardware mechanism to support intra-process memory isolation, and discuss how such a mechanism can empower the next wave of highly precise software security mitigations that rely on partially isolated information in a process.
A simplified skyline-based method for estimating the annual solar energy potential in urban environments

Architects, engineers and urban planners have today at their disposal several tools for simulating the energy yield of photovoltaic systems. These tools are based on mathematical models that perform repetitive calculations to determine the annual irradiation received by solar panels; hence when photovoltaic systems are installed in complex urban environments, the simulations become highly computationally demanding. Here we present a simplified and yet accurate model for the direct calculation of the annual irradiation and energy yield of photovoltaic systems in urban environments. Our model is based on the correlation between the solar radiation components and the shape of the skyline profile. We show how calculations can be simplified by quantifying the skyline using two indicators: the sky view factor and the sun coverage factor. Model performance is evaluated in different climates using measured data from different photovoltaic systems. Results indicate that the proposed model significantly reduces the required computation time while preserving a high estimation accuracy. The growing deployment of solar panels in urban areas requires tools to determine their optimal placement and electricity yield. Towards this end, this study presents a simplified model based on the sky view and sun coverage factors and validates it using real-world data from different systems in different climates.
Satellite Image De-Noising With Harris Hawks Meta Heuristic Optimization Algorithm and Improved Adaptive Generalized Gaussian Distribution Threshold Function

An image may be influenced by noise during capturing and transmitting process. Removing the possible noise from the image has always been a challenging issue due to this fact that further processing will not be possible unless by diminishing the noise from images. Many researchers attempted to remove the noise to improve the qualitative and also the quantitative results but these methods could not preserve the quality of images after applying de-noising techniques. In this paper, in the first stage, we utilized the most recent nature-inspired meta-heuristic optimization algorithm to get the optimal solutions for the parameters of thresholding function. Using the Harris hawk optimization (HHO) algorithm results in obtaining the optimized thresholded wavelet coefficients before applying the inverse wavelet transform. In the second stage, we proposed the improved adaptive generalized Gaussian distribution (AGGD) threshold, which is a data-driven function with an adaptive threshold value. This function can be fitted to any kind of images without using any shape tuning parameter. It is clear that the calculation of the threshold value does not require any optimization and LMS learning algorithm. The qualitative and quantitative results validate the superiority of the proposed method.
A machine learning approach to analyze customer satisfaction from airline tweets

Customer’s experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer’s experience. Features were extracted from the tweets using word embedding with Glove dictionary approach and n-gram approach. Further, SVM (support vector machine) and several ANN (artificial neural network) architectures were considered to develop classification model that maps the tweet into positive and negative category. Additionally, convolutional neural network (CNN) were developed to classify the tweets and the results were compared with the most accurate model among SVM and several ANN architectures. It was found that CNN outperformed SVM and ANN models. In the end, association rule mining have been performed on different categories of tweets to map the relationship with sentiment categories. The results show that interesting associations were identified that certainly helps the airline industries to improve their customer’s experience.
Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration

A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions.
EfficientDet: Scalable and Efficient Object Detection

Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multiscale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and better backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single model and single-scale, our EfficientDet-D7 achieves state-of-the-art 55.1 AP on COCO test-dev with 77M parameters and 410B FLOPs, being 4x - 9x smaller and using 13x - 42x fewer FLOPs than previous detectors. Code is available at https://github.com/google/automl/tree/master/efficientdet.
SIFRank: A New Baseline for Unsupervised Keyphrase Extraction Based on Pre-Trained Language Model

In the age of social media, faced with a huge amount of knowledge and information, accurate and effective keyphrase extraction methods are needed to be applied in information retrieval and natural language processing. It is difficult for traditional keyphrase extraction models to contain a large amount of external knowledge information, but with the rise of pre-trained language models, there is a new way to solve this problem. Based on the above background, we propose a new baseline for unsupervised keyphrase extraction based on pre-trained language model called SIFRank. SIFRank combines sentence embedding model SIF and autoregressive pre-trained language model ELMo, and it has the best performance in keyphrase extraction for short documents. We speed up SIFRank while maintaining its accuracy by document segmentation and contextual word embeddings alignment. For long documents, we upgrade SIFRank to SIFRank+ by position-biased weight, greatly improve its performance on long documents. Compared to other baseline models, our model achieves state-of-the-art level on three widely used datasets.
Agile Software Development: Methodologies and Trends

&lt;p&gt;Software engineering is a discipline that undergone many improvements that aims to keep up with the new advancements in technologies and the modern business requirements through developing effective approaches to reach the final software product, agile software development is one of these successful approaches. Agile software development is a lightweight approach that was proposed to overcome the convolutional development methods’ limitations and to reduce the overhead and the cost while providing flexibility to adopt the changes in requirements at any stage, this is done by managing the tasks and their coordination through a certain set of values and principles.&lt;/p&gt;&lt;p&gt;In this work, a comprehensive review that outlines the main agile values and principles, and states the key differences that distinguish agile methods over the traditional ones are presented. Then a discussion of the most popular agile methodologies; their life cycles, their roles, and their advantages and disadvantages are outlined. The recent state of art trends that adopts agile development especially in cloud computing, big data, and coordination are also explored. And finally, this work highlights how to choose the best suitable agile methodology that must be selected according to the task at hand, how sensitive the product is and the organization structure.&lt;/p&gt;
TLDR: Extreme Summarization of Scientific Documents

We introduce TLDR generation, a new form of extreme summarization, for scientific papers.TLDR generation involves high source compression and requires expert background knowledge and understanding of complex domain-specific language.To facilitate study on this task, we introduce SCITLDR, a new multi-target dataset of 5.4K TLDRs over 3.2K papers.SCITLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden.We propose CATTS, a simple yet effective learning strategy for generating TLDRs that exploits titles as an auxiliary training signal.CATTS improves upon strong baselines under both automated metrics and human evaluations.Data and code are publicly available at https://github.com/allenai/scitldr.1 TLDR is an acronym that stands for "too long; didn't read," which is often used in online informal discussion (e.g., Twitter or Reddit) about scientific papers.For visual clarity, we omit the semi-colon.
Ternary Compression for Communication-Efficient Federated Learning

Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and Internet of thing (IoT) devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. Theoretical proofs of the convergence of quantization factors, unbiasedness of FTTQ, as well as a reduced weight divergence are given. On the basis of FTTQ, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available data sets, and our results demonstrate that the proposed T-FedAvg is effective in reducing communication costs and can even achieve slightly better performance on non-IID data in contrast to the canonical federated learning algorithms.
A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

Abstract Cervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime importance for eradicating the disease from the patient’s body. However, regular population-wise screening of cancer is limited by its expensive and labour intensive detection process, where clinicians need to classify individual cells from a stained slide consisting of more than 100,000 cervical cells, for malignancy detection. Thus, Computer-Aided Diagnosis (CAD) systems are used as a viable alternative for easy and fast detection of cancer. In this paper, we develop such a method where we form an ensemble-based classification model using three Convolutional Neural Network (CNN) architectures, namely Inception v3, Xception and DenseNet-169 pre-trained on ImageNet dataset for Pap stained single cell and whole-slide image classification. The proposed ensemble scheme uses a fuzzy rank-based fusion of classifiers by considering two non-linear functions on the decision scores generated by said base learners. Unlike the simple fusion schemes that exist in the literature, the proposed ensemble technique makes the final predictions on the test samples by taking into consideration the confidence in the predictions of the base classifiers. The proposed model has been evaluated on two publicly available benchmark datasets, namely, the SIPaKMeD Pap Smear dataset and the Mendeley Liquid Based Cytology (LBC) dataset, using a 5-fold cross-validation scheme. On the SIPaKMeD Pap Smear dataset, the proposed framework achieves a classification accuracy of 98.55% and sensitivity of 98.52% in its 2-class setting, and 95.43% accuracy and 98.52% sensitivity in its 5-class setting. On the Mendeley LBC dataset, the accuracy achieved is 99.23% and sensitivity of 99.23%. The results obtained outperform many of the state-of-the-art models, thereby justifying the effectiveness of the same. The relevant codes of this proposed model are publicly available on GitHub .
Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm

Online sequential extreme learning machine (OS-ELM) learns data one-by-one or chunk-by-chunk, and the recursive least square (RLS) algorithm is commonly employed to train the topological parameters of OS-ELM. Since it is hard to guarantee the smallest estimation error of the state variable by the RLS, the regression performance of the OS-ELM easily fluctuates in practical applications. To address this gap, a new training approach of the OS-ELM using Kalman filter called KFOS-ELM is proposed, and state propagation is combined into extreme learning process to obtain the OS-ELM's topological parameters. Besides, an adaptive-weighted ensemble mechanism is developed and used to dynamically tune the weight coefficients of each KFOS-ELM in the learning network. The regression performance of the proposed methodology is evaluated using benchmark datasets. The simulation results show that proposed methods are superior to the OS-ELM and EOS-ELM in terms of the regression accuracy and stability without additional computational efforts. Furthermore, an enhanced multi-sensor prognostic model based on KFOS-ELM and logistic regression (LR) model is designed for remaining useful life (RUL) prediction of aircraft engine. The experimental results confirm our viewpoints.
Large-Scale Transfer Learning for Natural Language Generation

Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks.We study how these architectures can be applied and adapted for natural language generation, comparing a number of architectural and training schemes.We focus in particular on open-domain dialog as a typical high entropy generation task, presenting and comparing different architectures for adapting pretrained models with state of the art results.
Autorank: A Python package for automated ranking of classifiers

Analyses to determine differences in the central tendency, e.g., mean or median values, are an important application of statistics.Often, such comparisons must be done with paired samples, i.e., populations that are not dependent on each other.This is, for example, required if the performance different machine learning algorithms should be compared on multiple data sets.The performance measures on each data set are then the paired samples, the difference in the central tendency can be used to rank the different algorithms.This problem is not new and how such tests could be done was already described in the well-known article by Demšar (2006).