Reviewerly/exHarmony huggingface dataset:
Ensemble Attention Distillation for Privacy-Preserving Federated Learning

We consider the problem of Federated Learning (FL) where numerous decentralized computational nodes collaborate with each other to train a centralized machine learning model without explicitly sharing their local data samples. Such decentralized training naturally leads to issues of imbalanced or differing data distributions among the local models and challenges in fusing them into a central model. Existing FL methods deal with these issues by either sharing local parameters or fusing models via online distillation. However, such a design leads to multiple rounds of inter-node communication resulting in substantial band-width consumption, while also increasing the risk of data leakage and consequent privacy issues. To address these problems, we propose a new distillation-based FL frame-work that can preserve privacy by design, while also consuming substantially less network communication resources when compared to the current methods. Our framework engages in inter-node communication using only publicly available and approved datasets, thereby giving explicit privacy control to the user. To distill knowledge among the various local models, our framework involves a novel ensemble distillation algorithm that uses both final prediction as well as model attention. This algorithm explicitly considers the diversity among various local nodes while also seeking consensus among them. This results in a comprehensive technique to distill knowledge from various decentralized nodes. We demonstrate the various aspects and the associated benefits of our FL framework through extensive experiments that produce state-of-the-art results on both classification and segmentation tasks on natural and medical images.
Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Abstract Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necessary section and the results achieved with conventional methods sometime tender poor results. Hence, this research aims to implement the CNN-assisted image segmentation scheme to extract the leukocyte section from the RGB scaled hematological images. The proposed work employs various CNN-based segmentation schemes, such as SegNet, U-Net, and VGG-UNet. We used the images from the Leukocyte Images for Segmentation and Classification (LISC) database. In this work, five classes of the leukocytes are considered, and each CNN segmentation scheme is separately implemented and evaluated with the ground-truth image. The experimental outcome of the proposed work confirms that the overall results accomplished with the VGG-UNet are better (Jaccard-Index = 91.5124%, Dice-Coefficient = 94.4080%, and Accuracy = 97.7316%) than those of the SegNet and U-Net schemes Finally, the merit of the proposed scheme is also confirmed using other similar image datasets, such as Blood Cell Count and Detection (BCCD) database and ALL-IDB2. The attained result confirms that the proposed scheme works well on hematological images and offers better performance measure values.
Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Online media has an increasing presence on the restaurants’ activities through social media websites, coinciding with an increase in customers’ reviews of these restaurants. These reviews become the main source of information for both customers and decision-makers in this field. Any customer who is seeking such places will check their reviews first, which usually affect their final choice. In addition, customers’ experiences can be enhanced by utilizing other customers’ suggestions. Consequently, customers’ reviews can influence the success of restaurant business since it is considered the final judgment of the overall quality of any restaurant. Thus, decision-makers need to analyze their customers’ underlying sentiments in order to meet their expectations and improve the restaurants’ services, in terms of food quality, ambiance, price range, and customer service. The number of reviews available for various products and services has dramatically increased these days and so has the need for automated methods to collect and analyze these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the sentiments underlying these reviews. Usually, SA for customers’ reviews face imbalanced datasets challenge, as the majority of these sentiments fall into supporters or resistors of the product or service. This work proposes a hybrid approach by combining the Support Vector Machine (SVM) algorithm with Particle Swarm Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is applied as a machine learning classification technique to predict the sentiments of reviews by optimizing the dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran, a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study shows that the proposed PSO-SVM approach produces the best results compared to different classification techniques in terms of accuracy, F-measure, G-mean and Area Under the Curve (AUC), for different versions of the datasets.
Technology acceptance before and after COVID-19: no-touch service from hotel robots

Purpose This study aims to investigate the consumer acceptance of robots in hotels before and after COVID-19, with a specific emphasis on whether COVID-19 had a significant effect on the acceptance of robots by hotel guests and whether guests had higher levels of acceptance of hotel robots since the initial COVID-19 outbreak was brought under control in China. Design/methodology/approach The sample for this research included Chinese hotel guests before and after COVID-19, with 247 responses obtained before its outbreak and a further 601 responses gathered after. Several hypotheses were developed and tested in a pseudo-experimental design. Findings The results showed that COVID-19 increased hotel guest acceptance of robots. After COVID-19, the perceived importance of the usefulness, social influence, attitude and value of robots increased, while the perceived importance of the ease of use and anthropomorphism of robots decreased. As a contactless service, the usefulness of robots was more valued by customers. This led customers to lower their requirements for the ease of use of robots. In addition, people were more concerned about the social influences on robot use. Research limitations/implications Hotel guest attitudes and behavioral intentions toward robots and the services they can provide are changing. However, whether this change is purely ephemeral and motivated by a pragmatic stance triggered by COVID-19 remains to be established. Practical implications The hospitality industry is encouraged to create a new profile of guests in terms of their favorable or unfavorable disposition toward being served by robots. Hotels should consider the deployment of robots according to the demographic characteristics of customers (e.g. according to guest age levels). Originality/value This research demonstrated that major crises affect customer attitudes and behaviors toward new technologies. COVID-19 resulted in guests paying more attention to the advantages of services offered by hotel robots as a means of reducing the probability of contagion.
HyperInverter: Improving StyleGAN Inversion via Hypernetwork

Real-world image manipulation has achieved fantastic progress in recent years as a result of the exploration and utilization of GAN latent spaces. GAN inversion is the first step in this pipeline, which aims to map the real image to the latent code faithfully. Unfortunately, the majority of existing GAN inversion methods fail to meet at least one of the three requirements listed below: high reconstruction quality, editability, and fast inference. We present a novel two-phase strategy in this research that fits all requirements at the same time. In the first phase, we train an encoder to map the input image to StyleGAN2 W-space, which was proven to have excellent editability but lower reconstruction quality. In the second phase, we supplement the reconstruction ability in the initial phase by leveraging a series of hypernetworks to recover the missing information during inversion. These two steps complement each other to yield high reconstruction quality thanks to the hypernetwork branch and excellent editability due to the inversion done in the W-space. Our method is entirely encoder-based, resulting in extremely fast inference. Extensive experiments on two challenging datasets demonstrate the superiority of our method. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project page: https://di-mi-ta.github.io/HyperInverter
General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model

This paper proposes a new General Type-2 Radial Basis Function Neural Network (GT2-RBFNN) that is functionally equivalent to a GT2 Fuzzy Logic System (FLS) of either Takagi-Sugeno-Kang (TSK) or Mamdani type. The neural structure of the GT2-RBFNN is based on the α-planes representation, in which the antecedent and consequent part of each fuzzy rule uses GT2 Fuzzy Sets (FSs). To reduce the iterative nature of the Karnik-Mendel algorithm, the Enhaned-Karnik-Mendel (EKM) type-reduction and three popular direct-defuzzification methods, namely the 1) Nie-Tan approach (NT), the 2) Wu-Mendel uncertain bounds method (WU) and the 3) Biglarbegian-Melek-Mendel algorithm (BMM) are used. Hence, this paper provides four different architectures of the GT2-RBFNN and their parametric optimisation. Such optimisation is a two-stage methodology that first implements an Iterative Information Granulation (IIG) approach to estimate the antecedent parameters of each fuzzy rule. Secondly, each consequent part and the fuzzy rule base of the GT2-RBFNN is optimised using an Adaptive Gradient Descent method (AGD) respectively. A number of popular benchmark data sets, the identification of a nonlinear system and the prediction of chaotic time series are considered. The reported comparative analysis of experimental results is used to evaluate the performance of the suggested GT2 RBFNN with respect to other popular methodologies.
DIAT: Data Integrity Attestation for Resilient Collaboration of Autonomous Systems

Networks of autonomous collaborative embedded systems are emerging in many application domains such as vehicular ad-hoc networks, robotic factory workers, search/rescue robots, delivery and search drones.To perform their collaborative tasks the involved devices exchange various types of information such as sensor data, status information, and commands.For the correct operation of these complex systems each device must be able to verify that the data coming from other devices is correct and has not been maliciously altered.In this paper, we present DIAT -a novel approach that allows to verify the correctness of data by attesting the correct generation as well as processing of data using control-flow attestation.DIAT enables devices in autonomous collaborative networks to securely and efficiently interact, relying on a minimal TCB.It ensures that the data sent from one device to another device is not maliciously changed, neither during transport nor during generation or processing on the originating device.Data exchanged between devices in the network is therefore authenticated along with a proof of integrity of all software involved in its generation and processing.To enable this, the embedded devices' software is decomposed into simple interacting modules reducing the amount and complexity of software that needs to be attested, i.e., only those modules that process the data are relevant.As a proof of concept we implemented and evaluated our scheme DIAT on a state-of-the-art flight controller for drones.Furthermore, we evaluated our scheme in a simulation environment to demonstrate its scalability for large-scale systems.
Using Similarity Measures to Select Pretraining Data for

Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cecile Paris. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Spatiotemporal Modeling for Video Summarization Using Convolutional Recurrent Neural Network

In this paper, a novel neural network named CRSum for the video summarization task is proposed. The proposed network integrates feature extraction, temporal modeling, and summary generation into an end-to-end architecture. Compared with previous work on this task, the proposed method owns three distinctive characteristics: 1) it for the first time leverages convolutional recurrent neural network for simultaneously modeling spatial and temporal structure of video for summarization; 2) thorough and delicate features of video are obtained in the proposed architecture by trainable three-dimension convolutional neural networks and feature fusion; and 3) a new loss function named Sobolev loss is defined, aiming to constrain the derivative of sequential data and exploit potential temporal structure of video. A series of experiments are conducted to prove the effectiveness of the proposed method. We further analyze our method from different aspects by well-designed experiments.
Bringing Open Data to Whole Slide Imaging

Faced with the need to support a growing number of whole slide imaging (WSI) file formats, our team has extended a long-standing community file format (OME-TIFF) for use in digital pathology. The format makes use of the core TIFF specification to store multi-resolution (or "pyramidal") representations of a single slide in a flexible, performant manner. Here we describe the structure of this format, its performance characteristics, as well as an open-source library support for reading and writing pyramidal OME-TIFFs.
Detecting Potential Warning Behaviors of Ideological Radicalization in an Alt-Right Subreddit

Over the past few years, new ideological movements like the Alt-Right have captured the attention and concern of both mainstream media, policy makers, and scholars alike. Today, the methods by which right-wing extremists are radicalized are increasingly taking place within social media platforms and online communities. However, no research has yet investigated methods for proactively detecting online communities that may be displaying overall warning signs of mass ongoing ideological and political radicalization. In our work, we use a variety of text analysis methods to investigate the behavioral patterns of a radical right-wing community on Reddit (r/altright) over a 6-month period until right before it was banned for violation of Reddit terms of service. We find that this community showed aggregated behavioral patterns that aligned with past literature on warning behaviors of individual extremists in online environments, and that these behavioral patterns were not seen in a comparison group of eight other online political communities, similar in size and user engagement. Our research helps build upon the established literature on the detection of extremism in online environments, and has implications for proactive monitoring of online communities.
Extreme Multi-Label Legal Text Classification: A Case Study in

We consider the task of Extreme Multi-Label Text Classification (XMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, the European Union’s public document database, annotated with concepts from EUROVOC, a multidisciplinary thesaurus. The dataset is substantially larger than previous EURLEX datasets and suitable for XMTC, few-shot and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with self-attention outperform the current multi-label state-of-the-art methods, which employ label-wise attention. Replacing CNNs with BIGRUs in label-wise attention networks leads to the best overall performance.
Sharp Analysis for Nonconvex SGD Escaping from Saddle Points.

In this paper, we give a sharp analysis for Stochastic Gradient Descent (SGD) and prove that SGD is able to efficiently escape from saddle points and find an $(\epsilon, O(\epsilon^{0.5}))$-approximate second-order stationary point in $\tilde{O}(\epsilon^{-3.5})$ stochastic gradient computations for generic nonconvex optimization problems, when the objective function satisfies gradient-Lipschitz, Hessian-Lipschitz, and dispersive noise assumptions. This result subverts the classical belief that SGD requires at least $O(\epsilon^{-4})$ stochastic gradient computations for obtaining an $(\epsilon,O(\epsilon^{0.5}))$-approximate second-order stationary point. Such SGD rate matches, up to a polylogarithmic factor of problem-dependent parameters, the rate of most accelerated nonconvex stochastic optimization algorithms that adopt additional techniques, such as Nesterov's momentum acceleration, negative curvature search, as well as quadratic and cubic regularization tricks. Our novel analysis gives new insights into nonconvex SGD and can be potentially generalized to a broad class of stochastic optimization algorithms.
A Closer Look at Data Bias in Neural Extractive Summarization Models

In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models.Specifically, we first propose several properties of datasets, which matter for the generalization of summarization models.Then we build the connection between priors residing in datasets and model designs, analyzing how different properties of datasets influence the choices of model structure design and training methods.Finally, by taking a typical dataset as an example, we rethink the process of the model design based on the experience of the above analysis.We demonstrate that when we have a deep understanding of the characteristics of datasets, a simple approach can bring significant improvements to the existing stateof-the-art model.
Mining cost-effective patterns in event logs

High Utility Pattern Mining is a popular task for analyzing data. It consists of discovering patterns having a high importance in databases. A popular application of high utility pattern mining is to identify high utility (profitable) patterns in customer transaction data. Though such analysis can be useful to understand data, it does not consider the cost (e.g. effort, resources, money or time) required for obtaining the utility (benefits). In this paper, we argue that to discover interesting patterns in event sequences, it is useful to consider both a utility model and a cost model. For example, to identify cost-effective ways of treating patients from medical pathways data, it is desirable to consider not only the ability of treatments to inhibit symptoms or cure a disease (utility) but also the resources consumed and the time spent (cost) to provide these treatments. Based on this perspective, this paper defines a novel task of discovering Cost-Effective Event Sequences in event logs. In this task, cost is modeled as numeric values, while utility is represented either as binary or numeric values. Measures are proposed to evaluate the trade-off and correlation between cost and utility of patterns to identify cost-effective patterns (patterns having a low cost but providing a high utility). Three efficient algorithms called CEPB, corCEPB and CEPN are designed to extract these patterns. They rely on a tight lower-bound on the cost and a memory buffering technique to find patterns efficiently. Experiments show that the proposed algorithms achieve high efficiency, that proposed optimizations improve efficiency, and that insightful cost-effective patterns are found in real-life e-learning data.
Neural networks for power flow: Graph neural solver

Recent trends in power systems and those envisioned for the next few decades push Transmission System Operators to develop probabilistic approaches to risk estimation. However, current methods to solve AC power flows are too slow to fully attain this objective. Thus we propose a novel artificial neural network architecture that achieves a more suitable balance between computational speed and accuracy in this context. Improving on our previous work on Graph Neural Solver for Power System [1], our architecture is based on Graph Neural Networks and allows for fast and parallel computations. It learns to perform a power flow computation by directly minimizing the violation of Kirchhoff’s law at each bus during training. Unlike previous approaches, our graph neural solver learns by itself and does not try to imitate the output of a Newton-Raphson solver. It is robust to variations of injections, power grid topology, and line characteristics. We experimentally demonstrate the viability of our approach on standard IEEE power grids (case9, case14, case30 and case118) both in terms of accuracy and computational time.
Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension

Integrating distributed representations with symbolic operations is essential for reading comprehension requiring complex reasoning, such as counting, sorting and arithmetics, but most existing approaches are hard to scale to more domains or more complex reasoning. In this work, we propose the Neural Symbolic Reader (NeRd), which includes a reader, e.g., BERT, to encode the passage and question, and a programmer, e.g., LSTM, to generate a program that is executed to produce the answer. Compared to previous works, NeRd is more scalable in two aspects: (1) domain-agnostic, i.e., the same neural architecture works for different domains; (2) compositional, i.e., when needed, complex programs can be generated by recursively applying the predefined operators, which become executable and interpretable representations for more complex reasoning. Furthermore, to overcome the challenge of training NeRd with weak supervision, we apply data augmentation techniques and hard Expectation-Maximization (EM) with thresholding. On DROP, a challenging reading comprehension dataset that requires discrete reasoning, NeRd achieves 2.5%/1.8% absolute improvement over the state-of-the-art on EM/F1 metrics. With the same architecture, NeRd significantly outperforms the baselines on MathQA, a math problem benchmark that requires multiple steps of reasoning, by 25.5% absolute increment on accuracy when trained on all the annotated programs. More importantly, NeRd still beats the baselines even when only 20% of the program annotations are given.