Reviewerly/exHarmony huggingface dataset:
Convergence Time Optimization for Federated Learning Over Wireless Networks

In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step. Moreover, since each user has unique training data samples, the BS prefers to include all local user FL models to generate a converged global FL model. Hence, the FL training loss and convergence time will be significantly affected by the user selection scheme. Therefore, it is necessary to design an appropriate user selection scheme that can select the users who can contribute toward improving the FL convergence speed more frequently. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time and the FL training loss. To solve this problem, a probabilistic user selection scheme is proposed such that the BS is connected to the users whose local FL models have significant effects on the global FL model with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission at each given learning step, which enables the BS to improve the global model, the FL convergence speed, and the training loss. Simulation results show that the proposed approach can reduce the FL convergence time by up to 56% and improve the accuracy of identifying handwritten digits by up to 3%, compared to a standard FL algorithm.
A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification

Sarcasm identification on text documents is one of the most challenging tasks in natural language processing (NLP), has become an essential research direction, due to its prevalence on social media data.The purpose of our research is to present an effective sarcasm identification framework on social media data by pursuing the paradigms of neural language models and deep neural networks.To represent text documents, we introduce inverse gravity moment based term weighted word embedding model with trigrams.In this way, critical words/terms have higher values by keeping the word-ordering information.In our model, we present a three-layer stacked bidirectional long short-term memory architecture to identify sarcastic text documents.For the evaluation task, the presented framework has been evaluated on three-sarcasm identification corpus.In the empirical analysis, three neural language models (i.e., word2vec, fastText and GloVe), two unsupervised term weighting functions (i.e., term-frequency, and TF-IDF) and eight supervised term weighting functions (i.e., odds ratio, relevance frequency, balanced distributional concentration, inverse question frequency-question frequency-inverse category frequency, short text weighting, inverse gravity moment, regularized entropy and inverse false negative-true positive-inverse category frequency) have been evaluated.For sarcasm identification task, the presented model yields promising results with a classification accuracy of 95.30%.
DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection

Although the YOLOv2 method is extremely fast on object detection, its detection accuracy is restricted due to the low performance of its backbone network and the under-utilization of multi-scale region features. Therefore, a dense connection (DC) and spatial pyramid pooling (SPP) based YOLO (DC-SPP-YOLO) method is proposed in this paper for ameliorating the object detection accuracy of YOLOv2. Specifically, the backbone network of YOLOv2 adopts the dense connection of convolution layers, which strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an improved spatial pyramid pooling is introduced to pool and concatenate the multi-scale region features, so that the network learns the object features more comprehensively. The DC-SPP-YOLO model is established and trained based on a new loss function composed of MSE (mean square error) loss and cross-entropy loss. The experimental results indicate that the mAP (mean Average Precision) of DC-SPP-YOLO is higher than that of YOLOv2 on the PASCAL VOC datasets and the UA-DETRAC datasets. The effectiveness of DC-SPP-YOLO method proposed is demonstrated.
Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification

Classification is an important data mining technique with a wide range of applications to classify the various types of data existing in almost all areas of our lives.The purpose of this discovery study can be used to estimate the potential of having breast cancer by taking advantage of anthropometric data and collected routine blood analysis parameters.The study was performed using data from patients who were admitted to the clinic with the suspicion of breast cancer.The values of Age (years), BMI (kg/m2), Glucose (mg/dL), Insulin (µU/mL), HOMA, Leptin (ng/mL), Adiponectin (µg/mL), Resistin (ng/mL), MCP-1(pg/dL) were used.In our study, classification algorithms were applied to the data and they were asked to estimate the disease diagnosis.The classification performance of Artificial neural networks and Naïve Bayes classifiers which were applied to data with 9 inputs and one output were calculated and theperformance results were compared.This article sheds light on the performance evaluation based on correct and incorrect data classification examples using ANN and Naïve Bayes classification algorithm.When we look at the performances obtained, it is predicted that using the anthropometric data and the collected routine blood analysis parameters, the potential for diagnosing breast cancer is high using these data.
Opinion Fraud Detection in Online Reviews by Network Effects

User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However,review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets. Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike the vast majority of existing methods that focus on review text or behavioral analysis, (2) it consists of two complementary steps; scoring users and reviews for fraud detection, and grouping for visualization and sensemaking, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available, and (4) it is scalable to large datasets as its run time grows linearly with network size. We demonstrate the effectiveness of our framework on syntheticand real datasets; where FRAUDEAGLE successfully reveals fraud-bots in a large online app review database.
Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation

The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks. In this paper, we present a lightweight network to address this problem, namely LEDNet, which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation. More specifically, the encoder adopts a ResNet as backbone network, where two new operations, channel split and shuffle, are utilized in each residual block to greatly reduce computation cost while maintaining higher segmentation accuracy. On the other hand, an attention pyramid network (APN) is employed in the decoder to further lighten the entire network complexity. Our model has less than 1M parameters, and is able to run at over 71 FPS in a single GTX 1080Ti GPU. The comprehensive experiments demonstrate that our approach achieves state-of-the-art results in terms of speed and accuracy trade-off on CityScapes dataset.
Encoding a qubit in a trapped-ion mechanical oscillator

The stable operation of quantum computers will rely on error correction, in which single quantum bits of information are stored redundantly in the Hilbert space of a larger system. Such encoded qubits are commonly based on arrays of many physical qubits, but can also be realized using a single higher-dimensional quantum system, such as a harmonic oscillator1–3. In such a system, a powerful encoding has been devised based on periodically spaced superpositions of position eigenstates4–6. Various proposals have been made for realizing approximations to such states, but these have thus far remained out of reach7–11. Here we demonstrate such an encoded qubit using a superposition of displaced squeezed states of the harmonic motion of a single trapped 40Ca+ ion, controlling and measuring the mechanical oscillator through coupling to an ancillary internal-state qubit12. We prepare and reconstruct logical states with an average squared fidelity of 87.3 ± 0.7 per cent. Also, we demonstrate a universal logical single-qubit gate set, which we analyse using process tomography. For Pauli gates we reach process fidelities of about 97 per cent, whereas for continuous rotations we use gate teleportation and achieve fidelities of approximately 89 per cent. This control method opens a route for exploring continuous variable error correction as well as hybrid quantum information schemes using both discrete and continuous variables13. The code states also have direct applications in quantum sensing, allowing simultaneous measurement of small displacements in both position and momentum14,15. A single logical qubit is encoded, manipulated and read out using a superposition of displaced squeezed states of the harmonic motion of a trapped calcium ion.
LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images

We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images. Key to our approach is the generation and scoring of a number of pose proposals per image, which allows us to predict 2D and 3D poses of multiple people simultaneously. Hence, our approach does not require an approximate localization of the humans for initialization. Our Localization-Classification-Regression architecture, named LCR-Net, contains 3 main components: 1) the pose proposal generator that suggests candidate poses at different locations in the image; 2) a classifier that scores the different pose proposals; and 3) a regressor that refines pose proposals both in 2D and 3D. All three stages share the convolutional feature layers and are trained jointly. The final pose estimation is obtained by integrating over neighboring pose hypotheses, which is shown to improve over a standard non maximum suppression algorithm. Our method recovers full-body 2D and 3D poses, hallucinating plausible body parts when the persons are partially occluded or truncated by the image boundary. Our approach significantly outperforms the state of the art in 3D pose estimation on Human3.6M, a controlled environment. Moreover, it shows promising results on real images for both single and multi-person subsets of the MPII 2D pose benchmark and demonstrates satisfying 3D pose results even for multiperson images.
Explain Yourself! Leveraging Language Models for Commonsense Reasoning

Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of worldknowledge or reasoning over information not immediately present in the input.We collect human explanations for commonsense reasoning in the form of natural language sequences and highlighted annotations in a new dataset called Common Sense Explanations (CoS-E).We use CoS-E to train language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.CAGE improves the state-of-the-art by 10% on the challenging CommonsenseQA task.We further study commonsense reasoning in DNNs using both human and auto-generated explanations including transfer to out-of-domain tasks.Empirical results indicate that we can effectively leverage language models for commonsense reasoning. 1https://github.com/nazneenrajani/CoS-EQuestion: While eating a hamburger with friends, what are people trying to do? Choices: have fun, tasty, or indigestion CoS-E: Usually a hamburger with friends indicates a good time.Question: After getting drunk people couldn't understand him,it was because of his what?Choices: lower standards,slurred speech, or falling down CoS-E: People who are drunk have difficulty speaking.
Controllable and trustworthy blockchain-based cloud data management

In recent years, there have been efforts to deploy blockchain in a broad range of applications and in different domains, such as the critical infrastructure sectors. Generally, blockchain can be leveraged to establish a fair and transparent data sharing environment, where unauthorized modification to the data can be audited and traced. There are, however, known limitations of blockchain-based solutions, such as a significantly weakened networking control capability due to the distributed nature of such solutions. In addition, decisions recorded on a blockchain cannot be changed and there is the risk of majority attack (also known as 51% attack). Seeking to mitigate these limitations, in this paper we propose a controllable blockchain data management (CBDM) model that can be deployed in a cloud environment. We then evaluate its security and performance, in order to demonstrate utility.
A Comprehensive Analysis of Deep Regression

Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, together with the stochastic nature of the optimization procedures, produce notably different results, making extremely difficult to sift methods that significantly outperform others. This situation motivates the current study, in which we perform a systematic evaluation and statistical analysis of vanilla deep regression, i.e. convolutional neural networks with a linear regression top layer. This is the first comprehensive analysis of deep regression techniques. We perform experiments on four vision problems, and report confidence intervals for the median performance as well as the statistical significance of the results, if any. Surprisingly, the variability due to different data pre-processing procedures generally eclipses the variability due to modifications in the network architecture. Our results reinforce the hypothesis according to which, in general, a general-purpose network (e.g. VGG-16 or ResNet-50) adequately tuned can yield results close to the state-of-the-art without having to resort to more complex and ad-hoc regression models.
Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI

AI is remarkably successful and outperforms human experts in certain tasks, even in complex domains such as medicine. Humans on the other hand are experts at multi-modal thinking and can embed new inputs almost instantly into a conceptual knowledge space shaped by experience. In many fields the aim is to build systems capable of explaining themselves, engaging in interactive what-if questions. Such questions, called counterfactuals, are becoming important in the rising field of explainable AI (xAI). Our central hypothesis is that using conceptual knowledge as a guiding model of reality will help to train more explainable, more robust and less biased machine learning models, ideally able to learn from fewer data. One important aspect in the medical domain is that various modalities contribute to one single result. Our main question is "How can we construct a multi-modal feature representation space (spanning images, text, genomics data) using knowledge bases as an initial connector for the development of novel explanation interface techniques?". In this paper we argue for using Graph Neural Networks as a method-of-choice, enabling information fusion for multi-modal causability (causability – not to confuse with causality – is the measurable extent to which an explanation to a human expert achieves a specified level of causal understanding). The aim of this paper is to motivate the international xAI community to further work into the fields of multi-modal embeddings and interactive explainability, to lay the foundations for effective future human–AI interfaces. We emphasize that Graph Neural Networks play a major role for multi-modal causability, since causal links between features can be defined directly using graph structures.
A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks

There has been significant research in incorporating both blockchain and intrusion detection to improve data privacy and detect existing and emerging cyberattacks, respectively. In these approaches, learning-based ensemble models can facilitate the identification of complex malicious events and concurrently ensure data privacy. Such models can also be used to provide additional security and privacy assurances during the live migration of virtual machines (VMs) in the cloud and to protect Internet-of-Things (IoT) networks. This would allow the secure transfer of VMs between data centers or cloud providers in real time. This article proposes a deep blockchain framework (DBF) designed to offer security-based distributed intrusion detection and privacy-based blockchain with smart contracts in IoT networks. The intrusion detection method is employed by a bidirectional long short-term memory (BiLSTM) deep learning algorithm to deal with sequential network data and is assessed using the data sets of UNSW-NB15 and BoT-IoT. The privacy-based blockchain and smart contract methods are developed using the Ethereum library to provide privacy to the distributed intrusion detection engines. The DBF framework is compared with peer privacy-preserving intrusion detection techniques, and the experimental outcomes reveal that DBF outperforms the other competing models. The framework has the potential to be used as a decision support system that can assist users and cloud providers in securely migrating their data in a timely and reliable manner.
Incomplete Multiview Spectral Clustering With Adaptive Graph Learning

In this paper, we propose a general framework for incomplete multiview clustering. The proposed method is the first work that exploits the graph learning and spectral clustering techniques to learn the common representation for incomplete multiview clustering. First, owing to the good performance of low-rank representation in discovering the intrinsic subspace structure of data, we adopt it to adaptively construct the graph of each view. Second, a spectral constraint is used to achieve the low-dimensional representation of each view based on the spectral clustering. Third, we further introduce a co-regularization term to learn the common representation of samples for all views, and then use the k-means to partition the data into their respective groups. An efficient iterative algorithm is provided to optimize the model. Experimental results conducted on seven incomplete multiview datasets show that the proposed method achieves the best performance in comparison with some state-of-the-art methods, which proves the effectiveness of the proposed method in incomplete multiview clustering.
Breast cancer histopathological image classification using a hybrid deep neural network

Even with the rapid advances in medical sciences, histopathological diagnosis is still considered the gold standard in diagnosing cancer. However, the complexity of histopathological images and the dramatic increase in workload make this task time consuming, and the results may be subject to pathologist subjectivity. Therefore, the development of automatic and precise histopathological image analysis methods is essential for the field. In this paper, we propose a new hybrid convolutional and recurrent deep neural network for breast cancer histopathological image classification. Based on the richer multilevel feature representation of the histopathological image patches, our method integrates the advantages of convolutional and recurrent neural networks, and the short-term and long-term spatial correlations between patches are preserved. The experimental results show that our method outperforms the state-of-the-art method with an obtained average accuracy of 91.3% for the 4-class classification task. We also release a dataset with 3771 breast cancer histopathological images to the scientific community that is now publicly available at http://ear.ict.ac.cn/?page_id=1616. Our dataset is not only the largest publicly released dataset for breast cancer histopathological image classification, but it covers as many different subclasses spanning different age groups as possible, thus providing enough data diversity to alleviate the problem of relatively low classification accuracy of benign images.
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors

Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions. As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear. To better model human language, we first model expressive nonverbal representations by analyzing the fine-grained visual and acoustic patterns that occur during word segments. In addition, we seek to capture the dynamic nature of nonverbal intents by shifting word representations based on the accompanying nonverbal behaviors. To this end, we propose the Recurrent Attended Variation Embedding Network (RAVEN) that models the fine-grained structure of nonverbal subword sequences and dynamically shifts word representations based on nonverbal cues. Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition. We also visualize the shifted word representations in different nonverbal contexts and summarize common patterns regarding multimodal variations of word representations.
The Woman Worked as a Babysitter: On Biases in Language Generation

We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups.In this work, we introduce the notion of the regard towards a demographic, use the varying levels of regard towards different demographics as a defining metric for bias in NLG, and analyze the extent to which sentiment scores are a relevant proxy metric for regard.To this end, we collect strategically-generated text from language models and manually annotate the text with both sentiment and regard scores.Additionally, we build an automatic regard classifier through transfer learning, so that we can analyze biases in unseen text.Together, these methods reveal the extent of the biased nature of language model generations.Our analysis provides a study of biases in NLG, bias metrics and correlated human judgments, and empirical evidence on the usefulness of our annotated dataset.