Reviewerly/exHarmony huggingface dataset:
Graph classification based on structural features of significant nodes and spatial convolutional neural networks

Many real-world problems can be abstracted into graph classification problems. Recently, graph convolutional networks have achieved great success in the task of node classification and link prediction. However, when using graph convolution network to process the task of graph classification, either global topology information or local information is ignored. Therefore, designing graph convolutional networks to improve the accuracy of graph classification has attracted more and more attention. Inspired by the use of convolutional neural networks to process graph-structured data, we put forward a new spatial convolutional neural network architecture for graph classification. To be specific, we first design a comprehensive weighting method to measure the significance of vertices in the graph based on multiple indicators to choose the central node sequence. Then, the normalization process of the graph is realized by constructing the same size neighborhood graphs for the central vertices. After that, the structural characteristics of the graph are extracted from both local and global aspects. Finally, the tensors obtained after the above steps are respectively input into the following two spatial convolutional neural network architectures to perform classification, one is a simple CNN structure, which has only two convolution layers, one dense layer and one softmax layer. The other is to modify the architecture of CNN, and the channel concatenation layer is introduced to determine the classification result of the entire graph according to the category of the neighborhood graphs. Experimental results on two kinds of real-world datasets, bioinformatics and social network datasets, indicate that our approach obtains competitive results and is superior to some classic kernels and similar deep learning-based algorithms on 6 out of 8 benchmark data sets.
Post-comparison mitigation of demographic bias in face recognition using fair score normalization

Current face recognition systems achieve high progress on several benchmark tests. Despite this progress, recent works showed that these systems are strongly biased against demographic sub-groups. Consequently, an easily integrable solution is needed to reduce the discriminatory effect of these biased systems. Previous work mainly focused on learning less biased face representations, which comes at the cost of a strongly degraded overall recognition performance. In this work, we propose a novel unsupervised fair score normalization approach that is specifically designed to reduce the effect of bias in face recognition and subsequently lead to a significant overall performance boost. Our hypothesis is built on the notation of individual fairness by designing a normalization approach that leads to treating “similar” individuals “similarly”. Experiments were conducted on three publicly available datasets captured under controlled and in-the-wild circumstances. Results demonstrate that our solution reduces demographic biases, e.g. by up to 82.7% in the case when gender is considered. Moreover, it mitigates the bias more consistently than existing works. In contrast to previous works, our fair normalization approach enhances the overall performance by up to 53.2% at false match rate of 10−3 and up to 82.9% at a false match rate of 10−5. Additionally, it is easily integrable into existing recognition systems and not limited to face biometrics.
Outlier-robust multi-view clustering for uncertain data

Nowadays, multi-view clustering is drawn more and more attention in the area of machine learning because real-world datasets frequently consist of multiple views. Moreover, it provides complementary and consensus information across multiple views. So, owing to the efficacy of revealing the concealed patterns in uncertain data, multiple views are considered in this study. But, a multi-view clustering algorithm is not alone sufficient to increase accuracy. A similarity measure is equally important in uncertain data clustering. However, existing similarity functions for clustering uncertain data afflict with several problems. Geometric distance-based similarity function cannot correctly capture the change between uncertain data with their distributions when they are massively location-wise overlapped. On the other hand, the divergence-based similarity function cannot discriminate against the change between various duos of absolutely disjointed uncertain data. Thus, a self-adaptive mixture similarity function based on geometric distance and S-divergence is introduced for uncertain data clustering. The proposed similarity function is integrated with k-medoids based multi-view clustering. The proposed method reduces the effect of outliers and noises since it uses the threshold-based residual objective function in k-medoids. Finally, extensive experimental results on synthetic and real-world uncertain datasets illustrate that the proposed method consistently defeats the state-of-the-art clustering algorithms. Experimental results also demonstrate the effectiveness and robustness of the proposed method against noise and outliers.
CaSe4SR: Using category sequence graph to augment session-based recommendation

Session-based recommendation aims to predict next item based on users’ anonymous behavior sequence within a short time. Recent studies focus on modeling sequential dependencies or complex relations among items in a session via recurrent/convolutional/graph neural networks. However, the following problems still remain: for short sessions, limited interactions cannot manifest user’s intent clearly; for long sessions, user’s interest may drift but be blurred by complex transitions. Motivated by the observation that different items are often belong to only a few categories or that closely related, in this article, we tackle these challenges by leveraging item category information, which is a concise form of knowledge and readily available in many platforms. We propose a novel method CaSe4SR that utilizes category sequence graph to augment session-based recommendation. In CaSe4SR, we build an item graph and a category graph, from user behavior sequence and item category sequence. The latter summarizes the former at concept level, which reduces item-level user behavior noises and makes user’s interest clearer. Afterwards, graph neural networks are applied on item graph and category graph respectively to learn representations of items and categories. Then two alternative fusion strategies and attention mechanism are designed to integrate them, yielding global embedding of the session, which is further combined with representation of last item to get ultimate session representation. Extensive experiments on real-world datasets show that CaSe4SR outperforms other state-of-the-art methods consistently. Detailed analysis reveals that category sequence graph is beneficial for next-item recommendation in sessions with different lengths.
Mutual Information-Based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging

Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is an interesting and difficult challenge. This problem occurs frequently in medical imaging applications when attempts are made to deploy and improve deep learning models across different image acquisition devices, across acquisition parameters or if some classes are unavailable in new training databases. To address this problem, we propose Mutual Information-based Disentangled Neural Networks (MIDNet), which extract generalizable categorical features to transfer knowledge to unseen categories in a target domain. The proposed MIDNet adopts a semi-supervised learning paradigm to alleviate the dependency on labeled data. This is important for real-world applications where data annotation is time-consuming, costly and requires training and expertise. We extensively evaluate the proposed method on fetal ultrasound datasets for two different image classification tasks where domain features are respectively defined by shadow artifacts and image acquisition devices. Experimental results show that the proposed method outperforms the state-of-the-art on the classification of unseen categories in a target domain with sparsely labeled training data.
VisualCOMET: Reasoning About the Dynamic Context of a Still Image

Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggling to stay afloat in water, we can reason that the man fell into the water sometime in the past, the intent of that man at the moment is to stay alive, and he will need help in the near future or else he will get washed away. We propose VisualCOMET, (Visual Commonsense Reasoning in Time.) the novel framework of visual commonsense reasoning tasks to predict events that might have happened before, events that might happen next, and the intents of the people at present. To support research toward visual commonsense reasoning, we introduce the first large-scale repository of Visual Commonsense Graphs that consists of over 1.4 million textual descriptions of visual commonsense inferences carefully annotated over a diverse set of 59,000 images, each paired with short video summaries of before and after. In addition, we provide person-grounding (i.e., co-reference links) between people appearing in the image and people mentioned in the textual commonsense descriptions, allowing for tighter integration between images and text. We establish strong baseline performances on this task and demonstrate that integration between visual and textual commonsense reasoning is the key and wins over non-integrative alternatives.
Methods for correcting inference based on outcomes predicted by machine learning

Many modern problems in medicine and public health leverage machine-learning methods to predict outcomes based on observable covariates. In a wide array of settings, predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between observed and predicted outcomes. We call inference with predicted outcomes postprediction inference. In this paper, we develop methods for correcting statistical inference using outcomes predicted with arbitrarily complicated machine-learning models including random forests and deep neural nets. Rather than trying to derive the correction from first principles for each machine-learning algorithm, we observe that there is typically a low-dimensional and easily modeled representation of the relationship between the observed and predicted outcomes. We build an approach for postprediction inference that naturally fits into the standard machine-learning framework where the data are divided into training, testing, and validation sets. We train the prediction model in the training set, estimate the relationship between the observed and predicted outcomes in the testing set, and use that relationship to correct subsequent inference in the validation set. We show our postprediction inference (postpi) approach can correct bias and improve variance estimation and subsequent statistical inference with predicted outcomes. To show the broad range of applicability of our approach, we show postpi can improve inference in two distinct fields: modeling predicted phenotypes in repurposed gene expression data and modeling predicted causes of death in verbal autopsy data. Our method is available through an open-source R package: https://github.com/leekgroup/postpi.
Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition

Existing deep learning methods for action recognition in videos require a large number of labeled videos for training, which is labor-intensive and time-consuming. For the same action, the knowledge learned from different media types, e.g., videos and images, may be related and complementary. However, due to the domain shifts and heterogeneous feature representations between videos and images, the performance of classifiers trained on images may be dramatically degraded when directly deployed to videos. In this paper, we propose a novel method, named Deep Image-to-Video Adaptation and Fusion Networks (DIVAFN), to enhance action recognition in videos by transferring knowledge from images using video keyframes as a bridge. The DIVAFN is a unified deep learning model, which integrates domain-invariant representations learning and cross-modal feature fusion into a unified optimization framework. Specifically, we design an efficient cross-modal similarities metric to reduce the modality shift among images, keyframes and videos. Then, we adopt an autoencoder architecture, whose hidden layer is constrained to be the semantic representations of the action class names. In this way, when the autoencoder is adopted to project the learned features from different domains to the same space, more compact, informative and discriminative representations can be obtained. Finally, the concatenation of the learned semantic feature representations from these three autoencoders are used to train the classifier for action recognition in videos. Comprehensive experiments on four real-world datasets show that our method outperforms some state-of-the-art domain adaptation and action recognition methods.
Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective

Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this article, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensing framework, and bridge the gap between the NILM inference accuracy and differential privacy's parameters. We then derive the valid theoretical bounds to offer insights on how the differential privacy parameters affect the NILM performance. Moreover, we generalize our conclusions by proposing the hierarchical framework to solve the multi-shot NILM problem. Numerical experiments verify our analytical results and offer better physical insights of differential privacy in various practical scenarios. This also demonstrates the significance of our work for the general privacy preserving mechanism design.
Task-Aware Quantization Network for JPEG Image Compression

We propose to learn a deep neural network for JPEG image compression, which predicts image-specific optimized quantization tables fully compatible with the standard JPEG encoder and decoder. Moreover, our approach provides the capability to learn task-specific quantization tables in a principled way by adjusting the objective function of the network. The main challenge to realize this idea is that there exist non-differentiable components in the encoder such as run-length encoding and Huffman coding and it is not straightforward to predict the probability distribution of the quantized image representations. We address these issues by learning a differentiable loss function that approximates bitrates using simple network blocks—two MLPs and an LSTM. We evaluate the proposed algorithm using multiple task-specific losses—two for semantic image understanding and another two for conventional image compression—and demonstrate the effectiveness of our approach to the individual tasks.
BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA

Khandelwal et al. (2020) use a k-nearestneighbor (kNN) component to improve language model performance.We show that this idea is beneficial for open-domain question answering (QA).To improve the recall of facts encountered during training, we combine BERT (Devlin et al., 2019) with a traditional information retrieval step (IR) and a kNN search over a large datastore of an embedded text collection.Our contributions are as follows: i) BERT-kNN outperforms BERT on cloze-style QA by large margins without any further training.ii) We show that BERT often identifies the correct response category (e.g., US city), but only kNN recovers the factually correct answer (e.g., "Miami").iii) Compared to BERT, BERT-kNN excels for rare facts.iv) BERT-kNN can easily handle facts not covered by BERT's training set, e.g., recent events.
Circuit Compilation Methodologies for Quantum Approximate Optimization Algorithm

The quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid algorithm to solve hard combinatorial optimization problems. The multi-qubit CPHASE gates used in the quantum circuit for QAOA are commutative i.e., the order of the gates can be altered without changing the output state. This re-ordering leads to the execution of more gates in parallel and a smaller number of additional SWAP gates to compile the QAOA-circuit. Consequently, the circuit-depth and cumulative gate-count become lower which is beneficial for circuit execution time and noise resilience. A less number of gates indicates a lower accumulation of gate-errors, and a reduced circuit-depth means less decoherence time for the qubits. However, finding the best-ordered circuit is a difficult problem and does not scale well with circuit size. This paper presents four generic methodologies to optimize QAOA-circuits by exploiting gate re-ordering. We demonstrate a reduction in gate-count by ≈23.0% and circuit-depth by ≈53.0% on average over a conventional approach without incurring any compilation-time penalty. We also present a variation-aware compilation which enhances the compiled circuit success probability by ≈62.7% for the target hardware over the variation unaware approach. A new metric, Approximation Ratio Gap (ARG), is proposed to validate the quality of the compiled QAOA-circuit instances on actual devices. Hardware implementation of a number of QAOA instances shows ≈25.8% improvement in the proposed metric on average over the conventional approach on ibmq 16 melbourne.
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers

To build an interpretable neural text classifier, most of the prior work has focused on designing inherently interpretable models or finding faithful explanations.A new line of work on improving model interpretability has just started, and many existing methods require either prior information or human annotations as additional inputs in training.To address this limitation, we propose the variational word mask (VMASK) method to automatically learn task-specific important words and reduce irrelevant information on classification, which ultimately improves the interpretability of model predictions.The proposed method is evaluated with three neural text classifiers (CNN, LSTM, and BERT) on seven benchmark text classification datasets.Experiments show the effectiveness of VMASK in improving both model prediction accuracy and interpretability.
Analysis of concept drift in fake reviews detection

Online reviews have a substantial impact on decision making in various areas of society, predominantly in the arena of buying and selling of goods. As such, the truthfulness of internet reviews is critical for both consumers and vendors. Fake reviews not only mislead innocent clients and influence customers' choice, leading to inaccurate descriptions and sales. This raises the need for efficient fake review detection models and tools that can address these issues. Analysing a text data stream of fake reviews in concept drift appears to reduce the effectiveness of the detection models. Despite several efforts to develop algorithms for detecting fake reviews, one crucial aspect that has not been addressed is finding a real correlation between the concept drift score and the classification of performance over-time in the real-world data stream. Consequently, we have introduced a comprehensive analysis to investigate the concept drift problem within fake review detection. There are two methods to achieve this goal: benchmarking concept drift detection method and content-based classification methods. We conducted our experiment using four real-world datasets from Yelp.com. The results demonstrated that there is a strong negative correlation between concept drift and the performance of fake review detection/prediction models, which indicates the difficulty of building more efficient models.
ThymesisFlow: A Software-Defined, HW/SW co-Designed Interconnect Stack for Rack-Scale Memory Disaggregation

With cloud providers constantly seeking the best infrastructure trade-off between performance delivered to customers and overall energy/utilization efficiency of their data-centres, hardware disaggregation comes in as a new paradigm for dynamically adapting the data-centre infrastructure to the characteristics of the running workloads. Such an adaptation enables an unprecedented level of efficiency both from the standpoint of energy and the utilization of system resources. In this paper, we present - ThymesisFlow - the first, to our knowledge, full-stack prototype of the holy-grail of disaggregation of compute resources: pooling of remote system memory. Thymesis-Flow implements a HW/SW co-designed memory disaggregation interconnect on top of the POWER9 architecture, by directly interfacing the memory bus via the OpenCAPI port. We use ThymesisFlow to evaluate how disaggregated memory impacts a set of cloud workloads, and we show that for many of them the performance degradation is negligible. For those cases that are severely impacted, we offer insights on the underlying causes and viable cross-stack mitigation paths.
A modified teaching–learning-based optimization algorithm for solving optimization problem

In order to reduce the NOx emissions concentration of a circulation fluidized bed boiler, a modified teaching–learning-based optimization algorithm (MTLBO) is proposed, which introduces a new population group mechanism into the conventional teaching–learning based optimization algorithm. The MTLBO still has two phases: Teaching phase and Learning phase. In teaching phase, all students are divided into two groups based on the mean marks of the class, the two groups present different solution updating strategies, separately. In learning phase, all students are divided into two groups again, where the first group includes the top half of the students and the second group contains the remaining students. The two groups also have different solution updating strategies. Performance of the proposed MTLBO algorithm is evaluated by 14 unconstrained numerical functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed. In addition, the tuned extreme learning machine by MTLBO is applied to establish the NOx emission model. Based on the established model, the MTLBO is used to optimize the operation conditions of a 330 MW circulation fluidized bed boiler for reducing the NOx emissions concentration. Experimental results reveal that the MTLBO is an effective tool for reducing the NOx emissions concentration.