Reviewerly/exHarmony huggingface dataset:
Gated Channel Attention Mechanism YOLOv3 Network for Small Target Detection

In order to solve the problem of low recognition rate and high missed rate in current target detection task, this paper proposes an improved YOLOv3 algorithm based on a gated channel attention mechanism (GCAM) and adaptive up-sampling module. Firstly, darknet-53 is used as the backbone network to extract image basic features. Secondly, an adaptive up-sampling module is introduced to expand the low-resolution convolutional feature images, which effectively enhances the fusion efficiency of the convolutional feature images at different scales. Finally, GCAM is added to improve the network’s feature expression and detection capability for small targets before the three-scale channels output the prediction results. The results show that the improved method can adapt to multiscale target detection tasks in complex scenes and reduce the missing rate of a small target.
Learning Optimal Priors for Task-Invariant Representations in Variational Autoencoders

The variational autoencoder (VAE) is a powerful latent variable model for unsupervised representation learning. However, it does not work well in case of insufficient data points. To improve the performance in such situations, the conditional VAE (CVAE) is widely used, which aims to share task-invariant knowledge with multiple tasks through the task-invariant latent variable. In the CVAE, the posterior of the latent variable given the data point and task is regularized by the task-invariant prior, which is modeled by the standard Gaussian distribution. Although this regularization encourages independence between the latent variable and task, the latent variable remains dependent on the task. To reduce this task-dependency, the previous work introduced an additional regularizer. However, its learned representation does not work well on the target tasks. In this study, we theoretically investigate why the CVAE cannot sufficiently reduce the task-dependency and show that the simple standard Gaussian prior is one of the causes. Based on this, we propose a theoretical optimal prior for reducing the task-dependency. In addition, we theoretically show that unlike the previous work, our learned representation works well on the target tasks. Experiments on various datasets show that our approach obtains better task-invariant representations, which improves the performances of various downstream applications such as density estimation and classification.
Compute Like Humans: Interpretable Step-by-step Symbolic Computation with Deep Neural Network

Neural network capability in symbolic computation has emerged in much recent work. However, symbolic computation is always treated as an end-to-end blackbox prediction task, where human-like symbolic deductive logic is missing. In this paper, we argue that any complex symbolic computation can be broken down to a sequence of finite Fundamental Computation Transformations (FCT), which are grounded as certain mathematical expression computation transformations. The entire computation sequence represents a full human understandable symbolic deduction process. Instead of studying on different end-to-end neural network applications, this paper focuses on approximating FCT which further build up symbolic deductive logic. To better mimic symbolic computations with math expression transformations, we propose a novel tree representation learning architecture GATE (Graph Aggregation Transformer Encoder) for math expressions. We generate a large-scale math expression transformation dataset for training purpose and collect a real-world dataset for validation. Experiments demonstrate the feasibility of producing step-by-step human-like symbolic deduction sequences with the proposed approach, which outperforms other neural network approaches and heuristic approaches.
Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction

Opioids (e.g., oxycodone and morphine) are highly addictive prescription (aka Rx) drugs which can be easily overprescribed and lead to opioid overdose. Recently, the opioid epidemic is increasingly serious across the US as its related deaths have risen at alarming rates. To combat the deadly opioid epidemic, a state-run prescription drug monitoring program (PDMP) has been established to alleviate the drug over-prescribing problem in the US. Although PDMP provides a detailed prescription history related to opioids, it is still not enough to prevent opioid overdose because it cannot predict over-prescribing risk. In addition, existing machine learning-based methods mainly focus on drug doses while ignoring other prescribing patterns behind patients' historical records, thus resulting in suboptimal performance. To this end, we propose a novel model DDHGNN - Disentangled Dynamic Heterogeneous Graph Neural Network, for over-prescribing prediction. Specifically, we abstract the PDMP data into a dynamic heterogeneous graph which comprehensively depicts the prescribing and dispensing (P&D) relationships. Then, we design a dynamic heterogeneous graph neural network to learn patients' representations. Furthermore, we devise an adversarial disentangler to learn a disentangled representation which is particularly related to the prescribing patterns. Extensive experiments on a 1-year anonymous PDMP data demonstrate that DDHGNN outperforms state-of-the-art methods, revealing its promising future in preventing opioid overdose.
Semantic Aware Answer Sentence Selection Using Self-Learning Based Domain Adaptation

Selecting an appropriate and relevant context forms an essential component for the efficacy of several information retrieval applications like Question Answering (QA) systems. The problem of Answer Sentence Selection (AS2) refers to the task of selecting sentences, from a larger text, that are relevant and contain the answer to users' queries. While there has been a lot of success in building AS2 systems trained on open-domain data (e.g., SQuAD, NQ), they do not generalize well in closed-domain settings, since domain adaptation can be challenging due to poor availability and annotation expense of domain-specific data. This paper proposes SEDAN, an effective self-learning framework to adapt AS2 models for domain-specific applications. We leverage large pre-trained language models to automatically generate domain-specific QA pairs for domain adaptation. We further fine-tune a pre-trained Sentence-BERT architecture to capture semantic relatedness between questions and answer sentences for AS2. Extensive experiments demonstrate the effectiveness of our proposed approach (over existing state-of-the-art AS2 baselines) on different Question Answering benchmark datasets.
Big graph partitioning

At many levels, from storage infrastructures to programming paradigms, billion-node graphs pose considerable obstacles. It is necessary to provide a general-purpose graph-processing platform. A distributed memory system is thought to be a viable platform for both on-line and offline query processing and graph analytics. In this research, we look at how to partition a billion-node graph on such a platform, which is crucial because it affects load balancing and communication overhead directly. It's difficult not only because the graph is vast, but also because we can't assume that the data can be structured in any way to maximize the partitioning algorithm's speed. Instead, the algorithm must follow the system's and other applications' data and programming models. We present a multi-level label propagation (MLP) method for graph partitioning in this paper.
Paving the Way Towards Collective Intelligence at the IoT Edge

With the scale of data generated and the increasing computing capabilities at the IoT Edge, moving intelligence to end-devices and gateways in IoT networks is within the realms of possibility. There are tremendous hopes on how self-awareness in IoT networks could be helpful to solve a variety of smart city use case problems. Self-awareness implies self-teaching; the network devices autonomously become smarter with AI. Collective intelligence is a subset of self-awareness but not yet formalized and exploited at the Edge of IoT networks. In this paper, current mathematical models for Collective Intelligence are studied and compared. Their suitability for on-device machine learning deployment is also explored. Moreover, it attempts to lay down a framework using graph theory coupled with artificial neural networks to model a collective intelligence system applicable to a specific scenario.
Facilitating privacy-preserving activity recognition in age-friendly environments through low-power devices

Advances in the Internet of Things (IoT) technologies are being applied in various industries, but lately, they are also finding applications in home-based healthcare systems. Such pervasive healthcare systems aim to enable older adults to receive better and more cost-effective care in their preferred home environment. Battery-powered IoT devices are essential for low-cost deployment, especially in rural areas. However, one of the main challenges for any battery-powered device is energy management so that the period between battery changes is prolonged. This paper proposes an energy-saving approach for a non-invasive passive infrared (PIR) sensor. The proposed method can put the device into a deep sleep to minimize the energy consumption and use hardware wake-up interrupts to make it functional again. The analyzed sensor kit allows the detection of persons, including recognition of their actions, while preserving the privacy of the person. This is very important for age-friendly environments where privacy-preserving is essential.
New and Efficient Lightweight Cryptography Algorithm for Mobile and Web Applications

Essentially all forms of online communication involve the use of cryptography to ensure that only the intended recipient of a message can read it. Nearly every protocol includes message encryption as a part of its standard. Whether it is https, sh, or S[MIME, the most common types of connections have encryption enabled by default. Most computers can encrypt and decrypt information without any perceivable difference to the user. Processors found in home computers are fairly powerful, but home computers are not the only devices people have. There are an increasing number of smart devices in your average person's home. These smart IoT devices do not have as powerful hardware as the average PC. To maintain the expected instant transmission of data while providing security, lightweight crypto algorithms are needed for these devices.
17th International Workshop on Mining and Learning with Graphs (MLG)

The 17th International Workshop on Mining and Learning with Graphs (MLG) is held in Washington DC, USA on August 15, 2022 and is co-located with the Eighth International Workshop on Deep Learning on Graphs (DLG) as part of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. This workshop is a forum for exchanging ideas and methods for mining and learning with graphs, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. In doing so, we aim to better understand the overarching principles and the limitations of our current methods, and to inspire research on new algorithms and techniques for mining and learning with graphs. Topics of interest include, but are not limited to, graph mining, statistical relational learning, social network analysis, and network science. The target audience spans researchers and practitioners across academia, government, and industry.
Aligning Dual Disentangled User Representations from Ratings and Textual Content

Classical recommendation methods typically render user representation as a single vector in latent space. Oftentimes, a user's interactions with items are influenced by several hidden factors. To better uncover these hidden factors, we seek disentangled representations. Existing disentanglement methods for recommendations are mainly concerned with user-item interactions alone. To further improve not only the effectiveness of recommendations but also the interpretability of the representations, we propose to learn a second set of disentangled user representations from textual content and to align the two sets of representations with one another. The purpose of this coupling is two-fold. For one benefit, we leverage textual content to resolve sparsity of user-item interactions, leading to higher recommendation accuracy. For another benefit, by regularizing factors learned from user-item interactions with factors learned from textual content, we map uninterpretable dimensions from user representation into words. An attention-based alignment is introduced to align and enrich hidden factors representations. A series of experiments conducted on four real-world datasets show the efficacy of our methods in improving recommendation quality.
Synthesising Audio Adversarial Examples for Automatic Speech Recognition

Adversarial examples in automatic speech recognition (ASR) are naturally sounded by humans yet capable of fooling well trained ASR models to transcribe incorrectly. Existing audio adversarial examples are typically constructed by adding constrained perturbations on benign audio inputs. Such attacks are therefore generated with an audio dependent assumption. For the first time, we propose the Speech Synthesising based Attack (SSA), a novel threat model that constructs audio adversarial examples entirely from scratch, i.e., without depending on any existing audio to fool cutting-edge ASR models. To this end, we introduce a conditional variational auto-encoder (CVAE) as the speech synthesiser. Meanwhile, an adaptive sign gradient descent algorithm is proposed to solve the adversarial audio synthesis task. Experiments on three datasets (i.e., Audio Mnist, Common Voice, and Librispeech) show that our method could synthesise naturally sounded audio adversarial examples to mislead the start-of-the-art ASR models. Our web-page containing generated audio demos is at https://sites.google.com/view/ssa-asr/home.
DP-GAT: A Framework for Image-based Disease Progression Prediction

Predicting disease progression is key to provide stratified patient care and enable good utilization of healthcare resources. The availability of longitudinal images has enabled image-based disease progression prediction. In this work, we propose a framework called DP-GAT to identify regions containing significant biological structures and model the relationships among these regions as a graph along with their respective contexts. We perform reasoning via Graph Attention Network to generate representations that enable accurate disease progression prediction. We further extend DP-GAT to perform 3D medical volume segmentation. Experiments on real world medical image datasets demonstrate the advantage of our approach over strong baseline methods for both disease progression prediction and 3D segmentation tasks.
The DipEncoder: Enforcing Multimodality in Autoencoders

Hartigan's Dip-test of unimodality gained increasing interest in unsupervised learning over the past few years. It is free from complex parameterization and does not require a distribution assumed a priori. A useful property is that the resulting Dip-values can be derived to find a projection axis that identifies multimodal structures in the data set. In this paper, we show how to apply the gradient not only with respect to the projection axis but also with respect to the data to improve the cluster structure. By tightly coupling the Dip-test with an autoencoder, we obtain an embedding that clearly separates all clusters in the data set. This method, called DipEncoder, is the basis of a novel deep clustering algorithm. Extensive experiments show that the DipEncoder is highly competitive to state-of-the-art methods.
Avoiding Biases due to Similarity Assumptions in Node Embeddings

Node embeddings are vectors, one per node, that capture a graph's structure. The basic structure is the adjacency matrix of the graph. Recent methods also make assumptions about the similarity of unlinked nodes. However, such assumptions can lead to unintentional but systematic biases against groups of nodes. Calculating similarities between far-off nodes is also difficult under privacy constraints and in dynamic graphs. Our proposed embedding, called NEWS, makes no similarity assumptions, avoiding potential risks to privacy and fairness. NEWS is parameter-free, enables fast link prediction, and has linear complexity. These gains from avoiding assumptions do not significantly affect accuracy, as we show via comparisons against several existing methods on $21$ real-world networks. Code is available at https://github.com/deepayan12/news.
External Knowledge Infusion for Tabular Pre-training Models with Dual-adapters

Tabular pre-training models have received increasing attention due to the wide-ranging applications for tabular data analysis. However, most of the existing solutions are directly built upon the tabular data with a mixture of non-semantic and semantic contents. According to the statistics, only 30% of tabular data in wikitables are semantic entities that are surrounded and isolated by enormous irregular characters such as numbers, strings, symbols, etc. Despite the small portion, such semantic entities are crucial for table understanding. This paper attempts to enhance the existing tabular pre-training model by injecting common-sense knowledge from external sources. Compared with the knowledge injection in the natural language pre-training models, the tabular model naturally requires overcoming the domain gaps between external knowledge and tabular data with significant differences in both structures and contents. To this end, we propose the dual-adapters inserted within the pre-trained tabular model for flexible and efficient knowledge injection. The two parallel adapters are trained by the knowledge graph triplets and semantically augmented tables respectively for infusion and alignment with the tabular data. In addition, a path-wise attention layer is attached below to fuse the cross-domain representation with the weighted contribution. Finally, to verify the effectiveness of our proposed knowledge injection framework, we extensively test it on 5 different application scenarios covering both zero-shot and finetuning-based tabular understanding tasks over the cell, column, and tables levels.
CS-RAD: Conditional Member Status Refinement and Ability Discovery for Social Network Applications

In a social network environment, member status represents a member's social value in the network. A member's abilities represent the potential of a member projecting his/her social values to others, and also represent the level of credibility and authority for a member to hold certain status. Therefore, the concepts of status and ability are deeply related, and should be consistent with each other. In this paper, we establish the consistency models among different member status and their abilities through analyzing member data and integrating domain knowledge. We use these models to help our members refine their inconsistent status, at the same time, identify ability gaps. To reliably refine a member status, we introduce a practical and human-in-the-loop methodology to build status hierarchy. Conditioned on the hierarchical structure, our modeling process exploits the associations between status and abilities. We applied the technique to LinkedIn member titles -- one of the major types of the member status, and member skills -- the main ability representations at LinkedIn. We showed that our models are intuitive and perform well. The skill gaps identified are actionable and concise. In this paper, we also discuss the aspects of building such systems, and how we could deploy the models in production.
Toward Real-life Dialogue State Tracking Involving Negative Feedback Utterances

Recently, the research of dialogue systems has been widely concerned, especially task-oriented dialogue systems, which have received increased attention due to their wide application prospect. As a core component, dialogue state tracking (DST) plays a key role in task-oriented dialogue systems, and its function is to parse natural language dialogues into dialogue state formed by slot-value pairs. It is well known that dialogue state tracking has been well studied and explored on current benchmark datasets such as the MultiWOZ. However, almost all current research completely ignores the user negative feedback utterances that exist in real-life conversations when a system error occurs, which often contains user-provided corrective information for the system error. Obviously, user negative feedback utterances can be used to correct the inevitable errors in automatic speech recognition and model generalization. Thus, in this paper, we will explore the role of negative feedback utterances in dialogue state tracking in detail through simulated negative feedback utterances. Specifically, due to the lack of dataset involving negative feedback utterances, first, we have to define the schema of user negative feedback utterances and propose a joint modeling method for feedback utterance generation and filtering. Then, we explore three aspects of interaction mechanism that should be considered in real-life conversations involving negative feedback utterances and propose evaluation metrics related to negative feedback utterances. Finally, on WOZ2.0 and MultiWOZ2.1 datasets, by constructing simulated negative feedback utterances in training and testing, we not only verify the important role of negative feedback utterances in dialogue state tracking, but also analyze the advantages and disadvantages of different interaction mechanisms involving negative feedback utterances, lighting future research on negative feedback utterances.
DICE: Domain-attack Invariant Causal Learning for Improved Data Privacy Protection and Adversarial Robustness

The adversarial attack reveals the vulnerability of deep models by incurring test domain shift, while delusive attack relieves the privacy concern about personal data by injecting malicious noise into the training domain to make data unexploitable. However, beyond their successful applications, the two attacks can be easily defended by adversarial training (AT). While AT is not the panacea, it suffers from poor generalization for robustness. For the limitations of attack and defense, we argue that to fit data well, DNNs can learn the spurious relations between inputs and outputs, which are consequently utilized by the attack and defense and degrade their effectiveness, and DNNs can not easily capture the causal relations like humans to make robust decisions under attacks. In this paper, to better understand and improve attack and defense, we first take a bottom-up perspective to describe the correlations between latent factors and observed data, then analyze the effect of domain shift on DNNs induced by attack and finally develop our causal graph, namely Domain-attack Invariant Causal Model (DICM). Based on DICM, we propose a coherent causal invariant principle, which guides our algorithm design to infer the human-like causal relations. We call our algorithm Domain-attack Invariant Causal Learning (DICE) and the experimental results on two attacks and one defense task verify its effectiveness.