Reviewerly/exHarmony huggingface dataset:
Improving the co-training algorithm to enhance semi-supervised learning results

The co-training algorithm is one of the most common methods of semi-supervised learning in machine learning, which allows multiple learners to collaborate to discover the best information in unlabelled data. Co-training works well if the two views satisfy the sufficiency and independence assumptions. As a result of these assumptions of co-training and advancements in data classification algorithms, the performance of the underlying model could be improved. Specifically, view division, correlation between features in each view, domain knowledge, and label confidence estimation are introduced as key steps in improving co-training algorithms in this paper. Furthermore, we discuss the problems with the co-training methods currently being used, suggest some improvements, and speculate at how the algorithm could be improved going forward.
A SHAP-based Active Learning Approach for Creating High-Quality Training Data

Machine learning-based text classification models require labeled data for training. However, manual labeling is a costly and time-consuming process. This task is particularly difficult in domains such as banking, where outsourcing data labeling is generally not allowed due to privacy laws. We propose a novel active learning-based approach in which the most difficult instances in the pool of unlabeled data are selected based on the Shapley Additive Explanations (SHAP) values of the words in the texts to be classified and passed to human annotators for labeling. At each iteration of this human-in-the-loop strategy, newly labeled instances are added to the training set. We demonstrate the effectiveness of this approach in classifying customer comments in the banking domain surveys. Our experiments indicate that better results are achieved when the proposed approach is used to expand the training set, compared to a baseline strategy of expanding the training set with randomly selected instances. Further analysis shows that the difference in performance between the two approaches becomes more pronounced as class imbalance increases. This study suggests that human-in-the-loop based active learning is a powerful strategy for creating high-quality training datasets by effectively leveraging human annotation effort.
SHCN: Self-supervised General Hypergraph Clustering Network

Clustering is a fundamental and hot issue in the unsupervised learning area. With the rapid development of deep learning and graph neural networks (GNNs) techniques, researchers have proposed a series of effective clustering methods. However, most existing approaches adopt a conventional graph to aggregate the neighborhood information, where only the pairwise relations are considered. Moreover, the redundancy/noise in the raw data samples may result in less accurate sample relations and inferior clustering results. In this paper, we proposed a new GNNs based clustering method, which adopts the hypergraph learning approach to explore the high-order relationship for accurate relation learning. Specifically, we first construct two hypergraph representations based on the topology feature and attribute feature from data samples. Then, a self-supervised structure is integrated to learn a cross-correlation matrix from the original hypergraph to act as a higher-order neighborhood with reduced redundancy and noise. Finally the embedding representation of the clustering space is learned in the graph convolution. The proposed method has been evaluated on six public datasets for clustering tasks. Experimental results show that our proposed method outperforms the state-of-the-art ones.
EntityRank: Unsupervised Mining of Bilingual Named Entity Pairs from Parallel Corpora for Neural Machine Translation

As Neural Machine Translation (NMT) heavily relies on training data, finding an effective method to help NMT make better use of limited data is of great significance. In this paper, with the motivation of the famous Google’s PageRank algorithm, we propose a novel unsupervised method EntityRank for mining bilingual named entity pairs from parallel corpora, which involves three critical components (Generator, Scorer and Filter). To apply the pairs mined by EntityRank to NMT, we design a data augmentation strategy for the state-of-the-art (SOTA) model Transformer. From the experimental results on the CCMT20 English-Chinese and WMT14 English-German news parallel corpora, it can be seen that the unsupervised method EntityRank could obtain relatively high quality bilingual named entity pairs; and with the designed data augmentation strategy, the mined pairs could not only significantly improve the translation quality of their covered data, but also benefit the translation quality of the overall data.
Ishikawa, JESS, and Visual Analytics for Engineering

Domain-specific big data poses issues in knowledge discovery and decision support given its many Vs such as volume, variety, and visualization. In this work, we propose solutions via Ishikawa diagrams, JESS (Java Expert System Shell) with rule discovery, as well as visual analytics. We deploy data science and domain models, targeting engineering and scientific domains. Our results yield high accuracy, efficiency and cost-effectiveness.
RI-GCN: Review-aware Interactive Graph Convolutional Network for Review-based Item Recommendation

A wealth of semantic features exist in the reviews written by users, such as rich information on item features and implicit preferences of users. Existing review-based recommendation models usually employ Convolutional Neural Networks (CNNs) to learn representations of users and items from reviews. However, these CNNs-based models suffer from two main problems: (1) they only consider the information of the word itself during the convolution, ignoring the high-order contextual semantic information of the word; (2) they model user/item attributes in a static and independent way, ignoring the potential feature interaction between them. Therefore, we propose a novel Review-aware Interactive Graph Convolutional Network (RI-GCN) for review-based item recommendation. Specifically, we design a Review-aware GCN component to model the message propagation of graphs constructed from reviews, capturing the contextual features of words. A feature interactive GCN component is then proposed to capture the user/item high-order collaborative features in the user-item graph, enabling the model to further complement and refine u ser/item a ttributes. Finally, we adopt a Factorization Machine model for the recommendation task. Experimental results demonstrate that the proposed model is superior to state-of-the-art models.
Semantic-based Attention model for Hospital Readmission Prediction

Unexpected hospital readmissions are problematic to both hospitals and patients, and also costly in terms of money and resources as well. Thus, identifying whether or not a patient will be readmitted becomes an important task. Due to the limitation of traditional machine learning techniques, Recurrent neural networks (RNN) and attention mechanisms have been proposed to learn temporal relationships between patient admissions for readmission prediction. Especially, learning accurate representations to represent medical concepts plays a crucial role for generating accurate predictions. Existing works demonstrate that incorporating medical ontologies can be beneficial to learn accurate medical code representations. However, current works ignore the importance of the textual descriptions associated with the medical codes in medical ontologies. In fact, these textual descriptions provide valuable information about the meaning of the medical codes and also the semantic relations among them. In this paper, we propose a new model called Semantic-based Attention model for Medical Concept Embedding (SAMCE). It takes into consideration the semantic information provided in code descriptions to learn medical code representations based on which to generate readmission prediction via RNN and attention mechanisms. Experimental results show that the proposed SAMCE model improves not only the performance of readmission prediction but also the quality of medical code representations.
ABM: Attention-based Message Passing Network for Knowledge Graph Completion

Knowledge graph is ubiquitous and plays an important role in many real-world applications, including recommender systems, question answering, fact-checking, and so on. However, most of the knowledge graphs are incomplete which can hamper their practical usage. Fortunately, knowledge graph completion (KGC) can mitigate this problem by inferring missing edges in the knowledge graph according to the existing information. In this paper, we propose a novel KGC method named ABM (Attention-Based Message passing) which focuses on predicting the relation between any two entities in a knowledge graph. The proposed ABM consists of three integral parts, including (1) context embedding, (2) structure embedding, and (3) path embedding. In the context embedding, the proposed ABM generalizes the existing message passing neural network to update the node embedding and the edge embedding to assimilate the knowledge of nodes' neighbors, which captures the relative role information of the edge that we want to predict. In the structure embedding, the proposed method overcomes the shortcomings of the existing GNN method (i.e., most methods ignore the structural similarity between nodes.) by assigning different attention weights to different nodes while doing the aggregation. Path embedding generates paths between any two entities and treats these paths as sequences. Then, the sequence can be used as the input of the Transformer to update the embedding of the knowledge graph to gather the global role of the missing edges. By utilizing these three mutually complementary strategies, the proposed ABM is able to capture both the local and global information which in turn leads to a superb performance. Experiment results show that ABM outperforms baseline methods on a wide range of datasets.
Federated Learning Models using Flow Cytometry Data of Blood Test in Medical Decision Support

Medical big data has become important as many hospitals have been collecting massive amounts of medical information in daily treatment. We investigated the architecture of federated learning to construct the detection model of disease with blood test data formatted in flow cytometry standards to facilitate multi-site medical research. The big data characteristics of raw information in blood tests and privacy problems in sharing patient data make it hard to collect and share data into the central site to construct the generalized detection model. In this paper, we introduce the work-in-progress study, FedM-FCM, the federated learning of flow cytometry analysis with the pipeline from the data sources to the domain-shifted distribution of the federated learning model. We compose the major components of FedM-FCM with data representation of multi-dimensional flow cytometry, adoption of neural network models based on the data representation, and aggregation and distribution of learning parameters across the participating hospitals without data sharing.
MS<sup>2</sup>A: A Multi-Scale Spatial Aggregation Framework for Visual Place Recognition

Visual Place Recognition (VPR) is the task of estimating the location of a query image by searching for the most similar reference image. The ever-changing appearance and viewpoint pose a significant challenge to the VPR task. The mainstream methods estimate the distance between images by comparing their compact representations. However, the representations are usually generated by aggregating all deep local features, which lose detailed semantics and spatial relationships. As a result, it is hard to distinguish the location of images that are highly similar in global semantics, but differ in local semantics or viewpoints. To this end, we proposed a novel Multi-Scale Spatial Aggregation (MS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A) framework, which takes detailed and spatial information into account to generate representations. MS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A fuses multi-scale semantics, which calculates the feature map of an image only once and requires no fine-tuning, regardless of the number of scales and regions. In addition, we introduced an approach for quantifying the choice of multi-scale parameters and an efficient method to further reduce the dimension of MS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A representation. Experimental results on three large-scale benchmark datasets demonstrate that MS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A can achieve state-of-the-art performance.
Forensic Analysis of Amazon Alexa Echo Dot 4<sup>th</sup> Generation

Internet of Things devices such as Amazon Alexa have grown in popularity over the years due to the ease it gives to its’ user. As a result of the common occurrence of crimes taking place at homes, especially violent crimes there is a high possibility of the presence of an Alexa when a criminal investigation occurs; thus, forensic investigations on these devices are required to assess the evidential value to forensic examiners. This paper focused on the carrying out a forensic investigation of Amazon Alexa Echo Dot 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation on the software and the cloud level. This paper shows how forensic artifacts could be obtained from the Alexa application on an iPhone and an Android smartphone, as well as adapted and described an open source tool that can be used to collect evidence from an Amazon Alexa Echo Dot 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation at the cloud level.
Typhon: Parallel Transfer on Heterogeneous Datasets for Cancer Detection in Computer-Aided Diagnosis

We present Typhon, a new Deep Learning framework that trains a single model using multiple, heterogeneous datasets leveraging parallel transfer. This aims to improve the performance of Deep Learning methods in critical applications afflicted by data scarcity, such as computer-aided diagnosis for cancer detection, where large datasets are rare or unfeasible but many smaller datasets may be available. The key idea is to assemble sufficient data for t raining deep models by selecting a set of multiple, potentially smaller and heterogeneous datasets, as long as they all exhibit similar visual features, such as common with medical imaging applications. The Typhon model architecture is composed of a single Feature Extractor and multiple Decision Makers, in sequence but explicitly separated. The Feature Extractor is trained using all datasets with a focus on producing generic features which are useful across all datasets. The Decision Makers are each paired with a different dataset, and specialized to take decisions based on the output of the Feature Extractor. Our training method is based on the concept of parallel transfer: on each epoch, we train on just one batch from each dataset in turn. This is done by pairing the correct Decision Maker on top of the shared Feature Extractor, then training the resulting model end-to-end on the data batch using classical methods. The actual design is inherently more complex, as we had to overcome a set of major challenges such as dataset imbalance, moving target, catastrophic forgetting, and issues with initialization viability. Once made viable, however, this methods excels at strictly enforcing feature generalization and delaying or even preventing overfitting. We present our results on the widely adopted PROSTATEx MRI dataset for prostate cancer classification, using additional datasets of brain MRI and lung CT images to boost the model’s performance. Typhon improves on our previous work based on sequential transfer (Hydra) by over 7%, which compounds to a 15% improvement over classical methods and 12% over transfer learning, while only seeing 54% more samples than classical end-to-end training on a single dataset.
Adaptive Knowledge Transfer on Evolving Domains

In this paper, we study the dynamic transfer learning problem involving adaptive knowledge transfer from a static source domain to a time evolving target domain. One major challenge is the time evolving relatedness of the source domain and the current target domain as the target domain evolves over time. To address this challenge, we derive a generic error bound on the current target domain with flexible domain discrepancy measures. Moreover, we propose a label-informed $\mathcal{C}$-divergence to measure the shift of joint data distributions (over input features and output labels) across domains. The resulting tighter error bound with $\mathcal{C}$-divergence motivates us to develop a novel dynamic transfer learning algorithm TransLATE. Empirical results on various data sets confirm the effectiveness of our proposed algorithm in modeling the time evolving target domain.
PROFET: PROFiling-based CNN Training Latency ProphET for GPU Cloud Instances

Training a Convolutional Neural Network (CNN) model typically requires significant computing power, and cloud computing resources are widely used as a training environment. However, it is difficult for CNN algorithm developers to keep up with system updates and apply them to their training environment due to quickly evolving cloud services. Thus, it is important for cloud computing service vendors to design and deliver an optimal training environment for various training tasks to lessen system operation management overhead of algorithm developers. To achieve the goal, we propose PROFET, which can predict the training latency of arbitrary CNN implementation on various Graphical Processing Unit (GPU) devices to develop a cost-effective and time-efficient training cloud environment. Different from the previous training latency prediction work, PROFET does not rely on the implementation details of the CNN architecture, and it is suitable for use in a public cloud environment. Thorough evaluations reveal the superior prediction accuracy of PROFET compared to the state-of-the-art related work, and the demonstration service presents the practicality of the proposed system.
Adaptive and Dynamic Multi-Resolution Hashing for Pairwise Summations

In this paper, we propose Adam-Hash: an adaptive and dynamic multi-resolution hashing data-structure for fast pairwise summation estimation. Given a data-set X ⊂ ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> , a binary function f : ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> × ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> → ℝ, and a point y ∈ ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> , the Pairwise Summation Estimate $PS{E_X}(y): = \frac{1}{{\left| X \right|}}\sum\nolimits_{x \in X} {f(x,y)} $. For any given data-set X, we need to design a data-structure such that given any query point y ∈ ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> , the data-structure approximately estimates PSE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</inf> (y) in time that is sub-linear in |X|. Prior works on this problem have focused exclusively on the case where the data-set is static, and the queries are independent. In this paper, we design a hashing-based PSE data-structure which works for the more practical dynamic setting in which insertions, deletions, and replacements of points are allowed. Moreover, our proposed Adam-Hash is also robust to adaptive PSE queries, where an adversary can choose query q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</inf> ∈ ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> depending on the output from previous queries q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> , q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , …, q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j–1</inf> .
Detecting Malware Activity Using Public Search Data

The prevalence of malware on the Internet makes malware detection vital as an early warning system for organizations’ security. This paper presents a novel approach to linking knowledge from heterogeneous and specialized datasets using a sentence embedding approach. This paper also proposes a novel approach to detect malware activity using standardized and specialized datasets and people’s search interest data. We demonstrated the detection capabilities of our approach, assessing our models using four real attack study cases. We found an increase in Google searches and probabilities of our models seven days before and after an attack occurred. In addition, the web search volume and model probabilities time series are characterized by an increase in outliers around 14 days before and after the discovery of the attack. This work should pave the path for integrating domain-specific datasets and user-generated dynamic data for detecting malware activity.
Bilateral Trade Flow Prediction by Gravity-informed Graph Auto-encoder

The gravity models has been studied to analyze interaction between two objects such as trade amount between a pair of countries, human migration between a pair of countries and traffic flow between two cities. Particularly in the international trade, predicting trade amount is instrumental to industry and government in business decision making and determining economic policies. Whereas the gravity models well captures such interaction between objects, the model simplifies the interaction to extract essential relationships or needs handcrafted features to drive the models. Recent studies indicate the connection between graph neural networks (GNNs) and the gravity models in international trade. However, to our best knowledge, hardly any previous studies in the this domain directly predicts trade amount by GNNs. We propose GGAE (Gravity-informed Graph Auto-encoder) and its surrogate model, which is inspired by the gravity model, showing trade amount prediction by the gravity model can be formulated as an edge weight prediction problem in GNNs and solved by GGAE and its surrogate model. Furthermore, we conducted experiments to indicate GGAE with GNNs can improve trade amount prediction compared to the traditional gravity model by considering complex relationships.