Reviewerly/exHarmony huggingface dataset:
PriveTAB

Machine Learning has increased our ability to model large quantities of data efficiently in a short time. Machine learning approaches in many application domains require collecting large volumes of data from distributed sources and combining them. However, sharing of data from multiple sources leads to concerns about privacy. Privacy regulations like European Union's General Data Protection Regulation (GDPR) have specific requirements on when and how such data can be shared. Even when there are no specific regulations, organizations may have concerns about revealing their data. For example in cybersecurity, organizations are reluctant to share their network-related data to permit machine learning-based intrusion detectors to be built. This has, in particular, hampered academic research. We need an approach to make confidential data widely available for accurate data analysis without violating the privacy of the data subjects. Privacy in shared data has been discussed in prior work focusing on anonymization and encryption of data. An alternate approach to make data available for analysis without sharing sensitive information is by replacing sensitive information with synthetic data that behave as original data for all analytical purposes. Generative Adversarial Networks (GANs) are one of the well-known models to generate synthetic samples that can have the same distributional characteristics as the original data. However, modeling tabular data using GAN is a non-trivial task. Tabular data contain a mix of categorical and continuous variables and require specialized constraints as described in the CTGAN model. In this paper, we propose a framework to generate privacy-preserving synthetic data suitable for release for analytical purposes. The data is generated using the CTGAN approach, and so is analytically similar to the original dataset. To ensure that the generated data meet the privacy requirements, we use the principle of t-closeness. We ensure that the distribution of attributes in the released dataset is within a certain threshold distance from the real dataset. We also encrypt sensitive values in the final released version of the dataset to minimize information leakage. We show that in a variety of cases, models trained on this synthetic data instead of the real data perform nearly as well when tested on the real data. Specifically, we show that the machine learning models used for network event/attack recognition tasks do not have a significant loss in accuracy when trained on data generated from our framework in place of the real dataset.
Panoramic/Dual-Surface Digital Image Correlation Measurement Using a Single Camera

We propose a cost-effective and simple-to-implement mirror-assisted single-camera panoramic digital image correlation (DIC) method for panoramic/dual-surface profile and deformation measurement. Specifically, two planar mirrors and a single camera attached with a four-mirror adapter are used to capture stereo images of the front and rear surfaces of a test object. These stereo images can be processed by regular stereo-DIC to retrieve shape and kinematics fields of each surface. Further, with the speckle patterns prefabricated on the mirrors, reflection transformation matrices are obtained and applied to transform all reconstructed surfaces into a common world coordinate system. As such, panoramic/dual-surface shape and deformation measurements can be realized. For validation, a high-resolution smartphone camera and an industrial camera were, respectively, used to construct mirror-assisted single-camera panoramic DIC systems. Real experiments, including panoramic shape measurement of an aluminum cylinder, dual-surface shape measurement of an aluminum plate and uniaxial tensile tests of aluminum sheet specimens, were performed, confirming the feasibility and accuracy of the method. Since only a single camera and a few auxiliary reflective mirrors are required, the proposed method provides a cost-effective and convenient way for taking panoramic/dual-surface shape and deformation measurements of regular-sized cylindrical and bar samples.
DREW: Efficient Winograd CNN Inference with Deep Reuse

Deep learning has been used in various domains, including Web services. Convolutional neural networks (CNNs), which are deep learning representatives, are among the most popular neural networks in Web systems. However, CNN employs a high degree of computing. In comparison to the training phase, the inference process is more frequently done on low-power computing equipments. The limited computing resource and high computation pressure limit the effective use of CNN algorithms in industry. Fortunately, a minimal filtering algorithm called Winograd can reduce convolution calculations by minimizing multiplication operations. We find that Winograd convolution can be sped up further by deep reuse technique, which reuses the similar data and computation processes. In this paper, we propose a new inference method, called DREW, which combines deep reuse with Winograd for further accelerating CNNs. DREW handles three difficulties. First, it can detect the similarities from the complex minimal filtering patterns by clustering. Second, it reduces the online clustering cost in a reasonable range. Third, it provides an adjustable method in clustering granularity balancing the performance and accuracy. Experiments show that 1) DREW further accelerates the Winograd convolution by an average of 2.06 × speedup; 2) when DREW is applied to end-to-end Winograd CNN inference, it achieves 1.71 × the average performance speedup with no (<0.4%) accuracy loss; 3) DREW reduces the number of convolution operations to 11% of the original operations on average.
Joint Optimization for Mobile Edge Computing-Enabled Blockchain Systems: A Deep Reinforcement Learning Approach

A mobile edge computing (MEC)-enabled blockchain system is proposed in this study for secure data storage and sharing in internet of things (IoT) networks, with the MEC acting as an overlay system to provide dynamic computation offloading services. Considering latency-critical, resource-limited, and dynamic IoT scenarios, an adaptive system resource allocation and computation offloading scheme is designed to optimize the scalability performance for MEC-enabled blockchain systems, wherein the scalability is quantified as MEC computational efficiency and blockchain system throughput. Specifically, we jointly optimize the computation offloading policy and block generation strategy to maximize the scalability of MEC-enabled blockchain systems and meanwhile guarantee data security and system efficiency. In contrast to existing works that ignore frequent user movement and dynamic task requirements in IoT networks, the joint performance optimization scheme is formulated as a Markov decision process (MDP). Furthermore, we design a deep deterministic policy gradient (DDPG)-based algorithm to solve the MDP problem and define the multiple and variable number of consecutive time slots as a decision epoch to conduct model training. Specifically, DDPG can solve an MDP problem with a continuous action space and it only requires a straightforward actor-critic architecture, making it suitable for tackling the dynamics and complexity of the MEC-enabled blockchain system. As demonstrated by simulations, the proposed scheme can achieve performance improvements over the deep Q network (DQN)-based scheme and some other greedy schemes in terms of long-term transactional throughput.
Making Decision like Human: Joint Aspect Category Sentiment Analysis and Rating Prediction with Fine-to-Coarse Reasoning

Joint aspect category sentiment analysis (ACSA) and rating prediction (RP) is a newly proposed task (namely ASAP) that integrates the characteristics of both fine-grained and coarse-grained sentiment analysis. However, the prior joint models for the ASAP task only consider the shallow interaction between the two granularities. In this work, we gain the inspiration from human intuition, presenting an innovative from-fine-to-coarse reasoning framework for better joint task performance. Our system advances mainly in three aspects. First, we additionally make use of the category label text features, co-encoding them with the input document texts, allowing to accurately capture the key clues of each category. Second, we build a fine-to-coarse hierarchical label graph, modeling the aspect categories and the overall rating as a hierarchical structure for full interaction of the two granularities. Third, we propose to perform global iterative reasoning with a cross-collaboration between the hierarchical label graph and the context graphs, enabling sufficient communication between categories and review contexts. Based on the ASAP dataset, experimental results demonstrate that our proposed framework outperforms state-of-the-art baselines by large margins. Further in-depth analyses prove that our method is effective on addressing both the unbalanced data distribution and the long-text issue.
Profile Decomposition Based Hybrid Transfer Learning for Cold-Start Data Anomaly Detection

Anomaly detection is an essential task for quality management in smart manufacturing. An accurate data-driven detection method usually needs enough data and labels. However, in practice, there commonly exist newly set-up processes in manufacturing, and they only have quite limited data available for analysis. Borrowing the name from the recommender system, we call this process a cold-start process. The sparsity of anomaly, the deviation of the profile, and noise aggravate the detection difficulty. Transfer learning could help to detect anomalies for cold-start processes by transferring the knowledge from more experienced processes to the new processes. However, the existing transfer learning and multi-task learning frameworks are established on task- or domain-level relatedness. We observe instead, within a domain, some components (background and anomaly) share more commonality, others (profile deviation and noise) not. To this end, we propose a more delicate component-level transfer learning scheme, i.e., decomposition-based hybrid transfer learning ( DHTL ): It first decomposes a domain (e.g., a data source containing profiles) into different components (smooth background, profile deviation, anomaly, and noise); then, each component’s transferability is analyzed by expert knowledge; Lastly, different transfer learning techniques could be tailored accordingly. We adopted the Bayesian probabilistic hierarchical model to formulate parameter transfer for the background, and “ L 2,1 + L 1 ”-norm to formulate low dimension feature-representation transfer for the anomaly. An efficient algorithm based on Block Coordinate Descend is proposed to learn the parameters. A case study based on glass coating pressure profiles demonstrates the improved accuracy and completeness of detected anomaly, and a simulation demonstrates the fidelity of the decomposition results.
Interpreting BERT-based Text Similarity via Activation and Saliency Maps

Recently, there has been growing interest in the ability of Transformer-based models to produce meaningful embeddings of text with several applications, such as text similarity. Despite significant progress in the field, the explanations for similarity predictions remain challenging, especially in unsupervised settings. In this work, we present an unsupervised technique for explaining paragraph similarities inferred by pre-trained BERT models. By looking at a pair of paragraphs, our technique identifies important words that dictate each paragraph’s semantics, matches between the words in both paragraphs, and retrieves the most important pairs that explain the similarity between the two. The method, which has been assessed by extensive human evaluations and demonstrated on datasets comprising long and complex paragraphs, has shown great promise, providing accurate interpretations that correlate better with human perceptions.
Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads

Click-through rate (CTR) prediction plays a crucial role in sponsored search advertising (search ads). User click behavior usually showcases strong comparison patterns among relevant/competing items within the user awareness. Explicit user awareness could be characterized by user behavior sequence modeling, which however suffers from issues such as cold start, behavior noise and hidden channels. Instead, in this paper, we study the problem of modeling implicit user awareness about relevant/competing items. We notice that candidate items of the CTR prediction model could play as surrogates for relevant/competing items within the user awareness. Motivated by this finding, we propose a novel framework, named CIM (Candidate Item Modeling), to characterize users’ awareness on candidate items. CIM introduces an additional module to encode candidate items into a context vector and therefore is plug-and-play for existing neural network-based CTR prediction models. Offline experiments on a ten-billion-scale production dataset collected from the real traffic of a search advertising system, together with the corresponding online A/B testing, demonstrate CIM’s superior performance. Notably, CIM has been deployed in production at JD.com, serving the main traffic of hundreds of millions of users, which shows great application value. Our code and dataset are available at https://github.com/kaifuzheng/cim.
A Utility Framework for COVID-19 Online Forward Triage Tools: A Swiss Telehealth Case Study

The SARS-CoV-2 pandemic caused a surge in online tools commonly known as symptom checkers. The purpose of these symptom checkers was mostly to reduce the health system burden by providing worried people with testing criteria, where to test and how to self-care. Technical, usability and organizational challenges with regard to online forward triage tools have also been reported. Very few of these online forward triage tools have been evaluated. Evidence for decision frameworks may be of particular value in a pandemic setting where time frames are restricted, uncertainties are ubiquitous and the evidence base is changing rapidly. The objective was to develop a framework to evaluate the utility of COVID-19 online forward triage tools. The development of the online forward triage tool utility framework was conducted in three phases. The process was guided by the socio-ecological framework for adherence that states that patient (individual), societal and broader structural factors affect adherence to the tool. In a further step, pragmatic incorporation of themes on the utility of online forward triage tools that emerged from our study as well as from the literature was performed. Seven criteria emerged; tool accessibility, reliability as an information source, medical decision-making aid, allaying fear and anxiety, health system burden reduction, onward forward transmission reduction and systems thinking (usefulness in capacity building, planning and resource allocation, e.g., tests and personal protective equipment). This framework is intended to be a starting point and a generic tool that can be adapted to other online forward triage tools beyond COVID-19. A COVID-19 online forward triage tool meeting all seven criteria can be regarded as fit for purpose. How useful an OFTT is depends on its context and purpose.
A Gain-Tuning Dynamic Negative Sampler for Recommendation

Selecting reliable negative training instances is the challenging task in the implicit feedback-based recommendation, which is optimized by pairwise learning on user feedback data. The existing methods usually exploit various negative samplers (i.e., heuristic-based or GAN-based sampling) on user feedback data to improve the quality of negative samples. However, these methods usually focused on maintaining the hard negative samples with a high gradient for training, causing the false negative samples to be selected preferentially. The limitation of the false negative noise amplification may lead to overfitting and further poor generalization of the model. To address this issue, we propose a Gain-Tuning Dynamic Negative Sampling GDNS to make the recommendation more robust and effective. Our proposed model designs an expectational gain sampler, concerning the expectation of user’ preference gap between the positive and negative samples in training, to guide the negative selection dynamically. This gain-tuning negative sampler can effectively identify the false negative samples and further diminish the risk of introducing false negative instances. Moreover, for improving the training efficiency, we construct positive and negative groups for each user in each iteration, and develop a group-wise optimizer to optimize them in a cross manner. Experiments on two real-world datasets show our approach significantly outperforms state-of-the-art negative sampling baselines.
Establishment of ICU Mortality Risk Prediction Models with Machine Learning Algorithm Using MIMIC-IV Database

Objective: The mortality rate of critically ill patients in ICUs is relatively high. In order to evaluate patients' mortality risk, different scoring systems are used to help clinicians assess prognosis in ICUs, such as the Acute Physiology and Chronic Health Evaluation III (APACHE III) and the Logistic Organ Dysfunction Score (LODS). In this research, we aimed to establish and compare multiple machine learning models with physiology subscores of APACHE III-namely, the Acute Physiology Score III (APS III)-and LODS scoring systems in order to obtain better performance for ICU mortality prediction. Methods: A total number of 67,748 patients from the Medical Information Database for Intensive Care (MIMIC-IV) were enrolled, including 7055 deceased patients, and the same number of surviving patients were selected by the random downsampling technique, for a total of 14,110 patients included in the study. The enrolled patients were randomly divided into a training dataset (n = 9877) and a validation dataset (n = 4233). Fivefold cross-validation and grid search procedures were used to find and evaluate the best hyperparameters in different machine learning models. Taking the subscores of LODS and the physiology subscores that are part of the APACHE III scoring systems as input variables, four machine learning methods of XGBoost, logistic regression, support vector machine, and decision tree were used to establish ICU mortality prediction models, with AUCs as metrics. AUCs, specificity, sensitivity, positive predictive value, negative predictive value, and calibration curves were used to find the best model. Results: For the prediction of mortality risk in ICU patients, the AUC of the XGBoost model was 0.918 (95%CI, 0.915-0.922), and the AUCs of logistic regression, SVM, and decision tree were 0.872 (95%CI, 0.867-0.877), 0.872 (95%CI, 0.867-0.877), and 0.852 (95%CI, 0.847-0.857), respectively. The calibration curves of logistic regression and support vector machine performed better than the other two models in the ranges 0-40% and 70%-100%, respectively, while XGBoost performed better in the range of 40-70%. Conclusions: The mortality risk of ICU patients can be better predicted by the characteristics of the Acute Physiology Score III and the Logistic Organ Dysfunction Score with XGBoost in terms of ROC curve, sensitivity, and specificity. The XGBoost model could assist clinicians in judging in-hospital outcome of critically ill patients, especially in patients with a more uncertain survival outcome.
DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph

In the era of information explosion, it is difficult for people to obtain their desired information effectively. In tourism, a travel recommender system based on big travel data has been developing rapidly over the last decade. However, most work focuses on click logs, visit history, or ratings, and dynamic prediction is absent. As a result, there are significant gaps in both dataset and recommender models. To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system. We provided two linked data sets, namely, interaction sequence and dialogue data sets. The usage of the former data set was done to fully explore the static preference characteristics of users based on it, while the latter identified the dynamics changes in user preference from it. Then, we proposed and evaluated BERT-based baseline models for the travel conversational recommender system and compared them with several representative non-conversational and conversational recommender system models. Extensive experiments demonstrated the effectiveness and robustness of our approach regarding conversational recommendation tasks. Our work can extend the scope of the travel conversational recommender system and our annotated data can also facilitate related research.
Rating Distribution Calibration for Selection Bias Mitigation in Recommendations

Real-world recommendation datasets have been shown to be subject to selection bias, which can challenge recommendation models to learn real preferences of users, so as to make accurate recommendations. Existing approaches to mitigate selection bias, such as data imputation and inverse propensity score, are sensitive to the quality of the additional imputation or propensity estimation models. To break these limitations, in this work, we propose a novel self-supervised learning (SSL) framework, i.e., Rating Distribution Calibration (RDC), to tackle selection bias without introducing additional models. In addition to the original training objective, we introduce a rating distribution calibration loss. It aims to correct the predicted rating distribution of biased users by taking advantage of that of their similar unbiased users. We empirically evaluate RDC on two real-world datasets and one synthetic dataset. The experimental results show that RDC outperforms the original model as well as the state-of-the-art debiasing approaches by a significant margin.
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering

Over the past decades, for One-Class Collaborative Filtering (OCCF), many learning objectives have been researched based on a variety of underlying probabilistic models. From our analysis, we observe that models trained with different OCCF objectives capture distinct aspects of user-item relationships, which in turn produces complementary recommendations. This paper proposes a novel OCCF framework, named as ConCF, that exploits the complementarity from heterogeneous objectives throughout the training process, generating a more generalizable model. ConCF constructs a multi-branch variant of a given target model by adding auxiliary heads, each of which is trained with heterogeneous objectives. Then, it generates consensus by consolidating the various views from the heads, and guides the heads based on the consensus. The heads are collaboratively evolved based on their complementarity throughout the training, which again results in generating more accurate consensus iteratively. After training, we convert the multi-branch architecture back to the original target model by removing the auxiliary heads, thus there is no extra inference cost for the deployment. Our extensive experiments on real-world datasets demonstrate that ConCF significantly improves the generalization of the model by exploiting the complementarity from heterogeneous objectives.