Reviewerly/exHarmony huggingface dataset:
Introduction to Anaconda and Python: Installation and setup

Python has become one of the most popular programming languages for research in the past decade.Its free, open-source nature and vast online community are some of the reasons behind its success.Countless examples of increased research productivity due to Python can be found across a plethora of domains online, including data science, artificial intelligence and scientific research.This tutorial's goal is to help users get started with Python through the installation and setup of the Anaconda software.The goal is to set users on the path toward using the Python language by preparing them to write their first script.This tutorial is divided in the following fashion: a small introduction to Python, how to download the Anaconda software, the different content that comes with the installation, and a simple example related to implementing a Python script.
MANE: Model-Agnostic Non-linear Explanations for Deep Learning Model

Deep learning methods have shown great potentiality in the credit card fraud detection field. Unfortunately, they cannot explain their predictions, while a financial company expects to know the reason that a transaction is decided to be fraud. There have been some studies of the interpretability of deep learning methods in other fields, but we find that their effects are not very good when we directly use them to the credit card fraud detection problem, because they ignore the behavior patterns of the cardholder, and cannot deal with the nonlinear local boundary. In this paper, we propose a new method, MANE (Model-Agnostic Non-linear Explanations) for deep learning models, that can provide a set of interpretable features for each transaction. First, we use a cardholder's historical transactions to extract a set of behavior patterns for the cardholder via an aggregation strategy. Next, we use nonlinear model GBDT (Gradient Boosting Decision Tree) to mine cross features based on these behavior patterns, so that our explanation model can approximate the local boundary of a complex deep learning model. Finally, for every to-be-explained sample, we obtain its neighbors by using a random perturb method, then we train an interpretable linear regression model for the sample based on its neighbors, the behavior patterns of this cardholder and the related cross features. Our experiments on a real dataset demonstrate that our method achieves better effects.
Few-Shot Human-Object Interaction Recognition With Semantic-Guided Attentive Prototypes Network

Extreme instance imbalance among categories and combinatorial explosion make the recognition of Human-Object Interaction (HOI) a challenging task. Few studies have addressed both challenges directly. Motivated by the success of few-shot learning that learns a robust model from a few instances, we formulate HOI as a few-shot task in a meta-learning framework to alleviate the above challenges. Due to the fact that the intrinsical characteristic of HOI is diverse and interactive, we propose a Semantic-guided Attentive Prototypes Network (SAPNet) framework to learn a semantic-guided metric space where HOI recognition can be performed by computing distances to attentive prototypes of each class. Specifically, the model generates attentive prototypes guided by the category names of actions and objects, which highlight the commonalities of images from the same class in HOI. In addition, we design two alternative prototypes calculation methods, i.e., Prototypes Shift (PS) approach and Hallucinatory Graph Prototypes (HGP) approach, which explore to learn a suitable category prototypes representations in HOI. Finally, in order to realize the task of few-shot HOI, we reorganize 2 HOI benchmark datasets with 2 split strategies, i.e., HICO-NN, TUHOI-NN, HICO-NF, and TUHOI-NF. Extensive experimental results on these datasets have demonstrated the effectiveness of our proposed SAPNet approach.
E<sup>2</sup>bird: <u>E</u>nhanced <u>E</u>lastic <u>B</u>atch for <u>I</u>mproving <u>R</u>esponsiveness and Throughput of <u>D</u>eep Learning Services

We aim to tackle existing problems about deep learning serving on GPUs in the view of the system. GPUs have been widely adopted to serve online deep learning-based services that have stringent QoS(Quality-of-Service) requirements. However, emerging deep learning serving systems often result in poor responsiveness and low throughput of the inferences that damage user experience and increase the number of GPUs required to host an online service. Our investigation shows that the poor batching operation and the lack of data transfer-computation overlap are the root causes of the poor responsiveness and low throughput. To this end, we propose E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> bird, a deep learning serving system that is comprised of a GPU-resident memory pool, a multi-granularity inference engine, and an elastic batch scheduler. The memory pool eliminates the unnecessary waiting of the batching operation and enables data transfer-computation overlap. The inference engine enables concurrent execution of different batches, improving the GPU resource utilization. The batch scheduler organizes inferences elasticallyto guarantee the QoS. Our experimental results on an Nvidia Titan RTXGPU show that E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> bird reduces the response latency of inferences by up to 82.4 percent and improves the throughput by up to 62.8 percent while guaranteeing the QoS target compared with TensorFlow Serving.
Blockchain-based Farming Activities Tracker for Enhancing Trust in the Community Supported Agriculture Model

Community Supported Agriculture (CSA) model is an efficient solution that not only solves the problem of the agricultural product's origin but also provides a method to share the market risk between consumers and producers. In the CSA model, consumers order and pay for products in advance. Then, the producers will send packages of fresh products to consumers after a fixed period corresponding to the paid amount. However, the critical weakness of the CSA model is the lack of solutions for both sides to demonstrate the product's quality, which makes consumers unsure about the condition of receiving products. In consequence, consumers become hesitant to order and pay money in advance. In this paper, we will propose a novel solution that allows consumers to track their products through agricultural diaries recorded by farmers every day. The key difference of the proposed solution is to leverage Blockchain technology advantages in authenticating and protecting the integrity of information. Such that, consumers could track all steps in the production process quickly and reliably. Meanwhile, producers can build and increase their enterprise branding by transferring product information transparently and responsibly.
Satellite Multispectral and Hyperspectral Image De-Noising with Enhanced Adaptive Generalized Gaussian Distribution Threshold in the Wavelet Domain

The presence of noise in remote sensing satellite images may cause limitations in analysis and object recognition. Noise suppression based on thresholding neural network (TNN) and optimization algorithms perform well in de-noising. However, there are some problems that need to be addressed. Furthermore, finding the optimal threshold value is a challenging task for learning algorithms. Moreover, in an optimization-based noise removal technique, we must utilize the optimization algorithm to overcome the problem. These methods are effective at reducing noise but may blur some parts of an image, and they are time-consuming. This flaw motivated the authors to develop an efficient de-noising method to discard un-wanted noises from these images. This study presents a new enhanced adaptive generalized Gaussian distribution (AGGD) threshold for satellite and hyperspectral image (HSI) de-noising. This function is data-driven, non-linear, and it can be fitted to any image. Applying this function provides us with an optimum threshold value without using any least mean square (LMS) learning or optimization algorithms. Thus, it is possible to save the processing time as well. The proposed function contains two main parts. There is an AGGD threshold in the interval [−σn, σn], and a new non-linear function behind the interval. These combined functions can tune the wavelet coefficients properly. We applied the proposed technique to various satellite remote sensing images. We also used hyperspectral remote sensing images from AVIRIS, HYDICE, and ROSIS sensors for our experimental analysis and validation process. We applied peak signal-to-noise ratio (PSNR) and Mean Structural Similarity Index (MSSIM) to measure and evaluate the performance analysis of different de-noising techniques. Finally, this study shows the superiority of the developed method as compared with the previous TNN and optimization-based noise suppression methods. Moreover, as the results indicate, the proposed method improves PSNR values and visual inspection significantly when compared with various image de-noising methods.
Team Semantics for Interventionist Counterfactuals: Observations vs. Interventions

Abstract Team semantics is a highly general framework for logics which describe dependencies and independencies among variables. Typically, the (in)dependencies considered in this context are properties of sets of configurations or data records. We show how team semantics can be further generalized to support languages for the discussion of interventionist counterfactuals and causal dependencies, such as those that arise in manipulationist theories of causation (Pearl, Hitchcock, Woodward, among others). We show that the “causal teams” we introduce in the present paper can be used for modelling some classical counterfactual scenarios which are not captured by the usual causal models. We then analyse the basic properties of our counterfactual languages and discuss extensively the differences with respect to the Lewisian tradition.
Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis

Background Asthma is a major chronic disease that poses a heavy burden on health care. To facilitate the allocation of care management resources aimed at improving outcomes for high-risk patients with asthma, we recently built a machine learning model to predict asthma hospital visits in the subsequent year in patients with asthma. Our model is more accurate than previous models. However, like most machine learning models, it offers no explanation of its prediction results. This creates a barrier for use in care management, where interpretability is desired. Objective This study aims to develop a method to automatically explain the prediction results of the model and recommend tailored interventions without lowering the performance measures of the model. Methods Our data were imbalanced, with only a small portion of data instances linking to future asthma hospital visits. To handle imbalanced data, we extended our previous method of automatically offering rule-formed explanations for the prediction results of any machine learning model on tabular data without lowering the model’s performance measures. In a secondary analysis of the 334,564 data instances from Intermountain Healthcare between 2005 and 2018 used to form our model, we employed the extended method to automatically explain the prediction results of our model and recommend tailored interventions. The patient cohort consisted of all patients with asthma who received care at Intermountain Healthcare between 2005 and 2018, and resided in Utah or Idaho as recorded at the visit. Results Our method explained the prediction results for 89.7% (391/436) of the patients with asthma who, per our model’s correct prediction, were likely to incur asthma hospital visits in the subsequent year. Conclusions This study is the first to demonstrate the feasibility of automatically offering rule-formed explanations for the prediction results of any machine learning model on imbalanced tabular data without lowering the performance measures of the model. After further improvement, our asthma outcome prediction model coupled with the automatic explanation function could be used by clinicians to guide the allocation of limited asthma care management resources and the identification of appropriate interventions.
Utilizing deep learning and graph mining to identify drug use on Twitter data

Abstract Background The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. Methods Social media data (tweets and attributes) were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset of 3,696,150 rows. The predictive classification power of multiple methods was compared including SVM, XGBoost, BERT and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. Results To test the predictive capability of the model, SVM and XGBoost were first employed. The results calculated from the models respectively displayed an accuracy of 59.33% and 54.90%, with AUC’s of 0.87 and 0.71. The values show a low predictive capability with little discrimination. Conversely, the CNN-based classifiers presented a significant improvement, between the two models tested. The first was trained with 2661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. Conclusion Predictive analysis with a CNN is promising, whereas attribute-based models presented little predictive capability and were not suitable for analyzing text of data. This research found that the commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased accuracy scores and improves the predictive capability.
Deep Learning–Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors

Multimodal wearable technologies have brought forward wide possibilities in human activity recognition, and more specifically personalized monitoring of eating habits. The emerging challenge now is the selection of most discriminative information from high-dimensional data collected from multiple sources. The available fusion algorithms with their complex structure are poorly adopted to the computationally constrained environment which requires integrating information directly at the source. As a result, more simple low-level fusion methods are needed.In the absence of a data combining process, the cost of directly applying high-dimensional raw data to a deep classifier would be computationally expensive with regard to the response time, energy consumption, and memory requirement. Taking this into account, we aimed to develop a data fusion technique in a computationally efficient way to achieve a more comprehensive insight of human activity dynamics in a lower dimension. The major objective was considering statistical dependency of multisensory data and exploring intermodality correlation patterns for different activities.In this technique, the information in time (regardless of the number of sources) is transformed into a 2D space that facilitates classification of eating episodes from others. This is based on a hypothesis that data captured by various sensors are statistically associated with each other and the covariance matrix of all these signals has a unique distribution correlated with each activity which can be encoded on a contour representation. These representations are then used as input of a deep model to learn specific patterns associated with specific activity.In order to show the generalizability of the proposed fusion algorithm, 2 different scenarios were taken into account. These scenarios were different in terms of temporal segment size, type of activity, wearable device, subjects, and deep learning architecture. The first scenario used a data set in which a single participant performed a limited number of activities while wearing the Empatica E4 wristband. In the second scenario, a data set related to the activities of daily living was used where 10 different participants wore inertial measurement units while performing a more complex set of activities. The precision metric obtained from leave-one-subject-out cross-validation for the second scenario reached 0.803. The impact of missing data on performance degradation was also evaluated.To conclude, the proposed fusion technique provides the possibility of embedding joint variability information over different modalities in just a single 2D representation which results in obtaining a more global view of different aspects of daily human activities at hand, and yet preserving the desired performance level in activity recognition.
A Robust and Efficient Framework for Sports-Field Registration

We propose a novel framework to register sports-fields as they appear in broadcast sports videos. Unlike previous approaches, we particularly address the challenge of field- registration when: (a) there are not enough distinguishable features on the field, and (b) no prior knowledge is available about the camera. To this end, we detect a grid of key- points distributed uniformly on the entire field instead of using only sparse local corners and line intersections, thereby extending the keypoint coverage to the texture-less parts of the field as well. To further improve keypoint based homography estimate, we differentialbly warp and align it with a set of dense field-features defined as normalized distance- map of pixels to their nearest lines and key-regions. We predict the keypoints and dense field-features simultaneously using a multi-task deep network to achieve computational efficiency. To have a comprehensive evaluation, we have compiled a new dataset called SportsFields which is collected from 192 video-clips from 5 different sports covering large environmental and camera variations. We empirically demonstrate that our algorithm not only achieves state of the art field-registration accuracy but also runs in real-time for HD resolution videos using commodity hardware.
Research on pathways of expert finding on academic social networking sites

The advent of academic social networking sites (ASNS) has provided a more convenient way for users to seek out experts. This paper uses ResearchGate to explore the features of pathways through four different tasks, combined with questionnaires and interviews. These tasks are aimed at identifying the right experts with relevant expertise or experience for a given topic. We propose a pathway-based approach that takes into consideration both pages and navigations. The pathways show the whole process of how users find experts on ASNS for a variety of reasons. A user visiting ASNS will generate different types of pages, and navigation represents the act of moving from one page to another. A series of navigations between pages that can be linked together chronologically make up the pathways. A total of 94 valid pathways were used for the cluster analysis. The results show that the pages most frequently accessed by users are profile pages, search pages, and publication pages. A comparison of these three types found that the overall utilization of publication pages is low. Users pay more attention to research results pages within profile pages, and continuously adjust the queries on demand. Single seeking pathways, triangular seeking pathways and multiple seeking pathways are identified to understand the process of users' expert finding. Finally, this paper proposes implications for the interface design of ASNS: improve search pages, focus on users' personalized needs, and apply relational networks in the seeking process. Furthermore, the findings have a positive impact on encouraging interaction, collaboration and innovation in the research environment.
Vision-based People Counting for Attendance Monitoring System

In this paper, an automatic people detection and counting system using data collected from an over-head camera is proposed. The purpose of this research is to develop a fast and accurate intelligent people counting technique for attendance monitoring systems in offices and lecture rooms. The proposed method includes two stages working sequentially. First, the detection task is executed to find any person presented in the current frame. A deep learning architecture, MobileNetv2-SSD, was used to carry out the detecting phase. If there is any detected person, the tracking phase, which based on visual-tracking techniques, will be initialized, and keeps track of the people's position. Based on the tracked motion path of the detected people, we can determine if there is any person who has entered or exited the room. Therefore, we can monitor the number of attending people. The testing hardware was a Raspberry Pi computer and a camera. This work has been tested on different stages of a day and achieved real-time performance with sufficient accuracy.
Animal Behavior Classification Using DeepLabCut

In this paper, we introduce a method to classify animal behaviors from videos taken by a fixed-point camera. In order to classify animal behavior, it is necessary to detect and track the animals. Conventional approaches for detecting moving objects are based on background subtraction and frame subtraction. Conventional methods are not suitable for detection of animals kept indoors since they are susceptible to sunlight and shadow. We propose a method to track animals and classify their behavior using skeletal information obtained by DeepLabCut. The experimental results show that the proposed method is superior to the conventional method.