pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Bias in Machine Learning
Authors: Jindong Gu, Daniela Oelke
Date: 2019-09-02 00:00:00
Tasks: BIG-bench Machine Learning

Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or even resolve the problem. At the same time, machine learning experts warn that machine learning models can be biased as well. In this article, our goal is to explain the issue of bias in machine learning from a technical perspective and to illustrate the impact that biased data can have on a machine learning model. To reach such a goal, we develop interactive plots to visualizing the bias learned from synthetic data.
Soft Triangles for Expert Aggregation
Authors: Paul B. Kantor
Date: 2019-09-02 00:00:00

We consider the problem of eliciting expert assessments of an uncertain parameter. The context is risk control, where there are, in fact, three uncertain parameters to be estimates. Two of these are probabilities, requiring the that the experts be guided in the concept of "uncertainty about uncertainty." We propose a novel formulation for expert estimates, which relies on the range and the median, rather than the variance and the mean. We discuss the process of elicitation, and provide precise formulas for these new distributions.
Analysis of SparseHash: an efficient embedding of set-similarity via sparse projections
Authors: Diego Valsesia, Sophie Marie Fosson, Chiara Ravazzi, Tiziano Bianchi, Enrico Magli
Date: 2019-09-02 00:00:00

Embeddings provide compact representations of signals in order to perform efficient inference in a wide variety of tasks. In particular, random projections are common tools to construct Euclidean distance-preserving embeddings, while hashing techniques are extensively used to embed set-similarity metrics, such as the Jaccard coefficient. In this letter, we theoretically prove that a class of random projections based on sparse matrices, called SparseHash, can preserve the Jaccard coefficient between the supports of sparse signals, which can be used to estimate set similarities. Moreover, besides the analysis, we provide an efficient implementation and we test the performance in several numerical experiments, both on synthetic and real datasets.
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State Network
Authors: Niklas Heim, James E. Avery
Date: 2019-09-02 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

This work builds an automated anomaly detection method for chaotic time series, and more concretely for turbulent, high-dimensional, ocean simulations. We solve this task by extending the Echo State Network by spatially aware input maps, such as convolutions, gradients, cosine transforms, et cetera, as well as a spatially aware loss function. The spatial ESN is used to create predictions which reduce the detection problem to thresholding of the prediction error. We benchmark our detection framework on different tasks of increasing difficulty to show the generality of the framework before applying it to raw climate model output in the region of the Japanese ocean current Kuroshio, which exhibits a bimodality that is not easily detected by the naked eye. The code is available as an open source Python package, Torsk, available at https://github.com/nmheim/torsk, where we also provide supplementary material and programs that reproduce the results shown in this paper.
A Deep, Forgetful Novelty-Seeking Movie Recommender Model
Authors: Ruomu Zou
Date: 2019-09-02 00:00:00
Tasks: model, Movie Recommendation

As more and more people shift their movie watching online, competition between movie viewing websites are getting more and more intense. Therefore, it has become incredibly important to accurately predict a given user's watching list to maximize the chances of keeping the user on the platform. Recent studies have suggested that the novelty-seeking propensity of users can impact their viewing behavior. In this paper, we aim to accurately model and describe this novelty-seeking trait across many users and timestamps driven by data, taking into consideration user forgetfulness. Compared to previous studies, we propose a more robust measure for novelty. Our model, termed Deep Forgetful Novelty-Seeking Model (DFNSM), leverages demographic information about users, genre information about movies, and novelty-seeking traits to predict the most likely next actions of a user. To evaluate the performance of our model, we conducted extensive experiments on a large movie rating dataset. The results reveal that DFNSM is very effective for movie recommendation.
Analysis of Bias in Gathering Information Between User Attributes in News Application
Authors: Yoshifumi Seki, Mitsuo Yoshida
Date: 2019-09-02 00:00:00
Tasks: Articles, Attribute

In the process of information gathering on the web, confirmation bias is known to exist, exemplified in phenomena such as echo chambers and filter bubbles. Our purpose is to reveal how people consume news and discuss these phenomena. In web services, we are able to use action logs of a service to investigate these phenomena. However, many existing studies about these phenomena are conducted via questionnaires, and there are few studies using action logs. In this paper, we attempt to discover biases of information gathering due to differences in user demographic attributes, such as age and gender, from the behavior log of the news distribution service. First, we summarized the actions in the service for each user attribute and showed the difference of user behavior depending on the attributes. Next, the degree of correlation between the attributes was measured using the correlation coefficient, and a strong correlation was found to exist in the browsing tendency of the news articles between the attributes. Then, the bias of keywords between attributes was discovered, keywords with bias in behavior among the attributes were found using parameters of regression analysis. Since these discovered keywords are almost explainable by big news, our proposed method is effective in detecting biased keywords.
An Efficient Convolutional Neural Network for Coronary Heart Disease Prediction
Authors: Aniruddha Dutta, Tamal Batabyal, Meheli Basu, Scott T. Acton
Date: 2019-09-01 00:00:00
Tasks: Class-Specific Performance, Disease Prediction, Efficient Neural Network, General Classification

This study proposes an efficient neural network with convolutional layers to classify significantly class-imbalanced clinical data. The data are curated from the National Health and Nutritional Examination Survey (NHANES) with the goal of predicting the occurrence of Coronary Heart Disease (CHD). While the majority of the existing machine learning models that have been used on this class of data are vulnerable to class imbalance even after the adjustment of class-specific weights, our simple two-layer CNN exhibits resilience to the imbalance with fair harmony in class-specific performance. In order to obtain significant improvement in classification accuracy under supervised learning settings, it is a common practice to train a neural network architecture with a massive data and thereafter, test the resulting network on a comparatively smaller amount of data. However, given a highly imbalanced dataset, it is often challenging to achieve a high class 1 (true CHD prediction rate) accuracy as the testing data size increases. We adopt a two-step approach: first, we employ least absolute shrinkage and selection operator (LASSO) based feature weight assessment followed by majority-voting based identification of important features. Next, the important features are homogenized by using a fully connected layer, a crucial step before passing the output of the layer to successive convolutional stages. We also propose a training routine per epoch, akin to a simulated annealing process, to boost the classification accuracy. Despite a 35:1 (Non-CHD:CHD) ratio in the NHANES dataset, the investigation confirms that our proposed CNN architecture has the classification power of 77% to correctly classify the presence of CHD and 81.8% the absence of CHD cases on a testing data, which is 85.70% of the total dataset. ( (<1920 characters)Please check the paper for full abstract)
Stochastic Submodular Probing with State-Dependent Costs
Authors: Shaojie Tang
Date: 2019-09-01 00:00:00

In this paper, we study a new stochastic submodular maximization problem with state-dependent costs and rejections. The input of our problem is a budget constraint $B$, and a set of items whose states (i.e., the marginal contribution and the cost of an item) are drawn from a known probability distribution. The only way to know the realized state of an item is to probe that item. We allow rejections, i.e., after probing an item and knowing its actual state, we must decide immediately and irrevocably whether to add that item to our solution or not. Our objective is to sequentially probe/selet a best group of items subject to a budget constraint on the total cost of the selected items. We present a constant approximate solution to this problem. We show that our solution can be extended to an online setting.
3D Bounding Box Estimation for Autonomous Vehicles by Cascaded Geometric Constraints and Depurated 2D Detections Using 3D Results
Authors: Jiaojiao Fang, Lingtao Zhou, Guizhong Liu
Date: 2019-09-01 00:00:00
Tasks: 3D Object Detection, Autonomous Vehicles, Object, object-detection, Object Detection

3D object detection is one of the most important tasks in 3D vision perceptual system of autonomous vehicles. In this paper, we propose a novel two stage 3D object detection method aimed at get the optimal solution of object location in 3D space based on regressing two additional 3D object properties by a deep convolutional neural network and combined with cascaded geometric constraints between the 2D and 3D boxes. First, we modify the existing 3D properties regressing network by adding two additional components, viewpoints classification and the center projection of the 3D bounding box s bottom face. Second, we use the predicted center projection combined with similar triangle constraint to acquire an initial 3D bounding box by a closed-form solution. Then, the location predicted by previous step is used as the initial value of the over-determined equations constructed by 2D and 3D boxes fitting constraint with the configuration determined with the classified viewpoint. Finally, we use the recovered physical world information by the 3D detections to filter out the false detection and false alarm in 2D detections. We compare our method with the state-of-the-arts on the KITTI dataset show that although conceptually simple, our method outperforms more complex and computational expensive methods not only by improving the overall precision of 3D detections, but also increasing the orientation estimation precision. Furthermore our method can deal with the truncated objects to some extent and remove the false alarm and false detections in both 2D and 3D detections.
Employ Multimodal Machine Learning for Content quality analysis
Authors: Eric Du, Xiaoyong Li
Date: 2019-09-01 00:00:00
Tasks: BIG-bench Machine Learning

The task of identifying high-quality content becomes increasingly important, and it can improve overall reading time and CTR(click-through rate estimates). Generalizes quality analysis only focused on single Modal,such as image or text,but in today's mainstream media sites a lot of information is presented in graphic form.In this paper we propose a MultiModal quality recognition approach for the quality score. First we use two feature extractors,one for image and another for the text. After that we use an Siamese Network with the rank loss as the optimization objective.Compare with other approach,our approach get a more accuracy result.
Integration of returns and decomposition of customer orders in e-commerce warehouses
Authors: Albert H. Schrotenboer, Susanne Wruck, Iris F. A. Vis, Kees Jan Roodbergen
Date: 2019-09-01 00:00:00

In picker-to-parts warehouses, order picking is a cost- and labor-intensive operation that must be designed efficiently. It comprises the construction of order batches and the associated order picker routes, and the assignment and sequencing of those batches to multiple order pickers. The ever-increasing competitiveness among e-commerce companies has made the joint optimization of this order picking process inevitable. Inspired by the large number of product returns and the many but small-sized customer orders, we address a new integrated order picking process problem. We integrate the restocking of returned products into regular order picking routes and we allow for the decomposition of customer orders so that multiple batches may contain products from the same customer order. We thereby generalize the existing models on order picking processing. We provide Mixed Integer Programming (MIP) formulations and a tailored adaptive large neighborhood search heuristic that, amongst others, exploits these MIPs. We propose a new set of practically-sized benchmark instances, consisting of up to 5547 to be picked products and 2491 to be restocked products. On those large-scale instances, we show that integrating the restocking of returned products into regular order picker routes results in cost-savings of 10 to 15%. Allowing for the decomposition of the customer orders' products results in cost savings of up to 44% compared to not allowing this. Finally, we show that on average cost-savings of 17.4% can be obtained by using our ALNS instead of heuristics typically used in practice.
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
Authors: Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
Date: 2019-09-01 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, Reinforcement Learning

This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms. The aim of these algorithms is to jointly learn an approximation of the state-value function ($V$), alongside an approximation of the state-action value function ($Q$). Our analysis starts with a thorough study of the Deep Quality-Value Learning (DQV) algorithm, a DRL algorithm which has been shown to outperform popular techniques such as Deep-Q-Learning (DQN) and Double-Deep-Q-Learning (DDQN) \cite{sabatelli2018deep}. Intending to investigate why DQV's learning dynamics allow this algorithm to perform so well, we formulate a set of research questions which help us characterize a new family of DRL algorithms. Among our results, we present some specific cases in which DQV's performance can get harmed and introduce a novel \textit{off-policy} DRL algorithm, called DQV-Max, which can outperform DQV. We then study the behavior of the $V$ and $Q$ functions that are learned by DQV and DQV-Max and show that both algorithms might perform so well on several DRL test-beds because they are less prone to suffer from the overestimation bias of the $Q$ function.
Charged particle tracking with quantum annealing-inspired optimization
Authors: Alexander Zlokapa, Abhishek Anand, Jean-Roch Vlimant, Javier M. Duarte, Joshua Job, Daniel Lidar, Maria Spiropulu
Date: 2019-08-13 00:00:00
Tasks: Combinatorial Optimization

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework and to HL-LHC conditions. Furthermore, we develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML dataset are presented, demonstrating the successful application of a quantum annealing-inspired algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the LHC while leaving open the possibility of a quantum speedup for tracking.
Using Contextual Information to Improve Blood Glucose Prediction
Authors: Mohammad Akbari, Rumi Chunara
Date: 2019-08-24 00:00:00
Tasks: Gaussian Processes, Management, Prediction, Value prediction

Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the influence of diabetes pathologies, we need more research on data and methodologies to incorporate and evaluate signals about such temporal context into prediction models. Person-generated data sources, such as actively contributed surveys as well as passively mined data from social media offer opportunity to capture such context, however the self-reported nature and sparsity of such data mean that such data are noisier and less specific than physiological measures such as blood glucose values themselves. Therefore, here we propose a Gaussian Process model to both address these data challenges and combine blood glucose and latent feature representations of contextual data for a novel multi-signal blood glucose prediction task. We find this approach outperforms common methods for multi-variate data, as well as using the blood glucose values in isolation. Given a robust evaluation across two blood glucose datasets with different forms of contextual information, we conclude that multi-signal Gaussian Processes can improve blood glucose prediction by using contextual information and may provide a significant shift in blood glucose prediction research and practice.
An Empirical Study of the Relationships between Code Readability and Software Complexity
Authors: Duaa Alawad, Manisha Panta, Minhaz Zibran, Md. Rakibul Islam
Date: 2019-08-30 00:00:00
Tasks: BIG-bench Machine Learning

Code readability and software complexity are important software quality metrics that impact other software metrics such as maintainability, reusability, portability and reliability. This paper presents an empirical study of the relationships between code readability and program complexity. The results are derived from an analysis of 35 Java programs that cover 23 distinct code constructs. The analysis includes six readability metrics and two complexity metrics. Our study empirically confirms the existing wisdom that readability and complexity are negatively correlated. Applying a machine learning technique, we also identify and rank those code constructs that substantially affect code readability.
Single Training Dimension Selection for Word Embedding with PCA
Authors: Yu Wang
Date: 2019-08-30 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Question Answering, Sentiment Analysis, Word Embeddings

In this paper, we present a fast and reliable method based on PCA to select the number of dimensions for word embeddings. First, we train one embedding with a generous upper bound (e.g. 1,000) of dimensions. Then we transform the embeddings using PCA and incrementally remove the lesser dimensions one at a time while recording the embeddings' performance on language tasks. Lastly, we select the number of dimensions while balancing model size and accuracy. Experiments using various datasets and language tasks demonstrate that we are able to train 10 times fewer sets of embeddings while retaining optimal performance. Researchers interested in training the best-performing embeddings for downstream tasks, such as sentiment analysis, question answering and hypernym extraction, as well as those interested in embedding compression should find the method helpful.
Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context
Authors: Jie Zhang, Yin Zhao, Longjun Cai, Chaoping Tu, Wu Wei
Date: 2019-09-01 00:00:00

Predicting the emotional impact of videos using machine learning is a challenging task considering the varieties of modalities, the complicated temporal contex of the video as well as the time dependency of the emotional states. Feature extraction, multi-modal fusion and temporal context fusion are crucial stages for predicting valence and arousal values in the emotional impact, but have not been successfully exploited. In this paper, we propose a comprehensive framework with novel designs of modal structure and multi-modal fusion strategy. We select the most suitable modalities for valence and arousal tasks respectively and each modal feature is extracted using the modality-specific pre-trained deep model on large generic dataset. Two-time-scale structures, one for the intra-clip and the other for the inter-clip, are proposed to capture the temporal dependency of video content and emotion states. To combine the complementary information from multiple modalities, an effective and efficient residual-based progressive training strategy is proposed. Each modality is step-wisely combined into the multi-modal model, responsible for completing the missing parts of features. With all those improvements above, our proposed prediction framework achieves better performance on the LIRIS-ACCEDE dataset with a large margin compared to the state-of-the-art.
Visual Question Answering using Deep Learning: A Survey and Performance Analysis
Authors: Yash Srivastava, Vaishnav Murali, Shiv Ram Dubey, Snehasis Mukherjee
Date: 2019-08-27 00:00:00
Tasks: Common Sense Reasoning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

The Visual Question Answering (VQA) task combines challenges for processing data with both Visual and Linguistic processing, to answer basic `common sense' questions about given images. Given an image and a question in natural language, the VQA system tries to find the correct answer to it using visual elements of the image and inference gathered from textual questions. In this survey, we cover and discuss the recent datasets released in the VQA domain dealing with various types of question-formats and robustness of the machine-learning models. Next, we discuss about new deep learning models that have shown promising results over the VQA datasets. At the end, we present and discuss some of the results computed by us over the vanilla VQA model, Stacked Attention Network and the VQA Challenge 2017 winner model. We also provide the detailed analysis along with the challenges and future research directions.