Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural News Recommendation with Heterogeneous User Behavior

News recommendation is important for online news platforms to help users find interested news and alleviate information overload. Existing news recommendation methods usually rely on the news click history to model user interest. However, these methods may suffer from the data sparsity problem, since the news click behaviors of many users in online news platforms are usually very limited. Fortunately, some other kinds of user behaviors such as webpage browsing and search queries can also provide useful clues of users’ news reading interest. In this paper, we propose a neural news recommendation approach which can exploit heterogeneous user behaviors. Our approach contains two major modules, i.e., news representation and user representation. In the news representation module, we learn representations of news from their titles via CNN networks, and apply attention networks to select important words. In the user representation module, we propose an attentive multi-view learning framework to learn unified representations of users from their heterogeneous behaviors such as search queries, clicked news and browsed webpages. In addition, we use word- and record-level attentions to select informative words and behavior records. Experiments on a real-world dataset validate the effectiveness of our approach.
Advances in Monte Carlo Variational Inference and Applied Probabilistic Modeling

Galvanized by the accelerated pace and ease of data collection, researchers in more and more disciplines are turning to large, heterogeneous datasets to answer scientific questions. Divining insight from massive and complex data, however, requires flexible models and efficient inference of meaningful factors of variation. This thesis develops new statistical models and methods to help practitioners answer quantitative questions and more efficiently explore their data. The first part of this thesis presents three applied probabilistic modeling case studies in a diverse set of domains: astronomy, healthcare, and sports analytics. For each application we develop a probabilistic model for high-dimensional observations to address a particular goal—to make robust, portable predictions, find latent structure, or to organize and visualize interpretable factors of variation in the data. Guided by these examples, we discuss the common challenges of specifying interpretable-yet-flexible probabilistic models in applied settings. Motivated by the challenges of applying probabilistic models to large datasets, the second part of this thesis develops new algorithms for approximate Bayesian inference. We focus on improving variational inference, a widely used class of approximation algorithms. We develop two new techniques to improve the accuracy and computational efficiency of variational inference methods. We further generalize one technique into a class of computationally efficient Monte Carlo estimators.
The Impact of Product and Process Strategies on the Service Performance: The Mediating Role of Supply Chain Integrations

The main purpose of the current study is to investigate he relationship between product and process strategies and the service performance namely delivery performance and the support performance. In addition to that the study has also examined the mediating role of supply chain integrations namely supplier integration and customer integration in the relationship between product and process strategies and the service performance. In this research, Product Modularity is the specific product and Process Modularity is the process strategy, which has been selected. The performance of service is influenced by these two strategies in a direct and indirect way. The focus of the study is on the evaluation of the level to which the supplier (upstream) as well as customer (downstream) integration mediates the impact of process strategies on performance of services. The study is among the pioneering studies on the issues. So, current study has used SEM-PLS as statistical tool to answer the research questions raised in this study and research objectives envisaged in the current study. The findings of the current study have provided support to with the hypothesized results. This study will be helpful for policymakers and researchers in examining the link between product and process strategies and the service performance namely delivery performance and the support performance.
Accurate Segmentation of Synaptic Cleft with Contour Growing Concatenated with a Convnet

Synaptic cleft is an important area for neuroscientists to analyze the macromolecular complexes related to neurotransmitter transmission. However, the large amount of noise and low signal-to-noise ratio in raw electron micrographs make it challenging to extract this region automatically. In this paper, we propose a simple but effective framework to automatically extract accurate boundaries of synaptic cleft regions. Our approach concatenates a novel contour growing algorithm to a fully convolutional network (FCN), so that it takes both advantages of large receptive field of FCNs and fine-level localization of contour evolution. The contour growing algorithm is based on the flexible evolving tension and synchronous growing controlling to localize the opening contour of clef region. With consideration of both global localization and local segmentation, our approach is more robust to noisy electron micrographs and outperforms all existing single-model FCNs on accurate segmentation of synaptic clefts.
Implementation of Obfuscated Control Logic Circuit with LegUp and oLLVM

It is an important issue to protect the intellectual property of software. The authors proposed to conceal some part of software by implementing it as logic circuit. Though the security is further improved by obfuscating the logic circuit, it requires much effort to develop the dedicated obfuscation tool. Matsuoka et al. proposed using the software obfuscation tool, Obfuscator-LLVM (oLLVM), with C-backend (CBE) and Xilinx Vivado HLS (high-level synthesis) to generate the obfuscated logic circuit. This study proposes adopting another HLS, LegUp, to obfuscate logic circuit. The feasibility of these two methods are examined with 12 applications of CHStone benchmark, each of which were evaluated with four obfuscation methods (total 48 cases). In our experiments, Matsuoka’s method failed to generate the correct hardware in 5 cases out of 48 cases. Meanwhile, the proposed method successfully generated the correct hardware for all 48 cases. The average latency derived by the proposed method was 43% larger than that by Matsuoka’s method. The logic scale derived by the proposed method was also 42% larger (LUT) and 112% larger (FF) than that by Matsuoka’s method.
Cluster-Wise Removal of Reflection Artifacts in Large-Scale 3d Point Clouds Using Superpixel-Based Glass Region Estimation

Large-scale 3D point cloud (LS3DPC) models captured by LiDAR scanners suffer from reflection artifacts caused by glass in real-world scenes. In this paper, we propose a reflection artifact removal algorithm for LS3DPC models using a panoramic image. We first partition the panoramic color image corresponding to an input LS3DPC model into superpixels, and cluster the points projected to each superpixel according to their geometric positions. We determine a superpixel with multiple point clusters as a glass region. We also detect and remove the virtual clusters of reflection artifacts by evaluating the symmetry and geometry scores. The experiment results show that the proposed algorithm detects the glass regions more faithfully and removes the virtual objects more completely compared with the existing method.
Studying co-running avionic real-time applications on multi-core COTS architectures

For the last decades, industries from the safety-critical domain have been using Commercial Off-The-Shelf (COTS) architectures despite their inherent runtime variability. To guarantee hard real-time constraints in such systems, designers massively relied on resource over-provisioning and disabling the features responsible for runtime variability. The recent shift to multi-core architectures in the embedded COTS market worsened the runtime variability problem as contention on shared hardware resources brought new variability sources. Additionally, hiding this variability in additional safety margins as performed in the past will offset most if not all the multi-core performance gains. To enable the use of multi-cores in this domain, it has become essential to finely characterize at system level the application workload, as well as the possible contention on shared hardware resources. In this paper, we introduce measurement techniques based on a set of dedicated stressing benchmarks and architecture hardware monitors to characterize (1) the architecture, by identifying the shared hardware resources and their associated contention mechanisms. (2) the application, by identifying which shared hardware resources it is sensitive to. Such information would guide us toward identifying which applications can run smoothly together without endangering individual worst-case execution times.
CLOUD AGNOSTIC STREAM ANALYTICS

As machine learning is getting more and more mainstream, increasing number of public cloud providers are providing machine learning related APIs such as Text Analytics, ANN, Auto ML (pretrained) APIs which are on one hand make life easier for many application developers, but on the other hand brings API management complexity in hybrid or multi cloud environment. The current evolution of these ML (machine learning) APIs leads to more coherent integration with the current cloud provider and makes it harder to switch between the providers. These leads to a vendor locked in situation where it becomes difficult for application to easily switch the service provider. A cloud agnostic framework has been proposed here which would allow to develop modern cloud-based data science, machine learning, AI applications using the proposed framework or similar approaches where the application provider could seamlessly switch between various cloud vendors as well as native OS level APIs painlessly and programmatically. As the subject of data science is so broad that we will focus only on one aspect of it to show case the proof of concept of the framework using sentiment analysis APIs to demonstrate stream analytics capabilities. Same approach could easily be applied on other APIs as well.
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels

In low-resource settings, the performance of supervised labeling models can be improved with automatically annotated or distantly supervised data, which is cheap to create but often noisy. Previous works have shown that significant improvements can be reached by injecting information about the confusion between clean and noisy labels in this additional training data into the classifier training. However, for noise estimation, these approaches either do not take the input features (in our case word embeddings) into account, or they need to learn the noise modeling from scratch which can be difficult in a low-resource setting. We propose to cluster the training data using the input features and then compute different confusion matrices for each cluster. To the best of our knowledge, our approach is the first to leverage feature-dependent noise modeling with pre-initialized confusion matrices. We evaluate on low-resource named entity recognition settings in several languages, showing that our methods improve upon other confusion-matrix based methods by up to 9%.
A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers

The alternating direction method of multipliers (ADMM) has recently received tremendous interests for distributed large scale optimization in machine learning, statistics, multi-agent networks and related applications. In this paper, we propose a new parallel multi-block stochastic ADMM for distributed stochastic optimization, where each node is only required to perform simple stochastic gradient descent updates. The proposed ADMM is fully parallel, can solve problems with arbitrary block structures, and has a convergence rate comparable to or better than existing state-of-the-art ADMM methods for stochastic optimization. Existing stochastic (or deterministic) ADMMs require each node to exchange its updated primal variables across nodes at each iteration and hence cause significant amount of communication overhead. Existing ADMMs require roughly the same number of inter-node communication rounds as the number of in-node computation rounds. In contrast, the number of communication rounds required by our new ADMM is only the square root of the number of computation rounds.
Dressing for Attention: Outfit Based Fashion Popularity Prediction

analysis of fashion trends is crucial. However, existing predictive algorithms of fashion popularity are restricted to be feasible on the coarse style level but not a finer item level. That is, they are only predictive in the future popularity of a given type of fashion styles (e.g., Rocker), but cannot be precisely down to a particular outfit look chosen by individuals. This paper thus proposes the first solution directly aimed at predicting the fine-grained fashion popularity of an outfit look by taking social media as the learning source. Particularly, a deep temporal sequence learning framework is developed and the proposed framework is evaluated on a real dataset of 380,000 street fashion images collected from the fashion website lookbook.nu. The experimental results show that our proposed framework outperforms the state-of-the-art approaches, with a relative increase of 11.51% to 27.62% (MSE metric) and 7.02% to 32.61% (CSE metric) in the prediction accuracy.
Are Slim Models Doing Better? Advertising Effect of Model Body Size

Advertisements frequently contain images of persons (models) in a decorative role. Advertisers have to decide what characteristics these models should have (gender, age, pose, clothing, facial expression, etc.). Among these characteristics, the model’s body size is an important aspect. Numerous previous studies have shown that the model’s body size has an impact on how positively consumers evaluate the promoted product. One must admit that the model’s body size used in advertisements has evoked controversial debates in recent years. While using slim models was not strongly criticized some decades ago, severe public concerns have been expressed in recent years. Advertisers (and “media” in general) have been accused of fostering many problems by using very slim models: body dissatisfaction, eating disorders, bulimia, anorexia, and demand for plastic surgery, among others. As a response to these concerns, editors of some magazines decided to refrain from depicting very slim models anymore. Some brands used images of average-sized persons for promoting their brands. The most famous example was the “Real Women” campaign for Dove cosmetics, a Unilever brand. The overwhelming economic success of this campaign may be due to its innovativeness and originality and not due to the use of average-sized models per se, because this campaign was the first well-known campaign that used average-sized models. However, such campaigns could reduce potential detrimental effects when models are depicted; they could prevent consumers from developing a negative image of their body, i.e., they could avoid impairment of appearance self-esteem.Therefore, we investigated how young consumers respond at present to products when slim, average-sized, or heavy models are shown in advertisements that do not strongly emphasize the body size of the depicted models. From our studies, we derive the following recommendations: 1. If average-sized or heavy young consumers are targeted, average-sized models are advantageous. If the audience of the company consists of slim young consumers, slim models should be depicted. 2. This advice is not contingent on whether appearance-related or appearance-unrelated products are promoted.
A FITNESS BIKE MAINTENANCE TRAINING SYSTEM WITH THREE-DIMENSIONAL COMPUTER GRAPHICS

This article describes a fitness bike maintenance training system using 3D computer graphics. The proposed system provides customer engineers with a 360-degree view and enables interactive manipulation. The system has three training modes: learning mode, examination mode, and positional relation check mode. Flashing CG objects and interactive manipulation are used so that engineers can readily understand the maintenance work. Test usage by customer engineers, managers, and training instructors has resulted in several positive comments including praise for the clearly indicated instructions and good instructions for handling the bike in 360-degrees.
Trainable Active Contour Model for Histological Image Segmentation

Lesions analysis of mucous glands, which depends on the glands segmentation in histological images, is an important task of surgical pathology. This paper presents a hybrid method of glands object segmentation in histological images, based on the trainable active contour model. The hybrid method combines the use of both modern convolutional neural networks and classical methods of mathematical image processing. Also, within this hybrid method a special postprocessing algorithm is implemented, which allows to correctly segment stucked glands in the image. The proposed method was tested on PATH-DT-MSU dataset and demonstrated good results. The average value of IoU for all test images is 0.81.
The impact of autonomous vehicles on urban traffic network capacity: an experimental analysis by microscopic traffic simulation

ABSTRACT Urban commuters have been suffering from traffic congestion for a long time. In order to avoid or mitigate the congestion effect, it is significant to know how the introduction of autonomous vehicles (AVs) influence the road capacity . The effects that AVs bring to the macroscopic fundamental diagram (MFD) were investigated through microscopic traffic simulations. This is a key issue as the MFD is a basic model to describe road capacity in practical traffic engineering. Accordingly, the paper investigates how the different percentage of AVs affects the urban MFD. A detailed simulation study was carried out by using SUMO both with an artificial grid road network and a real-world network in Budapest. On the one hand, simulations clearly show the capacity improvement along with AVs penetration growth. On the other hand, the paper introduces an efficient modeling for MFDs with different AVs rates by using the generalized additive model (GAM).
Understanding sequential measurements in psi-epistemic ontological models

Since the famous debates of Einstein & Bohr, physicists have argued about the nature of the quantum state. Is it best thought of as describing the-way-things-are out there in the world, or merely as a description of our knowledge of such things? In more recent years, this has been termed the ψ-epistemic/ψ-ontic debate, and that distinction has been given a mathematical definition within the ontological models formalism. This formalism is a framework for describing a large class of interpretations of quantum mechanics. Here we show that consideration of sequential measurements, and the fact that the quantum state changes during measurements, has been a neglected topic in this area as it places nontrivial restrictions on the structure of ψ-epistemic ontological models. We do this by finding a general restriction on the structure of ψ-epistemic models, although not one that is strong enough to rule them out categorically. We then apply this restriction to all of the known examples of ψ-epistemic ontological models and show that they can’t represent sequential measurements. We also present a new version of the ontological models formalism, based on hidden Markov models, which we develop briefly and describe how it may be useful for either proving a no-go theorem (i.e. ‘ψ-epistemic models can’t exist’) or for learning more about the structure of ψ-epistemic models and how to construct them.
Attention manipulation in reinforcement learning agents

The ability to change others’ attention for our own benefit is referred to as attention manipulation and is known to be an important cognitive ability for coordination in cooperative tasks. In this work, we formulate attention manipulation in the context of reinforcement learning (RL) agents and argue that if the environment is complex enough agents will learn to use this skill. In particular, we first outline some of the characteristics in the environment that make it complex enough for this behavior to become relevant. Then, we test RL agents in two environments with such characteristics. Finally, we estimate a measure of attention manipulation using information theory functionals proposed to capture causal influence. Our results indicate that attention manipulation can be used by relatively simple RL agents to achieve better coordination in cooperative tasks.
jomo: A Flexible Package for Two-level Joint Modelling Multiple Imputation

Multiple imputation is a tool for parameter estimation and inference with partially observed data, which is used increasingly widely in medical and social research. When the data to be imputed are correlated or have a multilevel structure — repeated observations on patients, school children nested in classes within schools within educational districts — the imputation model needs to include this structure. Here we introduce our joint modelling package for multiple imputation of multilevel data, jomo, which uses a multivariate normal model fitted by Markov Chain Monte Carlo (MCMC). Compared to previous packages for multilevel imputation, e.g. pan, jomo adds the facility to (i) handle and impute categorical variables using a latent normal structure, (ii) impute level-2 variables, and (iii) allow for cluster-specific covariance matrices, including the option to give them an inverse-Wishart distribution at level 2. The package uses C routines to speed up the computations and has been extensively validated in simulation studies both by ourselves and others.