Reviewerly/exHarmony huggingface dataset:
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems

Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution. The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index. By indexing items in a tree hierarchy and training a user-node preference prediction model satisfying a max-heap like property in the tree, TDM provides logarithmic computational complexity w.r.t. the corpus size, enabling the use of arbitrary advanced models in candidate retrieval and recommendation. In tree-based recommendation methods, the quality of both the tree index and the user-node preference prediction model determines the recommendation accuracy for the most part. We argue that the learning of tree index and preference model has interdependence. Our purpose, in this paper, is to develop a method to jointly learn the index structure and user preference prediction model. In our proposed joint optimization framework, the learning of index and user preference prediction model are carried out under a unified performance measure. Besides, we come up with a novel hierarchical user preference representation utilizing the tree index hierarchy. Experimental evaluations with two large-scale real-world datasets show that the proposed method improves recommendation accuracy significantly. Online A/B test results at a display advertising platform also demonstrate the effectiveness of the proposed method in production environments.
Natural strategic ability

In game theory, as well as in the semantics of game logics, a strategy can be represented by any function from states of the game to the agent's actions. That makes sense from the mathematical point of view, but not necessarily in the context of human behavior. This is because humans are quite bad at executing complex plans, and rather unlikely to come up with such plans in the first place. A similar concern applies to artificial agents with limited memory and/or computational power. In this paper, we adopt the view of bounded rationality, and look at “simple” strategies in specification of agents' abilities. We formally define what “simple” means, and propose a variant of alternating-time temporal logic that takes only such strategies into account. We also study the model checking problem for the resulting semantics of ability. After that, we turn to the broader issue of natural strategic abilities in concurrent games with LTL-definable winning conditions, and study a number of decision problems based on surely winning and Nash equilibrium. We show that, by adopting the view of bounded rationality based on natural strategies, we significantly decrease the complexity of rational verification for multi-agent systems.
Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases

Entity-centric knowledge bases are large collections of facts about entities of public interest, such as countries, politicians, or movies. They find applications in search engines, chatbots, and semantic data mining systems. In this paper, we first discuss the knowledge representation that has emerged as a pragmatic consensus in the research community of entity-centric knowledge bases. Then, we describe how these knowledge bases can be mined for logical rules. Finally, we discuss how entities can be represented alternatively as vectors in a vector space, by help of neural networks.
Experimental interference of uncorrelated photons

Abstract The distinguishing of the multiphoton quantum interference effect from the classical one forms one of the most important issues in modern quantum mechanics and experimental quantum optics. For a long time, the two-photon interference (TPI) of correlated photons has been recognized as a pure quantum effect that cannot be simulated with classical lights. In the meantime, experiments have been carried out to investigate the classical analogues of the TPI. In this study, we conduct TPI experiments with uncorrelated photons with different center frequencies from a luminescent light source, and we compare our results with the previous ones of correlated photons. The observed TPI fringe can be expressed in the form of three phase terms related to the individual single-photon and two-photon states, and the fringe pattern is strongly affected by the two single-photon-interference fringes and also by their visibilities. With the exception of essential differences such as valid and accidental coincidence events within a given resolving time and the two-photon spectral bandwidth, the interference phenomenon itself exhibits the same features for both correlated and uncorrelated photons in the single-photon counting regime.
Meta Dropout: Learning to Perturb Latent Features for Generalization

A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we know how to optimally perturb training examples to account for test examples, we may achieve better generalization performance. However, obtaining such perturbation is not possible in standard machine learning frameworks as the distribution of the test data is unknown. To tackle this challenge, we propose a novel regularization method, meta-dropout, which learns to perturb the latent features of training examples for generalization in a meta-learning framework. Specifically, we meta-learn a noise generator which outputs a multiplicative noise distribution for latent features, to obtain low errors on the test instances in an input-dependent manner. Then, the learned noise generator can perturb the training examples of unseen tasks at the meta-test time for improved generalization. We validate our method on few-shot classification datasets, whose results show that it significantly improves the generalization performance of the base model, and largely outperforms existing regularization methods such as information bottleneck, manifold mixup, and information dropout.
Theory and Evaluation Metrics for Learning Disentangled Representations

We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluation. First, we characterize the concept “disentangled representations” used in supervised and unsupervised methods along three dimensions–informativeness, separability and interpretability–which can be expressed and quantified explicitly using information-theoretic constructs. This helps explain the behaviors of several well-known disentanglement learning models. We then propose robust metrics for measuring informativeness, separability and interpretability. Through a comprehensive suite of experiments, we show that our metrics correctly characterize the representations learned by different methods and are consistent with qualitative (visual) results. Thus, the metrics allow disentanglement learning methods to be compared on a fair ground. We also empirically uncovered new interesting properties of VAE-based methods and interpreted them with our formulation. These findings are promising and hopefully will encourage the design of more theoretically driven models for learning disentangled representations.
Collaborative Mobile-Learning Architecture Based on Mobile Agents

The connection between collaborative learning and the new mobile technology has become tighter. Mobile learning enhances collaborative learning as learners can access information and learning materials from anywhere and at any time. However, supporting efficient mobile learning in education is a critical challenge. In addition, incorporating technological and educational components becomes a new, complex dimension. In this paper, an efficient collaborative mobile-learning architecture based on mobile agents is proposed to enhance learning activity and to allow teachers and students to collaborate in knowledge and information transfer. A mobile agent can control its own actions, is able to communicate with other agents, and adapts in accordance with previous experience. The proposed model consists of four components: the learner agent, the teacher agent, the device agent and the social agent. The social agent plays the main role in the collaborative tasks since it is responsible for evaluating the collaborative interactions among different learners. Additionally, it offers an evaluation indicator for the learners’ collaboration and supplies the teacher with learner’s collaboration reports. The proposed model is evaluated by introducing a collaborative mobile-learning case study applied to two full classes of undergraduate students. To conduct the model experiments, students were asked to complete a questionnaire after they used the proposed model. The questionnaire results statistically revealed that the proposed architecture is easy to use and access, well-organized, convenient, and facilitates the learning process. The students thought the proposed m-learning application should complement rather than replace the traditional lectures. Moreover, the experimental results show that the proposed collaborative mobile learning model enhances the learner’s skills in problem solving, increases the learner’s knowledge in comparison with individual learning, and social agent encourages learners for more participation in the learning tasks. Based on the experiments conducted, the authors found that the proposed model can improve the quality of the learning process by assessing learners’ and groups’ collaboration, and it can help teachers make learners improve how they work in groups. This also provides various ways of assessing learners abilities and skills in groups. It is also possible to integrate the collaborative e-learning with the proposed collaborative m-learning.
Comparison of Multi-atlas Segmentation and U-Net Approaches for Automated 3D Liver Delineation in MRI

Segmentation of medical images is typically one of the first and most critical steps in medical image analysis. Manual segmentation of volumetric images is labour-intensive and prone to error. Automated segmentation of images mitigates such issues. Here, we compare the more conventional registration-based multi-atlas segmentation technique with recent deep-learning approaches. Previously, 2D U-Nets have commonly been thought of as more appealing than their 3D versions; however, recent advances in GPU processing power, memory, and availability have enabled deeper 3D networks with larger input sizes. We evaluate methods by comparing automated liver segmentations with gold standard manual annotations, in volumetric MRI images. Specifically, 20 expert-labelled ground truth liver labels were compared with their automated counterparts. The data used is from a liver cancer study, HepaT1ca, and as such, presents an opportunity to work with a varied and challenging dataset, consisting of subjects with large anatomical variations responding from different tumours and resections. Deep-learning methods (3D and 2D U-Nets) proved to be significantly more effective at obtaining an accurate delineation of the liver than the multi-atlas implementation. 3D U-Net was the most successful of the methods, achieving a median Dice score of 0.970. 2D U-Net and multi-atlas based segmentation achieved median Dice scores of 0.957 and 0.931, respectively. Multi-atlas segmentation tended to overestimate total liver volume when compared with the ground truth, while U-Net approaches tended to slightly underestimate the liver volume. Both U-Net approaches were also much quicker, taking around one minute, compared with close to one hour for the multi-atlas approach.
Local Differential Privacy: Tools, Challenges, and Opportunities

Local Differential Privacy (LDP), where each user perturbs her data locally before sending to an untrusted party, is a new and promising privacy-preserving model. Endorsed by both academia and industry, LDP provides strong and rigorous privacy guarantee for data collection and analysis. As such, it has been recently deployed in many real products by several major software and Internet companies, including Google, Apple and Microsoft in their mainstream products such as Chrome, iOS, and Windows 10. Besides industry, it has also attracted a lot of research attention from academia. This tutorial first introduces the rationale of LDP model behind these deployed systems to collect and analyze usage data privately, then surveys the current research landscape in LDP, and finally identifies several open problems and research directions in this community.
A Few-Shot Practical Behavioral Biometrics Model for Login Authentication in Web Applications

Risk based authentication has been advocated as complement to traditional authentication mechanisms in order to raise the bar against attackers in possession of stolen credentials.Behavioral biometrics has received attention in the literature in the past decade, however the best results have been obtained in the so-called continuous setting and with enough training data, usually spanning several hours of user interaction.In this paper we explore the more challenging scenario of behavioral biometrics as an effective risk-based authentication technique using both mouse and keyboard information at login time (static authentication), assuming only between 3 and 7 login sessions per user for training.In a controlled but realistic experiment with 89 subjects we achieve a FRR of 10.73% and FAR of 23.34% for a model trained using only 5 login attempts, each performed in less than 30 seconds on average.We also evaluate our prototype with 2000 users from production data in the banking domain.
VM Migration for Profit Maximization in Federated Cloud Data Centers

Federated cloud systems present a cost-effective solution for handling user requests in a scalable and cost-efficient manner, while avoiding the costs of large infrastructure to support peak workload. The main goal of each cloud provider in a federation is minimizing the operating cost while meeting the QoS requirements of the users. Virtual machine migration can be utilized for efficient resource management in the federated cloud where a cloud provider can rent resources from other cloud providers and migrate its virtual machines to achieve load balancing, better QoS, or minimize the operating cost. However, inter-data center migration brings an additional cost, and it may degrade the quality of the service. We propose an algorithm for VM migration between data centers in a federated cloud to reduce the operating cost of the cloud provider considering the cost of the migration and the QoS. Experimental results show the benefits of the federation for the cloud provider and the user. In addition, our proposed migration algorithm reduces the total operating cost of the cloud provider significantly with a minimal migration cost.
Graph convolutional networks for region of interest classification in breast histopathology

Deep learning-based approaches have shown highly successful performance in the categorization of digitized biopsy samples. The commonly used setting in these approaches is to employ convolutional neural networks for classification of data sets consisting of images all having the same size. However, the clinical practice in breast histopathology necessitates multi-class categorization of regions of interest (ROI) in biopsy samples where these regions can have arbitrary shapes and sizes. The typical solution to this problem is to aggregate the classification results of fixed-sized patches cropped from these images to obtain image-level classification scores. Another limitation of these approaches is the independent processing of individual patches where the rich contextual information in the complex tissue structures has not yet been sufficiently exploited. We propose a generic methodology to incorporate local inter-patch context through a graph convolution network (GCN) that admits a graph-based ROI representation. The proposed GCN model aims to propagate information over neighboring patches in a progressive manner towards classifying the whole ROI into a diagnostic class. The experiments using a challenging data set for a 4-class ROI-level classification task and comparisons with several baseline approaches show that the proposed model that incorporates the spatial context by using graph convolutional layers performs better than commonly used fusion rules.
Implementation of Data-optimized FPGA-based Accelerator for Convolutional Neural Network

Convolutional Neural Networks (CNNs) are widely used for image recognition, and FPGAs are considered suitable platform for CNNs due to their low power consumption and reconfigurability. While CNNs are mostly trained using floating point data type for high inference accuracy, fixed point data type can be used to reduce data size and take advantage of computation efficiency on FPGAs without any accuracy loss. In this paper, we propose an accelerator design for LeNet-5 CNN architecture [1] for MNIST handwritten digit recognition. The accelerator is synthesized with Xilinx Vivado High-Level Synthesis (HLS) tool (v2017.2), targeting xczu9eg-ffvb1156-2-i FPGA board. The proposed accelerator focuses on reducing latency and memory usage, and the performance is compared with a conventional floating point design. Our proposed accelerator can achieve latency reduction up to 90% and memory usage reduction up to 40% without any accuracy loss, compared to the conventional design.
Native Language Identification of Fluent and Advanced Non-Native Writers

Native Language Identification (NLI) aims at identifying the native languages of authors by analyzing their text samples written in a non-native language. Most existing studies investigate this task for educational applications such as second language acquisition and require the learner corpora. This article performs NLI in a challenging context of the user-generated-content (UGC) where authors are fluent and advanced non-native speakers of a second language. Existing NLI studies with UGC (i) rely on the content-specific/social-network features and may not be generalizable to other domains and datasets, (ii) are unable to capture the variations of the language-usage-patterns within a text sample, and (iii) are not associated with any outlier handling mechanism. Moreover, since there is a sizable number of people who have acquired non-English second languages due to the economic and immigration policies, there is a need to gauge the applicability of NLI with UGC to other languages. Unlike existing solutions, we define a topic-independent feature space, which makes our solution generalizable to other domains and datasets. Based on our feature space, we present a solution that mitigates the effect of outliers in the data and helps capture the variations of the language-usage-patterns within a text sample. Specifically, we represent each text sample as a point set and identify the top- k stylistically similar text samples (SSTs) from the corpus. We then apply the probabilistic k nearest neighbors’ classifier on the identified top- k SSTs to predict the native languages of the authors. To conduct experiments, we create three new corpora where each corpus is written in a different language, namely, English, French , and German . Our experimental studies show that our solution outperforms competitive methods and reports more than 80% accuracy across languages.
Mindtools: Affording Multiple Knowledge Representations for Learning

Numerous reports decry the deficiencies in thinking skills of students at all educational levels. Students had memorized and could apply the equations and problem-solving procedures without understanding the physics concepts they were representing mathematically. An effective method for supporting the representation of learner knowledge through multiple formalisms is to use computers as mindtools to represent their knowledge. Mindtools are computer software applications, like databases, spreadsheets, semantic networking programs, expert systems, systems modeling tools, microworlds, hypermedia authoring tools, and computer conferencing, that enable learners to represent what they have learned and know using different representational formalisms. Semantic organization tools enable learners to analyze and organize what they know. Two of the most commonly used semantic organization tools are databases and semantic networking tools. Database management systems are computerized filing systems designed to accelerate the storage and retrieval of information. Semantic networking tools provide visual screen tools for producing concept maps.
G-DAUG: Generative Data Augmentation for Commonsense Reasoning

Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance. However, manual curation of training examples is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit on. We investigate G-DAUG^C, a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting. Our approach generates synthetic examples using pretrained language models, and selects the most informative and diverse set of examples for data augmentation. In experiments with multiple commonsense reasoning benchmarks, G-DAUG^C consistently outperforms existing data augmentation methods based on back-translation, and establishes a new state-of-the-art on WinoGrande, CODAH, and CommonsenseQA. Further, in addition to improvements in in-distribution accuracy, G-DAUG^C-augmented training also enhances out-of-distribution generalization, showing greater robustness against adversarial or perturbed examples. Our analysis demonstrates that G-DAUG^C produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance. Our findings encourage future research toward generative data augmentation to enhance both in-distribution learning and out-of-distribution generalization.
Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Supervised learning in large discriminative models is a mainstay for modern computer vision. Such an approach necessitates investing in large-scale human-annotated datasets for achieving state-of-the-art results. In turn, the efficacy of supervised learning may be limited by the size of the human annotated dataset. This limitation is particularly notable for image segmentation tasks, where the expense of human annotation is especially large, yet large amounts of unlabeled data may exist. In this work, we ask if we may leverage semi-supervised learning in unlabeled video sequences and extra images to improve the performance on urban scene segmentation, simultaneously tackling semantic, instance, and panoptic segmentation. The goal of this work is to avoid the construction of sophisticated, learned architectures specific to label propagation (e.g., patch matching and optical flow). Instead, we simply predict pseudo-labels for the unlabeled data and train subsequent models with both human-annotated and pseudo-labeled data. The procedure is iterated for several times. As a result, our Naive-Student model, trained with such simple yet effective iterative semi-supervised learning, attains state-of-the-art results at all three Cityscapes benchmarks, reaching the performance of 67.8% PQ, 42.6% AP, and 85.2% mIOU on the test set. We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.