Reviewerly/exHarmony huggingface dataset:
REMAP: Multi-Layer Entropy-Guided Pooling of Dense CNN Features for Image Retrieval

This paper addresses the problem of very large-scale image retrieval, focusing on improving its accuracy and robustness. We target enhanced robustness of search to factors such as variations in illumination, object appearance and scale, partial occlusions, and cluttered backgrounds -particularly important when search is performed across very large datasets with significant variability. We propose a novel CNN-based global descriptor, called REMAP, which learns and aggregates a hierarchy of deep features from multiple CNN layers, and is trained end-to-end with a triplet loss. REMAP explicitly learns discriminative features which are mutually-supportive and complementary at various semantic levels of visual abstraction. These dense local features are max-pooled spatially at each layer, within multi-scale overlapping regions, before aggregation into a single image-level descriptor. To identify the semantically useful regions and layers for retrieval, we propose to measure the information gain of each region and layer using KL-divergence. Our system effectively learns during training how useful various regions and layers are and weights them accordingly. We show that such relative entropy-guided aggregation outperforms classical CNN-based aggregation controlled by SGD. The entire framework is trained in an end-to-end fashion, outperforming the latest state-of-the-art results. On image retrieval datasets Holidays, Oxford and MPEG, the REMAP descriptor achieves mAP of 95.5%, 91.5% and 80.1% respectively, outperforming any results published to date. REMAP also formed the core of the winning submission to the Google Landmark Retrieval Challenge on Kaggle.
DATAZERO: Datacenter With Zero Emission and Robust Management Using Renewable Energy

As the need for cloud services has been growing steadily, the size and energy consumption of datacenters have increased significantly over the past years. Due to economic and environmental constraints, energy efficiency in datacenters and greenhouse emissions have become a major concern. Renewable energy is widely seen as a promising solution to supply datacenters using local energy, without greenhouse gas emissions. However, the intermittent power generation resulting from the use of renewable energy imposes a paradigm change in the way energy and computation activities are managed. On the one hand, service placement and scheduling may be used on the IT (information technologies) side to adapt to the available power. On the other hand, the storage units may be used to lessen power generation variations. Existing literature and actual deployment mainly design optimization algorithms including the entire system (from cloud service to electrical management, the latter often being neglected or simplified). Conversely to these approaches, we propose a solution where each side optimizes its own objectives, both interacting through a negotiation loop process to reach a common agreement. In this paper, we present DATAZERO, a project developing this idea to ensure high availability of IT services, avoiding unnecessary redundancies, under the constraints due to the intermittent nature of electrical and cloud services flows.
Regularization via Mass Transportation

The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, overfitting is typically mitigated by adding regularization terms to the objective that penalize hypothesis complexity. In this paper we introduce new regularization techniques using ideas from distributionally robust optimization, and we give new probabilistic interpretations to existing techniques. Specifically, we propose to minimize the worst-case expected loss, where the worst case is taken over the ball of all (continuous or discrete) distributions that have a bounded transportation distance from the (discrete) empirical distribution. By choosing the radius of this ball judiciously, we can guarantee that the worst-case expected loss provides an upper confidence bound on the loss on test data, thus offering new generalization bounds. We prove that the resulting regularized learning problems are tractable and can be tractably kernelized for many popular loss functions. We validate our theoretical out-of-sample guarantees through simulated and empirical experiments.
Phishing in an academic community: A study of user susceptibility and behavior

We present an observational study on the relationship between demographic factors and phishing susceptibility at the University of Maryland, Baltimore County (UMBC).In spring 2018, we delivered phishing attacks to 450 randomly selected students on three different days (1,350 students total) to examine user click rates and demographics among UMBC's undergraduates.Participants were initially unaware of the study.We deployed the billing problem, contest winner, and expiration date phishing tactics.Experiment 1 impersonated banking authorities; Experiment 2 enticed users with monetary rewards; and Experiment 3 threatened users with account cancelation.We found correlations resulting in lowered susceptibility based on college affiliation, academic year progression, cyber training, involvement in cyber clubs or cyber scholarship programs, time spent on the computer, and age demographics.We found no significant correlation between gender and susceptibility.Contrary to our expectations, we observed a reverse correlation between phishing awareness and student resistance to clicking.Students who identified themselves as understanding the definition of phishing had a higher susceptibility rate than did their peers who were merely aware of phishing attacks, with both groups having a higher susceptibility rate than those with no knowledge whatsoever.Approximately 70% of survey respondents who opened a phishing email clicked on it, with 60% of student having clicked overall.
Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis

In various clinical scenarios, medical image is crucial in disease diagnosis and treatment. Different modalities of medical images provide complementary information and jointly helps doctors to make accurate clinical decision. However, due to clinical and practical restrictions, certain imaging modalities may be unavailable nor complete. To impute missing data with adequate clinical accuracy, here we propose a framework called self-supervised collaborative learning to synthesize missing modality for medical images. The proposed method comprehensively utilize all available information correlated to the target modality from multi-source-modality images to generate any missing modality in a single model. Different from the existing methods, we introduce an auto-encoder network as a novel, self-supervised constraint, which provides target-modality-specific information to guide generator training. In addition, we design a modality mask vector as the target modality label. With experiments on multiple medical image databases, we demonstrate a great generalization ability as well as specialty of our method compared with other state-of-the-arts.
Evaluating Performance of Software Durability through an Integrated Fuzzy-Based Symmetrical Method of ANP and TOPSIS

Acceptance of any new approach by the organizations depends upon the users’ needs. Currently, reducing the cost and time invested in maintenance is a major challenge for the practitioners. Moreover, symmetrical and optimal maintenance is the need of the hour and it can be achieved by increasing the durability of software. Many attributes of the quality may affect the durability of the software and identification of durability attributes is a crucial task at the early stage of software development. Thus, it is a problem that contains multi-criteria within it. With the help of quantitative estimation, software durability may be assessed symmetrically and increased. In this row, the authors of this article have attempted to posit an effective technique to assess the durability of software. Based on empirical data through research, the presenters of this article suggest that fuzzy-based symmetrical method of Analytic Network Process (ANP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) would be an accurate methodology for assessing the durability of software. For determining the efficacy of this assessment, the researchers took six alternative software of a University. The results, as presented in this paper, would serve as guidelines for the practitioners who aim at achieving the preferred software durability.
Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation

We propose a self-supervised representation learning model for the task of unsupervised phoneme boundary detection. The model is a convolutional neural network that operates directly on the raw waveform. It is optimized to identify spectral changes in the signal using the Noise-Contrastive Estimation principle. At test time, a peak detection algorithm is applied over the model outputs to produce the final boundaries. As such, the proposed model is trained in a fully unsupervised manner with no manual annotations in the form of target boundaries nor phonetic transcriptions. We compare the proposed approach to several unsupervised baselines using both TIMIT and Buckeye corpora. Results suggest that our approach surpasses the baseline models and reaches state-of-the-art performance on both data sets. Furthermore, we experimented with expanding the training set with additional examples from the Librispeech corpus. We evaluated the resulting model on distributions and languages that were not seen during the training phase (English, Hebrew and German) and showed that utilizing additional untranscribed data is beneficial for model performance.
Triangle Measure of Tripartite Entanglement

A previously overlooked constraint for the distribution of entanglement in three-qubit systems is exploited for the first time and used to reveal a new genuine tripartite entanglement measure. It is interpreted as the area of a so-called concurrence triangle and is compared with other existing measures. The new measure is found superior to previous attempts for different reasons. A specific example is illustrated to show that two tripartite entanglement measures can be inequivalent due to the high dimensionality of the Hilbert space.
Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning

The growing demand for robots able to act autonomously in complex scenarios has widely accelerated the introduction of Reinforcement Learning (RL) in robots control applications. However, the trial and error intrinsic nature of RL may result in long training time on real robots and, moreover, it may lead to dangerous outcomes. While simulators are useful tools to accelerate RL training and to ensure safety, they often are provided only with an approximated model of robot dynamics and of its interaction with the surrounding environment, thus resulting in what is called the reality gap (RG): a mismatch of simulated and real control-law performances caused by the inaccurate representation of the real environment in simulation. The most undesirable result occurs when the controller learnt in simulation fails the task on the real robot, thus resulting in an unsuccessful sim-to-real transfer. The goal of the present survey is threefold: (1) to identify the main approaches to face the RG problem in the context of robot control with RL, (2) to point out their shortcomings, and (3) to outline new potential research areas.
3-D RoI-Aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

Segmentation of colorectal cancerous regions from 3-D magnetic resonance (MR) images is a crucial procedure for radiotherapy. Automatic delineation from 3-D whole volumes is in urgent demand yet very challenging. Drawbacks of existing deep-learning-based methods for this task are two-fold: 1) extensive graphics processing unit (GPU) memory footprint of 3-D tensor limits the trainable volume size, shrinks effective receptive field, and therefore, degrades speed and segmentation performance and 2) in-region segmentation methods supported by region-of-interest (RoI) detection are either blind to global contexts, detail richness compromising, or too expensive for 3-D tasks. To tackle these drawbacks, we propose a novel encoder-decoder-based framework for 3-D whole volume segmentation, referred to as 3-D RoI-aware U-Net (3-D RU-Net). 3-D RU-Net fully utilizes the global contexts covering large effective receptive fields. Specifically, the proposed model consists of a global image encoder for global understanding-based RoI localization, and a local region decoder that operates on pyramid-shaped in-region global features, which is GPU memory efficient and thereby enables training and prediction with large 3-D whole volumes. To facilitate the global-to-local learning procedure and enhance contour detail richness, we designed a dice-based multitask hybrid loss function. The efficiency of the proposed framework enables an extensive model ensemble for further performance gain at acceptable extra computational costs. Over a dataset of 64 T2-weighted MR images, the experimental results of four-fold cross-validation show that our method achieved 75.5% dice similarity coefficient (DSC) in 0.61 s per volume on a GPU, which significantly outperforms competing methods in terms of accuracy and efficiency. The code is publicly available.
Generation of multiphoton quantum states on silicon

Multiphoton quantum states play a critical role in emerging quantum technologies and greatly improve our fundamental understanding of the quantum world. Integrated photonics is well recognized as an attractive technology offering great promise for the generation of photonic quantum states with high-brightness, tunability, stability, and scalability. Herein, we demonstrate the generation of multiphoton quantum states using a single-silicon nanophotonic waveguide. The detected four-photon rate reaches 0.34 Hz even with a low-pump power of 600 μW. This multiphoton quantum state is also qualified with multiphoton quantum interference, as well as quantum state tomography. For the generated four-photon states, the quantum interference visibilities are greater than 95%, and the fidelity is 0.78 ± 0.02. Furthermore, such a multiphoton quantum source is fully compatible with the on-chip processes of quantum manipulation, as well as quantum detection, which is helpful for the realization of large-scale quantum photonic integrated circuits (QPICs) and shows great potential for research in the area of multiphoton quantum science.
A tutorial on Dirichlet process mixture modeling

Bayesian nonparametric (BNP) models are becoming increasingly important in psychology, both as theoretical models of cognition and as analytic tools. However, existing tutorials tend to be at a level of abstraction largely impenetrable by non-technicians. This tutorial aims to help beginners understand key concepts by working through important but often omitted derivations carefully and explicitly, with a focus on linking the mathematics with a practical computation solution for a Dirichlet Process Mixture Model (DPMM)—one of the most widely used BNP methods. Abstract concepts are made explicit and concrete to non-technical readers by working through the theory that gives rise to them. A publicly accessible computer program written in the statistical language R is explained line-by-line to help readers understand the computation algorithm. The algorithm is also linked to a construction method called the Chinese Restaurant Process in an accessible tutorial in this journal (Gershman and Blei, 2012). The overall goals are to help readers understand more fully the theory and application so that they may apply BNP methods in their own work and leverage the technical details in this tutorial to develop novel methods.
Prioritizing marketing research in virtual reality: development of an immersion/fantasy typology

Purpose Virtual reality (VR) is of increasing interest to marketers because it can be used to explore and proactively shape long-term futures, co-create value with consumers, and foster consumer-brand engagement. Yet, to date, the field lacks a cohesive framework for approaching VR research; thus, the objective of this systematic literature review is to provide such a framework and highlight research opportunities. Design/methodology/approach First, after conducting a systematic literature review, we highlight VR themes instrumental to flow and propose a typology for VR research using realism-fantasy and immersion as dimensions. Next, we review the current state of empirical research for each quadrant. Finally, we synthesize research within each quadrant, specifying criteria and considerations for conducting research. In doing so, we propose an agenda for marketing research, centered on methodological, future studies, and consumer-related contributions. Findings VR themes instrumental to flow include the avatar, application quality, and interactivity. We find, contrary to some conceptualizations of VR, that all applications are capable of producing flow. Conflicting research and gaps are highlighted in the findings section and summarized in Table III. Additionally, while prior research seems to draw from findings of other VR applications in advancing knowledge in general, the results of the literature review suggest that VR applications should be treated uniquely. Finally, we propose highly immersive VR applications as more conducive to future studies research. Research limitations/implications Scholars can utilize the findings to prioritize future research studies in marketing. By following the typology and research opportunities, scholars can advance marketing theory and enhance the external validity of research studies through VR applications. Practical implications Managers can utilize the findings to ascertain consumers and societies‘ responses to various marketing stimuli, with implications for product development, branding, retail/service experiences, adoption of new technologies, tourism, and many other domains. VR applications offer managers more ways of testing concepts and processes in realistic fashion without the costs and risks associated with more traditional methods. Originality/value The objective of this paper is to examine varying opportunities for VR research given flow and fantasy potential and to prioritize VR research.
Deep Counterfactual Regret Minimization

Counterfactual Regret Minimization (CFR) is the leading framework for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with tabular CFR, and its solution is mapped back to the full game. This process can be problematic because aspects of abstraction are often manual and domain specific, abstraction algorithms may miss important strategic nuances of the game, and there is a chicken-and-egg problem because determining a good abstraction requires knowledge of the equilibrium of the game. This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game. We show that Deep CFR is principled and achieves strong performance in large poker games. This is the first non-tabular variant of CFR to be successful in large games.
Exploiting Monolingual Data at Scale for Neural Machine Translation

While target-side monolingual data has been proven to be very useful to improve neural machine translation (briefly, NMT) through back translation, source-side monolingual data is not well investigated.In this work, we study how to use both the source-side and targetside monolingual data for NMT, and propose an effective strategy leveraging both of them.First, we generate synthetic bitext by translating monolingual data from the two domains into the other domain using the models pretrained on genuine bitext.Next, a model is trained on a noised version of the concatenated synthetic bitext where each source sequence is randomly corrupted.Finally, the model is fine-tuned on the genuine bitext and a clean version of a subset of the synthetic bitext without adding any noise.Our approach achieves state-of-the-art results on WMT16, WMT17, WMT18 English$German translations and WMT19 German!French translations, which demonstrate the effectiveness of our method.We also conduct a comprehensive study on how each part in the pipeline works.
The Effect of Mobile Learning on Learning Performance: A Meta-Analysis Study

The importance of mobile technologies in the educational process has directed the attention of many researchers to this field and has created an important body of academic research. The main purpose of this study is to determine the effect of mobile learning on students' learning performance. In this study, the meta-analysis method was employed. The literature was reviewed through different databases in order to access the relevant researches within the scope of the study. After reviewing the literature, study aspects and inclusion criteria were applied. The studies to be included in the meta-analysis were examined, and 104 studies conducted between the years of 2009 and 2019 that met the inclusion criteria were subjected to a meta-analysis. Education level, course/subject and the implementation period of the studies were determined as moderating variables. The sample of the study consists of 7,568 participants. As a result of the analysis performed according to the random effects model, the mean effect size value was calculated as 0.85 with an error of 0.07. It was determined that there was no publication bias in the meta-analysis. According to the results of the moderator analysis, it was found that the effect of mobile learning on the learning performance of the students did not change according to the education level and the implementation period, but it changed according to the course/subject. In addition to the aforementioned research results, this article also contains descriptive analysis of the results of the studies included in the meta-analysis.
What Have We Achieved on Text Summarization?

Deep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years.However, gaps still exist between summaries produced by automatic summarizers and human professionals.Aiming to gain more understanding of summarization systems with respect to their strengths and limits on a fine-grained syntactic and semantic level, we consult the Multidimensional Quality Metric 1 (MQM) and quantify 8 major sources of errors on 10 representative summarization models manually.Primarily, we find that 1) under similar settings, extractive summarizers are in general better than their abstractive counterparts thanks to strength in faithfulness and factual-consistency; 2) milestone techniques such as copy, coverage and hybrid extractive/abstractive methods do bring specific improvements but also demonstrate limitations; 3) pre-training techniques, and in particular sequence-to-sequence pre-training, are highly effective for improving text summarization, with BART giving the best results.