Reviewerly/exHarmony huggingface dataset:
How to promote a new and sustainable food consumption model: A fuzzy cognitive map study

The amount of food wastage produced at the global level generates high environmental, economic and social costs, such as greenhouse gas emissions, soil degradation, waste generation, consumption of natural resources, as well as economic losses, inequality and poverty. Taking stock of these problems, this paper conducts an empirical investigation in order to identify and recommend the most effective policy actions and private initiatives that might modify the current unsustainable food consumption model, characterizing high income countries, in order to achieve a significant reduction in the amount of food wastage. Specifically, it performs a fuzzy inference simulation by means of a three-step methodology: analyzing the use of language within the food waste reduction/valorization debate to identify system variables; deducing the map of causal-effect relationships among the identified system variables through interviews to a pool of experts; and finally, performing a fuzzy inference simulation, to identify drivers potentially able to discourage current unsustainable consumer behaviors. Among other things, the fuzzy analysis shows how some policy drivers, as “Public food waste rules”, “Investments and infrastructure” and “Small-scale farming” are particularly effective in supporting a new and sustainable food consumption model. At the same time, the experts' knowledge allows highlighting the crucial role of “biorefinery” in fostering a transition towards a sustainable consumption model. This is an interesting result, as it points at the ability of biorefineries to enhance a general positive attitude of consumers which, in turn, would translate into more sustainable consumption practices.
Screen-Shooting Resilient Watermarking

This paper proposes a novel screen-shooting resilient watermarking scheme, which means that if the watermarked image is displayed on the screen and the screen information is captured by the camera, we can still extract the watermark message from the captured photo. To realize such demands, we analyzed the special distortions caused by the screen-shooting process, including lens distortion, light source distortion, and moiré distortion. To resist the geometric deformation caused by lens distortion, we proposed an intensity-based scale-invariant feature transform (I-SIFT) algorithm which can accurately locate the embedding regions. As for the loss of image details caused by light source distortion and moiré distortion, we put forward a small-size template algorithm to repeatedly embed the watermark into different regions, so that at least one complete information region can survive from distortions. At the extraction side, we designed a cross-validation-based extraction algorithm to cope with repeated embedding. The validity and correctness of the extraction method are verified by hypothesis testing. Furthermore, to boost the extraction speed, we proposed a SIFT feature editing algorithm to enhance the intensity of the keypoints, based on which, the extraction accuracy and extraction speed can be greatly improved. The experimental results show that the proposed watermarking scheme achieves high robustness for screen-shooting process. Compared with the previous schemes, our algorithm provides significant improvement in robustness for screen-shooting process and extraction efficiency.
Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning

Purpose To examine Generative Visual Rationales (GVRs) as a tool for visualizing neural network learning of chest radiograph features in congestive heart failure (CHF). Materials and Methods A total of 103 489 frontal chest radiographs in 46 712 patients acquired from January 1, 2007, to December 31, 2016, were divided into a labeled data set (with B-type natriuretic peptide [BNP] result as a marker of CHF) and unlabeled data set (without BNP result). A generative model was trained on the unlabeled data set, and a neural network was trained on the encoded representations of the labeled data set to estimate BNP. The model was used to visualize how a radiograph with high estimated BNP would look without disease (a "healthy" radiograph). An overfitted model was developed for comparison, and 100 GVRs were blindly assessed by two experts for features of CHF. Area under the receiver operating characteristic curve (AUC), κ coefficient, and mixed-effects logistic regression were used for statistical analyses. Results At a cutoff BNP of 100 ng/L as a marker of CHF, the correctly trained model achieved an AUC of 0.82. Assessment of GVRs revealed that the correctly trained model highlighted conventional radiographic features of CHF as reasons for an elevated BNP prediction more frequently than the overfitted model, including cardiomegaly (153 [76.5%] of 200 vs 64 [32%] of 200, respectively; P < .001) and pleural effusions (47 [23.5%] of 200 vs 16 [8%] of 200, respectively; P = .003). Conclusion Features of congestive heart failure on chest radiographs learned by neural networks can be identified using Generative Visual Rationales, enabling detection of bias and overfitted models. © RSNA, 2018 See also the editorial by Ngo in this issue.
Hippocampus Analysis by Combination of 3-D DenseNet and Shapes for Alzheimer's Disease Diagnosis

Hippocampus is one of the first involved regions in Alzheimer's disease (AD) and mild cognitive impairment (MCI), a prodromal stage of AD. Hippocampal atrophy is a validated, easily accessible, and widely used biomarker for AD diagnosis. Most of existing methods compute the shape and volume features for hippocampus analysis using structural magnetic resonance images (MRI). However, the regions adjacent to hippocampus may be relevant to AD, and the visual features of the hippocampal region are important for disease diagnosis. In this paper, we have proposed a new hippocampus analysis method to combine the global and local features of hippocampus by three-dimensional densely connected convolutional networks and shape analysis for AD diagnosis. The proposed method can make use of the local visual and global shape features to enhance the classification. Tissue segmentation and nonlinear registration are not required in the proposed method. Our method is evaluated with the T1-weighted structural MRIs from 811 subjects including 192 AD, 396 MCI (231 stable MCI and 165 progressive MCI), and 223 normal control in Alzheimer's disease neuroimaging initiative database. Experimental results show the proposed method achieves a classification accuracy of 92.29% and area under the ROC curve of 96.95% for AD diagnosis. Results comparison demonstrates the proposed method performs better than other methods.
Deep learning based personalized recommendation with multi-view information integration

With the rapid proliferation of images on e-commerce platforms today, embracing and integrating versatile information sources have become increasingly important in recommender systems. Owing to the heterogeneity in information sources and consumers, it is necessary and meaningful to consider the potential synergy between visual and textual content as well as consumers' different cognitive styles. This paper proposes a multi-view model, namely, Deep Multi-view Information iNtEgration (Deep-MINE), to take multiple sources of content (i.e., product images, descriptions and review texts) into account and design an end-to-end recommendation model. In doing so, stacked auto-encoder networks are deployed to map multi-view information into a unified latent space, a cognition layer is added to depict consumers' heterogeneous cognition styles and an integration module is introduced to reflect the interaction of multi-view latent representations. Extensive experiments on real world data demonstrate that Deep-MINE achieves high accuracy in product ranking, especially in the cold-start case. In addition, Deep-MINE is able to boost overall model performance compared with models taking a single view, further verifying the proposed model's effectiveness on information integration.
Blockchain Technology, Supply Chain Information, and Strategic Product Deletion Management

Products and associated materials are important supply chain flows. Product management greatly influences supply chain performance. Supply chain information is also critical for sound product management. Product deletion, rationalization, or discontinuation research is an important dimension often overlooked in product management. It is a critical issue for many managerial reasons, many espoused in this article. Product deletion is typically a multi-staged process including recognition, analysis and revitalization, evaluation and decision formation, and implementation. Each stage requires complicated information and data support from supply chain activities. Failure in information generating, understanding, and accuracy can prove risky for rational product deletion. Blockchain technology may help address information challenges. Blockchain technology provides traceability, transparency, security, accuracy, and smart execution, which can all contribute to the product deletion and rationalization decision. Application recommendations and managerial insights into product deletion decision making processes with blockchain technology are provided.
The Rise in Popularity of Cryptocurrency and Associated Criminal Activity

Cryptocurrency such as bitcoin, Ethereum, and, more recently, Monero has become the currency of choice for many drug dealers and extortionists. The criminal activities extend to tax evasion, money laundering, Ponzi schemes, and the theft of cryptocurrencies to kidnapping for ransom. As the demand for cryptocurrencies increases, it provides opportunities for criminals to hide behind the presumed privacy and anonymity. Identifying these cryptocurrency-related crimes have posed challenges for law enforcement due to the cross-border nature of transactions, the use of evasion technology to mask the identity of users, and inconsistent regulations. To address the role of cryptocurrencies in criminal activities, the study focused on four research questions: (1) What role do cryptocurrencies such as bitcoin (BTC) play in criminal activities? (2) What factors facilitate cryptocurrency-related criminal activities? (3) What role do politics play in regulating cryptocurrencies? and (4) What are the challenges they pose for regulators and law enforcement? To answer the questions, the study utilized a systematic content review of the news reports, court cases, scholarly articles, online search engines, and commentaries relevant to regulations and reforms. The findings help to understand the current climate of virtual currencies, their use in criminal activities, and the complexities involved in regulating cryptocurrencies.
LaSO: Label-Set Operations Networks for Multi-Label Few-Shot Learning

Example synthesis is one of the leading methods to tackle the problem of few-shot learning, where only a small number of samples per class are available. However, current synthesis approaches only address the scenario of a single category label per image. In this work, we propose a novel technique for synthesizing samples with multiple labels for the (yet unhandled) multi-label few-shot classification scenario. We propose to combine pairs of given examples in feature space, so that the resulting synthesized feature vectors will correspond to examples whose label sets are obtained through certain set operations on the label sets of the corresponding input pairs. Thus, our method is capable of producing a sample containing the intersection, union or set-difference of labels present in two input samples. As we show, these set operations generalize to labels unseen during training. This enables performing augmentation on examples of novel categories, thus, facilitating multi-label few-shot classifier learning. We conduct numerous experiments showing promising results for the label-set manipulation capabilities of the proposed approach, both directly (using the classification and retrieval metrics), and in the context of performing data augmentation for multi-label few-shot learning. We propose a benchmark for this new and challenging task and show that our method compares favorably to all the common baselines.
Realizing Virtual Reality Learning Environment for Industry 4.0

The scope of Industry 4.0 is to overlay a simulation on a real-time production line that can investigate poorly understood phenomena and aid the rectification of bottlenecks. VR application in Industry 4.0 allows companies to decrease design and production costs, maintain product quality and reduce the time needed to go from product concept to production. In this work, the authors present the VR’s technology aspects and limitations for supporting the VR developers for creating VR industrial environments that produce reliable/feasible simulations of the behavior of machines and real processes and stable operation of remotely real processes.
Trusting Virtual Agents

We present artificial intelligent (AI) agents that act as interviewers to engage with a user in a text-based conversation and automatically infer the user's personality traits. We investigate how the personality of an AI interviewer and the inferred personality of a user influences the user's trust in the AI interviewer from two perspectives: the user's willingness to confide in and listen to an AI interviewer. We have developed two AI interviewers with distinct personalities and deployed them in a series of real-world events. We present findings from four such deployments involving 1,280 users, including 606 actual job applicants. Notably, users are more willing to confide in and listen to an AI interviewer with a serious, assertive personality in a high-stakes job interview. Moreover, users’ personality traits, inferred from their chat text, along with interview context, influence their perception of and their willingness to confide in and listen to an AI interviewer. Finally, we discuss the design implications of our work on building hyper-personalized, intelligent agents.
SummCoder: An unsupervised framework for extractive text summarization based on deep auto-encoders

In this paper, we propose SummCoder, a novel methodology for generic extractive text summarization of single documents. The approach generates a summary according to three sentence selection metrics formulated by us: sentence content relevance, sentence novelty, and sentence position relevance. The sentence content relevance is measured using a deep auto-encoder network, and the novelty metric is derived by exploiting the similarity among sentences represented as embeddings in a distributed semantic space. The sentence position relevance metric is a hand-designed feature, which assigns more weight to the first few sentences through a dynamic weight calculation function regulated by the document length. Furthermore, a sentence ranking and a selection technique are developed to generate the document summary by ranking the sentences according to the final score obtained through the fusion of the three sentences selection metrics. We also introduce a new summarization benchmark, Tor Illegal Documents Summarization (TIDSumm) dataset, especially to assist Law Enforcement Agencies (LEAs), that contains two sets of ground truth summaries, manually created, for 100 web documents extracted from onion websites in Tor (The Onion Router) network. Empirical results show that, on DUC 2002, on Blog Summarization, and on TIDSumm datasets, our text summarization approach obtains comparable or better performance than the state-of-the-art methods for different ROUGE metrics.
Joint Entity and Event Extraction with Generative Adversarial Imitation Learning

We propose a new framework for entity and event extraction based on generative adversarial imitation learning—an inverse reinforcement learning method using a generative adversarial network (GAN). We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse. We utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth (expert) and the extractor (agent). Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods.
A multilayer path planner for a USV under complex marine environments

In this paper, a multilayer path planner (MPP) with global path-planning (GPP), collision avoidance (CA) and routine correction (RC) for an unmanned surface vehicle (USV) under complex marine environments including both coastal and surface constraints is presented. The main contributions of this paper are as follow: 1) An MPP framework consisting of multiple layers, i.e., backbone, CA and RC, is established, and achieves self-tuning path-planning which adapts time-varying environments. 2) To minimize yaw-cost for the USV within local path, a novel CA algorithm is developed by the B-Spline method. 3) For capturing environmental influences arisen from reefs around the coastline, a stochastic dynamic coastal environments (SDCE) model is built by virtue of Poisson distribution. In combination with the fast marching method (FMM) and the SCDE model, the RC algorithm is proposed to handle environmental uncertainties. Simulation results show that the proposed MPP achieves remarkable path-planning performance in terms of both collision avoidance and adaptability to complex environments.
Wide-Context Semantic Image Extrapolation

This paper studies the fundamental problem of extrapolating visual context using deep generative models, i.e., extending image borders with plausible structure and details. This seemingly easy task actually faces many crucial technical challenges and has its unique properties. The two major issues are size expansion and one-side constraints. We propose a semantic regeneration network with several special contributions and use multiple spatial related losses to address these issues. Our results contain consistent structures and high-quality textures. Extensive experiments are conducted on various possible alternatives and related methods. We also explore the potential of our method for various interesting applications that can benefit research in a variety of fields.
Stochastic Class-Based Hard Example Mining for Deep Metric Learning

Performance of deep metric learning depends heavily on the capability of mining hard negative examples during training. However, many metric learning algorithms often require intractable computational cost due to frequent feature computations and nearest neighbor searches in a large-scale dataset. As a result, existing approaches often suffer from trade-off between training speed and prediction accuracy. To alleviate this limitation, we propose a stochastic hard negative mining method. Our key idea is to adopt class signatures that keep track of feature embedding online with minor additional cost during training, and identify hard negative example candidates using the signatures. Given an anchor instance, our algorithm first selects a few hard negative classes based on the class-to-sample distances and then performs a refined search in an instance-level only from the selected classes. As most of the classes are discarded at the first step, it is much more efficient than exhaustive search while effectively mining a large number of hard examples. Our experiment shows that the proposed technique improves image retrieval accuracy substantially; it achieves the state-of-the-art performance on the several standard benchmark datasets.
Distinguishing Ore Deposit Type and Barren Sedimentary Pyrite Using Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Trace Element Data and Statistical Analysis of Large Data Sets

Faced with ongoing depletion of near-surface ore deposits, geologists are increasingly required to explore for deep deposits or those lying beneath surface cover. The result is increased drilling costs and a need to maximize the value of the drill hole samples collected. Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analysis of pyrite is one tool that is showing promise in deep exploration. Since the trace element content of pyrite approximates the composition of the fluid from which it precipitated and the crystallization mechanism, the trace element characteristics can be used to predict the type of deposit with which a pyritic sample is associated. This possibility, however, is complicated by overlapping trace element abundances for many deposit types. The solution lies with simultaneous comparison of multiple trace elements through rigorous statistical analysis. Specifically, we used LA-ICP-MS pyrite trace element data and Random Forests, an ensemble machine learning supervised classifier, to distinguish barren sedimentary pyrite and five ore deposit categories: iron oxide copper-gold (IOCG), orogenic Au, porphyry Cu, sedimentary exhalative (SEDEX), and volcanic-hosted massive sulfide (VHMS) deposits. The preferred classifier utilizes in situ Co, Ni, Cu, Zn, As, Mo, Ag, Sb, Te, Tl, and Pb measurements to train the Random Forests. Testing of the Random Forests classifier using additional data from the same deposits and sedimentary basins (test data set) yielded an overall accuracy of 91.4% (94.9% for IOCG, 78.8% for orogenic Au, 81.1% for porphyry Cu, 93.6% for SEDEX, 97.2% for sedimentary pyrite, 91.8% for VHMS). Similarly, testing of the Random Forests classifier using data from deposits and sedimentary basins that did not have analyses in the training data set yielded an overall accuracy of 88.0% (81.4% for orogenic Au, 95.5% for SEDEX, 90.0% for sedimentary pyrite, 73.9% for VHMS; insufficient data was available to perform a blind test on porphyry Cu and IOCG). The performance of the classifier was further improved by instituting criteria (at least 40% of total votes from the Random Forests needed for a conclusive identification) to remove uncertain or inconclusive classifications, increasing the classifier’s accuracy to 94.5% for the test data (94.6% for IOCG, 85.8% for orogenic Au, 87.8% for porphyry Cu, 95.4% for SEDEX, 98.5% for sedimentary pyrite, 94.6% for VHMS) and 93.9% for the blind test data (85.5% for orogenic Au, 96.9% for SEDEX, 96.7% for sedimentary pyrite, 84.6% for VHMS).