Reviewerly/exHarmony huggingface dataset:
Sequence and Time Aware Neighborhood for Session-based Recommendations

Recent advances in sequence-aware approaches for session-based recommendation, such as those based on recurrent neural networks, highlight the importance of leveraging sequential information from a session while making recommendations. Further, a session based k-nearest-neighbors approach (SKNN) has proven to be a strong baseline for session-based recommendations. However, SKNN does not take into account the readily available sequential and temporal information from sessions. In this work, we propose Sequence and Time Aware Neighborhood (STAN), with vanilla SKNN as its special case. STAN takes into account the following factors for making recommendations: i) position of an item in the current session, ii) recency of a past session w.r.t. to the current session, and iii) position of a recommendable item in a neighboring session. The importance of above factors for a specific application can be adjusted via controllable decay factors. Despite being simple, intuitive and easy to implement, empirical evaluation on three real-world datasets shows that STAN significantly improves over SKNN, and is even comparable to the recently proposed state-of-the-art deep learning approaches. Our results suggest that STAN can be considered as a strong baseline for evaluating session-based recommendation algorithms in future.
UniformFace: Learning Deep Equidistributed Representation for Face Recognition

In this paper, we propose a new supervision objective named uniform loss to learn deep equidistributed representations for face recognition. Most existing methods aim to learn discriminative face features, encouraging large inter-class distances and small intra-class variations. However, they ignore the distribution of faces in the holistic feature space, which may lead to severe locality and unbalance. With the prior that faces lie on a hypersphere manifold, we impose an equidistributed constraint by uniformly spreading the class centers on the manifold, so that the minimum distance between class centers can be maximized through complete exploitation of the feature space. To this end, we consider the class centers as like charges on the surface of hypersphere with inter-class repulsion, and minimize the total electric potential energy as the uniform loss. Extensive experimental results on the MegaFace Challenge I, IARPA Janus Benchmark A (IJB-A), Youtube Faces (YTF) and Labeled Faces in the Wild (LFW) datasets show the effectiveness of the proposed uniform loss.
An extensive study on the evolution of context-aware personalized travel recommender systems

Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.
The Omniglot challenge: a 3-year progress report

Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks. The model was not meant to be the final word on Omniglot; we hoped that the community would build on our work and develop new approaches. In the time since, we have been pleased to see wide adoption of the dataset. There has been notable progress on one-shot classification, but researchers have adopted new splits and procedures that make the task easier. There has been less progress on the other four tasks. We conclude that recent approaches are still far from human-like concept learning on Omniglot, a challenge that requires performing many tasks with a single model.
Data is Personal

Many of the guidelines that inform how designers create data visualizations originate in studies that unintentionally exclude populations that are most likely to be among the 'data poor'. In this paper, we explore which factors may drive attention and trust in rural populations with diverse economic and educational backgrounds - a segment that is largely underrepresented in the data visualization literature. In 42 semi-structured interviews in rural Pennsylvania (USA), we find that a complex set of factors intermix to inform attitudes and perceptions about data visualization - including educational background, political affiliation, and personal experience. The data and materials for this research can be found at https://osf.io/uxwts/
Blockchain-Based Identity Management: A Survey From the Enterprise and Ecosystem Perspective

Identity management is a core building block for the majority of software solutions and landscapes. Competing with existing identity-managing solutions, blockchain-based concepts and products have evolved in the context of verified claims and self-sovereign identities. The contribution of this paper is a systematic, criteria-driven survey of the solutions and technologies for this growing field and their comparison with the capabilities of established solutions. By including an extensive set of requirements covering ecosystem aspects, end-user functionality, mobility and overhead aspects, compliance/liability, EU regulations, standardization, and integration, this paper shows the highlights and the deficits of a wide array of solutions.
Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion

Using multi-source sensing data based on the Internet of Things (IoT) with artificial intelligence and big data processing technology to achieve predictive maintenance of mechanical equipment can remarkably improve the service life of the machine and reduce labor costs when diagnosing mechanical faults, and it has become a highly relevant research topic. In this paper, the multi-source sensing data fusion models and fusion algorithms are studied and discussed. First, the Joint Directors of Laboratories (JDL) fusion model and the Hierarchical fusion model are compared and analyzed. Then, various types of fusion algorithms based on Neural Networks and Deep Learning, including Dempster-Shafer (D-S) evidence theory and their applications in mechanical fault diagnosis and fault prediction, are studied and compared. The findings reveal that exploring and designing a more intelligent fusion model incorporating the beneficial characteristics of different fusion algorithms are challenging and have a certain value for promoting the development of mechanical fault diagnosis and prediction.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems

We propose Februus; a new idea to neutralize highly potent and insidious Trojan attacks on Deep Neural Network (DNN) systems at run-time. In Trojan attacks, an adversary activates a backdoor crafted in a deep neural network model using a secret trigger, a Trojan, applied to any input to alter the model’s decision to a target prediction—a target determined by and only known to the attacker. Februus sanitizes the incoming input by surgically removing the potential trigger artifacts and restoring the input for the classification task. Februus enables effective Trojan mitigation by sanitizing inputs with no loss of performance for sanitized inputs, Trojaned or benign. Our extensive evaluations on multiple infected models based on four popular datasets across three contrasting vision applications and trigger types demonstrate the high efficacy of Februus. We dramatically reduced attack success rates from 100% to near 0% for all cases (achieving 0% on multiple cases) and evaluated the generalizability of Februus to defend against complex adaptive attacks; notably, we realized the first defense against the advanced partial Trojan attack. To the best of our knowledge, Februus is the first backdoor defense method for operation at run-time capable of sanitizing Trojaned inputs without requiring anomaly detection methods, model retraining or costly labeled data.
An overview of data fusion techniques for Internet of Things enabled physical activity recognition and measure

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Measure (PARM) has been widely recognised as a key paradigm for a variety of smart healthcare applications. Traditional methods for PARM relies on designing and utilising Data fusion or machine learning techniques in processing ambient and wearable sensing data for classifying types of physical activity and removing their uncertainties. Yet they mostly focus on controlled environments with the aim of increasing types of identifiable activity subjects, improved recognition accuracy and measure robustness. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to an open and dynamic uncontrolled ecosystem by connecting heterogeneous cost-effective wearable devices and mobile apps and various groups of users [35]. Little is currently known about whether traditional Data fusion techniques can tackle new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand potential use and opportunities of Data fusion techniques in IoT enabled PARM applications, this paper will give a systematic review, critically examining PARM studies from a perspective of a novel 3D dynamic IoT based physical activity collection and validation model. It summarized traditional state-of-the-art data fusion techniques from three plane domains in the 3D dynamic IoT model: devices, persons and timeline. The paper goes on to identify some new research trends and challenges of data fusion techniques in the IoT enabled PARM studies, and discusses some key enabling techniques for tackling them.
Blockchain: The emerging technology of digital trust

Trust in individual relationships with blockchain has become an increasingly prominent issue. This study introduces a key heuristic used to assess trust in blockchain by analyzing how privacy and security concerns about blockchains have an impact on the user’s attitude and behavior. It proposes a blockchain user model by integrating security and privacy as primary influencing factors of trust and behavioral intent. The results from a user experience model of blockchain users confirm that the model explains user experience and predicts behavioral intent of blockchain. The results establish users’ cognitive role in embedding privacy and security in blockchain. The research contributes to the ongoing research by clarifying the role and dimension of trust in relation to security and privacy in blockchains and provides heuristic implications for academia and industry.
A new similarity measure for collaborative filtering based recommender systems

The objective of a recommender system is to provide customers with personalized recommendations while selecting an item among a set of products (movies, books, etc.). The collaborative filtering is the most used technique for recommender systems. One of the main components of a recommender system based on the collaborative filtering technique, is the similarity measure used to determine the set of users having the same behavior with regard to the selected items. Several similarity functions have been proposed, with different performances in terms of accuracy and quality of recommendations. In this paper, we propose a new simple and efficient similarity measure. Its mathematical expression is determined through the following paper contributions: 1) transforming some intuitive and qualitative conditions, that should be satisfied by the similarity measure, into relevant mathematical equations namely: the integral equation, the linear system of differential equations and a non-linear system and 2) resolving the equations to achieve the kernel function of the similarity measure. The extensive experimental study driven on a benchmark datasets shows that the proposed similarity measure is very competitive, especially in terms of accuracy, with regards to some representative similarity measures of the literature.
Max-Sliced Wasserstein Distance and Its Use for GANs

Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning. However, to model high-dimensional distributions, sequential training and stacked architectures are common, increasing the number of tunable hyper-parameters as well as the training time. Nonetheless, the sample complexity of the distance metrics remains one of the factors affecting GAN training. We first show that the recently proposed sliced Wasserstein distance has compelling sample complexity properties when compared to the Wasserstein distance. To further improve the sliced Wasserstein distance we then analyze its `projection complexity' and develop the max-sliced Wasserstein distance which enjoys compelling sample complexity while reducing projection complexity, albeit necessitating a max estimation. We finally illustrate that the proposed distance trains GANs on high-dimensional images up to a resolution of 256x256 easily.
VeriSolid: Correct-by-Design Smart Contracts for Ethereum

The adoption of blockchain based distributed ledgers is growing fast due to their ability to provide reliability, integrity, and auditability without trusted entities. One of the key capabilities of these emerging platforms is the ability to create self-enforcing smart contracts. However, the development of smart contracts has proven to be error-prone in practice, and as a result, contracts deployed on public platforms are often riddled with security vulnerabilities. This issue is exacerbated by the design of these platforms, which forbids updating contract code and rolling back malicious transactions. In light of this, it is crucial to ensure that a smart contract is secure before deploying it and trusting it with significant amounts of cryptocurrency. To this end, we introduce the VeriSolid framework for the formal verification of contracts that are specified using a transition-system based model with rigorous operational semantics. Our model-based approach allows developers to reason about and verify contract behavior at a high level of abstraction. VeriSolid allows the generation of Solidity code from the verified models, which enables the correct-by-design development of smart contracts.
Automatic Radiology Report Generation Based on Multi-view Image Fusion and Medical Concept Enrichment

Generating radiology reports is time-consuming and requires extensive expertise in practice. Therefore, reliable automatic radiology report generation is highly desired to alleviate the workload. Although deep learning techniques have been successfully applied to image classification and image captioning tasks, radiology report generation remains challenging in regards to understanding and linking complicated medical visual contents with accurate natural language descriptions. In addition, the data scales of open-access datasets that contain paired medical images and reports remain very limited. To cope with these practical challenges, we propose a generative encoder-decoder model and focus on chest x-ray images and reports with the following improvements. First, we pretrain the encoder with a large number of chest x-ray images to accurately recognize 14 common radiographic observations, while taking advantage of the multi-view images by enforcing the cross-view consistency. Second, we synthesize multi-view visual features based on a sentence-level attention mechanism in a late fusion fashion. In addition, in order to enrich the decoder with descriptive semantics and enforce the correctness of the deterministic medical-related contents such as mentions of organs or diagnoses, we extract medical concepts based on the radiology reports in the training data and fine-tune the encoder to extract the most frequent medical concepts from the x-ray images. Such concepts are fused with each decoding step by a word-level attention model. The experimental results conducted on the Indiana University Chest X-Ray dataset demonstrate that the proposed model achieves the state-of-the-art performance compared with other baseline approaches.
Sampling-Free Epistemic Uncertainty Estimation Using Approximated Variance Propagation

We present a sampling-free approach for computing the epistemic uncertainty of a neural network. Epistemic uncertainty is an important quantity for the deployment of deep neural networks in safety-critical applications, since it represents how much one can trust predictions on new data. Recently promising works were proposed using noise injection combined with Monte-Carlo sampling at inference time to estimate this quantity (e.g. Monte-Carlo dropout). Our main contribution is an approximation of the epistemic uncertainty estimated by these methods that does not require sampling, thus notably reducing the computational overhead. We apply our approach to large-scale visual tasks (\ie, semantic segmentation and depth regression) to demonstrate the advantages of our method compared to sampling-based approaches in terms of quality of the uncertainty estimates as well as of computational overhead.
Interactive Sketch &amp; Fill: Multiclass Sketch-to-Image Translation

We propose an interactive GAN-based sketch-to-image translation method that helps novice users easily create images of simple objects. The user starts with a sparse sketch and a desired object category, and the network then recommends its plausible completion(s) and shows a corresponding synthesized image. This enables a feedback loop, where the user can edit the sketch based on the network's recommendations, while the network is able to better synthesize the image that the user might have in mind. In order to use a single model for a wide array of object classes, we introduce a gating-based approach for class conditioning, which allows us to generate distinct classes without feature mixing, from a single generator network.
Canonical Surface Mapping via Geometric Cycle Consistency

We explore the task of Canonical Surface Mapping (CSM). Specifically, given an image, we learn to map pixels on the object to their corresponding locations on an abstract 3D model of the category. But how do we learn such a mapping? A supervised approach would require extensive manual labeling which is not scalable beyond a few hand-picked categories. Our key insight is that the CSM task (pixel to 3D), when combined with 3D projection (3D to pixel), completes a cycle. Hence, we can exploit a geometric cycle consistency loss, thereby allowing us to forgo the dense manual supervision. Our approach allows us to train a CSM model for a diverse set of classes, without sparse or dense keypoint annotation, by leveraging only foreground mask labels for training. We show that our predictions also allow us to infer dense correspondence between two images, and compare the performance of our approach against several methods that predict correspondence by leveraging varying amount of supervision.
Deep Sky Modeling for Single Image Outdoor Lighting Estimation

We propose a data-driven learned sky model, which we use for outdoor lighting estimation from a single image. As no large-scale dataset of images and their corresponding ground truth illumination is readily available, we use complementary datasets to train our approach, combining the vast diversity of illumination conditions of SUN360 with the radiometrically calibrated and physically accurate Laval HDR sky database. Our key contribution is to provide a holistic view of both lighting modeling and estimation, solving both problems end-to-end. From a test image, our method can directly estimate an HDR environment map of the lighting without relying on analytical lighting models. We demonstrate the versatility and expressivity of our learned sky model and show that it can be used to recover plausible illumination, leading to visually pleasant virtual object insertions. To further evaluate our method, we capture a dataset of HDR 360° panoramas and show through extensive validation that we significantly outperform previous state-of-the-art.
Beyond mere pollution source identification: Determination of land covers emitting soil heavy metals by combining PCA/APCS, GeoDetector and GIS analysis

In this work, we propose a method that is not limited in the identification of the type of pollution source but it also suggests the land covers that emit heavy metals into the surrounding soils by introducing a three-stage procedure, as follows: (a) the Principal Component Analysis/Absolute Principal Component Scores technique is applied to the spatial distribution of soil heavy metal accumulations to identify the type of source that is responsible for soil heavy metal accumulation, (b) based on the spatial distribution of the principal component scores and on four selected driving factors (land cover, distance to mine or smelter, distance to road, and topographic elevation), the Geographical Detector model was used to identify the effect intensity of the driving factors on soil heavy metal accumulation and obtain the landscape type of pollutant sources, and (c) GIS analysis (buffer and overlap analysis) was performed on the principal component scores around the suspected land covers linked to the landscape type of pollutant sources to determine the land covers that, in fact, emit heavy metals into the surrounding soils. Based on the proposed approach, four mining and metallurgy land or land groups were determined to be the actual sources of soil heavy metal pollution in Daye city, Hubei Province, China. Lastly, a Multiple Linear Regression model with decay function was proposed to quantify the contributions of previously identified pollution sources to soil heavy metals accumulation. It was found that the HuangJin mountain quarry, the Tonglu mountain cooper mine (together with some related mineral processing and smelting enterprises), the Lion mountain mining and mineral processing base, and the large Oujia mountain mine are the four sources that contributed 3.2%, 34.3%, 8.3%, and 44% of the total soil heavy metal accumulations in the study area.