Reviewerly/exHarmony huggingface dataset:
Physics‐constrained non‐Gaussian probabilistic learning on manifolds

Summary An extension of the probabilistic learning on manifolds (PLoM), recently introduced by the authors, has been presented: In addition to the initial data set given for performing the probabilistic learning, constraints are given, which correspond to statistics of experiments or of physical models. We consider a non‐Gaussian random vector whose unknown probability distribution has to satisfy constraints. The method consists in constructing a generator using the PLoM and the classical Kullback‐Leibler minimum cross‐entropy principle. The resulting optimization problem is reformulated using Lagrange multipliers associated with the constraints. The optimal solution of the Lagrange multipliers is computed using an efficient iterative algorithm. At each iteration, the Markov chain Monte Carlo algorithm developed for the PLoM is used, consisting in solving an Itô stochastic differential equation that is projected on a diffusion‐maps basis. The method and the algorithm are efficient and allow the construction of probabilistic models for high‐dimensional problems from small initial data sets and for which an arbitrary number of constraints are specified. The first application is sufficiently simple in order to be easily reproduced. The second one is relative to a stochastic elliptic boundary value problem in high dimension.
Next Generation Web for Alumni Web Portal

At some point, the understudies have insufficient information to proceed further in their career, so getting right exhortation from experienced individual is vital which will be accomplished by establishing an online alumni interface. With this the alumni can communicate to the students regarding job opportunities and the students can share their department activities to the alumni. The proposed system has the dynamic architecture and less static content, which can empower the full duplex association between all graduated students and understudies. This paper portrays how the system will function and collaborate the graduated class with the present understudies.
Predicting the Difficulty of Multiple Choice Questions in a High-stakes Medical Exam

Predicting the construct-relevant difficulty of Multiple-Choice Questions (MCQs) has the potential to reduce cost while maintaining the quality of high-stakes exams.In this paper, we propose a method for estimating the difficulty of MCQs from a high-stakes medical exam, where all questions were deliberately written to a common reading level.To accomplish this, we extract a large number of linguistic features and embedding types, as well as features quantifying the difficulty of the items for an automatic question-answering system.The results show that the proposed approach outperforms various baselines with a statistically significant difference.Best results were achieved when using the full feature set, where embeddings had the highest predictive power, followed by linguistic features.An ablation study of the various types of linguistic features suggested that information from all levels of linguistic processing contributes to predicting item difficulty, with features related to semantic ambiguity and the psycholinguistic properties of words having a slightly higher importance.Owing to its generic nature, the presented approach has the potential to generalize over other exams containing MCQs.
DD-Pose - A large-scale Driver Head Pose Benchmark

We introduce DD-Pose, the Daimler TU Delft Driver Head Pose Benchmark, a large-scale and diverse benchmark for image-based head pose estimation and driver analysis. It contains 330k measurements from multiple cameras acquired by an in-car setup during naturalistic drives. Large out-of-plane head rotations and occlusions are induced by complex driving scenarios, such as parking and driver-pedestrian interactions. Precise head pose annotations are obtained by a motion capture sensor and a novel calibration device. A high resolution stereo driver camera is supplemented by a camera capturing the driver cabin. Together with steering wheel and vehicle motion information, DD-Pose paves the way for holistic driver analysis. Our experiments show that the new dataset offers a broad distribution of head poses, comprising an order of magnitude more samples of rare poses than a comparable dataset. By an analysis of a state-of-the-art head pose estimation method, we demonstrate the challenges offered by the benchmark. The dataset and evaluation code are made freely available to academic and non-profit institutions for non-commercial benchmarking purposes.
Weakly Supervised Multilingual Causality Extraction from Wikipedia

We present a method for extracting causality knowledge from Wikipedia, such as Protectionism → Trade war, where the cause and effect entities correspond to Wikipedia articles.Such causality knowledge is easy to verify by reading corresponding Wikipedia articles, to translate to multiple languages through Wikidata, and to connect to knowledge bases derived from Wikipedia.Our method exploits Wikipedia article sections that describe causality and the redundancy stemming from the multilinguality of Wikipedia.Experiments showed that our method achieved precision and recall above 98% and 64%, respectively.In particular, it could extract causalities whose cause and effect were written distantly in a Wikipedia article.We have released the code and data for further research. TobaccoFrom Wikipedia, the free encyclopedia Tobacco is a product prepared from … Contents 1. Etymology 2. Harmful effects of tobacco 3.
Improving Fine-grained Entity Typing with Entity Linking

Hongliang Dai, Donghong Du, Xin Li, Yangqiu Song. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Forward-Secure Puncturable Identity-Based Encryption for Securing Cloud Emails

As one of the most important manners of personal and business communications, cloud emails have been widely employed due to its advantages of low-cost and convenience. However, with the occurrence of large-scale email leakage events and the revelation of long-term monitoring of personal communications, customers are increasingly worried about the security and privacy of their sensitive emails. In this paper, we first formalize a new cryptographic primitive named forward-secure puncturable identity-based encryption (fs-PIBE) for enhancing the security and privacy of cloud email systems. This primitive enables an email receiver to individually revoke the decryption capacity of a received email that was encrypted, while retaining the decryption capacity of those unreceived ones. Consequently, those received emails remain secure even if the secret key is comprised. Thus, it provides more practical forward secrecy than traditional forward-secure public key encryption, in which the decryption capacity of those received and unreceived emails is revoked simultaneously. Besides, we propose a concrete construction of fs-PIBE with constant size of ciphertext, and prove its security in the standard model. We present the performance analysis to demonstrate its merits.
A Solution Approach for UAV Fleet Mission Planning in Changing Weather Conditions

With a rising demand for utilizing unmanned aerial vehicles (UAVs) to deliver materials in outdoor environments, particular attention must be given to all the different aspects influencing the deployment of UAVs for such purposes. These aspects include the characteristics of the UAV fleet (e.g. size of fleet, UAV specifications and capabilities), the energy consumption (highly affected by weather conditions and payload) and the characteristics of the network and customer locations. All these aspects must be taken into account when aiming to achieve deliveries to customers in a safe and timely manner. However, at present, there is a lack of decision support tools and methods for mission planners that consider all these influencing aspects together. To bridge this gap, this paper presents a decomposed solution approach, which provides decision support for UAVs’ fleet mission planning. The proposed approach assists flight mission planners in aerospace companies to select and evaluate different mission scenarios, for which flight-mission plans are obtained for a given fleet of UAVs, while guaranteeing delivery according to customer requirements in a given time horizon. Mission plans are analyzed from multiple perspectives including different weather conditions (wind speed and direction), payload capacities of UAVs, energy capacities of UAVs, fleet sizes, the number of customers visited by a UAV on a mission and delivery performance. The proposed decision support-driven declarative model supports the selection of the UAV mission planning scenarios subject to variations on all these configurations of the UAV system and variations in the weather conditions. The computer simulation based experimental results, provides evidence of the applicability and relevance of the proposed method. This ultimately contributes as a prototype of a decision support system of UAVs fleet-mission planning, able to determine whether is it possible to find a flight-mission plan for a given fleet of UAVs guaranteeing customer satisfaction under the given conditions. The mission plans are created in such a manner that they are suitable to be sent to Air Traffic Control for flight approval.
Overview and Empirical Analysis of ISP Parameter Tuning for Visual Perception in Autonomous Driving

Image quality is a well understood concept for human viewing applications, particularly in the multimedia space, but increasingly in an automotive context as well. The rise in prominence of autonomous driving and computer vision brings to the fore research in the area of the impact of image quality in camera perception for tasks such as recognition, localization and reconstruction. While the definition of “image quality” for computer vision may be ill-defined, what is clear is that the configuration of the image signal processing pipeline is the key factor in controlling the image quality for computer vision. This paper is partly review and partly positional with demonstration of several preliminary results promising for future research. As such, we give an overview of what is an
Interactive double states emotion cell model for textual dialogue emotion prediction

Daily dialogues are full of emotions that control the trends of dialogues and influence the attitudes of interlocutors toward each other, and understanding the human emotions in dialogues is of great significance in emotional comfort, human–computer interaction and intelligent question-answering. This paper defines a new task called emotion prediction in textual dialogue. Different from the text emotion recognition task, which derives the current emotional state of interlocutor from the utterance, emotion prediction aims at predicting the future emotional state of interlocutor before the interlocutor utters something. Moreover, this paper summarizes and explains three notable characteristics of emotional propagation in text dialogue: context dependence, persistence and contagiousness. By considering these characteristics, a fully data-driven interactive double states emotion cell model (IDS-ECM) is proposed. The model has two layers. The first layer automatically extracts the emotional information of historical dialogue and is used to describe the contextual dependence of the textual dialogue emotion. The second layer models the change process of interlocutors’ emotional states during the dialogue and depicts the persistence and contagiousness of emotions. Experimental results on two manually annotated datasets show that the proposed model is superior to the baseline in the macro-averaged F1 evaluation metric and that the proposed model can simulate the emotional changes in the process of dialogue so as to predict the emotions with high accuracy. The experimental results also reveal the communication differences between different emotional categories in dialogue, which is of guiding significance for future research.
Deep learning-based classification of the polar emotions of "moe"-style cartoon pictures

The cartoon animation industry has developed into a huge industrial chain with a large potential market involving games, digital entertainment, and other industries. However, due to the coarse-grained classification of cartoon materials, cartoon animators can hardly find relevant materials during the process of creation. The polar emotions of cartoon materials are an important reference for creators as they can help them easily obtain the pictures they need. Some methods for obtaining the emotions of cartoon pictures have been proposed, but most of these focus on expression recognition. Meanwhile, other emotion recognition methods are not ideal for use as cartoon materials. We propose a deep learning-based method to classify the polar emotions of the cartoon pictures of the "Moe" drawing style. According to the expression feature of the cartoon characters of this drawing style, we recognize the facial expressions of cartoon characters and extract the scene and facial features of the cartoon images. Then, we correct the emotions of the pictures obtained by the expression recognition according to the scene features. Finally, we can obtain the polar emotions of corresponding picture. We designed a dataset and performed verification tests on it, achieving 81.9% experimental accuracy. The experimental results prove that our method is competitive.
GENPass: A Multi-Source Deep Learning Model for Password Guessing

The password has become today's dominant method of authentication. While brute-force attack methods such as HashCat and John the Ripper have proven unpractical, the research then switches to password guessing. State-of-the-art approaches such as the Markov Model and probabilistic context-free grammar (PCFG) are all based on statistical probability. These approaches require a large amount of calculation, which is time-consuming. Neural networks have proven more accurate and practical in password guessing than traditional methods. However, a raw neural network model is not qualified for cross-site attacks because each dataset has its own features. Our work aims to generalize those leaked passwords and improves the performance in cross-site attacks. In this paper, we propose GENPass, a multi-source deep learning model for generating “general” password. GENPass learns from several datasets and ensures the output wordlist can maintain high accuracy for different datasets using adversarial generation. The password generator of GENPass is PCFG+LSTM (PL). We are the first to combine a neural network with PCFG. Compared with Long short-term memory (LSTM), PL increases the matching rate by 16%-30% in cross-site tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The results demonstrate that the matching rate of GENPass is 20% higher than by simply mixing datasets in the cross-site test. Furthermore, we propose GENPass with probability (GENPass-pro), the updated version of GENPass, which can further increase the matching rate of GENPass.
STELA: A Real-Time Scene Text Detector With Learned Anchor

To achieve high coverage of target boxes, a normal strategy of conventional one-stage anchor-based detectors is to utilize multiple priors at each spatial position, especially in scene text detection tasks. In this work, we present a simple and intuitive method for multi-oriented text detection where each location of feature maps only associates with one reference box. The idea is inspired from the two-stage R-CNN framework that can estimate the location of objects with any shape by using learned proposals. The aim of our method is to integrate this mechanism into a one-stage detector and employ the learned anchor which is obtained through a regression operation to replace the original one into the final predictions. Based on RetinaNet, our method achieves competitive performances on several public benchmarks with a totally realtime efficiency (26.5fps at 800p), which surpasses all of anchor-based scene text detectors. In addition, with less attention on anchor design, we believe our method is easy to be applied on other analogous detection tasks. The code is publicly available at https://github.com/xhzdeng/stela.
Handwritten digits recognition with decision tree classification: a machine learning approach

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.
Domain-agnostic Question-Answering with Adversarial Training

Adapting models to new domain without finetuning is a challenging problem in deep learning.In this paper, we utilize an adversarial training framework for domain generalization in Question Answering (QA) task.Our model consists of a conventional QA model and a discriminator.The training is performed in the adversarial manner, where the two models constantly compete, so that QA model can learn domain-invariant features.We apply this approach in MRQA Shared Task 2019 and show better performance compared to the baseline model.
Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction

In this paper we tackle two unique challenges in biomedical relation extraction.The first challenge is that the contextual information between two entity mentions often involves sophisticated syntactic structures.We propose a novel graph convolutional networks model that incorporates dependency parsing and contextualized embedding to effectively capture comprehensive contextual information.The second challenge is that most of the benchmark data sets for this task are quite imbalanced because more than 80% mention pairs are negative instances (i.e., no relations).We propose a multi-task learning framework to jointly model relation identification and classification tasks to propagate supervision signals from each other and apply a focal loss to focus training on ambiguous mention pairs.By applying these two strategies, experiments show that our model achieves state-of-the-art F-score on the 2013 drug-drug interaction extraction task.
A hybrid recommendation system for Q&amp;A documents

Question and answer (Q&A) documents are a new type of knowledge document composed of a question part and an answer part. The questions represent knowledge needs, and the answers contain the knowledge that meets these knowledge needs. An overload of accumulated Q&A documents decreases the reuse of valuable knowledge. In this paper, we propose a novel hybrid system to recommend Q&A documents to alleviate overload. First, knowledge needs are partitioned, and current knowledge needs are identified by sequentially clustering the Q&A documents. Second, a content-based (CB) recommendation method, a collaborative filtering (CF) recommendation method and a complementarity-based recommendation method are used to find the Q&A documents that are potentially helpful for the user. Third, the three initial recommendation lists of Q&A documents derived from the three recommendation methods are combined to form a more comprehensive recommendation based on the Fermat point. Because reading all Q&A documents in the recommendation list consumes an enormous amount of time and users prefer to read Q&A documents one by one starting from the top, a novel ranking mechanism is proposed to ensure that users obtain comprehensive knowledge to the greatest extent possible from the limited number of Q&A documents at the top of the list. The proposed approach is evaluated and compared based on an experimental dataset. Our experimental results show that the approach is feasible, performs well, and provides a more effective way to recommend Q&A documents.
CoreCube: Core Decomposition in Multilayer Graphs

Many real-life complex networks are modelled as multilayer graphs where each layer records a certain kind of interaction among entities. Despite the powerful modelling functionality, the decomposition on multilayer graphs remains unclear and inefficient. As a well-studied graph decomposition, core decomposition is efficient on a single layer graph with a variety of applications on social networks, biology, finance and so on. Nevertheless, core decomposition on multilayer graphs is much more challenging due to the various combinations of layers. In this paper, we propose efficient algorithms to compute the CoreCube which records the core decomposition on every combination of layers. We also devise a hybrid storage method that achieves a superior trade-off between the size of CoreCube and the query time. Extensive experiments on 8 real-life datasets demonstrate our algorithms are effective and efficient.
H-INDEX: Hash-Indexing for Parallel Triangle Counting on GPUs

Triangle counting is a graph algorithm that calculates the number of triangles involving each vertex in a graph. Briefly, a triangle encompasses three vertices from a graph, where every vertex possesses at least one incidental edge to the other two vertices from the triangle. Consequently, list intersection, which identifies the incidental edges, becomes the core algorithm for triangle counting. At the meantime, attracted by the enormous parallel computing potential of Graphics Processing Units (GPUs), numerous efforts have been devoted to deploy triangle counting algorithms on GPUs. While state-of-the-art intersection algorithms, such as merge-path and binary-search, perform well on traditional multi-core CPU systems, deploying them on massively parallel GPUs turns out to be challenging. In particular, merge-path based approach experiences the hardship of evenly distributing the workload across vast GPU threads and irregular memory accesses. Binary-search based approach often suffers from the potential problem of high time complexity. Furthermore, both approaches require sorted neighbor lists from the input graphs, which involves nontrivial preprocessing overhead. To this end, we introduce H-INDEX, a hash-indexing assisted triangle counting algorithm that overcomes all the aforementioned shortcomings. Notably, HINDEX achieves 141.399 billion TEPS computing rate on a Protein K-mer V2a graph with 64 GPUs. To the best of our knowledge, this is the first work that advances triangle counting beyond the 100 billion TEPS rate.
Optimal Mach-Zehnder phase sensitivity with Gaussian states

We address in this work the phase sensitivity of a Mach-Zehnder interferometer with Gaussian input states. A squeezed-coherent plus squeezed vacuum input state allows us to unambiguously determine the optimal phase-matching conditions in order to maximize the quantum Fisher information. Realistic detection schemes are described and their performance compared in respect with the quantum Cram\'er-Rao bound. The core of this paper discusses in detail the most general Gaussian input state, without any apriori parameter restrictions. Prioritizing the maximization of various terms in the quantum Fisher information has the consequence of imposing the input phase-matching conditions. We discuss in detail when each scenario yields an optimal performance. Realistic detection scenarios are also considered and their performance compared to the theoretical optimum. The impact of the beam splitter types employed on the optimum phase-matching conditions is also discussed. We find a number of potentially interesting advantages of these states over the coherent plus squeezed vacuum input case.