Reviewerly/exHarmony huggingface dataset:
Improving Extreme Low-Light Image Denoising via Residual Learning

Taking a satisfactory picture in a low-light environment remains a challenging problem. Low-light imaging mainly suffers from noise due to the low signal-to-noise ratio. Many methods have been proposed for the task of image denoising, but they fail to work under extremely low-light conditions. Recently, deep learning based approaches have been presented that have higher objective quality than traditional methods, but they usually have high computational cost which makes them impractical to use in real-time applications or where the processing power is limited. In this paper, we propose a new residual learning based deep neural network for end-to-end extreme low-light image denoising that can not only significantly reduce the computational cost but also improve the quality over existing methods in both objective and subjective metrics. Specifically, in one setting we achieved 29x speedup with higher PSNR. Subjectively, our method provides better color reproduction and preserves more detailed texture information compared to state-of-the-art methods.
Knowledge Base Question Answering with Topic Units

Knowledge base question answering (KBQA) is an important task in natural language processing. Existing methods for KBQA usually start with entity linking, which considers mostly named entities found in a question as the starting points in the KB to search for answers to the question. However, relying only on entity linking to look for answer candidates may not be sufficient. In this paper, we propose to perform topic unit linking where topic units cover a wider range of units of a KB. We use a generation-and-scoring approach to gradually refine the set of topic units. Furthermore, we use reinforcement learning to jointly learn the parameters for topic unit linking and answer candidate ranking in an end-to-end manner. Experiments on three commonly used benchmark datasets show that our method consistently works well and outperforms the previous state of the art on two datasets.
A flexible method to defend against computationally resourceful miners in blockchain proof of work

Blockchain is well known as a decentralized and distributed public digital ledger, and is currently used by most cryptocurrencies to record transactions. One of the fundamental differences between blockchain and traditional distributed systems is that blockchain's decentralization relies on consensus protocols, such as proof of work (PoW). However, computation systems, such as application specific integrated circuit (ASIC) machines, have recently emerged that are specifically designed for PoW computation and may compromise a decentralized system within a short amount of time. These computationally resourceful miners challenge the very nature of blockchain, with potentially serious consequences. Therefore, in this paper, we propose a general and flexible PoW method that enforces memory usage. Specifically, the proposed method blocks computationally resourceful miners and retains the previous design logic without requiring one to replace the original hash function. We also propose the notion of a memory intensive function (MIF) with a memory usage parameter k (kMIF). Our scheme comprises three algorithms that construct a kMIF Hash by invoking any available hash function which is not kMIF to protect against ASICs, and then thwarts the pre-computation of hash results over a nonce. We then design experiments to evaluate memory changes in these three algorithms, and the findings demonstrate that enforcing memory usage in a blockchain can be an effective defense against computationally resourceful miners.
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization

Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues.However, the conventional reward ROUGE-L simply looks for exact n-grams matches between candidates and annotated references, which inevitably makes the generated sentences repetitive and incoherent.In this paper, instead of ROUGE-L, we explore the practicability of utilizing the distributional semantics to measure the matching degrees.With distributional semantics, sentence-level evaluation can be obtained, and semantically-correct phrases can also be generated without being limited to the surface form of the reference sentences.Human judgments on Gigaword and CNN/Daily Mail datasets show that our proposed distributional semantics reward (DSR) has distinct superiority in capturing the lexical and compositional diversity of natural language.
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation

We propose a fine-grained entity typing model with a novel attention mechanism and a hybrid type classifier.We advance existing methods in two aspects: feature extraction and type prediction.To capture richer contextual information, we adopt contextualized word representations instead of fixed word embeddings used in previous work.In addition, we propose a two-step mention-aware attention mechanism to enable the model to focus on important words in mentions and contexts.We also present a hybrid classification method beyond binary relevance to exploit type interdependency with latent type representation.Instead of independently predicting each type, we predict a low-dimensional vector that encodes latent type features and reconstruct the type vector from this latent representation.Experiment results on multiple data sets show that our model significantly advances the stateof-the-art on fine-grained entity typing, obtaining up to 6.6% and 5.5% absolute gains in macro averaged F-score and micro averaged Fscore respectively. 1
Constrained fixation point based segmentation via deep neural network

It is an explicit mode to use the clicking points by the mouse in the interactive image segmentation, while an implicit interaction mode is to use the fixation points from the eye-tracking device. Both modes can provide a series of points. Inspired by the similarity between these two interaction modes, we propose a novel human visual system (HVS) based neural network for transferring the constrained fixation point based segmentation to the clicking point based interactive segmentation. Briefly speaking, the sequence of information transmission and processing in our model is RGB image, VGG-16 backbone, LGN-like module (LGNL) and ConvLSTM block, which correspond to the pathway of stimulus transmission and processing, i.e. stimulus, retina, lateral geniculate nucleus (LGN) and visual cortex in the HVS. First, the RGB image is fed to the VGG-16 backbone to obtain the multiple-layer feature maps. Then the LGNL is adopted to effectively incorporate edge-aware features and semantic features from different layers of the VGG-16 backbone in multiple resolutions, so as to produce rich contextual features. Finally, with the guidance of the fixation density map transformed from the fixation points, the output feature maps of LGNL are utilized to generate the segmentation map via a stack of ConvLSTM blocks in a coarse-to-fine manner. Comprehensive experiments demonstrate that the proposed HVS based neural network achieves a higher segmentation performance and outperforms seven state-of-the-art methods, and prove that the transfer from constrained fixation points to clicking points is reasonable and valid.
Distance transform regression for spatially-aware deep semantic segmentation

Understanding visual scenes relies more and more on dense pixel-wise classification obtained via deep fully convolutional neural networks. However, due to the nature of the networks, predictions often suffer from blurry boundaries and ill-segmented shapes, fueling the need for post-processing. This work introduces a new semantic segmentation regularization based on the regression of a distance transform. After computing the distance transform on the label masks, we train a FCN in a multi-task setting in both discrete and continuous spaces by learning jointly classification and distance regression. This requires almost no modification of the network structure and adds a very low overhead to the training process. Learning to approximate the distance transform back-propagates spatial cues that implicitly regularizes the segmentation. We validate this technique with several architectures on various datasets, and we show significant improvements compared to competitive baselines.
3D Solar Potential in the Urban Environment: A Case Study in Lisbon

The assessment of solar potential in the urban environment is an important instrument for policy decision regarding renewable energy deployment in the city. This paper presents an experimentally validated 3D solar potential model for rooftops and facades from LIDAR data considering anisotropic diffuse irradiation. The data visualization is rendered in the ArcGIS platform using CityEngine to automatically generate 3D models from 2D geometries. The model is validated against summer and winter measurements of photovoltaic performance on a facade. A case study for two densely packed urban areas in Lisbon, Portugal, are presented. Facades are shown to increase the solar potential by 10 to 15%.
Progressive Sub-Band Residual-Learning Network for MR Image Super Resolution

High-resolution (HR) magnetic resonance images (MRI) provide more detailed information for clinical application. However, HR MRI is less available because of the longer scan time and lower signal-to-noise ratio. Spatial resolution is one of the key parameters of MRI. The image post-processing technique super-resolution (SR) is an alternative approach to improve the spatial resolution of MR images. Inspired by advanced deep learning based SR methods, we propose an MRI SR model named progressive sub-band residual learning SR network (PSR-SRN). The proposed model contains two parallel progressive learning streams, where one stream learns on missed high-frequency residuals by sub-band residual learning unit (ISRL) and the other focuses on reconstructing refined MR image. These two streams complement each other and enable to learn complex mappings between “Low-” and “High-” resolution MR images. Besides, we introduce brain-like mechanisms (in-depth supervision and local feedback mechanism) and progressive sub-band learning strategy to emphasize variant textures of MRI. Compared with traditional and deep learning MRI SR methods, our PSR-SRN model shows superior performance.
Assessment of eye fatigue caused by head-mounted displays using eye-tracking

Head-mounted displays (HMDs) and virtual reality (VR) have been frequently used in recent years, and a user's experience and computation efficiency could be assessed by mounting eye-trackers. However, in addition to visually induced motion sickness (VIMS), eye fatigue has increasingly emerged during and after the viewing experience, highlighting the necessity of quantitatively assessment of the detrimental effects. As no measurement method for the eye fatigue caused by HMDs has been widely accepted, we detected parameters related to optometry test. We proposed a novel computational approach for estimation of eye fatigue by providing various verifiable models.We implemented three classifications and two regressions to investigate different feature sets, which led to present two valid assessment models for eye fatigue by employing blinking features and eye movement features with the ground truth of indicators for optometry test. Three graded results and one continuous result were provided by each model, respectively, which caused the whole result to be repeatable and comparable.We showed differences between VIMS and eye fatigue, and we also presented a new scheme to assess eye fatigue of HMDs users by analysis of parameters of the eye tracker.
Rescue Method Based on V2X Communication and Human Pose Estimation

Advances in science and technologies not only improve our workflow and increase the quality of life but also change the way emergency rescue operates. The eCall system is the evidence of such a new evolutionary step. However, studies are not available about 'smart' rescue methods of independent human (neither pedestrian nor a driver) who turned out to be on the roadside for one or another reason and need first aid. The research questions were how to increase the quality and speed of the rescue process by using Vehicle-to-Everything (V2X) communication and the human pose estimation function and how to increase the reliability of transmitted information. In the article, we presented an overview about the current state-of-the-art in human pose estimation technology, discussed current methods of rescue, concept, and disadvantages of the current approach, and finally proposed our own method of rescue based on blending of vehicular communication networks with advancements in human pose estimation function. The research novelty in a scientific sense is a concept of a new information management method, where Autonomous Vehicles (AVs) act as witnesses itself without any human intervention. An example of SOS Packet Format has been designed. We proposed a novel view on the future ambulance.
Historical survey on metaheuristics algorithms

Metaheuristic algorithms have been an interesting and widely used area for scientists, researchers and academicians because of their specific and significant characteristics and capabilities in solving optimization problems. Metaheuristic algorithms are developed base on inspiration of some real world phenomenon in nature or on the behavior of living being (animal, insects, organic living beings). On the past many metaheuristic algorithms have been introduced and applied on various problems of various domains including real world optimization problems. This paper is aimed to provide a historical Survey on metaheuristic algorithms, it will provide a list of metaheuristic based algorithms ordered according to the foundation year, with the name of Authors and the algorithm abbreviations.Â
A novel learning cloud Bayesian network for risk measurement

Bayesian network (BN) is a popularly used approach for risk analysis. Because it is a graphic model being able to deal with randomness yet unable to model ambiguity, the fuzzy set theory is often combined with it to create a so-called fuzzy BN. Instead of using the classical fuzzy set theory, this paper intends to combine a normal Cloud model with the BN. In the normal Cloud model, an element belonging to a certain qualitative concept is not certain and precise as well. The Cloud BN is a generalization of the fuzzy BN. It is more adaptive for the uncertainty description of linguistic concepts, for example, the risks. Using the normal Cloud model, the following numerical characteristics of the variables can be estimated: the expectation, the dispersion degree compared with the expectation, and the dispersion degree of entropy. Consequently, the risk assessment contains a richer set of analytical information. Cloud BNs attract growing research interests. Compared to its precedents, the Cloud BN in this paper has a learning capability. Since the risk factors may have a combined effect, the causal relationships among the variables can be very complex, and hidden variables may exist. The learning mechanism allows for automatic structure discovery from data, giving rise to a dynamically evolving network. The proposed learning Cloud BN is able to represent the real risk situation better than its precedents. Its effectiveness and applicability are demonstrated by an illustrative case for risk prediction of the face instability in an underground tunnel construction project.
Educational smartphone apps for language learning in higher education: Students’ choices and perceptions

The popularity and use of smartphone apps for language learning have significantly grown in Iran over the past years. Despite this growth, empirical data on students’ choice of such apps, their perception towards them, and the impact of external factors on their attitude remains scant. In an attempt to contribute to this research base, a descriptive survey study was carried out to explore the type of language learning apps commonly used by 381 university students at Amirkabir University of Technology and their perception towards the effectiveness of these apps for developing learning language skills. Dictionary and lexical apps appeared to be the most popular application types amongst participants. While students were generally positive about the use of apps for language learning, they had different viewpoints regarding the potential of apps for developing different language skills. The results of ANOVA indicate that gender did not play a significant role in participants’ perception. On the contrary, the type of apps used significantly shaped students’ attitudes towards app-based language learning. The findings contribute to research on mobile assisted language learning and educational app design, by shedding more light on students’ choice of apps for learning different language skills.
Fine-Grained Entity Typing for Domain Independent Entity Linking

Neural entity linking models are very powerful, but run the risk of overfitting to the domain they are trained in. For this problem, a “domain” is characterized not just by genre of text but even by factors as specific as the particular distribution of entities, as neural models tend to overfit by memorizing properties of frequent entities in a dataset. We tackle the problem of building robust entity linking models that generalize effectively and do not rely on labeled entity linking data with a specific entity distribution. Rather than predicting entities directly, our approach models fine-grained entity properties, which can help disambiguate between even closely related entities. We derive a large inventory of types (tens of thousands) from Wikipedia categories, and use hyperlinked mentions in Wikipedia to distantly label data and train an entity typing model. At test time, we classify a mention with this typing model and use soft type predictions to link the mention to the most similar candidate entity. We evaluate our entity linking system on the CoNLL-YAGO dataset (Hoffart et al. 2011) and show that our approach outperforms prior domain-independent entity linking systems. We also test our approach in a harder setting derived from the WikilinksNED dataset (Eshel et al. 2017) where all the mention-entity pairs are unseen during test time. Results indicate that our approach generalizes better than a state-of-the-art neural model on the dataset.
Ensemble adversarial black-box attacks against deep learning systems

Deep learning (DL) models, e.g., state-of-the-art convolutional neural networks (CNNs), have been widely applied into security sensitivity tasks, such as face payment, security monitoring, automated driving, etc. Then their vulnerability analysis is an emergent topic, especially for black-box attacks, where adversaries do not know the model internal architectures or training parameters. In this paper, two types of ensemble-based black-box attack strategies, selective cascade ensemble strategy (SCES) and stack parallel ensemble strategy (SPES), are proposed to explore the vulnerability of DL system and potential factors that contribute to the high-efficiency attacks are explored. SCES adopts a boosting structure of ensemble learning and SPES employs a bagging structure. Moreover, two pairwise and non-pairwise diversity measures are adopted to examine the relationship between the diversity in substitutes ensembles and transferability of generated adversarial examples. Experimental results show that proposed ensemble adversarial black-box attack strategies can successfully attack the DL system with some defense mechanism, such as adversarial training and ensemble adversarial training. The experimental results also show the greater the diversity in substitute ensembles enables stronger transferability.
Distributed Blockchain-Based Message Authentication Scheme for Connected Vehicles

Vehicular ad-hoc networks (VANETs) have several security issues such as privacy preservation, secure authentication, and system reliability. In the VANET, a vehicle communicates with other vehicles or infrastructures using broadcasting messages. These messages contain not only normal traffic information, but also identification information of sender. In general, the identification information remains encrypted to ensure privacy. However, the conventional centralized system can decrypt the identification information using private information of the sender vehicle. As a result, the central server can often be targeted by adversaries. We propose a message authentication scheme for anonymity and decentralization of information using blockchain technology. Here, we introduce public-private key and message authentication code (MAC) for secure authentication. In this paper, we adopt consensus algorithms for composing blockchain system such as the proof of work (PoW) and Practical Byzantine Fault Tolerance (PBFT) into the proposed authentication process. Finally, we demonstrate that the proposed method is secure from the attacks which include impersonation from internal attacker as well as typical attacks.
Knowledge Graph Grounded Goal Planning for Open-Domain Conversation Generation

Previous neural models on open-domain conversation generation have no effective mechanisms to manage chatting topics, and tend to produce less coherent dialogs. Inspired by the strategies in human-human dialogs, we divide the task of multi-turn open-domain conversation generation into two sub-tasks: explicit goal (chatting about a topic) sequence planning and goal completion by topic elaboration. To this end, we propose a three-layer Knowledge aware Hierarchical Reinforcement Learning based Model (KnowHRL). Specifically, for the first sub-task, the upper-layer policy learns to traverse a knowledge graph (KG) in order to plan a high-level goal sequence towards a good balance between dialog coherence and topic consistency with user interests. For the second sub-task, the middle-layer policy and the lower-layer one work together to produce an in-depth multi-turn conversation about a single topic with a goal-driven generation mechanism. The capability of goal-sequence planning enables chatbots to conduct proactive open-domain conversations towards recommended topics, which has many practical applications. Experiments demonstrate that our model outperforms state of the art baselines in terms of user-interest consistency, dialog coherence, and knowledge accuracy.
Span Model for Open Information Extraction on Accurate Corpus

Open Information Extraction (Open IE) is a challenging task especially due to its brittle data basis. Most of Open IE systems have to be trained on automatically built corpus and evaluated on inaccurate test set. In this work, we first alleviate this difficulty from both sides of training and test sets. For the former, we propose an improved model design to more sufficiently exploit training dataset. For the latter, we present our accurately re-annotated benchmark test set (Re-OIE2016) according to a series of linguistic observation and analysis. Then, we introduce a span model instead of previous adopted sequence labeling formulization for n-ary Open IE. Our newly introduced model achieves new state-of-the-art performance on both benchmark evaluation datasets.