Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of DCGAN based Unsupervised Decoding and Image Generation

At present, deep learning is a fast growing research area of machine learning, which can extract more effective features by using a cascade of nonlinear layer units, some of them use deep convolutional neural network to process digital images. DCGAN stands for Deep Convolutional Generative Adversarial Networks, which is an unsupervised learning method for neural networks. The model consists of two networks. A generator that generates an image from a noise vector, and a discriminator that discriminates between a generated image and a real image. The generator model takes random input values and transforms them into images through a deconvolutional neural network, whereas DCGAN generates an image from random parameters. In this paper, an unsupervised learning method is proposed which is based on DCGAN to generate the image of codes and use the discriminator to encode the image, in the training stage, except the minimax cost function for the GAN network, the distance between input code of generator and the output code of discriminator is minimized. On MNIST dataset, this method has achieved good experiment result, experiments show that this architecture can learn the code of images and further prove the method has the ability to understand a set of image without extra knowledge, and can reconstruct the image using the generated code.
GPU-SPARC: Accelerating Parallelism in Multi-GPU Real-Time Systems

—GPU (General-Purpose computation on Graphics Processing Units) offers an effective computing platform to accelerate a wide class of data-parallel computing. Multi-GPU’s appear as an attractive platform to speed up the computation of data-parallel GPU. This paper aims to explore the feasibility of relaxing the task-level restriction of single GPU use in multi-GPU real-time systems. We develop a multi-GPU runtime support system, called GPU-SPARC , where GPU applications can be automatically split and run concurrently over multi-GPU’s. We present the prototype of GPU-SPARC on OpenCL runtime that can provide the service to existing OpenCL applications without any modiﬁcation to them unless global synchronization is employed. The multi-GPU parallel computing offers the potential for performance improvement but at the same time incurs additional resource consumption. Thereby, we analysis the beneﬁt and cost of executing a GPU application on multiple GPU’s and propose a GPU execution mode assignment policy from the perspective of system-wide schedulability. Our experiment results show that GPU-SPARC is able to improve schedulability in real-time multi-GPU systems by relaxing the single-GPU-per-task restriction and choosing better GPU execution modes.
Dataset Creation for Ranking Constructive News Comments

Ranking comments on an online news service is a practically important task for the service provider, and thus there have been many studies on this task. However, most of them considered users’ positive feedback, such as “Like”-button clicks, as a quality measure. In this paper, we address directly evaluating the quality of comments on the basis of “constructiveness,” separately from user feedback. To this end, we create a new dataset including 100K+ Japanese comments with constructiveness scores (C-scores). Our experiments clarify that C-scores are not always related to users’ positive feedback, and the performance of pairwise ranking models tends to be enhanced by the variation of comments rather than articles.
A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction

We present a new method for sentiment lexicon induction that is designed to be applicable to the entire range of typological diversity of the world’s languages. We evaluate our method on Parallel Bible Corpus+ (PBC+), a parallel corpus of 1593 languages. The key idea is to use Byte Pair Encodings (BPEs) as basic units for multilingual embeddings. Through zero-shot transfer from English sentiment, we learn a seed lexicon for each language in the domain of PBC+. Through domain adaptation, we then generalize the domain-specific lexicon to a general one. We show – across typologically diverse languages in PBC+ – good quality of seed and general-domain sentiment lexicons by intrinsic and extrinsic and by automatic and human evaluation. We make freely available our code, seed sentiment lexicons for all 1593 languages and induced general-domain sentiment lexicons for 200 languages.
Concentration and capacity effects on electricity market power

The paper reports experimental results for a computational double auction electricity market and compares them to the results that would hold under competitive equilibrium. The computational market is designed to simulate a deregulated electricity market. A genetic algorithm is used to simulate the learning mechanism used by buyers and sellers. Each simulation has parameters that can be set to investigate the relationships between different ratios of buyers and sellers with different ratios of buying and selling capacity. The market power of buyers and sellers is individually and collectively measured in terms of deviations from competitive equilibrium. We examine how this market power varies with changes in concentration and capacity.
Considerations for Large Authoritative DNS Server Operators

Recent research work has explored the deployment characteristics and con ﬁ guration of the Domain Name System (DNS). This document summarizes the conclusions from these research e ﬀ orts and o ﬀ ers speci ﬁ c, tangible considerations or advice to authoritative DNS server operators. Authoritative server operators may wish to follow these considerations to improve their DNS services. It is possible that the results presented in this document could be applicable in a wider context than just the DNS protocol, as some of the results may generically apply to any stateless/short-duration anycasted service.
A Neural Multi-digraph Model for Chinese NER with Gazetteers

Gazetteers were shown to be useful resources for named entity recognition (NER). Many existing approaches to incorporating gazetteers into machine learning based NER systems rely on manually defined selection strategies or handcrafted templates, which may not always lead to optimal effectiveness, especially when multiple gazetteers are involved. This is especially the case for the task of Chinese NER, where the words are not naturally tokenized, leading to additional ambiguities. To automatically learn how to incorporate multiple gazetteers into an NER system, we propose a novel approach based on graph neural networks with a multi-digraph structure that captures the information that the gazetteers offer. Experiments on various datasets show that our model is effective in incorporating rich gazetteer information while resolving ambiguities, outperforming previous approaches.
Developing and evaluating virtual anatomy resources for teaching allied health disciplines

Research in Learning Technology 2019. © 2019 Nicola Massy-Westropp et al. Research in Learning Technology is the journal of the Association for Learning Technology (ALT), a UK-based professional and scholarly society and membership organisation. ALT is registered charity number 1063519. http://www.alt. ac.uk/. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.
Visual odometry with high resolution time-of-flight cameras

We present a method that estimates the ego motion of a vehicle based on camera data of high resolution (207x204 pixels) Time-of-Flight cameras using visual odometry techniques. Translation and rotation of camera motion in six degrees of freedom are calculated. Point correspondences in consecutive amplitude image pairs are built. By consideration of the depth image of the camera 3D point correspondences are derived from the two dimensional point correspondences. Camera motion between the two images is then computed by registration of the two resulting point clouds. The process is optimized by incorporation of outlier removal and a multi sensor setup. The presented optimizations raise the precision and robustness of the method and enable visual odometry by Time-of-Flight camera data as an alternative to common odometry systems in low speed scenarios.
Deep multi-factor forensic face recognition

of the Dissertation Deep Multi-Factor Forensic Face Recognition by Zhengming Ding Doctor of Philosophy in Electrical and Computer Engineering Northeastern University, 2018 Dr. Yun Fu, Advisor Forensic science is any scientific field that is applied to the field of law. Due to the popularity of the digital media carriers such as images, videos, the facial recognition becomes another important forensic technique. The major issue of forensic face recognition is the unstable system performance due to internal factor, e.g., aging, and external factors, e.g., image resolution/modality, illumination, pose. In this thesis, we investigate a theoretical framework for forensic face recognition, subject to a variety of internal/external impact factors to tackle face recognition under different views, illuminations, resolutions, modalities, periods, when probe images are captured in the surveillance environments without collaborations. Specifically, we explore two scenarios as follows. First of all, we explore one dominant factor which hinders the forensic face recognition, which is view variance, e.g., pose and modality. Thus, we propose multi-view face recognition, which covers two settings in multi-view face recognition. On one hand, labeled data in multiple views are available in the training stage, which is the traditional multi-view learning setting. Specifically, we address the challenging but practical situation, in which the view information of the test data is unknown. On the other hand, some source views are labeled while target views are unlabeled, which satisfies transfer learning scenarios. Specifically, we explore the practical but challenging missing modality problem. Secondly, multiple factors are modeled as the noises as a whole. On one hand, conventional auto-encoder and its variants usually involve additive noises for training data to learn robust features, which, however, did not consider the already corrupted data. We propose a novel Deep Robust Encoder (DRE) through locality preserving low-rank dictionary to extract robust and discriminative features from corrupted data. Furthermore, we fight off one-shot face recognition, where we only have one training sample for some persons, by proposing a one-shot generative model to build a more effective face recognizer.
Computationally efficient exact remodeling of optimization programs with applications to autonomous driving

The future of autonomous vehicles is rapidly approaching and the published and available research, both from vehicle manufacturers and universities, is abundant. This new technology promises less pollution, lower accident rates, decreased congestion and the possibility to relax or work while a vehicle takes you where you need to go. In this thesis we use nonlinear model predictive control to control autonomous vehicles overtaking on highways and driving through intersections. One of the main disadvantages of model predictive control is that the optimal control problems can be computationally expensive to solve. This could certainly be the case for the exact temporal formulation of the intersection and highway problem since the modeling of for both applications include binary decisions; and thus, have mixed integer optimization programs as their optimal control problems. To decrease the computational complexity of these optimal control problems this thesis introduces a novel reformulation technique for optimal control problems which removes the integer decisions present due to the collision constraints; which results in a continuous, nonlinear control problem for both applications. The remodeling technique involves changing the independent variable from travel time to traveled distance, introducing travel time and inverse velocity as states and lastly by introducing new input signals. After the remodeling, the continuous, nonlinear optimal control problems are solved using sequential quadratic programming. Further, it is shown that the introduced remodeling technique guarantees that the subproblems of the sequential quadratic programming scheme provides feasible solutions to the original nonlinear program being solved; for both the intersection and overtaking problem. This makes it possible to stop the sequential quadratic programming scheme prematurely and still have access to a solution that is feasible in the nonlinear program; provided, of course, that the subproblems themselves are feasible.
The Effect of the Nonlinear Frequency Compression Methods on the Recognition of Monosyllabic Words in Persian Language

Article Info The Effect of the Nonlinear Frequency Compression Methods on the Recognition of Monosyllabic Words in Persian Language Noushin Zarza1, Nariman Rahbar2, Hassan Haddadzadeh Niri3 1. MSc Student, Department of Audiology, School of Rehabilitation Sciences, Iran University of Medical Science, Tehran, Iran 2. PhD, Department of Audiology, School of Rehabilitation Sciences, Iran University of Medical Science, Tehran, Iran 3. PhD Candidate, Department of Audiology, School of Rehabilitation Sciences, Iran University of Medical Science, Tehran, Iran Background and Objectives: The aim of all Frequency Lowering methods is access to high frequencies (HFs) for people with hearing loss. The advantage of these methods has been approved for improving speech perception and limitations reduction of affected people. One of these method is Nonlinear Frequency Compression that reduces the bandwidth of frequency band. This study was carried out to compare the Adaptive Nonlinear Frequency Compression (ANFC) with original algorithm of Nonlinear Frequency Compression (NFC) and Conventional Processing (CP). Materials and Methods: Thirty people in the range of 18-40 years old with skisloping hearing loss were evaluated. The presence of cochlear dead region at least in one of the frequencies of 1500, 2000, 3000 and 4000 Hz was proved in all cases by performing the Threshold Equalizing Noise test (TEN (HL)). The evaluation was carried out using monosyllabic balanced-word lists in Persian language. Each of Lists used for recognition in one of the three processing modes such as ANFC, NFC and CP which it has been performed in the free field at 90 centimeters distance of the speaker and 0° azimuth. Results: The scores of frequency compression algorithms are better than CP and it leads to improve recognition whereas the scores of NFC and ANFC were similar. In addition, it is not dependent to gender, the obtained scores in three processing modes. Conclusion: It is more useful for a person with ski-sloping hearing loss to use hearing aids with frequency compression technology rather than CP. In fact, frequency compression gives them a better speech perception, but the effect of the two different algorithms is similar.
Learning to Control the Fine-grained Sentiment for Story Ending Generation

Automatic story ending generation is an interesting and challenging task in natural language generation. Previous studies are mainly limited to generate coherent, reasonable and diversified story endings, and few works focus on controlling the sentiment of story endings. This paper focuses on generating a story ending which meets the given fine-grained sentiment intensity. There are two major challenges to this task. First is the lack of story corpus which has fine-grained sentiment labels. Second is the difficulty of explicitly controlling sentiment intensity when generating endings. Therefore, we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges. The sentiment analyzer adopts a series of methods to acquire sentiment intensities of the story dataset. The sentimental generator introduces the sentiment intensity into decoder via a Gaussian Kernel Layer to control the sentiment of the output. To the best of our knowledge, this is the first endeavor to control the fine-grained sentiment for story ending generation without manually annotating sentiment labels. Experiments show that our proposed framework can generate story endings which are not only more coherent and fluent but also able to meet the given sentiment intensity better.
Mobile Thermography-based Physiological Computing for Automatic Recognition of a Person’s Mental Stress

This thesis explores the use of Mobile Thermography1, a significantly less investigated sensing capability, with the aim of reliably extracting a person’s multiple physiological signatures and recognising mental stress in an automatic, contactless manner. Mobile thermography has greater potentials for real-world applications because of its light-weight, low computation-cost characteristics. In addition, thermography itself does not necessarily require the sensors to be worn directly on the skin. It raises less privacy concerns and is less sensitive to ambient lighting conditions. The work presented in this thesis is structured through a three-stage approach that aims to address the following challenges: i) thermal image processing for mobile thermography in variable thermal range scenes; ii) creation of rich and robust physiology measurements; and iii) automated stress recognition based on such measurements. Through the first stage (Chapter 4), this thesis contributes new processing techniques to address negative effects of environmental temperature changes upon automatic tracking of regions-of-interest and measuring of surface temperature patterns. In the second stage (Chapters 5,6,7), the main contributions are: robustness in tracking respiratory and cardiovascular thermal signatures both in constrained and unconstrained settings (e.g. respiration: strong correlation with ground truth, r=0.9987), and investigation of novel cortical thermal signatures associated with mental stress. The final stage (Chapters 8,9) contributes automatic stress inference systems that focus on capturing richer dynamic information of physiological variability: firstly, a novel respiration representation-based system (which has achieved state-of-the-art performance: 84.59% accuracy, two stress levels), and secondly, a novel cardiovascular representation-based system using short-term measurements of nasal thermal variability and heartrate variability from another sensing channel (78.33% accuracy achieved from 20seconds measurements). Finally, this thesis contributes software libraries and incrementally built labelled datasets of thermal images in both constrained and everyday ubiquitous settings. These are used to evaluate performance of our proposed computational methods across the three-stages.
Graph-Search Descent and Approach Trajectory Optimization Based on Enhanced Aircraft Energy Management

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Graph-Search Descent and Approach Trajectory Optimization Based on Enhanced Aircraft Energy Management Ramon Andreu Altava, Jean-Claude Mere, Daniel Delahaye, Thierry Miquel
Aprendizado não-supervisionado em redes neurais pulsadas de base radial: um estudo da capacidade de agrupamento para a classificação de pixels

Pulsed neural networks - networks that encode information in the timing of spikes - has been studied as a new and promising approach in the artificial neural networks paradigm, emergent from the cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. A learning algorithm was successfully applied to this pulsed network, which was able to map a sequence of input pulses into a sequence of output pulses. More recently, a method based on the use of Gaussian receptive fields was proposed to encode constant data into a temporal sequence of spikes. This method allowed this network to deal with computational data. The learning process of this new network is not completely understood and deeper investigations are necessary in order to situate this model in the machine learning context and also to establish the network abilities and limitations. This work investigates this new classifier and presents a study of the network capability in solving the three-dimensional clustering task, particularly looking for establishing its application domains and horizons in the computer vision field.
ProGAN: Network Embedding via Proximity Generative Adversarial Network

Network embedding has attracted increasing attention in recent few years, which is to learn a low-dimensional representation for each node of a network to benefit downstream tasks, such as node classification, link prediction, and network visualization. Essentially, the task of network embedding can be decoupled into discovering the proximity in the original space and preserving it in the low dimensional space. Only with the well-discovered proximity can we preserve it in the low-dimensional space. Thus, it is critical to discover the proximity between different nodes to learn good node representations. To address this problem, in this paper, we propose a novel proximity generative adversarial network (ProGAN) which can generate proximities. As a result, the generated proximity can help to discover the complicated underlying proximity to benefit network embedding. To generate proximities, we design a novel neural network architecture to fulfill it. In particular, the generation of proximities is instantiated to the generation of triplets of nodes, which encodes the similarity relationship between different nodes. In this way, the proposed ProGAN can generate proximities successfully to benefit network embedding. At last, extensive experimental results have verified the effectiveness of ProGAN.