Reviewerly/exHarmony huggingface dataset:
Richer Document Embeddings for Author Profiling tasks based on a heuristic search

In this study we propose a novel method to generate Document Embeddings (DEs) by means of evolving mathematical equations that integrate classical term frequency statistics. To accomplish this, we employed a Genetic Programming (GP) strategy to build competitive formulae to weight custom Word Embeddings (WEs), produced by cutting edge feature extraction techniques (e.g., word2vec, fastText, BERT), and then we create DEs by their weighted averaging. We exhaustively evaluated the proposed method over 9 datasets that are composed of several multilingual social media sources, with the aim to predict personal attributes of authors (e.g., gender, age, personality traits) in 17 tasks. In each dataset we contrast the results obtained by our method against state-of-the-art competitors, placing our approach at the top-quartile in all cases. Furthermore, we introduce a new numerical statistic feature called Relevance Topic Value (rtv), which could be used to enhance the forecasting of characteristics of authors, by numerically describing the topic of a document and the personal use of words by users. Interestingly, based on a frequency analysis of terminals used by GP, rtv turned out to be the most likely feature to appear alone in a single equation, then suggesting its usefulness as a WE weighting scheme.
Robust and structural sparsity auto-encoder with L21-norm minimization

The mean square error (MSE), as the most commonly used cost function for auto-encoder, is sensitive to outliers or impulsive noises in real-world application, which may misguide the training process. At the same time, stacked auto-encoder(SAE) is indeed a totally fully connected network, the parameters exponentially increase as the nodes and layers increase, which may cause over-fitting, huge computational complexity and storage overhead. So the robustness and sparseness problem of the auto-encoder need to be further investigated. In this paper, we develop a robust and structural sparsity stacked auto-encoder with L21-norm loss function and regularization (LR21-SAE). Our L21-norm loss function can alleviate the negative impact of outlier samples, thus show superior robust performance. Our L21-norm regularization can enforce some rows/columns of weight matrix shrink to zero entirely, thus promote to learn sparse features and choose compact network. We have validated our LR21-SAE model on several common datasets. Experimental results show that LR21-SAE is significantly robust to outlier noises for real-world data, it also can get sparse nodes connection deep neural network with notable less number of parameters than what the original non-sparse network has, while maintain outstanding performance.
An Interval Type-2 Fuzzy-Based DTC of IMD Using Hybrid Duty Ratio Control

This article provides an interval type-2 fuzzy-based direct torque control (IT2FDTC) of the induction motor (IM) drive, (IMD) in which a hybrid duty ratio control (HDRC) technique is used to activate a two-level inverter. Moreover, it is implemented with the help of DSPACE-1104 controller in a real-time application using a 2HP IM and compared its overall performance with a conventional proportional-integral direct torque control (CPIDTC) using space vector modulation (SVM) technique. The IT2FDTC can be provided less torque and flux ripples with a faster dynamic performance of IMD over a CPIDTC. This due to the fact that a three-dimensional Mamdani type-2 fuzzy controller along with the centroid method is used in the defuzzification process, for the proposed control method. The proposed method of study effectively controls the gains of PI and HDRC. Also, HDRC has a total of five possible duty ratios in which two novel duty ratios are introduced including two clamping and one SVM duty ratios to further improve the current/flux/torque harmonic profile performance of IMD than CPIDTC.
Real–Sim–Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning

Compared to traditional data-driven learning methods, recently developed deep reinforcement learning (DRL) approaches can be employed to train robot agents to obtain control policies with appealing performance. However, learning control policies for real-world robots through DRL is costly and cumbersome. A promising alternative is to train policies in simulated environments and transfer the learned policies to real-world scenarios. Unfortunately, due to the reality gap between simulated and real-world environments, the policies learned in simulated environments often cannot be generalized well to the real world. Bridging the reality gap is still a challenging problem. In this paper, we propose a novel real–sim–real (RSR) transfer method that includes a real-to-sim training phase and a sim-to-real inference phase. In the real-to-sim training phase, a task-relevant simulated environment is constructed based on semantic information of the real-world scenario and coordinate transformation, and then a policy is trained with the DRL method in the built simulated environment. In the sim-to-real inference phase, the learned policy is directly applied to control the robot in real-world scenarios without any real-world data. Experimental results in two different robot control tasks show that the proposed RSR method can train skill policies with high generalization performance and significantly low training costs.
Grayscale image formats for edge detection and for its FPGA implementation

The aim of this paper is analysis of image formats used for FPGA implementation of edge detection methods. All cameras used in FPGA applications give Raw RGB output video format, some cameras provide also YUV, YCbCr, RGB565/555 or compressed JPEG formats. If the FPGA circuit has limited number of configurable logic blocks (CLB) the JPEG format seems to be good solution how to increase the size of the processed image. On the other hand, using an image with lossy compression can more or less affect the overall result of image processing. The first goal of this paper is to show whether lossy image compression can affect the quality of edge detection. The results presented in this article show that lossy image compression can impair the efficiency of edge detection by up to six percent. Many researchers have proposed FPGA implementation of some edge detection methods. Usually their first step is RGB to grayscale conversion because they use edge detection methods for grayscale images. The second goal of this paper is to show that a performance of FPGA implementation can be improved if YUV, YCbCr or Raw RGB camera output formats are used instead of RGB format.
Summarizing egocentric videos using deep features and optimal clustering

In this paper, we address the problem of summarizing egocentric videos using deep features and an optimal clustering approach. Based on an augmented pre-trained convolutional neural network (CNN), each frame in an egocentric video is represented by deep features. An optimal clustering algorithm, based on a center-surround model (CSM) and an Integer Knapsack type formulation (IK) for K-means, termed as CSMIK K-means, is applied next to obtain the summary. In the center surround model, we compute difference in entropy and the optical flow values between the central region and that of the surrounding region of each frame. In the integer knapsack formulation, each cluster is treated as an item whose cost is assigned from the center surround model. A potential set of clusters in CSMIK K-means is obtained from the chi-square distance between color histograms of successive frames. CSMIK K-Means evaluates different cluster formations and simultaneously determines the optimal number of clusters and the corresponding summary. Experimental evaluation on four well-known benchmark datasets clearly indicate the superiority of the proposed method over several state-of-the-art approaches.
Query-by-Example On-Device Keyword Spotting

A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream. This paper presents a query-by-example on-device KWS system which is user-specific. The proposed system consists of two main steps: query enrollment and testing. In query enrollment step, phonetic posteriors are output by a small-footprint automatic speech recognition model based on connectionist temporal classification. Using the phonetic-level posteriorgram, hypothesis graph of finite-state transducer (FST) is built, thus can enroll any keywords thus avoiding an out-of-vocabulary problem. In testing, a log-likelihood is scored for input audio using the FST. We propose a threshold prediction method while using the user-specific keyword hypothesis only. The system generates query-specific negatives by rearranging each query utterance in waveform. The threshold is decided based on the enrollment queries and generated negatives. We tested two keywords in English, and the proposed work shows promising performance while preserving simplicity.
Enhancing Decision Tree Based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization

One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability. In this work, a practicable approach of gaining explainability of deep artificial neural networks (NN) using an interpretable surrogate model based on decision trees is presented. Simply fitting a decision tree to a trained NN usually leads to unsatisfactory results in terms of accuracy and fidelity. Using L1-orthogonal regularization during training, however, preserves the accuracy of the NN, while it can be closely approximated by small decision trees. Tests with different data sets confirm that L1-orthogonal regularization yields models of lower complexity and at the same time higher fidelity compared to other regularizers.
Real-time Bangla Sign Language Detection using Xception Model with Augmented Dataset

Bangla Sign language (BdSL) is the communication language used by the deaf and dumb of Bangladesh. In this paper, we present an optimal approach to recognize BdSL in real-time. First, we have developed BdSLInfinite dataset, which consists of 2,000 images of 37 different signs. Using this dataset, a convolutional neural network (CNN) based model is trained using Xception architecture that achieves 98.93% accuracy over the test-set, with response time of 48.53 ms on average. To the best of our knowledge, our proposed method outperforms all existing BdSL recognition methods in terms of both accuracy and speed.
End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation

Video super-resolution aims at generating a high-resolution video from its low-resolution counterpart. With the rapid rise of deep learning, many recently proposed video super-resolution methods use convolutional neural networks in conjunction with explicit motion compensation to capitalize on statistical dependencies within and across low-resolution frames. Two common issues of such methods are noteworthy. Firstly, the quality of the final reconstructed HR video is often very sensitive to the accuracy of motion estimation. Secondly, the warp grid needed for motion compensation, which is specified by the two flow maps delineating pixel displacements in horizontal and vertical directions, tends to introduce additional errors and jeopardize the temporal consistency across video frames. To address these issues, we propose a novel dynamic local filter network to perform implicit motion estimation and compensation by employing, via locally connected layers, sample-specific and position-specific dynamic local filters that are tailored to the target pixels. We also propose a global refinement network based on ResBlock and autoencoder structures to exploit non-local correlations and enhance the spatial consistency of super-resolved frames. The experimental results demonstrate that the proposed method outperforms the state-of-the-art, and validate its strength in terms of local transformation handling, temporal consistency as well as edge sharpness.
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting

Recently, there is a vast interest in developing unsupervised methods that are independent of the feature learning from the training data, e.g., deep image prior [26], zero-shot learning [23], and internal learning [21], [22]. These methods are based on the common goal of maxi-mizing the quality of image features learned from a single image despite inherent technical diversity. In this work, we bridge the gap between the various unsupervised approaches above and propose a general framework for image restoration and image retargeting. We use contextual feature learning and internal learning to improvise the structure similarity between the source and the target images. We perform image resizing application in the following setups: classical image resizing using super-resolution, a challenging image resizing where the low-resolution image contains noise, and content-aware image resizing using image retar-geting. We also compare our framework with relevant state-of-the-art methods.
Computer games as distraction from PAIN: Effects of hardware and difficulty on pain tolerance and subjective IMMERSION

Technology, such as computer games and virtual reality (VR), can be used to distract attention from pain. This type of non-pharmacological intervention is cost-effective, efficient and avoids complications arising from medication. However, the capacity of technology to capture attention and effectively distract from painful stimulation is determined by different factors related to the experience of immersion, such as: sensory immersion, i.e. the audio-visual presentation of the digital world, and challenge-based immersion, i.e. effortful engagement with goals in the digital world. Four studies were performed to explore the influence of both sensory and challenge-based immersion on pain tolerance using computer games in combination with the cold pressor test. Study One (N = 30) explored sensory immersion by contrasting pain tolerance during gameplay using VR display, 2D head-mounted micro-display and flatscreen TV, but no significant effect of display type on pain tolerance was observed. Study Two (N = 70) manipulated challenge-based immersion and reported a significant increase of pain tolerance when participants played a highly-demanding game compared to a game with low demand. Study Three (N = 60) simultaneously manipulated sensory immersion via screen display size (40″ vs 9″) and challenge-based immersion (game demand); pain tolerance increased in a linear fashion with demand but no significant effect of display size was reported. The fourth study (N = 40) also manipulated both forms of immersion via systematic manipulation of game music/sound volume (11.6 vs. 57.8 dB) and game demand, no effect for audio volume was observed but pain tolerance increased when the game was highly demanding. All studies included measures of cardiovascular psychophysiology and a subjective index of immersion. Analyses of the relationship between measures revealed that greater autonomic activation exerted a direct, positive effect on pain tolerance, i.e. higher activation = greater pain tolerance. It is concluded that challenge-based immersion is the primary means by which technology can distracts attention from pain.
CookGAN: Meal Image Synthesis from Ingredients

In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual list of its ingredients. Previous works on synthesis of images from text typically rely on pre-trained text models to extract text features, followed by generative neural networks (GAN) aimed to generate realistic images conditioned on the text features. These works mainly focus on generating spatially compact and well-defined categories of objects, such as birds or flowers, but meal images are significantly more complex, consisting of multiple ingredients whose appearance and spatial qualities are further modified by cooking methods. To generate real-like meal images from ingredients, we propose Cook Generative Adversarial Networks (CookGAN), CookGAN first builds an attention-based ingredients-image association model, which is then used to condition a generative neural network tasked with synthesizing meal images. Furthermore, a cycle-consistent constraint is added to further improve image quality and control appearance. Experiments show our model is able to generate meal images corresponding to the ingredients.
Resampling (detrital) zircon age distributions for accurate multidimensional scaling solutions

Multidimensional scaling (MDS) has become an important tool in detrital zircon geochronology for discriminating and comparing multiple samples, especially where large datasets are concerned. However, the non-parametric test statistics used in MDS (e.g., Kolmogorov–Smirnov [K-S] or Kuiper) to calculate differences between detrital zircon samples are sensitive to differences in grain age uncertainty, for example, when comparing compilations of U-Pb data produced via different analytical techniques. We illustrate the effect of different age uncertainties on comparison metrics and MDS analysis using synthetic populations. Additionally, we introduce two methods that reduce these limitations: an error resampling MDS (ERM) technique and bootstrap resampling with Procrustes rotation (BoPR) technique. These new resampling techniques reduce limitations associated with MDS. We demonstrate the utility of these new tools by comparing detrital zircon samples from the Mesoproterozoic Roper Group of Northern Australia with nearby potential source regions. Mitigating the limitations of MDS, and the underlying statistics by employing resampling techniques makes MDSa more potent means of addressing source to sink relationships in detrital zircon studies.
A robust computational DRM framework for protecting multimedia contents using AES and ECC

The last few years have witnessed a vast evolution in the communication and computational fields. The presence of social media makes data transferring more easier, which raises a flag of unauthorized usage and redistribution of digital contents. This copyright scenario is hardly affecting the publishing rights of authors and publishers. This paper presents a robust framework that protects the copyright property where only the authorized users legally use the data and prevents the illegal using or copying of data. The proposed framework is based on Digital Right Management (DRM) and includes two robust cryptosystems; Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) algorithms. The multimedia contents are encrypted using the AES-256 algorithm, while the shared key is encrypted using the ECC algorithm. The authorized user can utilize the copyrighted contents if the authorization is not expired. The license generation is done by a lightweight data-interchange format JavaScript Object Notation (JSON), and the license is validated using the Elliptic Curve Digital Signature Algorithm. The evaluation of the system has shown that it is fulfilling all the requirements of DRM technology with low cost, availability, platform independently, reliability and scalability since it is implemented on a cloud platform.
TKD: Temporal Knowledge Distillation for Active Perception

Deep neural network-based methods have been proved to achieve outstanding performance on object detection and classification tasks. Despite the significant performance improvement using the deep structures, they still require prohibitive runtime to process images and maintain the highest possible performance for real-time applications. Observing the phenomenon that human visual system (HVS) relies heavily on the temporal dependencies among frames from the visual input to conduct recognition efficiently, we propose a novel framework dubbed as TKD: temporal knowledge distillation. This framework distills the temporal knowledge from a heavy neural network-based model over selected video frames (the perception of the moments) to a light-weight model. To enable the distillation, we put forward two novel procedures: 1) a Long-short Term Memory (LSTM)-based keyframe selection method; and 2) a novel teacher-bounded loss design. To validate our approach, we conduct comprehensive empirical evaluations using different object detection methods over multiple datasets including Youtube-Objects and Hollywood scene dataset. Our results show consistent improvement in accuracy-speed trad- offs for object detection over the frames of the dynamic scene, compared to other modern object recognition methods. It can maintain the desired accuracy with the throughput of around 220 images per second. Implementation: https://github.com/mfarhadi/TKD-Cloud.
Few-Shot Learning of Video Action Recognition Only Based on Video Contents

The success of video action recognition based on Deep Neural Networks (DNNs) is highly dependent on a large number of manually labeled videos. In this paper, we introduce a supervised learning approach to recognize video actions with very few training videos. Specifically, we propose Temporal Attention Vectors (TAVs) which adapt various length videos to preserve the temporal information of the entire video. We evaluate the TAVs on UCF101 and HMDB51. Without training any deep 3D or 2D frame feature extractors on video datasets (only pre-trained on ImageNet), the TAVs only introduce 2.1M parameters but outperforms the state-of-the-art video action recognition benchmarks with very few labeled training videos (e.g. 92% on UCF101 and 59% on HMDB51, with 10 and 8 training videos per class, respectively). Furthermore, our approach can still achieve competitive results on full datasets (97.1% on UCF101 and 77% on HMDB51).
One Person, One Vote: Estimating the Prevalence of Double Voting in U.S. Presidential Elections

Beliefs about the incidence of voter fraud inform how people view the trade-off between electoral integrity and voter accessibility. To better inform such beliefs about the rate of double voting, we develop and apply a method to estimate how many people voted twice in the 2012 presidential election. We estimate that about one in 4,000 voters cast two ballots, although an audit suggests that the true rate may be lower due to small errors in electronic vote records. We corroborate our estimates and extend our analysis using data from a subset of states that share social security numbers, making it easier to quantify who may have voted twice. For this subset of states, we find that one suggested strategy to reduce double voting—removing the registration with an earlier registration date when two share the same name and birthdate—could impede approximately 300 legitimate votes for each double vote prevented.