Reviewerly/exHarmony huggingface dataset:
Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement Learning

Previous studies on image classification have mainly focused on the performance of the networks, not on real-time operation or model compression. We propose a Gaussian Deep Recurrent visual Attention Model (GDRAM) - a reinforcement learning based lightweight deep neural network for large scale image classification that outperforms the conventional CNN (Convolutional Neural Network) which uses the entire image as input. Highly inspired by the biological visual recognition process, our model mimics the stochastic location of the retina with Gaussian distribution. We evaluate the model on Large cluttered MNIST, Large CIFAR-10 and Large CIFAR-100 datasets which are resized to 128 in both width and height. The implementation of Gaussian RAM in PyTorch and its pretrained model are available at : https://github.com/dsshim0125/gaussian-ram
MALL: Resorting to Mobiles in the EFL Classroom

In the language learning environment, whereas the previous generation tools were computer-based systems (CALL for Computer Assisted Language Learning) dependent on University set ups, the mobile technology has revolutionised collaborative learning opportunities with Mobile Assisted Language Learning (MALL) taking its place. Teacher-student and student-student interaction via mobile applications such as WhatsApp Groups (WAGs) can optimise class time and individual problem solving, while at the same time, reversing the conventional pattern of learning where the teacher introduces new knowledge followed by student activities and homework, most of which is beyond teacher or collaborative assistance. Among other things, we are talking of the Flipped Classroom here where an idea is introduced via WhatsApp Group before the class, giving the learners time and opportunity to read up beforehand saving precious class time for interactive activities and learner participation (as opposed to class time being devoted to lecture). This paper examined the possibilities of using this tool to maximise language learning in the EFL class in Saudi Arabia, reviewed previous studies on the efficacy of ICT and other technological tools in foreign language learning and evaluated learning outcomes after using MALL for new vocabulary available in the language learning apps MyWordBook2 and Johny Grammarâ€™s Word Challenge over a period of six weeks with two groups of sixty participants each. The findings as evident in the pre and post tests indicated that learners could be engaged better with the learning process with the use of MALL. They also felt motivated towards the learning process and felt that their learning experience got enhanced with the new tool. Their vocabulary retention and usage also benefitted from the interventional tool.
Dense light field reconstruction algorithm based on dictionary learning

The camera array is an important tool to obtain the light field of target in space. The method of obtaining high angular resolution light field by a large-scaled dense camera array increases the difficulty of sampling and the equipment cost. At the same time, the demand for synchronization and transmission of a large number of data also limits the sampling rate of light field. In order to complete the dense reconstruction of sparse sampling of light field, we analyze the correlation and redundancy of multi-view images in the same scene based on sparse light field data, then establish an effective mathematical model of light field dictionary learning and sparse coding. The trained light field atoms can sparsely express the local spatial-angular consistency of light field, and the four-dimensional (4D) light field patches can be reconstructed from a two-dimensional (2D) local image patch centered around each pixel in the sensor. The global and local constraints of the four-dimensional light field are mapped into the low-dimensional space by the dictionary. These constraints are shown as the sparsity of each vector in the sparse representation domain, the constraints between the positions of non-zero elements and their values. According to the constraints among sparse encoding elements, we establish the sparse encoding recovering model of virtual angular image, and propose the sparse encoding recovering method in the transform domain. The atoms of light field in dictionary are screened and the patches of light field are represented linearly by the sparse representation matrix of the virtual angular image. In the end, the virtual angular images are constructed by image fusion after sparse inverse transform. According to multi-scene dense reconstruction experiments, the effectiveness of the proposed method is verified. The experimental results show that the proposed method can recover the occlusion, shadow and complex illumination in satisfying quality. That is to say, it can be used for dense reconstruction of sparse light field in complex scene. In our study, the dense reconstruction of linear sparse light field is achieved. In the future, the dense reconstruction of nonlinear sparse light field will be studied to promote the practical application of light field imaging.
Comparative Analysis of Different Approaches to Human Activity Recognition Based on Accelerometer Signals

Recently, automatic human activity recognition has drawn much attention. On one hand, this is due to the rapid proliferation and cost degradation of a wide variety of sensing hardware. On the other hand there are urgent growing and pressing demands from many application domains such as: in-home health monitoring especially for the elderly, smart cities, safe driving by monitoring and predicting driver’s behavior, healthcare applications, entertainment, assessment of therapy, performance evaluation in sports, etc. In this paper we focus on activities of daily living (ADL), which are routine activities that people tend to do every day without needing assistance. We have used a public dataset of acceleration data collected with a wrist-worn accelerometer for 14 different ADL activities. Our objective is to perform an extensive comparative study of the predictive power of several paradigms to model and classify ADL activities. To the best of our knowledge, almost all techniques for activity recognition are based on methods from the machine learning literature (particularly, supervised learning). Our comparative study widens the scope of techniques that can be used for automatic analysis of human activities and provides a valuation of the relative effectiveness and efficiency of a potentially myriad pool of techniques. We apply two different paradigms for the analysis and classification of daily living activities: (1) techniques based on supervised machine learning and (2) techniques based on estimating the empirical distribution of the time series data and use metric-theoretic techniques to estimate the dissimilarity between two distributions. We used several evaluation metrics including confusion matrices, overall accuracy, sensitivity and specificity for each activity, and relative computational performance. In each approach we used some of the well-known techniques in our experimentation and analysis. For example, in supervised learning we applied both support vector machines and random forests. One of the main conclusions from our analysis is that the simplest techniques, for example, using empirical discrete distributions as models, have almost the best performance in terms of both accuracy and computational efficiency.
Investigations on the Effects of Low Laser Infrared Marking Energy and Barcode Size on 2D Data Matrix Code Detection on Apples

Highlights Laser energy, type of marking on the product and product color affected the Data Matrix detection High laser marking energy resulted in ablation, browning and carbonization Working direction of the laser beam affected the print growth of the Data Matrix Proposed algorithm successfully decoded the barcodes on Golden Delicious apples Abstract . Product marking in horticulture aims at providing robust and permanent means of marking products and preventing theft, tampering and cheating by customers. Direct part marking has sought to provide solutions to these problems. However, unlike in industry where it has been successful, in horticulture there are still a lot of challenges that prevent successful marking and reading of directly marked barcodes on horticultural products. The laser energy, barcode size, product color and days of storage are important factors that affect the marking, quality and readability of directly marked Data Matrix (DM) on apples. Therefore, the objective of this study was to solve the aforementioned problems with these factors by using Synrad 48-5 CO2 laser (10,600 nm), to mark some apples using low energy levels. Laser energy, the skin of the product and the inertia of the laser beam affected the printing of the DM on the apples. Incomplete marking of the DM at some of the energies used resulted in the DM not being decoded. Generally, there was successful decoding on Golden Delicious compared to Kanzi and Red Jonaprince for 10 days of storage. On the average, the smaller barcode size produced a better detection of the code than the bigger size. The better detection on Golden Delicious can be attributed to the better contrast between the DM and its color. As the days of storage increased, detection decreased for Kanzi and Red Jonaprince. There is a future prospect for directly reading marked apples in real production systems. Keywords: Apple, Apple skin, Barcode size, Data matrix, Laser, Product marking.
People Counter with Area Occupancy Control for Covid-19

Governments have imposed social distancing regulations to counter the coronavirus pandemic. An automated occupancy control system to provide a more cost-effective and efficient way to abide with these safety regulations. With the generalization of the use of digital images, the analysis of movement in video sequences has proved to be an essential tool for various applications such as video surveillance, robotics etc. The advance in video processing algorithms and the fast computational capability, give a possibility to use a video tracking and counting people in real time. Estimating the number of people in real time is useful information for several applications such as security and health management. With the COVID-19 pandemic, the counting of people present in a region of interest is important to control the area occupancy in order to minimize human virus transmission. In this paper we present a finished solution for counting people present in the same area. The system is based on Raspberry Pi and a common camera. The method called BLOB (Binary Large Object) analysis is used. The performance of the system, achieving an average count rate between 95% and 98%.
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

Obtaining large annotated datasets is critical for training successful machine learning models and it is often a bottleneck in practice. Weak supervision offers a promising alternative for producing labeled datasets without ground truth annotations by generating probabilistic labels using multiple noisy heuristics. This process can scale to large datasets and has demonstrated state of the art performance in diverse domains such as healthcare and e-commerce. One practical issue with learning from user-generated heuristics is that their creation requires creativity, foresight, and domain expertise from those who hand-craft them, a process which can be tedious and subjective. We develop the first framework for interactive weak supervision in which a method proposes heuristics and learns from user feedback given on each proposed heuristic. Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels. We conduct user studies, which show that users are able to effectively provide feedback on heuristics and that test set results track the performance of simulated oracles.
Learning Volumetric Shape Super-Resolution for Cranial Implant Design

Cranioplasty is the process of repairing cranial defects or deformations, which may be the result of injuries or necessary medical treatments such as brain tumor surgery. For this procedure, it is necessary to generate a high-quality cranial implant, which needs to be shaped individually for each skull and each defect. This tends to be a very time consuming task and requires also in-depth knowledge of various CAM/CAD programs. In this work, we present a novel automatic three-stage implant generation pipeline. First, skull completion is conducted in low resolution using a trained artificial neural network (ANN). Second, the completed low-resolution skull is sent to a super-resolution network, which up-samples the low-resolution skull to higher resolution while, at the same time, filling the skull surface with geometric details. Finally, by simple subtraction and blob filtering, the desired high-resolution implant is generated.
Homography Estimation of a Moving Planar Scene From Direct Point Correspondence

Homography estimation is an important task in robotics applications, such as landing on moving platforms, docking and refueling, building inspection, and so on. Nonlinear observers depend on an estimate of the group algebra velocity as a feed-forward term to minimize lag in the filter response. When both the camera and the scene are moving, and for perspective image constructs, such as the homography, it is often impossible to directly measure the required group velocity. This article proposes a solution for the case where the motion is periodic, or approximately periodic, with a known period. The approach is based on the internal model principle, where the internal model can be expanded to include a sufficient harmonics of the desired period in order to model complex periodic motions. The novelty of this article lies in formalizing the internal model for observer design to the noncompact Lie group SL(3) and providing a demonstration of the effectiveness of the approach with real-world examples.
NASLib: A Modular and Flexible Neural Architecture Search Library

Neural Architecture Search (NAS) is one of the focal points for the Deep Learning community, but reproducing NAS methods is extremely challenging due to numerous low-level implementation details. To alleviate this problem we introduce NASLib, a NAS library built upon PyTorch. This framework offers high-level abstractions for designing and reusing search spaces, interfaces to benchmarks and evaluation pipelines, enabling the implementation and extension of state-of-the-art NAS methods with a few lines of code. The modularized nature of NASlib allows researchers to easily innovate on individual components (e.g., define a new search space while reusing an optimizer and evaluation pipeline, or propose a new optimizer with existing search spaces). As a result, NASLib has the potential to facilitate NAS research by allowing fast advances and evaluations that are by design free of confounding factors. To demonstrate that NASLib is a sound library, we implement and achieve state-of-the-art results with one-shot NAS optimizers (DARTS and GDAS) over the DARTS search space and the popular NAS-Bench-201 benchmark. Last but not least, we showcase how easily novel approaches are coded in NASLib, by training DARTS on a hierarchical search space.
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification

Recent work has shown that convolutional neural network classifiers overly rely on texture at the expense of shape cues. We make a similar but different distinction between shape and local image cues, on the one hand, and global image statistics, on the other. Our method, called Permuted Adaptive Instance Normalization (pAdaIN), reduces the representation of global statistics in the hidden layers of image classifiers. pAdaIN samples a random permutation $\pi$ that rearranges the samples in a given batch. Adaptive Instance Normalization (AdaIN) is then applied between the activations of each (non-permuted) sample $i$ and the corresponding activations of the sample $\pi(i)$, thus swapping statistics between the samples of the batch. Since the global image statistics are distorted, this swapping procedure causes the network to rely on cues, such as shape or texture. By choosing the random permutation with probability $p$ and the identity permutation otherwise, one can control the effect's strength. With the correct choice of $p$, fixed apriori for all experiments and selected without considering the test data, our method consistently outperforms baselines in multiple settings. In image classification, our method improves on both CIFAR100 and ImageNet using multiple architectures. In the setting of robustness, our method improves on both ImageNet-C and Cifar-100-C for multiple architectures. In the setting of domain adaptation and domain generalization, our method achieves state of the art results on the transfer learning task from GTAV to Cityscapes and on the PACS benchmark.
rocorl: Transferable Reinforcement Learning-Based Robust Control for Cyber-Physical Systems With Limited Data Updates

Autonomous control systems are increasingly using machine learning technologies to process sensor data, making timely and informed decisions about performing control functions based on the data processing results. Among such machine learning technologies, reinforcement learning (RL) with deep neural networks has been recently recognized as one of the feasible solutions, since it enables learning by interaction with environments of control systems. In this paper, we consider RL-based control models and address the problem of temporally outdated observations often incurred in dynamic cyber-physical environments. The problem can hinder broad adoptions of RL methods for autonomous control systems. Specifically, we present an RL-based robust control model, namely rocorl, that exploits a hierarchical learning structure in which a set of low-level policy variants are trained for stale observations and then their learned knowledge can be transferred to a target environment limited in timely data updates. In doing so, we employ an autoencoder-based observation transfer scheme for systematically training a set of transferable control policies and an aggregated model-based learning scheme for data-efficiently training a high-level orchestrator in a hierarchy. Our experiments show that rocorl is robust against various conditions of distributed sensor data updates, compared with several other models including a state-of-the-art POMDP method.
Constructing an interactive Old Norse text with LARA

We describe how the open-source Learning and Reading Assistant (LARA) platform was used to convert a classic Old Norse text, the Völuspá, into an interactive online form. The LARA version includes high-quality recorded audio, translations, notes on key words and phrases, an automatically generated concordance, and links to other online resources. The interactive text was created in two different editions, one with Modern Icelandic translations designed to support Icelandic school students who read the poem as a set text, and one with English translations designed for English readers with basic Old Norse who wish to able to appreciate the poem in the original as a piece of literature. Initial feedback from groups has been positive.
AI-Powered Digital Media Platform and Its Applications

Previously, the main method to create digital media such as animation was by humans. With the development of artificial intelligence technology, the work of making media content from raw materials is gradually replaced by computers. In this paper, we conceive a digital media production and interaction platform supported by artificial intelligence, based on some existing artificial intelligence and virtual reality display technologies. We also discuss the feasibility of some possible application scenarios for this platform.Based on our literature review and feasibility analysis, we conclude that current technology development is capable of supporting the AI-powered digital media platform that we propose in this paper.
A Zero-Knowledge Identification Scheme Based on the Discrete Logarithm Problem and Elliptic Curves

In cryptography, zero-knowledge proof has become frequently used in several types of transactions, for example, the authorization to access a server, the communication between a card and an automatic teller, and the verification of customers identity by banks. The idea of cryptographic identification protocols is that one entity, the claimant, proves its identity to another party, the verifier, by demonstrating the knowledge of a secret, of course without revealing the private key itself to the verifier during the protocol. In this work, a new identification method is presented, which bases its security on the discrete logarithm problem and partially on the Schnorr protocol. The security analysis and the complexity will be examined. An application to elliptic curves will be also given.
Improved machine learning classifiers combined with a stochastic local search for Web services classification

The Web services classification is the process that automatically assigns a category from a list of predefined categories to the Web service described as a WSDL document and where the purpose is to improve the Web services discovery process speed. The aim of this paper is to propose an optimization approach based on the attributes selection of Web services descriptions, to automatically classify Web services found in UDDI registers in predefined categories. The proposed approach combines the meta-heuristic of Stochastic Local Search (SLS) with a supervised learning method. The purpose of this work is to optimize the classification rate of the classifier by choosing the relevant attributes that best represents the Web service. First, we propose a classification method that uses a stochastic local search (SLS) for the attributes selection, then, in a second phase, the approach calls for a supervised classification method to perform the classification task. To this end, we studied six well-known classifiers which are: Support Vector Machine (SVM), Naïve Bayes (NB), k-Nearest Neighbors (k-NN), Bayesian Network (BN), Random Tree (RT), and Random Forests (RF). The six hybrid methods which are: SVM+SLS, NB+SLS, k-NN+SLS, BN+SLS, RT+SLS, and RF+SLS are evaluated on seven real datasets. The results are interesting and demonstrate the benefits of the proposed approaches for Web service classification.
CD$$^2$$: Combined Distances of Contrast Distributions for Image Quality Analysis

The quality of visual input impacts both human and machine perception. Consequently many processing techniques exist that deal with different distortions. Usually they are applied freely and unsupervised. We propose a novel method called CD $$^2$$ to protect against errors that arise during image processing. It is based on distributions of image contrast and custom distance functions which capture the effect of noise, compression, etc. CD $$^2$$ achieves excellent performance on image quality analysis benchmarks and in a separate user test with only a small data and computation overhead.