Reviewerly/exHarmony huggingface dataset:
The Pinkas Dataset

In historical document image processing, datasets account for a significant part of any research, and are crucial for the diversity and abundance of experimental results, which contribute to the development of new algorithms to meet the new challenge. Moreover, they are very important for benchmarking processing algorithms. Numerous publicly available document image datasets of different languages have been emerged. However, current segmentation and recognition performances are nearly saturated with respect to the present publicly available datasets. As such, collecting and labelling historical document images is a burden on historical document image processing researchers. This paper introduces a public historical document image dataset, Pinkas dataset, with new challenges to open room for improvement and identify strengths and weaknesses of available processing algorithms. It is the first dataset in medieval handwritten Hebrew and fully labeled at word, line and page level by an expert of historical Hebrew manuscripts. Pinkas dataset contributes to the diversity of benchmarking standards. In this paper we present meta features of Pinkas dataset and apply recent word spotting algorithms to analyze the room for improvement in terms of performance.
Chance-Constrained Trajectory Optimization for Non-linear Systems with Unknown Stochastic Dynamics

Iterative trajectory optimization techniques for non-linear dynamical systems are among the most powerful and sample-efficient methods of model-based reinforcement learning and approximate optimal control. By leveraging time-variant local linear-quadratic approximations of system dynamics and reward, such methods can find both a target-optimal trajectory and time-variant optimal feedback controllers. However, the local linear-quadratic assumptions are a major source of optimization bias that leads to catastrophic greedy updates, raising the issue of proper regularization. Moreover, the approximate models' disregard for any physical state-action limits of the system causes further aggravation of the problem, as the optimization moves towards unreachable areas of the state-action space. In this paper, we address the issue of constrained systems in the scenario of online-fitted stochastic linear dynamics. We propose modeling state and action physical limits as probabilistic chance constraints linear in both state and action and introduce a new trajectory optimization technique that integrates these probabilistic constraints by optimizing a relaxed quadratic program. Our empirical evaluations show a significant improvement in learning robustness, which enables our approach to perform more effective updates and avoid premature convergence observed in state-of-the-art algorithms.
Animal species classification using deep neural networks with noise labels

In this paper, we developed a robust learning method for animal classification from camera-trap images collected in highly cluttered natural scenes and annotated with noisy labels. We proposed two different network structures with and without clean samples to handle noisy labels. We use k-means clustering to divide the training samples into groups with different characteristics, which are then used to train different networks. These networks with enhanced diversity are then used to jointly predict or correct sample labels using max voting. We evaluate the performance of the proposed method on two public available camera-trap image datasets: Snapshot Serengeti and Panama-Netherlands datasets. Our experimental results demonstrate that our method outperforms the state-of-the-art methods from the literature and achieved improved accuracy on animal species classification from camera-trap images with high levels of label noise.
Volumetric Tree<sup>*</sup>: Adaptive Sparse Graph for Effective Exploration of Homotopy Classes

We present volumetric tree <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , a hybridization of sampling-based and optimization-based motion planning. Volumetric tree <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> constructs an adaptive sparse graph with volumetric vertices, hyper-spheres encoding free configurations, using a sampling-based motion planner for a homotopy exploration. The coarse-grained paths computed on the sparse graph are refined by optimization-based planning during the execution, while exploiting the probabilistic completeness of the sampling- based planning for the initial path generation. We also suggest a dropout technique probabilistically ensuring that the sampling- based planner is capable of identifying all possible homotopies of solution paths. We compare the proposed algorithm against the state-of-the-art planners in both synthetic and practical benchmarks with varying dimensions, and experimentally show the benefit of the proposed algorithm.
Classification of Daily Life Activities for Human Fall Detection: A Systematic Review of the Techniques and Approaches

Human fall detection systems are an important part of assistive technology, since daily living assistance are very often required for many people in today’s aging population. Human fall detection systems play an important role in our daily life, because falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. There has been several researches conducted using variety of sensors to develop systems to accurately classify unintentional human fall from other activities of daily life. The three basic approaches used to develop human fall detection systems include some sort of wearable devices, ambient based devices or non-invasive vision based devices using live cameras. This study reviewed the techniques and approaches employed to device systems to detect unintentional falls and classified them based on the approaches employed and sensors used.
A New Parallel Detection-Recognition Approach for End-to-End Scene Text Extraction

In this work, we present a new conceptually simple and flexible network for accurate scene text extraction, which handle text detection and text recognition concurrently. Apart from solving feature sharing by designing a unified network, we implement end-to-end training of the whole system by a set of new optimization strategies. More importantly, this method highlights its novel parallel detection-recognition structure, which constructs a loose connection between both detection and recognition. This loose connection is embodied in the definition of overall loss and derivative back propagation for the model parameter updating, which automatically balances the contribution of the two branches to the system performance. It is different from the existing end-to-end methods where two subtasks are connected serially and thus yielding heavy dependence of the predecessor text detection task on the follow-up text recognition task and sensitivity of recognition to detection noise. In addition, a simple Mask-Rectifier mechanism is applied to easily adapt our system to incidental text recognition with arbitrary orientation and shape. Experiment results on Incidental Scene Text ICDAR2015 dataset surpass the current state-of-the-art FOTS method, as suggests the effectiveness of the proposed approach.
Extraction of Math Expressions from PDF Documents Based on Unsupervised Modeling of Fonts

This paper proposes a multi-stage architecture to extract math expressions (ME) from PDF documents based on font analysis. The unsupervised algorithm starts from symbol level analysis based on metadata of PDF objects, including font size, font name, and glyph name. Two subsequent stages utilize a group of spatial and semantic heuristics to merge multiple ME symbols into both inline ME and displayed ME. The algorithm is tested on the Marmot dataset (amended with missing cases). For displayed ME, the proposed method achieved 93.6% precision, 99.4% recall, and 96.4% F1-score. For inline ME, the method achieved 92.2% precision, 91.9% recall, and 92.1% F1-score. In addition, the algorithm only takes an average of 1.09s to process a page, which is faster than other existing methods.
Shift-And-Decorrelate Lifting: CAMRA for Lossless Intra Frame CFA Video Compression

In this letter, we improve lossless intra-frame compression of color filter array (CFA) video based on Camera-Aware Multi-Resolution Analysis (CAMRA). CAMRA-based compression leverages the correlation between LH and HL subbands of wavelet-transformed CFA video frames. The key contribution of this letter is an analysis showing that the chrominance components shared by LH and HL subbands of LeGall 5/3 wavelet transform are misaligned, negatively impacting the coding efficiency. To decorrelate the subbands more effectively with minimal dynamic range growth, we developed a new lifting scheme that corrects for this misalignment. We validated our theoretical analysis and the performance of the proposed compression scheme using videos of natural scenes captured in a raw format. The experimental results verify that our proposed transform improves the coding efficiency of CFA intra-frame lossless compression.
Follow The Robot: Modeling Coupled Human-Robot Dyads During Navigation

Many robot applications being explored involve robots leading humans during navigation. Developing effective robots for this task requires a way for robots to understand and model a human's following behavior. In this paper, we present results from a user study of how humans follow a guide robot in the halls of an office building. We then present a data-driven Markovian model of this following behavior, and demonstrate its generalizability across time interval and trajectory length. Finally, we integrate the model into a global planner and run a simulation experiment to investigate the benefits of coupled human-robot planning. Our results suggest that the proposed model effectively predicts how humans follow a robot, and that the coupled planner, while taking longer, leads the human significantly closer to the target position.
Position-based monocular visual servoing of an unknown target using online self-supervised learning

Visual servoing, i.e. control with visual information, is a valuable capability in many robotic applications. In particular, position based visual servoing (PBVS) estimates position information from the observed image to generate visual servo control. However, the estimation of the position of an unknown target using monocular images is still difficult due to the complexity of the image information. For the target estimation problem, we propose to integrate three complementary techniques for monocular visual servoing. First, to estimate the probability of a target's existence, the learning model with spatial features from convolution neural network is proposed. Second, the extended Kalman filter based on epipolar geometry estimates the 3D position of the target; moreover, from this 3D position, the perception model is trained online by self-generated virtual ground-truth. Finally, visual servo control is generated, and the resulting movement helps to construct epipolar geometry. Finally. the experimental validation is performed in a challenging setting involving occlusion and target's shape change.
Hyperparameter Optimisation with Early Termination of Poor Performers

It is typical for a machine learning system to have numerous hyperparameters that affect its learning rate and prediction quality. Finding a good combination of the hyperparameters is, however, a challenging job. This is mainly because evaluation of each combination is extremely expensive computationally; indeed, training a machine learning system on real data with just a single combination of hyperparameters usually takes hours or even days. In this paper, we address this challenge by trying to predict the performance of the machine learning system with a given combination of hyperparameters without completing the expensive learning process. Instead, we terminate the training process at an early stage, collect the model performance data and use it to predict which of the combinations of hyperparameters is most promising. Our preliminary experiments show that such a prediction improves the performance of the commonly used random search approach.
Design of e-Government Security Governance System Using COBIT 2019 : (Trial Implementation in Badan XYZ)

e-Government is needed to actualize clean, effective, transparent and accountable governance as well as quality and reliable public services. The implementation of e-Government is currently constrained because there is no derivative regulation, one of which is the regulation for e-Government Security. To answer this need, this study aims to provide input on performance management and governance systems for e-Government Security with the hope that the control design for e-Government Security can be met. The results of this study are the e-Government Security Governance System taken from 28 core models of COBIT 2019. The 28 core models were taken using CSF and risk. Furthermore, performance management for this governance system consists of capability and maturity levels which is an extension of the evaluation process in the e-Government Evaluation Guidelines issued by the Ministry of PAN & RB. The evaluation of the design carried out by determining the current condition of capability and maturity level in Badan XYZ. The result of the evaluation shows that the design possible to be implemented and needed.
Combination of Canny Edge Detection and Blob Processing Techniques for Shrimp Larvae Counting

This paper presents the proposed method to count the number of the shrimp larvae using computer technology based approach. The high demand of computer aids in counting Shrimp larvae are required in calculating survival rate of Shrimp larvae. Currently, the process of handling shrimp from post to adult larvae stage is still conducted with a tedious and labor-intensive task especially in calculating the number of shrimp larvae. However, the use of the manual counting approach has suffered with some drawbacks such as inaccurate, time consuming, laborious, human intervention, sequential processes and over-estimation. Since that, many researchers are interested into this the fundamental research and development challenges. In this study, we present an image processing technique and blob analysis for counting the number of shrimp larvae. The use of image processing technique helps for object counting specifically on image enhancement and image segmentation. Meanwhile, a Blob analysis algorithm is used as connected component to find and count shrimp larvae in the image through the experiment. The performance of the proposed method is evaluated by comparing the results with the manual counting system using all ninety samples dataset. All ninety samples of shrimp larvae (Penaeus vannamei) were obtained from the Freshwater Hatchery of University Malaysia Terengganu. The experimental results demonstrate that the proposed method is outperformed compared to the manual counting approach through the experiment by reducing the analysis time with minimizing the sequential process task.
Lexicon-Based Sentiment Analysis for Movie Review Tweets

Sentiment analysis is a computational process to identify and classify subjective information such as positive, negative and neutral from the source material. It is able to extract feeling and emotion from a piece of a sentence. This technology has been widely used to extract valuable information from people's views on social media. Hence, this project aims to classify movie reviews into positives, negatives and neutral polarity using lexicon-based method which used R as the language and development framework. Twitter data is used as the source material. Firstly, tweets were extracted using RStudio and Twitter API. Then data pre-processing was done by removing all the stop words and noises. Next was the tokenization process, which separates the words and matches the separated words with positive and negative words vocabulary. Finally, the result of the sentiment analysis is produced into positive, negative and neutral polarities. The results were evaluated using standard evaluation metrics that are the precision, recall, F1 score and accuracy. After all, it is found that the basic lexicon-based method is able to classify sentiment quite well with 52% accuracy. Apparently, the accuracy value achieved in our experiment is not impressive enough, but it is worth corresponding to the simplicity and minimal cost of development for sentiment analysis on Twitter data for movies.
A Behavior Tree Cognitive Assistant System for Emergency Medical Services

This paper presents a cognitive assistant system for emergency medical services (EMS) that can serve as a rescue robot or virtual assistant, helping with improving situational awareness of the first responders through automated collection and analysis of data from the incident scene and providing suggestions to them. The proposed system relies on a Behavior Tree (BT) framework that combines the knowledge of EMS protocol guidelines with speech recognition, natural language processing, and machine learning methods to (i) extract critical information from responders' conversations and verbalized observations, (ii) infer the incident context, and (iii) decide on safe and effective response interventions to perform. We use a data-set of 8302 real EMS call records from an urban, high volume regional ambulance agency in the U.S. to evaluate the responsiveness and cognitive ability of the system and assess the safety of the suggestions provided to the responders. The experimental results show that the developed cognitive assistant achieves an average top-3 accuracy of 89% in selecting the correct EMS protocols and an average F1-score of 71% in suggesting the protocol specific interventions while providing transparency and evidence for the suggestions.
Quantized Adversarial Training: An Iterative Quantized Local Search Approach

Studies find that deep learning models are vulnerable to deliberate adversarial manipulations by attackers. Adversarial training is an effective approach to address this problem. Previous works quantize the input sample space to find appropriate perturbations on the benign samples so as to generate adversarial samples for adversarial training. However, since only the input sample space is quantized with the perturbation space being still continuous, finding the optimal perturbation noise is still a non-convex and computationally expensive problem. Moreover, in this case, the found perturbation noise that will be used to generate an adversarial sample may be strong in the continuous search space, but may become weak after quantization in the input sample space. In this paper, we first develop an Iterative Quantized Local Search (IQLS) algorithm that finds strong perturbation noises by quantizing both the input space and perturbation space. Then, we theoretically analyze and prove the upper bound on the number of iterations needed for the IQLS algorithm, based on which we devise an efficient and effective Quantized Adversarial Training (QAT) scheme. Experiment results on six public datasets show that our proposed scheme outperforms state-of-the-art methods to defend against different adversarial attacks. Particularly, QAT improves the system performance by 14%, 11%, 16% on average on CIFAR-10, SVHN, and CIFAR-100 datasets respectively compared with the existing defense schemes, and reduces the computing time by about 60%.
Fast Deep Learning Neural Networks

Algorithm of network topology construction and training of two-dimensional fast neural networks with additional switched planes is considered. The constructed topology is ideologically close to the topology of convolutional neural networks of deep learning, but it has regular topology with the number of layers established by the factor representation of the dimensions of the image and the output plane of the classes. The learning algorithm has an analytical representation; it is absolutely stable and converges in a finite number of steps. Additional planes extend the information capacity of the tunable transformation to the maximum possible. Control of the planes in the training and processing mode is realized by numerical coordinate codes of the output plane.
Efficient Sketching Algorithm for Sparse Binary Data

Recent advancement of the WWW, IOT, social network, e-commerce, etc. have generated a large volume of data. These datasets are mostly represented by high dimensional and sparse datasets. Many fundamental subroutines of common data analytic tasks such as clustering, classification, ranking, nearest neighbour search, etc. scale poorly with the dimension of the dataset. In this work, we address this problem and propose a sketching (alternatively, dimensionality reduction) algorithm - BinSketch (Binary Data Sketch) - for sparse binary datasets. BinSketch preserves the binary version of the dataset after sketching and maintains estimates for multiple similarity measures such as Jaccard, Cosine, Inner-Product similarities, and Hamming distance, on the same sketch. We present a theoretical analysis of our algorithm and complement it with extensive experimentation on several real-world datasets. We compare the performance of our algorithm with the state-of-the-art algorithms on the task of mean-square-error and ranking. Our proposed algorithm offers a comparable accuracy while suggesting a significant speedup in the dimensionality reduction time, with respect to the other candidate algorithms. Our proposal is simple, easy to implement, and therefore can be adopted in practice.
Thermal Image Degradation Influence on R-CNN Face Detection Performance

Visible light face detection systems have been well researched and in controlled environments can reach excellent accuracy. Variation in illumination conditions results in performance degradation and illumination is the one of the major limitations in visible light face detection systems. Using thermal infrared cameras one can provide a solution to this problem. Recent studies show that deep learning approaches can achieve impressive performance on object detection tasks, and face detection in particular. The goal of this paper is to find an effective way to take advantages from thermal IR spectra and provide a comparative analysis of various image degradation influence on thermal face detection performance in a system based on R-CNN.