Reviewerly/exHarmony huggingface dataset:
Optimal Developmental Learning for Multisensory and Multi-Teaching Modalities

Most intelligent systems require the use of multiple sensors and motors, but training these systems becomes costly as the number and complexity of sensors and motors increase. Reinforcement learning promises to make the training process easier on human trainers but can take significantly more time. While many learning systems are either directly supervised or supervised through reinforcement, few can learn through multiple teaching modalities. Furthermore, many neural network systems do not allow for frame-incremental learning. This work introduces a real-time, frame-incremental framework that uses multiple sensors and multiple teaching modes (motor-supervision, reinforcement, or self-practice). The Developmental Network (DN) used in this work is optimal in the sense of maximum likelihood throughout a “lifetime”, under three conditions: (1) an incremental learning framework, (2) a training experience and (3) a limited amount of computational resources. Because the DN is free from the local minima problem, all DNs are performance equivalent and we record “lifetime” errors of a single trained network, removing a need for post-selection–post-selecting the luckiest network from many randomly initialized and trained neural networks according to their performances on test sets.
Automatic Measurement of Fetal Head Biometry from Ultrasound Images Using Deep Neural Networks

Gestational age (GA) is an illustrative indicator of fetal growth. The GA is estimated through biometric parameters, including the head circumference (HC) and Biparietal diameter (BPD). This paper proposes a deep learning-based method for automatic measurement of the BPD and HC based on the segmentation of the fetal head from ultrasound images. We utilized an efficient convolutional network architecture, named multi-feature pyramid Unet (MFP-Unet) previously proposed for left ventricle segmentation from echocardiography images. The proposed network tackles the main drawback of U-net, which ignores the contribution of all semantic strengths in the segmentation procedure. To train and evaluate MFP-Unet, a set of ultrasound images was used from the HC18 challenge dataset (fetal head circumference challenge). To evaluate the quantitative accuracy of anatomy segmentation, several metrics, including Dice similarity coefficient (DSC) and Hausdorff distance (HD) were employed. MFP-Unet achieved a DSC of 0.95 and HD of 4.5, which indicates the performance of the MFP-Unet algorithm in segmenting fetal ultrasound images for automatic measurement of biparietal diameter (BPD) and head circumference (HC).
Research on Kalman Filter for One-dimensional Discrete Data

Abstract Kalman filter processes the input and observation signals with noise on the basis of linear state space representation to obtain the system state or real signal. In the one-dimensional model, due to the lack of multi-dimensional description of the target, the prior estimate of the state is often the measured value of the previous moment. When the target state changes, the divergence phenomenon will occur. Aiming at the problem that the one-dimensional traditional Kalman filter lacks the target observation dimension, which leads to the divergence or imprecision of the filter, this paper focuses on improving the estimation method of the target state, and proposes a real-time prediction model based on the cascade structure. This model can improve the response of Kalman filter to the change of target state and dynamically adjust the Kalman iterative domain to improve the measurement accuracy. The digital signal filtering simulation is carried out and the performance of the filter is verified based on LabVIEW. Experimental results show that the algorithm can maintain the accuracy and real-time performance of filtering when only one dimension observation results are obtained.
Designing a random S-box with the mixed spatiotemporal chaos

Abstract A random S-box is designed by using the mixed linear-nonlinear spatiotemporal chaotic system. Firstly, a chaotic sequence produced by the mixed chaotic system is used to generate the elements of the S-box randomly. Then the elements of the S-box are permuted by using the mixed chaotic system. Owing to the high dimensional feature of the mixed chaotic system, the generated S-box has a strong randomness property. Finally, we analyse six properties of the designed S-box, such as bijective property, nonlinearity, strict avalanche criterion, outputs bit independence criterion, input/output XOR distribution and linear approximation probability. The experimental results indicate that the designed S-box by using the mixed chaotic system has better performance.
Swarm Intelligence-Based Feature Selection: An Improved Binary Grey Wolf Optimization Method

Feature selection can effectively reduce the number of features and improve the accuracy of classification, so that reducing the computational burden and improving the performance of machine learning. In this paper, we propose an improved binary grey wolf optimization (IBGWO) algorithm for a wrapper-based feature selection method. Aiming at the shortcomings of grey wolf optimization (GWO) for feature selection, we first propose a enhanced opposition-based learning (E-OBL) initialization method to enhance the performance of initial solutions. Second, a local search strategy is introduced to balance the exploitation and exploration abilities of the IBGWO. Finally, a novel update mechanism is proposed for improving the population diversity and exploration capability of the algorithm. Simulations are conducted by using 16 well-known datasets, and the results show that the proposed method outperforms other benchmark algorithms on 12 datasets, and the introduced improved factors are suitable and effective.
Multi-Agent Path Finding using Dynamic Distributed Deep Learning Model

Multi-Agent Pathfinding (MAPF) is the task of finding paths for more than one agent in a common environment such that each agent reaches its goal and does not collide. Multi-Agent Path Finding is a problem that is related to various applications ranging from robot movements, game simulations, self-driving vehicles, etc. Most of the existing solutions to these problems are providing paths to each agent which they shall follow. These paths are optimal but are calculated once for a certain fixed and known scenario. There might be collisions and time-based overlapping paths if another agent or an obstacle is dynamically added to the grid. Thus, there is a need to have a system that deals with such situations while providing the most possible optimal path. Hence, we propose a solution to this problem by using a Deep Learning Model trained on a dataset made using an A* pathfinding algorithm that can take decisions at run time for each agent while making sure it does not collide with any obstacle or any other agent and handles all sort of dynamic variations in the environment at any point of time while not relying on precomputation.
Self-Dual 2-Quasi Abelian Codes

A class of self-dual quasi-abelian codes of index 2 over any finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> is introduced. By counting the number of such codes and the number of the codes in this class whose relative minimum weights are small, such codes are proved to be asymptotically good provided −1 is a square in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> . Moreover, a class of self-orthogonal quasi-abelian codes of index 2 is defined; and such codes always exist. In a way similar to that for self-dual quasi-abelian codes of index 2, it is proved that these self-orthogonal quasi-abelian codes of index 2 are asymptotically good.
Comprehensive Online Network Pruning Via Learnable Scaling Factors

One of the major challenges in deploying deep neural network architectures is their size which has an adverse effect on their inference time and memory requirements. Deep CNNs can either be pruned width-wise by removing filters or depth-wise by removing layers and blocks. Width wise pruning (filter pruning) is commonly performed via learnable gates or switches and sparsity regularizers whereas pruning of layers has so far been performed arbitrarily by manually designing a smaller network usually referred to as a student network. We propose a comprehensive pruning strategy that can perform both width-wise as well as depth-wise pruning. This is achieved by introducing gates at different granularities (neuron, filter, layer, block) which are then controlled via an objective function that simultaneously performs pruning at different granularity during each forward pass. Our approach is applicable to wide-variety of architectures without any constraints on spatial dimensions or connection type (sequential, residual, parallel or inception). Our method has resulted in a compression ratio of 70% to 90% without noticeable loss in accuracy when evaluated on benchmark datasets.
The “Delegate of Data Protection”: Strategic Planning in information security Case Ecuador

Abstract Information security over time has been taking important scenarios in the world, along with regulations and laws have had to adapt to this evolution in such a way that protection, compliance, and sanctions are guaranteed. A sample of this is the new European Data Protection Regulation in force since May 25, 2018, where it administers the function of “Data Protection Delegate” (DPD) or Data Protection Officer (DOP). Since this position is new to the rest of the world, concerns arise about the functions that it will have to fulfill in Latin American countries, where most of them already have a Data Protection Law but not with the position of Delegate as such. This study aims to justify the need for this new position, through a SOWA analysis of PESTEL, compilation of reports, interested stakeholders of the project, in addition to publicizing the guidelines, roles, fields of action, risk prevention and audit in terms of compliance.
RRT-SMP: Socially-encoded Motion Primitives for Sampling-based Path Planning

In this paper we propose a new method of encoding social norms and rules into sampling-based motion planners. Inspired from the social force model (SFM), we modify and use it as a social intention model (SIM) to reshape the motion primitives (MP) of the rapidly-exploring random tree (RRT) motion planner for the socially aware robot navigation. We also introduce a new benchmark for evaluating social planning performance, so called as the social effort index (SEI). The experimental results show that the socially-guided motion primitives-based RRT increases safe and social interactions between the robot and human agents about 50% compared to the typical RRT-embedded MP (RRT-MP) in human populated environments.
Introduction to Data Science

In this chapter, we are going to get an introduction about what is data science and how it is needed in our day-to-day life in concern of real-life uses, engineering purposes and medical fields.
Multimodal Encoders for Food-Oriented Cross-Modal Retrieval

The task of retrieving across different modalities plays a critical role in food-oriented applications. Modality alignment remains a challenging component in the whole process, in which a common embedding feature space between two modalities can be learned for effective comparison and retrieval. Recent studies mainly utilize adversarial loss or reconstruction loss to align different modalities. However, insufficient features may be extracted from different modalities, resulting in low quality of alignments. Unlike these methods, this paper proposes a method combining multimodal encoders with adversarial learning to learn improved and efficient cross-modal embeddings for retrieval purposes. The core of our proposed approach is the directional pairwise cross-modal attention that latently adapts representations from one modality to another. Although the model is not particularly complex, experimental results on the benchmark Recipe1M dataset show that our proposed method is superior to current state-of-the-art methods.
Video Summarization for Multiple Sports Using Deep Learning

This paper proposes a computationally inexpensive method for automatic key event extraction and subsequent summarization of sports videos using scoreboard detection. A database consisting of 1300 images was used to train a supervised learning-based object detection algorithm, You Only Look Once (YOLO). Then, for each frame of the video, once the scoreboard was detected using YOLO, the scoreboard was cropped out of the image. After this, image processing techniques were applied on the cropped scoreboard to reduce noise and false positives. Following that, the processed image was passed through an optical character recognizer (OCR) to get the score. A rule-based algorithm was run on the output of the OCR to generate the timestamps of key events based on the game. Finally, clips of length 30 s around every detected event (15 s before and after the event) were extracted from the video file and stitched together to form the highlights video. The proposed method is best suited for people who want to analyze the games and want precise timestamps of the occurrence of important events. The performance of the proposed design was tested on videos of Bundesliga, English Premier League, ICC WC 2019, IPL 2019, and Pro Kabaddi League. An average F1 score of 0.979 was achieved during the simulations. The model was also tested on different video formats, and these were .mp4, .avi, .mov, and .mkv. The algorithm was trained on five different classes of three separate games (soccer, cricket, and kabaddi). The design was implemented using Python 3.7.
A Lesion Classification Method Using Deep Learning Based on NICE Classification for Computer-Aided Diagnosis System in Colorectal NBI Endoscopy

Currently, video image diagnosis by NBI (Narrow Band Imaging) is commonly used for colonoscopy. The purpose of this paper is to develop a CAD (computer-aided diagnosis) system that can reduce the variability of diagnosis due to differences in clinical doctor's experience by presenting quantitative inference results to the clinical doctor. As a part of this system development, we create a classifier that classifies lesion images into NICE (NBI International Colorectal Endoscopic) classification using deep learning. We can achieve to develop a CNN (Convolutional Neural Network) based classifier in which five performance indicators (Accuracy, Recall, Specificity, PPV, and NPV) are satisfied more than 90 % quality.
Depth Map Super-Resolution By Multi-Direction Dictionary And Joint Regularization

Depth maps acquired by 3D cameras usually suffer from low resolution and insufficient quality, which makes it difficult to be directly used in visual depth perception and 3D reconstruction. To handle this problem, we propose a novel multi-direction dictionary and joint regularization model for high quality depth recovery. To enhance the sparsity, image is divided into classified patches according to the same geometrical direction and a compact dictionary is trained within each class. Then for a patch to be coded, the most relevant dictionary can be selected according to its geometrical direction. We further introduce two regularization terms into the reconstruction model. One is the anisotropic total variation (TV) defined by the local gradients of color/depth pair. The other is the nonlocal similarity to provide nonlocal constraint to the local structure. Experimental results demonstrate that our method outperforms other state-of-the-art methods in terms of both subjective quality and objective quality.
Real-time Pedestrian Detection Algorithm Based on Improved YOLOv3

Abstract As a research hotspot in the field of current computer vision, pedestrian detection is widely applied to many fields, such as video surveillance and autonomous driving. However, the accuracy of pedestrian detection under video surveillance is poor, and the miss rate of small target pedestrians is high. In this paper, an improves the YOLOv3 algorithm and a YOLOv3-Multi pedestrian detection model had been proposed. First, referring to the residual structure of DarkNet, the shallow features and deep features had been up-sampled and connected to obtain a multi-scale detection layer. Then, according to different special detection categories, the spatial pyramid pool (SPP) is introduced to strengthen the detection of small targets. The experimental results show that our method improves the average accuracy by 2.54%, 6.43% and 8.99%compared with YOLOv3, SSD and YOLOv2 on the VOC dataset.
A Detailed Analysis of Adaptive Kernel Density-Based Outlier Detection in Volatile Time Series

Preprocessing of historical observations is an essential step before any data-based analysis. Most power system studies using statistical methods use historical data of multiple variables collected from different places and time instants. A preprocessing method that detects outliers in a multivariate dataset in a single step is generally preferred. This paper presents a comprehensive analysis of a popular density-based preprocessing approach, i.e., the adaptive kernel density-based method applied to multivariate datasets. An attempt is made to explain the aforesaid method’s algorithm in detail and the significance of its crucial parameters. The applicability of the approach is elucidated by applying it to various kinds of datasets and evaluating the performance using different measures. Furthermore, the analysis demonstrates the impact of crucial parameters on the method’s performance. This paper will help a novice researcher apply and evaluate the aforesaid method according to the dataset type.
A cloud-scale per-flow backpressure system via FPGA-based heavy hitter detection

Virtual private clouds provide sharing resources to a massive number of tenants for economics of scale. In such clouds, off-the-shelf x86 boxes are widely deployed as network intermediate nodes. However, due to rapid growth of cloud traffic and significant slowdown of CPU improvement in recent years, although horizontal scaling is still leveraged, CPU overload and packet losses caused by heavy hitters are occasionally observed in production environment, which seriously damage tenant's SLAs. To address this, we propose a cloud-scale per-flow backpressure system designed in Alibaba Cloud. The basic idea is to (1) trigger the heavy-hitter flow acquisition at the intermediate node in an on-demand manner only when the CPU utilization exceeds a predefined threshold and (2) backpressure the identified heavy-hitter flow to the traffic source via rate limiting at sender's NIC or hypervisor. To handle the extremely large traffic rate of cloud traffic, we leverage a high-speed FPGA for heavy hitter detection acceleration. To accommodate highly concurrent flows in the cloud, we design a hierarchical memory system for accurate heavy hitter counting during a large time window. Under the per-flow backpressure mechanism, the rate of the heavy-hitter flow is accurately throttled while the rate of mice flows is completely unaffected during the backpressure.
Extraction and Recognition of Handwritten Gujarati Characters and Numerals from Images Using Deep Learning

Automated recognition of Indian scripts from a document or image is a challenging problem. Gujarati is an Indian script meant for expressing the language Gujarati. Gujarati is the official language of the west Indian state of Gujarat and is spoken and written by nearly 60.3 million people. Gujarati script can be considered as more complicated for machine recognition owing to the presence of complex consonants, vowel modifiers compound character called ‘Jodakshar’ and absence of ‘Shirorekha’. The problem becomes all the more challenging when the script to be recognized is handwritten rather than printed. Two basic approaches exist in the literature on the topic: Offline as well as online handwritten Gujarati script recognition. The online method takes the input in real time, whereas offline systems work on scanned images making the recognition task more lengthy and complicated. The main aim of this paper is twofold. First, a dataset of images of handwritten Gujarati characters and numerals is made available, and second, offline Gujarati character recognition from images of handwritten Gujarati text. Hence, the primary objective of the paper is to present an approach that could extract Gujarati characters from an image and then recognize them with deep learning. The accuracy of the system obtained in most of the cases is more than 98%. The best accuracy obtained when tested with unseen character images is 82.15%.
Predicting Next Local Appearance for Video Anomaly Detection

We present a local anomaly detection method in videos. As opposed to most existing methods that are computationally expensive and are not very generalizable across different video scenes, we propose an adversarial framework that learns the temporal local appearance variations by predicting the appearance of a normally behaving object in the next frame of a scene by only relying on its current and past appearances. In the presence of an abnormally behaving object, the reconstruction error between the real and the predicted next appearance of that object indicates the likelihood of an anomaly. Our method is competitive with the existing state-of-the-art while being significantly faster for both training and inference and being better at generalizing to unseen video scenes.
GI-AEE: GAN Inversion Based Attentive Expression Embedding Network For Facial Expression Editing

Facial expression editing aims to modify facial expression by specific conditions. Existing methods adopt an encoder-decoder architecture under the guidance of expression condition to process the desired expression. However, these methods always tend to produce artifacts and blurs in expression-intensive regions due to simultaneously modifying images in expression changed regions and ensuring the consistency of other attributes with the source image. To address these issues, we propose a GAN inversion based Attentive Expression Embedding Network (GI-AEE) for facial expression editing, which decouples this task utilizing GAN inversion to alleviate the strong effect of the source image on the target image and produces high-quality expression editing results. Furthermore, different from existing methods that directly embed the expression condition into the network, we propose an Attentive Expression Embedding module to embed corresponding expression vectors into different facial regions, producing more plausible results. Qualitative and quantitative experiments demonstrate our method outperforms the state-of-the-art expression editing methods.