Reviewerly/exHarmony huggingface dataset:
Offline Kannada Handwritten Character Recognition Using Convolutional Neural Networks

Handwritten characters are still far from being replaced with the digital form. The occurrence of handwritten text is abundant. With a wide scope, the problem of handwritten letter recognition using computer vision and machine learning techniques has been a well pondered upon topic. The field has undergone phenomenal development, since the emergence of machine learning techniques. This work on a major scale devises to bridge the gap between the state-of-the-art technology, of deep learning, to automate the solution to handwritten character recognition, using convolutional neural networks. Convolutional neural networks have been known to have performed extremely well, on the vintage classification problem in the field of computer vision. Using the advantages of the architecture and leveraging on the preprocessing free deep learning techniques, we present a robust, dynamic and swift method to solve the problem of handwritten character recognition, for Kannada language. We discuss the performance of the network on two different approaches with the dataset. The obtained accuracy measured upto 93.2 % and 78.73 % for the two different types of datasets used in the work. The results obtained have been plotted and presented in the relevant sections.
Optimal Path Planning Based on Hybrid Genetic-Cuckoo Search Algorithm

Three-dimensional path planning is one of the most important factor to decide the efficiency of the space robot moving. early research through mathematical modeling got some mathematical model to solve this problem. However, With different scenarios of the constraints on the robot path gradually increased. Intelligent algorithm which has global optimization increased advantages and ability to deal with multiple constraints has gradually become the mainstream. In this paper, a hybrid intelligent algorithm-Hybrid Genetic-cuckoo search algorithm is proposed, which can take into account the actual size of the robot and the strong global search ability of the genetic algorithm as the premise, and combine with the adaptive cuckoo algorithm to enhance the local search ability of the algorithm in the later stage, so as to improve the practicability of the intelligent algorithm. Simulation results show that the proposed algorithm can avoid obstacles reasonably in multi-constrained three dimensions (3D) environment, and the result is better than the single intelligent optimization algorithm.
A lightweight face detector by integrating the convolutional neural network with the image pyramid

Recently, significant improvements on face detection have been achieved by convolutional neural networks. However, the speed of a CNN-based face detector is hampered by the large computational complexity and the huge number of parameters. It is still challenging to achieve real-time face detection as well as maintain high accuracy. In this work, we introduce a single-stage face detector with an extremely lightweight CNN to achieve fast and accurate detection. Specifically, our method has a structure to integrate the network with the image pyramid for fully utilizing the calculated features. Benefiting from weight sharing, the network size still can keep small. We also analysis the detection capability of anchors with various scales, and reserve the most effective anchors in our model. Besides, to avoid the too difficult training samples which the small network can’t learn, each ground truth face is assigned with a 0-or-1 weight during training. When tested on WIDERFACE and FDDB, it outperforms existing lightweight face detectors on accuracy with the smallest model size. The outstanding detection performance and lightweight model size signify its effectiveness and practicability.
Contextualizing Consensus Protocols in Blockchain: A Short Survey

Consensus protocols are mechanisms that enable a group of entities to reach an agreement on a specific subject. In blockchain systems, consensus protocols are the cornerstone of achieving security and scalability. They not only enable nodes in the network to agree on the valid information that can be added to the ledger while keeping all nodes synchronized, they also establish the sequence of order in which blocks (and consequently transactions) are affixed. In this paper, we provide a comprehensive review of the working principles of the most commonly used consensus protocols in blockchain-based cryptocurrencies. We categorize them based on their permission requirements and perform a thorough comparative evaluation. More specifically, we identify computational difficulty, barrier for entry, platform utilization, and weaknesses in terms of scalability and fairness.
Extended target Poisson multi-Bernoulli mixture trackers based on sets of trajectories

The Poisson multi-Bernoulli mixture (PMBM) is a multi-target distribution for which the prediction and update are closed. By applying the random finite set (RFS) framework to multi-target tracking with sets of trajectories as the variable of interest, the PMBM trackers can efficiently estimate the set of target trajectories. This paper derives two trajectory RFS filters for extended target tracking, called extended target PMBM trackers. Compared to the extended target PMBM filter based on sets on targets, explicit track continuity between time steps is provided in the extended target PMBM trackers.
Poster: Social-Psychological Readiness of Engineering University Students for Academic Mobility to European Countries

The paper focuses on theoretical substantiation and experimental testing of the pedagogical conditions for effective formation and development of the social-psychological readiness of future engineers for academic mobility, and also on developing the stages for gradual formation of the social-psychological readiness of engineering university students for academic mobility in European countries. We have presented the pedagogical conditions for phased social-psychological preparation of engineering university students for academic mobility. These conditions reflect the gradual formation of the motivational, cognitive, personality-active, and communicative components of social-psychological readiness of future engineers for academic mobility. We have developed a new academic discipline “The Psychology of Success” and its didactic support. This discipline is introduced into the curriculum of future engineers at Kazan National Research Technological.
FFConv

Image classification is known to be one of the most challenging problems in the domain of computer vision. Significant research is being done on developing systems and algorithms improving accuracy, performance, area, and power consumption for related problems. Convolutional Neural Networks (CNNs) have shown to give outstanding accuracies for problems such as image classification, object detection, and semantic segmentation. While CNNs are pioneering the development of high accuracy systems, their excessive computational complexity presents a barrier for a more permeated deployment. Although Graphical Processing Units (GPUs), due to their massively parallel architecture, have shown to give performance orders of magnitude better than general purpose processors, the former are limited by their high power consumption and generality. Consequently, Field Programmable Gate Arrays (FPGAs) are being explored to implement CNN architectures, as they also provide massively parallel logic resources but with a relatively lower power consumption than GPUs. In this article, we present FFConv, an efficient FPGA-based fast convolutional layer accelerator for CNNs. We design a pipelined, high-throughput convolution engine based on the Winograd minimal filtering (also called Fast Convolution) algorithms for computing the convolutional layers of three popular CNN architectures: VGG16, Alexnet, and Shufflenet. We implement our accelerator on a Virtex-7 FPGA platform where we exploit the computational parallelization to the maximum while exploring optimizations aimed at improving performance. The resultant design loses only 0.43%, 0.47%, and 0.61% Top-1 classification accuracy for VGG16, Alexnet, and Shufflenet-v1, respectively, while significantly improving throughput, resource, and power efficiency compared to previous state-of-the-art designs.
An Evaluation of Tile Selection Methods for Viewport-Adaptive Streaming of 360-Degree Video

360-degree video has become increasingly popular nowadays. For effective transmission of bandwidth-intensive 360-degree video over networks, viewport-adaptive streaming has been introduced. In this article, we evaluate, for the first time, ten existing methods to understand the effectiveness of tile-based viewport adaptive streaming of 360-degree video. Experimental results show that tile-based methods can improve the average V-PSNR by up to 4.3 dB compared to a non-tiled method under low delay settings. Here, the V-PSNR is computed as the peak signal-to-noise ratio of the adapted viewport compared to the corresponding origin viewport. Also, different methods show different tradeoffs between average viewport quality and viewport quality variations. Especially, the performances of most tile-based methods decrease quickly as the segment duration and/or buffer size increase for the content with no main focus. Even, under long delay settings like HTTP Adaptive Streaming, it is found that the simple non-tiled method appears to be the best one. For the content with a strong viewing focus, it is found that the tile-based methods are less influenced by the segment duration and the buffer size. In addition, a comparison of the performances of the tile selection methods using two popular viewport estimation methods is conducted. It is interesting that there is only little difference found in performances of tile selection methods. The findings of this study are useful for service providers to make decisions on deployment of streaming solutions.
Simultaneous denoising and enhancement for X-ray angiograms by employing spatial-frequency filter

X-ray angiograms, which suffer from low-contrast and noise, need to be improved by both the image enhancement and denoising techniques. However, the goals of these two tasks usually conflict, which makes it difficult to efficiently combine the enhancement and denoising in one scheme. To deal with this problem, we propose a novel spatial-frequency filtering (SFF) scheme for high-contrast X-ray cardiovascular angiogram image restoration. The proposed scheme includes the following four parts: (1) low-quality X-ray angiogram images are decomposed into the base layer with strong structures and redundant parts by employing a relative total variation (RTV) method. (2) the base layer is enhanced by a proposed improved histogram equalization (IHE) method. (3) The detail layer with weak structures is extracted by using a short-time Fourier transform (STFT) and is further enhanced by a proposed adaptive correction parameter. (4) The improved image is the combination of results obtained by the base layer and the detail layer. Experimental results in both qualification and quantitation validate that the proposed method performs better than the state-of-the-arts on contrast enhancement, structure preservation, and noise reduction.
Autonomous recommender system architecture for virtual learning environments

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.
Tailoring Recommendations to Groups of Viewers on Smart TV: A Real-Time Profile Generation Approach

The recommender systems predict and calculate user preferences for recommendations. However, such predictions and calculations are neither accurate nor viable in the context of smart TV due to the reasons that it is a lean-back, non-personalized device, and normally enjoyed in groups. Hence, group recommendations have utmost importance, specifically from the perspectives of watching smart TV. The existing group recommendation techniques predict the individual's preferences and then create a virtual group profile for recommendations. However, identifying and satisfying every group member is challenging. Numerous techniques have been proposed, such as face detection and recognition systems, but these systems lead to security and privacy issues. This paper proposes a smart TV-based recommender system that aims to identify the individuals and group members from their “age,” “gender,” and “number” information to overcome the biases that occur due to predictions and estimations of user's preferences. The study proposes a novel formula and age-gender matrix for generating anonymous, consolidated, and secure profiles, including group profiles on a smart TV. This study further proposes a novel method for finding a dominant character in a group by utilizing the user's ratings. Results show that the group decision has a significant impact on supplying social metadata, such as ratings, comments, etc., which in turn improve recommendation results. For materializing the proposed work, smart TV's processing, storage, and camera are utilized. The prototypical implementation has been tested and analyzed with improved recommendation results and viewer(s) satisfaction.
Blockchain-based semi-autonomous ransomware

Blockchain’s benefits and advantages have been extensively studied in literature, but far fewer works can be found on the dishonest uses of them. In this paper, we present the first blockchain-based ransomware schemes, which use smart contracts and simple cryptographic primitives to provide a limited degree of automation and fair exchange. Specifically, the use of smart contracts would enable new capabilities for ransomware, such as the possibility of paying for individual files or the refund of the ransom to the victim if the decryption keys are not received within a specified period of time. To demonstrate their feasibility, both technically and economically, these proposals have been implemented in the Ethereum Ropsten test network. The results show that running a full ransomware campaign similar to WannaCry, with more than 300,000 affected users, would have an additional cost of only 3 cents of a dollar per victim. Finally, we show that there are no feasible countermeasures if these schemes are implemented in public blockchains. Therefore, we firmly believe that it is increasingly urgent to recognize and study this matter, in order to create new policies and technical countermeasures.
Video-based Real-time Intrusion Detection System using Deep-Learning for Smart City Applications

There is a huge demand of video surveillance based intelligent security systems which can automatically detect the unauthorized entry or mal-intentional intrusion to the unattended sensitive areas and notify to the concerned authorities in real-time. A novel video-based Intrusion Detection System (IDS) using deep learning is proposed. Here, You Only Look Once (YOLO) algorithm is used for object detection and intrusion is decided using our proposed algorithm based on the shifted center of mass of the detected object. Further, Simple Online and Real-time Tracking (SORT) algorithm is used for the tracking of the intruder in real-time. The developed system is also implemented and tested for live video stream using NVIDIA Jetson TX2 development platform with an accuracy of 97% and average fps of 30. Here, the proposed IDS is a generic one where the user can select the region of interest (the area to be intrusion free) of any size and shape from the reference (starting) frame and potential intruders such as a person, vehicle, etc. from the list of trained object classes. Hence, it can have a wide range of smart city applications such as person intrusion free zone, no vehicle entry zone, no parking zone, smart home security, etc.
PEA265: Perceptual Assessment of Video Compression Artifacts

The most widely used video encoders share a common hybrid coding framework that includes block-based motion estimation/compensation and block-based transform coding. Despite their high coding efficiency, the encoded videos often exhibit visually annoying artifacts, denoted as Perceivable Encoding Artifacts (PEAs), which significantly degrade the visual Quality-of-Experience (QoE) of end users. To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs. In this work, we make the first attempt to build a large-scale subject-labeled database composed of H.265/HEVC compressed videos containing various PEAs. The database, namely the PEA265, includes 4 types of spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types of temporal PEAs (i.e. flickering and floating). Each containing at least 60,000 image or video patches with positive and negative labels. Based on the PEA265 database, we develop and optimize Convolutional Neural Networks (CNNs) to objectively recognize different types of PEAs. Experiments show that our architecture is capable of identifying the 6 types of PEAs with an accuracy over 86%. To further demonstrate its application, we explore the relationship between collected PEA intensities and subjective quality scores of compressed videos. A quality metric is consequently proposed with superior performance in terms of correlation to Mean Opinion Score (MOS) values. We believe that the PEA265 database and our findings will benefit the future development of video quality assessment methods and perceptually motivated video encoders.
Performance evaluation of text-mining models with Hindi stopwords lists

Nowadays, several news portals, government websites, and social media sites are generating a massive amount of digitalized Hindi textual information. Stopword removal is a significant factor in text mining tasks that helps the miner to enhance the performance of a system. This paper attempts to construct the corpus specific stopwords lists for Hindi text documents using statistical and knowledge-based methods. In order to prepare the stopwords list, the proposed method considers the ranking of the words given by different methods followed by normalization of the outcomes of these methods using the social choice theory based vote ranking method. Further, we propose an evaluation method to evaluate the prepared stopword lists and investigate their behavior using text mining models. We also compare our prepared stopword lists with the baselines and conclude that the technique which fetches the best features does not necessarily identify the candidate stop words. To the best of our knowledge, the proposed approach guarantees the removal of candidate stop words and has the least information dissipation.
Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning

We propose a new online multi-object visual tracker based on a Gaussian mixture Probability Hypothesis Density (GM-PHD) filter in combination with a similarity Convolutional Neural Network (CNN). The GM-PHD filter estimates the states and cardinality of an unknown and time varying number of targets in the scene handling target birth, death, clutter (false alarms) and missing detections in a unified framework, and has a linear complexity with the number of targets. However, it lacks the identity of targets. We combine spatio-temporal and visual similarities obtained from object bounding boxes and deep CNN appearance features, respectively, to alleviate its shortcoming of labelling targets across frames. We apply this developed method for tracking multiple targets in video sequences acquired under varying environmental conditions and targets density using a tracking-by-detection approach. Finally, we carry out extensive experiments on Multiple Object Tracking 2016 (MOTI6) and 2017 (MOTI7) benchmark datasets and find out that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy and precision.
A Gaussian mixture based hidden Markov model for motion recognition with 3D vision device

Recognizing human motion is an import research area in computer vision. Hidden Markov Model (HMM) is widely used in object recognition and tracking due to its richness in mathematical theory. However, the poses of artistic performance are more complex compared to conventional human motion. To address this issue, in this paper, we present a Gaussian Mixture based Hidden Markov Model (GMM-HMM) for the artistic motion recognition of Peking opera. In order to filter the abnormal data and repair the missing data, a local weighted linear regression method is designed for improving the motion data captured by the 3D vision device OptiTrack. We construct a GMM-HMM model as the recognition method, which can get the exact number of hidden states based on the key frames to achieve high accuracy of recognition. In addition, we apply multiple multi-dimensional gaussian distribution functions to train the motion data, avoiding the large computational load and the discretization error. The results show that our method can identify and enable the interactions with some important motion movements in Peking opera performances.
Comprehensive Instructional Video Analysis: The COIN Dataset and Performance Evaluation

Thanks to the substantial and explosively inscreased instructional videos on the Internet, novices are able to acquire knowledge for completing various tasks. Over the past decade, growing efforts have been devoted to investigating the problem on instructional video analysis. However, the most existing datasets in this area have limitations in diversity and scale, which makes them far from many real-world applications where more diverse activities occur. To address this, we present a large-scale dataset named as "COIN" for COmprehensive INstructional video analysis. Organized with a hierarchical structure, the COIN dataset contains 11,827 videos of 180 tasks in 12 domains (e.g., vehicles, gadgets, etc.) related to our daily life. With a new developed toolbox, all the videos are annotated efficiently with a series of step labels and the corresponding temporal boundaries. In order to provide a benchmark for instructional video analysis, we evaluate plenty of approaches on the COIN dataset under five different settings. Furthermore, we exploit two important characteristics (i.e., task-consistency and ordering-dependency) for localizing important steps in instructional videos. Accordingly, we propose two simple yet effective methods, which can be easily plugged into conventional proposal-based action detection models. We believe the introduction of the COIN dataset will promote the future in-depth research on instructional video analysis for the community. Our dataset, annotation toolbox and source code are available at http://coin-dataset.github.io.
From Generalized Linear Models to Neural Networks, and Back

We present how to enhance classical generalized linear models by neural network features. On the way there, we highlight the traps and pitfalls that need to be avoided to get good statistical models. This includes the non-uniqueness of sufficiently good regression models, the balance property, and representation learning, which brings us back to the concept of the good old generalized linear models. This paper has been integrated into SSRN Manuscript 3822407.