Reviewerly/exHarmony huggingface dataset:
Image Segmentation in Constrained IoT Servers

Image segmentation forms an important concept in the computer vision technology. Image segmentation breaks the image into boundaries that differentiate meaningful components. For computer vision to realize its full potential it is essential that the image segmentation algorithms give accurate results in a fast and efficient way. In hierarchical architecture based IoT networks set up to “see” the world, methods of computer vision need more analysis. The need for low cost setup for IoT networks in terms of memory and their computational capabilities demands research for developing methods that are resource sensitive and can be successfully integrated into such networks of low end IoT servers. Addressing this need, a refined graph cut segmentation technique for low to medium resolution images and for constrained devices is presented in the paper. Implementation and analysis of the refined graph cut segmenter for linux based IoT servers is discussed. A comparison with the contemporary segmentation methods under similar constraints is also presented.
Cigarette Detection Algorithm Based on Improved Faster R-CNN

In view of the problems of high missed detection rate and inaccurate position of small targets in the cigarette detection algorithm based on Faster Regions Convolutional Neural Networks(Faster R-CNN), a cigarette detection algorithm based on Feature pyramid networks (FPN) and Faster R-CNN is proposed. The feature map with high-level semantic information and low-resolution of the last layer is adopted by the Faster R-CNN as the input of Region Proposal Network (RPN), resulting in low recognition rate of small targets. The improved Faster R-CNN framework combined with FPN algorithm continuously fuses the high-level feature maps with the feature maps of the front layer through up-sampling, and constructs the feature pyramid model of different scales as the input of RPN network, which improves the detection effect of cigarette effectively.
Weakly Supervised Attention Inference Generative Adversarial Network for Text-to-Image

Text-to-Image is a significant problem in computer vision. Recently, there are some problems in the quality and semantic consistency of the generated image. In this paper we propose an approach for Text-to-Image synthesis by focusing on the perception. We use text embeddings to generate semantic feature maps before target images synthesis instead of generating target images directly. The ground truth semantic layouts are calculated by interpretable classification network, and we will learn to generate semantic layouts before inferring target images from them. We have trained our approach on the CUB2011 dataset and verified the quality of its generation and the interpretability of the network in simple background and small scale feature generation.
An Ensemble Technique for Better Decisions Based on Data Streams and its Application to Data Privacy

In this work, we address the problem of making decisions based on data streams, i.e., choosing an action when a new value is recorded. For instance, actions can be trading decisions in financial markets, choices of controllers in dynamic systems or perturbations of the data stream itself. To start with, we propose a language that allows individuals to formulate requirements on the action space. We use prediction techniques to identify the best possible action. However, for many scenarios there is not just one technique that predicts the future precisely, and different techniques behave quite differently. Thus, since there is no technique that dominates all the others, our conclusion is to take multiple predictions generated by different techniques into account. While ensemble techniques aggregating the predictions seem promising, existing techniques have issues, such as unnecessary information losses or the need for a predefined quality measure. Thus, we propose a new ensemble approach that weights predictions techniques according to requirements and solves an optimization problem that derives decisions directly from weighted predictions. We apply our solution to data privacy on data streams. For this setting, the benefits provided by prediction techniques have not been studied yet. In three case studies, we show that our solution consistently achieves better decision-making quality than approaches from related work.
A Genetic Algorithm Based Approach for Word Sense Disambiguation Using Fuzzy WordNet Graphs

Due to the ever-evolving nature of human languages, the ambiguity in it needs to be dealt with by the researchers. Word sense disambiguation (WSD) is a classical problem of natural language processing which refers to identifying the most appropriate sense of a given word in the concerned context. WordNet graph based approaches are used by several state-of-art methods for performing WSD. This paper highlights a novel genetic algorithm based approach for performing WSD using fuzzy WordNet graph based approach. The fitness function is calculated using the fuzzy global measures of graph connectivity. For proposing this fitness function, a comparative study is performed for the global measures edge density, entropy and compactness. Also, an analytical insight is provided by presenting a visualization of the control terms for word sense disambiguation in the research papers from 2013 to 2018 present in Web of Science.
Image Security Using Triple Key Chaotic Encryption and SPIHT Compression Technique in Steganography

In recent trend biometric authentication is been used extensively to authenticate the person remotely. So, it is necessary to secure the sensitive secret data by encryption, before transmitting over medium. This paper proposed a stout, highly secure authentication technique based on semantic segmentation, Triple key chaotic encryption and data hiding. If a user wants to be authenticated remotely, then firstly image is segmented to extract head and body parts through semantic segmentation. Second, take A’s fingerprint and encrypt it using Triple key chaotic encryption technique. Furthermore, qualified significant wavelet tree is used to place the encrypted signal in the most significant wavelet coefficient of image. Finally, Inverse Discrete Wavelet Transform is applied to obtain stego image. The output image is further compressed before transmission over wireless medium using Set Partition in Hierarchical Tree compression technique. This technique has good resistance against lossless transmission and compression. The performance measures are Peak signal to noise ratio, correlation index, information entropy and compression ratio.
VR archaeological museum with applying at student education processes

The article aimed to provide a sort of new education process including virtual reality based application. At present, in accordance with the established ways of archaeological research, archaeologists are forced to transfer the found samples for long-term storage. In such notation, there is a challenging issue to create a virtual museum with deepening experience user interaction. The modern approaches of the virtual reality were implemented by applying technologies such as the Unreal Engine (UE) and Leap Motion (LM). In the paper, we give the scheme of the implemented development workflow. The ability of interaction with objects using the interface and hand gestures on LM on UE was given.
Encoding in Neural Networks

Data transforms, parameter re-normalization, and activation functions have gained significant attention in the neural network community over the past several years for improving convergence speed. The results in the literature are for computer vision applications, with batch-normalization (BN) and the Rectified Linear Unit (ReLU) activation attracting attention. In this paper, we present a new approach in data transformation in the context of regression during the synthesis of Head-related Transfer Functions (HRTFs) in the field of audio. The encoding technique whitens the real-valued input data delivered to the first hidden layer of a fully-connected neural network (FCNN) thereby providing the training speedup. The experimental results demonstrate, in a statistically significant way, that the presented data encoding approach outperforms other forms of normalization in terms of convergence speed, lower mean-square error, and robustness to network parameter initialization. Towards this, we used some popular first-and second-order gradient techniques such as scaled conjugate gradient, Extreme Learning Machine (ELM), and stochastic gradient descent with momentum and batch normalization. The improvements, as shown through t-SNE based depiction and analysis on the input covariance matrix, confirm the reduction in the condition number of the input covariance matrix (a process similar to whitening).
A Metric Reckon Algorithm for Bit Forwarding Techniques

Public-Key Cryptography is playing tremendously an important role in modern digital electronics world. Modular Exponentiation is the indispensable operation in all public-key cryptographic techniques, which is composed as a sequence of modular multiplications. Hence, the performance of public-key cryptosystem is influenced by the competent implementation of modular exponentiation. It is improved by reducing the number of modular multiplications needed to evaluate modular exponentiation. Several techniques have been developed to evaluate modular exponentiation. Sliding window techniques and Bit forwarding techniques are competent methods for evaluating modular exponentiation. Bit Forwarding Techniques are directly compatible to implement in hardware. This paper proposes a Metric Reckon Algorithm for Bit Forwarding Techniques. This algorithm finds the number of modular multiplications required to evaluate modular exponentiation using Bit Forwarding Techniques. This proposal is also proves that the Bit forwarding techniques out performs Sliding window techniques.
Detecting Malicious Websites by Query Templates

With the development of the Internet, web content is exponentially increasing. Along with this, web-based attacks such as drive-by download attacks and phishing have grown year on year. To prevent such attacks, URL blacklists are widely used. However, URL blacklists are not enough because they lack the ability to detect newly generated malicious URLs. In this paper, we propose an automatic query template generation method to detect malicious websites. Our method focus on URL query strings that contained similarities on malicious website groups. Additionally, we evaluate our proposed method with large-scale dataset and verify effectiveness. Consequently, our proposed method can grasp the characteristics of malicious campaigns; it can detect 11,292 malicious unique domains not detected by Google Safe Browsing. Moreover, our method achieved high precision in the seven months of experiments.
The complexity of identifying characteristic formulae

We introduce the completeness problem for Modal Logic (possibly with fixpoint operators) and examine its complexity. A formula is called complete, if any two satisfying processes are bisimilar. The completeness problem is closely connected to the characterization problem, which asks whether a given formula characterizes a given process up to bisimulation equivalence. We discover that completeness, characterization, and validity have the same complexity — with some exceptions for which there are, in general, no complete formulae. To prove our upper bounds, we present a non-deterministic procedure with an oracle for validity that combines tableaux and a test for bisimilarity, and determines whether a formula is complete.
BM25-AH

The Virginia Military Institute (VMI) uses Google search to provide webpage search service in the VMI website. As Google search is a general-purpose service, it does not consider VMI-specific information and in turn, often fails to retrieve relevant information. To address the limitation, in this paper, we propose the BM25-AH algorithm, an extension of the BM25 ranking algorithm that utilizes domain-specific knowledge by adding four new features to the existing BM25 algorithm. The implementation results show us the potential of the proposed algorithm and the new VMI search engine.
Region Based Anomaly Detection with Real-Time Training and Analysis

We present a method of anomaly detection that is capable of real-time operation on a live stream of images. The real-time performance applies to the training of the algorithm as well as subsequent analysis, and is achieved by substituting the region proposal mechanism used in [9] with one that makes the overall method more efficient. where they generate thousands of regions per image, we generate far fewer but better targeted regions. We also propose a 'convolutional' variant which does away with region extraction altogether, and propose improvements to the density estimation phase used in both variants.
Analyzing Key Tools for Digital Transformation of Educational and Scientific Organizations

Digital technologies are actively developing currently, and educational organizations can no longer stand aside. Digital transformation has affected almost all spheres of life, from daily tasks and work processes up to complex medical interventions, etc. A new job market requires new level specialists, and it is impossible to prepare them unless radical changes in the personnel training are made. Educational digitalization, the ICT use in the educational process, and the creation of new campuses generation indicate the urgent need for the higher educational institutions to be digitally transformed. Digital transformation is not just the interactive whiteboards appliance or the electronic class-book usage. It implies a systematic change in key processes that could lead to the creation of a perfect new education system, which allows the preparation of people, able to learn and retrain without spending much time on this. This article analyzes the key tools for higher educational institutions digital transformation. The key ways of using the toolkit are highlighted, as well as its implementation effect. Further study stages are also presented.
A Generic Architectural Framework for Machine Learning on Data Streams

In the past years, the importance of processing data streams increased with the emergence of new technologies and application domains. The Internet of Things provides many examples in which processing and analyzing data streams are critical success factors. With the growing amount of data, the usage of machine learning (ML) algorithms has become an essential part of data analysis. However, the high volume and velocity of data presents new challenges, which need to be addressed, e.g. frequent model changes, concept drift or insufficient time to train models. From our point of view, these challenges cannot be tackled alone by using an algorithm-centric approach, i.e. to focus solely on finding appropriate algorithms, and neglecting the structure of the overall processing system. Therefore, we propose a generic architectural framework, which describes common components and their interactions with each other in order to apply ML technologies to streaming data. Furthermore, we implement essential components in two real-world use cases to highlight the feasibility of our approach.
GraphOpt: a Framework for Automatic Parameters Tuning of Graph Processing Frameworks

Finding the optimal configuration of a black-box system is a difficult problem that requires a lot of time and human labor. Big data processing frameworks are among the increasingly popular systems whose tuning is a complex and time consuming. The challenge of automatically finding the optimal parameters of big data frameworks attracted a lot of research in recent years. Some of the studies focused on optimizing specific frameworks such as distributed stream processing [1], [2], or finding the best cloud configurations [3], while others proposed general services for optimizing any black-box system [4]. In this paper, we introduce a new use case in the domain of automatic parameter tuning: optimizing the parameters of distributed graph processing frameworks. This task is notably difficult given the particular challenges of distributed graph processing that include the graph partitioning and the iterative nature of graph algorithms. To address this challenge, we designed and implemented GraphOpt: an efficient and scalable black-box optimization framework that automatically tunes distributed graph processing frameworks. GraphOpt implements state-of-the-art optimization algorithms and introduces a new hill-climbing-based search algorithm. These algorithms are used to optimize the performance of two major graph processing frameworks: Giraph and GraphX. Extensive experiments were run on GraphOpt using multiple graph benchmarks to evaluate its performance and show that it provides up to 47.8% improvement compared to random search and an average improvement of up to 5.7%.
Using Machine Learning to Improve Surgical Outcomes

Predicting the severity of patient's condition helps providing accurate clinical care. Mortality prediction is one of the challenges due to distinct characteristics of the patient's data. It is a challenging problem to evaluate the patient's data which is highly sparse, highly biased and imbalanced, and highly mixed. In this paper, we are focusing on processing large volumes of data using neural networks which can be further used for analysis to obtain useful insights, such as identifying the major features contributing to certain outcomes of events or classifying different objects based on the presences of certain attributes and their measurements.
Characterizing the Outlying Feature Set of Groups

Outlying feature set of groups is useful in many applicationscenarios.However, most of existing literatures focusedoncharacterizing outlying feature set of individuals rather than group level. A method that can identify outlying feature setof groups effectively from large scale dataset is not yet available. This paper aims to tackle this challenge by proposing a novelgroup outlying feature set identification algorithm, named GOFSI, which can identify the outlying feature set at the group level automatically. The Experiments on both synthetic and real-world data sets confirmed the effectiveness of ourmethod.
Learnable Visual Rhythms Based on the Stacking of Convolutional Neural Networks for Action Recognition

Recent deep learning techniques have achieved satisfactory results for various image-related problems. However, many research questions remain open in tasks involving video sequences. Several applications demand the understanding of complex events in videos, such as traffic monitoring, person re-identification, security and surveillance. In this work, we address the problem of human action recognition in videos through a multi-stream network that incorporates both spatial and temporal information. The main contribution of our work is a stream based on a new variant of the visual rhythm, called Learnable Visual Rhythm (LVR). We employ a deep network to extract features from the video frames in order to generate the rhythm. The features are collected at multiple depths of the network to enable the analysis of different abstraction levels. This strategy significantly outperforms the handcrafted version on the UCF101 and HMDB51 datasets. Experiments conducted on these datasets show that our final multi-stream network achieved competitive results compared to state-of-the-art approaches.
Your Body Signals Expose Your Fall

Fall is a common cause of severe injuries that may lead to irreversible body damage and even death. A real-time fall monitoring system can reveal a fall in time for timely medical aid to a victim. This is particularly important in the context of mobile healthcare. Fall detection with most contemporary wearable devices relied solely on acceleration signals, often not flexible and robust enough. In this paper, we propose to deploy body signals in a multi-modality approach. Besides the common acceleration signals, we also make use of physiological signals returned by wearable devices for multiple modalities. Fall detection would not fail easily even if some acceleration signals become ineffective. Our experiment results indicate that we are able to attain an accuracy of more than 96%. An in-depth evaluation demonstrates that physiological signals can contribute in distinguishing falls from actions generating similar acceleration signals, such as jumps, sit-downs and walking-downstairs.
Detection of Critical Camera Configurations for Structure from Motion Using Random Forest

This paper presents an approach for the detection of critical camera configurations in unorganized image sets with (approximately) known internal camera parameters. Critical configurations are caused by an insufficient distance between cameras compared to the distance of the observed scene and can cause problems in triangulation-based structure from motion application. We give a summary of existing techniques and propose a new approach for the detection of image pairs with a critical camera configuration based on classification using a random forest. To this end, several features characterizing the quality of the reconstructed 3D points as well as the estimated camera poses have been defined and evaluated for various configurations. The proposed approach is integrated into the structure from motion framework COLMAP demonstrating its potential on an independent real-world dataset.
Evaluation of Wearable Visual Assistance System for Manual Automotive Assembly

Automotive manual assembly cannot avoid operations with limited sight for the associate. To complete the process, the associate often must rely on tactile feel or to assume ergonomically challenging positions. This work aims to provide a wearable camera system which can record a field of view near an associate's hand and display it remotely to the associate and determine user preference and acceptance. We present three camera systems: (1) wearable camera with arm wearable display, (2) wearable camera with external stationary display, and (3) wearable camera with smart glasses screen. The conducted evaluations included novice user evaluation of accuracy, timing, user preferences, and other performance results conducted on and off the assembly line providing insight on accuracy, timing, user acceptance, preferences, and performance potential in the production environment. This work is intended to assist assembly associates to better view their work area and to complete tasks faster, more reliably, and with higher quality. Our results show an improvement in performance metrics for novice users, with them experiencing increased accuracy and reduced timing with a positive acceptance rate for assistive technologies.
Recognition of Handwritten Kannada Characters Using Unsupervised Learning Method

In the digital era, recognition of handwritten Kannada characters and words and converting them to machine editable form is a challenging research. It has applications like reading aid for blind, digitization of the handwritten documents, postal mail segregation and author, and handedness identification in forensics. The aim of this paper is to build the dataset of handwritten Kannada vowels using unsupervised learning technique. To build the dataset by moving the characters to their respective folders which is done by feature extraction methods like Histogram of Oriented Gradients (HOG), Run Length Count (RLC), Chain Code (CC), Local Binary Pattern (LBP). Classification of characters is achieved with clustering, an unsupervised learning method. To achieve a higher recognition rate, combination of feature extraction methods have been applied. The combined features of HOG and RLC yielded the best recognition rate of 86.92%. Experiment is carried out with the data collected from 100 people.