Reviewerly/exHarmony huggingface dataset:
Non-uniform Illumination Document Image Binarization Using K-Means Clustering Algorithm

Good binarization result is of great help to the afterwords document image analysis and optical character recognition(OCR). However, non-uniform illumination document image binarization is a very challenging task due to high variation between the document background and foreground. This paper describes a new K-Means clustering based algorithm for non-uniform illumination document image binarization to solve this problem. In the proposed technique, we firstly obtain the combined edge map by take intersection of Canny’s edge map and local image contrast. Then divide the document image into small blocks, each block is classified as text and non-text block using our proposed algorithm. Finally, binarize the text block using K-Means clustering centroids. The proposed technique has been evaluated over nine Non-uniform illumination document images extracted from DIBCO datasets and one scene light reflection document image. Experimental results show that our proposed method achieves competitive performance among other six state-of-the-art binarization algorithm.
It Is All In The Weights: Robust Rotation Averaging Revisited

Rotation averaging is the problem of recovering 3D camera rotations from a number of pairwise relative rotation estimates. The state-of-the-art method of [51 involves robust averaging in the Lie-algebra of 3D rotations using an $\ell_{\frac{1}{2}}$ loss function which is carried out using an iteratively reweighted least squares (IRLS) minimization. In this paper we argue that the performance of IRLS-based rotation averaging is intimately connected with two factors: a) the nature of the robust loss function used, and b) the initialization. We make two contributions. Firstly, we analyse the pitfalls associated with the unbounded weights in IRLS minimization of $\ell_{p}(0\lt p\lt 2)$ loss functions in the context of rotation averaging. We elucidate the design choices and modifications implicit to the state-of-the-art method of [5] that overcomes these problems. Secondly, we argue that the $\ell_{\frac{1}{2}}$ -based IRLS method is inflexible in adapting to the specific noise characteristics of individual datasets, leading to poorer performance. We remedy this limitation by means of a Geman-McClure loss function embedded in a graduated optimization framework. We present results on a number of large-scale real-world datasets to demonstrate that our proposed method outpetforms state-of-the-art methods in terms of both efficiency and accuracy.
Availability Assessment of a Cloud Server System: Comparing Markov and Semi-Markov Models

Nowadays Markov Modelling Processes (MMP) found widely use into realm pertaining to availability assessments of diverse complex technical systems and devices. In this regard, Cloud Server Systems (CSSs) are no exception. However, in order to leverage MMPs for the availability evaluation of CSSs the researchers should take into account that this assessment procedure based on the utilization of just one distribution, that is exponential distribution of times to failure, times to repair etc. It is a serious constraint, which would be need preliminary substantiate considering different negative events and impacts. In that regard, to note that some groups of researchers into realm pertaining to CSSs availability are increasingly trying to leverage approaches based on Semi-Markov Modelling Processes (SMMPs). Using SMMPs the researchers can consider more number of parametric characteristics and have a realistic picture as regards change of availability level for the server system. In addition, seeking to improve overall availability of CSSs, the researchers can determine accuracy of numerical modeling results for SMMPs in the so-called borders' states, when Non-Markovian Process transforms at Markovian Process. By considering significance of these aspects, authors of this paper propose to perform mathematical modeling of behavior of a cloud server system based on joint usage of MMPs and SMMPs and get availability assessments considering diverse influence factors on states of the concrete CSS. This information is to be studded and can be used by developers in order to as well as design of the CSSs considering different influence factors.
Computer Vision for Elderly Care Based on Deep Learning CNN and SVM

Abstract Computer vision has wide application in medical sciences such as health care and home automation. This study on computer vision for elderly care is based on a Microsoft Kinect sensor considers an inexpensive, three dimensional, non-contact technique, that is comfortable for patients while being highly reliable and suitable for long term monitoring. This paper proposes a hand gesture system for elderly health care based on deep learning convolutional neural network (CNN) that is used to extract features and to classify five gestures according to five categories using a support vector machine (SVM). The proposed system is beneficial for elderly patients who are voiceless or deaf-mute and unable to communicate with others. Each gesture indicates a specific request such as “Water”, “Meal”, “Toilet”, “Help” and “Medicine” and translates as a command sending to a Microcontroller circuit that sends the request to the caregiver’s mobile phone via the global system for mobile communication (GSM). The system was tested in an indoor environment and provides reliable outcomes and a useful interface for older people with disabilities in their limbs to communicate with their families and caregivers.
Modified Group Delay Cepstral Coefficients for Voice Liveness Detection

Liveness detection has advanced for many biometrics, such as face, iris, hand geometry, etc. However, less emphasis is given to liveness detection for voice biometrics, i.e., Voice Liveness Detection (VLD). Pop noise is produced due to the spontaneous breathing while uttering a certain class of phonemes (such as fricative, affricates, plossive, nasal, etc.) which has low frequency characteristics. In this paper, phase-based information for analysis of pop noise is used as a feature for VLD task. We have use modified group delay function based cepstral coefficients (MGDCC) as a feature for VLD task. Experiments are performed using two different types of classifiers, i.e., Gaussian Mixture Model (GMM) and Convolutional Neaural Network (CNN). Our results indicate an overall improvement in accuracy by 17.16% and 1.38% for MGDCC-CNN and MGDCC-GMM systems respectively.
A niche for null models in adaptive resource management

Abstract As global systems rapidly change, our collective ability to predict future ecological dynamics will become increasingly important for successful natural resource management. By merging stakeholder objectives with system uncertainty, and by adapting actions to changing systems and knowledge, adaptive resource management (ARM) provides a rigorous platform for making sound decisions in a changing world. Critically, however, applications of ARM could be improved by employing benchmarks (i.e., points of reference) for determining when learning is occurring through the cycle of monitoring, modeling, and decision‐making steps in ARM. Many applications of ARM use multiple model‐based hypotheses to identify and reduce systematic uncertainty over time, but generally lack benchmarks for gauging discovery of scientific evidence and learning. This creates the danger of thinking that directional changes in model weights or rankings are indicative of evidence for hypotheses, when possibly all competing models are inadequate. There is thus a somewhat obvious, but yet to be filled niche for including benchmarks for learning in ARM. We contend that carefully designed “ecological null models,” which are structured to produce an expected ecological pattern in the absence of a hypothesized mechanism, can serve as suitable benchmarks. Using a classic case study of mallard harvest management that is often used to demonstrate the successes of ARM for learning about ecological mechanisms, we show that simple ecological null models, such as population persistence ( N t +1 = N t ), provide more robust near‐term forecasts of population abundance than the currently used mechanistic models. More broadly, ecological null models can be used as benchmarks for learning in ARM that trigger the need for discarding model parameterizations and developing new ones when prevailing models underperform the ecological null model. Identifying mechanistic models that surpass these benchmarks will improve learning through ARM and help decision‐makers keep pace with a rapidly changing world.
Deep Progressive Fusion Stereo Network

Stereo matching depth estimation for rectified image pairs is of great importance to many compute vision tasks, specifically in autonomous driving. With the flourishing of convolution neural networks, responsible depth estimation of stereo matching with artificial intelligence is the most severe challenge for autonomous driving in recent years. Previous research on end-to-end trainable stereo matching networks has usually used cascading convolution blocks with down-sampling or pooling operations to extract the unary features required for matching cost construction. Such approaches lack a reconstruction stage for increasing feature map pixel-wise alignment and strength, factors which play an important role in representing the similarity between stereo image pairs. To address this issue, in this paper, we propose the progressive fusion stereo matching network (PFSM-Net). We exploit an encoder-decoder feature extraction network architecture for multi-stage and -scale dynamic feature extraction. Moreover, we propose a group-wise concatenation method to construct the cost volume, which provides a more efficient cost volume for cost aggregation. Furthermore, we propose the use of multi-scale cost aggregation networks with a progressive fusion strategy. The aggregated cost volume is progressively fused with the multi-stage and -scale cost volume as the size of the cost volume increases. Multi-stage and -scale outputs are supervised with and learned in a coarse-to-fine manner. Experimental results demonstrate that our method outperforms previous methods on the SceneFlow, KITTI 2012, and KITTI 2015 datasets.
Secure Outsourcing for Normalized Cuts of Large-Scale Dense Graph in Internet of Things

With popularity and growth of cloud computing, outsourcing computation, as an important cloud service, has been applied in the field of academic and industry. It allows the resource-constrained IoT devices to outsource the computationally intensive problems to the cloud server. The smallest normalized cuts of the large-scale graph is a fundamental issue in graph theory, which is often used in various fields, such as community discovery, image segmentation, and network partition. Minimizing the normalized cuts of graph, as an NP-hard problem, can be approximately solved by the spectral decomposition. However, carrying out the spectral decomposition is very time-consuming and complicated for some IoT devices. In this article, we design a secure and efficient algorithm for outsourcing the spectral decomposition to an untrusted cloud server. We utilize a series of elementary matrices to protect both the input’s privacy and the output’s privacy from being disclosed to the cloud server. In order to ensure the correctness of the returned results, we design an efficient verification algorithm, which allows the client to detect the invalid results with a probability approximately 1. Our proposed algorithm not only reduces computational overhead on the client side, but also does not bring extra computational overhead on the cloud server side. Then, we give a theoretical analysis about correctness and privacy. Finally, we also provide some experimental results to show the feasibility of our proposed algorithm.
RDLNet: A Regularized Descriptor Learning Network

Local image descriptor learning has been instrumental in various computer vision tasks. Recent innovations lie with similarity measurement of descriptor vectors with metric learning for randomly selected Siamese or triplet patches. Local image descriptor learning focuses more on hard samples since easy samples do not contribute much to optimization. However, few studies focus on hard samples of image patches from the perspective of loss functions and design appropriate learning algorithms to obtain a more compact descriptor representation. This article proposes a regularized descriptor learning network (RDLNet) that makes the network focus on the learning of hard samples and compact descriptor with triplet networks. A novel hard sample mining strategy is designed to select the hardest negative samples in mini-batch. Then batch margin loss concerned with hard samples is adopted to optimize the distance of extreme cases. Finally, for a more stable network and preventing network collapsing, orthogonal regularization is designed to constrain convolutional kernels and obtain rich deep features. RDLNet provides a compact discriminative low-dimensional representation and can be embedded in other pipelines easily. This article gives extensive experimental results for large benchmarks in multiple scenarios and generalization in matching applications with significant improvements.
Custom emoji based emotion recognition system for dynamic business webpages

Purpose One of the contributions of artificial intelligent (AI) in modern technology is emotion recognition which is mostly based on facial expression and modification of its inference engine. The facial recognition scheme is mostly built to understand user expression in an online business webpage on a marketing site but has limited abilities to recognise elusive expressions. The basic emotions are expressed when interrelating and socialising with other personnel online. At most times, studying how to understand user expression is often a most tedious task, especially the subtle expressions. An emotion recognition system can be used to optimise and reduce complexity in understanding users' subconscious thoughts and reasoning through their pupil changes. Design/methodology/approach This paper demonstrates the use of personal computer (PC) webcam to read in eye movement data that includes pupil changes as part of distinct user attributes. A custom eye movement algorithm (CEMA) is used to capture users' activity and record the data which is served as an input model to an inference engine (artificial neural network (ANN)) that helps to predict user emotional response conveyed as emoticons on the webpage. Findings The result from the error in performance shows that ANN is most adaptable to user behaviour prediction and can be used for the system's modification paradigm. Research limitations/implications One of the drawbacks of the analytical tool is its inability in some cases to set some of the emoticons within the boundaries of the visual field, this is a limitation to be tackled within subsequent runs with standard techniques. Originality/value The originality of the proposed model is its ability to predict basic user emotional response based on changes in pupil size between average recorded baseline boundaries and convey the emoticons chronologically with the gaze points.
A Novel High-Capacity Information Hiding Scheme Based on Improved U-Net

With the gradual introduction of deep learning into the field of information hiding, the capacity of information hiding has been greatly improved. Therefore, a solution with a higher capacity and a good visual effect had become the current research goal. A novel high-capacity information hiding scheme based on improved U-Net was proposed in this paper, which combined improved U-Net network and multiscale image analysis to carry out high-capacity information hiding. The proposed improved U-Net structure had a smaller network scale and could be used in both information hiding and information extraction. In the information hiding network, the secret image was decomposed into wavelet components through wavelet transform, and the wavelet components were hidden into image. In the extraction network, the features of the hidden image were extracted into four components, and the extracted secret image was obtained. Both the hiding network and the extraction network of this scheme used the improved U-Net structure, which preserved the details of the carrier image and the secret image to the greatest extent. The simulation experiment had shown that the capacity of this scheme was greatly improved than that of the traditional scheme, and the visual effect was good. And compared with the existing similar solution, the network size has been reduced by nearly 60%, and the processing speed has been increased by 20%. The image effect after hiding the information was improved, and the PSNR between the secret image and the extracted image was improved by 6.3 dB.
Uncertainty assessment for detection of spoofing attacks to speaker verification systems using a Bayesian approach

There has been tremendous progress in automatic speaker verification systems over the last decade. Still, spoofing attacks pose a significant challenge to their deployment. Even though there are various attack techniques such as voice conversion and speech synthesis, replay attacks pose one of the most important types since they can be done without significant expertise in speech technology. Moreover, replay attacks are hard to detect because they are done with simple replay of the original audio. The problem has gained more attention since the introduction of the ASV spoof 2017 challenge, which included a well-designed database with realistic replay attack conditions. Even though many different deep network types and acoustic features were proposed since the challenge, one key issue, which is model uncertainty around the neural networks’ decision is largely ignored. This is a result of using the softmax function with the cross-entropy loss, which is widely used in many domains. Here, we propose using evidential deep learning, which is a recently proposed method that is rapidly gaining popularity, for assessing the model uncertainty around the network’s decision. Experimental results show that the investigated network architectures perform better in terms of equal error rate with the new loss function. Moreover, reliability of measured uncertainty is shown by filtering samples out of the test set using the Bayesian uncertainty measure, which resulted with a consistent decrease in EER with decreasing threshold. • The Bayesian approach has decreased the uncertainty of the countermeasure system. • The Max Feature Map function has improved the robustness of the countermeasure system. • The batch normalization and dropout were effective against noisy speech signals.
DEVELOPMENT OF READINESS FOR ORGANIZATIONAL INVOLVEMENT OF А FUTURE GRADUATE OF THE UNIVERSITY AS AN IMPERATIVE OF MODERN HIGHER EDUCATION

Staff turnover as one of the key problems of modern Russian labor market is discussed in the paper. The article highlights the relevance of the development of readiness for organizational involvement of a future university graduate in the process of higher education. A contradiction between the need for specialists with a sufficient level of involvement in work and the insufficiently realized potential of tertiary education in the formation and development of the level of readiness to be involved in the life of organizations was revealed. The aim of the research is to substantiate the characteristics and pedagogical conditions for the development of readiness for organizational involvement of a future university graduate in learning process. In the course of the study, analytical and synthetic methods were used. The theoretical significance of the research lies in the clarification of the concept of organizational involvement in the pedagogical context, the directions of development of its level. The scientific novelty of the work consists in identifying the features and developing pedagogical conditions for the formation of a future specialist's readiness for organizational involvement in a university environment. The implementation of new approaches to the organization of the formation of the readiness of university students to be involved in the life of the organization proves the practical implications of the study. The forms and methods of implementing pedagogical conditions are illustrated by the example of training future information security specialists in a university environment.
Team Performance due to Agent Conflicts: E-CARGO Simulations

Team management is a highly complex decision-making procedure, which is normally accomplished by highly intelligent human personnel based on their knowledge, experience, communication skills, and wisdom. Task (Role) assignment is a crucial element of team management and can help decision-makers understand or predict future team performance.From the standpoint of the Environments &#x2013; Classes, Agents, Roles, Groups, and Objects (E-CARGO) model as well as the Role-Based Collaboration (RBC) methodology, this paper uses four different approaches to conducting task assignment respectively: Group Role Assignment (GRA), GRA with Conflicting Agents on Roles (GRACAR), Best Agents for each Role (BAR), and BAR with Conflicting Agents (BARCA). After formalizations, we provide a comprehensive comparison among these methods of role assignments by simulations.The simulation results reveal interesting conclusions that help decision-makers understand the complexity of the assignment and choose a pertinent way when conducting team management.
Fast Approximation of Color Morphology

The basic filters in mathematical morphology are dilation and erosion. They are defined by a flat or non-flat structuring element that is usually shifted pixel-wise over an image and a comparison process that takes place within the corresponding mask. The algorithmic complexity of fast algorithms that realise dilation and erosion for color images usually depends on size and shape of the structuring element.
A Hybrid Security Framework to Preserve Multilevel Security on Public Cloud Networks

Cloud computing uses the “pay as you go model” to provide on-demand services to its users, especially data storage, computing power, network, and others. In recent years, Cloud Technology, a decentralized network has become one of the optimal solutions to store and process large amounts of data. Cloud Networks store and process data by achieving security in terms of authentication, integrity and privacy, a great challenge in today’s world. This paper had proposed a Multilevel level hybrid security framework to preserve security with the use of session key generation, cryptography algorithms, chain of hashes, and storing data with the use of decentralized approaches such as BlockChain. This framework preserves all the security services and mitigates the number of passive and active attacks such as modification, fabrication, session hijacking, network jamming, DOS attack, and attempts to modify or manipulate paths for gaining access.