Reviewerly/exHarmony huggingface dataset:
An adaptive illumination normalization using non-linear regression for robust person identification under varying illuminations

The precision of person recognition using facial images in complex illumination conditions deteriorates severely. Various illumination normalization and compensation techniques alleviate the classification performance by linear and static modeling of illumination variations thus result in inappropriate illumination normalization. Here, the authors present novel and adaptive illumination normalization method with the help of kernel extreme learning machine (KELM). Few low frequency discrete-Cosine-transform (DCT) coefficients are used as input to KELM for learning the non-linear regression model for illumination variations. The trained KELM is used to find the illumination variations present in the input face image which is represented by the regression value. Depending upon the regression value corresponding to the illumination variations in the test image, the number of low frequency DCT coefficients is selected adaptively. The selected low frequency DCT coefficients are processed by a fuzzy filter. The processed face images are classified by KELM with polynomial kernel. The evaluation of the proposed illumination normalization method is conducted on Extended Yale B face database. Zero, 0.19 and 1.11 percent error rate on subset 3, 4 and 5, respectively, of this database proves the efficacy of the proposed method.
A Literature Review of Steering Angle Prediction Algorithms for Self-driving Cars

A crucial requirement for intelligent, driverless cars is to maneuver without moving out of its drivable region of the road. It is well known that steering angle calculation plays an important role in maintaining the vehicle in the center of the road or within the boundary lanes to meet safety critical requirements. This paper presents a review of autonomous steering techniques for self driving cars which is a relatively unexplored task in the fields of computer vision, robotics and machine learning. Our principle aim is to find out the state-of-the-art models in traditional computer vision approach and end-to-end Deep learning approach. Subsequently we have analyzed and compared the performance of each model based on the reported experimental results. Our research investigations lead us to conclude that ResNet50 Deep network combined with event cameras can be assumed to give better prediction of the due wheel angle in comparison to the use of traditional cameras. An overview of future research direction and applications is also given.
LabVIEW Platform based Real-time Speed Control of a DC Servo Motor With Fuzzy-PI Controller

DC servo motor has become popular for many applications in industrial, robotics, consumer, etc. Controllers are developed to control the position and speed of servo motors. Some of the traditional controllers such as PI, PID, and fuzzy logic controller are used. This project proposed, a fuzzy logic-based controller has been developed to control the speed of the dc servo motor. The fuzzy control is based on fuzzy-PI, a logical of a fuzzy system that is very nearer to human thought and simple language resembled the classical control method. The model of the servomotor is obtained using a system identification approach. The execution of the suggested controller is validated in simulation as well as in a real-time situation.
Convolutional Neural Networks for Placenta Cell Classification

The placenta is a complicated organ that plays several roles during fetal evolution. There was a little awareness regarding the connection of anatomical placental disorders with fetal biology. In this particular work, we introduce an open source mathematically traceable deep learning pipeline to examine cell-level placenta histology using neural convolution networks. Also, we have a tendency to learn deep embedded encoding makeup information that's capable of each stratifying 5 distinct cell populations and learn interclass makeup variance. We anticipate that the automation of such a pipeline to population-scale placenta histology research does have the potential to benefit our understanding of the basic cellular placental biology, substantially its role in forecasting adverse birth results. The objective of the present work is to classify the cell populations into 5 classes using the convolution neural network.
Invariance vs Robustness of Neural Networks

Neural network models are known to be vulnerable to small, adversarial pixel-wise perturbations. More recently, they have been shown to be vulnerable to even random spatial transformations (e.g., translations, rotations). Spatial robustness to random translations and rotations is commonly attained via equivariant models (e.g., StdCNNs, GCNNs) and training augmentation. In this paper, we prove a quantitative trade-off between spatial and adversarial robustness in a simple statistical setting. We complement this by empirically studying the following two cases: (a) change in adversarial robustness as we improve only the spatial robustness via training augmentation in equivariant models, (b) change in spatial robustness as we improve only the adversarial robustness using adversarial training. We observe that the spatial robustness of equivariant models improves when their training is augmented with progressively larger transformations but while doing so, it progressively worsens their adversarial robustness. On the other hand, we take state-of-the-art adversarially trained models, and observe that adversarial training with progressively larger perturbations results in a progressive drop in their spatial robustness.
How Important is Immersion for Learning in Computer Science Replugged Games?

By following the idea of not using computers at all, Computer Science Unplugged has set the course for many interactive, social, and hands-on activities dealing with concepts and problems of Computer Science Education. Through recent developments in immersive media, technologies like Virtual and Augmented Reality could enhance such activities or even enable new ones. When providing immersive educational media that induce a sense of presence in the virtual environment, the illusion of unmediated learning experience can be delivered. Hence, the concept of "Computer Science Replugged'' can benefit from the affordances that Computer Science Unplugged thrives on while facilitating or enabling activities that might be impossible, dangerous, or expensive to carry out in reality. This paper presents three concepts from Computer Science Education that have been modeled as 3-D immersive educational virtual environments: components of a computer, asymmetric encryption/decryption, and finite state machines. To get a first impression of the effectiveness of these approaches and in order to determine the importance of the level of immersion for the learning process, a study with 78 participants was conducted in which the software was tested on different devices. All activities were found to be significantly effective with regards to the pre- and post-tests. When analyzing these results on the basis of comparing the least immersive setting (laptop) with the most immersive setting (head-mounted-display), the findings indicate different effects with effect sizes betweenβ = -.17 andβ = .41. This raises two questions: "Which topics from Computer Science Education can benefit from immersive technology?'' and "What are the opportunities and challenges of the didactical design of Computer Science Replugged activities?'' that have to be adressed in further research.
An Evolutionary Computing Approach to Solve Object Identification Problem in Image Processing Applications

In the given image identifying the existence of a required object is the concern of the object detection process. This is quite natural for Human without any effort, however making a machine to detect an object in image is tedious. To make machines to recognize the objects, the feature descriptor algorithms are to be implemented. The general object detection approaches use collection of local and global descriptors to represent an image. Difficulties arise during this process when there is variation in lightening, positioning, rotation, mirroring, occlusion, scaling etc., of the same object in different image scenes. To overcome these difficulties, we need combination of features that detects the object in the image scene. But there exist lot of descriptors that can be used. Hence, finding the required number of feature descriptors for object detection is a crucial task. The question that comes out here is how to select the optimum number of descriptors to achieve optimum accuracy? The answer for the question is an optimization algorithm, which can be employed to select the best combination of the descriptors with maximum detection accuracy. This paper proposing an Evolutionary Computation (EC) based approach with the Differential Evolution (DE) algorithm to find the optimal combination of feature descriptors to achieve optimal object detection accuracy. The proposed approach is implemented and its superiority is verified with four different images and results obtained are presented in this paper.
ICT Development, E-government Development and Economic Development. Does Institutional Quality Matter?

Interest in the role and contribution of ICT development, e-government development, and the quality of a nation’s institutions on economic development is profound. While extant research shows that the impact of institutions on national development is different across countries generally, the literature overlooks the role institutional quality plays in either the ICT development–economic development or the e-government–economic development nexuses. Relying on publicly available archival data, this study explores the mediating relationship of institutional quality by using Partial Least Squares Structural Equation Modeling (PLS-SEM). Our results show a significant relationship between ICT development and e-government development, e-government development and institutional quality, e-government development and economic development, as well as institutional quality on economic development. Also, the contribution of ICT development to economic prosperity can further be strengthened indirectly via the impacts of institutional quality. This study contributes to the theoretical discourse on the impact of ICT development and e-government development on economic development and the mediating effects of institutional quality. It offers implications for practice and policy.
Segmentation of 4D images via space-time neural networks

Medical imaging techniques currently produce 4D images that portray the dynamic behaviors and phenomena associated with internal structures. The segmentation of 4D images poses challenges different from those arising in segmenting 3D static images due to different patterns of variation of object shape and appearance in the space and time dimensions. In this paper, different network models are designed to learn the pattern of slice-to-slice change in the space and time dimensions independently. The two models then allow a gamut of strategies to actually segment the 4D image, such as segmentation following just the space or time dimension only, or following first the space dimension for one time instance and then following all time instances, or vice versa, etc. This paper investigates these strategies in the context of the obstructive sleep apnea (OSA) application and presents a unified deep learning framework to segment 4D images. Because of the sparse tubular nature of the upper airway and the surrounding low-contrast structures, inadequate contrast resolution obtainable in the magnetic resonance (MR) images leaves many challenges for effective segmentation of the dynamic airway in 4D MR images. Given that these upper airway structures are sparse, a Dice coefficient (DC) of ~0.88 for their segmentation based on our preferred strategy is similar to a DC of &lt;0.95 for large non-sparse objects like liver, lungs, etc., constituting excellent accuracy.
Detection of Hijacked Authoritative DNS Servers by Name Resolution Traffic Classification

Authoritative DNS server hijacking has been a critical threat which can be hardly prevented by DNSSEC. In this work, we propose a machine learning based detection method against DNS responses replied from hijacked external authoritative DNS servers by DNS traffic data classification and heuristic analysis. The proposed method consists of header, answer, authority and additional section analysis each of which is combined with the corresponding question section. The decision maker decides if a DNS query-response pair has been related to a hijacked external authoritative DNS server by conducting heuristic analysis on the classified DNS traffic data with comparing with old cached DNS data. We have setup a local experimental network and achieved DNS traffic data (A records) of the top 500 FQDNs listed on Alexa web site for about one month and confirmed that the required features can be attracted from the DNS traffic data. Accordingly, we confirmed that it was expectable to conduct the DNS traffic classification and heuristic analysis in order to detect DNS responses replied from hijacked external authoritative DNS servers. The future work includes the DNS traffic data training and the evaluations on a local experimental network as well as in a large scale real network environment.
Using Blockchain in IoT: Is It a Smooth Road Ahead for Real?

The rise of our smart life, along with the popularity of smart devices, has made the Internet of Things (IoT) a prevalent research topic in recent years. Even though it increases the intelligence level of society to a very great extent, it still has many flaws, especially when it comes to the security domain. The birth of blockchain has brought a new evolution for IoT. The decentralized and traceable feature of blockchain is entirely in sync with the need for the decentralized IoT devices in the future. Currently, there are many applications of blockchain that include but not limited to, smart homes, smart cities, banking, insurance, and financial services. However, there are still many challenges regarding the use of blockchain in IoT, as many things require to be proved and realized. Therefore, the paper is going to research the challenges of blockchain in IoT to find out about its causes and the cures. The changes in the identity of IoT during its lifetime is also covered and how to supervise the governance of data and privacy in a smart device. Data protection law is also taken into consideration when it comes to legal and compliance issues. To solve these difficulties, several perspectives are worth considering, like how to optimize the data authentication, ways to secure communication, methods to improve the smart contract, etc. The paper also aims to help the people from the industry to have better clarity and understanding of the scope of using blockchain in IoT.
Web-scale discovery and Google Scholar: A study in use patterns

J. Paul Leonard Library at San Francisco State University launched a web-scale discovery service, branded as OneSearch in 2013. Before that, Leonard library had been using Google Scholar since 2006. This study gathered usage data on OneSearch and Google Scholar from the library OpenURL link resolver, SFX, and the interlibrary loan server, ILLiad, from 2011 to 2017. The findings demonstrated that OneSearch had become an essential library search tool, especially among students. Data revealed fewer patrons used Google Scholar after the implementation of OneSearch. However, Google Scholar remained an important research tool that complemented OneSearch.
Weakly Supervised Domain Adaptation with Point Supervision in Histopathological Image Segmentation

When a model learned in a domain is applied to a different domain, even if in the same task, there is no guarantee of accuracy. This is a very important issue when deep learning and machine learning are applied in the field. In medical applications, there is a wide variety of domain bias, making it very difficult to create a model appropriate for all domains. Furthermore, semantic segmentation needs fine annotation and its high labor cost makes its application difficult. Histopathological image segmentation enables drug discovery and medical image analysis, but it is expensive due to its annotation cost and the need for the skills of histopathological experts. In this paper, we focus on a weakly supervised method using point annotation unique to histopathological image segmentation, and tackled on weakly supervised domain adaptation to suppress domain gaps. Providing point level annotation instead of fine annotation decreases the high cost of labor normally required.
Flexible service discovery based on multiple matching algorithms

Traditional web service discovery approaches rely on logic or non-logic matching techniques. In general, logic approaches can achieve satisfactory precision levels, but they result in modest recall scores. In contrast, non-logic approaches may ensure more balanced scores in terms of recall and precision, but they need additional aggregation schemes or optimisation methods. To improve the discovery performance, we need to combine multiple matching algorithms and fuse their results into a single ranked list of services. This combination must avoid the well-known side effects of fusion, such as overfitting or noise sensitivity. To tackle the service-discovery issue, we propose a solution based on two key ideas: first, we propose a majority voting model based on the 'Condorcet' paradigm to fuse a set of individual ranked lists (provided by the matching functions). Second, we leverage a probabilistic extension of the dominance relationship to ensure comparison between the services. The experimental evaluations indicate the proposed solution, 'probabilistic Condorcet', outperforms all individual matching functions, as well as many concurrent fusion algorithms.
Alignment of cortical vessels viewed through the surgical microscope with preoperative imaging to compensate for brain shift

Brain shift is a non-rigid deformation of brain tissue that is affected by loss of cerebrospinal fluid, tissue manipulation and gravity among other phenomena. This deformation can negatively influence the outcome of a surgical procedure since surgical planning based on pre-operative image becomes less valid. We present a novel method to compensate for brain shift that maps preoperative image data to the deformed brain during intra-operative neurosurgical procedures and thus increases the likelihood of achieving a gross total resection while decreasing the risk to healthy tissue surrounding the tumor. Through a 3D/2D non-rigid registration process, a 3D articulated model derived from pre-operative imaging is aligned onto 2D images of the vessels viewed through the surgical miscroscopic intra-operatively. The articulated 3D vessels constrain a volumetric biomechanical model of the brain to propagates cortical vessel deformation to the parenchyma and in turn to the tumor. The 3D/2D non-rigid registration is performed using an energy minimization approach that satisfies both projective and physical constraints. Our method is evaluated on real and synthetic data of human brain showing both quantitative and qualitative results and exhibiting its particular suitability for real-time surgical guidance.
Identification of kernels in a convolutional neural network: connections between the level set equation and deep learning for image segmentation

Medical image segmentation remains a difficult, time-consuming task; currently, liver segmentation from abdominal CT scans is often done by hand, requiring too much time to construct patient-specific treatment models for hepatocellular carcinoma. Image segmentation techniques, such as level set methods and convolutional neural networks (CNN), rely on a series of convolutions and nonlinearities to construct image features: neural networks that use strictly mean-zero finite difference stencils as convolution kernels can be treated as upwind discretizations of differential equations. If this relationship can be made explicit, one gains the ability to analyze CNN using the language of numerical analysis, thereby providing a well-established framework for proving properties such as stability and approximation accuracy. We test this relationship by constructing a level set network, a type of CNN whose architecture describes the expansion of level sets; forward-propagation through a level set network is equivalent to solving the level set equation; the level set network achieves comparable segmentation accuracy to solving the level set equation, while not obtaining the accuracy of a common CNN architecture. We therefore analyze which convolution filters are present in a standard CNN, to see whether finite difference stencils are learned during training; we observe certain patterns that form at certain layers in the network, where the learned CNN kernels depart from known convolution kernels used to solve the level set equation.
Blockchain Consensus Algorithm Design Based on Consistent Hash Algorithm

Blockchain, the concept from Bitcoin created by Satoshi Nakamoto, has the potential to decentralise traditionally centralised systems. Blockchain is a distributed ledger for recording information, stored by many nodes without a central organization through distributed systems and cryptography. The consensus algorithm is a protocol that guarantees the consistency of all data in a blockchain system. It is a key for building a blockchain system and an important part that affects the performance of the blockchain system. In this paper, we firstly compare the usage scenarios of different consensus algorithms, their advantages and disadvantages. After that, we present a new consensus algorithm in permissioned blockchain based on consistent hashing. For blockchain system construction, we propose a new design of the hash ring. The pseudo-randomness of the hash operation is used to ensure the randomness of the electoral leadership node in the blockchain system. It avoids the security risk of the fixed leadership node model. Our algorithm is applicable to blockchain systems containing Byzantine nodes and has a high throughput, low delay and many other advantages. Its communication complexity is O(n), significantly better than that of the practical Byzantine fault tolerance algorithm whose communication complexity is O(n2).
Effects of Digitalisation on the System of Vocational Education and Training

The effects of the Fourth Industrial revolution and digitalisation have had impact on education, on evolution and development of educational and retraining needs. The objective of the suggested research was to analyse and generalize on-going and future changes in the context of transformation of qualification requirements addressed to graduates of educational programs, particularly regarding their digital skills, as well as digitalisation of the university environment. Different interpretations of terms referred to digitalisation process are suggested. The role of human of capital at a new stage of technology development is highlighted. Based on generalisation of some approaches within international researches the article differentiates key effects of digitalisation regarding prospects for introduction of new learning technology and methods in the sphere of education, followed by expansion of educational environment, accelerated development of VR technology, artificial intelligence, mass open online courses, individualisation of learning. The core approach suggests system adaptation of the university activity in all fields to new conditions, considering the views of international experts about the risk areas and threats that the universities might encounter in the course of digitalisation process. The article describes the program of development of educational, scientific and expert activity of Russian transport universities worked out within the framework of development of digital economy in Russia, arguing that it is focused on prospective advantages of inclusion of digital technology into the academic ecosystem.