Reviewerly/exHarmony huggingface dataset:
CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion

Label propagation is a popular technique for anatomical segmentation. In this work, we propose a novel deep framework for label propagation based on non-local label fusion. Our framework, named CompareNet, incorporates subnets for both extracting discriminating features, and learning the similarity measure, which lead to accurate segmentation. We also introduce the voxel-wise classification as an unary potential to the label fusion function, for alleviating the search failure issue of the existing non-local fusion strategies. Moreover, CompareNet is end-to-end trainable, and all the parameters are learnt together for the optimal performance. By evaluating CompareNet on two public datasets IBSRv2 and MICCAI 2012 for brain segmentation, we show it outperforms state-of-the-art methods in accuracy, while being robust to pathologies.
A multilevel description of textbook linguistic complexity across disciplines: Leveraging NLP to support disciplinary literacy

Disciplinary Literacy as a pedagogy and research program aims to understand and teach the linguistic differences amongst disciplines. Limited studies have been done, however, on the differing linguistic complexity demands of disciplines. In this study, linguistic complexity is measured at the clause, phrase and lexical levels, across eight disciplines represented by a corpus of secondary school textbooks. Innovative natural language processing systems extract an unprecedented number of complexity measures, and discriminant function analysis describes features that best differentiate disciplinary writing. Results indicate disciplines vary along different clines of complexity. The first tends to discriminate humanities from science subjects, along features such as academic phraseology, possessive noun phrases, auxiliary verbs and clause dependents. Other clines show that subjects such as history and physics can be similarly complex in features such as their prepositional expansion of noun phrases. The paper concludes with detailed and specific pedagogical takeaways for teachers.
Approaches on Partial Face Recognition: A Literature Review

Face recognition technology has gained attention over past years owing to its broad applications. It is a potential research area in the scope of image analysis, computer vision and pattern recognition. The requirement of identifying an authentic user through different authentication mechanisms are increasing rapidly following the intense concerns about security, and rapid advancement in networking, communication, and mobility. Face recognition is an area which has attained attention over the last few decades. The traditional applications of face recognition system, especially those used for access control, authentication, and surveillance, usually need maximum information of face to attain exceptional performance in recognition. Occasionally it is difficult to get access of a full face under certain restricted situations. For example, a non-cooperative face can lead to face occlusion. For recognizing an arbitrary image captured in an unconstrained environment is a more challenging condition in video surveillance, handheld devices etc. Numerous methods have been devised for unconstrained face recognition with remarkable achievements. This paper provides an overview of different partial face recognition approaches proposed.
Adaptive LSB substitution Steganography technique based on PVD

Advancement in Internet technology has caused large text as well as multimedia data transfer over the Internet. To protect this data from unauthorized users, usage of security mechanisms like digital image steganography is highly essential. We have proposed a new steganography method deploying adaptive least significant bit (LSB) substitution and PVD. The gray scale image is partitioned in 3 ×3 or 3×3 plus 2×2 pixel blocks. In a block one pixel is termed as reference pixel, where 4 LSB's are replaced. Thereafter, difference values between the central pixel and each neighbor pixel is calculated. Based on these difference values adaptive LSB substitution in the neighboring pixels is applied. This technique was proposed after discovering errors in Shukla et al.'s OPVD method. Experiments showed that the hiding capacity is 4.025 bpp with an acceptable PSNR value of 32.63 dB. Furthermore, we ratified that the proposed method tolerates to RS analysis and PDH analysis.
Towards Transparency and Trustworthy: A Used-Car Deposit Platform Based on Blockchain

This paper describes a used car trading system based on Blockchain technology, which is a case studied for exploring the potential of Blockchain technologies in emerging landscapes and industrial applications. We are committed to solving the problem of lack of transparency and lack of trust in the current transaction of used cars. The Blockchain has played a positive role in improving business efficiency by helping to solve problems such as trust and data sharing. We hope that through our solutions, the used car market will flourish and make emerging Internet technologies more convenient for our lives.
Similarity Analysis of Law Documents Based on Word2vec

With the increasing demand for computer-assisted wisdom in justice, deep learning has gradually become an effective means of helping intelligent justice. The similarity analysis of law documents is the basis of intelligent justice, while law documents based on several types of cases are quite different in terms of format and length, which causes trouble in analyzing similarities. For that we propose a more specific approach to dealing with law documents, combining Word2vec with legal documents corpus. To measure the efficiency of the proposed method, we designed two sets of controls. The experimental results show that the Word2vec model can improve the accuracy by 0.20 compared with the bag of words (BOW) model, and the equipped law documents corpus can increase by 0.05-0.10 based on the Word2vec model. Thus, the combination of Word2vec and the law documents corpus is more compatible with the simple and efficient application of similarity analysis of law documents.
Utilizing Block Chain Technology in Various Application Areas of Machine Learning

The BlockChain is a New Era Technology that provides secure technological impacts on various exchanges. It totally revolves over the distributed database. That framework helps to eliminate the failure at any single point that may affect in saving time and for the reduction of cost. Till now most of the work regarding BlockChain is limited to inventing technologies that are related to the encrypted form of money. The famous crypto currency like Bitcoin and Etherium are examples that are utilizing this technology. These are widely known as cryptographic money. The exchanges are done with the help of the Blockchain technique eliminate the presence of middleman. With the advancement of various smart devices and the field of Artificial Intelligence and Machine Learning, the interdisciplinary collaboration with Blockchain Technology may be very beneficial for future researches. As Blockchain technology provides a decentralized structure and huge amount of data is to be analyzed in Machine learning area, there are huge exploring abilities in the collaboration of the technology. In this paper, various aspects of the utilization of the Blockchain technology framework in the context of machine learning are discussed. The application areas of Machine learning where the implementation of Blockchain is beneficial are also explored.
Towards answering “Am I on the right track?” automatically using program synthesis

Students learning to program often need help completing assignments and understanding why their code does not work as they expect it to. One common place where they seek such help is at teaching assistant office hours. We found that teaching assistants in introductory programming (CS1) courses frequently answer some variant of the question ``Am I on the Right Track?''. The goal of this work is to develop an automated tool that provides similar feedback for students in real-time from within an IDE as they are writing their program. Existing automated tools lack the generality that we seek, often assuming a single approach to a problem, using hand-coded error models, or applying sample fixes from other students. In this paper, we explore the use of program synthesis to provide less constrained automated answers to ``Am I on the Right Track'' (AIORT) questions. We describe an observational study of TA-student interactions that supports targeting AIORT questions, as well as the development of and design considerations behind a prototype integrated development environment (IDE). The IDE uses an existing program synthesis engine to determine if a student is on the right track and we present pilot user studies of its use.
Ovarian Toxicity Assessment in Histopathological Images Using Deep Learning

As ovarian toxicity is often a safety concern for cancer therapeutics, identification of ovarian pathology is important in early stages of preclinical drug development, particularly when the intended patient population include women of child-bearing potential. Microscopic evaluation by pathologists of hematoxylin and eosin (H&amp;E)–stained tissues is the current gold standard for the assessment of organs in toxicity studies. However, digital pathology and advanced image analysis are being explored with greater frequency and broader applicability to tissue evaluations in toxicologic pathology. Our objective in this work was to develop an automated method that rapidly enumerates rat ovarian corpora lutea on standard H&amp;E-stained slides with comparable accuracy to the gold standard assessment by a pathologist. Herein, we describe an algorithm generated by a deep learning network and tested on 5 rat toxicity studies, which included studies that both had and had not previously been diagnosed with effects on number of ovarian corpora lutea. Our algorithm could not only enumerate corpora lutea accurately in all studies but also revealed distinct trends for studies with and without reproductive toxicity. Our method could be a widely applied tool to aid analysis in general toxicity studies.
A fast fused part-based model with new deep feature for pedestrian detection and security monitoring

In recent years, pedestrian detection based on computer vision has been widely used in intelligent transportation, security monitoring, assistance driving and other related applications. However, one of the remaining open challenges is that pedestrians are partially obscured and their posture changes. To address this problem, deformable part model (DPM) uses a mixture of part filters to capture variation in view point and appearance and achieves success for challenging datasets. Nevertheless, the expensive computation cost of DPM limits its ability in the real-time application. This study propose a fast fused part-based model (FFPM) for pedestrian detection to detect the pedestrians efficiently and accurately in the crowded environment. The first step of the proposed method trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. These six response feature maps are combined with full-body model to produce spatial deep features. The second step of the proposed method uses the deep features as an input to support vector machine (SVM) to detect pedestrian. A variety of strategies is introduced in the proposed model, including part-based to full-body method, spatial filtering, and multi-ratios combination. Experiment results show that the proposed FFPM method improves the computation speed of DPM and maintains the performance in detection.
Do Reputational Sanctions Deter Negligence in Information Security Management? A Field Quasi‐Experiment

Security negligence, a major cause of data breaches, occurs when an organization's information technology management fails to adequately address security vulnerabilities. By conducting a field quasi‐experiment using outgoing spam as a focal security issue, this study investigates the effectiveness of reputational sanctions in reducing security negligence in a global context. In the quasi‐experiment, a reputational sanction mechanism based on outgoing spam was established for four countries, and for each country, reputational sanctions were imposed on the 10 organizations with the largest outgoing spam volumes—that is, these organizations were listed publicly. We find that because of our reputational sanction mechanism, organizations in the four countries, including those that were not listed, reduced outgoing spam significantly compared to those in similar countries. Within each country, the listed organizations, whose reputations were actually sanctioned, reduced spam to a greater extent than those that were not listed. The spam reduction in the not‐listed organizations is mainly driven by increased security awareness, while the reduction in the listed organizations is primarily due to reputation effect. Among the listed organizations, those ranked lower were more responsive to the reputational sanctions. Moreover, we find that reputational sanctions have a stronger effect on large organizations and important organizations that provide network access and transit to others.
Analysis of Image Segmentation Algorithms for the Effective Detection of Leukemic Cells

Image segmentation plays a vital role in medical image processing. Different pre-processing methods yield different results. The pre-processing methods such as histogram stretching with erosion and dilation, average filter and median filter along with histogram stretching is applied to the four different segmentation algorithms which are Otsu's thresholding, Watershed based segmentation, Canny edge detection and K-mean clustering. These algorithms are used to segment Acute Lymphoblastic Leukemia datasets and the parameters such as precision, accuracy and sensitivity of the results are calculated so as to find a better algorithm which is suitable for segmentation of the leukemic cells.
Analysis of Facial Landmark Features to determine the best subset for finding Face Orientation

The number of applications which use human face analysis are going up by the day and face orientation or pose detection is an important and upcoming research in this area. This paper uses a mathematical technique which compares real world coordinates of facial feature points with that of 2D points obtained from an image or live video using a projection matrix and Levenberg-Marquardt optimization to determine the Euler angles of the face. Further, this technique is used to find the best set of facial landmarks which give the maximum range of detection. The preliminary steps of the face orientation technique are face detection and facial landmark detection. For face detection, the Haar Cascade and Deep Neural Network techniques are experimented. Based on the analysis it is concluded that DNN is more robust, accurate and optimal. Facial landmarks are extracted by passing an image or video frame through a cascade of pre-trained regression trees. After analyzing various sets of facial features for their use in face orientation detection techniques and testing the results of each, a set of six facial points nose tip, chin, corner points of the eyes and corner points of the mouth are found to be enough for the algorithm to be able to detect the orientation of the face in a wide range of view with lesser computations.
A Study of Colormaps in Network Visualization

Color is a widely used visual channel for encoding data in visualization design. It is important to select the appropriate type of color mapping to better understand the data. While several studies have investigated the effects of colormaps in various types of information visualization, there have been no studies on their effects on network visualization. Thus, in this paper, we investigate the effects of several colormaps in network visualization using node-link diagrams. Specifically, we compare four different single- and multi-hue colormaps for node attributes, and evaluate their effectiveness in terms of task completion time and correctness rate. Our results show that participants complete their tasks significantly faster with blue (single-hue, sequential) as compared to viridis (multi-hue, sequential), RdYlBu (divergent, red-yellow-blue), and jet (rainbow) colormaps. Additionally, the overall correctness rate shows significant differences between colormaps, with viridis being the least error-prone among the colormaps studied.
A faster convergence and concise interpretability TSK fuzzy classifier deep-wide-based integrated learning

Hierarchical TSK fuzzy system was proposed to approach the exponential growth of IF-THEN rules which named "fuzzy rule explosion". However, it could not get better performance in few layers for instability of TSK fuzzy system, such that hierarchical TSK fuzzy system suffers from bad interpretability and slow convergence along with too much layers. To get a better solution, this study employs a faster convergence and concise interpretability TSK fuzzy classifier deep-wide-based integrated learning (FCCI-TSK) which has a wide structure to adopt several ensemble units learning in a meantime, and the best performer will be picked up to transfer its learning knowledge to next layer with the help of stacked generalization principle. The ensemble units are integrated by negative correlation learning (NCL). FCCI-TSK adjusts the input of the next layer with a better guidance such that it can quicken the speed of convergence and reduce the number of layers. Besides, leading with guidance, it can achieve higher accuracy and better interpretability with more simple structure. The contributions of this study include: (1) To enhance the performance of fuzzy classifier, we mix NCL and stacked generalization principle together in FCCI-TSK; (2) To overcome the phenomenon of "fuzzy rule explosion", we adopt deep-wide integrated learning and information discarding to accelerate convergence and obtain concise interpretability in the meantime. Comparing with other 11 algorithms, the results on twelve UCI datasets show that FCCI-TSK has the best performance overall and the convergence of FCCI-TSK is also examined.
Generative Adversarial Network for Segmentation of Motion Affected Neonatal Brain MRI

Automatic neonatal brain tissue segmentation in preterm born infants is a prerequisite for evaluation of brain development. However, automatic segmentation is often hampered by motion artifacts caused by infant head movements during image acquisition. Methods have been developed to remove or minimize these artifacts during image reconstruction using frequency domain data. However, frequency domain data might not always be available. Hence, in this study we propose a method for removing motion artifacts from the already reconstructed MR scans. The method employs a generative adversarial network trained with a cycle consistency loss to transform slices affected by motion into slices without motion artifacts, and vice versa. In the experiments 40 T2-weighted coronal MR scans of preterm born infants imaged at 30 weeks postmenstrual age were used. All images contained slices affected by motion artifacts hampering automatic tissue segmentation. To evaluate whether correction allows more accurate image segmentation, the images were segmented into 8 tissue classes: cerebellum, myelinated white matter, basal ganglia and thalami, ventricular cerebrospinal fluid, white matter, brain stem, cortical gray matter, and extracerebral cerebrospinal fluid. Images corrected for motion and corresponding segmentations were qualitatively evaluated using 5-point Likert scale. Before the correction of motion artifacts, median image quality and quality of corresponding automatic segmentations were assigned grade 2 (poor) and 3 (moderate), respectively. After correction of motion artifacts, both improved to grades 3 and 4, respectively. The results indicate that correction of motion artifacts in the image space using the proposed approach allows accurate segmentation of brain tissue classes in slices affected by motion artifacts.
Scalable Blockchain-based Architecture for Massive IoT Reconfiguration

With billions of IoT devices expected in the next few years, their management is an important issue to be resolved and, while being praised in the past decades, the centralized approach of cloud computing may not be adequate at this massive scale. In this context, the introduction of blockchain technology with a distributed approach has raised a lot of hypes in solving this scalability problem. This paper proposes a blockchain-based architecture design for scalable reconfiguration of massive IoT devices. A REST API event-based publish/subscribe mechanism is used to decouple the IoT devices from the blockchain operations for reducing resource utilization. Moreover, smart contracts, reconfiguration workflows are developed to facilitate the blockchain-based update process. To evaluate the feasibility and performance of the proposed architecture, a proof-of-concept testbed has been developed. Experimental results illustrate that the proposed architecture is capable of providing a scalable solution for delivering on-demand configuration changes with a negligible effect on the resource utilization on IoT devices.
Automated Guided Vehicle Using Robot Operating Systems

This paper focuses on proof of concept prototype modeling and development of fully autonomous guided robot using embedded controller integrated with GUI Robotic operating systems, which can be used to carry goods and materials at industry, hotels and so many environment. AGV is made up with latest sensors like Oblu which is an IMU sensor, Kinect and software for localization and mapping parallelly. The software part of this robot is mainly implemented using the python programming language and software frameworks, such as Robot operating system(ROS), Open-CV, Open-Ni, Qt. The proposed control system includes solution for the self-localization, automatic accurate path finding and motion control. From the experimental result robot can work freely in indoor environment and reach the goal accurately is proved. We will present the complete overview of prototype architecture and mechanism. This AGV equipped with 3D vision camera sensor and IMU oblu sensor which is capable of travelling self guided path. It is fundamentally in charge of assignment allotment, planning and path management. also we will show a proof of concept with actual model. Oblu gives the 98% accurate reading of velocity, distance and angle by which the path planning accuracy gets high.