Reviewerly/exHarmony huggingface dataset:
Triangular fuzzy grading method and its software

The purpose of this study was to develop a new grading method for assessment of undergraduate learning outcomes and to compare this new method with the traditional (scoring) grading method. A triangular fuzzy set was applied in a purposive grading method called the triangular fuzzy grading method. This method is used to integrate all evaluation scores (raw scores) from multiple assessment domains under an educational qualifications framework into a total (fuzzy) score. Findings from numerical examples in this study show that the total (fuzzy) score can indicate the grade of an undergraduate student more equitably and rationally than the traditional (scoring) grading method, and the study concludes that the triangular fuzzy grading method is more effective for assigning grades that match students' performance. In addition, a Scilab program that applies the series of steps used in the triangular fuzzy grading method was created as a tool for grading for teachers/lecturers. Its program leads to more accurate results and time reduction.
Channel-Wise Reconstruction-Based Anomaly Detection Framework for Multi-channel Sensor Data

Anomaly detection is the task of learning patterns of normal data and identifying data with other characteristics. As various types of sensors are attached to vehicle, healthcare equipment, production facilities, etc., detecting anomalies in multi-channel sensor data has become very important. In sensor data, abnormal signals occur temporally during certain intervals of a few channels. It is very important to capture the characteristics of individual channel and cross-channel relationship in order to detect abnormal signals that occur locally for a short time interval. We propose a channel-wise reconstruction-based anomaly detection framework which consists of two parts: channel-wise reconstruction part with convolutional autoencoder (CAE) and anomaly scoring part with machine learning algorithms, isolation forest (iForest) and local outlier factor (LOF). CAE learns the features of normal signal data and measures channel-wise reconstruction error. We applied the symmetric skip-connections technique to build a CAE model for higher reconstruction performance. Given the channel-wise reconstruction error as an input, iForest and LOF summarize it to anomaly score. We present our results on data collected from real sensors attached to vehicle and show that the proposed framework outperforms traditional reconstruction-based anomaly detection methods and one-class classification methods.
Orthogonal Affine Invariants from Gaussian-Hermite Moments

We propose a new kind of moment invariants with respect to an affine transformation. The new invariants are constructed in two steps. First, the affine transformation is decomposed into scaling, stretching and two rotations. The image is partially normalized up to the second rotation, and then rotation invariants from Gaussian-Hermite moments are applied. Comparing to the existing approaches – traditional direct affine invariants and complete image normalization – the proposed method is more numerically stable. The stability is achieved thanks to the use of orthogonal Gaussian-Hermite moments and also due to the partial normalization, which is more robust to small changes of the object than the complete normalization. Both effects are documented in the paper by experiments. Better stability opens the possibility of calculating affine invariants of higher orders with better discrimination power. This might be useful namely when different classes contain similar objects and cannot be separated by low-order invariants.
A survey on the most in-demand books in Moscow libraries

The article presents a survey of readers’ demand for books and periodicals conducted in Moscow libraries in 2018, which analyzes readers’ tastes and preferences. The most in-demand serious fiction writers include E. Vodolazkin, A. Ivanov, Z. Prilepin, A. Rubanov, D. Rubina and G. Yakhina. The author enumerates the reasons for a particular writer, book or journal to keep their top position in the readers’ ratings over a long period of time. Also described are writers’ advertising strategies, as well as the influence of television and screen adaptations on readers’ demand for new books. Noviy Mirhas long established itself as the main thick literary journal. The article also raises the issue of dwindling circulation of literary journals, and offers advice to writers, editors, publishers and librarians about promoting their products. As a separate topic, the article examines a growing demand for translated literature (published, among others, in Inostrannaya Literatura), as well as for children’s books.
Pilot Study on Experience of Engineering Students in Multimedia-enhanced Introductory Physics Labs

Dr. Popovic has expertise in design and development of multimodal medical imaging systems for surgical guidance and diagnostics.He enjoys working with undergraduate students on independent research projects, and has supervised students' work on topics ranging
δ‐GLMB filter based on DI in a clutter

For the problem that the performance of existing multi-target tracking algorithm's serious degrades in a dense clutter environment, a novel Doppler information assistant δ- generalised labelled multi-Bernoulli (DI-δ- GLMB) filter is proposed. By introducing DI, a new measurement likelihood function is established, and the improved update equation based on the δ- GLMB filter framework is derived. In addition, a sequential Monte Carlo implementation method is given under the non-linear model. Simulation results show that compared with the DI probability hypothesis density filter and the standard δ- GLMB filter, the estimation of the proposed algorithm is more accurate and stable.
Part of speech tagging for Polish

Abstract In this paper we discuss the current state of the art in part-of-speech tagging for Polish. We introduce the problem of POS tagging and point out the key issues in tagging inflected languages, which make this task more difficult in the case of Polish than e.g. English. We also discuss the most important language resources connected with POS tagging, as well as the task of morphological analysis, as it is commonly used as a preliminary step in tagging. We describe the methods that have been applied to the problem of POS tagging for Polish to date and discuss the most current, neural-network based methods in more detail. Finally, we conclude with a general view of this field in the context of Polish and discuss possible future research directions.
A Causality-Based Approach to Assessing Inconsistency for Multi-context Systems

Nonmonotonic multi-context systems provide a promising starting point to interlink heterogeneous and decentralized knowledge contexts effectively by modeling the information exchange among contexts instead of logics of contexts uniformly by virtue of bridge rules. Inconsistency handling has been considered as one of the important issues in multi-context systems, since inconsistency makes a multi-context system useless. In this paper, we propose an approach to assessing the responsibility of each bridge rule of a multi-context system for the inconsistency of that system, which helps us better understand roles of bridge rules involved in inconsistency from the point of view of causality.
5-K-Dimensional Tree for Human Activity Recognition in Smart Home

It is difficult to recognize complex and changeable human activities in smart home, and the K-Dimensional Tree (K-D Tree) algorithm is utilized to better deal with the difficulty. The depth and computational complexity of the K-D Tree algorithm are affected by the quantity of samples. When the quantity of samples is large, the retrieval time will increase and the accuracy will decrease, so an improved algorithm-5K-Dimensional Tree (5-K-D Tree) algorithm is proposed. Both algorithms will be applied to the field of activity recognition. The minimum Redundancy Maximum Relevance (mRMR) algorithm is also utilized in the feature selection. The results indicate that compared with the classifier based on K-D Tree, the classifier based on 5-K-D Tree increases the overall recognition rate and the single recognition rate, and also reduces the dimension of the optimal feature subset. This paper provides a new method for human activity recognition.
A Web-Based System to Assess Texture Analysis Methods and Datasets

Texture analysis is an active area of research in computer vision and image processing, being one of the most studied topics for image characterization. When facing with texture analysis in a novel application a researcher needs to evaluate different texture methods and classifiers to verify which are the most suitable for each type of image. This usually leads the researcher to spend time setting code to make comparisons and tests. In this context, we propose a research and collaboration platform for the study, analysis, and comparison of texture descriptors and image datasets. This web-based application eases the creation of experiments in texture analysis that consists of extracting texture features and performing a classification over these features. It has a collection of methods, datasets, and classification algorithms while also allows the user to upload the code of new descriptors, the files of new texture datasets and to perform various tasks over them. Another interesting feature of this application is its interactive confusion matrix in which the researcher can identify correctly and incorrectly classified images.
Blind quality assessment of on-board reconnaissance images

Due to imaging platform and conditions constraints, multiple types of hybrid distortions exist in on-board reconnaissance images. Research on quality assessment of reconnaissance images can provide important quantitative basis and reference for performance optimization of subsequent processing and imaging system. By analyzing characteristics of reconnaissance images, 11 kinds of relevant features from 3 categories such as camera shake, structure changes, and color loss are extracted in conditions of multi-degree freedom and multi-attitude changes of imaging platform. Here we use high resolution mapping images as the original image set, and extract features of image patches. Benchmark distribution characteristics are obtained by multivariate Gaussian fitting. Using the learned multivariate Gaussian model, a Mahalanobis distance is used to measure the quality of each patch of on-board reconnaissance images, then overall quality score is obtained by average pooling. When tested images from real on-board imaging platform, the proposed method is shown to correlate highly with human judgments of quality and have superior quality-prediction performance to state-of-the-art blind image quality assessment methods.
Multilayer ROP Protection Via Microarchitectural Units Available in Commodity Hardware

This paper presents a multilayer protection approach to guard programs against Return-Oriented Programming (ROP) attacks. Upper layers validate most of a program's control flow at a low computational cost; thus, not compromising runtime. Lower layers provide strong enforcement guarantees to handle more suspicious flows; thus, enhancing security. Our multilayer system combines techniques already described in the literature with verifications that we introduce in this paper. We argue that modern versions of x86 processors already provide the microarchitectural units necessary to implement our technique. We demonstrate the effectiveness of our multilayer protection on a extensive suite of benchmarks, which includes: SPEC CPU2006; the three most popular web browsers; 209 benchmarks distributed with LLVM and four well-known systems shown to be vulnerable to ROP exploits. Our experiments indicate that we can protect programs with almost no overhead in practice, allying the good performance of lightweight security techniques with the high dependability of heavyweight approaches.
Dhana Labha: A Financial Management Application to Underbanked Communities in Rural Sri Lanka

This paper presents findings from field trial of a mobile application called 'Dhana Labha' in a rural Sri Lankan community. Dhana Labha was designed to be used by the community members to manage their personal finances, oversee their performance in managing multiple microfinance loans and assist in loan collection. We distributed the application among thirty eight microfinance clients and studied their use over a period of six months. Our findings show that the use of Dhana Labha had a positive impact on existing local practices and financial awareness while prompting the participants to develop unexpected new practices around microfinance workflows. Our findings highlight the importance of understanding existing sociocultural practices for designing applications, as they strongly affect and shape the use of technology in a constrained setting.
Deep transfer learning for MR image feature point descriptors

In order to solve the internal feature matching problem of nonlinear flexible biological tissues in MR images, This paper proposes a feature point descriptor generation model based on transfer learning and convolutional neural networks TBNet . Firstly, the Siamese network structure model is combined with transfer learning to obtain a pre-trained CNN model and then this paper proposes a batch-by-batch model fine-tuning strategy. Secondly, the extracted feature point descriptor is obtained using the fine-tuned model. Finally, Experiments show that the TBNet has higher robustness and accuracy than traditional SIFT, SURF and the state-of-the-art VGG16-based models.
Distance Dependent Maximum Margin Dirichlet Process Mixture

We propose distance dependent maximum margin Dirichlet Process Mixture (STANDPM), a nonparametric Bayesian clustering model that combines distance-based priors with the discriminatively learned likelihood of the Maximum Margin Dirichlet Process Mixture. STANDPM generalizes the distance-based prior introduced in the distance dependent Chinese Restaurant Process for non-sequential distances and allows modeling of complex dependencies between data points and clusters. The generalized distance-based prior is formulated as an abstract similarity measurement between a data point and a cluster. Empirical results show that the STANDPM model with abstract similarity achieves state-of-the-art performances on a number of challenging clustering datasets.
CCG Supertagging Using Morphological and Dependency Syntax Information

After presenting a new CCG supertagging algorithm based on morphological and dependency syntax information, we use this algorithm to create a CCG French Tree Bank corpus (20,261 sentences) based on the FTB corpus by Abeillé et al. We then use this corpus, as well as the Groningen Tree Bank corpus for the English language, to train a new BiLSTM+CRF neural architecture that uses (a) morphosyntactic input features and (b) feature correlations as input features. We show experimentally that for an inflected language like French, dependency syntax information allows significant improvement of the accuracy of the CCG supertagging task, when using deep learning techniques.
Combining Convolutional Neural Networks for Multi-context Microcalcification Detection in Mammograms

Breast cancer is the most frequent cancer among women, and also causes the greatest number of cancer-related deaths. One effective way to reduce breast-cancer related deaths is to use mammography as a screening strategy. In this framework, cluster of microcalcifications can be an important indicator of breast cancer. To help radiologists in their diagnostic operations, Computer Aided Detection systems have been proposed, which are based Deep Learning methodologies. Such solutions showed remarkable performance, but further improvements can be gained if the design of the detector takes advantage of specific knowledge on the problem. We present an approach for the automated detection of microcalcifications in Full Field Digital Mammograms which involves an ensemble of CNN. The rationale is to employ one CNN trained on ROIs strictly containing the lesions to be detected together with other CNNS trained on ROIs centered on the same lesions, but progressively larger. In this way, shallower networks become specialized in learning local image features, whereas deeper ones are well suited to learn patterns of the contextual background tissues. Once trained, the detectors are combined together to obtain a final ensemble that can effectively detect lesions with a substantial reduction of false positives. Experiments made on a publicly available dataset showed that our approach obtained significantly better performance with respect to the best single detector in the ensemble, so demonstrating its effectiveness.
Robust Histogram Estimation Under Gaussian Noise

We present a novel approach to description of a multidimensional image histogram insensitive with respect to an additive Gaussian noise in the image. The proposed quantities, although calculated from the histogram of the noisy image, represent the histogram of the original clear image. Noise estimation, image denoising and histogram deconvolution are avoided. We construct projection operators, that divide the histogram into non-Gaussian and Gaussian part, which is consequently removed to ensure the invariance. The descriptors are based on the moments of the histogram of the noisy image. The method can be used in a histogram-based image retrieval systems.
Big Data Security Challenges and Solution of Distributed Computing in Hadoop Environment: A Security Framework

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.
A Negotiation Strategy Based on Compromising Degree

This paper proposed a compromising strategy based on constraint relaxation for automated negotiating agents. A lot of studies including international competitions have been made on automated negotiating agents. basically, automated negotiating agents are adjusting a threshold to accept their opponents’ offers. Because merely a threshold is adjusted, it is very difficult to show how and what the agent conceded even after an agreement has been reached. To address this issue, we propose an explainable concession process using a constraint relaxation process. Authors also describe methods min, distance and distance min, random for the size of the removed constraint. Experimental results demonstrate that distance strategies are effective.
Balanced ternary and quaternary sequence pairs of odd period with three-level correlation

Sequence pairs with good correlation have wide applications in communication systems. In this paper, ternary and quaternary sequence pairs of odd period with three-level correlation are constructed based on cyclotomy. In our constructions, sequences are both balanced and the maximum out-of-phase cross-correlation magnitude is shown to be √7 for ternary sequence pairs and to be √5 for quaternary sequence pairs, which are both better than the known sequence pairs of odd period with three-level correlation.
Blockchain in Media—A True Calling?

In recent years, blockchain has topped the chart of disruptive technologies. The promises of a decentralized, self-organized, trusted party that removes the need for a central organization to validate transactions has inflated the hopes of several companies and industries on the real potential of blockchain and its applications. Companies rushed to board the blockchain train (with the financial industry in the lead), trying hard to fit the technology to any use case at hand in a desperate attempt not to miss a new wave of technological changes.
Learning the Graph Edit Distance Parameters for Point-Set Image Registration

Alignment of point sets is frequently used in pattern recognition when objects are represented by sets of coordinate points. The idea behind this problem is to be able to compare two objects regardless of the effect of a given transformation on their coordinate data. This paper presents a method to align point sets based on the graph edit distance. The main idea is to learn the edit costs (in a learning step) and then apply graph edit distance (in a pattern recognition step) with the learned edit costs. Thus, the edit cost would have to incorporate the transformation parameters. In the experimental section, we show that the method is competitive if the graph edit distance parameters are automatically learned considering the learning set. These parameters are the insertion and deletion costs and also the weights on the substitution costs.
Vehicle color recognition based on superpixel features

In this paper, a novel methodology is presented to settle the region of interest (ROI) detection problem in vehicle color recognition so as to remove the redundant components of vehicles that interfere greatly with color recognition. In order to make full use of the local color and spatial information, vehicle images are divided into different superpixels at first. The spatial relationship between superpixels and the outermost pixels is then used for the background removal of vehicle images. By comparing with the vehicle window clustering centroids obtained by k-means, the superpixels close to the universal color characteristics of windows are removed so that the dominant color superpixels are determined. Finally, a linear Support Vector Machine classifier is trained for color recognition. The experiments demonstrate that the proposed methodology is effective for color region of interest detection and thus contribute to vehicle color recognition.
studentlife: Tidy Handling and Navigation of a Valuable Mobile-Health Dataset

Smartphones have become ubiquitous, and nearly constantly accompany their owners as they go about their daily lives.This sensor-rich and computationally-efficient technology allows quantifying continuous streams of human behaviors and activities unobtrusively and in situ, a process referred to as digital phenotyping (Onnela & Rauch, 2016).Two main types of data collected through this approach can be distinguished: on the one hand, active data that involve the voluntary participation of users, as required for surveys; and on the other hand, passive data that do not involve any dedicated action from users, as is the case for data generated by various sensors embedded in the smartphones.