Reviewerly/exHarmony huggingface dataset:
A novel neural network based image descriptor for texture classification

Nowadays, image processing and artificial intelligence have become popular science areas. The one of the major problems of the image processing is texture classification. Therefore, many methods have been presented about texture classification. In this article, a new textural feature extraction method is proposed. In this method, the feed forward neural networks are utilized as a feature extractor. The main purpose of the proposed method is to show feature extraction capability of the feed forward neural network. This descriptor consists of 3 x 3 overlapping blocks division, creating feature extraction network by using row and column pixels of the block, calculating feature value, normalization and histogram extraction. Firstly, image is divided into 3 x 3 size of overlapping blocks and pixels of each block are selected to create feed forward networks. To calculate the weights, neighbor pixel values and the signum function are used together. Tangent hyperbolic function is utilized as activation function in these networks. PCA (Principle Component Analysis) reduces feature dimensionality and LDA (Linear Discriminant Analysis) is chosen as classifier. In order to obtain the experimental results, the commonly used texture datasets were used with variable parameters. These datasets are UIUC, Outex and USPTex. The classification accuracies were calculated as 90.82%, 89.62% and 93.83% for these datasets respectively. The results were compared with the related 16 methods and the proposed method achieved the best performance among them. The space complexity of this method also calculated and the cost of the proposed method was given in the experiments. The computational costs result of this method demonstrates that the proposed neural network based image descriptor method has low complexity. The results clearly illustrated that the proposed textural image descriptor extracts distinctive features with short execution time, has simple mathematical background, is good discriminator and outperforms.
Bayesian and Gaussian Process Neural Networks for Large Vocabulary Continuous Speech Recognition

The hidden activation functions inside deep neural networks (DNNs) play a vital role in learning high level discriminative features and controlling the information flows to track longer history. However, the fixed model parameters used in standard DNNs can lead to over-fitting and poor generalization when given limited training data. Furthermore, the precise forms of activations used in DNNs are often manually set at a global level for all hidden nodes, thus lacking an automatic selection method. In order to address these issues, Bayesian neural networks (BNNs) acoustic models are proposed in this paper to explicitly model the uncertainty associated with DNN parameters. Gaussian Process (GP) activations based DNN and LSTM acoustic models are also used in this paper to allow the optimal forms of hidden activations to be stochastically learned for individual hidden nodes. An efficient variational inference based training algorithm is derived for BNN, GPNN and GPLSTM systems. Experiments were conducted on a LVCSR system trained on a 75 hour subset of Switchboard I data. The best BNN and GPNN systems outperformed both the baseline DNN systems constructed using fixed form activations and their combination via frame level joint decoding by 1% absolute in word error rate.
Auto-MeDiSine: an auto-tunable medical decision support engine using an automated class outlier detection method and AutoMLP

With advanced data analysis techniques, efforts for more accurate decision support systems for disease prediction are on the rise. According to the World Health Organization, diabetes-related illnesses and mortalities are on the rise. Hence, early diagnosis is particularly important. In this paper, we present a framework, Auto-MeDiSine, that comprises an automated version of enhanced class outlier detection using a distance-based algorithm (AutoECODB), combined with an ensemble of automatic multilayer perceptron (AutoMLP). AutoECODB is built upon ECODB by automating the tuning of parameters to optimize outlier detection process. AutoECODB cleanses the dataset by removing outliers. Preprocessed dataset is then used to train a prediction model using an ensemble of AutoMLPs. A set of experiments is performed on publicly available Pima Indian Diabetes Dataset as follows: (1) Auto-MeDiSine is compared with other state-of-the-art methods reported in the literature where Auto-MeDiSine realized an accuracy of 88.7%; (2) AutoMLP is compared with other learners including individual (focusing on neural network-based learners) and ensemble learners; and (3) AutoECODB is compared with other preprocessing methods. Furthermore, in order to validate the generality of the framework, Auto-MeDiSine is tested on another publicly available BioStat Diabetes Dataset where it outperforms the existing reported results, reaching an accuracy of 97.1%.
Survey on the approaches based geometric information for 3D face landmarks detection

Facial landmarks detection is an important and basic step in many face analysis applications. For this reason, it is considered a challenging task as the final results of the analysis depend on the accuracy of the landmarks detection. Decades of research have investigated approaches for two-dimensional (2D) facial landmarks detection but; however, the good obtained results, they still suffer from some weakness regarding the pose and illumination variations. Recently, the large availability of 3D scans makes the use of 3D face models easier hence, overcome the problems caused using 2D images. Many papers have studied the problem of 3D facial landmarks detection; nevertheless, there is a lack of literature reviews allowing an overview of the studies and researches related to the 3D face landmarks detection. In this study, the authors present a detailed survey of the latest (2010–2018) approaches based geometric information for 3D face landmarks detection, including the limitations and strengths of each work.
Adversarial Watermarking to Attack Deep Neural Networks

Watermark is one of the most fundamental approaches for avoiding potential copyright infringement activities. However, whether its introduction would effect the understanding of deep learning models remains unstudied. In this work, we propose a visible adversarial attack method that transforms and places a provided watermark on the target image to interfere the classification result from an Inception V3 model, which is pretrained on ImageNet. Specifically, the watermark is adjusted iteratively on location, transparency, color, angle and size which are determined by only 9 parameters. We define two types of attack to better simulate the watermark approaches in reality, respectively the watermark is constrained in either transparency or size. Experiments show that the generated adversarial samples are not only capable of fooling the Inception V3 model with high success rates, but also transferable to other models with high confidence, such as the Rekognition developed by Amazon.
Kahoot with Smartphones in Testing and Assessment of Language Teaching and Learning, the Need of Training on Mobile Devices for Vietnamese Teachers and Students

The underlying purpose of this research was examining the use of Kahoot in practice to test and revise what students had been learning at class to determine its viability as a supplement to existing materials.Twenty teachers and twenty students of English major were asked about Kahoot and its uses in teaching and learning.Pre-survey questionnaires showed that none teachers or students had used Kahoot in testing or assessment, only one teacher had heard of Kahoot before but had not actually used it.Regarding the students' habit of technology in learning, all twenty students were engaged with using their smartphones to revise content covered in class.All students agreed to take part in a three-week pilot study using Kahoot as a supporting tool.This involved training teachers in using Kahoot, requiring teachers to design quizzes and brief tasks to test and assess the students' outcomes after classes.The students created a Kahoot account for an analysis of student usage.Technical and networking problems did hinder usage of Kahoot, but both teachers and learners indicated it was still beneficial.The results of using Kahoot in testing and assessment are discussed in terms of the implications for teachers, students, and the curriculum.
Object Counting in Video Surveillance Using Multi-scale Density Map Regression

In this paper, we present an effective convolutional neural network (CNN) for object counting in video surveillance, namely multi-scale density map regressor (MSDMR). In contrast to existing CNN-based methods that achieve high accuracy by means of empirically increasing the model capacity with more complex structures/layers, we focus on a compact CNN. Specifically, the MSDMR is mainly designed with the supervision of multi-scale outputs, in which two CNN stacks estimate coarse- and fine-scale density maps, respectively. The integral of the fine density map provides the count of objects. The two stacks are connected in a cascaded manner and jointly trained such that the overall model can learn discriminative and complementary features to produce expressive performance. Experimental results show that the proposed MSDMR can achieve higher accuracy compared with state-of-the-art methods on the surveillance datasets.
System security requirements: A framework for early identification, specification and measurement of related software requirements

One of the responsibilities of developers is the early definition of non-functional requirements (NFR) at the system level and their related allocation as functional user requirements (FUR) at the software level. To identify some of the widely consensual security elements that could be used in a standards-based security framework, the security-related terminology and views from three sets of international standards (ECSS, IEEE and ISO) are analyzed and integrated. Next, the set of concepts adopted by ISO 19761 for describing software functionality at a lower level are introduced, thereby ensuring that the proposed framework is designed for measurement purposes as well. For the capture of security concepts, the proposed framework is designed using soft-goal interdependency graphs (SIG) and three main system NFR-security types: system availability, confidentiality and integrity. This standards-based system security framework at the function and service level can support software developers to derive such requirements in the early stages of the development process. Finally, an ATM example for the measurement of system security NFR allocated as software FUR within a service-oriented architecture (SOA) is presented.
A Survey on Research of Code Comment

Code comments are one of the effective means for assisting programmers to understand the source code. High-quality code comments play an important role in areas such as software maintenance and software reuse. Good code comments can help programmers understand the role of source code and facilitate comprehension of programs and software maintenance tasks quickly. But in reality, most programmers only pay attention to the code and ignore comments and documents, which greatly reduce the program's readability and maintainability. This paper has compiled the relevant research on code comments so far, mainly including four aspects: automatic generation of code comments, consistency of code comments, classification of code comments, and quality evaluation of code comments. By analyzing relevant methods in this research field, it provides more complete information for future research.
Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients

Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated pipeline to interrogate heterogeneous data to evaluate the use of bone scans using a two different Natural Language Processing (NLP) approaches.Our cohort was divided into risk groups based on Electronic Health Records (EHR). Information on bone scan utilization was identified in both structured data and free text from clinical notes. Our pipeline annotated sentences with a combination of a rule-based method using the ConText algorithm (a generalization of NegEx) and a Convolutional Neural Network (CNN) method using word2vec to produce word embeddings.A total of 5500 patients and 369,764 notes were included in the study. A total of 39% of patients were high-risk and 73% of these received a bone scan; of the 18% low risk patients, 10% received one. The accuracy of CNN model outperformed the rule-based model one (F-measure = 0.918 and 0.897 respectively). We demonstrate a combination of both models could maximize precision or recall, based on the study question.Using structured data, we accurately classified patients' cancer risk group, identified bone scan documentation with two NLP methods, and evaluated guideline adherence. Our pipeline can be used to provide concrete feedback to clinicians and guide treatment decisions.
Identification of Drop Out Students Using Educational Data Mining

Education makes a human being steady, stable and prosperous in his way of leading life. In the same way, the number of higher educated persons in a country can contribute to the development of the country. However, this number decreases due to dropout of students at early stage of the education. Furthermore, if a student can't continue or drop out, the resources of a nation is attenuated. Although nowadays the rate of drop out students is diminishing, till now it is a huge challenge for an educational institution to identify the dropout students at the beginning. To address this issue, several approaches have been discussed in educational data mining to identify the rate of drop out students. Following this line in this paper, a threshold based approach has been proposed to identify dropout students that outperforms than the existing approaches.
RadViz: Improvements on Radial-Based Visualizations++

RadViz is one of the few methods in Visual Analytics able to project high-dimensional data and explain formed structures in terms of data variables. However, RadViz methods have several limitations in terms of scalability in the number of variables, ambiguities created in the projection by the placement of variables along the circular design space, and ability to segregate similar instances into visual clusters. To address these limitations, we propose RadViz++, a set of techniques for interactive exploration of high-dimensional data using a RadViz-type metaphor. We demonstrate the added value of our method by comparing it with existing high-dimensional visualization methods, and also by analyzing a complex real-world dataset having over a hundred variables.
The role of Sprint planning and feedback in game development projects: Implications for game quality

Game development projects adopt Scrum to leverage their flexibility, as game concepts and the customer preferences are highly abstract and unpredictable. The most desirable features in an original game will not be easily identified during the first phase of development but will emerge later in a clear pattern as developers and testers continuously playtest the game. Thus, game development projects use feedback from game testers to understand what they think of various features and concepts, to obtain a better understanding of problem spaces. This study proposes that game tester feedback moderates the effect of Sprint planning on game quality. A field study was conducted using a pair-matched questionnaire in which 102 game development projects participated. Results showed that Sprint planning has a positive effect on game quality. The results also revealed that iterative feedback has a moderating effect on the relationship between Sprint planning and game quality. Theoretical and practical implications are discussed.
Deep Speaker Representation Using Orthogonal Decomposition and Recombination for Speaker Verification

Speech signal contains intrinsic and extrinsic variations such as accent, emotion, dialect, phoneme, speaking manner, noise, music, and reverberation. Some of these variations are unnecessary and are unspecified factors of variation. These factors lead to increased variability in speaker representation. In this paper, we assume that unspecified factors of variation exist in speaker representations, and we attempt to minimize variability in speaker representation. The key idea is that a primal speaker representation can be decomposed into orthogonal vectors and these vectors are recombined by using deep neural networks (DNN) to reduce speaker representation variability, yielding performance improvement for speaker verification (SV). The experimental results show that our proposed approach produces a relative equal error rate (EER) reduction of 47.1% compared to the use of the same convolutional neural network (CNN) architecture on the Vox-Celeb dataset. Furthermore, our proposed method provides significant improvement for short utterances.
Evolutional Methods for Creating Artificial Intelligence of Robotic Technical Systems

This paper considers evolutionary methods of symbolic regression for the creation of artificial intelligence of robotic systems. Methods of symbolic regression are reviewed and the features of their application to the solution of the problem of synthesis of control of robotic systems are indicated. The measure of the complexity of artificial intelligence is determined and the advantage of using the principle of small variations of the basic solution is shown, while creating intelligent control systems. A method of variational analytic programming is described and an example of its use for the synthesis of intellectual control is given.
JourneyCam

Recent HCI research has investigated how digital technologies might enable citizens to identify and express matters of civic concern. We extend this work by describing JourneyCam, a smartphone-based system that enables powered wheelchair users to capture video and sensor data about their experiences of mobility. Thirteen participants used JourneyCam to document journeys, after which the data they collected was used to support discussions around their experiences. Our findings highlight how the system facilitated the articulation of complex embodied experiences, and how the collected data might have particular value in surfacing these experiences to help inform urban design and policymaking. Participants valued the ways in which JourneyCam's moving image and sensor data made hard-to-express sensations apparent, as well as how it enabled them to surface previously unrecognised issues. We conclude by highlighting future opportunities for how such tools might enable citizens to inform and influence civic governance.
Adaptive Online Learning With Regularized Kernel for One-Class Classification

In the past few years, kernel-based one-class extreme learning machine (ELM) receives quite a lot of attention by researchers for offline/batch learning due to its noniterative and fast learning capability. This paper extends this concept for adaptive online learning with regularized kernel-based one-class ELM classifiers for detection of outliers, and are collectively referred to as ORK-OCELM. Two frameworks, viz., boundary and reconstruction, are presented to detect the target class in ORK-OCELM. The kernel hyperplane-based baseline one-class ELM model considers whole data in a single chunk, however, the proposed one-class classifiers are adapted in an online fashion from the stream of training samples. The performance of ORK-OCELM is evaluated on a standard benchmark as well as synthetic datasets for both types of environments, i.e., stationary and nonstationary. While evaluating on stationary datasets, these classifiers are compared against batch learning-based one-class classifiers. Similarly, while evaluating on nonstationary datasets, the comparison is done with incremental learning-based online one-class classifiers. The results indicate that the proposed classifiers yield better or similar outcomes for both. In the nonstationary dataset evaluation, adaptability of the proposed classifiers in a changing environment is also demonstrated. It is further shown that the proposed classifiers have large stream data handling capability even under limited system memory. Moreover, the proposed classifiers gain significant time improvement compared to traditional online one-class classifiers (in all aspects of training and testing). A faster learning ability of the proposed classifiers makes them more suitable for real-time anomaly detection.
Image Co-Segmentation via Locally Biased Discriminative Clustering

Object co-segmentation aims at simultaneously extracting common objects appeared in multiple images. In this paper, we propose a novel object co-segmentation method in which we formulate the image co-segmentation as a locally biased discriminative clustering problem. Specifically, we add a seed vector and a constraint term into the framework of discriminative clustering to constrain the segmentation result bias to this seed vector. In order to deal with the co-segmentation problem with indefinite number of common foreground objects, we design a Markov Random Field (MRF) based method to extract common objects prior. The extracted common objects prior is then added into the discriminative clustering and treated as the seed vector to constrain the segmentation result biased to it. Under the supervision of this prior, the segmentation result can find more common objects and becomes more complete and meaningful. In addition, we present a new segmentation process where we alternately update the MRF-based model and discriminative clustering to refine the common objects prior and the segmentation result. Finally, we test the proposed method on three benchmark datasets, iCoseg, Coseg-Rep, and THUR15K. The experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.
Improvement of (n, n)-multi-secret image sharing schemes based on Boolean operations

In a (n, n)-secret image sharing scheme, a secret image is distributed among n parties in such a way that cooperation of all the shares is needed for reconstruction of the original secret image. Moreover, in these schemes, we have the security requirement which states that fewer than n shares should not reveal any information about the secret. Recently, three efficient (n, n)-multi-secret image sharing schemes based on Boolean operations are proposed. The goal of this paper is to prove that these schemes don't satisfy the threshold security, i.e., with knowledge of (n−1) or fewer shares, considerable information about the secret image can be obtained using efficient computations. We further propose an improvement which successfully overcomes the reported security flaw. We show that the improved scheme generates pseudo-random shares and outperforms the recent secure Boolean-based schemes in computational cost.