Reviewerly/exHarmony huggingface dataset:
Decision Support for Breast Cancer Detection: Classification Improvement Through Feature Selection

Several statistical-based approaches have been developed to support medical personnel in early breast cancer detection. This article presents a method for feature selection aimed at classifying cases into categories based on patients' breast tissue measures and protein microarray. The effectiveness of this feature selection strategy was evaluated against the commonly used Wisconsin Breast Cancer Database-WBCD (with several patients and fewer features) and a new protein microarray data set (with several features and fewer patients). Features were ranked according to a feature importance index that combines parameters emerging from the unsupervised method of principal component analysis and the supervised method of Bhattacharyya distance. Observations of a training set were iteratively categorized into malignant and benign cases through 3 classification techniques: k-Nearest Neighbor, linear discriminant analysis, and probabilistic neural network. After each classification, the feature with the smallest importance index was removed, and a new categorization was carried out until there was only one feature left. The subset yielding maximum accuracy was used to classify observations in the testing set. Our method yielded average 99.17% accurate classifications in the testing set while retaining average 4.61 out of 9 features in the WBCD, which is comparable to the best results reported by the literature on that data set, with the advantage of relying on simple and widely available multivariate techniques. When applied to the microarray data, the method yielded average accuracy of 98.30% while retaining average 2.17% of the original features. Our results can aid health-care professionals during early diagnosis of breast cancer.
Understanding Emoji Interpretation through User Personality and Message Context

Emojis are commonly used as non-verbal cues in texting, yet may also lead to misunderstandings due to their often ambiguous meaning. User personality has been linked to understanding of emojis isolated from context, or via indirect personality assessment through text analysis. This paper presents the first study on the influence of personality (measured with BFI-2) on understanding of emojis, which are presented in concrete mobile messaging contexts: four recipients (parents, friend, colleague, partner) and four situations (information, arrangement, salutory, romantic). In particular, we presented short text chat scenarios in an online survey (N=646) and asked participants to add appropriate emojis. Our results show that personality factors influence the choice of emojis. In another open task participants compared emojis found as semantically similar by related work. Here, participants provided rich and varying emoji interpretations, even in defined contexts. We discuss implications for research and design of mobile texting interfaces.
A Session-Packets-Based Encrypted Traffic Classification Using Capsule Neural Networks

With the enhancement of network security awareness and excellent applicability of encryption protocols, identifying encrypted traffic is a critical and fundamental task for many network protection applications. Conventional port-based and deep packet inspection (DPI) approaches can't classify encrypted traffic effectively. Recent studies show that the approaches based on machine learning especially deep learning are effective for the task. However, these studies ignore the feature attributes of traffic like the location of fixed strings and change the effective features behind the traffic. In this paper, we propose a novel session-packets-based encrypted network traffic classification model using capsule neural networks (CapsNet), called SPCaps. The SPCaps introduces a twice-segmentation mechanism to dilute the interference traffic and increase the weight of effective traffic. And then it learns the spatial characteristics of encrypted traffic using CapsNet and outputs the results of encrypted traffic classification by a softmax classifier. We evaluate the proposed model for encrypted traffic classification in terms of service and application on publicly available ISCX VPN-nonVPN dataset. The experimental results demonstrate that SPCaps outperforms the state-of-the-art encrypted traffic classification approaches.
When Wireless Video Streaming Meets AI: A Deep Learning Approach

Wireless multimedia big data contains valuable information on users' behavior, content characteristics and network dynamics, which can drive system design and optimization. The fundamental issue is how to mine data intelligence and further incorporate them into wireless multimedia systems. Motivated by the success of deep learning, in this work we propose and present an integration of wireless multimedia systems and deep learning. We start with decomposing a wireless multimedia system into three components, including end-users, network environment, and servers, and present several potential topics to embrace deep learning techniques. After that, we present deep learning based QoS/QoE prediction and bitrate adjustment as two case-studies. In the former case, we present an end-to-end and unified framework that consists of three phases, including data preprocessing, representation learning, and prediction. It achieves significant performance improvement in comparison to the best baseline algorithm (88 percent vs. 80 percent). In the latter case, we present a deep reinforcement learning based framework for bitrate adjustment. Evaluating the performance with a real wireless dataset, we show that the perceived video QoE average bitrate, rebuffering time and bitrate variation can be improved significantly.
Deep Domain Adaptation for Vulnerable Code Function Identification

Due to the ubiquity of computer software, software vulnerability detection (SVD) has become crucial in the software industry and in the field of computer security. Two significant issues in SVD arise when using machine learning, namely: i) how to learn automatic features that can help improve the predictive performance of vulnerability detection and ii) how to overcome the scarcity of labeled vulnerabilities in projects that require the laborious labeling of code by software security experts. In this paper, we address these two crucial concerns by proposing a novel architecture which leverages deep domain adaptation with automatic feature learning for software vulnerability identification. Based on this architecture, we keep the principles and reapply the state-of-the-art deep domain adaptation methods to indicate that deep domain adaptation for SVD is plausible and promising. Moreover, we further propose a novel method named Semi-supervised Code Domain Adaptation Network (SCDAN) that can efficiently utilize and exploit information carried in unlabeled target data by considering them as the unlabeled portion in a semi-supervised learning context. The proposed SCDAN method enforces the clustering assumption, which is a key principle in semi-supervised learning. The experimental results using six real-world software project datasets show that our SCDAN method and the baselines using our architecture have better predictive performance by a wide margin compared with the Deep Code Network (VulDeePecker) method without domain adaptation. Also, the proposed SCDAN significantly outperforms the DIRT-T which to the best of our knowledge is currently the-state-of-the-art method in deep domain adaptation and other baselines.
Approximate Graph Laplacians for Multimodal Data Clustering

One of the important approaches of handling data heterogeneity in multimodal data clustering is modeling each modality using a separate similarity graph. Information from the multiple graphs is integrated by combining them into a unified graph. A major challenge here is how to preserve cluster information while removing noise from individual graphs. In this regard, a novel algorithm, termed as CoALa, is proposed that integrates noise-free approximations of multiple similarity graphs. The proposed method first approximates a graph using the most informative eigenpairs of its Laplacian which contain cluster information. The approximate Laplacians are then integrated for the construction of a low-rank subspace that best preserves overall cluster information of multiple graphs. However, this approximate subspace differs from the full-rank subspace which integrates information from all the eigenpairs of each Laplacian. Matrix perturbation theory is used to theoretically evaluate how far approximate subspace deviates from the full-rank one for a given value of approximation rank. Finally, spectral clustering is performed on the approximate subspace to identify the clusters. Experimental results on several real-life cancer and benchmark data sets demonstrate that the proposed algorithm significantly and consistently outperforms state-of-the-art integrative clustering approaches.
Informative variable identifier: Expanding interpretability in feature selection

There is nowadays an increasing interest in discovering relationships among input variables (also called features) from data to provide better interpretability, which yield more confidence in the solution and provide novel insights about the nature of the problem at hand. We propose a novel feature selection method, called Informative Variable Identifier (IVI), capable of identifying the informative variables and their relationships. It transforms the input-variable space distribution into a coefficient-feature space using existing linear classifiers or a more efficient weight generator that we also propose, Covariance Multiplication Estimator (CME). Informative features and their relationships are determined analyzing the joint distribution of these coefficients with resampling techniques. IVI and CME select the informative variables and then pass them on to any linear or nonlinear classifier. Experiments show that the proposed approach can outperform state-of-art algorithms in terms of feature identification capabilities, and even in classification performance when subsequent classifiers are used.
Implementation of Hand Gesture Recognition Device Applicable to Smart Watch Based on Flexible Epidermal Tactile Sensor Array

Ever since the development of digital devices, the recognition of human gestures has played an important role in many Human-Computer interface applications. Various wearable devices have been developed, and inertial sensors, magnetic sensors, gyro sensors, electromyography, force-sensitive resistors, and other types of sensors have been used to identify gestures. However, there are different drawbacks for each sensor, which affect the detection of gestures. In this paper, we present a new gesture recognition method using a Flexible Epidermal Tactile Sensor based on strain gauges to sense deformation. Such deformations are transduced to electric signals. By measuring the electric signals, the sensor can estimate the degree of deformation, including compression, tension, and twist, caused by movements of the wrist. The proposed sensor array was demonstrated to be capable of analyzing the eight motions of the wrist, and showed robustness, stability, and repeatability throughout a range of experiments aimed at testing the sensor array. We compared the performance of the prototype device with those of previous studies, under the same experimental conditions. The result shows our recognition method significantly outperformed existing methods.
A 3D Reconstruction Pipeline of Urban Drainage Pipes Based on MultiviewImage Matching Using Low-Cost Panoramic Video Cameras

Urban drainage pipe networks have complex spatial contributions, andthey are now facing problems such as damage, defects, and aging. A rapid and high-precision pipe inspection strategy is thekey to ensuring thesustainable development of urban water supply and drainage system. In this paper, a three-dimensional (3D) reconstruction pipeline of urban drainage pipes based on multiview image matching using low-cost panoramic video cameras is proposed, which provides an innovative technical approach for pipe inspection. Firstly, we extracted frames from the panoramic video of the pipes andcorrected the geometric distortion using a spherical reprojection to obtain multiview pipe images. Second, the robust feature matching method using support lines and affine-invariant ratios isintroduced to conduct pipe image matching. Finally, the photogrammetric processing, using structure from motion (SfM) and dense reconstruction, wasintroduced to achieve the 3D modeling of drainage pipes. Several typical drainage pipes and shafts of the real scenes were taken for the 3D reconstruction experiments. Theresults show that our strategy can realize high-precision 3D reconstruction of different types of pipes, which can provide effective technical support for rapid and efficient inspection of urban pipes with broad application prospects in the daily management of sustainable urban drainage systems (SUDSs).
Use of Motion Capture in 3D Animation: Motion Capture Systems, Challenges, and Recent Trends

In today's world capturing of motion from a video stream is a growing and challenging area. Motion capture system (Mocap) is widely used in today's realistic world including sports, entertainment, video gaming and many more. In this paper, firstly we provide an overview of different types of Mocap on the basis of Marker-based Mocap and Markerless Mocap along with their merits and demerits. Secondly, we discuss about the current trends for detection and tracking based on their motion in realistic scenarios and then, current challenging issues which are still an open challenge to researchers, academicians and experts in this area.
User Story Extraction from Online News for Software Requirements Elicitation: A Conceptual Model

To specify good requirements, system analysts need to understand the domain knowledge of the system. There are several techniques in requirements elicitation to improve domain knowledge understanding, such as user interviews, questioners, document analysis, and brainstorming. Most of these techniques require profound stakeholder involvement. However, not all software projects can do this task due to limited time or availability of stakeholders. In agile software development, the user story is the de facto standard used for capturing and writing functional requirements. The user story is an appropriate format and easy to understand for writing the results of requirements elicitation. This study purposes a conceptual model to extract user story from online news for improving domain knowledge understanding. The information in the online news contained lesson learned related to certain events. This information may improve the functionality of the software products. The user story consists of three aspects, namely: who, what, and why. Aspect of who represents the role or user, aspect of what shows the purpose or feature, while the aspect of why explains the reason. This format can summarize the lessons learned in the news. Our experimental results indicate that this conceptual model can extract user story from online news. The model manages to extract 105 user stories from 92 aspects of what/why candidate and 109 aspects of who candidate.
An Approach to Classify Human Activities in Real-time from Smartphone Sensor Data

According to the scenario of the last few decades, sensor-based human activity recognition has gained considerable research attention due to its novel applications in health care, machine learning, human-computer interaction, etc. In this research, we have investigated several data pre-processing methods to recognize static and dynamic activities more accurately based on smartphone sensor data. The addition of magnitude and jerk-based features ensure the recognition of activities independent of the orientation of the smartphone in daily life. Besides, we have proposed a new feature namely Average peak-trough-distance (APTD), which ensures better recognition accuracy. We have also discussed a sliding window technique without overfitting, which utilizes observation of test data from previous windows with 50% overlapping for the detection of activity in real-time. Moreover, we have evaluated our proposed method showing a comparison between four classifiers. We have found the best accuracy for HASC2010corpus dataset using our proposed feature along with the multi-class Support Vector Machine, where we have utilized the Grid Search Cross Validation optimization technique to tune the hyper-parameters. We have also shown that the proposed approach outperforms the naive methods for the detection of activity using smartphones more precisely in real-time applications.
Driver assistance systems: analysis, tests and the safety case. ISO 26262 and ISO PAS 21448

Abstract Modern automotive system must comply with strict safety requirements. This paper focuses on two aspects of safety: functional safety per ISO 26262 (FS) and the safety of the intended functionality (SOTIF) per ISO PAS 21448. The FS encompasses a lifecycle ensuring the absence of unreasonable risks due to internal failures of the system. SOTIF concentrates on non-deterministic parts and algorithms (e.g. neural networks), as full specification of their performance is out of reach now. At the same time, FS and its lifecycle are better known to the community and have better history of implementation. This paper presents an approach to integration of FS and SOTIF requirements based on FS lifecycle.
Exergaming in VR: The Impact of Immersive Embodiment on Motivation, Performance, and Perceived Exertion

Digital games which are controlled by physical movements - so-called exergames - have beneficial effects on the player's motivation to exercise. Since VR exergames can facilitate the potential impact on the player's motivation, we investigate the role of immersion and related constructs for a room-scale VR exergame. Thus, we developed four different versions of a digital exergame by manipulating the immersion (room-scale VR vs. common screen), perspective (first vs. third person) and tracking fidelity (HTC Vive Tracker vs. Microsoft Kinect). Our results of a study with 47 participants suggest that immersion and perspective in particular can significantly increase the player's presence, embodiment, motivation, and performance independent of the perceived exertion which was not affected by any condition. Our research gives novel insights which advantages modern room-scale VR systems can offer and how immersion influences the relationship between motivation, performance, and perceived exertion.
HFA-Net: 3D Cardiovascular Image Segmentation with Asymmetrical Pooling and Content-Aware Fusion

Automatic and accurate cardiovascular image segmentation is important in clinical applications. However, due to ambiguous borders and subtle structures (e.g., thin myocardium), parsing fine-grained structures in 3D cardiovascular images is very challenging. In this paper, we propose a novel deep heterogeneous feature aggregation network (HFA-Net) to fully exploit complementary information from multiple views of 3D cardiac data. First, we utilize asymmetrical 3D kernels and pooling to obtain heterogeneous features in parallel encoding paths. Thus, from a specific view, distinguishable features are extracted and indispensable contextual information is kept (rather than quickly diminished after symmetrical convolution and pooling operations). Then, we employ a content-aware multi-planar fusion module to aggregate meaningful features to boost segmentation performance. Further, to reduce the model size, we devise a new DenseVoxNet model by sparsifying residual connections, which can be trained in an end-to-end manner. We show the effectiveness of our new HFA-Net on the 2016 HVSMR and 2017 MM-WHS CT datasets, achieving state-of-the-art performance. In addition, HFA-Net obtains competitive results on the 2017 AAPM CT dataset, especially on segmenting subtle structures among multi-objects with large variations, illustrating the robustness of our new segmentation approach.
A New Activation Function for Deep Neural Network

Following machine learning, deep learning is a new emerging field of research in data science. The accuracy of deep learning models is high as compared to machine learning models due to the concept of hidden layers, activation functions, changing associated weights, decay functions. In this paper the focus is specifically at activation functions. Along with the introspection of different activation functions, a new function is also being introduced and explored experimentally.
A Non-Linear Technique for Effective Software Effort Estimation using Multi-Layer Perceptrons

An effective predictor for software effort estimation is highly desirable for the successful delivery of product in time and within the budget. Both either the underestimation or the overestimation endangers the entire business and market value too. Number of studies has been made in this direction but no univocal conclusion has been attained. It is still an open problem. In this work, a non-linear technique is implemented for the effort estimation using Multi-Layer Perceptrons with back propagation algorithm. The dataset used for this work is Maxwell Dataset. Then the comparison is made with the linear regression technique. The result shows that nonlinear technique is much better than linear technique with the value of 0.13 for MdMRE whereas the linear technique results the value of 0.36 for MdMRE. The results show that attribute selection also affects the overall performance.
Rapid evaluation of micro-scale photovoltaic solar energy systems using empirical methods combined with deep learning neural networks to support systems’ manufacturers

Solar energy is becoming one of the most attractive renewable sources. In many cases, due to a wide range of financial or installation limitations, off-grid small scale micro power panels are favoured as modular systems to power lighting in gardens or to be integrated together to power small devices such as mobile phone chargers and distributed smart city facilities and services. Manufacturers and systems' integrators have a wide range of options of micro-scale photo voltaic panels to choose from. This makes the selection of the right panel a challenging task and risky investment. To address this and to help manufacturers, this paper suggests and evaluates a novel approach based on integrating empirical lab-testing with short-term real data and neural networks to assess the performance of micro-scale photovoltaic panels and their suitability for a specific application in specific environment. The paper outlines the combination of lab testing power output under seasonal and hourly conditions during the year combined with environmental and operating conditions such as temperature, dust accumulation and tilt angle performance. Based on the lab results, a short in-situ experimental work is implemented and the performance over the year in the selected location in Kuwait is evaluated using deep learning neural networks. The findings of this approach are compared with simulation and long-term real data. The results show a maximum error of 23% of the neural network output when compared with the actual data, and a correlation values with previous work within 87.3% and 91.9% which indicate that the proposed approach could provide an experimental rapid and accurate assessment of the expected power output. Hence, supporting the rapid decision-making process for manufacturers and reducing investment risks.