Reviewerly/exHarmony huggingface dataset:
Focused Laser Differential Interferometer Response to a Controlled Phase Object

In this paper, we characterize the response of the focused laser differential interferometer (FLDI) instrument to a controlled phase object. A thin, uni-directional, controlled phase object is created by vibrating a cylindrical lens in the beam path. Attached to this lens is an accelerometer from which displacement may be measured and, with knowledge of the lens geometry, the phase response of a differential interferometer may be calculated. We compare the phase response derived from the accelerometer to that measured by the FLDI. This accelerometer-derived phase change was found to be in excellent agreement with the FLDI-derived phase change for both single and double FLDI setups with different phase-object strengths (focal lengths) in both amplitude and frequency.
Generalized Orthogonal Discrete W Transform and Its Fast Algorithm

Based on the generalized discrete Fourier transform, the generalized orthogonal discrete W transform and its fast algorithm are proposed and derived in this paper. The orthogonal discrete W transform proposed by Zhongde Wang has only four types. However, the generalized orthogonal discrete W transform proposed by us has infinite types and subsumes a family of symmetric transforms. The generalized orthogonal discrete W transform is a real-valued orthogonal transform, and the real-valued orthogonal transform of a real sequence has the advantages of simple operation and facilitated transmission and storage. The generalized orthogonal discrete W transforms provide more basis functions with new frequencies and phases and hence lead to more powerful analysis and processing tools for communication, signal processing, and numerical computing.
A new approach based on the discriminant system of polynomial for robust stability and stabilization of two-dimensional systems

In this paper, we present necessary and sufficient stability and robust stability conditions for two-dimensional (2D) systems described by the Fornasini-Marchesini (FM) second model in terms of the discriminant systems of polynomial. This paper simplifies the traditional method of stability into a tractable method by the fractional linear transformation (FLT). More specifically, we reduce the stability analysis to a easy issue whether some polynomials are positive definite. Then we use the same idea to consider the stabilization and robust stabilization issues. Finally, the effectiveness of the proposed results is demonstrated by a practical example and two numerical examples.
Non-Rigid Point Set Registration Based on New Shape Context and Local Structure Constraint

Point set registration is an important issue in many fields. The point set registration methods based on Gaussian mixture models (GMM) is popular in recent years. In this kind of method, the two point sets are regarded as the centroids of GMM and data points, respectively. In this paper, we use a new shape context to assign the membership probabilities of GMM and the Expectation-Maximization method is used to solve GMM. To improve the precision of transformation parameters, we add the local structure constraint in our method. Experimental results on several datasets show the advantages of the proposed method.
Dual-Features Student-<i>t</i> Distribution Mixture Model Based Remote Sensing Image Registration

In the work, we present a novel multiviewpoint and multitemporal remote sensing image registration method based on a dual-features Student- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> distribution mixture model (DSMM) under a variational Bayesian (VB) framework. The main contributions of the work are: 1) guided image filter (GIF) is adopted to smooth edges and strengthen ridges of images for heightening characteristics of feature point; 2) a Student- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> distribution mixture model (SMM) based DSMM designs a global and local descriptor to estimate correspondences from local to global scale; and 3) local structure constraints are designed to preserve relationships of neighbors of points and the scale of neighborhood structure of points to constrain transformation. The experimental results demonstrate the better performance of our DSMM against five state-of-the-art methods.
Phrase Embedding Based Multi Document Summarization with Reduced Redundancy using Maximal Marginal Relevance

In the Internet Era of Information due to the exponential increase of textual data, Multi Document Summarization (MDS) is becoming an inevitable NLP task that aims to produce a concise representation of the main idea of multiple related documents. MDS becomes difficult and challenging to produce a non-redundant summary because of the lexical diversity of multiple authors. This paper proposes a new multi-document summarization system based on phrase embedding and greedy Maximal Marginal Relevance (MMR) algorithm. This approach considers phrases as the basic meaningful semantic unit of the sentences to understand and summarize documents. Embedding techniques are employed to learn the vector representation of phrases to identify similar phrases semantically. Finally, an MMR based greedy algorithm is used to select sentences with important phrases while reducing the redundancy among similar phrases. Experimental results on the benchmark dataset DUC 2004 show better performance gains compared with the state-of-the-art baselines.
Cockpit Display Graphics Symbol Detection for Software Verification Using Deep Learning

In Software Development Life-cycle, Verification and Validation plays a very important role, especially in the case of Safety-Critical Industries like Aerospace. Display dashboard consists of multiple static and dynamic objects having affine transformation, graphics overlap, shadows and less inter symbol discriminative features compared to natural images. Manual Software graphics verification is an error-prone and time-consuming activity. In this paper, we propose a novel software graphics verification pipeline to verify graphics symbols and alphanumeric objects as per Software requirements. To the best of our knowledge, our proposed approach is the first study on deep learning-based graphics symbol detection from complex synthetic background which requires high model accuracy. We experiment using Single-shot Multibox Detector (SSD) and You Only Look Once (YOLO v2) to detect different Graphical symbols from display simulator real-time captured video frames. These detected objects are further classified based on their nature. Objects containing alphanumeric digits can be recognized using Optical Character Recognition and dynamic symbols are detected using object detection to infer other properties. Finally, all the extracted properties can be compared with test expectations to verify their correctness. The result shows superior accuracy of the SSD algorithm over other state-of-the-art object detection algorithms for detecting real-time graphics symbols.
Iqra reading verification with mel frequency cepstrum coefficient and dynamic time warping

Abstract Al-Quran is a holy book of guidance from Allah SWT that was revealed through Prophet Muhammad SAW for Muslims. Muslims should learn to read Al-Quran properly and correctly. One of the methods in learning the Al-Quran is the Iqra method. Iqra method is learning in reading and writing the Al-Quran by using Iqra books delivered classically and individually. Learning methods will continue to be developed to adapt to current conditions. As technology develops today, the method of learning the Al-Quran has also developed, one of which is using sound. In this research, the writer will create a system that verifies the sound of reading the Iqra which is one of the basic methods in learning to read the Al-Quran. The system is designed using the Mel Frequency Cepstrum Coefficient (MFCC) method as a voice feature extension and the Dynamic Time Warping (DTW) method for matching the results of the voice features. In this study, the best accuracy in the Iqra reading sound was found to be 82% with 13 MFCC coefficients.
Multi Modal Genre Classification of Movies

With the emergence of platforms like Netftix which keep users engaged through their sophisticated recommendation algorithms, it is of growing importance to accurately predict the genre a movie belongs to, in order to push the right content to viewers. Until recently, content creators found their customers by communicating the plot of a movie through expressive posters or by publishing a short overview of the movie. For an algorithm to try and replicate the perceptions drawn by humans to predict which movies a consumer will be interested in, it is necessary to treat this as a multi-label problem and attribute multiple genres to a single movie. The principal aim of this paper is to reliably predict the different genres under which a movie can be classified, and further check whether a combination of poster and overview of a movie can provide more accurate results than a poster and overview used individually. We evaluate this task by performing a series of experiments that compare and contrast the importance of both, textual features from movie overviews and visual features from the poster. We propose that the ensemble use of both these features using a ResNet50 model and GloVe embeddings, outperforms conventional models that use these features individually, with an average F1-Score of 0.674.
Framework to Measure Agile Software Process Effectiveness in Critical Systems Development

Adopting Agile development life cycle to develop critical systems was investigated in previous research, however the effectiveness of software development process was not systematically measured in the development of critical systems. In this paper, a measurement framework is proposed to evaluate the effectiveness of the Agile software development process and culture when adopted to develop a critical system. The research also discusses how to apply the proposed framework on different case studies.
Smart Ecosystem to Facilitate the Elderly in Ambient Assisted Living

Ambient Assisted Living (AAL) facilities offer personalized care to inhabitants using their profile and surrounding environments. The services provided by AAL listed as health, indoor activities, daily routines and many others. The current focus of the research is to analyze the user’s behaviour to offer different services to them. The AAL system mostly uses various sensors to capture the user’s information. The smart environment uses environmental sensors, object sensors, body sensors and visual sensors as the primary devices. The collected information generally used to detect the activity of daily living (ADL). The research community requires to address added parameters to extend the services offered. The work presented here explores some new strategies to use the firings of environmental sensors. The environmental readings are useful to find the time of detection of any disease using the particular season, indoor air quality, the intensity of sun-rays and finally, the probability calculation of various illnesses during the year. This paper describes the potential role of AAL systems in elderly care by focusing on specific issues using a scenario-based approach.
An Automatic Approach to Measure and Visualize Coupling in Object-Oriented Programs

The task of measuring coupling in software systems is important to evaluate the software quality such as maintainability and reusability. The software engineers and developers spend much time and effort in order to accomplish this task. Measuring the coupling automatically helps in getting rid of the manual measurement inadequacy. In this paper, we proposed an approach to automatically measure and visualize the coupling among classes of object-oriented programs. The generated representations provide an overall recognition of the coupling in an effective and interactive way. The approach parses the program source code, using an existing tool, into an XML file, and extracts the class tokens according to the definitions of the coupling metrics. Then, it determines the coupling relationships through matching these tokens with other classes tokens. Finally, it generates interactive visualizations of the coupling utilizing several charts. A case study has been conducted to validate the proposed approach. The results indicated that the generated visualizations facilitate a comprehensive realization of the program coupling, and leads to a proper estimation of the software quality in terms of its coupling measure.
Human activity recognition using robust adaptive privileged probabilistic learning

In this work, a supervised probabilistic approach is proposed that integrates the learning using privileged information (LUPI) paradigm into a hidden conditional random field (HCRF) model, called HCRF+, for human action recognition. The proposed model employs a self-training technique for automatic estimation of the regularization parameters of the objective function. Moreover, the method provides robustness to outliers by modeling the conditional distribution of the privileged information by a Student’s t-density function, which is naturally integrated into the HCRF+ framework. The proposed method was evaluated using different forms of privileged information on four publicly available datasets. The experimental results demonstrate its effectiveness concerning the state of the art in the LUPI framework using both hand-crafted and deep learning-based features extracted from a convolutional neural network.
RGB-depth Fusion Framework for Object Detection in Autonomous Vehicles

We present an early fusion framework for robust object detection in autonomous vehicles. This framework firstly employs Monodepth as a self-supervised learning method to automatically infer a dense depth image from a single color input image. Then, the RGB image and its corresponding depth image are processed by a deep Convolutional Neural Networks (CNNs) to predict multiple 2D bounding boxes. We conduct experiments on the challenging KITTI benchmark dataset. The experimental results show that the features learnt from our fusion framework, when fused with the features learnt from depth-only and RGB-only architectures, outperform the state of the art on RGB-depth category recognition. We also investigated on performance of our fusion framework when it utilizes various sources (such as monocular and stereo imagery or both imageries) for generating the depth image.
Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function

Images recorded during the lifetime of computer vision based systems undergo a wide range of illumination and environmental conditions affecting the reliability of previously trained machine learning models. Image normalization is hence a valuable preprocessing component to enhance the models' robustness. To this end, we introduce a new strategy for the cost function formulation of encoder-decoder networks to average out all the unimportant information in the input images (e.g. environmental features and illumination changes) to focus on the reconstruction of the salient features (e.g. class instances). Our method exploits the availability of identical sceneries under different illumination and environmental conditions for which we formulate a partially impossible reconstruction target: the input image will not convey enough information to reconstruct the target in its entirety. Its applicability is assessed on three publicly available datasets. We combine the triplet loss as a regular- izer in the latent space representation and a nearest neighbour search to improve the generalization to unseen illuminations and class instances. The importance of the aforementioned post-processing is highlighted on an automotive application. To this end, we release a synthetic dataset of sceneries from three different passenger compartments where each scenery is rendered under ten different illumination and environmental conditions: https://sviro.kl.dfki.de.
Classification of Corpus Callosum Layer in Mid-saggital MRI Images Using Machine Learning Techniques for Autism Disorder

Autism is a neuro developmental disorder that affects the social interaction and communication skills of the children. It is characterized by repetitive behavior, lack of eye contact and unusual facial expressions. Corpus Callosum (CC) is the largest white matter area in the central nervous system that helps in transmission of information between both the hemispheres of brain. In autism kids, CC in the brain region shrinks and shape variations occur, making it as the region of interest with respect to diagnosis of autism disorder. Though there are many methods to segment and classify CC, there is still a need for accurate segmentation and automatic classification of CC. Since CC shares similar intensity and close proximity to other parts of the brain, segmentation of only CC region becomes challenging. To address this challenge, in the proposed work level set segmentation technique is used to segment Corpus callosum and the segmented images are validated against the ground truth using jaccard and dice index. From the segmented images geometric, texture and statistical features are extracted. Feature reduction methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are incorporated for selecting the most significant set of features. Machine learning algorithms such as Support vector machine (SVM) and Extreme learning machine (ELM) are proposed to classify the image as normal and abnormal. The proposed algorithm demonstrates the classification accuracy of 97% and 96.5% using SVM and ELM respectively.
No-reference quality assessment of HEVC video streams based on visual memory modelling

Providing adequate Quality of Experience (QoE) to end-users is crucial for streaming service providers. In this paper, in order to realize automatic quality assessment, a No-Reference (NR) bitstream Human-Vision-System-(HVS)-based video quality assessment (VQA) model is proposed. Inspired by discoveries from the neuroscience community, which suggest there is a considerable overlap between active areas of the brain when engaging in video quality assessment and saliency detection tasks, saliency maps are used in the proposed method to improve the quality assessment accuracy. To this end, saliency maps are first generated from features extracted from the HEVC bitstream. Then, saliency map statistics are employed to create a model of visual memory. Finally, a support vector regression pipeline learns an estimate of the video quality from the visual memory, saliency, and frame features. Evaluations on SJTU dataset indicate that the proposed bitstream based no-reference video quality assessment algorithm achieves a competitive performance.
Hierarchical GNC for High Cardinality Random Finite Set Based Teams with Autonomous Mission Planning

A hierarchical structure for Guidance, Navigation, and Control (GNC) of high cardinality teams is proposed. This hierarchy separates the team behavior from the individual agent's GNC and provides an end-to-end framework for the GNC system. It makes use of a central observer/command center to observe all agents and calculate team/group behavior. Random finite set theory is used by the observer to model the team and provide an abstraction from the individual agents by grouping them into Gaussians. This abstraction is a key part of the proposed hierarchy, and allows scalability to high cardinality teams. Additionally, an automated mission planner is designed for optimal group target assignment and trajectory optimization. The combined hierarchy and mission planner are tested against three scenarios, beginning with a simple case of equal targets, groups, and desired distributions of agents, up to unequal desired distributions, group obstacles, and agent death. The algorithm is shown to provide the desired team behavior while separating actions implementable by agents and actions implementable by a central observer.
Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living

Wearable systems have unlocked new sensing paradigms in various applications such as human activity recognition, which can enhance effectiveness of mobile health applications. Current systems using wearables are not capable of understanding their surroundings, which limits their sensing capabilities. For instance, distinguishing certain activities such as attending a meeting or class, which have similar motion patterns but happen in different contexts, is challenging by merely using wearable motion sensors. This article focuses on understanding user's surroundings, i.e., environmental context, to enhance capability of wearables, with focus on detecting complex activities of daily living (ADL). We develop a methodology to automatically detect the context using passively observable information broadcasted by devices in users’ locale. This system does not require specific infrastructure or additional hardware. We develop a pattern extraction algorithm and probabilistic mapping between the context and activities to reduce the set of probable outcomes. The proposed system contains a general ADL classifier working with motion sensors, learns personalized context, and uses that to reduce the search space of activities to those that occur within a certain context. We collected real-world data of complex ADLs and by narrowing the search space with context, we improve average F1-score from 0.72 to 0.80.
Enabling Maritime Risk Assessment Using Natural Language Processing-based Deep Learning Techniques

Maritime vessels around the world risk encountering dangerous situations during their voyages such as piracy, kidnappings, hijackings, and smugglings. Such dangers may put crews at risk and potentially cause extensive damage to both the vessel and its cargo. Maritime risk assessment is a process of analyzing such factors to positively impact the safety and timeliness of operations. Analyzing the high-risk areas in seas and oceans can help vessels route around areas with previously recorded high levels of dangerous activities. This information is scraped and extracted from various maritime documents, including newspaper articles, social media, incident blogs, and operational reports. After textual data sources are parsed, it is possible to use this extracted information as training datasets into a Machine Learning (ML) model using Natural Language Processing (NLP) Deep Learning (DL) techniques. With these methods, we can perform document classification, risk extraction, text summarization, topic modeling, and information extraction in an automated manner. Methods that are used include the bag-of words (BOW) approach and, in particular, the use of Named Entity Recognition (NER) to identify specific areas around the world that are considered a risk to a particular voyage. Once this information is processed, the model can determine and aggregate specific areas of high-risk activity. We propose a Maritime Event Log (MEL) pipeline using DL methods. This pipeline includes binary document classification (DC) to extract positive instances of incidents, an incident type classification (IC,) and an information extraction module (IE) including date and location and ship type extraction. With such information available, it is possible to avoid dangerous situations, which may have a significant level of impact on the safety and success rate of maritime operations.