Reviewerly/exHarmony huggingface dataset:
Mixtures of peaked power Batschelet distributions for circular data with application to saccade directions

Circular data are encountered throughout a variety of scientific disciplines, such as in eye movement research as the direction of saccades. Motivated by such applications, mixtures of peaked circular distributions are developed. The peaked distributions are a novel family of Batschelet-type distributions, where the shape of the distribution is warped by means of a transformation function. Because the Inverse Batschelet distribution features an implicit inverse that is not computationally feasible for large or complex data, an alternative called the Power Batschelet distribution is introduced. This distribution is easy to compute and mimics the behavior of the Inverse Batschelet distribution. Inference is performed in both the frequentist framework, through Expectation–Maximization (EM) and the bootstrap, and the Bayesian framework, through MCMC. All parameters can be fixed, which may be done by assumption to reduce the number of parameters. Model comparison can be performed through information criteria or through bridge sampling in the Bayesian framework, which allows performing a wealth of hypothesis tests through the Bayes factor. An R package, flexcircmix, is available to perform these analyses.
Minimal Unique Substrings and Minimal Absent Words in a Sliding Window

A substring u of a string T is called a minimal unique substring (MUS) of T if u occurs exactly once in T and any proper substring of u occurs at least twice in T. A string w is called a minimal absent word (MAW) of T if w does not occur in T and any proper substring of w occurs in T. In this paper, we study the problems of computing MUSs and MAWs in a sliding window over a given string T. We first show how the set of MUSs can change in a sliding window over T, and present an $$O(n\log \sigma )$$-time and O(d)-space algorithm to compute MUSs in a sliding window of width d over T, where $$\sigma $$ is the maximum number of distinct characters in every window. We then give tight upper and lower bounds on the maximum number of changes in the set of MAWs in a sliding window over T. Our bounds improve on the previous results in Crochemore et al. (2017).
Inverse Dynamics Modeling of Robotic Manipulator with Hierarchical Recurrent Network

Inverse dynamics modeling is a critical problem for the computed-torque control of robotic manipulator. This paper presents a novel recurrent network based on the modified Simple Recurrent Unit (SRU) with hierarchical memory (SRU-HM), which is achieved by the nested SRU structure. In this way, it enables the capability to retain the long-term information in the distant past, compared with the conventional stacked structure. The hidden state of SRU is able to provide more complete information relevant to current prediction. Experimental results demonstrate that the proposed method can improve the accuracy of dynamics model greatly, and outperforms the state-of-the-art methods.
Using evolutionary algorithms to select text features for mining design rationale

Abstract At its heart, design is a decision-making process. These decisions, and the reasons for making them, comprise the design rationale (DR) for the designed artifact. If available, DR provides a comprehensive record of the reasoning behind the decisions made during the design. Unfortunately, while this information is potentially quite valuable, it is usually not explicitly captured. Instead, it is often buried in other design and development artifacts. In this paper, we study how to identify rationale from text documents, specifically software bug reports and design discussion transcripts. The method we examined is statistical text mining where a model is built to use document features to classify sentences. Choosing which features are most likely to be good predictors is important. We studied two evolutionary algorithms to optimize feature selection – ant colony optimization and genetic algorithms. We found that for many types of rationale, models built with an optimized feature set outperformed those built using all the document features.
Research on Coverless Text Steganography Based on Multi-rule Language Models Alternation

Information hiding technology usually uses video, audio, image, and text as carriers. Among them, text redundancy is less and it is difficult to hide information. However, the text is widely used, so text-based information hiding has very important research significance. In this paper, a coverless text steganography based on multi-rule language models alternation is proposed. It is different from the traditional steganographic text generation method based on a single gram model. The method proposed in the paper tries to combine models under different language rules alternately, and tries to extract more language features from the training text. In order to verify the effectiveness of the method, we design some relevant experiments. The results show that, compared with other text steganography methods, the method proposed in the paper not only satisfies the concealment but also improves the hiding capacity.
Real-Time Hand Detection using Convolutional Neural Networks for Costa Rican Sign Language Recognition

Sign language is the natural language for the deaf, something that comes naturally as a form of non-verbal communication between signers, ruled by a set of grammars that is in constant evolution as the universe of signs represents a small fraction of all words in Spanish. This limitation combined with the lack of knowledge in sign language by verbal speakers creates a separation where both parties (signers and non-signers) are unable to efficiently communicate, a problem that increases under a specific context such as emergency situations, where first-response teams such as EMTs, firefighters or police officers might be unable to properly attend an emergency as interactions between the involved parties becomes a barrier for decision making when time is scarce. Developing a cognitive-capable tool that serves to recognize sign language in a ubiquitous way, is a must to reduce barriers between the deaf and emergency corps under this context. Hand detection is the first step toward building a Costa Rican sign language (LESCO) recognition framework. Important advances in computing, particularly in the area of deep learning, open a new frontier for object recognition that can be leveraged to build a hand detection module. This study trains the MobileNet V1 convolutional neural network against the EgoHands dataset from Indiana University's UI Computer Vision Lab to determine if the dataset itself is sufficient to detect hands in LESCO videos, from five different signers that wear short-sleeve shirts and under complex backgrounds. Those requirements are key to determine the usefulness of the solution as consulted bibliography performs tests with single-color backgrounds and long-sleeve shirts that ease the classification tasks under controlled environments only. The two-step experiment obtained 1) a mean average precision of 96.1% for the EgoHands dataset and 2) a 91% average accuracy for hand detection across the five LESCO videos. Despite the high accuracy reported by the tests in this paper, the hand detection module was unable to detect certain hand shapes such as closed fists and open hands pointing perpendicular to the camera lens, which suggests that the complex egocentric views as captured in the EgoHands dataset might be insufficient for proper hand detection for Costa Rican sign language.
Intelligent Real-Time Multimodal Fall Detection in Fog Infrastructure Using Ensemble Learning

As the data and computing power increases with surge in sensors and IoT devices to making things smart, the research trend is moving in the direction to help the elderly as a part of the smart home infrastructure. The elderly people of age over 60 years are at a high risk level due to adverse effects when fall occurs. There has been various researches in the field of automatic fall detection with the help of sensors, video surveillance, wearable devices etc. The detection and analysis has to be in near real-time to handle the fall efficiently. Decision making with the help of data from multiple sources tends to be more effective than decision from a single source. Hence, the use of multimodal fall detection has been in research which takes advantage of the data from multiple sources for optimal detection. Our proposed methodology helps in fall detection and relevant decision making in near real-time by reducing the processing latency. The low latency decision making is realized using the intermediate mist and fog layer for data filtering and immediate processing. The video data is analyzed at the edge and the fall is detected using edge processing. The use of the multimodal processing and detection increases the accuracy of prediction. Thus along with the video data, the data from the wearable sensors are also considered for detection. The detection is done using ensemble learning and compressed Deep neural networks for detection and image processing respectively. The blend of the fog infrastructure and the proposed algorithm helps improve the accuracy and reduce detection and decision making time.
Generation of Automated Sports Commentary from Live Sports Data

We have developed a method of generating “automated sports commentary” that conveys an objective situation with synthesized speech based on live sports data. Our method can be applied to a wide variety of sports events by preparing “commentary templates” for each of them. The results of a subjective assessment demonstrated that the automated sports commentary generated with our method is effective for helping listeners understand the game situation. In addition, a public live streaming with the automated commentary showed that our proposed method can provide practical service for live sports broadcasting.
Forecasting of Photovoltaic Power Generation: Techniques and Key Factors

The aim of this paper is first, to provide a detailed overview on conventional and modern techniques of solar photovoltaic (PV) forecasting and then, to determine the predominating factors that affect the generation of PV power. To mitigate the depletion of fossil fuel and reduce greenhouse gas emissions, renewable energy is being deployed all over the world. Amongst various options, PV is one of the leading renewable generation sources. Due to variability, uncertainty and dependence on weather condition, PV power forecasting is treated as a challenging task. To this end, several PV forecasting techniques have been developed. Before the widespread application of machine learning tools, conventional methods viz. statistical, autoregressive moving average, regression etc. were used to forecast PV power. However, with recent advancement of machine learning approaches and access to big data, Artificial Neural network, Support Vector machine and hybrid techniques have become prevalent. To better understand the insights of PV prediction, this paper explores the working principle and applicability of various PV forecasting methods. In addition, the key factors affecting the PV generation are estimated using practical data.
Natural Image Deblurring using Recursive Deep Convolutional Neural Network (R-DbCNN) and Second-Generation Wavelets

Deconvolution in blind digital images is a common issue in image enhancement techniques, which basically was a notion of many researches. In this study, spatial varying blind deconvloution is stated and implemented. In addition, image noise removal approach which utilizes the normalized platform of second-generation wavelet transform is applied as pre-processing step. The low and high frequencies are decomposed in this step in order to be extracted. Practically, the main merit of wavelet transform is its efficiency in reduction of data redundancy in digital images. This feature helps a lot in terms of data classification where it is easy to distinguish the signal from its noisy counterpart. The second step, a recursive deep convolutional neural network (R-DbCNN) is implemented to suppress any image blur affected by second-generation wavelet transform to further remove the blur of noisy image. The experimental results depict that the suggested method outperforms recent blur removal techniques for different bluer image types in terms of image quality and time consumption.
HierCon: Hierarchical Organization of Technical Documents Based on Concepts

In this work we study the hierarchical organization of technical documents, where given a set of documents and a hierarchy of categories, the goal is to assign documents to their corresponding categories. Unlike prior work on supervised hierarchical document categorization that relies on large amount of labeled training data, which is expensive to obtain in closed technical domain and tends to stale as new knowledge emerges, we study this problem in a weak supervision setting, by leveraging semantic information from concepts. The core idea is to project both documents and categories into a common concept embedding space, where their fine-grained similarity can be easily and effectively computed. Experiments over real-world datasets from the subject of computer science, physics & mathematics, and medicine demonstrated the superior performance of our approach over a wide range of state of the art baseline approaches.
Arguing Security of Autonomous Robots

Autonomous robots are already being used, for example, as tour guides, receptionists, or office-assistants. The proximity to humans and the possibility to physically interact with them highlights the importance of developing secure robot applications. It is crucial to consider security implications to be an important part of the robot application's development process. Adding security later in the application's life-cycle usually leads to high costs, or is not possible due to earlier design decisions. In this work, we present the Robot Application Security Process (RASP) as a lightweight process that enables the development of secure robot applications. Together with RASP we introduce the role of a Security Engineer (SecEng) as an important stakeholder in any robot application development process. RASP enables the SecEng to verify the completeness of his work and allows him to argue about the application's security with other stakeholders. Furthermore, we demonstrate how the RASP supports the SecEng and also other developers in their daily work.
Industrial text analytics for reliability with derivative-free optimization

Maintenance work order records provide valuable insights into chemical plants and production efficiency. These records are manually created in computerized management systems for routine and emergency maintenance. However, since the records are manually created, recording errors are not uncommon. The resulting datasets are additionally imbalanced, i.e., they have significantly more instances of certain classes than other minority classes. It is very challenging to use such datasets for classification and prediction of future events. In this paper, we propose a modeling framework that uses derivative-free optimization (DFO) to optimize the performance of classification models based on datasets that may be imbalanced. We apply our modeling framework to 15 real-world work order datasets. We also evaluate ten mixed-integer box-bounded DFO solvers for their ability to optimize machine learning models from industrial datasets. Compared to standard solutions, our results show dramatic improvements in the prediction accuracies of the models.
Time-Optimal Trajectory Generation for Dynamic Vehicles: A Bilevel Optimization Approach

This paper presents a general framework to find time-optimal trajectories for dynamic vehicles like drones and autonomous cars. Hindered by its nonlinear objective and complex constraints, this problem is hard even for state-of the-art nonlinear programming (NLP) solvers. The proposed framework addresses the problem by bilevel optimization. Specifically, the original problem is divided into an inner layer, which computes a time-optimal velocity profile along a fixed geometric path, and an outer layer, which refines the geometric path by a Quasi-Newton method. The inner optimization is convex and efficiently solved by interior-point methods. A novel variable reordering method is introduced to accelerate the optimization of the velocity profile. The gradients of the outer layer can be derived from the Lagrange multipliers using sensitivity analysis of parametric optimization problems. The method is guaranteed to return a feasible solution at any time, and numerical experiments on a ground vehicle with friction circle dynamics model show that the proposed method performs more robustly than general NLP solvers.
A Systematic Literature Review on Blockchain-Based Solutions for IoT Security

Nowadays, we are facing an exponential growth in the Internet of Things (IoT). There are over 8 billion IoT devices such as physical devices, vehicles and home appliances around the world. Despite its impact and benefits, IoT is still vulnerable against privacy and security threats, due to the limited resources of most IoT devices, lack of privacy considerations and scalability issues, thus making traditional security and privacy approaches ineffective for IoT. The main goal of this article is to investigate whether the Blockchain technology can be employed to address security challenges of IoT. At first, a Systematic Literature Review was conducted on Blockchain with the aim of gathering knowledge on the state-of-the-art usages of Blockchain technology. We found 44 use cases of Blockchain in the literature from which, 18 use cases were specifically designed for the application of Blockchain in addressing IoT-related security issues. We classified the state-of-the-art use cases in four domains, namely, smart cities, smart home, smart economy and smart health. We highlight the achievements, present the methodologies and lessons learnt, and identify limitations and research challenges.
iShake: Imitation-Resistant Secure Pairing of Smart Devices via Shaking

In conventional device-to-device (D2D) communication through wireless channels, it is an essential demand to authenticate with each other and establish spontaneous secure connections among the smart devices. In this paper, we propose an imitation-resistant mutual authentication and key generation framework for smart devices, by shaking these devices together. According to the multi-sensor data collected from smart devices, these devices are able to authenticate each other and generate a unique and consistent symmetric key if and only if they are shaken together. We have conducted comprehensive experimental study on shaking various devices, illustrated several novel observations and extracted some important clues for efficient key generation. We propose a series of novel techniques to make the key generation robust to noise and privacy-preserving, and generate highly distinctive and fully randomized symmetric keys among these devices. Realistic experiment results indicate that our solution is able to authenticate with each other and generate the symmetric keys with high accuracy and time-efficiency.
Visualization of Data from HACC Simulations by Paraview

In this project, we visualize and analyze data of Hybrid Accelerated Cosmology Code (HACC) cosmology simulations. We investigate in point rendering as well as Smoothed-particle hydrodynamics (SPH) interpolation and apply them to visualize the data in a 3D environment. Paraview was used for the rendering task. Some parameters of points in the software were adjusted to find well-qualified visualizations. These approaches provide a clear vision to the impact of Active Galactic Nuclei (AGN) on surrounding matter, and thus, we can gain more information for understanding the nature of AGNs and the universe.
Transductive Learning for Reading Handwritten Tibetan Manuscripts

We examine the use case of performing handwritten character recognition (HCR) on a newly compiled collection of Tibetan historical documents, which presents multiple challenges, including inherent challenges such as image quality and the lack of word separation, and dataset challenges such as a lack of supervised training data. To tackle these challenges, we introduce an end-to-end unsupervised full-document HCR approach composed of unsupervised line segmentation and a convolutional recurrent neural network, trained using solely synthetic data. Various augmentations are applied to these synthesized images, and we compare the effect of each augmentation on the HCR results. Since we work on a collection of historical manuscripts, we can fit the model to the available test data. During training, our network has access to both the labeled synthetic training data and the unlabeled images of the test set, and we adapt and evaluate four different semi-supervised learning and domain adaptation approaches for transductive learning in HCR. We test our approach on a set of 167 images from the "Kadam" collection, containing 829 lines. We show that correct data augmentation is crucial for the success of HCR trained solely on synthetic data and that using an effective transductive learning approach drastically improves results.
Local Receptive Field-Extreme Learning Machine based Adult Content Detection

Adult contents have become available everywhere whether in social networks, TV channels and websites. Children protection from pornographic contents is required in all societies and environments. Inappropriate visual contents have an impact on children's psychological development. Parents' censorship is important to solve the problem but this task is time consuming and needs full time employment for activities monitoring. Automatic censorship is highly required to be integrated in all types of media such as TV channels. This paper proposes an automated censorship system that is able to be implemented on low cost embedded system. The public dataset NPDI was used to train and evaluate the performance of the proposed deep model for visual contents detection. The key frames selected from videos are used for porn/non-porn classification. Efficient features have to be extracted in order to perform accurate classification. The paper utilizes a local Receptive Field-Extreme Learning Machine (LRF-ELM) model which is a fast deep learning model to extract features and map them to specific classes in a short time. Our proposed method was found to give good performance in terms of accuracy and training time. The accuracy of 82.9% was achieved by using a normal Central Processing Unit (CPU).
A Feature Extraction Method based on Local Binary Pattern Preprocessing and Wavelet Transform

Face recognition has been extensively studied by many scholars in the recent decades. Local binary pattern (LBP) is one of the most popular local descriptors and has been widely applied to face recognition. Wavelet transform is also more and more active in the field of pattern recognition. In this paper, a novel feature extraction method is proposed to overcome illumination influence. First, a given face image is processed by the LBP operator, and an LBP image is obtained. Second, wavelet transform is used to extract discriminant feature from the LBP image. The experiment results on LFW, Extended YaleB and CMU-PIE face databases show that the proposed method outperforms several popular face recognition methods, and the preprocessing step plays an important role to extract effective features for classification.