Reviewerly/exHarmony huggingface dataset:
Deeply feature learning by CMAC network for manipulating rehabilitation robots

Human action recognition is a key component in modern artificial intelligent systems that greatly enhance the manipulating of various robots, such as rehabilitation robtos and industrial robots. Existing action recognition algorithms mainly depend on a predefined spatial sequence code book, which may fail to discover discriminative spatial–temporal features to mimic robots. In this paper, we propose to engineer the spatial–temporal action features that can deeply encode the similarity of within-class human actions and dissimilarity of between-class human actions. Specifically, given a series of training action video samples, we first segment each video into multiple key sections based on human contour. These sections of a video are related to time and space. Then, local robot action and appearance information are combined using a cerebellar model articulation controller (CMAC) to represent each video section. We quantize these extracted features into a feature vector, which can represent category-specific robot actions. Subsequently, we develop an improved linear discriminative analysis to project the data points to a subspace, where data points with the same label are close while data points with different labels are far from each other. Experimental results on the well-known HMDB51 and KTH datasets have shown the effectiveness and robustness of our method. Moreover, our action recognition can be applied as a key module in the real-world rehabilitation robots.
Body and Hand–Object ROI-Based Behavior Recognition Using Deep Learning

Behavior recognition has applications in automatic crime monitoring, automatic sports video analysis, and context awareness of so-called silver robots. In this study, we employ deep learning to recognize behavior based on body and hand–object interaction regions of interest (ROIs). We propose an ROI-based four-stream ensemble convolutional neural network (CNN). Behavior recognition data are mainly composed of images and skeletons. The first stream uses a pre-trained 2D-CNN by converting the 3D skeleton sequence into pose evolution images (PEIs). The second stream inputs the RGB video into the 3D-CNN to extract temporal and spatial features. The most important information in behavior recognition is identification of the person performing the action. Therefore, if the neural network is trained by removing ambient noise and placing the ROI on the person, feature analysis can be performed by focusing on the behavior itself rather than learning the entire region. Therefore, the third stream inputs the RGB video limited to the body-ROI into the 3D-CNN. The fourth stream inputs the RGB video limited to ROIs of hand–object interactions into the 3D-CNN. Finally, because better performance is expected by combining the information of the models trained with attention to these ROIs, better recognition will be possible through late fusion of the four stream scores. The Electronics and Telecommunications Research Institute (ETRI)-Activity3D dataset was used for the experiments. This dataset contains color images, images of skeletons, and depth images of 55 daily behaviors of 50 elderly and 50 young individuals. The experimental results showed that the proposed model improved recognition by at least 4.27% and up to 20.97% compared to other behavior recognition methods.
Measuring Learning in a Cyber Security Exercise

In recent years, cyber security exercises have established themselves as an integral part of cyber security education. Cyber security professionals usually work as a part of a team that monitors and responds to incidents in the environment. A sufficiently realistic complex learning environment is necessary for collaborative learning at the expert level. Evaluating the learning outcomes of complex exercises is an important task for both assessing how individuals met the learning objectives, and how to improve the exercise to better serve those goals. This requires the assessment of multiple skill and knowledge categories independently. We leveraged the NIST NICE Cybersecurity Workforce Framework as a base for building knowledge categories for questionnaire use. However, the NICE framework is comprehensive and detailed requiring that the areas of competence assessment needed to be simplified for questionnaire use. We summarized the NICE framework into 44 questions addressed to the individuals who participated in the exercise. A web-based questionnaire was used to query 21 participants' skill level before and after the exercise, as well as their familiarity and experience with the topic before and during the exercise. The results indicate that cyber security exercises will increase the knowledge of the participant in the knowledge areas that were present in the exercise. This increase was more prominent in cases where the participant was more likely to recognize, and experience events related to that category during the exercise. Furthermore, we concluded that the NICE framework can be used to assess individual know-how and as a basis for knowledge-related questionnaires.
Text Line Extraction Using Fully Convolutional Network and Energy Minimization

Text lines are important parts of handwritten document images and easier to analyze by further applications. Despite recent progress in text line detection, text line extraction from a handwritten document remains an unsolved task. This paper proposes to use a fully convolutional network for text line detection and energy minimization for text line extraction. Detected text lines are represented by blob lines that strike through the text lines. These blob lines assist an energy function for text line extraction. The detection stage can locate arbitrarily oriented text lines. Furthermore, the extraction stage is capable of finding out the pixels of text lines with various heights and interline proximity independent of their orientations. Besides, it can finely split the touching and overlapping text lines without an orientation assumption. We evaluate the proposed method on VML-AHTE, VML-MOC, and Diva-HisDB datasets. The VML-AHTE dataset contains overlapping, touching and close text lines with rich diacritics. The VML-MOC dataset is very challenging by its multiply oriented and skewed text lines. The Diva-HisDB dataset exhibits distinct text line heights and touching text lines. The results demonstrate the effectiveness of the method despite various types of challenges, yet using the same parameters in all the experiments.
Thermal radiation elimination method for high-temperature digital image correlation using polarization camera

Abstract Digital image correlation (DIC) is a material displacement and strain measurement technology based on visible light illumination. At high temperatures, the problem of thermal radiation seriously affects the quality of acquired images and restricts the development of high-temperature DIC technology which is increasingly applied in the field of high-temperature measurement due to stringent measurement temperature requirements. A thermal radiation elimination method based on the use of a polarization camera for high-temperature DIC measurements is proposed in this study. This method uses a polarization camera combined with a filter set to achieve clear image acquisition at 1200 °C and effectively eliminates the effects of thermal radiation on image acquisition. The gray average method and an image inverse filtering algorithm are adopted in this study to eliminate high-temperature thermal disturbances. Finally, a high-temperature DIC measurement system is independently designed, and a rigid-body displacement experiment is carried out on an FV566 steel specimen to obtain time–displacement curves. A set of uniaxial tensile tests is also performed on FV566 steel material to explore its strain field at 1200 °C.
Object Detection and Tracking for Community Surveillance using Transfer Learning

Automation of Video surveillance is gaining extensive interest recently, considering the public security issues. In recent times, a systematic and exact detection of an object is a foremost point in computer vision technology. With the unfolding of recent deep learning techniques, the precision of detecting an object has increased greatly thereby igniting the interest in this area to large extent. Also, with the advent of automatic driving electric cars, the accurate detection of objects has gained phenomenal importance. The main aim is to integrate the state-of-the-art deep learning method on pedestrian object detection in real-time with improved accuracy. One of the crucial problems in deep learning is using computer vision techniques, which tend to slow down the process with trivial performance. In this work, an improved Yolov3 transfer learning-based deep learning technique is used for object detection. It is also shown that this approach can be used for solving the problem of object detection in a sustained manner having the ability to further separate occluded objects. Moreover, the use of this approach has enhanced the accuracy of object detection. The network used is trained on a challenging data set and the output obtained is fast and precise which is helpful for the application that requires object detection.
Utilizing Topic-Based Similar Commit Information and CNN-LSTM Algorithm for Bug Localization

With the use of increasingly complex software, software bugs are inevitable. Software developers rely on bug reports to identify and fix these issues. In this process, developers inspect suspected buggy source code files, relying heavily on a bug report. This process is often time-consuming and increases the cost of software maintenance. To resolve this problem, we propose a novel bug localization method using topic-based similar commit information. First, the method determines similar topics for a given bug report. Then, it extracts similar bug reports and similar commit information for these topics. To extract similar bug reports on a topic, a similarity measure is calculated for a given bug report. In the process, for a given bug report and source code, features shared by similar source codes are classified and extracted; combining these features improves the method’s performance. The extracted features are presented to the convolutional neural network’s long short-term memory algorithm for model training. Finally, when a bug report is submitted to the model, a suspected buggy source code file is detected and recommended. To evaluate the performance of our method, a baseline performance comparison was conducted using code from open-source projects. Our method exhibits good performance.
An AI based Chatbot to Self-Learn and Self-Assess Performance in Ordinary Level Chemistry

Education is one of the fast-growing fields in the global perspective. Advancement of technology can be used in this sector to provide an effective and a valuable education system. In general, the students are more attracted to displays rather than the textbooks. In Sri Lanka, there is an inadequacy of resources and teachers cannot provide one on one attention to the students. Sri Lanka is not equipped with any platform to self-learn or self-evaluate their performance using an application either. Fortunately, “Edubot” acts as a solution for the stated research gap by providing a self-learning and self-evaluating AI based chatbot platform for Ordinary Level students in Chemistry domain. The self-learning component will provide the students a classroom environment by providing interactive tutorials. Explanatory responses would be given by Edubot by capturing doubts raised by the students and the self-evaluating component will provide an exam-based environment in which the Edubot auto generates the question and answers. The research finding shows that each component has an accuracy of more than 70 percent and helps to achieve the main goal of increasing the resources available to the ordinary level students in the Chemistry domain. This would then lead to an increase in the pass rate of the chemistry subject in the G.C.E Ordinary Level exam.
WITHDRAWN: An adaptive optimization based meta-heuristic approach for tracing software requirements

In software development, stakeholders usually collaborate asynchronously by sharing artifacts. Traceability system links artifacts and henceforth offers assist collaboration among system requirements. Various stakeholders show their interest towards various kinds of traceability and system requirements. With analysis towards various investigations, system requirements have to be fulfilled by prioritizing tasks. However, it is extremely complex to maintain or fulfil system requirements. But, none of the background investigations have been analyzed with heuristic based requirement traversing. This work investigates traversing of users’ functional requirements with Meta-heuristic approach termed as adaptive optimization and with optimal priority scheduling. With the assistance of traceability concept, functional requirements, design, test artifacts, defects, code inspection and system construction are considered and come under maintenance phase. It is also essential to analyze huge industrial firms to depict link and traceability types. Traceability deals with relationship among requirements, their source and system design. Bi-directional traceability, which deals with various associated requirements enables user to find origin of requirements and track every changes that are made to the requirements. The foremost goal of this work is to establish traceability links to provide support for decision making, to resolve inconsistency problem and to demonstrate quality requirements with improved system lifetime. The anticipated model shows better trade-off in contrast to prevailing models.
Real- Time Location based Augmented Reality Advertising Platform

Augmented Reality (AR) is growing rapidly and is becoming more mature and robust technology, combining virtual information with a real-time performance environment. Most of the Augmented reality applications available today & popular because of the interactive virtual objects placed in the real environment. For education, navigation, tourism & many sectors use this technology due to clear understand of real objects appear as it is as virtual objects, in front of you. Like that, the Marketing sector also uses AR technology to brand themselves interactively. Most of them are marker-based AR applications which the virtual contents are showing when the AR camera directs to a target such as paper advertisement. On other hand marker-less AR advertising applications are developed for individual businesses from AR supported plugins, apps rare to see & as unique published app. From this research, I proposed a real-time marker-less augmented reality platform, streaming & showcasing virtual marketing assets in front of shops for common business use. The main objective of this research is to develop a real-time location-based Augmented Reality platform to improve marketing & sales aspects of businesses. The users can easily find the exact location of the shop though AR objects. This novel marketing concept engages more customers to business and enhances the usability of AR application among users though easy to access on their selling products. The users can use app in native platforms(both android & IOS) and ready to access interactive virtual 3D objects with animation as marketing materials placed in front of shops. This platform solved the existing problems of location-based AR application which are interactivity of AR & adequately perform as real-time platform extract data from a live real-time server & show the locations through AR camera.
Discovering Outstanding Subgroup Lists for Numeric Targets Using MDL

The task of subgroup discovery (SD) is to find interpretable descriptions of subsets of a dataset that stand out with respect to a target attribute. To address the problem of mining large numbers of redundant subgroups, subgroup set discovery (SSD) has been proposed. State-of-the-art SSD methods have their limitations though, as they typically heavily rely on heuristics and/or user-chosen hyperparameters.
Water wave optimization algorithm for autonomous underwater vehicle path planning problem

In this paper, a water wave optimization (WWO) algorithm is proposed to solve the autonomous underwater vehicle (AUV) path planning problem to obtain an optimal or near-optimal path in the marine environment. Path planning is a prerequisite for the realization of submarine reconnaissance, surveillance, combat and other underwater tasks. The WWO algorithm based on shallow wave theory is a novel evolutionary algorithm that mimics wave motions containing propagation, refraction and breaking to obtain the global optimization solution. The WWO algorithm not only avoids jumps out of the local optimum and premature convergence but also has a faster convergence speed and higher calculation accuracy. To verify the effectiveness and feasibility, the WWO algorithm is applied to solve the randomly generated threat areas and generated fixed threat areas. Compared with other algorithms, the WWO algorithm can effectively balance exploration and exploitation to avoid threat areas and reach the intended target with minimum fuel costs. The experimental results demonstrate that the WWO algorithm has better optimization performance and is robust.
Real-Time Path Planning Through Q-learning's Exploration Strategy Adjustment

As the usefulness of reinforcement learning has been confirmed through AlphaGo, research on applying reinforcement learning to the field of autonomous driving is actively progressing. In this paper, we implemented path planning using Q-learning, one of the reinforcement learning algorithms. Path planning using Q-learning has a limitation in that it is difficult to store tables for all environments. To overcome this limitation, we used real-time Q-learning that does not store tables in advance. We Adjustment our exploration strategy to the learning speed required for real-time Q-learning. It was confirmed that Q-learning using an appropriate exploration strategy enables real-time path planning.
LAC: LSTM AUTOENCODER with Community for Insider Threat Detection

The employees of any organization, institute or industry, spend a significant amount of time on computer network, where they develop their own routine of activities in the form of network transactions over a time period. Insider threat detection involves identifying deviations in the routines or anomalies which may cause harm to the organization in the form of data leaks and secrets sharing. If not automated, this process involves feature engineering for modeling human behavior which is a tedious and time-consuming task. Anomalies in the human behavior are forwarded to a human analyst for final threat classification. We developed an unsupervised deep neural network model using LSTM AUTOENCODER which learns to mimic the behavior of individual employees from their day-wise time stamped sequence of activities. It predicts the threat scenario via significant loss from anomalous routine. Employees in a community tend to align their routine with each other rather than the employees outside their communities, this motivates us to explore a variation of the AUTOENCODER, LSTM AUTOENCODER- trained on the interleaved sequences of activities in the Community (LAC). We evaluate the model on the CERT v6.2 dataset and perform analysis on the loss for normal and anomalous routine across 4000 employees. The aim of our paper is to detect the anomalous employees as well as to explore how the surrounding employees are affecting that employees’ routine over time.
Data-driven efficient solvers for Langevin dynamics on manifold in high dimensions

We study the Langevin dynamics of a physical system with manifold structure $\mathcal{M}\subset\mathbb{R}^p$ based on collected sample points $\{\mathsf{x}_i\}_{i=1}^n \subset \mathcal{M}$ that probe the unknown manifold $\mathcal{M}$. Through the diffusion map, we first learn the reaction coordinates $\{\mathsf{y}_i\}_{i=1}^n\subset \mathcal{N}$ corresponding to $\{\mathsf{x}_i\}_{i=1}^n$, where $\mathcal{N}$ is a manifold diffeomorphic to $\mathcal{M}$ and isometrically embedded in $\mathbb{R}^\ell$ with $\ell \ll p$. The induced Langevin dynamics on $\mathcal{N}$ in terms of the reaction coordinates captures the slow time scale dynamics such as conformational changes in biochemical reactions. To construct an efficient and stable approximation for the Langevin dynamics on $\mathcal{N}$, we leverage the corresponding Fokker-Planck equation on the manifold $\mathcal{N}$ in terms of the reaction coordinates $\mathsf{y}$. We propose an implementable, unconditionally stable, data-driven finite volume scheme for this Fokker-Planck equation, which automatically incorporates the manifold structure of $\mathcal{N}$. Furthermore, we provide a weighted $L^2$ convergence analysis of the finite volume scheme to the Fokker-Planck equation on $\mathcal{N}$. The proposed finite volume scheme leads to a Markov chain on $\{\mathsf{y}_i\}_{i=1}^n$ with an approximated transition probability and jump rate between the nearest neighbor points. After an unconditionally stable explicit time discretization, the data-driven finite volume scheme gives an approximated Markov process for the Langevin dynamics on $\mathcal{N}$ and the approximated Markov process enjoys detailed balance, ergodicity, and other good properties.
An Image of Encryption Algorithm Using Graph Theory and Speech Signal Key Generation

Abstract In this paper, a color image of encryption algorithm used a graph theory has been proposed. The proposed algorithm depends on the audio files as a basis for exploiting a set of keys for encoding color images. The key generation system was built by passing the audio signal in several stages based on the graph theory. The results revealed that the proposed algorithm has generated the encrypted images with uniform distribution in pixel histograms with information entropy closes to 8, which resists different attacks. The comparison experiments with other recent algorithms were performed. The statistical results show that the proposed algorithm has a strong security against attacks.
MUMBO: MUlti-task Max-Value Bayesian Optimization

We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, the challenge is to perform efficient optimization by evaluating low-cost functions somehow related to our true target function. This is a broad class of problems including the popular task of multi-fidelity optimization. However, while information-theoretic acquisition functions are known to provide state-of-the-art Bayesian optimization, existing implementations for multi-task scenarios have prohibitive computational requirements. Previous acquisition functions have therefore been suitable only for problems with both low-dimensional parameter spaces and function query costs sufficiently large to overshadow very significant optimization overheads. In this work, we derive a novel multi-task version of entropy search, delivering robust performance with low computational overheads across classic optimization challenges and multi-task hyper-parameter tuning. MUMBO is scalable and efficient, allowing multi-task Bayesian optimization to be deployed in problems with rich parameter and fidelity spaces.