Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Shot Boundary Detection in News Streams Based on Fuzzy Petri Nets

ABSTRACT With the advent of a digital era, people have encountered some difficulty in using and absorbing overwhelming information generated by technological advances in multimedia. Thus, the development of video summarization enables people to catch a general idea about videos in a short time. In this paper, we focus on the shot change, a part of the video summarization, to conduct an experimental sample on news programs. Moreover, a high-level fuzzy Petri net model is presented to describe the frame combination which indicates a shot boundary used for a video frame sequence in order to detect both cut transitions and gradual transitions. This study has used feature functions to estimate the direct shot change in consideration of video shot boundary detection which adopts the HLFPN model to find a threshold value. The experimental results manifest that the proposed system saves a lot of time and reduces the occurrence of improper shot changes caused by the motions of objects and cameras when comparing the proposed approach with other existing ones.
Comparisons of MVAC and MVDC Systems in Dynamic Operation, Fault Protection and Post-Fault Restoration

One of the most significant obstacles preventing the large-scale application of direct-current (DC) technology in medium voltage (MV) distribution networks is their fault protection. The existing AC relay protection needs to be changed or redesigned to protect the future overlay MVAC and MVDC distribution networks. Therefore, a comprehensive understanding of the dynamic and fault behavior and post-fault restoration strategies of MVAC and MVDC systems are critically important. Moreover, a comparison of MVAC and MVDC systems during a fault will also contribute to designing the protection systems of hybrid MV AC/DC systems. In this paper, the challenges of protecting DC faults of MVDC systems and possible solutions are first introduced. Then, the fault characteristics and post-fault restoration of MVDC and MVAC distribution systems are compared and investigated through case studies. Time-domain simulations have been conducted in PSCAD/EMTDC. The work in this paper will be valuable for the protection design for future hybrid MV AC/DC systems.
AUTOMATIC EXTRACTION OF POWER LINES FROM UAV LIDAR POINT CLOUDS USING A NOVEL SPATIAL FEATURE

Abstract. UAV LiDAR systems have unique advantage in acquiring 3D geo-information of the targets and the expenses are very reasonable; therefore, they are capable of security inspection of high-voltage power lines. There are already several methods for power line extraction from LiDAR point cloud data. However, the existing methods either introduce classification errors during point cloud filtering, or occasionally unable to detect multiple power lines in vertical arrangement. This paper proposes and implements an automatic power line extraction method based on 3D spatial features. Different from the existing power line extraction methods, the proposed method processes the LiDAR point cloud data vertically, therefore, the possible location of the power line in point cloud data can be predicted without filtering. Next, segmentation is conducted on candidates of power line using 3D region growing method. Then, linear point sets are extracted by linear discriminant method in this paper. Finally, power lines are extracted from the candidate linear point sets based on extension and direction features. The effectiveness and feasibility of the proposed method were verified by real data of UAV LiDAR point cloud data in Sichuan, China. The average correct extraction rate of power line points is 98.18%.
The modular electronic health record dashboard: a novel approach on interoperability and usability

Electronic health records have transformed the way care is delivered. Compared to the old paper medical record, there are many benefits to be gained. These benefits include a higher quality of care, increased adherence to medical guidelines, and a reduced amount of medical errors. Also, costs can be saved. The rise of mobile technology can further benefit the health care sector by allowing care delivery from a distance. There are also disadvantages associated with digital health systems. One example are its high acquisition and maintenance costs. Other cost increases are associated with the training of the medical staff to work with the software. This adaptation period is responsible for a loss in productivity. Another unintended consequence of these systems is an increase in medical errors. A prototype was designed to tackle these last two drawbacks in particular. A lack of system interoperability and good usability are to blame for some of the drawbacks of electronic health record systems. The ability of a system to communicate with other systems is defined by its interoperability, whereas usability refers to the ease of use and learnability of a system. The design of the prototype describes a modular dashboard wherein the clinician can freely choose its functionality according to his/her workflow. Interoperability was provided by having a loosely coupled back end and a base component to speed up front end development. Several principles defined by literature were kept in mind to provide good usability. Six individuals participated in a usability test of the prototype. The participants were spread across several health care units, such as nursing and emergency response. The results indicated that the dashboard had good usability, together with many opportunities to improve. However, the interoperability aspect could not be tested. Based on these results, several topics of future work were highlighted.
Learned Turbo Message Passing for Affine Rank Minimization and Compressed Robust Principal Component Analysis

This paper is focused on the efficient algorithm design for affine rank minimization (ARM) and compressed robust principal component analysis (CRPCA). Given the proliferation of the literature on the ARM and CRPCA problems, the existing algorithms mostly take a model-based approach in the algorithm design, and so are sensitive to the generation model of the linear measurement operator or to the generation model of the low-rank matrix to be recovered. It is known that, all these algorithms do not work well, e.g., when the low-rank matrix is ill-conditioned. As inspired by the success of learned iterative soft-thresholding (LIST) and learned approximate message passing (LAMP), we develop learning-based message passing algorithms, namely, the learned turbo message passing (LTMP) algorithm for affine rank minimization to cope with the ARM problem and the LTMP algorithm for the CRPCA problem. The LTMP algorithms learn their parameters from data, and hence are robust to the generation models of the linear operator and the low-rank matrix. We derive analytical expressions for the partial derivatives involved in training the LTMP network. Given the large size of the low-rank matrix in a typical ARM/CRPCA problem, these analytical expressions are of essential importance for the development of computationally feasible network training. Numerical results demonstrate that LTMP significantly outperforms the state-of-the-art counterparts for various generation models of the linear operator and the low-rank matrix, especially when the low-rank matrix is ill-conditioned.
Quantum Representations of MCGs and Their Applications to Quantum Computing

Author(s): Bloomquist, Wade | Advisor(s): Wang, Zhenghan | Abstract: We explore how skein theoretic techniques can be applied to the study of quantumrepresentations of mapping class groups. Of particular interest will be looking into theasymptotic faithfulness property of quantum representations coming from unimodal versionsof representation categories of quantum groups. We then introduce a combinatorialproperty on the graphical calculus of these representation categories which implies asymptoticfaithfulness. We proceed to show that this property is satisfied in some specific cases,in short we provide support for the conjecture that these quantum representations willalways be asymptotically faithful. This will lead into a discussion of other applicationswithin low dimensional topology. Finally applications to topological quantum computingwill be given, introducing a potential encoding of qudits making use of these quantumrepresentations.
THE GOOD, THE BAD AND THE UGLY: OPINION MINING ANALYSIS ON USER TWEETS IN TWITTER

Opinion mining evaluates people's opinions, sentiments, assessments, outlooks, and feelings from a written language. One of the most dynamic research areas in natural language processing, it is also extensively studied in fields of data mining like web mining and text mining. Opinion mining also finds its vast applications in management sciences and social sciences due to its importance to business and society asa whole. The emergent importance of opinion analysis parallelswithexpansion of socialmedia such as Facebook, Twitter, and social networks. This research suggests a stand-alone platform which enables users to perform opinion analysis which makes use of Twitter API to gather information aboutthe tweets and its users. The proposed model is used to analyse whether the opinion in a text is neutral, positive ornegative. As users tend to post frank and blunt comments in twitter, it has emerged as a platform to check people’s feedback.The inclusion of hashtags to attract social attention also is a trigger for opinion mining. Twitter Search and Streaming APIs with the Stanford NLPlibrary are used to opine the data collected. The pre-processed data passes through the opinion generation phase where opinion classes are defined for polarity. Words are labelled with opinions, and the general opinion of a tweet is thus analysed, in this research paper. IndexTerms Twitter, Opinion Mining, Opinion Classes.
Knowledge reuse for deep reinforcement learning.

With the rise of Deep Learning the field of Artificial Intelligence (AI) Research has entered a new era. Together with an increasing amount of data and vastly improved computing capabilities, Machine Learning builds the backbone of AI, providing many of the tools and algorithms that drive development and applications. While we have already achieved many successes in the fields of image recognition, language processing, recommendation engines, robotics, or autonomous systems, most progress was achieved when the algorithms were focused on learning only a single task with little regard to effort and reusability. Since learning a new task from scratch often involves an expensive learning process, in this work, we are considering the use of previously acquired knowledge to speed up the learning of a new task. For that, we investigated the application of Transfer Learning methods for Deep Reinforcement Learning (DRL) agents and propose a novel framework for knowledge preservation and reuse. We show, that the knowledge transfer can make a big difference if the source knowledge is chosen carefully in a systematic approach. To get to this point, we provide an overview of existing literature of methods that realize knowledge transfer for DRL, a field which has been starting to appear frequently in the relevant literature only in the last two years. We then formulate the Case-based Reasoning methodology, which describes a framework for knowledge reuse in general terms, in Reinforcement Learning terminology to facilitate the adaption and communication between the respective communities. Building on this framework, we propose Deep Case-based Policy Inference (DECAF) and demonstrate in an experimental evaluation the usefulness of our approach for sequential task learning with knowledge preservation and reuse. Our results highlight the benefits of knowledge transfer while also making aware of the challenges that come with it. We consider the work in this area as an important step towards more stable general learning agents that are capable of dealing with the most complex tasks, which would be a key achievement towards Artificial General Intelligence.
The Effectiveness of KWL and SQ3R Techniques in Teaching Reading Comprehension

Reading is considered as an important aspect for students because of its valuable benefit in developing knowledge. There are approaches of reading such as top-down, bottom up, and interactive approach. Some studies was conducted in applying interactive strategy such as SQ3R technique and KWL technique. For the purpose of teaching narrative, descriptive and news item text, the researcher try to observe and analyze the effectivenes of SQ3R and KWL techniques. The study was conducted at SMA Darussalam Blokagung Banyuwangi. This research used quasi experimental method. The researcher did a treatment by applying KWL technique to the experimental class and SQ3R technique to the control class. The data obtained were calculated by using a t-test for independent sample through SPSS 20 for windows to test the hyphothesis. After applying the statistical computation, the result shows that the probability was ,564 or 5,6 % > 5% and the t value was 0,57 > 0,5. it can be concluded that the difference effect between the two technique is not significant. However, the improvement of the mean obtained by the experimental and control group from pretest and posttest show that there is variance between the students’ starting skill and the students’ achievement after getting the treatments. Thus, the interactive approach by using KWL and SQ3R technique is effective to improve students’ reading skill.
An intelligent system for gastrointestinal polyp detection in endoscopic video using fusion of bidimensional empirical mode decomposition and convolutional neural network features

This paper presents an intelligent system for gastrointestinal polyp detection in endoscopic video. Video endoscopy is a popular diagnostic modality in assessing the gastrointestinal polyps. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer‐aided polyp detection is promising to reduce the miss detection rate of polyp and thus improve the accuracy of diagnosis results. The proposed method illustrates an automatic system based on a new color feature extraction scheme as a support for gastrointestinal polyp detection. The scheme is the combination of color empirical mode decomposition features and convolutional neural network features extracted from video frames. The features are fed into a linear support vector machine to train the classifier. Experiments on standard public databases show that the proposed scheme outperforms the previous conventional methods, gaining accuracy of 99.53%, sensitivity of 99.91%, and specificity of 99.15%.
Unmixing based Change Detection for Hyperspectral Images with Endmember Variability

Unmixing based change detection (UBCD) provides subpixel level information on the nature of the changes that occur in a temporal image series, in addition to providing a multi-output change detection map. These advantages have recently carried UBCD to prominence among change detection approaches for hyperspectral images. However, most, if not all, works on UBCD operate with the assumption that the endmembers do not vary in character temporally or locally. This assumption often fails to uphold, as intrinsic variability of endmembers is a significant concern for most real datasets, in addition to endmember variability occurring in temporal images due to changes in lighting or acquisition conditions. This paper proposes unmixing with the recently proposed extended linear mixture model (ELMM) to address spectral variability for UBCD and highlights its advantage with respect to UBCD with the linear mixture model (LMM) through experiments on synthetic and real datasets.
Neodroid Playground: Designing environments and tasks for learning robots in virtual reality

This thesis describes a Unity project that aims to simplify the development of machine learning agents’ environments and tasks. There are two parts of the task. It requires external developers to be able to extend it with relative ease. Users of the system need to be able to load up a scene and annotate a task with the use of VR. One of the more difficult issues to handle is implicit construction of an evaluation function for the AI. We solved this in our system by using conditions that describe what the desired goal state of the environment is. This makes it possible to describe any task through our interface in VR. The Neodroid platform is intended for use in academic research, as it is open source, but potentially also to solve issues in the industry. Automation in the industry is something people have been striving towards for several decades. There are a lot of man-hours of manual labour that can be automated if there is a reasonable solutiton to them. This will save time and money, but additionally, it will help reduce damage potential for labour that is done in hazardous working environments. However, automating manual labour is not very straightforward, and the standard in the industry is that simple tasks that do not need any consideration of the environment can easily be done by a hard-coded robot. This becomes a problem when the tasks have complex issues that are too advanced for hard-coded robots to resolve. A solution to these issues with automation is to simulate the robot in this environment and apply machine learning to it. That way, it will get years of experience through a simulated environment that is approximating the environment the task needs to be automated within. Our vision for the Neodroid Playground is to simplify and generalize a way to describe these tasks within virtual reality, such that a robot can learn how to do it in the simulated environment.
JAMS-SG

Time-triggered automotive networks use time-triggered protocols (FlexRay, TTEthernet, etc.) for periodic message transmissions that often originate from safety and time-critical applications. One of the major challenges with time-triggered transmissions is jitter, which is the unpredictable delay-induced deviation from the actual periodicity of a message. Failure to account for jitter can be catastrophic in time-sensitive systems, such as automotive platforms. In this article, we propose a novel scheduling framework (JAMS-SG) that satisfies timing constraints during message delivery for both jitter-affected time-triggered messages and high-priority event-triggered messages in automotive networks. At design time, JAMS-SG performs jitter-aware frame packing (packing of multiple signals from Electronic Control Units (ECUs) into messages) and schedules synthesis with a hybrid heuristic. At runtime, a Multi-Level Feedback Queue (MLFQ) handles jitter-affected time-triggered messages and high-priority event-triggered messages that are scheduled using a runtime scheduler. Our simulation results, based on messages and network traffic data from a real vehicle, indicate that JAMS-SG is highly scalable and outperforms the best-known prior work in the area in the presence of jitter.
Cyclic nucleotide-regulated channels (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

Cyclic nucleotide-gated (CNG) channels are responsible for signalling in the primary sensory cells of the vertebrate visual and olfactory systems.CNG channels are voltage-independent cation channels formed as tetramers. Each subunit has 6TM, with the pore-forming domain between TM5 and TM6. CNG channels were first found in rod photoreceptors [69, 98], where light signals through rhodopsin and transducin to stimulate phosphodiesterase and reduce intracellular cyclic GMP level. This results in a closure of CNG channels and a reduced ‘dark current’. Similar channels were found in the cilia of olfactory neurons [153] and the pineal gland [60]. The cyclic nucleotides bind to a domain in the C terminus of the subunit protein: other channels directly binding cyclic nucleotides include HCN, eag and certain plant potassium channels.Hyperpolarisation-activated, cyclic nucleotide-gated (HCN)The hyperpolarisation-activated, cyclic nucleotide-gated (HCN) channels are cation channels that are activated by hyperpolarisation at voltages negative to ~-50 mV. The cyclic nucleotides cyclic AMP and cyclic GMP directly activate the channels and shift the activation curves of HCN channels to more positive voltages, thereby enhancing channel activity. HCN channels underlie pacemaker currents found in many excitable cells including cardiac cells and neurons [56, 164]. In native cells, these currents have a variety of names, such as Ih, Iq and If. The four known HCN channels have six transmembrane domains and form tetramers. It is believed that the channels can form heteromers with each other, as has been shown for HCN1 and HCN4 [2]. High resolution structural studies of CNG and HCN channels has provided insight into the the gating processes of these channels [117, 121]. A standardised nomenclature for CNG and HCN channels has been proposed by the NC-IUPHAR subcommittee on voltage-gated ion channels [88].
Selection of base wavelet and classifier for accurate identification of power quality events

1The present study comprehensively investigates Power Quality (PQ) identification problem and proposes the optimum combination of base wavelet and Machine Learning Algorithm (MLA) which would yield highest classification accuracy. Although this problem has been studied by various researchers in the recent past, the selection of appropriate base wavelet and MLA, which would give better classification accuracy, have received comparatively less attention. This study bridges this gap by investigating the classification performance of 110 wavelets and 7 well-known MLAs across various noise levels using over 3500 PQ events generated as per IEEE Standard 1159. The results of this investigation demonstrate that the choice of base wavelet does significantly affect the classification performance. Further, it was observed that a single base wavelet does not provide optimum performance across all MLAs at various noise levels. In contrast, each MLA gives the maximum accuracy with a distinct base wavelet. The robustness of MLA against noise is studied which establishes that the simple MLAs, such as Decision Tree (DT) and Naive-Bayes (NB), are more robust against noise compared to the other intricate MLAs. Finally, several recommendations are drawn for the selection of base wavelet and MLA which yields the best possible accuracy. Part of this report has been accepted for publication in IET Science, Measurement & Technology, 2018 and available at DOI: 10.1049/iet-smt.2018. 5044
Non-functional Requirements for Real World Big Data Systems - An Investigation of Big Data Architectures at Facebook, Twitter and Netflix.

This research represents a unique contribution to the field of Software Engineering for Big Data in the form of an investigation of the big data architectures of three well-known real-world companies: Facebook, Twitter and Netflix. The purpose of this investigation is to gather significant non-functional requirements for real-world big data systems, with an aim to addressing these requirements in the design of our own unique architecture for big data processing in the cloud: MC-BDP (Multi-Cloud Big Data Processing). MC-BDP represents an evolution of the PaaS-BDP architectural pattern, previously developed by the authors. However, its presentation is not within the scope of this paper. The scope of this comparative study is limited to the examination of academic papers, technical blogs, presentations, source code and documentation officially published by the companies under investigation. Ten non-functional requirements are identified and discussed in the context of these companies' architectures: batch data, stream data, late and out-of-order data, processing guarantees, integration and extensibility, distribution and scalability, cloud support and elasticity, fault-tolerance, flow control, and flexibility and technology agnosticism. They are followed by the conclusion and considerations for future work.