Reviewerly/exHarmony huggingface dataset:
Interactive Storyboarding for Rapid Visual Story Generation

Artificial Intelligence (AI) technologies have impacted almost every domain and its systems, including the entertainment industry. Although AI-based systems are expected to offer significant benefits in making content, it is still challenging to build a real-world AI application that can effectively contribute to content production. In this paper, we present a novel approach for developing a storyboard; a sequence of images displayed for previsualizing a motion picture, animation, motion graphic, or interactive media. We implement a prototype system, Gennie, that can interact with users and suggest AI-generated sketches for each scene of the storyboard.
Predicting Diffuse Horizontal Radiation with Neural Networks

Solar energy predictions are vital for low-cost evaluations of the long-term solar energy potential in a particular location. Solar radiation measurements acquired using various sensors are often deemed expensive, and therefore, affordable solutions for obtaining valid solar radiation data are sought after. There are three types of solar radiation (i.e., global, direct and diffuse), and each type requires different sensors and mechanisms for measuring it. If a solar station has sensors for measuring global solar radiation, it can use a Neural Network (NN) for predicting diffuse solar radiation without the need for expensive diffuse solar radiation sensors. Along with the global solar radiation, a station needs to have access to the zenith and azimuth angles, extraterrestrial radiation on a horizontal surface, extraterrestrial radiation normal to the sun, precipitable water, and aerosol optical depth. Using all those measurements, we predict diffuse solar radiation with high accuracy, which allows solar stations to have diffuse radiation data without oftentimes prohibitive costs.
Face recognition using deep learning feature injection: An accurate hybrid network combining neural networks based on feature extraction with convolutional neural network

In this work we evaluate, compare and combine several face recognition models. Three multi-layer neural networks are trained with different descriptors: Histograms of Gradient Orientation (HOG), Scale Invariant Feature Transform (SIFT) and Local Binary Pattern Histograms (LBPH). A fourth model uses a Convolutional Neural Networks (CNN), trained with the raw images. The combination of these networks into a hybrid network results in a noticeable accuracy improvement, not only by comparing the results with the individual networks, but also with respect to the current state of the art. To validade our hybrid model, we include different sets of images, acquired under different conditions such as lighting, pose and expression..
Communities of Practice in Large-Scale Agile Development: A Systematic Literature Mapping

Agile software development has become an attractive option for companies trying to improve their performance, even though its methods were originally designed for small, individual teams. Current literature does not provide a cohesive picture of how agile methods scale into account, like large and often distributed software development projects. Some studies claim that Communities of Practice (CoPs) can be essential to successfully adopting agile practices, especially in large organizations. CoPs are groups of experts who share a common interest or topic and collectively wish to deepen their knowledge. This study highlights how CoPs are applied and used to promote and scale agile methods in large projects. To answer the research question, “How have Communities of Practice been used to support large-scale agile development teams?” we conducted a Systematic Literature Mapping (SLM) in the context of “Largescale Agile Methods” and “Communities of Practice”. Thus, from twenty-one studies, it was possible to synthesize three different themes and 13 categories that promote good practices in the adoption of CoPs to scale agile methods, including “coordination between teams”, “knowledge sharing”, and “open community”.
Resilient Architecture Framework for Robotic Systems

Robotic architectures in use today provide some mechanisms for evolution but fall short of their capabilities for dynamic reconfiguration and adaptation during run-time. Adding new modes of operation, components, and connections allows for supporting the evolution of a product throughout long periods. We analyze and discuss the possibilities of performing architecture modifications on the fly without rebooting the systems since a reboot may be undesirable or impossible. For this, we rely on strict component interfaces and a component manager subsystem that can make the transition from one architecture to another while the robot is operating. Our study focuses on modern systems such as ROS2 and PX4 and assumes a publish-subscribe mechanism for communication. The ability to modify the software architecture allows for adaptive solutions that work in different environments and are less susceptible to specific attacks. Switching to a safer architecture can limit some functionality but can increase security. Working with structurally defined interfaces, we set the possibility of using COTS components that comply with our requirements to build new architectures. This can make reuse and composition easier and improve time-to-market while guaranteeing reliability and safety.
Bitcoin Price Prediction using Long Short Term Memory Neural Networks

Cryptocurrency investment has been on trend for the last many years. Many studies have been done on cryptocurrency price prediction based on their parameters and factors. In this research, we concentrate on bitcoin as one of the well-known cryptocurrencies. In this regard, some machine learning approaches predicate the bitcoin price. Neural Networks and Recurrent Neural Networks seem great in bitcoin price prediction and relatively accurate because of their ability to work on time-series data. In this paper, the survey on the performance of LSTM (Long Short-Term Memory), which is one of the Recurrent Neural Networks and is suitable for time-series problems, has proven it can be used for bitcoin price prediction investigation. In this paper, to fulfil this measurement task, a model has been implemented to train and test the dataset; the last year's data was only used because of the fluctuation of the price in recent years. The result shows that LSTM can predict the price remarkably with acceptable accuracy.
Power Systems Coherency Representation and Identification as Particles

Electric power systems are complex structures with a continuously varying status of the system. Because of this, they require fast and innovative online monitoring methods to observe the evolving status of the system. Within the power system, online monitoring of the generators coherency and transmission adequacy represent some of the biggest challenges. Furthermore, to assist the Transmission System Operators (TSOs), a functionality for the ease of understanding and visualization of such phenomena must be included in the solution. This paper proposes a novel online measurement-based representation method for visualizing the coherent generation groups within a power system by mapping the simulation/data time-series into particles motion. The mapping also allows a reduction of the space-temporal dimension to a coordinate system that permits the visualization of other properties of the generators' signals such as the direction, linking-distance, overlapping, and the notion of inertia by their rapid movement. Additionally, a modified version of a clustering method specialized on particles - the so-called Friends of Friends (FoF) algorithm - is applied. This algorithm allows computation of the aforementioned properties and rapidly identifies the formed coherent groups. The method is applied to the well-known IEEE 9-bus test system for verifying the coherent groups reflected in the time-shots. Results show that the proposed visualization method is suitable for rapidly tracking the coherency features.
Measuring the effectiveness of blockchain smart contracts

Blockchain smart contracts have quickly become a focal point of research and development. Their autonomous, decentralized, transparent and secure nature allows for enforcement of agreement between multiple parties, with no need for trust as a prerequisite and no intermediaries to facilitate the relationship. However, this shift in paradigm, the significant difference with conventional software, and a variety of decentralization specifics has made development of reliable and effective smart contracts extremely difficult. The tendency has been shown by the short, yet turbulent history of smart contracts, full of numerous attacks, exploits, thefts, and failures. In this paper, the authors address this by proposing a formal way to evaluate the effectiveness of smart contracts. They specify the factors that impact effectiveness. First, parameters that serve as the input to a specially constructed evaluation model are identified. Then, the algorithm used by the model to calculate the effectiveness is shown. The result of this process is a special output metric called Smart Contract Index of Effectiveness, which quantifies the level of potential risk to the effectiveness of a smart contract. Lastly, the authors validate the model by applying it to popular smart contracts and show that it correctly classifies them as effective or not. Direction for future development of the model and further specification of its scope and accuracy is also provided.
Comparison of compiler efficiency with SSE and AVX instructions

In this paper, we present a comparison between the execution times of code generated by three widely used compilers for C/C++ for specific class of problems connected to our research. We juxtapose the execution time of the software compiled by clang, gcc, and msvc on Windows OS and Ubuntu OS. The software has explicit use of the SSE4.1 and AVX2 instruction sets and calculates the weight spectrum of a linear [n,k] code over the finite field GF(2). The build platform used for the projects is CMake which allows us to easily control the compilation parameters. The conclusions are made based on efficiency of the compiler
Automated Music Generation using Deep Learning

Generating music using Deep Neural Networks has been an active field of study for the past few decades. This paper discusses how to create computer-based monophonic musical content using a Recurrent Neural Network with the extension of Long Short-Term Memory. The objective of this study is to generate music that has a melodic nature to it and can be played by humans using sheet notation. The neural network is fed with input from the video game series: Final Fantasy for creating the melody. The selection of instruments is piano, which will be the same for the input and output. The main focus of this paper is on constrained music, differentiating notes from chords to help the architecture learn to distinguish one from the other and generate music with a mix of both, thus creating a pleasant listening experience.
Text Data Analysis Using Generalized Linear Mixed Model and Bayesian Visualization

Many parts of big data, such as web documents, online posts, papers, patents, and articles, are in text form. So, the analysis of text data in the big data domain is an important task. Many methods based on statistics or machine learning algorithms have been studied for text data analysis. Most of them were analytical methods based on the generalized linear model (GLM). For the GLM, text data analysis is performed based on the assumption of the error included in the given data and follows the Gaussian distribution. However, the GLM has shown limitations in the analysis of text data, including data sparseness. This is because the preprocessed text data has a zero-inflated problem. To solve this problem, we proposed a text data analysis using the generalized linear mixed model (GLMM) and Bayesian visualization. Therefore, the objective of our study is to propose the use of GLMM to overcome the limitations of the conventional GLM in the analysis of text data with a zero-inflated problem. The GLMM uses various probability distributions as well as Gaussian for error terms and considers the difference between observations by clustering. We also use Bayesian visualization to find meaningful associations between keywords. Lastly, we carried out the analysis of text data searched from real domains and provided the analytical results to show the performance and validity of our proposed method.
Visual servoing control of omnidirectional mobile robots based on disturbance observer

This study studies a critical issue that linear velocity and steering angular velocity of the wheels of omni-directional mobile robot are easily affected by the unknown external disturbances such as wind direction and road flatness during the visual servoing stabilization task. To overcome such challenging problems, this study proposes a disturbance observer based visual servoing control strategy. In configuration, the controller is divided into two parts: the quasi-min-max model predictive controller is designed to accommodate the state constraint and input constraint of the system, and complete the visual stabilization task without disturbance. A nonlinear disturbance observer is designed to estimate the external disturbance and feedback compensate the effect of the actual disturbance on the wheel speed. The simulation results and real experiments show that the proposed control strategy can stably accomplish the visual servoing stabilization task even with a large external disturbance.
Oral cancer detection model in distributed cloud environment via optimized ensemble technique

A new Two-tier m-healthcare oral cancer detection framework is constructed in a distributed cloud environment in this study. The following is the detection model: The supplied data is pre-processed to reduce noise as well as any undesired artifacts. The region of interest is separated from the backdrop via image segmentation. The segmentation in this research is done utilizing the Region Growing Technique. Textural features, Graph features, and Morphological features are also retrieved. A suggested Improved Linear Discriminant Analysis (ILDA) is used to pick the features in this study. For cancer classification, the selected features are exposed to ensemble classifiers (EC). In EC, stage 1 includes the Support Vector Machine (SVM) and Multi-layer Perceptron (MLP)) modeled for disease classification. The stage 2 phase includes the optimized CNN, which makes the final decisions regarding the presence/ absence of oral cancer. The optimized CNN is trained with outcomes acquired from SVM and the input for optimal CNN is MLP, which will provide the final detected outcomes. Since, the CNN is the final decision makes, its weight of it is fine-tuned using a new hybrid optimization model- Aquila Exploration Updated with Local Movement (AEULM), this ensures enhanced detection accuracy. The traditional Aquila Optimizer (AO) and the proposed hybrid optimization method are conceptually combined to form Wildebeest Herd Optimization (WHO). To confirm the effectiveness of the predicted model, a comparative assessment is completed.
A Novel Knowledge Base Question Answering Method Based on Graph Convolutional Network and Optimized Search Space

Knowledge base question answering (KBQA) aims to provide answers to natural language questions from information in the knowledge base. Although many methods perform well when dealing with simple questions, there are still two challenges for complex questions: huge search space and information missing from the query graphs’ structure. To solve these problems, we propose a novel KBQA method based on a graph convolutional network and optimized search space. When generating the query graph, we rank the query graphs by both their semantic and structural similarities with the question. Then, we just use the top k for the next step. In this process, we specifically extract the structure information of the query graphs by a graph convolutional network while extracting semantic information by a pre-trained model. Thus, we can enhance the method’s ability to understand complex questions. We also introduce a constraint function to optimize the search space. Furthermore, we use the beam search algorithm to reduce the search space further. Experiments on the WebQuestionsSP dataset demonstrate that our method outperforms some baseline methods, showing that the structural information of the query graph has a significant impact on the KBQA task.
AXIOMATIZABILITY OF PROPOSITIONALLY QUANTIFIED MODAL LOGICS ON RELATIONAL FRAMES

Abstract Propositional modal logic over relational frames is naturally extended with propositional quantifiers by letting them range over arbitrary sets of worlds of the relevant frame. This is also known as second-order propositional modal logic. The propositionally quantified modal logic of a class of relational frames is often not axiomatizable, although there are known exceptions, most notably the case of frames validating the strong modal logic $\mathrm {S5}$ . Here, we develop new general methods with which many of the open questions in this area can be answered. We illustrate the usefulness of these methods by applying them to a range of examples, which provide a detailed picture of which normal modal logics define classes of relational frames whose propositionally quantified modal logic is axiomatizable. We also apply these methods to establish new results in the multimodal case.
Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories

Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.
TCMFVis: A visual analytics system toward bridging together traditional Chinese medicine and modern medicine

Although traditional Chinese medicine (TCM) and modern medicine (MM) have considerably different treatment philosophies, they both make important contributions to human health care. TCM physicians usually treat diseases using TCM formula (TCMF), which is a combination of specific herbs, based on the holistic philosophy of TCM, whereas MM physicians treat diseases using chemical drugs that interact with specific biological molecules. The difference between the holistic view of TCM and the atomistic view of MM hinders their combination. Tools that are able to bridge together TCM and MM are essential for promoting the combination of these disciplines. In this paper, we present TCMFVis, a visual analytics system that would help domain experts explore the potential use of TCMFs in MM at the molecular level. TCMFVis deals with two significant challenges, namely, (i) intuitively obtaining valuable insights from heterogeneous data involved in TCMFs and (ii) efficiently identifying the common features among a cluster of TCMFs. In this study, a four-level (herb-ingredient-target-disease) visual analytics framework was designed to facilitate the analysis of heterogeneous data in a proper workflow. Several set visualization techniques were first introduced into the system to facilitate the identification of common features among TCMFs. Case studies on two groups of TCMFs clustered by function were conducted by domain experts to evaluate TCMFVis. The results of these case studies demonstrate the usability and scalability of the system.
Social Recommendation Based on Quantified Trust and User’s Primary Preference Space

Social recommendation has received great attention recently, which uses social information to alleviate the data sparsity problem and the cold-start problem of recommendation systems. However, the existing social recommendation methods have two deficiencies. First, the binary trust network used by current social recommendation methods cannot reflect the trust level of different users. Second, current social recommendation methods assume that users only consider the same influencial factors when purchasing goods and establishing friendships, which does not match the reality, since users may have different preferences in different scenarios. To address these issues, in this paper, we propose a novel social recommendation framework based on trust and preference, named TPSR, including a trust quantify method based on random walk with restart (TQ_RWR) and a user’s primary preference space model (UPPS). Our experimental results in four public real-world datasets show that TQ_RWR can improve the utilization of trust information, and improve the recommended accuracy. In addition, compared with current social recommendation methods/studies, TPSR can achieve a higher performance in different metrics, including root mean square error, precision, recall and F1 value.
Pose Detection and Automatic Deviation Correction Control Strategy of Crawler Walking Equipment in Coal Mines

Autonomous navigation technology is the basis of underground crawler walking equipment automation. Based on research of the sector laser pose parameter detection method, a pose detection system based on a cross laser is proposed. The mathematical model between pose parameters, laser receiver measurement data and tilt sensor measurement data is established by vector algorithm and the expression of key pose parameters is deduced. This paper studies the deviation correction control strategy of crawler walking equipment and puts forward the point deviation correction control strategy based on roadway excavation technology. Firstly, the end position of the path is determined according to the initial pose of the roadheader. Then, Bessel curve is used to plan the path between the starting point and the endpoint, and the pure tracking algorithm is used to track the planned path. The measurement errors of X and Y coordinates and yaw angle of roadheader within 10 80 m shall not exceed 10 mm, 32 mm and 0.65°. Using the point deviation correction strategy, the positioning error of the roadheader at the end of the path is less than 13 mm, and the yaw angle error is less than 0.16°. In the deviation correction process, the maximum angular velocity of the roadheader is 0.07 rad/s, which is less than 0.82 rad/s of the conventional deviation correction strategy. The results show that the point deviation correction strategy can not only ensure the accuracy of motion control, but also improve the stability of equipment motion.