Reviewerly/exHarmony huggingface dataset:
Research on Identification of Handwritten Mathematical Formulas

At present, handwriting recognition is making rapid progress, and accurate recognition of complex formulas has always been a difficulty, among which mathematical formulas are one of the representative examples. Due to the two-dimensional distribution of mathematical formulas, how to improve their accuracy has been a hot topic in handwritten recognition research. The research direction of this paper is to establish a structural system for better recognition of complex formulas in images, which is suitable for offline image recognition. The first part of this paper summarizes the methods of mathematical expression recognition and the application of various grammars. The second part introduces the system designed in this paper, including grammar, participle hypothesis generator, parsing algorithm and spatial relations.
Improved Filter Method for Feature Selection

Abstract To solve the problem of high data feature dimension in intrusion detection, a hybrid feature selection method has been proposed to reduce the feature dimension. This approach combines the filter and sequence floating forward search methods. Firstly, the original feature series is sorted by different filter methods, and the top ranked features are selected as the next original feature set. Based on this, the sequence floating forward search method is used to select the optimal feature subset with the Support Vector Machine (SVM) as the classifier. The method can avoid the selection and screening of the features based on the threshold and the evaluation value of single feature and categorical variable. Thereby the high evaluation characteristics obtained by the Filter method may be complementary to the low evaluation characteristics. The result shows that the proposed method can not only effectively reduce the number of features, but also can achieve a better classification performance.
A novel non-negative matrix factorization technique for decomposition of Chinese characters with application to secret sharing

The decomposition of Chinese characters is difficult and has been rarely investigated in the literature. In this paper, we propose a novel non-negative matrix factorization (NMF) technique to decompose a Chinese character into several graphical components without considering the strokes of the character or any semantic or phonetic properties of the components. Chinese characters can usually be represented as binary images. However, traditional NMF is only suitable for representing general gray-level or color images. To decompose a binary image using NMF, we force all of the elements of the two matrices (obtained by factorizing the binary image/matrix to be decomposed) as close to 0 or 1 as possible. As a result, a Chinese character can be efficiently decomposed into several components, where each component is semantically unreadable. Moreover, our NMF-based Chinese character decomposition method is suitable for applications in visual secret sharing by distributing the shares (different character components) among multiple parties, so that only when the parties are taken together with their respective shares can the secret (the original Chinese character(s)) be reconstructed. Experimental results have verified the decomposition performance and the feasibility of the proposed method.
An Efficient Classification Method of Uncertain Data with Sampling

Current research on the classification for uncertain data mainly focuses on the structural changes of the classification algorithms. Existing methods have achieved encouraging results; however, they do not take an effective trade-off between accuracy and running time, and they do not have good portability. This paper proposed a new framework to solve the classification problem of uncertain data from data processing point. The proposed algorithm represents the distribution of raw data by a sampling method, which means that the uncertain data are converted into determined data. The proposed framework is suitable for all classifiers, and then, XGBoost is adopted as a specific classifier in this paper. The experimental results show that the proposed method is an effective way of handling the classification problem for uncertain data.
Nucleus - Deploying Research Data Management Infrastructure At The Los Alamos National Laboratory

Research data management (RDM) efforts, including the implementation of tools, development of best practices, and training of scholars, have taken center stage in many academic libraries. Evaluating and serving the RDM needs at federally funded organizations have also become a priority since the release of the 2013 U.S. Office of Science and Technology Policy memo. At the Los Alamos National Laboratory, a U.S. Department of Energy laboratory, the Research Library has launched a collaborative data management pilot called "Nucleus", based on a local installation of the open source software Open Science Framework. In this poster we present a preliminary assessment of Nucleus' implementation, including user feedback and lessons learned.
Reaching Consensus on A Group Decision Using Dialogue and Arguments

Argumentation systems are special tools that enable agents to reason under uncertainty. Especially in a society of agents, arguments can be sent in the exchange of messages representing a particular point of view with a justification for this. An argument has a set of premises that lead to a conclusion through a mechanism of logical inference. In a scenario where group decision using consensus must be established, it is important to determine the extent that each piece of information in an argument is accepted in the group of agents, influencing in the strength of arguments and in the decision taking. This paper proposes a computational dialogue model that allows the agents to form common knowledge by consenting the information that is supported by majority in the group and to approximate their knowledge so that a consensual decision making can be achieved.
HYPERSPECTIVE: Shaping Experiences beyond Perspectives

Embodied knowledge of one's experiences are broadly categorized as tacit knowledge, that can be acquired through active physical experiences only and not by conversations or visual media. We hypothesize that shaping a person's bodily sensation, such as physical representation or a physiological characteristic, to that of another person, including a child or a patient, while allowing active, embodied, and social interactions with the surrounding objects and people in a real-world environment would provide an authentic as well as empathic knowledge to school teachers, product designers, and medical staff when designing living environments or communicating with them. We propose and demonstrate a new style to experience one's perspectives, called HYPERSPECTIVE (hyper + perspective), that can be achieved with wearable devices, and discuss its challenges, benefits, and feasibility in real scenarios.
Resource auto-scaling for SQL-like queries in the cloud based on parallel reinforcement learning

Cloud computing is a technology that provides on-demand services in which the number of assigned resources can be automatically adjusted. A key challenge is how to choose the right number of resources so that the overall monetary cost is minimised. This problem, known as auto-scaling, was addressed in some existing works but most of them are dedicated to web applications. In these applications, it is assumed that the queries are atomic and each of them uses a single resource for a short period of time. However, this assumption cannot be considered for database applications. A query, in this case, contains many dependent and long tasks so several resources are required. We propose in this work an auto-scaling method based on reinforcement learning. The method is coupled with placement-scheduling. In the experimental section, we show the advantage of coupling the auto-scaling to the placement-scheduling by comparing our work to an existing auto-scaling method.
Studying Algebraic Structures Using Prover9 and Mace4

In this chapter we present a case study, drawn from our research work, on the application of a fully automated theorem prover together with an automatic counter-example generator in the investigation of a class of algebraic structures. We will see that these tools, when combined with human insight and traditional algebraic methods, help us to explore the problem space quickly and effectively. The counter-example generator rapidly rules out many false conjectures, while the theorem prover is often much more efficient than a human being at verifying algebraic identities. The specific tools in our case study are Prover9 and Mace4; the algebraic structures are generalisations of Heyting algebras known as hoops. We will see how this approach helped us to discover new theorems and to find new or improved proofs of known results. We also make some suggestions for how one might deploy these tools to supplement a more conventional approach to teaching algebra.
Research on Application of Microlecture to Teaching Reform of Higher Normal Education

Abstract The progress in computer sciences and information technology provides more teaching modes. Recently, the microlecture has drawn extensive attentions, which could make the knowledge points clear in a shorter teaching time than traditional ways. In addition, it is not limited by time and space. This study makes some explanations and discussions on microlecture in order to apply it to higher normal education. First, the basic concept of microlecture is presented to explain the background. Afterwards, the teaching mode and advantages of microlecture are discussed. Accordingly, its potential in higher normal education can be inferred. Finally, some feasible ways are suggested to apply microlecture to the higher normal education. Conclusions are drawn based on all these analyses. And it is promising that microlecure could make contributions to the teaching reform of higher normal education.
Information Technology Operational Risk: A Teaching Case

IT operational risk refers to any threat that could lead to the inappropriate modification, lack of availability, destruction, or theft of IT assets. This paper presents a real-world teaching case study, based upon an actual consulting engagement of risk assurance professionals working at a multinational professional services provider, to bring practical experience with managing IT operational risk into the classroom. Guidance for implementing this teaching case within an Information Systems course is provided. Students may work in teams, assuming the role of consultants, to provide recommendations involving an organization’s IT operational risks in operations, financial reporting, and compliance. Feedback from participating students revealed that the learning objectives of the case were met. These finding confirm that the Global CyberDerma Solutions case is a useful tool to teach students, who will be entering the workplace, about IT operational risks confronting organizations.
Interoperability Between SaaS and Data Layers: Enhancing the MIDAS Middleware

Nowadays, the volume of digital data grows exponentially. As a result, many organizations store and provide their data in cloud computing services. While Software as a Service (SaaS) is a typical model for application delivery, Data as a Service (DaaS) and Database as a Service (DBaaS) are models to provide data and database management systems on demand, respectively. Heterogeneity of these services makes it difficult to automate communication among them. In these cases, SaaS applications require additional efforts to access those data. Besides that, the lack of standardization from DaaS and DBaaS generates a problem of communication among cloud layers. In this paper, we propose an enhancing version of MIDAS (Middleware for DaaS and SaaS) that provides interoperability between Services (SaaS) and Data layers (DaaS and DBaaS). Our current version of MIDAS is concerned with (i) presenting a Description Logic representation of the middleware and (ii) detailing the Web Crawler. Experiments were carried out to evaluate execution time, overhead, interoperability, and correctness. Results demonstrated our effectiveness on addressing interoperability concerns in cloud computing environments.
Evaluation of Pointing Interfaces with an AR Agent for Multi-section Information Guidance

In educational settings such as art galleries or museums, Augmented Reality (AR) has the potential to provide detailed information about exhibits. However, dealing with items that contain information in multiple sections or areas is still a significant challenge. For example, a large painting may contain many minute details, which requires a system that can explain its broader features rather than just a generic description. To address this challenge, we introduce an AR guidance system that uses an embodied agent to point out items and explain each piece and part of exhibit items in detail. We also designed and tested 3 different pointing interfaces for the embodied agent: gesture only, gesture with a dot laser, and gesture with line laser. To evaluate this interface, we conducted a user experiment simulating painting guidance to test interest and exhibit memory. During the experiment, the agent pointed to various areas of interest in the painting and provided a detailed description to participants. The result shows that the search times for target positions were the fastest with the line laser. However, no particular interface outperformed others in memory recall of exhibit content.
Adaptive Multi-optimiser Cooperative Co-evolution for Large-Scale Optimisation

Large-scale optimisation is of great importance as most real-world optimisation problems involve a large number of decision variables. Cooperative co-evolution (CC) approaches are successful in this field as they can tackle thousands of decision variables. CC methods consist of decomposition, optimisation of decomposed sub-components and cooperative combination based on the `divide-and-conquer' mechanism. Sub-components are optimised cooperatively before the merge. Most of existing CCs optimise different sub-components using the same optimisation algorithm. In this study we propose an adaptive approach based on multi-optimiser cooperative co-evolution for large-scale optimisation problems. As different sub-components may have different characteristics and different contributions towards the global fitness function, they should to be handled differently considering the diversity and the quality of each sub-component. To achieve this effect our proposed approach incorporates four different optimisers adaptively according to the relative contribution. In each optimisation cycle, an adaptive selection strategy is used to allocate the most suited optimiser to each sub-component using quality-diversity measure and the historical performance of the algorithms. The performance of the proposed approach has been evaluated on large-scale optimisation benchmark problems. The results confirmed the effectiveness of the proposed adaptive approach as it can achieve comparable or better performance on almost of test cases compared to state-of-the-art methods.
Cooperative game concepts in solving global optimization

Nowadays, cooperative game theory has been applied to many domains of human activities. In this study, the cooperative game concept needed for calculating Shapley value is used in solving global optimization. Precisely, the marginal contribution that an agent carries by joining a coalition is calculated as an increase in population diversity of coalition. This concept is incorporated into differential evolution and its self-adaptive variants jDE in order to show that distributing the monolithic population of solutions into more coalitions and their parallel evolution can improve the results of the original algorithms.
Warping Space and Time-Reviving Educational Tools of the 19th Century

xR has the potential to warp both space and time. We demonstrate this potential by designing a mixed reality application for mobile devices for the Penn State's Obelisk, a historic landmark on the main Penn State campus that artistically reveals the geological history of Pennsylvania. Our AR application allows for placing a model of the Obelisk on any surface, interacting with the individual stones to reveal their geological characteristics and location of excavation, and changing to an immersive VR experience of this location based on 360° imagery. Originally conceptualized as a teaching tool for the School of Mines, our xR application revives the Obelisk's long forgotten mission and allows educators to integrate it once more into the curriculum as well as creatively expand its potential.
Development Trend of Software Process Model

Abstract With the continuous development of software technology, the scale and complexity of software have never been higher, and the difficulty of design and development has also increased. The software process changes the chaotic situation of software development and provides an effective roadmap for the software development team to make the development process more controllable and stable. This paper briefly introduces the software process, and introduces the existing software process model in detail. Finally, the development trend of software process model is forecasted.
Finite Inverted Beta-Liouville Mixture Models with Variational Component Splitting

Use of mixture models to statistically approximate data has been an interesting topic of research in unsupervised learning methods. Mixture models based on exponential family of distributions have gained popularity in recent years. In this chapter, we introduce a finite mixture model based on Inverted Beta-Liouville distribution which has a higher degree of freedom to provide a better fit for the data. We use a variational learning framework to estimate the parameters which decreases the computational complexity of the model. We handle the problem of model selection with a component splitting approach which is an added advantage as it is done within the variational framework. We evaluate our model against some challenging applications like image clustering, speech clustering, spam image detection, and software defect detection.
Enabling Efficient Automated Configuration Generation and Management

Creating and managing valid configurations is one of the main tasks in software product line engineering. Due to the often complex constraints from a feature model, some kind of automated configuration generation is required to facilitate the configuration process for users and developers. For instance, decision propagation can be applied to support users in configuring a product from a software product line (SPL) with less manual effort and error potential, leading to a semi-automatic configuration process. Furthermore, fully-automatic configuration processes, such as random sampling or t-wise interaction sampling can be employed to test or to optimize an SPL. However, current techniques for automated configuration generation still do not scale well to SPLs with large and complex feature models. Within our thesis, we identify current challenges regarding the efficiency and effectiveness of the semi- and fully-automatic configuration process and aim to address these challenges by introducing novel techniques and improving current ones. Our preliminary results show already show promising progress for both, the semi- and fully-automatic configuration process.
Precise Evaluation for Continuous Action Control in Reinforcement Learning

With the development of deep learning, reinforcement learning also gradually into the eye, reinforcement learning has made remarkable achievements in games, go games and other fields, but most of the control problems involved in these fields or tasks are discrete action control with sufficient rewards. Continuous action control in reinforcement learning is closer to the actual control problem, and is considered as one of the main channels leading to artificial intelligence, so it is also one of the research hotspots of researchers. The traditional continuous control algorithm for reinforcement learning evaluates the network with multiple outputs of a single scalar value. In this paper, an accurate evaluation mechanism and corresponding objective function are proposed to accelerate the reinforcement learning training process. The experimental results show that the accurate evaluation of log-cosh objective function can make the robot arm grasp the task more quickly, converge and complete the training task.
Hybrid HMM/DNN System for Arabic Handwriting Keyword Spotting

Deep Neural Networks (DNN) have been incorporated in handwriting recognition systems and have proven their effectiveness. This paper presents an efficient hybrid word spotting system for Arabic handwritten documents. We investigate the optimal integration of different discriminative DNN in the standard DNN-HMM hybrid system. Well-trained Bi-Directional Long Short-term Memory (BLSTM), Time Delay Neural Network (TDNN) and LSTM are compared in the discriminative stage. The second stage is based on HMM and integrates a context dependent character modeling. Experiments are made with Arabic handwritten document text lines extracted from the publicly available dataset KHATT. We conclude that the hybrid approach appears to be well suited for Arabic handwriting word spotting compared to the classical GMM-HMM model.
Template Matching for Kinship Verification in the Wild

Kinship verification is a new research problem in the field of computer vision. It has gained attention from different research communities due to the fact that parent/child facial resemblance could be utilized to perform kinship verification. This paper proposes kinship verification based on template matching. We present two different methods for template matching on specific facial parts of parent/child image pair. First method selects each facial part in parent image and computes similarity score with corresponding facial part in child image. It analyzes and compares role of each facial part in parent/child image pair for kinshipGoyal, Aarti verification. Second method performs fusion of all facial parts and selects the one with maximum similarity score. Experiments are performedMeenpal, T. on baseline KinFaceW-I/II datasets to show effectiveness of facial parts in kinship verification.
Comparative Analysis of Privacy Preserving Approaches for Collaborative Data Processing

Data collection by public and private organizations is increasing for extracting hidden knowledge from it that may be used for assisting decision making process. Moreover, availability of high speed internet and sophisticated data mining tools make sharing of this collected data across various organizations possible. As a consequence, these organizations may share and combine their datasets to retrieve the improved result from the combined data using collaborative data processing. Sharing of such data as it is between collaborative organizations may compromise individual’s privacy as the collected data may contain sensitive information about individuals in its original form. To address this challenge, two main categories of privacy preserving approaches viz. the non-cryptography based approach and the cryptography based approach can be utilized. This paper aims to discuss an insight of these approaches and to highlight the parametric comparison.