Reviewerly/exHarmony huggingface dataset:
MECANISMOS INTUITIVO E RACIONAL: VALIDAÇÃO DE ESCALA PARA TOMADA DE DECISÃO

Although there is significant acceptance of triple structure by artificial intelligence studies, current literature converges to dual processing.In this way, discussions are still needed about the components of the mind.Therefore, the present work proposes to validate a scale for the measurement of Cognitive Systems, deepening the understanding about its divisions.To do so, it relied on the "Rational Experimental Inventory (REI)" to measure individual differences in thinking styles, with a total of 2,843 observations.As results, the scale could then be validated with 3 factors of significant reliability.It was realized that, although intuition has been grouped into a single factor, rationality has been divided into two factors.The sample seems to perceive rationality as coming from two different mechanisms: a logical, and a deeper and more demanding.The REI was statistically recognized by finding three factors, two related to rationality and one to intuition, with a relationship between the two rationalities.
2D Extreme–Point Symmetric Mode Decomposition Method

Abstract This paper proposes a kind of 2D extension of Extreme–Point Symmetric Mode Decomposition Method. The 2D-sifting process is performed by 4 steps: 1 Extrema points detection (by neighboring window). 2 Delaunay triangulation is done over the grid of all extrema points. 3 Mid points of the triangulation are obtained. 4 Thin plate splines are used to interpolate the mid points. 5 Sifting processing is carried out. Finally, experiments are done with real images to show the efficiency of this 2D Extreme–Point Symmetric Mode Decomposition Method.
Room vs greenDAO for Android : A comparative analysis of performance of Room and greenDAO

Android developers often store relational data using databases. There are a couple of database frameworks from which an Android developer can choose. Application performance is often considered whe ...
Relative Impact of Key Rendering Parameters on Perceived Quality of VR Imagery Captured by the Facebook Surround 360 Camera

High quality, 360 capture for Cinematic VR is a relatively new and rapidly evolving technology. The field demands very high quality, distortion- free 360 capture which is not possible with cameras that depend on fish- eye lenses for capturing a 360 field of view. The Facebook Surround 360 Camera, one of the few “players” in this space, is an open-source license design that Facebook has released for anyone that chooses to build it from oﬀ-the-shelf components and generate 8K stereo output using open-source licensed rendering software. However, the components are expensive and the system itself is extremely demanding in terms of computer hardware and software. Because of this, there have been very few implementations of this design and virtually no real deployment in the field. We have implemented the system, based on Facebook’s design, and have been testing and deploying it in various situations; even generating short video clips. We have discovered in our recent experience that high quality, 360 capture comes with its own set of new challenges. As an example, even the most fundamental tools of photography like “exposure” become diﬃcult because one is always faced with ultra-high dynamic range scenes (one camera is pointing directly at the sun and the others may be pointing to a dark shadow). The conventional imaging pipeline is further complicated by the fact that the stitching software has diﬀerent eﬀects on various as- pects of the calibration or pipeline optimization. Most of our focus to date has been on optimizing the imaging pipeline and improving the qual- ity of the output for viewing in an Oculus Rift headset. We designed a controlled experiment to study 5 key parameters in the rendering pipeline– black level, neutral balance, color correction matrix (CCM), geometric calibration and vignetting. By varying all of these parameters in a combinatorial manner, we were able to assess the relative impact of these parameters on the perceived image quality of the output. Our results thus far indicate that the output image quality is greatly influenced by the black level of the individual cameras (the Facebook cam- era comprised of 17 cameras whose output need to be stitched to obtain a 360 view). Neutral balance is least sensitive. We are most confused about the results we obtain from accurately calculating and applying the CCM for each individual camera. We obtained improved results by using the average of the matrices for all cameras. Future work includes evaluating the eﬀects of geometric calibration and vignetting on quality.
Space distance intelligent perception device based on stereoscopic visual recognition technology

The operation of power construction and maintenance is dangerous. Every year, there are personal injury and death, line power failure and equipment damage accidents due to power construction maintenance accident and wrong operation, which causes great losses. The device described in this paper can automatically identify the vehicles and personnel targets of the power construction maintenance site, and based on the two-eye stereo vision detection technology, and it can achieve the timely monitoring and tracking of the safe distance of the crane in the process of moving around the scene. So as to avoid safety accidents caused by the failure to alarm in time, this device can realize the sense connection, and can be deployed in large numbers, forming a power construction maintenance operation sense terminal under the power Internet of Things.
Speaking Skills Learning Based on Politeness for Conflict Resolution Education in Higher Education

Abstract. Speaking skills are an important aspect for every individual to develop themselves socially and professionally. From the social side, speaking skills will help individuals establish harmonious relationships with other individuals/ groups for a variety of interests. From a professional perspective, speaking skills as part of communication skills are key factors that support career success. Inter-student conflict is one of the social problems facing universities today, mainly in South Sulawesi. Data shows that during 2003–2011 there were 60 student conflicts. Politeness values that grow from cultural roots can be used to build mutual respect, respect, and tolerance between students through lectures on speaking skills. Conflict resolution is one aspect that can be instilled through learning activities. From various approaches to conflict resolution education, the most essential goal is to instill awareness in students of the importance of establishing humanitarian interactions and relations and actively involving them in resolving conflicts. To prevent conflict, everyone should understand the culture and ways of communicating that apply in certain community groups. Learning language skills, especially speaking skills, is one of the media for planting anti-conflict values. Through learning politeness-based speaking skills, students can develop themselves to communicate politely and with dignity to avoid conflict.
LibGuides: Library Instruction, Services for Faculty: Free e-textbooks during COVID-19

SCC Faculty Guide to Teaching Online Many academic and technical publishers are making textbooks and other learning resources free during this time. Some platforms and instructions are listed here. Contact a librarian if you can't easily find what you need.
A Comparative Study of Moving Target Detection Algorithms

Abstract This paper wants to analyze and compare the mainstream algorithms for moving target detection and lay a foundation for algorithm improvements as well as for such research directions as intelligent transportation system and traffic calculation, this paper selects three target detection algorithms for comparative study: methods of interframe difference, background difference and optical flow. It conducts simulation experiment on traffic surveillance videos with MATLAB programming, selects the threshold for frame difference method suitable for the current video and improves detection accuracy by binaryzation, expansion, corrosion and other processing methods. Interframe difference method compares the effects between the original difference image and grayscale difference image and considers bad difference caused by excessive moving distance. Background difference method compares the effect images after morphological processing, and reduces the impact of noises. The advantages are shown by comparing the original image with the labeled optical flow diagram using the optical flow method. By comparing the recognition effect images and processing time of these three algorithms, it analyzes and concludes the strengths and weaknesses as well as the ranges of application in intelligent transportation system.
KGRAM-BASED COMPOSITE SECRET SIGN SEARCH OVER ENCRYPTED CLOUD INFORMATION

A Large number of data owners have moved our data into cloud servers. Cloud data owners prefer to outsource documents in an encrypted form for the function of confidentiality preserving. Therefore it is essential to develop efficient and reliable cipher text search method. One challenge is that the relationship between documents will be normally concealed in the procedure of encryption, which will lead to major search accuracy performance degradation. All access the data from cloud by using the keyword based search. The secure multi-keyword ranked search from the encrypted data from the cloud, top-k search problem for big data encryption against privacy breaches, and attempt to identify an efficient and secure solution to this problem. It accessible operations like update, delete, insertion of documents. Here using tree structure and nebulous search method for retrieve the data from the cloud. These types of techniques are used to solve the problem of keyword guessing attack. The blowfish algorithm for the encryption process. We propose a group multi-keyword top-k search scheme based on the idea of partition, where a group of tree-based indexes are constructed for all documents. We combine these methods together into an efficient and secure approach to address our proposed top-k similarity search here to reduce statistical attacks. The extensive experimental results on real-life data sets demonstrate that our approach can significantly improve the capability of defending the privacy breaches, the scalability and the time efficiency of query processing over the state-of-the-art methods. It can achieve sub-linear search time and the search result like a number of file retrieval also deal with deletion and insertion of documents flexibly.
Influence of Customer Incivility on Employees’ Emotional Dissonance and Counterproductive Work Behaviors (CWB)

The aims of this study was to examine the relationships amongst customer incivility, emotional dissonance, and counterproductive work behaviors (CWB). The concept of counterproductive work behaviors was reviewed in literature review, and then the research model was provided based on the review of past studies. In the main survey, validity and reliability were ensured, and 202 valid survey responses were used to test the hypothesized structural model. The results showed that although the hypothesized model was obtained with a good model fit, the relationship between ‘emotional dissonance’ and ‘coworker-CWB’ was found to be not significant. Also, others had significant relationships amongst constructs. Based on the study’ findings, theoretical implications and practical implications were discussed, and limitations of the study and further study directions were also suggested.
Best Practices from World Libraries Photo Gallery Project: Engaging Global Libraries

This article reports on the “2019 Best Practices from World Libraries” Photo Gallery Project initiated by the International Connections Committee (ICC) of the International Relations Round Table (IRRT) at the American Library Association (ALA). The project was inspired by a 2018 ALA-IRRT Emerging Leader’s project, Toward Increasing Engagement of International New Professional Leaders in ALA Activities, which followed a survey study developed to explore IRRT’s ability to engage its international members. The article shares the project planning process and the key actions taken to build a virtual and physical gallery for the 2019 ALA Annual Conference in Washington, DC, as well as highlights example programs from the project which demonstrate how libraries around the world are building stronger communities. Finally, the authors consider how the project may be improved upon and further developed in the future.
Clustering for Continuous-Time Hidden Markov Models

We develop clustering procedures for longitudinal trajectories based on a continuous-time hidden Markov model (CTHMM) and a generalized linear observation model. Specifically in this paper, we carry out infinite mixture model-based clustering for CTHMM and achieve inference using Markov chain Monte Carlo (MCMC). Specifically, for Bayesian nonparametric inference using a Dirichlet process mixture model, we utilize restricted Gibbs sampling split-merge proposals to expedite the MCMC algorithm. We employ the proposed algorithm to the simulated data as well as a large real data example, and the results demonstrate the desired performance of the new sampler.
Current Trends in Eye Tracking Research in Mathematics Education: A PME Literature Review : A PME Survey

Eye tracking (ET) is a research method that receives growing interest in mathematics education research (MER). This paper aims to give a literature overview, specifically focusing on the evolution ...
Alternative Income Generating Activities for Financial Sustainability of Higher Education Institutions

The huge increase in the cost of universities are starting to seriously ward off potential customers and alienate the public, whose support of colleges is waning. For the last five years, aggregate universities' enrollments in Kenya have fallen, and alternative Income Generation Activities (IGAs) to supplement the tuition income have become a necessity for institutions to grow economically and be sustainable in their business. This paper aims to examine better ways Higher Education Institutions (HEIs) approach financial management, including HEIs choice in allocating institutional resources. In today’s world, competition is a driving force for innovation and entrepreneurship with higher education not spared to unfair practices. For example, Africa, including Kenya, loses approximately 23,000 qualified professionals to the west every year.  This is a theoretical paper; based on financial management theory, that explains the gap between the educational knowledge and business practices in the education industry. An expenditure-based budget allocation model for HEIs' resource allocation is developed as a new financial management framework. It is based on the premise that HEIs exists not only to educate students, but also to contribute to the development and furtherance of knowledge, and contribution to the nation's economic growth in terms of expertise and commercialization of intellectual property; this paper contributes to conversations and policies on financial management practices and alternative IGAs for HEIs. Keywords: Sustainability; Financial sustainability; and Financial Innovations.
Quantum Computing: Architectures, Circuits, Algorithms

The aim of this paper is to provide a brief overview of Quantum Computing development with focus on quantum architectures, gates, circuits, tools, and quantum error correction. We also discuss research challenges and needs in quantum computing pertaining to architectures, circuits, tool developments and algorithms.
Edge Detection-Based Depth Analysis Using TD-WHOG Scheme

In current trends, detection and tracking of moving object is significant for video surveillance services. The surveillance concentrates on highly prohibited regions and secured environments. These machine vision systems were widely incorporated in automotive surveillance and traffic monitoring. There are two most key challenges need to be addressed to solve the real-time issues in video surveillance systems are memory requirements to store sequence of information and moving object detection. Hence, the edge detection-based moving object tracking provides better feature vector and higher classification rate for further process. This work discusses the research contribution on edge detection-based depth analysis using TD-WHOG scheme for compression. Also, the conventional standards were failed to exploit better spatio-temporal redundancies, which may not produce considerable coding efficiency for dynamic texture video sequences. So, the prime objective is to suppress the redundancy; dimensionality reduction is the mode of process for increasing the effectiveness.
NSH extensions for local distributed SFC control

Service function chaining (SFC) allows the instantiation of an orderedset of service functions and subsequent steering of traffic throughthem. In order to set up and maintain SFC instances, a control planeis required, which typically is centralized. In certain environments,such as fog computing ones, such centralized control might not befeasible, calling for distributed SFC control solutions. This documentspecifies several NSH extensions to provide in-band SFC controlsignaling.
Metrics for Analyzing Variability and Its Implementation in Software Product Lines: A Systematic Literature Review.

Abstract Context: Software Product Line (SPL) development requires at least concepts for variability implementation and variability modeling for deriving products from a product line. These variability implementation concepts are not required for the development of single systems and, thus, are not considered in traditional software engineering. Metrics are well established in traditional software engineering, but existing metrics are typically not applicable to SPLs as they do not address variability management. Over time, various specialized product line metrics have been described in literature, but no systematic description of these metrics and their characteristics is currently available. Objective: This paper describes and analyzes variability-aware metrics, designed for the needs of software product lines. More precisely we restrict the scope of our study explicitly to metrics designed for variability models, code artifacts, and metrics taking both kinds of artifacts into account. Further, we categorize the purpose for which these metrics were developed. We also analyze to what extent these metrics were evaluated to provide a basis for researchers for selecting adequate metrics. Method: We conducted a systematic literature review to identify variability-aware implementation metrics. We discovered 42 relevant papers reporting metrics intended to measure aspects of variability models or code artifacts. Results: We identified 57 variability model metrics, 34 annotation-based code metrics, 46 code metrics specific to composition-based implementation techniques, and 10 metrics integrating information from variability model and code artifacts. For only 31 metrics, an evaluation was performed assessing their suitability to draw any qualitative conclusions. Conclusions: We observed several problematic issues regarding the definition and the use of the metrics. Researchers and practitioners benefit from the catalog of variability-aware metrics, which is the first of its kind. Also, the research community benefits from the identified observations in order to avoid those problems when defining new metrics.
Research on Face Recognition Based on Vertex Energy Minimum Patterns

Aiming at the loss of the geometric information of the three-dimensional surface caused by the existing three-dimensional face recognition algorithm in the process of dimension reduction, by studying the conformal geometry related knowledge, this chapter adopts the conformal mapping method based on Ricci curvature flow to reduce the three-dimensional face surfaces to two-dimensional planar disk. The geometric information in the 3D surface is not lost as much as possible thus improving the recognition efficiency in the subsequent recognition process. In this chapter, a three-dimensional face recognition method based on minimum vertex energy pattern is proposed. Each point on the surface will be given its unique energy value in the calculation process of conformal mapping through Ricci curvature flow. By counting the variation of the energy value of each vertex, the feature histogram is generated, which is used as the basis of recognition. The method achieves better recognition effect. The recognition rate has been increased to more than 95%.
Pengaruh Kompetensi, Pemahaman Sistem Informasi Akuntansi, Pendidikan Dan Pelatihan Terhadap Penggunaan Sistem Informasi Akuntansi (Studi Empiris Di Opd Kota Pekanbaru)

This research is conducted by at office of OPD in City of Pekanbaru. Intention of research is to know influence of interest, education and understanding and training to usage of Information System Accountancy at office of OPD City of Pekanbaru. Population in this research is Organization Peripheral of Area (OPD) in City of Pekanbaru spread over in 45 OPD, while sampel amount to counted 135 people. Intake of sampel by using method of purposive sampling. Technique data collecting use method interview and kuesioner. To analyse data, hence writer use doubled linear regression method constructively program of SPSS Version 22.0. Pursuant to result of research known that interest variable, understanding variable and education variable and training have an effect on to Usage of Information System Accountancy in Organization Peripheral Of Area City of Pekanbaru. Pursuant to result of Coefficient test of Determinasi (R2) known that value of R Square equal to 0,780. Its meaning is that contribution influence of independent variable to variable of dependen is equal to 78 %. While the rest 22 % influenced by other variable which do not be packed into model of this regresi.Keywords: Usage Of Information System Accountancy, Interest, Understanding of, Education and Training.
State-based Policy Representation for Deep Policy Learning

Author(s): Liu, Fangchen | Advisor(s): Su, Hao | Abstract: Reinforcement Learning has achieved noticeable success in many fields, such as video game playing, continuous control, and the game of Go. One the other hand, current approaches usually require large sample complexity, and also lack the transferability to similar tasks. Imitation learning, also known as ``learning from demonstrations'', is possible to mitigate the former problem by providing successful experiences. However, current methods usually assume the expert and imitator are the same, which lack flexibility and robustness when the dynamics change.Generalizability is the core of artificial intelligence. An agent should be able to apply its knowledge for novel tasks after training in similar environments, or providing related demonstrations. Given current observation, it should have the ability to predict what can happen (modeling), and what needs to happen (planning). This brings out challenges on how to represent the knowledge and how to utilize the knowledge by learning from interactions or demonstrations.In this thesis, we will systematically study two important problems, the universal goal-reaching problem and the cross-morphology imitation learning problem, which are representative challenges in the field of reinforcement learning and imitation learning. Laying out our research work that attends to these challenging tasks unfolds our roadmap towards the holy-grail goal: make the agent generalizable by learning from observations and model the world.