Reviewerly/exHarmony huggingface dataset:
Design and Simulation of Solar Photovoltaic Directional Tracking Device

With the rapid development of solar photovoltaic systems, the application and demand for solar tracking systems are growing accordingly. This paper, from an astronomical point of view, analyses movement of the sun and designs an integrated device for solar photovoltaic directional tracking based on the general formulas of solar declination, time angle, azimuth, and elevation angle. To improve tracking accuracy and efficiency, the device is designed by using a double-degree-of-freedom tracking system, so that it can track the sun in both horizontal and vertical directions. Besides, a hybrid tracking mode is applied to achieve accurate tracking of the sun movement, in which the solar trajectory tracking is used as the primary mode, while the photoelectric monitoring is taken as a supplementary, to improve the accuracy and stability of the algorithm. The results from the simulation carried out by Proteus software reveals that the device as well as the control system both operate in a stable and reliable manner, during which, the solar panel is perpendicular to the sun's direct rays within an acceptable range of deviation, realizing the automatic tracking of the sun's rays all the time, which significantly improves the receiving efficiency of solar energy.
Learning Invariants through Soft Unification

Human reasoning involves recognising common underlying principles across many examples. The by-products of such reasoning are invariants that capture patterns such as if went then they are there, expressed using variables someone and somewhere instead of mentioning specific people or places. Humans learn what variables are and how to use them at a young age. This paper explores whether machines can also learn and use variables solely from examples without requiring human pre-engineering. We propose Unification Networks, an end-to-end differentiable neural network approach capable of lifting examples into invariants and using those invariants to solve a given task. The core characteristic of our architecture is soft unification between examples that enables the network to generalise parts of the input into variables, thereby learning invariants. We evaluate our approach on five datasets to demonstrate that learning invariants captures patterns in the data and can improve performance over baselines.
UCF Research Guides: New to "U"? (University of Central Florida Libraries): Finding Books & eBooks

Whether you're a new DirectConnect to UCF transfer student or a new transfer student from another college or university, here's information to help you locate the library resources you'll need for research assignments.
Fast Search with Poor OCR

The indexing and searching of historical documents have garnered attention in recent years due to massive digitization efforts of important collections worldwide. Pure textual search in these corpora is a problem since optical character recognition (OCR) is infamous for performing poorly on such historical material, which often suffer from poor preservation. We propose a novel text-based method for searching through noisy text. Our system represents words as vectors, projects queries and candidates obtained from the OCR into a common space, and ranks the candidates using a metric suited to nearest-neighbor search. We demonstrate the practicality of our method on typewritten German documents from the WWII era.
Prototype of Student Attendance Application Based on Face Recognition Using Eigenface Algorithm

Prototype of face recognition based attendance application that has been developed to overcome weaknesses in DTETI UGM student manual attendance system has several weaknesses. These weaknesses are a decrease in facial recognition accuracy when operating under conditions of varying environmental light intensity and in condition of face rotating towards z axis rotation centre. In addition, application prototype also does not yet have a database to store attendance results. In this paper, a new application prototype has been developed using Eigenface face detection and recognition algorithm and Haar-based Cascade Classifier. Meanwhile, to overcome prototype performance weaknesses of the previously developed application, a pre-processing method was proposed in another study was added. Processes in the method were geometry transformation, histogram levelling separately, image smoothing using bilateral filtering, and elliptical masking. The test results showed that in the category of various environmental light intensity conditions, face recognition accuracy from developed application prototypes was 16.71% better than previous application prototypes. Meanwhile, in category of face slope conditions at z axis rotation centre, face recognition accuracy from developed application prototype was 38.47% better. Attendance database system was also successfully implemented and running without error.
Development of Cubesat For Quantum and Classical Communication

The use of satellites is considered a perspective view for laser communication. We present a space-to-ground quantum key distribution (QKD) concept to overcome the limitations for the distance of the secure communication.
An Overview of Wearable Computing

This chapter provides a high-level user-oriented overview of wearable computing. It begins by defining wearable computing and describing its key characteristics. It then provides an historical view of wearable computing devices, beginning with an abacus ring from the 17th century and progressing to modern wearable computing devices. Lessons learned from this history and their implications for future wearable devices are discussed. Key application areas for wearable computing are then introduced, and sample applications from each area are described. Two case studies are provided to demonstrate the role of data analytic methods, and how they can yield more powerful wearable applications. The chapter concludes with a summary of the current state of wearable technology.
Behavioural evidence for the existence of a spatiotopic free-viewing saliency map

Humans gather high-resolution visual information only in the fovea, therefore they must make eye movements to explore the visual world. Inspired by results in attention research (Treisman 1980), it has been proposed that free-viewing fixations are driven by a spatial priority or “saliency” map. Whether this is the case has been debated for decades in neuroscience and psychology. One hypothesis states that priority values are assigned locally to image locations, independent of saccade history, and are only later combined with saccade history and other constraints to select the next fixation location. A second hypothesis is that there are interactions between saccade history and image content that cannot be summarised by a single value. For example, if after long saccades different content drives the next fixation than after short saccades, then it is impossible to assign a single saliency value to image locations. Here we discriminate between these possibilities in a data-driven manner. We extend the DeepGaze II model (Kümmerer et al., 2017) to a new model of scanpath prediction. First, we extract features from the VGG deep neural network that are used in a small “readout network” to predict one or multiple saliency maps. These saliency maps are then processed in a second readout network together with information on the scanpath history to predict upcoming saccade landing positions. We train the model using human free-viewing scan path data and achieve state-of-the-art performance compared to previous scanpath models. We find that using multiple saliency maps gives no advantage in scanpath prediction compared to a single saliency map. Since the number of saliency maps the network can use imposes strong qualitative constraints on what the model is able to predict, this suggests that for free-viewing a single saliency map may exist that does not depend on either current or previous gaze locations.
8 ways to protect your data from hackers

It’s impossible to guarantee your data will be safe, but there are best practices you can follow to reduce your chance of a hack or breach
Lyrics Analysis of Korean Popular Songs and Application for Language and Culture Education.

This study aims to analyze the decrease of the vocabulary frequency and the increase of the lyrics from the 1970s to the 2000s. We examined the frequency of representative vocabularies in nouns, verbs and adjectives, and examined the characteristics of emotions. The purpose of this study is to investigate the application of cultural literacy education to Korean culture education by examining the change of vocabulary frequency and observing the contemporary social phenomenon and the latent desire of the masses. From the above, it can be understood that Korean people can plan their understanding of Korean culture and society through popular songs that contain the cultural context of Korea. The lyrics of Korean pop songs can be used as a multimedia education material that enhances cultural sensitivity so that learners’ Korean language ability can be expanded not only because they include current living language, but also because they can critically understand popular culture. As a result, the Korean popular song is a cultural product, which is valuable as language and cultural education material that can understand the situation of the times.
New Trends and Challenges in CAPTCHA Programming

This chapter describes new challenging methods which are related to the usability of the CAPTCHA test. Firstly, a new strategy for the automatic prediction of the response time to solve the CAPTCHA is presented and described. The proposed strategy is based on regression trees for the prediction of the response time given known personal and demographic features of the Internet users: (i) age, (ii) education level, and (iii) Internet experience level. The involved CAPTCHA types are new trends of the CAPTCHA test: (i) game-based CAPTCHA, and (ii) image-based CAPTCHA. The prediction accuracy of the model is evaluated on collected data from a population of Internet users. The obtained results show that the proposed model is promising in predicting the response time to solve the CAPTCHA test. Secondly, the advanced study of the CAPTCHA usability which employs the association rule mining is extended to the new trends of the CAPTCHA test, i.e. the image-based CAPTCHA. Both approaches represent an important step in the CAPTCHA programming for designing new types of CAPTCHA which can be customized to specific classes of Internet users.
Data Mapping for Restricted Boltzmann Machine.

Restricted Boltzmann machine (RBM) is two-layer neural nets constructed as a probabilistic model and its training is to maximize a product of probabilities by the contrastive divergence (CD) scheme. In this paper a data mapping is used to describe the relationship between visible and hidden layers and the training is to minimize a squared error of the reconstructed visible layer by the gradient descent or a finite difference approximation. This paper presents three new findings: 1) nodes on visible and hidden layers can take real-valued matrix data without a probabilistic interpretation; 2) the famous CD1 is a finite difference approximation of gradient descent after ignoring the second-order error; 3) activation can take non-sigmoid functions such as identity, relu and softsign. The data mapping provides a unified framework on dimensionality reduction, feature extraction and data representation pioneered and developed by Hinton and his colleagues. As an approximation of gradient descent, the finite difference learning is applicable to both directed and undirected graphs. Numerical results are performed to confirm these new findings on very low dimensionality reduction, matrix data and flexible activations.
ANTHONY W. LEE (ed.), New Essays on Samuel Johnson: Revaluation

ANTHONY W. LEE (ed.), New Essays on Samuel Johnson: Revaluation Anthony W Lee (ed.), New Essays on Samuel Johnson: Revaluation. Pp. xx + 261. Newark: University of Delaware Press, 2018. £65.00 (ISBN 978 1 611 49679 6). Philip smallwood Philip smallwood Bristol University Email: philip.smallwood60@googlemail.com Search for other works by this author on: Oxford Academic Google Scholar Notes and Queries, Volume 66, Issue 4, December 2019, Pages 603–605, https://doi.org/10.1093/notesj/gjz149 Published: 20 September 2019
Blockchain: Overview, Practical Implementation & Its Uses

Advanced from the Merkle Tree, Blockchain Technology is a completely decentralized computerized register which keeps a protected history of information trades. The decentralization part of Blockchain Technology does away the need of any focal specialist for overseeing it. In this paper we present an exhaustive diagram on blockchain innovation. We initially start by revealing insight into the basics of Blockchain Technology then we investigate some run of the mill calculations utilized in different blockchains. Blockchain, the establishment of Bitcoin, has gotten broad consideration as of late. Being an ineradicable information putting away innovation, Blockchain can be utilized in budgetary resources as well as anything which has some worth. In any case, being a human development, drawbacks are even here in the blockchain innovation, for example, versatility issues, security issues, and not really easy to use for non-specialized individuals. Next, with regular specialized issues we have discussed the ongoing advances. We in conclusion finish up this paper by spreading out conceivable future improvements of blockchain innovation.
Anonymous Proxy Server

This Final Year Project is about Anonymous Proxy Server. The aim of this thesis is to implement multiple instances of Tor and Privoxy with Squid. With Tor users are able to preserve their identity on the internet such as IP address and location, and to evade internet censorship or blocked websites. This project was conducted using the Waterfall software development methodology that allows step by step approach in completing the project. It enables testing to be performed when all of the project’s requirement has been met.Something interesting that occurred was the multiple instances of Tor and Privoxy allows each window or tab of a browser to use different instances of Tor and Privoxy. This is a great deal since it allows the users to be able to have up to eight different IP addresses all coming from every parts of the world. Which means, they can be browsing two different websites at the same time and each website is being connected from somewhere else even though the users are only browsing in Malaysia.
METODE DEMONSTRASI DAPAT MENINGKATKAN HASIL BELAJAR SISWA TENTANG BANGUN RUANG SEDERHANA PADA PELAJARAN MATEMATIKA

The problem in this study was the low mathematics learning outcomes of students at SD Negeri 29 Rimau. The purpose of this study was to improve the mathematics learning outcomes of fourth grade students through the application of demonstration methods. The type of research used is Classroom Action Research (CAR) with the stages of each cycle consisting of: planning, implementation, observation, and reflection. This data collection uses observation sheets and formative test questions. Data collection techniques were carried out by non-test techniques and test techniques. This study uses data analysis techniques in the form of qualitative data analysis and quantitative data analysis. The results of the study indicate that the application of demonstration methods can improve mathematics learning outcomes. The percentage of student learning outcomes in the first cycle of Less High category, in the second cycle becomes High.
Cyber Safe Curricula and Online Harassment

Abstract In this article, Heather Greenhalgh‐Spencer analyzes three examples of curricula aimed at cultivating good cyber behavior and countering online harassment. These curricula are branded as addressing civic needs, cultivating civic duty, and developing skills to help students understand and react to online threats. Here, Greenhalgh‐Spencer offers two critiques of these, and other similar, cyber safe curricula. First, current cyber safe curricula do not adequately address the ways that identity — particularly gender and sexuality — shape one's experience of online spaces and exposure to online threats. Second, current cyber safe curricula do not adequately consider nuances of how to “react” to online threats. The preponderance of cyber curricula teach students that all forms of offensive speech online should be abolished or pushed against. These curricula miss the opportunity to cultivate conversations around the tensions between the need to feel safe and the need to protect freedoms of speech.
Properties of invariant object recognition in human one-shot learning suggests a hierarchical architecture different from deep convolutional neural networks

Recognition invariant to transformations can be a significant advantage for a visual system. It is important, however, to distinguish between intrinsic invariance due to the underlying representation and example-based invariance for familiar objects that have been previously seen under different viewpoints. To characterize invariance in humans, we conducted psychophysical experiments measuring object invariant recognition performance in one-shot learning scheme. We report tolerance to scale and position changes by analyzing recognition accuracy of Korean letters presented in a flash to non-Korean subjects, who had no previous experience with Korean letters. We found that humans have significant scale-invariance at the center of the visual field after only a single exposure to a novel object. The degree of translation-invariance is limited, depending on the size and position of objects. We represent the range of invariance as the window of invariance, and compare it with the window of visibility, which is obtained by testing Korean subjects under the same experimental conditions as for the one-shot learning task. This comparison revealed that the window of invariance lies within the window of visibility. In addition, to understand the underlying brain computation associated with the invariance properties, we compared experimental data with computational modeling results. For the computational model, we tested Eccentricity-dependent Neural Network (ENN), which we hypothesized it exploits the intrinsic invariance properties observed in the human experiments. Our modeling results suggest that to explain invariant recognition by humans, artificial neural networks require to explicitly incorporate built-in scale-invariance, by encoding different scale channels as well as eccentricity-dependent representation captured with neurons’ receptive field sizes that change with eccentricity as in ENN. Our psychophysical experiments and related simulations strongly suggest that the human visual system uses a different computational strategy than current convolutional deep learning architectures, which is more data efficient and strongly reliant on eye-movements.
The Peripheral View Melts Facial Emotion into a Blur: Investigating the Role of Spatial Frequency in Younger and Older Adults’ Peripheral Emotion Detection

Facial emotion detection declines when stimuli are presented in more distal peripheral relative to more central foveal and parafoveal locations. Younger and older adults display similar decline in facial emotion detection with increased display distance from the center of the visual field when the stimuli used include a broad range of spatial frequencies. The current study examined younger and older adults’ emotion detection performance when low or high spatial frequencies have been filtered from the facial stimuli. Younger (n = 37, ages 18–29) and older (n = 40, ages 61–80) adults observed either angry and neutral or happy and neutral facial expressions presented at 5, 10, and 15 degrees from the center of the visual field. Spatial frequency filters were applied to the images so that participants observed stimuli consisting of broad, low, or high spatial frequencies at each location. Happiness was more easily detected than anger, and, as a result, happiness detection was more greatly impacted by stimulus distance from the center of the visual field than was anger. Likewise, emotion was more easily detected in broad and low spatial frequency stimuli at peripheral locations than in high spatial frequency stimuli. Younger adults outperformed older adults at detecting happiness and anger in high spatial frequency facial stimuli presented at 5 and 10 degrees from the center of the visual field, but not at 15 degrees, where both age groups were equally poor at detecting emotion. Younger and older adults did not differ in their ability to detect anger and happiness in broad and low spatial frequency expressions. Overall, these findings suggest that peripheral emotion detection relies more heavily on low spatial frequency information in facial images, and that this tendency is maintained throughout adulthood.
Research Guides: Professional Seminar in Teaching: Home

This research guide is intended to help students enrolled in the Professional Seminar in Teaching find resources in literature, education, and related fields.
Use of Multiple Data Sources in Collaborative Data Mining

Agriculture is one of the domains that depend on weather forecasts and which would improve its performance if some features could be predicted. Because of that, we try to define a concept that is capable of generating predictive results for temperature, based on multiple sources and use of virtual machine learning. We define collaborative approaches in data mining that use multiple sources and we analyze the results from a comparative point of view with the independent process. For each approach, we use a machine learning algorithm which is applied to the combination of data sources from the weather stations. The research proposes a model of the data mining process for a collaborative variant with multiple sources. This leads to the conclusion that a collaborative approach generates better results than a standalone one.
A study on the effect of flipped learning using SNS in college English education

The aim of this study is to apply flipped learning in teaching General English courses in university and examine its impact on English self-efficacy and students’ English achievement. The researcher implemented flipped learning by recording videos based on the students’ textbook and up-loading those videos on the SNS before each lesson to provide opportunities for active interactions in class. The students of the experimental group (n=56) were required to learn through SNS the videos to learn in the pre-class. They also practiced what they had learned under the teacher’s supervision by completing collaborative tasks in groups or pairs. Meanwhile, the control group students (n=66) received in-class only traditional English teaching. The statistical analysis of the results showed that adopting the flipped learning strategy appeared to play a role in enhancing the students’ English self-efficacy and English achievement. Also the students’ responses indicated that their satisfaction towards the flipped learning is positive.