Reviewerly/exHarmony huggingface dataset:
Automatic Integration of Ubiquitous Access Address in Camera Surveillance System Using Natural Language Processing

One of the issues rarely discussed in the IP camera is the system's difficulties in accessing the IP camera from different manufacturers. This problem is caused by the fact that each IP camera has an individual manufacturing preset access addresses that make the users unable to integrate IP cameras from other manufacturers automatically. It is complicated to access ubiquitous IP cameras automatically in an advanced surveillance system. This research proposes a selection of IP camera access links using the NLP extraction approach. TF-IDF is one of the NLP extraction techniques used in this research, which has been combined with a statistical approach as a novel method in order to select the reliable candidate address from the address data of the IP camera provided by the ISPY dataset. The experiment has been conducted using several schemes based on the percentage of degradation of the original access address. There are four proposed links results, which are 242 links (25%), 288 links (50%), 363 links (75%), and 388 links (100%). The evaluation using 10-fold validation in several simulations reveals that the proposed list can produce the range around 89% to 97% of average accuracy. Moreover, brute force complexity evaluation has denoted that the selected access address has a lower complexity than the original one.
Perspective Compensation of 2D-DIC Measurements by Combination with Speckle Imaging

Two-dimensional Digital Image Correlation (2D-DIC) is a popular method for determining object in-plane motion and deformation by tracing surface texture. However, the applicability of the method is limited by perspective errors that may be generated by object surface curvature, out-of-plane motions and non-perpendicular camera alignment. The resulting variations in camera magnification lead to errors in the measured surface motion signals. Speckle imaging is a closely related DIC method where the motions of a laser-illuminated surface are tracked by analyzing the scattered interference speckle pattern. When the camera is in focus, the speckles move at the same rate as the surface. However, if the camera is defocused, the speckle motion rate deviates from the actual surface motion. Speckle imaging measurement sensitivity can thus be controlled by varying the amount of defocus. Interestingly, speckle imaging sensitivity increases with near-focus, in contrast to sensitivity reduction in 2D-DIC caused by perspective change. Therefore, if the surface motions are analyzed using both DIC and speckle imaging, the resulting sensitivity difference can be utilized to compensate any perspective errors present. The amount of defocus must be moderated in order to achieve a balance between sensitivity and sufficient spatial resolution for full-field measurements. Therefore, it is important to study how speckle imaging behavior changes as a function of defocus. Here, the speckle imaging characteristics are experimentally investigated by recording speckle patterns at varying defocus distances and with different lens aperture sizes (f-numbers). The resulting speckle patterns are characterized by computing the average speckle diameters. The results reveal how at small defocus distances, speckle size is linearly dependent on lens f-number and inversely proportional to defocus distance. At large defocus distances, on the other hand, speckle size is independent on f-number and linearly proportional to defocus distance.
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing

In the problem of active sequential hypothesis testing (ASHT), a learner seeks to identify the true hypothesis from among a known set of hypotheses. The learner is given a set of actions and knows the random distribution of the outcome of any action under any true hypothesis. Given a target error $δ&gt;0$, the goal is to sequentially select the fewest number of actions so as to identify the true hypothesis with probability at least $1 - δ$. Motivated by applications in which the number of hypotheses or actions is massive (e.g., genomics-based cancer detection), we propose efficient (greedy, in fact) algorithms and provide the first approximation guarantees for ASHT, under two types of adaptivity. Both of our guarantees are independent of the number of actions and logarithmic in the number of hypotheses. We numerically evaluate the performance of our algorithms using both synthetic and real-world DNA mutation data, demonstrating that our algorithms outperform previously proposed heuristic policies by large margins.
Cloud Computing in Big Data

Big data analytics is the use of advanced analytic techniques against data sets of very high volume, high diversity and high velocity, which can be used to extract useful information. Recently, many companies use big data analytics to understand about their customers in order to provide better customer services. As times goes on, the sizes of data sets increase rapidly, the requirements on computation and storage increase rapidly too. Recently, cloud computing attracts much attentions. It is because cloud platform can provide the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. In this talk, we explore the available resources on cloud platforms and discuss how cloud platforms can improve the performance of big data applications.
Probabilistic Failure Analysis of Door System Using Bayesian Network

Unlike other products, these electric vehicles have undergone a period of reliability growth by manufacturer and operating company for a long time. Through many years, reliable field failure data is continuously stored in specific program. When uncertain failure of particular system is detected during this period, we suggest probabilistic method of reasoning that can show dependent relationship with subparts and estimate the reliability of system. In this study, failure mode effects and criticality analysis (FMECA) was conducted to extract the major failure and degradation factors of passenger door system (Centered on new manufactured vehicle line 2). In addition, Failure rates of sub parts are estimated by means of NSWC handbook and Field failure data. To show structural failure mode, we used fault tree analysis (FTA). Based on the fault states and failure rates, we estimated reliability of Passenger Door System using Bayesian network (BN). The goal is to execute dependability analysis for system reliability.
탈북청소년의 신앙발달과정에 관한 근거론적 연구

By using grounded theory, this study proposes a step-by-step curriculum for North Korean defectors’ faith development by examining the process of Christian spiritual development after entering South Korean society and uncovering their developmental patterns. At the time of defection, North Korean defectors have an identity as Christians, but often remain unconstitutionalized by their faith. They are members of our future society, especially those who will serve as a bridge for the unified generation as well as a resource to resolve heterogeneity between the two Korea after unification. Helping North Korean adolescents to develop Christian spirituality not only helps them restore healthy self-identity, but also grow into positive influencer to unified society.
Fundamentals of Entrepreneurship (ENT300) Business Plan : Custom Station / Ammar Firdaus Md Amran

Custom Station is a new business that allows people to custom design a t-shirt (specifically the design on the front or back) any way that they would like. By intelligently leveraging cutting edge technology, Custom Station will harness the power of computer sublimation to allow custom t-shirt printing in production runs as small as one unit. Custom Station will offer customers a variety of options for creating their own custom shirts. The majority of orders will be for t-shirts, however other style shirts will be available. My business will use Canon printer sublimation technology that allows a computer image to be applied to a t-shirt in a high quality, high resolution, economically feasible manner. This technology creates an image durable enough to withstand thousands of washing. Its photo-like quality, due to significantly higher printer resolution than anything on the market, will show off any image. The technology is cost effective enough to offer customers the ability to order just one unit. Most other competitors' costs prohibit printing custom shirts in one-off production runs. Finally, the customer may choose from an extensive library of existing images, supply their own image, or have an artist create an image for them. Competitive advantage is I setup this business in a very strategic place, it is because there is no other printing or custom services like I did in my place. Because of this is rural area, the nearest printing or custom shop will take everybody about an hour to get the service done.
Multimodal fusion using sparse CCA for breast cancer survival prediction

Effective understanding of a disease such as cancer requires fusing multiple sources of information captured across physical scales by multimodal data. In this work, we propose a novel feature embedding module that derives from canonical correlation analyses to account for intra-modality and inter-modality correlations. Experiments on simulated and real data demonstrate how our proposed module can learn well-correlated multi-dimensional embeddings. These embeddings perform competitively on one-year survival classification of TCGA-BRCA breast cancer patients, yielding average F1 scores up to 58.69% under 5-fold cross-validation.
AKUNTABILITAS DAN TRANSPARANSI KEUANGAN PENGELOLAAN DANA BOPDA SDN WONOREJO III/314 SURABAYA

This research aimed to describe Financial Accountability on Fund Management of Regional Education Operational Assistance (REOA) of SDN Wonorejo III/314 Surabaya, 2019: 1). REOA’s Fund Planning. 2). REOA’s Implementation. 3). REOA’s Fund Monitoring and Evaluation. 4). REOA’s Fund Reporting. The research was descriptive-qualitative. When, the population was SDN Wonorejo III/314 Surabaya, with subjects include Principal, School Treasurer, Teachers, and School Committees. Moreover, the data collection technique used observation, documentation, and interview. The research result showed as follows: 1) During REOA’s planning, the management had arranged its RKAS before having RAPBS, 2) There were two step in implementation of REOA’s arrangement, i.e salary for contract teachers and service goods. 3) Monitoring and Evaluation was done internally of school commitees and inspectorate. 4) REOA’s Fund Repoting was applied every quarter through its accountability repot; along with the attachment. This publication was done as it posted REOA’s summary on bulletin board and give copy of paper about the usage of REOA’s fund to parents.Keywords: reoa’s budget, planning, implementation, monitoring and evaluation, reoa’s reporting
LibGuides: Remote Research Resources: Primary Sources

Conducting research remotely? The New York Public Library has you covered. This guide includes resources to support your research 24/7 no matter where you are.
Spatial Ising machine: photonic acceleration and adiabatic evolution

Spatial Ising Machines are simple optical acceleration devices enabling hard combinatorial optimization with millions of spins. We review our results, including noise acceleration and adiabatic evolution.
An approach to using Instagram in secondary education

In this paper, the authors study the possibility of applying Instagram in education andincreasing the level of knowledge of high school students. The use of the Instagram app aimsto motivate students to gain and improve knowledge of economics, develop environmentalawareness and increase awareness of the importance of caring for their health using modernservices. The possibility of improving the teaching process in secondary education byintroducing mobile and pervasive technologies that would make the teaching process moreeffective and attractive to students was tested by posting Challenges on Instagram. Thispaper presents the research that was done with students of the third grade of secondaryvocational school in the period March - May 2019.
DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection

Street scene change detection continues to capture researchers' interests in the computer vision community. It aims to identify the changed regions of the paired street-view images captured at different times. The state-of-the-art network based on the encoder-decoder architecture leverages the feature maps at the corresponding level between two channels to gain sufficient information of changes. Still, the efficiency of feature extraction, feature correlation calculation, even the whole network requires further improvement. This paper proposes the temporal attention and explores the impact of the dependency-scope size of temporal attention on the performance of change detection. In addition, based on the Temporal Attention Module (TAM), we introduce a more efficient and light-weight version - Dynamic Receptive Temporal Attention Module (DRTAM) and propose the Concurrent Horizontal and Vertical Attention (CHVA) to improve the accuracy of the network on specific challenging entities. On street scene datasets `GSV', `TSUNAMI' and `VL-CMU-CD', our approach gains excellent performance, establishing new state-of-the-art scores without bells and whistles, while maintaining high efficiency applicable in autonomous vehicles.
Massively parallel amplitude-only Fourier neural network

Here we introduce a novel amplitude-only Fourier-optical processor paradigm capable of processing large-scale ~(1,000 × 1,000) matrices in a single time-step and 100 microsecond-short latency. We exemplary realize a convolutional neural network (CNN) performing classification tasks on 2-Megapixel large matrices at 10 kHz rates, which latency-outperforms current GPU and phase-based display technology by one and two orders of magnitude, respectively. Training this optical convolutional layer on image classification tasks and utilizing it in a hybrid optical-electronic CNN, shows classification accuracy of 98% (MNIST) and 54% (CIFAR-10).
EF Core and SQL Server

The ultimate goal of our web application is to work against a real SQL database. Up until now, we have only been working with our TestData service. You will remember that we created this service to mimic the workings of a real SQL database, just while we were busy developing the foundation of our application.
A COMPARATIVE PERFORMANCE STUDY OF MACHINE LEARNING ALGORITHMS, FOR EFFICIENT DATA MINING MANAGEMENT OF INTRUSION DETECTION SYSTEMS

Data mining provide decision support for intrusion management, and also help in Intrusion Detection System (IDS) in detecting of new vulnerabilities and intrusions by discovering unknown patterns of attacks or intrusions.In this paper, we have compared four algorithms of Machine Learning models which are namely: Naives Bayes (NB), Decision trees (J48), Support Vector machines (SVN), and Sequential Machine optimization (SMO).The realistic models were evaluated and compared using data sets as obtained from NSL-KDDCup.The method takes into consideration the relative sizes of the classes to each other in the dataset.which allows the user of the IDS to evaluate how well they will predict the classes given the distribution of the dataset.In addition, graphs were plotted in order to efficiently analyze the results obtained of the various models depicted in Figures.The simulation results were obtained using WEKA.The simulation parameters were filtered into various tables as depicted also in Figures so as to achieve a visual conception.Finally, we carried out various computational analyses to give us semblance of graphical constructions that are related to some parameters (time, kappa characteristics, ROC etc.) of our experiments respectively.
Solar potential evaluation and political, economic and social analysis (PES) in the department Cesar – Colombia

This research shows the estimation of direct, diffuse and total irradiation in the department of Cesar (Colombia) through the use of the Bird and Hulstrom model and parameterizations of the Mächler and Iqbal model. The data used for this purpose were collected by the meteorological stations installed by the Institute of Hydrology, Meteorology and Environmental Studies of Colombia (IDEAM) corresponding to the range of collection and analysis with more than 20 years between 1993 and 2013, a period of study suitable for a comprehensive solarimetric study. Irradiation data were estimated taking into account four types of climate scenarios that depended on the type of atmosphere present in the study area. It was found that the solar potential of the department of Cesar, located on the Colombian Caribbean Coast, presents average total irradiations ranging from 6.2 kWh/m2day in very turbid atmospheres to 6.8 kWh/m2 day in extremely clean atmospheres, making it one of the regions with the highest irradiation values in Colombia. This panorama allows to visualize the great potential that exists in the region for the implementation of projects with both thermal and photovoltaic solar technologies. This study was also complemented with a political, economic and social analysis of Colombia compared to the United State to determine the improvement opportunities in Colombia.
Does Cognitive Load Affect Eye Movements/Oculomotor Behavior in Natural Scenes?

Abstract Cognitive neuroscience researchers have identified relationships between cognitive load and eye movement behavior that are consistent with oculomotor biomarkers for neurological disorders. We develop an adaptive visual search paradigm that manipulates task difficulty and examine the effect of cognitive load on oculomotor behavior in healthy young adults. Participants (N=30) free-viewed a sequence of 100 natural scenes for 10 seconds each, while their eye movements were recorded. After each image, participants completed a 4 alternative forced choice task in which they selected a target object from the previously viewed scene, among 3 distracters of the same object type but from alternate scenes. Following two correct responses, the target object was selected from an image increasingly farther back (N-back) in the image stream; following an incorrect response, N decreased by 1. N-back thus quantifies and individualizes cognitive load. The results show that response latencies increased as N-back increased, and pupil diameter increased with N-back, before decreasing at very high N-back. These findings are consistent with previous studies and confirm that this paradigm was successful in actively engaging working memory, and successfully adapts task difficulty to individual subject’s skill levels. We hypothesized that oculomotor behavior would covary with cognitive load. However, there were no significant differences between the number or duration of fixations and saccades for high/low performing subjects, or between high/low performing trials for a given subject. Similarly, oculomotor behavior did not act as a predictor of correct/incorrect responses with increasing demand from the N-back task. Similarly, the proportion of each scene viewed was not related to N-back and was not a significant predictor of accuracy. These results suggest that cognitive load can be tracked with an adaptive visual search task, but that oculomotor strategies generally do not change as a result of greater cognitive demand in healthy adults.
Blended Learning: Impact on Student Motivation and Understanding

Blended learning is a combination of learning or a combination of face-to-face and online learning elements in a coherent manner.This method can be a solution to the impact of a pandemic on lectures.Therefore, this paper seeks to describe the impact of using the blended learning method on student motivation and understanding in lectures.Primary data applied in this paper to see students' perceptions regarding the learning methods applied and the obstacles they feel during lectures.The population is all participants in the International Law class, Department of International Relations, Universitas Andalas.This paper shows that blended learning can be a choice for lecturers that can increase student understanding and motivation in learning.The implementation of this method requires detailed lecture instructions so that students can focus on learning.This blended learning method depends on a stable internet connection network and a stable Learning Management System (LMS) server as a place for conducting online lectures.However, based on the survey, students had problems accessing the LMS because the servers were often down.This problem makes it difficult for students to carry out class activities that support the achievement of learning outcomes.
Self-supervised Symmetric Nonnegative Matrix Factorization

Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be a powerful method for data clustering. However, SNMF is mathematically formulated as a non-convex optimization problem, making it sensitive to the initialization of variables. Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering results, we propose self-supervised SNMF (S$^3$NMF), which is capable of boosting clustering performance progressively by taking advantage of the sensitivity to initialization characteristic of SNMF, without relying on any additional information. Specifically, we first perform SNMF repeatedly with a random nonnegative matrix for initialization each time, leading to multiple decomposed matrices. Then, we rank the quality of the resulting matrices with adaptively learned weights, from which a new similarity matrix that is expected to be more discriminative is reconstructed for SNMF again. These two steps are iterated until the stopping criterion/maximum number of iterations is achieved. We mathematically formulate S$^3$NMF as a constraint optimization problem, and provide an alternative optimization algorithm to solve it with the theoretical convergence guaranteed. Extensive experimental results on $10$ commonly used benchmark datasets demonstrate the significant advantage of our S$^3$NMF over $12$ state-of-the-art methods in terms of $5$ quantitative metrics. The source code is publicly available at https://github.com/jyh-learning/SSSNMF.
High-Performance Secure Access and Data Privacy Protection Method for 5G Border Routing Gateway

Abstract With the substantial increase in the amount of data information and the development of 5G technology, boundary computing technology based on 5G, as a real-time and efficient data processing method, has broad application prospects. The aggregation gateway further aggregates the power injection data of all electric energy storage units and sends it to the power company. The power company uses its own private key to access the aggregated data. Row decryption to obtain the total power data recovered in each time slot. The paper proposes a new solution using Lagrangian interpolation to ensure data privacy and security. The security shows that the algorithm can not only protect the privacy of the electric energy storage unit, but also realize the confidentiality and integrity of the injected data. Efficiency analysis shows that this algorithm can significantly reduce computational complexity and improve communication efficiency.
Signature of precious metal mineralization in hydrocarbon fluids, Central Scotland

Bitumens in a former ore deposit in central Scotland contain inclusions of silver, mercury, bismuth and uranium minerals, and are also enriched in gold. The deposit was mined for silver, and the immediate region exhibits gold and mercury anomalies. Thus the metal enrichments in the bitumen are a signature of the regional mineralization. Metal-hydrocarbon interaction probably occurred at the deposit, where a faulted boundary juxtaposed hydrocarbon fluids from a subsiding sedimentary basin with metals in a volcanic terrane.