Reviewerly/exHarmony huggingface dataset:
Optimizing the Whole-life Cost in End-to-end CNN Acceleration

The acceleration of CNNs has gained increasing atten-tion since their success in computer vision. With the heterogeneous functional layers that cannot be pro-cessed by the accelerators proposed for convolution layers only, modern end-to-end CNN acceleration so-lutions either transform the diverse computation into matrix/vector arithmetic, which loses data reuse op-portunities in convolution, or introduce dedicated functional unit to each kind of layer, which results in underutilization and high update expense. To enhance the whole-life cost efficiency, we need an acceleration solution that is efficient in processing CNN layers and has the generality to apply to all kinds of existing and emerging layers. To this end, we pro-pose GCONV Chain, a method to convert the entire CNN computation into a chain of standard general convolutions (GCONV) that can be efficiently pro-cessed by the existing CNN accelerators. This paper comprehensively analyzes the GCONV Chain model and proposes a full-stack implementation to support GCONV Chain. On one hand, the results on seven var-ious CNNs demonstrate that GCONV Chain improves the performance and energy efficiency of existing CNN accelerators by an average of 3.4x and 3.2x re-spectively. On the other hand, we show that GCONV Chain provides low whole-life costs for CNN accelera-tion, including both developer efforts and total cost of ownership for the users.
Violence Detection in Public Places using Machine Learning

Day by day modern technology is advancing and helping people to make their life easier. In recent years a large amount of surveillance cameras has been installed at various public places like traffic signals, offices, schools and so on. They are also being used during various public events like concerts, marriages, rallies, etc. The goal of this research article is to develop a violence detection system using these pre-installed surveillance cameras. We have put forward a system that detects violence from these videos and alerts the authorised person when violence is detected so that it can be handled and managed timely, causing the least harm to the public. The concept of machine learning is utilized here.
Analyzing Persuasive Strategies Used in e-Brochures

This research aims to analyze persuasive strategies used by the students of the English Department of Politeknik Negeri Padang (PNP) in creating e-Brochures.Ten brochures are chosen in this qualitative descriptive research.The data are the persuasion strategies applied in the brochures.After analyzing descriptively using Aristotle's rhetorical 'proof; ethos, logos, and pathos are found in the brochures.It is interesting to see that these brochures which talk about the natural view as the tourism destination tend to have some combination of persuasive strategies.It is implied that to create a good brochure, the writer has to consider the norm and value, then convince the reader with some argument or reason, and show his/her feeling about the object of the brochure.
Definition of End-to-end Encryption

End-to-end encryption (E2EE) is an application of cryptography incommunications systems between endpoints. E2EE systems are unique inproviding features of confidentiality, integrity and authenticity forusers. Improvements to E2EE strive to maximise the system'ssecurity while balancing usability and availability. Users of E2EEcommunications expect trustworthy providers of secure implementationsto respect and protect their right to whisper.
푸드표현활동 프로그램이 경로당 이용 노인의 주관적 기억력, 우울, 자기표현에 미치는 효과

This study is an experimental study on the single group pre-post test design for the effects of the food expression activity program on subjective memory, depression, and self-expression of the elderly. 36 elderly people attending Senior Citizen Club from two locations in S City participated. A total of six session programs were applied from January 8 to February 17, 2020. The questionnaire survey conducted subjective momory, depression, and self-expression before and after the program. The contents of the intervention consisted of physical relaxation, cognitive activities, and various food activities. In the result of this study, the food expression activity program were statistically significant differences in subjective memory(t=2.67, p=.011), depression(t=3.88, p<.001), and self-expression(t=2.09, p=.044). Therefore, it is expected that the food expression activity program can be applied as a nursing intervention to reduce depression and improve subjective memory and self-expression in the elders attending senior citizen clubs.
Compilation-based Solvers for Multi-Agent Path Finding: a Survey, Discussion, and Future Opportunities

Multi-agent path finding (MAPF) attracts considerable attention in artificial intelligence community as well as in robotics, and other fields such as warehouse logistics. The task in the standard MAPF is to find paths through which agents can navigate from their starting positions to specified individual goal positions. The combination of two additional requirements makes the problem computationally challenging: (i) agents must not collide with each other and (ii) the paths must be optimal with respect to some objective. Two major approaches to optimal MAPF solving include (1) dedicated search-based methods, which solve MAPF directly, and (2) compilation-based methods that reduce a MAPF instance to an instance in a different well established formalism, for which an efficient solver exists. The compilation-based MAPF solving can benefit from advancements accumulated during the development of the target solver often decades long. We summarize and compare contemporary compilation-based solvers for MAPF using formalisms like ASP, MIP, and SAT. We show the lessons learned from past developments and current trends in the topic and discuss its wider impact.
Distribution of Traffic Engineering Extended Administrative Groups using BGP-LS

Administrative groups are link attributes advertised used for trafficengineering. This document defines an extension to BGP-LS foradvertisement of extended administrative groups (EAGs).
Sistem Seleksi Calon Siswa Bidik Misi Menggunakan Metode Simple Additive Weighting

Vocational Senior High School of Muhammadiyah 2 Sukoharjo, opens Student Scholarship of Bidik Misi to accept new students in every admission period of academic year. This school has difficulty to decide the candidates of scholarship grantee. In this research, there is solution by creating selection system to select the candidate of scholarship grantee using Simple Additive Weighting method. This research uses parents’ income, family living cost, students’ grades, and students’ achievement. This research also uses a Usecase Diagram to design the system. To test the system, this research uses Blackbox testing method. There are 25 valid questions in research result so that this application is suitable to select the new students. Based in its result, additive simple weighting method can be used as a method to support decision, especially in the selection of scholarship grantee candidates.Keywords: Simple Additive Weighting, Decision Support System, New student selection.
Identifying change points for linear mixed models: a solution through evolutionary algorithms

Mathematical models are used to describe the relationship between two or more variables or features over the target population. Statistically, Simple Linear regression model has been extensively applied and the properties of their estimators are well known. However, this kind of model is not correctly applied in most cases, such as a longitudinal setting. Linear mixed models (LMMs) are useful when the measurements have been done over a specific interval of time. One of the most important assumptions, on both models, has been established as that the model holds for the whole data. In latter case, we could find one or several points which the function changes into. This proposal allows us to estimate the points where the model changes by minimizing a specific risk function or a loss function associated with the fitted model.
[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias

Singh et al. (2020) point out the dangers of contextual bias in visual recognition datasets. They propose two methods, CAM-based and feature-split, that better recognize an object or attribute in the absence of its typical context while maintaining competitive within-context accuracy. To verify their performance, we attempted to reproduce all 12 tables in the original paper, including those in the appendix. We also conducted additional experiments to better understand the proposed methods, including increasing the regularization in CAM-based and removing the weighted loss in feature-split. As the original code was not made available, we implemented the entire pipeline from scratch in PyTorch 1.7.0. Our implementation is based on the paper and email exchanges with the authors. We found that both proposed methods in the original paper help mitigate contextual bias, although for some methods, we could not completely replicate the quantitative results in the paper even after completing an extensive hyperparameter search. For example, on COCO-Stuff, DeepFashion, and UnRel, our feature-split model achieved an increase in accuracy on out-of-context images over the standard baseline, whereas on AwA, we saw a drop in performance. For the proposed CAM-based method, we were able to reproduce the original paper's results to within 0.5$\%$ mAP. Our implementation can be found at https://github.com/princetonvisualai/ContextualBias.
Component design of word report generation based on LabWindows/CVI

Abstract In order to realize the automation and diversification of word report generation business in the field of test and measurement, this paper first proposes a report generation collaborative processing mechanism based on a series of functional components through the demand analysis of report generation business and the design of software function allocation. Then, based on the development resources provided by LabWindows/CVI, this paper discusses the realization ways of the bookmark management and the automatic report generation based on word template. Finally, the practical effect of the word report generation business is given.
Efficiency of stochastic coordinate proximal gradient methods on nonseparable composite optimization

This paper deals with composite optimization problems having the objective function formed as the sum of two terms, one has Lipschitz continuous gradient along random subspaces and may be nonconvex and the second term is simple and differentiable, but possibly nonconvex and nonseparable. Under these settings we design a stochastic coordinate proximal gradient method which takes into account the nonseparable composite form of the objective function. This algorithm achieves scalability by constructing at each iteration a local approximation model of the whole nonseparable objective function along a random subspace with user-determined dimension. We outline efficient techniques for selecting the random subspace, yielding an implementation that has low cost per-iteration while also achieving fast convergence rates. We present a probabilistic worst-case complexity analysis for our stochastic coordinate proximal gradient method in convex and nonconvex settings, in particular we prove high-probability bounds on the number of iterations before a given optimality is achieved. Extensive numerical results also confirm the efficiency of our algorithm.
Multi-scale Self-calibrated Network for Image Light Source Transfer

Image light source transfer (LLST), as the most challenging task in the domain of image relighting, has attracted extensive attention in recent years. In the latest research, LLST is decomposed three sub-tasks: scene reconversion, shadow estimation, and image re-rendering, which provides a new paradigm for image relighting. However, many problems for scene reconversion and shadow estimation tasks, including uncalibrated feature information and poor semantic information, are still unresolved, thereby resulting in insufficient feature representation. In this paper, we propose novel down-sampling feature self-calibrated block (DFSB) and up-sampling feature self-calibrated block (UFSB) as the basic blocks of feature encoder and decoder to calibrate feature representation iteratively because the LLST is similar to the recalibration of image light source. In addition, we fuse the multi-scale features of the decoder in scene reconversion task to further explore and exploit more semantic information, thereby providing more accurate primary scene structure for image re-rendering. Experimental results in the VIDIT dataset show that the proposed approach significantly improves the performance for LLST.
The Decision Support System of Grade Promotion For The Students At Islamic Boarding School Foundation Tahfidzul Qur'an Mathlaul Huda Using Analytic Hierarchy Process Method

Decision support system is the system which is very helpful for decision-maker through data processing and decision model in solving a problem which is semi-structured or unstructured. In determining whether the student will graduate or stay in the class, it is necessary to pay attention to the thing which greatly affects the student activity. In this research, there are five assessment criteria used, namely Memorization, Semester Value, Morals, Imlak, and Reading the Alkitab. There are ten alternatives used, and in this research, the range of values used for graduating students is> 29.99, so that the results obtained by the 9 th student alternative and the 10 th student alternative stay in the class because the value is <29.99. This objective assessment, hopefully, it can help the teachers, ustadz and kiyai in assessing students who have extraordinary potential, so that students can find out their abilities and potential both in the academic and non-academic fields.
Blood Group Identification Using Machine Learning

Abstract- Recognizing the blood group is very important in medical emergency cases. The method based on image processing and machine learning technology at present are very fast and it has widely used in the medical field. In this system, we are proposing the more accurate and fast recognition of human blood group based on computer vision and machine learning technology. Sometimes the human eye may give us an inaccurate result, but if we detect the blood group using computer vision with machine learning technology then the small error in the results which are calculated and given by humans is eliminated. Using computer vision technology, we can give the best result as this technology is growing faster and faster. This technique can quickly and accurately identify the blood group.
Two algorithms based on modular arithmetic: lattice basis reduction and Hermite normal form computation

We verify two algorithms for which modular arithmetic plays an essential role: Storjohann's variant of the LLL lattice basis reduction algorithm and Kopparty's algorithm for computing the Hermite normal form of a matrix. To do this, we also formalize some facts about the modulo operation with symmetric range. Our implementations are based on the original papers, but are otherwise efficient. For basis reduction we formalize two versions: one that includes all of the optimizations/heuristics from Storjohann's paper, and one excluding a heuristic that we observed to often decrease efficiency. We also provide a fast, self-contained certifier for basis reduction, based on the efficient Hermite normal form algorithm.
INFORMATION SUPPORT AS A PROCESS OF FORMING MODERN IDEAS ABOUT NEW KNOWLEDGE, MATERIAL AND SPIRITUAL VALUES

The article states that no matter how scientific and technological progress changes or accelerates, a person does not act in accordance with the information provided to him, but according to his worldview at the moment, education and personal characteristics, supported by information. The manifestation of creativity demonstrates a key role in the system of moral values and beliefs of a person who makes a deliberate decision. This takes a special place in the formation of information support. Taking a creative decision, a person always takes the next step in his development, constantly enriching the information sphere.
Method of simultaneous calibration of dual view 3D measurement system

In view of the limitations of the existing methods when the camera has no common field of view, this paper proposes a method of using two plane calibration plates to calibrate two cameras at the same time. By deriving the coordinate transformation between the two cameras and two calibration plates, the solution of the relative pose relationship between any camera and the reference camera is transformed into a more mature hand-eye calibration equation. The experimental results show that this method can achieve simultaneous calibration of two cameras, and the absolute error is less than 0.089 mm. In the dual vision 3D measurement system, the cumulative error with phase height is less than 0.116 mm, which can provide a reliable initial value for the next step of data fusion.
Apparel Recommender System based on Bilateral image shape features

Probabilistic matrix factorization (PMF) is a well-known model of recommender systems. With the development of image recognition technology, some PMF recommender systems that combine images have emerged. Some of these systems use the image shape features of the recommended products to achieve better results compared to those of the traditional PMF. However, in the existing methods, no PMF recommender system can combine the image features of products previously purchased by customers and of recommended products. Thus, this study proposes a novel probabilistic model that integrates double convolutional neural networks (CNNs) into PMF. For apparel goods, two trained CNNs from the image shape features of users and items are combined, and the latent variables of users and items are optimized based on the vectorized features of CNNs and ratings. Extensive experiments show that our model predicts outcome more accurately than do other recommender models.
The Changing Nature of New Employee Orientation: 'Staff Retention and Turnover Reduction' an Empirical Study in the Gambia's Public and Private Sectors

Purpose: One of the major areas on HRM is how to develop employees and get the most efficient out of them by retaining employees and reducing an organization's turnover rate. People leave organizations all the times, depending on various reasons that might be personal and out of the control of the employee, and/or either due to conflict of interest or employees seeking a change in their careers. There are various possible factors of the range of control of the organization. However, it has been observed that organizations can keep their employees longer if they are oriented and well- trained. In this paper, investigation will be on the impact of employees' orientation as a tool towards reducing high turnover rates.Methodology: Both primary and secondary data were used to gather information for this investigation. The secondary data was obtained from articles, blogs, papers online, and textbooks. The questionnaires were sent to respondents electronically via Google forms. A link was produced: Https://forms.gle/GEgBCklkmlkMXmL9 after the survey was built, it was shared amongst respondents with an attached letter of clarification explaining the process of participation which was on voluntary basis. The survey was opened for a span of one week and within a week 144 surveys were filled and submitted above the benchmark which targeted 100 participates. The targeted audience was a random selection of The Gambia’s workforce, there was no specific organization under reviewed.Findings: The Statistics of this survey shows that very few numbers of employees stayed with an organization for more than 5 years of employment which indicates a high turnover. Over 73% of the respondents received orientation prior to job engagement with little and/or less information about how to do their jobs. Whereas, over 27% of employees never received any orientation. Results also show that management does not get involved often in ensuring that their newly hired employees understand what-to-do? And how-to-do-their jobs, rather most employees ended up learning their jobs by themselves. Finally, this survey recorded 38% for employees’ feedback and 66% of the time management does not respond to staff feedback. Feedback is one of the most ideal ways of improvement but management concentrates less on it. On the side of training and development, 42% of the respondents believe that orientation helps in reducing the rate at which people leave an organization. While 36% feel neutral about this statement and 50% of the respondents believe that orientation helps in retaining employees.
LibGuides: Blackboard Quick Start Guide: How to find your course

If you need to quickly migrate material for a traditional, face-to-face course online, this guide will help you get started.
Classification of Cancerous and Non Cancerous Cells in H & E Breast Cancer Images Using Structure Descriptors

Computer-assisted earlier detection and diagnosis of breast cancer are possible by analyzing morphological features in histopathology images. In this article, we propose computer-aided tool for accurate detection of the abnormal cells using shapes and morphological based features extracted from the segmented region of the input image. Different segmentation techniques have been performed and their performance is compared to find out the best segmentation algorithm for further processing ie., classification. Morphological descriptor such as major and minor-axis length, cell area, perimeter and eccentricity are extracted from the segmented region of the image. By analyzing the cell mass and contour of cells in the given input image, cells are classified as cancerous and non-cancerous. The results are validated using benchmark dataset images taken from Mitosis Atypia14 grand challenge in which 180 images are taken for training and 120 images for testing. The proposed method provides improved F-score of 94.12% compared with other previously proposed frameworks.
Coresets for $k$-median clustering under Fréchet and Hausdorff distances

We give algorithms for computing coresets for $(1+\varepsilon)$-approximate $k$-median clustering of polygonal curves (under the discrete and continuous Fréchet distance) and point sets (under the Hausdorff distance), when the cluster centers are restricted to be of low complexity. Ours is the first such result, where the size of the coreset is independent of the number of input curves/point sets to be clustered (although it still depends on the maximum complexity of each input object). Specifically, the size of the coreset is $Θ\left(\frac{k^3lm^δd}{\varepsilon^2}\log\left( \frac{kl}{\varepsilon}\right)\right)$ for any $δ&gt; 0$, where $d$ is the ambient dimension, $m$ is the maximum number of points in an input curve/point set, and $l$ is the maximum number of points allowed in a cluster center. We formally characterize a general condition on the restricted space of cluster centers -- this helps us to generalize and apply the importance sampling framework, that was used by Langberg and Schulman for computing coresets for $k$-median clustering of $d$-dimensional points on normed spaces in $\mathbb{R}^d$, to the problem of clustering curves and point sets using the Fréchet and Hausdorff metrics. Roughly, the condition places an upper bound on the number of different combinations of metric balls that the restricted space of cluster centers can hit. We also derive lower bounds on the size of the coreset, given the restriction that the coreset must be a subset of the input objects.