Reviewerly/exHarmony huggingface dataset:
Optimizing Memory Mapping for Dataflow Inference Accelerators: Efficient Memory Utilization on FPGAs

Convolutional Neural Network (CNN) inference has gained a significant amount of traction for performing tasks like speech recognition and image classification. To improve the accuracy with which these tasks can be performed, CNNs are typically designed to be deep, encompassing a large number of neural network layers. As a result, the computational intensity and storage requirements increase dramatically, necessitating hardware acceleration to reduce the execution latency. Field-Programmable Gate Arrays (FPGAs) in particular are well-suited for hardware acceleration of CNN inference, since the underlying hardware can be tailored to rapidly and efficiently perform the required operations. To this end, Xilinx introduced the FINN (Fast, Scalable Quantized Neural Network Inference on FPGAs) framework to leverage FPGAs for Neural Network (NN) inference. The FINN end-to-end deep learning framework converts high-level descriptions of CNN models into fast and scalable FPGA inference accelerator designs that are based on a custom dataflow architecture. In this dataflow architecture the input data are streamed in a feed forward fashion through a pipeline of per-layer dedicated compute units, that each have on-chip access to the associated NN parameters. In order to keep the compute units occupied, specific throughput requirements have to be satisfied by the memory subsystem. These throughput requirements directly dictate the shapes of the on-chip buffers that contain the NN parameter values. Especially for accelerators that exploit a high degree of parallelism, these memory shapes map poorly to the available on-chip memory resources of FPGA devices. As a result, these resources are typically underutilized, which leads an On-Chip Memory (OCM) deficiency, and limits the amount of parallelism that can be exploited. In this thesis, a methodology is proposed that improves the mapping efficiency of NN parameter buffers to the embedded Block RAM (BRAM) resources on FPGAs, without negatively impacting the accelerator throughput. To accomplish this, an architecture is proposed where the memory subsystem and compute units are decoupled, and operate as a producer-consumer system. Within this architecture, the memory subsystem functions at a higher clock frequency relative to the compute units, which enables the memory subsystem to match the consumption rate of the compute units when multiple NN parameter buffers are clustered within the same BRAM instance. Furthermore, a genetic algorithm is used to find optimal group arrangements for these clusters such that the mapping efficiency is improved, and the throughput requirements are still met. The proposed methodology has been applied to a number of CNN accelerators, and demonstrates BRAM reductions of up to 30%. The observed BRAM reductions enable existing FINN accelerator designs to be ported to smaller FPGA devices while maintaining the computational throughput.
MENINGKATKAN KEMAMPUAN MEMBACA PEMAHAMAN MELALUI PENDEKATAN WHOLE LANGUAGE DI KELAS V SEKOLAH DASAR NEGERI BENDUNGAN HILIR 01 PAGI JAKARTA PUSAT

The classroom action research aims to improve reading comprehension skill through a whole language approach in fifth grade students of Sekolah Dasar Negeri Bendungan Hilir 01 Pagi Central Jakarta. The sample of this research is thirty students of fifth grade. According to the result of this research is obtained that reading comprehension skill’s percentage in first cycle is 67%, then increase in second cycle to 87%. In addition to improve reading comprehension skill, the result of observing teacher’s activities data in first cycle is 75% then increase in second cycle to 93%. Besides, the result of observing student’s activities in first cycle is 70%, then increase in the second cycle to 90%. Therefore, using whole language approach can improve reading comprehension skill in fifth grade students of Sekolah Dasar Negeri Bendungan Hilir 01 Pagi Central Jakarta.
BIHL:A Fast and High Performance Object Proposals based on Binarized HL Frequency

In recent years, the use of object proposal as a preprocessing step for target detection to improve computational efficiency has become an effective method. Good object proposal methods should have high object detection recall rate and low computational cost, as well as good localization quality and repeatability. However, it is difficult for current advanced algorithms to achieve a good balance in the above performance. For this problem, we propose a class-independent object proposal algorithm BIHL. It combines the advantages of window scoring and superpixel merging, which not only improves the localization quality but also speeds up the computational efficiency. The experimental results on the VOC2007 data set show that when the IOU is 0.5 and 10,000 budget proposals, our method can achieve the highest detection recall and an mean average best overlap of 79.5%, and the computational efficiency is nearly three times faster than the current fastest method. Moreover, our method is the method with the highest average repeatability among the methods that achieve good repeatability to various disturbances.
LibGuides: Academic Resource Center: RESERVE A STUDY ROOM

The ARC hosts the College's Library and Learning/Tutoring Center. The Library's study room holds up to 6 people, and the Learning Center's study room holds up to 10 people.
LibGuides: Staley Library & COVID-19: Home

This guide provides information about current library services, resources, and research & instruction support in light of the COVID-19 epidemic.
Analyzing Video Streams on the Raspberry Pi

In this chapter, you’ll learn how to analyze video streams using concepts taken from functional programming. Specifically, you’ll use the Filter interface and combine it with a Pipeline object and then apply them to the video stream.
ACCIDENT ALERT SYSTEM USING BLOCK CHAIN APPROACH

Emergencies are unpredictable and rapid response is the key requirement in emergency management. Poor road infrastructure, failure to comply with speed limits, driver inattentiveness, growing drinking and driving habits is among the main factors contributing to deaths from road crashes. The motivation behind the project travolution is an attempt to make a block chain base system which is to bring a positive difference in the field of road safety and road discipline. Block chain is a new emerging technology that can bring a variety of benefits to enterprise car applications. The project tackles some major causes of road accidents such as breaking traffic signals, speed control in different areas, horn control in horn prohibited zones, no parking alerts and monitoring the heart rate of the driver. GSR sensor can be fitted inside vehicles to predict heart stroke of the driver. In our proposed system, Block chain acts as a digital ledger and it is used not only for sending data but also for parsing data in the Node MCU. If the car entered into the above mentioned area our block chain technology hold the car function up to the signal area.
Gaussian Mixture Fitting Filter for Non-Gaussian Measurement Environment

In this paper, a novel Gaussian mixture fitting filter (GMFF) is proposed to copy with the nonlinear state estimation problem with non-Gaussian measurement environment. The core of GMFF is to use Gaussian mixture regression model to model the unknown measurement likelihood probability, which represents the combination of Gaussian mixture model and linear regression process. In the variational inference framework, through iteratively and alternatively achieving the fitting of the measurement model and the compensation of linear regression error, the estimation accuracy and adaptiveness can be enhanced gradually. The superior performance of GMFF is demonstrated in the simulations.
Python API for Altair Inspire Studio with functionality of capturing 3D models from RGBD sensors

This thesis shows how we implemented the Python support to write plugins and to interact with Altair Inspire Studio, in addition to the C++ SDK they have been offering for years. Then, the thesis shows how we used this novel api to develop a plugin to capture 3D models from reality, using RGBD sensors.
Research on the Status Quo and Problems of Power Grid Production in China

The intelligent production of the power grid is the core of the development of smart grids. The current production status of the power grid is in a transitional phase. During the transition period, there are many problems in the power grid foundation due to the application of new technologies. After our investigation, the big data storage and protection problems, the different generation devices coexistence problem, and scene image classification and processing problems taken by drone inspection are the most prominent problems in the current power grid. This paper elaborates on the above issues and proposes feasible solutions according to the development of modern technology. The possible solutions for our observed problems are to establish a big data cloud platform inside the power grid, to propose a unified standard for the power grid device, and to effectively utilize the two-dimensional code for image classification. This will not only effectively solve the various problems faced by the power grid at the current stage, but also lay the foundation for the development of smart grid.
LibGuides: Citation Management: BibMe

This adaption of the Citation and Research Management Tools LibGuide by Marie Sciangula and Andrew Whitis is granted under the same Creative Commons Attibution-NonCommerical 3.0 license. BibMe
Research Guides: Shapiro Library Free & Fun Resources : "Follow" for Fun

Library staff share free and fun resources we have both in the library and that we've found around the web. Check them out! Instagram, YouTube, Facebook, and other social media to follow to keep you smiling.
Project Interdisciplinarity in Legal Students Education of Technological University

Modern engineering education aims at developing in students the ability to solve technical, organizational, and managerial complex multidisciplinary professional problems.
Visualization of solution of heat equation by different interpolation methods

Images are used to analyse the solution of two dimensional heat equation. A remarkable observation is that image of the solution detected an ambiguity in existing solution. Therefore better solution is proposed. The images are formed at three different sampling rates of 1000 × 1000, 100 × 100 and 10 × 10 pixels per unit for the space variables. Peak Signal to Noise Ratio (PSNR) is used as quantitative measure for relative analysis of two images and images are shown to the users for the analysis of solution. It has been observed that both analysis by using the images of sampling rate of 1000 × 1000 are rational with the mathematical solution. Average computation time (in seconds) to form a solution image of sampling rates of 1000 × 1000, 100 × 100 and 10 × 10 are approximately 100, 1 and 0.01 respectively. Different interpolation methods namely nearest, bilinear, bicubic, lanczos2 and lanczos3 are used to upscale and compare the images of sampling rate of 100 × 100 and 10 × 10 with 1000 × 1000. Average PSNR and computation time to upscale an image of sampling rate of 100 × 100 and 10 × 10 are 40 dB, 20 dB and 0.02 seconds, 0.02 seconds respectively for lanczos3 interpolation. It is observed that visual information of images of sampling rate of 1000 × 1000 and 100 × 100 are very close and computation time to form the images of sampling rate of 100 × 100 is very less as compared to 1000 × 1000.
Artificial Neural Network-based Method for Stereoscopic 3D Reconstruction

This study proposes a three-dimensional (3D) position measurement method that uses two cameras fixed at arbitrary positions. Unlike many existing techniques, the proposed method allows a direct 3D position computation from the image coordinates. The direct image-to-space relations are redundantly obtained and can be used to make efficient 3D computations. The proposed model can be easily implemented using multilayer feedforward artificial neural networks due to directness. In our test, four small neural networks were constructed using different combinations of image coordinates as the network inputs. The most accurate network was selected. As a result, we could easily train the neural networks and obtain better results compared to existing methods.
Pemanfaatan City Tour sebagai Sumber Belajar dalam Mata Pelajaran Tour Guiding di Kelas XI Jurusan Usaha Perjalanan Wisata di SMK Negeri 57 Jakarta

This study aims to describe the utilization of city tour on tour guiding subject in grade XI tourism program at SMK Negeri 57 Jakarta. The method used in this study is descriptive qualitative with case study approach and data collection techniques through questionnaire, interview, observation and documentation. The result of this study indicates that the teacher has taken advantage of city tour as a source of learning on tour guiding subject grade XI. Data obtained are described in percentage and narrative analysis. The utilization of city tour is described through the six steps of utilization model ASSURE which includes 1) Analyze learners, 2) Select objective, 3) Select method, media and materials, 4) Utilize media and materials, 5) Require learners’ participation, 6) Evaluate and revise.
Educational media utilisation and reading habit of private secondary school students in Oyo state, Nigeria

The study investigated the relationship between educational media utilisation and reading habit of secondary school students in Oyo State, Nigeria. The study adopted the survey research design while questionnaire was used as the main instrument of data collection. Findings revealed television, Internet, computers, radio and smart phones as the most used types of educational media by the students. On the types of books being read by the students, textbooks and class notes were found to be the commonly read books by the students while the major purposes of reading among the students were, urge to get information, learn new things and to pass examinations. Findings from the study further revealed that the students preferred to read late at night for between -2 hours. A positive relationship was established between educational media utilisation and reading habit of the students.Keywords: Educational media utilisation, Reading habit, Secondary school students in Oyo State
Using Spelling Bee Game in Teaching Pronunciation. (An Experimental Research at Eighth Grade Students of MTsN 1 Kota Serang-Banten)

Amalia Tresna Viviyani. 152301789. 2019. Using Spelling Bee Game in Teaching Pronunciation. (An Experimental Research at Eighth Grade Students of MTsN 1 Kota Serang-Banten.) This research investigates the use of Spelling bee game in teaching pronunciation. This research was find out whether teaching pronunciation by using spelling bee game can improve students pronunciation ability. This research used quasi experimental design and quantitative method. The instrument used in this research was tests (pre-test and post-test). This research involves 58 students as sample among second grade of MTsN 1 Kota Serang as population. The research finding of the last result showed that to that the writer got from the calculation is higher than t-table both at significance level 5%. Significance 5% t- observation 2,329 and t-table 1,68 or 2,329 > 1,68. In addition, the result of the observation showed that the students more motivated in learning English. Based on the result above, it can be concluded that spelling bee game can be used in teaching pronunciation. The result of this researcher shows that the students teaching pronunciation using spelling bee game achieve better performance than who don’t use it.
Research Guides: CCC Library's services (Fall 2021): Free Chromebooks, printing, & internet

This guide details CCC Library's adjusted services and resources during the College's COVID-19-related closure.
Secure Sharing Of PHR Data Using Reencryption

The Personal Health Record (PHR) is an emerging framework of health information exchange, which is often stored at cloud servers. But there are still various privacy problems as personal health information could be discovered to unauthorized people. To guarantee the patients control over to their own PHRs, it is a method to encrypt the PHRs before storing on cloud. But still issues such as risks of privacy, efficiency in key administration, flexible access and efficient user administration, have still remained the important challenges toward achieving better, cryptographically imposed data access control. ABE. This paper we proposes a methodology called SeSPHR for secure sharing of the PHRs in the cloud. The SeSPHR scheme ensures patient-centric control on the PHRs and preserves the confidentiality of the PHRs. The patients store the encrypted PHRs on the un-trusted cloud servers and selectively grant access to different types of users on different portions of the PHRs. A semi-trusted proxy called Setup and Re-encryption Server (SRS) is introduced to set up the public/private key pairs and to produce the re-encryption keys. Moreover, the methodology is secure against insider threats and also enforces a forward and backward access control. Furthermore, we formally analyze and verify the working of SeSPHR methodology through the High Level Petri Nets (HLPN).
A Local Image Feature Approach as a Step of a Top-Down RGBD Semantic Segmentation Method

Semantic segmentation is a per-pixel class labeling problem, a method to assign a class label from a set of classes to each pixel on an image.Recent works have shown great progress using RGB images.However, indoor environments is still a challenging problem for the state-ofthe-art algorithms due to the high variability of the scenarios.Additionally, semantic segmentation architectures based on Convolutional Neural Networks have reported to be vulnerable to adversarial attacks.However, elements of these architectures are similar to hand-crafted features and pipelines used in computer vision.In this paper, we explore the use of local image features by making an analysis and proposing a combination of feature detectors to make a robust classification of these features.This approach is a step of a top-down RGBD semantic segmentation method.Experiments on indoor environments show that the mean classification accuracy of feature descriptors can be improved by up to 3.3% with respect to the performance of a single feature detector.Also, using a balanced dataset an applying a cross-validation technique could improve up to 5.5% of the average of mean accuracy, obtaining better performance than just applying a single feature matching algorithm.
BREAST TUMOR CLASSIFICATION IN SHEAR WAVE ELASTOGRAPHY IMAGES USING CONVOLUTIONAL NEURAL NETWORK

Abstract Background:Breast cancer is the most common type of cancer amongwomen. About one in eight women are diagnosed with breast cancer during their lifetime. A malignant tissue is stiffer than normal and benign tissues. This stiffness could be evaluated by elastography. Patients and Methods:A comprehensive dataset of shear wave elastography (SWE) images of breast tissue using an Esaote MyLab™ 9 system was provided. 100 images were related to breasts with benign tumors and 100 images contain malignant tumors. The gold standard for evaluation of proposed algorithm was biopsy, which was performed on all of examining lesions.A convolutional neural network was applied to the dataset, to extract the visual features of the images. The architecture was based on Densenet architecture, which is modified for our purpose. We have used the network in both pre-trained and end-to-end training strategies and the results were compared. The network was pre-trained on the Imagenet dataset, due to the lack of sufficient dataset. On the other hand, with data augmentation the network underwent  a full training strategy. Finally, the classification layer, which decides about the benignity or malignancy of the lump, is a softmax layer. Results:The results of the proposed methods are satisfying in both pre-trained and end-to-end training approaches. We have used various evaluation metrics contain precision, recall, F1-score, ROC curve, and training time for both strategies. The precision, recall, and F1-score were 0.87, 0.91, and 0.89 for the Densenet architecture trained from scratch and 0.93, 0.95,  and 0.94 for the transfer learning approach. The ROC curves were plotted for both approaches and the area under the curves (AUCs) were calculated. The transfer learning approach yielded a 0.97 of AUC, whereas this number was 0.91 for the fully- trained approach. At last, the training time of transfer learning approach was less than training from scratch, as it was anticipated. Conclusion:The results show the superiority of the transfer learning approach in tumor classification. Higher statistical metrics with lower training time makes this approach more compatible with SWE images.
PENERAPAN METODE THINK PAIR AND SHARE PADA PEMBELAJARAN MENULIS CERITA PENDEK SISWA SMK

The research is motivated by the researcher's curiosity about the process and results of improving students' skills in short story writing skills. What is the process and results of students' ability to write short story texts, namely by applying the think pair and share method in the learning process? This study aims to determine the process and results of short story text production in class XI students using the think pair and share method. The method used in this study is the quasi-experimental design method. The sample used in this study is class XI, with 25 students. Data collection techniques using the initial test and final test. Based on the results of research students' ability to write short story texts before using the think pair and share method obtained an average initial test score of 47 and after using the think pair and share method obtained an average final test score of 59.84. The results of the ability to write short story texts increased, so conclusions can be drawn using the think pair and share method in class XI can improve students' ability to write short story texts.
Experimental demonstration of a scalable M-ary quantum receiver (Conference Presentation)

We report on the first experimental implementation of a scalable M-ary quantum receiver with time-resolved photon detection and real-time adaptive displacement using off-the-shelf components. We implemented M=4, 6, 8, 12, and 16 coherent frequency shift keying communication links with optimal parameters for maximal energy sensitivity. Then, we compared the measured sensitivity with the best sensitivity that can be achieved with a perfect classical measurement (shot noise limit). We observe unconditional, below the shot-noise limit sensitivity and a favorable sensitivity scaling with the alphabet length M.
IMPLEMENTASI METODE WARD DAN PEPPARD DALAM PERENCANAAN SI/TI DI KANTOR ARSIP DAN PERPUSTAKAAN KOTA TASIKMALAYA

Development Strategy of Information Systems and Technology (SI/TI) in an organization of Government as well as a pemanfaata absolutely necessary to continue to improve the performance of employees in running business processes such as in the areas of coaching and Development Office of the archives and library of the town of Tasikmalaya don't have a business process excellence. Therefore, design kerangaka do it with in the planning SI/TI Using the method of analysis of the ward and Peppard has been in the form of results define strategy SI/TI The design of the framework of planning strategies SI/TI featuring the resulting Roadmap to future Application Portfolio. This future application can serve as a reference for the development of information systems are integrated to support the needs of the SI/TI Office of archives and library of the town of Tasikmalaya.