Reviewerly/exHarmony huggingface dataset:
EPLQ: Efficient Privacy-Preserving Location-based Query over Outsourced Encrypted Data

With the inescapability of PDAs, location-based administrations (LBS) have gotten extensive consideration and turn out to be progressively prevalent and indispensable as of late. Be that as it may, the utilization of LBS like-wise represents a potential danger to client's area security. In this paper, going for spatial range inquiry, a mainstream LBS giving data about POIs (Points Of Interest) inside a given separation, we present a productive and protection safeguarding area based inquiry arrangement, called EPLQ. Area based administration (LBS) is blasting up lately with the quick development of cell phones furthermore, the rising of distributed computing worldview. Alongside the difficulties to build up LBS and the client protection issue turns into the most vital concern. So fruitful protection saving LBS must be secure and give precise inquiry results. In particular, to accomplish privacy-preserving spatial range inquiry, we propose the first predicate only encryption plot for internal item extend, which can be used to recognize whether a position is inside a given round zone in a security safeguarding way. To lessen question inactivity, we further plan a security safeguarding tree file structure in EPLQ. Point by point security examination affirms the security properties of EPLQ. Likewise, broad tests are led, and the outcomes exhibit that EPLQ is exceptionally productive in privacy-preserving spatial range inquiry over re-appropriated encoded information. Specifically, for a portable LBS client utilizing an Android telephone, around 0.9 second is expected to create an inquiry; and it likewise as it were requires a product workstation, which assumes the job of the cloud in our tests, a couple of moments to look POIs
HYBRID METODE BOOSTRAP DAN TEKNIK IMPUTASI PADA METODE C4-5 UNTUK PREDIKSI PENYAKIT GINJAL KRONIS

Missing values is a serious problem that most often found in real data today. The C4.5 method is a popular classification predictive modeling used because of its ease of implementation. However, C4.5 is still weak when testing data that contains large missing. In this study we used a hybrid approach the bootstrap method and k-NN imputation to overcome missing values. The proposed method tested using Chronic Kidney Disease (CKD) data, and evaluated using accuracy and AUC. The results showed that the proposed method was superior in overcoming missing values in CKD. It can be concluded that the proposed method is able to overcome missing values for chronic kidney disease prediction.
Using Elasticsearch for full-text searches on unstructured data

In order to perform effective searches on large amounts of data it is not viable to simply scan through all of said data. A well established solution for this problem is to generate an index based ...
LibGuides: NoodleTools for Students: Primary, Secondary, Tertiary - What's the Difference?

Gives the definition of a primary source, and explains the differences between primary, secondary, and tertiary sources and gives examples of each
LibGuides: English Language Learners - Resources for Elementary Teachers: Home

This LibGuide provides English Language Learner (ELL) resources for the elementary level teacher.
LibGuides: Retrieval Practice: Home

Retrieval Practice is a research-based teaching and learning technique. Implement RP in your face-to-face or online class to increase student learning AND teach students HOW to study and learn!
RookieOven Academy Launch Night

Last night marked the launch of the inaugural RookieOven Academy as the home of Glasgow’s tech community welcomed a group of ambitious young people looking to further their abilities in digital.
Empowering people through a new way of interacting with medical data

This project explored using human-centered design methods how can we change the way healthcare delivers information to people about their health so that they feel empowered and educated about their ...
Three-dimensional modeling of microcalciﬁcation clusters using breast tomosynthesis: a preliminary study

Computer aided diagnosis (CADx) systems for digital mammography mostly rely on 2D techniques. With the emergence of three-dimensional (3D) breast imaging modalities, such as digital breast tomosynthesis (DBT), there is an opportunity to create 3D models and analyze 3D features to classify microcalcications (MC) clusters to help the early detection of breast cancer. We adopted the 3L algorithm for implicit B-spline (IBS) ing to investigate the robustness of 3D models of microcalcication (MC) clusters for classifying benign and malignant cases. Point clouds were initially generated from tomosynthesis slices. Two additional oset points were generated to support the original point clouds for detailed 3D modeling. Before ing the splines, the point clouds were normalized into a unit cube laice. Aer modeling individual MCs into a unit cubic laice, they are all located in a 3D space according to their spatial location in the tomosynthesis images to form a cluster. Features were extracted from the 3D model of MC clusters. With selected features we obtained 80% classication accuracy.
Development and evaluation of wearable electro-goniometer for the assessment of repetitive efforts and measurement of joint trajectories during carpet weaving

The aim is to construct, calibrate and validate a low-cost sensor-embedded glove and elbow brace. This article establishes a common thread between electro-goniometry and postural assessment techniques. The output voltages from the sensors were converted into angles by implementing LabVIEW-Interface-for-Arduino based graphical environment. Ten experienced weavers participated in the study. Dynamic recordings of the finger, wrist and elbow bending trajectories were measured using sensory electro-goniometer. Assessment of repetitive task technique was used to identify the postural risks. Based on data output from each sensor, the voltage to angle optimal linear models were obtained for the measurement range of 0°-90° with 5° steps. The accuracy of the sensors was found within the limits of traditional goniometry. The cost-benefit breakdown revealed that wearable electro-goniometer is cost-effective as compared to commercially available electro-goniometers. The low-cost electro-goniometric glove and elbow brace for measurements of hand movements could be a promising tool for postural assessment and safety systems.
An Efficient Algorithm on Quantum Computing With Quantum Key Distribution for Secure Communication

Currently many symmetric key cryptographic tools which are known to be quantum-safe. They all share Secret symmetric keys through an untrusted medium which is usually done with public key methods which are prone to quantum attacks. One among the recommended solutions to the key distribution problem is Quantum Key Distribution (QKD). QKD utilizes an authenticated Communication channel along with a quantum communication channel so that a secret key is generated. Whatever may be the protocol to implement proposed QKD, they all need both a quantum channel, and an authenticated classical. OTP (One Time Pad) is used when two parties wish to communications have to share a key called pad. This pad is a randomly generated key and the length of the key should be equal to the message so it can be sent. The proposed QKD parameters that result in almost 35.67% smaller keys for quantum security and an implementation of QKD in network which is publicly available and usable in practice.
Download FaceTime for PC Windows 10/7/8 Laptop (%%currentmonth%% %%currentyear%% Official)

Snapchat is a social media and a multimedia messaging app that is popular today. It is competing head-to-head with GB Instagram app, Facebook, WhatsApp ...
A Densely Connected Transformer for Machine Translation

Recent work has shown that the network for natural language processing (NLP) task can be deeper as well as more accurate by applying attention mechanism with residual. However, the original features of training data will be lost after multiple operations on deep network. We propose a new model structure, Densely Connected Transformer (DCT), which is based on the Transformer to solve this problem. We connect each encoder/decoder layer to other layers, and sum outputs of all previous layers as the input of next layer. Our model encourages feature reuse and improves the information flow between layers. We apply this model to machine translation (MT) tasks and evaluate it on both IWSLT 2016 German-English and IWSLT 2016 German-English. The experiment results show that our model obtains a higher BLEU scores than the basic Transformer after training for 20 epochs. And the convergence speed of our model is faster than the basic Transformer.
PENINGKATAN PRESTASI BELAJAR IPS SISWA MELALUI METODE STUDENT TIMES ACHIEVMENT DIVISION (STAD)

Abstract This study aims to improve social studies learning achievements through the Student Times Achievement Division (STAD) method in third grade students of Ngraho State Elementary School Kedungtuban Blora. The subject of this study was 15 students, consisting of 8 male students and 7 female students. This study is a Classroom Action Research model Kemnis and Mc. Taggart. This research was conducted in 2 cycles, each cycle consisting of 2 meetings. In each cycle there are 4 stages, namely planning, action, observation and reflection. The research instrument used was the test item, observation sheet and student response questionnaire. The test results data were analyzed quantitatively descriptively using the average learning achievement and the percentage of mastery learning. Data from observations and student responses were analyzed by calculating percentages. In the pre-cycle students who completed KKM were 5 students (33.33%). After the action of the first cycle increased to 11 students (73.12%), while in the second cycle increased to 13 students (86.66%). From cycle I to cycle II it increased 13.33%. The KKM completeness increased because students' social studies learning achievements improved from pre-cycle to cycle I and cycle I to cycle II. The average student learning achievement and the number of students who reach KKM have met the indicators of success, thus this study was stopped in cycle II. Keywords: Social Studies Learning Achievement, Student Times Achievment Division (STAD) Method.
Tense and Aspects in Tyap and English

This study examines the formation of Tense and Aspect in English and Tyap with a view to predicting thelikely areas of difficulty Tyap learners of English as a second language may encounter while learning theuse of tense and aspect in English. The study adopted Carl’s analytic comparison model. Descriptiveapproach was employed in the analysis of the data collected and contrast of those linguistic features thatare present in English with those in Tyap with the view to showing areas of differences. The findings showthat the difference between Tyap and English language lies in the use of inflections. While English uses thefollowing inflections -“s” or –“es” in simple present tense and “-d” or “-ed” as simple past tense, the Tyapuses the uninflected form of the verb. Also, the present tense is not used in relation with the person(s) actingas the subject of the sentence in Tyap language, whereas in English, the present tense is used in relationwith the person(s) acting as the subject of the sentence. The present tense used with the 3rd person singulartakes the inflected form of the tense morpheme or verb, which is either “-s” or “-es”. The results are usedto make conclusions about the linguistic relationship that exist between the two languages.The studytherefore, recommends that language teachers should concentrate on the areas of differences in bothlanguages while teaching and then use appropriate teaching methods and teaching aids to ensure the learnerseffective understanding
Improving mammogram visualization by dimming text annotations

In digital mammogram visualization, text is usually among the brightest objects. When bright text is near an area of interest in the tissue image, it can annoy the readers and may reduce the perception of tissue details. To mitigate this effect, we propose segmentation of the frame buffer to annotation text and mammographic image using artificial intelligence. An appropriate luminance can be re-assigned to each area (i.e., dim annotations and/or brighten mammographic image to the maximum luminance). Existing text detection and/or segmentation tools we tested did not work for this purpose because they produce false positives (i.e., parts of breast are detected as text and dimmed). That is perhaps because such methods were designed (or trained to, in case of deep learning methods) for natural images. We investigated two state-of-the-art segmentation architectures DeepLabV3+ and Mask R-CNN, as well as a “shallow” text detection method based on maximally stable external regions (MSER). We generate the training data by adding random text to the background of publicly available mammographic images. DeepLabV3+ trained to our data produced promising results while Mask R-CNN and MSER did not. Keywords: Convolutional neural
LibGuides: Library Resources for Your Canvas Course: Home

A guide to embedding library resources and services in your online courses, for Valencia College faculty.
Training Bayesian Neural Networks

Although deep learning has made advances in a plethora of fields, ranging from financial analysis to image classification, it has some shortcomings for cases of limited data and complex models. In these cases the networks tend to be overconfident in their prediction even when erroneous - something that exposes its applications to risk. One way to incorporate an uncertainty measure is to let the network weights be described by probability distributions rather than point estimates. These networks, known as Bayesian neural networks, can be trained using a method called variational inference, allowing one to utilize standard optimization tools, such as SGD, Adam and learning rate schedules. Although these tools were not developed with Bayesian neural networks in mind, we will show that they behave similarly. We will confirm some best practices for training these networks, such as how the loss should be scaled and evaluated. Moreover, we see that one should avoid using Adam in favor of SGD and AdaBound. Wee see that one should also group the learnable parameters in order to use custom learning rates for the different groups. (Less)
Using Bezier Curve analysis in context of ExpressionAnalysis

Affective computing is an area of research underincreasing demand in the field of computer vision. Expressionanalysis, in particular, is a topic that has been undergoing researchfor many years. In this paper, an algorithm for expressiondetermination and analysis is performed for the detection of sevenexpressions: sadness, anger, happiness, neutral, fear, disgust andsurprise. First, the 68 landmarks of the face are detected and theface is realigned and warped to obtain a new image. Next, featureextraction is performed using LPQ. We then use a dimensionalityreduction algorithm followed by a dual RBF-SVM and Adaboostclassification algorithm to find the interest points in the featuresextracted. We then plot bezier curves on the regions of interestobtained. The curves are then used as the input to a CNN and thisdetermines the facial expression. The results showed the algorithmto be extremely successful
LibGuides: Continuity of Library Research & Instruction: Video Tutorials

Library Resources and services available at SUNY Erie in preparation of increased distance learning Spring/Fall 2020
LibGuides: How to Use Interlibrary Loan: Book Requests

This guide will help you determine whether Interlibrary Loan is the right choice as well as create an Interlibrary Loan request. Basics on creating an ILL book request.
Computerized determination scheme for histological classification of masses on breast ultrasonographic images using combination of CNN features and morphologic features

It can be difficult for clinicians to correctly determine biopsy or follow-up for masses on breast ultrasonographic images. The purpose of this study was to develop a computerized determination scheme for histological classification of masses using a combination of CNN (convolutional neural network) features and morphologic features. The database consisted of 585 breast ultrasonographic images. It included 288 malignant masses (218 invasive carcinomas and 70 noninvasive carcinomas) and 297 benign masses (182 fibroadenomas and 115 cysts). In the proposed method, CNN features and morphologic features were first determined from a mass. The CNN features were defined by reducing the dimensionality of the output of the final pooling layer in GoogLeNet using a principal component analysis. The morphologic features were also defined by taking into account image features commonly used for describing masses on breast ultrasonographic images. A support vector machine (SVM) with the CNN features and the morphologic features was employed to classify among histological classifications of masses. Three-fold cross validation method was used for training and testing the GoogLeNet and the SVM. The classification accuracies with the proposed method were 84.4% (184/218) for invasive carcinomas, 72.9% (51/70) for noninvasive carcinomas, 85.7% (156/182) for fibroadenomas, and 87.8% (101/115) for cysts, respectively. The sensitivity and the specificity were 87.2% (251/288) and 93.3% (277/297), whereas the positive predictive value and the negative predictive value were 92.6% (251/271) and 88.2% (277/314). The proposed method yielding high classification accuracies would be useful in the differential diagnosis of masses on ultrasonographic images as diagnosis aid.
Implementation and Comparison of Effective Area Efficient Architectures for CSLA

Carry Select Adder (CSLA) is one of the fastest efficient adders which are used in many data-processing processors to perform fast arithmetic functions. CSLA is called efficient adder because of less delay and reduced size. In this paper 16 bit, 32 bit, 64 bit and 128 bit Regular Linear CSLA, Modified Linear CSLA, Regular Square-root CSLA (SQRT CSLA) and Modified SQRT CSLA architectures have been developed and compared. Comparing the Regular Linear CSLA with Regular SQRT CSLA, the Regular SQRT CSLA has reduced area as well as comparing the Modified Linear CSLA with Modified SQRT CSLA The proposed work conveys that it uses a simple and efficient gate-level modification to significantly reduce the area and power of the CSLA. The performance factors of the proposed design are evaluated in terms of delay, area, power and their products by simulation tool and implemented in FPGA kit. The results analysis shows that the proposed modified BEC CSLA structure is better than the regular BEC SQRT CSLA. This paper introduces an unique method that replaces the BEC using common Boolean logic. This paper proposes an efficient method which replaces the BEC using D latch. Experimental results are compared and the result analysis shows that the proposed architecture achieves the three folded advantages in terms of area, delay and power..
LibGuides: A Beginner's Guide to Myers-Briggs Type Indicator (MBTI): Home

An introduction to MBTI, its origins, uses and criticisms The history of how MBTI was developed and the theory it is based upon
An Interview with Dr. Charu Aggarwal, SIGKDD Innovation Award Winner

Dr. Charu Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM Thomas J. Watson Research Center in Yorktown Heights, New York. He earned a Bachelor of Technology in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research, with a focus on mathematical optimization, from the Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining, with particular interest in data streams, privacy, uncertain data and social network analysis. He has authored eight books, over 400 papers in refereed venues, and has applied for, or been granted, over 80 patents. Dr. Aggarwal sat down with SIGKDD Explorations to discuss how he first became involved in the KDD conference, his work at the IBM T.J. Watson Center and what excites him about the future of machine learning, data science and artificial intelligence.
DSC Library: Falcon Self-Service- Faculty Center: Grade Roster

This is an info-guide created for the Faculty Center Located in the Falcon Self Service area. This was creaed as a resource for IT Training. This is an overview of submitting grades in the Faculty center within Falcon Self-Service.
Deep learning-based segmentation of mammary gland region in digital mammograms of scattered mammary glands and fatty breasts

This study is aimed to automatically segment mammary gland region into scattered mammary glands and fatty breasts using deep learning method. Total 433 mediolateral oblique-view mammograms of Japanese women were collected and confirmed for scattered mammary glands or fatty breasts; using BI-RADS’s classification. First, manually contoured mammary gland regions were determined for all mammograms as ground truths by three certified radiological technologists. Second, the U-net model was employed to segment the mammary gland region automatically. This model is a type of convolutional neural network (CNN) mainly aimed at medical image segmentation. The segmentation accuracies were assessed based on five criteria, Dice coefficients, breast densities, mean gray values, centroids, and sizes of mammary gland region. The Dice coefficient was 0.915. The mean size of mammary gland regions obtained by the Unet was 8.7% larger than that of the ground truths. The mean centroid coordinates of mammary gland regions by the U-net were shifted 1.6 and 5.4 mm on average in mediolateral and craniocaudal directions, respectively from ground truths. The mean gray value of mammary gland regions obtained by the U-net was only 0.4% higher compared with ground truths. The resultant difference was 0.4% on average in breast densities between ground truths and the segmented mammary gland regions. We found significant similarity in the ground truths and the data generated by deep learning on all the parameters, thereby attesting the efficacy of this method for segmenting the mammary gland regions of not only the dense breasts but also the scattered mammary gland- and fatty- breasts.
LibGuides: Wegner Library Interlibrary Loan-ILLiad: Find Article Full-Text in PubMed

A how-to guide for creating, navigating and placing a request with Wegner's Interlibrary Loan System, ILLiad.
Uso de TIC en estudiantes universitarios chilenos: enfoque desde la adultez emergente

Emerging adulthood (EA) is a stage of development in which, having finished adolescence, the assumption of adulthood is postponed. There is evidence that the use of information and communication technologies (ICT) can be related to the tasks of the EA. Using the EA approach, the present quantitative study aimed to characterize the use of ICT in emerging adults, all students of Chilean universities, and examine the effect of sex, socioeconomic status and age in such use. An intentional sample of 1,469 students answered a questionnaire that investigated different aspects of the experience with ICT. The results show that, compared to the general population, students access more to the computer and use ICT more frequently for a set of activities. In the time of daily use of ICT an effect of sex was observed but not of socioeconomic level nor of age. In addition, four types of ICT use were identified: communicative, informative, hedonistic and utilitarian. Men make greater hedonistic use and less utilitarian use than women. The socioeconomic level is related to the informative and hedonistic use; on the other hand, age is associated with communicative and informative use.