Reviewerly/exHarmony huggingface dataset:
Defect Forecasting by Specification Mining Using Code Quality Metrics

Formal specifications can help with program testing, optimization, refactoring, documentation, and, most importantly, debugging and repair.However, they are difficult to write manually, and automatic mining techniques suffer from 90-99 percent false positive rates.To address this problem, we propose to augment a temporalproperty miner by incorporating code quality metrics.We measure code quality by extracting additional information from the software engineering process and using information from code that is more likely to be correct, as well as code that is less likely to be correct.When used as a preprocessing step for an existing specification miner, our technique identifies which input is most indicative of correct program behavior, which allows off-the-shelf techniques to learn the same number of specifications using only 45 percent of their original input.As a novel inference technique, our approach has few false positives in practice (63 percent when balancing precision and recall, 3 percent when focused on precision), while still finding useful specifications (e.g., those that find many bugs) on over 1.5 million lines of code.
Enkripsi Citra Bitmap Menggunakan Algoritma Kompresi Lampel-Ziv-Welch (LZW)

&lt;p&gt;&lt;em&gt;The use of data in the form of image files has become more widespread in various fields. Therefore, security of image files from unauthorized persons is important. Various techniques to reduce file size, including securing files, have been developed, one of which is compression. Compression technique becomes important in processing large images such as Bitmap images. Ziv Welch seal is a loseless compression algorithm that can compress images without any loss of pixel elements in them or the resulting compression image is identical to the original image. The image will be encrypted first and then performed the compression technique using the Ziv Welch Lempel algorithm. The resulting compression is no longer in the form of images but in the form of files with the * .mat extension. The different file formats from images to non-image files can help avoid cryptanalysis&lt;/em&gt;&lt;/p&gt;
Prediction of Accident Severity Using Artificial Neural Network: A Comparison of Analytical Capabilities between Python and R

Large amount of data has been generated by Organizations.Different Analytical Tools are being used to handle such kind of data by Data Scientists.There are many tools available for Data processing, Visualisations, Predictive Analytics and so on.It is important to select a suitable Analytic Tool or Programming Language to carry out the tasks.In this research, two of the most commonly used Programming Languages have been compared and contrasted which are Python and R. To carry out the experiment two data sets have been collected from Kaggle and combined into a single Dataset.This study visualizes the data to generate some useful insights and prepare data for training on Artificial Neural Network by using Python and R language.The scope of this paper is to compare the analytical capabilities of Python and R.An Artificial Neural Network with Multilayer Perceptron has been implemented to predict the severity of accidents.Furthermore, the results have been used to compare and tried to point out which programming language is better for data visualization, data processing, Predictive Analytics, etc.
Chess, mathematics and patterns

Chess columns have appeared in many popular publications because chess players come from so many different backgrounds. In the past, chess problems could be found in English publications as varied as 'Cricket and Football Field, The Lancet, Law Gazette, Amateur Musician, and The Illustrated Science Monthly'. Chess problems often appeared in newspapers in Australia. 'The West Australian' newspaper has a longstanding chess column by David Ellis.
Aplikasi Stok Barang Pada CV. Wijaya Lesmana Sejahtera Interior Contractor

Nowadays, the development of information technology has entered all sectors of human life. One of the current uses of technology is the infrastructure sector. The increasing use of technology in the sector is proven by the number of companies that have used technology to facilitate the management of stock goods done. However, not all companies utilize technology that has grown rapidly. For example, CV. Wijaya Lesmana Sejahtera Interior Contractor who has not used technology in the registration of stock goods because it is newly established. The application development uses a waterfall system development model as well as using Visual Studio 2010 as its appearance and MySQL to process the database. Waterfall is chosen as a method of system development in the application of stock goods that will be made for CV. Wijaya Lesmana Sejahtera Interior Contractor because since the beginning of application development there is no change in demand and the availability of data Complete, so that the steps for the model of waterfall system development can be fulfilled namely communication, planning, modeling, construction, and deployment.Results obtained from this research in the form of stock applications that provide several features. Features that are provided in the application that is the Data menu of the item in the form of logging all existing stocks, the incoming stock menu is the logging of incoming goods, outgoing stock menu in the form of outgoing items, Report menu reports of incoming and outgoing data, and the password reset menu for program admins if you want to change app passwords. This application is useful for CV. Wijaya Lesmana Sejahtera Interior Contractor because it facilitates the admin in the record of stock goods and the preparation of stock report of goods on CV. Wijaya Lesmana Sejahtera Interior Contractor.
Implementasi Teknik Equivalence Partitioning pada Pengujian Aplikasi E-learning Berbasis Web

Testing is a verification process carried out to determine the quality of a software. In this case data validation becomes necessary, because errors that occur can make the system not optimal and can harm the owner or user of the system. For example, a compilation error the user can upload any file on the system. This can make the system corrupt and this cannot be known and tested. Black Box Testing has 4 methods, namely Equivalence Partitioning, Pairwise Testing, Boundary Value Analysis and Error Guessing. Each technique has advantages. However, in this study the Equality method was chosen because it was considered the most suitable way to conduct testing on E-learning Applications at Pamulang University. This method returns the system by dividing the input space or range into 2, valid and invalid. Can be obtained trial results obtained. This research begins by determining the functionality that will transfer, determining the data to be contested, determining the input can consist of numerical values, range of values and files, conducting test experiments, documenting research results and repeating results. From the results of tests that have been done, it can be concluded that this device can be used properly and perfectly. However, this research was only carried out using a number of sample forms. For Temporary Devices this can be used properly without any obstacles.
고등학생들의 인터넷을 활용한 역사지식 탐색 과정

The article looked at the process of 20 high school students using the Internet to find out how to access historical knowledge or information. When students read digital text and solve assignments, they generally showed the following characteristics:Students searched for historical information based on their prior knowledge of the Internet or search engine. And rather than peruse long sentences sequentially, the students looked around various sites for the information they wanted by browsing and searching. In addition, rather than checking the accuracy and reliability of text content, the purpose-oriented search was conducted to quickly find the desired information.As such, students used a nonlinear structure of hypertext to find the best information in the shortest time. And in an era when the filter bubble phenomenon accelerated by critically not reading text, it is more likely than before that the ability to make a wise distinction between information and fake historical news is more vulnerable. For this reason, the ability to identify the source of a website or to read digital texts critically is even more necessary.In the future, we need to explore the characteristics of digital history literacy that reflects this point. Further, follow-up research needs to be carried out, such as a practical study that can foster historical literacy capabilities in the digital age, and a study that expands or intensifies its meaning by adding the characteristics of digital text to the historical literacy.
Examining the Relationships Among Elementary Mathematics Teachers’ Self-Efficacy Beliefs, Constructivist Beliefs, and Years of Experience

This study aimed to examine the relationships among elementary mathematics teachers’ self-efficacy beliefs, constructivist beliefs, and years of experience. This study used the primary data set of 299 Korean elementary school teachers. Exploratory and confirmatory factor analysis, Pearson’s correlation test, multivariate analysis of variance, and structural equation modeling were conducted. This study found that mathematics teachers’ self-efficacy beliefs were positively related to their years of experience and constructivist beliefs, whereas there was no significant association between teachers’ years of experience and constructivist beliefs. Additionally, teachers’ self-efficacy beliefs significantly mediated the relationship between years of experience and constructivist beliefs.
LibGuides: For Faculty: COVID-19 Instructional Continuity in Spring 2020: Instructional Continuity

This guide is a copy of the original Instructional Continuity Guide used during the move to remote teaching in Spring 2020 due to COVID-19. It exists to retain and share this collection of resources for anyone who needs them.
Weighted Mean Variant with Exponential Decay Function of Grey Wolf Optimizer on Applications of Classification and Function Approximation Dataset

Nature-Inspired Meta-heuristic algorithms are optimization algorithms those are becoming famous day by day from last two decades for the researcher with many key features like diversity, simplicity, proper balance between exploration and exploitation, high convergence rate, avoidance of stagnation, flexibility, etc. There are many types of nature inspired meta-heuristics algorithms employed in many different research areas in order to solve complex type of problems that either single-objective or multi-objective in nature. Grey Wolf Optimizer (GWO) is one most powerful, latest and famous meta-heuristic algorithm which mimics the leadership hierarchy which is the unique property that differentiates it from other algorithms and follows the hunting behavior of grey wolves that found in Eurasia and North America. To implement the simulation, alpha, beta, delta, and omega are four levels in the hierarchy and alpha is most powerful and leader of the group, so forth respectively. No algorithm is perfect and hundred percent appropriate, i.e. replacement, addition and elimination are required to improve the performance of each and every algorithm. So, this work proposed a new variant of GWO namely, Weighted Mean GWO (WMGWO) with an exponential decay function to improve the performance of standard GWO and their many variants. The performance analysis of proposed variant is evaluated by standard benchmark functions. In addition, the proposed variant has been applied on Classification Datasets and Function Approximation Datasets. The obtained results are best in most of the cases.
LIFELONG EDUCATION - NECESSITY AND DESIDERATUM OF THE PERSONALITY

The article presents the problem of the need to ensure education throughout the life of the personality. There are highlighted various definitions of lifelong education given by various researchers, there is specified the purpose, objectives, characteristics, contents of lifelong education, there are described the forms and factors of lifelong education and it is elucidated the methodology of lifelong education.
Massively Parallel Amplitude-Only Fourier Neural Network.

Machine-intelligence has become a driving factor in modern society. However, its demand outpaces the underlying electronic technology due to limitations given by fundamental physics such as capacitive charging of wires, but also by system architecture of storing and handling data, both driving recent trends towards processor heterogeneity. Here we introduce a novel amplitude-only Fourier-optical processor paradigm capable of processing large-scale ~(1,000 x 1,000) matrices in a single time-step and 100 microsecond-short latency. Conceptually, the information-flow direction is orthogonal to the two-dimensional programmable-network, which leverages 10^6-parallel channels of display technology, and enables a prototype demonstration performing convolutions as pixel-wise multiplications in the Fourier domain reaching peta operations per second throughputs. The required real-to-Fourier domain transformations are performed passively by optical lenses at zero-static power. 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). Interestingly, the amplitude-only CNN is inherently robust against coherence noise in contrast to phase-based paradigms and features an over 2 orders of magnitude lower delay than liquid crystal-based systems. Beyond contributing to novel accelerator technology, scientifically this amplitude-only massively-parallel optical compute-paradigm can be far-reaching as it de-validates the assumption that phase-information outweighs amplitude in optical processors for machine-intelligence.
Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis

For multiple aspects scenario of aspect-based sentiment analysis (ABSA), existing approaches typically ignore inter-aspect relations or rely on temporal dependencies to process aspect-aware representations of all aspects in a sentence. Although multiple aspects of a sentence appear in a non-adjacent sequential order, they are not in a strict temporal relationship as natural language sequence, thus the aspect-aware sentence representations should not be treated as temporal dependency processing. In this paper, we propose a novel non-temporal mechanism to enhance the ABSA task through modeling inter-aspect dependencies. Furthermore, we focus on the well-known class imbalance issue on the ABSA task and address it by down-weighting the loss assigned to well-classified instances. Experiments on two distinct domains of SemEval 2014 task 4 demonstrate the effectiveness of our proposed approach.
PENENTUAN ZONASI DAERAH RAWAN BENCANA LONGSOR STUDI KASUS DI KECAMATAN TUMPANG, KABUPATEN MALANG

Tumpang Subdistrict is one of the sub-districts in Malang Regency which has many steep slopes especially in the east of Tumpang Subdistrict, this area has a high potential for natural disasters, one of which disasters is landslides, so it needs to be repaired. Landslides by tracing the areas that caused landslides in order to minimize losses caused. The purpose of this study is to know the cause and distribution areas which has high potential of landslides. This research method is scoring and weighting, and overlapping with physical digital basic map Tumpang to get areas that use landslides, which are processed using ArcGis 10.2 software. The results of this study are Landslide Prone Maps which are maps that describe areas with the level of landslide vulnerability assessment in the study area. With this result, then it becomes a reference in landslide disaster mitigation to assist the government in diverting landslide disasters in the area
R-GCN Based Function Inference for Gate-level Netlist

Graph neural networks are a type of deep-learning model for classification of graph domains. To infer arithmetic functions in a netlist, we applied relational graph convolutional networks (R-GCN), which can directly treat relations between nodes and edges. However, because original R-GCN supports only for node level labeling, it cannot be directly used to infer set of functions in a netlist. In this paper, by considering the distribution of labels for each node, we show a R-GCN based function inference method and data augmentation technique for netlist having multiple functions. According to our result, 91.4% accuracy is obtained from 1, 000 training data, thus demonstrating that R-GCN-based methods can be effective for graphs with multiple functions.
The Wisdom and Persuadability of Threads

Online discussion threads are important means for individual decision-making and for aggregating collective judgments, e.g. the `wisdom of crowds'. Empirical investigations of the wisdom of crowds are currently ambivalent about the role played by social information. While some findings suggest that social information undermines crowd accuracy due to correlated judgment errors, others show that accuracy improves. We investigate experimentally the accuracy of threads in which participants make magnitude estimates of varying difficulty while seeing a varying number of previous estimates. We demonstrate that, for difficult tasks, seeing preceding estimates aids the wisdom of crowds. If, however, participants only see extreme estimates, wisdom quickly turns into folly. Using a Gaussian Mixture Model, we assign a persuadability score to each participant and show that persuadability increases with task difficulty and with the amount of social information provided. In filtered threads, we see an increasing gap between highly persuadable participants and skeptics.
On the distribution of reducible polynomials

Let Y_n(t) denote the set of all polynomials over the ring Z which are reducible over the field Q and of degree n>1 and of height not greater than t. We show that the true order of magnitude of |Y_n(t)| equals t^2 log t in the special case n=2 and it equals t^n for each n>2. We also determine the true order of magnitude of the size of certain interesting subsets of Y_n(t).
TEACHER PROFESSIONALITY STRATEGY USING APPLICATION OF WHATSAPP (WA) IN PANDEMIC COVID-19 IN MTS PRIVATE DARUL ARAFAH LAU BAKERI KUTALIMBARU DELI SERDANG

At this time one of the instantaneous programs inclusive of WhatsApp or WA has been so effortlessly implemented and preferred by numerous layers of age. Most of the users of this software are young adults of faculty age from early adolescence to students. The COVID-19 pandemic has changed numerous elements of human life today, especially inside the area of education. This calls for all elements of schooling to conform and maintain the rest of the semester. The reason of this have a look at is an overview of mastering in the course of the COVID-19 pandemic. They have an impact on of WhatsApp is very sturdy on the getting to know machine in School. Generally WA users point out the cause about selecting this utility is because of the availability of numerous conveniences that are further to no longer voicing alias free. This studies makes use of descriptive analysis research method. Online gaining knowledge of is an effective answer for activating classrooms even though colleges have closed. This gaining knowledge of approach with WhatsApp media is essential for instructor professionalism in step with the continuity of coaching and mastering activities inside the contemporary COVID-19 pandemic conditions
LibGuides: Copyright: Home

Information/resources about copyright, policies on copyright within LANL and the DOE, and how copyright and fair use impact your research
Abstraction And Language Model

This article discusses abstraction, its essence, methods of abstraction and the role of abstraction in the formation of language models.
From Rerandomizability to Sequential Aggregation: Efficient Signature Schemes Based on SXDH Assumption.

An aggregate signature allows one to generate a short aggregate of signatures from different signers on different messages. A sequential aggregate signature (SeqAS) scheme allows the signers to aggregate their individual signatures in a sequential manner. All existing SeqAS schemes that do not use the random oracle assumption either require a large public key or the security depends upon some non-standard interactive/static assumptions. In this paper, we present an efficient SeqAS scheme with constant-size public key under the SXDH assumption. In the process, we first obtain an optimized (and more efficient) variant of Libert et al.’s randomizable signature scheme. While both the schemes are more efficient than the currently best ones that rely on some static assumption, they are only slightly costlier than the most efficient ones based on some interactive assumption.
Revisiting the Hardness of Binary Error LWE.

Binary error LWE is the particular case of the learning with errors (LWE) problem in which errors are chosen in \(\{0,1\}\). It has various cryptographic applications, and in particular, has been used to construct efficient encryption schemes for use in constrained devices. Arora and Ge showed that the problem can be solved in polynomial time given a number of samples quadratic in the dimension n. On the other hand, the problem is known to be as hard as standard LWE given only slightly more than n samples.
Research Guides: Library Resources for Distance Learning - Videos: eJournals

Library resources can be accessed remotely via the Leonard Lief Library website - here are videos showing you how.
SYNTHETIC VISION SYSTEM CALIBRATION FOR CONFORM PROJECTION ON THE PILOT’S HEAD-UP DISPLAY

Abstract. Situational awareness of the crew is critical for the safety of the air flight. Head-up display allows providing all required flight information in front of the pilot over the cockpit view visible through the cockpit’s front window. This device has been created for solving the problem of informational overload during piloting of an aircraft. While computer graphics such as scales and digital terrain model can be easily presented on the such display, errors in the Head-up display alignment for correct presenting of sensor data pose challenges. The main problem arises from the parallax between the pilot’s eyes and the position of the camera. This paper is focused on the development of an online calibration algorithm for conform projection of the 3D terrain and runway models on the pilot’s head-up display. The aim of our algorithm is to align the objects visible through the cockpit glass with their projections on the Head-up display. To improve the projection accuracy, we use an additional optical sensor installed on the aircraft. We combine classical photogrammetric techniques with modern deep learning approaches. Specifically, we use an object detection neural network model to find the runway area and align runway projection with its actual location. Secondly, we re-project the sensor’s image onto the 3D model of the terrain to eliminate errors caused by the parallax. We developed an environment simulator to evaluate our algorithm. Using the simulator we prepared a large training dataset. The dataset includes 2000 images of video sequences representing aircraft’s motion during takeoff, landing and taxi. The results of the evaluation are encouraging and demonstrate both qualitatively and quantitatively that the proposed algorithm is capable of precise alignment of the 3D models projected on a Head-up display.