Reviewerly/exHarmony huggingface dataset:
A Decision Tree Lifted Domain for Analyzing Program Families with Numerical Features

Abstract Lifted ( family-based ) static analysis by abstract interpretation is capable of analyzing all variants of a program family simultaneously, in a single run without generating any of the variants explicitly. The elements of the underlying lifted analysis domain are tuples, which maintain one property per variant. Still, explicit property enumeration in tuples, one by one for all variants, immediately yields combinatorial explosion. This is particularly apparent in the case of program families that, apart from Boolean features, contain also numerical features with large domains, thus giving rise to astronomical configuration spaces. The key for an efficient lifted analysis is a proper handling of variability-specific constructs of the language (e.g., feature-based runtime tests and $$\texttt {\#if}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>#</mml:mo> <mml:mi>if</mml:mi> </mml:mrow> </mml:math> directives). In this work, we introduce a new symbolic representation of the lifted abstract domain that can efficiently analyze program families with numerical features. This makes sharing between property elements corresponding to different variants explicitly possible. The elements of the new lifted domain are constraint-based decision trees , where decision nodes are labeled with linear constraints defined over numerical features and the leaf nodes belong to an existing single-program analysis domain. To illustrate the potential of this representation, we have implemented an experimental lifted static analyzer, called SPLNum $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> Analyzer , for inferring invariants of C programs. An empirical evaluation on BusyBox and on benchmarks from SV-COMP yields promising preliminary results indicating that our decision trees-based approach is effective and outperforms the baseline tuple-based approach.
The Effectiveness of a Micro-Learning Strategy in Developing the Skills of Using Augmented Reality Applications among Science Teachers in Jeddah

This study aime`d at identifying the effectiveness of a micro-learning strategy-based instructional program in developing the skills of using augmented reality applications among science teachers in Jeddah. The study used the semi-experimental approach based on the experimental design of one group (pre-posttests), and the instruments were an achievement test and the observation card. The sample of the study consisted of (42) science teachers in the intermediate stage in Jeddah. After conducting the study, the results showed statistically significant differences between the mean scores of the study group in the pre and post-application of the achievement test and the observation card on the skills of using augmented reality applications. The differences were in favor of the posttest. The study recommended the importance of the micro-learning strategy in providing advanced practical solutions to training and education problems in line with modern technological development.
Experimental Fock-state bunching capability of non-ideal single-photon states

Advanced quantum technologies, as well as fundamental tests of quantum physics, crucially require the interference of multiple single photons in linear-optics circuits. This interference can result in the bunching of photons into higher Fock states, leading to a complex bosonic behaviour. These challenging tasks timely require to develop collective criteria to benchmark many independent initial resources. Here we determine whether n independent imperfect single photons can ultimately bunch into the Fock state $|n \rangle$. We thereby introduce an experimental Fock-state bunching capability for single-photon sources, which uses phase-space interference for extreme bunching events as a quantifier. In contrast to autocorrelation functions, this operational approach takes into account not only residual multi-photon components but also vacuum admixture and the dispersion of the individual photon statistics. We apply this approach to high-purity single photons generated from an optical parametric oscillator and show that they can lead to a Fock-state capability of at least 14. Our work demonstrates a novel collective benchmark for single-photon sources and their use in subsequent stringent applications.
Implementation Of The K-Nearest Neighbor (KNN) Algorithm For Classification Of Obesity Levels

Obesity or overweight is a health problem that can affect anyone. In research in several journals, it was found that obesity can be influenced by many factors, but the most dominant factors are lifestyle and diet. Obesity should not only be considered as a consequence of an unhealthy lifestyle, but obesity is a disease that can lead to other dangerous diseases. Therefore, it is important to know the level of obesity in order to take early prevention.&#x0D; To determine the level of obesity, a classification method is used, namely K-Nearest Neighbor (KNN) to classify the level of obesity. In this study, classification was carried out with 16 test parameters, namely Gender, Age, Height, Weight, Family History With Overweight, FAVC, FCVC, NCP, CAEC, Smoke, CH2O, SCC, FAF, TUE, CALC, Mtrans and 1 class attribute, namely Nobesity. From tests carried out using the KNN algorithm, the results obtained are 78.98% accuracy with a value of k = 2.&#x0D; Keywords: Obesity, KNN, Classification
Brain Tumor Segmentation Using Dual-Path Attention U-Net in 3D MRI Images

Semantic segmentation plays an essential role in brain tumor diagnosis and treatment planning. Yet, manual segmentation is a time-consuming task. That fact leads to hire the Deep Neural Networks to segment brain tumor. In this work, we proposed a variety of 3D U-Net, which can achieve comparable segmentation accuracy with less graphic memory cost. To be more specific, our model employs a modified attention block to refine the feature map representation along the skip-connection bridge, which consists of parallelly connected spatial and channel attention blocks. Dice coefficients for enhancing tumor, whole tumor, and tumor core reached 0.752, 0.879 and 0.779 respectively on the BRATS- 2020 valid dataset.
A study of the roles of metadata standard and data repository in science, technology, engineering and mathematics researchers' data reuse

Purpose This research investigates how the availabilities of both metadata standards and data repositories influence researchers' data reuse intentions either directly or indirectly as mediated by the norms of data reuse and their attitudes toward data reuse. Design/methodology/approach The theory of planned behavior (TPB) was employed to develop the research model of researchers' data reuse intentions, focusing on the roles of metadata standards, data repositories and norms of data reuse. The proposed research model was evaluated using the structural equation modeling (SEM) method based on the survey responses received from 811 STEM (science, technology, engineering and mathematics) researchers in the United States. Findings This research found that the availabilities of both metadata standards and data repositories significantly affect STEM researchers' norm of data reuse, which influences their data reuse intentions as mediated by their attitudes toward data reuse. This research also found that both the availability of data repositories and the norm of data reuse have a direct influence on data reuse intentions and that norm of data reuse significantly increases the effect of attitude toward data reuse on data reuse intention as a moderator. Research limitations/implications The modified model of TPB provides a new perspective in apprehending the roles of resource facilitating conditions such as the availabilities of metadata standards and data repositories in an individual's attitude, norm and their behavioral intention to conduct a certain behavior. Practical implications This study suggests that scientific communities need to develop more supportive metadata standards and data repositories by considering their roles in enhancing the community norm of data reuse, which eventually lead to data reuse behaviors. Originality/value This study sheds light on the mechanism of metadata standard and data repository in researchers' data reuse behaviors through their community norm of data reuse; this can help scientific communities and academic institutions to better support researchers in their data sharing and reuse behaviors. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2020-0431
In&amp;Out : Diverse Image Outpainting via GAN Inversion

Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting -- filling in missing pixels in a way coherent with the neighboring pixels -- outpainting can be achieved in more diverse ways since the problem is less constrained by the surrounding pixels. Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image. To outpaint an image, we seek for multiple latent codes not only recovering available patches but also synthesizing diverse outpainting by patch-based generation. This leads to richer structure and content in the outpainted regions. Furthermore, our formulation allows for outpainting conditioned on the categorical input, thereby enabling flexible user controls. Extensive experimental results demonstrate the proposed method performs favorably against existing in- and outpainting methods, featuring higher visual quality and diversity.
Vertical Fragmentation of High-Dimensional Data Using Feature Selection

Fragmentation in a distributed database is a design technique that reduces query processing time by keeping the relation size small. When it comes to storing high-dimensional data in a distributed manner, the processing time increases. This is due to the huge attribute size. In this paper, a method is proposed which can reduce the size of high-dimensional data by using feature selection technique. This technique reduces dimensions by removing irrelevant or correlated attributes from the dataset without removing any relevant data. The algorithm used for feature selection and vertical fragmentation is the random forest and Bond Energy Algorithm (BEA), respectively. Experiments show that our method can produce better fragments.
Blockchain for Management of Natural Resources Using Energy Trading as a Platform

Nowadays, the need and necessity of renewable energy are one of the most important techniques which play a major role in our day-to-day life. Renewable energy is one of the best natural resources. In the earth, energy is available in the form of light, breeze, rain, surfs, and thermal. Resources are naturally renewable and it can be renovated. The proposed work mainly concentrates on blockchain technology in rainwater management because the limitation of water is one of the major reasons which is mainly caused by the climatic changes in the weather, floods, and amplified pollution. Blockchain helps to make a perfect decision by the assortment of data of water quantity and water quality. It empowers clean water projects to tap investors around the world with unparalleled breadth and ease. By giving a strong fireproof cabinet, safeguards transparency, and distributed ledger to document relations among the festivities, this branch of knowledge might deeply indicate a change in the way of rainwater resources which can be achieved and traded easily and one example is the use of the smart water grid (SWG) which is a two-way mechanism. One of the best is network transmission between the sensors and the mechanism that is constant and distant used for observing the water supply mechanism. The other one is the smart grid that can display many alternative parameters such as coercion, an improvement in the quality, rate of flow, a temperature which is more efficient toward this technology. By making use of this technology, a model based on an energy trading system helps the growth of industries from small-scale industries to large-scale industries that provides outstanding practice for energy efficiency measures is proposed. It mainly helps in monitoring the truth value of water. By the use of smart grid and sensors, the data can be fetched within a fraction of seconds. Finally, blockchain collects the exact record of water transaction and this makes as an account which helps in the management of water resource.
Temporal association rules discovery algorithm based on improved index tree

Abstract With the rapid increase of information generated from all kinds of sources, temporal big data mining in business area has been paid more and more attention recently. A novel data mining algorithm for mining temporal association is proposed. Mining temporal association can not only provide better predictability for customer behaviour but also help organisations with better strategies and marketing decisions. To compare the proposed algorithm, two methods to mine temporal association are presented. One is improved based on a traditional mining algorithm, Apriori. The other is based on an Index-Tree. Moreover, the proposed method is extended to mine temporal association in multi-dimensional space. The experimental results show that the Index-Tree method outperforms the Apriori-modified method in all cases.
Confusion plot for the confusion matrix

There is a well known 2 by 2 confusion matrix which is widely used in biostatistics or credit assessment. Using these, not only the true positive rate (TPR) and true negative rate (TNR) but also the positive predictive value (PPV) and negative predictive value (NPV) statistics are obtained. In this study, we propose the confusion plot which is a graphical method can geometrically describe various statistics defined from the confusion matrix. The confusion plot consists of six right-angled triangles using equations expressed as the sum of rows and columns of the confusion matrix. In particular, it is found that TPR, TNR, PPV and NPV can be described as acute angles of right-angled triangles in the confusion plot. This confusion plot can evaluate the classification model and its performance similarly to the well known ROC curve. Therefore, the confusion plot could be useful in evaluating the classification models similar to the ROC curve.
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) transfers predictive models from a fully-labeled source domain to an unlabeled target domain. In some applications, however, it is expensive even to collect labels in the source domain, making most previous works impractical. To cope with this problem, recent work performed instance-wise cross-domain self-supervised learning, followed by an additional fine-tuning stage. However, the instance-wise self-supervised learning only learns and aligns low-level discriminative features. In this paper, we propose an end-to-end Prototypical Cross-domain Self-Supervised Learning (PCS) framework for Few-shot Unsupervised Domain Adaptation (FUDA). PCS not only performs cross-domain low-level feature alignment, but it also encodes and aligns semantic structures in the shared embedding space across domains. Our framework captures category-wise semantic structures of the data by in-domain prototypical contrastive learning; and performs feature alignment through cross-domain prototypical self-supervision. Compared with state-of-the-art methods, PCS improves the mean classification accuracy over different domain pairs on FUDA by 10.5%, 3.5%, 9.0%, and 13.2% on Office, Office-Home, VisDA-2017, and DomainNet, respectively. Our project page is at http://xyue.io/pcs-fuda/index.html
DCAN: Deep Co-Attention Network by Modeling User Preference and News Lifecycle for News Recommendation

Personalized news recommendation systems aim to alleviate information overload and provide users with personalized reading suggestions. In general, each news has its own lifecycle that is depicted by a bell-shaped curve of clicks, which is highly likely to influence users’ choices. However, existing methods typically depend on capturing user preference to make recommendations while ignoring the importance of news lifecycle. To fill this gap, we propose a Deep Co-Attention Network DCAN by modeling user preference and news lifecycle for news recommendation. The core of DCAN is a Co-Attention Net that fuses the user preference attention and news lifecycle attention together to model the dual influence of users’ clicked news. In addition, in order to learn the comprehensive news representation, a Multi-Path CNN is proposed to extract multiple patterns from the news title, content and entities. Moreover, to better capture user preference and model news lifecycle, we present a User Preference LSTM and a News Lifecycle LSTM to extract sequential correlations from news representations and additional features. Extensive experimental results on two real-world news datasets demonstrate the significant superiority of our method and validate the effectiveness of our Co-Attention Net by means of visualization.
PROFESSIONAL DEFICIENCIES IN MATHEMATICS TEACHERS: AN ANALYSIS OF REGIONAL RESEARCH RESULTS

Purpose: to describe the results of a regional study of subject and methodological skills of teachers of mathematics in the Krasnoyarsk Territory; highlight the professional difficulties of teachers in the subject area "Mathematics"; determine a list of corrective measures to eliminate the identified problems. Methods: theoretical (analysis of scientific literature on the problem of research, systematization and generalization of the results of scientific research) and empirical (ascertaining pedagogical experiment, analysis and interpretation of the results). Results: a program for diagnosing the subject and methodological skills of the professional activity of a mathematics teacher was developed. A diagnostic toolkit has been developed, which is based on the following principles: orientation to current educational results and modern requirements for the labor actions of a teacher, a system-activity approach for determining deficiencies in the field of methods of teaching mathematics, the possibility of designing an individual trajectory of a teacher's professional development based on the results of diagnostics. The analysis of the research results and recommendations for eliminating the identified professional deficiencies are presented. Scientific novelty: developed and substantiated an approach to the formation of control and measuring materials for the diagnosis of professional deficiencies in mathematics teachers. Practical significance: the main results and conclusions of the article can be used to create a regional system for monitoring professional deficits of mathematics teachers, as well as to develop modular training programs for teachers that provide personalized assistance to teachers in eliminating professional difficulties.
Analysis of Air Temperature and Humidity in Kedunggalar Against BMKG Data Based on DHT11 Sensor

Abstract This research was conducted to compare the temperature and humidity data presented by the BMKG against realtime data based on the DHT11 sensor readings in Kedunggalar, Ngawi. Data was collected for 14 days (8-21 September 2020) at the same time interval between 07-10 AM according to the data update on the BMKG website. Temperature and humidity readings are recorded periodically with the DHT11 sensor every second for one minute, so that 60 data generated every one day. The average temperature and humidity data produced by the DHT11 sensor is 29.18 °C and 68.16% with a comparison of the average data from the BMKG website 28.79 °C and 65.36%. So that from the comparison of direct measurements of DHT11 and from the website it has a sensor reading error against a temperature of 1.59 °C and a humidity of 5.24%. The conclusion is that the measurement of temperature and humidity in Kedunggalar with the DHT11 sensor is very valid against the data on the BMKG website. Even though the humidity exceeds the accuracy limit of the DHT11 sensor which is a maximum tolerance value of 5%, on the other hand the temperature reading is very accurate because the tolerance value is less than 2 °C.
Session Based Recommendations Using Recurrent Neural Networks - Long Short-Term Memory

This paper describes the use of long short-term memory (LSTM) for session-based recommendations. This paper aims to test and propose the best solution using word-level LSTM as a real-time recommendation service. Our method is for general use. Our model is composed of embedding, two LSTM layers and dense layer. We employ the mean of squared errors to assess the prediction results. Also, we tested our prediction of recall and precision metrics. The best performing network has been a trainer for the last year of likes on an image-based social platform and contained about 2000 classes. Our best model has resulted in recall value 0.0213 and precision value 0.0052 on twenty items.
An Approach to Recognize Handwritten Hindi Characters Using Substantial Zernike Moments With Genetic Algorithm

A technique to recognize off-line handwritten Hindi character is suggested by employing Zernike complex moments like a tool to describe the characteristics of a character. Further, an algorithm for selecting the features is employed to identify the substantial image moments from the extracted moments, as the extracted moments may have some insignificant ones. Insignificant moments can increase the computational time and can also degrade the classification accuracy. Thus, the objectives of the study are twofold: (1) to find the important Zernike moments by employing the Genetic algorithm (GA) and (2) the classification of each character is performed using neural network. This way, the performance of the proposed technique is evaluated on two parameters (i.e., speed and recognition accuracy). Further, the efficacy of GA for selecting the moment features is assessed, and the efficacy of selected Zernike complex moments using GA is analyzed for handwritten Hindi characters. Here, the authors used a resilient backpropagation learning algorithm (RPROP) as a classification model.
SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices

With the introduction of edge analytics, IoT devices are becoming smarter and ready for AI applications. However, any increase in the training data results in a linear increase in the space complexity of the trained Machine Learning (ML) models, which means they cannot be deployed on IoT devices that have limited memory. To alleviate such memory issues, we recently proposed an SRAM-optimized classifier porting, stitching, and efficient deployment method in [3]. This is currently the most resource-friendly approach that enables large classifiers to be comfortably executed on microcontroller unit (MCU) based IoT devices, and perform ultra-fast classifications (1--4x times faster than state-of-the-art libraries) while consuming 0 bytes of SRAM.