Reviewerly/exHarmony huggingface dataset:
Priority Queue Based Real-time Task Scheduling in Virtualized Cloud Environment

The rich resources, virtualization, and pay-as-you-go method of cloud environment assure solutions to large-scale scientific cloud transactions. Many applications that are moving to the cloud demands both functional and temporal correctness. For profit-driven Infrastructure-as-a-Service (IaaS) cloud providers, minimizing the execution time (or makespan) and monetary cost while scheduling real-time tasks is a significant concern. However, acknowledging the complexity of resource configuration, deployment, and provisioning in the cloud environment, the existing task scheduling strategies have limited uses to address the said concern. In this regard, this work presents an algorithm that focuses on optimizing the execution time and execution cost incurred while running the real-time tasks in a cloud environment. The simulation results signify the effectiveness and applicability of the proposed approach for real-time task scheduling in the cloud environment.
Eigen Vectors based Rotation Invariant Multi-Object Deep Detector

In this paper, we propose an accurate, scalable data-free approach based on eigenvectors and Convolutional Neural Networks (CNNs) for rotated object detection. Detecting an arbitrarily diverted object poses a challenging problem, as features extracted by CNNs are variant to small changes in shift and scale. They lack in performance for images at orientation different from input data. Hence, we introduce a novel two-step architecture, which detects multiple objects at any angle in an image efficiently. We utilize eigenvector analysis on the input image based on bright pixel distribution. The vertical and horizontal vectors are used as a reference to detect the deviation of an image from the original orientation. This analysis gives four orientations of the input image which, that pass through a pre-trained YOLOv3 with proposed decision criteria. Our approach referred to as "Eigen Vectors based Rotation Invariant Multi-Object Deep Detector" (EVRI-MODD), produces rotation invariant detection without any additional training on augmented data and also determines actual image orientation without any prior information. The proposed network achieves high performance on Pascal-VOC 2012 dataset. We evaluate our network performance on three differently rotated angles, 90°, 180°, and 270°, and achieves a significant gain in accuracy by 48%, 50%, and 47% respectively, over YOLOv3.
An Empirical Study on the Science Education Students' Learning Attitude on Science

In China, the five most well-known normal universities have set a special curriculum for a long time, which combines general education and teaching training for education students. In this study, we used the Colorado Learning Attitude about Science Survey (CLASS) to study the learning attitudes on science changes of the education students in the curriculum structure from 8 CLASS dimensions. The results show that: (a) the learning attitudes on science of education students increase with their seniority; (b) in the curriculum structure, the general education has a little effect on improving the learning attitudes on science of the education students, and they gain a lot after teaching training; (c) compared to masters of education, the undergraduates education needs to strengthen in some aspects.
Training set effect on super resolution for automated target recognition

Single Image Super Resolution (SISR) is the process of mapping a low-resolution image to a high-resolution image. This inherently has applications in remote sensing as a way to increase the spatial resolution in satellite imagery. This suggests a possible improvement to automated target recognition in image classification and object detection. We explore the effect that different training sets have on SISR with the network, Super Resolution Generative Adversarial Network (SRGAN). We train 5 SRGANs on different land-use classes (e.g. agriculture, cities, ports) and test them on the same unseen dataset. We attempt to find the qualitative and quantitative differences in SISR, binary classification, and object detection performance. We find that curated training sets that contain objects in the test ontology perform better on both computer vision tasks while having a complex distribution of images allows object detection models to perform better. However, Super Resolution (SR) might not be beneficial to certain problems and will see a diminishing amount of returns for datasets that are closer to being solved.
The Five Levels of Data Destruction: A Paradigm for Introducing Data Recovery in a Computer Science Course

Digital forensics has become a fundamental piece of many cyber security programs across the US, and data recovery is an integral building block of digital forensics. Data recovery can be a difficult topic to cover without a system or organization to the different methods of recovery. The following manuscript offers a structure for introducing data recovery in a digital forensics or information technology course and a method for evaluating the admissibility of recovered files as court evidence based on how the data were recovered. This offers both a framework for teaching data recovery and a way for discussing evidence admissibility. The five levels of destruction paradigm is a result of over a decade of teaching digital forensics in vocational and academic environments in a computer science program. The authors offer up this paradigm in hopes it will be useful to other computer science and digital forensics educators.
A Recognition System for Subjects' Signature Using the Spatial Distribution of Signature Body

This investigation proposed an identification system of offline signature by utilizing rotation compensation depending on the features that were saved in the database. The proposed system contains five principle stages, they are: (1) data acquisition, (2) signature data file loading, (3) signature preprocessing, (4) feature extraction, and (5) feature matching. The feature extraction includes determination of the center point coordinates, and the angle for rotation compensation (θ), implementation of rotation compensation, determination of discriminating features and statistical condition. During this work seven essential collections of features are utilized to acquire the characteristics: (i) density (D), (ii) average (A), (iii) standard deviation (S) and integrated between them (iv) density and average (DA), (v) density and standard deviation (DS), (vi) average and standard deviation (AS), and finally (vii) density with average and standard deviation (DAS). The determined values of features are assembled in a feature vector used to distinguish signatures belonging to different persons. The utilized two Euclidean distance measures for matching stage are: (i) normalized mean absolute distance (nMAD) (ii) normalized mean squared distance (nMSD). The suggested system is tested by a public dataset collect from 612 images of handwritten signatures. The best recognition rate (i.e., 98.9%) is achieved in the proposed system using number of blocks (21×21) in density feature set. With the same number of blocks (i.e., 21×21) the maximum verification accuracy obtained is (100%).
Improving Texture Image Retrieval with Redundant Contourlet Features

In this paper, we develop RCT-Plus, a new variant of the contourlet transform in which augmented directional selectivity, redundancy and Gaussian filtering are meant to enhance texture characterization and feature extraction for content-based image retrieval (CBIR). Two modeling methodologies namely, generalized Gaussian distribution and energy computation, are applied on contourlet coefficients. The features that are derived from the estimated model parameters may rely to both directional and approximation sub-bands, providing thereby various CBIR frameworks. Experimental results show that RCT-Plus achieves superior retrieval performance compared to other methods using standard contourlets. Therefore, the contribution of RCT-Plus is significant to the relevance of the features extracted from textures.
In favour of or against multi-lingual Q&amp;A sites? Exploring the evidence from user and knowledge perspectives

Many Q&A sites initially run only in English, and then gradually release their multi-lingual variants to serve users who speak other languages. The launch of such multi-lingual sites always lead to an intense dispute about the pros and cons of multi-lingual sites. Although all arguments and concerns sound reasonable, people can rarely provide solid evidence to convince each other. In this paper, from users' comments about the launch of several non-English Stack Overflow sites, we first identify three major concerns including community split, knowledge needs and interests in other languages, and knowledge fragmentation and duplication. To validate these three concerns, we conduct an evidence-based data analysis and comparison of user characteristics, tag usage and cross-site links between the Russian Stack Overflow and the English Stack Overflow on these three concerns. Our study sheds light on the existence value and risks of multi-lingual Q&A sites.
Balancing Human and Machine Performance When Analyzing Image Cover

In computer vision, it is often unclear how humans should annotate data in a way that optimizes both human effort and machine performance. This is particularly true in image cover applications, in which the goal is to determine what percentage of the image is covered by a certain class of objects (e.g. trees or grass). In this paper, we present the first study comparing the efficacy of fundamentally different annotation styles when applied to image cover problems. Our results on two real-world marine science problems indicate that the most accurate human annotations do not necessarily result in the best machine learning performance, and that by carefully selecting the format of the annotations one can achieve good performance even with limited human effort.
Design and implementation of secure communication protocols for Internet of Things systems

The security of Internet of Things (IoT) systems is a major issue for protection of data and safety of people. This requires developing solutions that address security issues related to the design of communication protocols between objects but also in the collection, processing, routing and storage of data. In this paper, we propose a methodology for the design and implementation of secure protocols for the Internet of Things which currently represents a major part of our daily life. We propose to minimize vulnerabilities in the design and implementation of communication protocols in IoT platforms by combining simulations of fault injection attacks at the binary level combined with model checking of binaries. Our methodology takes into account the characteristics nature of IoT systems.
Large-scale storage of whole slide images and fast retrieval of tiles using DRAM

The U.S. Food and Drug Administration (FDA) has approved two digital pathology systems for primary diagnosis. These systems produce and consume whole slide images (WSIs) constructed from glass slides using advanced digital slide scanners. WSIs can greatly improve the work ow of pathologists through the development of novel image analytics software for automatic detection of cellular and morphological features and disease diagnosis using histopathology slides. However, the gigabyte size of a WSI poses a serious challenge for storage and retrieval of millions of WSIs. In this paper, we propose a system for scalable storage of WSIs and fast retrieval of image tiles using DRAM. A WSI is partitioned into tiles and sub-tiles using a combination of a space-filling curve, recursive partitioning, and Dewey numbering. They are then stored as a collection of key-value pairs in DRAM. During retrieval, a tile is fetched using key-value lookups from DRAM. Through performance evaluation on a 24-node cluster using 100 WSIs, we observed that, compared to Apache Spark, our system was three times faster to store the 100 WSIs and 1,000 times faster to access a single tile achieving millisecond latency. Such fast access to tiles is highly desirable when developing deep learning-based image analytics solutions on millions of WSIs.
User-Oriented Description of Emerging Services in Ambient Systems

Ambient intelligence aims at providing users the right services at the right time. Our solution composes software components and their services, automatically and on the fly, and makes composite services emerge from the environment. An important question is their intelligible presentation to an average user (not a service composition expert). Our approach consists in the automatic generation of user-oriented descriptions from unit descriptions of components and services. For that, we propose a domain-specific language for component and service descriptions and a combining method.
MaxHash for Fast Face Recognition and Retrieval

This paper presents a fast method for recognition and retrieval of face images in large face datasets. In this approach, we generate binary hash codes for deep face features extracted using FaceNet model. Unlike the distance of real valued feature vectors, the distance in the Hamming space is computed with a simple XOR operation. We generate a candidate set using the Hamming distance metric, then a fine grained search is used to find the corresponding face picture in this small set. Experiments on LFW dataset show that for 64 bit hash codes an image of the corresponding person is always in the candidates set of only 48 items.
THE ADOPTION OF E-GOVERNMENT IN THE TAX ADMINISTRATION: A SCOPING REVIEW

With the spread of information, technology and the Internet, we have found that the tax administration is moving towards online or digital services. The study aims to identify what has worked in the adoption of e-government in tax services. The key aspects include the design of e-government in tax procedures, acceptance and usability factors, impacts, and challenges in the implementation. A scoping review was designed to map the existing evidence on the application of information and technology system in delivering tax services, or known as e-government. Key search terms were developed and mapped. Selected databases and key journals were used in this scoping review. Using the criteria that were determined, we identified 79 articles that most closely demonstrated the purpose of this study. However, we only reported the detailed of the findings from ten included articles. The identified studies reflected the majority of articles investigated the reasons behind the success or failure of e-government projects in the tax administration. This review concludes that in an environment in which the government faced many challenges with the limited resources, e-government adoption in tax administration offers the potential benefit to strengthen the government finances. A greater understanding of the purpose, adoption &amp; acceptance factors, the impact and challenges in the application of e-government projects in tax services is a priority for research, policy and practice.
Fuzzy Logic Based Dryer Controller

The fuzzy logic control system determines the real world constraint of the dryer's heating rate to the dryer driver circuit which is constructed with the pulse width modulation (PWM) technique.Input and output operations to and from the computer system are carried out via parallel interfacing and Turbo C++ programming.The humidity sensing circuit is constructed around a 555 timer with astable operation which is a non-sinusoidal oscillator.This humidity sensing circuit produces a rectangular wave with the frequency indirectly proportional to the sensed humidity.In order to achieve the perfectively symmetrical square wave as well as to reduce the frequency value, the clocked flip-flop, CD4017BC that is divided by 10 Johnson counter with 10 decoded outputs and a carry out bit is added to the timer's output.The resultant frequency is applied into the computer system integrating with the fuzzy logic and it determines the frequency value and its rate of change which are input to the fuzzy logic control system.
Blockchain in Cybersecurity, Pros, and Cons

The blockchain is a new innovative technology which is in the phase of being adopted by organizations for managing records. The technology which was initially started as a concept of digital currency is now being watched as an alternative to traditional record keeping techniques. Since the development of blockchain application to solve the problem of integrity is still in infant stage we need to do a lot of research before we apply this technology to the real-world application.The blockchain is a distributed ledger where all the changes and updates on the network are verified by all the nodes on the network, it can solve the problem of integrity. It is almost impossible to tamper the data stored on the blockchain because of this feature there is a huge room to grow for this technology. We can develop an application on top of blockchain which will solve the problem of integrity, lack of trust and a single point of failure. Although blockchain technology can be used to solve the current problem of security and trust in the computer applications there are few limitations and dark side of this technology. There are many cryptocurrencies developed on the blockchain technology which are being used for money laundering and the method of the transaction in the dark web. Also, there is a certain rise on the malware and ransomware that uses the resources of the computer to mine and demand the cryptocurrencies from users and there is a rise in 51% attack on the blockchain network. The purpose of this research paper is to analyze the benefits of blockchain to solve the current problem in cyberspace and to finds, it's threats and limitation.
On the generalized Hamming weights of certain Reed–Muller-type codes

Abstract There is a nice combinatorial formula of P. Beelen and M. Datta for the r -th generalized Hamming weight of an a ne cartesian code. Using this combinatorial formula we give an easy to evaluate formula to compute the r -th generalized Hamming weight for a family of a ne cartesian codes. If 𝕏 is a set of projective points over a finite field we determine the basic parameters and the generalized Hamming weights of the Veronese type codes on 𝕏 and their dual codes in terms of the basic parameters and the generalized Hamming weights of the corresponding projective Reed–Muller-type codes on 𝕏 and their dual codes.
A Seed Usage Issue on Using catR for Simulation and the Solution

The R package catR is a useful developmental and testing platform for adaptive tests. Developers and researchers have been using catR for their assessments or research projects. However, there is a flaw in catR that can potentially cause misleading results. This article shows the flaw and provides a solution. Suggestions in using seeds for random numbers were also provided.
RecNets: Channel-wise Recurrent Convolutional Neural Networks.

In this paper, we introduce Channel-wise recurrent convolutional neural networks (RecNets), a family of novel, compact neural network architectures for computer vision tasks inspired by recurrent neural networks (RNNs). RecNets build upon Channel-wise recurrent convolutional (CRC) layers, a novel type of convolutional layer that splits the input channels into disjoint segments and processes them in a recurrent fashion. In this way, we simulate wide, yet compact models, since the number of parameters is vastly reduced via the parameter sharing of the RNN formulation. Experimental results on the CIFAR-10 and CIFAR-100 image classification tasks demonstrate the superior size-accuracy trade-off of RecNets compared to other compact state-of-the-art architectures.
Penerapan Rapidminer Pada Data Mining Klastering (Kasus: Distribusi Persentase Rumah Tangga Menurut Kabupaten/Kota Dan Bahan Bakar Untuk Memasak)

Fuel is any material that can be converted into energy. For example in daily life humans often use energy sources as fuel for cooking including Gas/LPG, Electricity, Kerosene, Charcoal/Briquettes, Wood and others. The purpose of this study is to classify the distribution of the percentage of fuel used in each district/city in Northern Sumatra. This study discusses the analysis of the K-Means method in the case of the distribution of household percentages by district/city and cooking fuel in North Sumatra through the North Sumatra Central Statistics Agency website. The data is processed into 2 clusters namely high level (C1) and low-level clusters (C2). Thus obtained from 34 districts/cities in North Sumatra 23 regions are grouped in high-level clusters (C1) and 10 regions are grouped in low-level clusters (C2).This needs to be done so that it becomes input in the form of information to the government to find out villages that still have low understanding and have not been fulfilled in a district/city in the Province of North Sumatra.
THE EFFECTIVENESS OF CREATIVE PROBLEM SOLVING ON LOGICAL THINKING ABILITY AND MASTERY OF CONCEPTS

This research aims to determine the effect of Creative Problem Solving (CPS) on logical thinking ability and concept achievement. The design was a nonequivalent pretest-posttest control group design. The samples were 60 students of X MIA 1 and X MIA 3 selected through cluster random sampling technique. Data on logical thinking ability obtained using the Test of Logical Thinking (TOLT and data on concept achievement (cognitive aspects) gathered using pretest and posttest. Both data were analyzed by Independent Sample t-Test at a 5% confidence level. The results showed that CPS give effect significantly the experimental class logical thinking ability (0.41± 0.13) was higher than the control class (0.21 ± 0.07). The mastery of concepts on the experiment class (0.61 ± 0.20) higher than the control class (0.41 ± 0.20). It means that CPS influences students’ logical thinking ability and mastery of concepts.
DOCUMENT CLASSIFICATION AND SUMMARIZER USING PROBABILISTIC CLASSIFIER.

In this paper, we propose a straightforward component extraction algorithm that can accomplish high archive order precision with regards to advancement driven subjects.Document classification has become an important field of research due to the increase of unstructured text documents available in digital form.Programmed content synopsis turns into a significant method for finding applicable data correctly in huge content in a brief span with little endeavors.In the proposed system,we take PDF as input,classify it according to it''s domain and summarize it using power of Natural Language Processing and Machine Learning.Here we are providing options for selecting required line of summary,the prediction result will send to the user email with summary and classified domain.Having generated summary help us to tell whether to deep dive in detail or not.The main motive of project to save time required in document classification and understanding it.