Reviewerly/exHarmony huggingface dataset:
A new approximate method for mining frequent itemsets from big data

Mining frequent itemsets in transaction databases is an important task in many applications. It becomes more challenging when dealing with a large transaction database because traditional algorithms are not scalable due to the limited main memory. In this paper, we propose a new approach for the approximately mining of frequent itemsets in a big transaction database. Our approach is suitable for mining big transaction databases since it uses the frequent itemsets from a subset of the entire database to approximate the result of the whole data, and can be implemented in a distributed environment. Our algorithm is able to efficiently produce high-accurate results, however it misses some true frequent itemsets. To address this problem and reduce the number of false negative frequent itemsets we introduce an additional parameter to the algorithm to discover most of the frequent itemsets contained in the entire data set. In this article, we show an empirical evaluation of the results of the proposed approach.
Application of Analytical Hierarchy Process in Teaching Quality Analysis of English Writing

There are two major problems in the evaluation of the teaching quality of English writing: the weak logic of the evaluation system and the low reliability of the evaluation model. To solve the problems, this paper put forward an evaluation method for the teaching quality of English writing based on the analytical hierarchy process (AHP). Firstly, the authors reviewed the current evaluation methods for the teaching quality of English writing. Next, hierarchical evaluation systems were established for the teaching quality of English writing from the perspectives of teachers and students, respectively. After that, the AHP method and the grey theory were introduced to set up an evaluation model for the teaching quality of English writing. Finally, several strategies were presented to improve the teaching quality of English writing. The proposed evaluation systems and model enriched the theories on teaching quality evaluation of English writing, and promoted the teaching quality of English writing.
Covid 19 and Campus Experience : Survey on Online Learning and Time Spent During the Movement Control Order (MCO) among Malaysian Postgraduates

COVID19 has transformed the worldwide education norms of teaching and learning The customary face-to-face method is currently being substitute with online webinars such as Webex and its counterparts A survey has been conducted to investigate the experience of online learning among postgraduate in Malaysia The survey is divided into two categories of issues related to online teaching and learning and time spent on activities during the Movement Control Order (MCO) Thematic analysis is employed to analyse the data pattern Three main themes on online learning experience had emerged, namely IT facilities, learning engagement and personal adjustment This survey also investigates the type of activities pursued during the Movement Control Order (MCO) and found that all the participants spent their time to complete assignments as well as highly video streaming especially from the youtube channel Though online teaching and learning received a positive response, most students still prefer the face-to-face method compare to fully adopting the online method of teaching and learning © 2020, Institute of Advanced Scientific Research, Inc All rights reserved
Abandoned Object Detection using Frame Differencing and Background Subtraction

Tracking objects over fixed surveillance cameras are widely used for security purposes in public areas such as train stations, airports, parking areas, and public transportation for the prevention of terrorism.Once the object is accurately detected in the image scene, we can use various visual algorithms to find a number of applications.In this paper, we introduce a model for tracking the multiple objects along with detecting the abandoned luggage in the real time environment.In our model, we used the initial frames to model the background scene.Next, we used the motion model that is background subtraction to detect and track moving objects such as the owner and the luggage.The proposed model also maintains the position history of moving objects followed by the frame differencing technique to find out the luggage history and detect the abandoned luggage by a human.We have used PETS2006 and PETS2007 dataset for the testing of the proposed system in various indoor and outdoor environments with varying lighting conditions.
Research of GIS partial discharge type evaluation based on convolutional neural network

This paper presents a GIS (Gas Insulated Switchgear) partial discharge type evaluation based on the convolutional neural network. It is significant to study GIS partial discharge and effectively classify the types of GIS partial discharge. In order to effectively and efficiently distinguish the types of GIS partial discharge, phase resolved partial discharge is researched and evaluated based on the convolutional neural network. First, the hardware of the evaluation system is introduced in detail. Second, the partial discharge signal is analyzed. Third, the partial discharge signal evaluation based on the convolutional neural network is studied. Finally, the experiment of the evaluation is presented to verify that the evaluation system can effectively and accurately perform GIS partial discharge monitoring and improve the reliability of power grid operation.
Recognition of Intrusive Alphabets to the Arabic Language Using a Deep Morphological Gradient

The optical character recognition field was one of the key areas of evidence for deep learning methods and has become one of the most successful applications of this technology.Despite that the Arabic is among the most spoken languages in the world today.the optical recognition of Arabic manuscript characters by the algorithms of deep learning remains insufficient.Recently, some studies are moving towards this side and give remarkable results either for the recognition of alphabets or Arabic numbers.We present a deep architecture to solve the problem of the recognition of intrusive handwritten characters to Arabic language.We use a fusion between the morphological gradient method to detect the contours of the alphabets, and multi-layer perceptron (MLP) network with regularization parameters like batch normalization.We apply this model for a database that we created.The classification accuracy was 100% with a very small loss of 0.2%.
GENE2D: A NoSQL Integrated Data Repository of Genetic Disorders Data

There are few sources from which to obtain clinical and genetic data for use in research in Saudi Arabia. Numerous obstacles led to the difficulty of integrating these data from silos and scattered sources to provide standardized access to large data sets for patients with common health conditions. To this end, we sought to contribute to this area and offer a practical and easy-to-implement solution. In this paper, we aim to design and implement a “not only SQL” (NoSQL) based integration framework to generate an Integrated Data Repository of Genetic Disorders Data (GENE2D) to integrate data from various genetic clinics and research centers in Saudi Arabia and provide an easy-to-use query interface for researchers to conduct their studies on large datasets. The major components involved in the GENE2D architecture consists of the data sources, the integrated data repository (IDR) as a central database, and the application interface. The IDR uses a NoSQL document store via MongoDB (an open source document-oriented database program) as a backend database. The application interface called Query Builder provides multiple services for data retrieval from the database using a custom query to answer simple or complex research questions. The GENE2D system demonstrates its potential to help grow and develop a national genetic disorders database in Saudi Arabia.
PERFORMANCE EVALUATION OF A MOBILE MAPPING APPLICATION USING SMARTPHONES AND AUGMENTED REALITY FRAMEWORKS

Abstract. In this paper, we present a performance evaluation of our smartphone-based mobile mapping application based on an augmented reality (AR) framework in demanding outdoor environments. The implementation runs on Android and iOS devices and demonstrates the great potential of smartphone-based 3D mobile mapping. The application includes several functionalities such as device tracking, coordinate, and distance measuring as well as capturing georeferenced imagery. We evaluated our prototype system by comparing measured points from the tracked device with ground control points in an outdoor environment with four different campaigns. The campaigns consisted of open and closed-loop trajectories and different ground surfaces such as grass, concrete and gravel. Two campaigns passed a stairway in either direction. Our results show that the absolute 3D accuracy of device tracking with state-of-the-art AR framework on a standard smartphone is around 1% of the travelled distance and that the local 3D accuracy reaches sub-decimetre level.
METODE k-MEDOIDS CLUSTERING DENGAN VALIDASI SILHOUETTE INDEX DAN C-INDEX (Studi Kasus Jumlah Kriminalitas Kabupaten/Kota di Jawa Tengah Tahun 2018)

The k-medoids method is a non-hierarchical clustering to classify n object into k clusters that have the same characteristics. This clustering algorithm uses the medoid as its cluster center. Medoid is the most centrally located object in a cluster, so it’s robust to outliers. In cluster analysis the objects are grouped by the similarity. To measure the similarity, it can be used distance measures, euclidean distance and cityblock distance. The distance that is used in cluster analysis can affect the clustering results. Then, to determine the quality of the clustering results can be used the internal criteria with silhouette width and C-index. In this research the k-medoids method to classify of regencies/cities in Central Java based on type and number of crimes. The optimal cluster at k= 4 use euclidean distance, where the silhouette index= 0,3862593 and C-index= 0,043893. Keywords: Clustering, k-Medoids, Euclidean distance, Cityblock distance, Silhouette index, C-index, Crime
Survey on Homomorphic Encryption and Address of New Trend

Encryption is the process of disguising text to ensure the confidentiality of data transmitted from one party to another.Homomorphic encryption is one of the most important encryption-related processes which allows performing operation over encrypted data.Using different public key algorithms, homomorphic encryption can be implemented in any scheme.There are many encryption algorithms to secure operations and data storage, which after calculations can obtain the same results.While there is a considerable contribution and enhancement in the field of homomorphic encryption for various performance metrics, there is still an necessity to clarify the applications dealing with this technology.Recently, many distinguished research papers have been filed to address the need for various applications of homomorphic encryption.Recently, many distinguished research papers have been filed to address the need for various applications of homomorphic encryption.Example of these applications but not limited to : Vehicle to Vehicle (v2v) secure communication, cloud security, Vehicular ad-hoc networks (VANET), Blockchain, E-Voting, Data mining with privacy preserving and healthcare sector.This article aims to introduce a literature survey to close the gap in homomorphic encryption systems and their applications in the protection of privacy.We focus on abovementioned applications and present our recommendations for future work.
Super-Resolution using Deep Learning to Support Person Identification in Surveillance Video

Recently, video surveillance systems have been perceived as important technical tools that play a fundamental role in protecting people and assets.In particular, the recorded surveillance video sequences are used as evidence to solve violation, theft and criminal cases.Therefore, the identification of the person present on the crime scene becomes a critical task.In this paper, we proposed a Deep learning-based Super-Resolution system that aims to enhance the faces images captured from surveillance video in order to support suspect identification.The proposed system relies on an image processing technique called Super-Resolution that consists of recovering high-resolution images from low-resolution ones.More specifically, we used the Very-Deep Super-Resolution (VDSR) neural network to enhance the image quality.The proposed model was trained with CelebA faces dataset and used to enhance the resolution of the QMUL-SurvFace dataset.It yielded a Peak Signal-to-Noise Ratio (PSNR) improvement of 7% and Structural Similarity Index (SSIM) improvement of 3%.Most importantly, it increased the face recognition rate by 45.7%.
Using Metaheuristic Algorithms to Improve k-Means Clustering: A Comparative Study

Finding the most suitable centroids for k-means clustering is one of the most important criteria for successful clustering operation.We are always looking for the best centroids.Since, clustering problem and finding best centroids are an NP-hard problems, using metaheuristic algorithms can be an appropriate tool to deal with these issues.Many authors have solved this issue with metaheuristic algorithms.Common and popular algorithms have very good solutions.But which of the metaheuristic algorithms really provides the best solution?To answer this question, in this comparative study, ten popular metaheuristic algorithms are compared.The comparisons are performed on synthetic and ten real-world datasets.To find significant differences between the results obtained by algorithms, statistical analysis is used.Comparison results are presented with suitable tables.
LiveScreen: Video Chat Liveness Detection Leveraging Skin Reflection

The rapid advancement of social media and communication technology enables video chat to become an important and convenient way of daily communication. However, such convenience also makes personal video clips easily obtained and exploited by malicious users who launch scam attacks. Existing studies only deal with the attacks that use fabricated facial masks, while the liveness detection that targets the playback attacks using a virtual camera is still elusive. In this work, we develop a novel video chat liveness detection system, LiveScreen, which can track the weak light changes reflected off the skin of a human face leveraging chromatic eigenspace differences. We design an inconspicuous challenge frame with minimal intervention to the video chat and develop a robust anomaly frame detector to verify the liveness of the remote user in the video chat using the response to the challenge frame. Furthermore, we propose resilient defense strategies to defeat both naive and intelligent playback attacks leveraging spatial and temporal verification. We implemented a prototype over both laptop and smartphone platforms and conducted extensive experiments in various realistic scenarios. We show that our system can achieve robust liveness detection with accuracy and false detection rates 97.7% (94.8%) and 1% (1.6%) on smartphones (laptops), respectively.
INTELLIGENT FAILURES: CLIPPY MEMES AND THE LIMITS OF DIGITAL ASSISTANTS

Often hated during its lifespan in product (1996-2006), Clippy – Microsoft’s Office Assistant, became a pop-culture icon in its afterlife. Delving into the plethora of memes featuring Clippy, we ask: why should a questionable character from a software program that has been out of use for well over a decade have so vibrant an afterlife? If Clippy has become a rhetorical resource, what is it being used to do? We propose that Clippy’s dual status as the original natural-language digital assistant, one that fell critically short in its ability to actually assist, makes it an ideal vehicle for critique of today’s ubiquitous assistants. An analysis of 1,148 meme instances collected from five sites led to a twofold argument: First, Clippy humor relies on the contrast between types of intelligence; Clippy is often too good at one kind, while lacking in another. In particular, Clippy lacks interpersonal intelligence: it serves as a disruptive mediator between its user and the world, as well as other human beings.&#x0D; Yet this failure in “knowing its limits” and adapting to its environment is also what gives Clippy character. This suggests that digital assistants must attend to multiple kinds of intelligences; attending to any one over others may create an endearing character but not an effective digital assistant. Furthermore, the fact that the unbending yet personality-filled character of Clippy remains ungendered or male gives us insight into the pliant and empty characters of the female gendered Alexa, Siri, and Cortana.
A GLOBAL SHAPE MODEL FOR SATURN’S MOON ENCELADUS FROM A DENSE PHOTOGRAMMETRIC CONTROL NETWORK

Abstract. A planetary body’s global shape provides both insight into its geologic evolution, and a key element of any Planetary Spatial Data Infrastructure (PSDI). NASA’s Cassini mission to Saturn acquired more than 600 moderate- to high-resolution images (&lt;500 m/pixel) of the small, geologically active moon Enceladus. The moon’s internal global ocean and intriguing geology mark it as a candidate for future exploration and motivates the development of a PSDI. Recently, two PSDI foundational data sets were created: geodetic control and orthoimages. To provide the third foundational data set, we generate a new shape model for Enceladus from Cassini images and a dense photogrammetric control network (nearly 1 million tie points) using the U.S. Geological Survey’s Integrated Software for Imagers and Spectrometers (ISIS) and the Ames Stereo Pipeline (ASP). The new shape model is near-global in extent and gridded to 2.2 km/pixel, ∼50 times better resolution than previous global models. Our calculated triaxial shape, rotation rate, and pole orientation for Enceladus is consistent with current International Astronomical Union (IAU) values to within the error; however, we determined a new prime meridian offset (Wo) of 7.063°. We calculate Enceladus’ long-wavelength topography by subtracting the best-fit triaxial ellipsoid from our shape model. The result is comparable to previous global models but can resolve topographic features as small as 5–7 km across in certain areas. To evaluate the spatially varying quality of the model, we calculate the point density (variable from 5 to more than 50 points per pixel), normalized median absolute deviation of the points within each pixel (typically less than 100 m), and the minimum expected vertical precision of each point (ranging from 29 m to 2 km).
Combination of Hybrid Cryptography In One Time Pad (OTP) Algorithm And Keyed-Hash Message Authentication Code (HMAC) In Securing The Whatsapp Communication Application

Whatsapp is a cross-platform messaging application that allows us to exchange messages without SMS fees, because WhatsApp uses the same internet data package for email, web browsing, and more. The WhatsApp application reportedly has improved the security system of applications that are end to end encryption (E2EE). This proves that the WhatsApp manager paid considerable attention to privacy and security issues for its users. In addition, it also implies an important matter where business actors have begun to realize that the application of cryptographic services is part of a very promising business strategy or even determines the present and future consequences. The consequence of implementing E2EE is the description encryption process that takes place at the application layer (OSI layer). Cryptography is a study that studies methods to send messages in secret (that is, encrypted or disguised) so that only the intended recipient of the message can decode and read the message. For this reason, cirptography is one method that can implement E2EE on whatsapp. One Time Pad (OTP) is a type of symmetry algorithm, each key is only used for one message. If the key is random (cannot be reproduced) and only used once, then the algorithm is perfectly safe. The MAC (Message Authentication Code) is an identifier to prove the authenticity of a document obtained by using meaningless messages obtained from processing some of the contents of the document using a private key .
Improved Reversible Data Hiding in Medical images using Interpolation and Threshold based Embedding Strategy

Reversible Data Hiding (RDH) techniques have become an active research topic in the field of Data Security.The two important factors in defining the efficiency of data hiding method are stego quality and payload capacity.Recently interpolation based data hiding techniques (IRDH) are useful for achieving high embedding capacity by up-scaling the carrier image to create additional space for embedding.Existing interpolation-based hiding techniques concentrate mainly on natural images.But IRDH can also be utilized for sensitive medical images to hide patient details inside them for achieving privacy protection.Thus, this paper encompasses an effective embedding procedure for interpolation based reversible data hiding schemes in medical images.The objective of our proposed scheme is to provide high visual quality as well as increased payload.The given medical image is interpolated without changing its original pixels.For embedding patient details, we propose a novel Threshold-based Embedding Strategy (TES) to increase the number of bits that can be embedded in an interpolated pixel.Experimental results show that the proposed interpolation based reversible data hiding technique achieves high stego quality and capacity than many state-of-the-art methods for medical images.
Toward Large-Scale Image Segmentation on Summit

Semantic segmentation of images is an important computer vision task that emerges in a variety of application domains such as medical imaging, robotic vision and autonomous vehicles to name a few. While these domain-specific image analysis tasks involve relatively small image sizes (∼ 102 × 102), there are many applications that need to train machine learning models on image data with extents that are orders of magnitude larger (∼ 104 × 104). Training deep neural network (DNN) models on large extent images is extremely memory-intensive and often exceeds the memory limitations of a single graphical processing unit, a hardware accelerator of choice for computer vision workloads. Here, an efficient, sample parallel approach to train U-Net models on large extent image data sets is presented. Its advantages and limitations are analyzed and near-linear strong-scaling speedup demonstrated on 256 nodes (1536 GPUs) of the Summit supercomputer. Using a single node of the Summit supercomputer, an early evaluation of a recently released model parallel framework called GPipe is demonstrated to deliver ∼ 2X speedup in executing a U-Net model with an order of magnitude larger number of trainable parameters than reported before. Performance bottlenecks for pipelined training of U-Net models are identified and mitigation strategies to improve the speedups are discussed. Together, these results open up the possibility of combining both approaches into a unified scalable pipelined and data parallel algorithm to efficiently train U-Net models with very large receptive fields on data sets of ultra-large extent images.