Reviewerly/exHarmony huggingface dataset:
Updatable Oblivious Key Management for Storage Systems.

We introduce Oblivious Key Management Systems (KMS) as a much more secure alternative to traditional wrapping-based KMS that form the backbone of key management in large-scale data storage deployments. The new system, that builds on Oblivious Pseudorandom Functions (OPRF), hides keys and object identifiers from the KMS, offers unconditional security for key transport, provides key verifiability, reduces storage, and more. Further, we show how to provide all these features in a distributed threshold implementation that enhances protection against server compromise. We extend this system with updatable encryption capability that supports key updates (known as key rotation) so that upon the periodic change of OPRF keys by the KMS server, a very efficient update procedure allows a client of the KMS service to non-interactively update all its encrypted data to be decryptable only by the new key. This enhances security with forward and post-compromise security, namely, security against future and past compromises, respectively, of the client's OPRF keys held by the KMS. Additionally, and in contrast to traditional KMS, our solution supports public key encryption and dispenses with any interaction with the KMS for data encryption (only decryption by the client requires such communication). Our solutions build on recent work on updatable encryption but with significant enhancements applicable to the remote KMS setting. In addition to the critical security improvements, our designs are highly efficient and ready for use in practice. We report on experimental implementation and performance.
A method for detecting and locating the gross error of tidal based on wavelet analysis

Tidal harmonic constants are necessary data for the evaluation of tidal model, tidal prediction and chart datum. Compared with the wavelet multi-scale analysis model, a new method is put forward to detect and locate tidal discrete and continuous gross error. Based on the properties of the wavelets, the wavelet suitable for detecting the gross error of tidal is selected. And taking db6 wavelet as an example, the feasibility and effectiveness of this method are proved by experiments. The results show that the method can not only simultaneously detect and locate discrete gross error and continuous gross based on high frequency information, and can detect and locate the systematic deviation caused by the zero drift according to the low frequency information. Experimental result shows that the method is more simple, the efficiency and accuracy of detecting and locating gross error are improved.
Pengembangan Media Pembelajaran Berbasis Android Dalam Materi Besaran Pada Mata Pelajaran Fisika Kelas X SMK AL Falah Winong

Physics is one of the subjects that contains theoretical and calculation material. Based on the results of observations made at Al Falah Winong Vocational School that the media used in the learning process, ie LKS, has not used learning media. This study aims to determine the feasibility of the media and the effect of using android-based learning media on learning outcomes in physics subjects. The method used in this research is Research and Development with the ADDIE (Analysis-Design-Development-Implementation-Evaluation) development model. This study uses a pretest posttest. Media validation is categorized as feasible with an average value of 77.5 included in the good category. Material validation with an average value of 82.5 included in the excellent category. Student assessments get an average value of 81.9375 which is included in the excellent category. The assessment was obtained from the SUS (System Usability Scale) questionnaire. The conclusion of this research is the use of an Android-based learning media application is appropriate to be used in teaching and learning activities because it gives a big influence on student learning outcomes compared to conventional methods.
YANG Data Node Self Explanation Tags

This document defines a method to tag data node associated withtelemetry data in YANG Modules. This YANG data node tagging method canbe used to provide input, instruction, indication to selection filterand filter queries of operational state on a server during a pub/subservice for YANG datastore updates and provide multiple dimensionalnetwork visibility analysis when the state of all subscriptions of aparticular Subscriber to be fetched is huge, so that the amount ofdata to be streamed out to the destination can be greatly reduced andonly targeted to the characteristics data. An extension statement tobe used to indicate YANG data node self explanation tags that SHOULDbe added by the module implementation automatically (i.e., outside ofconfiguration). A YANG module [RFC7950] is defined, which augmentsModule tag model and provides a list of data node entries to allow foradding or removing of data node self explanation tags as well asviewing the set of self explanation tags associated with a YANGmodule.
Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells

In this thesis, we examine the performance of different forecasting methods. We use dataof monthly house prices from the larger Stockholm area and the municipality of Uppsalabetween 2005 and early ...
Comprehensive Study For Upgrading Of Smart Campus In Polytechnic

Today, cities and campuses share many of the common goals with objectives.  They are trying to create services and applications that uplift  the lives of citizens/students and keep them safer. And, they are seeking to stand out from the crowd by demonstrating ideas and innovations. “Smart Campus” can provide a stable foundation to enhance all of these goals.  With the latest development of technology like 5G, Internet of Things and cloud computing, the conceiving of Smart Campus is going to be the trend setter for educational institutions. Within the city of Guwahati the interest for Smart Campus is slowly emerging. However, most institution are still laid back  in implementing Smart Campus  as part of their strategy and in developing performance indicators to track their progress in higher technical education. Guwahati as a Smart City can be a  reality by making all the facilities in its administration  smart. Technical institutions are one of the key growth pole in a Smart City which play a pivotal role in grooming students for a better tomorrow. Smart Campuses will give a platform various social challenges of technical education like low enrolment among female and other disadvantaged groups, access to institutions of higher education from remote villages. A Smart Campus with new experiences can position any institution as a state-of-the-art environment—the kind that students, faculties and staffs want to be part of it.  It would also help the technical education of Assam in serving the institutional needs like improvement in the quality of technical education and greater impetus for excellence in skill development, resources for faculty development and student campus placement.  The comprehensive study for the upgrading of Smart Campus in the framework of  existing campuses could think of alternatives for positively impacting the student in  the  ways for a better socio-economic society for tomorrow. Based on our research we propose the concept of a Smart Campus that integrates new models of learning, smart sharing of resources and the use of buildings, security and energy efficiency. Furthermore it is advisable that the campus is in itself an object for study for students and faculties and Smart Campus  should be made an integral part of the attitude of all stakeholders. The liveable Smart Campus concept provides a path  that  institutions in technical  education can apply in developing their own  strategy. Best alternatives  are interacted and can be implemented across various polytechnics in the state thereby realizing not only a more ubiquitous  environment but also changing the attitude that students  and staff  have towards sustainable development.
Differentially Private Convex Optimization with Piecewise Affine Objectives Using Projections and Average Subgradients

Han et al. introduced an iterative, differentially private method for solving convex constrained optimization problems with piecewise affine objectives: the differentially private subgradient method. In this paper, we propose extensions to this method to guarantee solutions stay within the feasible region and introduce a hyperparameter () to allow the optimization algorithm to take an average subgradient (the differentially private average subgradient method). We find evidence that tuning and using projections can increase performance.
Infrastructural artefacts in community health: a case study of pregnancy care infrastructures in south India

The work of frontline health workers providing access to pregnancy care services to women in South India is highly distributed and often overlooked in the design of healthcare infrastructures. Unlike clinical and nonclinical personnel who engage in different care practices within and across hospital departments with clearly established work roles, the work of frontline workers is performed across different geographical areas beyond the boundaries of the hospital and with loosely defined roles and resources making the coordination of work more complex. Based on a case study investigating the work of frontline health workers, we report a number of material infrastructural arrangements (the Thayi Card, physical and digital registers, and mobile phones) that played a major role supporting community health practices. We conclude by discussing the opportunities that these artefacts offer for the design of healthcare infrastructures.
Computer Experimental Course Teaching Reform Design Based on Cloud Computing

Abstract This paper begins with an analysis of the advantages of cloud computing technology in the teaching of experimental courses in colleges and universities. In order to make full use of the role of cloud computing technology in computer-assisted teaching, this paper combines the cloud computing technology with the teaching reform of computer basic courses in colleges and universities. The Design and Implementation of Computer Experiment Course Teaching in the Environment of Cloud Computing Technology. The program builds a computer experiment teaching environment on the basis of cloud computing technology and launches a new teaching reform model based on cloud computing technology. On the basis of cloud computing technology, this paper discusses the practical cases of collaborative learning model and shows that the teaching reform design of computer experiment courses based on cloud computing technology can achieve good experimental teaching effects, further improve the quality of computer experimental teaching, and make students’ innovation and practice. The continuous improvement of capabilities is of great significance to the construction of a resource-saving experimental environment, curriculum teaching reform, and innovative experimental teaching modes.
The Concept of Smart Motorways

By 2050, Two thirds of the world's population are soon to be residing in cities, this means a rise in the number of people and cars on roads, currently 55% of the world's population currently live in urban areas and this percentage is estimated to rise to 68% over the next 30 years. Smart motorways are one of the implemented strategies to accommodate for the population growth, smart motorways were introduced in 2006 and was first implemented on the M42 in the West Midlands in UK. Smart motorways are well known in the UK as there are many schemes constructing them at this time, the popular question which is asked by the public is what is a smart motorway and why is it smart? This paper is aimed to evaluate smart motorways, how they operate and what makes them smart. This paper will be beneficial to any researchers on technology in smart cities and widen knowledge on smart motorways. This paper also explores issued faced with contractors when constructing these smart motorways, the technologies used within the smart motorways will be discussed and technologies used within the construction stage will also be analysed. This paper will conclude on smart cities, are they worth it?
Super-Resolution for Practical Automated Plant Disease Diagnosis System

Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of symptoms are lost. In this paper, we first propose an effective pre-processing method for improving the performance of automated plant disease diagnosis systems using super-resolution techniques. We investigate the efficiency of two different super-resolution methods by comparing the disease diagnostic performance on the practical original high-resolution, low-resolution, and super-resolved cucumber images. Our method generates super-resolved images that look very close to natural images with 4$\times$ upscaling factors and is capable of recovering the lost detailed symptoms, largely boosting the diagnostic performance. Our model improves the disease classification accuracy by 26.9% over the bicubic interpolation method of 65.6% and shows a small gap (3% lower) between the original result of 95.5%.
ANALISIS PENGARUH BIAYA MARKETING TERHADAP SALES VOLUME

&lt;div class="WordSection1"&gt;&lt;p&gt;This study was conducted to determine whether the national marketing costs of vocational colleges have an impact on increasing Sales Volume. The process of acquiring Sales volume is carried out in stages starting from the acquisition of Applicants, Lists and finally Sales Volume which is the number of students ready for college. Data analysis was performed on marketing data and financial data over a period of 5 (five) years, using simple linear regression. Based on the calculation results of marketing costs correlation with the acquisition of Sales Volume is known to be 84.9%, where the value shows a meaningful and very strong relationship, while the results of the coefficient of determination are known that Marketing Costs have a 72.0% influence on the acquisition of Sales Volume, and the remaining 28.0 % is influenced by other factors not examined. The results of calculations using SPSS data analysis obtained linear regression Y= 21762.14+0.00000204X, where Marketing Costs (X) contributed positively to the acquisition of Sales Volume (Y) of 0.00000204 units, and the results of the hypothesis t -calculate is 2.778 values greater than t-table values at the significance level of 0.05 (2.778&amp;gt;1.982). Thus the research hypothesis can be accepted that marketing costs have a positive effect on the acquisition of Sales Volume.&lt;/p&gt;&lt;/div&gt;&lt;p dir="ltr"&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: Marketing Costs, Sales Volume&lt;/p&gt;
FSE/CACM Rebuttal 2 : Correcting A Large-Scale Study of Programming Languages and Code Quality in GitHub.

Ray, Devanbu and Filkov issued a rebuttal of our TOPLAS paper On the Impact of Programming Languages on Code Quality: Reproduction Study. Our paper reproduced A Large-Scale Study of Programming Languages and Code Quality in GitHub, which appeared at FSE 2014 and was subsequently republished as a CACM research highlight in 2017. This article is a rebuttal to that rebuttal.
Pengolahan Kain Lurik Pada Produk Busana Ready To Wear Wanita Dengan Teknik Zero Waste

Abstract Lurik is one of a traditional Indonesian fabrics, derived from Java. The use of lurik in the past, usually used as a tool to carry, beskap, kebaya, kemben and jarit. Nowadays, lurik weaving fabric are revafored. Lurik is processed into more modern fashion and processing tends to apply zero waste method with draping technique.Zero Waste method is a way to optimize the use of fabrics. Because the variety of processing is still limited, so there is a potential to cultivate a lurik cloth with drafting techniques, in order to enrich variations in the treatment of lurik fabric in today's fashion products.The study uses qualitative methods by conducting literature studies, interviews, observations and explorations.The final product in this research is fashion ready to wear woman with striated cloth using zero waste design technique and drafting technique to give a value and variation on fashion with striated cloth that is also modern. Keywords Lurik weaving, zero waste, ready to wear
Image Super-Resolution with Adversarial Learning

Single-image super-resolution refers to the problem of generating a high-resolution image from a low-resolution one. In this work we address to the problem of single-image super-resolution of degraded low-resolution images. We face this task by adopting Convolutional Neural Networks, in particular we combine unsupervised adversarial learning with techniques aimed to preserve the color information and the spatial smoothness on the produced images.
Edge Orientation Driven Depth Super-Resolution for View Synthesis

The limited resolution of depth images is a constraint for most of practical computer vision applications. To solve this problem, in this paper, we present a novel depth super-resolution method based on machine learning. The proposed super-resolution method incorporates an edge-orientation based depth patch clustering method, which classifies the patches into several categories based on gradient strength and directions. A linear mapping between the low resolution (LR) and high resolution (HR) patch pairs is learned for each patch category by minimizing the synthesis view distortion. Since depth maps are not viewed directly, they are used to generate the virtual views, our method takes synthesis view distortion as the optimization strategy. Experimental results show that our proposed depth super-resolution approach performs well on depth super-resolution performance and the view synthesis compared to other depth super-resolution approaches.
Hitting the Target: How Mississippi State University Libraries Improved Electronic Resource Processing and Workflows

At the 2019 ALA Annual Conference, Mississippi State University librarian Preston Salisbury presented on behalf of Bonnie Thornton at the ALCTS Electronic Resources Interest Group meeting. The pres...
A Load Balancing Algorithm for Distributed Crawler System

To address the problem of load balance in the distributed crawler system, we propose a new load balancing algorithm. First, we investigate the impact of different features among nodes of distributed crawler system on running time. Based on these features, a runtime model is established. Second, the minimum variance of running time of each node predicted by the model is regarded as the load-balancing objective function. Finally, we utilize an approximate gradient descent (AGD) to optimize the objective function and produce a sequence of task allocation to the nodes. The experimental results show an improvement in the running time of the distributed crawler system.
Efficiently Semantic-Aware Pairwise Similarity: an Applicable Use-Case

Pairwise similarity is an essential operation in multidisciplinary fields of study. However, its operation is expensive due to its complexity as well as the evolution of big data. Besides, similarity scores without semantics leads to neither accurate nor practical results. In this paper, we study the problem of pairwise similarity in terms of semantics, efficiency, and increment. More concretely, we build our own synonym database and organize it in a reflexive synonym index to support us in semantic-aware pairwise similarity. In addition, we employ cache to perform incremental similarity computing in the process of pair-by-pair similarity search. Moreover, we design our feature-based hierarchy to eliminate irrelevant candidate objects before doing pairwise similarity. Furthermore, we analyze a practical case and apply our method into such social network-based applications in order to demonstrate its feasibility and efficiency in reality.
A Cooperative Co-evolution based Scalable Framework for Solving Large-Scale Global optimization Problems

The Cooperative Co-evolution framework is an effective approach for decomposing large scale global optimization problems into multiple sub-components. Every subcomponent uses different optimization algorithms which evolve cooperatively and are independent of each other. These subcomponents contribute in a different way to the overall improvement of the optimal solution. Hence, the computation cost can be decreased by separating out the stagnant subcomponents of the population. Therefore, it is appropriate to allocate resources in an intelligent manner to increase the computational efficiency. In this paper, we illustrate a decomposition strategy to solve large scale global optimization problems which is scalable to millions of variables. The proposed strategy improves computational efficiency and enables embracing parallelization. The framework presented in this paper constitutes Cooperative Co-evolution based Genetic Algorithm with Scalar Distance Grouping technique (CCGA-SDG) derived from Cooperative Co-evolution based Genetic Algorithm (CCGA). In our proposed scheme, a novel scalar distance grouping technique is employed that collates the dependent variables together. The stagnant sub-components of the population are detected using this grouping method and resource reallocation is performed accordingly to increase the computational efficiency. Using our proposed methodology, a benchmark function $f_{6}$ of CEC’08 benchmark composed of 10 million variables is evaluated in 1759.79 seconds using 04 processors connected with Message Passing Interface (MPI) exhibiting better accuracy as compared to other methods. Moreover, for ${f}3$ and $f_{6}$ functions we achieve a better accuracy and for rest of the benchmark functions we achieve acceptable solutions.
A Common Semantic Scoring Method for Chinese Subjective Questions

The following topics are dealt with: formal verification; formal specification; program testing; automata theory; programming language semantics; object-oriented programming; program debugging; theorem proving; temporal logic; multi-agent systems.
Learning combined features for automatic facial expression recognition

Facial expressions are one of the most natural and powerful means for the human being in his social communications, whether to share his internal emotional states or to display his moods or intentions, which, in fact, may be true or simply played in a theatrical way. Given the numerous and variety of applications that can be easily planned, building a system able to automatically recognising facial expressions from images has been an intense field of study in recent years. In this paper, we propose a new framework for automatic facial expression recognition based on combined features and deep learning method. Before the feature extraction, we use Haar feature-based cascade classifier in order to detect then crop the face in the images. Next, we extract pyramid of histogram of gradients (PHOG) as shape descriptors and local binary patterns (LBP) as appearance features to form hybrid feature vectors. Finally, we use those vectors for training deep learning algorithm called deep belief network (DBN). The experimental results on publicly available datasets show promising accuracy in recognising all expression classes, even for experiments which are evaluated on more than seven basic expressions.