Reviewerly/exHarmony huggingface dataset:
Progressive Memory Adjustment with Performance Guarantee in Virtualized Systems

As applications are often allocated more memory than they actually used to cope with peak memory demand due to the workload dynamics in virtualized systems running multiple virtual machines, memory adjustment by reclaiming inactive memory in virtual machines is an effective way to enable memory overcommitment so as to reduce the cost. However, existing schemes are designed based on one-shot adjustment, which may reclaim a large size in a single operation, and they are unaware of both memory access dynamics and memory sensitivity of different applications, so they usually result in excessive reclamation and lead to severe performance slowdown. To address this issue, we propose PMA, a progressive memory adjustment scheme that takes into consideration both memory access dynamics and memory sensitivity and leverages virtual machine performance feedback to progressively reclaim inactive memory to avoid performance slowdown. Besides, PMA is designed based on ballooning (i.e., the balloon driver) so it preserves the good isolation between host and virtual machines for full virtualization systems. We also implement a prototype in host user space, and experiments show that PMA effectively limits the performance drop of memory overcommitment (e.g., the performance drop of every virtual machine is limited within 10% for up to 33% memory overcommitment), which is already very close to the optimal case of having enough memory, so PMA is efficient to enable memory overcommitment with performance guarantee in full virtualization systems.
Evaluation of Similarity Measures in Semantic Web Service Discovery

The semantic Web service discovery is the process of finding services that could potentially meet the consumer requirements by choosing between several services. The matchmaking between the consumer request and the semantic Web services is the main task of any semantic Web service discovery mechanism. To make this, many works use similarity measures to choose a similar semantic Web service and the consumer’s request. In this paper, we assess similarity measures to help us define their problems and determine which measure is most appropriate for Web service discovery approaches. For this matter, we used a test collection of semantic Web services with different domains. The results of our evaluation show the weakness of similarity measures to have an interesting efficiency.
An efficient local outlier detection optimized by rough clustering

Outlier detection is a hot issue in data mining, which has plenty of real-world applications. LOF (Local Outlier Factor) can capture the abnormal degree of objects in the dataset with different density levels, and many extended algorithms have been proposed in recent years. However, the LOF needs to search the nearest neighborhood of each object on the whole dataset, which greatly increases the time cost. Most of these extended algorithms only consider the distance between an object and its neighborhood, but ignore the local distribution of an object within its neighborhood, resulting in a high false-positive rate. To improve the running speed, a rough clustering based on triple fusion is proposed, which divides a dataset into several subsets and outlier detection is performed only on each subset. Then, considering the local distribution of an object within its neighborhood, a new local outlier factor is constructed to estimate the abnormal degree of each object. Finally, the experimental results indicate that the proposed algorithm has better performance and lower running time than the others.
Residual Attention Fusion Network for Single Image Super-Resolution

Abstract Recently, a very deep convolutional neural network (CNN) demonstrated influential performance in the field of single image super-resolution (SISR). However, most of the CNN-based methods focus on designing deeper and wider network structures alone and do not use the hierarchical and global features in the input image. Therefore, we proposed a residual attention fusion network (RAFN), which is an improved residual fusion (RF) framework, to effectively extract hierarchical features for use in single-image super-resolution. The proposed framework comprises two residual fusion structures composed of several residual and fusion modules, and a continuous memory mechanism is realized by adding a long and short jump connection. The network focuses on learning more effective features. Furthermore, to maximize the power of the RF framework, we introduced global context attention (GCA) module that can model the global context and capture long-distance dependencies. The final RAFN was constructed by applying the proposed RF framework to the GCA blocks. Extensive experiments showed that the proposed network achieved improved performance in the SISR method with fewer parameters, as compared to the methods proposed in previous studies.
Plausible Deniability for Anonymous Communication

The rigorous analysis of anonymous communication protocols and formal privacy goals have proven to be difficult to get right. Formal privacy notions as in the current state of the art based on indistinguishability games simplify analysis. Achieving them, however can incur prohibitively high overhead in terms of latency. Definitions based on function views, albeit less investigated, might imply less overhead but aren't directly comparable to state of the art notions, due to differences in the model.
Detection and Avoidance of Barriers in Driverless Cars using Deep Learning Techniques

In the advance of the technology and implantation of Internet of Things (IoT), the realization of smart city seems to be very needed. One of the key parts of a cyber-physical system of urban life is transportation. Such mission-critical application has led to inquisitiveness in researchers to develop autonomous robots from academicians and industry. In the domain of autonomous robot, intelligent video analytics is very crucial. By the advent of deep learning many neural ¬¬¬networks-based learning approaches are considered. One of the advanced Single Shot Multibox Detector (SSD) method is exploited for real-time video/image analysis using an IOT device and vehicles/any barrier avoidance on road is done using image processing. The proposed work makes use of SSD algorithm which is responsible for object detection and image processing to control the car, based on its current input. Thus, this work aims to develop real-time barrier detection and barrier avoidance for autonomous robots using a camera and barrier avoidance sensor in an unstructured environment.
Arabic Computational Linguistics

This chapter presents an easily followed overview of computational linguistics and where Arabic fits into it. Computational linguistics, often referred to interchangeably as natural language processing (NLP) or human language technologies, is a large and growing interdisciplinary field of research that lies at the intersection of linguistics, computer science, electrical engineering, cognitive science, psychology, pedagogy, and mathematics, among other fields. Research and work on Arabic computational linguistics has lagged behind English and other languages. This is despite a tremendous increase in the relative growth of Arabic NLP in the period between 2012 and 2016. The reason for its slow start is that Arabic presents a series of difficulties to programmers, those being morphological richness, orthographic ambiguity, dialectal variations, orthographic noise, and resource poverty. Those problems have been or are being overcome, and a new generation of researchers has made great strides in the field. This has partly to do with the growing interest in language technologies for opinion mining and translation in social media, which features dialectal Arabic more than MSA. Another motivation is that commercial giants like Apple and Google are interested in applications of Arabic as it is spoken.
Multi-task Sequence Classification for Disjoint Tasks in Low-resource Languages

Multi-task learning (MTL) has been successfully utilized in numerous NLP tasks, including sequence labeling. In this work, we utilize three transformer-based models (XLM-R, HerBERT, mBERT) to improve recognition quality using MTL for selected low-resource language (Polish) and three disjoint sequence labeling tasks with different levels of inter-annotator agreement. Our best MTL model outperforms single-task models both within the tasks domain and overall performance.
Examining stakeholder perceptions of oyster ecosystem services using fuzzy cognitive mapping

Abstract Nature provides numerous ecosystem services to people, yet the prioritization of these services often depends on the goals of various stakeholder groups. The eastern oyster ( Crassostrea virginica ) is an ecologically important species along the Gulf and Atlantic coasts of the United States, where it provides essential fish habitat and may mitigate against climatic variations in urban areas. The eastern oyster also supports a multimillion dollar aquaculture industry in coastal communities. Recent declines in eastern oyster populations, however, have spurred widespread restoration activities. Here, we look at three expert stakeholder groups (academics, nongovernmental organizations, and governmental agencies) in Rhode Island (United States) to understand how stakeholder perceptions of oyster ecosystem services differ. Stakeholders' mental models showed differences among the groups' topologies and components, although the terms “Water Quality” and “Habitat/Structural Complexity” were prioritized in all the groups. Our results suggest that there is substantial intergroup variation, but that there are common threads around which future oyster restoration and management programs can be designed. By making these mental models of ecosystem services explicit, we illuminate tacit assumptions held by different stakeholders of the oyster stakeholder community in Rhode Island. In doing so, we highlight opportunities for more efficient collaboration around commonly shared goals for sustainable social and ecological management.
A Naive Extractive Text Summarizer for Assamese Language

Text summarization is a pertinent problem of Natural Language Processing that deals with finding the summary of an input text or a set of text by using extractive or abstractive technique to find the important points of the text while maintaining the context and coherence. This paper describes the use of a naive extractive technique to summarize texts written in Assamese language, which is an Indo-Aryan language. The process starts with preprocessing of text, which is done manually and the term frequency of the words are considered to find the sentence scores, which are used to filter out the top-ranked sentences, based on which the summary is prepared. The feature of the system is that a user can insert their own document to summarize as well as can select the number of sentences in which they will get the summary. A set of 12 text is used to experiment on the code and although it does not incorporate semantic information into account, the results are quite promising, showing the validity of this naive extractive summarization approach.
The role of innovation in the growth of the company: A case of the emerging country

Major technological changes, the development of management, and its functions have influenced companies to launch more and more innovative products and services every day, even if these are inventions or improvements in their specific products or services. Innovation is a difficult process, but very valuable and effective for achieving the intended results. Knowing that Kosovo has had a difficult history in the recent past, from this paper we can see that there has been a pretty good technological development and an increase in knowledge by managing quite well the knowledge that has served in the establishment level of service and production, attracting foreign investors, and thus influencing a better economic development thanks to innovations from the above factors. This study examines all types of innovation whether they are product or process and service including their forms which are incremental or marginal innovations and any other form related to innovation and in any form that has influenced or is expected to positively affect the performance of the organization. These conclusions could also be used for the purposes of any business plan analysis for opening a new business or expanding an existing business, comparing search results with current ones and new business expectations. Very little research has been done on the impact of innovation on the growth of firms in Kosovo, but this paper shows concretely this impact by understanding them closely through interviews conducted with firms
Topologically-Informed Atlas Learning

We present a new technique that enables manifold learning to accurately embed data manifolds that contain holes, without discarding any topological information. Manifold learning aims to embed high-dimensional data into a lower dimensional Euclidean space by learning a coordinate chart, but it requires that the entire manifold can be embedded in a single chart. This is impossible for manifolds with holes. In such cases, it is necessary to learn an atlas: a collection of charts that collectively cover the entire manifold. We begin with many small charts, and combine them in a bottom-up approach, where charts are only combined if doing so will not introduce problematic topological features. When it is no longer possible to combine any charts, each chart is individually embedded with standard manifold learning techniques, completing the construction of the atlas. We show the efficacy of our method by constructing atlases for challenging synthetic manifolds; learning human motion embeddings from motion capture data; and learning kinematic models of articulated objects.
Security Issues of Web Services of Large Scale Distributed Database

Cloud computing is viewed as one of the most promising technologies in computing today. This is a new concept of large scale distributed computing. It provides an open platform for every user on the pay-per-use basis. Cloud computing provides number of interfaces and APIs to interact with the services provided to the users. With the development of web services distributed application, Security of data is another important subject in various layers of distributed computing. In this study, security of data that can be used during the access of distributed environment over various layers will be described.
Development and Evaluation of a System for showing Star Attribute Information with Sounds on a Virtual Planetarium

Currently, there are many contents related to celestial bodies in the world. However, their systems introduce constellations mainly. Most of them don’t introduce information about each star. Existing systems read aloud names of stars and constellations and show their color and brightness only visually. Therefore, in this research, we developed a system that can quickly present information of the color and brightness of stars by associating them with pitches and volumes of sounds. In the evaluation experiment, we compared experimental group using this system with control group using the voice reading system. We verified the speed and accuracy of grasping the information of stars in each system.
EDMODO IMPACTS: MEDIATING DIGITAL CLASS AND ASSESSMENT IN ENGLISH LANGUAGE TEACHING

This study aims at investigating the impact of the use of Edmodo on the digital class in the English learning assessment. This type of research is the method library research. This study uses journals and articles as reference to collecting data. To answer the literature review, the writer used data that is collected, studied, and treated then combined to get valid and relevant results by using the Internet as a medium to obtain data. In this paper there are three parts used to respond to the literature review: Terminology of Edmodo, Teaching Learning Process and Edmodo, and Edmodo Impact. Research shows that Edmodo's impact on digital classes in the English learning assessments students in the form of learning and made it easier for teachers to convey lessons, content, tasks, and file sharing easy access to all students. And with CBT, it makes it easier for teachers to assess a student's work with computer help. Edmodo became another evaluation so that the results of the examination were objective and accurate. Through Edmodo as a student's performance assessment mode, there is no cheating that students do in studying because students are working independently
Robust Multilayer Parallel Covariance Intersection Fusion Predictor for MultiChannel AR signal with Colored Noises and Uncertain Noise Variances and Unknown Cross-Covariances

This paper is concerned with robust multilayer parallel covariance intersection fusion prediction problem for a class of multisensor multichannel AR signal systems with colored noises, uncertain noise variances and unknown cross-covariances. By the state space method, the original system is converted into a multi-model system with only uncertain noise variances and unknown cross-covariances. According to the mini-max robust estimation principle, based on the worst-case system with conservative upper bounds of the noise variances, the local robust Kalman signal predictors are presented. And the MPCI fusion predictor is presented according to the MPCI fusion algorithm presented in this paper. The robustness of the proposed local and MPCI fused predictors are proved such that their actual prediction error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. The robust accuracies among the local and MPCI predictors are proved and they are compared by the traces of the variance matrices and the covariances ellipsoids, respectively. Finally, a simulation example shows the correctness and effectiveness of the proposed results.
The Method Based on Spectrogram's Image Classification for Chinese Dialect's Tone Recognition

In China, linguists are facing the dilemma of the rapid disappearance of dialects. The protection and research of dialects are of great significance to Chinese regional culture, history, nationality and language. At present, the tones of Chinese dialects are recognized by the professional trained researcher's human ear. Although some speech signal processing techniques are also used to observe the fundamental frequency, spectrogram, frequency, amplitude and other characteristics of speech signals, these data cannot be used as the description standard of tone characteristics. From another point of view, this paper uses the spectrogram, which represents the comprehensive characters of speech, to identify dialect tones. The characters classification of speech signals is transformed into the feature classification of images. A simple convolutional neural network model is proposed for tone recognition of dialects, and a good recognition rate is achieved with a small amount of data.
No-Reference Image Quality Assessment for Contrast Distorted Images

Image contrast distortion is a common type of distortion in digital images. However, there is almost no research on the no-reference image quality assessment (NR-IQA) algorithm for image contrast. Therefore, we propose a histogram-based NR-IQA algorithm for contrast distorted images. Firstly, we analyze the image sequence with gradually changing contrast, and the image features are extracted from two aspects: objective statistical attribute, subjective perception attribute. And then we propose three statistical features, including image local contrast, histogram shape and image brightness, which can describe the image quality more simply and intuitively. Furthermore, we introduce the Just noticeable difference (JND) model, which makes the proposed algorithm have a higher matching degree between the human vision system (HVS) and the objective features in the algorithm. Finally, the support vector regression (SVR) is utilized to obtain the mapping relationship between the quantified features and the subjective scores to predict the quality of contrast distorted images. The outstanding performance of the proposed algorithm have been proved on CSIQ, TID2013 and CCID2014 databases.
FaceCognize: An approach to Face Recognition for low resolution image

In recent times, with the rise of the digital surveillance system, there is abundant digital forensics in the form of CCTV footage which can be utilized for the task of recognizing individuals. But due to the image resolution and environmental conditions, most of the data is rendered futile. Our proposed system is specifically aimed at efficiently leveraging this low-quality data and utilize this data from CCTV or other sources to track the individual. This system comprises of up scaling the low-quality images, extracting and aligning the faces, converting them into a feature vector, and finally using linear separators to distinguish them. This system can not only play a very important role in tracking criminal for law enforcement but also have wide-ranging application in access control, security, finding a missing person, and tracking attendance. This paper was developed as part of the Cisco ThinQbator 2020 program which is a platform to share ideas and collaborate.
Cybersecurity Incident Response and Management

This chapter presents a systematic literature review on best practices regarding cybersecurity incident response handling and incident management. The study identifies incident handling models that are used worldwide when responding to any type of cybersecurity incident. The authors highlight the importance of understanding the current cyber threat landscape in any incident response team and their standard operations procedures. The chapter provides guidelines for building a cybersecurity incident team in terms of incident categorization, capabilities, tasks, incident cost calculation, and metrics.
Exponential Hashing with Different Penalty for Hamming Space Retrieval

Hamming space retrieval enables efficient constant-time search through hash table lookups constructed by hash codes, where in response to each query, all data points within a small given Hamming radius are returned as relevant data. However, in Hamming space retrieval, the search performance of the existed hashing schemes based on linear scan dropped when the length of the hash codes increases. The reason is that the Hamming space becomes very sparse and it is difficult to pull the similar data into the Hamming ball and to push the dissimilar data outside the ball. Currently, the existing deep hashing methods based on hash table lookups pay too much attention to similar samples outside the ball and ignore the learning of dissimilar samples inside the ball, leading to a biased model. In this paper, we introduce discriminatory penalty into the exponential loss functions to optimize the Hamming space, leading to Exponential Hashing with Discriminatory Penalty (EHDP), which discriminately penalizes similar/dissimilar data inside and outside the Hamming ball. Technically, EHDP capitalizes on exponential function to discriminatively encourage similar/dissimilar data approaching/away and to up-weight/down-weight the dissimilar data inside/outside the ball. Extensive experiments demonstrate that the proposed EHDP obtains superior results on three benchmark datasets.