Reviewerly/exHarmony huggingface dataset:
Neural Network Based Regression Model for Virtual Machines Migration Method Selection

Live virtual machines migration has been one of the main strategies in cloud systems management to efficiently utilize data centers resources, reduce power consumption and unutilized resources in data centers, as well as providing the least interruption to customers in the events of migrating virtual machines or their data between different hosts in the same or different data centers. Many migration methods, with different characteristics, have been proposed to migrate virtual machines. Pre-copy and Post-copy are the main classical migration methods that transfer VMs and their memory between different hosts. The main VMs migration performance metrics include the total migration time, downtime, and amount of transmitted data. Machine learning algorithms have been widely used to make systems intelligent. Neural Network is one of the key machine learning algorithms used for classification and regression. In this paper, we propose neural network-based adaptive models that predict the key performance metrics of VM migration for Pre-copy and Post-copy methods, and for different application workload types running over the virtual machine. Based on the prediction, one of the two migration methods can be selected to migrate a specific virtual machine.
Advantage and Misuse of Vision Augmentation – Exploring User Perceptions and Attitudes using a Zoom Prototype

Consequences that deter adoption, such as asymmetrical encounters between wearers and bystanders, need to be explored in order to make Ubiquitous Augmented Reality (UAR) acceptable. In our work we outline how social perception is based on a Head Mounted Displays (HMD) capability, appearance, and the role of the wearer. We fixed the device capability to zooming in AR and explored the privacy implications in 12 interviews through a prototype with the mocked ability to ”super humanly” zoom in on targets. Next, we used the resulting themes to survey 100 participants to deeper explore augmented zoom while we permutate on the device appearance housed in three form-factors: contact lenses, glasses, and helmet and role of wearer based on level of involvement in an abstracted scenario transpiring in a public space. Our results showed that explicit visibility of an AR system provides social translucence as it is rated least likely to cause misuse but also perceived as least likely to have an advantage.
Development of Solving the ECDLP

The study and analysis of the Elliptic curve Discrete Logarithm Problem (ECDLP) is very important, because one of the strongest cryptosystems is based on this problem, which is elliptic curve cryptosystem. This aspect has led to make a clear change in cryptography and open new research field for treatment with special group that is the group of elliptic curve points.This paper proposes a method for solving ECDLP, depending on an important theorem in abstract algebra. This method works in special groups, which are the cyclic group of elliptic curve points. As a result, we found a criterion that gives additional strength to the elliptic curve cryptosystems, which is the choice of the private key.
Research on software defect prediction technology based on deep learning

To solve the problem that the traditional feature selection methods, such as PCA and LDA, are unable to get the nonlinear relationship between characteristics. Deep belief networks cannot eliminate the noise and missing value, which affect the accuracy of the software defect prediction (SDP) model. Not only the methods of feature selection, but data preprocessing and learning algorithm can also affect the precision of the defect prediction model. This thesis uses deep belief networks and SVM to construct an SDP model (DBN-SVM) to increase prediction precision. Using denoising autoencoders and SVM to build an SDP model (DA - SVM), compared with the DBN - SVM, DA - SVM model not only improves the prediction precision, but also enhance the robustness of the model. The thesis also proposes an SDP model framework which includes data preprocessing, feature selection and learning algorithm.
VocEditor - An Integrated Environment to Visually Edit, Validate and Versioning RDF Vocabularies

In this paper, we present an early stage of an integrated environment allowing the user to edit an RDF vocabulary visualizing the relations among classes, properties, and SHACL shapes. It also includes automatic generation of SHACL shapes based on the SHACL classes and properties as well as the validation of the instances according to the SHACL constraints. The proposed environment is integrated with a versioned triple store which allows to have multiple versions of the vocabulary while tracking provenance information. To the best of our knowledge, this is the first integrated environment for visualizing, Validating, and Versioning RDF vocabularies.
The Need For Simultaneous Tracking And Recognition In Drone Surveillance Radar

This paper focuses on the need for good target recognition in order to provide effective tracking and on good tracking to provide effective recognition in the application of radar to drone surveillance. The joint function, merging drone tracking and recognition, referred to as Simultaneous Tracking and Recognition (STaR) of drones is discussed and the benefits are presented through examples. This includes real measurements from Aveillant's Gamekeeper Radar.
The Tensor Rank of 5x5 Matrices Multiplication is Bounded by 98 andIts Border Rank by 89

We present a non-commutative algorithm for the product of 3 x 5 by 5 x 5 matrices using 58 multiplications. This algorithm allows to construct a non-commutative algorithm for multiplying 5 x 5 (resp. 10 x 10, 15 x 15) matrices using 98 (resp. 686, 2088) multiplications. Furthermore, we describe an approximate algorithm that requires 89 multiplications and computes this product with an arbitrary small error.
Rating Prediction of Product Reviews of Bangla Language using Machine Learning Algorithms

The only way to provide feedback about a product is through reviews. When a new shopper proceeds to an online shop to purchase a product but does not have adequate time to study the reviews provided by other shoppers to get an opinion about the product, the shopper determines whether to buy the product or not on the number rating. Through reviews, shoppers can acquaint everyone about the product&#x0027;s quality and the manufacturer can enhance their products and business by interpreting that review. However, manufacturers demand a number review more than a text review for business analysis. This paper represents a machine learning based model for predicting the number rating from written text for Bangla product review. This study performs on a dataset collected manually from Daraz.com.bd, a Bangladeshi leading e-commerce shop. We have implemented Support Vector Machine (SVM), Random Forest, XGBoost, and Logistic Regression with Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer on our collected dataset and record all the performance metrics like accuracy, precision, recall and f1-score. From these above four algorithms, SVM showed more outstanding results than others in terms of performance metrics. SVM achieved 90&#x0025; accuracy on the applied dataset. The other SVM performance metrics are 0.90, 0.92, and 0.91 for precision, recall and f1-score respectively.
Look to the future now, it’s only just begun. The changing role of libraries during and after COVID-19

There was a time when libraries were at the global digital forefront, with initiatives like the United Kingdom's Electronic Libraries Programme (eLib—Rusbridge, 1995) and the joint Digital Libraries Initiative (Griffin, 1998) in America. However, as the Internet and digital technologies have become more and more embedded in our daily lives it has sometimes felt as though libraries and perhaps even librarians may be an endangered species—Blockbuster in a Netflix age. The COVID-19 era has accelerated and enforced that digital shift which was taking place already, forcing many libraries to move to new operating models—from pure digital to "click and collect" with many other permutations in between. In this chapter I will look at how libraries can play a catalytic role in helping us to bounce back from the pandemic, and how the very nature of what we think of as a library is changing—perhaps for good.
Using mobile phone applications to teach and learn organic chemistry

In the last decade mobile phone applications (apps)—from tools to games—have become ubiquitous in society and have likewise entered the organic chemistry classroom. This chapter introduces a variety of apps, including some designed specifically for the teaching of chemistry. After surveying the capabilities and functions of each application, evidence-based pedagogical uses are suggested. The apps are then contextualized within the organic chemistry curriculum, proposing ideas for implementation as tools for teaching, learning, reviewing, and assessment.
(Genetically) Improving Novelty in Procedural Story Generation

Procedural story generation (PCG) tailors a unique narrative experience for a player and can be accomplished via multiple techniques, from matching storylets to grammar-based generation. There exists a rich opportunity for evolutionary algorithms to be applied to this domain for intelligently constructing game narratives. This paper describes a conceptual procedure for applying genetic improvement to a grammar-driven procedural narrative within the context of a browser-based game.
Comparison and analysis of two measurement systems of horizontal atmospheric extinction of solar radiation

Direct normal irradiance is the component of solar radiation exploited by concentrating solar power plants. However, solar radiation reflected by heliostats can be partially extinguished on its way to the receivers in solar power tower plants. These energy losses are accentuated with the distance travelled by the light. The growing development of solar power tower plants has highlighted the interest in determining this phenomenon. This paper presents the results of a six-month intercomparison campaign of the two most promising extinction measuring systems. The system developed at Plataforma Solar de Almería (SE Spain) is based on a direct measurement methodology by using two digital cameras. The second system indirectly estimates the extinction from forward-scatter meter (FSM) measurements. Two FSMs were used in this study. Both FSMs provided the same Meteorological Optical Range (MOR) trends, with differences into declared error margins. A selected number of days corresponding to medium to high aerosol loads have been used to assess the performance of both types of systems. Results show that, in these days, the atmospheric extinction coefficient values derived from the two-camera system were on average 2.1 times higher than those determined with the FSMs. Semi-empirical and empirical corrections for the aerosol spectral characteristics and for the content of water vapour in the atmosphere have been applied to the FSM measurements so that both systems provide similar values of horizontal attenuation.
Content dependent spatial resolution selection for MPEG DASH segmentation

The need for standardization emerged with the fast development of video streaming technologies. MPEG Dynamic Adaptive Streaming over Hypertext Transfer Protocol (MPEG DASH) is a standard for adaptive video streaming. Applications developed in compliance with MPEG DASH ensure smooth playback to end-users while being hardware agnostic, cost-efficient, and easy to maintain. The client side of the MPEG DASH system houses the adaptation logic which has been investigated intensively over the last several years. The server side stores representation sets of video sequences as well as accompanying Media Presentation Description and initialization file. Compared to the client side, the server side has not been yet sufficiently explored. Although there are some papers investigating the parameter selection for representation sets, most solutions are proprietary, do not take into account the content, and do not give the comprehensive methodology and its notation that can be applied to other codecs and encoder parameters. Also, most of the currently available solutions are costly and require large computational power. This article provides a comprehensive analysis and methodology for selecting the most appropriate spatial resolution switching points, which define the bitrates where switching to the higher spatial resolution is optimal. Furthermore, it presents a spatial resolution selection model for the estimation of switching points, which considers the spatial and temporal activity of video sequences. With the developed model, the optimal representation selection process is simplified and the segmentation process in MPEG DASH is improved. The methodology proposed for the development of this content-dependent spatial resolution selection model can be used for encoders and coding parameters not covered by the presented research. Quality of the video sequences segmentation using the proposed switching points were tested using two different test cases for network conditions and two different adaptation algorithms: Basic Adaptation algorithm and Segment Aware Rate Adaptation algorithm. The streamed videos that used the proposed segmentation have achieved better SSIM (Structural Similarity Index) results in 83.33% of cases in comparison to the videos that used segmentation presented in the relevant literature.
Representation Learning With Dual Autoencoder for Multi-Label Classification

Multi-label classification aims to deal with the problem that an object may be associated with one or more labels, which is a more difficult task due to the complex nature of multi-label data.The crucial problem of multi-label classification is the more robust and higher-level feature representation learning, which can reduce non-helpful feature attributes from the input space prior to training.In recent years, deep learning methods based on autoencoders have achieved excellent performance in multi-label classification for the advantages of powerful representations learning ability and fast convergence speed.However, most existing autoencoder-based methods only rely on the single autoencoder model, which pose challenges for multi-label feature representations learning and fail to measure similarities between data spaces.To address this problem, in this paper, we propose a novel representation learning method with dual autoencoder for multi-label classification.Compared to the existing autoencoder-based methods, our proposed method can capture different characteristics and more abstract features from data by the serially connection of two different types of autoencoders.More specifically, firstly, the algorithm of Reconstruction Independent Component Analysis (RICA) in sparse autoencoder is trained on patches on all training and test dataset for robust global feature representations learning.Secondly, with the output of RICA, stacked autoencoder with manifold regularization (SAMR) is introduced to ameliorate the quality of multi-label features learning.Comprehensive experiments on several real-world data sets demonstrate the effectiveness of our proposed approach compared with several competing state-of-the-art methods.
f-Slip: an efficient privacy-preserving data publishing framework for 1:M microdata with multiple sensitive attributes

Privacy-preserving data publishing is a process of releasing the anonymized dataset for various purposes of analysis and research. Earlier, researchers have dealt with datasets considering it would contain only one record for an individual [1:1 dataset], which is uncompromising in various applications. Later, many researchers concentrate on the dataset, where an individual has multiple records [1:M dataset]. In the paper, a model f-slip was proposed that can address the various attacks such as Background Knowledge (bk) attack, Multiple Sensitive attribute correlation attack (MSAcorr), Quasi-identifier correlation attack(QIcorr), Non-membership correlation attack(NMcorr) and Membership correlation attack(Mcorr) in 1:M dataset and the solutions for the attacks. In f-slip, the anatomization was performed to divide the raw table into two sub-tables (1) quasi-identifier and (2) sensitive attributes. The correlation of sensitive attributes is computed to anonymize the sensitive attributes without breaking the linking relationship. Further, the quasi-identifier table was divided and k-anonymity was implemented on it. An efficient anonymization technique, frequency-slicing, was also developed to anonymize the sensitive attributes. The novel approach in the f-slip model is the slicing of records according to the frequency of occurrences of sensitive attribute values in each sub-table. The workload experiment proves that the f-slip model is consistent as the number of records increases. Extensive experiments were performed on a real-world dataset Informs and proved that the f-slip model outstrips the state-of-the-art techniques in terms of utility loss, efficiency and also acquires an optimal balance between privacy and utility.
Interactive Survey Design Using Pidgin and GIFS

An online survey is a growing phenomenon in the digital age because it is time-efficient and attracted little or no cost, contrary to traditional survey method which is time-consuming and expensive. Despite the awareness and importance of online questionnaire, most survey design often pay less attention to elements of fun, interactivity, and persuasiveness of the survey as well as the impact of the survey design on respondents. This paper therefore addressed the effectiveness of using local and informal languages such as Pidgin English and image-based fun elements such as animated Graphics Interchange Format (GIF)s on research survey design. The methodology includes pilot study, sampling technique, survey questionnaire translation, and online survey administration. Two different sampling techniques namely purposive and snowballing were used during the administration of questionnaires, with a total of 1,533 students responding to the survey. Descriptive and Inferential statistical analysis were also carried out. Results from our descriptive statistical analysis with an approximate mean of 4 on a scale of 1-5 shows that perception of user's interactivity of the use of GIFS and pidgin for the survey design is high. This implies that the use of GIFs and Pidgin for survey design is perceived as highly interactive. A further analysis using the ordinal logistic regression model and the traditional threshold value of computed p-value ≤ 0.05, show that user's discipline influences their interactivity perception while other independent variables such as gender, degree of study and level of study does not influence interactivity perception. Lastly, from the evaluation of respondents’ comments, themes such as fun, persuasiveness and innovation emerged as benefits of using GIFS and informal/local language such as Pidgin English for the survey design.
Multi-Domain Conditional Image Translation: Translating Driving Datasets from Clear-Weather to Adverse Conditions

Vision systems for fully autonomous navigation must perform well even in unstructured and degraded scenarios. In most driving datasets today, there is a bias toward clear-weather conditions as compared with extreme-weather owing to the difficulty in capturing and annotating large-scale image datasets degraded by adverse weather. While there has been extensive research on techniques such as deraining, dehazing and on tasks such as segmentation and domain adaptation, there has been minimal attention toward methods to effectively translate clear-weather driving datasets to extreme-weather domains. To address this, we present a method that builds on recent advances in Generative Networks and Self-Supervised Learning to perform conditional multi-domain image translation. We evaluate our method on the semantic scene understanding task and demonstrate quantitatively superior translation results from clear-weather conditions to adverse-weather shifted domains such as Rain, Night and Fog conditions. From our experiments, we show improved domain invariant content disentanglement, and segmentation methods trained with datasets translated using the proposed method have improved performance over single and multi-domain image translation baselines on real-world adverse weather data.
Improved Artifact Detection in Endoscopy Imaging Through Profile Pruning

Endoscopy is a highly operator dependent procedure. During any endoscopic surveillance of hollow organs, the presence of several imaging artifacts such as blur, specularity, floating debris and pixel saturation is inevitable. Artifacts affect the quality of diagnosis and treatment as they can obscure features relevant for assessing inflammation and morphological changes that are characteristic to precursors of cancer. In addition, they affect any automated analysis. It is therefore desired to detect and localise areas that are corrupted by artifacts such that these frames can either be discarded or the presence of these features can be taken into account during diagnosis. Such an approach can largely minimise the amount of false detection rates. In this work, we present a novel bounding box pruning approach that can effectively improve artifact detection and provide high localisation scores of diverse artifact classes. To this end, we train an EfficientDet architecture by minimising a focal loss, and compute the Bhattacharya distance between probability density of the pre-computed instance specific mean profiles of 7 artifact categories with that of the predicted bounding box profiles. Our results show that this novel approach is able to improve commonly used metrics such as mean average precision and intersection-over-union, by a large margin.
Towards Usable Attribute Scaling for Latency Compensation in Cloud-based Games

Cloud-based games have advantages in convenience over traditional computer games, but have the disadvantage of added latency from the thin client to the cloud-based server and back. This added latency has been shown to decrease player performance. New latency compensation techniques can help by scaling game attributes to make the game easier, exactly counteracting the difficulty added by the latency. We conduct a user study measuring attribute scaling for two games -- a first-person shooter and a rhythm game -- each having a different attribute scaling method: spatial and temporal. Data from the study shows a decrease in accuracy with an increase in latency and game difficulty, and an increase in accuracy with an increase in attribute scaling. More importantly, we derive a model from the data whereby a pre-determined accuracy can be chosen -- say, by the game designer -- and the model then outputs the scaling factor to meet that desired target accuracy.
Pixel-level mapping method in high dynamic range imaging system based on DMD modulation

In order to build pixel-to-pixel correspondence between the CCD camera and digital micromirror device (DMD) in high dynamic range (HDR) imaging system, we proposed an absolute phase mapping method based on the first partial derivatives of phase-shifted composite fringes in this paper. Compared with the conventional phase mapping method, the needed fringe patterns of our method are reduced by one time, which improves the mapping efficiency. Meanwhile, experimental results show the proposed method has an accuracy improvement over the existing methods. The root mean square error is within 0.11 pixels in both the horizontal and vertical directions, reaching the sub-pixel mapping accuracy and indicating the superiority of our method. Finally, we combined the proposed mapping method with the imaging system and achieved high dynamic range imaging by photographing the surface of highlighted object, which verified the validity and practicability of our method.