Reviewerly/exHarmony huggingface dataset:
Capacity of Desktop Clouds for Running HPC Applications: A Revisited Analysis

Desktop Clouds, such as UnaCloud and CernVM, run scientific applications on desktop computers installed in computer laboratories and offices. These applications run on virtual machines using the idle capacities of that desktops and networks. While some universities use desktop clouds to run bag of tasks (BoT), we have used these platforms to run High Performance Computing (HPC) applications that require coordination among the nodes and are sensible to communication delays. There, although a virtual machine with 4 virtual cores on computers released in 2012 may achieve more than 40 GFLOPs, the capacity of clusters with tens or hundreds of virtual machines cannot be determined by multiplying this value. In a previous work, we studied the capacity of desktop clouds for running applications non-intensive on communications on our computer labs. This paper presents a revisited analysis, focused on the capacity of desktop clouds for running HPC applications. The resulting information can be used for researchers deciding on investing on HPC clusters or using existing computer labs for running their applications, and those interested on designing desktop clusters that may achieve the maximum possible capacity.
Weakly Aggregative Modal Logic: Characterization and Interpolation

Weakly Aggregative Modal Logic ( $$\textsf {WAML}$$ ) is a collection of disguised polyadic modal logics with n-ary modalities whose arguments are all the same. $$\textsf {WAML}$$ has some interesting applications on epistemic logic and logic of games, so we study some basic model theoretical aspects of $$\textsf {WAML}$$ in this paper. Specifically, we give a van Benthem-Rosen characterization theorem of $$\textsf {WAML}$$ based on an intuitive notion of bisimulation and show that each basic $$\textsf {WAML}$$ system $$\mathbb {K}_n$$ lacks Craig Interpolation.
Incorporating 3D Information Into Visual Question Answering

We propose a tactic of advancing Visual Question Answering (VQA) task by incorporating 3D information via multi-view images. Conventional VQA approaches, which reply an answer in words against a linguistic question about a given RGB image, have less ability to recognize geometrical information so that they tend to fail to count things or guess positional relationship. Moreover, they have no ability to determine blinded space, so it is not feasible to invent VQA function to robots which will work in highly-occluded real-world environments. To achieve the situation, we introduce a new multi-view VQA dataset along with an approach that incorporating 3D scene information directly captured from multi-view images into VQA without using depth images or employing SLAM. Our proposed approach achieves strong performance with an overall accuracy of 95.4% on the challenging multi-view VQA dataset setup, which contains relatively severe occlusion. This work also demonstrates the promising aspects of bridging the gap between 3D vision and language.
Blackbox Secret Sharing Revisited: A Coding-Theoretic Approach with Application to Expansionless Near-Threshold Schemes

A blackbox secret sharing (BBSS) scheme works in exactly the same way for all finite Abelian groups G; it can be instantiated for any such group G and only black-box access to its group operations and to random group elements is required. A secret is a single group element and each of the n players' shares is a vector of such elements. Share-computation and secret-reconstruction is by integer linear combinations. These do not depend on G, and neither do the privacy and reconstruction parameters t, r. This classical, fundamental primitive was introduced by Desmedt and Frankel (CRYPTO 1989) in their context of "threshold cryptography." The expansion factor is the total number of group elements in a full sharing divided by n. For threshold BBSS with t-privacy ($$1\le t \le n-1$$), $$t+1$$-reconstruction and arbitrary n, constructions with minimal expansion $$O(\log n)$$ exist (CRYPTO 2002, 2005). These results are firmly rooted in number theory; each makes (different) judicious choices of orders in number fields admitting a vector of elements of very large length (in the number field degree) whose corresponding Vandermonde-determinant is sufficiently controlled so as to enable BBSS by a suitable adaptation of Shamir's scheme. Alternative approaches generally lead to very large expansion. The state of the art of BBSS has not changed for the last 17 years. Our contributions are two-fold. (1) We introduce a novel, nontrivial, effective construction of BBSS based on coding theory instead of number theory. For threshold-BBSS we also achieve minimal expansion factor $$O(\log n)$$. (2) Our method is more versatile. Namely, we show, for the first time, BBSS that is near-threshold, i.e., $$r-t$$ is an arbitrarily small constant fraction of n, and that has expansion factor O(1), i.e., individual share-vectors of constant length ("asymptotically expansionless"). Threshold can be concentrated essentially freely across full range. We also show expansion is minimal for near-threshold and that such BBSS cannot be attained by previous methods. Our general construction is based on a well-known mathematical principle, the local-global principle. More precisely, we first construct BBSS over local rings through either Reed-Solomon or algebraic geometry codes. We then "glue" these schemes together in a dedicated manner to obtain a global secret sharing scheme, i.e., defined over the integers, which, as we finally prove using novel insights, has the desired BBSS properties. Though our main purpose here is advancing BBSS for its own sake, we also briefly address possible protocol applications.
Organizational formalization and employee information security behavioral intentions based on an extended TPB model

Information security is a worldwide problem, and the psychological and behavioral factors of users are some of the most important reasons for the occurrence of information security incidents. Although many previous studies have discussed the influencing factors of information security behaviors, few have considered the impacts of organizational structures. In this study, the formation mechanism of information security behavioral intention was studied by integrating the Theory of Planned Behavior (TPB) and organizational formalization. Data analysis was performed using the Structural Equation Model (SEM) analysis, based on a survey of 261 company employees. The empirical results showed that organizational formalization had significant effect on cognitive processes theorized by TPB, and TPB was still applicable to predict and explain the information security behavioral intention. This study suggests that more formalized rules, procedures, and communications should be designed in order to increase employees' information security behavioral intentions.
Combinational Operation for Display Device using Acceleration Sensors on Fingertips

This paper describes a new method of combining multiple acceleration sensors to operate a display device. This paper proposes a method of detecting motions of two acceleration sensors attached to the fingertips to operate a display device screen. Experiments have shown that by synchronizing independent motions of the multiple acceleration sensors, the display screen can be operated accordingly. The experiments' results have verified that the proposed method is effective.
phasespace: n-body phase space generation in Python

Simulated particle decays are common in experimental particle physics.They are used to study a wide variety of aspects of a physics analysis, such as signal response, detector effects, and the efficiency of selection requirements, in a controlled manner.While it is possible to encode complex physics dynamics into these simulations at the cost of increased complexity and larger computer requirements, in many cases it is enough to generate these simulated samples as if only kinematic physics occurred, i.e., in an isotropic way.This type of generation, called "phase space generation", is very fast and offers simple and predictable patterns, making it an attractive first step in many physics analyses.
Language Identification for South African Bantu Languages Using Rank Order Statistics

Language identification is an important pre-process in many data management and information retrieval and transformation systems. However, Bantu languages are known to be difficult to identify because of lack of data and language similarity. This paper investigates the performance of n-gram counting using rank orders in order to discriminate among the different Bantu languages spoken in South Africa, using varying test and training data sizes. The highest average accuracy obtained was 99.3% with a testing size of 495 characters and training size of 600000 characters. The lowest average accuracy obtained was 78.72% when the testing size was 15 characters and learning size was 200000 characters.
Holistic User eXperience in Mobile Augmented Reality Using User eXperience Measurement Index

User eXperience (UX) evaluation in the field of Mobile Augmented Reality (MAR) is a challenging task, which requires the application of many heterogeneous methods, producing a variety of raw signals and subjective data. This multi-method approach is essential for capturing the holistic UX of any product, service or system. In order to convert this data into information and subsequently knowledge, a comprehensive and scalable system is required which can not only quantify the individual UX metrics but also produce a concise result, which is interpretable by anyone. We call this result, the User Experience Measurement Index, and in this paper we present the results of adopting the mixed method UX evaluation approach for evaluating a prototype MAR application using various methods and sensors, applied before, during, and after its usage. Additionally, we present the methodology and results for calculating the UXMI.
Data-intensive Workflow Execution using Distributed Compute Resources

Cloud computing has become a necessary utility for scientific and technical applications. Many diverse web services are published and subscribed using cloud data centers. It has become fairly easy to use services from Cloud Service Providers (CSPs) for computation and data processing. However, even with all their benefits, commercial cloud resources are not economical when large data processing is required. Hence, educators and researchers need guidance to use commercial cloud resources to run large data processing workflow applications within a budget. In this paper, we propose a framework to help users to leverage distributed compute resources to execute data-intensive application workflows, under budget constraints. We demonstrate how our framework can be used by users who may have access to small-scale compute resources in-house, to seamlessly interoperate with public cloud resources.
Light Field Compression using Eigen Textures

Light fields are becoming an increasingly popular method of digital content production for visual effects and virtual/augmented reality as they capture a view dependent representation enabling photo realistic rendering over a range of viewpoints. Light field video is generally captured using arrays of cameras resulting in tens to hundreds of images of a scene at each time instance. An open problem is how to efficiently represent the data preserving the view-dependent detail of the surface in such a way that is compact to store and efficient to render. In this paper we show that constructing an Eigen texture basis representation from the light field using an approximate 3D surface reconstruction as a geometric proxy provides a compact representation that maintains view-dependent realism. We demonstrate that the proposed method is able to reduce storage requirements by > 95% while maintaining the visual quality of the captured data. An efficient view-dependent rendering technique is also proposed which is performed in eigen space allowing smooth continuous viewpoint interpolation through the light field.
An outlier detection algorithm based on an integrated outlier factor

Outlier detection is an important task in data mining. In this paper, a novel outlier detection algorithm is proposed, which integrates the local density with the global distance seamlessly. In the proposed method, an integrated outlier factor is used to measure the detecting accuracy. A comprehens ive experimental study on both synthetic and real-life datasets shows that the proposed method is more effective than some typical outlier detection methods, including Relative Density-based Outlier Score (RDOS), INFLuenced Outlierness (INFLO), Local Outlier Factor (LOF) and Local Distance-based Outlier detection Factor (LDOF).
Learning Depth from Endoscopic Images

We propose an unsupervised approach to predict depth maps from images captured by a wireless endoscopic capsule. Recent advances in deep learning have shown that accurate depth maps can be predicted from a single image, where the deep network is trained via unsupervised or self-supervised learning by using monocular video sequences or stereo image pairs. However, directly applying these techniques to endoscopic images does not yield satisfactory results owing to the inherent difficulties of the wireless capsule imaging such as dim lighting and low-resolution of images, which are different from normal imaging conditions. For that reason, we exploit the environmental characteristics of endoscopic images - there is no external light source except ones attached to the capsule. Based on this condition, we propose the direct attenuation model-based depth map prediction scheme to guide depth prediction and to add meaningful cues to the loss function. We experimentally verify the proposed method with various endoscopic images.
SIR-Net: Scene-Independent End-to-End Trainable Visual Relocalizer

Visual relocalization addresses a problem of estimating the camera pose where a given query image is taken. In this paper, we focus on a scene-independent approach, which utilizes a database to maintain information of the scene. This approach thus enables adaptation to a new scene by switching a database. A standard procedure in this approach is to first retrieve images similar to the query from a database, then match 2D keypoints of the query image to 3D points visible in the retrieved images, and finally solve the perspective-n-point problem to estimate the pose. Recently, convolutional neural networks (CNNs) have been used for the retrieval and matching tasks in this process, and demonstrated promising accuracy and robustness. These CNNs are separately trained for retrieval and matching tasks, which could result in suboptimal relocalization accuracy. In this paper, we propose the first CNN-based relocalization framework, which is both scene-independent and end-to-end trainable. This framework jointly optimizes retrieval and matching tasks to maximize the relocalization accuracy by backpropagating the relocalization errors to both tasks. We demonstrate the effectiveness of the end-to-end training, robustness against new scenes, and the state-of-the-art accuracy on indoor and outdoor datasets, with computation done in real-time.
Application of Image Classification for Fine-Grained Nudity Detection

Many online social platforms need to use an image content filtering solution to detect nudity automatically. Existing solutions only focus on binary classification models to detect explicit nudity or pornography, which is not enough to distinguish between a great number of various racy outfits that people wear (swimsuit, summer outfit, etc.) that might be considered inappropriate according to user’s own preferences. This paper addresses the problem by proposing a robust technology which detects fine-grained human body parts (chest, back, abdomen, etc.) and assigns a level of nudity for each part using a multi-label image classification model. Since existing datasets were not sufficient for the given problem, we created a new dataset with a total of 37.872 images and 4.879.517 annotated labels that describe human body parts and nudity level (6 labels for each body part). We fine-tuned multiple state-of-the-art convolutional neural network models (VGG, ResNet, MobileNet) on our dataset for multi-label image classification. Our solution has a total accuracy of 98.1% on the test dataset and a low false positive rate of 0.8%.
Text Summarization in Indian Languages: A Critical Review

Everyday the internet is flooded with huge volume of data. This huge volume of text data is undeniably a valuable source of knowledge but at the same time it is not feasible for web users to go through all the information or data available due to time constraint. Thus, keeping in view the convenience of the web users, text summarization techniques have been adopted to condense and provide a compact version of the given original text data while ensuring its relevance. A good number of methodologies of text summarization have already been proposed and implemented in English language but in terms of Indian languages, researchers confront challenges. To say, text summarization is still at an early stage for Indian languages. This paper emphasizes on these challenges that researchers face and remedies are proposed while doing text summarization of Indian languages to derive better accurate results. It also describes the various methodologies and systems that could prove to be helpful in summarizing the varied Indian languages.
SENTIMENT ANALYSIS OF ‘INDONESIAN NO DATING CAMPAIGNS’ ON TWITTER USING NAÏVE BAYES ALGORITHM

The modern world has witnessed the widespread emergence of online social media. Many people use this kind of technology to share their view about anything. As consequence, it is easy to know public opinions on certain issue by utilizing social media data. One of trending issue in Indonesian Twitter user is about “Indonesian No Dating Campaign. It is interesting to know effectiveness of that campaign by analyzing public sentiment. In order to analyze the campaign, this study employ sentiment analysis using Naïve Bayes algorithm. This algorithm was chosen by considering its accuracy in several related studies. This research starts with data collection process, by crawling twitter's data that related to the campaign. The collected data will go through the data preprocessing, data classification based on its sentiment using Naïve Bayes. As the result, Naïve Bayes algorithm successfully classifies the twitter's data into 56% positive sentiment, negative sentiment 32% and neutral sentiment 12%. The accuracy of this classification is 74.77%.
On the Right Path: A Modal Logic for Supervised Learning

Formal learning theory formalizes the process of inferring a general result from examples, as in the case of inferring grammars from sentences when learning a language. Although empirical evidence suggests that children can learn a language without responding to the correction of linguistic mistakes, the importance of Teacher in many other paradigms is significant. Instead of focusing only on learner(s), this work develops a general framework—the supervised learning game (SLG)—to investigate the interaction between Teacher and Learner. In particular, our proposal highlights several interesting features of the agents: on the one hand, Learner may make mistakes in the learning process, and she may also ignore the potential relation between different hypotheses; on the other hand, Teacher is able to correct Learner's mistakes, eliminate potential mistakes and point out the facts ignored by Learner. To reason about strategies in this game, we develop a modal logic of supervised learning (SLL). Broadly, this work takes a small step towards studying the interaction between graph games, logics and formal learning theory.
Cavity-assisted squeezing and entanglement: non-adiabatic effects and optimal cavity-atomic ensemble matching

We investigate theoretically quantum entanglement of light with the collective spin polarization of a cold atomic ensemble in cavity-assisted Raman schemes. Previous works concentrated mostly on the bad cavity limit where the signals are much longer than the cavity field lifetime. In view of atomic relaxation and other imperfections, there may arise a need to speed-up the light-atoms interface operation. By increasing the cavity field lifetime, one can achieve better light–matter coupling and entanglement. In our work, we consider the non-adiabatic effects that become important beyond the bad cavity limit in both low-photon and continuous variables regime. We find classical control field time profiles that allow one to retrieve from the cavity an output quantized signal of a predefined time shape and duration, which is optimal for the homodyne detection, optical mixing or further manipulation. This is done for a wide range of the signal duration as compared to the cavity field lifetime. We discuss an optimal cavity–atomic ensemble matching in terms of the cavity field lifetime which allows one to apply less intense control field and to minimize a variety of nonlinear effects, such as AC light shifts, four-wave mixing, etc, which may be potentially harmful to an experiment.
C3-Sex: a Chatbot to Chase Cyber Perverts

The following topics are dealt with: learning (artificial intelligence); Internet of Things; cloud computing; data privacy; security of data; Internet; mobile computing; convolutional neural nets; social networking (online); feature extraction.
Facial Recognition System Employing Transform Implementations of Sparse Representation Method

A new discriminative sparse representation approach for robust face recognition via l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization (SRFR) was recently published. In this paper, a face recognition system implementation employing coefficients from two non-orthogonal transform domains, namely, Two-Dimensional Discrete Wavelet Transform (2D DWT) and 2D Discrete Cosine Transform (2D DCT), is presented. The use of these coefficients in this Mixed Wavelet Cosine Sparse Representation for Face Recognition (MWCSRFR) system as features shown to appreciably lower the computational complexity and the final storage size while maintaining the high recognition rate of the SRFR. Extensive simulations were carried out on five face databases, namely, ORL, YALE, FERET, Cropped AR, and Georgia Tech. The improved properties of the MWCSRFR are proved as shown in the given sample results.
Experimenting with AWS Direct Connect using Chameleon, ExoGENI, and Internet2 Cloud Connect

Many scientific research communities and institutions are adopting the cloud as a primary platform to support their computing needs. Rapid adoption of the cloud for scientific computing is a result of the simplicity with which an individual researcher can obtain large amounts of customized compute and storage resources. At the same time, most public cloud providers have rolled out many advanced networking services. Many of these services, like their compute services, are simple to access by any cloud user (e.g. routing between regions and private networking spaces within a cloud). However, it is not possible for most researchers to access expensive low-level, externally facing cloud network services without complicated support by campus IT staff, as well as national and regional network providers. This paper describes how to use Chameleon, ExoGENI, and Internet2's Cloud Connect service to deploy research experiments that use AWS Direct Connect without requiring a privately owned Direct Connect endpoint or support from local campus IT staff.