Reviewerly/exHarmony huggingface dataset:
Mobile Cloud Support for Semantic-Enriched Speech Recognition in Social Care

Nowadays, most users carry high computing power mobile devices where speech recognition is certainly one of the main technologies available in every modern smartphone, although battery draining and application performance (resource shortage) have a big impact on the experienced quality. Shifting applications and services to the cloud may help to improve mobile user satisfaction as demonstrated by several ongoing efforts in the mobile cloud area. However, the quality of speech recognition is still not sufficient in many complex cases to replace the common hand written text, especially when prompt reaction to short-term provisioning requests is required. To address the new scenario, this paper proposes a mobile cloud infrastructure to support the extraction of semantics information from speech recognition in the Social Care domain, where carers have to speak about their patients conditions in order to have reliable notes used afterward to plan the best support. We present not only an architecture proposal, but also a real prototype that we have deployed and thoroughly assessed with different queries, accents, and in presence of load peaks, in our experimental mobile cloud Platform as a Service (PaaS) testbed based on Cloud Foundry.
Distributed, end-to-end verifiable, and privacy-preserving internet voting systems

We present the D-DEMOS suite of distributed, privacy-preserving, and end-to-end verifiable e-voting systems; one completely asynchronous and one with minimal timing assumptions but better performance. Their distributed voting operation is human verifiable; a voter can vote over the web, using an unsafe web client stack, without sacrificing her privacy, and get recorded-as-cast assurance. Additionally, a voter can outsource election auditing to third parties, still without sacrificing privacy. We provide a model and security analysis of the systems, implement prototypes of the complete systems, measure their performance experimentally, demonstrate their ability to handle large-scale elections, and demonstrate the performance trade-offs between the two versions.
Tectonic and basin maps of the world

Geological maps and regional studies integrate a wide variety of geological, geophysical, and geochemical information. Today, this calls for astute use of computerized information systems. In the final analysis, most geological maps remain interpretations of observations that involve numerous assumptions. For this chapter, a series of global maps have been compiled to illustrate a limited choice of regionally relevant themes and show their interrelationships. We note here the importance and high quality of the work of the Commission for the Geological Map of the World (www.cgmw.net or [email protected]). For an understanding of the regional context, their maps are an outstanding, regularly updated source of information.
<i>Adia</i>: Achieving High Link Utilization with Coflow-Aware Scheduling in Data Center Networks

Link utilization has received extensive attention since data centers become the most pervasive platform for data-parallel applications. A specific job of such applications involves communication among multiple machines. The recently proposed coflow abstraction depicts such communication through a group of parallel flows, and captures application performance through corresponding communication requirements. Existing techniques to improve link utilization, however, either restrict themselves to achieving work conservation, or merely focus on flow-level metrics and ignore coflow-level performance. In this paper, we address the coflow-aware scheduling problem with the objective of maximizing link utilization. Through theoretic analyses, we formulate the coflow-aware scheduling problem as a NP-hard open shop scheduling problem with heterogeneous concurrency. We design Adia, a hierarchical scheduling framework to conduct both inter- and intra- link scheduling. The design of Adia leverages priority-based scheduling while guarantees work-conserving and starvation-free bandwidth allocation at the same time. We also prove Adia's algorithm is two-approximate in terms of link utilization. Extensive simulation results on ns3 further show that Adia outperforms both per-flow mechanisms coflow schemes in terms of link utilization, and achieves similar coflow performance in comparison with the state-of-art coflow scheduling schemes.
Smart VM co-scheduling with the precise prediction of performance characteristics

Traditional virtualization systems cannot effectively isolate the shared micro-architectural resources among VMs. Different types of CPU and memory-intensive VMs contending for these shared resources will lead to different levels of performance degradation, which decreases the system efficiency and Quality of Service (QoS) in the cloud. To address these problems, we design and implement a smart VM co-scheduling system with precise prediction of performance characteristics. First, we identify the performance interference factors and design synthetic micro-benchmarks. By co-running these micro-benchmarks with VMs, we decouple two kinds of VM performance characteristics: VM contention sensitivity and contention intensity. Second, based on the characteristics, we build VM performance prediction model using machine learning techniques to quantify the precise levels of performance degradation. By co-running large numbers of different VMs and collecting their performance scores, we train a robust performance prediction model. Finally, based on the prediction model, we design contention aware VM scheduling algorithms to improve system efficiency and guarantee the QoS of VMs in the cloud. Our experimental results show that the performance prediction model achieves high accuracy and the smart VM scheduling algorithms based on the prediction improves system efficiency and VM performance stability.
Probabilistic Modeling of Computing Demand for Service Level Agreement

Cloud computing applications must be allocated sufficient resources to comply with Service Level Agreements (SLAs). This paper considers data-driven probabilistic modeling of application resource demand for resource allocation. The modeling method is focused on peak demand and SLA violations and relies on a branch of statistics known as extreme value theory (EVT). Rigorous statistical validation of the proposed model shows that it generalizes better than the alternative. The paper presents a resource allocation algorithm using the model to ensure a given small SLA violation rate. For resource allocation in Yahoo data center, over 50 percent savings are demonstrated using the proposed approach.
CONFIRM – Clustering of noisy form images using robust matching

Identifying the type of a scanned form image greatly facilitates automated processing, including field segmentation and field recognition. Contrary to most prior work, we focus on unsupervised type identification, where the possible form types for a given collection are not known apriori. Our target domain is noisy collections of form images that contain structurally similar, yet objectively different, form types, which are challenging to differentiate in an unsupervised setting. This work presents a novel algorithm: CONFIRM (Clustering Of Noisy Form Images using Robust Matching), which simultaneously discovers the set of form types in a collection and assigns a type to each form. CONFIRM matches type-set text and rule lines between forms to create collection-specific features, which we show outperform the Bag of Visual Word (BoVW) approach employed by the current state-of-the-art in form image clustering. CONFIRM scales well to large document collections with a bootstrap clustering process, in which only a small subset of the data is clustered directly, and the rest of the data is assigned to clusters in linear time. We show that CONFIRM reduces cluster impurity on average by 44% compared to the state-of-the art on 5 collections of historical forms that contain structurally similar form types.
Multiple feature descriptors based model for individual identification in group photos

Detection of person in the crowd is most exigent facial recognition aspect. Faces in a group can comprise the criticality of face localization, partial occlusion, and facial overlapping and real time background scenes. In this paper, single and multiple persons recognition is provided for group pictures. This offered method at first localized the facial area of the individuals. These identified faces are divided in full or the partial view clusters by observing the oval map. While performing the recognition, separate algorithms will be applied to both facial groups. Full face recognition is here implied using LBP, Gabor and structured feature fusion based distance analysis method. These methods will be applied individually and jointly to identify the facial image. Partial face recognition is here implied using discriminative structural analysis. This structural formation is here obtained using structure points and base curve identification. Maximum structural points and curve ratio map is considered as the identified image. Group images are captured at home, office, classroom and roadside with multiple situations. The experimentation interpretation is measured disjointedly for full view and partial view images obtained from complex scenes and scenarios. The evaluation is here applied to perform the facial region identification and the group photo recognition. The facial region extraction is applied on eight datasets. These datasets are IMM, Caltech, CMU, Bau, FDDB, LFW, Pointing and Self Collected Dataset. The facial extraction is applied on complex background images with single and multiple faces. The comparative evaluation shows that the localization stage of proposed model has reduced the FAR and FRR. The complete recognition model is applied on real time family dataset, random web celebrity dataset associated with LFW and FDDB datasets. The evaluation is provided for recognition of front and partial face images in different scenarios. The comparative results of facial recognition are evaluated against PCA, ICA and PCA-LDA methods. The results conclude that the proposed methods have improved the accuracy for both full and partial faces.
EDOM: Improving energy efficiency of database operations on multicore servers

In this paper, we propose a toolkit called EDOM facilitating the evaluation and optimization of energy-efficient multicore-based database systems. The two core components in EDOM are a benchmarking toolkit and a multicore manager to improve energy efficiency of database systems running on multicore servers. We start this study by analyzing the energy efficiency of two popular database operations (i.e., cross join and outer join) processed on multicore processors. We describe the criteria and challenges of building an energy efficiency benchmark for databases on multicore servers. We build a benchmarking toolkit, which is comprised a configuration module, a test driver, and a power monitor. We develop a multicore manager to optimize the number of cores, thereby making good tradeoff between performance and energy efficiency in multicore database servers. At the heart of the multicore manager is a memory usage model that estimates memory utilization from queries and database characteristics. An appropriate number of cores is determined using the estimated memory usage to avert unnecessary memory swapping. We make use of the proposed benchmark toolkit to quantitatively evaluate the performance of our novel multicore manager. Our benchmarking tool of EDOM shows that the multicore and CPU utilizations have significant impacts on energy efficiency. More importantly, extensive experimental results show that our multicore manager in EDOM provides a simple yet powerful solution for improving energy efficiency of database applications running on multicore servers.
Topic profiling benchmarks in the linked open data cloud: Issues and lessons learned

Topical profiling of the datasets contained in the Linking Open Data (LOD) cloud has been of interest since such kind of data became available within the Web. Different automatic classification approaches have been proposed in the past, in order to overcome the manual task of assigning topics for e ach and every individual (new) dataset. Although the quality of those automated approaches is comparably sufficient, it has been shown, that in most cases a single topical label per dataset does not capture the topics described by the content of the dataset. Therefore, within the following study, we introduce a machine-learning based approach in order to assign a single topic, as well as multiple topics for one LOD dataset and evaluate the results. As part of this work, we present the first multi-topic classification benchmark for LOD cloud datasets, which is freely accessible. In addition, the article discusses the challenges and obstacles, which need to be addressed when building such a benchmark.
Smoothed Graphic User Interaction on Smartphones With Motion Prediction

The smoothness of human-smartphone interaction directly influences users experience and affects their purchase decisions. A commonly used method to improve user interaction of smartphones is to optimize the CPU scheduler. However, optimizing the CPU scheduler requires a modification of operating system. In addition, the improvement of the smoothness of human-smartphone interaction may be limited because the display subsystem is not optimized. Therefore, in this paper, we design a motion prediction queuing system, named MPQS, to improve the smoothness of human-smartphone interaction. For this, we use the information of vector, speed, movement, provided by the queuing mechanism of Android, to predict the movement of user-smartphone interaction. Based on the prediction, we then utilize available execution time between frames to perform image processing. We conducted a set of experiments on beagleboard-xM to evaluate the performance of MPQS. Our experiment results show that the proposed method can reduce the number of jank by up to 21.75%.
Decidability of Semi-Holonomic Prehensile Task and Motion Planning

In this paper, we define semi-holonomic controllability (SHC) and a general task and motion planning framework. We give a perturbation algorithm that can take a prehensile task and motion planning (PTAMP) domain and create a jointly-controllable-open (JC-open) variant with practically identical semantics. We then present a decomposition-based algorithm that computes the reachability set of a problem instance if a controllability criterion is met. Finally, by showing that JC-open domains satisfy the controllability criterion, we can conclude that PTAMP is decidable.
CloudFinder: A System for Processing Big Data Workloads on Volunteered Federated Clouds

The proliferation of private clouds that are often underutilized and the tremendous computational potential of these clouds when combined has recently brought forth the idea of volunteer cloud computing (VCC), a computing model where cloud owners contribute underutilized computing and/or storage resources on their clouds to support the execution of applications of other members in the community. This model is particularly suitable to solve big data scientific problems. Scientists in data-intensive scientific fields increasingly recognize that sharing volunteered resources from several clouds is a cost-effective alternative to solve many complex, data- and/or compute-intensive science problems. Despite the promise of the idea of VCC, it still remains at the vision stage at best. Challenges include the heterogeneity and autonomy of member clouds, access control and security, complex inter-cloud virtual machine scheduling, etc. In this paper, we present CloudFinder, a system that supports the efficient execution of big data workloads on volunteered federated clouds (VFCs). Our evaluation of the system indicates that VFCs are a promising cost-effective approach to enable big data science.
Conversion of Mersenne Twister to double-precision floating-point numbers

The 32-bit Mersenne Twister generator MT19937 is a widely used random number generator. To generate numbers with more than 32 bits in bit length, and particularly when converting into 53-bit double-precision floating-point numbers in [0,1) in the IEEE 754 format, the typical implementation concatenates two successive 32-bit integers and divides them by a power of 2. In this case, the 32-bit MT19937 is optimized in terms of its equidistribution properties (the so-called dimension of equidistribution with v-bit accuracy) under the assumption that one will mainly be using 32-bit output values, and hence the concatenation sometimes degrades the dimension of equidistribution compared with the simple use of 32-bit outputs. In this paper, we analyze such phenomena by investigating hidden F2-linear relations among the bits of high-dimensional outputs. Accordingly, we report that MT19937 with a specific lag set fails several statistical tests, such as the overlapping collision test, matrix rank test, and Hamming independence test.
Advantages of a modular high-level quantum programming framework

We review some of the features of the ProjectQ software framework and quantify their impact on the resulting circuits. The concise high-level language facilitates implementing even complex algorithms in a very time-efficient manner while, at the same time, providing the compiler with additional information for optimization through code annotation – so-called meta-instructions. We investigate the impact of these annotations for the example of Shor’s algorithm in terms of logical gate counts. Furthermore, we analyze the effect of different intermediate gate sets for optimization and how the dimensions of the resulting circuit depend on a smart choice thereof. Finally, we demonstrate the benefits of a modular compilation framework by implementing mapping procedures for one- and two-dimensional nearest neighbor architectures which we then compare in terms of overhead for different problem sizes.
Revisiting Outlier Rejection Approach for Non-Lambertian Photometric Stereo

Photometric stereo offers a single camera based approach to recover 3D information and has attracted wide range of applications in computer vision. Presence of non-Lambertian reflections in almost all the real-world objects limits the usage of the Lambertian model for surface normal vector estimation. Previous methods proposed to address such non-Lambertian phenomena employ an outlier rejection approach while more recent methods introduce BRDF models which can generate more accurate results. However, results with comparable accuracy can also be achieved by simply filtering the observed intensity values. This paper presents two novel outlier rejection techniques which attempt to identify the data which are more reliable and likely to be Lambertian. In the first technique, observed intensity values with less reliability are automatically eliminated. This reliability is determined by the responses from a newly introduced inter-relationship function. In the second technique, those photometric ratio equations which are less likely to be Lambertian are identified by observing the residue of the equations. By eliminating the data which is unreliable and likely to be non-Lambertian, surface normal vectors are more accurately estimated. Our comparative and reproducible experimental results using both real and synthetic datasets illustrate superior performance over the state-of-the-art methods, which validates our theoretical arguments presented in this paper.
Characterizing the performance of continuous-variable Gaussian quantum gates

The required set of operations for universal continuous-variable quantum computation can be divided into two primary categories: Gaussian and non-Gaussian operations. Furthermore, any Gaussian operation can be decomposed as a sequence of phase-space displacements and symplectic transformations. Although Gaussian operations are ubiquitous in quantum optics, their experimental realizations generally are approximations of the ideal Gaussian unitaries. In this work, we study different performance criteria to analyze how well these experimental approximations simulate the ideal Gaussian unitaries. In particular, we find that none of these experimental approximations converge uniformly to the ideal Gaussian unitaries. However, convergence occurs in the strong sense, or if the discrimination strategy is energy bounded, then the convergence is uniform in the Shirokov-Winter energy-constrained diamond norm and we give explicit bounds in this latter case. We indicate how these energy-constrained bounds can be used for experimental implementations of these Gaussian unitaries in order to achieve any desired accuracy.
Characterization of building materials by means of spectral remote sensing: The example of Carcassonne's defensive wall (Aude, France)

Geological and archaeological analysis of stone masonries in standing structures helps reveal information about use of natural resources. At the same time, the study of historical materials is useful for conservators and cultural heritage management. Geochemical and petrographic analysis of building material types is usually done through destructive analysis on a few selected samples and can be problematic due to the costs of operations and the size of buildings themselves. This paper demonstrates that the combination of hyperspectral imaging portable Near Infrared (NIR) spectroscopy and Energy Dispersive X-ray Fluorescence (ED-XRF) spectroscopy can be useful for analysing types of raw materials used in distinct construction phases of the inner defensive wall in the citadel of Carcassonne (Aude, France). Stratigraphic analysis of the architecture, short-range spectral remote sensing and portable ED-XRF measurements were combined in an interdisciplinary approach to classify sandstone elements. The experimental protocol for in situ non-destructive analysis and classification of the masonry types allows the investigation of the monument in a diachronic perspective, collecting information to delineate raw materials varieties and their use or re-use through time.
Does academic major matter in mobile assisted language learning? A quasi-experimental study

Purpose The purpose of this paper is to investigate the use of mobile assisted language learning (MALL) in vocabulary learning among English foreign language learners in China with a lens on academic major difference. This study hypothesizes that academic majors including Science, Technology, Engineering and Mathematics (STEM) and non-STEM differentiate the learners’ perception of computer self-efficacy (CSE) and MALL learning performance as well as moderate the effect of CSE on language learning performance. Design/methodology/approach Sample data for this study were collected from 200 university students enrolled in both STEM and non-STEM majors. Under the quasi-experiment, this study had been conducted vocabulary training, pre-test, post-test and a questionnaire. The Statistical Package for the Social Sciences, a statistical analysis technique based on ANOVA and regression were used to analyze the data. Findings The results support hypotheses that CSE, learning score and satisfaction are rated higher for STEM students than non-STEM ones and indicate the effect of CSE on language learning performance is stronger for STEM students than non-STEM ones in using MALL. Originality/value This paper addressed important issues in language learning on the academic major difference. The findings would be the guidelines for educational organizations to motivate and stimulate students with sustainable self-confidence important not only for improvement in students’ language learning achievements and outcomes but also for the advancement of mobile technology in language learning.