Miracle-dz/UniFAR-FTUs huggingface dataset:
How to see values in social computing: methods for studying values dimensions

Human values play an important role in shaping the design and use of information technologies. Research on values in social computing is challenged by disagreement about indicators and objects of study as researchers distribute their focus across contexts of technology design, adoption, and use. This paper draws upon a framework that clarifies how to see values in social computing research by describing values dimensions, comprised of sources and attributes of values in sociotechnical systems. This paper uses the framework to compare how diverse research methods employed in social computing surface values and make them visible to researchers. The framework provides a tool to analyze the strengths and weaknesses of each method for observing values dimensions. By detailing how and where researchers might observe interactions between values and technology design and use, we hope to enable researchers to systematically identify and investigate values in social computing.
Proliferation Imaging to Measure Early Cancer Response to Targeted Therapy

Positron emission tomography imaging using thymidine and analogues labeled with positron emitters provides noninvasive and quantitative estimates of regional cellular proliferation. This CCR Translations summary reviews the biological basis for proliferation imaging and discusses recent results using 18F-fluorothymidine-positron emission tomography to measure response to targeted therapy in the context of prior studies and potential future applications.
Visual Understanding via Multi-Feature Jointly Sharing Learning

Image/video data is usually represented by multiple visual features. Fusion of multi-sources information for establishing the identity has been widely recognized. Multi-feature visual recognition has recently received attention in multimedia applications. This paper studies visual understanding via a newly proposed -norm based multi-feature jointly sharing learning framework, which can simultaneously learn the global label matrix and explicit classifiers from the labeled visual data represented by multiple feature modalities. Additionally, a multi-modal group graph manifold regularizer formed by mixed Laplacian and Hessian graph is proposed for better preserving the manifold structure of different features on the labeled data, while preserving the label consistency and improving the label prediction power via semi-supervised learning. The merits of the proposed multi-feature learning framework lie in jointly sharing the structural information from multiple features in global classifier learning phase based on a mixed graph regularizer on one hand, and an efficient alternating optimization method for fast classifier training on the other hand. Experiments on several benchmark visual datasets, such as 17-category Oxford Flower dataset, the challenging 101-category Caltech dataset, YouTube & Consumer Videos dataset and large-scale NUS-WIDE dataset for multimedia understanding all demonstrate that the proposed approach compares favorably with state-of-the-art algorithms.
Automated directory assistance system - from theory to practice

Abstract The automated directory assistance system (ADAS) is traditionally formulated as an automatic speech recognition (ASR) problem. Recently, it has been formulated as a voice search problem, where a spoken utterance is firstly converted into text, which in turn is used to search for the listing. In this paper, we focus on the design and development of the utterance-to-listing component of ADAS. We show that many theoretical and practical issues need to be resolved when applying the basic idea of voice search to the development of ADAS. We share our experiences in addressing these issues, especially in pre-processing the listing database, generating a high performance LM, and developing efficient, accurate, and robust search algorithms. Field tests of our prototype system indicate that an 81% task completion rate can be achieved. Index Terms : speech recognition, directory assistance, voice search, TFIDF, spoken dialog system, vector space model 1. Introduction An automated directory assistance system (ADAS) [1] [2] [3] [5] [6] is a spoken dialog system that provides the caller with the phone number and/or address of the business or residential listing he/she requests. It is a very complicated system that involves automatic speech recognition (ASR), listing lookup, disambiguation, and dialog design. The core element of the ADAS is the utterance-to-listing (U2L) component that maps an utterance K
Delay-dependent robust H∞ stability and stabilization for uncertain singular system

This paper investigates the problem of delay-dependent robust Hinfin control for uncertain singular systems with time-varying state delay. The considered systems are not necessarily assumed to be regular and causal. In terms of linear matrix inequality (LMI)approach, a delay-dependent stabilizable criterion is given to ensure the nominal system to be regular, impulse-free and stable. Based on the criterion, the problem mentioned above is solved via state feedback controller, which guarantees that, for all admissible uncertainties, the closed-loop system is not only regular, causal and stable, but also satisfies a prescribed Hinfin performance condition. Some numerical examples are provided to demonstrate the efficiency and less conservition of the proposed methods in this paper.
Nonlinear estimation-based dipole source localization for artificial lateral line systems

As a flow-sensing organ, the lateral line system plays an important role in various behaviors of fish. An engineering equivalent of a biological lateral line is of great interest to the navigation and control of underwater robots and vehicles. A vibrating sphere, also known as a dipole source, can emulate the rhythmic movement of fins and body appendages, and has been widely used as a stimulus in the study of biological lateral lines. Dipole source localization has also become a benchmark problem in the development of artificial lateral lines. In this paper we present two novel iterative schemes, referred to as Gauss–Newton (GN) and Newton–Raphson (NR) algorithms, for simultaneously localizing a dipole source and estimating its vibration amplitude and orientation, based on the analytical model for a dipole-generated flow field. The performance of the GN and NR methods is first confirmed with simulation results and the Cramer–Rao bound (CRB) analysis. Experiments are further conducted on an artificial lateral line prototype, consisting of six millimeter-scale ionic polymer–metal composite sensors with intra-sensor spacing optimized with CRB analysis. Consistent with simulation results, the experimental results show that both GN and NR schemes are able to simultaneously estimate the source location, vibration amplitude and orientation with comparable precision. Specifically, the maximum localization error is less than 5% of the body length (BL) when the source is within the distance of one BL. Experimental results have also shown that the proposed schemes are superior to the beamforming method, one of the most competitive approaches reported in literature, in terms of accuracy and computational efficiency.
Cardiac Metabolic Pathways Affected in the Mouse Model of Barth Syndrome

Cardiolipin (CL) is a mitochondrial phospholipid essential for electron transport chain (ETC) integrity. CL-deficiency in humans is caused by mutations in the tafazzin (Taz) gene and results in a multisystem pediatric disorder, Barth syndrome (BTHS). It has been reported that tafazzin deficiency destabilizes mitochondrial respiratory chain complexes and affects supercomplex assembly. The aim of this study was to investigate the impact of Taz-knockdown on the mitochondrial proteomic landscape and metabolic processes, such as stability of respiratory chain supercomplexes and their interactions with fatty acid oxidation enzymes in cardiac muscle. Proteomic analysis demonstrated reduction of several polypeptides of the mitochondrial respiratory chain, including Rieske and cytochrome c1 subunits of complex III, NADH dehydrogenase alpha subunit 5 of complex I and the catalytic core-forming subunit of F0F1-ATP synthase. Taz gene knockdown resulted in upregulation of enzymes of folate and amino acid metabolic pathways in heart mitochondria, demonstrating that Taz-deficiency causes substantive metabolic remodeling in cardiac muscle. Mitochondrial respiratory chain supercomplexes are destabilized in CL-depleted mitochondria from Taz knockdown hearts resulting in disruption of the interactions between ETC and the fatty acid oxidation enzymes, very long-chain acyl-CoA dehydrogenase and long-chain 3-hydroxyacyl-CoA dehydrogenase, potentially affecting the metabolic channeling of reducing equivalents between these two metabolic pathways. Mitochondria-bound myoglobin was significantly reduced in Taz-knockdown hearts, potentially disrupting intracellular oxygen delivery to the oxidative phosphorylation system. Our results identify the critical pathways affected by the Taz-deficiency in mitochondria and establish a future framework for development of therapeutic options for BTHS.
The Combination of Tiotropium and Budesonide in the Treatment of Chronic Obstructive Pulmonary Disease

Because additive effects of inhaled corticosteroids and long-acting anticholinergics are unclear, we undertook this study to compare the efficacy of tiotropium alone and tiotropium plus budesonide in patients with chronic obstructive pulmonary disease. The study subjects were randomized to receive either tiotropium 18 µg once daily with or without budesonide 200 µg twice daily for 6 weeks. The efficacy variables were changes in trough forced expiratory volume in one second (FEV1), St. George's Respiratory Questionnaire (SGRQ), 6-minute walk distance (6MWD), and use of rescue medication. One hundred patients were randomized and 81 completed the study. The mean age was 64.0 yr, and the mean FEV1 was 39.7% predicted. Compared with tiotropium alone (N=40), the tiotropium/budesonide combination (N=41) was related to an improvement in the SGRQ total score (tiotropium -2.8 units and tiotropium/budesonide -5.6 units, p=0.003). 6MWD was improved by 13.5 m in the tiotropium group and by 22.5 m in the tiotropium/budesonide group (p=0.031). Changes in trough FEV1 and the use of rescue medication were similar between two groups. In conclusion, compared with tiotropium alone, the tiotropium/budesonide combination was related to an improved health-related quality of life. These data support that low-dose budesonide may enhance the efficacy of tiotropium.
Parallel SECONDO: scalable query processing in the cloud for non-standard applications

Secondo is an extensible DBMS prototype. It emphasizes the handling of spatial and moving objects data and provides sophisticated data models and query processing over such data, offering specialized types and operations. More generally, there is a wealth of techniques available, for example, (i) construction of a road network from OpenStreetMap data and map matching of trajectories against this network, (ii) symbolic trajectories with pattern matching and indexing techniques, (iii) spatio-temporal pattern queries, (iv) similarity search on M-trees, (v) combining raster data with trajectory data, to name but a few. Parallel Secondo combines Secondo with Hadoop to achieve scalable query processing in the cloud. In this architecture, there are many Secondo system instances on different computers of a network, called slaves, and one which is designated as the master. A user can issue at the master a query in the usual style of querying Secondo. This query may contain hadoopMap and hadoopReduce operators which take as parameters arbitrary Secondo queries designated for parallel execution. These are translated into Hadoop MapReduce jobs which are then, controlled by Hadoop, executed on all available Secondo slaves. The parallelization technique via Hadoop and Secondo as such are completely orthogonal. That means that everything Secondo can do, now or in the future, Parallel Secondo can do as well at large scale. Like Secondo, Parallel Secondo is freely available for download. In addition, plenty auxiliary tools are provided to deploy and manage the system at different scales, from a single computer to a local cluster, or the cloud.
Optimal landmark detection using shape models and branch and bound

Fitting statistical 2D and 3D shape models to images is necessary for a variety of tasks, such as video editing and face recognition. Much progress has been made on local fitting from an initial guess, but determining a close enough initial guess is still an open problem. One approach is to detect distinct landmarks in the image and initalize the model fit from these correspondences. This is difficult, because detection of landmarks based only on the local appearance is inherently ambiguous. This makes it necessary to use global shape information for the detections. We propose a method to solve the combinatorial problem of selecting out of a large number of candidate landmark detections the configuration which is best supported by a shape model. Our method, as opposed to previous approaches, always finds the globally optimal configuration. The algorithm can be applied to a very general class of shape models and is independent of the underlying feature point detector. Its theoretic optimality is shown, and it is evaluated on a large face dataset.
Hydrodynamic Computation with Hybrid Programming on CPU-GPU Clusters

The explosion of parallelism and heterogeneity in today’s computer architectures has created opportunities as well as challenges for redesigning legacy numerical software to harness the power of new hardware. In this paper we address the main challenges in redesigning BLAST – a numerical library that solves the equations of compressible hydrodynamics using high order finite element methods (FEM) in a moving Lagrangian frame – to support CPU-GPU clusters. We use a hybrid MPI + OpenMP + CUDA programming model that includes two layers: domain decomposed MPI parallelization and OpenMP + CUDA acceleration in a given domain. To optimize the code, we implemented custom linear algebra kernels and introduced an auto-tuning technique to deal with heterogeneity and load balancing at runtime. Our tests show that 12 Intel Xeon cores and two M2050 GPUs deliver a 24× speedup compared to a single core, and a 2.5× speedup compared to 12 MPI tasks in one node. Further, we achieve perfect weak scaling, demonstrated on a cluster with up to 64 GPUs in 32 nodes. Our choice of programming model and proposed solutions, as related to parallelism and load balancing, specifically targets high order FEM discretizations, and can be used equally successfully for applications beyond hydrodynamics. A major accomplishment is that we further establish the appeal of high order FEMs, which despite their better approximation properties, are often avoided due to their high computational cost. GPUs, as we show, have the potential to make them the method of choice, as the increased computational cost is also localized, e.g., cast as Level 3 BLAS, and thus can be done very efficiently (close to “free” relative to the usual overheads inherent in sparse computations).
Efficient Customized Privacy Preserving Friend Discovery in Mobile Social Networks

Mobile social networks have been increasingly popular with the explosive growth of mobile devices. Mobile users are allowed to interact with potential friends within a certain distance. Motivated by this feature, many exciting applications have been developed, yet the challenge of privacy protection is thus aroused. In this paper, we propose an efficient customized privacy preserving friend discovery mechanism, which not only protects the privacy of users' profile, but also establishes a verifiable secure communication channel between matched users. Besides, the initiator has the freedom to set a customized request profile by choosing the interested attributes and giving each attribute a specific value. Moreover, the request profile's privacy protection level is customized by the initiator according to his/her own privacy requirements. We also consider the collusion attacks among unmatched users. To the best of our knowledge, this is the first work to address such a security threat. Our protocol guarantees that only exactly matched users are able to communicate with the initiator securely, while little information can be obtained by other participants. To increase the matching efficiency, our design adopts the Bloom filter to efficiently exclude most unmatched users. As a result, our design effectively protects the profile privacy and efficiently decreases the computational overhead. Security analysis and performance evaluation are conducted to justify the superiority of our protocol.
The influence of a parasite community on the dynamics of a host population: a longitudinal study on willow ptarmigan and their parasites

Despite the fact that most host populations are infected by a community of different parasite species, the majority of empirical studies have focused on the interaction between the host and a single parasite species. Here, we explore the hypothesis that host population dynamics are affected both by single parasite species and by the whole parasite community. We monitored population density and breeding productivity of two populations of willow ptarmigan (Lagopus lagopus) in northern Norway for 8 and 11 years, respectively, and sampled eukaryotic endoparasites. We found that increasing abundances of the cestode Hymenolepis microps was associated with increased breeding mortality and reduced annual growth rate of the host population in both areas, and reduced host body mass and body condition in the area where such data were available. In one of the areas, the abundance of the nematode Trichostrongylus tenuis was associated with reductions in host body mass, body condition and breeding mortality and the filaroid nematode Splendidofilaria papillocerca was negatively related to host population growth rates. The parasite community was also negatively related to host fitness parameters, suggesting an additional community effect on host body mass and breeding mortality, although none of the parasites had a significant impact on their own. The prevalence of parasites with very different taxonomical origins tended to covary within years, suggesting that variability in the parasite community was not random, but governed by changes in host susceptibility or environmental conditions that affected exposure to parasites in general. Other variables including climate, plant production and rodent densities were not associated with the recorded demographic changes in the host population.
Exploring the relationship of history characteristics and defect count: an empirical study

During the lifetime of a project, a huge amount of information is generated, e.g. in versioning systems or bug data bases. When analysed appropriately, the knowledge about the previous project characteristics allows estimating the project's future evolution. For example, it is very valuable to know particular history characteristics of a file indicating its fault proneness because it helps testers to focus their testing effort on these specific files. In this paper, we present the results of an empirical study, exploring the relationship between history characteristics of software entities and their defects. For this purpose, we analyze 9 open source java projects. The results show that there are some history characteristics that highly correlate with defects in software, e.g. the number of changes and the number of distinct authors performing changes to a file. The number of co-changed files does not correlate with the defect count.
The evolutionary origins and psychology of charismatic leadership

ter verkrijging van de graad Doctor of Philosophy aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculty of Behavioral and Movement Sciences Department of Experimental and Applied Psychology op Dinsdag 29 Mei, 2018 om 15:30 uur in de Aula van de universiteit, De Boelelaan 1105
Generation of soil moisture patterns at the catchment scale by EOF interpolation

Abstract. Spatial patterns of soil moisture cannot be adequately characterized by direct measurement for most practical applications, so interpolation between observations is required. Interpolation of soil moisture is complicated because multiple hydrologic processes can affect soil moisture and these processes can introduce distinct modes of variation into the soil moisture patterns. In this paper, a new method to interpolate soil moisture data is presented. This method accepts a dataset of soil moisture at widely-spaced locations on multiple dates and produces fine-scale patterns of soil moisture on the same dates. The method first uses Empirical Orthogonal Function (EOF) analysis to decompose the dataset into a set of time-invariant patterns of covariation (EOFs) and a set of associated time series (called expansion coefficients or ECs) that indicate the importance of the patterns on each date. The method then uses a statistical test to retain only the most important EOFs, and these EOFs are interpolated to the desired resolution using a standard estimation or interpolation method. The interpolated EOFs are finally combined with the spatial averages and the ECs to construct the fine-scale soil moisture patterns. Using the Tarrawarra dataset, the EOF-based interpolation method is shown to outperform analogous direct interpolation methods, and this improved performance is observed when as few as two observation dates are available. The improved performance occurs because EOF analysis decomposes soil moisture roughly according to the controlling processes and the most important EOFs exhibit distinct but more consistent spatial structures than soil moisture itself. Less predictable variation is also separated into higher order EOFs, which are discarded by the method.
GIS and GPS Based Vehicle Guidance System

In order to enhance level of vehicle guidance system (VGS) in China, the framework, subsystem and critical technologies of VGS were studied, and geographic information system (GIS), Global Positioning Systems (GPS), etc. were applied into corresponding subsystems. Paying attention to the subsystem of route optimization, the mathematical model of road network was built. The storage structure of road network was ameliorated by adjacent node relation matrix and adjacent node weight matrix, which saves the storage space. The classical Dijkstra algorithm was improved with dynamic adjacent node relation matrix and adjacent node weight matrix, which increases the calculation efficiency, and the corresponding algorithm is given. Applying of GIS, GPS and so on, the studied VGS was realized. Test results show the studied VGS can exactly position vehicle, correctly find optimal route and guide travel in real road network.