Miracle-dz/UniFAR-FTUs huggingface dataset:
On Efficiently Transferring the Linear Secret-Sharing Scheme Matrix in Ciphertext-Policy Attribute-Based Encryption

Ciphertext-Policy Attribute-Based Encryption(CP-ABE) is a system for realizing complex access control on encrypted data, in which attributes are used to describe a user’s credentials and a party encrypting data determines a policy over attributes for who can decrypt. In CP-ABE schemes, access policy is attached to the ciphertext to be the input of the decryption algorithm. An access policy can be expressed in terms of monotone boolean formula or monotone access structure, and can be realized by a linear secretsharing scheme(LSSS). In recent provably secure and efficient CP-ABE schemes, the LSSS induced from monotone span program(MSP) is used, where the LSSS is a matrix whose rows are labeled by attributes. And a general algorithm for converting a boolean formula into corresponding LSSS matrix is described recently. However, when there are threshold gates in the access structure, the number of rows of the LSSS matrix generated by the algorithm will be unnecessary large, and consequently the ciphertext size is unnecessary large. In this paper, we give a more general and efficient algorithm that the number of rows of the LSSS matrix is as small as possible. And by some tricks, the boolean formula acts as the label function, so that only the boolean formula needs to be attached to the ciphertext, which decreases the communication cost drastically.
Avoiding Communication in the Lanczos Bidiagonalization Routine and Associated Least Squares QR Solver

Abstract : Communication - the movement of data between levels of memory hierarchy or between processors over a network - is the most expensive operation in terms of both time and energy at all scales of computing. Achieving scalable performance in terms of time and energy thus requires a dramatic shift in the field of algorithmic design. Solvers for sparse linear algebra problems, ubiquitous throughout scientific codes, are often the bottlenecks in application performance due to a low computation/communication ratio. In this paper we develop three potential implementations of communication-avoiding Lanczos bidiagonalization algorithms and discuss their different computational requirements. Based on these new algorithms, we also show how to obtain a communication-avoiding LSQR least squares solver.
Global hybrid multi-core-GPUs-resources platform (GHRP) in hard real time system

Graphics Processing Units (GPU) technologies have been adopted in many real-time applications, due to the developments in the architectures of GPU fit to be used in general purpose operation. Interesting with that we present a platform for hybrid multi-core-GPUs with global/local shared resources, where this platform divided into three parts; first, cluster the multi-core-GPUs-shared resources, where CPUs and GPUs grouped in one cluster with local shared resources and grouped the global shared resources by unique global cluster where the shared resources governed by global and local semaphore. Second, present an innovative mechanism for scheduling GPUs with different shared resources in multi-core system. Third, drive a precise blocking time. The GPU has been tested with many operations by OpenCL language and used the GPU performance with multi-core system depending on local and distributed semaphores evaluations and compare it with MPCP protocol.
Fault-containing network protocols

Self-stabilization is a simple and elegant approach towards designing fault-tolerant network protocols. While self-stabilization provides automatic recovery from arbitrary transient faults, self-stabilizing systems do not incorporate any optimization for more efficient recovery from limited transient faults, even though such limited faults are more likely to occur in practice than arbitrary faults. Fault-containing self-stabilizing protocols are proposed to efficiently contain the effects of more frequent, limited transient faults while retaining the desirable property of self-stabilization. In this paper, we propose a methodology for designing fault-containing selfstabilizing network protocols. The methodology views the properties of self-stabilization and fault-containment in separation. Since self-stabilizing protocols already exist for many important network problems, the design starts with an existing self-stabilizing protocol, and adds to it the fault-containment property. The feasibilitv of this technique is illustrated bv the design of a fault-containing self-stabilizing protocol for the breadthfirst-search (BFS) spanning tree problem. The applicability of the technique used in deriving the BFS protocol to other problems is also discussed.
The reception pattern of the balanced scorecard: accounting for interpretative viability

‘The role of fads and fashions in shaping management accounting practices in contemporary organizations requires further inquiry’ (Malmi, T. (1999). Activity based costing diffusion across organizations: an exploratory empirical analysis of Finnish firms. Accounting, Organizations and Society, 24, 669). Against the background of the evolving management fashion literature we discuss the ‘reception pattern’ of the Balanced Scorecard (BSC) in the Netherlands. To get insight into the organizational changes induced by and associated with the BSC-discourse, we systematically discovered how the concept, since its launch by Kaplan & Norton (1992), has grown into an umbrella that encompasses a variety of interpretations and uses. The paper offers a framework to show how BSC-discourse and its actual use have developed in interrelated yet loosely coupled ways.
Rapid speaker adaptation using probabilistic principal component analysis

In this letter, we propose a rapid speaker adaptation technique based on the probabilistic principal component analysis (PPCA). The PPCA is employed to obtain the canonical speaker models that provide the a priori knowledge of the training speakers. The proposed approach is conveniently incorporated into the Bayesian adaptation framework, where the parameters are adapted to the new speaker's speech according to the maximum a posteriori (MAP) criterion. Through a number of continuous digit recognition experiments, we can find the effectiveness of the PPCA-based approach compared to the other adaptation approaches with a small amount of adaptation data.
Phone Tag : Laser Tag for Mobile Devices

Mobile games now represent the largest sector of the game market when measured by revenue [1]. Ubiquitous smartphones with their wide range of integrated peripherals present designers with opportunities to use the devices in novel ways. Our goal was to build an Android app that turns a smartphone into the equipment to play a game similar to “laser tag”. A two-player game has been implemented as a demonstration. Android based mobile devices were selected due to the availability of advanced displays, cameras, network hardware and the accessibility of the software development environment. Algorithm selection was driven primarily by performance considerations. A fluid user experience was achieved without noticeable latency in game play or player feedback. Our work demonstrates that novel combinations of mobile device technology, computer vision, image processing, graphics, networking and myriad other capabilities can be realized while maintaining good performance in an application intended for entertainment. Keywords—laser tag; game; marker detection; object detection; OpenCV; Android; WifiP2P
Sputum Inflammatory Cell-Based Classification of Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Background Patients with chronic obstructive pulmonary disease (COPD) commonly suffer from acute exacerbations (AECOPD) and display varying disease severity. However, there is no available biomarker for the classification of AECOPD. This study is aimed at investigating the sputum cellular profiles to classify patients with AECOPD. Methods A total of 83 patients with AECOPD and 26 healthy controls were recruited. Their demographic and clinical characteristics were recorded, and their lung function was examined. The phenotypes of sputum inflammatory cells were characterised, and the concentrations of sputum and serum amyloid-A (SAA), C-reactive protein (CRP), interleukin-6 (IL-6), and matrix metalloproteinase-9 (MMP-9) were measured. Based on the sputum inflammatory cell profiles, individual patients were categorized into one of the four subgroups with inflammatory eosinophilic, neutrophilic, paucigranulocytic, and mixed granulocytic AECOPD. Most AECOPD patients were reevaluated within 12–14 months after discharge. Results There were 10 (12%) eosinophilic, 36 (43%) neutrophilic, 5 (6%) mixed granulocytic, and 32 (39%) paucigranulocytic AECOPD patients. The patients with mixed granulocytic or neutrophilic AECOPD had a higher BODE score, more sputum inflammatory cells, lower lung function, and longer hospital stay, accompanied by higher concentrations of sputum MMP-9, IL-6 and CRP, and serum SAA, IL-6 and CRP. Notably, 83% of patients with neutrophilic AECOPD displayed evidence of bacterial infection and many of them responded poorly to standard therapies. In addition, patients with mixed granulocytic or neutrophilic stable COPD remained at lower lung functions and higher levels of inflammation. Conclusion Patients with AECOPD display heterogeneous inflammation, and the profiles of sputum inflammatory cells may be used as valuable biomarkers for the classification of AECOPD patients.
Developing Corpus of Lecture Utterances Aligned to Slide Components

The approach which formulates the automatic text summarization as a maximum coverage problem with knapsack constraint over a set of textual units and a set of weighted conceptual units is promising. However, it is quite important and difficult to determine the appropriate granularity of conceptual units for this formulation. In order to resolve this problem, we are examining to use components of presentation slides as conceptual units to generate a summary of lecture utterances, instead of other possible conceptual units like base noun phrases or important nouns. This paper explains our developing corpus designed to evaluate our proposing approach, which consists of presentation slides and lecture utterances aligned to presentation slide components.
Decreased Vasopressin Gene Expression in the Biological Clock of Alzheimer Disease Patients With and Without Depression

Circadian rhythm disturbances are frequently present in Alzheimer disease (AD). In the present study, we investigated the expression of vasopressin (AVP) mRNA in the human suprachiasmatic nucleus (SCN). The in situ hybridization procedure on formalin-fixed paraffin-embedded material was improved to such a degree that we could, for the first time, visualize AVP mRNA expressing neurons in the human SCN and carry out quantitative measurements. The total amount of AVP mRNA expressed as masked silver grains in the SCN was 3 times lower in AD patients (n = 14; 2,135 ± 597 μm2) than in age- and time-of-death-matched controls (n = 11; 6,667 ± 1466 μm2) (p = 0.003). No significant difference was found in the amount of AVP mRNA between AD patients with depression (n = 7) and without depression (n = 7) (2,985 ± 1103 μm2 and 1,285 ± 298 μm2, respectively; p = 0.38). In addition, the human SCN AVP mRNA expressing neurons showed a marked day-night difference in controls under 80 years of age. The amount of AVP mRNA was more than 3 times higher during the daytime (9,028 ± 1709 μm2, n = 7) than at night (2,536 ± 740 μm2, n = 4; p = 0.02), whereas no clear diurnal rhythm of AVP mRNA in the SCN was observed in AD patients. There was no relationship between the amount of AVP mRNA in the SCN and age at onset of dementia, duration of AD and the neuropathological changes in the cerebral cortex. These findings suggest that the neurobiological basis of the circadian rhythm disturbances that are responsible for behavioral rhythm disorders is located in the SCN. It also explains the beneficial effects of light therapy on nightly restlessness in AD patients.
Derivative estimation with local polynomial fitting

We present a fully automated framework to estimate derivatives nonparametrically without estimating the regression function. Derivative estimation plays an important role in the exploration of structures in curves (jump detection and discontinuities), comparison of regression curves, analysis of human growth data, etc. Hence, the study of estimating derivatives is equally important as regression estimation itself. Via empirical derivatives we approximate the qth order derivative and create a new data set which can be smoothed by any nonparametric regression estimator. We derive L1 and L2 rates and establish consistency of the estimator. The new data sets created by this technique are no longer independent and identically distributed (i.i.d.) random variables anymore. As a consequence, automated model selection criteria (data-driven procedures) break down. Therefore, we propose a simple factor method, based on bimodal kernels, to effectively deal with correlated data in the local polynomial regression framework.
Server-Side Streaming Processing of WS-Security

With SOAP-based web services leaving the stadium of being an explorative set of new technologies and entering the stage of mature and fundamental building blocks for service-driven business processes-and in some cases even for mission-critical systems-the demand for nonfunctional requirements including efficiency as well as security and dependability commonly increases rapidly. Although web services are capable of coupling heterogeneous information systems in a flexible and cost-efficient way, the processing efficiency and robustness against certain attacks do not fulfill industry-strength requirements. In this paper, a comprehensive stream-based WS-Security processing system is introduced, which enables a more efficient processing in service computing and increases the robustness against different types of Denial-of-Service (DoS) attacks. The introduced engine is capable of processing all standard-conforming applications of WS-Security in a streaming manner. It can handle, e.g., any order, number, and nesting degree of signature and encryption operations, closing the gap toward more efficient and dependable web services.
The Finances of American Households in the Past Three Recessions: Evidence from the Survey of Consumer Finances

The downturn in economic activity in the U.S. that began in December 2007 (as determined by researchers with the National Bureau of Economic Research) has been noticeably deeper and has already lasted considerably longer than the prior two recessions--those beginning in July 1990 and in March 2001. In addition, a key difference between the current and the past two recessions is the extent to which consumer spending and residential investment have dropped since late 2007--that is, the extent to which the household sector appears to have "led" the drop in aggregate economic activity in this recession. This paper uses household-level data from the Federal Reserve Board's series of Surveys of Consumer Finances to document three factors that appear to have contributed to greater financial stress in the household sector in the current downturn compared with the prior two: 1) substantial and widespread reductions in home values that resulted in sizable erosions of home equity and net worth for many homeowners; 2) markedly expanded holdings of corporate equity among middle-income households which lost significant market value, on net, as stock prices sunk; and, 3) greater debt on household balance sheets and overall financial vulnerability around the onset of the 2008-09 recession, particularly for those in the middle of the income distribution.
New model for aircraft landing scheduling using real time algorithms scheduling

Expert systems are designed to solve non-regular complex problems using extracted cognitive data and inspiring from the human expertise and its best practices. They are based on the machine performance and its ability to carry out a very large number of complex iterations. The Aircraft Landing Scheduling (ALS) problem has been complex and challenging problem in air traffic control for a long time, In practice, it can formulated as a constrained optimization problem that needs to be solved in real-time. The choice of a task scheduling algorithm in a variable and unpredictable real-time system requires the use of an intelligent expert system, having an evolving knowledge base and a creative inference engine. In this paper we present a general architecture and conceptual concepts of our expert system. This expert system allows the choice of the most optimal scheduling algorithm for aircraft landing scheduling.
The Influence of Health Literacy and Depression on Diabetes Self-Management: A Cross-Sectional Study

Despite an increasing focus on health literacy in the clinical setting and in the literature, there is still ongoing debate about its influence on diabetes self-management. The aim of the study was to examine the relationships of sociodemographic, clinical, and psychological factors on health literacy and diabetes self-management. A cross-sectional survey was undertaken on 224 patients with type 2 diabetes at two diabetes centres in Sydney, Australia. Findings showed that people with low health literacy were more likely to (a) have lower educational attainment; (b) be migrants; and (c) have depressed mood. Unexpectedly, those who met HbA1c threshold of good glucose control were more likely to have low health literacy. Predictors of low diabetes self-management included (a) younger age group (AOR: 2.58, 95% CI: 1.24–4.64); (b) having postsecondary education (AOR: 2.30, 95% CI: 1.05–5.01); (c) low knowledge of diabetes management (AOR: 2.29, 95% CI: 1.25–4.20); and (d) having depressed mood (AOR: 2.30, 95% CI: 1.30–4.06). The finding that depressed mood predicted both low health literacy and low diabetes self-management stresses the importance of screening for depression. Increasing people's understanding of diabetes self-management and supporting those with depression are crucial to enhance participation in diabetes self-management.
Efficient Large-Scale Graph Analysis in MapReduce

Large-scale graph-structured datasets are commonplace in applications ranging from social sciences to systems biology. This has motivated efforts aimed at developing cloud-based software kernels for graph analyses. In this paper, we present a novel cloud-based implementation of topological similarity computation. The goal of this computation is to determine all pairwise similarities of vertices in a pair of networks. We analyze tradeoffs associated with graph representations and comprehensively profile execution costs (CPU utilization, communication overhead, and I/O) of kernels in MapReduce – a commonly used platform for distributed computations. We propose a number of optimizations to the baseline iterative topological similarity computation algorithm, including asynchronous iterations to reduce communications and inner-outer iterations to reduce global synchronizations. While these optimizations are known in parallel linear algebra literature, their performance benefits in the MapReduce cost model are significant, because of higher communication and synchronization costs. We apply these optimizations to our Network Similarity Decomposition (NSD) algorithm for topological similarity. We specifically show that: (i) our MapReduce optimized kernels for topological similarity are significantly faster, (ii) our MapReduce NSD formulation scales to large graphs and distributed platforms, and (iii) we demonstrate the use of our formulation to compute topological similarity of the largest graphs yet reported (over a billion nodes and twenty billion edges).
Service overlay network capacity adaptation for profit maximization

The considered Service Overlay Networks (SON) lease bandwidth with Quality of Service (QoS) guarantees from a multitude of Internet Autonomous Systems, through service level agreements (SLA) with Internet Service Providers (ISP). This bandwidth is used to establish SON links and deliver end-to-end QoS for real time service connections. The leased bandwidth amount influences both the admitted traffic and network cost, affecting the network profit. This gives the network operator the opportunity to optimize the profit by adapting the network resources to changing traffic and SLA costs conditions. We propose a novel approach that maximizes the network profit based on traffic measurements and SLA cost changes. The approach uses an economic model that integrates the network routing policy with the adaptation of the SON link capacities. While performing the adaptation of leased bandwidth, the connection blocking constraints are also maintained. The proposed adaptive optimization approach is based on a reward maximizing routing policy derived from the Markov Decision Process theory although it can be applied to other routing policies. Analytical models as well as simulation of a measurement based implementation of the proposed models are used to evaluate the performance of the proposed approach.
Achieving Energy Conservation, Coverage and Connectivity Requirements in Wireless Sensor Networks

critical application requirements in wireless sensor networks. Related researches have either concerned coverage, connectivity, and energy conservation separately or required sensing/communication range restrictions. In this paper, we aim to maximize the network lifetime, while maintaining coverage and connectivity simultaneously, without any sensing or communication range restriction. We propose MDS-MCC problem and we prove it is NP-complete. We also present a heuristic algorithm to solve MDS-MCC. Furthermore, we study MDS-MCC problem under some special conditions and obtain some important theoretical results.
Intent-aware temporal query modeling for keyword suggestion

This paper presents a data-driven approach for capturing the temporal variations in user search behaviour by modeling the dynamic query relationships using query-log data. The dependence between different queries (in terms of the query words and latent user intent) is represented using hypergraphs which allows us to explore more complex relationships compared to graph-based approaches. This time-varying dependence is modeled using the framework of probabilistic graphical models. The inferred interactions are used for query keyword suggestion - a key task in web information retrieval. Preliminary experiments using query logs collected from internal search engine of a large health care organization yield promising results. In particular, our model is able to capture temporal variations between queries relationships that reflect known trends in disease occurrence. Further, hypergraph-based modeling captures relationships significantly better compared to graph-based approaches.
Quantum margulis expanders

We present a simple way to quantize the well-known Margulis expander map. The result is a quantum expander which acts on discrete Wigner functions in the same way the classical Margulis expander acts on probability distributions. The quantum version shares all essential properties of the classical counterpart, e.g., it has the same degree and spectrum. Unlike previous constructions of quantum expanders, our method does not rely on non-Abelian harmonic analysis. Analogues for continuous variable systems are mentioned. Indeed, the construction seems one of the few instances where applications based on discrete and continuous phase space methods can be developed in complete analogy.