Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy-Preserving Decentralized Key-Policy Attribute-Based Encryption

Decentralized attribute-based encryption (ABE) is a variant of a multiauthority ABE scheme where each authority can issue secret keys to the user independently without any cooperation and a central authority. This is in contrast to the previous constructions, where multiple authorities must be online and setup the system interactively, which is impractical. Hence, it is clear that a decentralized ABE scheme eliminates the heavy communication cost and the need for collaborative computation in the setup stage. Furthermore, every authority can join or leave the system freely without the necessity of reinitializing the system. In contemporary multiauthority ABE schemes, a user's secret keys from different authorities must be tied to his global identifier (GID) to resist the collusion attack. However, this will compromise the user's privacy. Multiple authorities can collaborate to trace the user by his GID, collect his attributes, then impersonate him. Therefore, constructing a decentralized ABE scheme with privacy-preserving remains a challenging research problem. In this paper, we propose a privacy-preserving decentralized key-policy ABE scheme where each authority can issue secret keys to a user independently without knowing anything about his GID. Therefore, even if multiple authorities are corrupted, they cannot collect the user's attributes by tracing his GID. Notably, our scheme only requires standard complexity assumptions (e.g., decisional bilinear Diffie-Hellman) and does not require any cooperation between the multiple authorities, in contrast to the previous comparable scheme that requires nonstandard complexity assumptions (e.g., q-decisional Diffie-Hellman inversion) and interactions among multiple authorities. To the best of our knowledge, it is the first decentralized ABE scheme with privacy-preserving based on standard complexity assumptions.
iLamps: geometrically aware and self-configuring projectors

Projectors are currently undergoing a transformation as they evolve from static output devices to portable, environment-aware, communicating systems. An enhanced projector can determine and respond to the geometry of the display surface, and can be used in an ad-hoc cluster to create a self-configuring display. Information display is such a prevailing part of everyday life that new and more flexible ways to present data are likely to have significant impact. This paper examines geometrical issues for enhanced projectors, relating to customized projection for different shapes of display surface, object augmentation, and co-operation between multiple units.We introduce a new technique for adaptive projection on non-planar surfaces using conformal texture mapping. We describe object augmentation with a hand-held projector, including interaction techniques. We describe the concept of a display created by an ad-hoc cluster of heterogeneous enhanced projectors, with a new global alignment scheme, and new parametric image transfer methods for quadric surfaces, to make a seamless projection. The work is illustrated by several prototypes and applications.
Towards Enhancing In-Context Learning for Code Generation

In-contextlearning(ICL)withpre-trainedlanguagemodels (PTLMs) has shown great success in code generation. ICL does not require training. PTLMs take as the input a prompt consisting of a few requirement-code examples and a new requirement, and output a new program. However, existing studies simply reuse ICL techniques for natural language generation and ignore unique features of code generation. We refer to these studies as standard ICL . Inspired by observations of the human coding process, we pro-pose a novel ICL approach for code generation named AceCoder. Compared to standard ICL, AceCoder has two novelties. (1) Example retrieval. It retrieves similar programs as examples and learns programming skills ( e.g., algorithms, APIs) from them. (2) Guided Code Generation. It encourages PTLMs to output an intermediate preliminary ( e.g., test cases, APIs) before generating programs. The preliminary can help PTLMs understand requirements and guide the next code generation. We apply AceCoder to six PTLMs ( e.g., Codex) and evaluate it on three public benchmarks using the Pass@k. Results show that AceCoder can significantly improve the performance of PTLMs on code generation. (1) In terms of Pass@1, AceCoder outperforms standard ICL by up to 79.7% and fine-tuned models by up to 171%. (2) AceCoder is effective in PTLMs with different sizes ( e.g., 1B to 175B) and different languages ( e.g.,
Monitoring and modeling terrestrial arthropod diversity on the Kenai National Wildlife Refuge

The primary purpose of the Kenai National Wildlife Refuge (KENWR) is to “conserve fish and wildlife populations in their natural diversity,” where “fish and wildlife” explicitly includes arthropods. To this end, we developed a Long Term Ecological Monitoring Program (LTEMP), a collaborative effort with the USDA Forest Inventory and Analysis (FIA) Program. In 2004 and 2006, we sweep-netted terrestrial arthropods on 255 100m2 circular plots systematically distributed at 5-km intervals over the 805,000 ha KENWR. These samples yielded 15,136 specimens, which were sorted to families and to species when possible. The comprehensive spatial coverage of the LTEMP sampling design provided spatial data suitable for species distribution modeling, but we sought to improve upon this design by explicitly accounting for imperfect detection. We proposed a rotating panel design where each site would be visited once every ten years. Imperfect detection would be accounted for by spatial sub-sampling within plots. Using Monte-Carlo simulation, we assessed the proposed design of LTEMP for accurately monitoring changes in arthropod species distributions over time. Our simulations demonstrated that, for species that are likely to be collected in a single 50m2 sweep net sample where they are present, the proposed LTEMP sampling design should provide accurate estimates of species distributions and local rates of colonization and extinction over the long-term. In order to document the landscape-scale patterns of arthropod diversity over the KENWR, we modeled arthropod family richness using random forest regression. Arthropod family richness data were obtained from LTEMP and topographic, temporal, and productivity variables were obtained from GIS datasets. The resulting map explained 22% of variation of diversity of sweep net samples. Highest diversity was predicted on the margins of coastal wetlands and in productive hardwood and mixed forests; lowest diversity was predicted at barren alpine sites.
Changes in the glaciers of Chandra–Bhaga basin, Himachal Himalaya, India, between 1980 and 2010 measured using remote sensing

This study reports the glacier changes of Chandra–Bhaga basin, northwest Himalaya, India, from 1980 to 2010. Satellite remote-sensing data from the Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM), the Linear Imaging Self Scanning Sensor (LISS) and Advanced Wide Field Sensor (AWiFS) of the Indian Remote Sensing (IRS) series, and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) were used to study the changes in glacier parameters such as glacier area, length, snout elevation, and the impact of glacier topographical parameters (glacier slope, aspect, and altitude range) on the glacier changes. It was found that the total glaciated area had shrunk to 368.2 km2 in 2010 from 377.6 km2 in 1980, a loss of 2.5%. The average position of glacier terminuses retreated by 465.5 ± 169.1 m from 1980 to 2010 with an average rate of 15.5 ± 5.6 m year−1. The decadal scale analysis showed that the average rate of retreat had increased the most in the recent decade. A moraine-dammed lake located in the study region was found to have expanded in area from (0.65 ± 0.01) km2 in 1980 to (1.26 ± 0.03) km2 in 2010. Glaciers with steep slope and less altitude range have lost more area than the glaciers having gentle slope and greater altitude range.
TVM: End-to-End Optimization Stack for Deep Learning

Scalable frameworks, such as TensorFlow, MXNet, Caffe, and PyTorch drive the current popularity and utility of deep learning. However, these frameworks are optimized for a narrow range of server-class GPUs and deploying workloads to other platforms such as mobile phones, embedded devices, and specialized accelerators (e.g., FPGAs, ASICs) requires laborious manual effort. We propose TVM, an end-to-end optimization stack that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. We discuss the optimization challenges specific to deep learning that TVM solves: high-level operator fusion, low-level memory reuse across threads, mapping to arbitrary hardware primitives, and memory latency hiding. Experimental results demonstrate that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art libraries for low-power CPU and server-class GPUs. We also demonstrate TVM's ability to target new hardware accelerator back-ends by targeting an FPGA-based generic deep learning accelerator. The compiler infrastructure is open sourced.
A history of Japanese astronomy : Chinese background and Western impact

Eventually, you will very discover a new experience and finishing by spending more cash. nevertheless when? realize you believe that you require to get those all needs in the manner of having significantly cash? Why dont you try to get something basic in the beginning? Thats something that will guide you to comprehend even more approaching the globe, experience, some places, subsequently history, amusement, and a lot more?
Expert Elicitation of Adversary Preferences Using Ordinal Judgments

We introduce a simple elicitation process where subject-matter experts provide only ordinal judgments of the attractiveness of potential targets, and the adversary utility of each target is assumed to involve multiple attributes. Probability distributions over the various attribute weights are then mathematically derived (using either probabilistic inversion or Bayesian density estimation). This elicitation process reduces the burden of time-consuming orientation and training in traditional methods of attribute weight elicitation, and explicitly captures the existing uncertainty and disagreement among experts, rather than attempts to achieve consensus by eliminating them. We identify the relationship between the two methods and conduct sensitivity analysis to elucidate how these methods handle expert consensus or disagreement. We also present a real-world application on elicitation of adversarial preferences over various attack scenarios to show the applicability of our proposed methods.
Is Hosting the Games Enough to Win? A predictive economic model of medal wins at 2014 Winter Olympics

An econometric model which has first been estimated on medal wins at Summer Olympics and has predicted 88% of medal distribution at Beijing Games 2008, is revisited for Winter Olympics. After changing some variables to take into account the winter sports specificity, the model is estimated again on all Winter Games since 1964.Then it is used to predict (forecast) the medal distribution per country at the 2014 Sochi Winter Olympics.
Identification of dynamic properties of OII landfill

The performance of landfills during strong earthquake shaking is a matter of considerable concern and deserves to be studied more extensively. This paper investigates the dynamic properties of the 011 landfill materials using strong motion recordings, available field measurements, and simplified physical models. Although the earthquakes recorded at the 011 landfill generated shear strains as large as 0.08%, the landfill materials behaved as essentially a linear viscoelastic material, showing a negligible reduction in shear modulus with shear strain amplitude. The damping responsible for energy dissipation was found to depend on frequencies between 0.1 and 10 Hz. The physical origins of this frequency-dependent damping are not yet clearly understood. The results of this study are useful in analyzing the dynamic response of landfills and cap systems during small to moderate-size earthquakes. The present analysis is simplified because there were only two recording instruments at the 011 landfill. It is recommended that researchers deploy more field instruments at this and other landfills to document their dynamic response during future earthquakes, and develop large-scale laboratory tests to de­ termine landfill material properties under large static and dynamic strains.
Specification of the Ip Flow Information Export (ipfix) File Format

This document describes a file format for the storage of flow data based upon the IP Flow Information Export (IPFIX) protocol. It proposes a set of requirements for flat-file, binary flow data file formats, then specifies the IPFIX File format to meet these requirements based upon IPFIX Messages. This IPFIX File format is designed to facilitate interoperability and reusability among a wide variety of flow storage, processing, and analysis tools.
Modeling Nonlinear Audio Effects with End-to-end Deep Neural Networks

In the context of music production, distortion effects are mainly used for aesthetic reasons and are usually applied to electric musical instruments. Most existing methods for nonlinear modeling are often either simplified or optimized to a very specific circuit. In this work, we investigate deep learning architectures for audio processing and we aim to find a general purpose end-to-end deep neural network to perform modeling of nonlinear audio effects. We show the network modeling various nonlinearities and we discuss the generalization capabilities among different instruments.
Tsunamis: The Response of Harbours With Sloping Boundaries to Long Wave Excitation

The influence of sloping boundaries on the long wave response of bays and harbours is studied in this work. Laboratory experiments are performed to help validate the theoretical analysis which is applicable to nonbreaking waves. A set of long wave equations in the Lagrangian description is derived which includes terms to account for nonlinear, dispersive, and dissipative processes for wave propagation in two horizontal coordinates. A finite element model is developed based on these equations which is capable of treating arbitrary geometry and the runup of nonbreaking waves on a beach. An analytical harbour response model, capable of treating narrow rectangular harbours with variable bathymetry and sidewall geometry, is developed and applied to several simple geometries. The model shows that for a given harbour length and entrance width, the resonant frequencies and the response of a harbour are very dependent on the harbour sidewall geometry and bathymetry. Some of the nonlinear effects of the runup of nonbreaking periodic waves on a plane beach are discussed. In particular, the time average of the water surface time history at a fixed spatial location is negative and the wave crests are smaller than the troughs. Nonlinear effects do not alter the runup maxima or minima and the maximum fluid acceleration occurs at the point of maximum rundown of the wave. Laboratory experiments were performed to determine the long wave reponse of a narrow rectangular harbour whose still water depth decreases linearly between the harbour entrance and the shoreline. Good agreement with the finite element model was obtained, including the prediction of the depression of the mean water level within the harbour. A three-dimensional application of the finite element model treats the runup of solitary waves on a coastline with variable bottom topography and a curved shoreline. The results indicate that the model can predict the trapping of wave energy along a sloping coastal margin, a process of fundamental importance for predicting potential tsunami damage.
The CMU-UKA statistical machine translation systems for IWSLT 2007

This paper describes the CMU-UKA statistical machine translation systems submitted to the IWSLT 2007 evaluation campaign. Systems were submitted for three language-pairs: Japanese→English, Chinese→English and Arabic→English. All systems were based on a common phrase-based SMT (statistical machine translation) framework but for each language-pair a specific research problem was tackled. For Japanese→English we focused on two problems: first, punctuation recovery, and second, how to incorporate topic-knowledge into the translation framework. Our Chinese→English submission focused on syntaxaugmented SMT and for the Arabic→English task we focused on incorporating morphological-decomposition into the SMT framework. This research strategy enabled us to evaluate a wide variety of approaches which proved effective for the language pairs they were evaluated on.
A Perfect Secret Sharing Scheme for (r-1, r)-Uniform Hypergraph-Based Access Structures

A secret sharing scheme is a method to distribute a secret among a set of participants, such that only qualified subsets of the participants can recover the secret. A secret sharing scheme is perfect if any unqualified subset obtains no information regarding the master key. The collection of qualified subsets is called access structure. In a hypergraph, if the size of edges is equal to r, the hypergraph is called an r-uniform hypergraph. An r-uniform hypergraph-based access structure is an access structure which using an r-uniform hypergraph to show the access structure, where a vertex denote a participant and the edge set denote the minimal access structure of a secret sharing scheme. Given any r-uniform hypergraph G and Γ is the G-based access structure, this paper proposes a perfect secret sharing scheme for Γ, and this scheme is the most efficient secret sharing scheme for Γ up to now.
Computation of recurrent minimal genomic alterations from array-CGH data

MOTIVATION The identification of recurrent genomic alterations can provide insight into the initiation and progression of genetic diseases, such as cancer. Array-CGH can identify chromosomal regions that have been gained or lost, with a resolution of approximately 1 mb, for the cutting-edge techniques. The extraction of discrete profiles from raw array-CGH data has been studied extensively, but subsequent steps in the analysis require flexible, efficient algorithms, particularly if the number of available profiles exceeds a few tens or the number of array probes exceeds a few thousands. RESULTS We propose two algorithms for computing minimal and minimal constrained regions of gain and loss from discretized CGH profiles. The second of these algorithms can handle additional constraints describing relevant regions of copy number change. We have validated these algorithms on two public array-CGH datasets. AVAILABILITY From the authors, upon request. CONTACT celine@lri.fr SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Soil–pile separation effect on the performance of a pile group under static and dynamic lateral loads

The effect of soil–pile separation is studied with respect to the performance of a laterally loaded pile group. Full-scale tests, which consist of a combination of a single and a 3 × 5 group pile under static and dynamic lateral loads, present a unique opportunity and allow a rigorous study without arbitrary parameter back-fitting. The coupled soil–pile system is idealized through two-dimensional finite elements with soil models idealized by a hyperbolic-type multiple shear mechanism. Nonlinear spring elements are used to idealize the soil–pile interaction through a hysteretic nonlinear load–displacement relationship. Joint elements with a separation–contact mechanism are used to idealize the separation effect at the soil–pile interface. Ignoring soil–pile separation in static tests overestimates the ultimate lateral load–carrying capacity by 43% for a single pile and 73% for the trailing pile in a closely spaced pile group. Moreover, neglecting soil–pile separation in dynamic tests overestimates the tota.
Investment in Business Software and Perceived Utility: An Empirical Study

Authors have identified many different reasons why companies invest into IT and there has been much discussion of how IT can generate value. This paper adds to the discussion of IT value generation by investigating investments in business software for the support of business processes in Swiss SMEs. In an empirical survey on the future IT investment for process support, 917 questionnaires were collected and analysed. In a first step, using exploratory factor analysis, two factors representing different sets of business software modules were identified: (1) basic modules and (2) specialized modules. In a second step, using cluster analysis, we identified four typical characteristic company profiles regarding investments in business software: (1) the IT convinced, (2) the IT differentiators, (3) the IT sceptics, and (4) the IT pragmatists. To further explore and profile the clusters, we ran several bivariate analyses with selected questions from the questionnaire. This study has both theoretical and managerial relevance as it helps to understand firm attitudes towards investments and business software.
Dear Sir or Madam, May I introduce the YAFC Corpus: Corpus, Benchmarks and Metrics for Formality Style Transfer

Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus for a particular stylistic transfer (formality) and show that techniques from the machine translation community can serve as strong baselines for future work. We also discuss challenges of using automatic metrics.
Adversarial Manifold Matching via Deep Metric Learning for Generative Modeling

We propose a manifold matching approach to generative models which includes a distribution generator (or data generator) and a metric generator. In our framework, we view the real data set as some manifold embedded in a high-dimensional Euclidean space. The distribution generator aims at generating samples that follow some distribution condensed around the real data manifold. It is achieved by matching two sets of points using their geometric shape descriptors, such as centroid and p -diameter, with learned distance metric; the metric generator utilizes both real data and generated samples to learn a distance metric which is close to some intrinsic geodesic distance on the real data manifold. The produced distance metric is further used for manifold matching. The two networks are learned simultaneously during the training process. We apply the approach on both unsupervised and supervised learning tasks: in un-conditional image generation task, the proposed method obtains competitive results compared with existing generative models; in super-resolution task, we incorporate the framework in perception-based models and improve visual qualities by producing samples with more natural textures. Both theoretical analysis and real data experiments guarantee the feasibility and effectiveness of the proposed framework.