Miracle-dz/UniFAR-FTUs huggingface dataset:
An Operating System Analog to the Perl Data Tainting Functionality

Several recent Internet security incidents, such as the Melissa and ILOVEYOU virus outbreaks, demonstrate fundamental shortcomings in the security provided by general purpose operating systems deployed in the Internet computing environment. These incidents reveal that the security controls on downloaded software and data are often discretionary, relying on user vigilance and competence for their effectiveness. This does not provide sufficient protection. To address this deficiency, we propose extending operating system functionality to support a tainting file system that associates a level of trustworthiness with each software and data file stored on the computer. We also propose developing a set of mandatory trustworthiness policies that govern how trustworthiness is assigned and how files of a given trustworthiness are handled, along with implementing operating system support for such policies. Our scheme will be developed as an overlay for existing Unix environments that support loadable kernel modules. No source code modifications to the operating system will be required. It is anticipated that our approach could be ported to the Windows 95/98/NT environments or directly integrated into a future operating system. The tainting file system is motivated by the data tainting functionality in the Perl programming language and is intended to complement other approaches to computer and network security, including software wrappers, sandboxing, digital signatures, firewalls, and intrusion detection systems. It is envisioned to be one element of a network defense in depth strategy that encompasses all levels of computer and network architecture.
Dark Current and Signal-to-Noise Ratio in BDJ Image Sensors

In this paper, analytical expressions of dark currents and equivalent noise generators of a CMOS color image sensor are presented, and the signal-to-noise ratio (SNR) at both outputs is evaluated. Static measurements and simulations on Austria Micro Systems 0.35-mum CMOS test structures yield guidelines to increase the SNR of the buried double junction photodetector
A Data Management Approach for Dataset Selection Using Human Computation

As the number of applications that use machine learning algorithms increases, the need for labeled data useful for training such algorithms intensifies. Getting labels typically involves employing humans to do the annotation, which directly translates to training and working costs. Crowdsourcing platforms have made labeling cheaper and faster, but they still involve significant costs, especially for the cases where the potential set of candidate data to be labeled is large. In this paper we describe a methodology and a prototype system aiming at addressing this challenge for Web-scale problems in an industrial setting. We discuss ideas on how to efficiently select the data to use for training of machine learning algorithms in an attempt to reduce cost. We show results achieving good performance with reduced cost by carefully selecting which instances to label. Our proposed algorithm is presented as part of a framework for managing and generating training datasets, which includes, among other components, a human computation element.
Absolute Gravity Variation at Sakurajima Volcano from April 2009 through January 2011 and its Relevance to the Eruptive Activity of Showa Crater(＜Special Section＞Sakurajima Special Issue)

and its relevance to the eruptive activity of Showa crater Shuhei Okubo1, Takahito Kazama2, Keigo Yamamoto3, Masato Iguchi3, Yoshiyuki Tanaka1, Yuichi Imanishi1, Wenke Sun4, Mamoru Saka1, Atsushi Watanabe1, Shigeo Matsumoto5 1Earthquake Research Institute, the University of Tokyo, Japan, 2Graduate School of Science, Kyoto University, Japan, 3Disaster Prevention Research Institute, Kyoto University, Japan, 4Graduate University of Chinese Academy of Sciences, Japan, 5Sumiko Consultants Co., Ltd, Japan E-mail: okubo@eri.u-tokyo.ac. jp
Discriminative Reordering with Chinese Grammatical Relations Features

The prevalence in Chinese of grammatical structures that translate into English in different word orders is an important cause of translation difficulty. While previous work has used phrase-structure parses to deal with such ordering problems, we introduce a richer set of Chinese grammatical relations that describes more semantically abstract relations between words. Using these Chinese grammatical relations, we improve a phrase orientation classifier (introduced by Zens and Ney (2006)) that decides the ordering of two phrases when translated into English by adding path features designed over the Chinese typed dependencies. We then apply the log probability of the phrase orientation classifier as an extra feature in a phrase-based MT system, and get significant BLEU point gains on three test sets: MT02 (+0.59), MT03 (+1.00) and MT05 (+0.77). Our Chinese grammatical relations are also likely to be useful for other NLP tasks.
Kernel Selection using Multiple Kernel Learning and Domain Adaptation in Reproducing Kernel Hilbert Space, for Face Recognition under Surveillance Scenario

Face Recognition (FR) has been the interest to several researchers over the past few decades due to its passive nature of biometric authentication. Despite high accuracy achieved by face recognition algorithms under controlled conditions, achieving the same performance for face images obtained in surveillance scenarios, is a major hurdle. Some attempts have been made to super-resolve the low-resolution face images and improve the contrast, without considerable degree of success. The proposed technique in this paper tries to cope with the very low resolution and low contrast face images obtained from surveillance cameras, for FR under surveillance conditions. For Support Vector Machine classification, the selection of appropriate kernel has been a widely discussed issue in the research community. In this paper, we propose a novel kernel selection technique termed as MFKL (Multi-Feature Kernel Learning) to obtain the best feature-kernel pairing. Our proposed technique employs a effective kernel selection by Multiple Kernel Learning (MKL) method, to choose the optimal kernel to be used along with unsupervised domain adaptation method in the Reproducing Kernel Hilbert Space (RKHS), for a solution to the problem. Rigorous experimentation has been performed on three real-world surveillance face datasets : FR\_SURV, SCface and ChokePoint. Results have been shown using Rank-1 Recognition Accuracy, ROC and CMC measures. Our proposed method outperforms all other recent state-of-the-art techniques by a considerable margin.
On SAT Technologies for Dependency Management and Beyond

SAT solvers technology is now mature enough to be part of the engineer toolbox side by side with Mixed Integer Programming and Constraint Programming tools. As of June 2008, two great pieces of software are using SAT technology to manage dependency like problems: the open source Linux distribution OpenSuse 11.0, released on June 19, 2008, integrates a custom SAT solver in their dependency manager Zypp. The new update manager of Eclipse 3.4, called Equinox p2, also uses SAT technology to resolve dependencies in their OSGi platform. The use of SAT technology in Software Product Lines has already been pointed out by several authors. We believe that the current interest for solving dependency management problems in SAT technologies opens quite interesting challenges to the SAT community. First, since those problems are met in software with interactive use, SAT engines need to solve them within seconds. Second, providing one solution is usually not sufficient: finding the best solution is usually what users want. Finally, fully supported open source or commercial SAT engines are needed for a broader adoption in the software engineering community.
County-Scale Spatial Variability of Soil Nutrient Distribution and Determination of Reasonable Sampling Density

Spatial variations of soil organic matter, available nitrogen, phosphorus and potassium were discussed based on geostatistics combined with GIS technology in Wugong County. Spatial interpolation results from different sampling densities of organic matter were analyzed and compared by testing interpolation accuracy under different densities with the Root Mean Square Error(RMSE) and correlation coefficient(r) in order to determine a reasonable number of samples in the county. The results showed that there existed moderate spatial variability in the soil nutrient. The order of coefficient of variation of soil nutrient was available phosphorusavailable potassiumorganic matter available nitrogen. The soil nutrients, with positive base effect, showed moderate spatial variation and moderate spatial autocorrelation. Among them, the spatial variability of organic matter and potassium was little affected by human factors, but related with the soil parent material, soil type, climate conditions, respectively. Nevertheless, the spatial variability of available nitrogen and phosphorus was much influenced by human factors, and related with the tillage and fertilizer agricultural practices. With the increase of sampling density, kriging interpolation accuracy was improved. It was possible to reduce the number of samples to satisfy the need of the research regarding spatial variability of soil nutrients. Taking spatial prediction accuracy into consideration, more than 2213 samples should be the suitable number to evaluate the spatial variability of soil organic matter on county scale, and the maximum sampling unit was 17.8 hm2.
Adaptive oscillators with human-in-the-loop: Proof of concept for assistance and rehabilitation

Most recent findings in robot-assisted therapy suggest that the therapy is more successful if the patient actively participates to the movement (“assistance-as-needed”). In the present contribution, we propose a novel approach for designing highly flexible protocols based on this concept. This approach uses adaptive oscillators: a mathematical primitive having the capacity to learn the high-level features of a quasi-sinusoidal signal (amplitude, frequency, offset). Using a simple inverse model, we demonstrate that this method permits to synchronize with the torque produced by the user, such that the effort associated with the movement production is shared between the user and the assistance device, without specifying any arbitrary reference trajectory. Simulation results also establish the method relevance for helping patients with movement disorders. Since our method is specifically designed for rhythmic movements, the final target is the assistance/rehabilitation of locomotory tasks. As an initial proof of concept, this paper focuses on a simpler movement, i.e. rhythmic oscillations of the forearm about the elbow.
Assessment of MERRA-2 Land Surface Energy Flux Estimates

AbstractIn the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) system the land is forced by replacing the model-generated precipitation with observed precipitation before it reaches the surface. This approach is motivated by the expectation that the resultant improvements in soil moisture will lead to improved land surface latent heating (LH). Here we assess aspects of the MERRA-2 land surface energy budget and 2 m air temperatures (T2m). For global land annual averages, MERRA-2 appears to overestimate the LH (by 5 Wm−2 ), the sensible heating (by 6 Wm−2), and the downwelling shortwave radiation (by 14 Wm−2), while underestimating the downwelling and upwelling (absolute) longwave radiation (by 10-15 Wm−2 each). These results differ only slightly from those for NASA’s previous reanalysis, MERRA. Comparison to various gridded reference data sets over Boreal summer (June-July-August) suggests that MERRA-2 has particularly large positive biases (>20 Wm−2) where LH is energ.
Research on the Improvement of the Concurrency Control Protocol for Real-Time Transactions

The traditional lock-based concurrency control mechanism which focuses on the data’s consistency and the transactions’ concurrency cannot meet the demand that the real-time database systems make on the temporal consistency. After the improvement of the concurrency control protocol for real-time transactions, this paper will present a new concurrency control method which is based on the locking, multi-version and valid confirmation concurrency control mechanisms. This method can adopt different concurrency control mechanisms according to the idiographic situation. In this way it can effectively improve the concurrency of transactions and increase the amount of the transactions completed within the deadline. The feasible analysis denotes that this new method is better than the traditional one on performance?
Modification of polyethylene glycol onto solid lipid nanoparticles encapsulating a novel chemotherapeutic agent (PK-L4) to enhance solubility for injection delivery

Background The synthetic potential chemotherapeutic agent 3-Chloro-4-[(4-methoxyphenyl) amino]furo[2,3-b]quinoline (PK-L4) is an analog of amsacrine. The half-life of PK-L4 is longer than that of amsacrine; however, PK-L4 is difficult to dissolve in aqueous media, which is problematic for administration by intravenous injection. Aims To utilize solid lipid nanoparticles (SLNs) modified with polyethylene glycol (PEG) to improve the delivery of PK-L4 and investigate its biodistribution behavior after intravenous administration. Results The particle size of the PK-L4-loaded SLNs was 47.3 nm and the size of the PEGylated form was smaller, at 28 nm. The entrapment efficiency (EE%) of PK-L4 in SLNs with and without PEG showed a high capacity of approximately 100% encapsulation. Results also showed that the amount of PK-L4 released over a prolonged period from SLNs both with and without PEG was comparable to the non-formulated group, with 16.48% and 30.04%, respectively, of the drug being released, which fit a zero-order equation. The half-maximal inhibitory concentration values of PK-L4-loaded SLNs with and those without PEG were significantly reduced by 45%–64% in the human lung carcinoma cell line (A549), 99% in the human breast adenocarcinoma cell line with estrogen receptor (MCF7), and 95% in the human breast adenocarcinoma cell line (MDA-MB-231). The amount of PK-L4 released by SLNs with PEG was significantly higher than that from the PK-L4 solution (P < 0.05). After intravenous bolus of the PK-L4-loaded SLNs with PEG, there was a marked significant difference in half-life alpha (0.136 ± 0.046 hours) when compared with the PK-L4 solution (0.078 ± 0.023 hours); also the area under the curve from zero to infinity did not change in plasma when compared to the PK-L4 solution. This demonstrated that PK-L4-loaded SLNs were rapidly distributed from central areas to tissues and exhibited higher accumulation in specific organs. The highest deposition of PK-L4-loaded SLNs with PEG was found in the lung and spine. Conclusion Sufficient amounts of PK-L4 were entrapped in the SLNs, and the pharmacokinetic behavior of PK-L4-loaded SLNs was established. This formulation successfully resolved the delivery problem, and the drug was localized in particular organs.
Statistical methodology to identify optimal placement of on-chip process monitors for predicting fmax

In previous literatures, many approaches use ring oscillators or other process monitors to correlate the chip's maximum operating frequency (Fmax). But none of them focus on the placement of these on-chip process monitors (OPMs) on a chip. The placement will greatly influence the accuracy of a prediction model. In this paper, we first propose a simulation framework to sample a chip's Fmax and it's OPM result. These samples are used to develop our methodology of OPM placement and to verify the effectiveness of an OPM placement. Then, a model-fitting framework is presented to correlate the OPMs' result to chip's Fmax. Finally, we propose a methodology to idenify optimal placement of OPM for predicting Fmax. The experiments demonstrate the effectiveness of our methodology in both simulation and silicon data.
The evolution of barred spiral galaxies in the Hubble Deep Fields North and South

The frequency of barred spiral galaxies as a function of redshift contains important information on the gravitational influence of stellar disks in their dark matter halos and also may distinguish between contemporary theories for the origin of galactic bulges. In this paper we present a new quantitative method for determining the strength of barred spiral structure, and verify its robustness to redshift-dependent effects. By combining galaxy samples from the Hubble Deep Field North with newly available data from the Hubble Deep Field South, we are able to define a statistical sample of 18 objectively-defined low-inclination barred spiral systems with I814W < 23.2 mag. Analysing the proportion of barred spiral galaxies seen as a function of redshift, we find a significant decline in the barred fraction beyond redshifts z ≃ 0.5. The physical significance of this effect remains unclear, but several possibilities include dynamically hotter (or increasingly dark-matter dominated) high-redshift discs, or an enhanced efficiency in bar destruction at high redshifts. By investigating the formation of the “orthogonal” axis of Hubble’s classification tuning fork, our result complements studies of evolution in the early–late sequence, and pushes to later epochs the redshift at which the Hubble classification sequence is observed to be in place.
Effects of agronomic factors on yield and quality of whole corn plants and the impact of feeding high concentrations of corn silage in diets containing distillers grains to finishing cattle1

Abstract Corn plants were sampled over 2 consecutive years to assess the effects of corn hybrid maturity class, plant population, and harvest time on whole corn plant quality and yield in Nebraska. A finishing experiment evaluated the substitution of corn with corn silage in diets with corn modified distillers grains with solubles (MDGS). The first 2 harvest dates were at the mid- and late-silage harvest times whereas the final harvest was at the grain harvest stage of plant maturity. Whole plant yields increased as harvest time progressed (yr 1 quadratic P < 0.01; yr 2 linear P < 0.01). However, differences in TDN concentration in both years were quite minimal across harvest time, because grain percentage increased but residue NDF in-situ disappearance decreased as harvest time was delayed. In the finishing experiment, as corn silage inclusion increased from 15 to 55% (DM basis) by replacing dry rolled and high moisture corn grain with corn silage in diets containing 40% MDGS, DMI, ADG, and G:F linearly decreased (P ≤ 0.01), with the steers on the 15% corn silage treatment being 1.5%, 5.0%, and 7.7% more efficient than steers on treatments containing 30, 45, and 55% corn silage, respectively. Calculated dietary NEm and NEg decreased linearly as corn silage inclusion increased indicating that net energy values were greater for corn grain than for corn silage. In addition, dressing percentage decreased linearly (P < 0.01) as silage inclusion increased suggesting more fill as silage inclusion increases in diets. Cattle fed greater than 15% corn silage in finishing diets based on corn grain will gain slower and be slightly less efficient and likely require increased days to market at similar carcass fatness and size. When 30% silage was fed with 65% MDGS, DMI, and ADG were decreased (P < 0.01) compared to feeding 30% silage with 40% MDGS suggesting some benefit to including a proportion of corn in the diet. Conversely, when 45% silage was fed with 40% MDGS, ADG, and G:F were greater (P < 0.04) than when 45% silage was fed with just grain implying a greater energy value for MDGS than for corn grain. Substituting corn silage for corn grain in finishing diets decreased ADG and G:F which would increase days to finish to an equal carcass weight; however, in this experiment, increasing corn silage levels with MDGS present reduced carcass fat thickness without significantly decreasing marbling score.
Petrophysical parameters estimation and reservoir characterisation based on well log data in the Joya Mair Oil Field, Minwal X-01 Well, Potwar Sub-Basin and Upper Indus Basin of Pakistan

The aim of this study is to assess the reservoir potential of the Eocene age Chorgali and Sakesar formations encountered in Minwal X-01 Well Potwar Sub-Basin.The current study is concerned with the reservoir potential evaluation using petrophysical approach of Minwal X-01 located in Potwar basin. Minwal X-01 is selected for research, where the prevailing assessment involves identifying, marking, analysing and interpreting the reservoir zones from well logs and computational analysis through different formulas. Several well logs were used comprising sonic log, gamma ray log, density log, neutron log, and resistivity logs. The petrophysical parameters such as the shale volume (Vsh), total porosity (PHT), effective porosity (PHE), water saturation (Sw), hydrocarbon saturation (Sh), and pay zone were assessed from wireline logs. The best possible reservoirs in the study area are Chorgali and Sakesar formations in Minwal X-01. These reservoirs have good net pay zones thicknesses (20-52 feet). Based on well log data, these reservoirs are classified as excellent reservoirs owing to their high effective porosity and hydrocarbon saturation.
A Distributed Cyber-security Framework for Heterogeneous Environments

Evolving business models, computing paradigms, and management practices are rapidly re-shaping the usage models of ICT infrastructures, and demanding for more ﬂexibility and dynamicity in enterprise security, beyond the traditional “security perimeter” approach. Since valuable ICT assets cannot be easily enclosed within a trusted physical sandbox any more, there is an increasing need for a new generation of pervasive and capillary cyber-security paradigms over distributed and geographically-scattered systems. Following the generalized trend towards virtualization, automation, software-deﬁnition, and hardware/software disaggregation, in this paper we elaborate on a multi-tier architecture made of a common, programmable, and pervasive data-plane and a powerful set of multi-vendor detection and analysis algorithms. Our approach leverages the growing level of programmability of ICT infrastructures to create a common and uniﬁed framework that could be used to monitor and protect distributed heterogeneous environments, including legacy enterprise networks, IoT installations, and virtual resources deployed in the cloud.