Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive Security Framework for Global Threads Analysis

Cyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios.
Applying domain engineering using RAISE into a particular banking domain

Recently, several flavors of formal methods have been gaining industrial acceptance and production quality software tools have begun emerging. Domain Engineering (DE) has been introduced as one of outstanding ideas in software development. It serves Formal Methods (FMs) that provide a rigorous, mathematical based framework for specifying, defining, and verifying systems in the software development for critical systems. New software engineering includes three phases consisting of domain engineering, requirement engineering, and design and implementation. The purpose of this paper is to introduce the key concepts of domain engineering with providing overview information about formal methods and applying RAISE as a formal method that has been used successfully on various, diverse applications. Then, a particular banking domain is specified with using them. Formal model presented in this paper is provable to implement this domain. With merginging object oriented technology with domain engineering, this model can be extended for serving in distributed systems. It improves reusability and reliability in such environments.
Atomic vacancies significantly degrade the mechanical properties of phosphorene

Due to low formation energies, it is very easy to create atomic defects in phosphorene during its fabrication process. How these atomic defects affect its mechanical behavior, however, remain unknown. Here, we report on a systematic study of the effect of atomic vacancies on the mechanical properties and failure behavior of phosphorene using molecular dynamics simulations. It is found that atomic vacancies induce local stress concentration and cause early bond-breaking, leading to a significant degradation of the mechanical properties of the material. More specifically, a 2% concentration of randomly distributed mono-vacancies is able to reduce the fracture strength by ∼40%. An increase in temperature from 10 to 400 K can further deteriorate the fracture strength by ∼60%. The fracture strength of defective phosphorene is also found to be affected by defect distribution. When the defects are patterned in a line, the reduction in fracture strength greatly depends on the tilt angle and the loading direction. Furthermore, we find that di-vacancies cause an even larger reduction in fracture strength than mono-vacancies when the loading is in an armchair direction. These findings provide important guidelines for the structural design of phosphorene in future applications.
A "WATERSHED APPROACH" TO TMDL DEVELOPMENT

The State of Minnesota, in an effort to streamline the identification of impaired waters and sub- sequent TMDL development, has developed a watershed approach to condition monitoring and assessment similar to other states such as Ohio and Indiana. The watershed approach uses intensive biological monitoring (fish and macroinvertebrates) to determine biologically impaired stream reaches. Follow up water quality monitoring in the identified impaired reaches is then used to identify the cause of the impairment through a stressor identification process. The idea behind the watershed approach is to identify all of the impairments at once providing an oppor- tunity to address the impairments through a coordinated TMDL process. Although the "Watershed Approach" provides a focused framework for the identification of impaired wa- ters and TMDLs, this approach is highly dependent on the development of biological indices for the evaluation of biological communities. These indices require a signifi- cant investment in time and resources to develop. Fur- thermore, the results of the Stressor Identification process routinely results in identifying stressors such as lack of habitat or altered hydrology not easily addressed in the TMDL framework. As a result, a large amount of crea- tivity is required in the development of these TMDLs. This paper will review the "Watershed Approach" using several examples where the process has been ap- plied in Minnesota. The authors will review the necessary scientific information required for this approach, identify the strengths and weaknesses, and outline how this ap- proach may be applicable to Southeast US watersheds.
Leveraging Hybrid Recommenders with Multifaceted Implicit Feedback

Research into recommender systems has focused on the importance of con- sidering a variety of users' inputs for an efficient capture of their main interests. How- ever, most collaborative filtering efforts are related to latent factors and implicit feed- back, which do not consider the metadata associated with both items and users. This article proposes a hybrid recommender model which exploits implicit feedback from users by considering not only the latent space of factors that describes the user and item, but also the available metadata associated with content and individuals. Such de- scriptions are an important source for the construction of a user's profile that contains relevant and meaningful information about his/her preferences. The proposed model is generic enough to be used with many descriptions and types and characterizes users and items with distinguished features that are part of the whole recommendation process. The model was evaluated with the well-known MovieLens dataset and its composing modules were compared against other approaches reported in the literature. The results show its effectiveness in terms of prediction accuracy.
Integrated Chinese Word Segmentation in Statistical Machine Translation

A Chinese sentence is represented as a sequence of characters, and words are not separated from each other. In statistical machine translation, the conventional approach is to segment the Chinese character sequence into words during the pre-processing. The training and translation are performed afterwards. However, this method is not optimal for two reasons: 1. The segmentations may be erroneous. 2. For a given character sequence, the best segmentation depends on its context and translation. In order to minimize the translation errors, we take different segmentation alternatives instead of a single segmentation into account and integrate the segmentation process with the search for the best translation. The segmentation decision is only taken during the generation of the translation. With this method we are able to translate Chinese text at the character level. The experiments on the IWSLT 2005 task showed improvements in the translation performance using two translation systems: a phrase-based system and a finite state transducer based system. For the phrase-based system, the improvement of the BLEU score is 1.5% absolute.
The Time Use and Activity Levels of Inpatients in a Co-located Acute and Rehabilitation Stroke Unit: An Observational Study

Abstract Purpose: Stroke patients spend a large proportion of their day alone and inactive. In one rehabilitation unit, practice changes had been implemented over several years to improve patient activity levels and practice, yet measures of patient activity had not been recorded. The aim of this study was to obtain baseline measures of the time use and activity levels of inpatients in that co-located acute and rehabilitation stroke unit. Methods: A quantitative observational design was used. The behavior of 11 inpatients was mapped at 15-minute intervals over 4 days between 7 am and 7 pm. Observations were recorded across 7 categories, including physical activity, location and person(s) present, and 42 possible subcategories. Thirteen allied health and nursing professionals were trained to record these observations and interrater reliability was examined. Frequencies, cross-tabulations, and t tests were used for data analysis. Results: Stroke patients spent a large proportion of the day in their bedroom, engaged in solitary behavior and being inactive (76%, 44% and 62% of observed time, respectively). Patients spent 11% of their weekday with an allied health or nursing professional. Good interrater agreement was achieved between raters for 41 of the 42 observational categories. Conclusions: These data provide a baseline for comparison following implementation of more practice change. A trained team of health professionals was able to reliably collect data.
Cost optimization based network deployment strategies for future dense networks

Deploying dense small cell networks overlaying to the conventional macro cell networks is widely regarded as a key step towards network architecture revolution for improved coverage and user rate. Small cells have different kinds, and a target coverage can be achieved through deploying different kinds of small cells. Previous works mainly focus their attention on the optimization of spectrum and energy efficiency, which are less involved with the deployment cost and base station (BS) types. In this paper, we propose a theoretical methodology based on cost optimization to find the type and corresponding density of BS deployed using tools from stochastic geometry theory. Two cost optimization problems are formulated to obtain the optimal deployment cost which submitted to target user date rate (UDR) and coverage probability (CP) constraints. And the obtained equation of deployment cost provides insights on it can be affected by networking settings, such as BS density, user equipment (UE) density, BS transmit power and bandwidth. Based on the numerical simulation, we are able to find the optimal cell density and the corresponding optimal BS transmit power for achieving an optimal cost when other parameters are pre-setting.
Soil C and N availability determine the priming effect: microbial N mining and stoichiometric decomposition theories

The increasing input of anthropogenically derived nitrogen (N) to ecosystems raises a crucial question: how does available N modify the decomposer community and thus affects the mineralization of soil organic matter (SOM). Moreover, N input modifies the priming effect (PE), that is, the effect of fresh organics on the microbial decomposition of SOM. We studied the interactive effects of C and N on SOM mineralization (by natural 13C labelling adding C4‐sucrose or C4‐maize straw to C3‐soil) in relation to microbial growth kinetics and to the activities of five hydrolytic enzymes. This encompasses the groups of parameters governing two mechanisms of priming effects – microbial N mining and stoichiometric decomposition theories. In sole C treatments, positive PE was accompanied by a decrease in specific microbial growth rates, confirming a greater contribution of K‐strategists to the decomposition of native SOM. Sucrose addition with N significantly accelerated mineralization of native SOM, whereas mineral N added with plant residues accelerated decomposition of plant residues. This supports the microbial mining theory in terms of N limitation. Sucrose addition with N was accompanied by accelerated microbial growth, increased activities of β‐glucosidase and cellobiohydrolase, and decreased activities of xylanase and leucine amino peptidase. This indicated an increased contribution of r‐strategists to the PE and to decomposition of cellulose but the decreased hemicellulolytic and proteolytic activities. Thus, the acceleration of the C cycle was primed by exogenous organic C and was controlled by N. This confirms the stoichiometric decomposition theory. Both K‐ and r‐strategists were beneficial for priming effects, with an increasing contribution of K‐selected species under N limitation. Thus, the priming phenomenon described in ‘microbial N mining’ theory can be ascribed to K‐strategists. In contrast, ‘stoichiometric decomposition’ theory, that is, accelerated OM mineralization due to balanced microbial growth, is explained by domination of r‐strategists.
Seven N-terminal Residues of a Thermophilic Xylanase Are Sufficient to Confer Hyperthermostability on Its Mesophilic Counterpart

Xylanases, and especially thermostable xylanases, are increasingly of interest for the deconstruction of lignocellulosic biomass. In this paper, the termini of a pair of xylanases, mesophilic SoxB and thermophilic TfxA, were studied. Two regions in the N-terminus of TfxA were discovered to be potentially important for the thermostability. By focusing on Region 4, it was demonstrated that only two mutations, N32G and S33P cooperated to improve the thermostability of mesophilic SoxB. By introducing two potential regions into SoxB in combination, the most thermostable mutant, M2-N32G-S33P, was obtained. The M2-N32G-S33P had a melting temperature (Tm) that was 25.6°C higher than the Tm of SoxB. Moreover, M2-N32G-S33P was even three-fold more stable than TfxA and had a Tm value that was 9°C higher than the Tm of TfxA. Thus, for the first time, the mesophilic SoxB “pupil” outperformed its thermophilic TfxA “master” and acquired hyperthermostability simply by introducing seven thermostabilizing residues from the extreme N-terminus of TfxA. This work suggested that mutations in the extreme N-terminus were sufficient for the mesophilic xylanase SoxB to acquire hyperthermostability.
Future Climate Forcings and Olive Yield in a Mediterranean Orchard

The olive tree is one of the most characteristic rainfed trees in the Mediterranean region. Observed and forecasted climate modifications in this region, such as the CO2 concentration and temperature increase and the net radiation, rainfall and wind speed decrease, will likely alter vegetation water stress and modify productivity. In order to simulate how climatic change could alter soil moisture dynamic, biomass growth and fruit productivity, a water-driven crop model has been used in this study. The numerical model, previously calibrated on an olive orchard located in Sicily (Italy) with a satisfactory reproduction of historical olive yield data, has been forced with future climate scenarios generated using a stochastic weather generator and a downscaling procedure of an ensemble of climate model outputs. The stochastic downscaling is carried out using simulations of some General Circulation Models adopted in the fourth Intergovernmental Panel on Climate Change (IPCC) assessment report (4AR) for future scenarios. The outcomes state that climatic forcings driving potential evapotranspiration compensate for each other, resulting in a slight increase of this water demand flux; moreover, the increase of CO2 concentration leads to a potential assimilation increase and, consequently, to an overall productivity increase in spite of the growth of water stress due to the rainfall reduction.
Understanding the Intrinsic Memorability of Images

Artists, advertisers, and photographers are routinely presented with the task of creating an image that a viewer will remember. While it may seem like image memorability is purely subjective, recent work shows that it is not an inexplicable phenomenon: variation in memorability of images is consistent across subjects, suggesting that some images are intrinsically more memorable than others, independent of a subjects' contexts and biases. In this paper, we used the publicly available memorability dataset of Isola et al. [13], and augmented the object and scene annotations with interpretable spatial, content, and aesthetic image properties. We used a feature-selection scheme with desirable explaining-away properties to determine a compact set of attributes that characterizes the memorability of any individual image. We find that images of enclosed spaces containing people with visible faces are memorable, while images of vistas and peaceful scenes are not. Contrary to popular belief, unusual or aesthetically pleasing scenes do not tend to be highly memorable. This work represents one of the first attempts at understanding intrinsic image memorability, and opens a new domain of investigation at the interface between human cognition and computer vision.
Learning from Pairwise Preference Data using Gaussian Mixture Model

In this paper we propose a fast online preference learning algorithm capable of utilizing incomplete preference information. It is based on a Gaussian mixture model that learns soft pairwise label preferences via minimization of the proposed soft rank loss measure. Standard supervised learning techniques, such as gradient descent or Expectation Maximization can be used to find the unknown model parameters. Algorithm outputs are soft pairwise label preference predictions that need to be further aggregated to produce a total label ranking prediction, for which several existing algorithms can be used. The main advantages of the proposed learning algorithm are the ability to process a single training instance at a time, low time and space complexity, ease of implementation, and model reuse.
Identification of Electrical Degradation Products of 4-Chlorophenol in Water

The electrical decomposition of 4-chlorophenol in water was examined with iridium dioxide doped on a titanium electrode. A number of electrical degradation products of 4-chlorophenol, such as hydroquinone and chlorohydroquinone via the addition of hydroxyl radicals, and dichlorophenol through addition of chlorine radical, were observed as major products. Moreover, hydroxylated chlorobiphenylethers, hydroxylated dibenzo- p -dioxin/furans and hydroxylated chlorobiphenyls formed by a dimerization process during the electrolysis process of 4-chlorophenol were also observed. On the other hand, benzoquinone, muconic acid and aldehyde derivatives that were further oxidative products of hydroquinone formed by photocatalysis process, were not observed. The electrical decomposition products of 4-chlorophenol were trimethylsilylated and then identified by gas chromatography–mass spectrometry. The degradation rate of 4-chlorophenol in water by iridium oxide electrode was measured against the electrical process duration. After iridium electrical process for 120 min, about 50% of 4-chlorophenol was converted into a number of products through oxidation processes. On the basis of the identified products, the degradation pathways of 4-chlorophenol under electrolysis process were proposed.
Two types of mouse helper T cell clone. III. Further differences in lymphokine synthesis between Th1 and Th2 clones revealed by RNA hybridization, functionally monospecific bioassays, and monoclonal antibodies

Lymphokine synthesis patterns of a panel of 19 T cell clones have been evaluated, using mRNA hybridization methods to examine 11 different mRNAs induced by Con A. The two types of CD4+ Th cell clone described previously were clearly distinguished by this procedure, and the differences between the two types have now been extended to six induced products. With minor exceptions, only Th1 clones synthesized mRNA for IL-2, IFN-gamma, and lymphotoxin, and only Th2 clones synthesized mRNA for IL-4, IL-5, and another induced gene, P600. Four more induced products were expressed preferentially but not uniquely by one or another type of clone: mRNAs for GM-CSF, TNF, and another induced, secreted product (TY5) were produced in larger amounts by Th1 clones, whereas preproenkephalin was preferentially expressed by Th2 clones. IL- 3 was produced in similar amounts by both types of clone. mAbs were used to establish three bioassays that were functionally monospecific for IL-2, IL-3, and IL-4, and a new anti-IFN gamma mAb, XMG1.2, was used to establish an ELISA for IFN-gamma. These four assays were used to show that secreted protein and mRNA levels correlated well for all cell lines. The implications of these findings for normal T cells are discussed.
A Software-only Mechanism for Device Passthrough and Sharing

Network processing elements in virtual machines, also known as Network Function Virtualization (NFV) often face CPU bottlenecks at the virtualization interface. Even highly optimized paravirtual device interfaces fall short of the throughput requirements of modern devices. Passthrough devices, together with SR-IOV support for multiple device virtual functions (VF) and IOMMU support, mitigate this problem somewhat, by allowing a VM to directly control a device partition bypassing the virtualization stack. However, device passthrough requires high-end (expensive and power-hungry) hardware, places scalability limits on consolidation ratios, and does not support efficient switching between multiple VMs on the same host. We present a paravirtual interface that securely exposes an I/O device directly to the guest OS running inside the VM, and yet allows that device to be securely shared among multiple VMs and the host. Compared to the best-known paravirtualization interfaces, our paravirtual interface supports up to 2x higher throughput, and is closer in performance to device passthrough. Unlike device passthrough however, we do not require SR-IOV or IOMMU support, and allow fine-grained dynamic resource allocation, significantly higher consolidation ratios, and seamless VM migration. Our security mechanism is based on a novel approach called dynamic binary opcode subtraction.
Clinical Staging for Sleep-Disordered Breathing

OBJECTIVE: The purpose of this study was to identify prognostic indicators that would lead to stratification of patients likely to have successful surgery for sleep-disordered breathing (SDB) versus those destined to fail. STUDY DESIGN: We retrospectively reviewed 134 patients to correlate palate position and tonsil size to the success of the UPPP as based on postoperative polysomnography results. Similar to our previously published data on the Friedman Score as a predictor of the presence and severity of SDB, the palate position was determined on physical examination of the oral cavity and was graded for each patient. This grade combined with tonsil size was used to stage the patients. Stage I was defined as having palate position 1 or 2 combined with tonsil size 3 or 4. Stage II was defined as having palate position 3 or 4 and tonsil size 3 or 4. Stage III patients had palate position 3 or 4 and tonsil size 0, 1, or 2. Any patient with body mass index of greater than 40 was placed in the stage III group. The results of uvulopalatopharyngoplasty (UPPP) were then graded as success or failure and success rates were compared by stage. SETTING: Academically affiliated tertiary care referral center. RESULTS: Stage I patients who underwent UPPP had a success rate of 80.6%, stage II patients had a success rate of 37.9%, and stage III patients had a success rate of 8.1%. CONCLUSION: A clinical staging system for SDB based on palate position, tonsil size, and body mass index is presented. It appears to be a valuable predictor of the success of UPPP. Additional studies and wider use of the staging system will ultimately define its role in the treatment of SDB.