Miracle-dz/UniFAR-FTUs huggingface dataset:
Stealthy malware detection and monitoring through VMM-based “out-of-the-box” semantic view reconstruction

An alarming trend in recent malware incidents is that they are armed with stealthy techniques to detect, evade, and subvert malware detection facilities of the victim. On the defensive side, a fundamental limitation of traditional host-based antimalware systems is that they run inside the very hosts they are protecting (“in-the-box”), making them vulnerable to counter detection and subversion by malware. To address this limitation, recent solutions based on virtual machine (VM) technologies advocate placing the malware detection facilities outside of the protected VM (“out-of-the-box”). However, they gain tamper resistance at the cost of losing the internal semantic view of the host, which is enjoyed by “in-the-box” approaches. This poses a technical challenge known as the semantic gap. In this article, we present the design, implementation, and evaluation of VMwatcher—an “out-of-the-box” approach that overcomes the semantic gap challenge. A new technique called guest view casting is developed to reconstruct internal semantic views (e.g., files, processes, and kernel modules) of a VM nonintrusively from the outside. More specifically, the new technique casts semantic definitions of guest OS data structures and functions on virtual machine monitor (VMM)-level VM states, so that the semantic view can be reconstructed. Furthermore, we extend guest view casting to reconstruct details of system call events (e.g., the process that makes the system call as well as the system call number, parameters, and return value) in the VM, enriching the semantic view. With the semantic gap effectively narrowed, we identify three unique malware detection and monitoring capabilities: (i) view comparison-based malware detection and its demonstration in rootkit detection; (ii) “out-of-the-box” deployment of off-the-shelf anti malware software with improved detection accuracy and tamper-resistance; and (iii) nonintrusive system call monitoring for malware and intrusion behavior observation. We have implemented a proof-of-concept VMwatcher prototype on a number of VMM platforms. Our evaluation experiments with real-world malware, including elusive kernel-level rootkits, demonstrate VMwatcher's practicality and effectiveness.
Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicles

The control of nonlinear systems has been putting especial attention in the use of Artificial Intelligent techniques, where fuzzy logic presents one of the best alternatives due to the exploit of human knowledge. However, several fuzzy logic real-world applications use manual tuning (human expertise) to adjust control systems. On the other hand, in the Intelligent Transport Systems (ITS) field, the longitudinal control (throttle and brake management) is an important topic because external perturbations can generate uncomfortable accelerations as well as unnecessary fuel consumption. In this work, we utilize a neuro-fuzzy system to use human driving knowledge to tune and adjust the input-output parameters of a fuzzy if-then system. The neuro-fuzzy system considered in this work is ANFIS (Adaptive-Network-based Fuzzy Inference System). Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous manual tuned controller, mainly in comfort and efficient use of actuators.
Genetic control of biennial bearing in apple

Although flowering in mature fruit trees is recurrent, floral induction can be strongly inhibited by concurrent fruiting, leading to a pattern of irregular fruiting across consecutive years referred to as biennial bearing. The genetic determinants of biennial bearing in apple were investigated using the 114 flowering individuals from an F(1) population of 122 genotypes, from a 'Starkrimson' (strong biennial bearer)×'Granny Smith' (regular bearer) cross. The number of inflorescences, and the number and the mass of harvested fruit were recorded over 6 years and used to calculate 26 variables and indices quantifying yield, precocity of production, and biennial bearing. Inflorescence traits exhibited the highest genotypic effect, and three quantitative trait loci (QTLs) on linkage group (LG) 4, LG8, and LG10 explained 50% of the phenotypic variability for biennial bearing. Apple orthologues of flowering and hormone-related genes were retrieved from the whole-genome assembly of 'Golden Delicious' and their position was compared with QTLs. Four main genomic regions that contain floral integrator genes, meristem identity genes, and gibberellin oxidase genes co-located with QTLs. The results indicated that flowering genes are less likely to be responsible for biennial bearing than hormone-related genes. New hypotheses for the control of biennial bearing emerged from QTL and candidate gene co-locations and suggest the involvement of different physiological processes such as the regulation of flowering genes by hormones. The correlation between tree architecture and biennial bearing is also discussed.
Use of genetic algorithms for encoding efficient neural network architectures: neurocomputer implementation

In this study an attempt is being made to encode the architecture of a neural network in a chromosome string for evolving robust, fast-learning, minimal neural network architectures through genetic algorithms. Various attributes affecting the learning of the network are represented as genes. The performance of the networks is used as the fitness value. Neural network architecture design concepts are initially demonstrated using a backpropagation architecture with the standard data set of Rosenberg and Sejnowski for text to speech conversion on Adaptive Solutions Inc.'s CNAPS Neuro-Computer. The architectures obtained are compared with the one reported in the literature for the standard data set used. The study concludes by providing some insights regarding the architecture encoding for other artificial neural network paradigms.
Content-based image retrieval using stochastic paintbrush transformation

We propose a new content based image retrieval method. The novelty of our approach lies in the applied image similarity measure: unlike traditional features, such as color, texture or shape, our measure is based on a painted representation of the original image. We use paintbrush stroke parameters as features. These strokes are produced by a stochastic paintbrush algorithm which simulates a painting process. Stroke parameters include color, orientation and location. Therefore, it provides information not only about the color content but also about the structural properties of an image. Experimental results on a database of more than 500 images show that the CBIR method using paintbrush features has a higher retrieval rate than methods using color features only.
The Steps and Methodology of Identifying Master Data from Business Processes

In the fragmented and fast-paced word, enterprise data has become even more daunting. If companies are to survive in an increasingly competitive global marketplace, they must find ways not only to use but to exploit this useful enterprise data. As a popular technology, the state of the art MDM solution is claimed by the software company such as IBM, Oracle to provide the quality data for decision making. The MDM system is set up based on enterprise have finished informatization. However, the industrialization is developing in China, most of middle and small company have not information system. So a new design methodology that is purposed in this paper can capture master data from the enterprise business process, it’s utilized by all of enterprise whether have establish information system. To construct a MDM system, the metadata in a MDM system should be designed first. Therefore, a design methodology for metadata in MDM system is presented in this paper.
Connexionnisme et fonctionnalisme

Les reseaux connexionnistes, prises par les eliminativistes, peuvent aussi se calquer sur les conceptions fonctionnalistes. Leur deficit de compositionnalite et de systematicite doit etre relativise : ils manifestent une compositionnalite limitee a un contexte d'apprentissage et une systematicite dependante d'un domaine de pertinence. On peut grâce a eux determiner des mediations entre la description physicaliste et la description fonctionnelle (categorielle), en ne retenant comme proprietes physiques pertinentes que celles qui sont capables d'emergence contextuelle.
Control of Large-Scale Systems through Dimension Reduction

Automated physical resource management of large-scale Internet Technology (IT) systems requires dynamic configuration of both application-level and system-level parameters. The existence of large number of tunable parameters makes it difficult to design a feedback controller that adjusts these parameters effectively in order to achieve application-level performance targets. In this paper, we introduce a new approach for simplified control architecture of large-scale IT systems based on dimension reduction techniques. It combines online selection of critical control knobs through LASSO-a powerful L1-constrained fitting method/Compressive Sensing (CS)-a L1-optimization method, and adaptive control of the identified knobs. The latter relies on the online estimation of the input-output model with the selected control knobs using the recursive least square (RLS) method and a self-tuning linear quadratic (LQ) optimal controller for output regulation. The results of both a numerical simulation in Matlab and a realistic case are presented to demonstrate the effectiveness of our approach.
Privacy Preservation in Affect-Driven Personalization

Knowing exactly how a given audience feels about some content in addition to what mood they are in, can affect the type of user experience that is delivered in the form of personalized advertising and content delivery. As we continue to mine rich data sources and draw insights from emotion analytics, we often leave in our wake, a major privacy gap pertaining to the human subjects involved in our studies. Yes, the goal is to derive insights from a user's emotional state in a bid to select personalized content or predict what the end user is likely to find attractive at a given point in time. Yet, this has to be done in a way that is ubiquitous and above all preserves the privacy of the subjects in question. This literature details the visual privacy gaps in collecting and analyzing emotion data in support of personalized advertising and, consequently, offers a reference framework for preserving end-user privacy throughout the emotion analytics lifecycle.
Frequencygrams and multi-feature joint sparse representation for action and gesture recognition

Features play a vital role in human action recognition (HAR), as they encapsulate the underlying dynamics of the action. We propose the features (frequencygrams) based on frequency domain analysis of histograms of the motion and its spatiotemporal gradient (rate of change in motion flow). Feature extraction is quite simple and can be performed in real time using sparse or interest point motion flow. They are resilient to delayed initiated actions, scale variation, moving background, sudden illumination changes (high frequency noise) and avoid the overload of person detection and tracking. Being robust to camera motions, they also provide a natural, compact and discriminative representation for reciprocating motions by preserving comprehensive temporal information of the action sequences. As other global features also bear some action semantics, we fuse all these features together in a systematic way to improve the overall HAR performance, by employing the joint sparse representation with group sparsity regularization. The extensive experimental results, on three benchmark action datasets and one gesture recognition dataset, show the effectiveness and generality of the proposed method.
Effectiveness of special and inclusive teaching in early childhood education in Zimbabwe

ABSTRACT This study examined the effectiveness of special and inclusive teaching in Early Childhood Education in Zimbabwe. Embedded within international research findings on effective teaching in special and inclusive education, this descriptive study drew on a purposive sample of 21 special and inclusive ECE teachers. A constant comparative approach of data organisation with continual adjustment was used throughout the analysis to ensure that the codes captured the range of participants’ ideas. Overall, participants understood and held positive attitudes and commitment, although they were inadequately professionally prepared to manage special and inclusive teaching and the behavioural challenges presented by some children. Participants institutionalised effective special and inclusive teaching practices in ECE including diagnosis information-based child placement, teacher-made tests, child-centred pedagogy, behaviour management, collaboration, advocacy and diverse teaching strategies, approaches and techniques. This study serves as a springboard for future research on delivery of services in special and inclusive education in ECE.
Efficient Atmospheric Cleansing of Oxidized Organic Trace Gases by Vegetation

Volatiles Versus Vegetation Plants act as both global sources and sinks of highly reactive volatile organic compounds (VOCs). Models typically treat the uptake and degradation of these compounds as if they are mostly unreactive, like other more commonly studied biogenic gases such as ozone. A study by Karl et al. (p. 816, published online 21 October) suggests that VOCs may be more reactive than expected. By monitoring six field sites representing a range of deciduous ecosystems, several oxidized VOCs were found to have high deposition fluxes. Fumigation experiments in the laboratory confirmed that leaves are capable of oxidizing these compounds, and do so through an enzymatic detoxification or stress-response mechanism. Budgets for VOC flux in the atmosphere suggests that, on a global scale, plants may take up significant levels of VOCs in polluted regions, especially in the tropics. Deciduous trees enzymatically remove oxygenated volatile organic compounds from the atmosphere. The biosphere is the major source and sink of nonmethane volatile organic compounds (VOCs) in the atmosphere. Gas-phase chemical reactions initiate the removal of these compounds from the atmosphere, which ultimately proceeds via deposition at the surface or direct oxidation to carbon monoxide or carbon dioxide. We performed ecosystem-scale flux measurements that show that the removal of oxygenated VOC via dry deposition is substantially larger than is currently assumed for deciduous ecosystems. Laboratory experiments indicate efficient enzymatic conversion and potential up-regulation of various stress-related genes, leading to enhanced uptake rates as a response to ozone and methyl vinyl ketone exposure or mechanical wounding. A revised scheme for the uptake of oxygenated VOCs, incorporated into a global chemistry-transport model, predicts appreciable regional changes in annual dry deposition fluxes.
Selecting Genes by Test Statistics

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets.
A gramática como testemunha do ethos discursivo parlamentar: ele oculta, ela desvela.

A analise se desenvolve no confronto dos diferentes modos de apresentacao da realidade social feminina em textos pronunciados no plenario da Câmara Federal. Os dados analiticos, que focalizam o atentado sofrido pelas operarias da fabrica de camisas Triangle Shirtwaist, sao discutidos a luz da concepcao de discurso como pratica discursiva, pratica social e pratica linguistica. As discussoes que permeiam o estudo pretendem destacar o papel desempenhado pelas categorias linguistico-discursivas na construcao argumentativa dos pronunciamentos e na elucidacao de posicionamentos ideologicos assumidos pelos atores sociais envolvidos na pratica socio-discursiva em foco.
Optimal Design for Symbiotic Wearable Wireless Sensors

Sensors aesthetically embedded in accessories such as jewelry, piercings or contact lenses are being proposed recently. These symbiotic wearable wireless sensors are envisioned to operate on scarce harvested energy resources from the human body. In addition to the hardware and software constraints arising from the form-factor and low energy operations, there are safety requirements such as avoidance of physical injury. The design implications of these requirements are non-intuitive and may involve estimation of human physiological dynamics. The physical impact of a sensor operation can be controlled by appropriate design of multiple sensor components such as processor, radio, and optimization of data algorithm. For example, the risk of thermal injury to tissue can be reduced by limiting the sensing frequency, the computation power, and the radio duty cycle of body worn sensor. Hence, it is a challenging task to trace back a cause of a physical impact to hardware and software design decisions in a sensor. This paper proposes a novel non-linear optimization framework to consider safety and sustainability requirements that depend on the human physiology and derive system level design parameters of a sensor. We demonstrate our methodology using three case studies: a) continuously monitoring ECG sensor sustained by body heat, b) thermally safe network of implanted sensors, and c) infusion pump control algorithm to avoid hypoglycemia.
Towards the prioritization of system test cases

During software development, companies are frequently faced with lack of time and resources, which limits their ability to effectively complete testing efforts. This paper presents a system‐level, value‐driven approach to test case prioritization called the Prioritization of Requirements for Test (PORT). PORT involves analysing and assigning value to each requirement using the following four factors: requirements volatility, customer priority, implementation complexity, and fault proneness. System test cases are prioritized such that the test cases for requirements with higher priority are executed earlier during system test. PORT was applied to four student team projects as well as an industrial case study. The results show that PORT improves the rate of detection of severe failures over random prioritization. Additionally, the results indicate that customer priority was the most important contributor towards improved rate of failure detection. Copyright © 2013 John Wiley & Sons, Ltd.
A long-term five-year randomized controlled trial of hydroxychloroquine, sodium aurothiomalate, auranofin and penicillamine in the treatment of patients with rheumatoid arthritis.

OBJECTIVE To compare the efficacy of hydroxychloroquine, penicillamine, sodium aurothiomalate and auranofin in the treatment of active rheumatoid arthritis over a period of 5 yr. METHOD Five hundred and forty-one patients with definite or classical rheumatoid arthritis were entered into an open randomized controlled trial with a flexible dose regimen designed to reflect clinical practice. Decisions to stop treatment with any one of the disease-modifying anti-rheumatic drugs (DMARDs) were based on an agreed trial protocol which defined criteria for adverse reactions and therapeutic failure. The managing physicians' decisions were confirmed in a separate monitor clinic. RESULTS The proportion of patients who remained on their first DMARD or who were in remission at 5 yr was 53% for penicillamine, 34% for sodium aurothiomalate, 31%, for auranofin and 30% for hydroxychloroquine (P < 0.001). In patients who stayed on their first DMARD, all groups showed a 30-50% improvement in C-reactive protein, erythrocyte sedimentation rate, Ritchie Index and joint stiffness, and a deterioration in their Larsen score. There was no evidence of physician bias to explain the larger proportion of patients remaining on penicillamine for 5 yr. CONCLUSION Despite the increased popularity of sulphasalazine and inmmunosuppressives, the drugs in this study continue to be used worldwide. The natural history of rheumatoid arthritis requires long-term follow up to establish drug efficacy. Evidence is needed as to whether the newer regimens will prove to be more effective and safer in the longer term than the commonly prescribed DMARDs. The data from this trial will provide a reference for comparison with future studies.
Tetrapyrrole‐based drought stress signalling

Summary Tetrapyrroles such as chlorophyll and heme play a vital role in primary plant metabolic processes such as photosynthesis and respiration. Over the past decades, extensive genetic and molecular analyses have provided valuable insights into the complex regulatory network of the tetrapyrrole biosynthesis. However, tetrapyrroles are also implicated in abiotic stress tolerance, although the mechanisms are largely unknown. With recent reports demonstrating that modified tetrapyrrole biosynthesis in plants confers wilting avoidance, a component physiological trait to drought tolerance, it is now timely that this pathway be reviewed in the context of drought stress signalling. In this review, the significance of tetrapyrrole biosynthesis under drought stress is addressed, with particular emphasis on the inter‐relationships with major stress signalling cascades driven by reactive oxygen species (ROS) and organellar retrograde signalling. We propose that unlike the chlorophyll branch, the heme branch of the pathway plays a key role in mediating intracellular drought stress signalling and stimulating ROS detoxification under drought stress. Determining how the tetrapyrrole biosynthetic pathway is involved in stress signalling provides an opportunity to identify gene targets for engineering drought‐tolerant crops.
Space-time signaling in correlated channels

The performance of space-time codes under channel correlation has been studied in detail recently, and a variety of results have been produced. In this work, we simplify some of the existing derivations as well as present some new results. One of the novel issues raised in this investigation is the loss of the uniform error probability (UEP) property of many codes in the presence of transmit side correlation. Also our error expressions for the general case of jointly spatio-temporally correlated Rayleigh and Rician fading are new, to the best of our knowledge. We also apply our analysis to some of the more recently developed codes for the MIMO channel, including the super-orthogonal codes and the linear dispersion codes.