Miracle-dz/UniFAR-FTUs huggingface dataset:
Using field access frequency to optimize layout of objects in the JVM

Increasing data locality is a good way to alleviate the gap between memory latency and processor speed. Structure layout optimization is one way to improve spatial locality and consequently improve runtime performance by rearranging fields inside objects. This paper examines modifying IBM's JVM with the ability to reorder fields inside Java objects from access frequency information (hotness) in the presence of storage optimization. In order to decrease the overhead resulting from larger objects, the new layouts for Java objects are very similar to their default layouts in terms of size. The results are promising and we were able to reduce cache misses by as much as 12%, resulting in about 20% in performance improvement.
Investigating the mediating effect of work motivation on work engagement

This study examines the impact of differing social environments within a school setting on teachers’ work motivation and its impacts on work engagement. Self-determination theory (SDT) defines social environment as an important factor in an individual’s motivation and this along with a modified Job Demand-Resource (JD-R) model forms the basis of this investigation into the mediating effect of work motivation on work engagement. A paired questionnaire was distributed to 64 schools in Indonesia. Sixty-four headmasters and 294 teachers took part in the research. Results suggest that some aspects of the social environment within a school can affect teachers’ work motivation and their subsequent work engagement (paternalistic leadership and team relationships), whilst others (pupil related inputs) appear to have little impact on work motivation and engagement.
The multiple-access channel with partial state information at the encoders

This work studies the multiple-access channel (MAC) controlled by random parameters, with full side information at the decoder, and partial, rate limited, side information (SI) at the encoders. A single-letter characterization of the capacity region is derived, for the special case where the SI is degraded. Here degraded SI refers to the case where the SI available at one of the encoders is a subset of the SI available at the other encoder. Inner and outer bounds are derived on the capacity region of that channel for the general case where there are no restrictions on the structure of the SI at the two encoders. The techniques employed for coding the rate-limited SI, and the achievable regions so obtained, are closely related to the problems of hierarchical source coding, and multiple descriptions.
Variability in Teachers' Responses to Standards-Based Mathematics Reform.

Motivated by three separate policies (Goals 2000: Educate America Act of 1994, Colorado's Education Reform of 1993 House Bill 1313, and the Standards of the National Council of Teachers of Mathematics), Colorado school districts are grappling with implementation issues surrounding mathematics standards. Teachers in two school districts differing in socio-economic status, district size, and setting participated in this study of how standards-based reform impacted their curricular and instructional practices. Over 30 interviews and classroom observations, and numerous district and classroom documents produced the qualitative data for domain analyses. Results indicate that there is great variability among teachers' perceptions of standards-based reform, and that this variability is reflected in their classroom practices. Many teachers interpreted the reform as a requirement for surface level changes and followed through with classroom changes such as reorganizing and fine-tuning the curriculum. A few teachers understood standards-based reform to be more complex but usually their reform efforts were diminished by other pressing problems. Contains 22 references. (Author) ******************************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * ********************************************************************************
Meeting the Challenges in Evaluating Mobile Learning: A 3-Level Evaluation Framework

We propose six challenges in evaluating mobile learning: capturing and analyzing learning in context and across contexts, measuring mobile learning processes and outcomes, respecting learner/participant privacy, assessing mobile technology utility and usability, considering the wider organizational and socio-cultural context of learning, and assessing in/formality. A three-level framework for evaluating mobile learning is proposed, comprising a micro level concerned with usability, a meso level concerned with the learning experience, and a macro level concerned with integration within existing educational and organizational contexts. The article concludes with a discussion of how the framework meets the evaluation challenges and with suggestions for further extensions.
NH4+ toxicity in higher plants: a critical review

Summary Ammonium (NH 4 + ) toxicity is an issue of global ecological and economic importance. In this review, we discuss the major themes of NH 4 + toxicity, including the occurrence of NH 4 + in the biosphere, response differences to NH 4 + nutrition among wild and domesticated species, symptoms and proposed mechanisms underlying toxicity, and means by which it can be alleviated. Where possible, nitrate (NO 3 − ) nutrition is used as point of comparison. Particular emphasis is placed on issues of cellular pH, ionic balance, relationships with carbon biochemistry, and bioenergetics of primary NH 4 + transport. Throughout, we attempt to identify areas that are controversial, and areas that are in need of further examination.
Dynamic Modelling of Wheel-Terrain Interaction of a UGV

Understanding the vehicle-terrain interaction is essential for autonomous and safe operations of skid-steering unmanned ground vehicles (UGVs). This paper presents a comprehensive analysis of the dynamic processes involved in this interaction, using the vehicle kinetics and the theory of terramechanics to derive systematically shear displacement, reaction force, and load distribution for a wheel. The new model is then summarized in the form of an algorithm to allow for computation of characteristic performance of the interaction such as slip ratios, rolling resistance, and moment of turning resistance for a number of terrain types. Given the current state of the vehicle and terrain parameters, the model can be used to estimate its next states and to predict the vehicle running path. The development is illustrated by simulation and verified with experimental data.
Spatial and temporal variations of air pollution exposure risk based on Geo-information Technology Aided Proximity Model

This paper proposed a framework potential for assessing regional spatial and temporal variations of air pollution exposure risk by combing Geo-information Technology Aided Proximity Model (GTAPM) and GIS interpolation technology in areas with limited access to data inputs (e.g. emission data, meteorological data). In this process, the exposure risks to sulfur dioxide (SO2) for Dallas and Ellis counties in 1996 and 2002 were assessed firstly at annual scale based on GTAPM; then, the spatial and temporal variations of annual SO2 exposure risk in Dallas and Ellis counties were evaluated based on GIS spatial interpolation technology and overlay analysis operation. The results indicate that the annual SO2 exposure risk in Dallas and Ellis counties are spatially different in both 1996 and 2002. While areas with lower levels of variations were mainly distributed in most central parts of study area, the areas with relative higher levels of variations emerged in the northern part of Dallas county and southeastern of Ellis County. The results also suggest that while the annual SO2 exposure risk in minority areas with relative high exposure risks in 1996 seemed to be reduced over the period of 1996 to 2002, those in majority areas with relative lower exposure risks in 1996 increased a lot. The annual SO2 exposure risk of entire study area appeared to increase. Therefore, it can be concluded that GTAPM can effectively and efficiently evaluate air pollution exposure risk and its spatial and temporal variations in area without air quality observation data and related input data for air dispersion modeling.
Functions of Conserved Motifs in the RNA-Dependent RNA Polymerase of a Yeast Double-Stranded RNA Virus

ABSTRACT At least eight conserved motifs are visible in the totivirus RNA-dependent RNA polymerase (RDRP). We have systematically altered each of these in the Saccharomyces cerevisiaedouble-stranded RNA virus ScVL1 by substituting the conserved motifs from a giardiavirus. The results help define the conserved regions of the RDRP involved in polymerase function and those essential for other reasons.
Information-Theoretic Outlier Detection for Large-Scale Categorical Data

Outlier detection can usually be considered as a pre-processing step for locating, in a data set, those objects that do not conform to well-defined notions of expected behavior. It is very important in data mining for discovering novel or rare events, anomalies, vicious actions, exceptional phenomena, etc. We are investigating outlier detection for categorical data sets. This problem is especially challenging because of the difficulty of defining a meaningful similarity measure for categorical data. In this paper, we propose a formal definition of outliers and an optimization model of outlier detection, via a new concept of holoentropy that takes both entropy and total correlation into consideration. Based on this model, we define a function for the outlier factor of an object which is solely determined by the object itself and can be updated efficiently. We propose two practical 1-parameter outlier detection methods, named ITB-SS and ITB-SP, which require no user-defined parameters for deciding whether an object is an outlier. Users need only provide the number of outliers they want to detect. Experimental results show that ITB-SS and ITB-SP are more effective and efficient than mainstream methods and can be used to deal with both large and high-dimensional data sets where existing algorithms fail.
Scalable Arbiters and Multiplexers for On-FGPA Interconnection Networks

Soft on-FGPA interconnection networks are gaining increasing importance since they simplify the integration of heterogeneous components and offer, at the same time, a modular solution to the complex system-wide communication issues. The switches are the basic building blocks of such interconnection networks and their design critically affects the performance of the whole network. The way data traverse each switch is governed by the operation of the arbiter and the crossbar's multiplexers that need to be efficiently mapped on the FPGA fabric under tight area and delay constraints. This paper explores the design space of an arbiter and a multiplexer as a unified entity and proposes two new circuit alternatives that allow the design of scalable soft FPGA switches.
Towards a MacWilliams Identity for Convolutional Codes

The weight adjacency matrix associated with a convolutional code collects in a detailed manner information about the weight distribution of the code. We will formulate a MacWilliams Identity Conjecture stating that the adjacency matrix of a code fully determines the adjacency matrix of the dual code. An explicit formula for the transformation will be stated as well; it involves the MacWilliams matrix known from complete weight enumerators of block codes. The conjecture will be proven for the class of convolutional codes where either the code or its dual is a unit memory code. For the general case the conjecture is backed up by many examples, and a weaker version will be established.
Characterizing On/Off Pattern of Broadband Internet Users

It is well known that the duration of Internet user's sessions grows with the increase of broadband Internet access. Nevertheless, it is still not clear how much their usage increases, that is, whether the user issues requests during the whole session or there are periods during which users request nothing. This work presents a characterization of on/off pattern of broadband Internet users. We determine the statistical distribution of session on/off time and found that on-time is fits better to the Weibull distribution and offtime to the Lognormal. Furthermore, we model the on/off pattern with a state transition graph called CBMG (Customer Behavior Model Graph).
A Mining Method of Frequent Tree Sequences

With the development of frequent items and frequent sequences mining, the technology of data mining turns to solve the problem of structural pattern mining. In this paper, we introduce transformation rules and define a new representation of tree sequences by using transformation sequences. Then we propose an efficient approach to enumerate frequent transformation subsequences (FTSs) of trees from a given set of tree sequences. Its fundamental performance has been evaluated by using artificial datasets.
Addressing radiometric nonidealities: a unified framework

Cameras may have non-ideal radiometric aspects, including spatial non-uniformity, e.g., due to vignetting; a nonlinear radiometric response of the sensor; and temporal variations due to automatic gain control (AGC). Often, these characteristics exist simultaneously, and are typically unknown. They thus hinder consistent photometric measurements. In particular, they create annoying seams in image mosaics. Prior studies approached part of these problems while excluding others. We handle all these problems in a unified framework. We suggest an approach for simultaneously estimating the radiometric response, the spatial non-uniformity and the temporally varying gain. The approach does not rely on dedicated processes that intentionally vary exposure settings. Rather, it is based on an ordinary frame sequence acquired during camera motion. The estimated non-ideal characteristics are then compensated for. We state fundamental ambiguities associated with this recovery problem, while exposing a novel image invariance. The method is demonstrated in several experiments, where different frames are brought into mutual radiometric consistency. The accuracy achieved is sufficient for seamless mosaicing, with no need to resort to dedicated seam-feathering methods.
Online risk assessment and prediction models for Autonomic Cloud Intrusion srevention systems

The extensive use of virtualization in implementing cloud infrastructure brings unrivaled security concerns for cloud tenants or customers and introduces an additional layer that itself must be completely configured and secured. Intruders can exploit the large amount of cloud resources for their attacks. Most of the current security technologies do not provide the essential security features for cloud systems such as early warnings about future ongoing attacks, autonomic prevention actions, and risk measure. This paper discusses the integration of these three features to our Autonomic Cloud Intrusion Detection Framework (ACIDF). The early warnings are signaled through a new finite State Hidden Markov prediction model that captures the interaction between the attackers and cloud assets. The risk assessment model measures the potential impact of a threat on assets given its occurrence probability. The estimated risk of each security alert is updated dynamically as the alert is correlated to prior ones. This enables the adaptive risk metric to evaluate the cloud's overall security state. The prediction system raises early warnings about potential attacks to the autonomic component, controller. Thus, the controller can take proactive corrective actions before the attacks pose a serious security risk to the system. According to our experiments, both risk metric and prediction model have successfully signaled early warning alerts 39.6 minutes before the launching of the LLDDoS1.0 attack. This gives the system administrator or an autonomic controller ample time to take preventive measures.
Preferred Sites of Exocytosis and Endocytosis Colocalize during High- But Not Lower-Frequency Stimulation in Mouse Motor Nerve Terminals

The spatial relationship of exocytosis and endocytosis in motor nerve terminals has been explored, with varied results, mostly in fixed preparations and without direct information on the utilization of each exocytic site. We sought to determine these spatial properties in real time using synaptopHluorin (spH) and FM4-64. Earlier we showed that nerve stimulation elicits the appearance of spH fluorescence hot spots, which mark preferred sites of exocytosis. Here we show that nerve stimulation in the presence of the styryl dye FM4-64 evokes hot spots of FM4-64 fluorescence. Their size, density, and rate of appearance are similar to the spH hot spots, but their rate of disappearance after stimulation was much slower (t1/2 ∼9 min vs ∼10 s for spH hot spots), consistent with FM4-64 spots identifying bulk endocytosis and subsequent slow intracellular dispersion of nascent vesicles. Simultaneous imaging of both fluorophores revealed a strong colocalization of spH and FM4-64 spots, but only during high (100 Hz) stimulation. At 40 Hz stimulation, exocytic and endocytic spots did not colocalize. Our results are consistent with the hypothesis that hot spots of endocytosis, possibly in the form of bulk uptake, occur at or very near highly active exocytic sites during high-frequency stimulation.
A POSSIBILITY LINEAR PROGRAMMING APPROACH TO SOLVE A FUZZY SINGLE MACHINE SCHEDULING PROBLEM

This paper employs an interactive possibility linear programming approach to solve a single machine scheduling problem with imprecise processing times, due dates, as well as earliness and tardiness penalties of jobs. The proposed approach is based on a strategy of minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining a lower total costs, and minimizing the risk of obtaining higher total costs simultaneously. This approach is applicable to just-in-time systems, in which many firms face the need to complete jobs as close as possible to their due dates. The objective of the model is to minimize the total costs of earliness/tardiness penalties. In this paper, the proposed possibility linear programming approach is applied to a fuzzy single machine scheduling problem with respect to the overall degree of decision maker satisfaction. Due to the proposed model’s complexity, conventional optimization methods cannot be utilized in reasonable time. Hence, the particle swarm optimization method is applied toward its solution.
Comparison of first order statistical and autoregressive model features for activity prediction

Activity recognition is an important subject with many applications in health care, emergency care, and assisted living. Nowadays, activity information can be acquired using small accelerometers connected to the body, including the ones available in smartphones. In this study, we assessed the influence of autoregressive model parameters or features on activity detection or classification. Our results indicate that, compared to relatively simple features such as first order statistics, autoregressive model features have rather low impact in determining or improving performance of automatic activity detection using machine intelligence.
Relation of Parkinson's Disease Subtypes to Visual Activities of Daily Living

Abstract Visual perceptual problems are common in Parkinson's disease (PD) and often affect activities of daily living (ADLs). PD patients with non-tremor symptoms at disease onset (i.e., rigidity, bradykinesia, gait disturbance or postural instability) have more diffuse neurobiological abnormalities and report worse non-motor symptoms and functional changes than patients whose initial symptom is tremor, but the relation of motor symptom subtype to perceptual deficits remains unstudied. We assessed visual ADLs with the Visual Activities Questionnaire in 25 non-demented patients with PD, 13 with tremor as the initial symptom and 12 with an initial symptom other than tremor, as well as in 23 healthy control participants (NC). As expected, the non-tremor patients, but not the tremor patients, reported more impairment in visual ADLs than the NC group, including in light/dark adaptation, acuity/spatial vision, depth perception, peripheral vision and visual processing speed. Non-tremor patients were significantly worse than tremor patients overall and on light/dark adaptation and depth perception. Environmental enhancements especially targeted to patients with the non-tremor PD subtype may help to ameliorate their functional disability. (JINS, 2011, 17, 841–852)
Chemical, Biological and Radiological Hazard Assessment: A New Model of a Plume in a Complex Urban Environment

There has been a growing interest in recent years in the modelling of hazards arising from the atmospheric dispersion of chemical, biological and radiological (CBR) agents in the environment, and the threat that they pose to the population and military forces. This is a particularly challenging problem in an urban setting. Dispersion of CBR agents in an atmospheric boundary layer (ABL) over a heterogeneous (urban) canopy is a complex process to be described by advanced methods of fluid dynamics, turbulence theory, diffusion and statistics. Using comprehensive modelling is computationally intensive and too time consuming when applied to operational problems when reliable outcome has to be produced within a limited time frame. Plume characterisation requires the development of simplified analytical models of turbulent dispersion based on physical assumptions and “first principles” physics considerations. These models must still be simple enough to be easily treated numerically in an operationally viable way. Such models can also provide a theoretical foundation for “backtracking” problems, i.e. finding a CBR source in a complex canopy under various meteorological conditions. The purpose of this paper is to summarise the recent research conducted by DSTO (HPPD) in the development of such models.
Trawling Traffic under Attack, Overcoming DDoS Attacks by Target-Controlled Traffic Filtering

As more and more services are provided by servers via the Internet, Denial-of-Service (DoS) attacks pose an increasing threat to the Internet community. A DoS attack overloads the target server with a large volume of adverse requests, thereby rendering the server unavailable to “ well-behaved” users. Recently, the novel paradigm of traffic ownership that enables the clients of Internet service providers (ISP) to configure their own traffic processing policies has gained popularity. In this paper, we propose two algorithms belonging to this paradigm that allow attack targets to dynamically filter their incoming traffic based on a distributed policy. The proposed algorithms defend the target against DoS and distributed DoS (DDoS) attacks and simultaneously ensure that it continues to receive valuable users’ traffic. In a nutshell, a target can define a filtering policy which consists of a set of traffic classification rules and the corresponding amounts of traffic, measured in bandwidth units, which match each rule. The filtering algorithm is enforced by the ISP’s or the Network Service Provider’s (NSP) routers when a target is being overloaded with traffic. The goal is to maximize the amount of filtered traffic forwarded to the target, according to the filtering policy, from the ISP’s or the NSP’s network. The first algorithm we propose relies on complete collaboration among the ISP/NSP routers. It computes the filtering policy in polynomial time and delivers the best possible traffic mix to the target. The second algorithm is a distributed algorithm which assumes no collaboration among the ISP/NSP routers, each router only uses local information about its incoming traffic. We show the intuition behind the proof of lower bound on the second algorithm’s worst-case performance.