Miracle-dz/UniFAR-FTUs huggingface dataset:
A Coursework Support System for Offering Challenges and Assistance by Analyzing Students' Web Portfolios

Students can practice skills and acquire knowledge by doing coursework. However, in conventional coursework activities, each student is assigned the same exercises, without considering learners’ diversity. Moreover, students typically have difficulty in receiving assistance for completing their exercises after class. Therefore, some students cannot learn effectively when doing their coursework. This work presents a Web Coursework Support System (WCSS) to assist students doing coursework within their Zone of Proximal Development (ZPD). This system applies decision tree analysis methodology for selecting programming exercises at a level of difficulty suited to each student. The assigned exercises thus challenge students. To assist students in completing these assigned exercises, this system allows students to access distributed online learning materials related to a programming concept through links on a single web page and motivates students to answer posted questions in a discussion forum. Experimental results show that the exercises-completed rate and the rate of the assigned exercises completed with assistance were increased. They indicate that WCSS can increase likelihood that students do coursework within their ZPD by offering challenges and assistance. Furthermore, some students’ responses were insightful in understanding the benefits and limitations of this system.
Error-correction and crosstalk avoidance in DSM busses

Aggressive process scaling and increasing clock rates have made crosstalk noise an important issue in VLSI design. Switching on adjacent wires on long bus lines can increase delays and lead to logic faults, particularly when adjacent lines switch with opposite transitions. At the same time system-level interconnects have also become more susceptible to other less predictable forms of interference such as noise induced by power grid fluctuations, electromagnetic interference, and alpha particle radiation. Previous work has treated these systematic and non-systematic forms of noise separately.In this paper we propose to make system level interconnects more robust using encoding that simultaneously addresses error correction requirements and crosstalk noise avoidance. This is more efficient than satisfying these requirements separately. We give algorithms for obtaining optimal encodings, and present a practical class of codes called boundary shift codes. We evaluate the overhead of our method and make comparisons to using error correction with simple shielding.
Observed allocations of productivity and biomass, and turnover times in tropical forests are not accurately represented in CMIP5 Earth system models

c BNU-ESM Beijing Normal University Earth System Model Ji et al. [2014] CoLM-Common Land Model d CanESM2 Canadian earth system model, 2 generation Arora et al. [2011] CTEM-Canadian Terrestrial Ecosystem Model e CCSM4 Community Climate System Model version 4 Gent et al. [2011] CLM4-Community Land Model, v4 f CESM1-BGC Community Earth System Model, version 1-BGC Hurrell et al. [2013] includes active biogeochemistry CLM4
Speed Range Extension for Simplex Wave Winding Permanent-Magnet Brushless DC Machine

A new speed range extension method for the simplex wave winding permanent-magnet (PM) brushless DC machine is proposed in this paper. The machine is with PM excitation, electric commutation, and close-connected winding identical to the brushed DC machine (see Zhu , “A new simplex wave winding permanent-magnet brushless DC machine,” IEEE Transactions on Magnetics, vol. 47, no. 1, pp. 252-259, Jan. 2011). By properly controlling the status of power electronic switches, the number of coils being connected into the equivalent circuit can be reduced, and thus the speed range will be extended. For the prototype of 10-pole and 11-slot machine, besides operating with all the 11 coils, the machine can be controlled with only 8 or 6 coils operating for wider speed range. The experiments verify the new speed range extension method for simplex wave winding PM brushless DC machine.
A Parsing Methodology for the Implementation of Visual Systems

The Visual Language Compiler-Compiler (VLCC) is a grammar-based graphical system for the automatic generation of visual programming environments. In this paper the theoretical and algorithmic issues of VLCC are discussed in detail. The parsing methodology we present is based on the "positional grammar" model. Positional grammars naturally extend context-free grammars by considering new relations in addition to string concatenation. Thanks to this, most of the results from LR parsing can be extended to the positional grammars inheriting the well known LR technique efficiency. In particular, we provide algorithms to implement a YACC-like tool embedded in the VLCC system for automatic compiler generation of visual languages described by positional grammars.
Recurrent Synaptic Input and the Timing of Gamma-Frequency-Modulated Firing of Pyramidal Cells during Neocortical “UP” States

Gamma (γ) oscillation, a hallmark of cortical activity during sensory processing and cognition, occurs during persistent, self-sustained activity or “UP” states, which are thought to be maintained by recurrent synaptic inputs to pyramidal cells. During neocortical “UP” states, excitatory regular spiking (RS) (pyramidal) cells and inhibitory fast spiking (FS) (basket) cells fire with distinct phase distributions relative to the γ oscillation in the local field potential. Evidence suggests that γ-modulated RS → FS input serves to synchronize the interneurons and hence to generate γ-modulated FS → RS drive. How RS → RS recurrent input shapes both self-sustained activity and γ-modulated phasic firing, although, is unclear. Here, we investigate this by reconstructing γ-modulated synaptic input to RS cells using the conductance injection (dynamic clamp) technique in cortical slices. We find that, to show lifelike γ-modulated firing, RS cells require strongly γ-modulated, low-latency inhibitory inputs from FS cells but little or no γ-modulation from recurrent RS → RS connections. We suggest that this demodulation of recurrent excitation, compared with inhibition, reflects several possible effects, including distributed propagation delays and integration of excitation over wider areas of cortex, and maximizes the capacity for representing information by the timing of recurrent excitation.
NovaSearch at TREC 2014 Clinical Decision Support Track

Abstract : This paper describes the participation of the NovaSearch group at TREC Clinical Decision Support 2014. As this is the first edition of the track, we decided to assess the performance of multiple information retrieval techniques: retrieval functions, re-ranking, query expansion and classification of medical articles into question categories. The best performing run was based on an ensemble of state-of-the-art retrieval algorithms combined with unsupervised fusion. Our best run was based on the late fusion of runs using MeSH query expansion, pseudo-relevance feedback with terms from top retrieved results and multiple retrieval functions (BM25L, BM25+, TF-IDF and Language Models) combined with RRF fusion algorithm. We also tested an algorithm to measure article relevance to the target medical questions (diagnosis, test and treatment articles), based on the frequency of words to some categories. An additional experiment was based on pseudo relevance feedback based on each article's journal reputation. Although some techniques did not increase our baseline performance, we are satisfied with our global performance.
Qualified types for MLF

MLF is a type system that extends a functional language with impredicative rank-n polymorphism. Type inference remains possible and only in some clearly defined situations, a local type annotation is required. Qualified types are a general concept that can accommodate a wide range of type systems extension, for example, type classes in Haskell. We show how the theory of qualified types can be used seamlessly with the higher-ranked impredicative polymorphism of MLF, and give a solution to the non-trivial problem of evidence translation in the presence of impredicative datatypes.
Use of the Decision Support System for VA cost-effectiveness research.

BACKGROUND The Department of Veterans Affairs is adopting the Decision Support System (DSS), computer software and databases which include a cost-accounting system which determines the cost of health care products and patient encounters. OBJECTIVES A system for providing cost data for cost-effectiveness analysis should be provide valid, detailed, and comprehensive data that can be aggregated. METHODS The design of DSS is described and compared with those criteria. Utilization data from DSS was compared with other VA utilization data. Aggregate DSS cost data from 35 medical centers was compared with relative resource weights developed for the Medicare program. RESULTS Data on hospital stays at 3 facilities found that 3.7% of the stays in DSS were not in the VA discharge database, whereas 7.6% of the stays in the discharge data were not in DSS. DSS reported between 68.8% and 97.1% of the outpatient encounters reported by six facilities in the ambulatory care data base. Relative weights for each Diagnosis Related Group based on DSS data from 35 VA facilities correlated with Medicare weights (correlation coefficient of .853). CONCLUSIONS DSS will be useful for research if certain problems are overcome. It is difficult to distinguish long-term from acute hospital care. VA does not have a complete database of all inpatient procedures, so DSS has not assigned them a specific cost. The authority to access encounter-level DSS data needs to be centralized. Researchers can provide the feedback needed to improve DSS cost estimates. A comprehensive encounter-level extract would facilitate use of DSS for research.
Extracting Erosion and Exhumation Patterns from Detrital Thermochronology: an example from the eastern Himalaya

Detrital thermochronology, the core method explored in this thesis, is a relatively recent technique that can be applied to synorogenic sediments (both sedimentary basins and modern river detritus) to constrain the spatial time evolution of an “active” tectonic hinterlands. Detrital thermochronology can combine a large number of thermochronometric systems each with a different closure temperature. Some of the most commonly used thermochronometers are muscovite, hornblende, biotite, and microcline 40Ar/39Ar and zircon, apatite, and titanite U-Th/He and fi ssion-tracks. Each of these systems records different aspects of a geological history due to differences in their closure temperatures. The closure temperature of a geochronological system, as fi rst defi ned by Dodson (1973), is the temperature of the rock at the time of its calculated isotopic age. Isotopic dating developed in the 1950’s as the direct result of the discovery of radioactivity at the end of the 19th century. In this chapter, I will illustrate the analytical techniques that I used during this Ph.D. project to constrain the thermochronological history of selected sectors of two collisional belt: the Alps and the Himalayas. First, I will briefl y describe the historical background of the methods followed by the principles, applications, and limitations.
The Variation Tendency of Polyamines Forms and Components of Polyamine Metabolism in Zoysiagrass (Zoysia japonica Steud.) to Salt Stress with Exogenous Spermidine Application

To understand dynamic changes in polyamines (PAs) forms and components of polyamine metabolism in zoysiagrass (Zoysia japonica Steud.) response to salt stress with exogenous spermidine (Spd) application, two Chinese zoysia cultivars, z081 and z057, were exposed to sodium chloride stress for 2, 4, 6, and 8 days. The z057 cultivar possesses higher salinity tolerance than the z081 cultivar. Salt stress decreased the zoysiagrass fresh weight (FW) and increased free Spd and spermine (Spm) levels and soluble and insoluble putrescine (Put), Spd and Spm levels in both cultivars. Moreover, salt stress enhanced the activities of arginine decarboxylase (ADC), ornithine decarboxylase (ODC), S-adenosylmethionine decarboxylase (SAMDC), and diamine oxidase (DAO). Exogenous Spd increased PA metabolism and ADC, SAMDC, and DAO activities and decreased free Put levels under salt stress conditions in both cultivars. In addition, structural equation modeling (SEM) showed that ODC, SAMDC, and DAO contributed to PA metabolism, and endogenous Spd levels also contributed to endogenous Spm levels. Free PAs may be the primary factor influencing the variation of other PA forms. SEM also indicated that ADC and polyamine oxidase (PAO) play a limited role in enhancing zoysia salt tolerance via PA metabolism under salt stress.
Exploiting Rush Hours for Energy-Efficient Contact Probing in Opportunistic Data Collection

In many potential wireless sensor network applications, the cost of the base station infrastructure can be prohibitive. Instead, we consider the use of mobile devices carried by people in their daily life to collect sensor data opportunistically. As the movement of these mobile nodes is, by definition, uncontrolled, contact probing becomes a challenging task, particularly for sensor nodes which need to be aggressively duty-cycled to achieve long life. It has been reported that when the duty-cycle of a sensor node is fixed, SNIP, a sensor node-initiated probing mechanism, performs much better than mobile node-initiated probing mechanisms. Considering that the intended applications are delay-tolerant, mobile nodes tend to follow some repeated mobility patterns, and contacts are distributed unevenly in temporal, SNIP-RH is proposed in this paper to further improve the performance of contact probing through exploiting Rush Hours during which contacts arrive more frequently. In SNIP-RH, SNIP is activated only when the time is within Rush Hours and there are enough data to be uploaded in the next probed contact. As for the duty-cycle, it is selected based on the mean of contact length that is learned on line. Both analysis and simulation results indicate that under a typical simulated road-side wireless sensor network scenario, SNIP-RH can significantly reduce the energy consumed for probing the contacts, that are necessary for uploading the sensed data, or significantly increase the probed contact capacity under a sensor node's energy budget for contact probing.
Efficient volumetric segmentation method

In this paper we extend our previous work for planar images by adding a new step in the volumetric segmentation algorithm that allows us to determine regions closer to it. There are huge of papers for planar images and segmentation methods and most of them are graph-based for planar images and very few papers for volumetric segmentation methods. However, even if image segmentation is a heavily researched field, extending the algorithms to spatial has been proven not to be an easy task. A true volumetric segmentation remains a difficult problem to tackle due to the complex nature of the topology of spatial objects, the huge amount of data to be processed and the complexity of the algorithms that scale with the new added dimension. The problem of partitioning images into homogenous regions or semantic entities is a basic problem for identifying relevant objects. Visual segmentation is related to some semantic concepts because certain parts of a scene are pre-attentively distinctive and have a greater significance than other parts. A number of approaches to segmentation are based on finding compact regions in some feature space. A recent technique using feature space regions first transforms the data by smoothing it in a way that preserves boundaries between regions. The key to the whole own algorithm of volumetric segmentation is the honeycomb cells. The pre-processing module is used mainly to blur the initial RGB spatial image in order to reduce the image store and to make algorithms to be efficient. Then the volumetric segmentation module creates virtual cells of prisms with tree-hexagonal structure defined on the set of the image voxels of the input spatial image and a volumetric grid graph having tree-hexagons as cells of vertices. Early graph-based methods use fixed thresholds and local measures in finding a volumetric segmentation.
Making Them Remember—Emotional Virtual Characters with Memory

Research on virtual characters has been ongoing for the past 20 years. Early efforts focused mostly on making the characters move and speak that is, on body and facial animation. Simultaneously, researchers worked on making characters look convincing by adding animation and rendering hair, clothes, and muscles. The next step was to increase artists' interactive control over characters so that it was easier to create convincing video games and cinema.
Uncertainty Quantification and Global Sensitivity Analysis of Subsurface Flow Parameters to Gravimetric Variations During Pumping Tests in Unconfined Aquifers

We study the contribution of typically uncertain subsurface flow parameters to gravity changes that can be recorded during pumping tests in unconfined aquifers. We do so in the framework of a Global Sensitivity Analysis and quantify the effects of uncertainty of such parameters on the first four statistical moments of the probability distribution of gravimetric variations induced by the operation of the well. System parameters are grouped into two main categories, respectively, governing groundwater flow in the unsaturated and saturated portions of the domain. We ground our work on the three‐dimensional analytical model proposed by Mishra and Neuman (2011), which fully takes into account the richness of the physical process taking place across the unsaturated and saturated zones and storage effects in a finite radius pumping well. The relative influence of model parameter uncertainties on drawdown, moisture content, and gravity changes are quantified through (a) the Sobol' indices, derived from a classical decomposition of variance and (b) recently developed indices quantifying the relative contribution of each uncertain model parameter to the (ensemble) mean, skewness, and kurtosis of the model output. Our results document (i) the importance of the effects of the parameters governing the unsaturated flow dynamics on the mean and variance of local drawdown and gravity changes; (ii) the marked sensitivity (as expressed in terms of the statistical moments analyzed) of gravity changes to the employed water retention curve model parameter, specific yield, and storage, and (iii) the influential role of hydraulic conductivity of the unsaturated and saturated zones to the skewness and kurtosis of gravimetric variation distributions. The observed temporal dynamics of the strength of the relative contribution of system parameters to gravimetric variations suggest that gravity data have a clear potential to provide useful information for estimating the key hydraulic parameters of the system.
Altered Architecture and Enhanced Drought Tolerance in Rice via the Down-Regulation of Indole-3-Acetic Acid by TLD1/OsGH3.13 Activation1[C][W]

Plant architecture is determined by genetic and developmental programs as well as by environmental factors. Sessile plants have evolved a subtle adaptive mechanism that allows them to alter their growth and development during periods of stress. Phytohormones play a central role in this process; however, the molecules responsible for integrating growth- and stress-related signals are unknown. Here, we report a gain-of-function rice (Oryza sativa) mutant, tld1-D, characterized by (and named for) an increased number of tillers, enlarged leaf angles, and dwarfism. TLD1 is a rice GH3.13 gene that encodes indole-3-acetic acid (IAA)-amido synthetase, which is suppressed in aboveground tissues under normal conditions but which is dramatically induced by drought stress. The activation of TLD1 reduced the IAA maxima at the lamina joint, shoot base, and nodes, resulting in subsequent alterations in plant architecture and tissue patterning but enhancing drought tolerance. Accordingly, the decreased level of free IAA in tld1-D due to the conjugation of IAA with amino acids greatly facilitated the accumulation of late-embryogenesis abundant mRNA compared with the wild type. The direct regulation of such drought-inducible genes by changes in the concentration of IAA provides a model for changes in plant architecture via the process of drought adaptation, which occurs frequently in nature.
Model Counting: A New Strategy for Obtaining Good Bounds

Model counting is the classical problem of computing the number of solutions of a given propositional formula. It vastly generalizes the NP-complete problem of propositional satisfiability, and hence is both highly useful and extremely expensive to solve in practice. We present a new approach to model counting that is based on adding a carefully chosen number of so-called streamlining constraints to the input formula in order to cut down the size of its solution space in a controlled manner. Each of the additional constraints is a randomly chosen XOR or parity constraint on the problem variables, represented either directly or in the standard CNF form. Inspired by a related yet quite different theoretical study of the properties of XOR constraints, we provide a formal proof that with high probability, the number of XOR constraints added in order to bring the formula to the boundary of being unsatisfiable determines with high precision its model count. Experimentally, we demonstrate that this approach can be used to obtain good bounds on the model counts for formulas that are far beyond the reach of exact counting methods. In fact, we obtain the first non-trivial solution counts for very hard, highly structured combinatorial problem instances. Note that unlike other counting techniques, such as Markov Chain Monte Carlo methods, we are able to provide high-confidence guarantees on the quality of the counts obtained.
Profile Cloning Detection in Social Networks

Profile cloning is a severe security issue in social networks since it is used to make a profile identical to existing ones. Profile cloning detection creates a possibility to detect frauds that would use people's trust to gather social information. This paper proposes two novel methods of profile cloning detection and also presents state-of-the-art research. The first method is based on the similarity of attributes from both profiles and the second method is based on the similarity of relationship networks. The methods are further evaluated with experiments and the results clearly describes that the proposed methods are useful and efficient compared to existing methods. The paper also stress that profile cloning in Facebook is not only possible but also fairly easy to perform.
Automatic Detection and Recognition of Pig Wasting Diseases Using Sound Data in Audio Surveillance Systems

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an efficient data mining solution for the detection and recognition of pig wasting diseases using sound data in audio surveillance systems. In this method, we extract the Mel Frequency Cepstrum Coefficients (MFCC) from sound data with an automatic pig sound acquisition process, and use a hierarchical two-level structure: the Support Vector Data Description (SVDD) and the Sparse Representation Classifier (SRC) as an early anomaly detector and a respiratory disease classifier, respectively. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (even a cheap microphone can be used) and accurately (94% detection and 91% classification accuracy), either as a standalone solution or to complement known methods to obtain a more accurate solution.