Miracle-dz/UniFAR-FTUs huggingface dataset:
The Interaction of Standards-Based Reform and Inclusion in New Jersey's Fourth Grade Classrooms: Are They Perfect Together?.

This study examined how six teachers in inclusive classrooms who are being encouraged to deliver reform-oriented mathematics instruction modify mathematical tasks and provide academic interventions for students with cognitive disabilities. Qualitative data for this study were collected between April and June 1999 by means of observation and interviews. Two or three mathematics lessons taught by each of the six target teachers were observed. Semi-structured qualitative interviews were also conducted either in person or by telephone within two weeks of the observation. Findings indicated that task modification and the provision of separate assignments to students with disabilities appear to be rare occurrences in inclusive classrooms. Second, larger application and invention tasks seem to be more likely to trigger teacher interventions than the traditional American "drill and kill" problems. However, some of these non-traditional tasks may also be more flexible in accommodating wider ranges in ability. Third, academic interventions can be classified along a dimension of explicitness of the problem-solving strategy and by whether the teacher or student employs that strategy. (Contains 53 references.) Reproductions supplied by EDRS are the best that can be made from the original document. U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION lk CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. THE. INTERACTION OF STANDARDS-BASED REFORM AND INCLUSION IN NEW JERSEY'S FOURTH GRADE CLASSROOMS: ARE THEY PERFECT TOGETHER? By David Mayrowetz Rutgers University, Graduate School of Education
A comparative study of different classifiers for automatic personality prediction

Personality is described as a fairly fixed feature of an individual which indicates individual's preferences. Personality has been shown to be relevant to many types of interactions such as in predicting movie preferences, social relationships, personality and music, and correlation between personality and job performance. Predicting personality from social media become the current trend as the information extracted can be utilized to improve the users' experiences with various computerized interfaces. Thus, many algorithms have been performed to predict personality from social media. In this paper, we compared the performance of several classifiers provided in WEKA namely Bayes, Functions, Rules, Trees, and Meta in predicting student's personality. Based on adopted framework, the profile data of undergraduate students were extracted from Twitter, analyzed, and then classified in the automatic personality prediction. Four features with significant correlation from the profile data have been selected to map into Big Five personality model. Only extraversion dimension of the Big Five was considered in this study. A 10-fold cross validation was used to evaluate the classifiers. Several parameters that were observed in the performance of the classifiers are classification accuracy, F-measure, time taken to build the model, Kappa statistic, and training errors. Experimental evaluation demonstrated that OneR algorithm is the best classifier in terms of the accuracy, F-measure, and Kappa statistic.
Towards an Ontology Design Architecture

As the world enters the age of "big data", new ways to represent and reason on enormous amounts of data are demanded and expected. Work in developing ontologies and reasoning approaches have taken steps towards addressing these needs. However, ontology development is not usually perceived as an engineering activity. Developers often overlook fundamental questions and concerns of ontology design and an ad hoc "one-time use" mentality has emerged. In this position paper, we address this lack of design consideration in ontology development by adapting existing software design patterns. We propose an ontology design architecture based on the model-view-controller (MVC-II) architectural style in an effort to present a new engineering view of designing ontologies.
On the Reliability Function of the Common-Message Broadcast Channel with Variable-Length Feedback

We derive upper and lower bounds on the reliability function for the common-message discrete memoryless broadcast channel with variable-length feedback. We show that the bounds are tight when the broadcast channel is stochastically degraded. For the achievability part, we adapt Yamamoto and Itoh's coding scheme by controlling the expectation of the maximum of a set of stopping times. For the converse part, we adapt Burnashev's proof techniques for establishing the reliability functions for (point-to-point) discrete memoryless channels with variable-length feedback and sequential hypothesis testing.
NeuroLex.org: an online framework for neuroscience knowledge

The ability to transmit, organize, and query information digitally has brought with it the challenge of how to best use this power to facilitate scientific inquiry. Today, few information systems are able to provide detailed answers to complex questions about neuroscience that account for multiple spatial scales, and which cross the boundaries of diverse parts of the nervous system such as molecules, cellular parts, cells, circuits, systems and tissues. As a result, investigators still primarily seek answers to their questions in an increasingly densely populated collection of articles in the literature, each of which must be digested individually. If it were easier to search a knowledge base that was structured to answer neuroscience questions, such a system would enable questions to be answered in seconds that would otherwise require hours of literature review. In this article, we describe NeuroLex.org, a wiki-based website and knowledge management system. Its goal is to bring neurobiological knowledge into a framework that allows neuroscientists to review the concepts of neuroscience, with an emphasis on multiscale descriptions of the parts of nervous systems, aggregate their understanding with that of other scientists, link them to data sources and descriptions of important concepts in neuroscience, and expose parts that are still controversial or missing. To date, the site is tracking ~25,000 unique neuroanatomical parts and concepts in neurobiology spanning experimental techniques, behavioral paradigms, anatomical nomenclature, genes, proteins and molecules. Here we show how the structuring of information about these anatomical parts in the nervous system can be reused to answer multiple neuroscience questions, such as displaying all known GABAergic neurons aggregated in NeuroLex or displaying all brain regions that are known within NeuroLex to send axons into the cerebellar cortex.
Ground Wheat Grain for Midlactation Cows: Challenging a Common Wisdom

The objective was to determine the effects of ground wheat grain (GW) inclusion rate, grinding extent (GE), and their interaction on lactating cow performance. Eight midlactation cows in 3 × 4 m individual boxes were used in a 4 × 4 replicated Latin square design study with 4 21 d periods. GW was fed at either 10% or 20% of diet dry matter (DM), as either finer or coarser particles. DM intake increased and net energy for lactation (NEL) intake tended to increase when GW was fed at 10% instead of 20% of diet DM. Milk energy yield, milk solids content and yield, and urine pH were unaffected. Fecal pH tended to increase at 20% versus 10% GW. Total tract apparent NDF, but not DM, digestibility tended to be greater for coarsely than finely GW and tended to be greater at 10% versus 20% GW. GW at 10% versus 20% of diet DM decreased blood BHBA and increased blood concentrations of total proteins and albumin. Data provide novel evidence that both finely and coarsely ground WG can be safely fed up to 20% midlactation cows. Commercial accessibility and cost will determine feeding preference of wheat grain to dairy cows.
Location-Aware Collaborative Filtering for QoS-Based Service Recommendation

Collaborative filtering is one of widely used Web service recommendation techniques. In QoS-based Web service recommendation, predicting missing QoS values of services is often required. There have been several methods of Web service recommendation based on collaborative filtering, but seldom have they considered locations of both users and services in predicting QoS values of Web services. Actually, locations of users or services do have remarkable impacts on values of QoS factors, such as response time, throughput, and reliability. In this paper, we propose a method of location-aware collaborative filtering to recommend Web services to users by incorporating locations of both users and services. Different from existing user-based collaborative filtering for finding similar users for a target user, instead of searching entire set of users, we concentrate on users physically near to the target user. Similarly, we also modify existing service similarity measurement of collaborative filtering by employing service location information. After finding similar users and services, we use the similarity measurement to predict missing QoS values based on a hybrid collaborative filtering technique. Web service candidates with the top QoS values are recommended to users. To validate our method, we conduct series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that the location-aware method improves performance of recommendation significantly.
Efficient TSV repair method for 3D memories

Through-silicon-via (TSV) based 3D stacked memory is recognized as the next generation memory architecture but its low TSV yield is one of the manufacturing cost factors. In this paper, an efficient TSV repair method is proposed for 3D memories. The proposed method uses a new 2-dimensional 1-4 switching technique to enable efficient repair of clustered TSV faults using repair circuitry with reasonable area overhead. Therefore, the proposed TSV repair method can contribute the improvement of TSV yield for 3D memories.
Constituency and recursion in language

artificial languages, Christiansen and Chater (1999) show that the SRN’s general pattern of performance is relatively invariant across network size and training corpus, and conclude that the human-like pattern of performance derive from intrinsic constraints inherent in the SRN architecture. Connectionist models of recursive syntax typically use “toy” fragments of grammar and small vocabularies. Aside from raising concerns over scalingup, this makes it difficult to provide detailed fits with empirical data. Nonetheless, some attempts have recently been made toward fitting existing data and deriving new empirical predictions from the models. For example, the Christiansen and Chater (1999) SRN model fits grammaticality ratings data from several behavioral experiments, including an account of the relative processing difficulty associated with the processing center-embeddings (with the following relationship between nouns and verbs: N1N2N3V3V2V1) vs. cross-dependencies (with the following relationship between nouns and verbs: N1N2N3V1V2V3). Human data have shown that sentences with two centerembeddings (in German) were significantly harder to process than comparable sentences with two cross-dependencies (in Dutch). The simulation results demonstrated that the SRNs exhibited the same kind of qualitative processing difficulties as humans on these two types of complex recursive constructions. Just as the radical connectionist approach to constituency deviates from classical constituency, the above approach to recursion deviates from the classical notion of recursion. The radical models of recursion do not acquire “true” recursion because they are unable to process infinitely complex recursive constructions. However, the classic notion of recursion may be illsuited for capturing human recursive abilities. Indeed, the psycholinguistic data suggest that people’s performance may be better construed as being only “quasi-recursive”. The earlier mentioned semantic facilitation of recursive processing further suggests that human recursive performance may be partially context-sensitive; for example, the semantically biased ‘The bees that the hive that the farmer built housed stung the children’ is easier to comprehend than neutral ‘The chef that the waiter that the busboy offended appreciated admired the musicians’ even though both sentences contain two center-embeddings. This dovetails with the context-sensitive notion of constituency, and suggests that context-sensitivity may be a more pervasive feature of language processing than typically assumed by symbolic approaches.
The economic burden of prostate cancer in Canada: forecasts from the Montreal Prostate Cancer Model.

BACKGROUND We developed an economic model of prostate cancer management from diagnosis until death. We have used the Montreal Prostate Cancer Model to estimate the total economic burden of the disease in a cohort of Canadian men. METHODS Using this Markov state-transition simulation model, we estimated the probability of prostate cancer, annual prostate cancer progression rates and associated direct medical costs according to patient age, tumour stage and grade, and treatment modalities in a 1997 cohort of Canadian men. The estimated lifetime costs of prostate cancer included the costs of clinical staging, initial treatments and complications, follow-up cancer therapies, routine outpatient care, and palliative care following metastatic disease. RESULTS The clinical burden of prostate cancer forecasted using the model was similar to the projections of the National Cancer Institute. In the 1997 cohort of 5.8 million Canadian men between 40 and 80 years old, prostate cancer would be diagnosed in an estimated 701,491 men (12.1%) over their lifetime. Direct medical costs would total $9.76 billion, or $3.89 billion when discounted 5% annually. INTERPRETATION The Montreal Prostate Cancer Model indicates that the economic burden of prostate cancer to Canada's health care system will be substantial. Further analyses are needed to identify the most efficient means of treating this disease.
Brain–computer interfacing based on cognitive control

Brain–computer interfaces (BCIs) translate deliberate intentions and associated changes in brain activity into action, thereby offering patients with severe paralysis an alternative means of communication with and control over their environment. Such systems are not available yet, partly due to the high performance standard that is required. A major challenge in the development of implantable BCIs is to identify cortical regions and related functions that an individual can reliably and consciously manipulate. Research predominantly focuses on the sensorimotor cortex, which can be activated by imagining motor actions. However, because this region may not provide an optimal solution to all patients, other neuronal networks need to be examined. Therefore, we investigated whether the cognitive control network can be used for BCI purposes. We also determined the feasibility of using functional magnetic resonance imaging (fMRI) for noninvasive localization of the cognitive control network.
Control of Active Suspension System: An Interval Type -2 Fuzzy Approach

In this paper, we introduce a novel interval type-2 Fuzzy Logic System (FLS) for active automobile suspension control in presence of system unmodeling, measurement noises and external disturbances. In order to efficient reduction of noise effects and improving the reliability of suspension system, a fuzzy controller is designed. Interval type-2 FLS is used to improve the disturbance rejection of suspension system which can be caused by road shocks. Therefore, better control performance and car passengers comfort is achieved, in comparison with previous type-1 fuzzy controllers. In addition, it is shown that the proposed controller provide better responses even in comparison with similar type-2 fuzzy controllers. The designing procedure of proposed interval type-2 fuzzy controller is presented in detail and simulation examples are presented to verify the effectiveness of the method. Results are compared with some other methods proposed in the past research.
Binary Interaction Algorithms for the Simulation of Flocking and Swarming Dynamics

Microscopic models of flocking and swarming takes in account large numbers of interacting individ- uals. Numerical resolution of large flocks implies huge computational costs. Typically for $N$ interacting individuals we have a cost of $O(N^2)$. We tackle the problem numerically by considering approximated binary interaction dynamics described by kinetic equations and simulating such equations by suitable stochastic methods. This approach permits to compute approximate solutions as functions of a small scaling parameter $\varepsilon$ at a reduced complexity of O(N) operations. Several numerical results show the efficiency of the algorithms proposed.
Influence of antisocial and psychopathic traits on decision-making biases in alcoholics.

BACKGROUND Although decision-making processes have become a principal target of study among addiction researchers, few studies have specifically examined decision-making among individuals with alcohol dependence (AD) and findings to date are mixed. The present study examined the relationship between AD and decision-making, and tested whether different facets of antisocial and psychopathic traits explain this association. METHODS Participants were men with AD (n = 22), AD and comorbid antisocial personality disorder (AD + ASPD; n = 17), or a history of recreational alcohol use, but no current or lifetime symptoms of a substance use disorder, conduct disorder, or ASPD (n = 21). Decision-making was tested using the Iowa Gambling Task (IGT). RESULTS Across groups, participants reported similar levels of awareness of the contingencies of the task, but the AD groups with and without ASPD had poorer IGT performance compared with controls (p < 0.05). A block-by-block analysis revealed that while AD had slow but steady improvement across the task, AD + ASPD exhibited initial improvement followed by a significant decrease in advantageous decision-making during the last 20 trials (p < 0.05). This was further confirmed via evidence that impulsive/antisocial personality traits but not psychopathic traits mediated poor IGT performance beyond ASPD diagnosis. CONCLUSIONS Alcohol-dependent males favored risky choices regardless of whether they met criteria for ASPD. However, decision-making deficits were more pronounced among those with ASPD, and personality traits characterized by impulsive and antisocial tendencies mediated the relationship between AD and decision-making.
Self-Trained Supervised Segmentation of Subcortical Brain Structures Using Multispectral Magnetic Resonance Images

The aim of this paper is investigate the feasibility of automatically training supervised methods, such as k-nearest neighbor (kNN) and principal component discriminant analysis (PCDA), and to segment the four subcortical brain structures: caudate, thalamus, pallidum, and putamen. The adoption of supervised classification methods so far has been limited by the need to define a representative training dataset, operation that usually requires the intervention of an operator. In this work the selection of the training data was performed on the subject to be segmented in a fully automated manner by registering probabilistic atlases. Evaluation of automatically trained kNN and PCDA classifiers that combine voxel intensities and spatial coordinates was performed on 20 real datasets selected from two publicly available sources of multispectral magnetic resonance studies. The results demonstrate that atlas-guided training is an effective way to automatically define a representative and reliable training dataset, thus giving supervised methods the chance to successfully segment magnetic resonance brain images without the need for user interaction.
Reconstruction of Ribosomal RNA Genes from Metagenomic Data

Direct sequencing of environmental DNA (metagenomics) has a great potential for describing the 16S rRNA gene diversity of microbial communities. However current approaches using this 16S rRNA gene information to describe community diversity suffer from low taxonomic resolution or chimera problems. Here we describe a new strategy that involves stringent assembly and data filtering to reconstruct full-length 16S rRNA genes from metagenomicpyrosequencing data. Simulations showed that reconstructed 16S rRNA genes provided a true picture of the community diversity, had minimal rates of chimera formation and gave taxonomic resolution down to genus level. The strategy was furthermore compared to PCR-based methods to determine the microbial diversity in two marine sponges. This showed that about 30% of the abundant phylotypes reconstructed from metagenomic data failed to be amplified by PCR. Our approach is readily applicable to existing metagenomic datasets and is expected to lead to the discovery of new microbial phylotypes.
A Content-based Music Similarity Retrieval Scheme by Using BoW Representation and LSH-based Retrieval

This extended abstract paper presents detailed information about a content-based music similarity retrieval scheme, which is based on locality sensitive hashing (LSH). Our scheme considered MFCC and time histogram (TH) as two major features to represent the properties of audio music similarity. Next, each feature is depicted by Bag of Words (BoW), which k-means clustering summarizes extracted features. In order to enhance the computational performance and preserve the result quality as much as possible, we adopted LSH to retrieve similar songs from dataset.
Community-level impacts of the third sector: Does the local distribution of voluntary organizations influence the likelihood of volunteering?

Scholars have attributed various beneficial outcomes to the presence and density of the voluntary, third or non-profit sector in communities. One way in which the distribution of such organizations may benefit communities is through providing opportunities for volunteering. We hypothesize that the distribution of third-sector organizations has an influence, after controlling for relevant individual and area characteristics, on the likelihood of engaging in formal volunteering (defined as unpaid help given through, and to, third-sector organizations, rather than directly to individuals). Using administrative data from the Charity Commission, we classify organizations in terms of their geographical scale of operation. We then construct indicators of the distribution of charities, and their expenditures, for local authorities in England. We obtain data on volunteering by individuals from the Citizenship Survey and link this to administrative data contained in the Charity Commission register on the distribution of charities. We find that there is a positive relationship between the numbers of charities operating locally and the likelihood of volunteering. Other measures, however, including the distribution of charities operating either regionally or nationally, have no statistically significant effects. We find no relationship between a measure of the size of charities (the median expenditures of charities within local authorities) and the likelihood of volunteering. These findings are relevant beyond the UK to debates about the understanding of variations in voluntary action, and to discussions about the impact of the third sector upon communities.