Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Minority Serving Institutions in Transforming Teacher Education and Diversifying the Teaching Profession: A Literature Review and Research Agenda

Background Teacher education programs at Minority Serving Institutions – which include Historically Black Colleges and Universities, Native American and Tribal Colleges, Asian American and Pacific Islander Serving Institutions, and Hispanic Serving Institutions – are an under-researched resource. Purpose Our aim is to provide a foundation and set an agenda for future research on teacher education within the Minority Serving Institution context. Research Design The first part of this paper reviews the literature on the relationship between minority student achievement and teacher education, with an emphasis on the contribution and role of minority teacher candidates. The second part of this paper sets the agenda for future research on the role of Minority Serving Institutions’ teacher education programs, including what we already know and seek to learn about their unique and innovative approaches to recruitment, pedagogical approaches and course content, mentoring, academic support and remediation, and pre-service teaching experiences. Conclusion Some questions addressed include: In what specific ways are MSIs reframing teaching as a viable and accessible option for minority students? What are MSIs doing with regard to early recruitment of promising minority teacher candidates? What kinds of mentoring and support strategies are they offering, and to what do they attribute high rates of teacher retention in their programs?
On braided tensor categories of type BCD

Abstract We give a full classification of all braided semisimple tensor categories whose Grothendieck semiring is the one of Rep(O(∞)) (formally), Rep(O(N)), Rep(Sp(N)) or of one of its associated fusion categories. If the braiding is not symmetric, they are completely determined by the eigenvalues of a certain braiding morphism, and we determine precisely which values can occur in the various cases. If the category allows a symmetric braiding, it is essentially determined by the dimension of the object corresponding to the vector representation.
Embedded Incremental Feature Selection for Reinforcement Learning

Abstract : Classical reinforcement learning techniques become impractical in domains with large complex state spaces. The size of a domain's state space is dominated by the number of features used to describe the state. Fortunately, in many real-world environments learning an effective policy does not usually require all the provided features. In this paper we present a feature selection algorithm for reinforcement learning called Incremental Feature Selection Embedded in NEAT (IFSE-NEAT) that incorporates sequential forward search into neuroevolutionary algorithm NEAT. We provide an empirical analysis on a realistic simulated domain with many irrelevant and relevant features. Our results demonstrate that IFSE-NEAT selects smaller and more effective feature sets than alternative approaches, NEAT and FS-NEAT, and superior performance characteristics as the number of available features increases.
Discourse Ability and Brain Damage: Theoretical and Empirical Perspectives

The title of this book excited me. I thought, at last I would find out the truth about lateralization and localization of discourse abilities. I thought the chapters would c o n f m my suspicion that the right cerebral hemisphere is responsible for our ability to construct a narrative, understand a Story, draw inferences, and interpret ambiguous sentences. Needless to say, I was disappointed. Luckily for the future of neurolinguistics, the truth is never that simple. This collection of papers stems from a symposium at the 1986 Academy of Aphasia meeting. Additional authors were invited by the organizersleditors to contribute chapters representing the breadth of -research on discourse ability and brain damage. The editors have done an admirable job of selecting topics: Papers present data from adult aphasics, right brain-damaged adults, Alzheimer patients, and children with focal lesions. The contributions include discussion of most major areas of discourse research: inference, cohesion, coherence, story schema, narrative structure, and also some less traditional areas of pragmatic research, including code switching and gender-associated styles of verbal interaction. The book is organized into two sections. The first section (Chapters 1 4 ) is the “theoretical perspective” alluded to in the title. This section does not discuss brain damage directly, but rather reviews theoretical models of discourse. Considering the title, and my expectation that the bulk of the book would cover the intersection of Discourse Ability arid Brain Damage, it seemed to me that this section was rather overrepresented, and frankly, too long (110 pages of the 245 total). Chapter 1 (Discotirse Analysis iri Lirigriistics: Historical arid Tlieoretical Backgroiuid by Richard Patry and Jean-Luc Nespoulous) is a well-written historical overview which traces early approaches to discourse analysis from Vladimir Propp’s 1928 analysis of the structure of folk tales and the Prague School’s “functionalism” through current models of pragmatics and conversational analysis. This chapter defines and gives examples of many of the terms used recurrently throughout the text, including cohesion, coliererice, and macrostructure. It sets the tone for the entire book, emphasizing the necessity for exploring units of language structure beyond the sentence. Chapter 2 (The Psycltoliriguistics of Discourse Conipreliemiori by Gregory L. Murphy)
Cumulative Time Series Representation for Code Blue prediction in the Intensive Care Unit.

Patient monitors in hospitals generate a high number of false alarms that compromise patients care and burden clinicians. In our previous work, an attempt to alleviate this problem by finding combinations of monitor alarms and laboratory test that were predictive of code blue events, called SuperAlarms. Our current work consists of developing a novel time series representation that accounts for both cumulative effects and temporality was developed, and it is applied to code blue prediction in the intensive care unit (ICU). The health status of patients is represented both by a term frequency approach, TF, often used in natural language processing; and by our novel cumulative approach. We call this representation “weighted accumulated occurrence representation”, or WAOR. These two representations are fed into a L1 regularized logistic regression classifier, and are used to predict code blue events. Our performance was assessed online in an independent set. We report the sensitivity of our algorithm at different time windows prior to the code blue event, as well as the work-up to detect ratio and the proportion of false code blue detections divided by the number of false monitor alarms. We obtained a better performance with our cumulative representation, retaining a sensitivity close to our previous work while improving the other metrics.
Mining Software Metrics from Jazz

In this paper, we describe the extraction of source code metrics from the Jazz repository and the application of data mining techniques to identify the most useful of those metrics for predicting the success or failure of an attempt to construct a working instance of the software product. We present results from a systematic study using the J48 classification method. The results indicate that only a relatively small number of the available software metrics that we considered have any significance for predicting the outcome of a build. These significant metrics are discussed and implication of the results discussed, particularly the relative difficulty of being able to predict failed build attempts.
Management of Users' Privacy Preferences in Context

There has been intensive research on user-controlled privacy from the perspective of agent automation of privacy related user tasks. The W3C's platform for privacy preferences (P3P) specifies standards that can be used by P3P agents to automatically retrieve a Web-site's privacy policy on how the users' data is collected, stored, and managed, and then to determine whether they are compatible with the user's privacy preferences. Current approaches to managing user's privacy do not capture context, are not user-friendly, and do cater well to the dynamic nature of privacy preferences very well. Clearly, the user's privacy preferences depend on the context of the user's online activity and the user's preferences evolve with user's experience and changing levels of trust in various organizations and domains. We propose a model for user's privacy preferences that incorporates the context for user activity and we apply it using a case-based reasoning (CBR) approach that relates the current activity to previous activities stored in the case-base and thus forms an intuitive and understandable process. We describe the context model for privacy preferences, how CBR is used to create a new contextual case from the Web-site's privacy policy and the user's current activity, and how the CBR retrieves matching cases to be applied to the retrieved privacy policy.
Paleontological Society Huhatanka , a New Genus of Lobster ( Decapoda : Mecochiridae ) from the Kiowa Formation ( Cretaceous : Albian ) of Kansas Author ( s ) :

Reexamination of the type material of Squilla? kiowana Scott, 1970 and study of additional specimens of the same taxon indicates that it is a mecochirid decapod referable to a new genus, Huhatanka. This assignment brings to five the number of North American species referable to the Mecochiridae.
Supplemental Material for “ Discrete Graph Hashing ”

1 Proofs Lemma 1. If B (j) is the sequence of iterates produced by Algorithm 1, then f B (j+1) ≥ f B (j) holds for any integer j ≥ 0, and both f (B (j)) and B (j) converge. Proof. Since the B-subproblem in Eq. (5) of the main paper is to maximize a continuous function over a compact (i.e., closed and bounded) set, its optimal objective function value f * is finite. As B (j) is feasible for each j, the sequence {f (B (j))} is bounded from above.
Vision-based System for Head Pose Tracking in Indoor Immersive Environments

We describe an inexpensive, accurate, fast and scalable vision based indoor head pose tracking system. This system can be used for indoor tracking in VR and AR environments. Our approach uses video projectors to project a display grid pattern on the floor. The pattern consists of circular binary code color markers on a black and white checkerboard. A camera is attached to a user’s back, looking down. The camera looks at the projected pattern, and its position is calculated in tracking space based on the correspondence between the global position of the display grid markers and their image coordinates. We calibrate for the offset between the user’s head and the camera; hence head position can be calculated. The system has a mean position error of 4 millimeters and a mean jitter of less than 0.3 millimeters. We augment this position information with an inertial sensor to compute head rotation to achieve full 6DOF tracking.
Efficient low complexity power allocation policies for wireless communication systems guaranteeing QoS∗

We consider near-optimal policies for a single user transmitting on a wireless channel which minimize average queue length under average power constraint. The power is consumed in transmission of data only. We consider the case when the power used in transmission is a linear function of the data transmitted. The transmission channel may experience multipath fading. Later, we also extend these results to the multiuser case. We show that our policies can be used in a system with energy harvesting sources at the transmitter. Next we consider data users which require minimum rate guarantees. Finally we consider the system which has both data and real time users. Our policies have low computational complexity, closed form expression for mean delays and require only the mean arrival rate with no queue length information.
On diversity orders of distortion outage for coherent multi-access channels

In this paper we investigate the distortion outage performance of distributed estimation schemes in wireless sensor networks, where a distortion outage is defined as the event that the estimation error or distortion exceeds a pre-determined threshold. The sensors transmit their observation signals using analog amplify and forward through coherent multi-access channels to the fusion center, which reconstructs a minimum mean squared error (MMSE) estimate of the physical quantity observed. We consider three power allocation schemes - 1) equal power allocation (EPA), 2) short-term optimal power allocation (ST-OPA), and 3) long-term optimal power allocation (LT-OPA). We study their diversity orders of distortion outage in terms of increasing numbers of sensors, and show that under Rayleigh fading EPA and ST-OPA achieve the same diversity order of N logN, where N is the number of sensors. On the other hand, in LT-OPA, we find that for N > 1 the outage probability can be driven to zero with a finite amount of total power.
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes

Markov jump processes (MJPs) are used to model a wide range of phenomena from disease progression to RNA path folding. However, maximum likelihood estimation of parametric models leads to degenerate trajectories and inferential performance is poor in nonparametric models. We take a small-variance asymptotics (SVA) approach to overcome these limitations. We derive the small-variance asymptotics for parametric and nonparametric MJPs for both directly observed and hidden state models. In the parametric case we obtain a novel objective function which leads to non-degenerate trajectories. To derive the nonparametric version we introduce the gamma-gamma process, a novel extension to the gamma-exponential process. We propose algorithms for each of these formulations, which we call \emph{JUMP-means}. Our experiments demonstrate that JUMP-means is competitive with or outperforms widely used MJP inference approaches in terms of both speed and reconstruction accuracy.
Ubiquitous Mail

Bimap is a tool for synchronizing IMAP servers. It enables tw o or more IMAP mirrored servers to be modified independently and later on, synchronized. Bimap is versatile so, in additi on o synchronizing emails, it can be used for filtering and classi fying emails. For the sake of the example, the paper shows automati c emails classification and white-listing programmed with Bi map. Bimap is implemented in Scheme. The most important parts of its implementation are presented in this paper with the inte nded goal to demonstrate that Scheme is suited for programming ta sks that are usually devoted to scripting languages such as Perl or Python. With additional libraries, Scheme enables compact and efficient implementation of this distributed networked app lication because the main computations that require efficiency are ex ecuted in compiled code and only the user configurations are execute d in interpreted code.
Analysis of robust watermarking techniques: A retrospective

In this paper we focuses extensively on review of achievement of various non-blind invisible watermarking techniques. The main intention of the proposed techniques is to embed color logo into color host images or videos using transform domain techniques. And this concept is extended to video watermarking with multiple logos such as gray and color logos, to evince the supreme capacity of watermarking algorithm with least perceptual degradation of host video. In addition, watermarking algorithm was subjected to 12 different intentional and unintentional attacks such as frame averaging, dropping and swapping, compression and noise attacks, and common image processing attacks, which revealed that the hybrid video watermarking technique with multiple logos to be superior. Moreover, it is concluded that the projected technique is more robust under various subjected attacks. The most important objectives of digital watermarking techniques are capacity, robustness and imperceptibility. In all the proposed watermarking schemes, imperceptibility and robustness are estimated by measuring Peak Signal to Noise Ratio (PSNR) and Correlation Factor (CF). And this paper conveys the potency of the existing algorithms against various attacks.
1D Wavelet Transform of Projection Profiles for Isolated Handwritten Malayalam Character Recognition

This paper presents a novel method based on wavelet transform for offline recognition of isolated handwritten Malayalam characters. Thirty three characters from Malayalam, one of the South Indian languages, are chosen for the study. The scanned character images are first cropped and resized to fit into 32 times 64 window. This is followed by binarization and thinning. The preprocessed character images are then modeled with projection profile. One dimensional wavelet transformation is applied on the projection profile. The feature vector is formed from the smooth components of the transform coefficients. A Multi Level Perceptron network is used for classification. The results obtained for relatively large number of characters are encouraging and motivated us to investigate with different classifiers as our further study.
Temporal-Geospatial Cooperative Visual Analysis

Given the diverse set of pervasive tracking technologies available, temporal-geospatial data is being collected at an unprecedented rate. However, the effective visualization and interpretation of this data remains elusive. Visualizations have focused on showing an object's location, however more complex inter-entity queries also need to be supported, e.g. "did X and Y meet, and if so, where and when? ". We present Cooperative Visual Analysis, a combination of two novel visualizations, the Parallel Schedule View and the Braille Plot, working in synergy with a traditional 2D map. The Parallel Schedule View focuses on showing colocation (simultaneous or time separated), with the Braille Plot used to resolve position ambiguity and identify patterns and trends within a data trace (in addition to colocation). We present descriptions of each, and a user study showing support for these approaches. The study compared Cooperative Visual Analysis with a current approach, the Space Time Cube, and found the Cooperative Visual Analysis is an effective means for visualizing temporal-geospatial relationships in a data set, performing at or above the Space Time Cube, whilst being preferred by users.
Modeling Event Stream Hierarchies with Hierarchical Event Models

Compositional scheduling analysis couples local scheduling analysis via event streams. While local analysis has successfully been extended to include hierarchical scheduling strategies, event streams are still flat. In this paper, we generalize the concept of a stream hierarchy to embed different types of streams in a higher level structure. We explain why this extension is a natural match to model streams generated by communication stacks that are ubiquitous in networked embedded systems. We formally define the hierarchical event model and give operations to encode, combine, and extract stream properties that can be used in flat or hierarchical local scheduling analysis. Finally, we give an example and demonstrate that the proposed model enables superior analysis results.
Impact of Toll-Like Receptor 2 Deficiency on Survival and Neurological Function after Cardiac Arrest: A Murine Model of Cardiopulmonary Resuscitation

Background Cardiac arrest (CA) followed by cardiopulmonary resuscitation (CPR) is associated with poor survival rate and neurofunctional outcome. Toll-like receptor 2 (TLR2) plays an important role in conditions of sterile inflammation such as reperfusion injury. Recent data demonstrated beneficial effects of the administration of TLR2-blocking antibodies in ischemia/reperfusion injury. In this study we investigated the role of TLR2 for survival and neurofunctional outcome after CA/CPR in mice. Methods Female TLR2-deficient (TLR2-/-) and wild type (WT) mice were subjected to CA for eight min induced by intravenous injection of potassium chloride and CPR by external chest compression. Upon the beginning of CPR, n = 15 WT mice received 5 µg/g T2.5 TLR2 inhibiting antibody intravenously while n = 30 TLR2-/- and n = 31 WT controls were subjected to injection of normal saline. Survival and neurological outcome were evaluated during a 28-day follow up period. Basic neurological function, balance, coordination and overall motor function as well as spatial learning and memory were investigated, respectively. In a separate set of experiments, six mice per group were analysed for cytokine and corticosterone serum levels eight hours after CA/CPR. Results TLR2 deficiency and treatment with a TLR2 blocking antibody were associated with increased survival (77% and 80% vs. 51% of WT control; both P < 0.05). Neurofunctional performance was less compromised in TLR2-/- and antibody treated mice. Compared to WT and antibody treated mice, TLR2-/- mice exhibited reduced IL-6 (both P < 0.05) but not IL-1β levels and increased corticosterone plasma concentrations (both P < 0.05). Conclusion Deficiency or functional blockade of TLR2 is associated with increased survival and improved neurofunctional outcome in a mouse model of CA/CPR. Thus, TLR2 inhibition could provide a novel therapeutic approach for reducing mortality and morbidity after cardiac arrest and cardiopulmonary resuscitation.
Monster : a tool for analyzing the interaction between operating systems and computer architectures

To enable computer designers to better evaluate the architectural needs of operating systems, we have developed Monster , a tool which combines hardware and software monitoring techniques to unobtrusively obtain system performance data. This report is split into two major parts. In Part I, we argue the need for OS performance evaluation tools, summarize previous hardware and software based monitoring techniques, discuss our design of Monster and finally present an analysis of compilation workloads which test and demonstrate Monster’s capabilities. In Part II, we detail our plans for future studies in which Monster plays a central role. Monster: A Tool for Analyzing the Interaction between Operating Systems and Computer Architectures
Kurtosis Normalization in Feature Space for Robust Speaker Verification

The acoustic mismatch between the training and test environments will lead to the difference of the statistical characteristics of speech parameters. Since the statistical characteristics of the kurtosis can measure the non-Gaussianity of a random variable, kurtosis normalization will make the training and test speech parameters match the standard normal distribution in some sense. In this paper, a kurtosis normalization method using sigmoid functions (logit functions) in feature space is presented for GMM-UBM based text-independent speaker verification system. Experimental results on the 2004 NIST SRE database show that with the new method significant improvement can be achieved in not only equal error rate but also minimum detection cost compared with baseline system (more than 33% relative reduction for long speech)
Research on Transforming Relational Database into Enriched Ontology

A method transforming relational database into enriched ontology is proposed. Many methods merely focus on transforming of database schema, transformed ontology has weak semantics and inconsistency. In this paper, database is transformed into ontology by traditional methods firstly, then consistency check is applied on it, at last its semantics is enriched, this course ensures high quality of the transformed ontology. During this course, a predefined basis ontology is used to reduce ontology-mapping and improve consistency. Experiment has been done to prove quality of transformed ontology is improved.