Miracle-dz/UniFAR-FTUs huggingface dataset:
Crowdteaching: Supporting Teaching as Designing in Collective Intelligence Communities

The widespread availability of high-quality Web-based content offers new potential for supporting teachers as designers of curricula and classroom activities. When coupled with a participatory Web culture and infrastructure, teachers can share their creations as well as leverage from the best that their peers have to offer to support a collective intelligence or crowdsourcing community, which we dub crowdteaching . We applied a collective intelligence framework to characterize crowdteaching in the context of a Web-based tool for teachers called the Instructional Architect (IA). The IA enables teachers to find, create, and share instructional activities (called IA projects) for their students using online learning resources. These IA projects can further be viewed, copied, or adapted by other IA users. This study examines the usage activities of two samples of teachers, and also analyzes the characteristics of a subset of their IA projects. Analyses of teacher activities suggest that they are engaging in crowdteaching processes. Teachers, on average, chose to share over half of their IA projects, and copied some directly from other IA projects. Thus, these teachers can be seen as both contributors to and consumers of crowdteaching processes. In addition, IA users preferred to view IA projects rather than to completely copy them. Finally, correlational results based on an analysis of the characteristics of IA projects suggest that several easily computed metrics (number of views, number of copies, and number of words in IA projects) can act as an indirect proxy of instructionally relevant indicators of the content of IA projects.
Optimal networkdesign for synchronization ofKuramoto oscillators

In this paper, we study the problem of designing the natural frequencies and the coupling weights in a network of nonidentical Kuramoto oscillators. We define an objective function which represents the cost of tuning the design variables, and consider phase cohesiveness, defined as the asymptotic maximum phase difference across all the edges, as our design constraint. In this context, we develop a convex optimization framework to find the minimum cost design that guarantees a desired level of phase cohesiveness. To this end, we address two network design problems: (1) the nodal-frequency design problem, in which we design the natural frequencies of the oscillators for a fixed network structure, and (2 ) the edge-weight design problem, in which we design the link weights assuming that the natural frequencies are known to belong to a convex uncertainty set. We illustrate the applicability of the proposed framework by analyzing network design problems of practical interest, such as sparsity-promoting network design, robust network design for distributed wireless analog clocks, and the Braess’ paradox in power grids.
Broadcast control of multi-agent systems with instability phenomenon

This paper presents the instability problem of a broadcast control framework for multi-agent systems in executing certain motion coordination tasks. First, response of unstable solution using a standard broadcast control framework is demonstrated by choosing one motion coordination task as an example of the unstable case. In order to solve the problem, the standard broadcast control framework has been modified, where deterministic movements of the agents are limited to a certain value. Finally, the chosen motion coordination task has been simulated back using the modified broadcast control framework and numerical simulation illustrated the effectiveness of the modified broadcast control framework in handling the instability case.
Subgraph Rank: PageRank for Subgraph-Centric Distributed Graph Processing

The growth of Big Data has seen the increasing prevalence of interconnected graph datasets that reflect the variety and complexity of emerging data sources. Recent distributed graph processing platforms offer vertex-centric and subgraph-centric abstractions to compose and execute graph analytics on commodity clusters and Clouds. Naive translation of existing graph algorithms to these programming models can offer sub-optimal performance. We analyze the effectiveness of PageRank, a popular graph centrality measure, for a subgraph-centric programming model, and propose variations based on the existing BlockRank algorithm to improve the performance. We evaluate these algorithms on real-world graphs using the GoFFish platform on Amazon EC2 Cloud VMs, and demonstrate that the proposed Subgraph Rank algorithm outperforms the native PageRank and BlockRank algorithms, and is faster by 23--74% for most graphs we evaluated, while achieving an equivalent PageRank quality.
Models with a Step-Like Initial Power Spectrum

ABSTRACT We investigate the properties of clusters of galaxies in the ΛCDM models with a step-like initial power spectrum. We examine the mass function, the peculiar velocitiesand the power spectrum of clusters in models with diﬀerent values of the density pa-rameter Ω 0 , the normalized Hubble constant h and the spectral parameter p, whichdescribes the shape ofthe initial powerspectrum. The results are comparedwith obser-vations. We also investigate the rms bulk velocity in the models, where the propertiesof clusters are consistent with the observed data. We ﬁnd that the power spectrumof clusters is in good agreement with the observed power spectrum of the Abell-ACOclusters, if the spectral parameter p is in the range p = 0.6 − 0.8. The power spec-trum and the rms peculiar velocity of clusters are consistent with observations only ifΩ 0 < 0.4. The Ω 0 = 0.3 models are consistent with the observed properties of clusters,if h = 0.50−0.63. For h = 0.65, we ﬁnd that Ω 0 = 0.20−0.27. Key words: cosmology: theory – large-scale structure of Universe, cosmology: theory– dark matter, galaxies: clusters
Function-based ATPG for Path Delay Faults using the Launch-Off-Capture Scan Architecture

This paper presents a novel function-based test generation technique for path delay faults (PDFs) under the launch-off-capture (LOC) scan architecture. The LOC architecture imposes the condition that the second test pattern must be a functional response of the initial scan test pattern. The proposed function-based LOC methodology incorporates traditional function-based ATPG techniques alongside an implicit framework to efficiently identify testable PDFs under the LOC scan architecture, and avoids the complex backtracking performed by structural techniques which may abort PDF classifications for path intensive designs. The effectiveness and scalability of the proposed method is demonstrated on the path intensive ISCAS 89 benchmarks
Law and finance in transition economies 1

This paper offers the first comprehensive analysis of legal change in the protection of shareholder and creditor rights in transition economies and its impact on the propensity of firms to raise external finance. Following La Porta et al. (1998), the paper constructs an expanded set of legal indices to capture a range of potential conflicts between different stakeholders of the firm. It supplements the analysis of the law on the books with an analysis of the effectiveness of legal institutions. Our main finding is that the effectiveness of legal institutions has a much stronger impact on external finance than does the law on the books, despite legal change that has substantially improved shareholder and creditor rights. This finding supports the proposition that legal transplants and extensive legal reforms are not sufficient for the evolution of effective legal and market institutions. JEL classification: G15, K22, K42, O16, P51.
THE FUZZY DESCRIPTION LOGIC ALCFLH

In this paper, we present the fuzzy description logic ALCFLH . ALCFLH is based on ALCFH , but linear hedges are used instead of exponential ones. This allows to solve the entailment and the subsumption problem in a fuzzy description logic, where arbitrary concepts and roles may be modified.
Accelerating Random Kaczmarz Algorithm Based on Clustering Information

Kaczmarz algorithm is an efficient iterative algorithm to solve overdetermined consistent system of linear equations. During each updating step, Kaczmarz chooses a hyperplane based on an individual equation and projects the current estimate for the exact solution onto that space to get a new estimate.Many vairants of Kaczmarz algorithms are proposed on how to choose better hyperplanes.Using the property of randomly sampled data in high-dimensional space,we propose an accelerated algorithm based on clustering information to improve block Kaczmarz and Kaczmarz via Johnson-Lindenstrauss lemma. Additionally, we theoretically demonstrate convergence improvement on block Kaczmarz algorithm.
Modeling Invention by Analogy in ACT-R

We investigate some aspects of cognition involved in invention, more precisely in the invention of the telephone by Alexander Graham Bell. We propose the use of the Structure-BehaviorFunction (SBF) language for the representation of invention knowledge; we claim that because SBF has been shown to support a wide range of reasoning about physical devices, it constitutes a plausible account of how an inventor might represent knowledge of an invention. We further propose the use of the ACT-R architecture for the implementation of this model. ACT-R has been shown to very precisely model a wide range of human cognition. We draw upon the architecture for execution of productions and matching of declarative knowledge through spreadingactivation. Thus we present a model which combines the well-established cognitive validity of ACT-R with the powerful, specialized model-based reasoning methods facilitated by SBF.
Differences in Anthropometric Traits and Trend of Changes in High School Students

Objectives: Anthropometric traits are closely related to health improvement as well as motor skills, psychological and sociological development. Therefore, the aim of this study was to investigate the differences in anthropometric traits of high school students and trend of changes among them. Methods: The anthropometric parameters including weight, height and body mass index (BMI) were measured in 60 male and female students, aged 17 ± 0.5 years. Descriptive statistics and t-independent test was used to analyze the data. Results: The results suggested that there were statistically significant differences in the height (t = 7.40; P < 0.000) and body weight (t = 3.62; P < 0.001) of male and female students, while no significant difference was found in BMI values in the two groups (t = -0.33, P > 0.001). In addition, it was suggested that 68% of male and female high school with mean BMI of 25 and 13 respectively, had normal nutrition status. 14.6% of male and 10.5% of female students had malnutrition. 21% of female students and 17% of male students had an excessive malnutrition. Conclusions: Although, today’s development is moving towards the advancement of technology, the life style especially nutrition status and physical activity needs to be seriously considered. However, mush research is needed to understand the effective factors on life style of different age groups especially students who are so involved in threaten of hypokinesian lifestyle.
Phase I Pharmacokinetic and Pharmacodynamic Study of 17-N-Allylamino-17-Demethoxygeldanamycin in Pediatric Patients with Recurrent or Refractory Solid Tumors: A Pediatric Oncology Experimental Therapeutics Investigators Consortium Study

Purpose: Heat shock protein 90 (Hsp90) is essential for the posttranslational control of many regulators of cell growth, differentiation, and apoptosis. 17-N-Allylamino-17-demethoxygeldanamycin (17-AAG) binds to Hsp90 and alters levels of proteins regulated by Hsp90. We conducted a phase I trial of 17-AAG in pediatric patients with recurrent or refractory neuroblastoma, Ewing's sarcoma, osteosarcoma, and desmoplastic small round cell tumor to determine the maximum tolerated dose, define toxicity and pharmacokinetic profiles, and generate data about molecular target modulation. Experimental Design: Escalating doses of 17-AAG were administered i.v. over 1 to 2 h twice weekly for 2 weeks every 21 days until patients experienced disease progression or toxicity. harmacokinetic and pharmacodynamic studies were done during cycle 1. Results: Fifteen patients were enrolled onto dose levels between 150 and 360 mg/m2; 13 patients were evaluable for toxicity. The maximum tolerated dose was 270 mg/m2. DLTs were grade 3 transaminitis and hypoxia. Two patients with osteosarcoma and bulky pulmonary metastases died during cycle 1 and were not evaluable for toxicity. No objective responses were observed. 17-AAG pharmacokinetics in pediatric patients were linear; clearance and half-life were 21.6 ± 6.21 (mean ± SD) L/h/m2 and 2.6 ± 0.95 h, respectively. Posttherapy increases in levels of the inducible isoform of Hsp70, a marker of target modulation, were detected in peripheral blood mononuclear cells at all dose levels. Conclusion: 17-AAG was well tolerated at a dose of 270 mg/m2 administered twice weekly for 2 of 3 weeks. Caution should be used in treatment of patients with bulky pulmonary disease.
Tetracyclines and Neuromuscular Disorders

Tetracyclines are a class of antibiotics which could act as neuroprotective molecules in several neurological disorders, such as Huntington disease, Parkinson disease, stroke and multiple sclerosis. The main biological effects of tetracyclines are the inhibition of microglial activation, the attenuation of apoptosis and the suppression of reactive oxygen species production. The anti-apoptotic effect of tetracyclines involves the mitochondrion, and the major target for neuroprotective effects of tetracyclines lies within the complex network that links mitochondria, oxidative stress and apoptosis. Neuromuscular disorders are due to dysfunction of motor neurons, peripheral nerves, neuromuscular junction, or skeletal muscle itself. Animal studies have shown that minocycline could play neuroprotective effects in amyotrophic lateral sclerosis, but these positive findings have not been replicated in patients. Other neuromuscular disorders which tetracyclines may benefit are Guillain-Barré syndrome and other neuropathies, muscular dystrophies and mitochondrial disorders. However, well-designed double-blind controlled trials are still needed. Further studies are strongly needed to establish the most appropriate timing and dosage, as well as the indications for which tetracyclines could be effective and safe. Here, we review the neuroprotective effects of tetracyclines in animal models, the clinical studies in humans, and we focus on their potential application in patients with neuromuscular disorders.
Effect of downstream feedback on the achievable performance of feedback control loops for serial processes

This paper deals with feedback control of serial processes, that is, processes formed by the series connection of several subsystems. We focus on the determination of conditions under which a full multivariate controller may lead to performance improvements over a triangular controller. In other words, we study conditions under which it is advisable to include a control law with downstream feedback besides local control and feedforward action. The problem is addressed using an ℋ2 optimal performance criterion in a discrete-time framework. Our main result is a simple condition on the unstable poles, non minimum phase (NMP) zeros and delays of the discrete time plant model that ensures no penalization on the achievable performance under triangular control. As an application of this result, we show that for an application relevant class of serial processes, the condition can be expressed in terms of the time delays in each subsystem only. The derived condition may be useful for both process design and process control.
An intelligent recommender system using sequential Web access patterns

To provide intelligent personalized online services such as Web recommendations, it is usually necessary to model users' Web access behavior. To achieve this, one of the promising approaches is Web usage mining, which mines Web logs for user models and recommendations. Different from most Web recommender systems that are mainly based on clustering and association rule mining, this paper proposes an intelligent Web recommender system known as SWARS (sequential Web access-based recommender system) that uses sequential access pattern mining. In the proposed system, CS-mine, an efficient sequential pattern mining algorithm is used to identify frequent sequential Web access patterns. The access patterns are then stored in a compact tree structure, called Pattern-tree, which is then used for matching and generating Web links for recommendations. In this paper, the proposed SWARS system is described, and its performance is evaluated based on precision, satisfaction and applicability.
Badum Tss!: A DIY Paper-based Interaction to Augment Everyday Situations with Sound Effects

The DIY rollable soundboard is a paper-based instant button device, driven by a Raspberry Pi that allows for easily creating simple sound effects. With sound effects like a Badum Tss (the sound of drums which often appears for a bad joke in comedy shows), it augments everyday situations with a suitable sound while users are on the go. Since paper is quiet cheap and almost readily available, paper does not cause any inhibitions to touch it and create sounds. Unlike a simple instant button application on a private smartphone, the rollable soundboard invites people to explore it and to entertain their social environment. In this paper, we describe the design process for the development of a rollable, portable soundboard from the conceptual design down to the functional prototype and first usability tests, and discuss relevant areas of future work.
Acoustical ranging techniques in embedded wireless sensor networked devices

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments, where, typically, there is a need for a higher degree of accuracy. In this article, we focus on robust range estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation, and evaluation of acoustic (both ultrasound and audible) ranging systems. We distill the limitations of acoustic ranging and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler's effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20m (outdoor) and an average accuracy ≈2cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance.
Sentiment Identification Using Maximum Entropy Analysis of Movie Reviews

Sentiment classification is one of the most challenging problems in Natural Language Processing. A sentiment classifier recognizes patterns of word usage between different classes and attempts to put unlabeled text into one of these categories in an unsupervised manner. Therefore, the attempt is to classify documents not by topic but by overall sentiment. We have used reviews of movies to train and test our classifier. Our system uses the Maximum Entropy method of unsupervised machine learning. We present our observations, assumptions, and results in this paper. We conclude by looking at the challenges faced and the road ahead.
Increasing the artificial neural network based model building speed for supporting efficient technical diagnostics

The paper introduces an algorithm for increasing the speed for building up the general system model on Artificial Neural Network (ANN) basis. It finds that input/output configuration of the field analysed which realizes the most accurate estimation and explores the maximal number of dependencies among the related system parameters. The availability and the estimation capabilities of the needed system models are especially important for technical diagnostics in order to be able in differentiating between conform and non- conform situations. The performance of the novel solution is tested and evaluated under a field specific analysis applying the classical equations from the cutting theory. Experiments were done also for cutting diagnosis based on real measured parameters under varying conditions. These validations showed good empirical performance and practical applicability of the algorithm introduced. As result, the proposed algorithm increased significantly the speed of the model building stage.
Adjacency Cache: Fast Web Cache Indexing and Lookup

This paper describes the design and evaluation of a peer-to-peer cache indexing system, which can be used to integrate the resources of locally available web pages into a globally addressable index using a distributed hash table. The salient feature of the indexing system's design is the efficient dissemination of cache index information using a next-URL index which allows cache clients to determine ahead of time whether linked content is also available at a remote cache. In addition, conventional optimizations such as in browser caching and batching of index write requests are also used. These optimizations are evaluated using trace-driven simulation and the results show that these design trade-offs improve cache lookup performance.
Sentence-Forest Language Model: A Graph-theoretic Framework to Capture Local Term Dependencies

We describe a new probabilistic graph-based language Model that captures adaptively the local term dependencies in documents. The new model works by boosting scores of documents that contain topic-specific local dependencies and exhibits the behavior of the unigram model in the absence of such dependencies. New features of the approach include modeling the syntactic structure of sentences in documents and a computationally efficient algorithm for capturing the most significant within-sentence term dependencies using a Maximum Spanning Tree approach, similar to Keith van Rijsbergen’s [13] modeling of document-level term dependencies. We evaluated our model on three different tasks of the Topic Detection and Tracking (TDT) research program, namely Story Link Detection, Topic Tracking and New Event Detection. Our results show that the Local dependency language model consistently outperforms the basic unigram model on all three tasks.