Miracle-dz/UniFAR-FTUs huggingface dataset:
Trust-based backpressure routing in wireless sensor networks

In this paper, we apply a vector autoregression VAR based trust model over the backpressure collection protocol BCP, a collection mechanism based on dynamic backpressure routing in wireless sensor networks WSNs. The backpressure scheduling is known for being throughput optimal. In the presence of malicious nodes, the throughput optimality no longer holds. This affects the network performance in collection tree applications of sensor networks. We apply an autoregression based scheme to embed trust into the link weights, so that the trusted links are scheduled. We have evaluated our work in a real sensor network testbed and shown that by carefully setting the trust parameters, substantial benefit in terms of throughput can be obtained with minimal overheads. Our results show that even when 50% of network nodes are malicious, VAR trust offers approximately 73% throughput and ensures reliable routing, with a small trade-off in the end-to-end packet delay and energy consumptions.
Examining student satisfaction of interaction in online learning: Analyzing student satisfaction using the community of inquiry framework

EXAMINING STUDENT SATISFACTION OF INTERACTION IN ONLINE LEARNING: ANALYZING STUDENT SATISFACTION USING THE COMMUNITY OF INQUIRY FRAMEWORK Michelle Bacino Thiessen, Ed.D. Department of Counseling, Adult and Higher Education Northern Illinois University, 2015 Laura R. Johnson, Director This qualitative study explored the experiences of an online learner at a community college. The purpose of the study was to investigate community college students’ perception of online courses through an examination of their experiences with online learning. The community of inquiry theory was used as a guide to better understand the importance of student-teacher interaction, student-student interaction, and student-content interaction in online learning. The experiences of 15 community college students, who had completed an online course at a Midwestern community college, were examined to determine levels of interaction and presence. A focus group was interviewed and used for additional research into the examination of community college online learners. Finally, artifacts were collected and analyzed. The artifacts included essay assignments, syllabi, chapter questions, a research paper and discussion boards. The data collected provided three major findings. The first was that community college students enrolled in online courses had both positive and negative experiences. The second finding was that students preferred taking classes face-to-face rather than online and third, students placed greater importance on student-teacher interaction. The role of the teacher emerged as a pivotal component in the overall satisfaction of online courses. These findings suggest that future research could be done to further examine the design of an effective online course and the training for online teachers. NORTHERN ILLINOIS UNIVERSITY DEKALB, ILLINOIS
Bcl-xL increases mitochondrial fission, fusion, and biomass in neurons

Mitochondrial fission and fusion are linked to synaptic activity in healthy neurons and are implicated in the regulation of apoptotic cell death in many cell types. We developed fluorescence microscopy and computational strategies to directly measure mitochondrial fission and fusion frequencies and their effects on mitochondrial morphology in cultured neurons. We found that the rate of fission exceeds the rate of fusion in healthy neuronal processes, and, therefore, the fission/fusion ratio alone is insufficient to explain mitochondrial morphology at steady state. This imbalance between fission and fusion is compensated by growth of mitochondrial organelles. Bcl-xL increases the rates of both fusion and fission, but more important for explaining the longer organelle morphology induced by Bcl-xL is its ability to increase mitochondrial biomass. Deficits in these Bcl-xL–dependent mechanisms may be critical in neuronal dysfunction during the earliest phases of neurodegeneration, long before commitment to cell death.
Target design and low complexity signal detection for two-dimensional magnetic recording

Partial response maximum likelihood (PRML) scheme is a well known technique to equalize the data read from ID magnetic recording channels. The PRML scheme uses a linear equalizer followed by a maximum likelihood (ML) detector. This paper is novel in addressing the following aspects: a) We propose two different methods to design separable and non-separable 2D PR targets that help in signal detection, b) We propose an extension of ID Viterbi detector for signal detection in 2D ISI channels. We use the detector to study the efficacy of PR targets designed for a particular choice of 2D ISI channel.
Low Discrepancy Between Tissue Biopsy Plus Magnifying Endoscopy With Narrow-Band Imaging and Endoscopic Resection in the Diagnosis of Gastric Epithelial Neoplasia (STROBE)

AbstractTissue biopsy is often not very accurate for the diagnosis of gastric epithelial neoplasia (GEN), and the results differ notably from endoscopic resection (ER) in terms of the pathological diagnosis. The aims of this study were to evaluate the diagnostic performances of biopsy, magnifying endoscopy with narrow-band imaging (ME-NBI), and biopsy plus ME-NBI for GEN.This study retrospectively analyzed 101 cases diagnosed as GEN using ER samples. The discrepancies between biopsy and ER, as well as between biopsy plus ME-NBI and ER in the diagnosis of GEN were evaluated. Factors that contributed to such discrepancies were analyzed. The sensitivity and specificity of biopsy and ME-NBI for the diagnosis of high-grade neoplasia (HGN) were determined.The discrepancy in the pathological diagnosis between biopsy and ER was 39.6% for GEN and 54.2% for HGN. The discrepancy between biopsy combined with ME-NBI and ER was 15.9% for GEN and 10.2% for HGN. Factors that undermined the diagnostic accuracy of biopsy included the lesion size (⩽10 mm, odds ratio [OR] 1; 10–20 mm, OR 0.2, 95% confidence interval [CI] 0.1–0.7; >20 mm, OR 0.5, 95% CI 0.1–2.1, P = 0.03) and the number of biopsy fragments (OR 0.6, 95% CI 0.5–0.8, P = 0.001). The sensitivity and specificity for HGN were 45.8% (33.7%–58.3%) and 100% (87.5%–100%) for biopsy, and 88.1% (77.5%–94.1%) and 92.9% (81.0%–97.5%) for ME-NBI, respectively.In conclusion, biopsy-based diagnoses for GEN should be interpreted with caution. Biopsy combined with ME-NBI can contribute to the diagnosis of GEN, which improves diagnostic consistency with pathological result of ER specimens.
Dynamic Spectrotemporal Feature Selectivity in the Auditory Midbrain

The transformation of auditory information from the cochlea to the cortex is a highly nonlinear process. Studies using tone stimuli have revealed that changes in even the most basic parameters of the auditory stimulus can alter neural response properties; for example, a change in stimulus intensity can cause a shift in a neuron's preferred frequency. However, it is not yet clear how such nonlinearities contribute to the processing of spectrotemporal features in complex sounds. Here, we use spectrotemporal receptive fields (STRFs) to characterize the effects of stimulus intensity on feature selectivity in the mammalian inferior colliculus (IC). At low intensities, we find that STRFs are relatively simple, typically consisting of a single excitatory region, indicating that the neural response is simply a reflection of the stimulus amplitude at the preferred frequency. In contrast, we find that STRFs at high intensities typically consist of a combination of an excitatory region and one or more inhibitory regions, often in a spectrotemporally inseparable arrangement, indicating selectivity for complex auditory features. We show that a linear–nonlinear model with the appropriate STRF can predict neural responses to stimuli with a fixed intensity, and we demonstrate that a simple extension of the model with an intensity-dependent STRF can predict responses to stimuli with varying intensity. These results illustrate the complexity of auditory feature selectivity in the IC, but also provide encouraging evidence that the prediction of nonlinear responses to complex stimuli is a tractable problem.
A New Evidence Model for Missing Data Speech Recognition With Applications in Reverberant Multi-Source Environments

Conventional hidden Markov model (HMM) decoders often experience severe performance degradations in practice due to their inability to cope with uncertain data in time-varying environments. In order to address this issue, we propose the bounded-Gauss-Uniform mixture probability density function (pdf) as a new class of evidence model for missing data speech recognition. Exemplary for a hands-free speech recognition scenario, we illustrate how the parameters of the new mixture pdf can be estimated with the help of a multi-channel source separation front-end. In comparison with other models the new evidence pdf retains a fuller description of the available data and provides a more effective link between source separation and recognition. The superiority of the bounded-Gauss-Uniform mixture pdf over conventional approaches is demonstrated for a connected digits recognition task under varying test conditions.
Duo-graph: An efficient and robust method for large-scale mapping for visual-guided robots

In this paper, we present a duo-graph Simultaneous Localization and Mapping (SLAM) method that enables visual-guided robot to efficiently and robustly generate consistent 3D map in a large-scale environment. Recently the conditional independent graph (CI-GRAPH) has been proved to be an efficient and robust method for solving the large-scale SLAM problem. However, it stems from extended kalman filter (EKF) SLAM that suffers from high computational load when number of the landmarks becomes large. Moreover, the big noise in the measurement can lead EKF SLAM far from convergence. These disadvantages are the cases in the visual-guided robot. To solve these problems, we propose a duo-graph structure that sustains the feasibility of a hierarchical graph-based SLAM framework. It implements two level graph-based SLAM: local and global SLAM. Utilizing the characteristic of the duo-graph structure, the local SLAM can efficiently localize itself while the global SLAM can accurately close loops in the large scale environment. In addition, our method can filter massive noisy visual features and eliminate mismatches in the global SLAM process. To demonstrate the superiority of our method, both simulation and real experiments are carried out.
Pharmacological Characterization of Memoquin, a Multi-Target Compound for the Treatment of Alzheimer's Disease

Alzheimer's disease (AD) is characterized by progressive loss of cognitive function, dementia and altered behavior. Over 30 million people worldwide suffer from AD and available therapies are still palliative rather than curative. Recently, Memoquin (MQ), a quinone-bearing polyamine compound, has emerged as a promising anti-AD lead candidate, mainly thanks to its multi-target profile. MQ acts as an acetylcholinesterase and β-secretase-1 inhibitor, and also possesses anti-amyloid and anti-oxidant properties. Despite this potential interest, in vivo behavioral studies with MQ have been limited. Here, we report on in vivo studies with MQ (acute and sub-chronic treatments; 7–15 mg/kg per os) carried out using two different mouse models: i) scopolamine- and ii) beta-amyloid peptide- (Aβ-) induced amnesia. Several aspects related to memory were examined using the T-maze, the Morris water maze, the novel object recognition, and the passive avoidance tasks. At the dose of 15 mg/kg, MQ was able to rescue all tested aspects of cognitive impairment including spatial, episodic, aversive, short and long-term memory in both scopolamine- and Aβ-induced amnesia models. Furthermore, when tested in primary cortical neurons, MQ was able to fully prevent the Aβ-induced neurotoxicity mediated by oxidative stress. The results support the effectiveness of MQ as a cognitive enhancer, and highlight the value of a multi-target strategy to address the complex nature of cognitive dysfunction in AD.
Understanding the Complexity of E-Government : Multi-method Approaches to Social Phenomena

This paper contributes to the ongoing debate about multi-method approaches to studying social phenomena; in this contribution e-government is the social phenomenon of interest. A set of advantages and challenges to multi-method approaches are introduced and then used to frame a case analysis. Two case studies involving multi-method approaches to e-government research are presented to illustrate strategies for responding to implementation challenges in both large-scale and small-scale projects. The case discussion provides new insight into how the challenges to multi-method approaches can be managed.
Functional genomics by data mining in DNA microarray data

The DNA microarray is a new tool for rapid and efficient collection of gene expression data. Several thousand genes can be tested simultaneously, and up or down regulation of genes as a function of time and changes in experimental conditions can easily be identified. Such changes may be external stress, disease (e.g. cancer), drugs or simply natural development. From this information networks of gene regulation may be identified, as well as the potential function of novel genes. However, the large amount of information generated means that efficient and sophisticated techniques for computer-based data analysis have to be used. In this project we will use our knowledge on data mining, microarrays, sequence data and protein structures to develop new and improved methods for analysing microarray data.
Learning abstract snippet detectors with Temporal embedding in convolutional neural Networks

The prediction of periodical time-series remains challenging due to various types of scaling, misalignments and distortion effects. Here, we propose a novel model called Temporal embedding-enhanced convolutional neural Network (TeNet) to learn repeatedly-occurring-yet-hidden structural elements in periodical time-series, called abstract snippet detectors, to predict future changes. Our model effectively learns a new feature space for a time-series dataset. In the new feature space, distorted time-series that have implicit similarity but substantial differences in value and sequence to regular patterns are re-aligned to the regular patterns in the dataset, and subsequently contribute to a robust prediction mode. The model is robust to various types of distortions and misalignments and demonstrates strong prediction power for periodical time-series. We conduct extensive experiments and discover that the proposed model shows significant and consistent advantages over existing methods on a variety of data modalities ranging from human mobility to household power consumption records, when evaluated under four metrics. The model is also robust to various factors such as number of samples, variance of data, numerical ranges of data etc. The experiments verify that the intuition behind the model can be generalized to multiple data types and applications and promises significant improvement in prediction performance across the datasets studied.
Internal Model Controller for Scalar Controlled Induction Motor Drive: Design and Experiments JES

This paper deals with the performance analysis and implementation of a robust speed controller. The robustness is guaranteed by the use of the internal model controller (IMC). Verification of the proposed robust controller is provided by experimental realistic tests on scalar controlled induction motor drives. Robust speed control at low speeds and in field weakening region is studied in order to show the robustness of the speed controller with a wide range of load
Asymptotic Analysis of Lattice Boltzmann Boundary Conditions 1

In this article, we use a general method for the analysis of finite difference schemes to investigate lattice Boltzmann algorithms for Navier-Stokes problems with Dirichlet boundary conditions. Several link based boundary conditions for commonly used lattice Boltzmann BGK models are considered. With our method, the accuracy of the algorithms can be exactly predicted. Moreover, the analytical results can be used to construct new algorithms which is demonstrated with a corrected bounce back rule that requires only local evaluations but still yields second order accuracy for the velocity. The analysis is applicable to general geometries and instationary flows.
Committee-Based Sampling For Training Probabilistic Classi(cid:12)ers

In many real-world learning tasks, it is expensive to acquire a su(cid:14)cient number of labeled examples for training. This paper proposes a general method for e(cid:14)ciently training probabilistic classi(cid:12)ers, by selecting for training only the more informative examples in a stream of unlabeled examples. The method, committee-based sampling, evaluates the in-formativeness of an example by measuring the degree of disagreement between several model variants. These variants (the committee) are drawn randomly from a probability distribution conditioned by the training set selected so far (Monte-Carlo sampling). The method is particularly attractive because it evaluates the expected informationgain from a training example implicitly, making the model both easy to implement and generally applicable. We further show how to apply committee-based sampling for training Hidden Markov Model classi(cid:12)ers, which are commonly used for complex classi(cid:12)cation tasks. The method was implemented and tested for the task of tagging words in natural language sentences with parts-of-speech. Experimental evaluation of committee-based sampling versus standard sequential sampling showed a substantial improvement in training e(cid:14)ciency.
Using Technology Acceptance Model to assess teachers' attitude towards use of technology as teaching tool: a SEM Approach

To supplement the traditional teaching , in the last few years a steady growth of ICT applications and IT courseware development has taken place in India. Only a few studies have evaluated acceptance of such new technologies among teachers. An attempt is made to understand the attitude of mathematics teachers towards IT as an instructional tool in the State Schools of Kerala (India) , using the Technology Acceptance Model (TAM) framework. Results suggested that TAM being a robust model can be effectively used in this context too and a good fit for the proposed model for the data was found. The sample consisted of only the high school mathematics teachers, who seem to agree about the usefulness of computers in teaching mathematics, but were found not very conversant in using these as teaching tools. This factor leads to the finding of the study showing Teachers’ Perceived Ease of Use having significant impact on their perception about Usefulness and Attitude towards use of IT in teaching.
Alkylation of Zwitterionic Thiooxalic Acid Derivatives

The new S-alkyl thiooxal-1-hydrazono-2-amidrazonium halides 2-4 were synthesized by reaction of the corresponding zwitterionic thiooxalic acid derivatives 1 with alkyl halides in methanol. The structures of compounds 4b and 4d were proven by X-ray structural analysis. Both compounds form an interesting intermolecular network of hydrogen bonds in the solid state.
Improved upper bound on the stretch factor of delaunay triangulations

Let S be a finite set of points in the Euclidean plane. Let D be a Delaunay triangulation of S. The stretch factor (also known as dilation or spanning ratio) of D is the maximum ratio, among all points p and q in S, of the shortest path distance from p to q in D over the Euclidean distance ||pq||. Proving a tight bound on the stretch factor of the Delaunay triangulation has been a long standing open problem in computational geometry. In this paper we prove that the stretch factor of the Delaunay triangulation of a set of points in the plane is less than ρ = 1.998, improving the previous best upper bound of 2.42 by Keil and Gutwin (1989).
Implementing persistent objects in the Apertos operating system

The paper presents a way of providing users with a persistent object running under the Apertos operating system. The authors present an implementation of persistent objects by using object migration between metaspaces in the reflective object architecture. An Apertos object is stored into stable storage by migrating to a storage metaspace that is an abstraction of object storage. They also present the current status of the implementation. <<ETX>>
CCDM: Central Controller-Based Device Management Architecture and Method to Split Management Scripts

This paper presents CCDM, a new architecture for management of devices, which collect environmental information and give effects to the environment. CCDM provides easy management of devices and distributed execution of gateways for sudden network disruptions. We separate the management system to the control plane and the data plane.CCDM’s easy management is achieved by a logically centralized controller. The controller handles the control plane.All the policy of device management and network information are stored to 2 kinds of scripts; service scripts and resource scripts. The controller transforms the policy to work distributedly at each gateway. Data plane is the data communication between devices and gateways. Gateways,which operate devices directly, works at distributed manner based on the policy on the controller.
Virtual Network Construction with K-Shortest Path Algorithm and Prim’s MST Algorithm for Robust Physical Networks

—Network virtualization has attracted considerable attention currently, and it has been expected to be utilized as new-generation network technology. For the network virtualization, improved network robustness is indispensable for virtual networks and a physical network. Moreover, it is important to utilize network resources in the physical network and construct a lot of virtual networks. In this paper, we propose a new virtual network construction so as to construct a lot of virtual networks. Our proposed method utilizes K-shortest path algorithm and Prim’s MST algorithm. At ﬁrst, in the proposed method, K topologies of virtual networks are designed by using the K-shortest path algorithm and the Prim’s MST algorithm instead of the KMB algorithm. Because many topologies are designed, it is expected that a request of constructing a virtual network can be accepted with a high probability. We evaluate the performance of our proposed method for three different topologies with simulation. We investigate the effectiveness of our proposed method by comparing this method with the conventional method. In addition, we evaluate the computation time of the K-shortest path algorithm. In numerical examples, we show that our proposed method is effective in a large-scale backbone network.
TOURIST RISK RESEARCH AND ETHNOCENTRISM

Though risk perception theory has advanced a lot over the last decades, its preferred methodologies much of them closed-led questionnaires or intrusive instruments obscures the derived conclusions. This text aims not only to explore the problems and limitation of risk perception theory to understand the difference between fear, anxiety, panic and risk, but also the tourist-safety. The adoption of risk research, post 9/11 was based on quantitative methods alone. This creates a serious conceptual myopia to understand the connection of risk and late-capitalism. Our attempt to fulfill this gap is shown in this essay-review