Miracle-dz/UniFAR-FTUs huggingface dataset:
IEEE 802.15.4 Based QoS Support Slotted CSMA/CA MAC for Wireless Sensor Networks

In wireless sensor network, IEEE 802.15. 4 should operate in beacon-enabled mode in order to schedule periodic sleep and wake up to minimize energy consumption. In beacon enabled mode QoS is supported by using guaranteed time slots(GTS). However maximum number of GTS node cannot exceed seven, and the number is not sufficient for wireless sensor network which have high node density. In this paper, we present QoS support mechanism in beacon enabled mode using CSMA/CA back-off time. Our algorithm have compatibility with IEEE 802.15. 4 Standard, which means that there is no need to change hardware to support QoS on existing network. We will analysis the performance of our algorithm to design QoS parameters.
Evolving Robot Tank Controllers

In this paper, I describe the application of genetic programming to evolve a controller for a robotic tank in a simulated environment. The purpose is to explore how genetic techniques can best be applied to produce controllers based on subsumption and behavior oriented languages such as REX. As part of my implementation, I developed TableRex, a modification of REX that can be expressed on a fixed-length genome. Using a fixed subsumption architecture of TableRex modules, I evolved robots that beat some of the most competitive hand-coded adversaries.
Human Papilloma Viruses and Breast Cancer

Purpose Human papillomaviruses (HPV) may have a role in some breast cancers. The purpose of this study is to fill important gaps in the evidence. These gaps are: (i) confirmation of the presence of high risk for cancer HPVs in breast cancers, (ii) evidence of HPV infections in benign breast tissues prior to the development of HPV-positive breast cancer in the same patients, (iii) evidence that HPVs are biologically active and not harmless passengers in breast cancer. Methods RNA-seq data from The Cancer Genome Atlas (TCGA) was used to identify HPV RNA sequences in breast cancers. We also conducted a retrospective cohort study based on polymerase chain reaction (PCR) analyses to identify HPVs in archival specimens from Australian women with benign breast biopsies who later developed breast cancer. To assess whether HPVs in breast cancer were biologically active, the expression of the oncogenic protein HPV E7 was assessed by immunohistochemistry (IHC). Results Thirty (3.5%) low-risk and 20 (2.3%) high-risk HPV types were identified in 855 breast cancers from the TCGA database. The high risk types were HPV 18 (48%), HPV 113 (24%), HPV 16 (10%), HPV 52 (10%). Data from the PCR cohort study indicated that HPV type 18 was the most common type identified in breast cancer specimens (55% of 40 breast cancer specimens) followed by HPV 16 (13%). The same HPV type was identified in both the benign and subsequent breast cancer in 15 patients. HPV E7 proteins were identified in 72% of benign breast specimens and 59% of invasive breast cancer specimens. Conclusion There were four observations of particular interest: (i) confirmation by both NGS and PCR of the presence of high-risk HPV gene sequences in breast cancers, (ii) a correlation between high-risk HPV in benign breast specimens and subsequent HPV-positive breast cancer in the same patient, (iii) HPVs in breast cancer are likely to be biologically active (as shown by transcription of HPV DNA to RNA plus the expression of HPV E7 proteins), (iv) HPV oncogenic influences may occur early in the development of breast cancer.
Power system load forecasting based on BEMPSO chaotic neural network

Considering the chaotic characteristic of power system load, a method based on bee evolution modifying particle swarm optimization (BEMPSO) and chaotic neural network is presented for power system load forecasting to improve precision. In this paper, builds the chaotic neural network model and integrates bee evolution modifying with particle swarm optimization. The novel BEMPSO algorithm is proposed. It is used to train connection weights of multi-layer feed forward neural network until the learning error tends to be stable. Using the basic PSO algorithm and proposed BEMPSO algorithm, we simulate the prediction of power system load, the results shows that forecasting model based on the BEMPSO algorithm proposed in this paper have strong capacity of generalization and relatively high precision compared with the basic PSO algorithm.
Model based system engineering approach of a lightweight embedded TCP/IP

The use of embedded software is growing very rapidly. Accessing the internet is a necessary service which has large range of applications in many fields. The Internet is based on TCP/IP which is a very important stack. Although TCP/IP is very important there is not a software engineering model describing it. The common method in modeling and describing TCP/IP is RFCs which is not sufficient for software engineer and developers. Therefore there is a need for software engineering approach to help engineers and developers to customize their own web based applications for embedded systems. This research presents a model based system engineering approach of lightweight TCP/IP. The model contains the necessary phases for developing a lightweight TCP/IP for embedded systems. The proposed model is based on SysML as a model based system engineering language.
Prevalence of Clostridium difficile toxinotypes in infected patients at a tertiary care center in Lebanon.

INTRODUCTION Due to the increase in the incidence of Clostridium difficile associated diseases at a tertiary care center in Lebanon, this study was undertaken to determine the prevalent C. difficile toxinotypes. METHODOLOGY The immunocard method was used to test for toxins A and B in 88 collected stool samples, followed with API 20A to confirm for C. difficile. PCR amplification of the triose phosphate isomerase (tpi) gene, the toxin encoding genes tcdA, and tcdB, followed by toxinotyping, were performed on recovered isolates and stool specimens. RESULTS Out of the 88 stool samples obtained, 30 (65.2%) were Immunocard positive, culture and or tpi positive for C. difficile. Of the 30 isolates, 4 were PCR negative for the tcdA and tcdB genes (A-B-), and 26 were PCR positive for the tcdA and / or tcdB genes with 4 being A+B+, 1 A+B-, and 21 A-B+. The results of toxinotyping showed that 2 isolates belonged to toxinotype 0, 4 to toxinotype XI, 2 to toxinotype XII, 1 to toxinotype XVI, 1(A+B-) and twenty (A-B+) designated as toxinotype 0-like. C. difficile was detected in 65.2% of patients' stools with prevalence of toxinotype 0-like. CONCLUSION Identification of toxinotypes of C. difficile is important to determine the virulence potential of strains and control their spread.
Das Junktim von Forschen und Heilen in der Psychoanalyse

Im alten Streit um die Frage, ob die Psychonalyse eher zu den Naturwissenschaften oder eher zu den Geisteswissenschaften zahlt, favorisiert der Autor eine szientifische Position. Die Deutung in der analytischen Stunde versteht er als Eroffnung eines Spielraums fur Hypothesen, deren Validitat empirischer Uberprufung zuganglich ist. Fur die Technik der Deutung heist das, das Deutungen nicht nur knapp und einfach formuliert werden mussen, sondern auch so, das sie weniger auf die Verifizierung theoretischer Wahrheiten zielen als vielmehr auf die dyadenspezifische Wahrheit. Anhand einer Folge von langeren wortlich protokollierten Sequenzen einer Analyse demonstriert Etchegoyen die Moglichkeit, das der Analysand eine Deutung des Analytikers entweder bestatigen oder widerlegen kann. Eine gelungene Deutung, deren Hypothese dem Analysanden einleuchtet, ist zugleich eine wissenschaftliche Entdeckung und ein Heilungsfaktor.
QUERIES SNIPPET EXPANSION FOR EFFICIENT IMAGES RETRIEVAL

Technological and dynamic growths of digital media have increased volume of the multimedia corpus. Due to this growth, it is strongly urged for a system that can efficiently and effectively compiles the user demand, and retrieving the relevant images. Keyword based system retrieves an image on syntactic matching, i.e. string matching not concept. Content Based Image Retrieval (CBIR) systems retrieve the image based on low level features and still exist a gap is called semantic. This paper discussed snippet technique that covers the semantic gap as well as Word Sense disambiguation problems. It extracts user queries for expansion with the help of Knowledgebase WordNet and ConceptNet. Experiments performed on the open benchmark image dataset LabelMe. A substantial improvement has been achieved in terms of precision and recall. Remarkably outperformed of Results and showed 84% corrects.
City Notifications as a Data Source for Traffic Management

A common problem for cities of developing countries like India in managing traffic is the lack of basic automated instrumentation to track road conditions or vehicle locations. Still, to help their citizens make informed travel decisions based on changing city dynamics; many cities have an authorized, city-initiated, notification service in place to alert subscribing commuters about road conditions. Here, alternative means may be used to create informal textual notifications – e.g., inputs from field personnel, citizen updates, and pre-authorized events from city calendar. In this paper, the authors show that collections of such notifications, when processed with information extraction techniques, can turn them into a rich source of data for traffic managers. Specifically, they use Short Message Service (SMS) notifications from the city of Delhi, India to show promising insights.
Automated, Unobtrusive, Action-by-Action Assessment of Self-Regulation During Learning With an Intelligent Tutoring System

Assessment of students’ self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in the specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, help seeking while working with an intelligent tutoring system. The method relies on a computer-executable model of the targeted SRL strategy. The method was validated by showing that it converges with other measures of help seeking. Automated feedback on help seeking driven by this method led to a lasting improvement in students’ help-seeking behavior, although not in domain-specific learning. The method is unobtrusive, is temporally fine-grained, and can be applied on a large scale and over extended periods. The approach could be applied to other SRL strategies besides help seeking.
High Security for Manet Using Authentication and Intrusion Detection with Data Fusion

In Mobile Ad Hoc Network (MANET), Multimodal Biometric technology plays a vital role in giving security between user-to-device authentications. This paper concentrates on the Intrusion Detection and authentication with data fusion in MANET. To overcome the fault in unimodal biometric systems, Multimodal biometrics is set out to work with Intrusion Detection Systems. Each and every device has dimensions and estimation limitations, many devices to be selected and with the help of Dempster-Shafter theory for data fusion observation precision gets increased. Based on the security posture, system concludes which biosensor (IDS) to select and whether user authentication (or IDS input) is essential. By every authentication device and Intrusion Detection System (IDS), the decisions are made in a fully distributed manner. Simulation results are presented to show the effectiveness of the proposed scheme. Index Terms—Authentication, biometrics, intrusion detection, mobile ad hoc networks (MANETs), security.
Trajectory-based human activity recognition using Hidden Conditional Random Fields

This paper presents a new method for recognizing trajectory-based human activities. We use a discriminative latent variable model in our proposed method, which considers that human trajectories are made up of some specific motion regimes, and different activities have different switching patterns among the motion regimes. We model the trajectories using Hidden Conditional Random Fields (HCRFs) and the motion regimes act as sub-structures in the model. Experiments using both synthetic and real data sets demonstrate the superiority of our model in comparison with other methods, including Hidden Markov Models (HMM) and Conditional Random Fields (CRFs).
A search for precursors of ultracompact H II regions in a sample of luminous IRAS sources. III. Circumstellar dust properties

The James Clerk Maxwell Telescope has been used to obtain submillimeter and millimeter continuum photometry of a sample of 30 IRAS sources previously studied in molecular lines and centimeter radio continuum. All the sources have IRAS colours typical of very young stellar objects (YSOs) and are associated with dense gas. In spite of their high luminosities (L > 10 4 L), only ten of these sources are also associated with a radio counterpart. In 17 cases we could identify a clear peak of millimeter emission associated with the IRAS source, while in 9 sources the millimeter emission was either extended or faint and a clear peak could not be identified; upper limits were found in 4 cases only. The submm/mm observations allow us to make a more ac- curate estimate of the source luminosities, typically of the order of 10 4 L. Using simple greybody fitting to model the observed spectral energy distribution, we derive global properties of the circumstellar dust associated with the detected sources. We find that the dust temperature varies from 24 K to 45 K, while the exponent of the dust emissivity vs frequency power-law spans a range 1.56 < <2.38, characteristic of silicate dust; total circumstellar masses range up to more than 500 M. We present a detailed analysis of the sources associated with millimeter peaks, but without radio emission. In particular, we find that for sources with comparable luminosities, the total column densities derived from the dust masses do not distin- guish between sources with and without radio counterpart. We interpret this result as an indication that dust does not play a dominant role in inhibiting the formation of the Hii region. We examine several scenarios for their origin in terms of newborn ZAMS stars and although most of these (e.g. optically thick Hii regions, dust extinction of Lyman photons, clusters instead of single sources) fail to explain the observations, we cannot ex- clude that these sources are young stars already on the ZAMS with modest residual accretion that quenches the expansion of the Hii region, thus explaining the lack of radio emission in
Learning Bayesian Networks in Presence of Missing Data

Motivation: Signal Transduction Networks: The study of Signal Transduction Networks is one of the major subjects of interest in Systems Biology. Signal Transduction pathways are means of regulating numerous cellular functions in response to changes in the cell's chemical or physical environment. Signal transduction often involves a sequence of biochemical reactions inside the cell, which are carried out by proteins. The network of interacting proteins and phosphates relay information as well as amplifying them. Their interaction is by activation or inhibition of the next molecule in the chain. There are usually more than one pathways functioning as well as interacting in a cell at the same moment. The target proteins that can be gene regulatory proteins, ion channels, or components of a metabolic pathway have important affect on the cell behavior. In this project we try to show how Bayesian network learning methods are helpful in inferring the causal interactions between the molecules. To study the signal transduction network one needs to have a way of measuring proteins involved in the process and should also have a way to silence or force each of them or groups of them to a specific value in order to produce intervention data that is necessary to distinguish correlation from causality. Although Microarray technology has had a lot of progress and people are able to measure the expression of all the human genes in parallel, measuring the proteins that are active in cell is still a very challenging task. Current technology is capable of measuring only a few (around 20) proteins simultaneously in a single cell. Therefore it is of great value to develop methods that can extract the information about the structure of network as much as possible from the partially observed variables. This fact motivated me to work especially in learning the Bayesian network in presence of missing data. The data I have used to test my algorithms is from flow cytometry experiments (FACS). In FACS experiments they put fluorescent markers inside the cells that bind to proteins floating around inside. Then they put them inside a tiny tube with a fluid flowing through it that causes the cells to pass one by one through a number of lasers that can detect the quantity of proteins with markers bound to them, giving one an idea of the amount of each protein in the cell. What the scientists have done is to apply a number of activators and inhibitors to cell samples and then checking 11 different proteins in this manner. Each experiment has between 650 and 1000 cells in it and there were 9 different experiments. This data is published in [1].
Blunted excitability of aortic baroreceptor neurons in diabetic rats: involvement of hyperpolarization-activated channel.

AIMS Although dysfunction of arterial baroreflex occurs in human and animal models of type-1 diabetes (T1D), the mechanisms involved in the impairment of the baroreflex still remain unclear. The nodose ganglion (NG) contains the cell bodies of the aortic baroreceptor (AB) neurons. Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed in AB neurons and play an important role in regulating the cell excitability. We investigated whether the excitability of AB neurons is depressed in streptozotocin (STZ)-induced T1D rats and whether HCN channels are involved in this depression. METHODS AND RESULTS Using the whole-cell patch clamp technique, we found that AB neuron excitability (action potential frequency at 50 pA current stimulation) in the T1D rats was lower than that in the sham rats (0.4 +/- 0.5 vs. 4.8 +/- 0.6 spikes/s, P < 0.05; AB neurons were identified by DiI staining). In addition, HCN current density in AB neurons from the T1D rats was bigger than that from the sham rats (60.2 +/- 6.1 vs. 30.7 +/- 4.9 pA/pF at test pulse -140 from holding potential -40 mV, P < 0.05). Furthermore, HCN channel blockers (5 mM cesium chloride and 100 microM ZD7288) significantly reduced HCN currents and increased action potential frequency of the AB neurons in sham and T1D rats. Immunofluorescent and western blot analyses demonstrated that the expression of HCN1 and HCN2 channel protein in the NG from the T1D rats was higher than that from the sham rats. CONCLUSION These results indicate that the HCN channels influence the excitability of AB neurons, and more importantly, contribute to the decreased excitability of AB neurons in T1D rats.
Location of crossovers during gene targeting with insertion and replacement vectors

Gene targeting was used to introduce nonselectable genetic changes into chromosomal loci in mouse embryo-derived stem cells. The nonselectable markers were linked to a selectable marker in both insertion- and replacement-type vectors, and the transfer of the two elements to the Hprt locus was assayed. When insertion vectors were used as substrates, the frequency of transfer was highly dependent upon the distance between the nonselectable marker and the double-strand break in the vector. A marker located close to the vector ends was frequently lost, suggesting that a double-strand gap repair activity is involved in vector integration. When replacement vectors were used, cotransfer of a selectable marker and a nonselectable marker 3 kb apart was over 50%, suggesting that recombination between vector and target often occurs near the ends of the vector. To illustrate the use of replacement vectors to transfer specific mutations to the genome, we describe targeting of the delta F508 mutation to the CFTR gene in mouse embryo-derived stem cells.
Fault Injection Technology for Software Vulnerability Testing Based on Xen

Fault injection technology devotes an efficient way for verifying fault tolerance of computer and detecting the vulnerability of software system. In this paper, we present a Xen-based fault injection technology for software vulnerability test (XFISV) in order to build an efficient and general-purpose software test model, which injects faults into interactive layer between software applications and their environments. This technology has two main contributions: First, detecting the software vulnerability according to this model needs less number of fault test cases. Second, this model enhances the flexibility and the robustness of the fault injection tools with economical resource cost.
Automatic Learning Rate Maximization by On-Line Estimation of the Hessian's Eigenvectors

We propose a very simple, and well principled way of computing the optimal step size in gradient descent algorithms. The on-line version is very efficient computationally, and is applicable to large backpropagation networks trained on large data sets. The main ingredient is a technique for estimating the principal eigenvalue(s) and eigenvector(s) of the objective function's second derivative matrix (Hessian), which does not require to even calculate the Hessian. Several other applications of this technique are proposed for speeding up learning, or for eliminating useless parameters.
Profile-Based Adaptation for Cache Decay: A Technical Report

‘Cache decay’ is a set of leakage-reduction mechanisms that put cache lines that have not been accessed for a specific duration into a low-leakage standby mode. This duration is called the decay interval, and its optimal value varies across applications. This paper provides an extended discussion of the results previously presented in our journal paper [13]. It describes an adaptation technique that analytically finds the optimal decay interval through profiling, and shows that the most important variables required for finding the optimal decay interval can be estimated with a reasonable degree of accuracy using profiling. This work explicitly trades off the leakage power saved in putting both the ‘live’ and ‘dead’ lines into standby mode, against its performance and energy costs. It achieves energy savings close to what can be obtained with an omniscient choice of per-benchmark optimal decay interval.
Reciprocal regulation of Abl kinase by Crk Y251 and Abi1 controls invasive phenotypes in glioblastoma

Crk is the prototypical member of a class of Src homology 2 (SH2) and Src homology 3 (SH3) domain-containing adaptor proteins that positively regulate cell motility via the activation of Rac1 and, in certain tumor types such as GBM, can promote cell invasion and metastasis by mechanisms that are not well understood. Here we demonstrate that Crk, via its phosphorylation at Tyr251, promotes invasive behavior of tumor cells, is a prominent feature in GBM, and correlating with aggressive glioma grade IV staging and overall poor survival outcomes. At the molecular level, Tyr251 phosphorylation of Crk is negatively regulated by Abi1, which competes for Crk binding to Abl and attenuates Abl transactivation. Together, these results show that Crk and Abi1 have reciprocal biological effects and act as a molecular rheostat to control Abl activation and cell invasion. Finally, these data suggest that Crk Tyr251 phosphorylation regulate invasive cell phenotypes and may serve as a biomarker for aggressive GBM.