Miracle-dz/UniFAR-FTUs huggingface dataset:
RERAN: Timing- and touch-sensitive record and replay for Android

Touchscreen-based devices such as smartphones and tablets are gaining popularity, but their rich input capabilities pose new development and testing complications. To alleviate this problem, we present an approach and tool named Reran that permits record-and-replay for the Android smartphone platform. Existing GUI-level record-and-replay approaches are inadequate due to the expressiveness of the smartphone domain, in which applications support sophisticated GUI gestures, depend on inputs from a variety of sensors on the device, and have precise timing requirements among the various input events. We address these challenges by directly capturing the low-level event stream on the phone, which includes both GUI events and sensor events, and replaying it with microsecond accuracy. Moreover, Reran does not require access to app source code, perform any app rewriting, or perform any modifications to the virtual machine or Android platform. We demonstrate RERAN's applicability in a variety of scenarios, including (a) replaying 86 out of the Top-100 Android apps on Google Play; (b) reproducing bugs in popular apps, e.g., Firefox, Facebook, Quickoffice; and (c) fast-forwarding executions. We believe that our versatile approach can help both Android developers and researchers.
A 0.18-$\mu{\hbox {m}}$ Dual-Gate CMOS Device Modeling and Applications for RF Cascode Circuits

A merged-diffusion dual-gate CMOS device model is presented in this paper. The proposed large-signal model consists of two intrinsic BSIM3v3 nonlinear models and parasitic components. The parasitic elements, including the substrate networks, the distributed resistances, and the inductances, are extracted from the measured S-parameters. In order to verify the model accuracy, a cascode configuration with the proposed dual-gate device is employed in a low-noise amplifier. The dual-gate model is also evaluated with power sweep and load-pull measurements. In addition, a doubly balanced dual-gate mixer is successfully demonstrated using the proposed model. The measured results agree with the simulated results using the proposed device model for both linear and nonlinear applications. The advanced large-signal dual-gate CMOS model can be further used as an RF sub-circuit cell for simplifying the design procedure.
Matcher composition for identification of subsumption relations in ontology matching

Ontology matching is the process of identifying alignment between ontologies with the objective to facilitate interoperability and knowledge integration. A limitation of state of the art ontology matching systems is that the produced alignments usually only contain equivalence relations, while other relations, such as subsumption relations, are often neglected. Furthermore, an ontology matching system is normally composed of a set of differently tuned matching algorithms, but their appropriateness and order of execution typically require human judgement. The COMPOSE framework identifies subsumption relations automatically using a three-stage matcher composition process. These three processes are ontology analysis, matcher selection, and matcher combination. Within this framework we integrate existing techniques for all three processes with novel ones and evaluate the feasibility of the framework in an experiment involving six acknowledged ontologies.
Training a robot with evaluative feedback and unlabeled guidance signals

In this paper, we present a new method for training a robot by natural interaction using evaluative feedback and unlabeled guidance signals. Feedback signals are directly mapped to reward values and used for learning both the task and the meaning of the guidance signals. The learned guidance signals are used in return to bootstrap task learning. We propose to use unlabeled guidance signals as an alternative solution to preprogrammed guidance. We evaluate our method both in simulation and on a real robot.
Investigation of Recognition Methods in

Biometric is a very powerful technique used in modern technology for identifying a person. There are numerous traits in this technology, including face, palm, fingerprint, iris retina, hand geometry. This paper is addressing recognition methods in biometrics. Recognition is an automatic recognition based on a feature vector(s) derived from physiological and behavioural characteristic. Biometrics system should provide a reliable and efficient personal identification schemes to either confirm or determined identity of human beings. Application of the system include credit cards, mobile phones, computer security, and secure access to buildings, secure electronic banking, health and services. By using biometrics a person could be identified based on “who she/he is” and “what she/he has” (key, card, token) or “what she /he knows”(password, PIN) . In this paper a brief overview of biometric methods have been using from the beginning to recent days and also discuss about unimodal and multimodal systems and their advantages and disadvantages
Histogramming on a reconfigurable mesh computer

The authors develop reconfigurable mesh (RMESH) algorithms for window broadcasting, data shifts and consecutive sum. These are then used to develop efficient algorithms to compute the histogram of an image and to perform histogram modification. The histogram of an N*N image is computed by an N*N RMESH in O( square root B log /sub square root B/(N/ square root B) for B<N, O( square root N) for B=N, and O( square root B) for N<B<or=N/sup 2/. B is the number of gray scale values. Histogram modification is done in O( square root N) time by an N*N RMESH. <<ETX>>
Spatial Asymmetry and Short-Term Suppression Underlie Direction Selectivity of Synaptic Excitation in the Mouse Visual Cortex

Direction selectivity (DS) of neuronal responses is fundamental for motion detection. With in vivo whole-cell voltage-clamp recordings from layer (L)4 neurons in the mouse visual cortex, we observed a strong correlation between DS and spatial asymmetry in the distribution of excitatory input strengths. This raises an interesting possibility that the latter may contribute to DS. The preferred direction of excitatory input was found from the stronger to weaker side of its spatial receptive field. A simple linear summation of asymmetrically distributed excitatory responses to stationary flash stimuli however failed to predict the correct directionality: it at best resulted in weak DS with preferred direction opposite to what was observed experimentally. Further studies with sequential 2 flash-bar stimulation revealed a short-term suppression of excitatory input evoked by the late bar. More importantly, the level of the suppression positively correlated with the relative amplitude of the early-bar response. Implementing this amplitude-dependent suppressive interaction can successfully predict DS of excitatory input. Our results suggest that via nonlinear temporal interactions, the spatial asymmetry can be transformed into differential temporal integration of inputs under opposite directional movements. This mechanism may contribute to the DS of excitatory inputs to L4 neurons.
Enhancing Methodological Rigor for Computational Cognitive Science: Complexity Analysis

Complexity analysis provides a measure of how well the computations being performed by a cognitive model are supported by the constraints of biological computing. We argue that research in computational cognitive science will be greatly aided by treating computational complexity as a primary issue on the same order of importance as significance analysis in experimental work. In this paper, we give a brief review of computational complexity and its application in computer science. We then show how it can be applied to incorporate biological constraints abstractly into computational cognitive science research, even in the presence of massive uncertainty about the underlying neuroscience. To ground this discussion of complexity, examples are drawn from two recent pieces of work: Xu and Tenenbaum’s work applying Bayesian inference to word learning (Xu & Tenenbaum, 2007) and Forbus and Hinrich’s work on the construction of a large-scale analogical reasoner (K. D. Forbus & Hinrichs, 2006).
Developing leaders: Teaching about emotional intelligence and training in emotional skills

Despite years of denial, management scholars are now recognizing that emotions and affect comprise an inescapable component of organizational life (Barsade and Gibson, 2007). The Free Dictionary defines emotion as ‘A mental state that arises spontaneously rather than through conscious effort and is often accompanied by physiological changes. ’ 1 If it is indeed the case that emotions ‘arise spontaneously’, then this clearly will have major ramifications for the way people behave in general, and in their working life in particular. In this chapter we discuss the implications of the pervasiveness of emotion in organizations from the point of view of learning and education, with an emphasis on the role of emotions and emotional intelligence training in leadership programs. To accomplish this, we first define emotions, and then we review the recent literature on emotions as it pertains to organizational settings. In particular, the controversial notion of emotional intelligence has attained wide prominence, especially in the practitioner literature (e.g., Goleman, 1998), so we deal with this topic in some depth in this chapter, including a definition of emotional intelligence, discussion of the ongoing controversy, and elucidation of the role of emotion intelligence in the context of leadership. We then discuss issues surrounding emotional intelligence teaching, and specifically discuss how emotional skills may assist in the development of organizational leadership skills. We conclude with a discussion of the implications of emotions and emotional intelligence for management learning and education, and discuss some directions for the future development of this field.
Priority Based Pre-emptive Task Scheduling for Android Operating System

Android mobile operating system which is based on Linux Kernel 2.6, has open source license and adaptability to user driven applications. As all other operating systems it has all the basic features like process scheduling, memory management, process management etc associated with it. Any mobile platform works smoothly when the process scheduling is performed in a proper way. Ideal platform is that in which no resource conflict occurs. Thus scheduling in every manner is essential for the operating system to adapt itself with the requirement of a particular application. In this paper, priority based pre-emptive task scheduling is proposed for the SMS application. The idea is to define High priority to required contacts, for ex. Contact numbers of parents or teachers will be given High priority. If in case, any SMS from these High priority contacts is received, the application would flash the SMS on the active screen and redirect this High priority SMS to the Priority Inbox.
Biological function through network topology: a survey of the human diseasome

Abstract Molecular network data are increasingly becoming available, necessitating the development of well performing computational tools for their analyses. Such tools enabled conceptually different approaches for exploring human diseases to be undertaken, in particular, those that study the relationship between a multitude of biomolecules within a cell. Hence, a new field of network biology has emerged as part of systems biology, aiming to untangle the complexity of cellular network organization. We survey current network analysis methods that aim to give insight into human disease.
Black hole mass and velocity dispersion of narrow line region in active galactic nuclei and narrow line Seyfert 1 galaxies

Controversy regarding whether Narrow Line Seyfert 1 galaxies (NLS1s) follow the same black hole -galactic bulge relation as normal galaxies has been raised by Mathur et al. ([CITE]) and Ferrarese et al. ([CITE]). In this paper, the correlation between the black hole mass and the velocity dispersion of the narrow line region, indicated by the [OIII] line width for a heterogeneous sample of NLS1s, is examined. We show that the non-virial component subtracted [OIII] width, which may well represent the stellar velocity dispersion ( σ ), correlates with the estimated black hole mass M BH , made using the empirical law for the size of the broad line region from the reverberation mapping measurements. Considering measurement errors, we find that the relation between M BH and the [OIII] width in active galactic nuclei (both NLS1s in this paper and normal Seyferts in Nelson [CITE]) is consistent with that defined in nearby hot galaxies (ellipticals and spiral bulge) but with more scatter. It seems that there is no clear difference in the relation between M BH and σ (represented by the [OIII] width for AGN) in both NLS1s and normal broad line active galactic nuclei from the same relation defined by nearby hot galaxies.
3-D and package-conformal UHF RFID antennas with enhanced performance

Traditional UHF RFID tags follow the form-factor of a bar-code label. Constrained by the limited design space, the antennas are hard to be optimized and customized for specific product packages, especially for the packages containing RF non-friendly contents. In this paper, we proposed two 3-D and package conformal antenna designs. These antennas can either enhance the gain in the desired direction, or beam the radiation into the direction with less obstructions and shielding. The antenna elements can be fabricated on the same substrate and embedded into different facets of the package. When being folded into boxes, the 3-D antenna geometry will be placed into the right locations. Both HFSS simulation and field tests demonstrate that the performance of the proposed antennas is significantly enhanced.
Pairwise Alignment of Protein Interaction Networks

With an ever-increasing amount of available data on protein-protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Although available data on protein-protein interactions is currently limited, recently developed algorithms have been shown to convey novel biological insights through employment of elegant mathematical models. The main challenge in aligning PPI networks is to define a graph theoretical measure of similarity between graph structures that captures underlying biological phenomena accurately. In this respect, modeling of conservation and divergence of interactions, as well as the interpretation of resulting alignments, are important design parameters. In this paper, we develop a framework for comprehensive alignment of PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. We propose a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, the proposed framework facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, our model allows flexibility in adjusting parameters to quantify underlying evolutionary relationships. Based on the proposed model, we formulate PPI network alignment as an optimization problem and present fast algorithms to solve this problem. Detailed experimental results from an implementation of the proposed framework show that our algorithm is able to discover conserved interaction patterns very effectively, in terms of both accuracies and computational cost.
Lack of TAR-DNA binding protein-43 (TDP-43) pathology in human prion diseases

Aims: TAR‐DNA binding protein‐43 (TDP‐43) is the major ubiquitinated protein in the aggregates in frontotemporal dementia with ubiquitin‐positive, tau‐negative inclusions and motor neurone disease. Abnormal TDP‐43 immunoreactivity has also been described in Alzheimer's disease, Lewy body diseases and Guam parkinsonism–dementia complex. We therefore aimed to determine whether there is TDP‐43 pathology in human prion diseases, which are characterised by variable deposition of prion protein (PrP) aggregates in the brain as amyloid plaques or more diffuse deposits. Material and methods: TDP‐43, ubiquitin and PrP were analysed by immunohistochemistry and double‐labelling immunofluorescence, in sporadic, acquired and inherited forms of human prion disease. Results: Most PrP plaques contained ubiquitin, while synaptic PrP deposits were not associated with ubiquitin. No abnormal TDP‐43 inclusions were identified in any type of prion disease case, and TDP‐43 did not co‐localize with ubiquitin‐positive PrP plaques or with diffuse PrP aggregates. Conclusions: These data do not support a role for TDP‐43 in prion disease pathogenesis and argue that TDP‐43 inclusions define a distinct group of neurodegenerative disorders.
Investigation of the Mechanisms of G Protein: Effector Coupling by the Human and Mouse Prostacyclin Receptors

We recently identified a novel mechanism explaining how the mouse (m) prostacyclin receptor (IP) couples to Gαs, Gαi, and Gαq(Lawler, O. A., Miggin, S. M., and Kinsella, B. T. (2001) J. Biol. Chem. 276, 33596–33607) whereby mIP coupling to Gαi and Gαq is dependent on its initial coupling to Gαs and subsequent phosphorylation by cAMP-dependent protein kinase A (PKA) on Ser357. In the current study, the generality of that mechanism was investigated by examining the G protein coupling specificity of the human (h) IP. The hIP efficiently coupled to Gαs/adenylyl cyclase and to Gαq/phospholipase C activation but failed to couple to Gαi. Coupling of the hIP to Gαq, or indeed to Gαs or Gαi, was unaffected by the PKA or protein kinase C (PKC) inhibitors H-89 and GF 109203X, respectively. Thus, mIP and hIP exhibit essential differences in their coupling to Gαi and in their dependence on PKA in regulating their coupling to Gαq. Analysis of their primary sequences revealed that the critical PKA phosphorylation site within the mIP, at Ser357, is replaced by a PKC site within the hIP, at Ser328. Conversion of the PKC site of the hIP to a PKA site generated hIPQL325,326RP that efficiently coupled to Gαs and to Gαi and Gαq; coupling of hIPQL325,326RP to Gαi but not to Gαs or Gαq was inhibited by H-89. Abolition of the PKC site of the hIP generated hIPS328A that efficiently coupled to Gαs and Gαq but failed to couple to Gαi. Finally, conversion of the PKA site at Ser357 within the mIP to a PKC site generated mIPRP354,355QL that efficiently coupled to Gαs but not to Gαi or Gαq. Collectively, our data highlight critical differences in signaling by the mIP and hIP that are regulated by their differential phosphorylation by PKA and PKC together with contextual sequence differences surrounding those sites.
Verb Subcategorization Kernels for Automatic Semantic Labeling

Recently, many researches in natural language learning have considered the representation of complex linguistic phenomena by means of structural kernels. In particular, tree kernels have been used to represent verbal subcategorization frame (SCF) information for predicate argument classification. As the SCF is a relevant clue to learn the relation between syntax and semantic, the classification algorithm accuracy was remarkable enhanced. In this article, we extend such work by studying the impact of the SCF tree kernel on both PropBank and FrameNet semantic roles. The experiments with Support Vector Machines (SVMs) confirm a strong link between the SCF and the semantics of the verbal predicates as well as the benefit of using kernels in diverse and complex test conditions, e.g. classification of unseen verbs.
Classification of microarray data using Fuzzy inference system

The DNA microarray classification is one of the most popular technique among researchers and practitioners. In microarray data analysis, huge useful information may be lost due to irrelevant and insignificant features of the dataset. To overcome this drawback of the data set, only those features are selected which have high relevance with the classes and high significance in the feature set. In this paper, the t-statistic is used for feature selection with high relevance; and Fuzzy inference system (FIS) has been presented for classification purpose. FIS model is applied to classify the leukemia data set for gene classification. A comparative analysis of Fuzzy inference system (FIS) with different set of features (genes) have been presented. The comparison was performed on the basis of available performance parameters in literature such as: precision, recall, specificity, F-Measure, ROC curve and accuracy. The obtained results have been critically examined with the existing classifiers in the literature and it is observed that the proposed system obtained promising results with an increase in classification accuracy rate.
Optimisation of an epileptiform activity detector for ambulatory use

A detector originally designed for seizure detection is modified to detect epileptiform activity in adults. The detector is intended for ambulatory use, and as such an emphasis is placed on the computational load of the detector. A framework is proposed making use of genetic algorithms in order to select the features for a Gaussian mixture classifier. Feature subset selection was performed by incorporating the computational load of each feature. This resulted in an improvement in classification results (larger area under both the ROC and PR curves), while reducing the runtime of the algorithm by up to 2000 fold with respect to a detector using the full feature set.
Path Clearance

In military scenarios, agents (i.e., troops of soldiers, convoys, and unmanned vehicles) may often have to traverse environments with only a limited intelligence about the locations of adversaries. We study a particular instance of this problem that we refer to as path clearance problem.This article presents a survey of our work on scalable and suitable for real-time use approaches to solving the path clearance problem. In particular, in the first part of the article, we show that the path clearance problem exhibits clear preferences on uncertainty. It turns out that these clear preferences can be used to develop an efficient algorithm called probabilistic planning with clear preferences (PPCP). The algorithm is anytime usable, converges to an optimal solution under certain conditions, and scales well to large-scale problems. We briefly describe the PPCP algorithm and show how it can be used to solve the path clearance problem when no scouts are present. In the second part of the article, we show several strategies for how to use the PPCP algorithm in case multiple scouting unmanned aerial vehicles (UAVs) are available. The experimental analysis shows that planning with PPCP results in a substantially smaller execution cost than when ignoring uncertainty, and employing scouts can decrease this execution cost even further.
A Bypass Optimization Method for Network on Chip

Network-on-Chip (NoC) is proposed to solve the communication bottleneck for multi-core SoC. Performance is one of the most critical feature of the NoC. Many different approaches have been introduced to improve the performance of NoC. However, most of them focus on the network part of NoC architecture and neglect other important parts of the system, especially the processor core part. This paper proposes a new architecture: a transmission bypass framework. It adds the bypass path behind the EX stage, an execution stage of the instruction pipeline, to transmit the intermediate results and save transmission time. Experimental results show that when cache misses occur, the performance of send and receive operations can be improved by 15%–38%. The performance of Splash-2 applications can be improved by 28% at most.