Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging the Gap Between Object-Based Attention and Texton-Based Segmentation : How Attention Spreads Through Orientation-Defined Textures

Many recent studies of object-based attention (OBA) have suggested that the underlying units of attention are often discrete objects, through which attention readily spreads. However, the relationship of such experiments to the basic visual features (‘textons’) which guide the segmentation of visual scenes into ‘objects’ remains largely unexplored. Here we bridge this gap for one of the most conspicuous features of early vision: orientation. We do so in a study of how attention spreads through simple static orientation-defined textures (ODTs), and across texture-defined boundaries. Much work in the segmentation literature suggests that orientation-based texture segmentation (OBTS) is guided by orientation gradients, and our previous work suggests that it is also influenced by ODT curvatures. We suggest here that attention should respect ODT boundaries defined by both orientation and curvature differences, and we also predict that the flow of attention should not depend on the general direction (i.e. the ‘grain’) of the texture — in contradiction to previous findings in the OBA literature. We explore such predictions using both spatial-cueing and dividedattention paradigms on various ODTs, both uniform (one ‘object’) and discontinuous (two ‘objects’). Contrary to previous studies, we find that the texture’s ‘main direction’ has no effect: attention flows just as readily with vs. against the ‘grain’ of ODTs. At the same time, texture-defined discontinuities have a major effect: attention flows less readily across texture boundaries which are defined by either orientation or curvature. These effects replicated across multiple paradigms and dependent measures, and also held for jittered ODTs, wherein the effects must be due to global structure as opposed to local good continuation. We conclude that uniform ODTs are single objects from an attentional point of view, while discontinuous ODTs — with the discontinuities defined in either orientation or curvature — are processed as multiple objects. Collectively these experiments begin to reveal how the ‘objects’ of OBA are formed from simpler visual features.
The Neurocognition of Referential Ambiguity in Language Comprehension

Referential ambiguity arises whenever readers or listeners are unable to select a unique referent for a linguistic expression out of multiple candidates. In the current article, we review a series of neurocognitive experiments from our laboratory that examine the neural correlates of referential ambiguity, and that employ the brain signature of referential ambiguity to derive functional properties of the language comprehension system. The results of our experiments converge to show that referential ambiguity resolution involves making an inference to evaluate the referential candidates. These inferences only take place when both referential candidates are, at least initially, equally plausible antecedents. Whether comprehenders make these anaphoric inferences is strongly context dependent and co-determined by characteristics of the reader. In addition, readers appear to disregard referential ambiguity when the competing candidates are each semantically incoherent, suggesting that, under certain circumstances, semantic analysis can proceed even when referential analysis has not yielded a unique antecedent. Finally, results from a functional neuroimaging study suggest that whereas the neural systems that deal with referential ambiguity partially overlap with those that deal with referential failure, they show an inverse coupling with the neural systems associated with semantic processing, possibly reflecting the relative contributions of semantic and episodic processing to re-establish semantic and referential coherence, respectively.
Data-flow-based unit testing of aspect-oriented programs

The current research so far in aspect-oriented software development is focused on problem analysis, software design, and implementation techniques. Even though the importance of software testing is known, it has received little attention in the aspect-oriented paradigm. In this paper, we propose a data-flow-based unit testing approach for aspect oriented programs. Our approach tests two types of units for an aspect-oriented program, i.e., aspects that are modular units of crosscutting implementation of the program, and those classes whose behavior may be affected by one or more aspects. For each aspect or class, our approach performs three levels of testing, i.e., intra-module, inter-module, and intra-aspect or intra-class testing. For an individual module such as apiece of advice, a piece of introduction, and a method, we perform intra-module testing. For a public module along with other modules it calls in an aspect or class, we perform inter-module testing. For modules that can be accessed outside the aspect or class and can be invoked in any order by users of the aspect or class, we perform intra-aspect or intra-class testing. Our approach can handle unit testing problems that are unique to aspect-oriented programs. We use control flow graphs to compute def-use pairs of an aspect or class being tested and use such information to guide the selection of test for the aspect or class.
Using Social Networking for Educational and Cultural Adaptation: An Exploratory Study

This study investigates how an innovative technology, social networking, can be used in the process of building and maintaining social capital and exchanging knowledge in an educational setting. We employ a qualitative methodology, autoethnography, to examine how social networking can help students learn from other classmates and professors, exchange knowledge, and adjust both to a new program of study and to living in the United States. Using the theoretical foundation of social capital (Jacobs, 1960) and a social support framework created by Drentea and Moren-Cross (2005), autobiographical narratives are classified as instrumental support, emotional support, and community building. Our findings provide evidence that social networking sites can enhance social capital through these mechanisms in a doctoral education context, and our research serves as an important first step in addressing a gap in educational and cultural adaptation studies using social networking tools.
Ca2+–Secretion Coupling Is Impaired in Diabetic Goto Kakizaki rats

The Goto Kakizaki (GK) rat is a widely used animal model to study defective glucose-stimulated insulin release in type-2 diabetes (T2D). As in T2D patients, the expression of several proteins involved in Ca2+-dependent exocytosis of insulin-containing large dense-core vesicles is dysregulated in this model. So far, a defect in late steps of insulin secretion could not be demonstrated. To resolve this apparent contradiction, we studied Ca2+–secretion coupling of healthy and GK rat β cells in acute pancreatic tissue slices by assessing exocytosis with high time-resolution membrane capacitance measurements. We found that β cells of GK rats respond to glucose stimulation with a normal increase in the cytosolic Ca2+ concentration. During trains of depolarizing pulses, the secretory activity from GK rat β cells was defective in spite of upregulated cell size and doubled voltage-activated Ca2+ currents. In GK rat β cells, evoked Ca2+ entry was significantly less efficient in triggering release than in nondiabetic controls. This impairment was neither due to a decrease of functional vesicle pool sizes nor due to different kinetics of pool refilling. Strong stimulation with two successive trains of depolarizing pulses led to a prominent activity-dependent facilitation of release in GK rat β cells, whereas secretion in controls was unaffected. Broad-spectrum inhibition of PKC sensitized Ca2+-dependent exocytosis, whereas it prevented the activity-dependent facilitation in GK rat β cells. We conclude that a decrease in the sensitivity of the GK rat β-cell to depolarization-evoked Ca2+ influx is involved in defective glucose-stimulated insulin secretion. Furthermore, we discuss a role for constitutively increased activity of one or more PKC isoenzymes in diabetic rat β cells.
A trust model in cloud computing based on fuzzy logic

Trust is a vital factor, especially for service oriented systems in the area of Information Technology and Security. Several issues have been raised by enterprises and individuals concerning the reliability of the cloud resources. In cloud computing, trust helps the consumer to choose the service of a cloud provider for storing and processing their sensitive information. In this paper, a trust model is proposed which uses Quality of Service parameters (QoS) to evaluate trust. The fuzzy nature of trust has encouraged us to use fuzzy logic to calculate trust value of a provider in cloud environment, thus increasing the effectiveness of the system. QoS parameters such as turnaround time, availability and reliability are taken for trust evaluation.
Modeling Service Level Agreement with Ontology

the Negotiation and management of service level agreements between the customer and the service provider plays a very important role in the proper functioning of the network. SLA [1] defined between the various stakeholders (customer, provider, signing authority.) allows specifying the possible levels of service, compliance with this specification provides a guaranteed quality of service that the customer is seeking to meet
Cluster-based PLE areas for mobile cooperative localization in indoor wireless sensor network

Localization is an important requirement in wireless sensor networks to determine the position of the object. Localization based on RSSI (Received Signal Strength Indicator) has low accuracy is caused by attenuation of the transmitted signal as a function of the distance between nodes. In indoor environment, the PLE (path loss exponent) values are varied depend on the environment characteristic. This value is required to estimate the distance among nodes and it obtained from the measurement process. However, the fluctuation of PLE value based on RSSI is strongly influencing the output of estimation distance. In this paper we propose a cluster-based PLE areas measurement procedure for indoor mobile cooperation localization. The variation values of PLE can be used to calculate distance estimation from RSSI data of mobile object based on nearest distance from anchor node in cooperative localization. The simulation result shows that cluster-based PLE measurement values have capability in decreasing MSE localization up to 16.16 %, meanwhile without clustering the observation area the MSE localization has 32.39 % decreased. The improvement of PLE value on cluster-based area using linear regression yields 18.06 % of MSE localization.
Combining sparse appearance and Bayesian inference models for object tracking

In this paper, we present a method that combines a sparse appearance model into the Bayesian inference framework for tracking pedestrians in video sequences captured by a fixed camera. We formulate sparse appearance model as a linear combination of a set of 4D smoothed colour histograms for each pedestrian. These colour histograms are computed for all detection windows with different confidence values from human detector proposed by Dalal and Triggs. Object tracking is carried out using the Bayesian inference method. For occlusion handling, we integrate the Kalman filter to get the potential region containing target's observation and then use maximum a posteriori estimation to get the most likely observation. We test our tracking method on the benchmark video datasets. Our experimental results show that our tracking method outperforms one without using occlusion handling technique and can handle partial occlusion and false negative errors from human detector.
RoboSherlock: Unstructured information processing for robot perception

We present RoboSherlock, an open source software framework for implementing perception systems for robots performing human-scale everyday manipulation tasks. In RoboSherlock, perception and interpretation of realistic scenes is formulated as an unstructured information management (UIM) problem. The application of the UIM principle supports the implementation of perception systems that can answer task-relevant queries about objects in a scene, boost object recognition performance by combining the strengths of multiple perception algorithms, support knowledge-enabled reasoning about objects and enable automatic and knowledge-driven generation of processing pipelines. We demonstrate the potential of the proposed framework by three feasibility studies of systems for real-world scene perception that have been built on top of RoboSherlock.
Bimodal Gastroretentive Drug Delivery Systems of Lamotrigine: Formulation and Evaluation

Gastroretentive bimodal drug delivery systems of lamotrigine were developed using immediate release and extended release segments incorporated in a hydroxypropyl methylcellulose capsule and in vitro and in vivo evaluations were conducted. In vivo radiographic studies were carried out for the optimized formulation in healthy human volunteers with replacement of drug polymer complex by barium sulphate and the floating time was noted. Here the immediate release segment worked as loading dose and extended release segment as maintenance dose. The results of release studies of formulations with hydrophillic matrix to formulations with dual matrix hydroxypropyl methylcellulose acetate succinate shown that as the percentage of polymer increased, the release decreased. Selected formulation F2 having F-Melt has successfully released the drug within one hour and hydrophillic matrix composing polyethylene oxide with 5% hydroxypropyl methylcellulose acetate succinate showed a lag time of one hour and then extended its release up to 12th hour with 99.59% drug release following zero order kinetics with R2 value of 0.989. The Korsmeyer-Peppas equation showed the R2 value to be 0.941 and n value was 1.606 following non-Fickian diffusion pattern with supercase II relaxation mechanism. Here from extended release tablet the drug released slowly from the matrix while floating.
NLP-Based Requirements Modeling: Experiments on the Quality of the models

Conceptual models are used in a variety of areas within Computer Science, including Software Engineering, Databases and AI. A major bottleneck in broadening their applicability is the time it takes to build a conceptual model for a new application. Not surprisingly, a variety of tools and techniques have been proposed for reusing conceptual models, e.g. ontologies, or for building them semi-automatically from natural language (NL) descriptions. What has been left largely unexplored is the impact of such tools on the quality of the models that are being created. This paper presents the results of three experiments designed to assess the extent to which a Natural-Language Processing (NLP) tool improves the quality of conceptual models, specifically object-oriented ones. Our main experimental hypothesis is that the quality of a domain class model is higher if its development is supported by a NLP system. The tool used for the experiment – named NL-OOPS – extracts classes and associations from a knowledge base realized by a deep semantic analysis of a sample text. Specifically, NL-OOPS produces class models at different levels of detail by exploiting class hierarchies in the knowledge base of a NLP system and marks ambiguities in the text. In our experiments, we had groups working with and without the tool, and then compared and evaluated the final class models they produced. The results of the experiments – the first on this topic – give insights on the state of the art of linguistics-based Computer Aided Software Engineering (CASE) tools and allow identifying important guidelines to improve their performance. In particular it was possible to highlight which of the linguistic tasks are more critical to effectively support conceptual modelling.
Continuous noninvasive blood pressure measurement by pulse transit time

Blood pressure measurement is performed either invasively by an intra arterial catheter or noninvasively by cuff sphygmomanometry. The invasive method is continuous and accurate but has increased risk; the cuff is safe but less reliable and infrequent. A reliable continuous noninvasive blood pressure measurement is highly desirable. While the possibility of using pulse transit time to monitor blood pressure has previously been investigated, most studies were limited to calculating the correlation of the pulse transit time and blood pressure under rather static conditions. The relationship between the pulse transit time and blood pressure is yet to be clearly identified. This paper focuses on the modeling between the two values and presents results on cases where dramatic variation in blood pressure of the patient was induced by drug administration or surgical stimulation.
On-chip network evolution using NetC

Application specific on-chip network is a promising direction for networks on chips (NoCs) to solve the interconnection challenges that systems on chips (SoCs) will encounter in the billion-transistor age. This paper presents a novel design methodology named on-chip network evolution, which helps developing an on-chip network rapidly according to a specific application. To explore the network design space more efficiently, a new description language - NetC is introduced, which is a group of syntaxes that can be translated into SystemC programs.
Data Collection Method to Improve Energy Efficiency in Wireless Sensor Network

Wireless Sensor Networks (WSNs) are generally self-organized wireless ad hoc networks which incorporate a huge number of sensor nodes which are resource constraint. Among the tasks of WSN, one mostessentialtask is to collectthe data and transmits the gathered data to a distant base station (BS). The effectiveness of WSNs can be calculated in terms of network lifetime.Data collection is a frequentoperation but analytical and critical operation in many WSN’s application. To prolong network lifetime innovative technique that can improve energy efficiency are highly required. This paper presents a survey for designing Energy Efficient Data Collection Methods used for prolonging network lifetime in Wireless Sensor Network (WSN). The study highlights the importance of different Data conditions for various purposes like emergency response, medical monitoring, military applications, surveillance in volcanic or remote regions, etc.Different Data Collection methods like data aggregation clusters, data aggregation trees, network coding, correlation dominating set, etc. are considered in detail in this study. Furthermore a comparison of different Data Collection Method based on the network lifetime, energy efficiency, complexity of the algorithm, transmission cost and fusion cost is done.
Fast Frame-Accurate Mining for Repeating Video Clips

In the broad range of multimedia content analysis tasks the detection of recurring video sequences plays an important role. We introduce an algorithm for recognizing recurring video sequences frame-accurately in a highly effective and efficient way. It does not require temporal segmentation by shot detection. A 24 hour live-stream can be processed in about 4 hours including the computational expensive video decoding. The algorithm uses an inverted index for identifying similar frames rapidly. Gradient-based image features are mapped to the index by means of a hash function. The search algorithm consists of two steps: firstly searching for recurring short segments (e.g., 1 second long) and secondly assembling these small segments into the set of repeated whole video clips. In our experiments we investigate the sensitivity of the algorithm concerning all system parameters and apply it to the detection of unknown commercials within 24 hours of two different TV channels. It is shown that the method is an excellent alternative for searching for unknown commercials.
On modulation for magnetic induction based transmission in wireless underground sensor networks

Wireless underground sensor networks (WUSNs) are an emerging and promising research area. The aim of WUSNs is to establish an efficient wireless communication in the underground medium. A magnetic induction (MI)-based waveguide technique has been proposed to overcome the very harsh propagation conditions in WUSNs. In this approach, several resonant relay circuits are deployed between the two nodes to be connected. This technique allows for an extension of the transmission range. In this work, we investigate digital transmission schemes for MI-WUSNs. We analyze the influence of transmission parameters like symbol duration and modulation scheme and propose methods for their optimization.
ANALYSIS AND DETECTION OF METAMORPHIC COMPUTER VIRUSES

comparison between our approach and commercial virus scanners. I would also like to thank my friends and schoolmates for their technical and emotional support. I want to thank Yue Wang for performing the virus scanning, and Peter Hey for repairing my hard disk after it crashed at the most critical moment. Finally I want to thank my family for their understanding and support throughout my five years of graduate studies. They have shown the greatest care and patience which I truly appreciate. Abstract Computer virus writers commonly use metamorphic techniques to produce viruses that change their internal structure on each infection. It is generally believed that these metamorphic viruses are extremely difficult to detect. Metamorphic virus generating kits are readily available, so that little knowledge or skill is required to create these potentially devastating viruses. In this project, we first analyze four virus creation kits to determine the degree of metamorphism provided by each. We are able to precisely quantify the degree of metamorphism produced by these virus generators. While the best generator, the Next Generation Virus Creation Kit (NGVCK), produces virus variants that differ greatly from one another, the other three generators we examined are much less effective. We then show that three popular commercial virus scanners cannot detect any of the NGVCK viruses in our test set. We proceed to develop an effective metamorphic virus detection technique based on hidden Markov models (HMM). With this HMM detector, we are able to classify a given program as belonging to a particular virus family or not. Using this approach, we can detect all metamorphic viruses in our test set with extremely high accuracy. We also present a simpler detection method that detects metamorphic viruses with high accuracy. Our results show that the best available metamorphic generator is effective at morphing viral code and that the resulting morphed viruses are not detectable using popular commercial virus scanning software. Surprisingly, these viruses differ sufficiently from non-viral code so that they are detectable using a similarity technique that we present in this paper. It remains an interesting open question whether metamorphic viral code can be constructed which is undetectable using our techniques.
Global texture constrained active shape models for image interpretation

In this paper, we propose to improve active shape models (ASMs) by global texture constraint for reliable image interpretation. In the proposed method, ASM search strategy is firstly used, then, in order to evaluate the fitting degree of the current shape to the interpreted image, warped global texture subspace reconstruction error is exploited. When the current shape is more fitted than last iteration ASM search strategy will continue. Otherwise, global texture is used to predict the shape model parameters to get more fitted shape then the previous iteration, a strategy similar with active appearance model. By such an interleave iteration, our method takes the advantages of ASMs while fully utilizing the global texture information for accurate image interpretation. Experiments on our database containing 300 labeled face images significantly show the effectiveness of our method.