Miracle-dz/UniFAR-FTUs huggingface dataset:
Gradient-directed composition of multi-exposure images

In this paper, we present a simple yet effective method that takes advantage of the gradient information to accomplish the multi-exposure image composition in both static and dynamic scenes. Given multiple images with different exposures, the proposed approach is capable of producing a pleasant tonemapped-like high dynamic range (HDR) image by compositing them seamlessly with the guidance of gradient-based quality assessment. Especially, two novel quality measures: visibility and consistency, are developed based on the observations of gradient changes among different exposures. Experiments in various static and dynamic scenes are conducted to demonstrate the effectiveness of the proposed method.
Robotic Validation of an Inter-disciplinary Generic Model of Self-regulated Division of Labour in Social Systems ⋆

Division of labour (DoL) in multi-robot systems or multi-ro b t task allocation (MRTA) is a challenging research issue in the fiel d of multi-agent and multi-robot systems. We propose to solve MRTA problems usin g a set of previously published generic rules for DoL derived from the obser vation of ant, human and robotic social systems. These bottom-up rules describe the phenomenon of self-regulated DoL in terms of attractive fields between rob ots and tasks. The concrete form of these rules, the attractive filed model (AFM), avoids the strong dependence on local interactions found in many existing app roaches to MRTA. We present experimental results that constitute a first vali dation of AFM as a mechanism for MRTA and as a multi-disciplinary model of self -organisation in social systems. Our experiments used 16 e-puck robots in a 2m x 2 area.
Recursive Deep Learning for Sentiment Analysis over Social Data

Sentiment analysis has now become a popular research problem to tackle in NLP field. However, there are very few researches conducted on sentiment analysis for Chinese. Progress is held back due to lack of large and labelled corpus and powerful models. To remedy this deficiency, we build a Chinese Sentiment Treebank over social data. It concludes 13550 labeled sentences which are from movie reviews. Furthermore, we introduce a novel Recursive Neural Deep Model (RNDM) to predict sentiment label based on recursive deep learning. We consider the problem of classifying one sentence by overall sentiment, determining a review is positive or negative. On predicting sentiment label at sentence level, our model outperforms other commonly used baselines, such as Naïve Bayes, Maximum Entropy and SVM, by a large margin.
Providing multicast video on demand using native-mode asynchronous transfer mode

A network video server and client devices have been designed and prototyped that use multicast to deliver Motion-JPEG, MPEG-1, and MPEG-2 video on top of IP, IP-over-ATM and native-mode ATM (AAL5) protocols. The server runs on a workstation-class machine, while the clients have been implemented both as a multi-threaded software entity and as a stand-alone ATM network-attached playback device. Scalability of the video distribution network is obtained by using multicast features of the underlying network. Feedback from the clients and a single video frame retransmission mechanism are used to decrease end-to-end frame loss.
“I Just Can’t Do It Anymore” Patterns of Physical Activity and Cardiac Rehabilitation in African Americans with Heart Failure: A Mixed Method Study

Physical activity and cardiac rehabilitation (CR) are components of heart failure (HF) self-care. The aims of this study were to describe patterns of physical activity in African Americans (n = 30) with HF and to explore experience in CR. This was a mixed method, concurrent nested, predominantly qualitative study. Qualitative data were collected via interviews exploring typical physical activity, and CR experience. It was augmented by quantitative data measuring HF severity, self-care, functional capacity and depressive symptoms. Mean age was 60 ± 15 years; 65% were New York Heart Association (NYHA) class III HF. Forty-three percent reported that they did less than 30 min of exercise in the past week; 23% were told “nothing” about exercise by their provider, and 53% were told to do “minimal exercise”. A measure of functional capacity indicated the ability to do moderate activity. Two related themes stemmed from the narratives describing current physical activity: “given up” and “still trying”. Six participants recalled referral to CR with one person participating. There was high concordance between qualitative and quantitative data, and evidence that depression may play a role in low levels of physical activity. Findings highlight the need for strategies to increase adherence to current physical activity guidelines in this older minority population with HF.
An easy-to-use visualization system for huge cultural heritage meshes

The technology for the automatic shape reconstruction evolved rapidly in recent years, and huge mass of data can be easily produced. Due to the supported accuracy, works of art are one of the ideal fields of use of these devices. Given this particular application domain (Cultural Heritage), two issues arise: how to manage these complex data on commodity computers, and how to improve the ease of use of the visualization tools (as potential users are often not expert at all with interactive graphics). We present a new visualization system that allows naive users to inspect a large complex 3D model at interactive frame rates on off-the-shelf PC's. A main goal in the design of the system has been to provide the user with a very easy and natural interaction approach, based on a straightforward "point and click" metaphor. Visualization efficiency is obtained by adopting a LOD representation and on-line automatic selection of the best-fit level of detail (according to the current view frustum). The system has been used by restores and art curators for the inspection of some high resolution models of statues, and its evaluation was successful.
An Information-Theoretic Approach to Individual Sequential Data Sanitization

Fine-grained, personal data has been largely, continuously generated nowadays, such as location check-ins, web histories, physical activities, etc. Those data sequences are typically shared with untrusted parties for data analysis and promotional services. However, the individually-generated sequential data contains behavior patterns and may disclose sensitive information if not properly sanitized. Furthermore, the utility of the released sequence can be adversely affected by sanitization techniques. In this paper, we study the problem of individual sequence data sanitization with minimum utility loss, given user-specified sensitive patterns. We propose a privacy notion based on information theory and sanitize sequence data via generalization. We show the optimization problem is hard and develop two efficient heuristic solutions. Extensive experimental evaluations are conducted on real-world datasets and the results demonstrate the efficiency and effectiveness of our solutions.
Ontology based data integration of NoSQL datastores

NoSQL databases are suitable for network traffic loads experienced by today's websites. The important attributes that make them a favorable choice are inherent distributed nature, horizontal scalability and flexibility in data models. Hence, more data is now getting handled through NoSQL databases and sensible information extraction from these data stores is becoming a requirement. Information extraction may require sourcing data from multiple data sources, establishing relationship among them and querying across these data sources together. Ontology based semantic integration systems for Relational Database Management Systems (RDBMS) already exist that satisfies the above requirement. Many commercial systems are operational based on the above technique and tools and solutions for the above approach are very much mature. Hence, mapping the processing done in each stage of RDBMS based semantic integration systems to that of NoSQL systems can facilitate usage of existing tools and frameworks. The purpose of this work is to develop ontology based semantic integration system for a column-oriented NoSQL data store like HBase which is similar in architectural design to RDBMS.
Text Line detection and Segmentation in Handwritten Gurumukhi Scripts

Gurumukhi script is a two dimensional composition of symbols with connected and disconnected diacritics. Handwritten Gurumukhi script has some complexities like connected, overlapped text lines. It is one of the major reasons for errors during the recognition process. Text line segmentation is a challenging job in unconstrained writer independent handwritten document image processing. There is a great need for research in the area of Punjabi handwriting recognition to resolve challenging problems involved in it. In this paper we have proposed an algorithm for text line segmentation in handwritten Punjabi document that deals with the problems like overlapped and connected components in text line and extract text lines from handwritten document image. The text line detection algorithm is based on locating the most favourable segments of text line and associating it with its respective text line inserting a gap between neighbouring text lines.
So you know you're getting the best possible information: a tool that increases Wikipedia credibility

An experiment was conducted to study how credibility judgments about Wikipedia are affected by providing users with an interactive visualization (WikiDashboard) of article and author editing history. Overall, users who self-reported higher use of Internet information and higher rates of Wikipedia usage tended to produce lower credibility judgments about Wikipedia articles and authors. However, use of WikiDashboard significantly increased article and author credibility judgments, with effect sizes larger than any other measured effects of background media usage and attitudes on Wikiepedia credibility. The results suggest that increased exposure to the editing/authoring histories of Wikipedia increases credibility judgments.
Modeling Ontologies for Time-Dependent Applications

The typical approach for declaratively reasoning about phenomena that evolves in time is to use a cognitive system centered on the given problem domain ontology as a vehicle for knowledge representation and processing. Current ontological-based approaches are successful only for the static structural description of a given domain of discourse. We present a modeling approach suitable for building and processingontologies for applications where time-dependent evolution is of crucial importance. We also present the underlying concepts that form the basis of our modeling method and describe the temporal graph used for linking these concepts in order to encode the model evolution. That graph can be inspected in order to analyze the current and past events that a model generates, and their effects, and can be used for predicting he future model behavior. We present results on the capability of the modeling method to correctly describe temporal knowledge.
HVS-based structural similarity for image quality assessment

Objective quality assessment is important and widely used in image processing. Recently, the metric named structural similarity is proposed, which is based on the assumption that human visual perception is highly adapted for extracting structural information. This metric has a better performance than PSNR in many cases but fails in case evaluating the badly blurred images. This limitation is inconsistent with the characteristics of human visual system (HVS) and leads to our inspiration of applying HVS characters to images structural similarity. Our method, HVS-based structural similarity(HSSIM), employs the HVS characters both in frequency domain and spatial domain. It can be concluded in our experiment that HSSIM performs better than PSNR and SSIM, especially for badly blurred images.
Integration of Asphaltenes Flocculation Modeling Into Athos Reservoir Simulator

To respond to the ever-growing needs of reservoir simulation studies in a large range of applications, it has been considered necessary to integrate an asphaltenes flocculation model into the Athos software. Athos is a multipurpose simulator which provides a lot of different physical options. Operating companies wish to have available predictive tools, allowing them to plan a reservoir production when the asphaltenic crude it contains could induce porosity and permeability reduction. These companies can thus plan pressure maintenance technologies in order to avoid risks related to asphaltenes flocculation out of the reservoir crude. In the case of deep offshore reservoirs, the economic consequences of such strategies can be of primary importance. The work presented here describes procedures which have been implemented in order to compute asphaltene flocculation conditions within the Athos software. It is thus possible to determine the amount of asphaltenes deposit at different stages of a reservoir or of a field development. This would obviously improve the knowledge of porosity and permeability variation as a result of asphaltenes flocculation, and thus lead to a more reliable evaluation of the oil production.
Dopamine Transmission in the Human Striatum during Monetary Reward Tasks

Previous studies have demonstrated the ability of the [11C]raclopride positron emission tomography (PET) technique to measure behaviorally induced changes in endogenous dopamine transmission in humans. However, these studies have lacked well matched sensorimotor control conditions, making it difficult to know what sensory, cognitive, or motor features contributed to changes in dopaminergic activity. Here we report on [11C]raclopride PET studies in which healthy humans performed card selection tasks for monetary rewards. During separate scans, subjects completed a variable ratio (VR) reward schedule with a 25% reward rate in which they did not know the outcome of their responses in advance, a fixed ratio (FR) 25% reward schedule in which outcomes were fully predictable, and a sensorimotor control (SC) condition involving similar sensory and motor demands but no rewards. Relative to the SC condition, the FR schedule produced only modest increases in dopamine transmission and no decreases relative to the SC condition. In contrast, the VR schedule produced significant increases in dopamine transmission in the left medial caudate nucleus while simultaneously producing significant decreases in other areas of the caudate and putamen. These data indicate: (1) the feasibility of measuring alterations in dopamine transmission even after controlling for sensorimotor features and (2) the complex and regionally specific influence of VR schedules on dopamine transmission. The implications of these results are discussed in relation to conflicting models of dopaminergic functioning arising from studies using electrophysiological and microdialysis techniques in animals.
Accurate Prediction of Translation Initiation Sites by Universum SVM

In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions that start code for proteins. These points are called translation initiation sites (TIS). The task of recognizing TIS can be modeled as a classification problem. In this paper, we use a new pattern classification algorithm which has recently been proposed by Vapnik to deal with this problem. Numerical experiments proved the considerable improvement of this method compared with the leading existing approaches.
Survey on Data Mining

- In the Information Technology era information plays vital role in every sphere of the human life. It is very important to gather data from different data sources, store and maintain the data, generate information ,generate knowledge . To analyze this vast amount of data and drawing fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives overview of the data mining in which what research could be done in different areas and some of its applications.
When Creative Potentials are Being Undermined By Commercial Imperatives

The aim of this paper is to explore and discuss how newsroom restructuring in favour of cross-media news production is changing news production practices and to what extent media-specific norms, values and practices impede the strategy for change. In doing so, we use the structuration theory of Anthony Giddens as theoretical framework. With his concept of the dynamic relationship between structures and agency, and the analytical division of structures into recursively organised rules and resources, we look at planned newsroom reorganisation in order to analyse aspects leading to its implementation as well as factors resistant to change. Overall, our findings show that conflicts between journalistic norms (accurately researching content) and values (reliably informing citizens on socially relevant topics), and the requirement to produce more content within an ever shorter time, influence the implementation of the intended cross-media news production. Moreover, interviewees suggest that in addition to allocative resources (notably financial and human resources), authoritative resources, such as competencies and expert knowledge, have been relevant for adaptation or maintenance of newsroom structures.
Probability based models for estimation of wildfire risk

We present a probability-based model for estimating fire risk. Risk is defined using three probabilities: the probability of fire occurrence; the conditional probability of a large fire given ignition; and the unconditional probability of a large fire. The model is based on grouped data at the 1 km2-day cell level. We fit a spatially and temporally explicit non-parametric logistic regression to the grouped data. The probability framework is particularly useful for assessing the utility of explanatory variables, such as fire weather and danger indices for predicting fire risk. The model may also be used to produce maps of predicted probabilities and to estimate the total number of expected fires, or large fires, in a given region and time period. As an example we use historic data from the State of Oregon to study the significance and the forms of relationships between some of the commonly used weather and danger variables on the probabilities of fire. We also produce maps of predicted probabilities for the State of Oregon. Graphs of monthly total numbers of fires are also produced for a small region in Oregon, as an example, and expected numbers are compared to actual numbers of fires for the period 1989–1996. The fits appear to be reasonable; however, the standard errors are large indicating the need for additional weather or topographic variables.
Papercraft Models using Generalized Cylinders

We introduce an algorithm for approximating a 2-manifold 3D mesh by a set of developable surfaces. Each developable surface is a generalized cylinder represented as a strip of triangles not necessarily taken from the original mesh. Our algorithm is automatic, creates easy-to-assemble pieces, and provides L_\alpha global error bounds. The approximation quality is controlled by a user-supplied parameter specifying the allowed Hausdorff distance between the input mesh and its piecewise-developable approximation. The strips generated by our algorithm may be parameterized to conform with the parameterization of the original mesh, if given, to facilitate texture mapping. We demonstrate this by physically assembling papercraft models from the strips generated by our algorithm when run on several polygonal 3D mesh data sets.
T lymphomagenesis is determined by a dominant host gene thymic lymphoma susceptible mouse-1 (TLSM-1) in mouse models

Susceptibility to T lymphomas in mice is determined by a number of viral and host genetic factors. We analyzed the types and latent period of lymphomas spontaneously occurring in crosses between AKR/Ms, a T lymphoma-prone mouse strain, and SL/Kh, a pre-B lymphoma-prone strain. The incidence of T lymphomas in the F1 hybrids backcross to SL/Kh as well as F2 generation mice indicated that a dominant host gene thymic lymphoma susceptible mouse-1 (Tlsm-1) of AKR/Ms determined the type of lymphomas to be thymic. Linkage analysis with microsatellite markers assigned Tlsm-1 to the map position 61 cM from centromere of the chromosome 7. Close scrutiny of this region of AKXD recombinant inbred strains for spontaneous T lymphomas revealed the presence of Tlsm-1- like gene most likely between D7MIT71 (map position 62) and D7MIT13 (map position 70). On the other hand, a SL/Kh-derived recessive allele at a major histocompatibility complex (MHC)-linked locus accelerated development of both T and B lymphomas.
Divergence of sun-rays by atmospheric refraction at large solar zenith angles

For the determination of photolysis rates at large zenith angles it has been demonstrated that refraction by the earth's atmosphere must be taken into account. In fact, due to the modified optical path the optical transmittance is thereby increased in most instances. Here we show that in addition the divergence of sun-rays, which is also caused by refraction but which reduces the direct solar irradiance, should not be neglected. Our calculations are based on a spherically sym- metric atmosphere and include extinction by Rayleigh scat- tering, ozone, and background aerosol. For rays with 10 km tangent altitude the divergence yields a reduction of about 10% to 40% at solar zenith angles of 91 to 96 . More- over, we find that the divergence effect can completely cancel the relative enhancement caused by the increase of transmit- tance.
Brain-to-text: decoding spoken phrases from phone representations in the brain

It has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To-Text system described in this paper represents an important step toward human-machine communication based on imagined speech.
An efficient 210GHz compact harmonic oscillator with 1.4dBm peak output power and 10.6% tuning range in 130nm BiCMOS

A compact 210GHz harmonic oscillator is presented. By utilizing a device-centric bottom-up design methodology as well as the return-path gap coupler and self-feeding structure, the harmonic power generation is optimized. Fabricated with a 130nm SiGe BiCMOS process, the oscillator core occupies only 290×95 μm2 area. It achieves a 1.4dBm peak output power and 2.4% peak DC-to-RF efficiency. The output frequency can be tuned from 197.5GHz to 219.7GHz. The phase noise is measured to be -87.5dBc/Hz at 1MHz offset.