Miracle-dz/UniFAR-FTUs huggingface dataset:
Migrant participation in Norwegian health care. A qualitative study using key informants

Abstract Background: Little is known about how migrants adapt to first-world public health systems. In Norway, patients are assigned a registered general practitioner (RGP) to provide basic care and serve as gatekeeper for other medical services. Objectives: To explore determinants of migrant compliance with the RGP scheme and obstacles that migrants may experience. Methods: Individuals in leadership positions within migrant organizations for the 13 largest migrant populations in Norway in 2008 participated in this qualitative study. Semi-structured interviews, with migrants serving as key informants, were used to elucidate possible challenges migrant patients face in navigating the local primary health-care system. Conversations were structured using an interview guide covering the range of challenges that migrant patients meet in the health-care system. Results: According to informants, integration into the RGP scheme and adequacy of patient-physician communication varies according to duration of stay in Norway, the patient's country of origin, the reason for migration, health literacy, intention to establish permanent residence in Norway, language proficiency, and comprehension of information received about the health system. Informants noted as obstacles: doctor-patient interaction patterns, conflicting ideas about the role of the doctor, and language and cultural differences. In addressing noted obstacles, one strategy would be to combine direct intervention by migrant associations with indirect intervention via the public-health system. Conclusion: Our results will augment the interpretation of forthcoming quantitative data on migrant integration into the public-health system and shed light on particular obstacles.
Energy-aware cross-layer burst buffering for wireless communication

The massive explosion of mobile applications with the ensuing data exchange over the cellular infrastructure is not only a blessing to the mobile user but also has a price in terms of regular discharging of the device battery. A big contributor to this energy consumption is the power hungry wireless network interface. We leverage a measurement kit to perform accurate physical energy consumption measurements in a third generation (3G) telecommunication modem thus isolating the energy footprint of data transfers as opposed to other mobile phone-based measurement studies. Using the measurement kit we show how the statically configured network parameters, i.e., channel switch timers, and buffer thresholds, in addition to the transfer data pattern and the radio coverage, impact the communication energy footprint. We then demonstrate that being aware of static network parameters creates room for energy savings. This is done by devising a set of algorithms that (a) infer the network parameters efficiently, and (b) use the parameters in a new packet scheduler in the device. The combined regime is shown to transfer background uplink data, from real world traces of Facebook and Skype, with significant energy saving compared to the state-of-the-art.
Loongson IoT Gateway: A Technical Review

A prototype of Loongson IoT (Internet of Things) ZigBee gateway is already designed and implemented. However, this prototype is not perfect enough because of the lack of a number of functions. And a lot of things should be done to improve this prototype, such as adding widely used IEEE 802.11 function, using a fully open source ZigBee protocol stack to get rid of proprietary implement or using a fully open source embedded operating system to support 6LoWPAN, and implementing multiple interfaces.
Eigenvalue-Based Spectrum Sensing for Multiple Received Signals Under the Non-Reconstruction Framework of Compressed Sensing

Eigenvalue-based algorithm is generally acknowledged to be a promising method for spectrum sensing. However, it possesses high computational complexity, because the sampled covariance matrix and the corresponding eigenvalues are calculated. Furthermore, the detection performance of eigenvalue-based algorithms experiences a huge decline when the received signals are uncorrelated. Therefore, compressed sensing is adopted to reduce the computational complexity and introduce the relevance for multiple received signals. First, the sampled covariance matrix and its eigenvalues are calculated under the non-reconstruction framework of compressed sensing. The corresponding standard condition number of the eigenvalues is employed as test statistic to perform spectrum sensing. Then, the impact of the measurement matrix on the detection performance is discussed, and the computational complexity is analyzed. Next, the measurement matrix is optimized to improve the detection performance. In addition, a novel method of setting decision threshold is proposed to maintain a stable false alarm probability for the proposed spectrum sensing algorithm, and its computational complexity is compared with some existing methods. Finally, the corresponding simulations are performed to testify the theoretical results. Theoretical analysis and simulation results certify the effectiveness and validity of the proposed method.
Light on Body Image Treatment

The treatment of body image has to be multifaceted and should be directed toward the treatment of the whole individual—body, mind, and spirit—with an ultimate culmination of acceptance and compassion for the self. This article presents information on a mindful approach to the treatment of body image as it pertains to concerns with body size and shape. This approach fosters the idea that the treatment process should be one of observation, nonjudgment, neutrality, and acceptance. To this end, this article will depict the conceptualization of body image treatment from a mindful perspective, in which mindfulness serves as the foundation on which the multiple facets of treatment are built. The core components of body image treatment (i.e., cognitive, perceptual, behavioral, and emotional), in the context of mindfulness, are discussed as they relate to the treatment of body image disturbance. This article may be viewed as a theoretical overview of a new treatment concept for body image disturbance.
A compact routing scheme and approximate distance oracle for power-law graphs

Compact routing addresses the tradeoff between table sizes and stretch, which is the worst-case ratio between the length of the path a packet is routed through by the scheme and the length of an actual shortest path from source to destination. We adapt the compact routing scheme by Thorup and Zwick [2001] to optimize it for power-law graphs. We analyze our adapted routing scheme based on the theory of unweighted random power-law graphs with fixed expected degree sequence by Aiello et al. [2000]. Our result is the first analytical bound coupled to the parameter of the power-law graph model for a compact routing scheme. Let <i>n</i> denote the number of nodes in the network. We provide a <i>labeled</i> routing scheme that, after a stretch--5 handshaking step (similar to DNS lookup in TCP/IP), routes messages along stretch--3 paths. We prove that, instead of routing tables with <i>Õ</i>(<i>n</i><sup>1/2</sup>) bits (<i>Õ</i> suppresses factors logarithmic in <i>n</i>) as in the general scheme by Thorup and Zwick, expected sizes of <i>O</i>(<i>n<sup>γ</sup></i> log <i>n</i>) bits are sufficient, and that all the routing tables can be constructed at once in expected time <i>O</i>(<i>n<sup>1+γ</sup></i> log <i>n</i>), with γ = τ-22/τ-3 + ϵ, where τ∈(2,3) is the power-law exponent and ϵ 0 (which implies ϵ < γ < 1/3 + ϵ). Both bounds also hold with probability at least 1-1/<i>n</i> (independent of ϵ). The routing scheme is a labeled scheme, requiring a stretch--5 handshaking step. The scheme uses addresses and message headers with <i>O</i>(log <i>n</i> log log <i>n</i>) bits, with probability at least 1-<i>o</i>(1). We further demonstrate the effectiveness of our scheme by simulations on real-world graphs as well as synthetic power-law graphs. With the same techniques as for the compact routing scheme, we also adapt the approximate distance oracle by Thorup and Zwick [2001, 2005] for stretch-3 and we obtain a new upper bound of expected <i>Õ</i>(<i>n</i><sup>1+γ</sup>) for space and preprocessing for random power-law graphs. Our distance oracle is the first one optimized for power-law graphs. Furthermore, we provide a linear-space data structure that can answer 5--approximate distance queries in time at most <i>Õ</i>(<i>n</i><sup>1/4+ϵ</sup>) (similar to γ, the exponent actually depends on τ and lies between ϵ and 1/4 + ϵ).
Meteor: Seeding a Security-Enhancing Infrastructure for Multi-market Application Ecosystems

Application markets providing one-click software installation have become common to smartphones and are emerging on desktop platforms. Until recently, each platform has had only one market; however, social and economic pressures have resulted in multiple-market ecosystems. Multi-market environments limit, and in some cases eliminate, valuable security characteristics provided by the market model, including kill switches and developer name consistency. We outline a novel approach to retaining single-market security semantics while enabling the flexibility and independence of a multi-market environment. We propose Meteor as a security-enhancing application installation framework that leverages information (e.g., app statistics, expert ratings, developer history) from a configurable set of security information sources. We build a proof-of-concept Android application (Meteorite) to demonstrate the technical feasibility of our proposal. The Meteor approach provides valuable decisionmaking criteria useful not only for smartphone users, but technology consumers as a whole, as new and existing computing environments converge on a market-like model for software installation.
MULTISTAGE ALGORITHM FOR LOSSLESS COMPRESSION OF MULTISPECTRAL REMOTE SENSING IMAGES

A preliminary comparison between loss and lossless compression approaches for the remote sensing data processing was made. A three-stage lossless compression algorithm of multispectral remote sensing images based on wavelet transformations and intra-bands correlation is proposed and developed. It allows one to consider peculiarities of remote sensing data and to increase the compression ratio of the algorithm. The paper describes a modification of the compression algorithm aimed at considerable improvement of computational performance and based on bands trimmed enumeration and data selective use. A research of the three-stage algorithm performance was carried out in comparison with the universal compression algorithms such as WinRar, WinZip and JPEG2000 using data from various remote sensing systems showing to some extent a superiority in the compression ratio, as well as some insignificant lag of the computational performance was identified.
On Semantic Word Cloud Representation

We study the problem of computing semantic-preserving word clouds in which semantically related words are close to each other. While several heuris- tic approaches have been described in the literature, we formalize the underlying geometric algorithm problem: Word Rectangle Adjacency Contact (WRAC). In this model each word is a rectangle with fixed dimensions, and the goal is to rep- resent semantically related word pairs by contacts between their corresponding rectangles. We design and analyze efficient polynomial-time algorithms for vari- ants of the WRAC problem, show that some general variants are NP-hard, and describe several approximation algorithms. Finally, we experimentally demon- strate that our theoretically-sound algorithms outperform the early heuristics. 1 Introduction Word clouds and tag clouds are popular tools for visualizing text. The practical tool, Wordle (21) took word clouds to the next level with high quality design, graphics, style and functionality. Such word cloud visualizations provide an appealing way to sum- marize the content of a webpage, a research paper, or a political speech. Often such visualizations are used to contrast two documents; for example, word cloud visualiza- tions of the speeches given by the candidates in the 2008 US Presidential elections were used to draw sharp contrast between them in the popular media. While some of the more recent word cloud visualization tools aim to incorporate semantics in the layout, none provide any guarantees about the quality of the layout in terms of semantics. We propose a formal model of the problem, via a simple vertex- weighted and edge-weighted graph. The vertices in the graph are the words in the doc- ument, with weights corresponding to their frequency (or normalized frequency). The edges in the graph correspond to semantic relatedness, with weights corresponding to the strength of the relation. Each vertex must be drawn as a rectangle or box with fixed dimensions and with area determined by its weight. The goal is to "realize" as many edges as possible, by contacts between their corresponding rectangles; see Fig. 1.
Use of horseradish peroxidase- and fluorescein-modified cisplatin derivatives for simultaneous labeling of nucleic acids and proteins.

BACKGROUND Microarray platforms will change immunochemical and nucleic acid-based analysis of cell homogenates and body fluids compared with classic analyses. Microarrays use labeled target and immobilized probes, rather than fixed targets and labeled probes. We describe a method for simultaneous labeling of nucleic acids and proteins. METHODS Horseradish peroxidase- and fluorescein-modified cisplatin derivatives were used for labeling of nucleic acids and proteins. These reagents, called the Universal Linkage System (ULS), bind to sulfur- and nitrogen-donor ligands present in amino acids and nucleotides. For automated screening of proteins and nucleic acids on microarrays, it is advantageous to label these biomolecules without pre- or postpurification procedures. The labeling of antibodies and nucleic acids in whole serum was therefore pursued. RESULTS Immunoglobulins in nonpurified serum were labeled efficiently enough to be used for immunochemistry. To investigate whether protein-adapted labeling allowed nucleic acid labeling as well, 1 microg of plasmid DNA was added to 1 microL of serum. DNA and serum proteins were simultaneously labeled, and this labeled DNA could be used as a probe for direct fluorescence in situ hybridization. CONCLUSION ULS provides a direct labeling tool for the (simultaneous) modification of proteins and nucleic acids even in unpurified samples.
Hiding Sequences

The process of discovering relevant patterns holding in a database, was first indicated as a threat to database security by O' Leary in [20]. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent itemsets mining. Following many real-world data and applications demands, in this paper we shift, the problem of knowledge hiding to contexts where both the data arid the extracted knowledge have a sequential structure. We provide problem statement, some theoretical issues including NP-hardness of the problem, a polynomial sanitization algorithm and an experimental evaluation. Finally we discuss possible extensions that will allow to use this work as a basic building block for more complex kinds of patterns and applications.
First Person Action Recognition

Egocentric cameras are wearable cameras mounted on a person’s head or shoulder. With their ability to capture what the wearer is seeing, such cameras are spawning new set of exciting applications in computer vision. Recognising activity of the wearer from an egocentric video is an important but challenging problem. This problem is more challenging than third person activity recognition due to unavailability of wearer’s pose. Unstructured movement of the camera due to natural head motion of the wearer causes sharp changes in the visual field of the egocentric camera making problem even more challenging. This causes many standard third person action recognition techniques to perform poorly on such videos. On the other hand, objects present in the scene and hand gestures of the wearer are the most important cues for first person action recognition. However, such cues are difficult to segment and recognize in an egocentric video. Carefully crafted features based on hands and objects cues for the problem have been shown to be successful for limited targeted datasets. In the first part of our work, we propose a novel representation of the first person actions derived from feature trajectories. The features are simple to compute using standard feature tracking and does not assume segmentation of hand/objects or recognizing object or hand pose unlike in many previous approaches. We train a bag of words classifier with the proposed features and report a significant performance improvement on publicly available datasets. In the second part of the thesis, we propose convolutional neural networks (CNNs) for end to end learning and classification of wearers actions. The proposed network makes use of egocentric cues by capturing hand pose, head motion and saliency map. The proposed network model is compact and therefore can be trained from relatively small number of labeled egocentric videos that are available in egocentric settings. We show that the proposed network can generalize and give state of the art performance on various egocentric action datasets widely different from each other visually as well as dynamically.
The role of behavioural inhibition in preschoolers' emotion understanding

Behavioural inhibition to the unfamiliar is a temperamental style of reacting to novel situations, peers or adults with fear, wariness and avoidance. Childhood behavioural inhibition is a well-established risk factor for anxiety disorders, particularly social phobia. However, there is presently a gap in the literature with regard to possible links between behavioural inhibition and children’s social development, including their social cognition. There is evidence that behavioural inhibition poses a challenge for social interaction, yet research to date has not investigated relations of this temperamental trait with preschoolers’ social understanding (i.e., false-belief and emotion understanding). Ninetysix preschoolers between the ages of 3 1⁄2 and 5 years and their mothers participated in a study examining associations among behavioural inhibition, social understanding and maternal responses to children’s negative emotionality. Mothers completed questionnaires while preschoolers engaged in tasks measuring behavioural inhibition, false-belief understanding, emotion understanding and receptive language skills. It was expected that more inhibited preschoolers would demonstrate difficulties in emotion understanding while behavioural inhibition would be unrelated to false-belief understanding. It was further predicted that maternal responses to negative emotionality would moderate relations between behavioural inhibition and emotion understanding. As expected, behavioural inhibition, measured both observationally and by maternal report, was negatively associated with children’s emotion understanding, but was unrelated to false-belief understanding. Results held after controlling for the effects of age and language skills. Moreover, relations between behavioural inhibition and emotion understanding appeared to be largely attributable to difficulty understanding emotions in situations requiring children to inhibit their own typical emotional reaction when judging the feelings of another. With the exception of an additional predicted negative correlation between emotion understanding and maternal minimization responses, all additional hypotheses, including the moderation model were not supported. Implications of the results, including suggestions for early intervention and future research directions are discussed.
Citation recognition for scientific publications in digital libraries

A method based on part-of-speech tagging (PoS) is used for bibliographic reference structure. This method operates on a roughly structured ASCII file, produced by OCR. Because of the heterogeneity of the reference structure, the method acts in a bottom-up way, without an a priori model, gathering structural elements from basic tags to subfields and fields. Significant tags are first grouped in homogeneous classes according to their categories and then reduced in canonical forms corresponding to record fields: "authors", "title", "conference name", "date", etc. Nonlabeled tokens are integrated in one or another field by either applying PoS correction rules or using a interor intra-field model generated from well-detected records. The designed prototype operates with a great satisfaction on different record layouts and character recognition qualities. Without manual intervention, 96.6% words are correctly attributed, and about 75,9% references are completely segmented from 2,575 references.
Motile detergent-extracted cells of Tetrahymena and Chlamydomonas

Tetrahymena and Chlamydomonas cells treated with high (0.25-0.5%) concentrations of the detergent Nonidet P-40 in appropriate buffers retain the shape of the intact cells but are devoid of any ciliary activity unless supplied with MgATP. ATP causes them to swim actively, with beat parameters and swimming patterns indistinguishable from those of intact cells. Both types of detergent-extracted cells are completely devoid of ciliary membranes. The Tetrahymena preparations also lack all cellular membranes, whereas cellular membranes remain intact in the Chlamydomonas preparations. Experiments demonstrating the effects of ATP, ADP, vanadate, erythro-9-[3-2-(hydroxynonyl)]-adenine, and Ca++ are described to illustrate the use of these detergent-extracted cells in research on ciliary motility.
Detection and segmentation of sweeps in color graphics images

Business graphics are an important class of digital imagery. Such images are computer-generated, and comprise synthetic elements such as solid fills, line art, and color sweeps. Often these images are first printed and then scanned for further electronic reuse. The printing and scanning process destroys the synthetic structure of a graphics image, and furthermore introduces distortions due to halftoning and other forms of printer and scanner noise. Subsequent reproductions usually amplify these distortions thus resulting in rapid degradation of image quality. It would thus be desirable to detect and reconstruct the original synthetic structure from the scanned image. This paper presents an effort in this direction, namely a method to detect color sweeps in scanned images. Once detected, the synthetic signature of the sweep is derived, namely its starting and ending color. This information can be used to optimize subsequent image processing operations such as rendering to an output device, or image compression. This work represents a novel application of known image processing techniques to extract semantic information from graphics images.
Randall Selitto pressure algometry for assessment of bone‐related pain in rats

Deep pain is neglected compared with cutaneous sources. Pressure algometry has been validated in the clinic for assessment of bone‐related pain in humans. In animal models of bone‐related pain, we have validated the Randall Selitto behavioural test for assessment of acute and pathological bone pain and compared the outcome with more traditional pain‐related behaviour measures.