Miracle-dz/UniFAR-FTUs huggingface dataset:
An online forum as a user diary for remote workplace evaluation of a work-integrated learning system

This paper presents and discusses the use of an online diary for the remote evaluation at the workplace of a new knowledge management tool that supports self-directed learning at work, the second APOSDLE prototype. The workplace evaluation was carried out collaboratively in four different organizations, across different European countries. The online diary was built with the open source discussion forum software phpBB. Used in combination with other research methods, the diary allowed gathering data on the system design and performance as well as the user experience. Its flexibility met participants' preferences and needs. With its use, the diary became the communication tool between users, researchers and developers, giving voice to the users in the evaluation and redesign process.
Er mere meditation relateret til større behandlingseffekter? – Empirisk baserede anbefalinger til en mere kontekstuel meditationsforskning og interventionspraksis

Meditation is increasingly applied in health promotion and clinical interventions although the influence of meditation during multimodal interventions is unclear. Even among meditation-based interventions (MBI), theories and recommendations concerning meditation vary substantially. Our primary purpose is to discuss from an empirical base if MBI-participants’ amount of meditation practice is a substantial factor for the treatment effects. We investigate this especially by reviewing MBI-research on dose-effect relationships between meditation and outcome changes, and neuroimaging studies of meditators and MBI-participants. The clinical evidence shows that participants’ amount of meditation practice is not consistently related to treatment effects. Neuroscientific studies support this notion, since previous meditation practice has not been consistently or specifically related to structural or functional neural measures for MBI-participants or experienced meditators. The inconsistent dose-effect findings, however, do not lead us to discard meditation as a potentially important aspect of psychosocial interventions. Previous research is methodologically criticizable due to a lack of active control groups, studies investigating the importance of meditation as a function of participant characteristics, and qualitative studies of the experienced significance or quality of the meditation practices. In addition, meta-syntheses of qualitative studies of processes of change during MBI do conclude that meditation is experienced as effective, but in complex, systemic interactions with other treatment aspects. More complex models concerning the significance of meditation practices for different participants and clinical areas are thus needed. We present a contextual, functional and pragmatic approach to the application of meditation and provide recommendations for future research and practice.
Fusion: Design tradeoffs in coherent cache hierarchies for accelerators

Chip designers have shown increasing interest in integrating specialized fixed-function coprocessors into multicore designs to improve energy efficiency. Recent work in academia [11, 37] and industry [16] has sought to enable more fine-grain offloading at the granularity of functions and loops. The sequential program now needs to migrate across the chip utilizing the appropriate accelerator for each program region. As the execution migrates, it has become increasingly challenging to retain the temporal and spatial locality of the original program as well as manage the data sharing. We show that with the increasing energy cost of wires and caches relative to compute operations, it is imperative to optimize data movement to retain the energy benefits of accelerators. We develop FUSION, a lightweight coherent cache hierarchy for accelerators and study the tradeoffs compared to a scratchpad based architecture. We find that coherency, both between the accelerators and with the CPU, can help minimize data movement and save energy. FUSION leverages temporal coherence [32] to optimize data movement within the accelerator tile. The accelerator tile includes small per-accelerator L0 caches to minimize hit energy and a per-tile shared cache to improve localized-sharing between accelerators and minimize data exchanges with the host LLC. We find that overall FUSION improves performance by 4.3× compared to an oracle DMA that pushes data into the scratchpad. In workloads with inter-accelerator sharing we save up to 10× the dynamic energy of the cache hierarchy by minimizing the host-accelerator data ping-ponging.
Insights into the mechanisms of eukaryotic translation gained with ribosome profiling

The development of Ribosome Profiling (RiboSeq) has revolutionized functional genomics. RiboSeq is based on capturing and sequencing of the mRNA fragments enclosed within the translating ribosome and it thereby provides a ‘snapshot’ of ribosome positions at the transcriptome wide level. Although the method is predominantly used for analysis of differential gene expression and discovery of novel translated ORFs, the RiboSeq data can also be a rich source of information about molecular mechanisms of polypeptide synthesis and translational control. This review will focus on how recent findings made with RiboSeq have revealed important details of the molecular mechanisms of translation in eukaryotes. These include mRNA translation sensitivity to drugs affecting translation initiation and elongation, the roles of upstream ORFs in response to stress, the dynamics of elongation and termination as well as details of intrinsic ribosome behavior on the mRNA after translation termination. As the RiboSeq method is still at a relatively early stage we will also discuss the implications of RiboSeq artifacts on data interpretation.
Uses and management of poultry litter

The poultry industry is one of the largest and fastest growing agro-based industries in the world. This can be attributed to an increasing demand for poultry meat and egg products. However, a major problem facing the poultry industry is the large-scale accumulation of wastes including manure and litter which may pose disposal and pollution problems unless environmentally and economically sustainable management technologies are evolved. Most of the litter produced by the poultry industry is currently applied to agricultural land as a source of nutrients and soil amendment. However environmental pollution, resulting from nutrient and contaminant leaching can occur when poultry litter is applied under soil and climatic conditions that do not favour agronomic utilisation of the manure-borne nutrients. This review examines the composition of poultry litter in relation to nutrient content and environmental contaminants, its value as a nutrient source, soil amendment, animal feed and fuel source, and cost-effective innovative technologies for improving its value. Poultry litter provides a major source of nitrogen, phosphorus and trace elements for crop production and is effective in improving physical and biological fertility, indicating that land application remains as the main option for the utilisation of this valuable resource. The alternative use of poultry litter; as an animal feed and fuel source, is limited by contaminants, and high moisture content, respectively. The review proposes best management practices to mitigate environmental consequences associated with air and water quality parameters that are impacted by land application in order to maintain the continued productivity, profitability, and sustainability of the poultry industry.
Hybrid body representation for integrated pose recognition, localization and segmentation

We propose a hybrid body representation that represents each typical pose by both template-like view information and part-based structural information. Specifically, each body part as well as the whole body are represented by an off-line learned shape model where both region-based and edge-based priors are combined in a coupled shape representation. Part-based spatial priors are represented by a ldquostarrdquo graphical model. This hybrid body representation can synergistically integrate pose recognition, localization and segmentation into one computational flow. Moreover, as an important step for feature extraction and model inference, segmentation is involved in the low-level, mid-level and high-level vision stages, where top-down prior knowledge and bottom-up data processing is well integrated via the proposed hybrid body representation.
Removing the remaining ridges in fingerprint segmentation

Fingerprint segmentation is an important step in fingerprint recognition and is usually aimed to identify non-ridge regions and unrecoverable low quality ridge regions and exclude them as background so as to reduce the time expenditure of image processing and avoid detecting false features. In high and in low quality ridge regions, often are some remaining ridges which are the afterimages of the previously scanned finger and are expected to be excluded from the foreground. However, existing segmentation methods generally do not take the case into consideration, and often, the remaining ridge regions are falsely classified as foreground by segmentation algorithm with spurious features produced erroneously including unrecoverable regions as foreground. This paper proposes two steps for fingerprint segmentation aimed at removing the remaining ridge region from the foreground. The non-ridge regions and unrecoverable low quality ridge regions are removed as background in the first step, and then the foreground produced by the first step is further analyzed for possible remove of the remaining ridge region. The proposed method proved effective in avoiding detecting false ridges and in improving minutiae detection.
wProjects: Data-centric web development for female nonprogrammers

We evaluated the impacts of hands-on web development workshops organized for female undergraduates who had no programming background. In five groups ranging from 2-8 participants, the women used a special-purpose tool (wProjects) to edit database tables, build queries, and embed the queries in web pages. We gathered background information on a pre-test and assessed knowledge and perceptions in a post-test. In general, the students were successful, enjoyed the projects, and their attitudes about computer-related careers were enhanced. We discuss the potential for workshops such as these in light of the general problem of decreasing females in computer-related professions.
New Clinical and Research Perspectives on the Sexual Pain Disorders

A woman became unable to achieve penetration after repeated painful intercourse. This case illustrates the shortcomings of the present nomenclature and diagnostic criteria for the sexual pain disorders, dyspareunia and vaginismus. The Diagnostic and Statistical Manual of Mental Disorders, 4th edition, considers the sexual pain disorders as sexual dysfunctions, completely ignoring the pain component and leading to internally inconsistent descriptions of these conditions. The role of pain in dyspareunia and vaginismus and the implications of using a pain syndrome framework for diagnosis and research are discussed. Most important, pain syndrome framework focuses diagnosis on the presence or absence of pain and the history of the development of the pain. This approach does not deny a role for psychosocial factors, but rather considers them as critical components of the pain syndromes. From the pain syndrome perspective, preliminary data indicate that vulvar vestibulitis syndrome may be associated with nonpainful sensory abnormalities, and that women with vaginismus do not constitute a homogeneous group.
A Velocity-Based Rao-Blackwellized Particle Filter Approach to Monocular vSLAM

This paper presents a modified Rao-Blackwellized Particle Filter (RBPF) approach for the bearing-only monocular SLAM problem. While FastSLAM 2.0 is known to be one of the most computationally efficient SLAM approaches; it is not applicable to certain formulations of the SLAM problem in which some of the states are not explicitly expressed in the measurement equation. This constraint impacts the versatility of the FastSLAM 2.0 in dealing with partially ob-servable systems, especially in dynamic environments where inclusion of higher order but unobservable states such as velocity and acceleration in the filtering process is highly desirable. In this paper, the formulation of an enhanced RBPF-based SLAM with proper sampling and importance weights calculation for resampling distributions is presented. As an example, the new formulation uses the higher order states of the pose of a monocular camera to carry out SLAM for a mobile robot. The results of the experiments on the robot verify the improved performance of the higher order RBPF under low parallax angles conditions.
An Effective Epipolar Geometry Assisted Motion Estimation Technique for Multi-View Image and Video Coding

To efficiently encode data-intensive multi-view imaging content, conventional hybrid predictive coding methodologies choose to address the compression by exploiting temporal and inter-viewpoint redundancy. However, their key yet time-consuming component, motion estimation (ME), is usually not efficient in inter-viewpoint prediction because inter-viewpoint motion is quite different from temporal motion. In essence, inter-viewpoint correlation is subject to epipolar geometry, which provides constraints for multi-view image sequences. A fast inter-viewpoint ME technique is hence proposed in this paper to accelerate the encoding by employing epipolar geometry. Theoretical analysis and experimental results prove that the proposed ME algorithm can greatly reduce search region and effectively track large and irregular motion that is typical for convergent multi-view camera setups. As a result, compared with fast full search at large search size adopted in H.264, our proposed ME algorithm can obtain a similar coding efficiency while achieving a speedup ratio of 2.9.
Image denoising using bivariate wavelet packet zerotrees and neighbor dependency

This paper presents image denoising methods performed within wavelet domain scheme by using wavelet packet zerotrees, and at the same time incorporating neighbor and inter-subband dependencies through NeighShrink and BiShrink [1] shrinkage functions, respectively. In particular, we call our proposed method as adaptive wavelet bivariate maximum a posteriori estimator (MAP) with NeighShrink threshold function namely, MAP_NBShrink_WP. In our second method, adaptive wavelet bivariate minimum mean square error estimator (MMSE) with NeighShrink threshold function, namely MMSE_NBShrink_WP, is proposed for image-denoising. Experimental results show that our proposed methods, MAP_NBShrink_WP and MMSE_NBShrink_WP, have better PSNR than BiShrink [1], NeighShrink [6] and BayeShrink [3] in oscillatory images (such as Barbara).
Ghost removal in HDRI acquisition

This paper presents a fast, robust and completely automatic technique for creating high dynamic range images of non-static scenes. The technique is based on probability maps. These maps are calculated with comparison function from sequences of hand-held photographs. In practice, several basic problems occur when taking an image sequence. First, the camera is moving which causes images to misalign. This results in a blurry HDR image. Secondly, objects are in movement causing ghost artifacts. Finally, the dynamic range of a single image is decreased by sensor noise. Our solution is a GPU based application for compositing HDR images that can handle all aforementioned problems.
Interaction of Acidosis and Increased Extracellular Potassium on Action Potential Characteristics and Conduction in Guinea Pig Ventricular Muscle

We studied the individual and combined effects of extracellular acidosis and increases in extracellular potassium on action potential characteristics and conduction in order to gain a better understanding of the effects of acute ischemia. At each level of potassium between 2.7 and 17 ITIM, acidosis induced by increasing Pco2 (respiratory acidosis) and by decreasing HCO3- (metabolic acidosis) decreased resting membrane potential, the maximum rate of rise of the action potential upstroke (Vmax), and slowed conduction. Metabolic acidosis consistently and significantly lengthened the steady state action potential duration whereas respiratory acidosis did not. Respiratory acidosis caused changes in resting membrane potential, Vmax, and conduction velocity; which occurred more rapidly and were of greater magnitude than the changes induced by metabolic acidosis. The changes in Vmax induced by both types of acidosis were due to a change in the resting membrane potential Vmax relationship as well as to the changes in resting membrane potential. The conduction slowing induced by acidosis was greater when potassium was 9 and 13 HIM than when potassium was 5.4 mm. Our results suggest that acidosis causes important changes in the electrophysiological properties of ventricular fibers and that many of the known electrophysiological effects of acute ischemia can be mimicked by the combined effects of extracellular acidosis and an increase in extracellular potassium.
Automatic Programming Methodology for Program Reuse

This paper investigates the representation of program for program reuse. A new gene structure is proposed: head + body + tail, which allows the program with necessary complexity and putting some learning mechanism into the search process. A new homeotic gene structure is proposed, it not only can call for subroutines easily, but also can automatically perform programming. The concept of different homeotic gene, a multi-cellular structure is proposed. It can be used to describe the complex multi-level programs and to implement the complex subroutine calls. An estimation of distribution mutation operator for guiding search is proposed. It fuses statistic learning mechanism into the search process to accelerate the convergent process and improve the quality of solutions. Numerical experiments show that the new methods of automatic programming is very practical
Automatic image scaling for place recognition in changing environments

Robustness to variations in environmental conditions and camera viewpoint is essential for long-term place recognition, navigation and SLAM. Existing systems typically solve either of these problems, but invariance to both remains a challenge. This paper presents a training-free approach to lateral viewpoint- and condition-invariant, vision-based place recognition. Our successive frame patch-tracking technique infers average scene depth along traverses and automatically rescales views of the same place at different depths to increase their similarity. We combine our system with the condition-invariant SMART algorithm and demonstrate place recognition between day and night, across entire 4-lane-plus-median-strip roads, where current algorithms fail.
Efficient Searchable Symmetric Encryption for Storing Multiple Source Data on Cloud

Cloud computing has greatly facilitated large-scale data outsourcing due to its cost efficiency, scalability and many other advantages. Subsequent privacy risks force data owners to encrypt sensitive data, hence making the outsourced data no longer searchable. Searchable Symmetric Encryption (SSE) is an advanced cryptographic primitive addressing the above issue, which maintains efficient keyword search over encrypted data without disclosing much information to the storage provider. Existing SSE schemes implicitly assume that original user data is centralized, so that a searchable index can be built at once. Nevertheless, especially in cloud computing applications, user-side data centralization is not reasonable, e.g. an enterprise distributes its data in several data centers. In this paper, we propose the notion of Multi-Data-Source SSE (MDS-SSE), which allows each data source to build a local index individually and enables the storage provider to merge all local indexes into a global index afterwards. We propose a novel MDS-SSE scheme, in which an adversary only learns the number of data sources, the number of entire data files, the access pattern and the search pattern, but not any other distribution information such as how data files or search results are distributed over data sources. We offer rigorous security proof of our scheme, and report experimental results to demonstrate the efficiency of our scheme.
Four teachers' beliefs and strategies in teaching gifted students: A multiple case study

This multiple case study was conducted to explore how four regular classroom teachers, who have a reputation for implementing differentiated practices to meet the needs of gifted students, describe their educational experiences with gifted students in their regular classrooms at an elementary school in the Midwestern USA. The four teachers' beliefs and strategies for instructing gifted students and differentiating instruction, their willingness and readiness to embrace change, collaboration, and their advanced training and knowledge were discussed under the themes of the study. By illustrating the non-linear, complex, context-specific reality of the four teachers' teaching experience, this small case study tried to offer insight into the actual classroom and strategies in developing schema for future practice for our gifted minds. The findings of this study contribute to our understanding of the practices that teachers use to accommodate the needs of gifted students in regular classrooms
Multi-queue merging scheme and its application in Arabic script segmentation

It is an important topic for segmentation on how to merge the over-segmented subimages of characters into integral characters. The conventional character segmentation has been mostly "one dimensional" but this work attacks two-dimensionally complex character layout. For that, a multi-queue model is proposed. First, a positional relationship graph (PRG) is produced among graphemes, based on the positional constraints. With the PRG, several parallel queues of graphemes are generated. The merging problem is inherently a process of walking in segmentation space, which is generated from the multi-queue structure. With some defined metric, the optimization can be conducted to get the appropriate merging path. We test our model in Arabic script, and experiment results show the potential of our model
Localization in Wireless Sensor Networks Based on RSS Rank Vectors

Localization has been a fundermental and crucial issue for wireless sensor networks (WSNs). In this paper, we propose a distributed range-free localization scheme based on RSS rank vectors (RRV). In RRV, each normal node is first localized in an estimative rectangle based on the connectivity constraints. Then the estimative rectangle of the normal node is refined with the RSS rank vector. At last, the centroid of the estimated residence area is calculated as the location of the normal node. The performance of the proposed methods is validated through simulations. Results confirm that RRV outperforms competing localization approaches.
Activation light pattern helps detection

In this paper a CNN frameless computing algorithm is considered for feature detection via lighting activation. The uphill region of a convex bump is measured with an infrared active sensor array, when different deficiencies are present. In the case of global lighting (when all of the light sources of the array are shining), due to the applied deficiency (eq. roughness, ditch, or well) the system is unable to settle down to one, common output pattern; however, in the case of an up-moving periodic lighting wave, the system mostly converges to and remains at a specific output pattern. This pattern uniquely identifies the global uphill trend of the observed terrain.