Miracle-dz/UniFAR-FTUs huggingface dataset:
Big-Sensing-Data Curation for the Cloud is Coming: A Promise of Scalable Cloud-Data-Center Mitigation for Next-Generation IoT and Wireless Sensor Networks

Modern sensing devices play a pivotal role in achieving data acquisition, communication, and dissemination for the Internet of Things (IoT). Naturally, IoT applications and intelligent sensing systems supported by sensing devices, such as wireless sensor networks (WSNs), are closely coupled. Modern intelligent sensing systems generate huge volumes of sensing data, well beyond the processing capabilities of common techniques and tools. As a result, collecting, managing, and processing IoT big sensing data within an acceptable time duration is a new challenge for both research and industrial applications. The massive size, extreme complexity, and high speed of big sensing data bring new technical requirements including data collection, data storage, data organization, data analysis, and data publishing in real time when deploying real-world IoT applications. To better facilitate these IoT applications, the convergent research of WSNs, big data, the IoT, and cloud computing is a natural scientific development trend. In this article, we concentrate on big-sensing-data curation and preparation issues with cloud computing under the theme of the IoT. There are three especially critical issues that need to be addressed: scalable big-sensing-data cleaning, scalable big-sensing-data compression, and cloud-based data curation response for IoT device optimization. Viewed from the IoT side, all IoT sensing devices are integrated together in an adaptive solution and upload their data onto the cloud. The automatic responses from both the cloud and intelligent sensors will change the status or behavior of sensing devices and, therefore, the status of the IoT itself.
Screen Printing Process Design of Experiments for Fine Line Printing of Thick Film Ceramic Substrates

Screen printing has been the dominant method of thick film deposition because of its low cost. Many experiments in industry have been done and many models of the printing process have been developed since the 1960’s. With a growing need for denser packaging and a drive for higher pin count, screen printing has been refined to yield high resolution prints. However, fine line printing is still considered by industry to be difficult. In order to yield high resolution prints with high first pass yields and manufacturing throughput, the printing process must be controlled stringently. This paper focuses on investigating the effect of manufacturing process parameters on fine line printing through the use of statistical design of experiments (DOE). The process parameters include print speed, squeegee hardness, squeegee pressure, and snap-off distance. Response variables are mean width and standard deviation of 10 mil, 8 mil, and 5 mil lines in both parallel and perpendicular directions relative to the squeegee travel direction. It is concluded that the squeegee hardness has a statistically significant effect on both directions, while the squeegee speed has an effect only on the parallel direction. The implementation procedures of the experimental design are presented. The analysis of a 2 k factorial design with center points pertaining to the fine line printing experiment is discussed in detail.
Multimodal Assisted Living Environment

—The global aim of this project is to establish a mul-timodal environment for an assisted living smart space. We have designed the multimodal assisted living environment, where multiple people are tracked by omnidirectional video cameras installed in the environment, as well as speech and audio signals are processed by microphone arrays with an automatic system for speech recognition and acoustic event detection. The multiple objects tracking precision of the proposed video monitoring system was 0.78 and 0.73 for single person and three people scenario , respectively. The precision is measured as 62.81% and 72.31%, correspondingly. The proposed speech and audio event recognition system is multilingual one and it is able to recognize a set of voice commands both in English and Russian, as well as some non-speech acoustic events. Recognition accuracies for speech commands and non-speech acoustic events were 96.5% and 93.8%, correspondingly. The multimodal interfaces make possibility for users to access the assisted living information system by speech and gestures, combining several modalities in order to interpret requirements and behavior of the user.
Interruptible tasks: treating memory pressure as interrupts for highly scalable data-parallel programs

Real-world data-parallel programs commonly suffer from great memory pressure, especially when they are executed to process large datasets. Memory problems lead to excessive GC effort and out-of-memory errors, significantly hurting system performance and scalability. This paper proposes a systematic approach that can help data-parallel tasks survive memory pressure, improving their performance and scalability without needing any manual effort to tune system parameters. Our approach advocates interruptible task (ITask), a new type of data-parallel tasks that can be interrupted upon memory pressure---with part or all of their used memory reclaimed---and resumed when the pressure goes away. To support ITasks, we propose a novel programming model and a runtime system, and have instantiated them on two state-of-the-art platforms Hadoop and Hyracks. A thorough evaluation demonstrates the effectiveness of ITask: it has helped real-world Hadoop programs survive 13 out-of-memory problems reported on StackOverflow; a second set of experiments with 5 already well-tuned programs in Hyracks on datasets of different sizes shows that the ITask-based versions are 1.5--3x faster and scale to 3--24x larger datasets than their regular counterparts.
A nonlinear feature selection method based on kernel separability measure for hyperspectral image classification

Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification and other applications. In 2012, an automatic kernel parameter selection (APS) based on kernel-based within- and between-class separability measures were proposed. Moreover, the application for determining the kernel parameters of the full bandwidth RBF (FRBF) kernel was proposed. In this study, the bandwidths of the FRBF kernel were considered as the weights of the features when the feature values are rescaled by computing the z-scores. Experimental results on the Indian Pine Site dataset showed that the SVM based on the proposed feature subset outperforms than the SVMs based on the RBF kernel and FRBF kernel.
Interactive light field painting

Since the seminal SketchPad work of Sutherland [1964], direct interaction with a computer has been compelling: we can directly touch, move, and change what we see. Direct interaction is a major contribution to the success of smartphones and tablets; yet, the world is not flat. While existing technologies can display realistic multi-view stereoscopic 3D content reasonably well [Lueder 2012], interaction within the same 3D space often requires extensive additional hardware. We present a cheap and easy system that uses the same lenslet array for both multi-view autostereoscopic display and 3D light-pen position sensing.
Reflections on the Field: Imagining the Future of the Anthropology of Education if We Take Laura Nader Seriously

The large corpus of scholarship of cultural anthropologist Laura Nader is relevant to the contemporary practice of the anthropology of education. In particular, her work is relevant to contemporary debates about methodology and what constitutes “scientifically based” education research; to the prospect and need for more crossfertilization within the discipline of anthropology; and for the proud assertion of anthropology's distinctive suitability for understanding and responding to many contemporary educational challenges, including how to have anthropologically derived insights more favorably compete in the “marketplace of ideas” against less empirically grounded claims and strategies.
Direct Redistribution and Taxation in Oil-rich Economies : A Proposal

This paper proposes that, to enhance the efficiency of public spending in oil-rich economies, some or all of the oil revenues be transferred to citizens, and then this amount would be taxed to lead to increased citizens‘ scrutiny over public expenditures. The authors develop the case as follows. First, they confirm the wellknown result that public-expenditure efficiency is lower in oil-rich countries compared with other developing countries. Second, they develop a theoretical model to explain why citizen‘s scrutiny over public expenditure can be increased by transferring oil revenues to citizens and then taxing them. Being taxed, citizen‘s information about the level of government revenue available for public goods increases and the citizen wants to ensure that this certain loss of income which was transferred to government budget will be spent on public goods. Third, they confirm empirically that enhanced citizens‘ scrutiny is associated with more efficient government‘s spending decisions and that accountability is stronger in countries that rely more on direct taxation to finance public spending. The authors conclude that, while it may be difficult to implement such a proposal in existing oil producers, there is scope for introducing it in some of Africa‘s new oil producers.
Cerebral malaria: gamma-interferon redux

There are two theories that seek to explain the pathogenesis of cerebral malaria, the mechanical obstruction hypothesis and the immunopathology hypothesis. Evidence consistent with both ideas has accumulated from studies of the human disease and experimental models. Thus, some combination of these concepts seems necessary to explain the very complex pattern of changes seen in cerebral malaria. The interactions between malaria parasites, erythrocytes, the cerebral microvascular endothelium, brain parenchymal cells, platelets and microparticles need to be considered. One factor that seems able to knit together much of this complexity is the cytokine interferon-gamma (IFN-γ). In this review we consider findings from the clinical disease, in vitro models and the murine counterpart of human cerebral malaria in order to evaluate the roles played by IFN-γ in the pathogenesis of this often fatal and debilitating condition.
Algorithms for mining meaningful roles

Role-based access control (RBAC) offers significant advantages over lower-level access control policy representations, such as access control lists (ACLs). However, the effort required for a large organization to migrate from ACLs to RBAC can be a significant obstacle to adoption of RBAC. Role mining algorithms partially automate the construction of an RBAC policy from an ACL policy and possibly other information, such as user attributes. These algorithms can significantly reduce the cost of migration to RBAC. This paper proposes new algorithms for role mining. The algorithms can easily be used to optimize a variety of policy quality metrics, including metrics based on policy size, metrics based on interpretability of the roles with respect to user attribute data, and compound metrics that consider size and interpretability. The algorithms all begin with a phase that constructs a set of candidate roles. We consider two strategies for the second phase: start with an empty policy and repeatedly add candidate roles, or start with the entire set of candidate roles and repeatedly remove roles. In experiments with publicly available access control policies, we find that the elimination approach produces better results, and that, for a policy quality metric that reflects size and interpretability, our elimination algorithm achieves significantly better results than previous work.
Effects of shoes on kinetics and kinematics of the squash forward lunge in male players

Squash is associated with a high incidence of chronic injuries. Currently there is a trend in many sports for players to select minimalist footwear. The aim of the current investigation was to examine the effects of squashspecific, running shoes and minimalist footwear on the kinetics and 3-D kinematics of the lunge movement in squash players. Twelve male squash players performed lunge movements whilst wearing minimalist, running shoe and squash-specific footwear. 3-D kinematics of the lower extremities were measured using an eightcamera motion analysis system alongside kinetic and tibial acceleration information which were obtained using a force platform and an accelerometer. Differences between footwear were examined using one-way repeated measures ANOVA. The results show firstly that loading rate parameters were significantly greater in the minimalist (average = 85.36B.W/s and instantaneous = 179.09B.W/s) footwear in relation to the squashspecific (average = 38.66 B.W/s and instantaneous = 50.73B.W/s) and running footwear (average = 37.62B.W/s and instantaneous = 48.14B.W/s). In addition, tibial acceleration parameters were also significantly greater in the minimalist (peak tibial acceleration = 8.45 g and tibial acceleration slope = 422.28g/s) footwear in relation to the squash-specific (peak tibial acceleration = 4.33 g and tibial acceleration slope = 182.57g/s) and running footwear (peak tibial acceleration = 4.81 g and tibial acceleration slope = 226.72g/s). The significant increase in impact loading in the minimalist footwear therefore suggests this type of shoe may place squash players at an increased risk of developing impact-related chronic injuries. Key words: biomechanics, footwear, squash
Speech Recognition Based on Tree-Structured Clustering and Aspect Model in Multiple Noise Environments

In this paper, we propose speech recognition by using cluster-specific aspect model based on tree-structured clustering in multiple noise environments. Multi-condition hidden Markov model (MC-HMM) is one of the standard methods for speech recognition in noisy environment. While MC-HMM is pretty simple, it is known to be robust against various noises, thus this method is regarded as a “standard” of noise-robust acoustic model. However, it is difficult to train a model with large number of parameters to represent wide variabilities. We use tree-structured clustering method to avoid this problem. After training cluster models, cluster-specific aspect models are trained by using results of tree-structured clustering. Each cluster-specific aspect model can represent latent characteristic of specific noisy environments included in a certain cluster. The method for adaptation is based on the aspect model, which is a “mixtureof-mixture” model. To realize adaptation using extremely small amount of adaptation data (i.e., a few seconds), we first select the model according to the result of binary search of tree-structure and train a small number of mixture models which can be interpreted as models for “subclusters ” of cluster models. The experimental results showed that the adaptation based on the cluster-specific aspect model improved the word accuracy in a heavy noise environment.
Combining unidirectional long short-term memory with convolutional output layer for high-performance speech synthesis

In this paper, we target improving the accuracy of acoustic modelling for statistical parametric speech synthesis (SPSS) and introduce the convolutional neural network (CNN) due to its powerful capacity in locality modelling. A novel model architecture combining unidirectional long short-term memory (LSTM) and a time-domain convolutional output layer (COL) is proposed and employed to acoustic modelling. The two components complement each other and result in a high-performance synthesis system. Specifically, the unidirectional LSTM can learn expressive feature representations from history context and the COL ingeniously absorbs some of these representations within a look-ahead window to advance predictions. This complementary mechanism significantly improve the predictive accuracy and the quality of synthetic speech. In addition, the unique operation mechanism of convolution makes COL a fine parameter trajectory smoother between consecutive frames. Subjective preference tests show that the proposed architecture can synthesize natural sounding speech without dynamic features.
Spectral quality of light modulates emotional brain responses in humans

Light therapy can be an effective treatment for mood disorders, suggesting that light is able to affect mood state in the long term. As a first step to understand this effect, we hypothesized that light might also acutely influence emotion and tested whether short exposures to light modulate emotional brain responses. During functional magnetic resonance imaging, 17 healthy volunteers listened to emotional and neutral vocal stimuli while being exposed to alternating 40-s periods of blue or green ambient light. Blue (relative to green) light increased responses to emotional stimuli in the voice area of the temporal cortex and in the hippocampus. During emotional processing, the functional connectivity between the voice area, the amygdala, and the hypothalamus was selectively enhanced in the context of blue illumination, which shows that responses to emotional stimulation in the hypothalamus and amygdala are influenced by both the decoding of vocal information in the voice area and the spectral quality of ambient light. These results demonstrate the acute influence of light and its spectral quality on emotional brain processing and identify a unique network merging affective and ambient light information.
Lifestyles and discomfort in a sample of young Romanian students

Summary Introduction The 40.3% of the Romanian youth population is at risk of poverty or social exclusion, and, in addition, the abuse of substances increases. It was interesting to evaluate the attitudes shared by pupils as well as their knowledge of these substances with a view to analyzing causes and types of risky behaviour in young people. Methods This is an observational study on the harassment influence in the lifestyle of a sample of Craiova high-school students The Fagerström Test was applied to evaluate the degree of nicotine dependence, and the data processed to assess the Odds Ratio. Results The sample was composed by 1,980 students with an average age of 17, of which 1,727 correctly responded to the questionnaire. The 37.4% of students admit to smoke, and the 67% to drink alcohol. The Fagerström test showed that the 68.2% of respondents are not highly addicted to smoke while the 31.8% was associated with a middle-to-high addiction level. A worrying weekly consumption of beer has been registered in the students. Regarding psychological distress caused by harassment a high Odds Ratio was recorded between the smokers and the drug users. Conclusions More than half of students did not know about the health consequences of smoking, and the same result was recorded about the consumption of alcohol. A special attention should be done to the understanding of harassment problem in the young people. In fact, almost all the respondents declared to have suffered abuse and admits to use drug and cigarettes.
Accelerating Event-Driven Simulation of Spiking Neurons with Multiple Synaptic Time Constants

The simulation of spiking neural networks (SNNs) is known to be a very time-consuming task. This limits the size of SNN that can be simulated in reasonable time or forces users to overly limit the complexity of the neuron models. This is one of the driving forces behind much of the recent research on event-driven simulation strategies. Although event-driven simulation allows precise and efficient simulation of certain spiking neuron models, it is not straightforward to generalize the technique to more complex neuron models, mostly because the firing time of these neuron models is computationally expensive to evaluate. Most solutions proposed in literature concentrate on algorithms that can solve this problem efficiently. However, these solutions do not scale well when more state variables are involved in the neuron model, which is, for example, the case when multiple synaptic time constants for each neuron are used. In this letter, we show that an exact prediction of the firing time is not required in order to guarantee exact simulation results. Several techniques are presented that try to do the least possible amount of work to predict the firing times. We propose an elegant algorithm for the simulation of leaky integrate-and-fire (LIF) neurons with an arbitrary number of (unconstrained) synaptic time constants, which is able to combine these algorithmic techniques efficiently, resulting in very high simulation speed. Moreover, our algorithm is highly independent of the complexity (i.e., number of synaptic time constants) of the underlying neuron model.
Subspace Restricted Boltzmann Machine

The subspace Restricted Boltzmann Machine (subspaceRBM) is a third-order Boltzmann machine where multiplicative interactions are between one visible and two hidden units. There are two kinds of hidden units, namely, gate units and subspace units. The subspace units reect variations of a pattern in data and the gate unit is responsible for activating the subspace units. Additionally, the gate unit can be seen as a pooling feature. We evaluate the behavior of subspaceRBM through experiments with MNIST digit recognition task, measuring reconstruction error and classication error.
Scavenger receptor B1 (SR-B1) profoundly excludes high density lipoprotein (HDL) apolipoprotein AII as it nibbles HDL-cholesteryl ester

Reverse cholesterol transport (transfer of macrophage-cholesterol in the subendothelial space of the arterial wall to the liver) is terminated by selective high density lipoprotein (HDL)-cholesteryl ester (CE) uptake, mediated by scavenger receptor class B, type 1 (SR-B1). We tested the validity of two models for this process: “gobbling,” i.e. one-step transfer of all HDL-CE to the cell and “nibbling,” multiple successive cycles of SR-B1-HDL association during which a few CEs transfer to the cell. Concurrently, we compared cellular uptake of apoAI with that of apoAII, which is more lipophilic than apoAI, using HDL-[3H]CE labeled with [125I]apoAI or [125I]apoAII. The studies were conducted in CHO-K1 and CHO-ldlA7 cells (LDLR−/−) with (CHO-SR-B1) and without SR-B1 overexpression and in human Huh7 hepatocytes. Relative to CE, both apoAI and apoAII were excluded from uptake by all cells. However, apoAII was more highly excluded from uptake (2–4×) than apoAI. To distinguish gobbling versus nibbling mechanisms, media from incubations of HDL with CHO-SR-B1 cells were analyzed by non-denaturing PAGE, size-exclusion chromatography, and the distribution of apoAI, apoAII, cholesterol, and phospholipid among HDL species as a function of incubation time. HDL size gradually decreased, i.e. nibbling, with the concurrent release of lipid-free apoAI; apoAII was retained in an HDL remnant. Our data support an SR-B1 nibbling mechanism that is similar to that of streptococcal serum opacity factor, which also selectively removes CE and releases apoAI, leaving an apoAII-rich remnant.