Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile Real-Time Stress Detection

Prolonged exposure to stress can cause serious mental and physical illnesses. Therefore, it is important that people are aware of stressful situations, so that they can take necessary actions to cope with them. We introduce a mobile system that is able to detect stress in individuals in real-time based on electrocardiogram and electrodermal activity. The system is built around an Android smartphone and uses wearable, unobtrusive sensors, which include a smart watch and a heart rate belt that gather skin conductance and heart rate data, respectively. When a stressful situation is detected, the system alerts the user and records an audio clip relevant to the stressful event. Both physiological and audio data are stored in the cloud, enabling the user to review past stress-related data offline. Small scale user tests show 82.9% 10-fold and 82.2% between-subject stress detection accuracy. Furthermore, algorithms for processing the skin conductance and heart rate data, as well as an automated method for labeling the collected ‘stress’ and ‘non-stress’ experimental data are developed.
Energy-based fair queuing scheduling implementation for battery-limited mobile systems

Energy-based fair queuing (EFQ) is an implementation of the classical fair queuing scheduling algorithm in the energy domain. It is an energy-centric scheduling algorithm that manages energy as the first-class resource that is globally shared by different devices in the system. Recent high-level simulation and Linux-based implementation and experiment have proven that EFQ can achieve proportional power sharing by accounting the energy consumption on both CPU and I/O operations and scheduling tasks based on their normalized energy consumptions. It has also been demonstrated that EFQ can achieve effective and flexible time-constraint compliance. In this paper, the core Linux implementation of EFQ is introduced in detail, and the experimental platform as well as the benchmarks are presented for the experiments. Experimental results on EFQ scheduling show that it is more effective and flexible than the default Linux scheduler in optimizing the user experience of battery-limited systems1.
Extensionality 5 Normal Forms

14 that two variables denote distinct objects. Carpenter 1992] provides an analysis of inequations for the individual component of the current logic. This approach can be naturally extended to the description language containing sets. (1991). Living with classic: When and how to use a kl-one-like language. 13 instance, if we take to be our binary operator, the term t 1 t n , where the t i are terms simply translates into 9 1 9 n , where the i play the role of terms. In particular, the i can be variables, and thus denote arbitrary sets. Uniication, of course, translates to conjunction in our language. Normal form results for a theory with unions are much more complex than those for the theory without unions, and we leave them for another paper. But note that unlike the case in universal uniication theory Siekmann 1989], we are not after the set of most general satissers of our descriptions in the same way as Rounds and Kasper 1990]; the results of Livesay and Siekmann 1976] show that we are bound to fail at this task. Note that intersections and set diierence can be handled in exactly the same manner. Types We can straightforwardly extend Carpenter's 1991, 1992] type theory to the present situation. Carpenter, following the practice of hpsg, replaces the atomic individual descriptions with types. These types are assumed to be arranged in a bounded complete partial order (bcpo), so that uniication of consistent types is uniquely deened (though this condition can be relaxed at the expense of additional non-determinism Carpenter 1992]). Type declarations specify for each type which features are appropriate for it and what their appropriate values are. To adopt this arrangement to the current setting, we need to allow types to be deened for sets. The most straightforward way to do this is to assume that there is a hierarchy of set types, each of which provides a restriction on the type of its elements. With these assumptions, an eecient type inference algorithm can be provided following Carpenter 1992, 1993]. ] discusses the issue of extensionality, wherein two structures are assumed to be identical if they are of a type declared to be extensional and are structurally similar according to a notion of bisimulation. This allows the interpretation of Prolog II terms via innnite unfoldings Colmerauer 1984] to be accomodated within the present setting. As things stand, it's possible …
DRIVE: Dynamic Runtime Integrity Verification and Evaluation

Classic security techniques use patterns (e.g., virus scanner) for detecting malicious software, compiler features (e.g., canaries, tainting) or hardware memory protection features (e.g., DEP) for protecting software. An alternative approach is the verification of software based on the comparison between the binary code loaded before runtime and the actual memory image during runtime. The expected memory image is predictable based on the ELF-file, the loading mechanism, and its allocated memory addresses. Using binary files as references for verifying the memory during execution allows for the definition of white-lists based on the actual software used. This enables a novel way of detecting sophisticated attacks to executed code, which is not considered by current approaches. This paper presents the background, design, implementation, and verification of a non-intrusive runtime memory verification concept, which is based on the comparison of binary executables and the actual memory image.
CLASSIFYING DEFECTS IN PALLET STRINGERS BY ULTRASONIC SCANNING

Detecting and classifying defects are required to grade and sort pallet parts. Use of quality parts can extend the life cycle of pallets and can reduce long-term cost. An investigation has been carried out to detect and classify defects in yellow-poplar ( Liriodendron tulipifera , L.) and red oak ( Quercus rubra , L.) stringers using ultrasonic scanning. Data were collected for sound and unsound knots, bark pockets, decay, holes, and wane using rolling transducers in a pitch-catch arrangement. Data from eight ultrasonic variables—energy, pulse length, time of flight (TOF)-amplitude, TOF-energy, TOF-centroid, energy value, energy pulse value, and peak frequency—were used to classify defects. Three different types of classifiers were used to categorize defects—a multi-layer perceptron network (MLP), a probabilistic neural network (PNN), and a k-nearest neighbor (KNN) classifier. Mean values for the energy variables demonstrated statistically significant differences between clear wood and defects and among defect types. Mean values for the TOF variables did not differ significantly between clear wood and knots. All three types of classifiers were able to distinguish defected from clear wood in oak with accuracies above 95%; accuracies for yellow-poplar were somewhat lower for the MLP and PNN classifiers. Among the defect classes, decay exhibited the highest recognition rate for both yellow-poplar and oak. Wane and holes in oak were readily confused owing to their common loss of transducer contact. Overall accuracy at the data-point level varied from 69-78%. Simple post-processing operations are expected to improve that substantially. Based on accuracy performance alone, the MLP and KNN appear equally preferable for this task.
Optimum Power Allocation and Bit Loading for Systems with Suboptimum Receivers

Waterfllling and mercury/waterfilling power allocation policies maximize the mutual information of independent parallel Gaussian-noise channels under an average power constraint for Gaussian signals and for practical discrete constellations respectively. In this paper, the same criterion is considered for joint power allocation and bit loading in more general scenarios where suboptimum receivers introduce a mutual information loss. The proposed approach provides the optimum solution without resorting to greedy algorithms and has nearly the same complexity as the original mercury/waterfilling.
Validation of In utero Tractography of Human Fetal Commissural and Internal Capsule Fibers with Histological Structure Tensor Analysis

Diffusion tensor imaging (DTI) and tractography offer the unique possibility to visualize the developing white matter macroanatomy of the human fetal brain in vivo and in utero and are currently under investigation for their potential use in the diagnosis of developmental pathologies of the human central nervous system. However, in order to establish in utero DTI as a clinical imaging tool, an independent comparison between macroscopic imaging and microscopic histology data in the same subject is needed. The present study aimed to cross-validate normal as well as abnormal in utero tractography results of commissural and internal capsule fibers in human fetal brains using postmortem histological structure tensor (ST) analysis. In utero tractography findings from two structurally unremarkable and five abnormal fetal brains were compared to the results of postmortem ST analysis applied to digitalized whole hemisphere sections of the same subjects. An approach to perform ST-based deterministic tractography in histological sections was implemented to overcome limitations in correlating in utero tractography to postmortem histology data. ST analysis and histology-based tractography of fetal brain sections enabled the direct assessment of the anisotropic organization and main fiber orientation of fetal telencephalic layers on a micro- and macroscopic scale, and validated in utero tractography results of corpus callosum and internal capsule fiber tracts. Cross-validation of abnormal in utero tractography results could be achieved in four subjects with agenesis of the corpus callosum (ACC) and in two cases with malformations of internal capsule fibers. In addition, potential limitations of current DTI-based in utero tractography could be demonstrated in several brain regions. Combining the three-dimensional nature of DTI-based in utero tractography with the microscopic resolution provided by histological ST analysis may ultimately facilitate a more complete morphologic characterization of axon guidance disorders at prenatal stages of human brain development.
Self-monitored adaptive cache warm-up for microprocessor simulation

Simulation is the most important tool for computer architects to evaluate the performance of new computer designs. However, detailed simulation is extremely time consuming. Sampling is one of the techniques that effectively reduce simulation time. In order to achieve accurate sampling results, microarchitectural structure must be adequately warmed up before each measurement. In this paper, a new technique for warming up microprocessor caches is proposed. The simulator monitors the warm-up process of the caches and decides when the caches are warmed up based on simple heuristics. In our experiments the self-monitored adaptive (SMA) warm-up technique on average exhibits only 0.2% warm-up error in CPI. SMA achieves smaller warm-up error with only 1/2-1/3 of the warm-up length of previous methods. In addition, it is adaptive to the cache configuration simulated. For simulating small caches, the SMA technique can reduce the warm-up overhead by an order of magnitude compared to previous techniques. Finally, SMA gives the user some indicator of warm-up error at the end of the cycle-accurate simulation that helps the user to gauge the accuracy of the warm-up.
Multiembedding of Metric Spaces

Metric embedding has become a common technique in the design of algorithms. Its applicability is often dependent on how large the embedding's distortion is. For example, embedding finite metric space into trees may require linear distortion as a function of the size of the metric. Using probabilistic metric embeddings, the bound on the distortion reduces to logarithmic in the size of the metric. We make a step in the direction of bypassing the lower bound on the distortion in terms of the size of the metric. We define "multiembeddings" of metric spaces, in which a point is mapped onto a set of points, while keeping the target metric of polynomial size and preserving the distortion of paths. The distortion obtained with such multiembeddings into ultrametrics is at most $O(\log \Delta\log\log \Delta)$, where $\Delta$ is the aspect ratio of the metric. In particular, for expander graphs, we are able to obtain constant distortion embeddings into trees, in contrast with the $\Omega(\log n)$ lower bound for all previous notions of embeddings. We demonstrate the algorithmic application of the new embeddings for two optimization problems: group Steiner tree and metrical task systems.
Ensemble based active annotation for named entity recognition

Active Learning is an important prospect of machine learning for information extraction to deal with the problems of high cost of collecting labeled examples. It makes more efficient use of the learner's time by asking them to label only instances that are most useful for the trainer. We propose a novel method for solving this problem and show that it favorably results in the increased performance. Our proposed framework is based on an ensemble approach, where Support Vector Machine and Conditional Random Field are used as the base learners. The intuition is that both learning approaches are somewhat orthogonal in their advantages, so a combination of them can yield superior results. The proposed approach is applied for solving the problem of named entity recognition (NER) in two Indian language, namely Hindi and Bengali. Results show that the proposed technique indeed improves the performance of the system.
Low-impact air-to-ground free-space optical communication system design and first results

An air-to-ground free-space optical communication system has been designed and partially developed. The design covers both the communications between the airborne and the ground station, and the acquisition, tracking and pointing. A strong effort has been made in order to achieve the minimum payload power, size and weight, for which a MEMS modulating retroreflector has been chosen. In the ground station, a new technique for fine pointing, based on a liquid crystal device, is proposed and will be demonstrated, as well as other improvements with the aim of optimizing the ground station performance.
Sevoflurane affects evoked electromyography monitoring in cerebral palsy

Abstract Background To explore the effect of sevoflurane inhalation anesthesia on evoked electromyography monitoring of spinal nerve root in children associated with cerebral palsy. Methodology Children with cerebral palsy (n=40) were selected and further divided into 1MAC (minimum alveolar concentration) sevoflurane group and 2MAC sevoflurane group. Following the induction of anesthesia, Nicolet Endeavor-CR16 channel electrophysiological monitor was used to implement three times of successive electrical stimulation with interval of 5 sec at 3.50 mA. Results Our results suggested a statistical significance of amplitude retention ratio and latency in the sevoflurane inhalation time (P<0.01), with an interaction effect between the sevoflurane inhalation time and concentration for amplitude retention ratio (P<0.01), while there is no interaction effect between the sevoflurane inhalation time and concentration for latency (P>0.05). Compared to 1MAC sevoflurane group, the amplitude retention ratio of 2MAC sevoflurane group decreased remarkably (P<0.01) and the latency of 2MAC sevoflurane group extended at T3 and T4 (P<0.05 or P<0.01). Conclusions In evoked electromyography monitoring of spinal nerve root in children with cerebral palsy, with the increasing of concentration and duration of sevoflurane inhalation, evoked electromyogram retention ratio reduces gradually, latency extends and the retention ratio has more changes than the latency.
An intrusion detection method based on decision tree

How to find the intrusion behaviors is a problem that troubled the intrusion detection field for years. Until now, there is not a good method to solve it, epically in a realistic context. Most methods are effective on small data sets, but when used to the massive data of IDS, the effectiveness seems to be unsatisfactory. In this paper, a new method based on decision tree is discussed to solve the problem of low detection rate of massive data.
Specific Glial Populations Regulate Hippocampal Morphogenesis

The hippocampus plays an integral role in spatial navigation, learning and memory, and is a major site for adult neurogenesis. Critical to these functions is the proper organization of the hippocampus during development. Radial glia are known to regulate hippocampal formation, but their precise function in this process is yet to be defined. We find that in Nuclear Factor I b (Nfib)-deficient mice, a subpopulation of glia from the ammonic neuroepithelium of the hippocampus fail to develop. This results in severe morphological defects, including a failure of the hippocampal fissure, and subsequently the dentate gyrus, to form. As in wild-type mice, immature nestin-positive glia, which encompass all types of radial glia, populate the hippocampus in Nfib-deficient mice at embryonic day 15. However, these fail to mature into GLAST- and GFAP-positive glia, and the supragranular glial bundle is absent. In contrast, the fimbrial glial bundle forms, but alone is insufficient for proper hippocampal morphogenesis. Dentate granule neurons are present in the mutant hippocampus but their migration is aberrant, likely resulting from the lack of the complete radial glial scaffold usually provided by both glial bundles. These data demonstrate a role for Nfib in hippocampal fissure and dentate gyrus formation, and that distinct glial bundles are critical for correct hippocampal morphogenesis.
Keyword-based concept search on consumer photos by web-based kernel function

In light of the strong demands for semantic search over large-scale consumer photos, which generally lack reliable user-provided annotations, we investigate the feasibility and challenges entailed by the new paradigm, concept search - retrieving visual objects by large-scale automatic concept detectors with keywords. We investigate the problem in three folds: (1) the effective concept mapping and selection methods over large-scale concept ontology; (2) the quality and feasibility of the pre-trained concept detectors applying on cross-domain consumer data (i.e., Flickr photos); (3) the search quality by fusing automatic concepts and user-annotated data (tags). Through experiments over large-scale benchmarks, TRECVID and Flickr550, we confirm the effectiveness of concept search in the proposed framework, where the semantic mapping by web-based kernel function over Google snippets significantly outperforms conventional WordNet-like methods both in accuracy and efficiency.
Distance Oracles for Spatial Networks

The popularity of location-based services and the need to do real-time processing on them has led to an interest in performing queries on transportation networks, such as finding shortest paths and finding nearest neighbors. The challenge is that these operations involve the computation of distance along a spatial network rather than "as the crow flies. " In many applications an estimate of the distance is sufficient, which can be achieved by use of an oracle. An approximate distance oracle is proposed for spatial networks that exploits the coherence between the spatial position of vertices and the network distance between them. Using this observation, a distance oracle is introduced that is able to obtain the epsilon-approximate network distance between two vertices of the spatial network. The network distance between every pair of vertices in the spatial network is efficiently represented by adapting the well-separated pair technique to spatial networks. Initially, use is made of an epsilon-approximate distance oracle of size O( n / epsilon^d ) that is capable of retrieving the approximate network distance in O(logn) time using a B-tree. The retrieval time can be theoretically reduced further to O(1) time by proposing another e-approximate distance oracle of size O(n log n / epsilon^d) that uses a hash table. Experimental results indicate that the proposed technique is scalable and can be applied to sufficiently large road networks. A 10%-approximate oracle (epsilon = 0.1) on a large network yielded an average error of 0.9% with 90% of the answers making an error of 2% or less and an average retrieval timeof 68µ seconds. Finally, a strategy for the integration of the distance oracle into any relational database system as well as using it to perform a variety of spatial queries such as region search, k-nearest neighbor search, and spatial joins on spatial networks is discussed.
Visceral Emotions, Within-Community Communication, and (Ill-Judged) Endorsement of Financial Propositions

The 2007-08 financial crisis exposed poignant examples of ill-judged risk accretion in both tails of the Lorenz curve: concentrations of inappropriate mortgages within low-income neighborhoods, and concentrations of Bernard Madoff’s victims within wealthy, predominantly Jewish country-club communities. These examples share three key elements. First, individual behavioral decision makers take decisions privately but contribute to the build-up of risk within the community. Second, sales agents employ psychological persuasion techniques (bypassing logical processes), and trigger visceral emotions (overriding rational deliberation). Third, community membership immerses individuals within information flows that trigger invidious visceral emotions, and leads to biased inferences due to sample-size illusion and persuasion bias. We develop a closed-form model based on Signal-Detection Theory (SDT) that incorporates all three abovementioned elements: it is behavioral in employing a Prospect Theory (PT) objective function; peripheral-route persuasion and visceral emotions are incorporated through their impacts on discriminability d ′ ; and sample-size illusion and persuasion bias are incorporated through their effects on the score θ. This PT-SDT model predicts that visceral-emotion-charged hot states can short-circuit the capacity to practice caveat emptor, carrying implications for regulation and for our understanding of US household-borrowing growth 2001–2006.
Using an interactive interface to support Web search for improving user experience

With rapidly developing of the Internet, the current Web search engines are lack of interactivity for advanced users, thus the user experience cannot be satisfied. Exactly, in most of Web search engines, they only return a large number of search result pages, or suggest several correlative words according to their large-scale searching histories at the same time. For instance, Google, Yahoo! and MSN, they are the most popular and well-known search engines in the world. However, they are not enough for the advanced users who require user experience, much more correlative words, or interactive operations applied to returned results such as re-ranking and zooming. To focus on this idea of interactivity, we proposed and implemented an interactive interface to improve user experience for supporting Web search. It is a nice interface supporting dynamically changing re-ranking, related word zooming, and related word emphasizing and deemphasizing. It has been served as online search service recently. Our experimental evaluation confirms its convenience and good online performance.