Miracle-dz/UniFAR-FTUs huggingface dataset:
DBMSs on a Modern Processor : “ Where Does Time Go ? ” Revisited

In 1999, the performances of four commercial DBMSs were analyzed and broken down into the amount of time spent doing useful computation and the amount of time spent stalling for data to be retrieved from memory. In this paper, we aim to revisit two of those DBMSs and see if their interactions with the underlying hardware architecture has improved. We will use the same metrics as used in the previous paper to facilitate comparison between the two papers. We divide total execution time into time spent on useful computation, L1 and L2 cache misses, resource (functional unit) stalls, and branch misprediction (pipeline draining). It’s important to note that we are not interested in analyzing how “fast” these DBMSs are. Instead we are interested in determining the relative contributions of stalls and letting both DBMS and microprocessor designers locate these performance bottlenecks in their systems and ameliorate them. We find that despite the performance optimizations found in today’s database systems, they are not able to take full advantage of many recent improvements in processor technology and data placement.
Media Streams Planning with Transcoding

High-quality video, both uncompressed and compressed with relatively low compression ratio, is used in interactive environments, where minimum end-to-end latency is required. The media streams planning solves multi-point data routing for such applications even when bit rate of streams is comparable to capacity of network links. Recent advances in acceleration of high-throughput low-latency video processing enable on-the-fly video transcoding even for 4K and 8K video streams, thus allowing individual adaptation of data stream for each recipient without affecting the others. Stimulated by these advances, we present a novel media streams planning with transcoding on network nodes, and a mixed integer programming approach to solve the problem optimally. Our approach also enhances traditional end-to-end network model of the whole system with known topology of certain network regions. Our solution was implemented in the Co Universe, which is middleware for orchestration of collaborative environments. Measurements show that performance of the new solver is sufficient for interactive solving of most practical use-cases, thus enabling self-organization of high-performance collaborative environments.
Mechanism Design Based Nodes Selection Model for Threshold Key Management in MANETs

The design of threshold based distributed Certification Authority (CA) has been proposed to provide secure and efficient key management service in mobile ad hoc networks (MANETs). However, most of previous works ignore the efficiency and effectiveness and assuming there are always honest nodes performing the service. Focusing on the development of a model which can be used to select a coalition of nodes dynamically and optimally to carry out the threshold key management service during its operation in MANETs with selfish nodes, this paper formulates the dynamic nodes selection problem as combinatorial optimization problem with the objectives of maximizing the success ratio of key management service and minimizing the nodes' cost of security and energy firstly. Then, to ensure truth-telling is the dominant strategy for any node in our scenario, we extend the payment structure of the classical Vickrey, Clarke, and Groves (VCG) mechanism design framework and divide the payment to nodes in the coalition of nodes with the consideration of the actual execution effectiveness of each one. Simulations show that proposed model enjoys an improvement of both the success ratio of key management service and lifetime of the network, and a reduction of both the cost of participating nodes and compromising probability of MANETs, compared with existing works in the presence of selfish nodes.
An enhanced botnet detection technique for mobile devices using log analysis

Botnets have fully integrated and adapted in mobile devices. Security of mobile devices are not designed to detect threats such as botnets. This allows botnets to infect devices and go unnoticed. In this paper we propose an enhanced botnet detection technique called “Logdog” for mobile devices using log analysis. Our approach relies on analyzing logs of mobile devices to find evidence of botnet activities. We implement the technique in android, taking advantage of the android logging system using logcat command. The effectiveness of this method is shown by experimenting on android devices.
Exploring API method parameter recommendations

A number of techniques have been developed that support method call completion. However, there has been little research on the problem of method parameter completion. In this paper, we first present a study that helps us to understand how developers complete method parameters. Based on our observations, we developed a recommendation technique, called Parc, that collects parameter usage context using a source code localness property that suggests that developers tend to collocate related code fragments. Parc uses previous code examples together with contextual and static type analysis to recommend method parameters. Evaluating our technique against the only available state-of-the-art tool using a number of subject systems and different Java libraries shows that our approach has potential. We also explore the parameter recommendation support provided by the Eclipse Java Development Tools (JDT). Finally, we discuss limitations of our proposed technique and outline future research directions.
Building Russian Word Sketches as Models of Phrases

The paper describes the writing of Sketch Grammar for the Russian language as a part of the Sketch Engine system. The Sketch Engine representing itself a corpus tool which takes as input a corpus of any language and corresponding grammar patterns. The system gives information about a word’s collocability on concrete dependency models, and generates lists of the most frequent phrases for a given word based on appropriate models. The papers deals with different approaches to writing rules for the grammar, based on morphological and syntactic information, and also with applying word sketches to the Russian language. The results show that word sketches and information about collocation behaviour could facilitate lexicographic work with the Russian language.
Extension of Yeast Chronological Lifespan by Methylamine

Background Chronological aging of yeast cells is commonly used as a model for aging of human post-mitotic cells. The yeast Saccharomyces cerevisiae grown on glucose in the presence of ammonium sulphate is mainly used in yeast aging research. We have analyzed chronological aging of the yeast Hansenula polymorpha grown at conditions that require primary peroxisome metabolism for growth. Methodology/Principal Findings The chronological lifespan of H. polymorpha is strongly enhanced when cells are grown on methanol or ethanol, metabolized by peroxisome enzymes, relative to growth on glucose that does not require peroxisomes. The short lifespan of H. polymorpha on glucose is mainly due to medium acidification, whereas most likely ROS do not play an important role. Growth of cells on methanol/methylamine instead of methanol/ammonium sulphate resulted in further lifespan enhancement. This was unrelated to medium acidification. We show that oxidation of methylamine by peroxisomal amine oxidase at carbon starvation conditions is responsible for lifespan extension. The methylamine oxidation product formaldehyde is further oxidized resulting in NADH generation, which contributes to increased ATP generation and reduction of ROS levels in the stationary phase. Conclusion/Significance We conclude that primary peroxisome metabolism enhanced chronological lifespan of H. polymorpha. Moreover, the possibility to generate NADH at carbon starvation conditions by an organic nitrogen source supports further extension of the lifespan of the cell. Consequently, the interpretation of CLS analyses in yeast should include possible effects on the energy status of the cell.
Dynamic Shapes Average

Abstract : A framework for computing shape statistics in general, and average in particular, for dynamic shapes is introduced in this paper. Given a metric d(-,-) on the set of static shapes, the empirical mean of N static shapes, C(sub 1),. ,C(sub N), is defined by arg min(sub C) 1/N SIGMA (sup N, sub i=1) d(C,C(sub i))(exp 2). The purpose of this paper is to extend this shape average work to the case of N dynamic shapes and to give an efficient algorithm to compute it. The key concept is to combine the static shape statistics approach with a time-alignment step. To align the time scale while performing the shape average the authors use dynamic time warping, adapted to deal with dynamic shapes. The proposed technique is independent of the particular choice of the shape metric d(.,.). They present the underlying concepts, a number of examples, and conclude with a variational formulation to address the dynamic shape average problem. They also demonstrate how to use these results for comparing different types of dynamics. Although only average is addressed in this paper, other shape statistics can be similarly obtained following the framework proposed here.
Facial action unit detection using kernel partial least squares

In this work, we propose a framework for simultaneously detecting the presence of multiple facial action units using kernel partial least square regression (KPLS). This method has the advantage of being easily extensible to learn more face related labels, while at the same time being computationally efficient. We compare the approach to linear and non-linear support vector machines (SVM) and evaluate its performance on the extended Cohn-Kanade (CK+) dataset and the GEneva Multimodal Emotion Portrayals (GEMEP-FERA) dataset, as well as across databases. It is shown that KPLS achieves around 2% absolute improvement over the SVM-based approach in terms of the two alternative forced choice (2AFC) score when trained on CK+ and tested on CK+ and GEMEP-FERA. It achieves around 6% absolute improvement over the SVM-based approach when trained on GEMEP-FERA and tested on CK+. We also show that KPLS is handling non-additive AU combinations better than SVM-based approaches trained to detect single AUs only.
Named Entity Extraction with Conjunction Disambiguation

The recognition of named entities is now a well-developed area, with a range of symbolic and machine learning techniques that deliver high accuracy extraction and categorisation of a variety of entity types. However, there are still some named entity phenomena that present problems for existing techniques; in particular, relatively little work has explored the disambiguation of conjunctions appearing in candidate named entity strings. We demonstrate that there are in fact four distinct uses of conjunctions in the context of named entities; we present some experiments using machine-learned classifiers to disambiguate the different uses of the conjunction, with 85% of test examples being correctly classified.
Motor Decline in Clinically Presymptomatic Spinocerebellar Ataxia Type 2 Gene Carriers

Background Motor deficits are a critical component of the clinical characteristics of patients with spinocerebellar ataxia type 2. However, there is no current information on the preclinical manifestation of those motor deficits in presymptomatic gene carriers. To further understand and characterize the onset of the clinical manifestation in this disease, we tested presymptomatic spinocerebellar ataxia type 2 gene carriers, and volunteers, in a task that evaluates their motor performance and their motor learning capabilities. Methods and Findings 28 presymptomatic spinocerebellar ataxia type 2 gene carriers and an equal number of control volunteers matched for age and gender participated in the study. Both groups were tested in a prism adaptation task known to be sensible to both motor performance and visuomotor learning deficits. Our results clearly show that although motor learning capabilities are intact, motor performance deficits are present even years before the clinical manifestation of the disease start. Conclusions The results show a clear deficit in motor performance that can be detected years before the clinical onset of the disease. This motor performance deficit appears before any motor learning or clinical manifestations of the disease. These observations identify the performance coefficient as an objective and quantitative physiological biomarker that could be useful to assess the efficiency of different therapeutic agents.
Cross-lingual query classification: a preliminary study

The non-English Web is growing at breakneck speed, but available language processing tools are mostly English based. Taxonomies are a case in point: while there are plenty of commercial and non-commercial taxonomies for the English Web, taxonomies for other languages are either not available or of very limited quality. Given that building taxonomies in all non-English languages is prohibitively expensive, it is natural to ask whether existing English taxonomies can be leveraged, possibly via machine translation, to enable information processing tasks in other languages. Preliminary results presented in this paper indicate that the answer is affirmative with respect to query classification, a task which is essential both for understanding the user intent and thus provide better search results, and for better targeting of search-based advertising, the economic underpinning of commercial Web search engines. We propose a robust method for classifying non-English queries against an English taxonomy and classifier using widely available, off-the-shelf machine translation systems. In particular, we show that by viewing the search results in the query's original language as independent sources of information, we can alleviate the impact of poor quality or erroneous machine translations. Empirical results for Chinese queries show that we achieve remarkably encouraging results.
Multimodal retinal image registration using edge map and feature guided Gaussian mixture model

In this paper, we propose a method for multimodal retinal image registration based on feature guided Gaussian mixture model (GMM) and edge map. We extract two sets of feature points from the edge maps of two images, and formulate image registration as the estimation of a feature guided mixture of densities: a GMM is fitted to one point set, such that both the centers and local features of the Gaussian densities are constrained to coincide with the other point set. The problem is solved under a maximum-likelihood framework together with an iterative EM algorithm initialized by confident feature matches, where the image transformation is modeled by an affine function. Extensive experiments on various retinal images show the robustness of our method, which consistently outperforms other state-of-the-arts, especially when the data is badly degraded.
Emotional Intelligence and Personality Traits: A Correlational Study of MYEIT and BFI

The objective of this paper is to study the relationship between Emotional Intelligence (EI) and the personality traits using Malaysian Youth Emotional Intelligence Test (MYEIT) and Big Five Inventory (BFI). MYEIT is a newly developed measures to meets the need of a reliable instrument to measure EI among Malaysian samples. The dimensions of this newly developed MYEIT are similar to the well known SSEIT and TEIQue and however the items have been designed to suit the Malaysian socio-politics and cultures. Although MYEIT is new instrument but, it’s has the capacity to linked to mainstream scientific models in differential psychology such as Big Five. The finding of this study is shows that there are strong relationships between EI and Big Five, consistent with previous studies. Conscientiousness and Agreeableness strongly correlates with EI, followed by Extraversion, Openness and Neuroticism. Regression analyses further confirm that EI measured by MYEIT is overlap with the higher–order personality dimensions.
College Connectedness: The Student Perspective.

: Connectedness and integration are essential elements of student satisfaction, academic success, and retention. Despite its importance in the lives of college students, research on connectedness has approached the concept from definitional perspectives other than those of students. This multi-study explores connectedness from the student perspective while drawing from social identity theory and student involvement theory. In Study 1, students described their experiences and perceptions of connectedness in focus groups. Study 2 built on the qualitative findings of Study 1 and empirically tested connectedness as defined by the students. Results of the study indicate the need for a student definition of connectedness and provide practical suggestions for institutions of higher education.
To reply or not to reply : Predicting action on an email message

What characteristics of an email message predict a users action on that message? Participants in a survey of university faculty, staff, and students provided data on the characteristics of new email messages and their actions based on the messages. Statistical analyses of responses revealed several factors that were important in predicting the fate of a message. These were: importance of a message, number of recipients, sender characteristics, and the nature of the message content. Factors influencing user perception of message importance were also examined. Important messages were from high communication frequency work contacts requesting action, providing a status update, or scheduling a meeting.
Effect of Atlas Vertebrae Realignment in Subjects with Migraine: An Observational Pilot Study

Introduction. In a migraine case study, headache symptoms significantly decreased with an accompanying increase in intracranial compliance index following atlas vertebrae realignment. This observational pilot study followed eleven neurologist diagnosed migraine subjects to determine if the case findings were repeatable at baseline, week four, and week eight, following a National Upper Cervical Chiropractic Association intervention. Secondary outcomes consisted of migraine-specific quality of life measures. Methods. After examination by a neurologist, volunteers signed consent forms and completed baseline migraine-specific outcomes. Presence of atlas misalignment allowed study inclusion, permitting baseline MRI data collection. Chiropractic care continued for eight weeks. Postintervention reimaging occurred at week four and week eight concomitant with migraine-specific outcomes measurement. Results. Five of eleven subjects exhibited an increase in the primary outcome, intracranial compliance; however, mean overall change showed no statistical significance. End of study mean changes in migraine-specific outcome assessments, the secondary outcome, revealed clinically significant improvement in symptoms with a decrease in headache days. Discussion. The lack of robust increase in compliance may be understood by the logarithmic and dynamic nature of intracranial hemodynamic and hydrodynamic flow, allowing individual components comprising compliance to change while overall it did not. Study results suggest that the atlas realignment intervention may be associated with a reduction in migraine frequency and marked improvement in quality of life yielding significant reduction in headache-related disability as observed in this cohort. Future study with controls is necessary, however, to confirm these findings. Clinicaltrials.gov registration number is NCT01980927.
Extraction of Foreground in images with Similar Background using Grab Cut in HSV Color Space

—Interactive image segmentation of images has great practical importance. Grab cut is an approach based on optimization by graph cut is implemented by Rother et. al. Grab cut is faster technique and it only requires a rough initial trimap by user interaction. But Grab Cut fails for the images having same foreground and background color Intensity Here we proposed a variation of Grab cut by applying the Grab Cut on other Color model. Detail of the approach is given in the paper.
An Efficient Face Recognition System based on the Hybridization of Pose Invariant and Illumination Process

In the previous decade, one of the most effectual applications of image analysis and indulgent that attracted significant consideration is the human face recognition. One of the diverse techniques used for identifying an individual is the Face recognition. Normally the image variations for the reason that of the change in face identity are less than the variations between the images of the same face under different illumination and viewing angle. Among several factors that manipulate face recognition, illumination and pose are the two major challenges. Pose and illumination variations harshly affect the performance of face recognition. Considerably less effort has been taken to deal with the problem of mutual variations of pose and illumination in face recognition, while several algorithms have been proposed for face recognition from fixed points. In this paper we intend a face recognition method that is forceful to pose and illumination variations. We first put forward a simple pose estimation method based on 2D images, which uses a proper classification rule and image representation to classify a pose of a face image. After that, the image can be assigned to a pose class by a classification rule in a low-dimensional subspace constructed by a feature extraction method. We offer a shadow compensation method that compensates for illumination variation in a face image so that the image can be predictable by a face recognition system designed for images under normal illumination condition. Starting the accomplishment result, it is obvious that our projected technique based on the hybridization system recognizes the face images effectively.
Approaches to Enhance Graph Coloring Register Allocation

The relationship between run-time performance and effecti v use of a machine’s register set is well understood. Graph col oring method, which pioneered by Chaitin, has been widely practiced. In this paper, we describe three improvements to Chaitin-style graph coloring register allocators. The firs t is a more precise definition on interference. The second is calle d rename, which can be easily incorporated into Chaitin’s all ocator to reduce complexity of interference graph. In the thi rd we propose some possibilities to perform program transformation to reduce live ranges of variables thus giving them more opportunities to be allocated some registers. These techniques have been successfully applied in Motorola VeComP701 C Compiler.
Predicting Daily Behavior via Wearable Sensors

We report on ongoing research into how to statistically represent the experiences of a wearable computer user for the purposes of day-today behavior prediction. We combine natural sensor modalities (camera, microphone, gyros) with techniques for automatic labeling from sparsely labeled data. We have also taken the next required step to build robust statistical models by beginning an extensive data collection experiment, the " I Sensed " series, a 100 day data set consisting of full surround video, audio, and orientation.