Miracle-dz/UniFAR-FTUs huggingface dataset:
Software quality research: From processes to model-based techniques

In this article we state that cyber-physical systems and the Internet of Things pose challenges to software quality research. In the emerging real-time digital economy companies need to gain deep customer insight. The state of the art in modelbased systems and model-based testing allows software engineers for product-based and quantitative control of quality and for increased productivity. These gains can be invested to better understand the domain and business conditions. We argue that cyber-physical systems will pose an excellent basis for conducting collaborative research in model-based systems and model-based testing in the near future and report on lessons learnt in three areas of software (quality) research: (1) process-oriented quality, (2) model-based systems and (3) model-based testing.
Bayesian Approach for Spectrum Sensing in Cognitive Radio

Unused portions of allocated fixed spectrum are called spectrum holes or white spaces. These spectrum holes or white spaces are used by cognitive radio technology in an opportunistic manner. It will solve the global problem of spectrum scarcity. Cognitive radio assigns spectrum or radio resources in a manner to keep interference between CR devices and licensed users within limit. The scope of this paper is to achieve higher spectrum utilization in cognitive radio networks, using an optimal Bayesian Detector. If the primary user is highly inactive and the primary signals are digitally modulated, we will derive the optimal detector structure. And further suboptimal detectors in low and high SNR scenario. The Bayesian Detector has better performance than the Energy Detector in high signal to noise ratio and same performance as ED in low SNR for spectrum utilization. We provide Detection probability and false alarm probability analysis.
Effects of E‐Commerce on Greenhouse Gas Emissions: A Case Study of Grocery Home Delivery in Finland

In this article, we present a literature review of the general and environmental effects of e‐commerce in various parts of the demand‐supply chain. These are further translated into effects on greenhouse gas (GHG) emissions in the food production and consumption system. The literature study revealed many opportunities for e‐commerce to reduce GHG emissions in the food production and consumption system. Some possibly negative effects were also identified. Electronic grocery shopping (e‐grocery) home delivery service was chosen as the subject of a case study because of its direct and indirect potential for reducing the GHG emissions in the food production and consumption system.
Location l-Diversity against Multifarious Inference Attacks

The increasing use of location-based services leads these users to publish their locations unintentionally. Adversarial attackers can identify the user and find sensitive locations where the user often visits. Although l-diversity can be applied to location data and protect the user's privacy by preserving variations of locations, it does not consider the difference of the adversary's knowledge and does not properly address each sensitive location. The sensitive locations vary depending on the adversary's knowledge which reflects the relationship between the adversary and the user. In this paper, we introduce multi-dimensional l-diversity (MDlD), an enhancement of location l-diversity, to control the privacy risk from published locations by considering the specific knowledge that an adversary has on the user. We also propose an anonymization algorithm that adopts both generalization and suppression of locations to satisfy the MDlD. To reduce information loss and to preserve the number of locations for each user as much as possible, our algorithm applies generalization preferentially to suppression. We also show the practicality of our algorithm based on experimental results which used two real world datasets. The results show the fact that MDlD enables the publication of precise enough location information while still preserving user's privacy.
Unknown Word Guessing and Part-of-Speech Tagging Using Support Vector Machines

The accuracy of part-of-speech (POS) tagging for unknown words is substantially lower than that for known words. Considering the high accuracy rate of up-to-date statistical POS taggers, unknown words account for a non-negligible portion of the errors. This paper describes POS prediction for unknown words using Support Vector Machines. We achieve high accuracy in POS tag prediction using substrings and surrounding context as the features. Furthermore, we integrate this method with a practical English POS tagger, and achieve accuracy of 97.1%, higher than conventional approaches.
A Petri Net-based Approach to OWL Ontology Representation

Ontologies are an essential component to construct the Semantic Web . Therefore, their accuracy and quality must be assured. From the point of view of several ﬁelds of study, models (or representations ) are useful to achieve that goal. Petri Nets are a graphical and mathematical modeling tool whose capabilities to express important systems characteristics makes them valuable for ontology representation, veriﬁcation and reasoning. However, given that their application has been reduced to depict taxonomies, the veriﬁcation and reasoning process are partially achieved. So, there is a need to have a comprehensive ontology representation. In this paper, we introduce the Ontology Conditional Coloured Petri Net (OCCPN) model, a Petri Net extension to represent ontologies. Unlike existing proposals, OCCPN not only gives details of the taxonomy but, by evaluating conditions, also provides modeling primitives to create complex classes and describe properties as well as additional restrictions.
Contextual binarization for syntax-based machine translation

In this paper, by relabeling nodes generated during binarization of syntactic trees, contexts can be easily and systematically integrated. This not only helps to restructure syntactic trees to obtain smaller rules, that can be acquired and exploited for translation, also helps to determine which rules are most suitable for translation. By contextual bina-rization, high-quality translation could be easily generated from the contextual rules, if available; otherwise the translation just falls back on original syntax-based model without performance loss. Experimental results on the NIST Chinese-to-English corpus show promising improvements, the system applying contextual binarization outperforms over both the original syntax-based system and the original one with right binarization.
The velocity-curvature power law in Drosophila larval locomotion

We report the discovery that the locomotor trajectories generated by crawling fruit fly larvae follow the same power law relationship between speed and curvature previously found in the human motor control of hand-drawing, walking, eye movements and speech. Using high resolution behavioral tracking of individual flies in different sensory environments, we tested the power law by making maggots trace different trajectory types in naturalistic conditions, from reaching-like movements to scribbles. In all these conditions, we found that the law holds, and also that the exponent of the larval scaling law approaches 3/4, rather than the usual 2/3 exponent found in almost all human situations. This is consistent with recent findings on humans drawing ellipses on water, where dynamic effects related to medium viscosity have been shown to increase the exponent that would emerge from purely kinematic-geometric constraints. To our knowledge, the speed-curvature power law has only been studied in human and non-human primates, our work then being the first demonstration of the speed-curvature scaling principle in other species. As there are still different competing hypotheses for the origin of such law in humans (one invoking complex cortical computations in primates; another postulating its emergence from the coupling of viscoelastic muscle properties with simple central pattern generation) our findings in the larva demonstrate that the law is possible in an animal with a nervous system orders of magnitude simpler than that of humans, thus supporting the latter view. Given that our discovery is in Drosophila (amenable to precise genetic manipulations, electron microscopy reconstruction of neural circuits, imaging in behaving animals, electrophysiology, and other techniques) this opens great potential for uncovering the mechanistic implementation of the velocity-curvature power law. Such scaling laws might exist because natural selection favors processes that remain behaviorally efficient across a wide range of contexts in distantly related species. Our work is an effort to search for shared principles of animal behavior across phyla.
Face location with a extended set of LBP

Due to improve the rate of accuracy of face detection when illuminating condition, face position, face expression was changeable, proposed LTP (local ternary patterns) features of image which is a generalization of the local binary patterns (LBP) and its local texture descriptor is more discriminate and less sensitive to noise. Because LTP feature is very robust to illustration change and rotation of face, it could be used to solve the low rate of recognition that Haar-like method detects face under un-normal conditions. It experimented on a large of image under difficult conditions, the results show that this method is more efficient and robust than LBP, and when illumination is changeable and there is a lot of noise the results of method are more effective and utility than Haar-like feature.
Extracellular calcium transients and action potential configuration changes related to post-stimulatory potentiation in rabbit atrium

Extracellular calcium transients were monitored with 2 mM tetramethylmurexide at low calcium (250 microM total, 130 microM free), and action potentials were monitored together with developed tension at normal calcium (1.3 mM) during the production and decay of post- stimulatory potentiation in rabbit left atrial strips. At normal calcium, the contractile potentiation produced by a brief burst of 4 Hz stimulation is lost in three to five post-stimulatory excitations, which correlate with a negative staircase of the late action potential. At low calcium, stimulation at 4 Hz for 3-8 s results in a net extracellular calcium depletion of 5-15 microM. At the subsequent potentiated contraction (1-45 s rest), total extracellular calcium increases by 4-8 microM. The contractile response at a second excitation is greatly suppressed and results in little or no further calcium shift; the sequence can be repeated immediately thereafter. Reducing external sodium to 60 mM (sucrose replacement) enhances post- rest contractions, suppresses the late action potential, nearly eliminates loss of contractility and net calcium efflux at post-rest excitations, and markedly reduces extracellular calcium depletion during rapid stimulation. 4-Aminopyridine (1 mM) markedly suppresses the rapid early repolarization of this preparation at post-rest excitations and the loss of contractility at post-rest stimulation from the rested state; during a post-stimulatory potentiation sequence at low calcium, replenishment of extracellular calcium takes several post- stimulatory excitations. Ryanodine (10 nM to 5 microM) abolishes the post-stimulatory contraction at rest periods of greater than 5 s. If the initial repolarization is rapid, ryanodine suppresses the late action potential, calcium efflux during quiescence is greatly accelerated, and subsequent excitations do not result in an accumulation of extracellular calcium. A positive staircase of the early action potential correlates with the magnitude of net extracellular calcium depletion. These findings demonstrate that negative contractile staircases at post-rest stimulation correspond closely to an accumulation of extracellular calcium at activation and a negative staircase of the late action potential; the correlation of these three events suggests that electrogenic sodium-calcium exchange is the common underlying mechanism.
The IMPACT of ICT on LEARNING and TEACHING

This review set out to identify and evaluate relevant strategies in local, national and international research and initiatives related to measuring and demonstrating the impact of ICT in schools with regard to: students, learning and the learning environment; teachers and teaching strategies; organisational change; and other areas relevant to teaching and learning in WA government schools. This will include the presentation of definitions of common terminology. As a result a framework was developed to articulate the areas of impact of ICT in schools and strategies for monitoring and evaluating each of the areas of impact at the school and system levels. This framework is provided in a separate but associated document.
An empirical analysis of database recovery costs

The time required for recovery from a failure is heavily inuenced by hardware setup and workload characteristics. In bad but still realistic cases, the recovery required during restart can take hours. For a database system based on write-ahead logging, we performed a qualitative study of how hardware and software congurations aect the behavior of the database and, consequently, how this behavior aects recovery time after a system crash. With the relevant parameters identied in the qualitative study, we performed an empirical quantitative analysis of recovery costs in multiple scenarios. We show that recovery costs tend to get worse as hardware and software improve in eciency, and we discuss possible approaches to make recovery time independent of system congurations and workload characteristics.
From motion to emotion: synthesis of interactivity with gestural primitives

We discuss the motion-based gestural primitives. Unlike the problems in facial expressions or communicative gestures which are subjected to the agreeable interpretation of "what it means", our problems are raised in the development of a human-machine performance system. We put an emphasis on dynamics and kinetic elements among interacting entities that comprise a complex environment for Human-Machine performance. In that environment the performers actively change the sensory information by means of movements; the performance movements that are the changes of the states of the performers induce the changes in system states. The paper introduces the concepts of synthesis of interactivity, extended circularity, and tonmeister kinesthetic. Gestural primitives are classified into three classes with respect to performer’s orientation; trajectory-based, force-based, and pattern-base primitives.
Hand gesture recognition based on SOM and ART

Gesture recognition is needed for a variety of applications such as human-computer interfaces, communication aids for the deaf, etc. In this paper, we present a SOMART system for the recognition of hand gestures. The sequence of a hand gesture is first projected into a 2-dimensional trajectory on a self-organizing feature map (SOM). Then the problem of recognizing hand gestures is transformed to the problem of recognizing hand-written characters. An ART-like algorithm generates multiple templates for each hand gesture. Finally, an unknown gesture is classified to be the gesture with the maximum similarity in the vocabulary via a template matching technique. A database consisted of 47 static hand gestures and 8 dynamic hand gestures was tested to demonstrate the performance of the proposed method.
A Robust Dynamic Watermarking Scheme

A new data structure dynamic watermarking scheme is proposed in this paper. Using Shamir threshold scheme and block encryption, the watermark is divided into several shares. Then these shares are embedded into the dynamic branch structure of program. Code obfuscation technique is applied during embedding. Our scheme provides watermarks with increased robustness, decreased visibility and higher cost performance ratio. Keywords-software watermark;shamir scheme;block encryption;dynamic branch structure;
Socio-cognitive Heterogeneity in American Public Opinion

Americans’ political beliefs present a long observed paradox. Whereas the mainstream political discourse is structured on a clearly defined polarity between conservative and liberal views, in practice, most people exhibit ideologically incoherent belief patterns. This paper challenges the notion that political beliefs are necessarily defined by a singular ideological continuum. It applies a new, network-based method for detecting heterogeneity in collective patterns of opinion, relational class analysis (RCA), to Americans’ political attitudes as captured by the American National Election Studies. By refraining from making a-priori assumptions about how beliefs are interconnected, RCA looks for opinion structures, belief networks, that are not necessarily congruent with received wisdom. It finds that in the twenty years between 1984 and 2004 Americans’ political attitudes were consistently structured by two alternative belief systems: one that is strongly aligned with the liberal-conservative divide, another that rejects the association between moral (e.g. abortion) and economic (e.g. redistribution) attitudes. An interaction between class and religious participation explains this variance. High earners with weak religious commitments, and vice versa, tend to exhibit the latter approach; those with seemingly conflicting moral and economic worldviews are also more likely to self-identify as Republicans. These findings are particularly relevant for recent debates on the roles of moral and economic beliefs in shaping political behavior, suggesting that different sociodemographic groups understand the political debate differently, and consequently emphasize different issues in deciding their party identification. ∗Please do not quote without permission. We thank Paul DiMaggio for useful comments. The usual disclaimer does apply. Direct all correspondence to Delia Baldassarri, 147 Wallace Hall, Princeton, NJ 08544. (dbalda@princeton.edu). †Assistant Professor, Department of Sociology, Princeton University. ‡Ph.D candidate, Department of Sociology, Princeton University. [These] studies represent our effort to make sense of the ordinary citizens effort to make sense of politics. (Sniderman et al. 1991, 30)
Capture of arm-muscle deformations using a depth-camera

Modeling realistic skin deformations due to underneath muscle bulging has a wide range of applications in medicine, entertainment and art. Current acquisition systems based on dense markers and multiple synchronized cameras are able to record and reproduce fine-scale skin deformations with sufficient quality. However, the complexity and the high cost of these systems severely limit their applicability. In this paper, we propose a method for reconstructing fine-scale arm muscle deformations using the Kinect depth camera. The captured data from the depth camera has no temporal contiguity and suffers from noise and sensory artifacts, and thus unsuitable by itself for potential applications in visual media production or biomechanics. We process noisy depth input to obtain spatio-temporally consistent 3D mesh reconstructions showing fine-scale muscle bulges over time. Our main contribution is the incorporation of statistical deformation priors into the spatiotemporal mesh registration progress. We obtain these priors from a previous dataset of a limited number of physiologically different actors captured using a high fidelity acquisition setup, and these priors help provide a better initialization for the ultimate non-rigid surface refinement that models deformations beyond the range of the previous dataset. Thus, our method is an easily scalable framework for bootstrapping the statistical muscle deformation model, by extending the set of subjects through a Kinect based acquisition process. We validate our spatio-temporal surface registration method on several arm movements performed by people of different body shapes.
RXTE observations of 3C 273 between 1996 and 2000: variability time-scale and jet power

We present the results of a long-look monitoring of 3C 273 with RXTE between 1996 and 2000. A total of 230 observations amounts to a net exposure of 845 ks, with this spectral and variability analysis of 3C 273 covering the longest observation period available at hard X-ray energies. Flux variations by a factor of 4 have been detected over 4 yr, whereas flux variations of less than 30 per cent have been observed for individual flares on time-scales of ∼ 3d . Two temporal methods, the power spectrum density (PSD) and the structure function (SF), have been used to study the variability characteristics of 3C 273. The hard X-ray photon spectra generally show a power-law shape with a differential photon index of � � 1.6 ± 0.1. In 10 of 261 data segments, exceptions to power-law behaviour have been found: (i) an additional soft excess below 4 keV; and (ii) a broad Fe fluorescent line feature with EW ∼ 100‐200 eV. Our new observations of these previously reported X-ray features may imply that 3C 273 is a unique object whose hard X-ray emission occasionally contains a component that is not related to a beamed emission (Seyfert-like), but most hard X-rays are likely to originate in inverse Compton radiation from the relativistic jet (blazar-like). Multifrequency spectra from radio to γ -rays are presented in addition to our RXTE results. The X-ray time variability and spectral evolution are discussed in the framework of the beamed, synchrotron self-Compton picture. We consider the ‘power balance’ (both radiative and kinetic) between the accretion disc, the sub-parsec-scale jet and the 10-kpc-scale jet.
Micro-blogging Sentiment Detection by Collaborative Online Learning

We study the online micro-blog sentiment detection problem, which aims to determine whether a micro-blog post expresses emotions. This problem is challenging because a micro-blog post is very short and individuals have distinct ways of expressing emotions. A single classification model trained on the entire corpus may fail to capture characteristics unique to each user. On the other hand, a personalized model for each user may be inaccurate due to the scarcity of training data, especially at the very beginning where users have just posted a few entries. To overcome these challenges, we propose learning a global model over all micro-bloggers, which is then leveraged to continuously refine the individual models through a collaborative online learning way. We evaluate our algorithm on a real-life micro-blog dataset collected from the popular micro-blog site – Twitter. Results show that our algorithm is effective and efficient for timely sentiment detection in real micro-blogging applications.
Optimal design of photovoltaic-wind hybrid renewable energy system using a discrete geometric selective harmony search

Photovoltaic (PV) and wind energies are renewable and clean, which can be alternative to fossil fuels. The PV/wind hybrid system optimal design problem is the hot topic nowadays. The aim of the optimal design is to develop an independent energy supply system with minimum total annual cost and loss power supply probability by determining the number of PV panels, wind turbines and backup batteries. This paper presents a discrete geometric selective harmony search (DGSHS) algorithm combined with Deb's constraint handling technique to tackle this problem. Optimal design under different requirements are investigated in this study and DGSHS are compared with 3 previous methods, namely discrete harmony search(DHS), discrete harmony search -based simulated annealing (DHSSA) and discrete chaotic harmony search-based simulated annealing (DCHSSA). The simulation results validate the superior performance of the DGSHS algorithm.