Miracle-dz/UniFAR-FTUs huggingface dataset:
Android Security: A Survey of Issues, Malware Penetration, and Defenses

Smartphones have become pervasive due to the availability of office applications, Internet, games, vehicle guidance using location-based services apart from conventional services such as voice calls, SMSes, and multimedia services. Android devices have gained huge market share due to the open architecture of Android and the popularity of its application programming interface (APIs) in the developer community. Increased popularity of the Android devices and associated monetary benefits attracted the malware developers, resulting in big rise of the Android malware apps between 2010 and 2014. Academic researchers and commercial antimalware companies have realized that the conventional signature-based and static analysis methods are vulnerable. In particular, the prevalent stealth techniques, such as encryption, code transformation, and environment-aware approaches, are capable of generating variants of known malware. This has led to the use of behavior-, anomaly-, and dynamic-analysis-based methods. Since a single approach may be ineffective against the advanced techniques, multiple complementary approaches can be used in tandem for effective malware detection. The existing reviews extensively cover the smartphone OS security. However, we believe that the security of Android, with particular focus on malware growth, study of antianalysis techniques, and existing detection methodologies, needs an extensive coverage. In this survey, we discuss the Android security enforcement mechanisms, threats to the existing security enforcements and related issues, malware growth timeline between 2010 and 2014, and stealth techniques employed by the malware authors, in addition to the existing detection methods. This review gives an insight into the strengths and shortcomings of the known research methodologies and provides a platform, to the researchers and practitioners, toward proposing the next-generation Android security, analysis, and malware detection techniques.
Real-Time Simulation of Multihead Tape Units

The main result of this paper is that, given a Turing machine with several read-write heads per tape, one can effectively construct an equivalent multitape Turing machine with a single read-write head per tape, which runs at precisely the same speed. This result implies that serial storage may be used to handle files requiring several points of immediate two-way read-write access without interruptions for rewinds, etc. It also yields simplified proofs of several results in the literature of computational complexity.
A Block Diagram Approach to Reference Frame Transformation of Converter Dynamic Models

This paper proposes a reference frame transformation method which applies appropriate rotational operators to the block diagram representation of a converter space vector model to transform it from one reference frame to another. Unlike reference frame transformation using matrices, it can easily be applied to converter models of average complexity. A table of reference frame transform pairs and three examples are provided to demonstrate the use of this method. Time domain simulation results are used to validate the reference frame transformation in each example
Resolving the Brainstem Contributions to Attentional Analgesia

Previous human imaging studies manipulating attention or expectancy have identified the periaqueductal gray (PAG) as a key brainstem structure implicated in endogenous analgesia. However, animal studies indicate that PAG analgesia is mediated largely via caudal brainstem structures, such as the rostral ventromedial medulla (RVM) and locus coeruleus (LC). To identify their involvement in endogenous analgesia, we used brainstem optimized, whole-brain imaging to record responses to concurrent thermal stimulation (left forearm) and visual attention tasks of titrated difficulty in 20 healthy subjects. The PAG, LC, and RVM were anatomically discriminated using a probabilistic atlas. Pain ratings disclosed the anticipated analgesic interaction between task difficulty and pain intensity (p < 0.001). Main effects of noxious thermal stimulation were observed across several brain regions, including operculoinsular, primary somatosensory, and cingulate cortices, whereas hard task difficulty was represented in anterior insular, parietal, and prefrontal cortices. Permutation testing within the brainstem nuclei revealed the following: main effects of task in dorsal PAG and right LC; and main effect of temperature in RVM and a task × temperature interaction in right LC. Intrasubject regression revealed a distributed network of supratentorial brain regions and the RVM whose activity was linearly related to pain intensity. Intersubject analgesia scores correlated to activity within a distinct region of the RVM alone. These results identify distinct roles for a brainstem triumvirate in attentional analgesia: with the PAG activated by attentional load; specific RVM regions showing pronociceptive and antinociceptive processes (in line with previous animal studies); and the LC showing lateralized activity during conflicting attentional demands. SIGNIFICANCE STATEMENT Attention modulates pain intensity, and human studies have identified roles for a network of forebrain structures plus the periaqueductal gray (PAG). Animal data indicate that the PAG acts via caudal brainstem structures to control nociception. We investigated this issue within an attentional analgesia paradigm with brainstem-optimized fMRI and analysis using a probabilistic brainstem atlas. We find pain intensity encoding in several forebrain structures, including the insula and attentional activation of the PAG. Discrete regions of the rostral ventromedial medulla bidirectionally influence pain perception, and locus coeruleus activity mirrors the interaction between attention and nociception. This approach has enabled the resolution of contributions from a hub of key brainstem structures to endogenous analgesia.
Evolutionary multi-objective clustering with adaptive local search

In many real-world applications, the accurate number of clusters in the data set may be unknown in advance. In addition, clustering criteria are usually high dimensional, nonlinear and multi-model functions and most existing clustering algorithms are only able to achieve a clustering solution that locally optimizes them. Therefore, a single clustering criterion sometimes fails to identify all clusters in a data set successfully. This paper presents a novel multi-objective evolutionary clustering algorithm based on adaptive local search that mitigates the above disadvantages of existing clustering algorithms. Unlike the conventional local search, the proposed adaptive local search scheme automatically determines whether local search is used in an evolutionary cycle or not. Experimental results on several artificial and real data sets demonstrate that the proposed algorithm can identify the accurate number of clusters in the data sets automatically and simultaneously achieves a high quality clustering solution. The superiority of the proposed algorithm over some single-objective clustering algorithms and existing multi-objective evolutionary clustering algorithms is also confirmed by the experimental results.
No-arbitrage bounds on American Put Options with a single maturity

We consider in this thesis the problem of pricing American Put Options in a modelfree framework where we do not make any assumptions about the price dynamics of the underlying except those implied by the no-arbitrage conditions. Our goal is to obtain bounds on the price of an American put option with a given strike and maturity directly from the prices of other American put options with the same maturity but different strikes and the current price of the underlying. We proceed by first investigating the structural properties of the price curve of American Put Options of a fixed maturity and derive necessary and sufficient conditions that strike price pairs of these options must satisfy in order to exclude arbitrage. Using these conditions, we can find tight bounds on the price of the option of interest by solving a very tractable Linear Programming Problem. We then apply the methods developed to real market data. We observe that the quality of bounds that we obtain compares well with the quoted bid-ask spreads in most cases. Thesis Supervisor: Dimitris J. Bertsimas Title: Boeing Professor of Operations Research, Sloan School of Management
An empirical analysis of taxpayers' attitudes and behavioral intentions regarding compliance with federal income tax laws

The purpose o f this study was to analyze variables that potentially affect taxpayers' attitudes about income tax compliance. Phase one o f the study focused on the relationships between four groups o f variables and ethical perceptions o f compliance behaviors. The four groups o f variables follow: demographic variables such as age. education, and household income; personal characteristic variables such as idealism, relativism, conservatism, and religiosity; situational variables including an overall attitude about paying federal income taxes and impressions o f the effectiveness o f a number o f law changes intended to make the IRS more responsive; and. consequential variables comprised o f five dimensions o f moral intensity, expectations o f audit, and additional IRS assessments. Phase two of the study focused on the relationship between ethical perceptions and expected compliance behaviors. The study sample was drawn from responses to a national survey o f 3,109 individuals selected randomly throughout the United States. The primary analytical technique employed was hierarchical regression. The results o f phase one were mixed. No support was found with respect to demographic variables. Regarding personal characteristics, limited support was found with respect to both conservatism and religiosity. Among the situational variables, partial support was identified for two o f the tax law changes—extension, in some circumstances,
A context-aware collaborative filtering approach for service recommendation

It is a challenge to recommend Web services under multiple contexts. To address this challenge, we propose a context-aware collaborative filtering (CaCF) approach for service recommendation. Three types of contextual information, i.e. time, location and interest of user, are considered. In this approach, users' interests are extracted from service invocation records and represented as term-weight vectors. Neighbors are chosen according to the Cosine similarities of these vectors. Then, neighbors are filtered into close neighbors by location and time. At last, these close neighbors recommend service to a target user. We evaluate our method through comparing with other service recommendation approaches. The experimental results show that it achieves better precision and satisfaction rate than other two methods.
Low Rank SVM

Recently, some research try to incorporate the 2D structure of images into dimensionality reduction process, like 2DPCA [3] and CSA [4]. Some work use high order tensor to represent image ensembles, where the factors may include different faces, facial expression, viewpoints and illuminations [5, 6]. All these efforts indeed provide good performance but tend to separate the dimensionality reduction stage from supervised learning stage.
An unexpected outburst from A0535+262

A0535+262 is a transient Be/X-ray binary system which was in a quiescent phase from 1994‐ 2005. In this paper we report on the timing and spectral properties of the INTEGRAL detection of the source in October 2003. The source is detected for∼6000 seconds in the 18‐100 keV energy band at a luminosity of∼3.8× 10 35 ergs s −1 ; this is compatible with the high end of the range of luminosities expected for quiescent emission. The system is observed to be outside of the centrifugal inhibition regime and pulsati ons are detected with periodicity, P=103.7± 0.1 seconds. An examination of the pulse history of the source shows that it had been in a constant state of spin-down since it entered the quiescent phase in 1994. The rate of spin-down implies the consistent presence of an accretion disk supplying torques to the pulsar. The observations show that the system is still activ e and highly variable even in the absence of recent Type I or Type II X-ray outbursts.
The combination of CCA and PSDA detection methods in a SSVEP-BCI system

In recent years, based on the steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) have generated significant interest, due to their shorter calibration times and higher information transfer rates. Target identification is the core signal processing task in BCIs. Power spectral density analysis (PSDA) and canonical correlation analysis (CCA) are the most popular and widely used classification methods in SSVEP-BCI systems. In this paper, we first combined these two methods for detecting the SSVEP signals. Moreover, we compared the proposed method with PSDA, CCA method, respectively. The results showed that the proposed method can improve the accuracy and the transfer rate of BCIs.
Modeling and Requirements Formulation for Submarine Control Surface Actuation Systems

This paper examines methods that can be adopted in early stage or retrot design of actuator subsystems, and is motivated by the modern trend toward retrotting legacy hydraulic actuation of submarine control surfaces with electromechanical systems. This problem highlights the familiar need designers have to relate system response requirements to selected subsystems to enable focused development of the latter. This paper shows the utility of adopting a model basis, and specically uses a bond graph approach in combination with two-port immittance relations. Bond graphs benet study of multidisciplinary systems and provide insight into system interconnection and causality. In combination with bond graphs, immittance functions enable key relations to be established based on the overall system description, and provide a way to specify response requirements. Immittance functions also enable adopting network synthesis methods, which are used in this paper to show how passive and active compensation can be integrated to benet proposed actuation schemes. It is shown how this combination of methods provides designers with a systematic way of relating system response requirements to targeted subsystems. The methods described are demonstrated using a simplied control surface actuation problem, and simulation results of key static and dynamic requirements are presented. The derived passive and active compensation are used to augment conventional feedback controlled actuator models, and simulations demonstrate how compensation can reduce energy consumed by actuation subsystems.
Inference of large phylogenetic trees on parallel architectures

Due to high computational demands, the inference of large phylogenetic trees from molecular sequence data requires the use of HPC systems in order to obtain the necessary computational power and memory. The continuous explosive accumulation of molecular data, which is driven by the development of cost-effective sequencing techniques, amplifies this requirement additionally. Furthermore, a continuously increasing degree of parallelism is necessary in order to exploit the performance of emerging multi-core processors efficiently. This dissertation describes scalable parallelization schemes for the inference of large phylogenetic trees as well as tangible implementations of those which also eliminate memory requirements as a limiting factor for phylogenetic analyses. Additionally, it pinpoints the properties of current multi-core shared and distributed memory architectures and describes novel approaches for their efficient exploitation.
Functional and structural correlates of the aging brain: Relating visual cortex (V1) gamma band responses to age‐related structural change

The gamma band response is thought to be a key neural signature of information processing in the mammalian brain, yet little is known about how age‐related maturation influences the γ‐band response. Recent MRI‐based studies have shown that brain maturation is accompanied by clear structural changes in both gray and white matter, yet the correspondence of these changes to brain function is unclear. The objective of this study was to relate visual cortex (V1) γ‐band responses to age‐related structural change. We evaluated MEG measured γ‐band responses to contrast gratings stimuli and structural MRIs from participants observed from two separate research centers (MEG lab at CUBRIC, Cardiff University, UK, and the Lurie Family Foundations MEG Imaging Center, (CHOP) at the Children's Hospital of Philadelphia). Pooled participant data (N = 59) ranged in age from 8.7 to 45.3 years. We assessed linear associations between age and MEG γ‐band frequency and amplitude, as well as between age and MRI volumetric parameters of the occipital lobe. Our MEG findings revealed a significant negative correlation for gamma band frequency versus age. Volumetric brain analysis from the occipital lobe also revealed significant negative correlations between age and the cortical thickness of pericalcarine and cuneus areas. Our functional MEG and structural MRI findings shows regionally specific changes due to maturation and may thus be informative for understanding physiological processes of neural development, maturation, and age‐related decline. In addition, this study represents (to our knowledge), the first published demonstration of multicenter data sharing across MEG centers. Hum Brain Mapp 33:2035–2046, 2012. © 2011 Wiley Periodicals, Inc.
Run-Time Reconfiguration in Wireless Sensor Networks

A wireless sensor network (WSN) consists of multiple small and simple computers (nodes), whose performance is tightly linked to its unpredictable deployment environment. It is nearly impossible to design a WSN that performs well in every scenario; instead they are developed for a specific context, with performance rapidly decreasing when environment properties move away from the optimum. Enabling a WSN to adapt to a changing environment could be a solution for this problem. The goal of this thesis project is to develop software for sensor nodes that is able to reconfigure a wireless sensor network at run-time, allowing the sensor network to perform within its requirements under changing conditions. An approach is proposed in which developers provide knowledge about the WSN’s requirements to sensor nodes during design-time, enabling the nodes to reason about their configuration at run-time. A middleware solution is designed that combines the run-time environment data with the knowledge about the requirements and indicates which reconfiguration is required. A proof-of-concept implementation is developed which reasons about reconfiguration within milliseconds. The performed evaluation includes the implementation of a reconfigurable modal analysis application and shows the processing and memory overhead introduced by the middleware remains low. The approach proves to be a solid basis for future developments on reconfigurable sensor networks. Graduation Committee Prof.dr. K.G. Langendoen (chair) Delft University of Technology Dr.ir. G.N. Gaydadjiev Delft University of Technology Dr.ir. Z. Papp (daily supervisor) TNO Dr. M. Woehrle (university supervisor) Delft University of Technology
SIMPLE CONNECTIVITY MEASURES IN SPATIAL ECOLOGY

Connectivity is a fundamental concept that is widely utilized in spatial ecology. The majority of connectivity measures used in the recent ecological literature only consider the nearest neighbor patch/population, or patches within a limited neighborhood of the focal patch (a buffer). Meta-analysis suggests that studies using nearest neighbor connectivity measures are much less likely to find statistically significant effects of connectivity than studies that use more complex measures. Here we compare simple connectivity measures in their ability to predict colonization events in two large and good-quality empirical data sets. The nearest neighbor distance to an occupied patch is found to be an inferior measure. Buffer measures do much better, but their performance is found to be sensitive to the estimate of the buffer radius. For highly fragmented habitats, the best and most consistent performance is found for a measure that takes into account the size of the focal patch and the sizes of and distances to all potential source populations. When experimenting with reduced data sets, it was discovered that nearest neighbor measures fail to find a statistically significant effect of connectivity for a large range of data set sizes for which the more complex measures still detect a highly significant effect. We conclude that the simplicity of a nearest neighbor measure is not an adequate compensation for poor performance.
Mycoplasma pneumoniae: Current Knowledge on Nucleic Acid Amplification Techniques and Serological Diagnostics

Mycoplasma pneumoniae (M. pneumoniae) belongs to the class Mollicutes and has been recognized as a common cause of respiratory tract infections (RTIs), including community-acquired pneumonia (CAP), that occur worldwide and in all age groups. In addition, M. pneumoniae can simultaneously or sequentially lead to damage in the nervous system and has been associated with a wide variety of other acute and chronic diseases. During the past 10 years, the proportion of LRTI in children and adults, associated with M. pneumoniae infection has ranged from 0 to more than 50%. This variation is due to the age and the geographic location of the population examined but also due to the diagnostic methods used. The true role of M. pneumoniae in RTIs remains a challenge given the many limitations and lack of standardization of the applied diagnostic tool in most cases, with resultant wide variations in data from different studies. Correct and rapid diagnosis and/or management of M. pneumoniae infections is, however, critical to initiate appropriate antibiotic treatment and is nowadays usually done by PCR and/or serology. Several recent reviews, have summarized current methods for the detection and identification of M. pneumoniae. This review will therefore provide a look at the general principles, advantages, diagnostic value, and limitations of the most currently used detection techniques for the etiological diagnosis of a M. pneumoniae infection as they evolve from research to daily practice.
Antipodal paraunitary matrices and their application to OFDM systems

Paraunitary (PU) matrices and filterbanks have played an important role in many applications. This paper studies a special class of PU matrices, called antipodal paraunitary (APU) matrices. APU matrices are PU matrices whose coefficient matrices consist of /spl plusmn/1 only. Several new methods will be introduced for the construction of APU matrices. In particular, a new method based on the butterfly structure that has a low cost implementation is proposed. Moreover, one application of APU matrices in precoded orthogonal frequency division multiplexing (OFDM) systems will be considered. By using an APU precoding matrix, the complexity will be very low, and experiments show that the use of APU matrices can greatly enhance the performance.