Miracle-dz/UniFAR-FTUs huggingface dataset:
Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.
A Salmonella Regulator Modulates Intestinal Colonization and Use of Phosphonoacetic Acid

Many microorganisms produce phosphonates, molecules characterized by stable carbon-phosphorus bonds that store phosphorus or act as antimicrobials. The role of phosphonates in the marine biosphere is well characterized but the role of these molecules in the intestine is poorly understood. Salmonella enterica uses its virulence factors to influence the host immune response to compete with the host and normal microflora for nutrients. Salmonella cannot produce phosphonates but encodes the enzymes to use them suggesting that it is exposed to phosphonates during its life cycle. The role of phosphonates during enteric salmonellosis is unexplored. We have previously shown that STM3602, encoding a putative regulator of phosphonate metabolism, is needed for colonization in calves. Here, we report that the necessity of STM3602 in colonization of the murine intestine results from multiple factors. STM3602 is needed for full activation of the type-3 secretion system-1 and for optimal invasion of epithelial cells. The ΔSTM3602 mutant grows poorly in phosphonoacetic acid (PA) as the sole phosphorus source, but can use 2-aminoethylphosphonate. PhnA, an enzyme required for PA breakdown, is not controlled by STM3602 suggesting an additional mechanism for utilization of PA in S. Typhimurium. Finally, the requirement of STM3602 for intestinal colonization differs depending on the composition of the microflora. Our data suggest that STM3602 has multiple regulatory targets that are necessary for survival within the microbial community in the intestine. Determination of the members of the STM3602 regulon may illuminate new pathways needed for colonization of the host.
Design of 40–108-GHz Low-Power and High-Speed CMOS Up-/Down-Conversion Ring Mixers for Multistandard MMW Radio Applications

In this paper, a pair of broadband, low-LO-power, low-dc-power, and high-speed up/down-conversion ring mixers are presented for multistandard millimeter-wave (MMW) radio applications. By employing a weak inversion biasing technique, the ring mixer can operate at a low LO drive level and low dc power while maintaining reasonable conversion gain performance. In addition, an IF transimpedance amplifier (TIA) buffer and wideband RF design are introduced to increase the operation speed of the mixer for MMW wireless Gigabit transmission. Using a 90-nm CMOS low-power process, the up-/down-conversion ring mixers are designed and fabricated based on the presented topology. The down-conversion ring mixer and up-conversion ring mixer exhibit flat measured conversion gain of -1 ± 2 dB and 0 ± 2 dB and dB from 40 to 110 GHz and 40 to 108 GHz, respectively. After biasing the transistors of the ring mixer core at weak inversion region, the presented down-conversion and up-conversion ring mixers can operate at low LO drive power, -2 and 0 dBm, respectively, even up to 100 GHz. For MMW wireless gigabit communication, gigabit binary phase-shift keying modulation signal test is successfully performed through a direct-conversion system in this work. The presented ring mixers are suitable for 60-GHz wireless personal area network, -E-band (71-76 GHz, 81-86 GHz, and 92-95 GHz) wireless fiber, and 77-GHz anti-collision radar applications.
Toward anonymity in Delay Tolerant Networks: Threshold Pivot Scheme

Delay Tolerant Networks (DTNs) remove traditional assumptions of end-to-end connectivity, extending network communication to intermittently connected mobile, ad-hoc, and vehicular environments. This work considers anonymity as a vital security primitive for viable military and civilian DTNs. DTNs present new and unique anonymity challenges since we must protect physical location information as mobile nodes with limited topology knowledge naturally mix. We develop a novel Threshold Pivot Scheme (TPS) for DTNs to address these challenges and provide resistance to traffic analysis, source anonymity, and sender-receiver unlinkability. Reply techniques adapted from mix-nets allow for anonymous DTN communication, while secret sharing provides a configurable level of anonymity that enables a balance between security and efficiency. We evaluate TPS via simulation on real-world DTN scenarios to understand its feasibility, performance, and overhead while comparing the provided anonymity against an analytically optimal model.
K-Connection Maintenance Algorithm for Balanced Routing In Mobile Ad Hoc Networks

In this paper we propose a connection balanced routing in mobile ad-hoc networks (MANETs) using backbone update messages. Virtual-Backbone (VB) construction is very familiar, to overcome broadcast storm problem in wireless networks. In real time, during the movement of nodes in MANET, more number of nodes added in a particular node (say a VB node), it leads poor quality of service in multi-hop wireless environment. To overcome this problem connection balanced is required; in terms of reduction of nodes attachment in a backbone node and attach these nodes with new VB, to maintain connectivity. Most load balancing routing (LBR) protocols are based on reactive routing and evaluate the routes by weight, which depends on the traffic load information collected by each intermediate nodes. The collection of data for load information from all the nodes creates more delay and processing time, leads to extra overhead and result in performance degradation. Existing research works deals with construction of virtual backbone only, no one give the solution for connection overhead problem. To overcome this we propose a localized algorithm to support reducing the number of connection and maintain these connections by nearby VB nodes or identify the new VB for maintain the k-connections.our results are witnessed by simulation.
Enhanced dynamic spectrum sharing for multi-cell heterogeneous networks

In order to efficiently exploit the spectrum utility, we propose a multi-cell frequency reusing approach for heterogeneous networks based on dynamic spectrum sharing (DSS) technology. Actually in the traditional DSS scheme there are still several types of interference that cannot be eliminated completely, especially the severe inter-cell interference that the cell-edge users suffer from. To improve the cell edge throughput, an enhanced DSS scheme based on fractional frequency reuse (FFR) is proposed. The numerical results demonstrate that the proposed scheme can mitigate inter-cell interference and improve the cell-edge throughput efficiently.
The Impact of IT on Quality of Care: Evaluation of an Integrated Chronic Disease Management System

Fragmentation of health care service delivery is one of the main challenges in North American health systems. IT is often seen as a solution to fragmentation in care delivery. In this paper, we study the impact of electronically integrating primary care physicians, specialists, hospitals, and pharmacists, on the quality of care delivered to patients. We hypothesize that an electronically integrated prescription, drug, and disease management system will have both a direct and an indirect effect on quality of care. The results of our study based on data from before and after electronic integration, from a matched set of 15,626 patients with electronic integration and 15, 626 patients in a control group, who visited over 95 physicians in a large North American health network, show that patients treated through the electronically integrated system had better quality of care in the follow-up period and a higher continuity of care, compared to the control group.
Learning Representations for Text-level Discourse Parsing

In the proposed doctoral work we will design an end-to-end approach for the challenging NLP task of text-level discourse parsing. Instead of depending on mostly hand-engineered sparse features and independent components for each subtask, we propose a unified approach completely based on deep learning architectures. To train more expressive representations that capture communicative functions and semantic roles of discourse units and relations between them, we will jointly learn all discourse parsing subtasks at different layers of our architecture and share their intermediate representations. By combining unsupervised training of word embeddings with our layer-wise multi-task learning of higher representations we hope to reach or even surpass performance of current state-of-the-art methods on annotated English corpora.
Does politics impact carbon emissions

Do political variables influence long-term environmental transitions? The discussion on the determinants of the environmental performance of countries has been dominated by a focus on the Environmental Kuznets curve. This concept concentrated primarily on the role of economic factors, in particular per capita income levels. By contrast, we outline both conceptually and empirically how political factors can affect long-term carbon trajectories. Our findings from an error-correction model suggest that political factors are an important explanatory variable for carbon emissions in over 100 countries during the period 1970-2004. The results show that political capacity reduces carbon emission in OECD countries whereas political constraints, democracy and the Kyoto Protocol reduce long-term carbon emission in the group of all countries as well as in non-OECD countries.
Examining the Impact of Leadership Style and School Climate on Student Achievement.

EXAMINING THE IMPACT OF LEADERSHIP STYLE AND SCHOOL CLIMATE ON STUDENT ACHIEVEMENT Tina Robinson Old Dominion University, 2010 Director: William A. Owings The purpose of this quantitative study was to investigate whether or not leadership style and school climate are significant predictors of student achievement. The target population consisted of elementary and high school teachers from Virginia public schools who had taught under the leadership of their respective current principals for at least 4 years. Nine school divisions were randomly selected from each of the following regions—rural, suburban, and urban—within the Commonwealth of Virginia. Data were collected using two surveys. Bass's (1985) Multifactor Leadership Questionnaire (Form 5X) was used to assess principals' leadership style—transactional, transformational, or laissez-faire—as perceived by teachers. The Organizational Climate Description Questionnaire (OCDQ), which was originally developed by Halpin and Croft (1963), was used to assess school climate. The OCDQ-RE was administered to elementary school participants; the OCDQ-RS was administered to high school participants. Both versions of the OCDQ identify overall school climate type: open, engaged, disengaged, or closed climate. All participants were also asked to complete a demographics questionnaire. Student achievement was measured using 3-year average scores on the Virginia Standards of Learning tests in reading and math for grades 3 and 11.
An Effective Image Retrieval Technique Based on Color Perception

In content-based image retrieval (CBIR), color is the most intuitive image features and it is widely used. But The current color feature can describe the semantics of the whole image effectively, but does not reflect characteristics of the color salience objects in an image. For the purpose of giving prominence to the color characteristics of salience objects, this paper proposes a new color feature descriptor based on color visual perception model. By analyzing the human color visual perception process, the color perception model is proposed at first. This model integrates the intensity, the color contrast and self saliency, sparsity and centricity saliency to describe human color visual perception of the image. Then, the new color feature descriptor is calculated by weighting the significant bit-plane histograms with color perception map. Finally, similarity measure is presented for the new color feature. Experiment results show that the proposed color feature is more accurate and efficient in retrieving images with user-interested color objects. Compared with the other three retrieval methods, the proposed technique improves the retrieval accuracy effectively.
Attack Patterns as a Knowledge Resource for Building Secure Software

Building software with an adequate level of security assurance for its mission becomes more and more challenging every day as the size, complexity, and tempo of software creation increases and the number and the skill level of attackers continues to grow. These factors each exacerbate the issue that, to build secure software, builders must ensure that they have protected every relevant potential vulnerability; yet, to attack software, attackers often have to find and exploit only a single exposed vulnerability. To identify and mitigate relevant vulnerabilities in software, the development community needs more than just good software engineering and analytical practices, a solid grasp of software security features, and a powerful set of tools. All of these things are necessary but not sufficient. To be effective, the community needs to think outside of the box and to have a firm grasp of the attacker’s perspective and the approaches used to exploit software [Hoglund 04, Koizol 04].
Bootstrapping the Software Engineering Industry into E-Commerce

This paper discusses the changes that need to occur in the software engineering industry in order for it to be able to broadly adopt electronic commerce and digitise its value chains. A framework is proposed that encompasses these changes and suggests the potential to achieve large productivity gains by making the transition from primary producer to secondary industry. 1 INTRODUCTION The potential benefits of e-commerce (incorporating electronic distribution of the product itself, to truncate distribution chains) to the industry are obvious – reduced delivery times, fewer profit markups , lower costs for distribution and return/recall, and (initially) increased developer profitability. The digitisation of the distribution chain increases consumers' location-utility. Materials management is reduced or eliminated, so capital is not tied up in inventory and there are no materials handling costs. This increases short-term profitability and the cash available to operations. Customers are exposed to the developer's web-site, increasing brand recognition (and customer retention) and facilitating flow-on sales. Despite these benefits, e-fulfilment in the software engineering industry is limited compared to its potential extent. Both bespoke and off-the-shelf software are shipped largely as boxed CD-ROM binaries suitable for installation and execution on a single OS/processor family – such as " Win32 on a Pentium-conformant architecture ". A single package may contain several binaries supporting different platforms. The use of File Transfer Protocol (FTP) servers for software distribution is only widely adopted for zero-cost, shareware, open-source, or trial-version software most of which are not commercial. It is apparent, therefore, that changes must need to occur in the industry before it can widely adopt e-commerce. This paper postulates what these changes might be and proposes an industry framework both as a potential change agent and as a 'next-generation' evolution in industry practice that incorporates e-commerce to produce higher-quality software more cheaply, reliably and quickly. The proposed framework is essentially an electronic iterative just-in-time document refinement system. The system operates on a micro-level as a participant in (and a driver of) the information exchanged between each vendor and consumer, and on a macro-level by instrumenting itself and organising system information into useful reports (e.g. catalogues). Section 2 presents an analysis of the software engineering value chain and considers the roles e-commerce might play. This is followed by consideration of the possible factors inhibiting broader use of e-commerce in the software engineering industry. Section 4 presents the proposed industry framework that facilitates and is facilitated …
BLOCK-SPARSE BASIS SETS FOR IMPROVED AUDIO CONTENT ESTIMATION

Unsupervised lexicon learning techniques for audio-in-the-wild typically assume that only one of the lexical units is active at any given point in time (hard quantization) or use soft counts to avoid committing to one unit (soft quantization). In reality, the audio will usually be produced as a mixture of the different audio concepts in the lexicon. In this paper, we propose a model where the audio content is assumed to be generated by a mixture of a sparse subset of the lexical units thus guiding the system toward a better estimate of presence of the concepts. We present an approach that builds on current lexicon learning frameworks, and develop a novel algorithm to estimate the contribution of different sources by imposing block-sparsity constraints on the lexicon. Our proposed framework shows significant improvement over the standard lexicon learning framework on a retrieval task for audio-in-the-wild.
Mycotoxin Determination in Foods Using Advanced Sensors Based on Antibodies or Aptamers

Mycotoxin contamination threatens health and life of humans and animals throughout the food supply chains. Many of the mycotoxins have been proven to be carcinogens, teratogens and mutagens. The reliable and sensitive sensing methods are requested to monitor mycotoxin contamination. Advanced sensors based on antibodies or aptamers boast the advantages of high sensitivity and rapidity, and have been used in the mycotoxin sensing. These sensors are miniaturized, thereby lowering costs, and are applicable to high-throughput modes. In this work, the latest developments in sensing strategies for mycotoxin determination were critically discussed. Optical and electrochemical sensing modes were compared. The sensing methods for single mycotoxin or multiple mycotoxins in food samples were reviewed, along with the challenges and the future of antibody or aptamer-based sensors. This work might promote academic studies and industrial applications for mycotoxin sensing.
Chromospherically Active Stars. XXI. The Giant, Single-lined Binaries HD 89546 And HD 113816

We have obtained spectroscopy and photometry of the chromospherically active, single-lined spectroscopic binaries HD 89546 and HD 113816. HD 89546 has a circular orbit with a period of 21.3596 days. Its primary has a spectral type of G9 III and is somewhat metal-poor with [Fe/H] ∼ -0.5. HD 113816 has an orbit with a period of 23.6546 and a low eccentricity of 0.022. Its mass function is extremely small, 0.0007 M⊙, consistent with a very low inclination. The primary is a slightly metal-poor K2 III. A decade or more of photometric monitoring with an automatic telescope demonstrates that both systems display brightness variations due to rotational modulation of the visibility of photospheric star spots, as well as light-curve changes resulting from the redistribution of star spots by differential rotation and long-term changes in the filling factor of the spots. We determined rotation periods for each season when the observations were numerous enough. Our mean rotation periods of 21.3 and 24.1 days for HD 89546 and HD 113816, respectively, confirm that the giants in each system are synchronously rotating. The orbital elements and properties of the giant components of these two systems, including levels of surface magnetic activity, are quite similar. However, the two rotational inclinations are rather different, 57° for HD 89546 and 13° for HD 113816. Thus the latter giant is seen nearly pole on. We analyzed the light curves for similarities and differences that result from viewing these two systems from quite different inclinations.
Occlusion handling based on sub-blobbing in automated video surveillance system

Object tracking with occlusion handling is a challenging problem in automated video surveillance. In particular, occlusion handling and tracking have been often considered as separate modules. This paper proposes a tracking method in the context of video surveillance, where occlusions are automatically detected and handled to solve ambiguities. Hence, the tracking process can continue to track the different moving objects correctly. The proposed approach is based on sub-blobbing, that is, blobs representing moving objects are segmented into sections whenever occlusions occur. These sub-blobs are then treated as blobs with the occluded ones ignored. By doing so, the tracking of objects has become more accurate and less sensitive to occlusions. We have also used a feature-based framework for identifying the tracked objects, where several flexible attributes were involved. Experiments on several videos have clearly demonstrated the success of the proposed method.
A Novel Method of Outliers within Data Streams Based on Clustering Evolving Model for Detecting Intrusion Attacks of Unknown Type

It is an important issue to detect the intrusion attacks for the security of network communication. The clustering-based methods usually are proposed to cope with the problem of intrusion detections. However, how to detect the unknown intrusion attacks within stream data has come to be a challenge. In this paper, we consider the intrusion attacks as outliers and propose a novel approach (called DOExMiCluster) based on clustering data stream to detect the outliers of unknown type. The new micro-cluster concept, normalization data technology and k-mean measure are only used to learn the normal sub micro-clusters online till the event that two special micro-clusters are merged and a new micro-cluster is created doesn’t appear, and then system recognizes the instances which cannot fall into any micro-clusters as outliers.
Experimental study on dereverberation and noise reduction for distant speech recognition

Room reverberation and environment noise pose significant challenges for both speech enhancement and distant speech recognition (DSR). However, the rapid progresses achieved recently in the two fields are somehow ignored by each other. Thus it is important to examine the benefit of the state-of-the-art front-end speech enhancement can provide for a typical DSR system. In this paper, we employ a recently-reported state-of-the-art dereverberation and noise reduction approach for DSR. This method can be evolved to several famous enhancement methods by choosing different parameters. We examine the correlations between seven commonly-used objective measures for enhancement evaluation and the speech recognition error rate of a typical DSR system. We show that, besides the substantial effectiveness of the enhancement approach for DSR, the perceptual evaluation of speech quality (PESQ) has the highest correlation with the speech recognition error rate over other object measures.