Query: The Analysis of Android Malware Behaviors

Relevant passages:
On lightweight mobile phone application certification <=SEP=> Users have begun downloading an increasingly large number of mobile phone applications in response to advancements in handsets and wireless networks. The increased number of applications results in a greater chance of installing Trojans and similar malware. In this paper, we propose the Kirin security service for Android, which performs lightweight certification of applications to mitigate malware at install time. Kirin certification uses security rules, which are templates designed to conservatively match undesirable properties in security configuration bundled with applications. We use a variant of security requirements engineering techniques to perform an in-depth security analysis of Android to produce a set of rules that match malware characteristics. In a sample of 311 of the most popular applications downloaded from the official Android Market, Kirin and our rules found 5 applications that implement dangerous functionality and therefore should be installed with extreme caution. Upon close inspection, another five applications asserted dangerous rights, but were within the scope of reasonable functional needs. These results indicate that security configuration bundled with Android applications provides practical means of detecting malware.

DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android Malware Analysis <=SEP=> The prevalence of mobile platforms, the large market share of Android, plus the openness of the Android Market makes it a hot target for malware attacks. Once a malware sample has been identified, it is critical to quickly reveal its malicious intent and inner workings. In this paper we present DroidScope, an Android analysis platform that continues the tradition of virtualization-based malware analysis. Unlike current desktop malware analysis platforms, DroidScope reconstructs both the OSlevel and Java-level semantics simultaneously and seamlessly. To facilitate custom analysis, DroidScope exports three tiered APIs that mirror the three levels of an Android device: hardware, OS and Dalvik Virtual Machine. On top of DroidScope, we further developed several analysis tools to collect detailed native and Dalvik instruction traces, profile API-level activity, and track information leakage through both the Java and native components using taint analysis. These tools have proven to be effective in analyzing real world malware samples and incur reasonably low performance overheads.

AppIntent: analyzing sensitive data transmission in android for privacy leakage detection <=SEP=> Android phones often carry personal information, attracting malicious developers to embed code in Android applications to steal sensitive data. With known techniques in the literature, one may easily determine if sensitive data is being transmitted out of an Android phone. However, transmission of sensitive data in itself does not necessarily indicate privacy leakage; a better indicator may be whether the transmission is by user intention or not. When transmission is not intended by the user, it is more likely a privacy leakage. The problem is how to determine if transmission is user intended. As a first solution in this space, we present a new analysis framework called AppIntent. For each data transmission, AppIntent can efficiently provide a sequence of GUI manipulations corresponding to the sequence of events that lead to the data transmission, thus helping an analyst to determine if the data transmission is user intended or not. The basic idea is to use symbolic execution to generate the aforementioned event sequence, but straightforward symbolic execution proves to be too time-consuming to be practical. A major innovation in AppIntent is to leverage the unique Android execution model to reduce the search space without sacrificing code coverage. We also present an evaluation of AppIntent with a set of 750 malicious apps, as well as 1,000 top free apps from Google Play. The results show that AppIntent can effectively help separate the apps that truly leak user privacy from those that do not.

Towards automatic generation of vulnerability-based signatures <=SEP=> In this paper we explore the problem of creating vulnerability signatures. A vulnerability signature matches all exploits of a given vulnerability, even polymorphic or metamorphic variants. Our work departs from previous approaches by focusing on the semantics of the program and vulnerability exercised by a sample exploit instead of the semantics or syntax of the exploit itself. We show the semantics of a vulnerability define a language which contains all and only those inputs that exploit the vulnerability. A vulnerability signature is a representation (e.g., a regular expression) of the vulnerability language. Unlike exploit-based signatures whose error rate can only be empirically measured for known test cases, the quality of a vulnerability signature can be formally quantified for all possible inputs. We provide a formal definition of a vulnerability signature and investigate the computational complexity of creating and matching vulnerability signatures. We also systematically explore the design space of vulnerability signatures. We identify three central issues in vulnerability-signature creation: how a vulnerability signature represents the set of inputs that may exercise a vulnerability, the vulnerability coverage (i.e., number of vulnerable program paths) that is subject to our analysis during signature creation, and how a vulnerability signature is then created for a given representation and coverage. We propose new data-flow analysis and novel adoption of existing techniques such as constraint solving for automatically generating vulnerability signatures. We have built a prototype system to test our techniques. Our experiments show that we can automatically generate a vulnerability signature using a single exploit which is of much higher quality than previous exploit-based signatures. In addition, our techniques have several other security applications, and thus may be of independent interest

Computer security technology planning study <=SEP=> Approved for pubJic reJease; distribution unri mited. When U.S. Government drawings, specifications or other data are used for any purpose other than a definitely related government procurement operation, the government thereby incurs no responsibility nor any obligation whatsoever; and the fact that the government may have formulated, fui·nished, or in any way sup­ plied the said drawings, specifications, or other data is not to be regarded by implication or otherwise as in any manner licensing the holder or any other person or conveying any rights or permission to manufacture, use, or sell any patented invention that may in any way be related thereto.

Ghost hunters: ambient light and hand gesture utilization in mobile augmented reality games <=SEP=> This paper presents the current status of work-in-progress in developing Ghost Hunters that is targeted to explore the possibilities of incorporating gesture control and ambient lighting in a mobile augmented reality game played with smart glasses. We present the design and implementation of the current prototype, together with a small feasibility evaluation of battery consumption and gesture control.

An Expressive (Zero-Knowledge) Set Accumulator <=SEP=> We present a new construction of an expressive set accumulator. Unlike existing cryptographic accumulators, ours provides succinct proofs for a large collection of operations over accumulated sets, including intersection, union, set difference, SUM, COUNT, MIN, MAX, and RANGE, as well as arbitrary nestings of the above. We also show how to extend our accumulator to be zero-knowledge. The security of our accumulator is based on extractability assumptions and other assumptions that hold in the generic group model. Our construction has asymptotically optimal verification complexity and proof size, constant update complexity, and public verifiability/updatability—namely, any client who knows the public key and the last accumulator value can verify the supported operations and update the accumulator. The expressiveness of our accumulator comes at the cost of quadratic prover time. However, we show that the cryptographic operations involved are cheap compared to those incurred by generic approaches (e.g., SNARKs) that are equally expressive: our prover runs faster for sets of up to 5 million items. Our accumulator serves as a powerful cryptographic tool with many applications. For example, it can be applied to efficiently support verification of a rich collection of SQL queries when used as a drop-in replacement in existing verifiable database systems (e.g., IntegriDB, CCS 2015).

Effectiveness of fundamental frequency (F0) and strength of excitation (SOE) for spoofed speech detection <=SEP=> Current countermeasures used in spoof detectors (for speech synthesis (SS) and voice conversion (VC)) are generally phase-based (as vocoders in SS and VC systems lack phase-information). These approaches may possibly fail for non-vocoder or unit-selection-based spoofs. In this work, we explore excitation source-based features, i.e., fundamental frequency (F0) contour and strength of excitation (SoE) at the glottis as discriminative features using GMM-based classification system. We use F0 and SoE1 estimated from speech signal through zero frequency (ZF) filtering method. Further, SoE2 is estimated from negative peaks of derivative of glottal flow waveform (dGFW) at glottal closure instants (GCIs). On the evaluation set of ASVspoof 2015 challenge database, the F0 and SoEs features along with its dynamic variations achieve an Equal Error Rate (EER) of 12.41%. The source features are fused at score-level with MFCC and recently proposed cochlear filter cepstral coefficients and instantaneous frequency (CFCCIF) features. On fusion with MFCC (CFCCIF), the EER decreases from 4.08% to 3.26% (2.07% to 1.72%). The decrease in EER was evident on both known and unknown vocoder-based attacks. When MFCC, CFCCIF and source features are combined, the EER further decreased to 1.61%. Thus, source features captures complementary information than MFCC and CFCCIF used alone.

Quantitative trait loci for glucosinolate accumulation in Brassica rapa leaves. <=SEP=> Glucosinolates and their breakdown products have been recognized for their effects on plant defense, human health, flavor and taste of cruciferous vegetables. Despite this importance, little is known about the regulation of the biosynthesis and degradation in Brassica rapa. Here, the identification of quantitative trait loci (QTL) for glucosinolate accumulation in B. rapa leaves in two novel segregating double haploid (DH) populations is reported: DH38, derived from a cross between yellow sarson R500 and pak choi variety HK Naibaicai; and DH30, from a cross between yellow sarson R500 and Kairyou Hakata, a Japanese vegetable turnip variety. An integrated map of 1068 cM with 10 linkage groups, assigned to the international agreed nomenclature, is developed based on the two individual DH maps with the common parent using amplified fragment length polymorphism (AFLP) and single sequence repeat (SSR) markers. Eight different glucosinolate compounds were detected in parents and F(1)s of the DH populations and found to segregate quantitatively in the DH populations. QTL analysis identified 16 loci controlling aliphatic glucosinolate accumulation, three loci controlling total indolic glucosinolate concentration and three loci regulating aromatic glucosinolate concentrations. Both comparative genomic analyses based on Arabidopsis-Brassica rapa synteny and mapping of candidate orthologous genes in B. rapa allowed the selection of genes involved in the glucosinolate biosynthesis pathway that may account for the identified QTL.

Can Smartwatches Replace Smartphones for Posture Tracking? <=SEP=> This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch's ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches' ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed.

API design for machine learning software: experiences from the scikit-learn project <=SEP=> scikit-learn is an increasingly popular machine learning library. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.

Social support: a conceptual analysis. <=SEP=> Using the methodology of Walker and Avant, the purpose of this paper was to identify the most frequently used theoretical and operational definitions of social support. A positive relationship between social support and health is generally accepted in the literature. However, the set of dimensions used to define social support is inconsistent. In addition, few measurement tools have established reliability and validity. Findings from this conceptual analysis suggested four of the most frequently used defining attributes of social support: emotional, instrumental, informational, and appraisal. Social network, social embeddedness, and social climate were identified as antecedents of social support. Social support consequences were subsumed under the general rubric of positive health states. Examples were personal competence, health maintenance behaviours, effective coping behaviours, perceived control, sense of stability, recognition of self-worth, positive affect, psychological well-being, and decreased anxiety and depression. Recommendations for future research were made.

Child sexual abuse in sub-Saharan Africa: a literature review. <=SEP=> OBJECTIVE
This article reviews the English-language literature on child sexual abuse in sub-Saharan Africa (SSA). The focus is on the sexual abuse of children in the home/community, as opposed to the commercial sexual exploitation of children.


METHODS
English language, peer-reviewed papers cited in the Social Sciences Citation Index (SSCI) are examined. Reports from international and local NGOs and UN agencies are also examined.


RESULTS
Few published studies on the sexual abuse of children have been conducted in the region, with the exception of South Africa. Samples are predominantly clinical or University based. A number of studies report that approximately 5% of the sample reported penetrative sexual abuse during their childhood. No national survey of the general population has been conducted. The most frequent explanations for the sexual abuse of children in SSA include rapid social change, AIDS/HIV avoidance strategies and the patriarchal nature of society. Child sexual abuse is most frequently perpetrated by family members, relatives, neighbors or others known to the child.


CONCLUSIONS
There is nothing to support the widely held view that child sexual abuse is very rare in SSA-prevalence levels are comparable with studies reported from other regions. The high prevalence levels of AIDS/HIV in the region expose sexually abused children to high risks of infection. It is estimated that, approximately.6-1.8% of all children in high HIV-incidence countries in Southern Africa will experience penetrative sexual abuse by an AIDS/HIV infected perpetrator before 18 years of age.

Stabilizing Dynamic Controllers for Hybrid Systems: A Hybrid Control Lyapunov Function Approach <=SEP=> This paper proposes a dynamic controller structure and a systematic design procedure for stabilizing discrete-time hybrid systems. The proposed approach is based on the concept of control Lyapunov functions (CLFs), which, when available, can be used to design a stabilizing state-feedback control law. In general, the construction of a CLF for hybrid dynamical systems involving both continuous and discrete states is extremely complicated, especially in the presence of non-trivial discrete dynamics. Therefore, we introduce the novel concept of a hybrid control Lyapunov function, which allows the compositional design of a discrete and a continuous part of the CLF, and we formally prove that the existence of a hybrid CLF guarantees the existence of a classical CLF. A constructive procedure is provided to synthesize a hybrid CLF, by expanding the dynamics of the hybrid system with a specific controller dynamics. We show that this synthesis procedure leads to a dynamic controller that can be implemented by a receding horizon control strategy, and that the associated optimization problem is numerically tractable for a fairly general class of hybrid systems, useful in real world applications. Compared to classical hybrid receding horizon control algorithms, the proposed approach typically requires a shorter prediction horizon to guarantee asymptotic stability of the closed-loop system, which yields a reduction of the computational burden, as illustrated through two examples.

Familial hypertrichosis cubiti: hairy elbows syndrome. <=SEP=> Genetically determined hypertrichosis is uncommon, but several forms of familial hairiness have been described. In some kindreds the unusual hairiness has been generalized, while in others, the hypertrichosis has been confined to a specific site on the body. The purpose of this paper is to report the occurrence of undue hairiness of the elbow regions in members of an Amish family, and to discuss the genetic significance of the condition. No previous descriptions of hypertrichosis of this distribution could be found in the literature, and it is therefore suggested that the condition be termed familial hypertrichosis cubiti or the hairy elbows syndrome.

Instance Based Clustering of Semantic Web Resources <=SEP=> The original Semantic Web vision was explicit in the need for intelligent autonomous agents that would represent users and help them navigate the Semantic Web. We argue that an essential feature for such agents is the capability to analyse data and learn. In this paper we outline the challenges and issues surrounding the application of clustering algorithms to Semantic Web data. We present several ways to extract instances from a large RDF graph and computing the distance between these. We evaluate our approaches on three different data-sets, one representing a typical relational database to RDF conversion, one based on data from a ontologically rich Semantic Web enabled application, and one consisting of a crawl of FOAF documents; applying both supervised and unsupervised evaluation metrics. Our evaluation did not support choosing a single combination of instance extraction method and similarity metric as superior in all cases, and as expected the behaviour depends greatly on the data being clustered. Instead, we attempt to identify characteristics of data that make particular methods more suitable.

An Evaluation of Fine and Gross Motor Skills in Adolescents with Down Syndromes <=SEP=> Down syndrome (DS) is the most common genetic cause of intellectual disability. Motor development of children with DS is delayed. The aim of the present study was to evaluate of fine and gross motor skills of adolescents with DS. The study sample of a total of 34 participants aged between 14 to 20 years comprised 16 adolescents with DS and a normally developing group of 18 adolescents without DS. Fine and gross motor skills of the participants were assessed by Bruininks-Oseretsky Test of Motor Proficiency, second edition short form (BOT-2 SF). The highest score of the test is 88 and higher score shows higher performance. The average age of adolescents with DS and without DS who participated in the study were 17.06±2.79 and 16.56±1.09, respectively. All partcipants were male. It was found a significant differences for all BOT-2 SF subtests and total scores when compared between adolescents with DS and adolescents without DS (p<0.05). Adolescents without DS has higher scores than adolescents with DS. In conclusion, both fine and gross motor skill performance of adolescents with DS is lower than normally developing adolescents. This study stresses the importance of interventions facilitating motor skills.

Deep Continuous Fusion for Multi-sensor 3D Object Detection <=SEP=> In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art.

Wideband Excitation Technology of ${\rm TE}_{20}$ Mode Substrate Integrated Waveguide (SIW) and Its Applications <=SEP=> The higher order mode substrate integrated waveguide (SIW) has advantages in the applications, which are: simplifying the structure with reduced fabrication cost and enhancing the stability of performance by relaxed fabrication tolerance. In this paper, two wideband TE20 mode excitation structures are presented and investigated for the SIW. A slot along the mid line in the longitudinal direction of the SIW is employed to convert the electromagnetic field pattern between slotline and the TE20 mode SIW. The second structure is based on the slot aperture coupling and can realize wideband direct transition between the TE20 mode SIW and microstrip line. Both transitions have a simple and compact structure, as well as broadband characteristics. The wideband transition performance is demonstrated in this paper. As application examples of the transitions, two broadband substrate integrated baluns are presented and fabricated based on the TE20 mode excitation structures and TE20 mode characteristics. The 180 ° out-of-phase and equal magnitude of the balun outputs in a wide frequency range can be achieved inherently. The balun based on the slotline excitation has a fractional bandwidth of 50.2%, from 7.3 to 12.2 GHz, with measured return loss, amplitude imbalance, and phase imbalance better than 10 and 0.45 dB and 3.8 °. The balun based on the aperture coupling excitation shows a fractional bandwidth of 50.3%, from 7 to 11.7 GHz, with measured return loss, insertion loss, amplitude imbalance, and phase imbalance better than 10, 1, and 0.27 dB and 2.4 °, respectively. Both baluns not only show good performance, but also demonstrate the existence of the TE20 mode in the SIW.

Intuitionistic fuzzy sets and L-fuzzy sets <=SEP=> This paper proves that the concepts of intuitionistic fuzzy sets and intuitionistic L-fuzzy sets and the concept of L-fuzzy sets are equivalent. c © 2000 Elsevier Science B.V. All rights reserved

Games and Agents: Designing Intelligent Gameplay <=SEP=> There is an attention shift within the gaming industry toward more natural (long-term) behavior of nonplaying characters (NPCs). Multiagent system research offers a promising technology to implement cognitive intelligent NPCs. However, the technologies used in game engines and multiagent platforms are not readily compatible due to some inherent differences of concerns. Where game engines focus on real-time aspects and thus propagate efficiency and central control, multiagent platforms assume autonomy of the agents. Increased autonomy and intelligence may offer benefits for a more compelling gameplay and may even be necessary for serious games. However, it raises problems when current game design techniques are used to incorporate state-of-the-art multiagent system technology. In this paper, we will focus on three specific problem areas that arise from this difference of view: synchronization, information representation, and communication. We argue that the current attempts for integration still fall short on some of these aspects. We show that to fully integrate intelligent agents in games, one should not only use a technical solution, but also a design methodology that is amenable to agents. The game design should be adjusted to incorporate the possibilities of agents early on in the process.

Towards Enhancing the Security of OAuth Implementations in Smart Phones <=SEP=> With the roaring growth and wide adoption of smart mobile devices, users are continuously integrating with culture of the mobile applications (apps). These apps are not only gaining access to information on the smartphone but they are also able gain users' authorization to access remote servers on their behalf. The Open standard for Authorization (OAuth) is widely used in mobile apps for gaining access to user's resources on remote service providers. In this paper, we analyze the different OAuth implementations adopted by the SDKs of the popular resource providers on smartphones and demonstrate possible attacks on most OAuth implementations. By analyzing source code of more than 430 popular Android apps we summarized the trends followed by the service providers and by the OAuth development choices made by application developers. In addition, we propose an application-based OAuth Manager framework, that provides a secure OAuth flow in smartphones that is based on the concept of privilege separation and does not require high overhead.

Improving Cancer Treatment via Mathematical Modeling: Surmounting the Challenges Is Worth the Effort <=SEP=> Drug delivery schedules are key factors in the efficacy of cancer therapies, and mathematical modeling of population dynamics and treatment responses can be applied to identify better drug administration regimes as well as provide mechanistic insights. To capitalize on the promise of this approach, the cancer field must meet the challenges of moving this type of work into clinics.

Unsupervised learning of feature hierarchies <=SEP=> The applicability of machine learning methods is often limi ted by the amount of available labeled data, and by the ability (or inability) of the de signer to produce good internal representations and good similarity measures for the input data vectors. The aim of this thesis is to alleviate these two limitations by proposing al gorithms tolearngood internal representations, and invariant feature hierarchies from u nlabeled data. These methods go beyond traditional supervised learning algorithms, and rely on unsupervised, and semi-supervised learning. In particular, this work focuses on “deep learning” methods , a et of techniques and principles to train hierarchical models. Hierarchical mod els produce feature hierarchies that can capture complex non-linear dependencies among the observed data variables in a concise and efficient manner. After training, these mode ls can be employed in real-time systems because they compute the representation by a very fast forward propagation of the input through a sequence of non-linear transf ormations. When the paucity of labeled data does not allow the use of traditional supervi s d algorithms, each layer of the hierarchy can be trained in sequence starting at the bott om by using unsupervised or semi-supervised algorithms. Once each layer has been train ed, the whole system can be fine-tuned in an end-to-end fashion. We propose several unsu pervised algorithms that can be used as building block to train such feature hierarchi es. We investigate algorithms that produce sparse overcomplete representations a nd fe tures that are invariant to known and learned transformations. These algorithms are designed using the Energy-

Deep convolutional neural network for latent fingerprint enhancement <=SEP=> In this work, we propose a novel latent fingerprint enhancement method based on FingerNet inspired by recent development of Convolutional Neural Network (CNN). Although CNN is achieving superior performance in many computer vision tasks from low-level image processing to high-level semantic understanding, limited attention has been paid in fingerprint community. The proposed FingerNet has three major parts: one common convolution part shared by two different deconvolution parts, which are the enhancement branch and the orientation branch. The convolution part is to extract fingerprint features particularly for enhancement purpose. The enhancement deconvolution branch is employed to remove structured noise and enhance fingerprints as its task. The orientation deconvolution branch performs the task of guiding enhancement through a multi-task learning strategy. The network is trained in the manner of pixels-to-pixels and end-to-end learning, that can directly enhance latent fingerprint as the output. We also study some implementation details such as single-task learning, multi-task learning, and the residual learning. Experimental results of the FingerNet system on latent fingerprint dataset NIST SD27 demonstrate effectiveness and robustness of the proposed method.

IRIS performer: a high performance multiprocessing toolkit for real-time 3D graphics <=SEP=> This paper describes the design and implementation of IRIS Performer, a toolkit for visual simulation, virtual reality, and other real-time 3D graphics applications. The principal design goal is to allow application developers to more easily obtain maximal performance from 3D graphics workstations which feature multiple CPUs and support an immediate-mode rendering library. To this end, the toolkit combines a low-level library for high-performance rendering with a high-level library that implements pipelined, parallel traversals of a hierarchical scene graph. While discussing the toolkit architecture, the paper illuminates and addresses performance issues fundamental to immediate-mode graphics and coarse-grained, pipelined multiprocessing. Graphics optimizations focus on efficient data transfer to the graphics subsystem, reduction of mode settings, and restricting state inheritance. The toolkit's multiprocessing features solve the problems of how to partition work among multiple processes, how to synchronize these processes, and how to manage data in a pipelined, multiprocessing environment. The paper also discusses support for intersection detection, fixed-frame rates, run-time profiling and special effects such as geometric morphing.

Tversky loss function for image segmentation using 3D fully convolutional deep networks <=SEP=> Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks.

Implementation of FC-TCR for Reactive Power Control <=SEP=> This paper deals with the simulation and implementation of fixed capacitor thyristor controlled reactor system(SVC) . The TCR system is simulated using MATLAB and the simulation results are presented. SVC is basically a shunt connected static var generator whose output is adjusted to exchange capacitive or inductive current so as to maintain or control specific power variable. In this paper, simple circuit model of Thyristor Controlled Reactor is modeled and simulated using MATLAB. The simulation results are presented. The current drawn by the TCR varies with the variation in the firing angle. The simulation results are compared with the theoretical results.

Deep Automatic Portrait Matting <=SEP=> We propose an automatic image matting method for portrait images. This method does not need user interaction, which was however essential in most previous approaches. In order to accomplish this goal, a new end-to-end convolutional neural network (CNN) based framework is proposed taking the input of a portrait image. It outputs the matte result. Our method considers not only image semantic prediction but also pixel-level image matte optimization. A new portrait image dataset is constructed with our labeled matting ground truth. Our automatic method achieves comparable results with state-of-the-art methods that require specified foreground and background regions or pixels. Many applications are enabled given the automatic nature of our system.

Prediction of Primary Pupil Enrollment in Government School Using Data Mining Forecasting Technique <=SEP=> This research concentrates upon predictive analysis of enrolled pupil using forecasting based on data mining technique. The Microsoft SQL Server Data Mining Add-ins Excel 2007 was employed as a software mining tool for predicting enrollment of pupils. The time series algorithm was used for experiment analysis. U-DISE (Unified District Information System for Education) Dataset of Aurangabad district in Maharashtra (India) was obtained from SSA (Sarva Shiksha Abhiyan) and was used for analysis. Data mining for primary pupil enrollment research provides a feasible way to analyze the trend and to addresses the interest of pupil. The dataset was studied and analyzed to forecast registered pupils in government for upcoming years. We conclude that for upcoming year, pupil strength in government school will be dropped down and number of teachers will be supersizing in the said district. Keywords— Data mining, Microsoft SQL Server Data Mining Add-ins Excel 2007, Time series algorithm, U-DISE