title: Discovery of driving patterns by trajectory segmentation

abstract: Telematics data is becoming increasingly available due to the ubiquity of devices that collect data during drives, for different purposes, such as usage based insurance (UBI), fleet management, navigation of connected vehicles, etc. Consequently, a variety of data-analytic applications have become feasible that extract valuable insights from the data. In this paper, we address the especially challenging problem of discovering behavior-based driving patterns from only externally observable phenomena (e.g. vehicle's speed). We present a trajectory segmentation approach capable of discovering driving patterns as separate segments, based on the behavior of drivers. This segmentation approach includes a novel transformation of trajectories along with a dynamic programming approach for segmentation. We apply the segmentation approach on a real-word, rich dataset of personal car trajectories provided by a major insurance company based in Columbus, Ohio. Analysis and preliminary results show the applicability of approach for finding significant driving patterns.


title: Unsupervised Learning of Stereo Matching

abstract: Convolutional neural networks showed the ability in stereo matching cost learning. Recent approaches learned parameters from public datasets that have ground truth disparity maps. Due to the difficulty of labeling ground truth depth, usable data for system training is rather limited, making it difficult to apply the system to real applications. In this paper, we present a framework for learning stereo matching costs without human supervision. Our method updates network parameters in an iterative manner. It starts with a randomly initialized network. Left-right check is adopted to guide the training. Suitable matching is then picked and used as training data in following iterations. Our system finally converges to a stable state and performs even comparably with other supervised methods.


title: Doping-Less Tunnel Field Effect Transistor: Design and Investigation

abstract: Using calibrated simulations, we report a detailed study of the doping-less tunnel field effect transistor (TFET) on a thin intrinsic silicon film using charge plasma concept. Without the need for any doping, the source and drain regions are formed using the charge plasma concept by choosing appropriate work functions for the source and drain metal electrodes. Our results show that the performance of the doping-less TFET is similar to that of a corresponding doped TFET. The doping-less TFET is expected to be free from problems associated with random dopant fluctuations. Furthermore, fabrication of doping-less TFET does not require a high-temperature doping/annealing processes and therefore cuts down the thermal budget, opening up possibilities for fabricating TFETs on single crystal silicon-on-glass substrates formed by wafer scale epitaxial transfer.


title: Shape retrieval using 3d zernike descriptors

abstract: Abstract We advocate the usage of 3D Zernike invariants as descriptors for 3D shape retrieval. The basis polynomials of this representation facilitate computation of invariants under rotation, translation and scaling. Some theoretical results have already been summarized in the past from the aspect of pattern recognition and shape analysis. We provide practical analysis of these invariants along with algorithms and computational details. Furthermore, we give a detailed discussion on influence of the algorithm parameters like the conversion into a volumetric function, number of utilized coefficients, etc. As is revealed by our study, the 3D Zernike descriptors are natural extensions of recently introduced spherical harmonics based descriptors. We conduct a comparison of 3D Zernike descriptors against these regarding computational aspects and shape retrieval performance using several quality measures and based on experiments on the Princeton Shape Benchmark.


title: Classic and Heuristic Approaches in Robot Motion Planning – A Chronological Review

abstract: This paper reviews the major contributions to the Motion Planning (MP) field throughout a 35-year period, from classic approaches to heuristic algorithms. Due to the NP-Hardness of the MP problem, heuristic methods have outperformed the classic approaches and have gained wide popularity. After surveying around 1400 papers in the field, the amount of existing works for each method is identified and classified. Especially, the history and applications of numerous heuristic methods in MP is investigated. The paper concludes with comparative tables and graphs demonstrating the frequency of each MP method’s application, and so can be used as a guideline for MP researchers. Keywords—Robot motion planning, Heuristic algorithms.


title: Earnings Quality and Financial Reporting Credibility: An Empirical Investigation

abstract: Existing research indicates that firms with high accruals are more likely to experience future earnings reversals and SEC enforcement actions for GAAP violations, but that investors do not appear to anticipate these consequences. In this paper, we directly examine the published opinions of two types of professional investor intermediaries to see if they anticipate the consequences of high accruals. First, we examine the earnings forecasts of sell-side analysts. We show that analysts' earnings forecasts do not anticipate the future earnings reversals associated with high accruals. Second, we examine the audit opinions of independent auditors. We find no evidence that auditors signal the higher likelihood of GAAP violations through their audit opinions. Overall, our evidence indicates that even professional investor intermediaries act as if they do not anticipate the consequences of high accruals.


title: Grammatical Gender and Number Agreement in Spanish: An ERP Comparison

abstract: The role of grammatical gender and number representations in syntactic processes during reading in Spanish was studied using the event-related potentials (ERPs) technique. The electroencephalogram was recorded with a dense array of 128 electrodes while Spanish speakers read word pairs (Experiment 1) or sentences (Experiment 2) in which gender or number agreement relationships were manipulated. Disagreement in word pairs formed by a noun and an adjective (e.g., faro–alto [lighthouse–high]) produced an N400-type effect, while word pairs formed by an article and a noun (e.g., el–piano [ the-piano]) showed an additional left anterior negativity effect (LAN). Agreement violations with the same words inserted in sentences (e.g., El piano estaba viejo y desafinado [them-spiano m-swas old m-sand off-key]) resulted in a pattern of LAN–P600. This effect was found both when the violation occurred in the middle of the sentence (at the adjective), as well as when this happened at the beginning of the sentence (at the noun), but the last segment of the P600 effect was greater for the middle sentence position, which could indicate differences in the complexity of reanalysis processes. Differences between grammatical gender and number disagreement were found in late measures. In the word pairs experiment, P3 peak latency varied across conditions, being later for gender than for number disagreement. Similarly, in the sentence experiment, the last segment of the P600 effect was greater for gender than for number violations. These event-related potentials (ERPs) effects lend support to the idea that reanalysis or repair processes after grammatical disagreement detection could involve more steps in the case of gender disagreement, as grammatical gender is a feature of the lexical representation in contrast to number, which is considered a morphological feature that combines with the stem of the word.


title: Survival outcomes and prognostic factors in mycosis fungoides/Sézary syndrome: validation of the revised International Society for Cutaneous Lymphomas/European Organisation for Research and Treatment of Cancer staging proposal.

abstract: PURPOSE ::: We have analyzed the outcome of mycosis fungoides (MF) and Sézary syndrome (SS) patients using the recent International Society for Cutaneous Lymphomas (ISCL)/European Organisation for Research and Treatment of Cancer (EORTC) revised staging proposal. ::: ::: ::: PATIENTS AND METHODS ::: Overall survival (OS), disease-specific survival (DSS), and risk of disease progression (RDP) were calculated for a cohort of 1,502 patients using univariate and multivariate models. ::: ::: ::: RESULTS ::: The mean age at diagnosis was 54 years, and 71% of patients presented with early-stage disease. Disease progression occurred in 34%, and 26% of patients died due to MF/SS. A significant difference in survival and progression was noted for patients with early-stage disease having patches alone (T1a/T2a) compared with those having patches and plaques (T1b/T2b). Univariate analysis established that (1) advanced skin and overall clinical stage, increased age, male sex, increased lactate dehydrogenase (LDH), and large-cell transformation were associated with reduced survival and increased RDP; (2) hypopigmented MF, MF with lymphomatoid papulosis, and poikilodermatous MF were associated with improved survival and reduced RDP; and (3) folliculotropic MF was associated with an increased RDP. Multivariate analysis established that (1) advanced skin (T) stage, the presence in peripheral blood of the tumor clone without Sézary cells (B0b), increased LDH, and folliculotropic MF were independent predictors of poor survival and increased RDP; (2) large-cell transformation and tumor distribution were independent predictors of increased RDP only; and (3) N, M, and B stages; age; male sex; and poikilodermatous MF were only significant for survival. ::: ::: ::: CONCLUSION ::: This study has validated the recently proposed ISCL/EORTC staging system and identified new prognostic factors.


title: A 20-ps Time-to-Digital Converter (TDC) Implemented in Field-Programmable Gate Array (FPGA) with Automatic Temperature Correction

abstract: This paper presents an automatic temperature correction design for carry chain based time-to-digital converter (TDC) in field-programmable gate array (FPGA). The bin-by-bin calibrations under different temperatures are performed for both plain TDC and Wave Union TDC to characterize the influence of temperature variation on the delay time of carry chain. Accordingly, a simplified temperature correction scheme by using a dedicated correction channel to measure the coefficient and correct fine time result for all TDC channels is implemented and tested. This method shows only few picosecond errors for both simulation and measurement. With this correction approach, a TDC precision of 21 ps has been achieved in Cyclone II FPGA under a wide ambient temperature range from 0 $^\circ{\rm C}$ to 60 $^\circ{\rm C}$ . Several design key points are also described in this paper.


title: Threat of plastic pollution to seabirds is global, pervasive, and increasing

abstract: Plastic pollution in the ocean is a global concern; concentrations reach 580,000 pieces per km(2) and production is increasing exponentially. Although a large number of empirical studies provide emerging evidence of impacts to wildlife, there has been little systematic assessment of risk. We performed a spatial risk analysis using predicted debris distributions and ranges for 186 seabird species to model debris exposure. We adjusted the model using published data on plastic ingestion by seabirds. Eighty of 135 (59%) species with studies reported in the literature between 1962 and 2012 had ingested plastic, and, within those studies, on average 29% of individuals had plastic in their gut. Standardizing the data for time and species, we estimate the ingestion rate would reach 90% of individuals if these studies were conducted today. Using these results from the literature, we tuned our risk model and were able to capture 71% of the variation in plastic ingestion based on a model including exposure, time, study method, and body size. We used this tuned model to predict risk across seabird species at the global scale. The highest area of expected impact occurs at the Southern Ocean boundary in the Tasman Sea between Australia and New Zealand, which contrasts with previous work identifying this area as having low anthropogenic pressures and concentrations of marine debris. We predict that plastics ingestion is increasing in seabirds, that it will reach 99% of all species by 2050, and that effective waste management can reduce this threat.


title: When higher working memory capacity hinders insight.

abstract: Higher working memory capacity (WMC) improves performance on a range of cognitive and academic tasks. However, a greater ability to control attention sometimes leads individuals with higher WMC to persist in using complex, attention-demanding approaches that are suboptimal for a given task. We examined whether higher WMC would hinder insight problem solving, which is thought to rely on associative processes that operate largely outside of close attentional control. In addition, we examined whether characteristics of the insight problems influence whether this negative relationship will be revealed. In Experiment 1, participants completed matchstick arithmetic problems, which require a similar initial problem representation for all problems. Higher WMC was associated with less accurate insight problem solving. In Experiment 2, participants completed insight word problems, which require substantially different representations for each problem. Higher WMC was again negatively associated with insight, but only after statistically controlling for shared variance between insight and incremental problem-solving accuracy. These findings suggest that WMC may benefit performance on fundamental processes common to both incremental and insight problem solving (e.g., initial problem representation), but hinder performance on the processes that are unique to insight (e.g., solution and restructuring). By considering the WMC of the individual, and the nature of the insight task, we may better understand the process of insight and how to best support it.


Abstract P5-03-08: Use of natural language processing on mammography and pathology findings to supplement BI-RADS to improve clinical decision making in breast cancer care


title: Four\mathbb {Q} on FPGA: New Hardware Speed Records for Elliptic Curve Cryptography over Large Prime Characteristic Fields

abstract: We present fast and compact implementations of Four\(\mathbb {Q}\) (ASIACRYPT 2015) on field-programmable gate arrays (FPGAs), and demonstrate, for the first time, the high efficiency of this new elliptic curve on reconfigurable hardware. By adapting Four\(\mathbb {Q}\)’s algorithms to hardware, we design FPGA-tailored architectures that are significantly faster than any other ECC alternative over large prime characteristic fields. For example, we show that our single-core and multi-core implementations can compute at a rate of 6389 and 64730 scalar multiplications per second, respectively, on a Xilinx Zynq-7020 FPGA, which represent factor-2.5 and 2 speedups in comparison with the corresponding variants of the fastest Curve25519 implementation on the same device. These results show the potential of deploying Four\(\mathbb {Q}\) on hardware for high-performance and embedded security applications. All the presented implementations exhibit regular, constant-time execution, protecting against timing and simple side-channel attacks.


title: Risk of venous thromboembolism in hospitalized patients with peripherally inserted central catheters.

abstract: BACKGROUND: ::: Peripherally inserted central catheters (PICC) are increasingly used in hospitalized patients. The benefit can be offset by complications such as upper extremity deep vein thrombosis (UEDVT). ::: ::: METHODS: ::: Retrospective study of patients who received a PICC while hospitalized at the Methodist University Hospital (MUH) in Memphis, TN. All adult consecutive patients who had PICCs inserted during the study period and who did not have a UEDVT at the time of PICC insertion were included in the study. A UEDVT was defined as a symptomatic event in the ipsilateral extremity, leading to the performance of duplex ultrasonography, which confirmed the diagnosis of UEDVT. Pulmonary embolism (PE) was defined as a symptomatic event prompting the performance of ventilation-perfusion lung scan or spiral computed tomography (CT). ::: ::: RESULTS: ::: Among 777 patients, 38 patients experienced 1 or more venous thromboembolisms (VTEs), yielding an incidence of 4.89%. A total of 7444 PICC-days were recorded for 777 patients. This yields a rate of 5.10 VTEs/1000 PICC-days. Compared to patients whose PICC was inserted in the SVC, patients whose PICC was in another location had an increased risk (odds ratio = 2.61 [95% CI = 1.28-5.35]) of VTE. PICC related VTE was significantly more common among patients with a past history of VTE (odds ratio = 10.83 [95% CI = 4.89-23.95]). ::: ::: CONCLUSIONS: ::: About 5% of patients undergoing PICC placement in acute care hospitals will develop thromboembolic complications. Thromboembolic complications were especially common among persons with a past history of VTE. Catheter tip location at the time of insertion may be an important modifiable risk factor. Journal of Hospital Medicine 2009;4:417–422. © 2009 Society of Hospital Medicine.


title: Mental motor imagery: a window into the representational stages of action

abstract: Abstract The physiological basis of mental states can be effectively studied by combining cognitive psychology with human neuroscience. Recent research has employed mental motor imagery in normal and brain-damaged subjects to decipher the content and the structure of covert processes preceding the execution of action. The mapping of brain activity during motor imagery discloses a pattern of activation similar to that of an executed action.


title: Biomedical Applications of Layer-by-Layer Assembly: From Biomimetics to Tissue Engineering†

abstract: The design of advanced, nanostructured materials at the molecular level is of tremendous interest for the scientific and engineering communities because of the broad application of these materials in the biomedical field. Among the available techniques, the layer-by-layer assembly method introduced by Decher and co-workers in 1992 has attracted extensive attention because it possesses extraordinary advantages for biomedical applications: ease of preparation, versatility, capability of incorporating high loadings of different types of biomolecules in the films, fine control over the materials’ structure, and robustness of the products under ambient and physiological conditions. In this context, a systematic review of current research on biomedical applications of layer-by-layer assembly is presented. The structure and bioactivity of biomolecules in thin films fabricated by layer-by-layer assembly are introduced. The applications of layer-bylayer assembly in biomimetics, biosensors, drug delivery, protein and cell adhesion, mediation of cellular functions, and implantable materials are addressed. Future developments in the field of biomedical applications of layer-by-layer assembly are also discussed.


title: Addressing Concept-Evolution in Concept-Drifting Data Streams

abstract: The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two such well studied aspects are infinite length and concept-drift. Since a data stream may be considered a continuous process, which is theoretically infinite in length, it is impractical to store and use all the historical data for training. Data streams also frequently experience concept-drift as a result of changes in the underlying concepts. However, another important characteristic of data streams, namely, concept-evolution is rarely addressed in the literature. Concept-evolution occurs as a result of new classes evolving in the stream. This paper addresses concept-evolution in addition to the existing challenges of infinite-length and concept-drift. In this paper, the concept-evolution phenomenon is studied, and the insights are used to construct superior novel class detection techniques. First, we propose an adaptive threshold for outlier detection, which is a vital part of novel class detection. Second, we propose a probabilistic approach for novel class detection using discrete Gini Coefficient, and prove its effectiveness both theoretically and empirically. Finally, we address the issue of simultaneous multiple novel class occurrence, and provide an elegant solution to detect more than one novel classes at the same time. We also consider feature-evolution in text data streams, which occurs because new features (i.e., words) evolve in the stream. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach.


title: Armenian grapevines: cytoembryological, morphological and chemical analysis

abstract: The objectives of our study was to phenotype Armenian grapevines on the base of cytoembryological, morphometric and phenolic content analysis of 10 wine and 10 table cultivars (Vitis vinifera L.). The presented results suggest that Armenian table grape cultivars have higher level of abnormalities in the process of seed formation than wine cultivars. However, during ovule development, the observed differences between table and wine varieties were not significant. The berry morphometric analysis demonstrated that table cultivars formed significantly larger and heavier berries than wine cultivars. The obtained results show that wine grapes contained significantly higher phenol concentrations than table grapes, in both colored and white grapevine cultivars. The study of phenol composition, an important chemical descriptor in grapevine phenotyping, provides oenological information useful to improve the wine quality. Cultivar characterization could be used as marker for the selection of table and wine grape breeding programs. These results will be upgraded in the database for Armenian grapevine varieties. In future Armenian germplasm will be compared with datasets of neighboring countries, to determine the varietal origins relationships.


title: Apophysitis of the ischial tuberosity mimicking a neoplasm on magnetic resonance imaging

abstract: We present multimodality imaging features of an ischial tuberosity apophysitis in a 13-year-old boy who was an active baseball pitcher. Roentgenography of the pelvis and computed tomography showed mild irregularity in the inferior margin of the left ischial tuberosity. T1-weighted MRI showed a wide area with low signal intensity in the left ischial body; T2-weighted fat-suppression images showed areas with markedly high signal intensity in the ischial apophysis and body and the surrounding periosteum; contrast-enhanced T1-weighted fat-suppression MRI showed that the ischial body, surrounding periosteum, and origin of the hamstring muscles strongly enhanced; technetium-99m scintigraphic scans showed increased isotope uptake in the entire ischial body. Histological specimens obtained from the bone showed increased osteoblastic activity, edema, and proliferation of benign spindle cells and small vessels in the bone marrow spaces. In the present case, because MR imaging demonstrated extensive signal abnormalities involving the apophysis, periosteum, and intramedullary portion of bone, a neoplasm could not be excluded, and a biopsy was undertaken.


title: Modeling Dynamic Transcriptional Circuits with CRISPRi

abstract: Targeted transcriptional repression with catalytically inactive Cas9 (CRISPRi) can be used to build gene regulatory nets similar in principle to those made with traditional transcription factors, and promises to do so with better orthogonality, programmability, and extensibility. We use a simple dynamical model of CRISPRi to understand its behavior and requirements, and to show that CRISPRi can recapitulate several classic gene regulatory circuits, including the repressilator, a toggle switch, and an incoherent feed-forward loop pulse generator. Our model also predicts that these circuits are highly sensitive to promoter leak, but that promoter leak can be offset with active degradation of dCas. We provide specifications for required fold-repression and dCas degradation rates for several dynamic circuits. Our modeling reveals key engineering requirements and considerations for the construction of dynamic CRISPRi circuits, and provides a roadmap for building those circuits.


title: PBFT vs Proof-of-Authority: Applying the CAP Theorem to Permissioned Blockchain

abstract: Permissioned blockchains are arising as a solution to federate companies prompting accountable interactions. A variety of consensus algorithms for such blockchains have been proposed, each of which has different benefits and drawbacks. Proof-of-Authority (PoA) is a new family of Byzantine fault-tolerant (BFT) consensus algorithms largely used in practice to ensure better performance than traditional Practical Byzantine Fault Tolerance (PBFT). However, the lack of adequate analysis of PoA hinders any cautious evaluation of their effectiveness in real-world permissioned blockchains deployed over the Internet, hence on an eventually synchronous network experimenting Byzantine nodes. In this paper, we analyse two of the main PoA algorithms, named Aura and Clique, both in terms of provided guarantees and performances. First, we derive their functioning including how messages are exchanged, then we weight, by relying on the CAP theorem, consistency, availability and partition tolerance guarantees. We also report a qualitative latency analysis based on message rounds. The analysis advocates that PoA for per- missioned blockchains, deployed over the Internet with Byzantine nodes, do not provide adequate consistency guarantees for scenarios where data integrity is essential. We claim that PBFT can fit better such scenarios, despite a limited loss in terms of performance.


title: RASP: efficient multidimensional range query on attack-resilient encrypted databases

abstract: Range query is one of the most frequently used queries for online data analytics. Providing such a query service could be expensive for the data owner. With the development of services computing and cloud computing, it has become possible to outsource large databases to database service providers and let the providers maintain the range-query service. With outsourced services, the data owner can greatly reduce the cost in maintaining computing infrastructure and data-rich applications. However, the service provider, although honestly processing queries, may be curious about the hosted data and received queries. Most existing encryption based approaches require linear scan over the entire database, which is inappropriate for online data analytics on large databases. While a few encryption solutions are more focused on efficiency side, they are vulnerable to attackers equipped with certain prior knowledge. We propose the Random Space Encryption (RASP) approach that allows efficient range search with stronger attack resilience than existing efficiency-focused approaches. We use RASP to generate indexable auxiliary data that is resilient to prior knowledge enhanced attacks. Range queries are securely transformed to the encrypted data space and then efficiently processed with a two-stage processing algorithm. We thoroughly studied the potential attacks on the encrypted data and queries at three different levels of prior knowledge available to an attacker. Experimental results on synthetic and real datasets show that this encryption approach allows efficient processing of range queries with high resilience to attacks.


title: BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era

abstract: Digital currencies have emerged as a new fascinating phenomenon in the financial markets. Recent events on the most popular of the digital currencies – BitCoin – have risen crucial questions about behavior of its exchange rates and they offer a field to study dynamics of the market which consists practically only of speculative traders with no fundamentalists as there is no fundamental value to the currency. In the paper, we connect two phenomena of the latest years – digital currencies, namely BitCoin, and search queries on Google Trends and Wikipedia – and study their relationship. We show that not only are the search queries and the prices connected but there also exists a pronounced asymmetry between the effect of an increased interest in the currency while being above or below its trend value.


title: CSI-Based Indoor Localization

abstract: Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in orthogonal frequency division multiplexing systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to build a propagation model and a fingerprinting system at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.


title: PhishStorm: Detecting Phishing With Streaming Analytics

abstract: Despite the growth of prevention techniques, phishing remains an important threat since the principal countermeasures in use are still based on reactive URL blacklisting. This technique is inefficient due to the short lifetime of phishing Web sites, making recent approaches relying on real-time or proactive phishing URLs detection techniques more appropriate. In this paper we introduce PhishStorm, an automated phishing detection system that can analyse in real-time any URL in order to identify potential phishing sites. PhishStorm can interface with any email server or HTTP proxy. We argue that phishing URLs usually have few relationships between the part of the URL that must be registered (low level domain) and the remaining part of the URL (upper level domain, path, query). We show in this paper that experimental evidence supports this observation and can be used to detect phishing sites. For this purpose, we define the new concept of intra-URL relatedness and evaluate it using features extracted from words that compose a URL based on query data from Google and Yahoo search engines. These features are then used in machine learning based classification to detect phishing URLs from a real dataset. Our technique is assessed on 96,018 phishing and legitimate URLs that results in a correct classification rate of 94.91% with only 1.44% false positives. An extension for a URL phishingness rating system exhibiting high confidence rate (> 99%) is proposed. We discuss in the paper efficient implementation patterns that allow real time analytics using Big Data architectures like STORM and advanced data structures based on Bloom filter.


title: Integration Strategies for Enterprise Knowledge Graphs

abstract: Semantic computing and enterprise Linked Data have recently gained traction in enterprises. Although the concept of Enterprise Knowledge Graphs (EKGs) has meanwhile received some attention, a formal conceptual framework for designing such graphs has not yet been developed. By EKG we refer to a semantic network of concepts, properties, individuals and links representing and referencing foundational and domain knowledge relevant for an enterprise. Through the efforts reported in this paper, we aim to bridge the gap between the increasing need for EKGs and the lack of formal methods for realising them. We present a thorough study of the key concepts of knowledge graphs design along with an analysis of the advantages and disadvantages of various design decisions. In particular, we distinguish between two polar approaches towards data fusion, i.e., the unified and the federated approach, describe their benefits and point out shortages.


title: PCB defect detection USING OPENCV with image subtraction method

abstract: In the PCB manufacturing industry, one of the most important aspect of the production is quality checking. PCB boards goes through lots of production process from panel cutting to laminating and one single defect in a board will make the entire board become obsolete. With the rise of electronic appliances demand every day, the demand for higher quality components is rising. With the high price tag of commercial AOI, lots of manufacturers are not able to do an automated inspection. This is where Computer Vision can provide an alternative for commercial AOI to assist small scale manufacturers to do an automated inspection. Image Subtraction and Blob detection enable users to easily find numerous visual defects in PCBs especially with a complex method. By dissecting pictures and pointing out the differences on the output image, PCBs can be inspected quickly to find out the defective parts. And thus, this thesis will focus on the basis that a basic image subtraction will be enough to assist Manufacturers in Quality Inspections.


title: The PHQ-9: A New Depression Diagnostic and Severity Measure

abstract: and treatable mental disorders presenting in general medical as well as specialty settings. There are a number of case-finding instruments for detecting depression in primary care, ranging from 2 to 28 items.1,2 Typically these can be scored as continuous measures of depression severity and also have established cutpoints above which the probability of major depression is substantially increased. Scores on these various measures tend to be highly correlated3, with little evidence that one measure is superior to any other.1,2,4


title: Violent video game exposure and aggression A literature review

abstract: Over the last 30 years, the video game industry has grown into a multi-billion dollar business. More children and adults are spending time playing computer games, consoles games, and online games than ever before. Violence is a dominant theme in most of the popular video games. This article reviews the current literature on effects of violent video game exposure on aggression-related variables. Exposure to violent video games causes increases in aggressive behavior, cognitions, and affect. Violent video game exposure also causes increases in physiological desensitization to real-life violence and decreases in helping behavior. The current video game literature is interpreted in terms of the general aggression model (GAM). Differences between violent video game exposure and violent television are also discussed.


SAFE: Fast, Verifiable Sanitization for SSDs Or: Why encryption alone is not a solution for sanitizing SSDs


A Comparison of Dimensionality Reduction Techniques for Unstructured Clinical Text Yoni Halpern


title: The Unwritten Contract of Solid State Drives

abstract: We perform a detailed vertical analysis of application performance atop a range of modern file systems and SSD FTLs. We formalize the "unwritten contract" that clients of SSDs should follow to obtain high performance, and conduct our analysis to uncover application and file system designs that violate the contract. Our analysis, which utilizes a highly detailed SSD simulation underneath traces taken from real workloads and file systems, provides insight into how to better construct applications, file systems, and FTLs to realize robust and sustainable performance.


title: Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction

abstract: Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from Twitter. Medical information extraction from social media is challenging, mainly due to short and highly informal nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction rely on supervised learning methods, which suffer from labeled data scarcity problem. The state-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which is Long-Short-Term-Memory network (LSTM). Deep neural networks, due to their large number of free parameters rely heavily on large annotated corpora for learning the end task. But in the real-world, it is hard to get large labeled data, mainly due to the heavy cost associated with the manual annotation. To this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction. In this study, we tackle the problem of labeled data scarcity for Adverse Drug Reaction mention extraction from social media and propose a novel semi-supervised learning based method which can leverage large unlabeled corpus available in abundance on the web. Through empirical study, we demonstrate that our proposed method outperforms fully supervised learning based baseline which relies on large manually annotated corpus for a good performance.


title: A wireless mesh communication protocol for smart-metering

abstract: Worldwide there has been increasing interest over the past few years for so-called “Smart Meters”, in academia, governments and in industry. Such smart-metering systems need a way to communicate the collected data reliably and cost efficiently to the back-office for analysis. Several competing technologies exist and are in use world-wide. Mesh-networks have been the winning technology in the USA and Australia for the past two years, and are gaining interest in Europe at the moment due to its reduced costs and increased reliability. In this paper we present and evaluate a real-life implementation of a new routing protocol for use in smart-metering mesh-network grids. The routing protocol we present is designed with both technological constraints and legislative requirements as posed by the application area in mind. Our evaluation of the protocol is based on real-world experience and data collected from real-world devices, in combination with simulation studies of the protocol. Our evaluation shows that the protocol is a robust, reliable solution for communicating collected data in difficult scenarios, showing great resilience against both bit-errors and node-failures.


title: Improving SSL Warnings: Comprehension and Adherence

abstract: Browsers warn users when the privacy of an SSL/TLS connection might be at risk. An ideal SSL warning would empower users to make informed decisions and, failing that, guide confused users to safety. Unfortunately, users struggle to understand and often disregard real SSL warnings. We report on the task of designing a new SSL warning, with the goal of improving comprehension and adherence. We designed a new SSL warning based on recommendations from warning literature and tested our proposal with microsurveys and a field experiment. We ultimately failed at our goal of a well-understood warning. However, nearly 30% more total users chose to remain safe after seeing our warning. We attribute this success to opinionated design, which promotes safety with visual cues. Subsequently, our proposal was released as the new Google Chrome SSL warning. We raise questions about warning comprehension advice and recommend that other warning designers use opinionated design.


title: Detecting Accounting Frauds in Publicly Traded U.S. Firms: New Perspective and New Method

abstract: We propose a new perspective and a new method to detect accounting frauds out of sample. We show that a logistic regression that directly uses raw accounting data as regressors outperforms the traditional logistic regression that uses expert-identified financial ratios. Using the same raw data as inputs, ensemble learning, a state-of-the-art machine learning method, further outperforms the logistic regression model. The ensemble learning method also outperforms a support vector machine (SVM) with a financial kernel that maps the same raw accounting data into a broader set of ratios and changes in ratios. Overall, our results suggest that the existing fraud prediction models haven’t fully utilized the information from publicly available financial statement data. In addition, we show that it is possible to extract such useful information by adopting better fraud prediction models that rely on raw accounting data rather than financial ratios as model inputs.


title: Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners

abstract: Abstract Numerous studies have begun to address how the brain's gray and white matter may be shaped by meditation. This research is yet to be integrated, however, and two fundamental questions remain: Is meditation associated with altered brain structure? If so, what is the magnitude of these differences? To address these questions, we reviewed and meta-analyzed 123 brain morphology differences from 21 neuroimaging studies examining ∼300 meditation practitioners. Anatomical likelihood estimation (ALE) meta-analysis found eight brain regions consistently altered in meditators, including areas key to meta-awareness (frontopolar cortex/BA 10), exteroceptive and interoceptive body awareness (sensory cortices and insula), memory consolidation and reconsolidation (hippocampus), self and emotion regulation (anterior and mid cingulate; orbitofrontal cortex), and intra- and interhemispheric communication (superior longitudinal fasciculus; corpus callosum). Effect size meta-analysis (calculating 132 effect sizes from 16 studies) suggests a global ‘medium’ effect size (Cohen's d ¯ = 0.46 ; r ¯ = .19 ). Publication bias and methodological limitations are strong concerns, however. Further research using rigorous methods is required to definitively link meditation practice to altered brain morphology.


title: Measuring risk in stealth games

abstract: Level design for stealth games requires the ability to explore and understand the possible paths players may take through a given scenario and how they are impacted by different design choices. Good tool support can help by demonstrating the existence of such paths, but for rapid, interactive design, the relative difficulty of possible solutions also needs to be quantified, in a way that correlates well with human perception of risk. Here we propose and evaluate three different metrics for defining and quantifying the risk of stealthy paths. We validate and compare these measures through a small human study, showing that a simple path-distance measure correlates best with human judgement. An evaluation of a non-trivial stealth scenario demonstrates the practicality of our approach, and shows how such measures can be useful in understanding a level design.


Clustering Time Series with Hidden Markov Models and Dynamic Time Warping


title: Invited Commentary: Estimating Population Impact in the Presence of Competing Events

abstract: The formal approach in the field of causal inference has enabled epidemiologists to clarify several complications that arise when estimating the effect of an intervention on a health outcome of interest. When the outcome is a failure time or longitudinal process, researchers must often deal with competing events. In this issue of the Journal, Picciotto et al. (Am J Epidemiol. 2015;181(8):563-570) use structural nested failure time models to assess potential population effects of hypothetical interventions and censor competing events. In the present commentary, we discuss 2 interpretations that result from treating competing events as censored observations and how they relate to measures of public health impact. We also comment on 2 alternative approaches for handling competing events: an inverse probability weighting estimator of the survivor average causal effect and the parametric g-formula, which can be used to estimate a functional of the subdistribution of the event of interest. We argue that careful consideration of the tradeoff between the interpretation of the parameters from each approach and the assumptions required to estimate these parameters should guide researchers on the various ways to handle competing events in epidemiologic research.


title: Delay-Aware Quality Optimization in Cloud-Assisted Video Streaming System

abstract: Cloud-assisted video streaming has emerged as a new paradigm to optimize multimedia content distribution over the Internet. This article investigates the problem of streaming cloud-assisted real-time video to multiple destinations (e.g., cloud video conferencing, multi-player cloud gaming, etc.) over lossy communication networks. The user diversity and network dynamics result in the delay differences among multiple destinations. This research proposes D ifferentiated cloud- A ssisted VI deo S treaming (DAVIS) framework, which proactively leverages such delay differences in video coding and transmission optimization. First, we analytically formulate the optimization problem of joint coding and transmission to maximize received video quality. Second, we develop a quality optimization framework that integrates the video representation selection and FEC (Forward Error Correction) packet interleaving. The proposed DAVIS is able to effectively perform differentiated quality optimization for multiple destinations by taking advantage of the delay differences in cloud-assisted video streaming system. We conduct the performance evaluation through extensive experiments with the Amazon EC2 instances and Exata emulation platform. Evaluation results show that DAVIS outperforms the reference cloud-assisted streaming solutions in video quality and delay performance.


title: Neural Network Based Next-Song Recommendation

abstract: Recently, the next-item/basket recommendation system, which considers the sequential relation between bought items, has drawn attention of researchers. The utilization of sequential patterns has boosted performance on several kinds of recommendation tasks. Inspired by natural language processing (NLP) techniques, we propose a novel neural network (NN) based next-song recommender, CNN-rec, in this paper. Then, we compare the proposed system with several NN based and classic recommendation systems on the next-song recommendation task. Verification results indicate the proposed system outperforms classic systems and has comparable performance with the state-of-the-art system.


title: A new image thresholding method based on Gaussian mixture model

abstract: In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then to fit the Gaussian mixtures to the histogram of image, the expectation maximization (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms and their corresponding parameterization. Finally, the optimal threshold which is the average of these Gaussian mixture means is chosen. And the experimental results show that the new algorithm performs better.


title: Practical Evaluations of a ZVS-PWM DC–DC Converter With Secondary-Side Phase-Shifting Active Rectifier

abstract: This paper presents a feasibility investigation of a zero-voltage switching (ZVS) pulsewidth modulation (PWM) dc-dc converter with secondary-side phase-shifting power control scheme. The ZVS-PWM dc-dc converter treated here can achieve soft commutation in all the power devices under the wide range of output power variation. By the phase-shifting control that is based on the secondary-side rectifier linked with a high-frequency planar transformer, the effective reduction of idling power in the primary-side inverter as well as snubber-less rectifications in the secondary-side rectifier can be actually attained. The essential experimental data obtained from a 100-kHz/2.5-kW prototype are described herein to validate the soft-switching circuit and control scheme, and then the effectiveness of the dc-dc converter is discussed and evaluated from a practical point of view.


title: Style2Vec: Representation Learning for Fashion Items from Style Sets

abstract: With the rapid growth of online fashion market, demand for effective fashion recommendation systems has never been greater. In fashion recommendation, the ability to find items that goes well with a few other items based on style is more important than picking a single item based on the user's entire purchase history. Since the same user may have purchased dress suits in one month and casual denims in another, it is impossible to learn the latent style features of those items using only the user ratings. If we were able to represent the style features of fashion items in a reasonable way, we will be able to recommend new items that conform to some small subset of pre-purchased items that make up a coherent style set. We propose Style2Vec, a vector representation model for fashion items. Based on the intuition of distributional semantics used in word embeddings, Style2Vec learns the representation of a fashion item using other items in matching outfits as context. Two different convolutional neural networks are trained to maximize the probability of item co-occurrences. For evaluation, a fashion analogy test is conducted to show that the resulting representation connotes diverse fashion related semantics like shapes, colors, patterns and even latent styles. We also perform style classification using Style2Vec features and show that our method outperforms other baselines.


Visualizing Higher-Layer Features of a Deep Network


title: Gaussian particle filtering

abstract: Sequential Bayesian estimation for nonlinear dynamic state-space models involves recursive estimation of filtering and predictive distributions of unobserved time varying signals based on noisy observations. This paper introduces a new filter called the Gaussian particle filter. It is based on the particle filtering concept, and it approximates the posterior distributions by single Gaussians, similar to Gaussian filters like the extended Kalman filter and its variants. It is shown that under the Gaussianity assumption, the Gaussian particle filter is asymptotically optimal in the number of particles and, hence, has much-improved performance and versatility over other Gaussian filters, especially when nontrivial nonlinearities are present. Simulation results are presented to demonstrate the versatility and improved performance of the Gaussian particle filter over conventional Gaussian filters and the lower complexity than known particle filters.


A Hybrid Approach To Detect Code Smells using Deep Learning


title: Fair Division: From Cake-Cutting to Dispute Resolution

abstract: Cutting a cake, dividing up the property in an estate, determining the borders in an international dispute - such problems of fair division are ubiquitous. Fair Division treats all these problems and many more through a rigorous analysis of a variety of procedures for allocating goods (or 'bads' like chores), or deciding who wins on what issues, when there are disputes. Starting with an analysis of the well-known cake-cutting procedure, 'I cut, you choose', the authors show how it has been adapted in a number of fields and then analyze fair-division procedures applicable to situations in which there are more than two parties, or there is more than one good to be divided. In particular they focus on procedures which provide 'envy-free' allocations, in which everybody thinks he or she has received the largest portion and hence does not envy anybody else. They also discuss the fairness of different auction and election procedures.


title: Support vector machines combined with feature selection for breast cancer diagnosis

abstract: Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. Research efforts have reported with increasing confirmation that the support vector machines (SVM) have greater accurate diagnosis ability. In this paper, breast cancer diagnosis based on a SVM-based method combined with feature selection has been proposed. Experiments have been conducted on different training-test partitions of the Wisconsin breast cancer dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, positive and negative predictive values, receiver operating characteristic (ROC) curves and confusion matrix. The results show that the highest classification accuracy (99.51%) is obtained for the SVM model that contains five features, and this is very promising compared to the previously reported results.


title: A key management scheme for wireless sensor networks using deployment knowledge

abstract: To achieve security in wireless sensor networks, it is important to he able to encrypt messages sent among sensor nodes. Keys for encryption purposes must he agreed upon by communicating nodes. Due to resource constraints, achieving such key agreement in wireless sensor networks is nontrivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks. Pre-distribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. Recently, a random key pre-distribution scheme and its improvements have been proposed. A common assumption made by these random key pre-distribution schemes is that no deployment knowledge is available. Noticing that in many practical scenarios, certain deployment knowledge may be available a priori, we propose a novel random key pre-distribution scheme that exploits deployment knowledge and avoids unnecessary key assignments. We show that the performance (including connectivity, memory usage, and network resilience against node capture) of sensor networks can he substantially improved with the use of our proposed scheme. The scheme and its detailed performance evaluation are presented in this paper.


title: Hierarchical Online Intrusion Detection for SCADA Networks

abstract: We propose a novel hierarchical online intrusion detection system (HOIDS) for supervisory control and data acquisition (SCADA) networks based on machine learning algorithms. By utilizing the server-client topology while keeping clients distributed for global protection, high detection rate is achieved with minimum network impact. We implement accurate models of normal-abnormal binary detection and multi-attack identification based on logistic regression and quasi-Newton optimization algorithm using the Broyden-Fletcher-Goldfarb-Shanno approach. The detection system is capable of accelerating detection by information gain based feature selection or principle component analysis based dimension reduction. By evaluating our system using the KDD99 dataset and the industrial control system dataset, we demonstrate that HOIDS is highly scalable, efficient and cost effective for securing SCADA infrastructures.


SumeCzech: Large Czech News-Based Summarization Dataset.


title: The ‘SIGn-and-MAc’ approach to authenticated Diffie-Hellman and its use in the IKE protocols

abstract: We present the SIGMA family of key-exchange protocols and the SIGn-and-MAc approach to authenticated Diffie-Hellman underlying its design. The SIGMA protocols provide perfect forward secrecy via a Diffie-Hellman exchange authenticated with digital signatures, and are specifically designed to ensure sound cryptographic key exchange while providing a variety of features and trade-offs required in practical scenarios (such as optional identity protection and reduced number of protocol rounds). As a consequence, the SIGMA protocols are very well suited for use in actual applications and for standardized key exchange. In particular, SIGMA serves as the cryptographic basis for the signature-based modes of the standardized Internet Key Exchange (IKE) protocol (versions 1 and 2). This paper describes the design rationale behind the SIGMA approach and protocpls, and points out to many subtleties surrounding the design of secure key-exchange protocols in general, and identity-protecting protocols in particular. We motivate the design of SIGMA by comparing it to other protocols, most notable the STS protocol and its variants. In particular, it is shown how SIGMA solves some of the security shortcomings found in previous protocols.


title: Anomaly detection and classification for hyperspectral imagery

abstract: Anomaly detection becomes increasingly important in hyperspectral image analysis, since hyperspectral imagers can now uncover many material substances which were previously unresolved by multispectral sensors. Two types of anomaly detection are of interest and considered in this paper. One was previously developed by Reed and Yu to detect targets whose signatures are distinct from their surroundings. Another was designed to detect targets with low probabilities in an unknown image scene. Interestingly, they both operate the same form as does a matched filter. Moreover, they can be implemented in real-time processing, provided that the sample covariance matrix is replaced by the sample correlation matrix. One disadvantage of an anomaly detector is the lack of ability to discriminate the detected targets from another. In order to resolve this problem, the concept of target discrimination measures is introduced to cluster different types of anomalies into separate target classes. By using these class means as target information, the detected anomalies can be further classified. With inclusion of target discrimination in anomaly detection, anomaly classification can be implemented in a three-stage process, first by anomaly detection to find potential targets, followed by target discrimination to cluster the detected anomalies into separate target classes, and concluded by a classifier to achieve target classification. Experiments show that anomaly classification performs very differently from anomaly detection.


title: Stacked-Patch Dual-Polarized Antenna for Triple-Band Handheld Terminals

abstract: In this letter, a novel design of a stacked-patch triple-band antenna in both circular and linear polarizations that can be used on a handheld terminal for surveying and geo-informatics applications is presented. The inculcation of corner truncation and I-slot in both the lower and middle patches has achieved better impedance bandwidth and axial ratio at GPS L1, L2, and GSM 1800 resonant frequency bands. A prototype of the proposed design is fabricated, and its performance is verified in measurement.


title: MIDWIVES, FOSTER-MOTHERS, STEPMOTHERS? CLINICAL PSYCHOLOGISTS TEACHING JUNIOR DOCTORS PSYCHOTHERAPY

abstract: Teaching junior doctors psychotherapy is a difficult task. This paper charts the progress of clinical psychologists setting out on this task, initially finding it impossible, but thinking and working together to develop creative, working groups in which the junior doctors and group leaders learned and developed. ::: ::: ::: ::: We detail how we run the teaching groups and what it is like to be a member of such a group. We consider the costs and benefits of clinical psychologists teaching junior doctors and the differing cultures of psychiatry, psychology and psychotherapy. We discuss institutional and group defences which create obstacles to learning. We propose a training model based on maternal containment as opposed to pseudo-paternal identifications. Containment of anxiety appears to be necessary for the creation and maintenance of a safe space for junior doctors to learn about psychotherapy.


title: HOG-Based Decision Tree for Facial Expression Classification

abstract: We address the problem of human emotion identification from still pictures taken in semi-controlled environments. Histogram of Oriented Gradient (HOG) descriptors are considered to describe the local appearance and shape of the face. First, we propose a Bayesian formulation to compute class specific edge distribution and log-likelihood maps over the entire aligned training set. A hierarchical decision tree is then built using a bottom-up strategy by recursively clustering and merging the classes at each level. For each branch of the tree we build a list of potentially discriminative HOG features using the log-likelihood maps to favor locations that we expect to be more discriminative. Finally, a Support Vector Machine (SVM) is considered for the decision process in each branch. The evaluation of the present method has been carried out on the Cohn-Kanade AU-Coded Facial Expression Database, recognizing different emotional states from single picture of people not present in the training set.


title: Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization

abstract: Learning to remember long sequences remains a challenging task for recurrent neural networks. Register memory and attention mechanisms were both proposed to resolve the issue with either high computational cost to retain memory differentiability, or by discounting the RNN representation learning towards encoding shorter local contexts than encouraging long sequence encoding. Associative memory, which studies the compression of multiple patterns in a fixed size memory, were rarely considered in recent years. Although some recent work tries to introduce associative memory in RNN and mimic the energy decay process in Hopfield nets, it inherits the shortcoming of rule-based memory updates, and the memory capacity is limited. This paper proposes a method to learn the memory update rule jointly with task objective to improve memory capacity for remembering long sequences. Also, we propose an architecture that uses multiple such associative memory for more complex input encoding. We observed some interesting facts when compared to other RNN architectures on some well-studied sequence learning tasks.


title: Seamless Handover in Software-Defined Satellite Networking

abstract: Satellites have largely been designed as application-specific and isolated for the past decades. Though with certain benefits, it might lead to resource under utilization and limited satellite applications. As an emerging networking technology, software-defined networking has recently been introduced into satellite networks. In this letter, we propose a software-defined satellite networking (SDSN) architecture, which simplifies networking among versatile satellites and enables new protocols to be easily tested and deployed. In particualr, we propose a seamless handover mechanism based on SDSN and conduct physical layer simulation, which shows significant improvement over the existing hard handover and hybrid handover mechanisms in terms of handover latency, throughput, and quality of experience of users.


title: A survey and analysis of Electronic Healthcare Record standards

abstract: Medical information systems today store clinical information about patients in all kinds of proprietary formats. To address the resulting interoperability problems, several Electronic Healthcare Record standards that structure the clinical content for the purpose of exchange are currently under development. In this article, we present a survey of the most relevant Electronic Healthcare Record standards, examine the level of interoperability they provide, and assess their functionality in terms of content structure, access services, multimedia support, and security. We further investigate the complementarity of the standards and assess their market relevance.


title: A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks

abstract: Cognitively demanding tasks that evoke activation in the brain's central-executive network (CEN) have been consistently shown to evoke decreased activation (deactivation) in the default-mode network (DMN). The neural mechanisms underlying this switch between activation and deactivation of large-scale brain networks remain completely unknown. Here, we use functional magnetic resonance imaging (fMRI) to investigate the mechanisms underlying switching of brain networks in three different experiments. We first examined this switching process in an auditory event segmentation task. We observed significant activation of the CEN and deactivation of the DMN, along with activation of a third network comprising the right fronto-insular cortex (rFIC) and anterior cingulate cortex (ACC), when participants perceived salient auditory event boundaries. Using chronometric techniques and Granger causality analysis, we show that the rFIC-ACC network, and the rFIC, in particular, plays a critical and causal role in switching between the CEN and the DMN. We replicated this causal connectivity pattern in two additional experiments: (i) a visual attention “oddball” task and (ii) a task-free resting state. These results indicate that the rFIC is likely to play a major role in switching between distinct brain networks across task paradigms and stimulus modalities. Our findings have important implications for a unified view of network mechanisms underlying both exogenous and endogenous cognitive control.


title: Type B RCD with a simplified magnetic modulation/demodulation method

abstract: In this paper, a type of Type B RCD applying a simplified magnetic modulation/demodulation method is designed to detect residual currents of sinusoidal AC, pulsating DC and smooth DC to protect person against indirect/direct contact and fire hazards. A modulation method based on RL self-oscillation circuit is applied to modulate AC and DC residual current with square-wave pulse signal. A simplified demodulation method, involving a 2 nd -order LPF (Low Pass Filter) circuit, is designed to easily measure AC and DC residual current. Type B RCD presented in the paper fulfills the AC and DC residual current protections complying with IEC60947-2 Annex B and IEC60755. Furthermore, this Type B RCD characterizes a simple structure, small size and low cost.


title: A new multi-human detection algorithm using an IR-UWB radar system

abstract: This paper presents a multi-human detection algorithm using an impulse radio ultra-wideband (IR-UWB) radar system. The algorithm operates in a similar manner to the conventional clean algorithm that has mainly been used for estimating a channel impulse response (CIR) by computing deconvolution from the clutter subtracted received signal. Unlike the clean algorithm, the algorithm attempts to divide the clutter subtracted received signal into a number of coherent clusters so that the detection success rate of the multi-humans can be improved without a priori knowledge of the number of existing humans. The algorithm performs quite well in dense multipath environments that take place almost always in practice. The validity of the algorithm is demonstrated by successfully tracking the distances of randomly walking people using a commercial IR-UWB radar system. The detection error rate is also discussed based on the normalized histogram obtained from the experimentally measured data.


title: Molecular mechanism of diabetic cardiomyopathy and modulation of microRNA function by synthetic oligonucleotides

abstract: Diabetic cardiomyopathy (DCM) is a chronic complication in individuals with diabetes and is characterized by ventricular dilation and hypertrophy, diastolic dysfunction, decreased or preserved systolic function and reduced ejection fraction eventually resulting in heart failure. Despite being well characterized, the fundamental mechanisms leading to DCM are still elusive. Recent studies identified the involvement of small non-coding small RNA molecules such as microRNAs (miRs) playing a key role in the etiology of DCM. Therefore, miRs associated with DCM represents a new class of targets for the development of mechanistic therapeutics, which may yield marked benefits compared to other therapeutic approaches. Indeed, few miRs currently under active clinical investigation, with many expressing cautious optimism that miRs based therapies will succeed in the coming years. The major caution in using miRs based therapy is the need to improve the stability and specificity following systemic injection, which can be achieved through chemical and structural modification. In this review, we first discuss the established role of miRs in DCM and the advances in miRs based therapeutic strategies for the prevention/treatment of DCM. We next discuss the currently employed chemical modification of miR oligonucleotides and their utility in therapies specifically focusing on the DCM. Finally, we summarize the commonly used delivery system and approaches for assessment of miRNA modulation and potential off-target effects.


title: Lightweight Emulation of Virtual Channels using Swaps

abstract: Virtual Channels (VCs) are a fundamental design feature across networks, both on-chip and off-chip. They provide two key benefits - deadlock avoidance and head-of-line (HoL) blocking mitigation. However, VCs increase the router critical path, and add significant area and power overheads compared to simple wormhole routers. This is especially challenging in the era of energy-constrained many-core chips. The number of VCs required for deadlock avoidance is unavoidable, but those required for mitigating HoL depend on runtime factors such as the distribution and size of single and multi-flit packets, and their intended destinations. In some cases more VCs are beneficial, while in others they may actually harm performance, as we demonstrate. In this work, we provide a low-cost microarchitectural technique to emulate the HoL mitigation behavior of VCs inside routers, without requiring the expensive data path or control path (vc state and vc allocation) for VCs. We augment wormhole routers with the ability to do an in-place swap of blocked packets to the head of the queue. Our design (SwapNoC) can operate at low area and power specs like wormhole designs, without incurring their HoL challenges.


title: EdVidParse : detecting people and content in educational videos

abstract: Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.


title: Game AI is Dead. Long Live Game AI!

abstract: This paper discusses how the recent advances in game AI technology can help benefit industry and the academia. Some of these advances include large-scale automated art and content generation, automated storytelling, goal-driven virtual actors, and the ability to adapt to the player's preferences and mental state


title: An Autonomous Time-Dependent SLA Negotiation Strategy for Cloud Computing

abstract: Cloud service level agreement negotiation is a process of joint decision-making between cloud clients and providers to resolve their conflicting objectives. With the advances of cloud technology, operations such as discovery, scaling, monitoring and decommissioning are accomplished automatically. Therefore,negotiationbetweencloudclientsandproviderscanbeabottleneckifitiscarriedoutmanually. Our objective is to propose a state-of-the-art solution to automate the negotiation process for cloud environments and specifically infrastructure as a service category. The proposed negotiation strategy is based on a time-dependent tactic. For cloud providers, the strategy uniquely considers utilization of resources when generating new offers and automatically adjusts the tactic’s parameters to concedemoreonthepriceoflessutilizedresources.Inaddition,whilethepreviousnegotiationstrategiesinliteraturetrustofferedqualityofservicevaluesregardlessoftheirdependability,ourproposed strategy is capable of assessing reliability of offers received from cloud providers. Furthermore, to find the right configuration of the time-dependent tactic in cloud computing environments, we investigate the effect of modifying parameters such as initial offer value and deadline on negotiation outputsthatincluderatioofdealsmade,andinequalityindex.Theproposednegotiationstrategyistested with different workloads and in diverse market conditions to show how the time-dependent tactic’s settings can dynamically adapt to help cloud providers increase their profits.


title: Complex Network Analysis Using Parallel Approximate Motif Counting

abstract: Subgraph counting forms the basis of many complex network analysis metrics, including motif and anti-motif finding, relative graph let frequency distance, and graph let degree distribution agreements. Determining exact subgraph counts is computationally very expensive. In recent work, we present FASCIA, a shared-memory parallel algorithm and implementation for approximate subgraph counting. FASCIA uses a dynamic programming-based approach and is significantly faster than exhaustive enumeration, while generating high-quality approximations of subgraph counts. However, the memory usage of the dynamic programming step prohibits us from applying FASCIA to very large graphs. In this paper, we introduce a distributed-memory parallelization of FASCIA by partitioning the graph and the dynamic programming table. We discuss a new collective communication scheme to make the dynamic programming step memory-efficient. These optimizations enable scaling to much larger networks than before. We also present a simple parallelization strategy for distributed subgraph counting on smaller networks. The new additions let us use subgraph counts as graph signatures for a large network collection, and we analyze this collection using various subgraph count-based graph analytics.


THYROXINE-BINDING BY SERUMANDURINE PROTEINS IN NEPHROSIS


title: Comparison of LIFI and WIFI and study of smart meter-survey

abstract: Li-Fi stands for Light-Fidelity. Li-Fi innovation, which was suggested by Harald Haas, a German physicist, gives conduction of information over brightening through distribution of information via a LED light which changes in force quicker when compared to the vision of human beings which could take after. Wi-Fi is extraordinary for overall remote scope inside structures, while Li-Fi has been perfect for high thickness remote information scope in limited range besides for calming wireless impedance concerns. Smart meters are electronic devices which are used for recording consumption of electrical energy on a regular basis at an interval of an hour or less. In this paper, we motivate the need to learn and understand about the various new technologies like LiFi and its advantages. Further, we will understand the comparison between LiFi and Wi-Fi and learn about the advantages of using LiFi over WiFi. In addition to that we will also learn about the working of smart meters and its communication of the recorded information on a daily basis to the utility for monitoring and billing purposes.


title: Personality Judgments Based on Physical Appearance

abstract: Despite the crucial role of physical appearance in forming first impressions, little research has examined the accuracy of personality impressions based on appearance alone. This study examined the accuracy of observers’ impressions on 10 personality traits based on full-body photographs using criterion measures based on self and peer reports. When targets’ posture and expression were constrained (standardized condition), observers’ judgments were accurate for extraversion, self-esteem, and religiosity. When targets were photographed with a spontaneous pose and facial expression (spontaneous condition), observers’ judgments were accurate for almost all of the traits examined. Lens model analyses demonstrated that both static cues (e.g., clothing style) and dynamic cues (e.g., facial expression, posture) offered valuable personality-relevant information. These results suggest that personality is manifested through both static and expressive channels of appearance, and observers use this information to form...


title: Byzantine Chain Replication

abstract: We present a new class of Byzantine-tolerant State Machine Replication protocols for asynchronous environments that we term Byzantine Chain Replication. We demonstrate two implementations that present different trade-offs between performance and security, and compare these with related work. Leveraging an external reconfiguration service, these protocols are not based on Byzantine consensus, do not require majority-based quorums during normal operation, and the set of replicas is easy to reconfigure.


title: Augmented Piano Performance using a Depth Camera

abstract: We augment the piano keyboard with a 3D gesture space using Microsoft Kinect for sensing and top-down projection for visual feedback. This interface provides multi-axial gesture controls to enable continuous adjustments to multiple acoustic parameters such as those on the typical digital synthesizers. We believe that using gesture control is more visceral and aesthetically pleasing, especially during concert performance where the visibility of the performer’s action is important. Our system can also be used for other types of gesture interaction as well as for pedagogical applications.


title: Fast Convergence Strategy for Multi-Image Superresolution via Adaptive Line Search

abstract: Multi-image superresolution (SR) techniques produce a high-resolution image from several low-resolution observations. Previous reconstruction-based SR approaches focus more on the optimization models but have not adequately emphasized the mathematic-solving techniques for this typically ill-conditioned and under-determined large scale problem. Since step size plays an important role in the iterative SR process, and there is a tradeoff between less computation cost and higher accuracy, conventional SR methods either adopt a fixed step size to obtain a higher running speed, or use a computationally expensive line search algorithm to pursue an improvement in accuracy. Taking both cues into consideration, in this paper, we propose an adaptive line search strategy to realize the fast convergence of reconstruction-based SR. The approximate analytical expression of step size is introduced to prevent us from setting it empirically or running iterations to test a proper one. We further modify the proposed strategy to be more adaptive under different SR conditions. Using our strategy, one can accelerate the SR process and obtain the optimal solution with less iteration. Experiments are conducted on both synthetic data sets and real-world scenes. The results have demonstrated the effectiveness and outperformance of our proposed strategy compared with other line search strategies.


title: Affective Pacman: A Frustrating Game for Brain-Computer Interface Experiments

abstract: We present the design and development of Affective Pacman, ::: a game that induces frustration to study the effect of user state changes on the EEG signal. Affective Pacman is designed to induce frustration for short periods, and allows the synchronous recording of a wide range of sensors, such as physiological sensors and EEG in addition to the game state. A self-assessment is integrated in the game to track changes in user state. Preliminary results indicate a signicant effect of the frustration induction on the EEG.


title: A novel dual-rotor hybrid machine with synchronous reluctance and surface permanent magnet rotors

abstract: The goal of this paper is to investigate a proposed novel dual-rotor hybrid synchronous machine using two different types of rotors, i.e. the synchronous reluctance (SyR) and surface permanent magnet (SPM) rotors. Conventional design of dual-rotor machine utilizes SPM rotors on both inner and outer sides with toroidal winding configuration. However, there are limitations of using the permanent magnet in the inner rotor such as centrifugal forces on the magnets at high rotational speed conditions and relatively low torque density if ferrite permanent magnets are used. This research proposed a novel topology by using a well-designed SyR rotor to replace the inner SPM rotor in the conventional design. The principle of operation including combined space vector diagram of the novel dual-rotor hybrid machine is discussed. Sizing equations of the proposed machine are derived to show the calculation of output power from key dimensions and electrical and magnetic loading conditions. The N-N and N-S configurations of the novel dual-rotor machine are compared. The performance of the novel machines is studied against the conventional dual-rotor surface permanent magnet machine by finite element analysis (FEA) to demonstrate that the proposed machines achieve higher torque densities with less permanent magnet consumption. The proposed N-S machine is also scaled for the traction motor specification and compared to a conventional interior PM machine.


title: Congruences modulo 2 for dimensions of spaces of cusp forms

abstract: Abstract In this paper, we give some congruences modulo 2 for dimensions of spaces of Siegel cusp forms of degree one or two. First, we review some known results for congruences between dimensions of spaces of cusp forms and class numbers of imaginary quadratic fields. Next, we give our main result which is a congruence relation for the degree two case. It is related to Arthur's conjecture for PGSp ( 2 ) .


title: Parallel Execution of the ASP Computation - an Investigation on GPUs

abstract: This paper illustrates the design and implementation of a conflict-driven ASP solver that is capable of exploiting the Single-Instruction Multiple-Thread parallelism offered by General Purpose Graphical Processing Units (GPUs). Modern GPUs are multi-core platforms, providing access to large number of cores at a very low cost, but at the price of a complex architecture with non-trivial synchronization and communication costs. The search strategy of the ASP solver follows the notion of ASP computation, that avoids the generation of unfounded sets. Conflict analysis and learning are also implemented to help the search. The CPU is used only to pre-process the program and to output the results. All the solving components, i.e., nogoods management, search strategy, (non-chronological) backjumping, heuristics, conflict analysis and learning, and unit propagation, are performed on the GPU by exploiting SIMT parallelism. The preliminary experimental results confirm the feasibility and scalability of the approach, and the potential to enhance performance of ASP solvers.


title: ‘But can you prove it?’ – examining the quality of innocent suspects' alibis

abstract: Despite an assumption in the legal system that innocent people can generate accurate alibis, little research has examined the process of alibi generation. The current study examined this process with respect to three theoretical reasons why innocent suspects may fail to generate convincing alibis: they may lack the necessary memory, generate mistaken alibis, and generate weak or uncorroborable alibis. Undergraduates (N=255) were asked to report four initial alibis – each for a different time – along with corroborating physical and person evidence. Participants attempted to corroborate that evidence before returning 48 hours later. Upon return, participants reported their investigated alibis for the same four time periods. Results indicated that, despite participants' willingness to generate initial alibis, a substantial proportion of these alibis (36%) were mistaken, requiring either a change in narrative or a change in corroborating evidence. The majority of investigated alibis relied on evidence that ev...


title: Link prediction in multi-relational collaboration networks

abstract: Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this paper, we study the problem of link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new link prediction framework, Link Prediction using Social Features (LPSF), which weights the network using a similarity function based on features extracted from patterns of prominent interactions across the network.


title: A 24-GHz high-gain Yagi-Uda antenna array

abstract: A compact 24-GHz Yagi-Uda antenna has been developed using standard design tables and simple scaling to take into account the added capacitance due to the supporting dielectric substrate. The antenna results in a directivity of 9.3 dB, a front-to-back ratio of 11 dB, and a bandwidth of 2.5-3%. The Yagi-Uda antenna has been implemented in an 11-beam system using a planar array and a 2-inch Teflon spherical lens. The measured patterns show a 22 dB gain beam, a cross-polarization level of -24 dB, and a crossover level of -6 dB. The design method presented in this paper is quite straightforward, and can be used to develop low-, medium-, and even high-gain endfire Yagi-Uda antennas.


title: A Variance Reduced Stochastic Newton Method

abstract: Quasi-Newton methods are widely used in practise for convex loss minimization problems. These methods exhibit good empirical performance on a wide variety of tasks and enjoy super-linear convergence to the optimal solution. For large-scale learning problems, stochastic Quasi-Newton methods have been recently proposed. However, these typically only achieve sub-linear convergence rates and have not been shown to consistently perform well in practice since noisy Hessian approximations can exacerbate the effect of high-variance stochastic gradient estimates. In this work we propose Vite, a novel stochastic Quasi-Newton algorithm that uses an existing first-order technique to reduce this variance. Without exploiting the specific form of the approximate Hessian, we show that Vite reaches the optimum at a geometric rate with a constant step-size when dealing with smooth strongly convex functions. Empirically, we demonstrate improvements over existing stochastic Quasi-Newton and variance reduced stochastic gradient methods.


title: Infusion of Labeled Data into Distant Supervision for Relation Extraction

abstract: Distant supervision usually utilizes only unlabeled data and existing knowledge bases to learn relation extraction models. However, in some cases a small amount of human labeled data is available. In this paper, we demonstrate how a state-of-theart multi-instance multi-label model can be modified to make use of these reliable sentence-level labels in addition to the relation-level distant supervision from a database. Experiments show that our approach achieves a statistically significant increase of 13.5% in F-score and 37% in area under the precision recall curve.


title: Sensor-Cloud Infrastructure - Physical Sensor Management with Virtualized Sensors on Cloud Computing

abstract: Increasing numbers of physical sensors are used for various purposes. Those physical sensors are usually used by their own applications. Because each application manages both of physical sensors and their sensor data exclusively, other applications cannot use the physical sensors in the different party easily. We propose a new infrastructure called Sensor-Cloud infrastructure which can manage physical sensors on IT infrastructure. The Sensor-Cloud Infrastructure virtualizes a physical sensor as a virtual sensor on the cloud computing. Dynamic grouped virtual sensors on cloud computing can be automatic provisioned when the users need them. The approach to enable the sensor management capability on cloud computing. Since the resource and capability of physical sensor devices is limited, the cloud computing on the IT infrastructure can be behalf of the sensor management such as availability and performance of physical sensors. This paper describes the design of Sensor-Cloud Infrastructure, the system architecture and the implementation.


title: Phase transformation of programmed NiSi electrical fuse: Diffusion, agglomeration and thermal stability

abstract: An advanced CMOS technology process reliability qualification especially for the NiSi poly gated electrical fuse (eFuse) consists of electrical characterization, physical analyses and reliability evaluations. In this paper, insights are given on microstructural behaviors of the programmed NiSi poly gated eFuse induced by the high temperature storage (HTS) test. Both ex- and in-situ transmission electron microscopy (TEM) reveal that the improved post-resistance of the programmed eFuse is attributed to the low temperature growth of Ni 3 Si 2 during HTS test at 250°C. In addition, the Ni agglomeration, the propensity of Ni 3 Si 2 formation on the programmed eFuse with and without void appearance on the fuse link, is comprehensively investigated in conjunction with the eFuse reliability.


title: Integrating Multiple Data Sources to Enhance Sentiment Prediction

abstract: Understanding the sentiment conveyed by a person is an important part of any social interaction, and sentiment in text can provide valuable insight into an author's opinion. Sentiment analysis for text is a large field of research within machine learning, as it allows the sentiment of large numbers of text instances to be determined and used to answer various questions, such as election prediction. Typically, a sentiment classifier is trained using data from the same domain it is intended to be applied to; however, there may not be sufficient training data within the given domain. Additionally, using data from multiple sources, including other related domains, may help create a more generalized sentiment classifier that can be applied to multiple domains. To this aim, we conduct an empirical study using sentiment data from two sources, online reviews and tweets. We first test the performance of sentiment analysis models built using a single data source for both in-domain and cross-domain classification. Then, we evaluate classifiers trained using instances randomly sampled from both sources. Additionally, we evaluate sampling different quantities of instances from both data sources to determine how many instances should be included in a training data set. We apply statistical tests to verify the significance of our results and find that using a combination of instances from reviews and tweets is similar to, or better than any model trained from a single domain. Also, we found no significant difference in performance for classifiers 100,000 or more combined training instances. These results are important as they indicate a more robust classifier can be trained by using a smaller number of in-domain instances augmented with instances from a related domain, rather than using purely in-domain instances. Thus, we recommend using a training data set composed of both tweets and reviews, when training a sentiment classifier for use in predicting both tweet and review sentiment.


title: Process and environmental variation impacts on ASIC timing

abstract: With each semiconductor process node, the impacts on performance of environmental and semiconductor process variations become a larger portion of the cycle time of the product. Simple guard-banding for these effects leads to increased product development times and uncompetitive products. In addition, traditional static timing methodologies are unable to cope with the large number of permutations of process, voltage, and temperature corners created by these independent sources of variation. In this paper we will discuss the sources of variation; by introducing the concepts of systematic inter-die variation, systematic intra-die variation and intra-die random variation. We will show that by treating these forms of variations differently, we can achieve design closure with less guard-banding than traditional methods.


title: Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition

abstract: Fine-grained visual recognition typically depends on modeling subtle difference from object parts. However, these parts often exhibit dramatic visual variations such as occlusions, viewpoints, and spatial transformations, making it hard to detect. In this paper, we present a novel attention-based model to automatically, selectively and accurately focus on critical object regions with higher importance against appearance variations. Given an image, two different Convolutional Neural Networks (CNNs) are constructed, where the outputs of two CNNs are correlated through bilinear pooling to simultaneously focus on discriminative regions and extract relevant features. To capture spatial distributions among the local regions with visual attention, soft attention based spatial Long-Short Term Memory units (LSTMs) are incorporated to realize spatially recurrent yet visually selective over local input patterns. All the above intuitions equip our network with the following novel model: two-stream CNN layers, bilinear pooling layer, spatial recurrent layer with location attention are jointly trained via an end-to-end fashion to serve as the part detector and feature extractor, whereby relevant features are localized and extracted attentively. We show the significance of our network against two well-known visual recognition tasks: fine-grained image classification and person re-identification.


title: ZIGZAG: an efficient peer-to-peer scheme for media streaming

abstract: A peer-to-peer technique called ZIGZAG for single-source media streaming is designed . ZIGZAG allows the media server to distribute content to many clients by organizing them into an appropriate tree rooted at the server. This application-layer multicast tree has a height logarithmic with the number of clients and a node degree bounded by a constant. This helps reduce the number of processing hops on the delivery path to a client while avoiding network bottleneck. Consequently, the end-to-end delay is kept small. Although one could build a tree satisfying such properties easily, an efficient control protocol between the nodes must be in place to maintain the tree under the effects of network dynamics and unpredictable client behaviors. ZIGZAG handles such situations gracefully requiring a constant amortized control overhead. Especially, failure recovery can be done regionally with little impact on the existing clients and mostly no burden on the server.


title: Some philosophical problems from the standpoint of artificial intelligence

abstract: Abstract A computer program capable of acting intelligently in the world must have a general representation of the world in terms of which its inputs are interpreted. Designing such a program requires commitments about what knowledge is and how it is obtained. Thus, some of the major traditional problems of philosophy arise in artificial intelligence. More specifically, we want a computer program that decides what to do by inferring in a formal language that a certain strategy will achieve its assigned goal. This requires formalizing concepts of causality, ability, and knowledge. Such formalisms are also considered in philosophical logic. The first part of the paper begins with a philosophical point of view that seems to arise naturally once we take seriously the idea of actually making an intelligent machine. We go on to the notions of metaphysically and epistemo-logically adequate representations of the world and then to an explanation of can, causes, and knows in terms of a representation of the world by a system of interacting automata. A proposed resolution of the problem of freewill in a deterministic universe and of counterfactual conditional sentences is presented. The second part is mainly concerned with formalisms within which it can be proved that a strategy will achieve a goal. Concepts of situation, fluent, future operator, action, strategy, result of a strategy and knowledge are formalized. A method is given of constructing a sentence of first-order logic which will be true in all models of certain axioms if and only if a certain strategy will achieve a certain goal. The formalism of this paper represents an advance over McCarthy (1963) and Green (1969) in that it permits proof of the correctness of strategies that contain loops and strategies that involve the acquisition of knowledge; and it is also somewhat more concise. The third part discusses open problems in extending the formalism of part 2. The fourth part is a review of work in philosophical logic in relation to problems of artificial intelligence and a discussion of previous efforts to program ‘general intelligence’ from the point of view of this paper.


Automated Dynamic Enforcement of Synthesized Security Policies in Android