title: A Privacy-Aware Architecture at the Edge for Autonomous Real-Time Identity Reidentification in Crowds

abstract: The capability to perform identity reidentification in a crowd (e.g., from video feeds from a network of cameras, and social media platforms, such as Facebook and Instagram) efficiently and effectively is increasingly important, as evident in recent real-world events (e.g., terrorist attacks on places of mass gatherings in different countries). However, real-time reidentification in a network of cameras, such as those deployed in a smart city, and from other sources, such as social media platforms, remains a challenging task. In this paper, a new embedding algorithm pipeline is presented to extract and administrate the crowd-sourced facial image features (e.g., social media platforms and multicameras in a dense crowd, such as a stadium or airport). The proposed facial embedding is a privacy-aware parameterized function, which maps facial images to high-dimensional vectors in order to facilitate the identification and tracking of individuals. In other words, we are able to uniquely identify person(s) of interest, without the need to determine their true identity. To extract the facial embedding information in crowds, concurrent residual neural network (ResNet) embedding pipeline for each camera is proposed. Specifically, facial embedding feature vectors are generated in real-time by each camera using the proposed enhanced ResNet architecture, which is trained with vectorized-l2-loss function for face recognition. The multivariate kernel density estimation matching algorithm is then applied to facial embedding pipelines generated by cameras at the fog cloud for identity reidentification and security verification. This allows us to ensure the privacy of individuals captured by the camera without compromising on the capability for identity reidentification. Evaluations using mixed datasets in real-time demonstrate that our proposed approach achieves a 2.6% accuracy over other state-of-the-art approaches.


title: Resistive Switching by Voltage-Driven Ion Migration in Bipolar RRAM—Part I: Experimental Study

abstract: Resistive-switching random access memory (RRAM) based on the formation and the dissolution of a conductive filament (CF) through insulating materials, e.g., transition metal oxides, may find applications as novel memory and logic devices. Understanding the resistive-switching mechanism is essential for predicting and controlling the scaling and reliability performances of the RRAM. This paper addresses the set/reset characteristics of RRAM devices based on $\hbox{HfO}_{x}$ . The set process is analyzed as a function of the initial high-resistance state and of the current compliance. The reset process is studied as a function of the initial low-resistance state. Finally, the intermediate set states, obtained by set at variable compliance current, and reset states, obtained by reset at variable stopping voltage, are characterized with respect to their reset voltage, allowing for a microscopic interpretation of intermediate states in terms of different filament morphologies.


title: Once for All: A Two-Flow Convolutional Neural Network for Visual Tracking

abstract: The main challenges of visual object tracking arise from the arbitrary appearance of the objects that need to be tracked. Most existing algorithms try to solve this problem by training a new model to regenerate or classify each tracked object. As a result, the model needs to be initialized and retrained for each new object. In this paper, we propose to track different objects in an object-independent approach with a novel two-flow convolutional neural network (YCNN). The YCNN takes two inputs (one is an object image patch, the other is a larger searching image patch), then outputs a response map which predicts how likely and where the object would appear in the search patch. Unlike the object-specific approaches, the YCNN is actually trained to measure the similarity between the two image patches. Thus, this model will not be limited to any specific object. Furthermore, the network is end-to-end trained to extract both shallow and deep dedicated convolutional features for visual tracking. And once properly trained, the YCNN can be used to track all kinds of objects without further training and updating. As a result, our algorithm is able to run at a very high speed of 45 frames-per-second. The effectiveness of the proposed algorithm can also be proved by the experiments on two popular data sets: OTB-100 and VOT-2014.


title: Energy Optimal VM Placement in the Cloud

abstract: In this paper, we investigate the issue of minimizing data center energy usage. In particular, we formulate a problem of virtual machine placement with the objective of minimizing the total power consumption of all the servers. To do this, we examine a CPU power consumption model and then incorporate the model into an mixed integer programming formulation. In order to find optimal or near-optimal solutions fast, we resolve two difficulties: non-linearity of the power model and integer decision variables. We first show how to linearize the problem, and then give a relaxation and iterative rounding algorithm. Computation experiments have shown that the algorithm can solve the problem much faster than the standard integer programming algorithms, and it consistently yields near-optimal solutions. We also provide a heuristic min-cost algorithm, which finds less optimal solutions but works even faster.


title: STFU NOOB! Predicting Crowdsourced Decisions on Toxic Behavior in Online Games

abstract: One problem facing players of competitive games is negative, or toxic, behavior. League of Legends, the largest eSport game, uses a crowdsourcing platform called the Tribunal to judge whether a reported toxic player should be punished or not. The Tribunal is a two stage system requiring reports from those players that directly observe toxic behavior, and human experts that review aggregated reports. While this system has successfully dealt with the vague nature of toxic behavior by majority rules based on many votes, it naturally requires tremendous cost, time, and human efforts. In this paper, we propose a supervised learning approach for predicting crowdsourced decisions on toxic behavior with large-scale labeled data collections; over 10 million user reports involved in 1.46 million toxic players and corresponding crowdsourced decisions. Our result shows good performance in detecting overwhelmingly majority cases and predicting crowdsourced decisions on them. We demonstrate good portability of our classifier across regions. Finally, we estimate the practical implications of our approach, potential cost savings and victim protection.


title: SIFT: Scale Invariant Feature Transform (Review)

abstract: This paper presents a study on SIFT (Scale Invariant Feature transform) which is a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. There are various applications of SIFT that includes object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.


title: Issues and challenges in dense WiFi networks

abstract: The IEEE 802.11 Working Group has initiated a new study group known as IEEE 802.11ax which is aiming to devise ways to improve spectrum efficiency, in particular to enhance the system throughput in highly dense scenarios, frequently referred to as Overlapped Basic Service Set (OBSS). In this paper we revisit some of the common problems faced in traditional WiFi networks and show how their effects could be amplified in dense deployments, especially in co-channel scenarios. We then highlight our findings through a simulation based study and draw inferences from these. Some of the key insights from this study are: link suppression and deadlock effects could potentially amplify in co-channel deployments thereby significantly degrading the throughput performance. Also, increasing the concentration of APs in a given area may not always lead to better performance and therefore AP placement needs to be carefully managed in OBSS scenarios. Where AP placement cannot be controlled due to unmanaged environments, findings indicate the need for intelligent load balancing and channel selection algorithms to minimize the impact of the aforementioned effects.


Games and Learning Management Systems : A Discussion about Motivational Design and Emotional Engagement


title: Recognition of handprinted Chinese characters using Gabor features

abstract: A method for handprinted Chinese character recognition based on Gabor filters is proposed. The Gabor approach to character recognition is intuitively appealing because it is inspired by a multi-channel filtering theory for processing visual information in the early stages of the human visual system. The performance of a character recognition system using Gabor features is demonstrated on the ETL-8 character set. Mental results show that the Gabor features yielded an error rate of 2.4% versus the error rate of 4.4% obtained by using a popular feature extraction method.


title: Learning and Memory Impairment in Patients with Temporal Lobe Epilepsy: Relation to the Presence, Type, and Location of Brain Lesion

abstract: Summary: Purpose: To study the influence of epileptogenic lesions on learning and memory alterations in patients with temporal lobe epilepsy (TLE). ::: ::: ::: ::: Methods: We studied 131 patients (55 with left and 39 with right lesional TLE; 22 with left and 15 with right cryptogenic TLE) and 36 healthy subjects. We compared these groups by using a battery of tests to assess verbal and visual learning, delayed recall, and recall after the imposition of interfering activity. ::: ::: ::: ::: Results: Compared with the controls and patients with right TLE, the patients with left TLE were significantly impaired on all verbal tests. On visual tests, patients with right TLE were impaired compared with controls but not more so than patients with left TLE. Separate multivariate analyses of variance (MA-NOVAs) of patients' verbal and visual test scores, taking the TLE side and morphologic features of the temporal lobes (i.e., normal, hippocampal sclerosis, low-grade glioma, or cavernous angioma) as independent factors, did not show any significant effect of these features. Separate comparisons of verbal and visual test scores of patients with lesional TLE, taking the side and location (mesial or lateral) of the epileptogenic lesion as independent factors, did not show any significant effect of location. ::: ::: ::: ::: Conclusions: Our findings show that some learning and memory abilities are impaired in patients with TLE irrespective of the presence of overt damage. This supports the theory that focal epileptic discharges, rather than the lesions themselves, affect these functions. The pathologic characteristics and intratemporal location of an associated lesion do not seem to play an important role in determining learning and memory impairment when clinical and treatment-related factors are taken into account.


title: Translation Invariant Word Embeddings

abstract: This work focuses on the task of finding latent vector representations of the words in a corpus. In particular, we address the issue of what to do when there are multiple languages in the corpus. Prior work has, among other techniques, used canonical correlation analysis to project pre-trained vectors in two languages into a common space. We propose a simple and scalable method that is inspired by the notion that the learned vector representations should be invariant to translation between languages. We show empirically that our method outperforms prior work on multilingual tasks, matches the performance of prior work on monolingual tasks, and scales linearly with the size of the input data (and thus the number of languages being embedded).


title: Scalable Overlapping Co-clustering of Word-Document Data

abstract: Text clustering is used on a variety of applications such as content-based recommendation, categorization, summarization, information retrieval and automatic topic extraction. Since most pair of documents usually shares just a small percentage of words, the dataset representation tends to become very sparse, thus the need of using a similarity metric capable of a partial matching of a set of features. The technique known as Co-Clustering is capable of finding several clusters inside a dataset with each cluster composed of just a subset of the object and feature sets. In word-document data this can be useful to identify the clusters of documents pertaining to the same topic, even though they share just a small fraction of words. In this paper a scalable co-clustering algorithm is proposed using the Locality-sensitive hashing technique in order to find co-clusters of documents. The proposed algorithm will be tested against other co-clustering and traditional algorithms in well known datasets. The results show that this algorithm is capable of finding clusters more accurately than other approaches while maintaining a linear complexity.


title: Combining the benefits of function approximation and trajectory optimization

abstract: Neural networks have recently solved many hard problems in Machine Learning, but their impact in control remains limited. Trajectory optimization has recently solved many hard problems in robotic control, but using it online remains challenging. Here we leverage the high-fidelity solutions obtained by trajectory optimization to speed up the training of neural network controllers. The two learning problems are coupled using the Alternating Direction Method of Multipliers (ADMM). This coupling enables the trajectory optimizer to act as a teacher, gradually guiding the network towards better solutions. We develop a new trajectory optimizer based on inverse contact dynamics, and provide not only the trajectories but also the feedback gains as training data to the network. The method is illustrated on rolling, reaching, swimming and walking tasks.


title: DroidTrace: A ptrace based Android dynamic analysis system with forward execution capability

abstract: Android, being an open source smartphone operating system, enjoys a large community of developers who create new mobile services and applications. However, it also attracts malware writers to exploit Android devices in order to distribute malicious apps in the wild. In fact, Android malware are becoming more sophisticated and they use advanced “dynamic loading” techniques like Java reflection or native code execution to bypass security detection. To detect dynamic loading, one has to use dynamic analysis. Currently, there are only a handful of Android dynamic analysis tools available, and they all have shortcomings in detecting dynamic loading. The aim of this paper is to design and implement a dynamic analysis system which allows analysts to perform systematic analysis of dynamic payloads with malicious behaviors. We propose “DroidTrace”, a ptrace based dynamic analysis system with forward execution capability. Our system uses ptrace to monitor selected system calls of the target process which is running the dynamic payloads, and classifies the payloads behaviors through the system call sequence, e.g., behaviors such as file access, network connection, inter-process communication and even privilege escalation. Also, DroidTrace performs “physical modification” to trigger different dynamic loading behaviors within an app. Using DroidTrace, we carry out a large scale analysis on 36,170 dynamic payloads in 50,000 apps and 294 malware in 10 families (four of them are zero-day) with various dynamic loading behaviors.


title: Parallel Attentive Correlation Tracking

abstract: Psychological and cognitive findings indicate that human visual perception is attentive and selective, which may process spatial and appearance selective attentions in parallel. By reflecting some aspects of these attentions, this paper presents a novel correlation filter (CF)-based tracking approach, corresponding to processing a local and a semi-local background domains, respectively. In the local domain, inspired by the Gestalt principle of figure-ground segregation, we leverage an efficient Boolean map representation, which characterizes an image by a set of Boolean maps via randomly thresholding its color channels, yielding a location response map as a weighted sum of all Boolean maps. The Boolean maps capture the topological structures of target and its scene with different granularities, thereby enabling to effectively improve tracking of non-rectangular objects. Alternatively, in the semi-local domains, we introduce a novel distractor-resilient metric regularization into CF, which acts as a force to push distractors into negative space. Consequently, the unwanted boundary effects of CF can be effectively alleviated. Finally, both models associated with the local and the semi-local domains are seamlessly integrated into a Bayesian framework, and the tracked location is determined by maximizing its likelihood function. Extensive evaluations on the OTB50, OTB100, VOT2016, and VOT2017 tracking benchmarks demonstrate that the proposed method achieves favorable performance against a variety of state-of-the-art trackers with a speed of 45 fps on a single CPU.


title: TVQA: Localized, Compositional Video Question Answering

abstract: Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks. However, due to data limitations, there has been much less work on video-based QA. In this paper, we present TVQA, a large-scale video QA dataset based on 6 popular TV shows. TVQA consists of 152,545 QA pairs from 21,793 clips, spanning over 460 hours of video. Questions are designed to be compositional in nature, requiring systems to jointly localize relevant moments within a clip, comprehend subtitle-based dialogue, and recognize relevant visual concepts. We provide analyses of this new dataset as well as several baselines and a multi-stream end-to-end trainable neural network framework for the TVQA task. The dataset is publicly available at http://tvqa.cs.unc.edu.


title: An approach for passive testing of SFSM-based systems

abstract: Based on Simplified Finite State Machine (SFSM) as an improved model, an approach for passive testing consistency is proposed. The improved model is introduced and the algorithm complexity is analysed. Also, this approach is implemented with the EPON Tester, and proved to be feasible to analyze Multi-Point Control Protocol (MPCP) consistency.


title: Paired 3D Model Generation with Conditional Generative Adversarial Networks

abstract: Generative Adversarial Networks (GANs) are shown to be successful at generating new and realistic samples including 3D object models. Conditional GAN, a variant of GANs, allows generating samples in given conditions. However, objects generated for each condition are different and it does not allow generation of the same object in different conditions. In this paper, we first adapt conditional GAN, which is originally designed for 2D image generation, to the problem of generating 3D models in different rotations. We then propose a new approach to guide the network to generate the same 3D sample in different and controllable rotation angles (sample pairs). Unlike previous studies, the proposed method does not require modification of the standard conditional GAN architecture and it can be integrated into the training step of any conditional GAN. Experimental results and visual comparison of 3D models show that the proposed method is successful at generating model pairs in different conditions.


title: Automatically Generating Models for Botnet Detection

abstract: A botnet is a network of compromised hosts that is under the control of a single, malicious entity, often called the botmaster. We present a system that aims to detect bots, independent of any prior information about the command and control channels or propagation vectors, and without requiring multiple infections for correlation. Our system relies on detection models that target the characteristic fact that every bot receives commands from the botmaster to which it responds in a specific way. These detection models are generated automatically from network traffic traces recorded from actual bot instances. We have implemented the proposed approach and demonstrate that it can extract effective detection models for a variety of different bot families. These models are precise in describing the activity of bots and raise very few false positives.


title: Single-Pass PCA of Large High-Dimensional Data

abstract: Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning. For large and high-dimensional data, computing the PCA (i.e., the singular vectors corresponding to a number of dominant singular values of the data matrix) becomes a challenging task. In this work, a single-pass randomized algorithm is proposed to compute PCA with only one pass over the data. It is suitable for processing extremely large and high-dimensional data stored in slow memory (hard disk) or the data generated in a streaming fashion. Experiments with synthetic and real data validate the algorithm's accuracy, which has orders of magnitude smaller error than an existing single-pass algorithm. For a set of high-dimensional data stored as a 150 GB file, the proposed algorithm is able to compute the first 50 principal components in just 24 minutes on a typical 24-core computer, with less than 1 GB memory cost.


title: Extraction of neonicotinoid insecticides from environmental water samples with magnetic graphene nanoparticles as adsorbent followed by determination with HPLC

abstract: In this paper, a kind of graphene magnetic nanoparticle (G–Fe3O4) was prepared and used as the adsorbent for the preconcentration of the four neonicotinoid insecticides (thiamethoxam, imidacloprid, acetamiprid and thiacloprid) from environmental water samples. The properties of the magnetic adsorbent were characterized by scanning electron microscopy, transmission electron microscopy and X-ray diffraction. After preconcentration, the adsorbent can be conveniently separated from the aqueous samples by an external magnet, and the analytes desorbed were determined by high performance liquid chromatography-ultraviolet detection (HPLC-UV). Various parameters affecting the extraction efficiencies such as the concentration of G–Fe3O4, the extraction time, the pH of the sample solution, the salinity and the desorption conditions were investigated. Under the optimum conditions, the concentration factors for the analytes ranged between 3325 and 4644. The linearity of the method was in the range from 0.05 to 50 ng mL−1 with the correlation coefficients (r) ranging from 0.9990 to 0.9995. The limits of detection (S/N = 3) of the method for thiamethoxam, imidacloprid, acetamiprid and thiacloprid were 0.01, 0.006, 0.004 and 0.006 ng ml−1, respectively. The relative standard deviations (RSDs) varied from 4.3% to 7.4% (n = 6), and the recoveries of the method for the compounds at spiking levels of 0.5 and 5 ng mL−1 were in the range of 86%–110%. The method has been successfully applied to the analysis of the neonicotinoid insecticides in real water samples.


title: CURRENT CLAIM: Isolated Sleep Paralysis: A Web Survey

abstract: Isolated Sleep Paralysis (SP) occurs at least once in a lifetime in 40-50% of normal subjects, while as a chronic complaint it is an uncommon and scarcely known disorder. A series of messages written by subjects who experienced at least one episode of SP, containing more or less detailed descriptions of this disorder, were collected from the Sleep Web site of the University of California in Los Angeles between January 1996 and July 1998. Two hundred and sixty-four messages fulfilling the International Classification of Sleep Disorders (ICSD) (Thorpy, 1990) minimal criteria for SP were analyzed. A wide spectrum of severity was evident, with a frequency of episodes ranging from one in a lifetime to almost every night, and a variety of emotional and hallucinatory experiences associated with SP episodes were reported. Clinical similarities between the recurrent form of isolated SP and channelopathies (in particular, periodic paralyses) are discussed. An activation of limbic system structures is suggested in order to explain some of the most common subjective experiences associated with SP.


title: Wireless access monitoring and control system based on digital door lock

abstract: We propose a novel wireless access monitoring and control system based on the digital door lock, which is explosively used as a digital consumer device. Digital door lock is an electronic locking system operated by the combination of digital key, security password or number codes. This paper presents a prototype of the proposed system and shows a scheme for the implementation. To implement the system with ZigBee network protocol, four types of modules are developed, ZigBee module, digital door lock module, human detection module, and ZigBee relay module. ZigBee module is designed to support wireless sensor network and also used for the ZigBee tag to identify the access objects. Digital door lock module is implemented as a digital consumer device to control the access system as well as locking system. It is very convenient system for the consumers and has extensible and flexible characteristics. That is, it can be used as a home security system by the ZigBee network with additional sensor devices. Therefore, it can be a good practical product for the realization of an access monitoring and control system. It also can be applied to the real market for home networking system. Furthermore, the system can be extended to another service such as a connection between mobile phone and home networking system.


title: Intel® Software Guard Extensions (Intel® SGX) Support for Dynamic Memory Management Inside an Enclave

abstract: We introduce Intel® Software Guard Extensions (Intel® SGX) SGX2 which extends the SGX instruction set to include dynamic memory management support for enclaves. Intel® SGX is a subset of the Intel Architecture Instruction Set [1]. SGX1 allows an application developer to build a trusted environment and execute inside that space. However SGX1 imposes limitations regarding memory commitment and reuse of enclave memory. The software developer is required to allocate all memory at enclave instantiation. This paper describes new instructions and programming models to extend support for dynamic memory management inside an enclave.


title: Energy efficient neural networks for big data analytics

abstract: The world is experiencing a data revolution to discover knowledge in big data. Large scale neural networks are one of the mainstream tools of big data analytics. Processing big data with large scale neural networks includes two phases: the training phase and the operation phase. Huge computing power is required to support the training phase. And the energy efficiency (power efficiency) is one of the major considerations of the operation phase. We first explore the computing power of GPUs for big data analytics and demonstrate an efficient GPU implementation of the training phase of large scale recurrent neural networks (RNNs). We then introduce a promising ultrahigh energy efficient implementation of neural networks' operation phase by taking advantage of the emerging memristor technique. Experiment results show that the proposed GPU implementation of RNNs is able to achieve 2 ~ 11× speed-up compared with the basic CPU implementation. And the scaled-up recurrent neural network trained with GPUs realizes an accuracy of 47% on the Microsoft Research Sentence Completion Challenge, the best result achieved by a single RNN on the same dataset. In addition, the proposed memristor-based implementation of neural networks demonstrates power efficiency of > 400 GFLOPS/W and achieves energy savings of 22× on the HMAX model compared with its pure digital implementation counterpart.


title: SQLiteKV: An efficient LSM-tree-based SQLite-like database engine for mobile devices

abstract: SQLite has been deployed in millions of mobile devices from web to smartphone applications on various mobile operating systems. However, SQLite is not efficient with low transactions per second. In this paper, we for the first time propose a new SQLite-like database engine, called SQLiteKV, which adopts the LSM-tree-based data structure but retains the SQLite operation interfaces. With its SQLite interface, SQLiteKV can be utilized by existing applications without any modification, while providing high performance with its LSM-tree-based data structure. In SQLiteKV, we develop a light-weight SQLite to key-value compiler to solve the semantic mismatch, so SQL statements can be efficiently translated into KV operations. We also design a novel coordination caching mechanism so query results can be effectively cached inside SQLiteKV by alleviating the discrepancy of data management between front-end SQLite statements and back-end key-value data organization. We have implemented and deployed SQLiteKV on a Google Nexus 6P smartphone. Experiments results show that SQLiteKV outperforms SQLite up to 6 times.


Principles for Learning Controllable TTS from Annotated and Latent Variation


title: Load-Line Regulation With Estimated Load-Current Feedforward: Application to Microprocessor Voltage Regulators

abstract: A consistent framework for load-line regulation design is presented, applicable to microprocessor voltage regulators (VRs) using either electrolytic or ceramic output capacitors. With conventional feedback control, the loop bandwidth is limited by stability constraints linked to the switching frequency. The output capacitor has to be chosen sufficiently large to meet the stability requirement. Load-current feedforward can extend the useful bandwidth beyond that imposed by feedback stability constraints. With load-current feedforward, the size of the output capacitor can be reduced, since it is determined solely by large-signal and switching-ripple considerations which are shown to be less constraining than the feedback stability requirement. This work points to the feasibility of microprocessor VR implementations using only a small number of ceramic output capacitors, while running at sub-megahertz switching frequencies


title: Bogus currency authorization using HSV techniques

abstract: In present scenario, the Indian government has announced the demonetization of all Rs 500 and Rs 1000, in reserve bank notes of Mahatma Gandhi series. Indian government has introduced a new Rs 500 and Rs 2000, to reduce fund illegal activity in India. Even then the new notes of fake or bogus currency are circulated in the society. The main objective of this work is used to identify fake currencies among the real. From the currency, the strip lines or continuous lines are detected from real and fake note by using edge detection techniques. HSV techniques are used to saturate the value of an input image. To achieve the enhance reliability and dynamic way in detecting the counterfeit currency.


title: Seasonal freeze resistance of rainbow smelt (Osmerus mordax) is generated by differential expression of glycerol-3-phosphate dehydrogenase, phosphoenolpyruvate carboxykinase, and antifreeze protein genes.

abstract: In winter, rainbow smelt (Osmerus mordax) accumulate glycerol and produce an antifreeze protein (AFP), which both contribute to freeze resistance. The role of differential gene expression in the seasonal pattern of these adaptations was investigated. First, cDNAs encoding smelt and Atlantic salmon (Salmo salar) phosphoenolpyruvate carboxykinase (PEPCK) and smelt glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were cloned so that all sequences required for expression analysis would be available. Using quantitative PCR, expression of beta actin in rainbow smelt liver was compared with that of GAPDH in order to determine its validity as a reference gene. Then, levels of glycerol-3-phosphate dehydrogenase (GPDH), PEPCK, and AFP relative to beta actin were measured in smelt liver over a fall-winter-spring interval. Levels of GPDH mRNA increased in the fall just before plasma glycerol accumulation, implying a driving role in glycerol synthesis. GPDH mRNA levels then declined during winter, well in advance of serum glycerol, suggesting the possibility of GPDH enzyme or glycerol conservation in smelt during the winter months. PEPCK mRNA levels rose in parallel with serum glycerol in the fall, consistent with an increasing requirement for amino acids as metabolic precursors, remained elevated for much of the winter, and then declined in advance of the decline in plasma glycerol. AFP mRNA was elevated at the onset of fall sampling in October and remained elevated until April, implying separate regulation from GPDH and PEPCK. Thus, winter freezing point depression in smelt appears to result from a seasonal cycle of GPDH gene expression, with an ensuing increase in the expression of PEPCK, and a similar but independent cycle of AFP gene expression.


title: Contextual Anomaly Detection in Big Sensor Data

abstract: Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection technique for use in streaming sensor networks. The technique uses a well-defined content anomaly detection algorithm for real-time point anomaly detection. Additionally, we present a post-processing context-aware anomaly detection algorithm based on sensor profiles, which are groups of contextually similar sensors generated by a multivariate clustering algorithm. Our proposed research has been implemented and evaluated with real-world data provided by Powersmiths, located in Brampton, Ontario, Canada.


title: Plastic particles in coastal pelagic ecosystems of the Northeast Pacific ocean.

abstract: The purpose of this study was to examine the distribution, abundance and characteristics of plastic particles in plankton samples collected routinely in Northeast Pacific ecosystems, and to contribute to the development of ideas for future research into the occurrence and impact of small plastic debris in marine pelagic ecosystems. Plastic debris particles were assessed from zooplankton samples collected as part of the National Oceanic and Atmospheric Administration's (NOAA) ongoing ecosystem surveys during two research cruises in the Southeast Bering Sea in the spring and fall of 2006 and four research cruises off the U.S. west coast (primarily off southern California) in spring, summer and fall of 2006, and in January of 2007. Nets with 0.505 mm mesh were used to collect surface samples during all cruises, and sub-surface samples during the four cruises off the west coast. The 595 plankton samples processed indicate that plastic particles are widely distributed in surface waters. The proportion of surface samples from each cruise that contained particles of plastic ranged from 8.75 to 84.0%, whereas particles were recorded in sub-surface samples from only one cruise (in 28.2% of the January 2007 samples). Spatial and temporal variability was apparent in the abundance and distribution of the plastic particles and mean standardized quantities varied among cruises with ranges of 0.004-0.19 particles/m³, and 0.014-0.209 mg dry mass/m³. Off southern California, quantities for the winter cruise were significantly higher, and for the spring cruise significantly lower than for the summer and fall surveys (surface data). Differences between surface particle concentrations and mass for the Bering Sea and California coast surveys were significant for pair-wise comparisons of the spring but not the fall cruises. The particles were assigned to three plastic product types: product fragments, fishing net and line fibers, and industrial pellets; and five size categories: <1 mm, 1-2.5 mm, >2.5-5 mm, >5-10 mm, and >10 mm. Product fragments accounted for the majority of the particles, and most were less than 2.5 mm in size. The ubiquity of such particles in the survey areas and predominance of sizes <2.5 mm implies persistence in these pelagic ecosystems as a result of continuous breakdown from larger plastic debris fragments, and widespread distribution by ocean currents. Detailed investigations of the trophic ecology of individual zooplankton species, and their encounter rates with various size ranges of plastic particles in the marine pelagic environment, are required in order to understand the potential for ingestion of such debris particles by these organisms. Ongoing plankton sampling programs by marine research institutes in large marine ecosystems are good potential sources of data for continued assessment of the abundance, distribution and potential impact of small plastic debris in productive coastal pelagic zones.


title: An Efficient Algorithm for Easy-First Non-Directional Dependency Parsing

abstract: We present a novel deterministic dependency parsing algorithm that attempts to create the easiest arcs in the dependency structure first in a non-directional manner. Traditional deterministic parsing algorithms are based on a shift-reduce framework: they traverse the sentence from left-to-right and, at each step, perform one of a possible set of actions, until a complete tree is built. A drawback of this approach is that it is extremely local: while decisions can be based on complex structures on the left, they can look only at a few words to the right. In contrast, our algorithm builds a dependency tree by iteratively selecting the best pair of neighbours to connect at each parsing step. This allows incorporation of features from already built structures both to the left and to the right of the attachment point. The parser learns both the attachment preferences and the order in which they should be performed. The result is a deterministic, best-first, O(nlogn) parser, which is significantly more accurate than best-first transition based parsers, and nears the performance of globally optimized parsing models.


title: DietCam: Regular Shape Food Recognition with a Camera Phone

abstract: The purpose of this paper is to develop an automatic camera phone based multi-view food classifier as part of a food intake assessment system. Food intake assessment is important for obesity management, which has shown significant impacts in public healthcare. Conventional dietary record based food intake assessment methods exhibit insufficient popularity due to their low accuracy and high dependence on human interactions. Image based food recognition appears recently. But it is still under development and far away from field applications. This paper presents DietCam, a camera phone based application to evaluate food intakes automatically from multiple perspectives. Food recognition from images is afflicted currently with a low recognition accuracy caused by the uncertainties of food appearances. The deformable nature of food items together with the complex background environment makes the problem even harder. DietCam separates every food item through evaluating the best perspective and recognize each of them from multiple images with a probabilistic method. The recognition accuracy is increased through an enhanced joint distribution from every viewpoint. A prototype of DietCam has been implemented on iPhone. In the field experiments, it shows an accuracy of 84% for regular shape food items.


title: A General Factor of Personality From Multitrait–Multimethod Data and Cross–National Twins

abstract: In three studies, a General Factor of Personality (GFP) was found to occupy the apex of the hierarchical structure. In Study 1, a GFP emerged independent of method variance and accounted for 54% of the reliable variance in a multitrait–multimethod assessment of 391 Italian high school students that used self-, teacher-, and parent-ratings on the Big Five Questionnaire — Children. In Study 2, a GFP was found in the seven dimensions of Cloninger's Temperament and Character Inventory as well as the Big Five of the NEO PI-R, with the GFPtci correlating r = .72 with the GFPneo. These results indicate that the GFP is practically the same in both test batteries, and its existence does not depend on being extracted using the Big Five model. The GFP accounted for 22% of the total variance in these trait measures, which were assessed in 651 pairs of 14- to 30-year-old Japanese twins. In Study 3, a GFP accounted for 32% of the total variance in nine scales derived from the NEO PI-R, the Humor Styles Questionnaire, and the Trait Emotional Intelligence Questionnaire assessed in 386 pairs of 18- to 74-year-old Canadian and U.S. twins. The GFP was found to be 50% heritable with high scores indicating openness, conscientiousness, sociability, agreeableness, emotional stability, good humor and emotional intelligence. The possible evolutionary origins of the GFP are discussed.


title: The effect of Kahoot, Quizizz and Google Forms on the student's perception in the classrooms response system

abstract: Kahoot, Quizizz, and Google Forms are learning technology opens for new ways of teaching in the classroom. The teachers' laptops connected to a video projector, access to wireless network and the students smartphones, tablets or laptops can be utilized to enhance the interaction between the teacher and students, as well as boost the students motivation, engagement and learning. This paper shows the results from investigating the effect of using Kahoot, Quizizz, and Google Forms in classroom on how the students' perception of concentration, engagement, enjoyment, perceived learning, motivation, and satisfaction. The results show that students learned something from doing the quiz via Kahoot, Quizizz and Google Forms. But, there are significant differences in the concentration, engagement, enjoyment, motivation, and satisfaction. Kahoot and Quizizz has presented a lot of positives over Google forms when used in the classroom.


Three dimensions of emotion.


title: The adrenergic receptors of the intraocular muscles of the human eye.

abstract: The adrenergic receptors in man were analyzed using isolated sphincter, dilator, and ciliary muscle strips, dissected from eyebank eyes. The dilator is mainly alpha, the sphincter both alpha and beta, and the ciliary muscle predominantly beta adrenergic.


title: Drivable Road Detection with 3D Point Clouds Based on the MRF for Intelligent Vehicle

abstract: In this paper, a reliable road/obstacle detection with 3D point cloud for intelligent vehicle on a variety of challenging environments (undulated road and/or uphill/ downhill) is handled. For robust detection of road we propose the followings: 1) correction of 3D point cloud distorted by the motion of vehicle (high speed and heading up and down) incorporating vehicle posture information; 2) guideline for the best selection of the proper features such as gradient value, height average of neighboring node; 3) transformation of the road detection problem into a classification problem of different features; and 4) inference algorithm based on MRF with the loopy belief propagation for the area that the LIDAR does not cover. In experiments, we use a publicly available dataset as well as numerous scans acquired by the HDL-64E sensor mounted on experimental vehicle in inner city traffic scenes. The results show that the proposed method is more robust and reliable than the conventional approach based on the height value on the variety of challenging environment.


title: Implementation of RSA 2048-bit and AES 256-bit with digital signature for secure electronic health record application

abstract: This research addresses the implementation of encryption and digital signature technique for electronic health record to prevent cybercrime problem such as robbery, modification and unauthorized access. In this research, RSA 2048-bit algorithm, AES 256-bit and SHA 256 will be implemented in Java programming. Secure Electronic Health Record Information (SEHR) application design is intended to combine given services, such as confidentiality, integrity, authentication, and non-repudiation. Cryptography is used to ensure the file records and electronic documents for detailed information on the medical past, present and future forecasts that have been given only for the patients. The document will be encrypted using an encryption algorithm based on NIST Standard. In the application, there are two schemes, namely the protection and verification scheme. This research uses black-box testing and white-box testing to test the software input, output, and code without testing the process and design that occurs in the system. We demonstrated the implementation of cryptography in Secure Electronic Health Record Information (SEHR). The implementation of encryption and digital signature in this research can prevent archive thievery which is shown on implementation and is proven on the test.


Permanent-magnet variable-reluctance linear motor control


Malware Variants Identification in Practice


title: Design of a dual-band omni-directional microstrip antenna for smart mobile phones

abstract: A dual-band omni-directional antenna for smart mobile phones, ordered by a manufacturer of mobile phones in Vietnam, has been designed and tested. The results from the designed model, which fits the housing required by the manufacturer, have been compared with data from literature. A sample of the designed model has been fabricated and measured. Good agreement of simulated and measured data has been obtained.


title: Problem frames: analyzing and structuring software development problems

abstract: This book is a must-have for all IT professionals facing software development problems on a daily basis. If you are a systems analyst or requirements engineer it will provide an essential, practical guide from the task of identifying the problem to making the descriptions needed to resolve it.


title: Micromolding of shape-controlled, harvestable cell-laden hydrogels

abstract: Encapsulation of mammalian cells within hydrogels has great utility for a variety of applications ranging from tissue engineering to cell-based assays. In this work, we present a technique to encapsulate live cells in three-dimensional (3D) microscale hydrogels (microgels) of controlled shapes and sizes in the form of harvestable free standing units. Cells were suspended in methacrylated hyaluronic acid (MeHA) or poly(ethylene glycol) diacrylate (PEGDA) hydrogel precursor solution containing photoinitiator, micromolded using a hydrophilic poly(dimethylsiloxane) (PDMS) stamp, and crosslinked using ultraviolet (UV) radiation. By controlling the features on the PDMS stamp, the size and shape of the molded hydrogels were controlled. Cells within microgels were well distributed and remained viable. These shape-specific microgels could be easily retrieved, cultured and potentially assembled to generate structures with controlled spatial distribution of multiple cell types. Further development of this technique may lead to applications in 3D co-cultures for tissue/organ regeneration and cell-based assays in which it is important to mimic the architectural intricacies of physiological cell–cell interactions. r 2006 Elsevier Ltd. All rights reserved.


title: Convex hull based neuro-retinal optic cup ellipse optimization in glaucoma diagnosis

abstract: Glaucoma is the second leading cause of blindness. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Automatic calculation of optic cup boundary is challenging due to the interweavement of blood vessels with the surrounding tissues around the cup. A Convex Hull based Neuro-Retinal Optic Cup Ellipse Optimization algorithm improves the accuracy of the boundary estimation. The algorithm’s effectiveness is demonstrated on 70 clinical patient’s data set collected from Singapore Eye Research Institute. The root mean squared error of the new algorithm is 43% better than the ARGALI system which is the state-of-the-art. This further leads to a large clinical evaluation of the algorithm involving 15 thousand patients from Australia and Singapore.


title: Printed in the United States of America Deictic codes for the embodiment of cognition

abstract: To describe phenomena that occur at different time scales, computational models of the brain necessarily must incorporate different levels of abstraction. We argue that at time scales of approximately one-third of a second, orienting movements of the body play a crucial role in cognition and form a useful computational level, termed the embodiment level. At this level, the constraints of the body determine the nature of cognitive operations, since the natural sequentiality of body movements can be matched to the natural computational economies of sequential decision systems. The way this is done is through a system of implicit reference termed deictic, whereby pointing movements are used to bind objects in the world to cognitive programs. We show how deictic bindings enable the solution of natural tasks and argue that one of the central features of cognition, working memory, can be related to moment-by-moment dispositions of body features such as eye movements and hand movements.


title: Emotion rating from short blog texts

abstract: Being able to automatically perceive a variety of emotions from text alone has potentially important applications in CMC and HCI that range from identifying mood from online posts to enabling dynamically adaptive interfaces. However, such ability has not been proven in human raters or computational systems. Here we examine the ability of naive raters of emotion to detect one of eight emotional categories from 50 and 200 word samples of real blog text. Using expert raters as a 'gold standard', naive-expert rater agreement increased with longer texts, and was high for ratings of joy, disgust, anger and anticipation, but low for acceptance and 'neutral' texts. We discuss these findings in light of theories of CMC and potential applications in HCI.


title: An Induction Generator-Based AC/DC Hybrid Electric Power Generation System for More Electric Aircraft

abstract: In more electric aircraft (MEA) system, both ac and dc electric power with multiple voltage levels are required for various aircraft loads. This paper presents an induction generator-based ac/dc hybrid electric power generation system for MEA. In the proposed system architecure, a high-speed induction starter/generator and a low-speed induction generator are installed on the high pressure (HP) and low pressure (LP) spools of the engine, respectively. In generating mode of operation, all of the constant voltage variable frequency ac power is generated by the HP generator while the dc power demand is shared by both HP and LP generators. A control scheme is developed to regulate the ac load voltage and coordinate dc power generation between the two generators. The proposed induction generator based ac/dc hybrid generation system results in reduced hardware requirement compared with both ac and dc primary generation systems.


title: Iseult/INUMAC 11.7-T MRI Assembly Status

abstract: A new innovative whole-body 11.7-T MRI magnet is currently being manufactured at Alstom Belfort as part the Iseult/Inumac project, a French-German initiative focused on very high magnetic field molecular imaging. It will be installed at the end of year 2016 in a neuroscience research center with other very high field MRI equipment, operating in France at CEA Saclay since November 2006. The main coil constructed from a stack of 170 double pancakes of 2-m diameter, with a finished height of 4 m and 50 t in weight, has been completed within required tolerances. The two shielding coils, vacuum impregnated solenoids of 4 m in diameter and 10 t in weight, have been also completed within tolerances. A crack has been discovered inside the 2-m diameter mandrel of the cryogenic correction coils. A new mandrel has been manufactured, with delivery in July 2015. The main coil and the shielding coils have been integrated inside the helium vessel, and the assembly of thermal shield and vacuum vessel is due to start, with completion expected by the middle of 2016. The magnet will be serviced by a separate cryogenic and electrical facility; the installation of this external equipment will be completed by the end of 2015 when the first phase of the commissioning will start. Full tests and commissioning of the magnet at 1.8 K are expected at NeuroSpin at the beginning of 2017.


title: Facebook in context(s): Measuring emotional responses across time and space:

abstract: This article advances a contextual approach to understanding the emotional and social outcomes of Facebook use. In doing so, we address the ambiguity of previously reported relationships between Facebook use and well-being. We test temporal (shorter vs longer time spans) and spatial (at home vs away from home) dimensions of Facebook activity using an innovative approach. By triggering smartphone surveys in response to users’ naturalistic Facebook posting, we captured the immediate context of both mobile and desktop activities during daily life. Findings indicated positive—yet fleeting—emotional experiences up to 10 minutes after active posting and higher arousal for 30 minutes following posting at home. Nonetheless, Facebook activities predicted no changes in aggregate mood over 2 weeks, despite showing positive relationships to bridging social capital during the same period. Our results call attention to fleeting experiences (vs enduring consequences) and encourage future research to specify temporal and...


title: Semi-supervised Multinomial Naive Bayes for text classification by leveraging word-level statistical constraint

abstract: Multinomial Naive Bayes with Expectation Maximization (MNB-EM) is a standard semi-supervised learning method to augment Multinomial Naive Bayes (MNB) for text classification. Despite its success, MNB-EM is not stable, and may succeed or fail to improve MNB. We believe that this is because MNB-EM lacks the ability to preserve the class distribution on words. ::: ::: In this paper, we propose a novel method to augment MNB-EM by leveraging the word-level statistical constraint to preserve the class distribution on words. The word-level statistical constraints are further converted to constraints on document posteriors generated by MNB-EM. Experiments demonstrate that our method can consistently improve MNB-EM, and outperforms state-of-art baselines remarkably.


title: Computed tomography in diagnosis of diffuse axonal injury

abstract: Diffuse axonal injury (DAI) has been described in instances of prolonged traumatic coma on the basis of the neuropathological findings, but the same findings are also found in patients with cerebral concussion. Experimental studies confirm that the quality of survivors following trauma is directly proportional to the amount of primarily injured-axon. When the injured axon lies in a widespread area of the brain, outcome for the patient is always poor. In a series of 260 severely head-injured patients, based on their poor outcome, 69 (27%) were diagnosed as DAI. Because of their relatively good outcome, eighty-two patients (32%) were classified into non-DAI group. The predominant CT finding of DAI patients was intraparenchymal deep-seated hemorrhagic lesion. This was observed in 28 patients (41%). Normal CT was also observed in 11 patients (16%). On the other hand, 8 of the non-DAI group (10%) manifested deep-seated lesions. Diffuse cerebral swelling (DCS) appeared in both groups in the same incidence. Subarachnoid hematoma in the perimesencephalic cistern (SAH (PMC] and intraventricular hematoma (IVH) were observed in 64% of the DAI group, and in 23% of the non-DAI group. The available evidence indicates that various types of hematoma seen in the deep-seated structures of the brain do not have an absolute diagnostic value, but the frequency of hematoma is thought to increase in proportion to the amount of injured-axon.


title: AC/AC Buck Converter Instantaneous Value Control Modeling, System Analyzing and Designing

abstract: Close-loop control to the power electronics transformer (PET) was not well solved still now, we studied the basic topology of PET-AC/AC buck converter, built its small-signal model, analyzed its frequency characteristic, and pointed out that: under general inductive load condition, the open-loop model is an approximate second-order undamped oscillation system, it is marginally stable and output voltage oscillation can be induced by instant disturbances of input voltage and/or output current, therefore, we must find out a suitable close-loop adjustment. Here, we employed the classical PID control algorithm to instantaneously control output voltage following the reference signal. The close-loop system was stable and had good dynamic response characteristic. In addition, during one switching period, the line-frequency input/output voltage and output reactance were treated as constants, and a parameter-lambda, which is related to load feature, was introduced to simplify the modeling and analysis process, it is a valid method for AC/AC converter small-signal modeling and system dynamic analysis


title: Phase-Locked Loop Noise Reduction via Phase Detector Implementation for Single-Phase Systems

abstract: A crucial component of grid-connected converters is the phase-locked loop (PLL) control subsystem that tracks the grid voltage's frequency and phase angle. Therefore, accurate fast-responding PLLs for control and protection purposes are required to provide these measurements. This paper proposes a novel feedback mechanism for single-phase PLL phase detectors using the estimated phase angle. Ripple noise appearing in the estimated frequency, most commonly the second harmonic under phase-lock conditions, is reduced or eliminated without the use of low-pass filters, which can cause delays to occur and limits the overall performance of the PLL response to dynamic changes in the system. The proposed method has the capability to eliminate the noise ripple entirely and, under extreme line distortion conditions, can reduce the ripple by at least half. Other modifications implemented through frequency feedback are shown to decrease the settling time of the PLL up to 50%. Mathematical analyses with the simulated and experimental results are provided to confirm the validity of the proposed methods.


title: Thermal Face Recognition Under Temporal Variation Conditions

abstract: In this paper, we analyze the problems produced by temporal variations of infrared face images when used in face recognition systems. The temporal variations present in thermal face images are mainly due to different environmental conditions, physiological changes of the subjects, and differences of the infrared detectors’ responsivity at the time of the capture, which affect the performance of infrared face recognition systems. To perform this paper, we created two thermal face databases that include capture sessions with real and variable conditions. We also propose two criteria to quantify the temporal variations between data sets. The thermal face recognition systems have been developed using the following five methods: local binary pattern (LBP), Weber linear descriptor (WLD), Gabor jet descriptors, scale invariant feature transform, and speeded up robust features. The results indicate that the local matching-based methods (WLD and LBP) are mostly immune to temporal variations, which is noticeable when the face images have been acquired with a time lapse, while the rest of the methods are clearly affected and are not suitable for practical infrared face recognition.


title: A Cascaded High Step-Up DC–DC Converter With Single Switch for Microsource Applications

abstract: This paper proposes a new high step-up dc-dc converter designed especially for regulating the dc interface between various microsources and a dc-ac inverter to electricity grid. The figuration of the proposed converter is a quadratic boost converter with the coupled inductor in the second boost converter. The converter achieves high step-up voltage gain with appropriate duty ratio and low voltage stress on the power switch. Additionally, the energy stored in the leakage inductor of the coupled inductor can be recycled to the output capacitor. The operating principles and steady-state analyses of continuous-conduction mode and boundary-conduction mode are discussed in detail. To verify the performance of the proposed converter, a 280-W prototype sample is implemented with an input voltage range of 20-40 V and an output voltage of up to 400 V. The upmost efficiency of 93.3% is reached with high-line input; on the other hand, the full-load efficiency remains at 89.3% during low-line input.


title: Multi-scale Volumes for Deep Object Detection and Localization

abstract: This study aims to analyze the benefits of improved multi-scale reasoning for object detection and localization with deep convolutional neural networks. To that end, an efficient and general object detection framework which operates on scale volumes of a deep feature pyramid is proposed. In contrast to the proposed approach, most current state-of-the-art object detectors operate on a single-scale in training, while testing involves independent evaluation across scales. One benefit of the proposed approach is in better capturing of multi-scale contextual information, resulting in significant gains in both detection performance and localization quality of objects on the PASCAL VOC dataset and a multi-view highway vehicles dataset. The joint detection and localization scale-specific models are shown to especially benefit detection of challenging object categories which exhibit large scale variation as well as detection of small objects. HighlightsMulti-scale feature reasoning for deep object detection in images is analyzed.A multi-scale contextual reasoning approach is proposed using multi-scale volumes.Scale-specific, joint detection and localization models increase robustness.The approach efficiently handles challenging cases of large variation in scale.


title: Forced-choice decision-making in modified trolley dilemma situations: a virtual reality and eye tracking study

abstract: Based on the frameworks of dual-process theories, we examined the interplay between intuitive and controlled cognitive processes related to moral and social judgments. In a virtual reality setting we performed an experiment investigating the progression from fast, automatic decisions towards more controlled decisions over multiple trials in the context of a sacrificing scenario. We repeatedly exposed participants to a modified ten-to-one version and to three one-to-one versions of the trolley dilemma in virtual reality and varied avatar properties, such as their gender and ethnicity, and their orientation in space. We also investigated the influence of arousing music on decisions. Our experiment replicated the behavioral pattern observed in studies using text versions of the trolley dilemma, thereby validating the use of virtual environments in research on moral judgments. Additionally, we found a general tendency towards sacrificing male individuals which correlated with socially desirable responding. As indicated by differences in response times, the ten-to-one version of the trolley dilemma seems to be faster to decide than decisions requiring comparisons based on specific avatar properties as a result of differing moral content. Building upon research on music-based emotion induction, we used music to induce emotional arousal on a physiological level as measured by pupil diameter. We found a specific temporal signature displaying a peak in arousal around the moment of decision. This signature occurs independently of the overall arousal level. Furthermore, we found context-dependent gaze durations during sacrificing decisions, leading participants to look prolonged at their victim if they had to choose between avatars differing in gender. Our study confirmed that moral decisions can be explained within the framework of dual-process theories and shows that pupillometric measurements are a promising tool for investigating affective responses in dilemma situations.


title: REVIEW ARTICLE INTELLIGENT SYSTEMS IN TOURISM A Social Science Perspective

abstract: Intelligent systems sense their environment and learn from the actions they imple- ment to reach specific goals. They are increasingly used to support tourist information search and decision making as well as work processes. In order to model the tourism domain, these systems require a profound understanding of its nature. Looking at existing literature in tour- ism, this paper discusses critical gaps in the knowledge of the field to be filled so that intel- ligent system design can be informed and impacts understood. Specifically, it discusses the need to better conceptualize technology in tourism research and argues for a focus on uses and interactions. It challenges simplistic views of tourist information search and decision- making processes and calls for more research on potential impacts. Keywords: intelligent sys- tem design, group decision making, preferences, persuasion, technology-user interaction, pri- vacy. 2011 Elsevier Ltd. All rights reserved.


title: Securing Home IoT Environments with Attribute-Based Access Control

abstract: Rapid advances in iotn~ have led to the proliferation of several end-user iot devices. A modern day home iot environment now resembles a complete network ecosystem with a variety of devices co-existing and operating concurrently. It is necessary that these devices do not disrupt the operations of other devices, either accidentally or maliciously. Accidental disruptions are usually due to misconfigured devices, which may, for instance, result in a device sending network broadcasts and flooding the network. Malicious disruptions may be caused by devices being compromised by attackers or due to devices purchased from untrusted manufacturers. An intentional disruption can include sending control information to other devices to manipulate their operations, and requesting for sensitive information such as surveillance videos or camera pictures. One way of preventing such disruptions is by enforcing access control on iot devices. Attribute-Based Access Control is the most appropriate model because of its ability to enforce access control based on the attributes of the devices, users, and environment context. We consider the NIST Next Generation Access Control (NGAC) specification for our ABAC requirements because of several reasons, including its support for adaptive policies, efficiency, and ease of policy management.


title: An integrated 33.5dBm linear 2.4GHz power amplifier in 65nm CMOS for WLAN applications

abstract: An integrated linear 2.4GHz CMOS power amplifier is presented. With a 3.3v supply, the PA produces a saturated output power of 33.5dBm with peak drain and power-added efficiencies of 44.2% and 37.6%, respectively and has 40dB small-signal gain. By utilizing gm-linearization and digital pre-distortion, an EVM of −25dB is achieved at 26.4dBm with 22% PAE while transmitting 54Mbs OFDM. The chip is fabricated in standard 65nm CMOS and packaged in a 40-pin QFN package. The PA occupies 2.2mm2 active area.


title: Dog Breed Classification Using Part Localization

abstract: We propose a novel approach to fine-grained image classification in which instances from different classes share common parts but have wide variation in shape and appearance. We use dog breed identification as a test case to show that extracting corresponding parts improves classification performance. This domain is especially challenging since the appearance of corresponding parts can vary dramatically, e.g., the faces of bulldogs and beagles are very different. To find accurate correspondences, we build exemplar-based geometric and appearance models of dog breeds and their face parts. Part correspondence allows us to extract and compare descriptors in like image locations. Our approach also features a hierarchy of parts (e.g., face and eyes) and breed-specific part localization. We achieve 67% recognition rate on a large real-world dataset including 133 dog breeds and 8,351 images, and experimental results show that accurate part localization significantly increases classification performance compared to state-of-the-art approaches.


title: Academy for eating disorders position paper: The role of the family in eating disorders

abstract: Position It is the position of the Academy for Eating Disorders (AED) that whereas family factors can play a role in the genesis and maintenance of eating disorders, current knowledge refutes the idea that they are either the exclusive or even the primary mechanisms that underlie risk. Thus, the AED stands firmly against any etiologic model of eating disorders in which family influences are seen as the primary cause of anorexia nervosa or bulimia nervosa, and condemns generalizing statements that imply families are to blame for their child’s illness. The AED recommends that families be included in the treatment of younger patients, unless doing so is clearly ill advised on clinical grounds. The position articulated in this article is in line with the World Wide Charter for Action on Eating Disorders.


title: Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist.

abstract: To improve the completeness of reporting of mobile health (mHealth) interventions, the WHO mHealth Technical Evidence Review Group developed the mHealth evidence reporting and assessment (mERA) checklist. The development process for mERA consisted of convening an expert group to recommend an appropriate approach, convening a global expert review panel for checklist development, and pilot testing the checklist. The guiding principle for the development of these criteria was to identify a minimum set of information needed to define what the mHealth intervention is (content), where it is being implemented (context), and how it was implemented (technical features), to support replication of the intervention. This paper presents the resulting 16 item checklist and a detailed explanation and elaboration for each item, with illustrative reporting examples. Through widespread adoption, we expect that the use of these guidelines will standardise the quality of mHealth evidence reporting, and indirectly improve the quality of mHealth evidence.


title: Mechatronic Systems for Machine Tools

abstract: This paper reviews current developments in mechatronic systems for metal cutting and forming machine tools. The integration of mechatronic modules to the machine tool and their interaction with manufacturing processes are presented. Sample mechatronic components for precision positioning and compensation of static, dynamic and thermal errors are presented as examples. The effect of modular integration of mechatronic system on the reconfigurability and reliability of the machine tools is discussed along with intervention strategies during machine tool operation. The performance and functionality aspects are discussed through active and passive intervention methods. A special emphasis was placed on active and passive damping of vibrations through piezo, magnetic and electro-hydraulic actuators. The modular integration of mechatronic components to the machine tool structure, electronic unit and CNC software system is presented. The paper concludes with the current research challenges required to expand the application of mechatronics in machine tools and manufacturing systems.


title: Vision based smoke detection system using image energy and color information

abstract: Smoke detection is a crucial task in many video surveillance applications and could have a great impact to raise the level of safety of urban areas. Many commercial smoke detection sensors exist but most of them cannot be applied in open space or outdoor scenarios. With this aim, the paper presents a smoke detection system that uses a common CCD camera sensor to detect smoke in images and trigger alarms. First, a proper background model is proposed to reliably extract smoke regions and avoid over-segmentation and false positives in outdoor scenarios where many distractors are present, such as moving trees or light reflexes. A novel Bayesian approach is adopted to detect smoke regions in the scene analyzing image energy by means of the Wavelet Transform coefficients and Color Information. A statistical model of image energy is built, using a temporal Gaussian Mixture, to analyze the energy decay that typically occurs when smoke covers the scene then the detection is strengthen evaluating the color blending between a reference smoke color and the input frame. The proposed system is capable of detecting rapidly smoke events both in night and in day conditions with a reduced number of false alarms hence is particularly suitable for monitoring large outdoor scenarios where common sensors would fail. An extensive experimental campaign both on recorded videos and live cameras evaluates the efficacy and efficiency of the system in many real world scenarios, such as outdoor storages and forests.


title: Binary Artificial Bee Colony optimization using bitwise operation

abstract: Proposes a novel Binary Artificial Bee Colony optimization (BitABC) algorithm.BitABC has a similar framework to the original ABC.BitABC uses a bitwise operation for the movements of the bees. Artificial Bee Colony (ABC) algorithm has been applied to many scientific and engineering problems for its easy of use and efficiency. However, the original ABC technique cannot be used in a binary search space directly. This paper proposes an effective binary ABC algorithm, or BitABC, that has a similar framework to the original ABC but uses a bitwise operation for the movements of the employed bees and onlooker bees. Experiments have been carried out by comparing BitABC with three binary ABC variants, DisABC, normABC, and BinABC, and the most used binary search technique, GA, on a set of 13 selected benchmark functions. Results show that the proposed BitABC performs better than, or at least comparable to, the other algorithms in terms of the final solution accuracy, convergence speed, and robustness.


title: Self-Driven Synchronous Rectification System With Input Voltage Tracking for Converters With a Symmetrically Driven Transformer

abstract: Synchronous rectification (SR) is mandatory to achieve good efficiencies with low output voltages. If a transformer is driven asymmetrically without dead times, the self-driven SR (SDSR) is a very interesting solution. However, if the transformer is driven symmetrically, the synchronous rectifiers are off during the dead times, and as a consequence, the efficiency is lowered. This paper deals with the optimization of an SDSR system that keeps the rectifiers on even during the dead times. The input voltage is tracked, and the information is used to adapt the gate to the source voltage of the synchronous rectifiers and improve the efficiency. The system has been implemented in a prototype, and the results have been compared with the ones obtained in the same prototype without SDSR.


title: A Time-Triggered Network-on-Chip

abstract: In this paper we propose a time-triggered network-on-chip (NoC) for on-chip real-time systems. The NoC provides time predictable on-and off-chip communication, a mandatory feature for dependable real-time systems. A regular structured NoC with a pseudo-static communication schedule allows for a high bandwidth. In this paper we argue for a simple, time-triggered NoC structure to achieve maximum bandwidth. We have implemented the proposed TT-NoC in a low-cost FPGA. The base bandwidth is 29 Gbit/s and the peak bandwidth 230 Gbit/s for eight nodes. The idea is in line with current on-chip multiprocessor designs, such as the cell processor. The simple design of the network and the network interlace easiest certification of the proposed NoC for safety critical applications.


title: Content Centric Networking in tactical and emergency MANETs

abstract: Reliable and secure content distribution in a disruptive environment is a critical challenge due to high mobile and lossy channels. Traditional IP networking and wireless protocols tend to perform poorly. In this paper, we propose Content Centric Networking (CCN) for emergency wireless ad hoc environments. CCN is a novel communication architecture capable to access and retrieve content by name. This new approach achieves scalability, security, and efficient network resource management in large scale disaster recovery and battlefield networks. Existing Internet CCN schemes cannot be directly applied to wireless mobile ad hoc networks due to different environments and specific limitations. Thus, we must extend the CCN architecture by introducing features and requirements especially designed for disruptive networks. We prove feasibility and performance gain of the new design via implementation and experimentation.


title: The Development of a SMS-based Teaching and Learning System

abstract: The short message service (SMS) technology is one of the most stable mobile technologies around. Most of our tertiary students carry mobile phones with SMS facilities and can be used for teaching and learning. There are many projects of using SMS technologies in education as outlined in the literature review, but many publications do not provide the possible underlying technologies to implement such the teaching and learning systems. In this paper, the development of a SMS-based teaching and learning system is presented. The underlying technology of using GSM modems to build the SMS-based teaching and learning system is revealed. The system is capable of supporting administrative, teaching and learning activities via the SMS technology. Examples of various activities such as brainstorming, voting, and assessment activities supported by the system are illustrated.


title: Selection of Orthogonal Investment Portfolio Using Evolino RNN Trading Model

abstract: Abstract Investing in financial market require the reliable predicting of expecting returns, assessment of risk and reliability. Principle of portfolio orthogonality was using to reduce the risk of the investment. An artificial intelligence system may reveal new opportunities for using this principle. Prediction of recurrent neural networks ensemble is stochastically informative distribution, which is helpful for portfolio selection. Shape and parameters of distribution influence decision making in currency market. Assessment of portfolio riskiness, finding most orthogonal elements of portfolio, influence better results for trading in real market.


title: An Empirical Workload Model for Driving Wide-Area TCP/IP Network Simulations

abstract: Abstract We present an artificial workload model of wide-area internetwork traffic. The model can be used to drive simulation experiments of communication protocols and flow and congestion control experiments. The model is based on analysis of wide-area TCP/IP traffic collected from one industrial and two academic networks. The artificial workload model uses both detailed knowledge and measured characteristics of the user application programs responsible for the traffic. Observations drawn from our measurements contradict some commonly held beliefs regarding wide-area TCP/IP network traffic. The simulation techniques presented here will be useful in studying congestion control, routing algorithms, and other resource management schemes, for existing and future networks.


title: Design of a novel variable stiffness gripper using permanent magnets

abstract: This paper presents the design of a novel variable stiffness gripper with two parallel fingers (jaws). Compliance of the system is generated by using permanent magnets as the nonlinear springs. Based on the presented design, the position and stiffness level of the fingers can be adjusted simultaneously by changing the air gap between the magnets. The modeling of magnetic repulsion force and stiffness are presented and verified experimentally. An experiment is also conducted to demonstrate the functionality of the gripper to improve safety when a fragile object was grasped and the gripper collided with an obstacle.


title: A novel approach to ransomware: Designing a safe zone system

abstract: It is vital to protect the files and data for institutions, organizations and individuals. For this, people and institutions develop methods to provide both physical and digital security, and they spend thousands of dollars over the years for measures. Ransomware is undoubtedly one of the most frequently encountered malicious software that harms people and organizations. These viruses are used to encrypt all important data on the infected computer. Ransomware usually targets file types such as PDF, Word, Excel that can store important documents, bills, financial data, and makes these documents inaccessible by encrypting them. The restoration of these encrypted files is only possible with the keyword in the hand of the person who wrote the virus. This key word is given by an aggressive fee, which means ransom against documents. These fees are usually very high and there is no guarantee that the attacker will not want more fee. In this paper, it is aimed to find solutions to these problems and to minimize possible harm. In order to do so, an area called safezone has been created and prevented from being replaced by other software by moving important documents to this zone.


title: Video Summarization via Multi-view Representative Selection

abstract: Video contents are inherently heterogeneous. To exploit different feature modalities in a diverse video collection for video summarization, we propose to formulate the task as a multi-view representative selection problem. The goal is to select visual elements that are representative of a video consistently across different views (i.e., feature modalities). We present in this paper the multi-view sparse dictionary selection with centroid co-regularization (MSDS-CC) method, which optimizes the representative selection in each view, and enforces that the view-specific selections to be similar by regularizing them towards a consensus selection. The problem can be efficiently solved by an alternating minimizing optimization with the fast iterative shrinkage thresholding algorithm (FISTA). We also show how the formulation can be applied to category-specific video summarization by incorporating visual co-occurrence priors. Experiments on benchmark video datasets validate the effectiveness of the proposed approach in comparison with other video summarization methods and representative selection methods.


title: How to Use Search Engine Optimization Techniques to Increase Website Visibility

abstract: Research questions: This tutorial aims to answer two general questions: (1) What contributes to search engine rankings? and (2) What can web content creators and webmasters do to make their content and sites easier to find by audiences using search engines? Key concepts: Search engines' rankings are shaped by three classes of participants: search engine companies and programmers, search engine optimization practitioners, and search engine users. Key lessons: By applying three key lessons, professional communicators can make it easier for audiences to find their web content through search engines: (1) consider their web content's audiences and website's competitors when analyzing keywords; (2) insert keywords into web text that will appear on search engine results pages, and (3) involve their web content and websites with other web content creators. Implications: Because successful search engine optimization requires considerable time, professional communicators should progressively apply these lessons in the sequence presented in this tutorial and should keep up to date with frequently changing ranking algorithms and with the associated changing practices of search optimization professionals.


title: A clustering algorithm based on graph connectivity

abstract: We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. A similarity graph is defined and clusters in that graph correspond to highly connected subgraphs. A polynomial algorithm to compute them efficiently is presented. Our algorithm produces a solution with some provably good properties and performs well on simulated and real data.


title: Obesity in children and young people: a crisis in public health

abstract: Ten per cent of the world’s school-aged children are estimated to be carrying excess body fat (Fig. 1), with an increased risk for developing chronic disease. Of these overweight children, a quarter are obese, with a significant likelihood of some having multiple risk factors for type 2 diabetes, heart disease and a variety of other co-morbidities before or during early adulthood. The prevalence of overweight is dramatically higher in economically developed regions, but is rising significantly in most parts of the world. In many countries the problem of childhood obesity is worsening at a dramatic rate. Surveys during the 1990s show that in Brazil and the USA, an additional 0.5% of the entire child population became overweight each year. In Canada, Australia and parts of Europe the rates were higher, with an additional 1% of all children becoming overweight each year. The burden upon the health services cannot yet be estimated. Although childhood obesity brings a number of additional problems in its train – hyperinsulinaemia, poor glucose tolerance and a raised risk of type 2 diabetes, hypertension, sleep apnoea, social exclusion and depression – the greatest health problems will be seen in the next generation of adults as the present childhood obesity epidemic passes through to adulthood. Greatly increased rates of heart disease, diabetes, certain cancers, gall bladder disease, osteoarthritis, endocrine disorders and other obesityrelated conditions will be found in young adult populations, and their need for medical treatment may last for their remaining life-times. The costs to the health services, the losses to society and the burdens carried by the individuals involved will be great. The present report has been written to focus attention on the issue and to urge policy-makers to consider taking action before it is too late. Specifically, the report:


title: Agile Big Data Analytics Development: An Architecture-Centric Approach

abstract: Agile development for big data analytics has become the new normal. However, research questions remain: 1) how should a big data system be designed and developed to effectively support advanced analytics? and 2) how should the agile process be adapted for big data analytics development? This article contributes an Architecture-centric Agile Big data Analytics (AABA) development methodology evolved and validated in 10 case studies through Collaborative Practice Research. Our studies showed that architecture agility is the key for successful agile big data analytics development. Employing an architecture-centric approach, the AABA methodology integrates the Big Data system Design (BDD) method and Architecture-centric Agile Analytics with architecture-supported DevOps (AAA) model for effective value discovery and rapid continuous delivery of value. The uses of a design concepts catalog and architectural spikes are advancements to architecture design methods that have proven to be critical to agile big data analytics development.


title: SPR-based biosensors: a tool for biodetection of hormonal compounds

abstract: Novel cancer treatments, prevention of postmenopausal disorder, and prescription of oral contraceptives are the main developments in the design of synthetic estrogenic medication. The increasing consumption of these synthetic pharmaceuticals, in addition to human and animal natural estrogenic compound excretion, contribute to their environmental dissemination worldwide. Their assimilation as a result of consumption of food and water perturbs normal endocrine systems and leads to the emergence of human and animal diseases and malformations. These compounds are active in the organism at low concentrations. Accordingly, daily low-level exposure disrupts the natural equilibrium in the endocrine system. A method enabling quantification at such products at low levels (from pg L(-1) to ng L(-1)) is therefore required for these products. Surface plasmon resonance, essentially used for comprehension of molecular mechanisms and in drug discovery, can also be used for environmental pollutant monitoring. This technology has already been used for evaluation of the effects of chemical pollutants on specific nuclear receptors. It has been possible to determine the role of each individual compound on the disruption of the estrogen-activated cellular pathway. Development of SPR screening methods enables application of such an approach for quantification of these compounds in water.


title: GAN-D: Generative adversarial networks for image deconvolution

abstract: We propose new generative adversarial networks for generalized image deconvolution, GAN-D. Most of the previous researches concentrate to specific sub-topic of image deconvolution or generative image deconvolution models with a strong assumption. However, our network restores visual data from distorted images applied multiple dominant degradation problems such as noise, blur, saturation, compression without any prior information. As a generator, we leverage convolutional neural networks based ODCNN [12] which perform generalized image deconvolution with a decent performance, and we use VGGNet [11] to distinguish true/fake of an input image as a discriminator. We devise the loss function of the generator of GAN-D which combines mean square error (MSE) of network output and ground-truth images to traditional adversarial loss of GAN. This loss function and the presence of discriminator reinforces the generator to produce more high-quality images than the original model structured with a single convolutional neural network. During experiments with four datasets, we find that our network has higher PSNR/SSIM values and qualitative results than ODCNN.


title: High-throughput system for screening of high l-lactic acid-productivity strains in deep-well microtiter plates

abstract: For strain improvement, robust and scalable high-throughput cultivation systems as well as simple and rapid high-throughput detection methods are crucial. However, most of the screening methods for lactic acid bacteria (LAB) strains were conducted in shake flasks and detected by high-performance liquid chromatography (HPLC), making the screening program laborious, time-consuming and costly. In this study, an integrated strategy for high-throughput screening of high l-lactic acid-productivity strains by Bacillus coagulans in deep-well microtiter plates (MTPs) was developed. The good agreement of fermentation results obtained in the MTPs platform with shake flasks confirmed that 24-well U-bottom MTPs could well alternate shake flasks for cell cultivation as a scale-down tool. The high-throughput pH indicator (bromocresol green) and l-lactate oxidase (LOD) assays were subsequently developed to qualitatively and quantitatively analyze l-lactic acid concentration. Together with the color halos method, the pH indicator assay and LOD assay, the newly developed three-step screening strategy has greatly accelerated the screening process for LAB strains with low cost. As a result, two high l-lactic acid-productivity mutants, IH6 and IIIB5, were successfully screened out, which presented, respectively, 42.75 and 46.10 % higher productivities than that of the parent strain in a 5-L bioreactor.


title: Sentiment Analysis on Twitter Using Streaming API

abstract: In general, opinion mining has been used to know about what people think and feel about their products and services in social media platforms. Millions of users share opinions on different aspects of life every day. Spurred by that growth, companies and media organizations are increasingly seeking way to mine information. It requires efficient techniques to collect a large amount of social media data and extract meaningful information from them. This paper aims to provide an interactive automatic system which predicts the sentiment of the review/tweets of the people posted in social media using hadoop, which can process the huge amount of data. Till now, there are few different problems predominating in this research community, namely, sentiment classification, feature based classification and handling negations. A precise method is used for predicting sentiment polarity, which helps to improve marketing strategies. This paper deals with the challenges that appear in the process of Sentiment Analysis, real time tweets areconsidered as they are rich sources of data for opinion mining and sentiment analysis. This paper focus on Sentiment analysis, Feature based Sentiment classification and Opinion Summarization. The main objective of this paper is to perform real time sentimental analysis on the tweets that are extracted from the twitter and provide time based analytics to the user.


title: Software defined environments: an introduction

abstract: During the past few years, enterprises have been increasingly aggressive in moving mission-critical and performance-sensitive applications to the cloud, while at the same time many new mobile, social, and analytics applications are directly developed and operated on cloud computing platforms. These two movements are encouraging the shift of the value proposition of cloud computing from cost reduction to simultaneous agility and optimization. These requirements (agility and optimization) are driving the recent disruptive trend of software defined computing, for which the entire computing infrastructure--compute, storage and network--is becoming software defined and dynamically programmable. The key elements within software defined environments include capability-based resource abstraction, goal-based and policy-based workload definition, and outcome-based continuous mapping of the workload to the available resources. Furthermore, software defined environments provide the tooling and capabilities to compose workloads from existing components that are then continuously and autonomously mapped onto the underlying programmable infrastructure. These elements enable software defined environments to achieve agility, efficiency, and continuous outcome-optimized provisioning and management, plus continuous assurance for resiliency and security. This paper provides an overview and introduction to the key elements and challenges of software defined environments.


title: Quality Assessment of Academic Library Performance: the Case of an Iranian Academic Library

abstract: The present research aims to assess the performance of libraries at the Islamic Azad University, Sciences and Research Branch (IAUSRB) through gap analysis. To do this, a researcher-developed questionnaire which is based on available standards and quality assessment tools including ACRL standards, LibQUAL and ISO 11620, and some criteria included in the related literature was designed and distributed among users of IAUSRB libraries. Five service quality categories, were included in the questionnaire: "Environment, equipment and physical facilities", "Public services", "Non-book materials", "Staff (librarians and their co-workers)", and "Information literacy and use education". Based on research findings, the most expected library services prioritized by users are "operation time" (mean = 4.2300), "staff" (mean = 4.1461), and "circulation" (mean = 4.1208), while the least expected library services which are of lower importance from users' perspective are "press" (mean = 3.9734) and "audiovisual materials" (mean = 3.8796). The findings also indicate that services offered by the libraries surveyed had a relatively quality performance and average success (perceived quality is 2.9635≈50%). In other words, such a finding emphasizes an average performance of IAUSRB libraries. In addition, confirming five research hypotheses concerning gap analysis, there was a significant difference between expected quality of five service categories and their perceived quality based on users' viewpoint. It was also realized that the categories with better performance or perceived quality from users' perspective are "staff" (mean = 3.2240), "information literacy and user education" (mean = 2.9998), and "environment, equipment and physical facilities" (mean = 2.9871), and the two libraries "Theology and Philosophy" (mean = 2.2639) and "Medical Engineering" (mean = 2.4507) have offered poor services than the other IAUSRB libraries.


title: A security enforcement kernel for OpenFlow networks

abstract: Software-defined networks facilitate rapid and open innovation at the network control layer by providing a programmable network infrastructure for computing flow policies on demand. However, the dynamism of programmable networks also introduces new security challenges that demand innovative solutions. A critical challenge is efficient detection and reconciliation of potentially conflicting flow rules imposed by dynamic OpenFlow (OF) applications. To that end, we introduce FortNOX, a software extension that provides role-based authorization and security constraint enforcement for the NOX OpenFlow controller. FortNOX enables NOX to check flow rule contradictions in real time, and implements a novel analysis algorithm that is robust even in cases where an adversarial OF application attempts to strategically insert flow rules that would otherwise circumvent flow rules imposed by OF security applications. We demonstrate the utility of FortNOX through a prototype implementation and use it to examine performance and efficiency aspects of the proposed framework.