title: Perpetuating online sexism offline: Anonymity, interactivity, and the effects of sexist hashtags on social media

abstract: We assessed the effects of a sexist hashtag on the social media site Twitter.The affordances of anonymity and interactivity were manipulated.Anonymous participants reported more post-task sexism than identifiable participants.Writing sexist tweets (higher interactivity) led to more sexism than retweeting.Higher interactivity led to more negative evaluations of female job candidates. Sexism and sexual harassment are not uncommon in online environments such as social networking sites, forums, and video games. This experiment investigated whether users' anonymity and level of interactivity with sexist content on social media influenced sexist attitudes and offline behavior. Participants (N=172) used a Twitter account that was anonymous or had personally identifying details. They were asked to share (i.e., retweet) or write posts incorporating a sexist hashtag. After exposure, participants completed two purportedly unrelated tasks, a survey and a job hiring simulation in which they evaluated male and female candidates' resumes. Anonymous participants reported greater hostile sexism after tweeting than nonanonymous participants. Participants who composed sexist tweets reported greater hostile sexism and ranked female job candidates as less competent than those who retweeted, although this did not significantly affect their likelihood to hire.


PSO-BASED DBSCAN WITH OBSTACLE CONSTRAINTS


title: Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning

abstract: In this paper, we propose a novel framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom. ::: Our framework combines the state-of-the-art reinforcement learning approach (Asynchronous Advantage Actor-Critic (A3C) model) with curriculum learning. Our model is simple in design and only uses game states from the AI side, rather than using opponents' information. On a known map, our agent won 10 out of the 11 attended games and the champion of Track1 in ViZDoom AI Competition 2016 by a large margin, 35\% higher score than the second place.


title: Learning to generalize to new compositions in image understanding

abstract: Recurrent neural networks have recently been used for learning to describe images using natural language. However, it has been observed that these models generalize poorly to scenes that were not observed during training, possibly depending too strongly on the statistics of the text in the training data. Here we propose to describe images using short structured representations, aiming to capture the crux of a description. These structured representations allow us to tease-out and evaluate separately two types of generalization: standard generalization to new images with similar scenes, and generalization to new combinations of known entities. We compare two learning approaches on the MS-COCO dataset: a state-of-the-art recurrent network based on an LSTM (Show, Attend and Tell), and a simple structured prediction model on top of a deep network. We find that the structured model generalizes to new compositions substantially better than the LSTM, ~7 times the accuracy of predicting structured representations. By providing a concrete method to quantify generalization for unseen combinations, we argue that structured representations and compositional splits are a useful benchmark for image captioning, and advocate compositional models that capture linguistic and visual structure.


title: Treatment of congenital syndactyly of the fingers.

abstract: BACKGROUND ::: Syndactyly or webbed fingers is one of the most common congenital malformations of the upper extremities, but it comprises few new cases annually. The purpose of treatment is to enhance hand function. ::: ::: ::: METHOD ::: The article is based on current text books and literature searches in PubMed as well as the authors' clinical experience within this field. ::: ::: ::: RESULTS ::: The purpose of surgical treatment is to separate the fingers and reconstruct a webspace. It is difficult to indicate exact treatment results because of large variations in the extent of the deformity. For syndactyly involving only soft tissue (simple syndactyly), a good functional result is achieved with a less than 10% risk of complications. Syndactyly where also the bones have fused (complex syndactyly) or where there is additional bone formation between two digital rays (complicated syndactyly), gives a poorer functional outcome and a higher risk of complications. Gradual stretching of the tissue using a distraction device enables separation of fingers one was previously reluctant to separate. ::: ::: ::: INTERPRETATION ::: It should be possible to expect safe separation with a good and independent function of the fingers with surgical treatment. The parents should be informed that the surgical treatment is a reconstructive procedure that may require secondary corrections.


title: Exploiting deep residual networks for human action recognition from skeletal data

abstract: Abstract The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems. Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks. In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors. Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images. These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs. We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes. The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource. In particular, the proposed method surpasses previous approaches by a significant margin of 3.4% on MSR Action 3D dataset, 0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset.


title: Software rejuvenation: analysis, module and applications

abstract: Software rejuvenation is the concept of gracefully terminating an application and immediately restarting it at a clean internal state. In a client-server type of application where the server is intended to ran perpetually for providing a service to its clients, rejuvenating the server process periodically during the most idle time of the server increases the availability of that service. In a long-running computation-intensive application, rejuvenating the application periodically and restarting it at a previous checkpoint increases the likelihood of successfully completing the application execution. We present a model for analyzing software rejuvenation in such continuously-running applications and express downtime and costs due to downtime during rejuvenation in terms of the parameters in that model. Threshold conditions for rejuvenation to be beneficial are also derived. We implemented a reusable module to perform software rejuvenation. That module can be embedded in any existing application on a UNIX platform with minimal effort. Experiences with software rejuvenation in a billing data collection subsystem of a telecommunications operations system and other continuously-running systems and scientific applications in AT&T are described. >


title: RoboRay hand: A highly backdrivable robotic hand with sensorless contact force measurements

abstract: This paper presents a 14-DOF robotic hand including 5 fingers and a wrist. The hand has a new tendon-driven mechanism which minimizes frictional loss and maximizes efficiency and backdrivability. In order to accomplish high efficiency and backdrivability as well as human-like payload and dexterity in a compact size, two novel mechanical concepts are proposed. Firstly, the actuators are placed according to the functions of fingers - high power grasping and precise manipulation - instead of positioning at each joint. For the high power grasping, 7 high payload motors are positioned in the forearm, and 5 small size motors are positioned in the palm for the precise manipulation. Secondly, a new tension decoupling mechanism is proposed to the 2-DOF wrist joint, which delivers wire motions of the forearm motors to the fingers without frictional loss or coupling with wrist motion. A total weight including the forearm is 1.59kg which is similar to human. The fingertip force is 15N which is sufficient for most of household work. High backdrivability enables the contact force sensing by measuring a motor current without additional sensors. A detectable minimum contact force was 0.735N. In order to enhance the contact force sensing capability, a friction compensation algorithm was applied, which resulted to the minimum contact force as 0.196N. Theoretical and experimental analyses are also performed.


title: The Performance Impact of Elliptic Curve Cryptography on DNSSEC Validation

abstract: The domain name system (DNS) is a core Internet infrastructure that translates names to machine-readable information, such as IP addresses. Security flaws in DNS led to a major overhaul, with the introduction of the DNS security (DNSSEC) extensions. DNSSEC adds integrity and authenticity to the DNS using digital signatures. DNSSEC, however, has its own concerns. It suffers from availability problems due to packet fragmentation and is a potent source of distributed denial-of-service attacks. In earlier work, we argued that many issues with DNSSEC stem from the choice of RSA as default signature algorithm. A switch to alternatives based on elliptic curve cryptography (ECC) can resolve these issues. Yet switching to ECC introduces a new problem: ECC signature validation is much slower than RSA validation. Thus, switching DNSSEC to ECC imposes a significant additional burden on DNS resolvers, pushing load toward the edges of the network. Therefore, in this paper, we study the question: will switching DNSSEC to ECC lead to problems for DNS resolvers, or can they handle the extra load? To answer this question, we developed a model that accurately predicts how many signature validations DNS resolvers have to perform. This allows us to calculate the additional CPU load ECC imposes on a resolver. Using real-world measurements from four DNS resolvers and with two open-source DNS implementations, we evaluate future scenarios where DNSSEC is universally deployed. Our results conclusively show that switching DNSSEC to ECC signature schemes does not impose an insurmountable load on DNS resolvers, even in worst case scenarios.


title: Sketch-based image retrieval on a large scale database

abstract: The paper presents a simple and effective sketch-based algorithm for large scale image retrieval. One of the main challenges in image retrieval is to localize a region in an image which would be matched with the query image in contour. To tackle this problem, we use the human perception mechanism to identify two types of regions in one image: the first type of region (the main region) is defined by a weighted center of image features, suggesting that we could retrieve objects in images regardless of their sizes and positions. The second type of region, called region of interests (ROI), is to find the most salient part of an image, and is helpful to retrieve images with objects similar to the query in a complicated scene. So using the two types of regions as candidate regions for feature extraction, our algorithm could increase the retrieval rate dramatically. Besides, to accelerate the retrieval speed, we first extract orientation features and then organize them in a hierarchal way to generate global-to-local features. Based on this characteristic, a hierarchical database index structure could be built which makes it possible to retrieve images on a very large scale image database online. Finally a real-time image retrieval system on 4.5 million database is developed to verify the proposed algorithm. The experiment results show excellent retrieval performance of the proposed algorithm and comparisons with other algorithms are also given.


title: On Learning Where To Look

abstract: Current automatic vision systems face two major challenges: scalability and extreme variability of appearance. First, the computational time required to process an image typically scales linearly with the number of pixels in the image, therefore limiting the resolution of input images to thumbnail size. Second, variability in appearance and pose of the objects constitute a major hurdle for robust recognition and detection. In this work, we propose a model that makes baby steps towards addressing these challenges. We describe a learning based method that recognizes objects through a series of glimpses. This system performs an amount of computation that scales with the complexity of the input rather than its number of pixels. Moreover, the proposed method is potentially more robust to changes in appearance since its parameters are learned in a data driven manner. Preliminary experiments on a handwritten dataset of digits demonstrate the computational advantages of this approach.


title: You are what you tweet: Personality expression and perception on Twitter

abstract: Microblogging services such as Twitter have become increasingly popular in recent years. However, little is known about how personality is manifested and perceived in microblogs. In this study, we measured the Big Five personality traits of 142 participants and collected their tweets over a 1-month period. Extraversion, agreeableness, openness, and neuroticism were associated with specific linguistic markers, suggesting that personality manifests in microblogs. Meanwhile, eight observers rated the participants’ personality on the basis of their tweets. Results showed that observers relied on specific linguistic cues when making judgments, and could only judge agreeableness and neuroticism accurately. This study provides new empirical evidence of personality expression in naturalistic settings, and points to the potential of utilizing social media for personality research.


title: Dimensions of Social Media Addiction among University Students in Kuwait

abstract: This study aimed to examine social media addiction in a sample of university students. Based on the Internet addiction scale developed by Young (1996) the researcher used cross-sectional survey methodology in which a questionnaire was distributed to 1327 undergraduate students with their consent. Factor analysis of the self-report data showed that social media addiction has three independent dimensions. These dimensions were positively related to the users experience with social media; time spent using social media and satisfaction with them. In addition, social media addiction was a negative predictor of academic performance as measured by a student's GPA. Future studies should consider the cultural values of users and examine the context of social media usage.


title: Blockchain application and outlook in the banking industry

abstract: Blockchain technology is a core, underlying technology with promising application prospects in the banking industry. On one hand, the banking industry in China is facing the impact of interest rate liberalization and profit decline caused by the narrowing interest-rate spread. On the other hand, it is also affected by economic transformation, Internet development, and financial innovations. Hence, the banking industry requires urgent transformation and is seeking new growth avenues. As such, blockchains could revolutionize the underlying technology of the payment clearing and credit information systems in banks, thus upgrading and transforming them. Blockchain applications also promote the formation of “multi-center, weakly intermediated” scenarios, which will enhance the efficiency of the banking industry. However, despite the permissionless and self-governing nature of blockchains, the regulation and actual implementation of a decentralized system are problems that remain to be resolved. Therefore, we propose the urgent establishment of a “regulatory sandbox” and the development of industry standards.


title: Directional multiresolution image representations

abstract: We propose a new scheme, named contourlet, that provides a flexible multiresolution, local and directional image expansion. The contourlet transform is realized efficiently via a double iterated filter bank structure. Furthermore, it can be designed to satisfy the anisotropy scaling relation for curves, and thus offers a fast and structured curvelet-like decomposition. As a result, the contourlet transform provides a sparse representation for two-dimensional piecewise smooth signals resembling images. Finally, we show some numerical experiments demonstrating the potential of contourlets in several image processing tasks.


title: Light Field Reconstruction Using Shearlet Transform

abstract: In this article we develop an image based rendering technique based on light field reconstruction from a limited set of perspective views acquired by cameras. Our approach utilizes sparse representation of epipolar-plane images (EPI) in shearlet transform domain. The shearlet transform has been specifically modified to handle the straight lines characteristic for EPI. The devised iterative regularization algorithm based on adaptive thresholding provides high-quality reconstruction results for relatively big disparities between neighboring views. The generated densely sampled light field of a given 3D scene is thus suitable for all applications which require light field reconstruction. The proposed algorithm compares favorably against state of the art depth image based rendering techniques and shows superior performance specifically in reconstructing scenes containing semi-transparent objects.


Who they are and what they want: Understanding the reasons for MOOC enrollment


title: Efficient set joins on similarity predicates

abstract: In this paper we present an efficient, scalable and general algorithm for performing set joins on predicates involving various similarity measures like intersect size, Jaccard-coefficient, cosine similarity, and edit-distance. This expands the existing suite of algorithms for set joins on simpler predicates such as, set containment, equality and non-zero overlap. We start with a basic inverted index based probing method and add a sequence of optimizations that result in one to two orders of magnitude improvement in running time. The algorithm folds in a data partitioning strategy that can work efficiently with an index compressed to fit in any available amount of main memory. The optimizations used in our algorithm generalize to several weighted and unweighted measures of partial word overlap between sets.


title: Prediction of Compression Ratio for DCT-Based Coders With Application to Remote Sensing Images

abstract: A problem of predicting compression ratio (CR) for lossy image compression methods based on discrete cosine transform (DCT) is considered for remote sensing imaging as a main target application. We demonstrate that the noise presence in images leads to specific requirements to lossy image compression as well as to criteria used, and ways to meet these requirements. In particular, it is often desired to compress images in the neighborhood of optimal operation point (OOP) where a compressed image might be closer to the corresponding noise-free one compared to noisy (original, uncompressed) counterpart. In this paper, first, we consider a problem of predicting CR for compressing noisy images in OOP neighborhood. Several statistical parameters calculated in DCT domain in 8 × 8 pixel blocks are used for this. The factors that influence a prediction accuracy and the ways to improve this accuracy are discussed. Next, we show that there is a statistical parameter that does not require any a priori information on noise properties, and that can be used to predict CR with a high accuracy without any necessity to apply multiple image compression/decompression procedures. The proposed methods are thoroughly tested for two DCT-based image coders applied to component-wise lossy compression of hyperspectral data. Finally, a modification of the prediction approach for 3-D compression of multichannel images is proposed.


title: Computational Investigations of Multiword Chunks in Language Learning

abstract: Second-language learners rarely arrive at native proficiency in a number of linguistic domains, including morphological and syntactic processing. Previous approaches to understanding the different outcomes of first- versus second-language learning have focused on cognitive and neural factors. In contrast, we explore the possibility that children and adults may rely on different linguistic units throughout the course of language learning, with specific focus on the granularity of those units. Following recent psycholinguistic evidence for the role of multiword chunks in online language processing, we explore the hypothesis that children rely more heavily on multiword units in language learning than do adults learning a second language. To this end, we take an initial step toward using large-scale, corpus-based computational modeling as a tool for exploring the granularity of speakers' linguistic units. Employing a computational model of language learning, the Chunk-Based Learner, we compare the usefulness of chunk-based knowledge in accounting for the speech of second-language learners versus children and adults speaking their first language. Our findings suggest that while multiword units are likely to play a role in second-language learning, adults may learn less useful chunks, rely on them to a lesser extent, and arrive at them through different means than children learning a first language.


title: Revealing the City That We Cannot See

abstract: We here investigate the potential of participatory sensor networks derived from location sharing systems, such as Foursquare, to understand the human dynamics of cities. We propose the City Image visualization technique, which builds a transition graph mapping people's movements between location categories, and demonstrate its use to identify similarities and differences of human dynamics across cities by clustering cities according to their citizens' routines. We also analyze centrality metrics of the transition graphs built for different cities, considering transitions between specific venues. We show that these metrics complement the City Image technique, contributing to a deeper understanding of city dynamics.


title: Online Reputation and Polling Systems: Data Incest, Social Learning, and Revealed Preferences

abstract: This paper considers online reputation and polling systems where individuals make recommendations based on their private observations and recommendations of friends. Such interaction of individuals and their social influence is modeled as social learning on a directed acyclic graph. Data incest (misinformation propagation) occurs due to unintentional reuse of identical actions in the formation of public belief in social learning; the information gathered by each agent is mistakenly considered to be independent. This results in overconfidence and bias in estimates of the state. Necessary and sufficient conditions are given on the structure of information exchange graph to mitigate data incest. Incest removal algorithms are presented. Experimental results on human subjects are presented to illustrate the effect of social influence and data incest on decision-making. These experimental results indicate that social learning protocols require careful design to handle and mitigate data incest. The incest removal algorithms are illustrated in an expectation polling system where participants in a poll respond with a summary of their friends’ beliefs. Finally, the principle of revealed preferences arising in microeconomics theory is used to parse Twitter datasets to determine if social sensors are utility maximizers and then determine their utility functions.


Modeling and Solving Human-Robot Collaborative Tasks Using POMDPs


title: Deep Learning Approaches to Semantic Relevance Modeling for Chinese Question-Answer Pairs

abstract: The human-generated question-answer pairs in the Web social communities are of great value for the research of automatic question-answering technique. Due to the large amount of noise information involved in such corpora, it is still a problem to detect the answers even though the questions are exactly located. Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. Since both the questions and their answers usually contain a small number of sentences, the relevance modeling methods have to overcome the problem of word feature sparsity. In this article, the deep learning principle is introduced to address the semantic relevance modeling task. Two deep belief networks with different architectures are proposed by us to model the semantic relevance for the question-answer pairs. According to the investigation of the textual similarity between the community-driven question-answering (cQA) dataset and the forum dataset, a learning strategy is adopted to promote our models’ performance on the social community corpora without hand-annotating work. The experimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora.


title: Affective Music Information Retrieval

abstract: Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this chapter, we present a novel generative approach to music emotion modeling, with a specific focus on the valence–arousal (VA) dimension model of emotion. The presented generative model, called acoustic emotion Gaussians (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition, emotion-based music retrieval, and tag-to-VA projection. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.


title: Multipath Behavior of FM-UWB Signals

abstract: This paper analyzes the multipath behavior of FM-UWB signals and more particularly the effect of frequency selective fading upon the sub-carrier signal level in the FM-UWB receiver. The performance with CM1-CM4 channels is investigated and illustrated with MATLAB simulation results. Good and bad propagation channels for FM-UWB communication systems are identified. The effect of frequency selective fading is mainly determined by the channel transfer at the edges of the FM-UWB signal's bandwidth. It is shown that multipath is beneficial for the FM-UWB receiver performance.


title: Solving the pallet loading problem

abstract: Abstract This paper presents new bounds, heuristics, and an exact algorithm for the Pallet Loading Problem (PLP). PLP maximizes the number of boxes placed on a rectangular pallet. All boxes have identical rectangular dimensions and, when placed, must be located completely within the pallet. Boxes may be rotated 90° so long as they are placed with edges parallel to the pallet’s edges. The set of all PLP instances with an area ratio (pallet area divided by box area) less than 101 boxes can be represented by 3,080,730 equivalent classes. Our G5-heuristic finds optimal solutions to 3,073,724 of these 3,080,730 classes and in the remaining 7006 classes only differs from the best known bound by one box. We develop three other heuristics that solve another 54 instances. Finally, we solve the 6952 remaining classes with our exact HVZ algorithm. Only a subset of these classes has been solved previously.


title: Hypnosis as Adjunct Therapy in Conscious Sedation for Plastic Surgery

abstract: BACKGROUND AND OBJECTIVES: Sedation is often requested during local and regional anesthesia. However, some surgical procedures, such as plastic surgery, require conscious sedation, which may be difficult to achieve. Hypnosis, used routinely to provide conscious sedation in the authors' Department of Plastic Surgery, results in high patient and surgeon satisfaction. The authors conducted a retrospective study to investigate the benefits of hypnosis in supplementing local anesthesia. METHODS: The study included 337 patients undergoing minor and major plastic surgical procedures under local anesthesia and conscious intravenous sedation. Patients were divided into three groups depending on the sedation technique: intravenous sedation (n = 137) using only midazolam and alfentanil; hypnosis (n = 172), during which patients achieved a hypnotic trance level with age regression; and relaxation (n = 28), comprising patients in whom hypnosis was induced without attaining a trance level. In all three groups, midazolam and alfentanil were titrated to achieve patient immobility, in response to patient complaints, and to maintain hemodynamic stability. Midazolam and alfentanil requirements; intra- and postoperative pain scores; as well as pre-, intra-, and postoperative anxiety score, reported on a 10-cm visual analog scale, were recorded and compared in the three groups. RESULTS: Intraoperative anxiety reported by patients in the hypnosis group (0.7 +/- 0.11) and in the relaxation group (2.08 +/- 0.4) was significantly (P < .001) less than in the intravenous sedation group (5.6 +/- 1.6). Pain scores during surgery were significantly greater in the intravenous sedation group (4.9 +/- 0.6) than in the hypnosis group (1.36 +/- 0.12; P < .001) and the relaxation group (1.82 +/- 0.6; P < .01). Furthermore, midazolam requirements were significantly lower in the hypnosis group (P < .001) and in the relaxation group (P < .01) as compared with the intravenous sedation group: respectively, 0.04 +/- 0.002, 0.07 +/- 0.005, and 0.11 +/- 0.01 mg/kg/h. Alfentanil requirements were significantly decreased in the hypnosis group, as compared with the intravenous sedation group: 10.2 +/- 0.6 microgram/kg/h versus 15.5 +/- 2.07 micrograms/kg/h; P < .002. In the relaxation group, alfentanil requirements were 14.3 +/- 1.5 micrograms/kg/h (ns). Postoperative nausea and vomiting were reported by 1.2% of patients in the hypnosis group, 12.8% in the relaxation group and 26.7% in the intravenous sedation group. Greater patient satisfaction with the anesthetic procedure and greater surgical comfort were also reported in the hypnosis group. CONCLUSIONS: Successful hypnosis as an adjunct sedation procedure to conscious intravenous sedation provided better pain and anxiety relief than conventional intravenous sedation and allowed for a significant reduction in midazolam and alfentanil requirements. Patient satisfaction was significantly improved.


title: An Automated Garage Door and Security Management System (A dual control system with VPN IoT & Biometric Database)

abstract: Internet of Things (IoT) have revolutionized the entire way in which the industry operates. The authenticity and entry access to certain places of the industry plays a crucial role in maintaining the secrecy of the operations. Therefore providing limited access to such confidential places proves to be seemingly vital. The proposed work provides a dual security mechanism against unauthorized entry and hence enhances the integrity of the system. This system thus eradicates the common security threats such as Spoofing, Repudiation and trafficking from evaders especially in the current scenario of industry 4.0. This system uses two techniques for the controlled access. One via a fingerprint access that utilizes the raspberry pi3 and a biometric database system for control. This method is a thorough hardware based solution and could be utilized when there is a physical presence of a person. In case if the authenticator is not available in the arena, the alternate mechanism would be followed to provide a secured access authenticity through Virtual Point Network (VPN) & IoT. The utility of VPN facilitates the personnel to prove his / her credentials from any remote location. Thus this dual mode system provides a highly credential entry / exit system in an industrial arena.


title: Neural Segmental Hypergraphs for Overlapping Mention Recognition

abstract: In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. We show that our model built on top of such a new representation is able to capture features and interactions that cannot be captured by previous models while maintaining a low time complexity for inference. We also present a theoretical analysis to formally assess how our representation is better than alternative representations reported in the literature in terms of representational power. Coupled with neural networks for feature learning, our model achieves the state-of-the-art performance in three benchmark datasets annotated with overlapping mentions.


title: Augmented Reality Technology for Education

abstract: Nowadays, our educational institutions are frustratingly outmoded in terms of teaching students. The entire process of giving lectures, taking notes, reading facts from books, and taking final exams is a throwback to institutions of learning dating back to the Renaissance. Remarkably, very little has changed today: with notable exceptions, the vast majority of university professors continue to bore students with ineffective, non-interactive approaches to education that result in little more than the professor's notes becoming the students' notes without passing through the minds of either. True learning is experiential. Humans learn best by doing, not by reading or listening to lectures. The more senses are involved (sound, sight, touch, emotions, etc.), the more powerful the learning experience. That's why today's best teachers are those pioneering individuals who take the effort to engage their students in meaningful activities that reach students at multiple levels. In this sense, augmented reality appears as an emerging technology that promises to make "educational immersion" available to practically everyone. Augmented Reality (AR) is a technology that permits to overlay computer graphics onto the real world. Unlike immersive Virtual Reality, AR interfaces allow users to see the real world at the same time as virtual imagery attached to real locations and objects. In an AR interface, the user views the world through a handheld or head mounted display (HMD) that is either see-through or overlays graphics on video of the surrounding environment. AR interfaces enhance the real world experience, unlike other computer interfaces that draw users away from the real world and onto the screen. Through the use of advanced technology an empty space is turned into a very rich educational experience. AR interface is a visualization technology that can take advantage of the limitations offered by other visual means of communication for learning. Traditional methods of learning spatially-related content by viewing 2D diagrams create a sort of cognitive filter. This filter exists even when working with 3D objects on a computer screen because the manipulation of the objects in space is made through mouse clicks. 15


title: ARF-Predictor: Effective Prediction of Aging-Related Failure Using Entropy

abstract: Even well-designed software systems suffer from chronic performance degradation, also known as “software aging”, due to internal (e.g., software bugs) or external (e.g., resource exhaustion) impairments. These chronic problems often fly under the radar of software monitoring systems before causing severe impacts (e.g., system failures). Therefore, it is a challenging issue how to timely predict the occurrence of failures caused by these problems. Unfortunately, the effectiveness of prior approaches are far from satisfactory due to the insufficiency of aging indicators adopted by them. To accurately predict failures caused by software aging which are named as Aging-Related Failure (ARFs), this paper presents a novel entropy-based aging indicator, namely Multidimensional Multi-scale Entropy (MMSE) which leverages the complexity embedded in runtime performance metrics to indicate software aging. To the best of our knowledge, this is the first time to leverage entropy to predict ARFs. Based upon MMSE, we implement three failure prediction approaches encapsulated in a proof-of-concept prototype named ARF-Predictor. The experimental evaluations in a Video on Demand (VoD) system, and in a real-world production system, AntVision, show that ARF-Predictor can predict ARFs with a very high accuracy and a low Ahead-Time-To-Failure ( $ATTF$ ) . Compared to previous approaches, ARF-Predictor improves the prediction accuracy by about 5 times and reduces $ATTF$ even by 3 orders of magnitude. In addition, ARF-Predictor is light-weight enough to satisfy the real-time requirement.


Oral Teratoma with a Primitive Neuroectodermal Tumor Component: A Case Report


title: Improved partial distance search for k nearest-neighbor classification

abstract: An important method in pattern recognition is k nearest-neighbor classification. However, its computational complexity limits its real-time applications. The partial distance search is a solution to this problem. Although it is not very effective, it can be combined with other algorithms to reduce the complexity. The paper proposes an indexing method that uses the variance vector of feature vectors of a design set to improve the efficiency of the partial distance search. Experimental results indicate the effectiveness of this indexing preprocessing


On Blockchain Applications : Hyperledger Fabric And Ethereum


title: Emergent Tangled Graph Representations for Atari Game Playing Agents

abstract: Organizing code into coherent programs and relating different programs to each other represents an underlying requirement for scaling genetic programming to more difficult task domains. Assuming a model in which policies are defined by teams of programs, in which team and program are represented using independent populations and coevolved, has previously been shown to support the development of variable sized teams. In this work, we generalize the approach to provide a complete framework for organizing multiple teams into arbitrarily deep/wide structures through a process of continuous evolution; hereafter the Tangled Program Graph (TPG). Benchmarking is conducted using a subset of 20 games from the Arcade Learning Environment (ALE), an Atari 2600 video game emulator. The games considered here correspond to those in which deep learning was unable to reach a threshold of play consistent with that of a human. Information provided to the learning agent is limited to that which a human would experience. That is, screen capture sensory input, Atari joystick actions, and game score. The performance of the proposed approach exceeds that of deep learning in 15 of the 20 games, with 7 of the 15 also exceeding that associated with a human level of competence. Moreover, in contrast to solutions from deep learning, solutions discovered by TPG are also very ‘sparse’. Rather than assuming that all of the state space contributes to every decision, each action in TPG is resolved following execution of a subset of an individual’s graph. This results in significantly lower computational requirements for model building than presently the case for deep learning.


title: Performance comparison of TCP algorithms for D2D communication in LTE-A

abstract: Recent massive growth in the connected devices as well as the traffic on wireless cellular networks is posing serious issues in achieving higher data rates. Device-to-device (D2D) communication has received a great attention to meet such massive demands. D2D communications allow two devices to communicate directly without going through the base station. Therefore, D2D communications have potential contribution in offloading the traffic from the core network and improving the overall throughput. In this paper, we evaluate the performance of TCP flows in D2D communications in LTE-A networks using the system-level simulator called SimuLTE. This paper presents the results on the comparison of the throughputs obtained with various TCP implementations in D2D communications in LTE-A networks. It also provides explanation to the variations in the throughputs resulting from changing the receive window size and the way the mode switch is handled in different TCP implementations.


title: Lift-Based Bidding in Ad Selection

abstract: Real-time bidding has become one of the largest online advertising markets in the world. Today the bid price per ad impression is typically decided by the expected value of how it can lead to a desired action event to the advertiser. However, this industry standard approach to decide the bid price does not consider the actual effect of the ad shown to the user, which should be measured based on the performance lift among users who have been or have not been exposed to a certain treatment of ads. In this paper, we propose a new bidding strategy and prove that if the bid price is decided based on the performance lift rather than absolute performance value, advertisers can actually gain more action events. We describe the modeling methodology to predict the performance lift and demonstrate the actual performance gain through blind A/B test with real ad campaigns. We also show that to move the demand-side platforms to bid based on performance lift, they should be rewarded based on the relative performance lift they contribute.


title: Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game

abstract: This study investigated the relationships among incoming knowledge, persistence, affective states, in-game progress, and consequently learning outcomes for students using the game Physics Playground. We used structural equation modeling to examine these relations. We tested three models, obtaining a model with good fit to the data. We found evidence that both the pretest and the in-game measure of student performance significantly predicted learning outcome, while the in-game measure of performance was predicted by pretest data, frustration, and engaged concentration. Moreover, we found evidence for two indirect paths from engaged concentration and frustration to learning, via the in-game progress measure. We discuss the importance of these findings, and consider viable next steps concerning the design of effective learning supports within game environments. We model relations among various student variables and learning outcome in a game.Pretest and in-game performance significantly predict learning outcome.In-game performance is predicted by pretest data, frustration, and engagement.Two indirect paths involving frustration and engagement predict learning.


title: Online Learning with Predictable Sequences

abstract: We present methods for online linear optimization that take advantage of benign (as opposed to worst-case) sequences. Specically if the sequence encountered by the learner is described well by a known \predictable process", the algorithms presented enjoy tighter bounds as compared to the typical worst case bounds. Additionally, the methods achieve the usual worst-case regret bounds if the sequence is not benign. Our approach can be seen as a way of adding prior knowledge about the sequence within the paradigm of online learning. The setting is shown to encompass partial and side information. Variance and path-length bounds [11, 9] can be seen as particular examples of online learning with simple predictable sequences. We further extend our methods and results to include competing with a set of possible predictable processes (models), that is \learning" the predictable process itself concurrently with using it to obtain better regret guarantees. We show that such model selection is possible under various assumptions on the available feedback. Our results suggest a promising direction of further research with potential applications to stock market and time series prediction.


title: Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

abstract: We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call "groups." Collaborative Altering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformation of a group, shrinkage of the transform spectrum, and inverse 3D transformation. The result is a 3D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.


title: An Exploration of Heat Tolerance in Mice Utilizing mRNA and microRNA Expression Analysis

abstract: BACKGROUND ::: Individuals who rapidly develop hyperthermia during heat exposure (heat-intolerant) are vulnerable to heat associated illness and injury. We recently reported that heat intolerant mice exhibit complex alterations in stress proteins in response to heat exposure. In the present study, we further explored the role of genes and molecular networks associated with heat tolerance in mice. ::: ::: ::: METHODOLOGY ::: Heat-induced physiological and biochemical changes were assessed to determine heat tolerance levels in mice. We performed RNA and microRNA expression profiling on mouse gastrocnemius muscle tissue samples to determine novel biological pathways associated with heat tolerance. ::: ::: ::: PRINCIPAL FINDINGS ::: Mice (n = 18) were assigned to heat-tolerant (TOL) and heat-intolerant (INT) groups based on peak core temperatures during heat exposures. This was followed by biochemical assessments (Hsp40, Hsp72, Hsp90 and Hsf1 protein levels). Microarray analysis identified a total of 3,081 mRNA transcripts that were significantly misregulated in INT compared to TOL mice (p<0.05). Among them, Hspa1a, Dnajb1 and Hspb7 were differentially expressed by more than two-fold under these conditions. Furthermore, we identified 61 distinct microRNA (miRNA) sequences significantly associated with TOL compared to INT mice; eight miRNAs corresponded to target sites in seven genes identified as being associated with heat tolerance pathways (Hspa1a, Dnajb1, Dnajb4, Dnajb6, Hspa2, Hspb3 and Hspb7). ::: ::: ::: CONCLUSIONS ::: The combination of mRNA and miRNA data from the skeletal muscle of adult mice following heat stress provides new insights into the pathophysiology of thermoregulatory disturbances of heat intolerance.


title: Genome-Wide Comparative Analysis of Miniature Inverted Repeat Transposable Elements in 19 Arabidopsis thaliana Ecotype Accessions

abstract: Miniature inverted repeat transposable elements (MITEs) are prevalent in eukaryotic genomes. They are known to critically influence the process of genome evolution and play a role in gene regulation. As the first study concentrated in the transposition activities of MITEs among different ecotype accessions within a species, we conducted a genome-wide comparative analysis by characterizing and comparing MITEs in 19 Arabidopsis thaliana accessions. A total of 343485 MITE putative sequences, including canonical, diverse and partial ones, were delineated from all 19 accessions. Within the entire population of MITEs sequences, 80.7% of them were previously unclassified MITEs, demonstrating a different genomic distribution and functionality compared to the classified MITEs. The interactions between MITEs and homologous genes across 19 accessions provided a fine source for analyzing MITE transposition activities and their impacts on genome evolution. Moreover, a significant proportion of MITEs were found located in the last exon of genes besides the ordinary intron locality, thus potentially modifying the end of genes. Finally, analysis of the impact of MITEs on gene expression suggests that migrations of MITEs have no detectable effect on the expression level for host genes across accessions.


title: A real-time x-ray mobile application using augmented reality and google street view

abstract: X-ray view can be defined as the ability one has to see through real surfaces. Although this skill is often associated with superheroes and medical examination, there are several researches conducted to employ X-ray view in numerous applications. However, the generation of X-ray visualization includes numerous challenges regarding occlusion, realistic appearance, and depth perception. In this paper, we present a mobile application that uses Augmented Reality and Google Street View to allow users experience real-time X-ray vision. The proposed application was designed to enhance previous Augmented Reality X-ray systems, by introducing a silhouette computation method to provide visual context from the occluder and a perspective estimation system that improves the projection of occluded images into the real scene. Additionally, we implemented two usability studies to assess qualitative aspects of both silhouettes and perspective estimation to generate better X-ray effects. Results indicate good acceptance of the novel X-ray visualization method and a great usability score on the SUS scale for the mobile application.


title: AllConcur: Leaderless Concurrent Atomic Broadcast (Extended Version)

abstract: Many distributed systems require coordination between the components involved. With the steady growth of such systems, the probability of failures increases, which necessitates scalable fault-tolerant agreement protocols. The most common practical agreement protocol, for such scenarios, is leader-based atomic broadcast. In this work, we propose AllConcur, a distributed system that provides agreement through a leaderless concurrent atomic broadcast algorithm, thus, not suffering from the bottleneck of a central coordinator. In AllConcur, all components exchange messages concurrently through a logical overlay network that employs early termination to minimize the agreement latency. Our implementation of AllConcur supports standard sockets-based TCP as well as high-performance InfiniBand Verbs communications. AllConcur can handle up to 135 million requests per second and achieves 17x higher throughput than today's standard leader-based protocols, such as Libpaxos. Thus, AllConcur is highly competitive with regard to existing solutions and, due to its decentralized approach, enables hitherto unattainable system designs in a variety of fields.


title: Construction and Central Pattern Generator-Based Control of a Flipper-Actuated Turtle-Like Underwater Robot

abstract: This paper deals with the construction and control of a turtle-like underwater robot with four mechanical flippers. Each flipper consists of two joints generating a rowing motion by a combination of lead-lag and feathering motions. With cooperative movements of four flippers, the robot can propel and maneuver in any direction without rotation of its main body and execute complicated three-dimensional movements, including ascending, submerging, rolling and hovering. The control architecture is constructed based on a central pattern generator (CPG). A model for a system of coupled nonlinear oscillators is established to construct a CPG and has been successfully applied to the eight-joint turtle-like robot. The CPGs are modeled as nonlinear oscillators for joints and inter-joint coordination is achieved by altering the connection weights between joints. Rowing action can be produced by modulating the control parameters in the CPG model. The CPG-based method performs elegant and smooth transitions between swi...


title: A new generalized computational framework for finding object orientation using perspective trihedral angle constraint

abstract: This paper investigates a fundamental problem of determining the position and orientation of a three-dimensional (3-D) object using a single perspective image view. The technique is focused on the interpretation of trihedral angle constraint information. A new closed form solution based on Kanatani's formulation is proposed. The main distinguishing feature of the authors' method over the original Kanatani formulation is that their approach gives an effective closed form solution for a general trihedral angle constraint. The method also provides a general analytic technique for dealing with a class of problem of shape from inverse perspective projection by using "angle to angle correspondence information." A detailed implementation of the authors' technique is presented. Different trihedral angle configurations were generated using synthetic data for testing the authors' approach of finding object orientation by angle to angle constraint. The authors performed simulation experiments by adding some noise to the synthetic data for evaluating the effectiveness of their method in a real situation. It has been found that the authors' method worked effectively in a noisy environment which confirms that the method is robust in practical application. >


title: Saliency Detection with Recurrent Fully Convolutional Networks

abstract: Deep networks have been proved to encode high level semantic features and delivered superior performance in saliency detection. In this paper, we go one step further by developing a new saliency model using recurrent fully convolutional networks (RFCNs). Compared with existing deep network based methods, the proposed network is able to incorporate saliency prior knowledge for more accurate inference. In addition, the recurrent architecture enables our method to automatically learn to refine the saliency map by correcting its previous errors. To train such a network with numerous parameters, we propose a pre-training strategy using semantic segmentation data, which simultaneously leverages the strong supervision of segmentation tasks for better training and enables the network to capture generic representations of objects for saliency detection. Through extensive experimental evaluations, we demonstrate that the proposed method compares favorably against state-of-the-art approaches, and that the proposed recurrent deep model as well as the pre-training method can significantly improve performance.


title: Security and Privacy in Cloud Computing: A Survey

abstract: Cloud Computing is becoming a well-known buzzword nowadays. Many companies, such as Amazon, Google, Microsoft and so on, accelerate their paces in developing Cloud Computing systems and enhancing their services to provide for a larger amount of users. However, security and privacy issues present a strong barrier for users to adapt into Cloud Computing systems. In this paper, we investigate several Cloud Computing system providers about their concerns on security and privacy issues. We find those concerns are not adequate and more should be added in terms of five aspects (i.e., availability, confidentiality, data integrity, control, audit) for security. Moreover, released acts on privacy are out of date to protect users' private information in the new environment (i.e., Cloud Computing system environment) since they are no longer applicable to the new relationship between users and providers, which contains three parties (i.e., Cloud service user, Cloud service provider/Cloud user, Cloud provider). Multi located data storage and services (i.e., applications) in the Cloud make privacy issues even worse. Hence, adapting released acts for new scenarios in the Cloud, it will result in more users to step into Cloud. We claim that the prosperity in Cloud Computing literature is to be coming after those security and privacy issues having be resolved.


title: Color correction algorithm based on camera characteristics for multi-view video coding

abstract: Various types of multi-view camera systems have been proposed for capturing three dimensional scenes. Yet, color distributions among multi-view images remain inconsistent in most cases, degrading multi-view video coding performance. In this paper, we propose a color correction algorithm based on the camera characteristics to effectively solve such a problem. Initially, we model camera characteristics and estimate their coefficients by means of correspondences between views. To consider occlusion in multi-view images, correspondences are extracted via feature-based matching. During coefficient estimation with nonlinear regression, we remove outliers in the extracted correspondences. Consecutively, we generate lookup tables for each camera using the model and estimated coefficients. Such tables are employed for fast color converting in the final color correction process. The experimental results show that our algorithm enhances coding efficiency with gains of up to 0.9 and 0.8 dB for luminance and chrominance components, respectively. Further, the method also improves subjective viewing quality and reduces color distance between views.


title: Boosting concolic testing via interpolation

abstract: Concolic testing has been very successful in automatically generating test inputs for programs. However one of its major limitations is path-explosion that limits the generation of high coverage inputs. Since its inception several ideas have been proposed to attack this problem from various angles: defining search heuristics that increase coverage, caching of function summaries, pruning of paths using static/dynamic information etc. We propose a new and complementary method based on interpolation, that greatly mitigates path-explosion by subsuming paths that can be guaranteed to not hit a bug. We discuss new challenges in using interpolation that arise specifically in the context of concolic testing. We experimentally evaluate our method with different search heuristics using Crest, a publicly available concolic tester.


title: A spectral histogram model for texton modeling and texture discrimination

abstract: Abstract We suggest a spectral histogram, defined as the marginal distribution of filter responses, as a quantitative definition for a texton pattern. By matching spectral histograms, an arbitrary image can be transformed to an image with similar textons to the observed. We use the χ 2 -statistic to measure the difference between two spectral histograms, which leads to a texture discrimination model. The performance of the model well matches psychophysical results on a systematic set of texture discrimination data and it exhibits the nonlinearity and asymmetry phenomena in human texture discrimination. A quantitative comparison with the Malik–Perona model is given, and a number of issues regarding the model are discussed.


title: UNIFYing Cloud and Carrier Network Resources: An Architectural View

abstract: Cloud networks provide various services on top of virtualized compute and storage resources. The flexible operation and optimal usage of the underlying infrastructure are realized by resource orchestration methods and virtualization techniques developed during the recent years. In contrast, service deployment and service provisioning in carrier networks have several limitations in terms of flexibility, scalability or optimal resource usage as the built-in mechanisms are strongly coupled to the physical topology and special purpose hardware elements. Network Function Virtualization (NFV) opens the door between cloud and carrier networks by providing software-based telecommunication services which can run in virtualized environment on general purpose hardwares. Our main goal is to unify software and network resources in a common framework. In this paper, we propose a novel architecture supporting automated, dynamic service creation based on a fine-granular service chaining model, SDN and cloud virtualization techniques. First, we introduce the architecture with the main components. Second, the most important benefits are highlighted and compared to other state-of-the-art approaches. Finally, preliminary experiences with our proof-of-concept prototypes are presented.


title: Whole-function vectorization

abstract: Data-parallel programming languages are an important component in today's parallel computing landscape. Among those are domain-specific languages like shading languages in graphics (HLSL, GLSL, RenderMan, etc.) and "general-purpose" languages like CUDA or OpenCL. Current implementations of those languages on CPUs solely rely on multi-threading to implement parallelism and ignore the additional intra-core parallelism provided by the SIMD instruction set of those processors (like Intel's SSE and the upcoming AVX or Larrabee instruction sets). In this paper, we discuss several aspects of implementing dataparallel languages on machines with SIMD instruction sets. Our main contribution is a language- and platform-independent code transformation that performs whole-function vectorization on low-level intermediate code given by a control flow graph in SSA form. We evaluate our technique in two scenarios: First, incorporated in a compiler for a domain-specific language used in realtime ray tracing. Second, in a stand-alone OpenCL driver. We observe average speedup factors of 3.9 for the ray tracer and factors between 0.6 and 5.2 for different OpenCL kernels.


title: Active cyber defense with denial and deception: A cyber-wargame experiment

abstract: In January 2012, MITRE performed a real-time, red team/blue team cyber-wargame experiment. This presented the opportunity to blend cyber-warfare with traditional mission planning and execution, including denial and deception tradecraft. The cyber-wargame was designed to test a dynamic network defense cyber-security platform being researched in The MITRE Corporation's Innovation Program called Blackjack, and to investigate the utility of using denial and deception to enhance the defense of information in command and control systems. The Blackjack tool failed to deny the adversary access to real information on the command and control mission system. The adversary had compromised a number of credentials without the computer network defenders' knowledge, and thereby observed both the real command and control mission system and the fake command and control mission system. However, traditional denial and deception techniques were effective in denying the adversary access to real information on the real command and control mission system, and instead provided the adversary with access to false information on a fake command and control mission system.


title: A phd Filter for Tracking Multiple Extended Targets Using Random Matrices

abstract: This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets' extensions are modeled as random matrices. For this purpose, the random matrix framework developed recently by Koch is adapted into the extended target phd framework, resulting in the Gaussian inverse Wishart phd (giw-phd) filter. A suitable multiple target likelihood is derived, and the main filter recursion is presented along with the necessary assumptions and approximations. The particularly challenging case of close extended targets is addressed with practical measurement clustering algorithms. The capabilities and limitations of the resulting extended target tracking framework are illustrated both in simulations and in experiments based on laser scans.


title: Cloud Computing Uncovered: A Research Landscape MOHAMMAD HAMDAQA AND

abstract: Abstract The amalgamation of technology and service-based economy was the impetus of a new IT delivery model called “Cloud Computing.” Unsurprisingly, the new model created a state of confusion; new concepts are mixed with old ones, and some old technologies are being reinvented. Today, many research areas and projects under the cloud umbrella need to be sorted and classified. Additionally, many gaps need to be categorized so that each research area can be tackled by its experts. It is important to learn from what already exists, and to be able to distinguish between what is new and what can be reused. Accordingly, this survey article presents a unified view of the Cloud Computing main concepts, characteristics, models, and architectures. Moreover, it presents a landscape of research in Cloud Computing by highlighting relevant disciplines and some diligent research projects. This helps identify the underlying research gaps and elaborates on the corresponding challenges.


title: Lightweight and Secure Session-Key Establishment Scheme in Smart Home Environments

abstract: The proliferation of current wireless communications and information technologies have been altering humans lifestyle and social interactions—the next frontier is the smart home environments or spaces. A smart home consists of low capacity devices (e.g., sensors) and wireless networks, and therefore, all working together as a secure system that needs an adequate level of security. This paper introduces lightweight and secure session key establishment scheme for smart home environments. To establish trust among the network, every sensor and control unit uses a short authentication token and establishes a secure session key. The proposed scheme provides important security attributes including prevention of various popular attacks, such as denial-of-service and eavesdropping attacks. The preliminary evaluation and feasibility tests are demonstrated by the proof-of-concept implementation. In addition, the proposed scheme attains both computation efficiency and communication efficiency as compared with other schemes from the literature.


title: The Role of Extracellular Conductivity Profiles in Compartmental Models for Neurons: Particulars for Layer 5 Pyramidal Cells

abstract: With the rapid increase in the number of technologies aimed at observing electric activity inside the brain, scientists have felt the urge to create proper links between intracellular-and extracellular-based experimental approaches. Biophysical models at both physical scales have been formalized under assumptions that impede the creation of such links. In this work, we address this issue by proposing a multicompartment model that allows the introduction of complex extracellular and intracellular resistivity profiles. This model accounts for the geometrical and electrotonic properties of any type of neuron through the combination of four devices: the integrator, the propagator, the 3D connector, and the collector. In particular, we applied this framework to model the tufted pyramidal cells of layer 5 PCL5 in the neocortex. Our model was able to reproduce the decay and delay curves of backpropagating action potentials APs in this type of cell with better agreement with experimental data. We used the voltage drops of the extracellular resistances at each compartment to approximate the local field potentials generated by a PCL5 located in close proximity to linear microelectrode arrays. Based on the voltage drops produced by backpropagating APs, we were able to estimate the current multipolar moments generated by a PCL5. By adding external current sources in parallel to the extracellular resistances, we were able to create a sensitivity profile of PCL5 to electric current injections from nearby microelectrodes. In our model for PCL5, the kinetics and spatial profile of each ionic current were determined based on a literature survey, and the geometrical properties of these cells were evaluated experimentally. We concluded that the inclusion of the extracellular space in the compartmental models of neurons as an extra electrotonic medium is crucial for the accurate simulation of both the propagation of the electric potentials along the neuronal dendrites and the neuronal reactivity to an electrical stimulation using external microelectrodes.


title: Ability of preoperative serum prostatespecific antigen value to predict pathologic stage and DNA ploidy Influence of clinical stage and tumor grade

abstract: Although the preoperative serum prostate-specific antigen (PSA) concentration has been demonstrated to be directly proportional to pathologic stage, it has been found to be unreliable for predicting final pathologic stage on an individual basis. Statistical analysis was thus performed to determine whether combining various preoperative parameters could enhance the predictive power of PSA. Multivariate logistic regression analysis revealed that, indeed, local clinical stage (determined by digital rectal examination) and tumor grade (determined from the biopsy specimen) significantly enhanced the predictive power of PSA (P < 0.0001 and < 0.0001, respectively). These preoperative parameters were thus all combined into a model from which probability plots were generated. These probability plots allow the practicing urologist to estimate preoperatively for an individual patient the probability of a given pathologic stage and DNA ploidy status. For example, the predicted probability of organ-confined disease for a patient with a PSA value of 10 ng/mL, when used alone, is 53 percent. However, the predicted probability of organ-confined disease for a patient with a PSA value of 10 ng/mL, clinical Stage B2, and high-tumor grade is only 30 percent. Thus, the findings of this investigation allow the physician maximal use of the preoperative variables to discuss more accurately with the patient what might be expected at the time of operation and to plan more precisely the surgical procedure that will possibly ensure complete removal of the prostate cancer with the least compromise to normal physiologic function (continence and potency).


title: End-to-End Instance Segmentation and Counting with Recurrent Attention

abstract: While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. Techniques that combine large graphical models with low-level vision have been proposed to address this problem; however, we propose an end-to-end recurrent neural network (RNN) architecture with an attention mechanism to model a human-like counting process, and produce detailed instance segmentations. The network is jointly trained to sequentially produce regions of interest as well as a dominant object segmentation within each region. The proposed model achieves state-of-the-art results on the CVPPP leaf segmentation dataset and KITTI vehicle segmentation dataset.


Design and Implementation of Campus Gate Control System Based on RFID


title: Channel estimation algorithms for OFDM systems

abstract: In this work we have applied Adapive Boosting (AdaBoost) for channel estimation of Orthogonal Frequency Division Multiplexing (OFDM) systems. The result of the AdaBoost algorithm was compared with other algorithms such Least Square (LS), Best Linear Unbiased Estimator (BLUE) and Minimum Mean Square Error (MMSE).


title: Innate and adaptive immunity in teleost fish: a review.

abstract: The immune system of fish is very similar to vertebrates, although there are some important dif - ferences. Fish are free-living organisms from the embryonic stage of life in their aquatic environment. They have mechanisms to protect themselves from a wide variety of microorganisms. Consequently, fish rely on their innate immune system for an extended period of time, beginning at the early stages of embryogenesis. The components of the innate immune response are divided into physical, cellular and humoral factors and include humoral and cellular receptor molecules that are soluble in plasma and other body fluids. The lymphoid organs found in fish include the thymus, spleen and kidney. Immunoglobulins are the principal components of the immune response against pathogenic organisms. Immunomodulatory products, including nucleotides, glucans and probiotics, are increasingly used in aquaculture production. The use of these products reduces the need for therapeutic treatments, enhances the effects of vaccines and, in turn, improves the indicators of production. The aim of this review is to provide a review of the immune system in fish, including the ontogeny, mechanisms of unspecific and acquired immunity and the action of some immunomodulators.


title: Mobile marketing: A literature review on its value for consumers and retailers

abstract: The article describes the existing knowledge of how mobile marketing can increase the value for consumers and retailers. Mobile device shopping, and consumers' use of mobile devices while shopping is shown to be both an extension of consumers' shopping behaviours developed on Internet-connected desktop and laptop computers (PC), and potentially new behaviours based on a mobile devices' uniquely integrated features such as camera, scanners and GPS. The article focuses on how mobile marketing creates value for consumers and retailers, enabling more precise research and development of managerial concepts and tools while providing both managers and academics with increased understanding of mobile marketing and its value outcomes for retailers.


Comparison of maximum voluntary isometric contraction of the biceps on various posture and respiration conditions for normalization of electromyography data


title: On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

abstract: Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing efficient deep architectures to analyse volumetric medical images remains challenging. This work investigates efficient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images. Our experiments show that the proposed network architecture compares favourably with state-of-the-art volumetric segmentation networks while being an order of magnitude more compact. We consider the brain parcellation task as a pretext task for volumetric image segmentation; our trained network potentially provides a good starting point for transfer learning. Additionally, we show the feasibility of voxel-level uncertainty estimation using a sampling approximation through dropout.


title: Using morphological operators and inpainting for hair removal in dermoscopic images

abstract: The increasing incidence of melanoma has led to development of computer-aided diagnosis systems that classify dermoscopic images. A fundamental problem however during the pre-processing stage is the removal of artifacts such as hair. Hair strands introduce additional edges, which can be problematic when performing automatic skin lesion segmentation. This paper proposes a straightforward approach to automatic hair and consequently noise removal. The process starts with a median filter on each color space of RGB, a bottom hat filter, a binary conversion, a dilation and morphological opening, and then the removal of small connected pixels. The detected hair regions are then filled up using harmonic inpainting. Experiments were carried out on the PH2 datasets and compared to DullRazor. We also generated synthetic hair on skin images and measured the reconstruction quality using peak signal-to-noise ratio.


title: Context-based search for 3D models

abstract: Large corpora of 3D models, such as Google 3D Warehouse, are now becoming available on the web. It is possible to search these databases using a keyword search. This makes it possible for designers to easily include existing content into new scenes. In this paper, we describe a method for context-based search of 3D scenes. We first downloaded a large set of scene graphs from Google 3D Warehouse. These scene graphs were segmented into individual objects. We also extracted tags from the names of the models. Given the object shape, tags, and spatial relationship between pairs of objects, we can predict the strength of a relationship between a candidate model and an existing object in the scene. Using this function, we can perform context-based queries. The user specifies a region in the scene they are modeling using a 3D bounding box, and the system returns a list of related objects. We show that context-based queries perform better than keyword queries alone, and that without any keywords our algorithm still returns a relevant set of models.


title: Spinner: Scalable Graph Partitioning in the Cloud

abstract: Several organizations, like social networks, store and routinely analyze large graphs as part of their daily operation. Such graphs are typically distributed across multiple servers, and graph partitioning is critical for efficient graph management. Existing partitioning algorithms focus on finding graph partitions with good locality, but disregard the pragmatic challenges of integrating partitioning into large-scale graph management systems deployed on the cloud, such as dealing with the scale and dynamicity of the graph and the compute environment. ::: In this paper, we propose Spinner, a scalable and adaptive graph partitioning algorithm based on label propagation designed on top of the Pregel model. Spinner scales to massive graphs, produces partitions with locality and balance comparable to the state-of-the-art and efficiently adapts the partitioning upon changes. We describe our algorithm and its implementation in the Pregel programming model that makes it possible to partition billion-vertex graphs. We evaluate Spinner with a variety of synthetic and real graphs and show that it can compute partitions with quality comparable to the state-of-the art. In fact, by using Spinner in conjunction with the Giraph graph processing engine, we speed up different applications by a factor of 2 relative to standard hash partitioning.


title: An urban metabolism and ecological footprint assessment of Metro Vancouver

abstract: Abstract As the world urbanizes, the role of cities in determining sustainability outcomes grows in importance. Cities are the dominant form of human habitat, and most of the world's resources are either directly or indirectly consumed in cities. Sustainable city analysis and management requires understanding the demands a city places on a wider geographical area and its ecological resource base. We present a detailed, integrated urban metabolism of residential consumption and ecological footprint analysis of the Vancouver metropolitan region for the year 2006. Our overall goal is to demonstrate the application of a bottom-up ecological footprint analysis using an urban metabolism framework at a metropolitan, regional scale. Our specific objectives are: a) to quantify energy and material consumption using locally generated data and b) to relate these data to global ecological carrying capacity. Although water is the largest material flow through Metro Vancouver (424,860,000 m 3 ), it has the smallest ecological footprint (23,100 gha). Food (2,636,850 tonnes) contributes the largest component to the ecological footprint (4,514,400 gha) which includes crop and grazing land as well as carbon sinks required to sequester emissions from food production and distribution. Transportation fuels (3,339,000 m 3 ) associated with motor vehicle operation and passenger air travel comprises the second largest material flow through the region and the largest source of carbon dioxide emissions (7,577,000 tonnes). Transportation also accounts for the second largest component of the EF (2,323,200 gha). Buildings account for the largest electricity flow (17,515,150 MWh) and constitute the third largest component of the EF (1,779,240 gha). Consumables (2,400,000 tonnes) comprise the fourth largest component of the EF (1,414,440 gha). Metro Vancouver's total Ecological Footprint in 2006 was 10,071,670 gha, an area approximately 36 times larger than the region itself. The EFA reveals that cropland and carbon sinks (forested land required to sequester carbon dioxide emissions) account for 90% of Metro Vancouver's overall demand for biocapacity. The per capita ecological footprint is 4.76 gha, nearly three times the per capita global supply of biocapacity. Note that this value excludes national government services that operate outside the region and could account for up to an additional 2 gha/ca.


title: Plagiarism detection using feature-based neural networks

abstract: This paper focuses on the use of code features for automatic plagiarism detection. Instead of the text-based analyses employed by current plagiarism detectors, we propose a system that is based on properties of assignments that course instructors use to judge the similarity of two submissions. This system uses neural network techniques to create a feature-based plagiarism detector and to measure the relevance of each feature in the assessment. The system was trained and tested on assignments from an introductory computer science course, and produced results that are comparable to the most popular plagiarism detectors.


title: Supporting Listening Comprehension and Vocabulary Acquisition with Multimedia Annotations: The Students' Voice

abstract: This study extends Mayer’s (1997, 2001) generative theory of multimedia learning and investigates under what conditions multimedia annotations can support listening comprehension in a second language. This paper highlights students’ views on the effectiveness of multimedia annotations (visual and verbal) in assisting them in their comprehension and acquisition of vocabulary from aural texts. English-speaking college students listened to a 2 min 20 sec historical account in French presented by a computer program. Participants were randomly assigned to one of four listening treatments: the aural text (a) with no annotations, (b) with only verbal annotations, (c) with only visual annotations, and (d) with both visual and verbal annotations. For purposes of this paper, 20 students were purposively selected to participate in interviews. Overall, students remembered word translations and recalled the passage best when they had selected both verbal and visual annotations while listening. Students’ voices reflected these results and revealed that they should have options for viewing material in both a visual mode and a verbal mode in a multimedia listening comprehension environment. This study provides qualitative evidence for a generative theory of multimedia learning that suggests that the availability and the choice of visual and verbal annotations in listening comprehension activities enhances students’ abilities to comprehend the material presented and to acquire vocabulary.


title: The emotional power of music: How music enhances the feeling of affective pictures

abstract: Music is an intriguing stimulus widely used in movies to increase the emotional experience. However, no brain imaging study has to date examined this enhancement effect using emotional pictures (the modality mostly used in emotion research) and musical excerpts. Therefore, we designed this functional magnetic resonance imaging study to explore how musical stimuli enhance the feeling of affective pictures. In a classical block design carefully controlling for habituation and order effects, we presented fearful and sad pictures (mostly taken from the IAPS) either alone or combined with congruent emotional musical excerpts (classical pieces). Subjective ratings clearly indicated that the emotional experience was markedly increased in the combined relative to the picture condition. Furthermore, using a second-level analysis and regions of interest approach, we observed a clear functional and structural dissociation between the combined and the picture condition. Besides increased activation in brain areas known to be involved in auditory as well as in neutral and emotional visual-auditory integration processes, the combined condition showed increased activation in many structures known to be involved in emotion processing (including for example amygdala, hippocampus, parahippocampus, insula, striatum, medial ventral frontal cortex, cerebellum, fusiform gyrus). In contrast, the picture condition only showed an activation increase in the cognitive part of the prefrontal cortex, mainly in the right dorsolateral prefrontal cortex. Based on these findings, we suggest that emotional pictures evoke a more cognitive mode of emotion perception, whereas congruent presentations of emotional visual and musical stimuli rather automatically evoke strong emotional feelings and experiences.


title: Total Productive Maintenance (TPM) Implementation in a Machine Shop: A Case Study

abstract: Quality and Maintenance of manufacturing systems are closely related functions of any organization. Over a period of time two concepts have emerged which are Total Productive Maintenance (TPM) and Total Quality Management (TQM) along with other concepts to achieve World Class Manufacturing system. In this paper experience of implementing Total Productive Maintenance is shared and investigated for a company manufacturing automotive component. Concept is implemented in the machine shop having CNC turning centers of different capacity. Overall Equipment Effectiveness is used as the measure of success of TPM implementation. The losses associated with equipment effectiveness are identified. All the pillars of TPM are implemented in a phased manner eliminating the losses and thus improving the utilization of CNC machines.


3D Pose Estimation and Normalization for Face Recognition


title: Entire Information Attentive GRU for Text Representation

abstract: Recurrent Neural Networks~(RNNs), such as Long Short-Term Memory~(LSTM) and Gated Recurrent Unit~(GRU), have been widely utilized in sequence representation. However, RNNs neglect variational information and long-term dependency. In this paper, we propose a new neural network structure for extracting a comprehension sequence embedding by handling the entire representation of the sequence. Unlike previous works that put attention mechanism after all steps of GRU, we add the entire representation to the input of the GRU which means the GRU model takes the entire information of the sequence into consideration in every step. We provide three various strategies to adding the entire information which are the Convolutional Neural Network~(CNN) based attentive GRU~(CBAG), the GRU inner attentive GRU~(GIAG) and the pre-trained GRU inner attentive GRU~(Pre-GIAG). To evaluate our proposed methods, we conduct extensive experiments on a benchmark sentiment classification dataset. Our experimental results show that our models outperform state-of-the-art baselines significantly.


title: An Evaluation of Fine and Gross Motor Skills in Adolescents with Down Syndromes

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


Humanoid Robot Simulation and Walking Behavior Development in the Spark Simulator Framework


title: Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks

abstract: We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact. Adopting a straightforward decomposition of the problem into entity detection, entity linking, relation prediction, and evidence combination, we explore simple yet strong baselines. On the SimpleQuestions dataset, we find that baseline LSTMs and GRUs plus a few heuristics yield accuracies that approach the state of the art, and techniques that do not use neural networks also perform reasonably well. These results show that gains from sophisticated deep learning techniques proposed in the literature are quite modest and that some previous models exhibit unnecessary complexity.


title: Coverage evaluation of wireless networks for Unmanned Aerial Systems

abstract: Unmanned Aerial Vehicles (UAVs) gained a lot popularity during the last years and are used in many applications ranging from reconnaissance over disaster recovery to remote sensing [1][2]. In most applications some sort of communication link is needed to transmit telemetry and payload data. Especially for homeland security and civil operations the realization is sometimes very demanding due to missing radio frequencies. In order to overcome this constraint we investigate the usability of existing cellular networks. Considering these UAVs to communicate by means of cellular networks for establishing reliable air-to-ground links, claims an analysis and evaluation of the cellular channel characteristics in the low altitude operational area. In this paper we present a coverage analysis for cellular networks for altitudes up to 500m based on aerial RSSI measurements.


title: Autonomous Collaborative Mobile Manipulators: State of the Art

abstract: In order to extend the workspace of manipu- lators, the use of mobile platforms represents a good solu- tion. This is the idea behind the concept of Mobile Manip- ulator (MM). In spite of their progresses, MMs have limita- tions, that can concern control or mechatronic design, the former being often covered in literature. Up to now, MMs are limited to specific applications and not widely spread in industry. One of the possible breakouts of these machines into industrial applications can be the collaborative way of performing some tasks. In this paper, a literature overview of different researches on collaborative MMs is presented to help understand the advantages of these machines, their background, the ongo- ing developments and their limits. A classification of these systems is presented along with definitions of each of their mechatronics components. The paper includes a focus on experimental architectures of MMs and a review of the lim- its of the current state of the art and finishes with possible enhancements and research topics that could be considered for future projects. This work will help in the project of designing and controlling new collaborative MMs for in- dustrial and service applications.


title: Decomposition of MAC address structure for granular device inference

abstract: Common among the wide variety of ubiquitous networked devices in modern use is wireless 802.11 connectivity. The MAC addresses of these devices are visible to a passive adversary, thereby presenting security and privacy threats - even when link or application-layer encryption is employed. While it is well-known that the most significant three bytes of a MAC address, the OUI, coarsely identify a device's manufacturer, we seek to better understand the ways in which the remaining low-order bytes are allocated in practice. From a collection of more than two billion 802.11 frames observed in the wild, we extract device and model information details for over 285K devices, as leaked by various management frames and discovery protocols. From this rich dataset, we characterize overall device populations and densities, vendor address allocation policies and utilization, OUI sharing among manufacturers, discover unique models occurring in multiple OUIs, and map contiguous address blocks to specific devices. Our mapping thus permits fine-grained device type and model predictions for unknown devices solely on the basis of their MAC address. We validate our inferences on both ground-truth data and a third-party dataset, where we obtain high accuracy. Our results empirically demonstrate the extant structure of the low-order MAC bytes due to manufacturer's sequential allocation policies, and the security and privacy concerns therein.


title: Sequencing and beyond: integrating molecular 'omics' for microbial community profiling

abstract: In this Review, Huttenhower and colleagues discuss how integrating multi-omic data types — including genomics, transcriptomics, proteomics and metabolomics — enables a better characterization of the composition and function of human-associated and environmental microbial communities.


title: Neural Stain-Style Transfer Learning using GAN for Histopathological Images

abstract: Performance of data-driven network for tumor classification varies with stain-style of histopathological images. This article proposes the stain-style transfer (SST) model based on conditional generative adversarial networks (GANs) which is to learn not only the certain color distribution but also the corresponding histopathological pattern. Our model considers feature-preserving loss in addition to well-known GAN loss. Consequently our model does not only transfers initial stain-styles to the desired one but also prevent the degradation of tumor classifier on transferred images. The model is examined using the CAMELYON16 dataset.


title: Acute Graft-versus-Host Disease: Novel Biological Insights

abstract: Abstract Graft-versus-host disease (GVHD) continues to be a leading cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation. Recent insights into intestinal homeostasis and uncovering of new pathways and targets have greatly reconciled our understanding of GVHD pathophysiology and will reshape contemporary GVHD prophylaxis and treatment. Gastrointestinal (GI) GVHD is the major cause of mortality. Emerging data indicate that intestinal stem cells (ISCs) and their niche Paneth cells are targeted, resulting in dysregulation of the intestinal homeostasis and microbial ecology. The microbiota and their metabolites shape the immune system and intestinal homeostasis, and they may alter host susceptibility to GVHD. Protection of the ISC niche system and modification of the intestinal microbiota and metabolome to restore intestinal homeostasis may, thus, represent a novel approach to modulate GVHD and infection. Damage to the intestine plays a central role in amplifying systemic GVHD by propagating a proinflammatory cytokine milieu. Molecular targeting to inhibit kinase signaling may be a promising approach to treat GVHD, ideally via targeting the redundant effect of multiple cytokines on immune cells and enterocytes. In this review, we discuss insights on the biology of GI GVHD, interaction of microflora and metabolome with the hosts, identification of potential new target organs, and identification and targeting of novel T cell–signaling pathways. Better understanding of GVHD biology will, thus, pave a way to develop novel treatment strategies with great clinical benefits.