title: A malware instruction set for behavior-based analysis

abstract: We introduce a new representation for monitored behavior of malicious software called Malware Instruction Set (MIST). The representation is optimized for effective and efficient analysis of behavior using data mining and machine learning techniques. It can be obtained automatically during analysis of malware with a behavior monitoring tool or by converting existing behavior reports. The representation is not restricted to a particular monitoring tool and thus can also be used as a meta language to unify behavior reports of different sources.


title: Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining (Best Student Paper Award)

abstract: Since their introduction over a decade ago, time series motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns are arbitrarily dissimilar. In the discrete space, this is similar to extracting the text chain "hit, hot, dot, dog" from a paragraph. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time series chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce a robust definition of time series chains, and a scalable algorithm that allows us to discover them in massive datasets.


title: A Review of Soft Computing Based on Deep Learning

abstract: A review of deep learning based soft computing techniques in several applications is presented. On one hand, soft computing, defined as a group of methodologies, is an important element for constructing a new generation of computational intelligent system and has gained great success in solving practical computing problems. On the other hand, deep learning has become one of the most promising techniques in artificial intelligence in the past decade. Since soft computing is an evolving collection of methodologies, by presenting the latest research results of soft computing based on deep learning, this review not only reveals a promising direction for soft computing by incorporating deep learning, but also gives some suggestions for improving the performance of deep learning with soft computing techniques.


title: Online 3D acquisition and model integration

abstract: This paper presents a system which yields complete 3D models in a fast and inexpensive way. The major building blocks are a high-speed structured light range scanner and a registration module. The former generates ’raw’ range data whereas the latter performs a fast registration (ICP) and renders the partially integrated model as a preview of the final result. As the scanner uses only a single image to make a reconstruction, it is possible to scan while the object is moved. The use of an adaptive projection pattern gives a more robust behaviour. This allows the system to deal more easily with complicated geometry and texture than most other systems. During the scanning process the model is built up incrementally and rendered on the screen. This real-time visual feedback allows the user to check the current state of the 3D model. Holes or other flaws can be detected during the acquisition process itself. This speeds up the model building process, and solves indirectly the problem of viewplanning. The whole real-time pipeline comprising acquisition, merging and visualization only uses off-the-shelf hardware. A regular desktop PC connected to a camera and LCD projector is turned into a high-speed scanner and modeler. The current implementation has a throughput of approx. 5 fps.


title: Using information scent to model user information needs and actions and the Web

abstract: On the Web, users typically forage for information by navigating from page to page along Web links. Their surfing patterns or actions are guided by their information needs. Researchers need tools to explore the complex interactions between user needs, user actions, and the structures and contents of the Web. In this paper, we describe two computational methods for understanding the relationship between user needs and user actions. First, for a particular pattern of surfing, we seek to infer the associated information need. Second, given an information need, and some pages as starting pints, we attempt to predict the expected surfing patterns. The algorithms use a concept called “information scent”, which is the subjective sense of value and cost of accessing a page based on perceptual cues. We present an empirical evaluation of these two algorithms, and show their effectiveness.


title: Broadband multisection asymmetric 8.34-dB directional coupler with improved directivity

abstract: The paper presents the design of a broadband 8.34-dB directional coupler in a stripline technique. In order to obtain broadband frequency response a three-section asymmetric coupled-line directional coupler has been chosen. The influence of discontinuities in the regions of coupled and transmission line connections, as well as intersection connections, has been taken into account in the design stage. The parasitic reactances associated with the discontinuities have been compensated using the developed capacitive compensation technique which allows for directional couplers' design with improved directivity and return loss characteristics. The compensation technique has been for the first time applied for the design of directional couplers with the coupling weaker than 3 dB. The designed coupler has been manufactured and measured to validate the presented compensation technique.


title: A comparative user study on rating vs. personality quiz based preference elicitation methods

abstract: We conducted a user study evaluating two preference elicitation approaches based on ratings and personality quizzes respectively. Three criteria were used in this comparative study: perceived accuracy, user effort and user loyalty. Results from our study show that the perceived accuracy in two systems is not significantly different. However, users expended significantly less effort, both perceived cognitive effort and actual task time, to complete the preference profile establishing process in the personality quiz-based system than in the rating-based system. Additionally, users expressed stronger intention to reuse the personality quiz-based system and introduce it to their friends. After using these two systems, 53% of users preferred the personality quiz-based system vs. 13% of users preferred the rating-based system, since most users thought the former is easier to use.


title: A phishing vulnerability analysis of web based systems

abstract: Phishing, a criminal act of collecting personal, bank and credit card information by sending out forged e-mails with fake websites, has become the most popular practice among the criminals of the Web. Phishing attacks are becoming more and more sophisticated and are constantly on the rise. The impact of phishing is quite drastic since it involves the threat of identity theft and financial losses. A lot of groups and organizations are trying to study this act and also inform and update the public on what are the latest tactics being used in the phishing sector. According to industry estimates, phishing attacks are on the rise every year and the existing anti phishing solutions fall short in detecting phishing. Moreover, phishers come up with innovative methods of phishing everyday making it even more difficult to detect and prevent phishing. This paper explains in detail the various methods used in phishing. We perform a root-cause analysis of the methods used in phishing, the motivation for phishing and in the process come up with a fishbone diagram outlining the causes and methodologies used in phishing. This analysis is aimed at helping developers to design and develop better anti phishing solutions.


title: Rotational Projection Statistics for 3D Local Surface Description and Object Recognition

abstract: Recognizing 3D objects in the presence of noise, varying mesh resolution, occlusion and clutter is a very challenging task. This paper presents a novel method named Rotational Projection Statistics (RoPS). It has three major modules: local reference frame (LRF) definition, RoPS feature description and 3D object recognition. We propose a novel technique to define the LRF by calculating the scatter matrix of all points lying on the local surface. RoPS feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics (including low-order central moments and entropy) of the distribution of these projected points. Using the proposed LRF and RoPS descriptor, we present a hierarchical 3D object recognition algorithm. The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets. Our proposed techniques exhibited superior performance compared to existing techniques. We also showed that our method is robust with respect to noise and varying mesh resolution. Our RoPS based algorithm achieved recognition rates of 100, 98.9, 95.4 and 96.0 % respectively when tested on the Bologna, UWA, Queen’s and Ca’ Foscari Venezia Datasets.


title: From general discrimination to segmented inequality: Migration and inequality in urban China

abstract: Abstract Internal migration in China during the last three decades, the largest in human history, offers a rare opportunity to understand inequalities in the making. Using data spanning 10 years from China’s largest metropolis, Shanghai, this study assesses how enduring state institutions interplay with the spread of market forces to shape income inequality between migrants and native urban workers. Though the wages of both Chinese migrants and urban workers rose considerably, economic restructuring during the decade under study resulted in diminished privileges for urbanites and subsequently increased collision between migrants and urban workers in the private sectors. These shifts, rather than substantially reducing inequality, have led to an evolving form of inequality, from an initial general blatant discrimination against migrants across the board, to a new and more subtle form of inequality characterized by substantial segmented discrimination against migrants within economic sectors, with the degree of inequality varying from sector to sector. We discuss how this changing inequality reflects complementary rather than competing roles of the state and market institutions in inequality creation and maintenance.


title: Design of fractal based microstrip rectangular patch antenna for multiband applications

abstract: In this paper a multiband fractal based rectangular microstrip patch antenna is designed. FR4 substrate having thickness of 1.58 mm is used as substrate material for the design of proposed antenna and microstrip feed line provides the excitation to the antenna. The antenna operating frequency range is from 1 to 10 GHz. The proposed antenna resonate at twelve different frequencies as 1.86, 2.33, 3.67, 4.57, 5.08, 6.06, 7.03, 7.75, 8.08, 8.84, 9.56 and 10 GHz and the return losses are −15.39, −16.48, −10.02, −17.29, −13.15, −23.41, −10.22, −11.28, −17.02, −10.94, −15.15 and −15.48 dB respectively. The proposed antenna is designed and simulated by using the Ansoft HFSS V13 (high frequency structure simulator) software.


title: Total Generalized Variation

abstract: The novel concept of total generalized variation of a function $u$ is introduced, and some of its essential properties are proved. Differently from the bounded variation seminorm, the new concept involves higher-order derivatives of $u$. Numerical examples illustrate the high quality of this functional as a regularization term for mathematical imaging problems. In particular this functional selectively regularizes on different regularity levels and, as a side effect, does not lead to a staircasing effect.


title: Interpreting change on the WAIS-III/WMS-III in clinical samples

abstract: Abstract Clinicians should note that there is considerable variability in the reliabilities of the index and subtest scores derived from the third editions of the Wechsler Adult Intelligence Scale (WAIS-III) and the Wechsler Memory Scale (WMS-III). The purpose of this article is to review these reliabilites and to illustrate how they can be used to interpret change in patients' performances from test to retest. The WAIS-III IQ and Index scores are consistently the most reliable scores, in terms of both internal consistency and test–retest reliability. The most internally consistent WAIS-III subtests are Vocabulary, Information, Digit Span, Matrix Reasoning, and Arithmetic. Information and Vocabulary have the highest test–retest reliability. On the WMS-III, the Auditory Immediate Index, Immediate Memory Index, Auditory Delayed Index, and General Memory Index are the most reliable, in terms of both internal consistency and test–retest reliability. The Logical Memory I and Verbal Paired Associates I subtests are the most reliable. Data from three clinical groups (i.e., Alzheimer's disease, chronic alcohol abuse, and schizophrenia) were extracted from the Technical Manual [Psychological Corporation (1997). WAIS-III/WMS-III Technical Manual. San Antonio: Harcourt Brace] for the purpose of calculating reliable change estimates. A table of confidence intervals for test–retest measurement error is provided to help the clinician determine if patients have reliably improved or deteriorated on follow-up testing.


title: Data Provenance: A Categorization of Existing Approaches

abstract: In many application areas like e-science and data-warehousing detailed information about the origin of data is required. This kind of information is often referred to as data provenance or data lineage. The provenance of a data item includes information about the processes and source data items that lead to its creation and current representation. The diversity of data representation models and application domains has lead to a number of more or less formal definitions of provenance. Most of them are limited to a special application domain, data representation model or data processing facility. Not surprisingly, the associated implementations are also restricted to some application domain and depend on a special data model. In this paper we give a survey of data provenance models and prototypes, present a general categorization scheme for provenance models and use this categorization scheme to study the properties of the existing approaches. This categorization enables us to distinguish between different kinds of provenance information and could lead to a better understanding of provenance in general. Besides the categorization of provenance types, it is important to include the storage, transformation and query requirements for the different kinds of provenance information and application domains in our considerations. The analysis of existing approaches will assist us in revealing open research problems in the area of data provenance.


title: Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition

abstract: Social behavior and many cultural etiquettes are influenced by gender. There are numerous potential applications of automatic face gender recognition such as human-computer interaction systems, content based image search, video surveillance and more. The immense increase of images that are uploaded online has fostered the construction of large labeled datasets. Recently, impressive progress has been demonstrated in the closely related task of face verification using deep convolutional neural networks. In this paper we explore the applicability of deep convolutional neural networks on gender classification by fine-tuning a pretrained neural network. In addition, we explore the performance of dropout support vector machines by training them on the deep features of the pretrained network as well as on the deep features of the fine-tuned network. We evaluate our methods on the color FERET data collection and the recently constructed Adience data collection. We report cross-validated performance rates on each dataset. We further explore generalization capabilities of our approach by conducting cross-dataset tests. It is demonstrated that our fine-tuning method exhibits state-of-the-art performance on both datasets.


Assessing Effects of Task and Data Distribution on the Effectiveness of Visual Encodings


title: FEPR: fast energy projection for real-time simulation of deformable objects

abstract: We propose a novel projection scheme that corrects energy fluctuations in simulations of deformable objects, thereby removing unwanted numerical dissipation and numerical "explosions."


Development of hepatocellular carcinoma after clearance of hepatitis C virus with interferon therapy.


title: A biorobotic flapping fin for propulsion and maneuvering

abstract: A series of biorobotic fins has been developed based on the pectoral fin of the bluegill sunfish. These robotic fins model physical properties of the biological fin, and execute kinematics derived from sunfish motions that were identified to be most responsible for thrust. When the physical properties of the robotic fin are tuned appropriately to operating conditions, the robotic fin, like the sunfish, produces positive thrust throughout the entire fin beat. Due to having many degrees of freedom, these fins can be used to generate and control forces for propulsion and maneuvering.


title: Neural, mechanical, and geometric factors subserving arm posture in humans

abstract: When the hand is displaced from an equilibrium posture by an external disturbance, a force is generated to restore the original position. We developed a new experimental method to measure and represent the field of elastic forces associated with posture of the hand in the horizontal plane. While subjects maintained a given posture, small displacements of the hand along different directions were delivered by torque motors. The hand was held in the displaced positions and, at that time, we measured the corresponding restoring forces before the onset of any voluntary reaction. The stiffness in the vicinity of the hand equilibrium position was estimated by analyzing the force and displacement vectors. We chose to represent the stiffness both numerically, as a matrix, and graphically, as an ellipse characterized by three parameters: magnitude (the area), shape (the ratio of axis) and orientation (direction of the major axis). The latter representation captures the main geometrical features of the elastic force field associated with posture. We also evaluated the conservative and nonconservative components of this elastic force field. We found that the former were much larger than the latter and concluded that the behavior of the neuromuscular system of the multiarticular arm is predominantly spring-like. Our data indicated that the shape and orientation of the stiffness were invariant over subjects and over time. We also investigated the ability of our subjects to produce voluntary and adaptive changes in the stiffness. Our findings indicated that, when a disturbance acting along a fixed and predictable direction was imposed, the magnitude of the stiffness was increased but only minor changes in shape and orientation occurred. Taken together, all of these experiments represent a step toward the understanding of the interactions between geometrical and neural factors involved in maintaining hand posture and its interactions with the environment.


title: Fundamental Limitations of Small Antennas

abstract: A capacitor or inductor operating as a small antenna is theoretically capable of intercepting a certain amount of power, independent of its size, on the assumption of tuning without circuit loss. The practical efficiency relative to this ideal is limited by the "radiation power factor" of the antenna as compared with the power factor and bandwidth of the antenna tuning. The radiation power factor of either kind of antenna is somewhat greater than (1/6π) (Ab/l2) in which Ab is the cylindrical volume occupied by the antenna, and l is the radianlength (defined as 1/2π wavelength) at the operating frequency. The efficiency is further limited by the closeness of coupling of the antenna with its tuner. Other simple formulas are given for the more fundamental properties of small antennas and their behavior in a simple circuit. Examples for 1-Mc. operation in typical circuits indicate a loss of about 35 db for the I.R.E. standard capacitive antenna, 43 db for a large loop occupying a volume of 1 meter square by 0.5 meter axial length, and 64 db for a loop of 1/5 these dimensions.


title: Evaluating Mobile Apps with A/B and Quasi A/B Tests

abstract: We have seen an explosive growth of mobile usage, particularly on mobile apps. It is more important than ever to be able to properly evaluate mobile app release. A/B testing is a standard framework to evaluate new ideas. We have seen much of its applications in the online world across the industry [9,10,12]. Running A/B tests on mobile apps turns out to be quite different, and much of it is attributed to the fact that we cannot ship code easily to mobile apps other than going through a lengthy build, review and release process. Mobile infrastructure and user behavior differences also contribute to how A/B tests are conducted differently on mobile apps, which will be discussed in details in this paper. In addition to measuring features individually in the new app version through randomized A/B tests, we have a unique opportunity to evaluate the mobile app as a whole using the quasi-experimental framework [21]. Not all features can be A/B tested due to infrastructure changes and wholistic product redesign. We propose and establish quasi-experimental techniques for measuring impact from mobile app release, with results shared from a recent major app launch at LinkedIn.


title: The AVISPA Tool for the Automated Validation of Internet Security Protocols and Applications ⋆

abstract: AVISPA is a push-button tool for the automated validation of Internet security-sensitive protocols and applications. It provides a modular and expressive formal language for specifying protocols and their security properties, and integrates different back-ends that implement a variety of state-of-the-art automatic analysis techniques. To the best of our knowledge, no other tool exhibits the same level of scope and robustness while enjoying the same performance and scalability.


title: WordSeer: a knowledge synthesis environment for textual data

abstract: We describe WordSeer, a tool whose goal is to help scholars and analysts discover patterns and formulate and test hypotheses about the contents of text collections, midway between what humanities scholars call a traditional "close read'' and the new "distant read" or "culturomics" approach. To this end, WordSeer allows for highly flexible "slicing and dicing" (hence "sliding") across a text collection. The tool allows users to view text from different angles by selecting subsets of data, viewing those as visualizations, moving laterally to view other subsets of data, slicing into another view, expanding the viewed data by relaxing constraints, and so on. We illustrate the text sliding capabilities of the tool with examples from a case study in the field of humanities and social sciences -- an analysis of how U.S. perceptions of China and Japan changed over the last 30 years.


title: MIT Cheetah 3: Design and Control of a Robust, Dynamic Quadruped Robot

abstract: This paper introduces a new robust, dynamic quadruped, the MIT Cheetah 3. Like its predecessor, the Cheetah 3 exploits tailored mechanical design to enable simple control strategies for dynamic locomotion and features high-bandwidth proprioceptive actuators to manage physical interaction with the environment. A new leg design is presented that includes proprioceptive actuation on the abduction/adduction degrees of freedom in addition to an expanded range of motion on the hips and knees. To make full use of these new capabilities, general balance and locomotion controllers for Cheetah 3 are presented. These controllers are embedded into a modular control architecture that allows the robot to handle unexpected terrain disturbances through reactive gait modification and without the need for external sensors or prior environment knowledge. The efficiency of the robot is demonstrated by a low Cost of Transport (CoT) over multiple gaits at moderate speeds, with the lowest CoT of 0.45 found during trotting. Experiments showcase the ability to blindly climb up stairs as a result of the full system integration. These results collectively represent a promising step toward a platform capable of generalized dynamic legged locomotion.


title: Understanding and Controlling User Linkability in Decentralized Learning

abstract: Machine Learning techniques are widely used by online services (e.g. Google, Apple) in order to analyze and make predictions on user data. As many of the provided services are user-centric (e.g. personal photo collections, speech recognition, personal assistance), user data generated on personal devices is key to provide the service. In order to protect the data and the privacy of the user, federated learning techniques have been proposed where the data never leaves the user's device and"only"model updates are communicated back to the server. In our work, we propose a new threat model that is not concerned with learning about the content - but rather is concerned with the linkability of users during such decentralized learning scenarios. We show that model updates are characteristic for users and therefore lend themselves to linkability attacks. We show identification and matching of users across devices in closed and open world scenarios. In our experiments, we find our attacks to be highly effective, achieving 20x-175x chance-level performance. In order to mitigate the risks of linkability attacks, we study various strategies. As adding random noise does not offer convincing operation points, we propose strategies based on using calibrated domain-specific data; we find these strategies offers substantial protection against linkability threats with little effect to utility.


title: New algorithms for join and grouping operations

abstract: Traditional database query processing relies on three types of algorithms for join and for grouping operations. For joins, index nested loops join exploits an index on its inner input, merge join exploits sorted inputs, and hash join exploits differences in the sizes of the join inputs. For grouping, an index-based algorithm has been used in the past whereas today sort- and hash-based algorithms prevail. Cost-based query optimization chooses the most appropriate algorithm for each query and for each operation. Unfortunately, mistaken algorithm choices during compile-time query optimization are common yet expensive to investigate and to resolve. ::: ::: Our goal is to end mistaken choices among join algorithms and among grouping algorithms by replacing the three traditional types of algorithms with a single one. Like merge join, this new join algorithm exploits sorted inputs. Like hash join, it exploits different input sizes for unsorted inputs. In fact, for unsorted inputs, the cost functions for recursive hash join and for hybrid hash join have guided our search for the new join algorithm. In consequence, the new join algorithm can replace both merge join and hash join in a database management system. ::: ::: The in-memory components of the new join algorithm employ indexes. If the database contains indexes for one (or both) of the inputs, the new join can exploit persistent indexes instead of temporary in-memory indexes. Using database indexes to find matching input records, the new join algorithm can also replace index nested loops join. ::: ::: In addition to join operations, a very similar algorithm supports grouping ("group by" queries in SQL) and duplicate elimination. For unsorted inputs, candidate output records take on the role of one of the inputs in a join operation. Our goal is to define a single grouping algorithm that can replace grouping by repeated index searches, by sorting, and by hashing. In other words, our goal is to end mistaken algorithm choices not only for joins and other binary matching operations but also for grouping and other unary matching operations in database query processing. ::: ::: Finally, these new algorithms can be instrumental for efficient and robust data processing in a map-reduce environment, because `map' and `reduce' operations are similar in essentials to join and grouping operations. ::: ::: Results from an implementation of the core algorithm are reported.


title: Matching models across abstraction levels with Gaussian Processes

abstract: Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively concordant predictions over specific parametrisations, but it is generally unclear whether model predictions are quantitatively in agreement, and whether such agreement holds for different parametrisations. Here we present a generally applicable statistical machine learning methodology to automatically reconcile the predictions of different models across abstraction levels. Our approach is based on defining a correction map, a random function which modifies the output of a model in order to match the statistics of the output of a different model of the same system. We use two biological examples to give a proof-of-principle demonstration of the methodology, and discuss its advantages and potential further applications.


Multiple Turn Comprehension for the Bi-Directional Attention Flow Model


title: Multi-Rate Deep Learning for Temporal Recommendation

abstract: Modeling temporal behavior in recommendation systems is an important and challenging problem. Its challenges come from the fact that temporal modeling increases the cost of parameter estimation and inference, while requiring large amount of data to reliably learn the model with the additional time dimensions. Therefore, it is often difficult to model temporal behavior in large-scale real-world recommendation systems. In this work, we propose a novel deep neural network based architecture that models the combination of long-term static and short-term temporal user preferences to improve the recommendation performance. To train the model efficiently for large-scale applications, we propose a novel pre-train method to reduce the number of free parameters significantly. The resulted model is applied to a real-world data set from a commercial News recommendation system. We compare to a set of established baselines and the experimental results show that our method outperforms the state-of-the-art significantly.


title: Compact Resonant Slot Array Antenna Using Partial H-Plane Waveguide

abstract: In this letter, a resonant slot array antenna using a compact partial H-plane waveguide is proposed. To design the antenna, the conductance value of a longitudinal shunt slot on the partial H-plane waveguide is numerically estimated using a commercial electromagnetic (EM) simulator and the analytic conductance equation. The partial H-plane slot array antenna behaves as a resonant 1-D structure with longitudinal shunt slots and has almost the same characteristics as a conventional waveguide slot antenna even though it has a one-quarter cross section. The antenna is fabricated in the S-band, and the measured results are in good agreement with simulated results.


title: Beam-steering in microstrip patch antenna array using DGS based phase shifters at 5.2 GHz

abstract: Design and experimental analysis of beam steering in microstrip patch antenna array using dumbbell shaped Defected Ground Structure (DGS) for S-band (5.2 GHz) application was carried out in this study. The Phase shifting in antenna has been achieved using different size and position of dumbbell shape DGS. DGS has characteristics of slow wave, wide stop band and compact size. The obtained radiation pattern has provided steerable main lobe and nulls at predefined direction. The radiation pattern for different size and position of dumbbell structure in microstrip patch antenna array was measured and comparative study has been carried out.


title: Three-dimensional assumed strain solid element for piezoelectric actuator/sensor analysis

abstract: This paper deals with a fully coupled assumed strain solid element that can be used for simultaneous moiling of thin sensors and actuators. To solve fully coupled field problems, electric potential is regarded as a nodal degree of freedom in addition to three translations in an eighteen node assumed strain solid element. Therefore, the induced electric potential can be calculated for a prescribed deformation or an applied load. Since the original assumed strain solid element is free of locking, the element can be used to analyze behavior of very thin actuators without locking. Numerical examples, such as a typical bimorph actuator/sensor beam problem shows that the present element can handle fully coupled problems. Using the solid element, we have analyzed the actuation performance of THUNDER and compared the result with measured data. The comparison shows that the numerical estimation agrees well with measured displacement for simply supported boundary condition. It is also found that a particular combination of materials for layers and curvature of THUNDER improve actuation displacement.


title: AppShell: Making data protection practical for lost or stolen Android devices

abstract: Mobile apps continue to consume increasing amounts of sensitive data, such as banking credentials and classified documents. At the same time, the number of smartphone thefts is increasing at a rapid speed. As a result, there is an imperative need to protect sensitive data on lost or stolen mobile devices. In this work, we develop a practical solution to protect sensitive data on mobile devices. Our solution enables adaptive protection by pro-actively stepping up or stepping down data security based on perceived contextual risk of the device. We realize our solution for the Android platform in the form of a system called AppShell. AppShell does not require root privilege, nor need any modification to the underlying framework, and hence is a ready-to-deploy solution. It supports both in-memory and on-disk data protection by transparently encrypting the data, and discarding the encryption key, when required, for enhanced protection. We implement a working prototype of AppShell and evaluate it against several popular Android apps. Our results show that AppShell can successfully protect sensitive data in the lost devices with a reasonable performance overhead.


title: Semantic Granularity in Ontology-Driven Geographic Information Systems

abstract: The integration of information of different kinds, such as spatial and alphanumeric at different levels of detail, is a challenge. While a solution is not reached, it is widely recognized that the need to integrate information is so pressing that it does not matter if detail is lost, as long as integration is achieved. This paper shows the potential for information retrieval at different levels of granularity inside the framework of information systems based on ontologies. Ontologies are theories that use a specific vocabulary to describe entities, classes, properties and functions related to a certain view of the world. The use of an ontology, translated into an active information system component, leads to ontology-driven information systems and, in the specific case of GIS, leads to what we call ontology-driven geographic information systems.


title: The "Beyond the Fence" Musical and "Computer Says Show" Documentary

abstract: During 2015 and early 2016, the cultural application of Com- putational Creativity research and practice took a big leap for- ward, with a project where multiple computational systems were used to provide advice and material for a new musical theatre production. Billed as the world’s first ‘computer mu- sical ... conceived by computer and substantially crafted by computer’, Beyond The Fence was staged in the Arts Theatre in London’s West End during February and March of 2016. Various computational approaches to analytical and genera- tive sub-projects were used to bring about the musical, and these efforts were recorded in two 1-hour documentary films made by Wingspan Productions, which were aired on Sky Arts under the title Computer Says Show. We provide de- tails here of the project conception and execution, including details of the systems which took on some of the creative re- sponsibility in writing the musical, and the contributions they made. We also provide details of the impact of the project, including a perspective from the two (human) writers with overall control of the creative aspects the musical.


title: Experience-Driven Procedural Music Generation for Games

abstract: As video games have grown from crude and simple circuit-based artefacts to a multibillion dollar worldwide industry, video-game music has become increasingly adaptive. Composers have had to use new techniques to avoid the traditional, event-based approach where music is composed mostly of looped audio tracks, which can lead to music that is too repetitive. In addition, these cannot scale well in the design of today's games, which have become increasingly complex and nonlinear in narrative. This paper outlines the use of experience-driven procedural music generation, to outline possible ways forward in the dynamic generation of music and audio according to user gameplay metrics.


Sage: The New BBN Speech Processing Platform


title: A Novel Method for Islanding Detection in DC Networks

abstract: Several methods have been published aiming to detect islanding in ac distribution networks. On the other hand, islanding detection in dc networks appears more challenging, as many electric parameters, such as frequency and reactive power, are absent and thus, several methods used in ac networks are nonapplicable. This paper proposes a novel islanding detection method (IDM) that is effective in dc networks and at the same time provides an index to evaluate the effectiveness of the various IDMs. The proposed IDM is evaluated by simulation and experimental testing according to Standards IEEE 1547 and UL1741 proving its satisfactory performance.


title: StyleBank: An Explicit Representation for Neural Image Style Transfer

abstract: We propose StyleBank, which is composed of multiple convolution filter banks and each filter bank explicitly represents one style, for neural image style transfer. To transfer an image to a specific style, the corresponding filter bank is operated on top of the intermediate feature embedding produced by a single auto-encoder. The StyleBank and the auto-encoder are jointly learnt, where the learning is conducted in such a way that the auto-encoder does not encode any style information thanks to the flexibility introduced by the explicit filter bank representation. It also enables us to conduct incremental learning to add a new image style by learning a new filter bank while holding the auto-encoder fixed. The explicit style representation along with the flexible network design enables us to fuse styles at not only the image level, but also the region level. Our method is the first style transfer network that links back to traditional texton mapping methods, and hence provides new understanding on neural style transfer. Our method is easy to train, runs in real-time, and produces results that qualitatively better or at least comparable to existing methods.


title: Automation as Caregiver: A Survey of Issues and Technologies

abstract: In the United States, the number of people over 65 will double between now and 2030 to 69.4 million. Providing care for this increasing population becomes increasingly difficult as the cognitive and physical health of elders deteriorates. This survey article describes some of the factors that contribute to the institutionalization of elders, and then presents some of the work done towards providing technological support for this vulnerable community.


title: Self-assembly processes of superparamagnetic colloids in a quasi-two-dimensional system

abstract: Superparamagnetic colloids gather depending on the magnitude of the magnetic field applied, forming chains and ribbons in a quasi-two-dimensional chamber. The results presented in this work are in good agreement with recent experimental multistable data for the mean length of the aggregates in thermodynamic equilibrium.


title: On agent-based software engineering ✩

abstract: Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems. Yet, to date, there has been little systematic analysis of what makes the agent-based approach such an appealing and powerful computational model. Moreover, even less effort has been devoted to discussing the inherent disadvantages that stem from adopting an agent-oriented view. Here both sets of issues are explored. The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures.


The flipped classroom: A survey of the research


title: The poppy humanoid robot: Leg design for biped locomotion

abstract: We introduce a novel humanoid robotic platform designed to jointly address three central goals of humanoid robotics: 1) study the role of morphology in biped locomotion; 2) study full-body compliant physical human-robot interaction; 3) be robust while easy and fast to duplicate to facilitate experimentation. The taken approach relies on functional modeling of certain aspects of human morphology, optimizing materials and geometry, as well as on the use of 3D printing techniques. In this article, we focus on the presentation of the design of specific morphological parts related to biped locomotion: the hip, the thigh, the limb mesh and the knee. We present initial experiments showing properties of the robot when walking with the physical guidance of a human.


title: A Nonisolated Single-Phase UPS Topology With 110-V/220-V Input–Output Voltage Ratings

abstract: A circuit configuration of a single-phase nonisolated online uninterruptible power supply (UPS) with 110-V/220-V input–output voltage ratings is proposed, allowing the bypass operation without a transformer even if the input voltage is different from the output voltage. The converter consists of an ac–dc/dc–dc three-level boost converter combined with a double half-bridge inverter. In this type of configuration size, cost and efficiency are improved due to the reduced number of switches and batteries, and also, no low-frequency isolation transformer is required to realize bypass operation because of the common neutral connection. Both stages of the proposed circuit operate at high frequency by using a passive nondissipative snubber circuit in the boost converter and insulated-gate bipolar-transistor switches in the double half-bridge inverter, with low conduction losses, low tail current, and low switching losses. Principle of operation and experimental results for a 2.6-kVA prototype are presented to demonstrate the UPS performance.


title: Coding-based energy minimization for phase change memory

abstract: We devise new coding methods to minimize Phase Change Memory write energy. Our method minimizes the energy required for memory rewrites by utilizing the differences between PCM read, set, and reset energies. We develop an integer linear programming method and employ dynamic programming to produce codes for uniformly distributed data. We also introduce data-aware coding schemes to efficiently address the energy minimization problem for stochastic data. Our evaluations show that the proposed methods result in up to 32% and 44% reduction in memory energy consumption for uniform and stochastic data respectively.


title: A Real-Time Service-Oriented Architecture for Industrial Automation

abstract: Industrial automation platforms are experiencing a paradigm shift. New technologies are making their way in the area, including embedded real-time systems, standard local area networks like Ethernet, Wi-Fi and ZigBee, IP-based communication protocols, standard service oriented architectures (SOAs) and Web services. An automation system will be composed of flexible autonomous components with plug & play functionality, self configuration and diagnostics, and autonomic local control that communicate through standard networking technologies. However, the introduction of these new technologies raises important problems that need to be properly solved, one of these being the need to support real-time and quality-of-service (QoS) for real-time applications. This paper describes a SOA enhanced with real-time capabilities for industrial automation. The proposed architecture allows for negotiation of the QoS requested by clients from Web services, and provides temporal encapsulation of individual activities. This way, it is possible to perform an a priori analysis of the temporal behavior of each service, and to avoid unwanted interference among them. After describing the architecture, experimental results gathered on a real implementation of the framework (which leverages a soft real-time scheduler for the Linux kernel) are presented, showing the effectiveness of the proposed solution. The experiments were performed on simple case studies designed in the context of industrial automation applications.


title: Fuzzy rule based classification systems for big data with MapReduce: granularity analysis

abstract: Due to the vast amount of information available nowadays, and the advantages related to the processing of this data, the topics of big data and data science have acquired a great importance in the current research. Big data applications are mainly about scalability, which can be achieved via the MapReduce programming model.It is designed to divide the data into several chunks or groups that are processed in parallel, and whose result is "assembled" to provide a single solution. Among different classification paradigms adapted to this new framework, fuzzy rule based classification systems have shown interesting results with a MapReduce approach for big data. It is well known that the performance of these types of systems has a strong dependence on the selection of a good granularity level for the Data Base. However, in the context of MapReduce this parameter is even harder to determine as it can be also related with the number of Maps chosen for the processing stage. In this paper, we aim at analyzing the interrelation between the number of labels of the fuzzy variables and the scarcity of the data due to the data sampling in MapReduce. Specifically, we consider that as the partitioning of the initial instance set grows, the level of granularity necessary to achieve a good performance also becomes higher. The experimental results, carried out for several Big Data problems, and using the Chi-FRBCS-BigData algorithms, support our claims.


title: Learning metric-topological maps for indoor mobile robot navigation

abstract: Abstract Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are often difficult to learn and maintain in large-scale environments, particularly if momentary sensor data is highly ambiguous. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and naive Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms, the approach presented here gains advantages from both worlds: accuracy/consistency and efficiency. The paper gives results for autonomous exploration, mapping and operation of a mobile robot in populated multi-room environments.


title: TAPIR, a web server for the prediction of plant microRNA targets, including target mimics

abstract: Summary: We present a new web server called TAPIR, designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA-target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA-target duplex having a large loop, making them undetectable by traditional tools. ::: ::: Availability: The TAPIR web server can be accessed at: http://bioinformatics.psb.ugent.be/webtools/tapir ::: ::: Contact: yves.vandepeer@psb.vib-ugent.be ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.


title: Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data

abstract: Estimating urban traffic conditions of an arterial network with GPS probe data is a practically important while substantially challenging problem, and has attracted increasing research interests recently. Although GPS probe data is becoming a ubiquitous data source for various traffic related applications currently, they are usually insufficient for fully estimating traffic conditions of a large arterial network due to the low sampling frequency. To explore other data sources for more effectively computing urban traffic conditions, we propose to collect various traffic events such as traffic accident and jam from social media as complementary information. In addition, to further explore other factors that might affect traffic conditions, we also extract rich auxiliary information including social events, road features, Point of Interest (POI), and weather. With the enriched traffic data and auxiliary information collected from different sources, we first study the traffic co-congestion pattern mining problem with the aim of discovering which road segments geographically close to each other are likely to co-occur traffic congestion. A search tree based approach is proposed to efficiently discover the co-congestion patterns. These patterns are then used to help estimate traffic congestions and detect anomalies in a transportation network. To fuse the multisourced data, we finally propose a coupled matrix and tensor factorization model named TCE_R to more accurately complete the sparse traffic congestion matrix by collaboratively factorizing it with other matrices and tensors formed by other data. We evaluate the proposed model on the arterial network of downtown Chicago with 1,257 road segments whose total length is nearly 700 miles. The results demonstrate the superior performance of TCE_R by comprehensive comparison with existing approaches.


title: Inducing Value Sparsity for Parallel Inference in Tree-shaped Models

abstract: Single-core architectures are rapidly on the decline, and even the most common computational devices now contain multiple cores. With this easy access to parallelism, the machine learning community needs to go beyond treating the running time as the only computational resource and needs to study approaches that take this additional form of flexibility into account. In this work, we study inference in tree-shaped models. Specifically, we focus on balancing accuracy and efficient use of multiple cores when faced with running time constraints.


title: Predicting female physical attractiveness: Waist-to-hip ratio versus thinness

abstract: Introductory psychology students (120 females and 120 males) rated attractiveness and fecundity of one of six computer-altered female figures representing three body-weight categories (underweight, normal weight and overweight) and two levels of waist-to-hip ratio (WHR), one in the ideal range (0.72) and one in the non-ideal range (0.86). Both females and males judged underweight figures to be more attractive than normal or overweight figures, regardless of WHR. The female figure with the high WHR (0.86) was judged to be more attractive than the figure with the low WHR (0.72) across all body-weight conditions. Analyses of fecundity ratings revealed an interaction between weight and WHR such that the models did not differ in the normal weight category, but did differ in the underweight (model with WHR of 0.72 was less fecund) and overweight (model with WHR of 0.86 was more fecund) categories. These findings lend stronger support to sociocultural rather than evolutionary hypotheses.


title: Fine-Grained Access Control System Based on Outsourced Attribute-Based Encryption

abstract: As cloud computing becomes prevalent, more and more sensitive data is being centralized into the cloud for sharing, which brings forth new challenges for outsourced data security and privacy. Attribute-based encryption (ABE) is a promising cryptographic primitive, which has been widely applied to design fine-grained access control system recently. However, ABE is being criticized for its high scheme overhead as the computational cost grows with the complexity of the access formula. This disadvantage becomes more serious for mobile devices because they have constrained computing resources.


title: Analysis and Design of a Novel Circularly Polarized Antipodal Linearly Tapered Slot Antenna

abstract: This paper presents and characterizes a novel antipodal linearly tapered slot antenna for generating the circularly polarized (CP) field. By introducing two symmetrical slots on the top and bottom layers of a rectangular WR-15 waveguide, we could obtain circular polarization with an axial ratio bandwidth of 34.1%. The waveguide was used to feed the proposed antenna with very wideband impedance matching. The antenna can achieve impedance bandwidth of 40% for S(1,1) <−15 dB from 50 to 75 GHz. The simulation and measurement results show that the antenna can produce very good radiation patterns, with stable gain of 9.8 dBi ± 1.24 dBi, low back radiation, and narrow beamwidth over the operating frequency range. The working principle of this CP antenna is explained, using theoretical equations and explicit current distributions. The proposed antenna is easy to manufacture and is a compact candidate for high performance CP millimeter-wave antenna.


title: A novel highly compact dual-band branch-line coupler utilizing left and right-handed technique

abstract: A novel dual-band coupler is proposed by utilizing microstrip coupled transmission lines and entering into combination with complementary split ring resonator (CSRR). A lucid equivalent L-C circuit is synthesized and simulated for the analysis and the initial design of this proposed dual-band resonator. Additionally, a novel technique is introduced to shift the two pass bands towards the lower microwave frequencies, and subsequently, reduce the size of suchlike components. A dual-band microwave component is analyzed, designed and simulated by this technique. The simulated dual-band BLC demonstrated the merits which are mentioned as follows: a compact size more than that of the previous ones and a ratio of less than 2 for the two operating frequencies.


title: x.ent: R Package for Entities and Relations Extraction based on Unsupervised Learning and Document Structure

abstract: Relation extraction with accurate precision is still a challenge when processing full text databases. We propose an approach based on cooccurrence analysis in each document for which we used document organization to improve accuracy of relation extraction. This approach is implemented in a R package called \emph{x.ent}. Another facet of extraction relies on use of extracted relation into a querying system for expert end-users. Two datasets had been used. One of them gets interest from specialists of epidemiology in plant health. For this dataset usage is dedicated to plant-disease exploration through agricultural information news. An open-data platform exploits exports from \emph{x.ent} and is publicly available.


title: Unveiling Hardware-based Data Prefetcher, a Hidden Source of Information Leakage

abstract: Data prefetching is a hardware-based optimization mechanism used in most of the modern microprocessors. It fetches data to the cache before it is needed. In this paper, we present a novel microarchitectural attack that exploits the prefetching mechanism. Our attack targets Instruction pointer (IP)-based stride prefetching in Intel processors. Stride prefetcher detects memory access patterns with a regular stride, which are likely to be found in lookup table-based cryptographic implementations. By monitoring the prefetching activities near the lookup table, attackers can extract sensitive information such as secret keys from victim applications. This kind of leakage from prefetching has never been considered in the design of constant time algorithm to prevent side-channel attacks. We show the potential of the proposed attack by applying it against the Elliptic Curve Diffie-Hellman (ECDH) algorithm built upon the latest version of OpenSSL library. To the best of our knowledge, this is the first microarchitectural side-channel attack exploiting the hardware prefetching of modern microprocessors.


title: Named Entity Analysis and Extraction with Uncommon Words

abstract: Most previous research treats named entity extraction and classification as an end-to-end task. We argue that the two sub-tasks should be addressed separately. Entity extraction lies at the level of syntactic analysis while entity classification lies at the level of semantic analysis. According to Noam Chomsky's "Syntactic Structures," pp. 93-94 (Chomsky 1957), syntax is not appealed to semantics and semantics does not affect syntax. We analyze two benchmark datasets for the characteristics of named entities, finding that uncommon words can distinguish named entities from common text; where uncommon words are the words that hardly appear in common text and they are mainly the proper nouns. Experiments validate that lexical and syntactic features achieve state-of-the-art performance on entity extraction and that semantic features do not further improve the extraction performance, in both of our model and the state-of-the-art baselines. With Chomsky's view, we also explain the failure of joint syntactic and semantic parsings in other works.


title: Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards

abstract: Wireless sensor networks are an emerging technology for low-cost, unattended monitoring of a wide range of environments. Their importance has been enforced by the recent delivery of the IEEE 802.15.4 standard for the physical and MAC layers and the forthcoming ZigBee standard for the network and application layers. The fast progress of research on energy efficiency, networking, data management and security in wireless sensor networks, and the need to compare with the solutions adopted in the standards motivates the need for a survey on this field.


title: Local Gaussian Process Regression for Real Time Online Model Learning and Control

abstract: Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by local learning, we propose a method to speed up standard Gaussian Process regression (GPR) with local GP models (LGP). The training data is partitioned in local regions, for each an individual GP model is trained. The prediction for a query point is performed by weighted estimation using nearby local models. Unlike other GP approximations, such as mixtures of experts, we use a distance based measure for partitioning of the data and weighted prediction. The proposed method achieves online learning and prediction in real-time. Comparisons with other nonparametric regression methods show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and nu-SVR.


title: Distributed Energy Resources Topology Identification via Graphical Modeling

abstract: Distributed energy resources (DERs), such as photovoltaic, wind, and gas generators, are connected to the grid more than ever before, which introduces tremendous changes in the distribution grid. Due to these changes, it is important to understand where these DERs are connected in order to sustainably operate the distribution grid. But the exact distribution system topology is difficult to obtain due to frequent distribution grid reconfigurations and insufficient knowledge about new components. In this paper, we propose a methodology that utilizes new data from sensor-equipped DER devices to obtain the distribution grid topology. Specifically, a graphical model is presented to describe the probabilistic relationship among different voltage measurements. With power flow analysis, a mutual information-based identification algorithm is proposed to deal with tree and partially meshed networks. Simulation results show highly accurate connectivity identification in the IEEE standard distribution test systems and Electric Power Research Institute test systems.


title: Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning

abstract: Recent progress has been made in using attention based encoder-decoder framework for video captioning. However, most existing decoders apply the attention mechanism to every generated word including both visual words (e.g., "gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these non-visual words can be easily predicted using natural language model without considering visual signals or attention. Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of video captioning. To address this issue, we propose a hierarchical LSTM with adjusted temporal attention (hLSTMat) approach for video captioning. Specifically, the proposed framework utilizes the temporal attention for selecting specific frames to predict the related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and high-level language context information to support the video caption generation. To demonstrate the effectiveness of our proposed framework, we test our method on two prevalent datasets: MSVD and MSR-VTT, and experimental results show that our approach outperforms the state-of-the-art methods on both two datasets.


title: Recommender system based on workflow

abstract: This paper proposes a workflow-based recommender system model on supplying proper knowledge to proper members in collaborative team contexts rather than daily life scenarios, e.g., recommending commodities, films, news, etc. Within collaborative team contexts, more information could be utilized by recommender systems than ordinary daily life contexts. The workflow in collaborative team contains information about relationships among members, roles and tasks, which could be combined with collaborative filtering to obtain members' demands for knowledge. In addition, the work schedule information contained in the workflow could also be employed to determine the proper volume of knowledge that should be recommended to each member. In this paper, we investigate the mechanism of the workflow-based recommender system, and conduct a series of experiments referring to several real-world collaborative teams to validate the effectiveness and efficiency of the proposed methods.


title: Using the Canny edge detector for feature extraction and enhancement of remote sensing images

abstract: Edges are important features in an image since they represent significant local intensity changes. They provide important clues to separate regions within an object or to identify changes in illumination. Most remote sensing applications, such as image registration, image segmentation, region separation, object description, and recognition, use edge detection as a preprocessing stage for feature extraction. Real images, such as remote sensing images, can be corrupted with point noise. The real problem is how to enhance noisy remote sensing images and simultaneously extract the edges. Using the implemented Canny edge detector for feature extraction and as an enhancement tool for remote sensing images, the result was robust with a very high enhancement level.


title: 3D Soft Body Simulation Using Mass-spring System with Internal Pressure Force and Simplified Implicit Integration

abstract: In this paper, we propose a method to simulate soft bodies by using gravitational force, spring and damping forces between surface points, and internal molecular pressure forces. We consider a 3D soft body model composed of mesh points that define the body’s surface such that the points are connected by springs and influenced by internal molecular pressure forces. These pressure forces have been modeled on gaseous molecular interactions. Simulation of soft body with internal pressure forces is known to become unstable when high constants are used and is averted using an implicit integration method. We propose an approximation to this implicit integration method that considerably reduces the number of computations in the algorithm. Our results show that the proposed method realistically simulates soft bodies and improves performance of the implicit integration method.


title: MazeBase: A Sandbox for Learning from Games

abstract: This paper introduces MazeBase: an environment for simple 2D games, designed as a sandbox for machine learning approaches to reasoning and planning. Within it, we create 10 simple games embodying a range of algorithmic tasks (e.g. if-then statements or set negation). A variety of neural models (fully connected, convolutional network, memory network) are deployed via reinforcement learning on these games, with and without a procedurally generated curriculum. Despite the tasks' simplicity, the performance of the models is far from optimal, suggesting directions for future development. We also demonstrate the versatility of MazeBase by using it to emulate small combat scenarios from StarCraft. Models trained on the MazeBase version can be directly applied to StarCraft, where they consistently beat the in-game AI.


title: An Adaptive Relaying Scheme for Fuse Saving in Distribution Networks With Distributed Generation

abstract: In some situations, utilities may try to “save” the fuse of a circuit following temporary faults by de-energizing the line with the fast operation of an upstream recloser before the fuse is damaged. This fuse-saving practice is accomplished through proper time coordination between a recloser and a fuse. However, the installation of distributed generation (DG) into distribution networks may affect this coordination due to additional fault current contributions from the distributed resources. This phenomenon of recloser-fuse miscoordination is investigated in this paper with the help of a typical network that employs fuse saving. The limitations of a recloser equipped with time and instantaneous overcurrent elements with respect to fuse savings, in the presence of DG, are discussed. An adaptive relaying strategy is proposed to ensure fuse savings in the new scenario even in the worst fault conditions. The simulation results obtained by adaptively changing relay settings in response to changing DG configurations confirm that the settings selected theoretically in accordance with the proposed strategy hold well in operation.


title: CRRN: Multi-scale Guided Concurrent Reflection Removal Network

abstract: Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle this problem in a unified framework. Our proposed network integrates image appearance information and multi-scale gradient information with human perception inspired loss function, and is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes. Extensive experiments on a public benchmark dataset show that the proposed method performs favorably against state-of-the-art methods.


title: Autonomous long-range navigation in GNSS-denied environment with low-cost UAV platform

abstract: The capability of an Unmanned Aerial Vehicle to navigate in GNSS-denied environments is desired in many situations, especially in situation in which the GNSS signal is not trustable. In this work is presented a solution based on a low-cost UAV platform, that uses a Landmark Recognition algorithm to compensate the inertial sensors errors cumulation, aiming to perform a long-range navigation, independently of GNSS information. The results in a real-time simulation demonstrated the effectiveness of the integration between the UAV control and the computer vision, performing a successful navigation of 20.7 km long at 400 meters high, where the system has been able to estimate the coordinates during the whole route, with an average error lower than 6 meters.


title: Probabilistic video stabilization using Kalman filtering and mosaicing

abstract: The removal of unwanted, parasitic vibrations in a video sequence induced by camera motion is an essential part of video acquisition in industrial, military and consumer applications. In this paper, we present a new image processing method to remove such vibrations and reconstruct a video sequence void of sudden camera movements. Our approach to separating unwanted vibrations from intentional camera motion is based on a probabilistic estimation framework. We treat estimated parameters of interframe camera motion as noisy observations of the intentional camera motion parameters. We construct a physics-based state-space model of these interframe motion parameters and use recursive Kalman filtering to perform stabilized camera position estimation. A six-parameter affine model is used to describe the interframe transformation, allowing quite accurate description of typical scene changes due to camera motion. The model parameters are estimated using a p-norm-based multi-resolution approach. This approach is robust to model mismatch and to object motion within the scene (which are treated as outliers). We use mosaicking in order to reconstruct undefined areas that result from motion compensation applied to each video frame. Registration between distant frames is performed efficiently by cascading interframe affine transformation parameters. We compare our method' s performance with that of a commercial product on real-life video sequences, and show a significant improvement in stabilization quality for our method.


title: Radar cross section (RCS) modeling and simulation, part 1: a tutorial review of definitions, strategies, and canonical examples

abstract: Modeling and simulation strategies for radar cross section (RCS) prediction are reviewed, and a novel FDTD-based virtual RCS prediction tool is introduced in this two-part paper. Part 1 is reserved for a tutorial review. Concepts and definitions related to RCS modeling are outlined. Analytical approaches, i.e., high-frequency asymptotics (HFA), as well as powerful time- and frequency-domain numerical methods, are given. Canonical examples using the finite-difference time-domain (FDTD) Method, the method of moments (MoM), and physical optics (PO) are presented.


title: Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space

abstract: When computing alignments of DNA sequences to a large genome, a key element in achieving high processing throughput is to prioritize locations in the genome where high-scoring mappings might be expected. We formulated this task as a series of list-processing operations that can be efficiently performed on graphics processing unit (GPU) hardware.We followed this approach in implementing a read aligner called Arioc that uses GPU-based parallel sort and reduction techniques to identify high-priority locations where potential alignments may be found. We then carried out a read-by-read comparison of Arioc’s reported alignments with the alignments found by several leading read aligners. With simulated reads, Arioc has comparable or better accuracy than the other read aligners we tested. With human sequencing reads, Arioc demonstrates significantly greater throughput than the other aligners we evaluated across a wide range of sensitivity settings. The Arioc software is available at https://github.com/RWilton/Arioc. It is released under a BSD open-source license.


title: Enabling Low-Latency Applications in Fog-Radio Access Networks

abstract: The ultra low-latency operations of communications and computing enable many potential IoT applications, and thus have gained widespread attention recently. Existing mobile devices and telecommunication systems may not be able to provide the highly desired low-latency computing and communications services. To meet the needs of those applications, we introduce the Fog-Radio Access Network (F-RAN) architecture, which brings the efficient computing capability of the cloud to the edge of the network. By distributing computing-intensive tasks to multiple F-RAN nodes, F-RAN has the potential to meet the requirements of those ultra low-latency applications. In this article, we first introduce the F-RAN and its rationale in serving ultra low-latency applications. Then we discuss the need for a service framework for F-RAN to cope with the complex tradeoff among performance, computing cost, and communication cost. Finally, we illustrate the mobile AR service as an exemplary scenario to provide insights for the design of the framework. Examples and numerical results show that ultra low-latency services can be achieved by the F-RAN by properly handling the tradeoff.


title: CrescendoNet: A New Deep Convolutional Neural Network with Ensemble Behavior

abstract: We introduce a new deep convolutional neural network, CrescendoNet, by stacking simple building blocks without residual connections. Each Crescendo block contains independent convolution paths with increased depths. The numbers of convolution layers and parameters are only increased linearly in Crescendo blocks. In experiments, CrescendoNet with only 15 layers outperforms almost all networks without residual connections on benchmark datasets, CIFAR10, CIFAR100, and SVHN. Given sufficient amount of data as in SVHN dataset, CrescendoNet with 15 layers and 4.1M parameters can match the performance of DenseNet-BC with 250 layers and 15.3M parameters. CrescendoNet provides a new way to construct high performance deep convolutional neural networks with simple network architecture. Moreover, by investigating a various combination of subnetworks in CrescendoNet, we note that the high performance of CrescendoNet may come from its implicit ensemble behavior, which gives CrescendoNet an anytime classification property. Furthermore, the independence between paths in CrescendoNet allows us to introduce a new path-wise training procedure, which can reduce the memory needed for training.


title: Hydroxychloroquine and lichen planopilaris: Efficacy and introduction of Lichen Planopilaris Activity Index scoring system

abstract: Background Lichen planopilaris (LPP) and its variant frontal fibrosing alopecia (FFA) are primary lymphocytic cicatricial alopecias for which there is no evidence-based therapy. Objective We assessed the efficacy of hydroxychloroquine in active LPP and FFA using the LPP Activity Index (LPPAI), a numeric score that allows quantification of the symptoms and signs of the condition for statistical comparison. In addition, we determined with the LPPAI if any improvement (reduction) in the numeric score pretreatment and posttreatment reached statistical significance. Methods This was a retrospective, single-center chart review of 40 adult patients with LPP, FFA, or both who were treated with hydroxychloroquine for up to 12 months from 2004 to 2007 at the University of California, San Francisco Hair Center. Symptoms, signs, activity, and spreading were scored at each visit in the standardized cicatricial alopecia flow chart. A numeric score was assigned to these markers of disease activity and a numeric score was calculated at each visit. Results There was significant reduction ( P Limitations Retrospective analysis and uncontrolled study are limitations. Conclusions Hydroxychloroquine is effective in decreasing symptoms and signs in LPP and FFA as shown by significant reduction in the LPPAI in 69% and 83% of patients after 6 and 12 months of treatment, respectively.


title: On the use of visualization to support awareness of human activities in software development: a survey and a framework

abstract: This paper proposes a framework for describing, comparing and understanding visualization tools that provide awareness of human activities in software development. The framework has several purposes -- it can act as a formative evaluation mechanism for tool designers; as an assessment tool for potential tool users; and as a comparison tool so that tool researchers can compare and understand the differences between various tools and identify potential new research areas. We use this framework to structure a survey of visualization tools for activity awareness in software development. Based on this survey we suggest directions for future research.


title: A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization.

abstract: Text categorization has become a key research field in the NLP community. However, most works in this area are focused on Western languages ignoring other Semitic languages like Arabic. These languages are of immense political and social importance necessitating robust categorization techniques. In this paper, we present a novel three-stage technique to efficiently classify Arabic documents into different categories based on the words they contain. We leverage the significance of root-words in Arabic and incorporate a combination of Markov clustering and Deep Belief Networks to classify Arabic words into separate groups (clusters). Our approach is tested on two public datasets giving a F-Measure of 91.02%.


title: Multi-dimensional Workload Analysis and Synthesis for Modern Storage Systems

abstract: Modern computer systems produce and process an overwhelming amount of data at an increasing rates. The performance of storage hardware components, however, cannot keep up with the required speed at a practical cost. To mitigate this discrepancy, storage vendors incorporate many workload-driven optimizations in their products. Emerging applications cause workload patterns to change rapidly and significantly. One of the prominent examples is a rapid shift towards virtualized environments. Virtual machines mix I/O streams from different applications and perturb applications' access patterns. In addition, modern users demand more convenience features, such as deduplication, snapshotting, and encryption. Stringent performance requirements, changing I/O patterns, and the growing feature list increase the complexity of storage systems. The complexity of design, in turn, makes the evaluation of the storage systems a difficult task. Storage community needs practical evaluation tools and techniques to resolve this task timely and efficiently. This thesis first explores the complexity of evaluating storage systems. Second, the thesis proposes a Multi-Dimensional Histogram (MDH) workload analysis as a basis for designing a variety of evaluation tools. I/O traces are good sources of information about real-world workloads but are inflexible in representing more than the exact system conditions at the point the traces were captured. We demonstrate how MDH techniques can accurately convert I/O traces to workload models. Historically, most I/O optimizations focused on the metadata: e.g., I/O access patterns, arrival times, read/write sizes. Increasingly, storage systems must also consider the data and not just the metadata. For example, deduplication systems eliminate duplicates in the data to increase logical storage capacity. We use MDH techniques to generate realistic datasets for deduplication systems. The shift from physical to virtual clients drastically changes the I/O workloads seen by Network Attached Storage (NAS). Using MDH techniques we study workload changes caused by virtualization and synthesize a set of versatile NAS benchmarks. It is our thesis that MDH techniques are powerful for both workload analysis and synthesis. MDH analysis bridges the gap between the complexity of storage systems and the availability of practical evaluations tools.


Tagging Patient Notes With ICD-9 Codes


title: Translating research findings to clinical nursing practice

abstract: AIMS AND OBJECTIVES ::: To describe the importance of, and methods for, successfully conducting and translating research into clinical practice. ::: ::: ::: BACKGROUND ::: There is universal acknowledgement that the clinical care provided to individuals should be informed on the best available evidence. Knowledge and evidence derived from robust scholarly methods should drive our clinical practice, decisions and change to improve the way we deliver care. Translating research evidence to clinical practice is essential to safe, transparent, effective and efficient healthcare provision and meeting the expectations of patients, families and society. Despite its importance, translating research into clinical practice is challenging. There are more nurses in the frontline of health care than any other healthcare profession. As such, nurse-led research is increasingly recognised as a critical pathway to practical and effective ways of improving patient outcomes. However, there are well-established barriers to the conduct and translation of research evidence into practice. ::: ::: ::: DESIGN ::: This clinical practice discussion paper interprets the knowledge translation literature for clinicians interested in translating research into practice. ::: ::: ::: METHODS ::: This paper is informed by the scientific literature around knowledge translation, implementation science and clinician behaviour change, and presented from the nurse clinician perspective. We provide practical, evidence-informed suggestions to overcome the barriers and facilitate enablers of knowledge translation. Examples of nurse-led research incorporating the principles of knowledge translation in their study design that have resulted in improvements in patient outcomes are presented in conjunction with supporting evidence. ::: ::: ::: CONCLUSIONS ::: Translation should be considered in research design, including the end users and an evaluation of the research implementation. The success of research implementation in health care is dependent on clinician/consumer behaviour change and it is critical that implementation strategy includes this. ::: ::: ::: RELEVANCE TO PRACTICE ::: Translating best research evidence can make for a more transparent and sustainable healthcare service, to which nurses are central.


title: A very high energy-efficiency switching technique for SAR ADCs

abstract: A high energy-efficiency capacitor switching scheme for a successive approximation register (SAR) analog-to-digital converter (ADC) is presented in this paper. The proposed switching technique achieves a zero energy dissipation in the first 2 comparison cycles and a 4X reduction in total capacitance used in the digital-to-analog converter (DAC), i.e., for the same total capacitance, the proposed technique can produce 2 additional bits of resolution than a conventional SAR. The proposed method can achieve 95% savings in switching energy over a conventional SAR. The result has been verified through behavioral simulations.


title: A filter-based feature selection model for anomaly-based intrusion detection systems

abstract: Feature selection is an important factor in modeling anomaly-based intrusion detection systems. An irrelevant feature can result in overfitting and affect the modeling power of classification algorithms. The objective of feature selection is to remove irrelevant and redundant attributes from the dataset to improve the predictive power of a classification algorithm. In this paper, we introduce a filter-based feature selection model for anomaly-based intrusion detection systems. The proposed model evaluates the features based on information gain by considering consistency, dependency, information, and distance of each feature. The experimental results show that our proposed model has a key effect in reducing computational and time complexity. The accuracy of the proposed model was measured as 99.70 % and 99.90% for the ISCX and NSL-KDD datasets respectively.


title: Parsing Videos of Actions with Segmental Grammars

abstract: Real-world videos of human activities exhibit temporal structure at various scales, long videos are typically composed out of multiple action instances, where each instance is itself composed of sub-actions with variable durations and orderings. Temporal grammars can presumably model such hierarchical structure, but are computationally difficult to apply for long video streams. We describe simple grammars that capture hierarchical temporal structure while admitting inference with a finite-state-machine. This makes parsing linear time, constant storage, and naturally online. We train grammar parameters using a latent structural SVM, where latent subactions are learned automatically. We illustrate the effectiveness of our approach over common baselines on a new half-million frame dataset of continuous YouTube videos.


title: Efficient semantic image segmentation with multi-class ranking prior

abstract: Semantic image segmentation is of fundamental importance in a wide variety of computer vision tasks, such as scene understanding, robot navigation and image retrieval, which aims to simultaneously decompose an image into semantically consistent regions. Most of existing works addressed it as structured prediction problem by combining contextual information with low-level cues based on conditional random fields (CRFs), which are often learned by heuristic search based on maximum likelihood estimation. In this paper, we use maximum margin based structural support vector machine (S-SVM) model to combine multiple levels of cues to attenuate the ambiguity of appearance similarity and propose a novel multi-class ranking based global constraint to confine the object classes to be considered when labeling regions within an image. Compared with existing global cues, our method is more balanced between expressive power for heterogeneous regions and the efficiency of searching exponential space of possible label combinations. We then introduce inter-class co-occurrence statistics as pairwise constraints and combine them with the prediction from local and global cues based on S-SVMs framework. This enables the joint inference of labeling within an image for better consistency. We evaluate our algorithm on two challenging datasets which are widely used for semantic segmentation evaluation: MSRC-21 dataset and Stanford Background dataset and experimental results show that we obtain high competitive performance compared with state-of-the-art methods, despite that our model is much simpler and efficient.


title: Improved Cluster Head Selection for Efficient Data Aggregation in Sensor Networks

abstract: Large-scale Wireless Sensor Networks (WSN) is the focus of recent research and development efforts. Due to their benefits in monitoring physical environments, WSN find diverse applications from military usage to agriculture and scientific works. To maximize WSN's network life, data transfer paths are selected so that total energy consumed on the path is minimal. To ensure high scalability and improved data aggregation, sensor nodes are grouped into disjoint, non-overlapping subsets known as clusters. This study proposes improved Cluster Head (CH) selection for efficient sensor networks' data aggregation. The suggested hybrid algorithm is based on Bacterial Foraging Optimization (BFO) and Gravitational Search Algorithm (GSA). The proposed hybrid BFO is incorporated in Lower Energy Adaptive Clustering Hierarchy (LEACH).


title: 3D scene segmentation for autonomous robot grasping

abstract: We present an algorithm to segment an unstructured table top scene. Operating on the depth image of a Kinect camera, the algorithm robustly separates objects of previously unknown shape in cluttered scenes of stacked and partially occluded objects. The model-free algorithm finds smooth surface patches which are subsequently combined to form object hypotheses. We evaluate the algorithm regarding its robustness and real-time capabilities and discuss its advantages compared to existing approaches as well as its weak spots to be addressed in future work. We also report on an autonomous grasping experiment with the Shadow Robot Hand which employs the estimated shape and pose of segmented objects.


title: An efficient elastic distributed SDN controller for follow-me cloud

abstract: Follow Me Cloud (FMC) concept has emerged as a promising technology that allows seamless migration of services according to the corresponding users' mobility. Meanwhile, Software Defined Networking (SDN) is a new paradigm that permits to decouple the control and data planes of traditional network, and provides programmability and flexibility, allowing the network to dynamically adapt to changing traffic patterns and user demands. While the SDN implementations are gaining momentum, the control plane, however, is still suffering from scalability and performance concerns for a very large network. In this paper, we address these scalability and performance issues by introducing a novel SDN/OpenFlow-based architecture and control plane framework tailored for mobile cloud computing systems and more specifically for FMC-based systems where mobile nodes and network services are subject to constraints of movements and migrations. Contrary to centralized approach with single SDN controller, our approach permits to distribute the SDN/OpenFlow control plane on a two-level hierarchical architecture: a first level with a global controller G-FMCC, and second level with several local controllers L-FMCC(s). Thanks to our control plane framework and Network Function Virtual-ization concept (NFV), the L-FMCC(s) are deployed on-demand, where and when needed, depending on the global system load. Results obtained via analysis show that our solution ensures more efficient management of control plane, performances maintaining and network resources preservation.


title: Robust proprioceptive grasping with a soft robot hand

abstract: This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements along with a combined system which autonomously performs grasps. A highly compliant soft hand allows for intrinsic robustness to grasping uncertainties; the addition of internal sensing allows the configuration of the hand and object to be detected. The finger module includes resistive force sensors on the fingertips for contact detection and resistive bend sensors for measuring the curvature profile of the finger. The curvature sensors can be used to estimate the contact geometry and thus to distinguish between a set of grasped objects. With one data point from each finger, the object grasped by the hand can be identified. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps. A closed loop system uses a camera to detect approximate object locations. Compliance in the soft hand handles that uncertainty in addition to geometric uncertainty in the shape of the object.


title: Epitaxial growth, dielectric response, and microstructure of compositionally graded (Ba,Sr)TiO3 thin films grown on (100) MgO substrates by pulsed laser deposition

abstract: Compositionally graded (Ba1−xSrx)TiO3 (BST) thin films (with 0.0 x 0.25) were grown by pulsed laser deposition on the (100)MgO single-crystal substrates covered with a conductive La0.5Sr0.5CoO3 (LSCO) layer as a bottom electrode. Their epitaxial growth, dielectric response, and microstructure were characterized. The epitaxial relationships between the BST, LSCO, and MgO can be determined as [001]BST//[001]LSCO//[001]MgO and (100)BST//(100)LSCO//(100)MgO, from the x-ray diffraction (rocking curve, scans) and electron-diffraction patterns. Dielectric data showed that the room temperature values of the dielectric constant and dielectric loss of the graded BST films were 630 and 0.017 at 100 kHz, respectively. Cross-sectional transmission electron microscopy (TEM) images reveal that both the BST films and the LSCO bottom electrode grow with a columnar structure, and they have flat interfaces and overall uniform thickness across the entire specimen. Cross-sectional high-resolution TEM images reveal that at the LSCO/MgO(100) interface, an interfacial reaction is not seen, whereas edge-type interfacial dislocations with their extra half-planes residing in the LSCO side are observed with an average interval of 2.20 nm, close to the theoretical value of 2.15 nm. At/near the LSCO/BST interface, the graded BST films grow perfectly and coherently on the LSCO lattice because they have the same type of crystal structure and almost same lattice constants, and no interfacial dislocations are observed. Planar TEM images show that the graded films exhibit granular and/or polyhedral morphologies with an average grain size of 50 nm, and the aligned rectangular-shaped voids were also observed. High-resolution TEM images show that the length sizes of voids vary from 8 to 15 nm, and with width of 5 to 10 nm along the 〈001〉 direction in the (100) plane.


title: Overweight/obesity among school aged children in Bahir Dar City: cross sectional study

abstract: Developing countries, including Ethiopia are experiencing a double burden of malnutrition. There is limited information about prevalence of overweight/obesity among school aged children in Ethiopia particularly in Bahir Dar city. Hence this study aimed to assess the prevalence of overweight/obesity and associated factors among school children aged 6–12 years at Bahir Dar City, Northwest Ethiopia. A school based cross-sectional study was carried out. A total of 634 children were included in the study. Multi stage systematic random sampling technique was used. A multivariable logistic regression analysis was used to identify factors associated with overweight/obesity. The association between dependent and independent variables were assessed using odds ratio with 95% confidence interval and p-value ≤0.05 was considered statistically significant. The overall prevalence of overweight and/or obesity was 11.9% (95% CI, 9.3, 14.4) (out of which 8.8% were overweight and 3.1% were obese). Higher wealth status[adjusted OR = 3.14, 95% CI:1.17, 8.46], being a private school student [AOR = 2.21, 95% CI:1.09, 4.49], use of transportation to and from school [AOR = 2.53, 95% CI: 1.26,5.06], fast food intake [AOR = 3.88, 95% CI: 1.42,10.55], lack of moderate physical activity [AOR = 2.87, 95% CI: 1.21,6.82], low intake of fruit and vegetable [AOR = 6.45, 95% CI:3.19,13.06] were significant factors associated with overweight and obesity. This study revealed that prevalence of overweight/obesity among school aged children in Bahir Dar city is high. Thus, promoting healthy dietary habit, particularly improving fruit and vegetable intake is essential to reduce the burden of overweight and obesity. Furthermore, it is important to strengthen nutrition education about avoiding junk food consumption and encouraging regular physical activity.


An Evaluation Exercise for Word Alignment


title: Polarization-Adjustable Planar Array Antenna With SIW-Fed High-Order-Mode Microstrip Patch

abstract: This communication presents a high-order-mode microstrip patch antenna driven by substrate integrated waveguide (SIW) for millimeter-wave polarization-adjustable applications. Different polarization status can be yielded from variable excited signals, including vertical and horizontal linear, right-handed and left-handed circular polarizations. In the patch antenna configuration, an enlarged aperture coming from high-order mode makes it easy to be fed by orthogonal SIWs for dual polarizations. Loaded slots on the patch significantly improve the radiation patterns. The impedance bandwidth with dual resonances is originated from the SIW slot and patch modes. A Ka-band $2\times 2$ antenna array integrated with single-layer SIW feeding network is designed and tested to illustrate this technique. The results manifest that the planar integrated $2 \times 2$ antenna array features adjustable polarization with considerable gain. Furthermore, a two-layer scheme of orthogonal SIW feeding is developed, which would provide a more flexible topology for the scale-expandable array antenna with adjustable polarizations.