title: Comparing White-Box and Black-Box Test Prioritization

text: Although white-box regression test prioritization has been well-studied, the more recently introduced black-box prioritization approaches have neither been compared against each other nor against more well-established white-box techniques. We present a comprehensive experimental comparison of several test prioritization techniques, including well-established white-box strategies and more recently introduced black-box approaches. We found that Combinatorial Interaction Testing and diversity-based techniques (Input Model Diversity and Input Test Set Diameter) perform best among the black-box approaches. Perhaps surprisingly, we found little difference between black-box and white-box performance (at most 4% fault detection rate difference). We also found the overlap between black- and white-box faults to be high: the first 10% of the prioritized test suites already agree on at least 60% of the faults found. These are positive findings for practicing regression testers who may not have source code available, thereby making white-box techniques inapplicable. We also found evidence that both black-box and white-box prioritization remain robust over multiple system releases.


title: 4G- A NEW ERA IN WIRELESS TELECOMMUNICATION

text: 4G – “connect anytime, anywhere, anyhow” promising ubiquitous network access at high speed to the end users, has been a topic of great interest especially for the wireless telecom industry. 4G seems to be the solution for the growing user requirements of wireless broadband access and the limitations of the existing wireless communication system. The purpose of this paper is to provide an overview of the different aspects of 4G which includes its features, its proposed architecture and key technological enablers. It also elaborates on the roadblocks in its implementations. A special consideration has been given to the security concerns of 4G by discussing a security threat analysis model proposed by International Telecommunication Union (ITU). By applying this model, a detailed analysis of threats to 4G and the corresponding measures to counter them can be performed.


title: Collocated photo sharing, story-telling, and the performance of self

text: This article reports empirical findings from four inter-related studies, with an emphasis on collocated sharing. Collocated sharing remains important, using both traditional and emerging image-related technologies. Co-present viewing is a dynamic, improvisational construction of a contingent, situated interaction between story-teller and audience. The concept of performance, as articulated differently by Erving Goffman and Judith Butler, is useful understand the enduring importance of co-present sharing of photos and the importance of oral narratives around images in enacting identity and relationships. Finally, we suggest some implications for both HCI research and the design of image-related technologies. & 2009 Elsevier Ltd. All rights reserved.


title: DoS and DDoS in Named Data Networking

text: With the growing realization that current Internet protocols are reaching the limits of their senescence, several on-going research efforts aim to design potential next-generation Internet architectures. Although they vary in maturity and scope, in order to avoid past pitfalls, these efforts seek to treat security and privacy as fundamental requirements. Resilience to Denial-of-Service (DoS) attacks that plague today's Internet is a major issue for any new architecture and deserves full attention. In this paper, we focus on DoS in Named Data Networking (NDN) - a specific candidate for next-generation Internet architecture designs. By naming data instead of its locations, NDN transforms data into a first-class entity and makes itself an attractive and viable approach to meet the needs for many current and emerging applications. It also incorporates some basic security features that mitigate classes of attacks that are commonly seen today. However, NDN's resilience to DoS attacks has not been analyzed to-date. This paper represents a first step towards assessment and possible mitigation of DoS in NDN. After identifying and analyzing several new types of attacks, it investigates their variations, effects and counter-measures. This paper also sheds some light on the debate about relative virtues of self-certifying, as opposed to human-readable, names in the context of content-centric networking.


title: Introducing Serendipity in a Content-Based Recommender System

text: Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/ searched/ visited item and all the items stored in a repository. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen. This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions.


title: Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System

text: Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and empirical evaluation of NJFun, an experimental spoken dialogue system that provides users with access to information about fun things to do in New Jersey. Our results show that by optimizing its performance via reinforcement learning, NJFun measurably improves system performance.


title: ARROW: GenerAting SignatuRes to Detect DRive-By DOWnloads

text: A drive-by download attack occurs when a user visits a webpage which attempts to automatically download malware without the user's consent. Attackers sometimes use a malware distribution network (MDN) to manage a large number of malicious webpages, exploits, and malware executables. In this paper, we provide a new method to determine these MDNs from the secondary URLs and redirect chains recorded by a high-interaction client honeypot. In addition, we propose a novel drive-by download detection method. Instead of depending on the malicious content used by previous methods, our algorithm first identifies and then leverages the URLs of the MDN's central servers, where a central server is a common server shared by a large percentage of the drive-by download attacks in the same MDN. A set of regular expression-based signatures are then generated based on the URLs of each central server. This method allows additional malicious webpages to be identified which launched but failed to execute a successful drive-by download attack. The new drive-by detection system named ARROW has been implemented, and we provide a large-scale evaluation on the output of a production drive-by detection system. The experimental results demonstrate the effectiveness of our method, where the detection coverage has been boosted by 96% with an extremely low false positive rate.


title: A theorem on polygon cutting with applications

text: Let P be a simple polygon with N vertices, each being assigned a weight ∈ {0,1}, and let C, the weight of P, be the added weight of all vertices. We prove that it is possible, in O(N) time, to find two vertices a,b in P, such that the segment ab lies entirely inside the polygon P and partitions it into two polygons, each with a weight not exceeding 2C/3. This computation assumes that all the vertices have been sorted along some axis, which can be done in O(Nlog N) time. We use this result to derive a number of efficient divide-and-conquer algorithms for: 1. Triangulating an N-gon in O(Nlog N) time. 2. Decomposing an N-gon into (few) convex pieces in O(Nlog N) time. 3. Given an O(Nlog N) preprocessing, computing the shortest distance between two arbitrary points inside an N-gon (i.e., the internal distance), in O(N) time. 4. Computing the longest internal path in an N-gon in O(N2) time. In all cases, the algorithms achieve significant improvements over previously known methods, either by displaying better performance or by gaining in simplicity. In particular, the best algorithms for Problems 2,3,4, known so far, performed respectively in O(N2), O(N2), and O(N4) time.


title: A meta-analysis of continuing medical education effectiveness.

text:
INTRODUCTION
We undertook a meta-analysis of the Continuing Medical Education (CME) outcome literature to examine the effect of moderator variables on physician knowledge, performance, and patient outcomes.

METHODS
A literature search of MEDLINE and ERIC was conducted for randomized controlled trials and experimental design studies of CME outcomes in which physicians were a major group. CME moderator variables included the types of intervention, the types and number of participants, time, and the number of intervention sessions held over time.

RESULTS
Thirty-one studies met the eligibility criteria, generating 61 interventions. The overall sample-size weighted effect size for all 61 interventions was r = 0.28 (0.18). The analysis of CME moderator variables showed that active and mixed methods had medium effect sizes (r = 0.33 [0.33], r = 0.33 [0.26], respectively), and passive methods had a small effect size (r = 0.20 [0.16], confidence interval 0.15, 0.26). There was a positive correlation between the effect size and the length of the interventions (r = 0.33) and between multiple interventions over time (r = 0.36). There was a negative correlation between the effect size and programs that involved multiple disciplines (r = -0.18) and the number of participants (r = -0.13). The correlation between the effect size and the length of time for outcome assessment was negative (r = -0.31).

DISCUSSION
The meta-analysis suggests that the effect size of CME on physician knowledge is a medium one; however, the effect size is small for physician performance and patient outcome. The examination of moderator variables shows there is a larger effect size when the interventions are interactive, use multiple methods, and are designed for a small group of physicians from a single discipline.


title: Prediction of Yelp Review Star Rating using Sentiment Analysis

text: Yelp aims to help people find great local businesses, e.g. restaurants. Automated software is currently used to recommend the most helpful and reliable reviews for the Yelp community, based on various measures of quality, reliability, and activity. However, this is not tailored to each customer. Our goal in this project is to apply machine learning to predict a customer’s star rating of a restaurant based on his/her reviews, as well as other customers’ reviews/ratings, to recommend other restaurants to the customer, as shown in Figure 1.


title: An efficient algorithm for the "optimal" stable marriage

text: In an instance of size n of the stable marriage problem, each of n men and n women ranks the members of the opposite sex in order of preference. A stable matching is a complete matching of men and women such that no man and woman who are not partners both prefer each other to their actual partners under the matching. It is well known [2] that at least one stable matching exists for every stable marriage instance. However, the classical Gale-Shapley algorithm produces a marriage that greatly favors the men at the expense of the women, or vice versa. The problem arises of finding a stable matching that is optimal under some more equitable or egalitarian criterion of optimality. This problem was posed by Knuth [6] and has remained unsolved for some time. Here, the objective of maximizing the average (or, equivalently, the total) “satisfaction” of all people is used. This objective is achieved when a person's satisfaction is measured by the position of his/her partner in his/her preference list. By exploiting the structure of the set of all stable matchings, and using graph-theoretic methods, an O(n4) algorithm for this problem is derived.


Primary, Secondary, and Meta-Analysis of Research


title: Efficient AUV navigation fusing acoustic ranging and side-scan sonar

text: This paper presents an on-line nonlinear least squares algorithm for multi-sensor autonomous underwater vehicle (AUV) navigation. The approach integrates the global constraints of range to and GPS position of a surface vehicle or buoy communicated via acoustic modems and relative pose constraints arising from targets detected in side-scan sonar images. The approach utilizes an efficient optimization algorithm, iSAM, which allows for consistent on-line estimation of the entire set of trajectory constraints. The optimized trajectory can then be used to more accurately navigate the AUV, to extend mission duration, and to avoid GPS surfacing. As iSAM provides efficient access to the marginal covariances of previously observed features, automatic data association is greatly simplified — particularly in sparse marine environments. A key feature of our approach is its intended scalability to single surface sensor (a vehicle or buoy) broadcasting its GPS position and simultaneous one-way travel time range (OWTT) to multiple AUVs. We discuss why our approach is scalable as well as robust to modem transmission failure. Results are provided for an ocean experiment using a Hydroid REMUS 100 AUV co-operating with one of two craft: an autonomous surface vehicle (ASV) and a manned support vessel. During these experiments the ranging portion of the algorithm ran online on-board the AUV. Extension of the paradigm to multiple missions via the optimization of successive survey missions (and the resultant sonar mosaics) is also demonstrated.


title: Understanding the perceived logic of care by vaccine-hesitant and vaccine-refusing parents: A qualitative study in Australia

text: In terms of public health, childhood vaccination programs have benefits that far outweigh risks. However, some parents decide not to vaccinate their children. This paper explores the ways in which such parents talked about the perceived risks and benefits incurred by vaccinating (or not vaccinating) their children. Between 2013-2016 we undertook 29 in-depth interviews with non-vaccinating and/or 'vaccine hesitant' parents in Australia. Interviews were conducted in an open and non-judgmental manner, akin to empathic neutrality. Interviews focused on parents talking about the factors that shaped their decisions not to (or partially) vaccinate their children. All interviews were transcribed and analysed using both inductive and deductive processes. The main themes focus on parental perceptions of: 1. their capacity to reason; 2. their rejection of Western medical epistemology; and 3. their participation in labour intensive parenting practices (which we term salutogenic parenting). Parents engaged in an ongoing search for information about how best to parent their children (capacity to reason), which for many led to questioning/distrust of traditional scientific knowledge (rejection of Western medical epistemology). Salutogenic parenting spontaneously arose in interviews, whereby parents practised health promoting activities which they saw as boosting the natural immunity of their children and protecting them from illness (reducing or negating the perceived need for vaccinations). Salutogenic parenting practices included breastfeeding, eating organic and/or home-grown food, cooking from scratch to reduce preservative consumption and reducing exposure to toxins. We interpret our data as a 'logic of care', which is seen by parents as internally consistent, logically inter-related and inter-dependent. Whilst not necessarily sharing the parents' reasoning, we argue that an understanding of their attitudes towards health and well-being is imperative for any efforts to engage with their vaccine refusal at a policy level.


title: Computational Thinking in Practice : How STEM Professionals Use CT in Their Work

text: The goal of this study is to bring current computational thinking in STEM educational efforts in line with the increasingly computational nature of STEM research practices. We conducted interviews with STEM practitioners in various fields to understand the nature of CT as it happens in authentic research settings and to revisit a first iteration of our definition of CT in form of a taxonomy. This exploration gives us insight into how scientists use computers in their work and help us identify what practices are important to include in high school STEM learning contexts. Our findings will inform the design of classroom activities to better prepare today’s students for the modern STEM landscape that awaits them.


title: Wavelet filter evaluation for image compression

text: Choice of filter bank in wavelet compression is a critical issue that affects image quality as well as system design. Although regularity is sometimes used in filter evaluation, its success at predicting compression performance is only partial. A more reliable evaluation can be obtained by considering an L-level synthesis/analysis system as a single-input, single-output, linear shift-variant system with a response that varies according to the input location module (2(L),2(L)). By characterizing a filter bank according to its impulse response and step response in addition to regularity, we obtain reliable and relevant (for image coding) filter evaluation metrics. Using this approach, we have evaluated all possible reasonably short (less than 36 taps in the synthesis/analysis pair) minimum-order biorthogonal wavelet filter banks. Of this group of over 4300 candidate filter banks, we have selected and present here the filters best suited to image compression. While some of these filters have been published previously, others are new and have properties that make them attractive in system design.


title: Multimodal recognition of personality traits in social interactions

text: This paper targets the automatic detection of personality traits in a meeting environment by means of audio and visual features; information about the relational context is captured by means of acoustic features designed to that purpose. Two personality traits are considered: Extraversion (from the Big Five) and the Locus of Control. The classification task is applied to thin slices of behaviour, in the form of 1-minute sequences. SVM were used to test the performances of several training and testing instance setups, including a restricted set of audio features obtained through feature selection. The outcomes improve considerably over existing results, provide evidence about the feasibility of the multimodal analysis of personality, the role of social context, and pave the way to further studies addressing different features setups and/or targeting different personality traits.


title: Design Criteria for Electric Vehicle Fast Charge Infrastructure Based on Flemish Mobility Behavior

text: This paper studies the technical design criteria for fast charge infrastructure, covering the mobility needs. The infrastructure supplements the residential and public slow charging infrastructure. Two models are designed. The first determines the charging demand, based on current mobility behavior in Flanders. The second model simulates a charge infrastructure that meets the resulting fast charge demand. The energy management is performed by a rule-based control algorithm, that directs the power flows between the fast chargers, the energy storage system, the grid connection, and the photovoltaic installation. There is a clear trade-off between the size of the energy storage system and the power rating of the grid connection. Finally, the simulations indicate that 99.7% of the vehicles visiting the fast charge infrastructure can start charging within 10 minutes with a configuration limited to 5 charging spots, instead of 9 spots when drivers are not willing to wait.


title: A Field Study of Run-Time Location Access Disclosures on Android Smartphones

text: Smartphone users are increasingly using apps that can access their location. Often these accesses can be without users knowledge and consent. For example, recent research has shown that installation-time capability disclosures are ineffective in informing people about their apps’ location access. In this paper, we present a four-week field study (N=22) on run-time location access disclosures. Towards this end, we implemented a novel method to disclose location accesses by location-enabled apps on participants’ smartphones. In particular, the method did not need any changes to participants’ phones beyond installing our study app. We randomly divided our participants to two groups: a Disclosure group (N=13), who received our disclosures and a No Disclosure group (N=9) who received no disclosures from us. Our results confirm that the Android platform’s location access disclosure method does not inform participants effectively. Almost all participants pointed out that their location was accessed by several apps they would have not expected to access their location. Further, several apps accessed their location more frequently than they expected. We conclude that our participants appreciated the transparency brought by our run-time disclosures and that because of the disclosures most of them had taken actions to manage their apps’ location access.


title: Morphological and LSU rDNA sequence variation within the Gonyaulax spinifera-Spiniferites group (Dinophyceae) and proposal of G-elongata comb. nov and G-membranacea comb. nov

text: Cultures were established from cysts of the cyst-based taxa Spiniferites elongatus and S. membranaceus. Motile cells and cysts from both cultures and sediment samples were examined using light and scanning electron microscopy. The cyst theca relationship was established for S. elongatus. The motile cells have the tabulation pattern 2 pr, 4', 6", 6c, � 4s, 6"', I p, Inn, but they remain unattributable to previously described Gonyaulax species. There was large variation in process length and process morphology in cysts from both cultures and wild samples and there was variation in ornamentation and in the development of spines and flanges in motile cells. A new combination, G. elongata (Reid) Ellegaard et al. comb. nov. is proposed, following new rules of the International Code of Botanical Nomenclature that give genera based on extant forms priority over genera based on fossil forms. Extreme morphological variation in the cyst and motile stages of S. membranaceus is described and this species is also transferred to the genus Gonyaulax, as G. membranacea (Rossignol) Ellegaard et al. comb. nov. Approximately 1500 bp of large subunit (LSU) rDNA were determined for these two species and for G. baltica, G. cf. spinifera ( = S. ramosus) and G. digitalis ( = Bitectatodinium tepikiense). LSU rDNA showed sequence divergences similar to those estimated between species in other genera within the Gonyaulacales; a phylogeny for the Gonyaulacales was established, including novel LSU rONA sequences for Alexandrium margalefii, A. pseudogonyaulax and Pyrodinium bahamense var. compressum. Our results show that motile stages obtained from the germination of several cysts of the 'fossil-based' Spiniferites and B. tepikiense, which were previously attributed to 'Gonyaulax spinifera group undifferentiated', belong to distinct species of the genus Gonyaulax. These species show small morphological differences in the motile stage but relatively high sequence divergence. Moreover, this group of species is monophyletic, supported by bootstrap values of 100% in parsimony and maximum likelihood analyses.


title: BUSINESS-TO-BUSINESS E-BUSINESS MODELS : CLASSIFICATION AND TEXTILE INDUSTRY IMPLICATIONS

text: Since the introduction of the Internet and e-commerce in the mid-1990s, there has been a lot of hype surrounding e-business, the impact that it will have on the way that companies do business, and how it will change the global economy as a whole. Since the crash of the dotcom companies in 2001, there has been much less hype surrounding the use of the Internet for business. There seems to have been a realization that e-business may not be the answer to all of a company’s problems, but can be a great asset in the struggle to increase efficiencies in daily business dealings, and that the Web is primarily a new way of relating to customers and suppliers. This paper categorizes and discusses the different types of business-to-business electronic business models currently being used by businesses and discussed in the academic literature, and shows how these business models are being implemented within the textile industry. This paper is divided into three parts. Part I gives an overview and some important definitions associated with businessto-business e-business, and discusses some characteristics that are unique to doing business on the Internet. Risks and benefits associated with doing business online are also discussed. Part II analyzes the different types of e-business models seen in the academic literature. Based on the analysis of the literature, a taxonomy of e-business models was developed. This new classification system organized e-business models into the following categories: sourcing models, ownership models, service-based models, customer relationship management models, supply chain models, interaction Models and revenue models. Part III reviews how these e-business models are currently being used within the textile industry. A preliminary analysis of 79 textile manufacturing companies was conducted to identify the applications of e-business.


title: Test Results and Torque Improvement of the 50-kW Switched Reluctance Motor Designed for Hybrid Electric Vehicles

text: A switched reluctance motor (SRM) has been developed as one of the possible candidates of rare-earth-free electric motors. A prototype machine has been built and tested. It has competitive dimensions, torque, power, and efficiency with respect to the 50-kW interior permanent magnet synchronous motor employed in the hybrid electric vehicles (Toyota Prius 2003). It is found that competitive power of 50-kW rating and efficiency of 95% are achieved. The prototype motor provided 85% of the target torque. Except the maximum torque, the most speed-torque region is found to be covered by the test SRM. The cause of discrepancy in the measured and calculated torque values is examined. An improved design is attempted, and a new experimental switched reluctance machine is designed and built for testing. The results are given in this paper.


title: Matching Curves to Imprecise Point Sets using Fréchet Distance

text: Let P be a polygonal curve in R of length n, and S be a point-set of size k. The Curve/Point Set Matching problem consists of finding a polygonal curve Q on S such that the Fréchet distance from P is less than a given ε. We consider eight variations of the problem based on the distance metric used and the omittability or repeatability of the points. We provide closure to a recent series of complexity results for the case where S consists of precise points. More importantly, we formulate a more realistic version of the problem that takes into account measurement errors. This new problem is posed as the matching of a given curve to a set of imprecise points. We prove that all three variations of the problem that are in P when S consists of precise points become NP-complete when S consists of imprecise points. We also discuss approximation re-


Common LISP: the language, 2nd Edition


title: A Survey on Multicarrier Communications: Prototype Filters, Lattice Structures, and Implementation Aspects

text: Due to their numerous advantages, communications over multicarrier schemes constitute an appealing approach for broadband wireless systems. Especially, the strong penetration of orthogonal frequency division multiplexing (OFDM) into the communications standards has triggered heavy investigation on multicarrier systems, leading to re-consideration of different approaches as an alternative to OFDM. The goal of the present survey is not only to provide a unified review of waveform design options for multicarrier schemes, but also to pave the way for the evolution of the multicarrier schemes from the current state of the art to future technologies. In particular, a generalized framework on multicarrier schemes is presented, based on what to transmit, i.e., symbols, how to transmit, i.e., filters, and where/when to transmit, i.e., lattice. Capitalizing on this framework, different variations of orthogonal, bi-orthogonal, and non-orthogonal multicarrier schemes are discussed. In addition, filter designs for various multicarrier systems are reviewed considering four different design perspectives: energy concentration, rapid decay, spectrum nulling, and channel/hardware characteristics. Subsequently, evaluation tools which may be used to compare different filters in multicarrier schemes are studied. Finally, multicarrier schemes are evaluated from the perspective of practical implementation aspects, such as lattice adaptation, equalization, synchronization, multiple antennas, and hardware impairments.


title: 5GNOW: non-orthogonal, asynchronous waveforms for future mobile applications

text: This article provides some fundamental indications about wireless communications beyond LTE/LTE-A (5G), representing the key findings of the European research project 5GNOW. We start with identifying the drivers for making the transition to 5G networks. Just to name one, the advent of the Internet of Things and its integration with conventional human-initiated transmissions creates a need for a fundamental system redesign. Then we make clear that the strict paradigm of synchronism and orthogonality as applied in LTE prevents efficiency and scalability. We challenge this paradigm and propose new key PHY layer technology components such as a unified frame structure, multicarrier waveform design including a filtering functionality, sparse signal processing mechanisms, a robustness framework, and transmissions with very short latency. These components enable indeed an efficient and scalable air interface supporting the highly varying set of requirements originating from the 5G drivers.


title: Sparse code multiple access

text: Multicarrier CDMA is a multiplexing approach in which modulated QAM symbols are spread over multiple OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequence allowing us to take advantage of a near optimal ML receiver with practically feasible complexity. In this paper, we propose a new multiple access scheme so called sparse code multiple access (SCMA) which still enjoys the low complexity reception technique but with better performance compared to LDS. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to a multidimensional codeword of an SCMA codebook set. Each layer or user has its dedicated codebook. Shaping gain of a multidimensional constellation is the main source of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. In general, SCMA codebook design is an optimization problem. A systematic sub-optimal approach is proposed here for SCMA codebook design.


title: SCMA Codebook Design

text: Multicarrier CDMA is a multiple access scheme in which modulated QAM symbols are spread over OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequences allowing us to take advantage of a near optimal message passing algorithm (MPA) receiver with practically feasible complexity. Sparse code multiple access (SCMA) is a multi-dimensional codebook-based non-orthogonal spreading technique. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to multi-dimensional codewords of SCMA codebook sets. Each layer has its dedicated codebook. Shaping gain of a multi-dimensional constellation is one of the main sources of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. Meanwhile, like LDS, SCMA enjoys the low complexity reception techniques due to the sparsity of SCMA codewords. In this paper a systematic approach is proposed to design SCMA codebooks mainly based on the design principles of lattice constellations. Simulation results are presented to show the performance gain of SCMA compared to LDS and OFDMA.


title: LDS-OFDM an Efficient Multiple Access Technique

text: In this paper LDS-OFDM is introduced as an uplink multicarrier multiple access scheme. LDS-OFDM uses Low Density Signature (LDS) structure for spreading the symbols in frequency domain. This technique benefits from frequency diversity besides its ability of supporting parallel data streams up to 400% more than the number of subcarriers (overloaded condition). The performance of LDS-OFDM is evaluated and compared with conventional OFDMA systems over multipath fading channel. Monte Carlo based simulations for various loading conditions indicate significant performance improvement over OFDMA system.


title: Neural network controller based on PID using an extended Kalman filter algorithm for multi-variable non-linear control system

text: The Proportional Integral Derivative (PID) controller is widely used in the industrial control application, which is only suitable for the single input/single output (SISO) with known-parameters of the linear system. However, many researchers have been proposed the neural network controller based on PID (NNPID) to apply for both of the single and multi-variable control system but the NNPID controller that uses the conventional gradient descent-learning algorithm has many disadvantages such as a low speed of the convergent stability, difficult to set initial values, especially, restriction of the degree of system complexity. Therefore, this paper presents an improvement of recurrent neural network controller based on PID, including a controller structure improvement and a modified extended Kalman filter (EKF) learning algorithm for weight update rule, called ENNPID controller. We apply the proposed controller to the dynamic system including inverted pendulum, and DC motor system by the MATLAB simulation. From our experimental results, it shows that the performance of the proposed controller is higher than the other PID-like controllers in terms of fast convergence and fault tolerance that are highly required.


title: Can recursive bisection alone produce routable placements?

text:
This work focuses on congestion-driven placement of standard cells into rows in the fixed-die context. We summarize the state-of-the-art after two decades of research in recursive bisection placement and implement a new placer, called Capo, to empirically study the achievable limits of the approach. From among recently proposed improvements to recursive bisection, Capo incorporates a leading-edge multilevel min-cut partitioner [7], techniques for partitioning with small tolerance [8], optimal min-cut partitioners and end-case min-wirelength placers [5], previously unpublished partitioning tolerance computations, and block splitting heuristics. On the other hand, our “good enough” implementation does not use “overlapping” [17], multi-way partitioners [17, 20], analytical placement, or congestion estimation [24, 35]. In order to run on recent industrial placement instances, Capo must take into account fixed macros, power stripes and rows with different allowed cell orientations. Capo reads industry-standard LEF/DEF, as well as formats of the GSRC bookshelf for VLSI CAD algorithms [6], to enable comparisons on available placement instances in the fixed-die regime.
Capo clearly demonstrates that despite a potential mismatch of objectives, improved mincut bisection can still lead to improved placement wirelength and congestion. Our experiments on recent industrial benchmarks fail to give a clear answer to the question in the title of this paper. However, they validate a series of improvements to recursive bisection and point out a need for transparent congestion management techniques that do not worsen the wirelength of already routable placements. Our experimental flow, which validates fixed-die placement results by violation-free detailed auto-routability, provides a new norm for comparison of VLSI placement implementations.


title: Factors Affecting Repurchase Intention of Smartphone : A Case Study of Huawei Smartphone in Yangon , Myanmar

text: This study investigates the relationship between functional value, price consciousness, word of mouth, brand image, attitude towards product and repurchases intention of a smartphone brand. To do so, a survey was conducted by distributing 420 questionnaires in 7 different shopping malls in Yangon, Myanmar. The data collected was analyzed using SPSS and hypotheses were examined by employing the Pearson correlation and multiple linear regression. The results show that there is a positive and significant relationship among functional value, word of mouth, price consciousness towards attitude towards product and brand image, attitude towards product, word of mouth influences repurchase intention. Based on these results, it seems that the smart phone company needs to develop marketing strategy to increase repurchase intention.


title: Finding the face in the crowd: an anger superiority effect.

text: Facial gestures have been given an increasingly critical role in models of emotion. The biological significance of interindividual transmission of emotional signals is a pivotal assumption for placing the face in a central position in these models. This assumption invited a logical corollary, examined in this article: Face-processing should be highly efficient. Three experiments documented an asymmetry in the processing of emotionally discrepant faces embedded in crowds. The results suggested that threatening faces pop out of crowds, perhaps as a result of a preattentive, parallel search for signals of direct threat.


title: Combination of Texture and Geometric Features for Age Estimation in Face Images

text: Automatic age estimation from facial images has recently received an increasing interest due to a variety of applications, such as surveillance, human-computer interaction, forensics, and recommendation systems. Despite such advances, age estimation remains an open problem due to several challenges associated with the aging process. In this work, we develop and analyze an automatic age estimation method from face images based on a combination of textural and geometric features. Experiments are conducted on the Adience dataset (Adience Benchmark, 2017; Eidinger et al., 2014), a large known benchmark used to evaluate both age and gender classification approaches.


title: BIGT control optimisation for overall loss reduction

text: In this paper we present the latest results of utilizing MOS-control (MOSctrl) to optimize the performance of the Bi-mode Insulated Gate Transistor (BIGT) chip. The adaption of the BIGT technology enables higher output power per footprint. However, to enable the full performance benefit of the BIGT, the optimisation of the known standard MOS gate control is necessary. This optimisation is being demonstrated over the whole current and temperature range for the BIGT diode turn-off and BIGT turn-on operation. It is shown that the optimum control can offer a performance increase up to 20% for high voltage devices.


title: Evaluation of information technology investment: a data envelopment analysis approach

text: The increasing use of information technology (IT) has resulted in a need for evaluating the productivity impacts of IT. The contemporary IT evaluation approach has focused on return on investment and return on management. IT investment has impacts on different stages of business operations. For example, in the banking industry, IT plays a key role in effectively generating (i) funds from the customer in the forms of deposits and then (ii) profits by using deposits as investment funds. Existing approaches based upon data envelopment analysis (DEA) only measure the IT efficiency or impact on one specific stage when a multi-stage business process is present. A detailed model is needed to characterize the impact of IT on each stage of the business operation. The current paper develops a DEA non-linear programming model to evaluate the impact of IT on multiple stages along with information on how to distribute the IT-related resources so that the efficiency is maximized. It is shown that this non-linear program can be treated as a parametric linear program. It is also shown that if there is only one intermediate measure, then the non-linear DEA model becomes a linear program. Our approach is illustrated with an example taken from previous studies. 2004 Elsevier Ltd. All rights reserved.


title: Comparing memory systems for chip multiprocessors

text: There are two basic models for the on-chip memory in CMP systems:hardware-managed coherent caches and software-managed streaming memory. This paper performs a direct comparison of the two modelsunder the same set of assumptions about technology, area, and computational capabilities. The goal is to quantify how and when they differ in terms of performance, energy consumption, bandwidth requirements, and latency tolerance for general-purpose CMPs. We demonstrate that for data-parallel applications, the cache-based and streaming models perform and scale equally well. For certain applications with little data reuse, streaming scales better due to better bandwidth use and macroscopic software prefetching. However, the introduction of techniques such as hardware prefetching and non-allocating stores to the cache-based model eliminates the streaming advantage. Overall, our results indicate that there is not sufficient advantage in building streaming memory systems where all on-chip memory structures are explicitly managed. On the other hand, we show that streaming at the programming model level is particularly beneficial, even with the cache-based model, as it enhances locality and creates opportunities for bandwidth optimizations. Moreover, we observe that stream programming is actually easier with the cache-based model because the hardware guarantees correct, best-effort execution even when the programmer cannot fully regularize an application's code.


title: Positive and negative emotional verbal stimuli elicit activity in the left amygdala.

text: The human amygdala's involvement in negative emotion is well established, but relatively little is known regarding its role in positive emotion. Here we examined the neural response to emotionally positive, negative, and neutral words using fMRI. Relative to neutral words, positive and negative emotional words elicited greater activity in the left amygdala. Positive but not negative words elicited activity in dorsal and ventral striatal regions which have been linked in previous neuroimaging studies to reward and positive affect, including caudate, putamen, globus pallidus, and accumbens. These findings provide the first direct evidence that the amygdala is involved in emotional reactions elicited by both positive and negative emotional words, and further indicate that positive words additionally activate brain regions related to reward.


title: Broadband Radial Waveguide Spatial Combiner

text: A broadband eight-way spatial combiner using coaxial probes and radial waveguides has been proposed and designed. The simple electromagnetic modeling for the radial waveguide power divider/combiner has been developed using equivalent-circuit method. The measured 10-dB return loss and 1-dB insertion loss bandwidth of this waveguide spatial combiner are all demonstrated to be about 8 GHz.


title: Privacy Aspects of Recommender Systems

text: The popularity of online recommender systems has soared; they are deployed in numerous websites and gather tremendous amounts of user data that are necessary for the recommendation purposes. This data, however, may pose a severe threat to user privacy, if accessed by untrusted parties or used inappropriately. Hence, it is of paramount importance for recommender system designers and service providers to find a sweet spot, which allows them to generate accurate recommendations and guarantee the privacy of their users. In this chapter we overview the state of the art in privacy enhanced recommendations. We analyze the risks to user privacy imposed by recommender systems, survey the existing solutions, and discuss the privacy implications for the users of the recommenders. We conclude that a considerable effort is still required to develop practical recommendation solutions that provide adequate privacy guarantees, while at the same time facilitating the delivery of high-quality recommendations to their users. Arik Friedman NICTA, Australia, e-mail: arik.friedman@nicta.com.au Bart Knijnenburg UC Irvine, USA, e-mail: bart.k@uci.edu Kris Vanhecke iMinds Ghent University, Belgium, e-mail: kris.vanhecke@intec.ugent.be Luc Martens iMinds Ghent University, Belgium, e-mail: luc.martens@intec.ugent.be Shlomo Berkovsky CSIRO, Australia, e-mail: shlomo.berkovsky@csiro.au 1 Accepted to be included in the 2nd edition of the Recommender Systems handbook. Please do not share publicly, and consult the authors before citing this chapter.


title: ArgumenText: Searching for Arguments in Heterogeneous Sources

text: Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.


title: A qualitative study of Ragnarök Online private servers: in-game sociological issues

text: In the last decade, online games have garnered much attention as more and more players gather on game servers. In parallel, communities of illegal private server players and administrators have spread and might host hundreds of thousands of players. To study the Korean online game Ragnarök Online, we conducted interviews to collect qualitative data on two private servers as well as on the official French server for the game. This paper discusses some of the reasons why Ragnarök Online private servers might attract players and how examining private servers' characteristics could help improve official game servers.


title: A 28GHz SiGe BiCMOS phase invariant VGA

text: The paper describes a technique to design a phase invariant variable gain amplifier (VGA). Variable gain is achieved by varying the bias current in a BJT, while the phase variation is minimized by designing a local feedback network such that the applied base to emitter voltage has a bias-dependent phase variation which compensates the inherent phase variation of the transconductance. Two differential 28GHz VGA variants based on these principles achieve <;5° phase variation over 8dB and 18dB of gain control range, respectively, with phase invariance maintained over PVT. Implemented in GF 8HP BiCMOS technology, the VGAs achieve 18dB nominal gain, 4GHz bandwidth, and IP1dB > -13dBm while consuming 35mW.


title: Bacteriocins: developing innate immunity for food.

text: Bacteriocins are bacterially produced antimicrobial peptides with narrow or broad host ranges. Many bacteriocins are produced by food-grade lactic acid bacteria, a phenomenon which offers food scientists the possibility of directing or preventing the development of specific bacterial species in food. This can be particularly useful in preservation or food safety applications, but also has implications for the development of desirable flora in fermented food. In this sense, bacteriocins can be used to confer a rudimentary form of innate immunity to foodstuffs, helping processors extend their control over the food flora long after manufacture.


title: DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging

text: Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.


title: On the Traveling Salesman Problem with Simple Temporal Constraints

text: Many real-world applications require the successful combination of spatial and temporal reasoning. In this paper, we study the general framework of the Traveling Salesman Problem with Simple Temporal Constraints. Representationally, this framework subsumes the Traveling Salesman Problem, Simple Temporal Problems, as well as many of the frameworks described in the literature. We analyze the theoretical properties of the combined problem providing strong inapproximability results for the general problem, and positive results for


title: A Survey on Detection of Reasons Behind Infant Cry Using Speech Processing

text: Infant cry analysis is necessary to detect the reasons behind the cry. Infants can communicate through their cry only. They express their physical and emotional needs through their cry. The crying of an infant can be stopped if the reason behind their cry is known. Therefore, identifying the reason behind infant cry is very essential for medical follow up. In this paper, a literature survey is done to study, analyze, compare the existing approaches and their limitations. This survey helped us to propose a new approach for detecting the reasons behind Infant's cry. The proposed model will use input as the cry signal and from these signal patterns the unique features are extracted using MFCCs, LPCCs and Pitch, etc. These features will help discriminating the patterns from one another. These extracted features will be used to train Neural Network Multilayer classifier. This classifier will be used to identify multiple classes of reasons behind infant cry such as hunger, pain, sleep, discomfort, etc. Efforts will be made for achieving good accuracy results through this approach.


title: Convolutional neural networks: an illustration in TensorFlow

text: C onvolutional neural networks (CNNs) have interesting and widely varied architectures catering to the requirement of learning features from three dimensional and structured data volumes (with a particular example being images, whether single or multi-channel). However, there are certain design considerations common to all CNN architectures that enable us to craft correct implementations of the architectures suited to our objectives. It is more than just mathematical calculations—it's an art in itself! Thus, in the present tutorial, we'll put our knowledge of CNNs and their distinct layers in practice by making use of Google's open-source machine learning (ML) platform, TensorFlow. The tutorial is fairly simple and follows the steps detailed online [1] in light of added insights, which can be assimilated from the basics of CNN architectural concepts. The TensorFlow platform was released in November 2015, in a climate of stiff competition from well-established deep learning and ML packages such as Torch and Theano. (The performance comparisons between the packages goes beyond the scope of this piece). The platform has been undergoing constant improvements since its release, thanks in part to a nascent community of contributors. TensorFlow packs quite a punch owing to its significant positive characteristics. Firstly, the package's open source nature makes it transparent, customizable, and extensible by the end user. Secondly, the documentation support is rich and elaborate, which is a critical factor in accelerating user adoption. Lastly, the fact that the package has been backed by Google, with some of the best ML and AI researchers being among its active contributors, is the cherry on top. Additionally it's compatibility with newer versions of Python (version 3.3 and above), optional GPU utilization, parallelizable execution, and visualization support via TensorBoard further enrich its impressive repertoire. Its Achilles heel—which has prevented widespread adoption—is its unavailability on the Windows OS (TensorFlow is only available on Mac OSX and Linux platforms), and its non-operability in distributed environments, as of now (it's bound to change soon). The question as to why the MNIST dataset [2] was chosen for this tutorial is a very pertinent one. The answer is the handwritten digits dataset has been ubiquitous since its release in 1998 by Yann LeCunn [3]. Having since become the textbook dataset for neural network classification tasks, it is a good choice in our context of getting acquainted with CNNs. The dataset consists of 60,000 training examples and 10,000 test examples, each being a grayscale …


title: TextCatcher: a method to detect curved and challenging text in natural scenes

text: In this paper, we propose a text detection algorithm which is hybrid and multi-scale. First, it relies on a connected component-based approach: After the segmentation of the image, a classification step using a new wavelet descriptor spots the letters. A new graph modeling and its traversal procedure allow to form candidate text areas. Second, a texture-based approach discards the false positives. Finally, the detected text areas are precisely cut out and a new binarization step is introduced. The main advantage of our method is that few assumptions are put forward. Thus, “challenging texts” like multi-sized, multi-colored, multi-oriented or curved text can be localized. The efficiency of TextCatcher has been validated on three different datasets: Two come from the ICDAR competition, and the third one contains photographs we have taken with various daily life texts. We present both qualitative and quantitative results.


title: Put your money where your mouth is! Explaining collective action tendencies through group-based anger and group efficacy.

text: Insights from appraisal theories of emotion are used to integrate elements of theories on collective action. Three experiments with disadvantaged groups systematically manipulated procedural fairness (Study 1), emotional social support (Study 2), and instrumental social support (Study 3) to examine their effects on collective action tendencies through group-based anger and group efficacy. Results of structural equation modeling showed that procedural fairness and emotional social support affected the group-based anger pathway (reflecting emotion-focused coping), whereas instrumental social support affected the group efficacy pathway (reflecting problem-focused coping), constituting 2 distinct pathways to collective action tendencies. Analyses of the means suggest that collective action tendencies become stronger the more fellow group members "put their money where their mouth is." The authors discuss how their dual pathway model integrates and extends elements of current approaches to collective action.


title: Taxonomy of intrusion risk assessment and response system

text: In recent years, we have seen notable changes in the way attackers infiltrate computer systems compromising their functionality. Research in intrusion detection systems aims to reduce the impact of these attacks. In this paper, we present a taxonomy of intrusion response systems (IRS) and Intrusion Risk Assessment (IRA), two important components of an intrusion detection solution. We achieve this by classifying a number of studies published during the last two decades . We discuss the key features of existing IRS and IRA. We show how characterizing security risks and choosing the right countermeasures are an important and challenging part of designing an IRS and an IRA. Poorly designed IRS and IRA may reduce network performance and wrongly disconnect users from a network. We propose techniques on how to address these challenges and highlight the need for a comprehensive defense mechanism approach. We believe that this taxonomy will open up interesting areas for future research in the growing field of intrusion risk assessment and response systems.


title: Segmentation of Medical Ultrasound Images Using Convolutional Neural Networks with Noisy Activating Functions

text: The attempts to segment medical ultrasound images have had limited success than the attempts to segment images from other medical imaging modalities. In this project, we attempt to segment medical ultrasound images using convolutional neural networks (CNNs) with a group of noisy activation functions which have recently been demonstrated to improve the performance of neural networks. We report on the segmentation results using a U-Net-like CNN with noisy rectified linear unit (NReLU) functions, noisy hard sigmoid (NHSigmoid) functions, and noisy hard tanh (NHTanh) function on a small data set.


title: GSA: A Gravitational Search Algorithm

text: In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. 2009 Elsevier Inc. All rights reserved.


title: Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

text: Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.


title: Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution

text: The analysis of functional magnetic resonance imaging (fMRI) time-series data can provide information not only about task-related activity, but also about the connectivity (functional or effective) among regions and the influences of behavioral or physiologic states on that connectivity. Similar analyses have been performed in other imaging modalities, such as positron emission tomography. However, fMRI is unique because the information about the underlying neuronal activity is filtered or convolved with a hemodynamic response function. Previous studies of regional connectivity in fMRI have overlooked this convolution and have assumed that the observed hemodynamic response approximates the neuronal response. In this article, this assumption is revisited using estimates of underlying neuronal activity. These estimates use a parametric empirical Bayes formulation for hemodynamic deconvolution.


title: Common regions of the human frontal lobe recruited by diverse cognitive demands

text: Though many neuroscientific methods have been brought to bear in the search for functional specializations within prefrontal cortex, little consensus has emerged. To assess the contribution of functional neuroimaging, this article reviews patterns of frontal-lobe activation associated with a broad range of different cognitive demands, including aspects of perception, response selection, executive control, working memory, episodic memory and problem solving. The results show a striking regularity: for many demands, there is a similar recruitment of mid-dorsolateral, mid-ventrolateral and dorsal anterior cingulate cortex. Much of the remainder of frontal cortex, including most of the medial and orbital surfaces, is largely insensitive to these demands. Undoubtedly, these results provide strong evidence for regional specialization of function within prefrontal cortex. This specialization, however, takes an unexpected form: a specific frontal-lobe network that is consistently recruited for solution of diverse cognitive problems.


title: Prefrontal cortical function and anxiety: controlling attention to threat-related stimuli

text: Threat-related stimuli are strong competitors for attention, particularly in anxious individuals. We used functional magnetic resonance imaging (fMRI) with healthy human volunteers to study how the processing of threat-related distractors is controlled and whether this alters as anxiety levels increase. Our work builds upon prior analyses of the cognitive control functions of lateral prefrontal cortex (lateral PFC) and anterior cingulate cortex (ACC). We found that rostral ACC was strongly activated by infrequent threat-related distractors, consistent with a role for this area in responding to unexpected processing conflict caused by salient emotional stimuli. Participants with higher anxiety levels showed both less rostral ACC activity overall and reduced recruitment of lateral PFC as expectancy of threat-related distractors was established. This supports the proposal that anxiety is associated with reduced top-down control over threat-related distractors. Our results suggest distinct roles for rostral ACC and lateral PFC in governing the processing of task-irrelevant, threat-related stimuli, and indicate reduced recruitment of this circuitry in anxiety.


title: Other minds in the brain: a functional imaging study of “theory of mind” in story comprehension

text: The ability of normal children and adults to attribute independent mental states to self and others in order to explain and predict behaviour ("theory of mind") has been a focus of much recent research. Autism is a biologically based disorder which appears to be characterised by a specific impairment in this "mentalising" process. The present paper reports a functional neuroimaging study with positron emission tomography in which we studied brain activity in normal volunteers while they performed story comprehension tasks necessitating the attribution of mental states. The resultant brain activity was compared with that measured in two control tasks: "physical" stories which did not require this mental attribution, and passages of unlinked sentences. Both story conditions, when compared to the unlinked sentences, showed significantly increased regional cerebral blood flow in the following regions: the temporal poles bilaterally, the left superior temporal gyrus and the posterior cingulate cortex. Comparison of the "theory of mind" stories with "physical" stores revealed a specific pattern of activation associated with mental state attribution: it was only this task which produced activation in the medial frontal gyrus on the left (Brodmann's area 8). This comparison also showed significant activation in the posterior cingulate cortex. These surprisingly clear-cut findings are discussed in relation to previous studies of brain activation during story comprehension. The localisation of brain regions involved in normal attribution of mental states and contextual problem solving is feasible and may have implication for the neural basis of autism.


title: Daemo: A Self-Governed Crowdsourcing Marketplace

text: Crowdsourcing marketplaces provide opportunities for autonomous and collaborative professional work as well as social engagement. However, in these marketplaces, workers feel disrespected due to unreasonable rejections and low payments, whereas requesters do not trust the results they receive. The lack of trust and uneven distribution of power among workers and requesters have raised serious concerns about sustainability of these marketplaces. To address the challenges of trust and power, this paper introduces Daemo, a self-governed crowdsourcing marketplace. We propose a prototype task to improve the work quality and open-governance model to achieve equitable representation. We envisage Daemo will enable workers to build sustainable careers and provide requesters with timely, quality labor for their businesses.


title: Discriminative shape from shading in uncalibrated illumination

text: Estimating surface normals from just a single image is challenging. To simplify the problem, previous work focused on special cases, including directional lighting, known reflectance maps, etc., making shape from shading impractical outside the lab. To cope with more realistic settings, shading cues need to be combined and generalized to natural illumination. This significantly increases the complexity of the approach, as well as the number of parameters that require tuning. Enabled by a new large-scale dataset for training and analysis, we address this with a discriminative learning approach to shape from shading, which uses regression forests for efficient pixel-independent prediction and fast learning. Von Mises-Fisher distributions in the leaves of each tree enable the estimation of surface normals. To account for their expected spatial regularity, we introduce spatial features, including texton and silhouette features. The proposed silhouette features are computed from the occluding contours of the surface and provide scale-invariant context. Aside from computational efficiency, they enable good generalization to unseen data and importantly allow for a robust estimation of the reflectance map, extending our approach to the uncalibrated setting. Experiments show that our discriminative approach outperforms state-of-the-art methods on synthetic and real-world datasets.


title: System Integration and Power-Flow Management for a Series Hybrid Electric Vehicle Using Supercapacitors and Batteries

text: In this paper, system integration and power-flow management algorithms for a four-wheel-driven series hybrid electric vehicle (HEV) having multiple power sources composed of a diesel-engine-based generator, lead acid battery bank, and supercapacitor bank are presented. The super-capacitor is utilized as a short-term energy storage device to meet the dynamic performance of the vehicle, while the battery is utilized as a mid-term energy storage for the electric vehicle (EV) mode operation due to its higher energy density. The generator based on an interior permanent magnet machine (IPMM), run by a diesel engine, provides the average power for the normal operation of the vehicle. Thanks to the proposed power-flow management algorithm, each of the energy sources is controlled appropriately and also the dynamic performance of the vehicle has been improved. The proposed power-flow management algorithm has been experimentally verified with a full-scale prototype vehicle.


title: Linear AC LED driver with the multi-level structure and variable current regulator

text: This paper proposes a linear AC LED driver for LED lighting applications. The proposed circuit is small in size because the circuit structure consists of only semiconductors and resistors without any reactors and electrolytic capacitors. The current bypass circuit which is connected in parallel to the LED string consists of single MOSFET, single zener diode and two resistors. The MOSFET is operated in an active state by a self-bias circuit. Thus, an external controller and high voltage gate drivers are not required. The proposed circuit is experimentally validated by using a 7 W prototype. From the experimental results, the THD of input current is 2.1% and the power factor is 0.999. In addition, the simulation loss analysis demonstrates an efficiency of 87% for a 7 W prototype.


title: Space-Vector PWM Control Synthesis for an H-Bridge Drive in Electric Vehicles

text: This paper deals with a synthesis of space-vector pulsewidth modulation (SVPWM) control methods applied for an H-bridge inverter feeding a three-phase permanent-magnet synchronous machine (PMSM) in electric-vehicle (EV) applications. First, a short survey of existing architectures of power converters, particularly those adapted to degraded operating modes, is presented. Standard SVPWM control methods are compared with three innovative methods using EV drive specifications in the normal operating mode. Then, a rigorous analysis of the margins left in the control strategy is presented for a semiconductor switch failure to fulfill degraded operating modes. Finally, both classic and innovative strategies are implemented in numerical simulation; their results are analyzed and discussed.


title: WALNUT: Waging Doubt on the Integrity of MEMS Accelerometers with Acoustic Injection Attacks

text: Cyber-physical systems depend on sensors to make automated decisions. Resonant acoustic injection attacks are already known to cause malfunctions by disabling MEMS-based gyroscopes. However, an open question remains on how to move beyond denial of service attacks to achieve full adversarial control of sensor outputs. Our work investigates how analog acoustic injection attacks can damage the digital integrity of a popular type of sensor: the capacitive MEMS accelerometer. Spoofing such sensors with intentional acoustic interference enables an out-of-spec pathway for attackers to deliver chosen digital values to microprocessors and embedded systems that blindly trust the unvalidated integrity of sensor outputs. Our contributions include (1) modeling the physics of malicious acoustic interference on MEMS accelerometers, (2) discovering the circuit-level security flaws that cause the vulnerabilities by measuring acoustic injection attacks on MEMS accelerometers as well as systems that employ on these sensors, and (3) two software-only defenses that mitigate many of the risks to the integrity of MEMS accelerometer outputs. We characterize two classes of acoustic injection attacks with increasing levels of adversarial control: output biasing and output control. We test these attacks against 20 models of capacitive MEMS accelerometers from 5 different manufacturers. Our experiments find that 75% are vulnerable to output biasing, and 65% are vulnerable to output control. To illustrate end-to-end implications, we show how to inject fake steps into a Fitbit with a $5 speaker. In our self-stimulating attack, we play a malicious music file from a smartphone's speaker to control the on-board MEMS accelerometer trusted by a local app to pilot a toy RC car. In addition to offering hardware design suggestions to eliminate the root causes of insecure amplification and filtering, we introduce two low-cost software defenses that mitigate output biasing attacks: randomized sampling and 180 degree out-of-phase sampling. These software-only approaches mitigate attacks by exploiting the periodic and predictable nature of the malicious acoustic interference signal. Our results call into question the wisdom of allowing microprocessors and embedded systems to blindly trust that hardware abstractions alone will ensure the integrity of sensor outputs.


title: Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem

text: In this paper we introduce Ant-Q, a family of algorithms which present many similarities with Q-learning (Watkins, 1989), and which we apply to the solution of symmetric and asymmetric instances of the traveling salesman problem (TSP). Ant-Q algorithms were inspired by work on the ant system (AS), a distributed algorithm for combinatorial optimization based on the metaphor of ant colonies which was recently proposed in (Dorigo, 1992; Dorigo, Maniezzo and Colorni, 1996). We show that AS is a particular instance of the Ant-Q family, and that there are instances of this family which perform better than AS. We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP's are competitive with those obtained by other heuristic approaches based on neural networks or local search. Finally, we apply Ant-Q to some difficult asymmetric TSP's obtaining very good results: Ant-Q was able to find solutions of a quality which usually can be found only by very specialized algorithms.


title: All-at-once Optimization for Coupled Matrix and Tensor Factorizations

text: Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers’ social networks (e.g., Facebook friendships) and restaurant categories information (e.g., Thai or Italian) can also be used to make better recommendations. The task of fusing data, however, is challenging since data sets can be incomplete and heterogeneous, i.e., data consist of both matrices, e.g., the person by person social network matrix or the restaurant by category matrix, and higher-order tensors, e.g., the “ratings” tensor of the form restaurant by meal by person. In this paper, we are particularly interested in fusing data sets with the goal of capturing their underlying latent structures. We formulate this problem as a coupled matrix and tensor factorization (CMTF) problem where heterogeneous data sets are modeled by fitting outer-product models to higher-order tensors and matrices in a coupled manner. Unlike traditional approaches solving this problem using alternating algorithms, we propose an all-at-once optimization approach called CMTF-OPT (CMTF-OPTimization), which is a gradient-based optimization approach for joint analysis of matrices and higher-order tensors. We also extend the algorithm to handle coupled incomplete data sets. Using numerical experiments, we demonstrate that the proposed all-at-once approach is more accurate than the alternating least squares approach.


title: A Tagged Corpus and a Tagger for Urdu

text: In this paper, we describe a release of a sizeable monolingual Urdu corpus automatically tagged with part-of-speech tags. We extend the work of Jawaid and Bojar (2012) who use three different taggers and then apply a voting scheme to disambiguate among the different choices suggested by each tagger. We run this complex ensemble on a large monolingual corpus and release the tagged corpus. Additionally, we use this data to train a single standalone tagger which will hopefully significantly simplify Urdu processing. The standalone tagger obtains the accuracy of 88.74% on test data.


title: Learning as search optimization: approximate large margin methods for structured prediction

text: Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., linear chains) in which search and parameter estimation can be performed exactly. Unfortunately, in many complex problems, it is rare that exact search or parameter estimation is tractable. Instead of learning exact models and searching via heuristic means, we embrace this difficulty and treat the structured output problem in terms of approximate search. We present a framework for learning as search optimization, and two parameter updates with convergence the-orems and bounds. Empirical evidence shows that our integrated approach to learning and decoding can outperform exact models at smaller computational cost.


title: Bricolage versus breakthrough : distributed and embedded agency in technology entrepreneurship

text: We develop a perspective on technology entrepreneurship as involving agency that is distributed across different kinds of actors. Each actor becomes involved with a technology, and, in the process, generates inputs that result in the transformation of an emerging technological path. The steady accumulation of inputs to a technological path generates a momentum that enables and constrains the activities of distributed actors. In other words, agency is not only distributed, but it is embedded as well. We explicate this perspective through a comparative study of processes underlying the emergence of wind turbines in Denmark and in United States. Through our comparative study, we flesh out “bricolage” and “breakthrough” as contrasting approaches to the engagement of actors in shaping technological paths. © 2002 Elsevier Science B.V. All rights reserved.


Threshold selection using Renyi's entropy


title: UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes

text: The 2016 Clinical TempEval challenge addresses temporal information extraction from clinical notes. The challenge is composed of six sub-tasks, each of which is to identify: (1) event mention spans, (2) time expression spans, (3) event attributes, (4) time attributes, (5) events’ temporal relations to the document creation times (DocTimeRel), and (6) narrative container relations among events and times. In this article, we present an end-to-end system that addresses all six sub-tasks. Our system achieved the best performance for all six sub-tasks when plain texts were given as input. It also performed best for narrative container relation identification when gold standard event/time annotations were given.


title: Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks

text: Functional groups (FGs) serve as a foundation for analyzing chemical properties of organic molecules. Automatic discovery of FGs will impact various fields of research, including medicinal chemistry, by reducing the amount of lab experiments required for discovery or synthesis of new molecules. Here, we investigate methods based on graph convolutional neural networks (GCNNs) for localizing FGs that contribute to specific chemical properties. Molecules are modeled as undirected graphs with atoms as nodes and bonds as edges. Using this graph structure, we trained GCNNs in a supervised way on experimentally-validated molecular training sets to predict specific chemical properties, e.g., toxicity. Upon learning a GCNN, we analyzed its activation patterns to automatically identify FGs using four different methods: gradient-based saliency maps, Class Activation Mapping (CAM), gradient-weighted CAM (Grad-CAM), and Excitation Back-Propagation. We evaluated the contrastive power of these methods with respect to the specificity of the identified molecular substructures and their relevance for chemical functions. GradCAM had the highest contrastive power and generated qualitatively the best FGs. This work paves the way for automatic analysis and design of new molecules.


title: How to make large self-organizing maps for nonvectorial data

text: The self-organizing map (SOM) represents an open set of input samples by a topologically organized, finite set of models. In this paper, a new version of the SOM is used for the clustering, organization, and visualization of a large database of symbol sequences (viz. protein sequences). This method combines two principles: the batch computing version of the SOM, and computation of the generalized median of symbol strings.


title: Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising

text: Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the X-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of low-dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio, leading to strong noise and artifacts that down-grade the CT image quality. In this paper, we propose a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the LDCT image quality. Specifically, we incorporate 3-D volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve the structural and textural information in reference to the normal-dose CT images and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information and outperforms competing methods.


Massive parallelization of approximate nearest neighbor search on KD-tree for high-dimensional image descriptor matching


title: REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems

text: The package REGULARIZATION TOOLS consists of 54 Matlab routines for analysis and solution of discrete ill-posed problems, i.e., systems of linear equations whose coefficient matrix has the properties that its condition number is very large, and its singular values decay gradually to zero. Such problems typically arise in connection with discretization of Fredholm integral equations of the first kind, and similar ill-posed problems. Some form of regularization is always required in order to compute a stabilized solution to discrete ill-posed problems. The purpose of REGULARIZATION TOOLS is to provide the user with easy-to-use routines, based on numerical robust and efficient algorithms, for doing experiments with regularization of discrete ill-posed problems. By means of this package, the user can experiment with different regularization strategies, compare them, and draw conclusions from these experiments that would otherwise require a major programming effert. For discrete ill-posed problems, which are indeed difficult to treat numerically, such an approach is certainly superior to a single black-box routine. This paper describes the underlying theory gives an overview of the package; a complete manual is also available.


title: Transparent and Efficient CFI Enforcement with Intel Processor Trace

text: Current control flow integrity (CFI) enforcement approaches either require instrumenting application executables and even shared libraries, or are unable to defend against sophisticated attacks due to relaxed security policies, or both, many of them also incur high runtime overhead. This paper observes that the main obstacle of providing transparent and strong defense against sophisticated adversaries is the lack of sufficient runtime control flow information. To this end, this paper describes FlowGuard, a lightweight, transparent CFI enforcement approach by a novel reuse of Intel Processor Trace (IPT), a recent hardware feature that efficiently captures the entire runtime control flow. The main challenge is that IPT is designed for offline performance analysis and software debugging such that decoding collected control flow traces is prohibitively slow on the fly. FlowGuard addresses this challenge by reconstructing applications' conservative control flow graphs (CFG) to be compatible with the compressed encoding format of IPT, and labeling the CFG edges with credits in the help of fuzzing-like dynamic training. At runtime, FlowGuard separates fast and slow paths such that the fast path compares the labeled CFGs with the IPT traces for fast filtering, while the slow path decodes necessary IPT traces for strong security. We have implemented and evaluated FlowGuard on a commodity Intel Skylake machine with IPT support. Evaluation results show that FlowGuard is effective in enforcing CFI for several applications, while introducing only small performance overhead. We also show that, with minor hardware extensions, the performance overhead can be further reduced.


title: Predictive coding as a model of cognition

text: Previous work has shown that predictive coding can provide a detailed explanation of a very wide range of low-level perceptual processes. It is also widely believed that predictive coding can account for high-level, cognitive, abilities. This article provides support for this view by showing that predictive coding can simulate phenomena such as categorisation, the influence of abstract knowledge on perception, recall and reasoning about conceptual knowledge, context-dependent behavioural control, and naive physics. The particular implementation of predictive coding used here (PC/BC-DIM) has previously been used to simulate low-level perceptual behaviour and the neural mechanisms that underlie them. This algorithm thus provides a single framework for modelling both perceptual and cognitive brain function.


title: Fine-tuning Deep Convolutional Networks for Plant Recognition

text: This paper describes the participation of the ECOUAN team in the LifeCLEF 2015 challenge. We used a deep learning approach in which the complete system was learned without hand-engineered components. We pre-trained a convolutional neural network using 1.8 million images and used a fine-tuning strategy to transfer learned recognition capabilities from general domains to the specific challenge of Plant Identification task. The classification accuracy obtained by our method outperformed the best result obtained in 2014. Our group obtained the 4th position among all teams and the 10th position among 18 runs.


title: A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

text: Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.


title: Interpreting random forest models using a feature contribution method

text: Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For “black box” models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.


title: Methodology of band rejection / addition for microstrip antennas design using slot line theory and current distribution analysis

text: Radio or wireless communication means to transfer information over long distance without using any wires. Millions of people exchange information every day using pager, cellular, telephones, laptops, various types of personal digital assistance (PDAs) and other wireless communication products. The worldwide interoperability for microwave access (Wi-Max) aims to provide wireless data over a long distance in variety of ways. It was based on IEEE 802.16 standard. It is an effective metropolitan area access technique with many favorable features like flexibility, cost, efficiency and fast networking, which not only provides wireless access, but also serves to expand the access to wired network. The coverage area of Wi-Max is around 30-50 km. it can provide high 100 Mbps data rates in 20 MHz bandwidth on fixed and nomadic applications in the 2-11 GHz frequencies. In this paper, a methodology of band rejection/addition for microstrip antennas design using slot line theory and current distribution analysis has been introduced and analyzed. The analysis and design are done by a commercial software. The radiation characteristics, such as; return loss, VSWR, input impedance, and the surface current densities have been introduced and discussed. Finally, the proposed optimum antenna design structure has been fabricated and the measured S-parameters of the proposed structure can be analyzed with network analyzer and compared with simulation results to demonstrate the excellent performance and meet the requirements for wireless communication applications.


title: Distance Metric Learning with Application to Clustering with Side-Information

text: Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many “plausible” ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are found. For these and other applications requiring good metrics, it is desirable that we provide a more systematic way for users to indicate what they consider “similar.” For instance, we may ask them to provide examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in , learns a distance metric over that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us to give efficient, local-optima-free algorithms. We also demonstrate empirically that the learned metrics can be used to significantly improve clustering performance.


title: Comparing Clusterings - An Overview

text: As the amount of data we nowadays have to deal with becomes larger and larger, the methods that help us to detect structures in the data and to identify interesting subsets in the data become more and more important. One of these methods is clustering, i.e. segmenting a set of elements into subsets such that the elements in each subset are somehow ”similiar” to each other and elements of different subsets are ”unsimilar”. In the literature we can find a large variety of clustering algorithms, each having certain advantages but also certain drawbacks. Typical questions that arise in this context comprise:


title: Discriminant Adaptive Nearest Neighbor Classification

text: Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions We propose a locally adaptive form of nearest neighbor classification to try to finesse this curse of dimensionality. We use a local linear discriminant analysis to estimate an effective metric for computing neighborhoods. We determine the local decision boundaries from centroid information, and then shrink neighborhoods in directions orthogonal to these local decision boundaries, and elongate them parallel to the boundaries. Thereafter, any neighborhood-based classifier can be employed, using the modified neighborhoods. The posterior probabilities tend to be more homogeneous in the modified neighborhoods. We also propose a method for global dimension reduction, that combines local dimension information. In a number of examples, the methods demonstrate the potential for substantial improvements over nearest neighbour classification. Introduction We consider a discrimination problem with d classes and N training observations. The training observations consist of predictor measurements x = (zl,z2,...zp) on p predictors and the known class memberships. Our goal is to predict the class membership of an observation with predictor vector x0 Nearest neighbor classification is a simple and appealing approach to this problem. We find the set of K nearest neighbors in the training set to x0 and then classify x0 as the most frequent class among the K neighbors. Nearest neighbors is an extremely flexible classification scheme, and does not involve any pre-processing (fitting) of the training data. This can offer both space and speed advantages in very large problems: see Cover (1968), Duda & Hart (1973), McLachlan (1992) for background material on nearest neighborhood classification. Cover & Hart (1967) show that the one nearest neighbour rule has asymptotic error rate at most twice the Bayes rate. However in finite samples the curse of dimensionality can severely hurt the nearest neighbor rule. The relative radius of the nearest-neighbor sphere grows like r 1/p where p is the dimension and r the radius for p = 1, resulting in severe bias at the target point x. Figure 1 illustrates the situation for a simple example. Figure 1: The vertical strip denotes the NN region using only the X coordinate to find the nearest neighbor for the target point (solid dot). The sphere shows the NN region using both coordinates, and we see in this case it has extended into the class 1 region (and found the wrong class in this instance). Our illustration here is based on a 1-NN rule, but the same phenomenon ccurs for k-NN rules as well. Nearest neighbor techniques are based on the assumption that locally the class posterior probabilities are constant. While that is clearly true in the vertical strip using only coordinate X, using X and Y this is no longer true. The techniques outlined in the abstract are designed to overcome these problems. Figure 2 shows an example. There are two classes in two dimensions, one of which almost completely surrounds the other. The left panel shows a nearest neighborhood of size 25 at the target point (shown as origin), which is chosen to near the class boundary. The right panel shows the same size neighborhood using our discriminant adap142 KDD--95 From: KDD-95 Proceedings. Copyright © 1995, AAAI (www.aaai.org). All rights reserved.


title: Constrained K-means Clustering with Background Knowledge

text: Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be profitably modified to make use of this information. In experiments with artificial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance.