title: Pavlou & Fygenson/Extending the TPB RESEARCH ARTICLE UNDERSTANDING AND PREDICTING ELECTRONIC COMMERCE ADOPTION: AN EXTENSION OF THE THEORY OF PLANNED BEHAVIOR 1

abstract: This paper extends Ajzen's (1991) theory of planned behavior (TPB) to explain and predict the process of e-commerce adoption by consumers. The process is captured through two online consumer behaviors: (1) getting information and (2) purchasing a product from a Web vendor. First, we simultaneously model the association between these two contingent online behaviors and their respective intentions by appealing to consumer behavior theories and the theory of implementation intentions, respectively. Second, following TPB, we derive for each behavior its intention, attitude, subjective norm, and perceived behavioral control (PBC). Third, we elicit and test a comprehensive set of salient beliefs for each behavior. ::: ::: A longitudinal study with online consumers supports the proposed e-commerce adoption model, validating the predictive power of TPB and the proposed conceptualization of PBC as a higher-order factor formed by self-efficacy and controllability. Our findings stress the importance of trust and technology adoption variables (perceived usefulness and ease of use) as salient beliefs for predicting e-commerce adoption, justifying the integration of trust and technology adoption variables within the TPB framework. In addition, technological characteristics (download delay, Website navigability, and information protection), consumer skills, time and monetary resources, and product characteristics (product diagnosticity and product value) add to the explanatory and predictive power of our model. Implications for Information Systems, e-commerce, TPB, and the study of trust are discussed.


title: A new structure of a switching flux synchronous polyphased machine with hybrid excitation

abstract: The aim of this paper is to present the structure of a new flux switching synchronous machine with hybrid excitation. This machine uses the flux switching principle where all the active parts are located on the stator. The rotor is only a salient passive rotor and can be robust and made with a low cost technology. This new machine can be supplied with electricity by means of a traditional three phase voltage converter or can be associated with a diode rectifier. The hybrid excitation is an association of permanent magnets and a wound exciter.


title: THEORETICAL NOTES How Persuasive Is a Good Fit? A Comment on Theory Testing

abstract: Quantitative theories with free parameters often gain credence when they closely fit data. This is a mistake. A good fit reveals nothing about the flexibility of the theory (how much it cannot fit), the variability of the data (how firmly the data rule out what the theory cannot fit), or the likelihood of other outcomes (perhaps the theory could have fit any plausible result), and a reader needs all 3 pieces of information to decide how much the fit should increase belief in the theory. The use of good fits as evidence is not supported by philosophers of science nor by the history of psychology; there seem to be no examples of a theory supported mainly by good fits that has led to demonstrable progress. A better way to test a theory with free parameters is to determine how the theory constrains possible outcomes (i.e., what it predicts), assess how firmly actual outcomes agree with those constraints, and determine if plausible alternative outcomes would have been inconsistent with the theory, allowing for the variability of the data.


title: Data Mining Classification Techniques for Human Talent Forecasting

abstract: In knowledge management process, data mining technique can be used to extract and discover the valuable and meaningful knowledge from a large amount of data. Nowadays, data mining has given a great deal of concern and attention in the information industry and in society as a whole. This technique is an approach that is currently receiving great attention in data analysis and it has been recognized as a newly emerging analysis tool (Osei-Bryson, 2010; Park, 2001; Sinha, 2008; Tso & Yau, 2007; Wan, 2009; Zanakis, 2005; Zhuang et al., 2009). Additionally, among the major tasks in data mining are classification and prediction; concept description; rule association; cluster analysis; outlier analysis; trend and evaluation analysis; statistical analysis and others. Classification and prediction tasks are among the popular tasks in data mining; and widely used in many areas especially for trend analysis and future planning. In fact, classification technique is supervised learning, which is the class level or prediction target is already known. As a result, the classification model which is represented through rules structures will be constructed in the classification process. In this case, the constructed model will be representing the precious knowledge and it can be used for future planning. There are many areas which adapted this approach to solve their problems such as in finance, medical, marketing, stock, telecommunication, manufacturing, health care, customer relationship and etc. However, the data mining application has not attracted much attention from people in Human Resource (HR) field (Chien & Chen, 2008; Ranjan, 2008). Besides that, in our previous study, most of the prediction applications are used to predict stock, demand, rate, risk, event and others; but there are quite limited studies on human prediction. In addition prediction applications are mainly developed in business and industrious fields; and quite restricted studies involved human talent in an organization (Jantan et al., 2009). HR data can provide a rich resource for knowledge discovery and for decision support system development. Recently, an organization has to struggle effectively in term of cost, quality, service or innovation. All these depend on having enough right people with the right skills, employed


title: Cyber attacking tactical radio networks

abstract: Cyber attacks in the Internet are common knowledge for even nontechnical people. Same attack techniques can also be used against any military radio networks in the battlefield. This paper describes a test setup that can be used to test tactical radio networks against cyber vulnerabilities. The test setup created is versatile and can be adapted to any command and control system on any level of the OSI model. Test setup uses as much publicly or commercially available tools as possible. Need for custom made components is minimized to decrease costs, to decrease deployment time and to increase usability. With architecture described, same tools used in IP network testing can be used in tactical radio networks. Problems found in any level of the system can be fixed in co-operation with vendors of the system. Cyber testing should be adapted as part of acceptance tests of any new military communication system.


title: An automated truck platoon for energy saving

abstract: This paper presents an automated truck platoon that has been developed under a national ITS project named “Energy ITS.” The project, started in 2008, aims at energy saving and global warming prevention with ITS technologies. A platoon of 3 fully-automated trucks currently drives at 80 km/h with the gap of 10 m on a test truck and along an expressway before public use, under not only steady state driving but also lane changing. The lateral control is based on the lane marker detection by the computer vision, and the longitudinal control is based on the gap measurement by 76 GHz radar and lidar in addition to the inter-vehicle communications of 5.8 GHz DSRC. The radar and lidar also work as the obstacle detection. The feature of the technologies is the high reliability, aiming at the near future introduction. Fuel consumption measurement on a test track and along an expressway shows that the fuel can be saved by about 14 %. The evaluation simulation shows that the effectiveness of the platooning with the gap of 10 m when the 40 % penetration in heavy trucks is 2.1 % reduction of CO 2 along an expressway. The introduction scenario is also discussed.


title: Practical clickjacking with BeEF

abstract: A lot of effort has been put into researching client-side attacks, including vulnerabilities like cross-site scripting, cross-site request forgery, and more recently, clickjacking. Similar to other client-side attacks, a clickjacking vulnerability can use the browser to exploit weaknesses in cross domain isolation and the same origin policy. It does this by tricking the user to click on something that is actually not what the user perceives they are clicking on. In the most extreme cases, this vulnerability can cause an unsuspecting user to have their account compromised with a single click. Although there are protections available for clickjacking, the web applications implementing these mitigations are far and in between. Additionally, although the possibility for an attacker to frame a page is easy to detect, it is much more difficult to demonstrate or assess the impact of a clickjacking vulnerability than more traditional client-side vectors. Tools do not currently exist to reliably demonstrate clickjacking exploitation, and the rare demonstrations that are done typically use custom JavaScript and HTML for each individual vulnerability. Worse, many times this esoteric code is never made public, leaving everyone to rewrite their own from scratch. BeEF, known as the Browser Exploitation Framework, is a tool designed to help professional penetration testers easily demonstrate the impact of client-side security vulnerabilities. In this paper, we present a plugin module for BeEF which provides a way for penetration testers to easily demonstrate the impact of clickjacking vulnerabilities.


title: PointGuard™: Protecting Pointers from Buffer Overflow Vulnerabilities

abstract: Despite numerous security technologies crafted to resist buffer overflow vulnerabilities, buffer overflows continue to be the dominant form of software security vulnerability. This is because most buffer overflow defenses provide only partial coverage, and the attacks have adapted to exploit problems that are not well-defended, such as heap overflows. This paper presents PointGuard, a compiler technique to defend against most kinds of buffer overflows by encrypting pointers when stored in memory, and decrypting them only when loaded into CPU registers. We describe the PointGuard implementation, show that PointGuard's overhead is low when protecting real security-sensitive applications such as OpenSSL, and show that PointGuard is effective in defending against buffer overflow vulnerabilities that are not blocked by previous defenses.


title: Corporal punishment and physical abuse: population-based trends for three-to-11-year-old children in the United States

abstract: C orporal punishment is increasingly regarded as an act of violence against children. Corporal punishment includes any use of physical punishment against a child in response to misbehaviour. This most commonly includes spanking, smacking and slapping, but also includes the use of an object such as a rod or stick, hair pulling and ear twisting. A growing body of research has focused on discipline and the adverse effects of corporal punishment (Berlin et al., 2009; Lansford et al., 2009). The UN Convention on the Rights of the Child asserts that States take ‘all appropriate legislative, administrative, social, and educational measures to protect the child from all forms of physical or mental violence . . .’ (UNICEF, 1989; Article 19, Center for Effective Discipline, 2009). The Committee on the Rights of the Child (2006), in General Comment Number 8, has further clarifi ed that:


title: An initial study of automatic curb ramp detection with crowdsourced verification using Google street view images

abstract: In our previous research, we examined whether minimally trained crowd workers could find, categorize, and assess sidewalk accessibility problems using Google Street View (GSV) images. This poster paper presents a first step towards combining automated methods ( e.g., machine vision-based curb ramp detectors) in concert with human computation to improve the overall scalability of our approach.


title: A single-phase photovoltaic inverter topology with a series-connected power buffer

abstract: Module integrated converters (MICs) have been under rapid developement for single-phase grid-tied photovoltaic applications. The capacitive energy storage implementation for the double-line-frequency power variation represents a differentiating factor among existing designs. This paper introduces a new topology that places the energy storage block in a series-connected path with the line interface block. This design provides independent control over the capacitor voltage, soft-switching for all semiconductor devices, and full four-quadrant operation with the grid. The proposed approach is analyzed and experimentally demonstrated.


title: CAS-based lock-free algorithm for shared deques

abstract: This paper presents the first lock-free algorithm for shared double-ended queues (deques) based on the single-address atomic primitives CAS (Compare-and-Swap) or LL/SC (Load-Linked and Store-Conditional). The algorithm can use single-word primitives, if the maximum deque size is static. To allow the deque’s size to be dynamic, the algorithm employs single-address double-width primitives. Prior lock-free algorithms for shared deques depend on the strong DCAS (Double-Compare-and-Swap) atomic primitive, not supported on most processor architectures. The new algorithm offers significant advantages over prior lock-free shared deque algorithms with respect to performance and the strength of required primitives. In turn, lock-free algorithms provide significant reliability and performance advantages over lock-based implementations.


title: A probabilistic framework for modeling and real-time monitoring human fatigue

abstract: A probabilistic framework based on the Bayesian networks for modeling and real-time inferring human fatigue by integrating information from various sensory data and certain relevant contextual information is introduced. A static fatigue model that captures the static relationships between fatigue, significant factors that cause fatigue, and various sensory observations that typically result from fatigue is first presented. Such a model provides mathematically coherent and sound basis for systematically aggregating uncertain evidences from different sources, augmented with relevant contextual information. The static model, however, fails to capture the dynamic aspect of fatigue. Fatigue is a cognitive state that is developed over time. To account for the temporal aspect of human fatigue, the static fatigue model is extended based on dynamic Bayesian networks. The dynamic fatigue model allows to integrate fatigue evidences not only spatially but also temporally, therefore, leading to a more robust and accurate fatigue modeling and inference. A real-time nonintrusive fatigue monitor was built based on integrating the proposed fatigue model with a computer vision system developed for extracting various visual cues typically related to fatigue. Performance evaluation of the fatigue monitor using both synthetic and real data demonstrates the validity of the proposed fatigue model in both modeling and real-time inference of fatigue


title: Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks

abstract: We propose a framework for inferring the latent attitudes or preferences of users by performing probabilistic first-order logical reasoning over the social network graph. Our method answers questions about Twitter users like {\em Does this user like sushi?} or {\em Is this user a New York Knicks fan?} by building a probabilistic model that reasons over user attributes (the user's location or gender) and the social network (the user's friends and spouse), via inferences like homophily (I am more likely to like sushi if spouse or friends like sushi, I am more likely to like the Knicks if I live in New York). The algorithm uses distant supervision, semi-supervised data harvesting and vector space models to extract user attributes (e.g. spouse, education, location) and preferences (likes and dislikes) from text. The extracted propositions are then fed into a probabilistic reasoner (we investigate both Markov Logic and Probabilistic Soft Logic). Our experiments show that probabilistic logical reasoning significantly improves the performance on attribute and relation extraction, and also achieves an F-score of 0.791 at predicting a users likes or dislikes, significantly better than two strong baselines.


title: Using the WinWin Spiral Model: A Case Study

abstract: Fifteen teams used the WinWin spiral model to prototype, plan, specify, and build multimedia applications for USC's Integrated Library System. The authors report lessons learned from this case study and how they extended the model's utility and cost-effectiveness in a second round of projects.


title: Abstract UDT: UDP-based Data Transfer for High-Speed Wide Area Networks

abstract: In this paper, we summarize our work on the UDT high performance data transport protocol over the past four years. UDT was designed to effectively utilize the rapidly emerging high-speed wide area optical networks. It is built on top of UDP with reliability control and congestion control, which makes it quite easy to install. The congestion control algorithm is the major internal functionality to enable UDT to effectively utilize high bandwidth. Meanwhile, we also implemented a set of APIs to support easy application implementation, including both reliable data streaming and partial reliable messaging. The original UDT library has also been extended to Composable UDT, which can support various congestion control algorithms. We will describe in detail the design and implementation of UDT, the UDT congestion control algorithm, Composable UDT, and the performance evaluation.


Multiple Precision Integer Multiplication on GPUs


title: Multiband Handset Antenna Combining a PIFA, Slots, and Ground Plane Modes

abstract: A multiband handset antenna combining a PIFA and multiple slots on a ground plane is presented. It is shown by means of simulations that the slots on the ground plane have a double function: to tune the ground plane resonance at low frequencies (f ? 900 MHz) and to act as parasitic radiators at high frequencies (f ? 1800 MHz). A prototype is designed and built featuring a behavior suitable for low frequencies (GSM850 and GSM900) and for high frequencies spanning from DCS1800 to Bluetooth, and including, for instance, PCS1900, UMTS2000, and other possible systems. Reflection coefficient, efficiency, and radiation patterns are measured and compared with a design without slots to prove the advantages of the slotted ground plane. The component effect is investigated to determine critical areas where the placement is not recommended. Besides, the effect of the slot of the ground plane on SAR is investigated, by discussing the effect of the ground plane and slot modes for two phone positions. The total antenna volume of the proposed design is 40 × 15 × 6 mm3.


title: Chip design of a 12-bit 5MS/s fully differential SAR ADC with resistor-capacitor array DAC technique for wireless application

abstract: A 1.8-V 12-bit 5MS/s successive approximation register (SAR) analog-to-digital converter (ADC) implemented in TSMC 0.18-um CMOS process is presented. To reduce DAC switching energy and chip area, a hybrid resistor-capacitor DAC is applied. To save energy, asynchronous control logic to drive the ADC is used. A pre-amplifier based comparator circuit is built to reduce the kickback noise from the dynamic latch designs. With 1.8 V supply voltage and 5.0 MHz sampling rate, measured results achieve −0.55/0.72 LSB (Least Significant Bit) of DNL (differential nonlinearity) and −0.78/0.92 LSB of integral nonlinearity (INL) respectively, and 10.76 bits of an effective number of bits (ENOB) at 1MHz input frequency. The chip area is 0.83 mm2 including pads and the power consumption is 490µW for optical and wireless communications.


title: How to Select Good Neighboring Images in Depth-Map Merging Based 3D Modeling

abstract: Depth-map merging based 3D modeling is an effective approach for reconstructing large-scale scenes from multiple images. In addition to generate high quality depth maps at each image, how to select suitable neighboring images for each image is also an important step in the reconstruction pipeline, unfortunately to which little attention has been paid in the literature until now. This paper is intended to tackle this issue for large scale scene reconstruction where many unordered images are captured and used with substantial varying scale and view-angle changes. We formulate the neighboring image selection as a combinatorial optimization problem and use the quantum-inspired evolutionary algorithm to seek its optimal solution. Experimental results on the ground truth data set show that our approach can significantly improve the quality of the depth-maps as well as final 3D reconstruction results with high computational efficiency.


title: A Simplification-Translation-Restoration Framework for Cross-Domain SMT Applications

abstract: Integration of domain specific knowledge into a general purpose statistical machine translation (SMT) system poses challenges due to insufficient bilingual corpora. In this paper we propose a simplification-translation-restoration (STR) framework for domain adaptation in SMT by simplifying domain specific segments of a text. For an in-domain text, we identify the critical segments and modify them to alleviate the data sparseness problem in the out-domain SMT system. After we receive the translation result, these critical segments are then restored according to the provided in-domain knowledge. We conduct experiments on an English-toChinese translation task in the medical domain and evaluate each step of the STR framework. The translation results show significant improvement of our approach over the out-domain and the naive in-domain SMT systems.


title: Improving Online Multiple Object tracking with Deep Metric Learning.

abstract: Tracking by detection is a common approach to solving the Multiple Object Tracking problem. In this paper we show how deep metric learning can be used to improve three aspects of tracking by detection. We train a convolutional neural network to learn an embedding function in a Siamese configuration on a large person re-identification dataset offline. It is then used to improve the online performance of tracking while retaining a high frame rate. We use this learned appearance metric to robustly build estimates of pedestrian's trajectories in the MOT16 dataset. In breaking with the tracking by detection model, we use our appearance metric to propose detections using the predicted state of a tracklet as a prior in the case where the detector fails. This method achieves competitive results in evaluation, especially among online, real-time approaches. We present an ablative study showing the impact of each of the three uses of our deep appearance metric.


title: Nursing Scope of Practice: Descriptions and Challenges

abstract: The nursing workforce is faced with shortages of near crisis proportions, yet little is understood about the optimal utilization of various categories of nurses – Licensed Practical Nurses (LPNs), Registered Nurses (RNs) and Registered Psychiatric Nurses (RPNs). The primary purpose in this study was to elicit the perceptions of nurses (RNs, LPNs, and RPNs) of what “working to full scope of practice” meant to them. NURSING RESEARCH


title: Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment

abstract: This paper presents a precise localization algorithm for vehicles in 3D urban environment with only one 2D LIDAR and odometry information. A novel idea of synthetic 2D LIDAR is proposed to solve the localization problem on a virtual 2D plane. A Monte Carlo Localization scheme is adopted for vehicle position estimation, based on synthetic LIDAR measurements and odometry information. The accuracy and robustness of the proposed algorithm are demonstrated by performing real time localization in a 1.5 km driving test around the NUS campus area.


title: Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning

abstract: We study the online estimation of the optimal policy of a Markov decision process (MDP). We propose a class of Stochastic Primal-Dual (SPD) methods which exploit the inherent minimax duality of Bellman equations. The SPD methods update a few coordinates of the value and policy estimates as a new state transition is observed. These methods use small storage and has low computational complexity per iteration. The SPD methods find an absolute-$\epsilon$-optimal policy, with high probability, using $\mathcal{O}\left(\frac{|\mathcal{S}|^4 |\mathcal{A}|^2\sigma^2 }{(1-\gamma)^6\epsilon^2} \right)$ iterations/samples for the infinite-horizon discounted-reward MDP and $\mathcal{O}\left(\frac{|\mathcal{S}|^4 |\mathcal{A}|^2H^6\sigma^2 }{\epsilon^2} \right)$ for the finite-horizon MDP.


title: SELF-SERV: A Platform for Rapid Composition of Web Services in a Peer-to-Peer Environment

abstract: The automation of Web services interoperation is gaining a considerable momentum as a paradigm for effective Business-to-Business collaboration [2]. Established enterprises are continuously discovering new opportunities to form alliances with other enterprises, by offering value-added integrated services. However, the technology to compose Web services in appropriate time-frames has not kept pace with the rapid growth and volatility of available opportunities. Indeed, the development of integrated Web services is often ad-hoc and requires a considerable eort of low level programming. This approach is inadequate given the size and the volatility of the Web. Furthermore, the number of services to be integrated may be large, so that approaches where the development of an integrated service requires the understanding of each of the underlying services are inappropriate. In addition, Web services may need to be composed as part of a short term partnership, and disbanded when the partnership is no longer profitable. Hence, the integration of a large number of Web services requires scalable and flexible techniques, such as those based on declarative languages. Also, the execution of an integrated service in existing approaches is usually centralised, whereas the underlying services are distributed and autonomous. This calls for the investigation of distributed execution paradigms (e.g., peer-to-peer models), that do not suffer of the scalability and availability problems of centralised coordination [3]. ::: ::: Motivated by these concerns, we have developed the SELF-SERV platform for rapid composition of Web services [1]. In SELF-SERV, Web services are declaratively composed, and the resulting composite services are executed in a peer-to-peer and dynamic environment. In this paper we overview the design and implementation of the SELF-SERV system.


title: Dry mechanochemical synthesis of hydroxyapatites from dicalcium phosphate dihydrate and calcium oxide: a kinetic study.

abstract: Calcium phosphate ceramics have been used successfully as synthetic bone substitutes in orthopedics, dentistry, and maxillofacial surgery. One way of preparing these ceramics is the sintering of a calcium-deficient hydroxyapatite (CDHA), which can be obtained in different ways. Mechanochemistry is one possible means of synthesizing CDHA, with an expected molar calcium-to-phosphate (Ca/P) ratio +/- 0.005. The grinding can be carried out under dry or wet conditions. To optimize the experimental conditions of CDHA preparation by dry mechanosynthesis and for a better understanding of the DCPD/CaO mechanochemical reaction, we performed a kinetic study in which some of the experimental parameters were varied. Carried out with two different vertical rotating ball mills, this kinetic study showed that (1) experiments are reproducible and give as a final product a hydroxyapatite powder, formed of nano-sized crystals of around 20 nm, with a controlled Ca/P ratio; (2) the time for complete disappearance of DCPD and the time for complete reaction are in direct proportion to the mass of the ground powder; but (3) the time for complete disappearance of DCPD is independent of the Ca/P ratio while the time for complete reaction increases exponentially with the Ca/P ratio; and (4) the time for complete disappearance of DCPD corresponds to the time for complete reaction solely for Ca/P = 1.5. These observations suggest a reaction mechanism in two well differentiated stages: (First stage) CaO reacts with DCPD to give first an amorphous calcium phosphate (ACP) with a low Ca/P ratio that transforms into CDHA when its Ca/P ratio reaches 1.5. At the same time, CaO is hydrated into Ca(OH)(2) by the water produced by the reaction. (Second stage) If the Ca/P > 1.5 in the initial mixture, the excess Ca(OH)(2) is added to CDHA 1.5 by reacting with the HPO(4) group of CDHA until its Ca/P ratio reaches the expected value. The slower the reaction, the higher the Ca/P in the initial mixture.


title: CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos

abstract: Temporal action localization is an important yet challenging problem. Given a long, untrimmed video consisting of multiple action instances and complex background contents, we need not only to recognize their action categories, but also to localize the start time and end time of each instance. Many state-of-the-art systems use segment-level classifiers to select and rank proposal segments of pre-determined boundaries. However, a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. To this end, we design a novel Convolutional-De-Convolutional (CDC) network that places CDC filters on top of 3D ConvNets, which have been shown to be effective for abstracting action semantics but reduce the temporal length of the input data. The proposed CDC filter performs the required temporal upsampling and spatial downsampling operations simultaneously to predict actions at the frame-level granularity. It is unique in jointly modeling action semantics in space-time and fine-grained temporal dynamics. We train the CDC network in an end-to-end manner efficiently. Our model not only achieves superior performance in detecting actions in every frame, but also significantly boosts the precision of localizing temporal boundaries. Finally, the CDC network demonstrates a very high efficiency with the ability to process 500 frames per second on a single GPU server. Source code and trained models are available online at https://bitbucket.org/columbiadvmm/cdc.


title: Understanding tools: Task-oriented object modeling, learning and recognition

abstract: In this paper, we present a new framework - task-oriented modeling, learning and recognition which aims at understanding the underlying functions, physics and causality in using objects as “tools”. Given a task, such as, cracking a nut or painting a wall, we represent each object, e.g. a hammer or brush, in a generative spatio-temporal representation consisting of four components: i) an affordance basis to be grasped by hand; ii) a functional basis to act on a target object (the nut), iii) the imagined actions with typical motion trajectories; and iv) the underlying physical concepts, e.g. force, pressure, etc. In a learning phase, our algorithm observes only one RGB-D video, in which a rational human picks up one object (i.e. tool) among a number of candidates to accomplish the task. From this example, our algorithm learns the essential physical concepts in the task (e.g. forces in cracking nuts). In an inference phase, our algorithm is given a new set of objects (daily objects or stones), and picks the best choice available together with the inferred affordance basis, functional basis, imagined human actions (sequence of poses), and the expected physical quantity that it will produce. From this new perspective, any objects can be viewed as a hammer or a shovel, and object recognition is not merely memorizing typical appearance examples for each category but reasoning the physical mechanisms in various tasks to achieve generalization.


title: Rhetorical Structure Theory in Natural Language Generation

abstract: In an angiography, it is desirable to improve the resolution of the subtraction image. One approach to this requirement is attempted to subtract a series of the optical image by an optical subtraction method. A diagnostic apparatus includes at least first and second electrooptic devices, into which first and second optical images originated from a plurality of optical images are exposed so as to be stored therein, and thereafter from which those first and second optical images are optically read out, and an optical image subtraction device in which the above-mentioned first and second optical images are optically subtracted.


title: Simulating LTE/LTE-Advanced Networks with SimuLTE

abstract: In this work we present SimuLTE, an OMNeT++-based simulator for LTE and LTE-Advanced networks. Following well-established OMNeT++ programming practices, SimuLTE exhibits a fully modular structure, which makes it easy to be extended, verified, and integrated. Moreover, it inherits all the benefits of such a widely used and versatile simulation framework as OMNeT++, i.e., experiment support and seamless integration with the OMNeT++ network modules, such as INET. This allows SimuLTE users to build up mixed scenarios where LTE is only a part of a wider network. This paper describes the architecture of SimuLTE, with particular emphasis on the modeling choices at the MAC layer, where resource scheduling is located. Furthermore, we describe some of the verification and validation efforts and present an example of the performance analysis that can be carried out with SimuLTE.


title: Learning deep structured semantic models for web search using clickthrough data

abstract: Latent semantic models, such as LSA, intend to map a query to its relevant documents at the semantic level where keyword-based matching often fails. In this study we strive to develop a series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them. The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data. To make our models applicable to large-scale Web search applications, we also use a technique called word hashing, which is shown to effectively scale up our semantic models to handle large vocabularies which are common in such tasks. The new models are evaluated on a Web document ranking task using a real-world data set. Results show that our best model significantly outperforms other latent semantic models, which were considered state-of-the-art in the performance prior to the work presented in this paper.


title: CellJoin: a parallel stream join operator for the cell processor

abstract: Low-latency and high-throughput processing are key requirements of data stream management systems (DSMSs). Hence, multi-core processors that provide high aggregate processing capacity are ideal matches for executing costly DSMS operators. The recently developed Cell processor is a good example of a heterogeneous multi-core architecture and provides a powerful platform for executing data stream operators with high-performance. On the down side, exploiting the full potential of a multi-core processor like Cell is often challenging, mainly due to the heterogeneous nature of the processing elements, the software managed local memory at the co-processor side, and the unconventional programming model in general. In this paper, we study the problem of scalable execution of windowed stream join operators on multi-core processors, and specifically on the Cell processor. By examining various aspects of join execution flow, we determine the right set of techniques to apply in order to minimize the sequential segments and maximize parallelism. Concretely, we show that basic windows coupled with low-overhead pointer-shifting techniques can be used to achieve efficient join window partitioning, column-oriented join window organization can be used to minimize scattered data transfers, delay-optimized double buffering can be used for effective pipelining, rate-aware batching can be used to balance join throughput and tuple delay, and finally single-instruction multiple-data (SIMD) optimized operator code can be used to exploit data parallelism. Our experimental results show that, following the design guidelines and implementation techniques outlined in this paper, windowed stream joins can achieve high scalability (linear in the number of co-processors) by making efficient use of the extensive hardware parallelism provided by the Cell processor (reaching data processing rates of ?13 GB/s) and significantly surpass the performance obtained form conventional high-end processors (supporting a combined input stream rate of 2,000 tuples/s using 15 min windows and without dropping any tuples, resulting in ?8.3 times higher output rate compared to an SSE implementation on dual 3.2 GHz Intel Xeon).


title: Automatically Generating Personalized User Interfaces

abstract: User Interfaces for today's software are usually created in a one-size-fits-all manner, making implicit assumptions about the needs, abilities, and preferences of the "average user" and the characteristics of the "average device." I argue that personalized user interfaces, which are adapted to a person's devices, tasks, preferences, and abilities, can improve user satisfaction and performance. I have developed three systems: (1) SUPPLE, which uses decision-theoretic optimization to automatically generate user interfaces adapted to a person's device and long-term usage; (2) ARNAULD, which allows optimization-based systems to be adapted to users' preferences; and (3) ABILITY MODELER and an extension of SUPPLE that first performs a one-time assessment of a person's motor abilities and then automatically generates user interfaces predicted to be the fastest to use for that user. ::: My experiments show that these automatically generated, personalized user interfaces significantly improve speed, accuracy, and satisfaction for users with motor impairments compared to manufacturers' default interfaces. I also provide the first characterization of the design space of adaptive graphical user interfaces, and demonstrate how such interfaces can significantly improve the quality and efficiency of daily interactions for typical users.


title: Competitive Inner-Imaging Squeeze and Excitation for Residual Network

abstract: Residual networks, which use a residual unit to supplement the identity mappings, enable very deep convolutional architecture to operate well, however, the residual architecture has been proved to be diverse and redundant, which may leads to low-efficient modeling. In this work, we propose a competitive squeeze-excitation (SE) mechanism for the residual network. Re-scaling the value for each channel in this structure will be determined by the residual and identity mappings jointly, and this design enables us to expand the meaning of channel relationship modeling in residual blocks. Modeling of the competition between residual and identity mappings cause the identity flow to control the complement of the residual feature maps for itself. Furthermore, we design a novel inner-imaging competitive SE block to shrink the consumption and re-image the global features of intermediate network structure, by using the inner-imaging mechanism, we can model the channel-wise relations with convolution in spatial. We carry out experiments on the CIFAR, SVHN, and ImageNet datasets, and the proposed method can challenge state-of-the-art results.


title: HiDDeN: Hiding Data With Deep Networks

abstract: Recent work has shown that deep neural networks are highly sensitive to tiny perturbations of input images, giving rise to adversarial examples. Though this property is usually considered a weakness of learned models, we explore whether it can be beneficial. We find that neural networks can learn to use invisible perturbations to encode a rich amount of useful information. In fact, one can exploit this capability for the task of data hiding. We jointly train encoder and decoder networks, where given an input message and cover image, the encoder produces a visually indistinguishable encoded image, from which the decoder can recover the original message. We show that these encodings are competitive with existing data hiding algorithms, and further that they can be made robust to noise: our models learn to reconstruct hidden information in an encoded image despite the presence of Gaussian blurring, pixel-wise dropout, cropping, and JPEG compression. Even though JPEG is non-differentiable, we show that a robust model can be trained using differentiable approximations. Finally, we demonstrate that adversarial training improves the visual quality of encoded images.


title: An Lda and Synonym Lexicon Based Approach to Product Feature Extraction from Online Consumer Product Reviews

abstract: ABSTRACTConsumers are increasingly relying on other consumers' online reviews of features and quality of products while making their purchase decisions. However, the rapid growth of online consumer product reviews makes browsing a large number of reviews and identifying information of interest time consuming and cognitively demanding. Although there has been extensive research on text review mining to address this information overload problem in the past decade, the majority of existing research mainly focuses on the quality of reviews and the impact of reviews on sales and marketing. Relatively little emphasis has been placed on mining reviews to meet personal needs of individual consumers. As an essential first step toward achieving this goal, this study proposes a product feature-oriented approach to the analysis of online consumer product reviews in order to support feature-based inquiries and summaries of consumer reviews. The proposed method combines LDA (Latent Dirichlet Allocation) and a synonym lexicon to extract product features from online consumer product reviews. Our empirical evaluation using consumer reviews of four products shows higher effectiveness of the proposed method for feature extraction in comparison to association rule mining.Keywords: Online product reviews; Feature extraction; Latent Dirichlet Allocation; Synonym lexicon; Data mining(ProQuest: ... denotes formulae omitted.)1. IntroductionWith the rapid advance of Internet technology, online shopping becomes increasingly popular. iResearch reports that the e-commerce market worldwide reached US$1.99 billion in Fall 2012, which was 21.9% more than 2011 [iResearch 2012], However, online shopping makes it impossible for consumers to obtain the first-hand experience and knowledge about product quality through direct touching, seeing, and trying out products before purchase.With the rise of Web 2.0 technology, users have become more and more comfortable with sharing their opinions on products or services over the Internet. Examples of these Web 2.0 web sites include e-commerce sites such as Amazon.com, social network sites like Facebook.com, local service provider review sites like Yelp.com, and digital media distributors like Netflix.com. Empirical studies have shown that user reviews indeed have significant impact on sales, and users rely much more on reviews than on numeric ratings to decide if a product should be purchased [Chevalier & Mayzlin, 2006, Li et al., 2009], Consumers often rely on other consumers' online reviews of features and quality of products while making purchase decisions because those reviews offer more useful information about products than the descriptive information available on a shopping website provided by manufacturers [Park and Kim 2008, Hu et al. 2009], According to a survey, 70 percent of Americans regularly consult online consumer product reviews or ratings before making an important purchase [Ante 2009],The rapid growth of the volume of online consumer product reviews, however, makes browsing a large number of reviews to identify information of interest time consuming and cognitively demanding. It poses a significant challenge for taking advantage of those reviews. Review mining that uses data mining and text mining techniques for analyzing a large quantity of online consumer product reviews [Popescu and Etzioni 2007] has been extensively explored as a means of addressing this information overload problem. The majority of existing research, however, focuses on prediction of helpfulness of reviews, determining the polarity of consumer opinions on products, and the impact of reviews on sales and marketing, etc. Relatively little emphasis has been placed on mining reviews to understand consumers' opinions toward individual features of specific products. Product features include product attributes, components, component attributes, etc. [Xi et al. 2011],In reality, individual consumers may have different preferences for features of a product. …


Solving Traveling Salesman Problem Using Parallel Genetic Algorithm and Simulated Annealing


title: Enhancing Performance Prediction Robustness by Combining Analytical Modeling and Machine Learning

abstract: Classical approaches to performance prediction rely on two, typically antithetic, techniques: Machine Learning (ML) and Analytical Modeling (AM). ML takes a black box approach, whose accuracy strongly depends on the representativeness of the dataset used during the initial training phase. Specifically, it can achieve very good accuracy in areas of the features' space that have been sufficiently explored during the training process. Conversely, AM techniques require no or minimal training, hence exhibiting the potential for supporting prompt instantiation of the performance model of the target system. However, in order to ensure their tractability, they typically rely on a set of simplifying assumptions. Consequently, AM's accuracy can be seriously challenged in scenarios (e.g., workload conditions) in which such assumptions are not matched. In this paper we explore several hybrid/gray box techniques that exploit AM and ML in synergy in order to get the best of the two worlds. We evaluate the proposed techniques in case studies targeting two complex and widely adopted middleware systems: a NoSQL distributed key-value store and a Total Order Broadcast (TOB) service.


title: Using Support Vector Machines and Bayesian Filtering for Classifying Agent Intentions at Road Intersections

abstract: Ford Motor Company, Le Fonds Quebecois de la Recherche sur la Nature et ::: les Technologies (FQRNT)


title: A Metropolitan Area Network

abstract: Most high-speed multiple-access networks on the market, or those currently under design, operate in a limited geographical environment and are devoted either to data-only or voice-only transmission. However, challenges issued by the marketplace require the full integration of voice, data, and video in a real-time environment. This paper proposes a metropolitan area network that covers a campus environment and truly integrates voice, data, and video under a single transmission medium. Furthermore, the transmission is achieved through a single media-access schema. This paper also presents marketing issues, network requirements, and technology constraints. The proposed mediaaccess schema is also being considered by the IEEE 802.6 MAN Committee for Standardization.


title: Computer science concept inventories: past and future

abstract: Concept Inventories (CIs) are assessments designed to measure student learning of core concepts. CIs have become well known for their major impact on pedagogical techniques in other sciences, especially physics. Presently, there are no widely used, validated CIs for computer science. However, considerable groundwork has been performed in the form of identifying core concepts, analyzing student misconceptions, and developing CI assessment questions. Although much of the work has been focused on CS1 and a CI has been developed for digital logic, some preliminary work on CIs is underway for other courses. This literature review examines CI work in other STEM disciplines, discusses the preliminary development of CIs in computer science, and outlines related research in computer science education that contributes to CI development.


title: Community Detection Using Diffusion Information

abstract: Community detection in social networks has become a popular topic of research during the last decade. There exist a variety of algorithms for modularizing the network graph into different communities. However, they mostly assume that partial or complete information of the network graphs are available that is not feasible in many cases. In this article, we focus on detecting communities by exploiting their diffusion information. To this end, we utilize the Conditional Random Fields (CRF) to discover the community structures. The proposed method, community diffusion (CoDi), does not require any prior knowledge about the network structure or specific properties of communities. Furthermore, in contrast to the structure-based community detection methods, this method is able to identify the hidden communities. The experimental results indicate considerable improvements in detecting communities based on accuracy, scalability, and real cascade information measures.


title: IEEE 802.11ax uplink scheduler to minimize, delay: A classic problem with new constraints

abstract: In order to meet the continuously increasing demands for high throughput in wireless networks, IEEE 802 LAN/MAN Standard Committee is developing IEEE 802.11ax: a new amendment for the Wi-Fi standard. This amendment provides various ways to improve the efficiency of Wi-Fi. The most revolutionary one is OFDMA. Apart from obvious advantages, such as decreasing overhead for short packet transmission at high rates and improving robustness to frequency selective interference, being used for uplink transmission, OFDMA can increase power spectral density and, consequently, user data rates. However, the gain of OFDMA mainly depends on the resource scheduling between users. The peculiarities of OFDMA implementation in Wi-Fi completely change properties of classic schedulers used in other OFDMA systems, e.g. LTE. In the paper, we consider the usage of OFDMA in Wi-Fi for uplink transmission. We study peculiarities of OFDMA in Wi-Fi, adapt classic schedulers to Wi-Fi, explaining why they do not perform well. Finally we develop a novel scheduler, MUTAX, and evaluate its performance with simulation.


title: Smart pilgrim: A mobile-sensor-cloud based system to safeguard pilgrims through smart environment

abstract: Smart environment at holy places integrates various intelligent devices, infrastructures, and services to monitor and control pilgrims' activities with efficiency. Smart pilgrim management requires aggregation of surrounding informatics for sustainable development of pilgrimage tourism. Some conventional sensing techniques, such as sensor networks are used to gather real time data. The management of large congregation is a primary factor in ensuring human's safety. In this paper, we investigate how sensor, mobile, and cloud technologies can be coupled together to address the pilgrim's safety, especially sensing, storing and analyzing the enormous number of pilgrims' whereabouts information in the smart environment. The existing solutions require a tag to track pilgrim. These tags need expensive sensors to detect them which results in high infrastructure cost. We develop a portable sensing system, named Smart Pilgrim, which uses pilgrims' smartphone whenever possible instead of tags. For sensing pilgrims' smartphones, we develop a sensing unit consisting of an Android based smartphone and an external Wi-Fi adapter. An extensive set of experiments is performed to evaluate the proposed system. The system developed is cost efficient, and will be helpful in congregation management.


title: Impact of Social Media and Web 2.0 on Decision-Making

abstract: Information technology continues to provide opportunities to alter the decisionmaking behavior of individuals, groups and organizations. Two related changes that are emerging are social media and W...


title: On Staying Grounded and Avoiding Quixotic Dead Ends

abstract: The 15 articles in this special issue on The Representation of Concepts illustrate the rich variety of theoretical positions and supporting research that characterize the area. Although much agreement exists among contributors, much disagreement exists as well, especially about the roles of grounding and abstraction in conceptual processing. I first review theoretical approaches raised in these articles that I believe are Quixotic dead ends, namely, approaches that are principled and inspired but likely to fail. In the process, I review various theories of amodal symbols, their distortions of grounded theories, and fallacies in the evidence used to support them. Incorporating further contributions across articles, I then sketch a theoretical approach that I believe is likely to be successful, which includes grounding, abstraction, flexibility, explaining classic conceptual phenomena, and making contact with real-world situations. This account further proposes that (1) a key element of grounding is neural reuse, (2) abstraction takes the forms of multimodal compression, distilled abstraction, and distributed linguistic representation (but not amodal symbols), and (3) flexible context-dependent representations are a hallmark of conceptual processing.


title: The Effect of Maximum Compression Force and Drug Content on Compression Force-Time cProfile Parameters

abstract: AbstractCompression force-time profiles have been obtained for three series of directly compressed tablets containing various quantities of prednisone, phenobarbital and isoniazide in a microcrystalline cellulose matrix. The tablets were punched in an excentric press equipped with piezoelectric transducers, and the profiles were characterized by the parameters: area under curve, width at half height, inflection point ordinate and maximum positive slope, whose dependence on maximum compression force and drug content was evaluated. Since the effects of maximum compression force overwhelmed those of drug content, it was found preferable to normalize the variables area under curve, inflection point ordinate and maximum positive slope by dividing them by the maximum compression force. The strong correlation between the width at half height and the normalized area under the curve as regards their dependence on maximum compression force and drug content suggest that it is redundant to use both these variables.


Height as a measure of success in academe.


title: Managing Churn to Maximize Profits

abstract: Customer defection or churn is a widespread phenomenon that threatens firms across a variety of industries with dramatic financial consequences. To tackle this problem, companies are developing sophisticated churn management strategies. These strategies typically involve two steps – ranking customers based on their estimated propensity to churn, and then offering retention incentives to a subset of customers at the top of the churn ranking. The implicit assumption is that this process would maximize firm’s profits by targeting customers who are most likely to churn. However, current marketing research and practice aims at maximizing the correct classification of churners and non-churners. Profit from targeting a customer depends on not only a customer’s propensity to churn, but also on her spend or value, her probability of responding to retention offers, as well as the cost of these offers. Overall profit of the firm also depends on the number of customers the firm decides to target for its retention campaign. We propose a predictive model that accounts for all these elements. Our optimization algorithm uses stochastic gradient boosting, a state-of-the-art numerical algorithm based on stage-wise gradient descent. It also determines the optimal number of customers to target. The resulting optimal customer ranking and target size selection leads to, on average, a 115% improvement in profit compared to current methods. Remarkably, the improvement in profit comes along with more prediction errors in terms of which customers will churn. However, the new loss function leads to better predictions where it matters the most for the company’s profits. For a company like Verizon Wireless, this translates into a profit increase of at least $28 million from a single retention campaign, without any additional implementation cost.


title: Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions

abstract: In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular bandwidth and all communications take place through the base stations. In this article, we envision a two-tier cellular network that involves a macrocell tier (i.e., BS-to-device communications) and a device tier (i.e., device-to-device communications). Device terminal relaying makes it possible for devices in a network to function as transmission relays for each other and realize a massive ad hoc mesh network. This is obviously a dramatic departure from the conventional cellular architecture and brings unique technical challenges. In such a two-tier cellular system, since the user data is routed through other users? devices, security must be maintained for privacy. To ensure minimal impact on the performance of existing macrocell BSs, the two-tier network needs to be designed with smart interference management strategies and appropriate resource allocation schemes. Furthermore, novel pricing models should be designed to tempt devices to participate in this type of communication. Our article provides an overview of these major challenges in two-tier networks and proposes some pricing schemes for different types of device relaying.


title: Sociology of Design and EDA

abstract: Successful design of complex electronic systems increasingly requires the bi-directional flow of information among groups of design specialists who are becoming more dispersed geographically and organisationally. This affects the type of design flows we develop, the nature of the design tools, how design software is supported and the organisational structure of the EDA and electronics industries.Dr Rhines will provide examples and discuss issues that suggest how the interaction among designers and design organisations will affect the future evolution of design methodology and EDA.


Ontology-Based Security Standards Mapping Optimization by the Means of Graph Theory


title: Action bank: A high-level representation of activity in video

abstract: Abstract : Activity recognition in video is dominated by low- and mid-level features, and while demonstrably capable, by nature, these features carry little semantic meaning. Inspired by the recent object bank approach to image representation, we present Action Bank, a new high-level representation of video. Action bank is comprised of many individual action detectors sampled broadly in semantic space as well as viewpoint space. Our representation is constructed to be semantically rich and even when paired with simple linear SVM classifiers is capable of highly discriminative performance. We have tested action bank on four major activity recognition benchmarks. In all cases, our performance is better than the state of the art, namely 98.2 on KTH (better by 3.3 ), 95.0 on UCF Sports (better by 3.7 ), 57.9 on UCF50 (baseline is 47.9 ), and 26.9 on HMDB51 (baseline is 23.2 ). Furthermore, when we analyze the classifiers, we find strong transfer of semantics from the constituent action detectors to the bank classifier.


Isolation and identification of the spoilage microorganisms in Sichuan homemade Paocai and their impact on quality and safety


title: Web-scale information extraction in knowitall: (preliminary results)

abstract: Manually querying search engines in order to accumulate a large bodyof factual information is a tedious, error-prone process of piecemealsearch. Search engines retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse information from multiple documents. This paperintroduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner.The paper describes preliminary experiments in which an instance of KnowItAll, running for four days on a single machine, was able to automatically extract 54,753 facts. KnowItAll associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KnowItAll's architecture and reports on lessons learned for the design of large-scale information extraction systems.


title: Learning Programs: A Hierarchical Bayesian Approach

abstract: We are interested in learning programs for multiple related tasks given only a few training examples per task. Since the program for a single task is underdetermined by its data, we introduce a nonparametric hierarchical Bayesian prior over programs which shares statistical strength across multiple tasks. The key challenge is to parametrize this multi-task sharing. For this, we introduce a new representation of programs based on combinatory logic and provide an MCMC algorithm that can perform safe program transformations on this representation to reveal shared inter-program substructures.


title: A vehicle license plate recognition system based on analysis of maximally stable extremal regions

abstract: Vehicle License Plate Recognition (VLPR) system is a core module in Intelligent Transportation Systems (ITS). In this paper, a VLPR system is proposed. Considering that license plate localization is the most important and difficult part in VLPR system, we present an effective license plate localization method based on analysis of Maximally Stable Extremal Region (MSER) features. Firstly, MSER detector is utilized to extract candidate character regions. Secondly, the exact locations of license plates are inferred according to the arrangement of characters in standard license plates. The advantage of this license plate localization method is that less assumption of environmental illumination, weather and other conditions is made. After license plate localization, we continue to recognize the license plate characters and color to complete the whole VLPR system. Finally, the proposed VLPR system is tested on our own collected dataset. The experimental results show the availability and effectiveness of our VLPR system in locating and recognizing all the explicit license plates in an image.


title: Canine Intestinal Contents vs. Simulated Media for the Assessment of Solubility of Two Weak Bases in the Human Small Intestinal Contents

abstract: Canine intestinal contents vs. simulated media for the assessment of solubility of two weak bases in the human small intestinal contents.


Using Random Forest to Learn Imbalanced Data


title: A fast object recognition and categorization technique for robot grasping using the visual bag of words

abstract: We present in this paper a real-time method for visual categorization to do robot grasping. We describe an object database with SURF feature points which we quantify with the Kmeans clustering algorithm to make visual words. Then, we train a Support Vector Machine classifier having as entries the distribution of the bag of features extracted earlier. Likewise, we do object recognition using the SVM algorithm. The real-time implementation is done with the OpenCV GPU. The application aims to pick up an object and drop it using our robot manipulator equipped with a camera using our visual system. Finally, the results of our experiments of the object recognition show an average of recognition between 95% and 100%.


title: Determinants of customer repurchase intention in online shopping

abstract: Purpose – The purpose of this paper is to understand customers' repurchase intentions in online shopping. This study extends the technology acceptance model (TAM) by introducing e‐service quality dimensions, trust and enjoyment in the development of a theoretical model to study customers' repurchase intentions within the context of online shopping.Design/methodology/approach – Data collected from 360 PCHome online shopping customers provides strong support for the proposed research model. PLS (partial least squares, PLS‐Graph version 3.0) is used to analyse the measurement and structural models.Findings – The study shows that trust, perceived ease of use, perceived usefulness and enjoyment are significant positive predictors of customers' repurchase intentions.Research limitations/implications – The data are collected from a single online shopping store – the generalisation of the model and findings to other online stores requires additional research. Our findings imply that the five dimensions of e‐servi...


title: Design and Implementation of a GPON-Based Virtual OpenFlow-Enabled SDN Switch

abstract: Passive optical networks (PON) have become a promising solution for accessing networks because of the advantages they offer, such as high efficiency, security, and cost reduction. However, network management in PON is not yet automated and needs network operator intervention. In recent years, software-defined networking (SDN) has become an emerging technology. Through the separation of control and data plane in SDN switches, SDN provides dynamically fine-grained traffic control that enhances total network controllability and manageability. In this paper, we leverage the benefits of gigabit-capable passive optical network (GPON), while enhancing its capabilities on traffic management to the same level as an SDN switch. More specifically, we abstract the underlying physical GPON into an OpenFlow-enabled virtual SDN switch. The virtual switch can be used to connect multiple sites in widespread geographic locations. Similar to a real OpenFlow switch, a GPON virtual switch can be controlled by a standard OpenFlow controller. In our design, an embedded OpenFlow agent resides in the optical line termination (OLT) of the underlying GPON. The agent communicates with the external OpenFlow controller and simultaneously uses optical network unit management and control interface inside the OLT to manage ONUs. We created a prototype system based on a commodity GPON network. In the virtual switch, we implemented all the OpenFlow functions, including packet forwarding, bandwidth metering, statistical data collection, and status reporting. The experimental results show that the GPON virtual switch can correctly perform all the functions defined in the OpenFlow 1.3 specification. Its performance on flow entry modification time, dynamic bandwidth control, and switch status monitoring are comparable to the performance of a real OpenFlow switch.


title: ABC Reinforcement Learning

abstract: We introduce a simple, general framework for likelihood-free Bayesian reinforcement learning, through Approximate Bayesian Computation (ABC). The advantage is that we only require a prior distribution on a class of simulators. This is useful when a probabilistic model of the underlying process is too complex to formulate, but where detailed simulation models are available. ABC-RL allows the use of any Bayesian reinforcement learning technique in this case. It can be seen as an extension of simulation methods to both planning and inference. We experimentally demonstrate the potential of this approach in a comparison with LSPI. Finally, we introduce a theorem showing that ABC is sound.


title: Comparison of classification techniques for intrusion detection dataset using WEKA

abstract: As the network based applications are growing rapidly, the network security mechanisms require more attention to improve speed and precision. The ever evolving new intrusion types pose a serious threat to network security. Although numerous network security tools have been developed, yet the fast growth of intrusive activities is still a serious issue. Intrusion detection systems (IDSs) are used to detect intrusive activities on the network. Machine learning and classification algorithms help to design “Intrusion Detection Models” which can classify the network traffic into intrusive or normal traffic. In this paper we present the comparative performance of NSL-KDD based data set compatible classification algorithms. These classifiers have been evaluated in WEKA (Waikato Environment for Knowledge Analysis) environment using 41 attributes. Around 94,000 instances from complete KDD dataset have been included in the training data set and over 48,000 instances have been included in the testing data set. Garrett's Ranking Technique has been applied to rank different classifiers according to their performance. Rotation Forest classification approach outperformed the rest.


title: Treatment of oral lichen sclerosus with 1% pimecrolimus cream.

abstract: Lichen sclerosus is a chronic inflammatory mucocutaneous disorder predominately affecting prepubertal girls and postmenopausal women. Isolated lichen sclerosus affecting the oral mucosa is exceedingly rare, and only 13 patients with biopsy-proved isolated oral disease have been reported in the literature. We report on a 7-year-old Korean girl with a well-demarcated 1.2x1.2 cm atrophic white plaque with an erythematous border and focal telangiectasia on the left vermillion lip, extending to the labial mucosa. No other cutaneous surfaces, including genitalia, were involved. An incisional biopsy of the plaque on the lip revealed a patchy lichenoid infiltrate of lymphocytes associated with sclerosis of the papillary dermis and a thinned epidermis consistent with a diagnosis of linear orofacial lichen sclerosus. Treatment with a short course of 1% pimecrolimus cream effectively prevented the progression of this lesion.


UNSUPERVISED FEATURE LEARNING FOR SPEECH AND MUSIC DETECTION IN RADIO BROADCASTS


title: Segmentation using eigenvectors: a unifying view

abstract: Automatic grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated good performance on this task using methods that are based on eigenvectors of the affinity matrix. These approaches are extremely attractive in that they are based on simple eigendecomposition algorithms whose stability is well understood. Nevertheless, the use of eigendecompositions in the context of segmentation is far from well understood. In this paper we give a unified treatment of these algorithms, and show the close connections between them while highlighting their distinguishing features. We then prove results on eigenvectors of block matrices that allow us to analyze the performance of these algorithms in simple grouping settings. Finally, we use our analysis to motivate a variation on the existing methods that combines aspects from different eigenvector segmentation algorithms. We illustrate our analysis with results on real and synthetic images.


title: The Computation of All 4R Serial Spherical Wrists With an Isotropic Architecture

abstract: A spherical wrist of the serial type is said to be isotropic if it can attain a posture whereby the singular values of its Jacobian matrix are all identical and nonzero. What isotropy brings about is robustness to manufacturing, assembly, and measurement errors, thereby guaranteeing a maximum orientation accuracy. In this paper we investigate the existence of redundant isotropic architectures, which should add to the dexterity of the wrist under design by virtue of its extra degree of freedom. The problem formulation leads to a system of eight quadratic equations with eight unknowns. The Bezout number of this system is thus 2^8 = 256, its BKK bound being 192. However, the actual number of solutions is shown to be 32. We list all solutions of the foregoing algebraic problem. All these solutions are real, but distinct solutions do not necessarily lead to distinct manipulators. Upon discarding those algebraic solutions that yield no new wrists, we end up with exactly eight distinct architectures, the eight corresponding manipulators being displayed at their isotropic posture.


title: A deep learning approach for pose estimation from volumetric OCT data

abstract: Abstract Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images’ 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework. For this purpose, we design a new 3D convolutional neural network (CNN) architecture to directly predict the 6D pose of a small marker geometry from OCT volumes. We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression. We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. Specifically, we demonstrate that exploiting volume information for pose estimation yields higher accuracy than relying on 2D representations with depth information. Supporting this observation, we provide quantitative and qualitative results that 3D CNNs effectively exploit the depth structure of marker objects. Regarding the deep learning aspect, we present efficient design principles for 3D CNNs, making use of insights from the 2D deep learning community. In particular, we present Inception3D as a new architecture which performs best for our application. We show that our deep learning approach reaches errors at our ground-truth label’s resolution. We achieve a mean average error of 14.89 ± 9.3 µm and 0.096 ± 0.072° for position and orientation learning, respectively.


title: Design and evaluation of a 6.6 kW GaN converter for onboard charger applications

abstract: This paper presents a compact, lightweight, highly efficient, 6.6 kW isolated three-port dc-dc converter for onboard charger (OBC) applications. The converter was designed and fabricated using normally-off gallium nitride (GaN) transistors; a three-dimensional (3-D) printed cold plate; high-voltage heavy copper printed circuit board (PCB) power planes; low-voltage (14 V) and high-current PCB power planes; and a planar transformer. The prototype has a power density of 10.5 kW/L and specific power of 9.6 kW/kg. Test results show greater efficiency than a silicon-based counterpart, even at 2.5 times higher switching frequency. The isolated GaN converter was integrated with a 100 kW segmented traction inverter that uses silicon carbide MOSFETs and 3-D printed components to test the functionality as a level-2 OBC. Testing and evaluation of the integral onboard charging functionality was successfully completed at power levels up to 6.6 kW.


title: Immobility and falls.

abstract: Immobility is a common problem for hospitalized older adults. Excessive bed rest results in multiple adverse physiologic consequences and may contribute to functional decline and increased risk for falls in the hospital setting. About 2% of hospitalized older adults fall during hospitalization. Risk factors for in-hospital falls includes cognitive impairment, mobility impairment, specific diagnoses, multiple comorbidities, and psychotropic medications. Appropriate actions to prevent immobility and falls include increasing exercise and activity levels, improving the hospital environment, and decreasing the use of psychotropic medications. Bed alarms and increased supervision for high-risk patients also may help prevent falls.


title: Interventions to reduce social isolation and loneliness among older people: an integrative review

abstract: Loneliness and social isolation are major problems for older adults. Interventions and activities aimed at reducing social isolation and loneliness are widely advocated as a solution to this growing problem. The aim of this study was to conduct an integrative review to identify the range and scope of interventions that target social isolation and loneliness among older people, to gain insight into why interventions are successful and to determine the effectiveness of those interventions. Six electronic databases were searched from 2003 until January 2016 for literature relating to interventions with a primary or secondary outcome of reducing or preventing social isolation and/or loneliness among older people. Data evaluation followed Evidence for Policy and Practice Information and Co-ordinating Centre guidelines and data analysis was conducted using a descriptive thematic method for synthesising data. The review identified 38 studies. A range of interventions were described which relied on differing mechanisms for reducing social isolation and loneliness. The majority of interventions reported some success in reducing social isolation and loneliness, but the quality of evidence was generally weak. Factors which were associated with the most effective interventions included adaptability, a community development approach, and productive engagement. A wide range of interventions have been developed to tackle social isolation and loneliness among older people. However, the quality of the evidence base is weak and further research is required to provide more robust data on the effectiveness of interventions. Furthermore, there is an urgent need to further develop theoretical understandings of how successful interventions mediate social isolation and loneliness.


title: The Internet of Things vision: Key features, applications and open issues

abstract: Abstract The Internet of Things (IoT) is a new paradigm that combines aspects and technologies coming from different approaches. Ubiquitous computing, pervasive computing, Internet Protocol, sensing technologies, communication technologies, and embedded devices are merged together in order to form a system where the real and digital worlds meet and are continuously in symbiotic interaction. The smart object is the building block of the IoT vision. By putting intelligence into everyday objects, they are turned into smart objects able not only to collect information from the environment and interact/control the physical world, but also to be interconnected, to each other, through Internet to exchange data and information. The expected huge number of interconnected devices and the significant amount of available data open new opportunities to create services that will bring tangible benefits to the society, environment, economy and individual citizens. In this paper we present the key features and the driver technologies of IoT. In addition to identifying the application scenarios and the correspondent potential applications, we focus on research challenges and open issues to be faced for the IoT realization in the real world.


title: Chinese sentiment classification using a neural network tool — Word2vec

abstract: Sentiment classification is the main and popular task in the field of sentiment analysis. Most of the existing researches focus on how to extract the effective features, such as lexical features and syntactic features, while limited work has been done on the extraction of semantic features, which can make more contributions to sentiment classification. This paper presents a method for sentiment classification based on word2vec. Word2vec is a tool, which establishes the neural network models to learn the vector representations of words in the high dimensional vector space. So it can extract the deep semantic relationships between words. In this paper, firstly, we cluster the similar features together using word2vec. And then we use word2vec again to learn the word representations as candidate feature vectors. After feature selection, the SVMperf package is adopted to train and classify the comment texts. To conduct the experiments, we collect a large number of Chinese comments on clothing products as data set. The experimental results show that the accuracy of sentiment classification is over 90 percent, which proves the effectiveness of proposed method for Chinese sentiment classification.


title: Probabilistic spatio-temporal resource search

abstract: In this paper, we deal with the resource search problem in a probabilistic setting. In a resource search problem, there are spatially located static resources and a mobile agent. The agent looks to obtain one of the resources while minimizing the cost. This cost may consist of different types of costs the agent has to pay, from travel time to the cost of obtaining a certain resource. We assume that the agent has no knowledge of exact availability of the resources in real-time, but some prior or partial data gives estimations of this information. This model applies to many situations that arise in urban transportation systems, such as drivers looking for street parking, taxis looking for new customers, and electric vehicles looking for charging stations. Our approach to the resource search problem only employs uncertain information about resource availability, minimizes the expected cost, and utilizes concepts from decision theory. A simulation that uses real-world data is used to compare our approach to alternatives.


title: Cleaning up the record on the maximal information coefficient and equitability

abstract: Although we appreciate Kinney and Atwal’s interest in equitability and maximal information coefficient (MIC), we believe they misrepresent our work. We highlight a few of our main objections below. ::: ::: ::: ::: Fig. 1. ::: ::: Equitability of MIC and mutual information under a range of noise models. The equitability of MIC and mutual information across a subset of noise models analyzed in refs. 1 and 4. For each noise model, the relationships tested are as in ref. 4. In each ... ::: ::: ::: ::: Regarding our original paper (1), Kinney and Atwal (2) state “MIC is said to satisfy not just the heuristic notion of equitability, but also the mathematical criterion of R2 equitability,” the latter being their formalization of the heuristic notion that we introduced. This statement is simply false. We were explicit in our paper that our claims regarding MIC’s performance were based on large-scale simulations: “We tested MIC’s equitability through simulations….[These] show that, for a large collection of test functions with varied sample sizes, noise levels, and noise models, MIC roughly equals the coefficient of determination R2 relative to each respective noiseless function.” Although we mathematically proved several things about MIC, none of our claims imply that it satisfies Kinney and Atwal’s R2 equitability, which would require that MIC exactly equal R2 in the infinite data limit. Thus, their proof that no dependence measure can satisfy R2 equitability, although interesting, does not uncover any error in our work, and their suggestion that it does is a gross misrepresentation. ::: ::: Kinney and Atwal seem ready to toss out equitability as a useful criterion based on their theoretical result. We argue, however, that regardless of whether “perfect” equitability is possible, approximate notions of equitability remain the right goal for many data exploration settings. Just as the theory of NP completeness does not suggest we stop thinking about NP complete problems, but instead that we look for approximations and solutions in restricted cases, an impossibility result about perfect equitability provides focus for further research, but does not mean that useful solutions are unattainable. Similarly, as others have noted (3), Kinney and Atwal’s proof requires a highly permissive noise model, and so the attainability of R2 equitability under more limited noise models such as those in our work remains an open question. ::: ::: Finally, the authors argue that mutual information is more equitable than MIC. However, they provide as justification only a single noise model, only at limiting sample sizes (n≥5,000). As we’ve shown in follow-up work (4), which they themselves cite but fail to address, MIC is more equitable than mutual information estimation under many other realistic noise models even at a sample size of 5,000. Kinney and Atwal have stated, “…it matters how one defines noise” (5), and a useful statistic must indeed be robust to a wide range of noise models. Equally importantly, we’ve established in both our original and follow-up work that at sample size regimes less than 5,000, MIC is more equitable than mutual information estimates across all noise models tested. MIC’s superior equitability in these settings is not an “artifact” we neglected—as Kinney and Atwal suggest—but rather a weakness of mutual information estimation and an important consideration for practitioners. ::: ::: We expect that the understanding of equitability and MIC will improve over time and that better methods may arise. However, accurate representations of the work thus far will allow researchers in the area to most productively and collectively move forward.


title: Relationship Proposal Networks

abstract: Image scene understanding requires learning the relationships between objects in the scene. A scene with many objects may have only a few individual interacting objects (e.g., in a party image with many people, only a handful of people might be speaking with each other). To detect all relationships, it would be inefficient to first detect all individual objects and then classify all pairs, not only is the number of all pairs quadratic, but classification requires limited object categories, which is not scalable for real-world images. In this paper we address these challenges by using pairs of related regions in images to train a relationship proposer that at test time produces a manageable number of related regions. We name our model the Relationship Proposal Network (Rel-PN). Like object proposals, our Rel-PN is class-agnostic and thus scalable to an open vocabulary of objects. We demonstrate the ability of our Rel-PN to localize relationships with only a few thousand proposals. We demonstrate its performance on the Visual Genome dataset and compare to other baselines that we designed. We also conduct experiments on a smaller subset of 5,000 images with over 37,000 related regions and show promising results.


title: A Mechanism to Improve the Performance of Zone-Based Broadcasting Protocol in the Recovery Phase

abstract: Wireless Sensor Networks (WSNs) have been widely used in environment monitoring to collect useful information. Packet broadcasting is an essential communication behavior for establishing a communication path from sink node to an interested region of sensor nodes. ZBP is a cellular based broadcast protocol, and it successfully outperformed simple flooding manner broadcasting and common cellular based approach in two main aspects: first it reduced the number of sensor nodes that forward transmission packets and thus save energy consumption; second, it can efficiently avoid collision by properly arranging broadcasting schedule. However it still faces broadcasting failure caused by blank region of sensors. This paper improves ZBP by introducing an energy efficient recovery mechanism which recovers broadcasting when blank region interrupts the request forwarding. Compared with the simple over-network flooding recovery used by ZBP, our proposed protocol can reduce much energy consumption.


Enhancing metacognitive reinforcement learning using reward structures and feedback.


title: Practical Implementation of Delayed Signal Cancellation Method for Phase-Sequence Separation

abstract: In this paper, the use of a method for online detection of positive- and negative-sequence components of three-phase quantities, named the delayed signal cancellation (DSC) method, is investigated. Problems that arise in practical implementation of the DSC method in computer-controlled systems are investigated. Expressions of the detection error due to nonideal discretization are derived and calculations are verified experimentally. Two methods for reducing the detection error are presented and verified. It is also shown that the given expressions and proposed methods for reducing the detection errors can also be applied to the case of grid frequency variations


title: Corrosion Behaviour of Nitrogen-Implantation Ti-Ta-Nb Alloy in Physiological Solutions Simulating Real Conditions from Human Body

abstract: We applied a new nitrogen-implantation technique (trademark Hardion+) using a source of nitrogen ions, electron cyclotron resonance that assures higher energy and deeper implantation than the conventional techniques. The N-implantation surface of the new Ti-25Ta-25Nb alloy was analyzed as follows: for the phase identification by x-ray diffraction (XRD) in a glancing geometry (1°); for the hardness by the nano-indentation method; for the corrosion behaviour in Ringer solutions of different pH values (simulating the real conditions from the human body) by cyclic and linear polarization, electrochemical impedance spectroscopy and the monitoring of the open circuit potentials and corresponding potential gradients. XRD pattern was indexed with face-centred cubic TiN compound partially substituted with TaN and NbN. The hardness increased about 2 times for the N-implantation alloy. The implantation layer had a protection effect, increasing the corrosion and passivation potentials and decreasing the tendency to passivation and passive current density, due to its compactness, reinforcement action. The corrosion current density and rate decreased by about 10 times and the polarization resistance increased by about 2 times, indicative of a more resistant nitride layer. The porosity was much reduced and the protection efficiency had values closed to 90%, namely the implantation treatment led to the formation of a dense, resistant layer. Impedance spectra showed that the capacitive behaviour of the N-implantation alloy was more insulating and protective. An electric equivalent circuit with two times constants was modelled.


title: An Approach for Learning and Construction of Expressive Ontology from Text in Natural Language

abstract: In this paper, we present an approach based on Ontology Learning and Natural Language Processing for automatic construction of expressive Ontologies, specifically in OWL DL with ALC expressivity, from a natural language text. The viability of our approach is demonstrated through the generation of descriptions of complex axioms from concepts defined by users and glossaries found at Wikipedia. We evaluated our approach in an experiment with entry sentences enriched with hierarchy axioms, disjunction, conjunction, negation, as well as existential and universal quantification to impose restriction of properties. The obtained results prove that our model is an effective solution for knowledge representation and automatic construction of expressive Ontologies. Thereby, it assists professionals involved in processes for obtain, construct and model knowledge domain.


A Novel Implementation of Phase Control Technique for Speed Control of Induction Motor Using ARDUINO


The Role of Motivated Social Cognition in the Regulation of Affective States


title: Radar cross section prediction and measurement at 77 GHz

abstract: This paper presents a comparison of predicted and measured radar cross sections (RCS) of complex metallic objects at 77 GHz. For computing the RCS, a ray tracing algorithm combined with physical optics and the incremental length diffraction coefficient is used. The simulated results are compared against measurements, obtained using a 77-GHz frequency-modulated continuous-wave radar sensor.


title: Game & Watch: Are "Let's Play" Gaming Videos as Immersive as Playing Games?

abstract: Let's Play videos, where players record themselves playing games, are a new and popular way of experiencing game content. To investigate the experience of watching Let's Play videos, we had 40 participants watch a video of someone playing a racing game. The same participants also played the racing game and watched footage of an actual racing event. After each media experience, participants completed a modified version of the Immersive Experience Questionnaire (IEQ). Results show that IEQ scores were highest after participants played the game, and lowest after passively watching the non-gaming content; watching the Let's Play video was in between. When watching the Let's Play video, participants who were familiar with the game had lower IEQ scores than participants who were new to the game. These results show that actively controlling a game generates a deeper immersive experience than passively watching others play - watching is not as immersive as playing.


title: Software-Defined Edge Defense Against IoT-Based DDoS

abstract: Industries, people's activities and urban infrastructure rely more and more on "Internet of Things" (IoT) devices for all kinds of activities and operations. Although securing a computer network has always been a difficult task, it transmutes into a massive challenge with the exponentially-growing network size and heterogenity in these systems. A recent example in that vein is a major security violation in the form of DDoS attacks which were performed by a huge number of IoT devices infected with a botnet called "Mirai". Moreover, the role of these devices as control, sensing and communication substrate in critical infrastructures aggravate such problems. Nevertheless, there are some new networking concepts and technologies which promise to offer remedies for the aforementioned challenges; one is software-defined networking (SDN) and another one is fog computing, which primarily provide global network control and local services, respectively. In this paper, we introduce a edge-oriented detection/mitigation scheme against DDoS in IoT using SDN and Fog approaches while utilizing Mirai as the case study.


title: Digital entrepreneurship ecosystem as a new form of organizing: the case of Zhongguancun

abstract: Digital innovation is becoming increasingly important in today’s economy. Many digital innovations are developed not within organizations, but in innovation-driven entrepreneurial ecosystems, where various entrepreneurship related stakeholders collaborate and cooperate. Despite its significance, studies on digital entrepreneurship ecosystems (DEEs) are limited and the concept is largely undertheorized. This study intends to fill that gap by studying how a DEE organizes. This organizing issue is challenging, because stakeholders of a DEE are self-organizing and are not governed by any formal authority. To answer that question, we adopt forms of organizing as a theoretical lens, which provides structure to examine organizing issues. Through an in-depth case study of Zhongguancun, the Silicon Valley of China, we unveil eight processes around the themes of division of labor and integration of efforts. We further show that the forms of organizing feature a balance of centralized design and de-centralized emergence. This balanced view extends the forms of organizing literature, which takes an either/or perspective. Ecosystem architects and policy makers who intend to build entrepreneurship ecosystems to promote local economies can derive practical implications from our findings.


title: Regularized hyperalignment of multi-set fMRI data

abstract: Inter-subject correspondence is an important aspect of multi-subject fMRI studies. Recently, a new approach, called hyperalignment, has shown very promising results in fMRI functional alignment. Hyperalignment is based on Procrustean rotations and is connected, mathematically, to canonical correlation analysis. We review the core details of each approach, relate them through an SVD analysis, and indicate why they can yield different levels of performance. We then examine the effectiveness of regularization in mediating between the extremes of these methods. An inter-subject classification experiment based on functional aligned fMRI datasets illustrates the resulting improved performance.


title: Privacy preserving visualization for social network data with ontology information

abstract: Analyzing social network data helps sociologists understand the behaviors of individuals and groups as well as the relationships between them. With additional ontology information, the semantics behind the network structure can be further explored. Unfortunately, creating network visualizations with these datasets for presentation can inadvertently expose the private and sensitive information of individuals that reside in the data. To deal with this problem, we generalize conventional data anonymization models (originally designed for relational data) and formally apply them in the context of privacy preserving ontological network visualization. We use these models to identify the privacy leaks that exist in a visualization, provide graph modification actions that remove and/or perceptually minimize the effect of the identified leaks, and discuss strategies for what types of privacy actions to choose depending on the context of the leaks. We implement an ontological visualization interface with associated privacy preserving operations, and demonstrate with two case studies using real-world datasets to show that our approach can identify and solve potential privacy issues while balancing overall graph readability and utility.