title: Automated driving on public roads: Experiences in real traffic

abstract: Abstract Automated driving on public roads is affected by many foreseeable and unforeseeable driving situations. Depending on the driving task, the environmental and road conditions, and the behavior of other drivers, different actions have to be taken. This paper provides a high-level overview of the development of highly automated driving systems and illustrates challenging situations and use cases. We outlined the impact of these use cases on system design, key technologies, and their technical realization for a highly automated driving system. Furthermore, the paper demonstrates how certain aspects of the system design as well as their implementation are country specific and how continuous testing is required for robust implementation of the functionalities.


title: Blending Vertical and Web Results

abstract: Modern search engines aggregate results from specialized verticals into the Web search results. We study a setting where vertical and Web results are blended into a single result list, a setting that has not been studied before. We focus on video intent and present a detailed observational study of Yandex's two video content sources i.e., the specialized vertical and a subset of the general web index thus providing insights into their complementary character. By investigating how to blend results from these sources, we contrast traditional federated search and fusion-based approaches with newly proposed approaches that significantly outperform the baseline methods.


title: An Energy Efficiency Feature Survey of the Intel Haswell Processor

abstract: The recently introduced Intel Xeon E5-1600 v3 and E5-2600 v3 series processors -- codenamed Haswell-EP -- implement major changes compared to their predecessors. Among these changes are integrated voltage regulators that enable individual voltages and frequencies for every core. In this paper we analyze a number of consequences of this development that are of utmost importance for energy efficiency optimization strategies such as dynamic voltage and frequency scaling (DVFS) and dynamic concurrency throttling (DCT). This includes the enhanced RAPL implementation and its improved accuracy as it moves from modeling to actual measurement. Another fundamental change is that every clock speed above AVX frequency -- including nominal frequency -- is opportunistic and unreliable, which vastly decreases performance predictability with potential effects on scalability. Moreover, we characterize significantly changed p-state transition behavior, and determine crucial memory performance data.


title: Study of Sliding Mode Control of DC-DC Buck Converter

abstract: In this paper, a robust sliding mode controller for the control of dc-dc buck converter is designed and analyzed. Dynamic equations describing the buck converter are derived and sliding mode controller is designed. A two-loop control is employed for a buck converter. The robustness of the sliding mode controlled buck converter system is tested for step load changes and input voltage variations. The theoretical predictions are validated by means of simulations. Matlab/Simulink is used for the simulations. The simulation results are presented. The buck converter is tested with operating point changes and parameter uncertainties. Fast dynamic response of the output voltage and robustness to load and input voltage variations are obtained.


title: A novel clustering-based approach of indoor location fingerprinting

abstract: This study proposes a clustering-based Wi-Fi fingerprinting localization algorithm. The proposed algorithm first presents a novel support vector machine based clustering approach, namely SVM-C, which uses the margin between two canonical hyperplanes for classification instead of using the Euclidean distance between two centroids of reference locations. After creating the clusters of fingerprints by SVM-C, our positioning system embeds the classification mechanism into a positioning task and compensates for the large database searching problem. The proposed algorithm assigns the matched cluster surrounding the test sample and locates the user based on the corresponding cluster's fingerprints to reduce the computational complexity and remove estimation outliers. Experimental results from realistic Wi-Fi test-beds demonstrated that our approach apparently improves the positioning accuracy. As compared to three existing clustering-based methods, K-means, affinity propagation, and support vector clustering, the proposed algorithm reduces the mean localization errors by 25.34%, 25.21%, and 26.91%, respectively.


title: A Comparative Study of Graphical and Alphanumeric Passwords for Mobile Device Authentication

abstract: Mobile devices such as smartphones and tablets are widely used to perform security critical and privacy sensitive activities, such as mobile banking, mobile health care, mobile shopping, etc. Screen locks are used in mobile devices to protect sensitive information. Graphical password and alphanumeric password are two common types of screen locking schemes. The alphanumeric password scheme has shown some security and usability drawbacks. For example, a user may pick an easy to remember alphanumeric password that may also be easy to guess. On the contrary, if as user picks a password that is hard to guess it may also be hard to remember. Several alternative password mechanisms have been introduced. Graphical password is one of them, and it is based on pictures or patterns. However, graphical password is also vulnerable to certain types of attack. In this paper, we study an alphanumeric password method (i.e., PIN) and a graphical password method (i.e., pattern) in order to unravel security and usability issues related to mobile device authentication. The study uses observation and survey data to compare these two authentication methods on following criteria: creation time, memorability, and login time and login success rate. In addition, we also measure how the screen size of a mobile device affects usability and security aspects of screen locks by measuring differences on creation time, memorability, login time, login success rate for Android smartphone and tablet.


title: Agent-Based Modelling of Social-Ecological Systems: Achievements, Challenges, and a Way Forward

abstract: Understanding social-ecological systems (SES) is crucial to supporting the sustainable management of resources. Agent-based modelling is a valuable tool to achieve this because it can represent the behaviour and interactions of organisms, human actors and institutions. Agent-based models (ABMs) have therefore already been widely used to study SES. However, ABMs of SES are by their very nature complex. They are therefore difficult to parameterize and analyse, which can limit their usefulness. It is time to critically reflect upon the current state-of-the-art to evaluate to what degree the potential of agent-based modelling for gaining general insights and supporting specific decision-making has already been utilized. We reviewed achievements and challenges by building upon developments in good modelling practice in the field of ecological modelling with its longer history. As a reference, we used the TRACE framework, which encompasses elements of model development, testing and analysis. We firstly reviewed achievements and challenges with regard to the elements of the TRACE framework addressed in reviews and method papers of social-ecological ABMs. Secondly, in a mini-review, we evaluated whether and to what degree the elements of the TRACE framework were addressed in publications on specific ABMs. We identified substantial gaps with regard to (1) communicating whether the models represented real systems well enough for their intended purpose and (2) analysing the models in a systematic and transparent way so that model output is not only observed but also understood. To fill these gaps, a joint effort of the modelling community is needed to foster the advancement and use of strategies such as participatory approaches, standard protocols for communication, sharing of source code, and tools and strategies for model design and analysis. Throughout our analyses, we provide specific recommendations and references for improving the state-of-the-art. We thereby hope to contribute to the establishment of a new advanced culture of agent-based modelling of SES that will allow us to better develop general theory and practical solutions.


Attention and Augmented Recurrent Neural Networks


title: Improved transient response using HFFC in current-mode CFCOT control for Buck converter

abstract: In this paper, improved transient response using high-frequency feedback control (HFFC) in current-mode constant frequency constant on-time (CFCOT) control for Buck converter is proposed. The concept uses HFFC to filter out the Vout at the load transient to dynamically change the width of on-time to prevent the Vout from dropping markedly. Finally, HFFC is implemented using the experimental results and SIMPLIS simulation results to verify its viability and superiority.


title: On the Personality Traits of StackOverflow Users

abstract: In the last decade, developers have been increasingly sharing their questions with each other through Question and Answer (Q&A) websites. As a result, these websites have become valuable knowledge repositories, covering a wealth of topics related to particular programming languages. This knowledge is even more useful as the developer community evaluates both questions and answers through a voting mechanism. As votes accumulate, the developer community recognizes reputed members and further trusts their answers. In this paper, we analyze the community's questions and answers to determine the developers' personality traits, using the Linguistic Inquiry and Word Count (LIWC). We explore the personality traits of Stack Overflow authors by categorizing them into different categories based on their reputation. Through textual analysis of Stack Overflow posts, we found that the top reputed authors are more extroverted compared to medium and low reputed users. Moreover, authors of up-voted posts express significantly less negative emotions than authors of down-voted posts.


title: Cuckoo: a Computation Offloading Framework for

abstract: Offloading computation from smartphones to remote cloud resources has recently been rediscovered as a technique to enhance the performance of smartphone applications, while reducing the energy usage.


title: Counterexample-guided abstraction refinement for symbolic model checking

abstract: The state explosion problem remains a major hurdle in applying symbolic model checking to large hardware designs. State space abstraction, having been essential for verifying designs of industrial complexity, is typically a manual process, requiring considerable creativity and insight.In this article, we present an automatic iterative abstraction-refinement methodology that extends symbolic model checking. In our method, the initial abstract model is generated by an automatic analysis of the control structures in the program to be verified. Abstract models may admit erroneous (or "spurious") counterexamples. We devise new symbolic techniques that analyze such counterexamples and refine the abstract model correspondingly. We describe aSMV, a prototype implementation of our methodology in NuSMV. Practical experiments including a large Fujitsu IP core design with about 500 latches and 10000 lines of SMV code confirm the effectiveness of our approach.


title: Robust dimensions of anxiety sensitivity: Development and initial validation of the Anxiety Sensitivity Index-3.

abstract: Accumulating evidence suggests that anxiety sensitivity (fear of arousal-related sensations) plays an important role in many clinical conditions, particularly anxiety disorders. Research has increasingly focused on how the basic dimensions of anxiety sensitivity are related to various forms of psychopathology. Such work has been hampered because the original measure-the Anxiety Sensitivity Index (ASI)- was not designed to be multidimensional. Subsequently developed multidimensional measures have unstable factor structures or measure only a subset of the most widely replicated factors. Tlierefore, the authors developed, via factor analysis of responses from U.S. and Canadian nonclinical participants (n = 2,361), an 18-item measure, the ASI-3, which assesses the 3 factors best replicated in previous research: Physical, Cognitive, and Social Concerns. Factorial validity of the ASI-3 was supported by confirmatory factor analyses of 6 replication samples, including nonclinical samples from the United States and Canada, France, Mexico, the Netherlands, and Spain (n = 4,494) and a clinical sample from the United States and Canada (n = 390). The ASI-3 displayed generally good performance on other indices of reliability and validity, along with evidence of improved psychometric properties over the original ASI.


title: Patient-centered care: the key to cultural competence

abstract: Much of the early literature on 'cultural competence' focuses on the 'categorical' or 'multicultural' approach, in which providers learn relevant attitudes, values, beliefs, and behaviors of certain cultural groups. In essence, this involves learning key 'dos and don'ts' for each group. Literature and educational materials of this kind focus on broad ethnic, racial, religious, or national groups, such as 'African American', 'Hispanic', or 'Asian'. The problem with this categorical or 'list of traits' approach to clinical cultural competence is that culture is multidimensional and dynamic. Culture comprises multiple variables, affecting all aspects of experience. Cultural processes frequently differ within the same ethnic or social group because of differences in age cohort, gender, political association, class, religion, ethnicity, and even personality. Culture is therefore a very elusive and nebulous concept, like art. The multicultural approach to cultural competence results in stereotypical thinking rather than clinical competence. A newer, cross cultural approach to culturally competent clinical practice focuses on foundational communication skills, awareness of cross-cutting cultural and social issues, and health beliefs that are present in all cultures. We can think of these as universal human beliefs, needs, and traits. This patient centered approach relies on identifying and negotiating different styles of communication, decision-making preferences, roles of family, sexual and gender issues, and issues of mistrust, prejudice, and racism, among other factors. In the current paper, we describe 'cultural' challenges that arise in the care of four patients from disparate cultures, each of whom has advanced colon cancer that is no longer responding to chemotherapy. We then illustrate how to apply principles of patient centered care to these challenges.


title: The Promise of the Sharing Economy among Disadvantaged Communities

abstract: The digital-sharing economy presents opportunities for individuals to find temporary employment, generate extra income, increase reciprocity, enhance social interaction, and access resources not otherwise attainable. Although the sharing economy is profitable, little is known about its use among the unemployed or those struggling financially. This paper describes the results of a participatory-design based workshop to investigate the perception and feasibility of finding temporary employment and sharing spare resources using sharing-economy applications. Specifically, this study included 20 individuals seeking employment in a U.S. city suffering economic decline. We identify success factors of the digital-sharing economy to these populations, identify shortcomings and propose mitigation strategies based on prior research related to trust, social capital and theories of collective efficacy. Finally, we contribute new principles that may foster collaborative consumption within this population and identify new concepts for practical employment applications among these populations.


title: Map-matching for low-sampling-rate GPS trajectories

abstract: Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.


title: A Technical Report: Entity Extraction using Both Character-based and Token-based Similarity

abstract: Entity extraction is fundamental to many text mining tasks such as organisation name recognition. A popular approach to entity extraction is based on matching sub-string candidates in a document against a dictionary of entities. To handle spelling errors and name variations of entities, usually the matching is approximate and edit or Jaccard distance is used to measure dissimilarity between sub-string candidates and the entities. For approximate entity extraction from free text, existing work considers solely character-based or solely token-based similarity and hence cannot simultaneously deal with minor variations at token level and typos. In this paper, we address this problem by considering both character-based similarity and token-based similarity (i.e. two-level similarity). Measuring one-level (e.g. character-based) similarity is computationally expensive, and measuring two-level similarity is dramatically more expensive. By exploiting the properties of the two-level similarity and the weights of tokens, we develop novel techniques to significantly reduce the number of sub-string candidates that require computation of two-level similarity against the dictionary of entities. A comprehensive experimental study on real world datasets show that our algorithm can efficiently extract entities from documents and produce a high F1 score in the range of [0.91, 0.97].


The Effect of Class Distribution on Classifier Learning: An Empirical Study


title: Large scale author name disambiguation in digital libraries

abstract: Person name disambiguation is essential to distinguish between persons that share the same name where unique identifiers are not present. In many domains this is a common problem including digital libraries where the same name can refer to multiple unique authors. Correctly attributing work and citations requires the digital library's database to be disambiguated. In this work we describe a large scale framework for disambiguating author names efficiently and effectively. The framework uses a density based clustering algorithm with a random forest based distance function to clusters unique authors. Effective use of blocking functions allows the clustering algorithm to be run in parallel. In our experiments we show that the framework disambiguates authors of more than 4 million papers in 24 hours.


title: Summarizing Spoken and Written Conversations

abstract: In this paper we describe research on summarizing conversations in the meetings and emails domains. We introduce a conversation summarization system that works in multiple domains utilizing general conversational features, and compare our results with domain-dependent systems for meeting and email data. We find that by treating meetings and emails as conversations with general conversational features in common, we can achieve competitive results with state-of-the-art systems that rely on more domain-specific features.


title: Towards a SCADA Forensics Architecture

abstract: With the increasing threat of sophisticated attacks on critical infrastructures, it is vital that forensic investigations take place immediately following a security incident. This paper presents an existing SCADA forensic process model and proposes a structured SCADA forensic process model to carry out a forensic investigations. A discussion on the limitations of using traditional forensic investigative processes and the challenges facing forensic investigators. Furthermore, flaws of existing research into providing forensic capability for SCADA systems are examined in detail. The study concludes with an experimentation of a proposed SCADA forensic capability architecture on the Siemens S7 PLC. Modifications to the memory addresses are monitored and recorded for forensic evidence. The collected forensic evidence will be used to aid the reconstruction of a timeline of events, in addition to other collected forensic evidence such as network packet captures.


title: The Effectiveness of Data Augmentation in Image Classification using Deep Learning

abstract: In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification. Previous work has demonstrated the effectiveness of data augmentation through simple techniques, such as cropping, rotating, and flipping input images. We artificially constrain our access to data to a small subset of the ImageNet dataset, and compare each data augmentation technique in turn. One of the more successful data augmentations strategies is the traditional transformations mentioned above. We also experiment with GANs to generate images of different styles. Finally, we propose a method to allow a neural net to learn augmentations that best improve the classifier, which we call neural augmentation. We discuss the successes and shortcomings of this method on various datasets.


title: Sentence compression for aspect-based sentiment analysis

abstract: Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as document-level sentiment classification, we are interested in the fine-grained aspect-based sentiment analysis that aims to identify aspects that users comment on and these aspects' polarities. Aspect-based sentiment analysis relies heavily on syntactic features. However, the reviews that this task focuses on are natural and spontaneous, thus posing a challenge to syntactic parsers. In this paper, we address this problem by proposing a framework of adding a sentiment sentence compression (Sent_Comp) step before performing the aspect-based sentiment analysis. Different from the previous sentence compression model for common news sentences, Sent_Comp seeks to remove the sentiment-unnecessary information for sentiment analysis, thereby compressing a complicated sentiment sentence into one that is shorter and easier to parse. We apply a discriminative conditional random field model, with certain special features, to automatically compress sentiment sentences. Using the Chinese corpora of four product domains, Sent_Comp significantly improves the performance of the aspect-based sentiment analysis. The features proposed for Sent_Comp, especially the potential semantic features, are useful for sentiment sentence compression.


title: Mass Spectrometry-Based Label-Free Quantitative Proteomics

abstract: In order to study the differential protein expression in complex biological samples, strategies for rapid, highly reproducible and accurate quantification are necessary. Isotope labeling and fluorescent labeling techniques have been widely used in quantitative proteomics research. However, researchers are increasingly turning to label-free shotgun proteomics techniques for faster, cleaner, and simpler results. Mass spectrometry-based label-free quantitative proteomics falls into two general categories. In the first are the measurements of changes in chromatographic ion intensity such as peptide peak areas or peak heights. The second is based on the spectral counting of identified proteins. In this paper, we will discuss the technologies of these label-free quantitative methods, statistics, available computational software, and their applications in complex proteomics studies.


title: Suboptimal compliance with surgical safety checklists in Colorado: A prospective observational study reveals differences between surgical specialties

abstract: Surgical safety checklists (SSCs) are designed to improve team communication and consistency in care, ultimately avoiding complications. In Colorado, hospitals reported that use of SSCs was standard practice, but a statewide survey indicated that SSC use was inconsistent. The purpose of this project was to directly observe the compliance with the SSC in Colorado hospitals, through direct observation of the perioperative checklist process. Ten hospitals participated in a quality improvement initiative. Trained team members recorded compliance with each of the components of the SSC. Data analysis was performed using a chi-squared test or ANOVA, depending on the number of categorical variables, with p < 0.05 determining statistical significance. Ten hospitals representing statewide diversity submitted 854 observations (median 98, range 24–106). 83% of cases were elective, 13% urgent, and 4% emergent/trauma. There was significant variation across hospitals in: team introductions, cessation of activity, affirming correct procedure, assessing hypothermia risk, need for beta blocker, or VTE prophylaxis. Uniformly poor compliance was observed with respect to assessment of case duration, blood loss, anesthesiologists’ concerns, or display of essential imaging. Only 71% of observers reported active participation by physicians; 9% reported that “the majority did not pay attention” and 4% reported that the team was “just going through the motions”. There were significant differences among surgical specialty groups in the majority of the elements. SSCs have been implemented by the vast majority of hospitals in our state; however, compliance with SSC completion in the operating room has wide variation and is generally suboptimal. Although this study was not designed to correlate SSC compliance with outcomes, there are concerns about the risk of a sentinel event or unanticipated complication resulting from poor preparation.


title: Query based event extraction along a timeline

abstract: In this paper, we present a framework and a system that extracts events relevant to a query from a collection C of documents, and places such events along a timeline. Each event is represented by a sentence extracted from C, based on the assumption that "important" events are widely cited in many documents for a period of time within which these events are of interest. In our experiments, we used queries that are event types ("earthquake") and person names (e.g. "George Bush"). Evaluation was performed using G8 leader names as queries: comparison made by human evaluators between manually and system generated timelines showed that although manually generated timelines are on average more preferable, system generated timelines are sometimes judged to be better than manually constructed ones.


title: Quadcopter Control System Modelling and Implementation

abstract: The paper presents two different types of approach for mathematical modeling of quadcopter kinematics and dynamics. The first one is based on the equations of classical mechanics and the other one is derived from Denavit-Hartenberg formalism and Lagrangian mechanics. The obtained models were used to design the control of the quadcopter motion on one rotation axis. The paper also offers some details of the physical and software implementation of the system on which tests have been made.


title: TREX: Spatially distributed model to assess watershed contaminant transport and fate

abstract: Contaminant releases from upland areas can have adverse water quality and stream ecology impacts. TREX (Two-dimensional, Runoff, Erosion, and Export) is a spatially distributed, physically-based model to simulate chemical transport and fate at the watershed scale. TREX combines surface hydrology and sediment transport features from the CASC2D watershed model with chemical transport features from the WASP/IPX series of water quality models. In addition to surface runoff and sediment transport, TREX simulates: (1) chemical erosion, advection, and deposition; (2) chemical partitioning and phase distribution; and (3) chemical infiltration and redistribution. Floodplain interactions for water, sediment, and chemicals are also simulated. To demonstrate the potential for using TREX to simulate chemical transport at the watershed scale, a screening-level application was developed for the California Gulch watershed mine-waste site in Colorado. Runoff, sediment transport, and metals (Cu, Cd, Zn) transport were simulated for a calibration event and a validation event. The model reproduced measured peak flows, and times to peak at the watershed outlet and three internal locations. Simulated flow volumes were within approximately 10% of measured conditions. Model results were also generally within measured ranges of total suspended solid and metal concentrations. TREX is an appropriate tool for investigating multimedia environmental problems that involve water, soils, and chemical interactions in a spatially distributed manner within a watershed.


title: EdgeChain: An Edge-IoT Framework and Prototype Based on Blockchain and Smart Contracts

abstract: The emerging Internet of Things (IoT) is facing significant scalability and security challenges. On one hand, IoT devices are “weak” and need external assistance. Edge computing provides a promising direction addressing the deficiency of centralized cloud computing in scaling massive number of devices. On the other hand, IoT devices are also relatively “vulnerable” facing malicious hackers due to resource constraints. The emerging blockchain and smart contracts technologies bring a series of new security features for IoT and edge computing. In this paper, to address the challenges, we design and prototype an edge-IoT framework named “EdgeChain” based on blockchain and smart contracts. The core idea is to integrate a permissioned blockchain and the internal currency or “coin” system to link the edge cloud resource pool with each IoT device’ account and resource usage, and hence behavior of the IoT devices. EdgeChain uses a credit-based resource management system to control how much resource IoT devices can obtain from edge servers, based on predefined rules on priority, application types, and past behaviors. Smart contracts are used to enforce the rules and policies to regulate the IoT device behavior in a nondeniable and automated manner. All the IoT activities and transactions are recorded into blockchain for secure data logging and auditing. We implement an EdgeChain prototype and conduct extensive experiments to evaluate the ideas. The results show that while gaining the security benefits of blockchain and smart contracts, the cost of integrating them into EdgeChain is within a reasonable and acceptable range.


title: Banach Wasserstein GAN

abstract: Wasserstein Generative Adversarial Networks (WGANs) can be used to generate realistic samples from complicated image distributions. The Wasserstein metric used in WGANs is based on a notion of distance between individual images, which induces a notion of distance between probability distributions of images. So far the community has considered l^2 as the underlying distance. We generalize the theory of WGAN with gradient penalty to Banach spaces, allowing practitioners to select the features to emphasize in the generator. We further discuss the effect of some particular choices of underlying norms, focusing on Sobolev norms. Finally, we demonstrate the impact of the choice of norm on model performance and show state-of-the-art inception scores for non-progressive growing GANs on CIFAR-10.


title: A MORPHOLOGY OF THE ORGANISATION OF DATA GOVERNANCE

abstract: Both information systems (IS) researchers and practitioners consider data governance as a promisingapproach for companies to improve and maintain the quality of corporate data, which is seen ascritical for being able to meet strategic business requirements, such as compliance or integratedcustomer management. Both sides agree that data governance primarily is a matter of organisation.However, hardly any scientific results have been produced so far indicating what actually has to beorganised by data governance, and what data governance may look like. The paper aims at closingthis gap by developing a morphology of data governance organisation on the basis of acomprehensive analysis of the state of the art both in science and in practice. Epistemologically, themorphology represents an analytic theory, as it serves for structuring the research topic of datagovernance, which is still quite unexplored. Six mini case studies are used to evaluate the morphologyby means of empirical data. Providing a foundation for further research, the morphology contributesto the advancement of the scientific body of knowledge. At the same time, it is beneficial topractitioners, as companies may use it as a guideline when organising data governance.


title: Deep Learning for Joint Source-Channel Coding of Text

abstract: We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach to this problem in both theory and practice involves performing source coding to first compress the text and then channel coding to add robustness for the transmission across the channel. This approach is optimal in terms of minimizing end-to-end distortion with arbitrarily large block lengths of both the source and channel codes when transmission is over discrete memoryless channels. However, the optimality of this approach is no longer ensured for documents of finite length and limitations on the length of the encoding. We will show in this scenario that we can achieve lower word error rates by developing a deep learning based encoder and decoder. While the approach of separate source and channel coding would minimize bit error rates, our approach preserves semantic information of sentences by first embedding sentences in a semantic space where sentences closer in meaning are located closer together, and then performing joint source and channel coding on these embeddings.


title: Identifying Product Opportunities Using Social Media Mining: Application of Topic Modeling and Chance Discovery Theory

abstract: As an emerging voice of the customer (VOC) containing feedback, such as opinions and expectations about products, social media data have the potential use for product improvement and new product development. However, most prior studies have focused on determining customer concerns, while neglecting to incorporate them into a systematic approach to identify product opportunities. In response, this paper suggests an approach to identify product opportunities from customer reviews in social media. This approach employs topic modeling to identify the product topics discussed by customers from large-scale review posts related to a given product. A keygraph is then constructed based on the co-occurrences among the topics contained in each post. The chance discovery theory is then applied to generate new product opportunities from the chance nodes obtained from the keygraph. Our approach contributes to the systematic ideation process for product opportunity analysis based on large-scale and real-time VOC.


Oil and Development : Technology , Geology , and the ‘ Curse ’ of Natural Resources


title: Towards Self-organized Large-Scale Shape Formation: A Cognitive Agent-Based Computing Approach

abstract: Swarm robotic systems are currently being used to address many real-world problems. One interesting application of swarm robotics is the self-organized formation of structures and shapes. Some of the key challenges in the swarm robotic systems include swarm size constraint, random motion, coordination among robots, localization, and adaptability in a decentralized environment. Rubenstein et al. presented a system ("Programmable self-assembly in a thousand-robot swarm", Science, 2014) for thousand-robot swarm able to form only solid shapes with the robots in aggregated form by applying the collective behavior algorithm. Even though agent-based approaches have been presented in various studies for self-organized formation, however these studies lack agent-based modeling (ABM) approach along with the constraints in term of structure complexity and heterogeneity in large swarms with dynamic localization. The cognitive agent-based computing (CABC) approach is capable of modeling such self-organization based multi-agents systems (MAS). In this paper, we develop a simulation model using ABM under CABC approach for self-organized shape formation in swarm robots. We propose a shape formation algorithm for validating our model and perform simulation-based experiments for six different shapes including hole-based shapes. We also demonstrate the formal specification for our model. The simulation result shows the robustness of the proposed approach having the emergent behavior of robots for the self-organized shape formation. The performance of the proposed approach is evaluated by robots convergence rate.


title: Design of a lightweight, tethered, torque-controlled knee exoskeleton

abstract: Lower-limb exoskeletons show promise for improving gait rehabilitation for those with chronic gait abnormalities due to injury, stroke or other illness. We designed and built a tethered knee exoskeleton with a strong lightweight frame and comfortable, four-point contact with the leg. The device is structurally compliant in select directions, instrumented to measure joint angle and applied torque, and is lightweight (0.76 kg). The exoskeleton is actuated by two off-board motors. Closed loop torque control is achieved using classical proportional feedback control with damping injection in conjunction with iterative learning. We tested torque measurement accuracy and found root mean squared (RMS) error of 0.8 Nm with a max load of 62.2 Nm. Bandwidth was measured to be phase limited at 45 Hz when tested on a rigid test stand and 23 Hz when tested on a person's leg. During bandwidth tests peak extension torques were measured up to 50 Nm. Torque tracking was tested during walking on a treadmill at 1.25 m/s with peak flexion torques of 30 Nm. RMS torque tracking error averaged over a hundred steps was 0.91 Nm. We intend to use this knee exoskeleton to investigate robotic assistance strategies to improve gait rehabilitation and enhance human athletic ability.


title: How Secure Are Printed Circuit Boards Against Trojan Attacks?

abstract: Hardware Trojan attacks at the integrated circuit (IC) level have been studied extensively in recent times. Researchers have analyzed the impact of these attacks and explored possible countermeasures for ICs. However, vulnerability with respect to hardware Trojan attacks at higher levels of system abstraction, e.g., at printed circuit board (PCB) level, have not been reported earlier. Previous studies have covered security of PCBs against piracy and various post-fabrication tampering attacks. JTAG (Joint Test Access Group) and other field programmability features, e.g., probe pins, unused sockets and USB have been extensively exploited by hackers to gain access to internal features of the designs as well as snooping of secret key, collection of test responses, and manipulating JTAG test pins. One instance demonstrated that Xbox can be hacked by disabling the Digital Rights Management (DRM) policy using JTAG. The emerging business model of PCB design and fabrication that favors extensive outsourcing and integration of untrusted components/entities in the PCB life-cycle to lower manufacturing cost, makes hardware Trojan attacks in PCBs highly feasible.


title: Unknown Tag Identification in Large RFID Systems: An Efficient and Complete Solution

abstract: Radio-Frequency Identification (RFID) technology brings revolutionary changes to many fields like retail industry. One important research issue in large RFID systems is the identification of unknown tags, i.e., tags that just entered the system but have not been interrogated by reader(s) covering them yet. Unknown tag identification plays a critical role in automatic inventory management and misplaced tag discovery, but it is far from thoroughly investigated. Existing solutions either trivially interrogate all the tags in the system and thus are highly time inefficient due to re-identification of already identified tags, or use probabilistic approaches that cannot guarantee complete identification of all the unknown tags. In this paper, we propose a series of protocols that can identify all of the unknown tags with high time efficiency. We develop several novel techniques to quickly deactivate already identified tags and prevent them from replying during the interrogation of unknown tags, which avoids re-identification of these tags and consequently improves time efficiency. To our knowledge, our protocols are the first non-trivial solutions that guarantee complete identification of all the unknown tags. We illustrate the effectiveness of our protocols through both rigorous theoretical analysis and extensive simulations. Simulation results show that our protocols can save up to 70 percent time when compared with the best existing solutions.


title: Data replication policy in a cloud computing environment

abstract: In a cloud computing environment, data replication is frequently used for failure recovery. However, data replication can be used also to improve the performance of the execution of application. In this context, this article presents a policy for data replication for a computational cloud with bioinformatics data, such that the execution of application of bioinformatics is reduced.


title: Cache-aware Roofline model: Upgrading the loft

abstract: The Roofline model graphically represents the attainable upper bound performance of a computer architecture. This paper analyzes the original Roofline model and proposes a novel approach to provide a more insightful performance modeling of modern architectures by introducing cache-awareness, thus significantly improving the guidelines for application optimization. The proposed model was experimentally verified for different architectures by taking advantage of built-in hardware counters with a curve fitness above 90%.


title: Business process comparison: a methodology and case study

abstract: Business processes often exhibit a high degree of variability. Process variants may manifest due to the differences in the nature of clients, heterogeneity in the type of cases, etc. Through the use of process mining techniques, one can benefit from historical event data to extract non-trivial knowledge for improving business process performance. Although some research has been performed on supporting process comparison within the process mining context, applying process comparison in practice is far from trivial. Considering all comparable attributes, for example, leads to an exponential number of possible comparisons. In this paper we introduce a novel methodology for applying process comparison in practice. We successfully applied the methodology in a case study within Xerox Services, where a forms handling process was analyzed and actionable insights were obtained by comparing different process variants using event data.


title: Reasons to Use Virtual Reality in Education and Training Courses and a Model to Determine When to Use Virtual Reality

abstract: Many studies have been conducted on the use of virtual reality in education and training. This article lists examples of such research. Reasons to use virtual reality are discussed. Advantages and disadvantages of using virtual reality are presented, as well as suggestions on when to use and when not to use virtual reality. A model that can be used to determine when to use virtual reality in an education or training course is presented.


(Labex Agro: ANR-10-LABX-0001-01) STRUCTURE OF THE MALE AND FEMALE REPRODUCTIVE AND DIGESTIVE SYSTEMS OF RHIZOGLYPHUS ROBINI CLAPAREDE


title: Real-Time Signal Light Detection

abstract: One of most important issue in research for unmanned ground vehicle is detecting signal lights at crossroads. This paper presents new algorithm for signal light detection. It has high detection rate with real time and fast processing at low price. Experimental environment is built in real vehicle and real urban road, and we shall show that the proposed algorithm realizes driving with reliability in unmanned ground vehicle.


title: Formation of Nickel Silicide from Direct-Liquid-Injection Chemical-Vapor-Deposited Nickel Nitride Films

abstract: Smooth, continuous, and highly conformal nickel nitride (NiN{sub x}) films were deposited by direct liquid injection (DLI)-chemical vapor deposition (CVD) using a solution of bis(N,N{prime}-di-tert-butylacetamidinato)nickel(II) in tetrahydronaphthalene as the nickel (Ni) source and ammonia (NH{sub 3}) as the coreactant gas. The DLI-CVD NiNx films grown on HF-last (100) silicon and on highly doped polysilicon substrates served as the intermediate for subsequent conversion into nickel silicide (NiSi), which is a key material for source, drain, and gate contacts in microelectronic devices. Rapid thermal annealing in the forming gas of DLI-CVD NiNx films formed continuous NiSi films at temperatures above 400 C. The resistivity of the NiSi films was 15{mu}{Omega} cm, close to the value for bulk crystals. The NiSi films have remarkably smooth and sharp interfaces with underlying Si substrates, thereby producing contacts for transistors with a higher drive current and a lower junction leakage. Resistivity and synchrotron X-ray diffraction in real-time during annealing of NiNx films showed the formation of a NiSi film at about 440 C, which is morphologically stable up to about 650 C. These NiSi films could find applications in future nanoscale complementary metal oxide semiconductor devices or three-dimensional metal-oxide-semiconductor devices such as Fin-type field effect transistorsmore » for the 22 nm technology node and beyond.« less


title: DOI: 10.5829/idosi.wasj.2012.18.12.2803 Armature Current Control of DC Motor using Generic Model Control

abstract: DC motor speed can be indirectly controlled by controlling armature current. This paper describes the design of Generic Model Control (GMC) algorithm that is based on the mathematical model of the Direct Current (DC) motor. The main objective of this paper illustrates how the armature current of the DC motor and also the speed of DC motor can be controlled using GMC. For the armature control mode, the field current is held constant and an adjustable voltage is applied to the armature as a manipulated variable. Computer simulations were performed using (GMC) algorithm for the armature current speed control of DC motor. The performances of (GMC) are demonstrated using (Matlab-Simulink) software under various operating conditions.


title: Action observation and robotic agents: Learning and anthropomorphism

abstract: The ‘action observation network’ (AON), which is thought to translate observed actions into motor codes required for their execution, is biologically tuned: it responds more to observation of human, than non-human, movement. This biological specificity has been taken to support the hypothesis that the AON underlies various social functions, such as theory of mind and action understanding, and that, when it is active during observation of non-human agents like humanoid robots, it is a sign of ascription of human mental states to these agents. This review will outline evidence for biological tuning in the AON, examining the features which generate it, and concluding that there is evidence for tuning to both the form and kinematic profile of observed movements, and little evidence for tuning to belief about stimulus identity. It will propose that a likely reason for biological tuning is that human actions, relative to non-biological movements, have been observed more frequently while executing corresponding actions. If the associative hypothesis of the AON is correct, and the network indeed supports social functioning, sensorimotor experience with non-human agents may help us to predict, and therefore interpret, their movements.


title: Detecting P2P botnets through network behavior analysis and machine learning

abstract: Botnets have become one of the major threats on the Internet for serving as a vector for carrying attacks against organizations and committing cybercrimes. They are used to generate spam, carry out DDOS attacks and click-fraud, and steal sensitive information. In this paper, we propose a new approach for characterizing and detecting botnets using network traffic behaviors. Our approach focuses on detecting the bots before they launch their attack. We focus in this paper on detecting P2P bots, which represent the newest and most challenging types of botnets currently available. We study the ability of five different commonly used machine learning techniques to meet online botnet detection requirements, namely adaptability, novelty detection, and early detection. The results of our experimental evaluation based on existing datasets show that it is possible to detect effectively botnets during the botnet Command-and- Control (C&C) phase and before they launch their attacks using traffic behaviors only. However, none of the studied techniques can address all the above requirements at once.


13 The Supply Chain Process Management Maturity Model – SCPM 3


title: Course Match: A Large-Scale Implementation of Approximate Competitive Equilibrium from Equal Incomes for Combinatorial Allocation

abstract: Combinatorial allocation involves assigning bundles of items to agents when the use of money is not allowed. Course allocation is one common application of combinatorial allocation, in which the bundles are schedules of courses and the assignees are students. Existing mechanisms used in practice have been shown to have serious flaws, which lead to allocations that are inefficient, unfair, or both. A recently developed mechanism is attractive in theory but has several features that limit its feasibility for practice. This paper reports on the design and implementation of a new course allocation mechanism, Course Match, that is suitable in practice. To find allocations, Course Match performs a massive parallel heuristic search that solves billions of mixed-integer programs to output an approximate competitive equilibrium in a fake-money economy for courses. Quantitative summary statistics for two semesters of full-scale use at a large business school the Wharton School of Business, which has about 1,700 students and up to 350 courses in each semester demonstrate that Course Match is both fair and efficient, a finding reinforced by student surveys showing large gains in satisfaction and perceived fairness.


title: A Review of Productivity Factors and Strategies on Software Development

abstract: Since the late seventies, efforts to catalog factors that influences productivity, as well as actions to improve it, has been a huge concern for both academy and software development industry. Despite numerous studies, software organizations still do not know which the most significant factors are and what to do with it. Several studies present the factors in a very superficial way, some others address only the related factors or there are those that describe only a single factor. Actions to deal with the factors are spread and frequently were not mapped. Through a literature review, this paper presents a consolidated view of the main factors that have affected productivity over the years, and the strategies to deal with these factors nowadays. This research aims to support software development industry on the selection of their strategies to improve productivity by maximizing the positive factors and minimizing or avoiding the impact of the negative ones.


title: Social engineering attack framework

abstract: The field of information security is a fast growing discipline. Even though the effectiveness of security measures to protect sensitive information is increasing, people remain susceptible to manipulation and the human element is thus a weak link. A social engineering attack targets this weakness by using various manipulation techniques in order to elicit sensitive information. The field of social engineering is still in its infancy stages with regards to formal definitions and attack frameworks. This paper proposes a social engineering attack framework based on Kevin Mitnick's social engineering attack cycle. The attack framework addresses shortcomings of Mitnick's social engineering attack cycle and focuses on every step of the social engineering attack from determining the goal of an attack up to the successful conclusion of the attack. The authors use a previously proposed social engineering attack ontological model which provides a formal definition for a social engineering attack. The ontological model contains all the components of a social engineering attack and the social engineering attack framework presented in this paper is able to represent temporal data such as flow and time. Furthermore, this paper demonstrates how historical social engineering attacks can be mapped to the social engineering attack framework. By combining the ontological model and the attack framework, one is able to generate social engineering attack scenarios and to map historical social engineering attacks to a standardised format. Scenario generation and analysis of previous attacks are useful for the development of awareness, training purposes and the development of countermeasures against social engineering attacks.


title: An Overview of Machine Teaching

abstract: In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is presented as varying along a dimension. The collection of dimensions then form the problem space of machine teaching, such that existing teaching problems can be characterized in this space. We hope this organization allows us to gain deeper understanding of individual teaching problems, discover connections among them, and identify gaps in the field.


title: Normalized face image generation with perceptron generative adversarial networks

abstract: This paper presents a deep neural architecture for synthesizing the frontal and neutral facial expression image of a subject given a query face image with arbitrary expression. This is achieved by introducing a combination of feature space perceptual loss, pixel-level loss, adversarial loss, symmetry loss, and identity-preserving loss. We leverage both the frontal and neutral face distributions and pre-trained discriminative deep perceptron models to guide the identity-preserving inference of the normalized views from expressive profiles. Unlike previous generative methods that utilize their intermediate features for the recognition tasks, the resulting expression- and pose-disentangledface image has potential for several downstream applications, such as facial expression or face recognition, and attribute estimation. We show that our approach produces photorealistic and coherent results, which assist the deep metric learning-based facial expression recognition (FER) to achieve promising results on two well-known FER datasets.


title: Spinal curvature determination from scoliosis X-Ray image using sum of squared difference template matching

abstract: Scoliosis is a disorder in which there is a sideways curve of the spine. Curve are often S-shaped or C-shaped. One of the methods to ascertain a patient with scoliosis is through using Cobb angle measurement. The importance of the automatic spinal curve detection system is to detect any spinal disorder quicker and faster. This study is intended as a first step based on computer-aided diagnosis. The spinal detection method that we propose is using template matching based on Sum of Squared Difference (SSD). This method is used to estimate the location of the vertebra. By using polynomial curve fitting, a spinal curvature estimation can be done. This paper discusses the performance of SSD method used to detect a variety of data sources of X-Ray from numerous patients. The results from the implementation indicate that the proposed algorithm can be used to detect all the X-ray images. The best result in this experiment has 96.30% accuracy using 9-subdivisions poly 5 algorithm, and the average accuracy is 86.01%.


Applications of DEC-MDPs in multi-robot systems


The Utilibot Project: An Autonomous Mobile Robot Based on Utilitarianism


title: Low-degree Graph Partitioning via Local Search with Applications to Constraint Satisfaction, Max Cut, and Coloring

abstract: We present practical algorithms for constructing partitions of graphs into a xed number of vertex-disjoint subgraphs that satisfy particular degree constraints. We use this in particular to nd k-cuts of graphs of


title: A single-chip linear CMOS power amplifier for 2.4 GHz WLAN

abstract: A single-chip linear CMOS PA for OFDM WLAN applications adopts a fully differential topology with transformer-type output matching and operates from a 3.3V supply. All of the components, including the input balun and output transformer, are integrated on a single 0.18mum CMOS die and no off-chip component is required


title: 60-GHz Wideband Substrate-Integrated-Waveguide Slot Array Using Closely Spaced Elements for Planar Multisector Antenna

abstract: A new wideband waveguide slot array is proposed for covering a wide elevation-angle range with a planar sector antenna of a multigigabit wireless LAN in the 60-GHz band. The requirement of millimeter-wave sector antennas is to realize both wide bandwidth of 11.2% and wide elevation-angle coverage with a required gain of over 10 dBi. To satisfy both the required gain and bandwidth, the important structural features of the proposed antenna are that multiple slot elements are successively arranged at an interval of the length less than one quarter guide wavelengths and that the slot length increases from the shorted waveguide end to the feed end. The operational principle of the slot array is based on multisection quarter-wavelength reflection cancellation, so the slot array can achieve the required bandwidth. Furthermore, this antenna produces a unidirectional radiation pattern by using the radiators and the reflectors, resulting in the required gain over a wide elevation-angle range. In the design, the structural parameters of the slot array are selected to meet the requirements for 60-GHz-band sector antenna. The developed substrate-integrated-waveguide (SIW) slot array achieves both the 10 dB return-loss bandwidth of 15% and the specified gain in 88-96% of the one-sector coverage range of 59-66 GHz. The effectiveness of the proposed slot array for a sector antenna is verified by the good agreement between the simulated and measured results.


title: How the ExpertFit distribution-fitting software can make your simulation models more valid

abstract: In this paper, we discuss the critical role of simulation input modeling in a successful simulation study. Two pitfalls in simulation input modeling are then presented and we explain how any analyst, regardless of their knowledge of statistics, can easily avoid these pitfalls through the use of the ExpertFit distribution-fitting software. We use a set of real-world data to demonstrate how the software automatically specifies and ranks probability distributions, and then tells the analyst whether the "best" candidate distribution is actually a good representation of the data. If no distribution provides a good fit, then ExpertFit can define an empirical distribution. In either case, the selected distribution is put into the proper format for direct input to the analyst's simulation software.


Joint Learning of Speaker and Phonetic Similarities with Siamese Networks.


title: Text summarization using rough sets

abstract: Text Summarization aims to generate concise and compressed form of original documents. The techniques used for text summarization may be categorized as extractive summarization and abstractive summarization. We consider extractive techniques which are based on selection of important sentences within a document. A major issue in extractive summarization is how to select important sentences, i.e., what criteria should be defined for selection of sentences which are eventually part of the summary. We examine this issue using rough sets notion of reducts. A reduct is an attribute subset which essentially contains the same information as the original attribute set. In particular, we defined and examined three types of matrices based on an information table, namely, discernibility matrix, indiscernibility matrix and equal to one matrix. Each of these matrices represents a certain type of relationship between the objects of an information table. Three types of reducts are determined based on these matrices. The reducts are used to select sentences and consequently generate text summaries. Experimental results and comparisons with existing approaches advocates for the use of the proposed approach in generating text summaries.


title: Reconfigurable Transmitarray Design Approaches for Beamforming Applications

abstract: Recent advances in wireless sensing and communications have resulted in the need for antennas capable of high-directivity reconfigurable beamforming. Transmitarrays have been shown to be viable architectures, and two general design approaches have emerged: the layered-scatterer approach and the guided-wave approach. In this paper, we first investigate the beamforming capability of the layered-scatterer approach, generalizing the approach using impedance surfaces. Using Floquet mode analysis, we show that when a structure of this type is used to produce a pencil beam at angles greater than 20 degrees off-broadside, significant side-lobes are produced at large angles, regardless of the aperture size. Next, we present a fully reconfigurable 6 × 6 transmitarray based on a guided-wave approach that experimentally demonstrates both pencil beam scanning over a 100 × 100-degree window as well as shaped-beam synthesis, over a 10 percent fractional bandwidth. The conclusion from our investigations into these two approaches is that for general beamforming applications, the guided-wave approach is superior, achieving good bandwidth, scanning range, insertion loss, and small structure thickness, with only moderate fabrication complexity.


title: Fast and accurate border detection in dermoscopy images using statistical region merging

abstract: As a result of advances in skin imaging technology and the development of suitable image processing ::: techniques during the last decade, there has been a significant increase of interest in the computer-aided ::: diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, ::: since the accuracy of the subsequent steps crucially depends on it. In this paper, a fast and unsupervised ::: approach to border detection in dermoscopy images of pigmented skin lesions based on the Statistical ::: Region Merging algorithm is presented. The method is tested on a set of 90 dermoscopy images. The ::: border detection error is quantified by a metric in which a set of dermatologist-determined borders is ::: used as the ground-truth. The proposed method is compared to six state-of-the-art automated methods ::: (optimized histogram thresholding, orientation-sensitive fuzzy c-means, gradient vector flow snakes, ::: dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method) ::: and borders determined by a second dermatologist. The results demonstrate that the presented method ::: achieves both fast and accurate border detection in dermoscopy images.


title: Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks

abstract: Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is difficult since many cases of AF are usually clinically silent and undiagnosed. In particular paroxysmal AF is a form of AF that occurs occasionally, and has a higher probability of being undetected. In this work, we present an attention based deep learning framework for detection of paroxysmal AF episodes from a sequence of windows. Time-frequency representation of 30 seconds recording windows, over a 10 minute data segment, are fed sequentially into a deep convolutional neural network for image-based feature extraction, which are then presented to a bidirectional recurrent neural network with an attention layer for AF detection. To demonstrate the effectiveness of the proposed framework for transient AF detection, we use a database of 24 hour Holter Electrocardiogram (ECG) recordings acquired from 2850 patients at the University of Virginia heart station. The algorithm achieves an AUC of 0.94 on the testing set, which exceeds the performance of baseline models. We also demonstrate the cross-domain generalizablity of the approach by adapting the learned model parameters from one recording modality (ECG) to another (photoplethysmogram) with improved AF detection performance. The proposed high accuracy, low false alarm algorithm for detecting paroxysmal AF has potential applications in long-term monitoring using wearable sensors.


title: SocialTransfer: cross-domain transfer learning from social streams for media applications

abstract: The usage and applications of social media have become pervasive. This has enabled an innovative paradigm to solve multimedia problems (e.g., recommendation and popularity prediction), which are otherwise hard to address purely by traditional approaches. In this paper, we investigate how to build a mutual connection among the disparate social media on the Internet, using which cross-domain media recommendation can be realized. We accomplish this goal through SocialTransfer---a novel cross-domain real-time transfer learning framework. While existing transfer learning methods do not address how to utilize the real time social streams, our proposed SocialTransfer is able to effectively learn from social streams to help multimedia applications, assuming an intermediate topic space can be built across domains. It is characterized by two key components: 1) a topic space learned in real time from social streams via Online Streaming Latent Dirichlet Allocation (OSLDA), and 2) a real-time cross-domain graph spectra analysis based transfer learning method that seamlessly incorporates learned topic models from social streams into the transfer learning framework. We present as use cases of \emph{SocialTransfer} two video recommendation applications that otherwise can hardly be achieved by conventional media analysis techniques: 1) socialized query suggestion for video search, and 2) socialized video recommendation that features socially trending topical videos. We conduct experiments on a real-world large-scale dataset, including 10.2 million tweets and 5.7 million YouTube videos and show that \emph{SocialTransfer} outperforms traditional learners significantly, and plays a natural and interoperable connection across video and social domains, leading to a wide variety of cross-domain applications.


title: Skinput: appropriating the body as an input surface

abstract: We present Skinput, a technology that appropriates the human body for acoustic transmission, allowing the skin to be used as an input surface. In particular, we resolve the location of finger taps on the arm and hand by analyzing mechanical vibrations that propagate through the body. We collect these signals using a novel array of sensors worn as an armband. This approach provides an always available, naturally portable, and on-body finger input system. We assess the capabilities, accuracy and limitations of our technique through a two-part, twenty-participant user study. To further illustrate the utility of our approach, we conclude with several proof-of-concept applications we developed.


title: A high speed, 500 frames/s, 1024/spl times/1024 CMOS active pixel sensor

abstract: The paper presents a high-speed (500 f/s) large-format 1 K/spl times/1 K 8 bit 3.3 V CMOS active pixel sensor (APS) with 1024 ADCs integrated on chip. The sensor achieves an extremely high output data rate of over 500 Mbytes per second and a low power dissipation of 350 mW at the 66 MHz master clock rate. Principal architecture and circuit solutions allowing such a high throughput are discussed along with preliminary results of the chip characterization.


title: Learning by Association — A Versatile Semi-Supervised Training Method for Neural Networks

abstract: In many real-world scenarios, labeled data for a specific machine learning task is costly to obtain. Semi-supervised training methods make use of abundantly available unlabeled data and a smaller number of labeled examples. We propose a new framework for semi-supervised training of deep neural networks inspired by learning in humans. Associations are made from embeddings of labeled samples to those of unlabeled ones and back. The optimization schedule encourages correct association cycles that end up at the same class from which the association was started and penalizes wrong associations ending at a different class. The implementation is easy to use and can be added to any existing end-to-end training setup. We demonstrate the capabilities of learning by association on several data sets and show that it can improve performance on classification tasks tremendously by making use of additionally available unlabeled data. In particular, for cases with few labeled data, our training scheme outperforms the current state of the art on SVHN.


title: Agile manufacturing : a taxonomy of strategic and technological imperatives

abstract: Agile Manufacturing (AM) is a relatively new operations concept that is intended to improve the competitiveness of firms. Manufacturing/service processes based on AM are characterized by customer-supplier integrated processes for product design, manufacturing, marketing, and support services. Agile manufacturing requires enriching of the customer; cooperating with competitors; organizing to manage change, uncertainty and complexity; and leveraging people and information. In recent years, a number of research papers have been published in the area of AM. The term 'agile' was coined in 1991. However, there are still some serious concerns that prevent companies from taking an entirely different direction from AM. Considering the potential importance of agile manufacturing in 21st century manufacturing competitiveness, an attempt has been made in this paper to reexamine the scope, definitions and strategies of AM. In addition, a framework has been presented as a basis for understanding the major strategies and relevant technologies of AM.


title: Hardware Acceleration of OpenSSL Cryptographic Functions for High-Performance Internet Security

abstract: The Transport Layer Security (TLS) protocol is currently the predominant method of implementing Internet security. This paper proposes an FPGA-based embedded system integrating hardware that accelerates the cryptographic algorithms used in the SSL/TLS protocol. OpenSSL, an open source implementation of the SLL v3 and TLS v1 protocol, is deployed in the proposed embedded system powered with a Nios-2 embedded soft-core processor. Nios2-Linux RTOS is applied, which serves to provide Ethernet connectivity, multitasking, and support for the OpenSSL library. Key cipher functions used in SSL-driven connections, which include AES-256 symmetric encryption, SHA-2 hashing, RSA-2048 publickey cryptography, are accelerated in hardware. The embedded cryptosystem is prototyped completely on an Altera Stratix II FPGA development board. Experimental results show significant improvements in performance of the SSL transactions when the proposed embedded cryptosystem is deployed in the networking system.


title: Fruit Recognition using Color and Texture Features

abstract: The computer vision strategies used to recognize a fruit rely on four basic features which characterize the object: intensity, color, shape and texture. This paper proposes an efficient fusion of color and texture features for fruit recognition. The recognition is done by the minimum distance classifier based upon the statistical and co-occurrence features derived from the Wavelet transformed sub- bands. Experimental results on a database of about 2635 fruits from 15 different classes confirm the effectiveness of the proposed approach.


title: Traffic light recognition using deep neural networks

abstract: Conventional traffic light detection methods often suffers from false positives in urban environment because of the complex backgrounds. To overcome such limitation, this paper proposes a method that combines a conventional approach, which is fast but weak to false positives, and a DNN, which is not suitable for detecting small objects but a very powerful classifier. Experiments on real data showed promising results.


title: Pharmacokinetic and tolerability profile of twice-daily saquinavir hard gelatin capsules and saquinavir soft gelatin capsules boosted with ritonavir in healthy volunteers

abstract: Objective ::: ::: To evaluate the pharmacokinetics and safety of a boosted saquinavir (SQV)/ritonavir (RTV) combination, administered as either the hard gelatin capsule (HGC) or soft gelatin capsule (SGC) formulation of SQV, in 24 healthy volunteers. ::: ::: ::: ::: Methods ::: ::: This was a single-centre, open-label, randomized, 2 × 2 crossover study. Twelve subjects were randomized to receive SQV/RTV 1000 mg/100 mg twice daily (BID) orally for 10 days, as either the HGC or SGC formulation. The pharmacokinetic profile of SQV was determined on day 10. Subjects then crossed over to the opposite SQV formulation, and the pharmacokinetic profile was determined again on day 20. The primary analysis was the assessment of bioequivalence based on logarithmically transformed values for AUC(0−24 h) and Cmax for the two formulations. ::: ::: ::: ::: Results ::: ::: There was a statistically significant increase in the geometric means of all the pharmacokinetic variables evaluated for SQV-HGC/RTV compared with SQV-SGC/RTV. A mean AUC0−24 h-value of 15.798 µg/mL/h was reported for the HGC formulation compared with 11.655 µg/mL/h for the SGC formulation (P = 0.0043). The SQV-HGC/RTV combination was better tolerated in terms of gastrointestinal system disorders. Furthermore, no elevations in triglycerides or total cholesterol were reported with SQV/RTV during the entire study period. ::: ::: ::: ::: Conclusion ::: ::: In healthy volunteers, RTV boosting of SQV-HGC produces plasma exposures at least comparable to SQV-SGC, which is accompanied by an improvement in gastrointestinal system disorders.


title: Evaluation of registration methods on two-dimensional forward-looking sonar imagery

abstract: Two-dimensional forward-looking sonars such as Blueview or DIDSON are becoming a standard sensor in both remotely-operated and autonomous underwater vehicles. Registration of imagery obtained from this sensors is of great interest since it constitutes a key step in several applications like the generation of acoustic mosaics or the extraction of vehicle motion estimates from sonar imagery, specially on poor visibility conditions. However, the characteristics of these sonar images, such as low signal-to-noise ratio, low resolution and intensity alterations due to viewpoint changes pose a challenge to the traditional registration techniques applied on optical images. In this paper, the performance of popular registration methods commonly used in photomosaicing are evaluated on real sonar data, including feature-based methods and an area-based approach. Experiments are carried out on different environments, from man-made structured scenarios to more natural and featureless areas, and under challenging conditions such as viewpoint changes and the presence of different sonar-specific artifacts. After assessing the impact of all these factors on the different registration techniques, we show that Fourier-based registration method stands as the more robust option to register acoustic imagery.


title: That’s your evidence?: Classifying Stance in Online Political Debate

abstract: A growing body of work has highlighted the challenges of identifying the stance that a speaker holds towards a particular topic, a task that involves identifying a holistic subjective disposition. We examine stance classification on a corpus of 4731 posts from the debate website ConvinceMe.net, for 14 topics ranging from the playful to the ideological. We show that ideological debates feature a greater share of rebuttal posts, and that rebuttal posts are significantly harder to classify for stance, for both humans and trained classifiers. We also demonstrate that the number of subjective expressions varies across debates, a fact correlated with the performance of systems sensitive to sentiment-bearing terms. We present results for classifying stance on a per topic basis that range from 60% to 75%, as compared to unigram baselines that vary between 47% and 66%. Our results suggest that features and methods that take into account the dialogic context of such posts improve accuracy.


title: Automatic Transcription of Flamenco Singing from Polyphonic Music Recordings

abstract: Automatic note-level transcription is considered one of the most challenging tasks in music information retrieval. The specific case of flamenco singing transcription poses a particular challenge due to its complex melodic progressions, intonation inaccuracies, the use of a high degree of ornamentation, and the presence of guitar accompaniment. In this study, we explore the limitations of existing state of the art transcription systems for the case of flamenco singing and propose a specific solution for this genre: We first extract the predominant melody and apply a novel contour filtering process to eliminate segments of the pitch contour which originate from the guitar accompaniment. We formulate a set of onset detection functions based on volume and pitch characteristics to segment the resulting vocal pitch contour into discrete note events. A quantised pitch label is assigned to each note event by combining global pitch class probabilities with local pitch contour statistics. The proposed system outperforms state of the art singing transcription systems with respect to voicing accuracy, onset detection, and overall performance when evaluated on flamenco singing datasets.


title: Improved Bilateral Texture Filtering With Edge-Aware Measurement

abstract: Texture filtering depends on high-quality texture measurement to separate structures from textures. However, the existing methods employ axis-aligned box windows for texture measurement, which may cover different texture regions, and so lowering the measurement quality because structure edges are not always parallel to the axes. Additionally, the existing texture measurements consider intensity contrast at the pixel level and do not account for the linear characteristics of structure edges in filtering windows; thus, their measurement effectiveness is limited. This results in a dilemma for texture filtering. Large-scale textures are not smoothed using smaller windows, while small structures are removed using larger windows. In this paper, we present edge-aware measures to improve texture measurement. Edge-aware windows are constructed such that each window is inside a texture region to the greatest extent possible, and the linear characteristics of structure edges are accounted for in the texture measurement. Furthermore, we use large box windows for texture filtering and long and narrow edge-aware small windows for texture measurement to filter out large-scale textures while preserving small structures. The experimental results show improved texture filtering with our method compared with existing methods.


title: Supporting Fraud Analysis in Mobile Telecommunications Using Case-Based Reasoning

abstract: Fraud in mobile telecommunications is a complex and dynamic problem for Telecom operators. These companies have developed and are exploring new ways of making the fraud detection process more efficient. Most of these attempts are based in fraud management systems, capable of detecting fraudulent communications. In this paper, we present a case-based reasoning system that aids fraud analysts in the classification of potential fraud cases. The system developed, presents to the analyst the most similar past cases, representing suspicious communication episodes that were previously investigated. We also describe an example of how the system is used.


title: Fast and Accurate Estimation of Quality of Results in High-Level Synthesis with Machine Learning

abstract: While high-level synthesis (HLS) offers sophisticated techniques to optimize designs for area and performance, HLS-estimated resource usage and timing often deviate significantly from actual quality of results (QoR) achieved by FPGA-targeted designs. Inaccurate HLS estimates prevent designers from performing meaningful design space exploration without resorting to the time-consuming downstream implementation process. To address this challenge, we first build a large collection of C-to-FPGA results from a diverse set of realistic HLS applications and identify relevant features from HLS reports for estimating post-implementation metrics. We then leverage these features and data to train and compare a number of promising machine learning models to effectively and efficiently bridge the accuracy gap. Experiments demonstrate that our proposed approach is able to dramatically reduce the estimation errors for different families of FPGA devices. By extracting domain-specific insights from our experiments, we explore the implications of our models and predictive influence of various features for enabling fast and accurate QoR estimation in HLS. We have released our dataset to springboard future efforts in this area.


title: On the Performance of Integrator Handover Algorithm in LTE Networks

abstract: The 3GPP UTRAN Long Term Evolution (LTE) system utilizes packet based architecture with distributed mobility management, where the E-UTRANNode-B (eNB) takes the responsibility of making the hard handover/handoff (HO) decisions based on the user equipment (UE) measurements. To cope with the corner effect due to the loss of the line of sight (LOS), a fast HO decision algorithm is required which can guarantee the LTE seamless HO requirement and keep the quality of service (QoS) criteria. In this paper, a HO decision algorithm, the integrator algorithm, has been evaluated in the Manhattan scenario, and is compared with the traditional power budget (PBGT) handover. The study focuses on the LTE intra-frequency HO scenario and uses the reference symbol received power (RSRP) measurement as input. The results show that the integrator algorithm has the same performance as the PBGT algorithm based on the number of HOs per UE and signal to interference plus noise ratio (SINR) evaluations for different UE speeds.


Alternatives to Theory Development


title: Development of Low-Profile Patch and Semi-Circular SIW Cavity Hybrid Antennas

abstract: Two low-profile hybrid antennas are presented. One of the structures provides linearly polarized (LP) wave and the other one produces circularly polarized (CP) radiated field. half-mode substrate integrated waveguide (HMSIW) technique is used to make a semi-circular cavity for reducing the antenna size. Using proximity effect, TM010 mode of a rectangular patch is excited by the HMSIW cavity. An inset microstrip line is used to excite the structure which leads to capability of planar circuit integration. The effects of the HMSIW cavity-backed antenna with and without patches are investigated. The simulated results show that adding the patch increases bandwidth and improves antenna gain. Both proposed hybrid antennas are made on a single-layer substrate using printed circuit board (PCB) process. Two prototypes of the proposed antennas are designed, simulated and fabricated. It is shown that broadband impedance bandwidth of 10% and maximum gain of 7.5 dBi is obtained for the LP antenna. In the case of CP antenna, axial ratio (AR) bandwidth is at least 1% with maximum gain of 6.8 dBi. The proposed hybrid antennas are low profile and offer attractive features such as high gain, low cross polarization level and high front-to-back ratio.


title: Facial Expression Recognition From Depth Video With Patterns of Oriented Motion Flow

abstract: In this paper, we propose a novel feature representation method by a new feature descriptor, named patterns of oriented motion flow (POMF) from the optical flow information, to recognize the proper facial expression from the facial video. The POMF computes different directional motion information and encodes the directional flow information with enhanced local texture micro patterns. As it captures the spatial temporal changes of facial movements through optical flow and enables us to observe both local and global structures, it shows its robustness in recognizing facial information. Finally, the POMF histogram is used to train the expression model through the hidden Markov model (HMM). To train through the HMM, the objective sequences are produced by the generation of a codebook using the K-means clustering technique. The performance of the proposed method has been evaluated over the RGB and depth camera-based video. Experimental results demonstrate that the proposed POMF descriptor is more robust in extracting facial information and provides a higher classification rate than other existing promising methods.


title: Towards Verified Artificial Intelligence

abstract: Verified artificial intelligence (AI) is the goal of designing AI-based systems that are provably correct with respect to mathematically-specified requirements. This paper considers Verified AI from a formal methods perspective. We describe five challenges for achieving Verified AI, and five corresponding principles for addressing these challenges.


title: The Fellini Manner: Open Form and Visual Excess

abstract: Probably the most salient fact about Federico Fellini's work is that viewers who have seen two or three of his movies feel confident they can identify any other of his films or can correctly designate films by other filmmakers as felliniesque. They feel, in other words, that they can recognize Fellini's particular way of putting together his movies: his manner. The issue of a filmmaker's manner is a complicated one in that a filmmaker may be supposed to have a narrative manner and a visual one. Narrative manner refers to the filmmaker's way of playing out his story material to his viewers, what theorists have called the "discourse" or the "syuzhet" of a work.' A study of a filmmaker's discourse or syuzhet would typically involve asking questions like the following: Does the filmmaker pursue a strictly chronological order in telling his story, or does he begin in medias res and allow the past to unfold through flashbacks, for example, as the work moves toward its conclusion in the future? Is there a cause-and-effect pattern between the sequences that leads us from one initial problem through its complications to a final solution (a discourse of resolution, as generally favored in classical Hollywood films), or are the sequences linked more loosely according to some other principle, say, the desire to investigate a diverse range of material about a theme or character (a discourse of revelation, as sometimes favored in certain European films, especially those connected with the art cinema of the 1950s and 1960s).2 The visual manner of a filmmaker is more often called his style. It could involve such elements of mise-en-scene as costumes, sets, lighting, and the blocking out of character movement before the camera; or the filmmaker's use of the camera itself; of his method and rhythm of editing his shots within a sequence and his method of editing his sequences within the structure of the entire film. In Fellini's case, it might make good sense to discuss an evolution of his narrative and visual styles as he and members of his production team have changed over the years. We might be able to trace a curve-and a fairly familiar one at thatfrom simplicity to complexity to self-parody. However, it is probably more interesting and more useful to hypothesize a manner that is perhaps never perfectly attained in any particular film but is approached from one angle or another in almost all of Fellini's movies. This manner depends on an open


title: A Survey on Intrusion Detection Systems for Cloud Computing Environment

abstract: Cloud Computing is a newly emerged technology. It is getting popularity day by day due to its amazing services. The applications and services based on the cloud are emerging day by day. Due to networked nature of the cloud, resources, data and applications are vulnerable to the attack in cloud environment. So Intrusion Detection Systems (IDS) are employed in the cloud to detect malicious behaviour in the network and in the host. IDS monitors network or host system activities by collecting network information, and analyzes this information for malicious activities and generate alarms, if intrusion takes place. In this paper we surveyed various types of Intrusion Detection Systems proposed over the years for Cloud Computing environment.


title: Investigation of Data Mining Techniques in Fraud Detection: Credit Card

abstract: In recent times the more secure data transfer takes place almost by means of internet. Apart from the corporate companies, publics also started using the network media. At the same time the risk also increases in secure data transfer. One of the major issue among them is credit card fraud detection systems which has a significant percentage of transactions labeled as fraudulent are in fact legitimate. Thus this may delay the fraudulent transaction detection. Due to ever increasing volumes of data needed to be analyzed using data mining methods and techniques which are being used more and more. The aim of this study is to analyze the five most frequently used classification techniques in fraudulent detection. Neural Network, Decision Tree, Naive Bayes, K-nn and Support Vector Machine are taken in to consideration. This paper discusses on each techniques and their limitations. Still they suffer from the problem of false detection rate highly.


Efficient On-Line Processor Scheduling for a Class of IRIS (Increasing Reward with Increasing Service.) Real-Time Tasks


title: Trust Region Policy Optimization

abstract: In this article, we describe a method for optimizing control policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified scheme, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is effective for optimizing large nonlinear policies such as neural networks. Our experiments demonstrate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input. Despite its approximations that deviate from the theory, TRPO tends to give monotonic improvement, with little tuning of hyperparameters.


title: Momentary Pleasure or Lasting Meaning?: Distinguishing Eudaimonic and Hedonic User Experiences

abstract: User experience (UX) research has expanded our notion of what makes interactive technology good, often putting hedonic aspects of use such as fun, affect, and stimulation at the center. Outside of UX, the hedonic is often contrasted to the eudaimonic, the notion of striving towards one's personal best. It remains unclear, however, what this distinction offers to UX research conceptually and empirically. We investigate a possible role for eudaimonia in UX research by empirically examining 266 reports of positive experiences with technology and analyzing its relation to established UX concepts. Compared to hedonic experiences, eudaimonic experiences were about striving towards and accomplishing personal goals through technology use. They were also characterized by increased need fulfillment, positive affect, meaning, and long-term importance. Taken together, our findings suggest that while hedonic UX is about momentary pleasures directly derived from technology use, eudaimonic UX is about meaning from need fulfilment.


title: An Integrated Four-Element Slot-Based MIMO and a UWB Sensing Antenna System for CR Platforms

abstract: This communication presents a novel integrated antenna system for cognitive radio (CR) applications. The design consists of a compact four-element reconfigurable annular slot-based multiple-input-multiple-output (MIMO) antenna system integrated within an ultrawideband (UWB) sensing antenna. All the antenna elements are planar in structure and designed on a single substrate ( RO-4350 ) with dimensions $60\times 120\times 1.5$ mm3. The frequency reconfigurable slot-based MIMO antenna system is tuned over a wide frequency band from 1.77 to 2.51 GHz while the UWB sensing antenna is covering from 0.75 to 7.65 GHz The proposed antenna system is suitable for CR enabled wireless devices. The envelop correlation coefficient did not exceed 0.248 in the entire operating band of the MIMO antenna part. The maximum measured gain of the MIMO antenna is 3.2 dBi with a maximum efficiency of 81%.


title: Fluoride toothpastes for preventing dental caries in children and adolescents

abstract: BACKGROUND: Fluoride toothpastes have been widely used for over three decades and remain a benchmark intervention for the prevention of dental caries. OBJECTIVES: To determine the effectiveness and safety of fluoride toothpastes in the prevention of caries in children and to examine factors potentially modifying their effect. SEARCH STRATEGY: We searched the Cochrane Oral Health Group's Trials Register (May 2000), the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library Issue 2, 2000), MEDLINE (1966 to January 2000), plus several other databases. We handsearched journals, reference lists of articles and contacted selected authors and manufacturers. SELECTION CRITERIA: Randomized or quasi-randomized controlled trials with blind outcome assessment, comparing fluoride toothpaste with placebo in children up to 16 years during at least one year. The main outcome was caries increment measured by the change in decayed, missing and filled tooth surfaces (D(M)FS). DATA COLLECTION AND ANALYSIS: Inclusion decisions, quality assessment and data extraction were duplicated in a random sample of one third of studies, and consensus achieved by discussion or a third party. Authors were contacted for missing data. The primary measure of effect was the prevented fraction (PF) that is the difference in caries increments between the treatment and control groups expressed as a percentage of the increment in the control group. Random effects meta-analyses were performed where data could be pooled. Potential sources of heterogeneity were examined in random effects meta-regression analyses. MAIN RESULTS: Seventy-four studies were included. For the 70 that contributed data for meta-analysis (involving 42,300 children) the D(M)FS pooled PF was 24% (95% confidence interval (CI), 21 to 28%; p<0.0001). This means that 1.6 children need to brush with a fluoride toothpaste (rather than a non-fluoride toothpaste) over three years to prevent one D(M)FS in populations with caries increment of 2.6 D(M)FS per year. In populations with caries increment of 1.1 D(M)FS per year, 3.7 children will need to use a fluoride toothpaste for three years to avoid one D(M)FS. There was clear heterogeneity, confirmed statistically (p<0.0001). The effect of fluoride toothpaste increased with higher baseline levels of D(M)FS, higher fluoride concentration, higher frequency of use, and supervised brushing, but was not influenced by exposure to water fluoridation. There is little information concerning the deciduous dentition or adverse effects (fluorosis). REVIEWER'S CONCLUSIONS: Supported by more than half a century of research, the benefits of fluoride toothpastes are firmly established. Taken together, the trials are of relatively high quality, and provide clear evidence that fluoride toothpastes are efficacious in preventing caries.


title: Features for Audio and Music Classification

abstract: Four audio feature sets are evaluated in their ability to classify five general audio classes and seven popular music genres. The feature sets include low-level signal properties, mel-frequency spectral coefficients, and two new sets based on perceptual models of hearing. The temporal behavior of the features is analyzed and parameterized and these parameters are included as additional features. Using a standard Gaussian framework for classification, results show that the temporal behavior of features is important for both music and audio classification. In addition, classification is better, on average, if based on features from models of auditory perception rather than on standard features.