title: Semi-Supervised Learning

abstract: In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction. Adaptive Computation and Machine Learning series


title: Monte Carlo Integration with Subtraction

abstract: This paper investigates a class of algorithms for numerical integration of a function in d dimensions over a compact domain by Monte Carlo methods. We construct a histogram approximation to the function using a partition of the integration domain into a set of bins specified by some parameters. We then consider two adaptations; the first is to subtract the histogram approximation, whose integral we may easily evaluate explicitly, from the function and integrate the difference using Monte Carlo; the second is to modify the bin parameters in order to make the variance of the Monte Carlo estimate of the integral the same for all bins. This allows us to use Student's t-test as a trigger for rebinning, which we claim is more stable than the \chi-squared test that is commonly used for this purpose. We provide a program that we have used to study the algorithm for the case where the histogram is represented as a product of one-dimensional histograms. We discuss the assumptions and approximations made, as well as giving a pedagogical discussion of the myriad ways in which the results of any such Monte Carlo integration program can be misleading.


title: Effect of beta-alanine supplementation on muscle carnosine concentrations and exercise performance

abstract: High-intensity exercise results in reduced substrate levels and accumulation of metabolites in the skeletal muscle. The accumulation of these metabolites (e.g. ADP, Pi and H(+)) can have deleterious effects on skeletal muscle function and force generation, thus contributing to fatigue. Clearly this is a challenge to sport and exercise performance and, as such, any intervention capable of reducing the negative impact of these metabolites would be of use. Carnosine (beta-alanyl-L-histidine) is a cytoplasmic dipeptide found in high concentrations in the skeletal muscle of both vertebrates and non-vertebrates and is formed by bonding histidine and beta-alanine in a reaction catalysed by carnosine synthase. Due to the pKa of its imidazole ring (6.83) and its location within skeletal muscle, carnosine has a key role to play in intracellular pH buffering over the physiological pH range, although other physiological roles for carnosine have also been suggested. The concentration of histidine in muscle and plasma is high relative to its K (m) with muscle carnosine synthase, whereas beta-alanine exists in low concentration in muscle and has a higher K (m) with muscle carnosine synthase, which indicates that it is the availability of beta-alanine that is limiting to the synthesis of carnosine in skeletal muscle. Thus, the elevation of muscle carnosine concentrations through the dietary intake of carnosine, or chemically related dipeptides that release beta-alanine on absorption, or supplementation with beta-alanine directly could provide a method of increasing intracellular buffering capacity during exercise, which could provide a means of increasing high-intensity exercise capacity and performance. This paper reviews the available evidence relating to the effects of beta-alanine supplementation on muscle carnosine synthesis and the subsequent effects on exercise performance. In addition, the effects of training, with or without beta-alanine supplementation, on muscle carnosine concentrations are also reviewed.


A Corpus Analysis of Social Connections and Social Isolation in Adolescents Suffering from Depressive Disorders


title: Evidence of Middle Neoproterozoic extensional tectonic settings along the western margin of the Siberian craton: Implications for the breakup of Rodinia

abstract: The new results of U–Pb and 40Ar/39Ar dating and petrological and geochemical studies of rocks from the Yenisey regional shear zone (YRSZ) in the Transangarian part of the Yenisey Ridge were used to identify a dike belt at the western margin of the Siberian craton and constrain the timing of formation of rift-related structures accompanied by within-plate magmatism. The magmatism of this epoch produced felsic and mafic intrusive rocks. The extension processes are recorded by the 797–787 Ma dike swarms. The rift origin of the rocks studied here is confirmed by their occurrence as dikes and sills confined to the YRSZ, bimodal composition of magmatic rock associations, and petrochemical characteristics of rocks typical of within-plate settings. The formation of the 800 Ma dike belt within the study area could be associated with the Neoproterozoic extensional tectonic events along the western margin of the Siberian craton which coincided with the onset of the breakup of the Rodinia supercontinent. These events provide the upper age limit for the collisional metamorphism that took place at the final stage of orogeny. The evolutionary phases identified in the region agree well with the sequence and style of tectonothermal events within the Valhalla Orogen at the Arctic margin of Rodinia and provide further support for the spatial proximity between Siberia and North Atlantic cratons during this epoch, as indicated by recent paleomagnetic data.


title: Database Design Strategies

abstract: Database design methodologies and tools should facilitate database modeling, effectively support database processing, database redesign and transform a conceptual schema of the database to a high-performance database schema in the model of the corresponding DBMS. Since the late 1970’s, various tools for database design have been introduced. Most of them, however, are dependent on the knowledge, comprehension and experience of the database analyst and their knowledge in normalization theory. The systems (DB)2 and RAD developed in our groups do not require the user to understand the theory, the implementational restrictions and the programming problems in order to design a database scheme. A novice designer can create a database design successfully using the system. These tools are based on an extended entity-relationship model. The entity-relationship model is extended to the Higher-order Entity-Relationship Model (HERM) by adding structural constructs and using integrity constraints and operations. The system RAD has a component which enables the user to choose his design strategy according to his experience and abilities. Different database design methodologies are developed based on the HERM approach. This paper demonstrates how different database design strategies can be developed and supported. This paper surveys further the strategy support of the design system RAD and proposes the design methodology Design-By-Units.


title: A Smart Parking System based on IoT protocols and emerging enabling technologies

abstract: Enabling a sustainable mobility is one of primary goals of the so-called Smart City vision, and the development of intelligent parking systems represents a key aspect. To this purpose, this paper presents a novel Smart Parking System based on the jointly use of different technologies, such as RFID, WSN, NFC, Cloud, and mobile. It is able to collect, in real time, both environmental parameters and information about the occupancy state of parking spaces, and to direct drivers to the nearest vacant parking spot by using a customized software application. This last one leverages a NFC-based e-wallet system to allow users to pay for parking fees. Furthermore, a customized software application, installed on a cloud platform, is able to manage alert events (e.g. improper use of a reserved space or expiration of the purchased time). In such a case, it promptly informs the traffic cops through an Android mobile app, which has been designed ad hoc for the considered scenario.


title: A Novel Semi-Active Multi-Modal Vibration Control Law for a Piezoceramic Actuator

abstract: In this paper, a novel semi-active energy rate multi-modal vibration control technique is developed for a piezoceramic actuator coupled to a switching resistor/inductor shunt. The technique works by briefly connecting a resistor/inductor shunt to a piezoceramic actuator in order to apply the necessary signed charge to allow energy dissipation. The switch timing is determined by a control scheme that observes the rate of energy change in controlled modes. The control scheme is developed in the paper, and is simplified to enable practical implementation. This new multi-modal control law is applied to both a simple numerical and an experimental test structure. The results from the numerical and experimental tests show that the energy rate multi-mode control law is able to dissipate energy from one, two and three modes of the flexible structures using a single actuator.


title: Numerical and Linguistic Prediction of Time Series With the Use of Fuzzy Cognitive Maps

abstract: In this paper, we introduce a novel approach to time-series prediction realized both at the linguistic and numerical level. It exploits fuzzy cognitive maps (FCMs) along with a recently proposed learning method that takes advantage of real-coded genetic algorithms. FCMs are used for modeling and qualitative analysis of dynamic systems. Within the framework of FCMs, the systems are described by means of concepts and their mutual relationships. The proposed prediction method combines FCMs with granular, fuzzy-set-based model of inputs. One of their main advantages is an ability to carry out modeling and prediction at both numerical and linguistic levels. A comprehensive set of experiments has been carried out with two major goals in mind. One is to assess quality of the proposed architecture, the other to examine the influence of its parameters of the prediction technique on the quality of prediction. The obtained results, which are compared with other prediction techniques using fuzzy sets, demonstrate that the proposed architecture offers substantial accuracy expressed at both linguistic and numerical levels.


title: Carpooling: A Multi-Agent Simulation In Netlogo

abstract: The WiSafeCar (Wireless Traffic Safety Network between Cars) project aims at increasing the performance and reliability of the wireless transport and to provide traffic safety improvements. Within the context of this project, we have designed a Dynamic Carpooling System that will optimize the transport utilization by the ride sharing among people who usually cover the same route. An initial prototype of the system has been developed by using NetLogo. The information obtained from this simulator will be used to study the functioning of the clearing services, the current business models and to propose new ones. The first results seem encouraging, and the users have many economical advantages thanks to the sharing of costs which allows the individuals to retrench expenses and to contribute to the use of green technologies.


Modeling plant life in computer graphics


title: Contextually Customized Video Summaries Via Natural Language

abstract: The best summary of a long video differs among different people due to its highly subjective nature. Even for the same person, the best summary may change with time or mood. In this paper, we introduce the task of generating contextually customized video summaries through simple text. First, we train a deep architecture to effectively learn semantic embeddings of video frames by leveraging the abundance of image-caption data via a progressive manner, whereby our algorithm is able to select semantically relevant video segments for a contextually meaningful video summary, given a user-specific text description or even a single sentence. In order to evaluate our customized video summaries, we conduct experimental comparison with baseline methods that utilize ground-truth information. Despite the challenging baselines, our method still manages to show comparable or even exceeding performance. We also demonstrate that our method is able to automatically generate semantically diverse video summaries even without any text input.


title: Oxytocin and vulnerable romantic relationships

abstract: Abstract Oxytocin (OT) has been implicated in the formation and maintenance of various social relationships, including human romantic relationships. Competing models predict, alternatively, positive or negative associations between naturally-occurring OT levels and romantic relationship quality. Empirical tests of these models have been equivocal. We propose a novel hypothesis (‘Identify and Invest’) that frames OT as an allocator of psychological investment toward valued, vulnerable relationships, and test this proposal in two studies. In one sample of 75 couples, and a second sample of 148 romantically involved individuals, we assess facets of relationships predicting changes in OT across a thought-writing task regarding one's partner. In both studies, participants' OT change across the task corresponded positively with multiple dimensions of high relationship involvement. However, increases in participants' OT also corresponded to their partners reporting lower relationship involvement. OT increases, then, reflected discrepancies between assessments of self and partner relationship involvement. These findings are robust in a combined analysis of both studies, and do not significantly differ between samples. Collectively, our findings support the ‘Identify and Invest’ hypothesis in romantic couples, and we argue for its relevance across other types of social bonds.


title: A Pattern Based Approach to Answering Factoid, List and Definition Questions

abstract: Finding textual answers to open-domain questions in large text collections is a difficult problem. In this paper we concentrate on three types of questions: factoid, list, and definition questions and present two systems that make use of regular expression patterns and other devices in order to locate possible answers. While the factoid and list system acquires patterns in an off-line phase using the Web, the definition system uses a library of patterns identified by corpus analysis to acquire knowledge in an on-line stage for each new question. Results are reported over the question sets from the question answering track of the 2002 and 2003 Text REtrieval Conference (TREC).


title: Design and development of IGBT resonant inverters for domestic induction heating applications

abstract: In this paper, resonant inverters for induction heating are developed for domestic applications. The design is based on half and full bridge single phase inverter of 1 and 4 kW rating. First, the power circuits of the two topologies are analyzed using computer aided design based on Simplorer package. Then, an experimental system was built and tested using microcontroller chip (PiccoloTMS320F28027). The performances of the topologies are highlighted and assessed. The obtained results show that two systems are able to operate satisfactory under soft switching at high frequency with high efficiency. The power control is more interesting for full bridge particularly when the angle phase control is considered. Further, the experimental results agree well with those obtained with the CAD design. This validates the design model used for the purpose. The experimental system shows high integration and flexibility to incorporate other control strategies whereby higher efficiency can be achieved.


title: Relationship among family environment, self-control, friendship quality, and adolescents’ smartphone addiction in South Korea: Findings from nationwide data

abstract: Background ::: Many studies have examined the negative impact on smartphone addiction in adolescents. Recent concerns have focused on predictors of smartphone addiction. This study aimed to investigate the association of adolescents’ smartphone addiction with family environment (specifically, domestic violence and parental addiction). We further investigated whether self-control and friendship quality, as predictors of smartphone addiction, may reduce the observed risk. ::: ::: ::: Methods ::: We used the 2013 national survey on internet usage and utilization data from the National Information Agency of Korea. Information on exposure and covariates included self-reported experience of domestic violence and parental addiction, sociodemographic variables, and other variables potentially related to smartphone addiction. Smartphone addiction was estimated using a smartphone addiction proneness scale, a standardized measure developed by national institutions in Korea. ::: ::: ::: Results ::: Adolescents who had experienced domestic violence (OR = 1.74; 95% CI: 1.23–2.45) and parental addiction (OR = 2.01; 95% CI: 1.24–3.27) were found to be at an increased risk for smartphone addiction after controlling for all potential variables. Furthermore, on classifying adolescents according to their level of self-control and friendship quality the association between domestic violence and parental addiction, and smartphone addiction was found to be significant in the group with adolescents with lower levels of self-control (OR = 2.87; 95% CI: 1.68–4.90 and OR = 1.95; 95% CI: 1.34–2.83) and friendship quality (OR = 2.33; 95% CI: 1.41–3.85 and OR = 1.83; 95% CI: 1.26–2.64). ::: ::: ::: Conclusion ::: Our findings suggest that family dysfunction was significantly associated with smartphone addiction. We also observed that self-control and friendship quality act as protective factors against adolescents’ smartphone addiction.


title: UML-Based integration testing

abstract: Increasing numbers of software developers are using the Unified Modeling Language (UML) and associated visual modeling tools as a basis for the design and implementation of their distributed, component-based applications. At the same time, it is necessary to test these components, especially during unit and integration testing. At Siemens Corporate Research, we have addressed the issue of testing components by integrating test generation and test execution technology with commercial UML modeling tools such as Rational Rose; the goal being a design-based testing environment. In order to generate test cases automatically, developers first define the dynamic behavior of their components via UML Statecharts, specify the interactions amongst them and finally annotate them with test requirements. Test cases are then derived from these annotated Statecharts using our test generation engine and executed with the help of our test execution tool. The latter tool was developed specifically for interfacing to components based on COM/DCOM and CORBA middleware. In this paper, we present our approach to modeling components and their interactions, describe how test cases are derived from these component models and then executed to verify their conformant behavior. We outline the implementation strategy of our TnT environment and use it to evaluate our approach by means of a simple example.


title: Projected augmentation - augmented reality using rotatable video projectors

abstract: In this paper, we propose a new way of augmenting our environment with information without making the user carry any devices. We propose the use of video projection to display the augmentation on the objects directly. We use a projector that can be rotated and in other ways controlled remotely by a computer, to follow objects carrying a marker. The main contribution of this paper is a system that keeps the augmentation displayed in the correct place while the object or the projector moves. We describe the hardware and software design of our system, the way certain functions such as following the marker or keeping it in focus are implemented and how to calibrate the multitude of parameters of all the subsystems.


title: High-precision heliostat for long-path light tracking

abstract: A heliostat has been designed and built for use in optical remote sensing of the atmosphere. The heliostat uses two flat mirrors to track the sun and direct the sunlight to optical instruments. A stepper motor driven horizontal turntable is used to track the sun in azimuth and support an elevation assembly and a mechanical tower. The stepper motor driven elevation assembly drives an acquisition mirror that tracks the sun in elevation. This mirror directs the solar beam to a secondary mirror fixed on the mechanical tower. The secondary mirror then directs the solar beam along the axis of the tracker for use in measurements. A sensitive, high resolution CCD camera, receives a small part of the solar beam to analyze for fine servo-control. Ground based tests have demonstrated this instrument"s tracking capability for the sun, the moon, stars and for long pathlength sources. Presently, this system is coupled with a high-resolution Brucker 120M spectrometer used to obtain solar absorption spectra. The heliostat directs the solar radiation along the spectrometer optical axis. The pointing precision was measured to be better than 0.5 arcsec. A description of the heliostat is presented, as well as the results of ground tests.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


Generating Discourse Structures For Written Text


title: Power electronics as efficient interface in dispersed power generation systems

abstract: The global electrical energy consumption is rising and there is a steady increase of the demand on the power capacity, efficient production, distribution and utilization of energy. The traditional power systems are changing globally, a large number of dispersed generation (DG) units, including both renewable and nonrenewable energy sources such as wind turbines, photovoltaic (PV) generators, fuel cells, small hydro, wave generators, and gas/steam powered combined heat and power stations, are being integrated into power systems at the distribution level. Power electronics, the technology of efficiently processing electric power, play an essential part in the integration of the dispersed generation units for good efficiency and high performance of the power systems. This paper reviews the applications of power electronics in the integration of DG units, in particular, wind power, fuel cells and PV generators.


title: Projection of Romantic and Sexual Desire in Opposite-Sex Friendships: How Wishful Thinking Creates a Self-Fulfilling Prophecy

abstract: In two studies, the authors examined the projection of romantic and sexual desire in opposite-sex friendships. In both studies, perceivers who strongly desired their friends projected this desire onto their friends, believing that their desire was more reciprocated than was actually the case. In turn, projection of desire appeared to motivate perceivers into enacting relationship initiation behaviors, which predicted changes in targets’ romantic and sexual desires over time (Study 2). Projection was elevated for perceivers who saw themselves as high in mate value, and targets appeared to be influenced by perceivers’ overtures primarily when they believed perceivers were high in mate value. This research suggests that, for perceivers high in mate value, romantic and sexual desire creates biased perceptions that initiate self-fulfilling prophecies.


title: Parametrizing filters of a CNN with a GAN

abstract: It is commonly agreed that the use of relevant invariances as a good statistical bias is important in machine-learning. However, most approaches that explicitly incorporate invariances into a model architecture only make use of very simple transformations, such as translations and rotations. Hence, there is a need for methods to model and extract richer transformations that capture much higher-level invariances. To that end, we introduce a tool allowing to parametrize the set of filters of a trained convolutional neural network with the latent space of a generative adversarial network. We then show that the method can capture highly non-linear invariances of the data by visualizing their effect in the data space.


title: Two-stage spectrum sensing for cognitive radios

abstract: We consider a two-stage sensing scheme for cognitive radios where coarse sensing based on energy detection is performed in the first stage and, if required, fine sensing based on cyclostationary detection in the second stage. We design the detection threshold parameters in the two sensing stages so as to maximize the probability of detection, given constraints on the probability of false alarm. We compare this scheme with ones where only energy detection or cyclostationary detection is performed. The performance comparison is made based on the probability of detection, probability of false alarm and mean detection time.


title: Multi-level Anomaly Detection in Industrial Control Systems via Package Signatures and LSTM Networks

abstract: We outline an anomaly detection method for industrial control systems (ICS) that combines the analysis of network package contents that are transacted between ICS nodes and their time-series structure. Specifically, we take advantage of the predictable and regular nature of communication patterns that exist between so-called field devices in ICS networks. By observing a system for a period of time without the presence of anomalies we develop a base-line signature database for general packages. A Bloom filter is used to store the signature database which is then used for package content level anomaly detection. Furthermore, we approach time-series anomaly detection by proposing a stacked Long Short Term Memory (LSTM) network-based softmax classifier which learns to predict the most likely package signatures that are likely to occur given previously seen package traffic. Finally, by the inspection of a real dataset created from a gas pipeline SCADA system, we show that an anomaly detection scheme combining both approaches can achieve higher performance compared to various current state-of-the-art techniques.


title: Area-Preservation Mapping using Optimal Mass Transport

abstract: We present a novel area-preservation mapping/flattening method using the optimal mass transport technique, based on the Monge-Brenier theory. Our optimal transport map approach is rigorous and solid in theory, efficient and parallel in computation, yet general for various applications. By comparison with the conventional Monge-Kantorovich approach, our method reduces the number of variables from O(n2) to O(n), and converts the optimal mass transport problem to a convex optimization problem, which can now be efficiently carried out by Newton's method. Furthermore, our framework includes the area weighting strategy that enables users to completely control and adjust the size of areas everywhere in an accurate and quantitative way. Our method significantly reduces the complexity of the problem, and improves the efficiency, flexibility and scalability during visualization. Our framework, by combining conformal mapping and optimal mass transport mapping, serves as a powerful tool for a broad range of applications in visualization and graphics, especially for medical imaging. We provide a variety of experimental results to demonstrate the efficiency, robustness and efficacy of our novel framework.


title: An unusual case of monozygotic epigastric heteropagus twinning

abstract: Abstract Epigastric heteropagus refers to unequal and asymmetric conjoined twins in which the dependent component (parasite) is smaller and usually attached to the epigastrium of the dominant component (autosite). Epigastric heteropagus is a very rare type of conjoined twins. It is generally assumed that conjoined twins represent a form of monozygotic twinning, but there has been no confirmation of this monozygotic hypothesis. Epigastric heteropagus twins differ in several ways from symmetrical conjoined twins. These include male preponderance, and no major connection of vessels, bowels, or bones. These significant characteristics suggest to the authors that the fusion of two fertilized ova and the atrophy of one fetus resulted in this interesting malformation. However, a DNA study indicated monozygotic origin in their case. The possible pathogenesis was ischemic atrophy of the body structure of the monozygotic conjoined twins at an early gestational age.


Culture-Based Creativity in the Regional Strategy of Development: Is Russia in Game?


title: Two privacy-preserving approaches for data publishing with identity reservation

abstract: Many approaches have been proposed for publishing useful information while preserving data privacy. Among them, the privacy models of identity-reserved (k, l)-anonymity and identity-reserved \((\alpha , \beta )\)-anonymity have been proposed to handle the situation where an individual could have multiple records. However, the two models fail to prevent attribute disclosure. To this end, we propose two new privacy models: enhanced identity-reserved l-diversity and enhanced identity-reserved \((\alpha , \beta )\)-anonymity. Moreover, to implement the two privacy models we design a general anonymization algorithm, called DAnonyIR, with clustering technique by calling different decision functions, which can decrease the information loss caused by generalization. Further, we compare DAnonyIR concerning our two privacy models with existing generalization method GeneIR concerning identity-reserved (k, l)-anonymity and identity-reserved \((\alpha , \beta )\)-anonymity, respectively. The experimental results show that our two approaches provide stronger privacy preservation, and their information loss and relative error ratio of query answering are less than those of GeneIR.


title: Software architecture: foundations, theory, and practice

abstract: Software architecture has become a centerpiece subject for software engineers, both researchers and practitioners alike. At the heart of every software system is its software architecture, i.e., "the set of principal design decisions about the system". Architecture permeates all major facets of a software system, for principal design decisions may potentially be made at any time during a system's lifetime, and potentially by any stakeholder. Such decisions encompass structural concerns, such as the system's high-level building blocks---components, connectors, and configurations; the system's deployment; the system's non-functional properties; and the system's evolution patterns, including runtime adaptation. Software architectures found particularly useful for families of systems---product lines---are often codified into architectural patterns, architectural styles, and reusable, parameterized reference architectures. This tutorial affords the participant an extensive treatment of the field of software architecture, its foundation, principles, and elements, including those mentioned above. Additionally, the tutorial introduces the participants to the state-of-the-art as well as the state-of-the-practice in software architecture, and looks at emerging and likely future trends in this field. The discussion is illustrated with numerous real-world examples. One example given prominent treatment is the architecture of the World Wide Web and its underlying architectural style, REpresentational State Transfer (REST).


title: Smart Ideas for Smart Cities: Investigating Crowdsourcing for Generating and Selecting Ideas for ICT Innovation in a City Context

abstract: Within this article, the strengths and weaknesses of crowdsourcing for idea generation and idea selection in the context of smart city innovation are investigated. First, smart cities are defined next to similar but different concepts such as digital cities, intelligent cities or ubiquitous cities. It is argued that the smart city-concept is in fact a more user-centered evolution of the other city-concepts which seem to be more technological deterministic in nature. The principles of crowdsourcing are explained and the different manifestations are demonstrated. By means of a case study, the generation of ideas for innovative uses of ICT for city innovation by citizens through an online platform is studied, as well as the selection process. For this selection, a crowdsourcing solution is compared to a selection made by external experts. The comparison of both indicates that using the crowd as gatekeeper and selector of innovative ideas yields a long list with high user benefits. However, the generation of ideas in itself appeared not to deliver extremely innovative ideas. Crowdsourcing thus appears to be a useful and effective tool in the context of smart city innovation, but should be thoughtfully used and combined with other user involvement approaches and within broader frameworks such as Living Labs.


title: Dependence of Voltage and Size on Write Error Rates in Spin-Transfer Torque Magnetic Random-Access Memory

abstract: The dependence of the write-error rate (WER) on the applied write voltage, write pulse width, and device size was examined in individual devices of a spin-transfer torque (STT) magnetic random-access memory (MRAM) 4 kbit chip. We present 10 ns switching data at the ${10^{ - 6}}$ error level for 655 devices, ranging in diameter from 50 nm to 11 nm, to make a statistically significant demonstration that a specific magnetic tunnel junction stack with perpendicular magnetic anisotropy is capable of delivering good write performance in junction diameters range from 50 to 11 nm. Furthermore, write-error-rate data on one 11 nm device down to an error rate of $7{\times}10^{ - 10}$ was demonstrated at 10 ns with a write current of $7.5\;\upmu{\rm A}$ , corresponding to a record low switching energy below 100 fJ.


title: Multi-level network embedding with boosted low-rank matrix approximation

abstract: As opposed to manual feature engineering which is tedious and difficult to scale, network embedding has attracted a surge of research interests as it automates the feature learning on graphs. The learned low-dimensional node vectors ease the knowledge discovery on graphs by enabling various off-the-shelf machine learning tools to be directly applied. Recent research has shown that the past decade of network embedding approaches either explicitly factorize a carefully designed matrix or are closely related to implicit matrix factorization, with the fundamental assumption that the factorized node connectivity matrix is low-rank. Nonetheless, the global low-rank assumption does not necessarily hold especially when the factorized matrix encodes complex node interactions, and the resultant single low-rank embedding matrix is insufficient to capture all the observed connectivity patterns. In this regard, we propose a novel multi-level network embedding framework BoostNE, which can learn multiple node embeddings of different granularity from coarse to fine without imposing the prevalent global low-rank assumption. The proposed BoostNE method is also in line with the successful gradient boosting method in ensemble learning. We demonstrate the superiority of the proposed BoostNE framework by comparing it with existing state-of-the-art network embedding methods on various datasets.


title: Large Scale Implementations for Twitter Sentiment Classification

abstract: Sentiment Analysis on Twitter Data is indeed a challenging problem due to the nature, diversity and volume of the data. People tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide spectrum of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since no one can invest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample is not representative in order to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this work, we develop two systems: the first in the MapReduce and the second in the Apache Spark framework for programming with Big Data. The algorithm exploits all hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification method of diverse sentiment types in a parallel and distributed manner. Moreover, the sentiment analysis tool is based on Machine Learning methodologies alongside Natural Language Processing techniques and utilizes Apache Spark’s Machine learning library, MLlib. In order to address the nature of Big Data, we introduce some pre-processing steps for achieving better results in Sentiment Analysis as well as Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Finally, the proposed system was trained and validated with real data crawled by Twitter, and, through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable while confirming the quality of our sentiment identification.


title: Sneak-path constraints in memristor crossbar arrays

abstract: In a memristor crossbar array, a memristor is positioned on each row-column intersection, and its resistance, low or high, represents two logical states. The state of every memristor can be sensed by the current flowing through the memristor. In this work, we study the sneak path problem in crossbars arrays, in which current can sneak through other cells, resulting in reading a wrong state of the memristor. Our main contributions are a new characterization of arrays free of sneak paths, and efficient methods to read the array cells while avoiding sneak paths. To each read method we match a constraint on the array content that guarantees sneak-path free readout, and calculate the resulting capacity.


title: The complete genome of hyperthermophile Methanopyrus kandleri AV19 and monophyly of archaeal methanogens

abstract: We have determined the complete 1,694,969-nt sequence of the GC-rich genome of Methanopyrus kandleri by using a whole direct genome sequencing approach. This approach is based on unlinking of genomic DNA with the ThermoFidelase version of M. kandleri topoisomerase V and cycle sequencing directed by 2′-modified oligonucleotides (Fimers). Sequencing redundancy (3.3×) was sufficient to assemble the genome with less than one error per 40 kb. Using a combination of sequence database searches and coding potential prediction, 1,692 protein-coding genes and 39 genes for structural RNAs were identified. M. kandleri proteins show an unusually high content of negatively charged amino acids, which might be an adaptation to the high intracellular salinity. Previous phylogenetic analysis of 16S RNA suggested that M. kandleri belonged to a very deep branch, close to the root of the archaeal tree. However, genome comparisons indicate that, in both trees constructed using concatenated alignments of ribosomal proteins and trees based on gene content, M. kandleri consistently groups with other archaeal methanogens. M. kandleri shares the set of genes implicated in methanogenesis and, in part, its operon organization with Methanococcus jannaschii and Methanothermobacter thermoautotrophicum. These findings indicate that archaeal methanogens are monophyletic. A distinctive feature of M. kandleri is the paucity of proteins involved in signaling and regulation of gene expression. Also, M. kandleri appears to have fewer genes acquired via lateral transfer than other archaea. These features might reflect the extreme habitat of this organism.


title: Uncovering Clusters in Crowded Parallel Coordinates Visualizations

abstract: The one-to-one strategy of mapping each single data item into a graphical marker adopted in many visualization techniques has limited usefulness when the number of records and/or the dimensionality of the data set are very high. In this situation, the strong overlapping of graphical markers severely hampers the user's ability to identify patterns in the data from its visual representation. We tackle this problem here with a strategy that computes frequency or density information from the data set, and uses such information in parallel coordinates visualizations to filter out the information to be presented to the user, thus reducing visual clutter and allowing the analyst to observe relevant patterns in the data. The algorithms to construct such visualizations, and the interaction mechanisms supported, inspired by traditional image processing techniques such as grayscale manipulation and thresholding are also presented. We also illustrate how such algorithms can assist users to effectively identify clusters in very noisy large data sets


title: Ensemble Knowledge Transfer for Semantic Segmentation

abstract: Semantic segmentation networks are usually learned in a strictly supervised manner, i.e., they are trained and tested on similar data distributions. Performance drops drastically in the presence of domain shifts. In this paper, we explore methods for learning across train and test distributions that dramatically differ in scene structure, viewpoints, and objects statistics. Motivated by the proliferation of aerial drone robotics, we consider the target task of semantic segmentation from aerial viewpoints. Inspired by the impact of Cityscapes [11], we introduce AeroScapes, a new dataset of 3269 images of aerial scenes (captured with a fleet of drones) annotated with dense semantic segmentations. Our dataset differs from existing segmentation datasets (that focus on ground-view or indoorscene domains) in terms of viewpoint, scene composition, and object scales. We propose a simple but effective approach for transferring knowledge from such diverse domains (for which considerable annotated training data exists) to our target task. To do so, we train multiple models for aerial segmentation via progressive fine-tuning through each source domain. We then treat these collections of models as an ensemble that can be aggregated to significantly improve performance. We demonstrate large absolute improvements (8.12%) over widely-used standard baselines.


title: A Ka-Band Single-Chip SiGe BiCMOS Phased-Array Transmit/Receive Front-End

abstract: This paper presents the detailed design and demonstration of a Ka-band single-chip transmit (TX)/receive (RX) front-end in 0.13- $\mu \text{m}$ SiGe BiCMOS technology. The front-end includes single-pole double-throw (SPDT) switches, low-noise amplifier, loss compensation amplifiers (LCAs), phase shifter, and power amplifier. Distributed structures are utilized in gain amplifiers to ensure broadband performance while stacked structure is adopted in power amplifier to deliver high output power in the TX mode. A 5-b phase shifter with design strategies for low rms phase/gain errors serves as the common leg of the RX and TX paths. In the RX mode, measurements show a gain of 17 dB, an output $P_{\mathrm { {-1~dB}}}$ of −1 dBm, an rms phase error less than 4°, and an rms gain error less than 0.6 dB with 0.528-W dc power from 30 to 40 GHz. In the TX mode, measurements show a gain of 14 dB, an output $P_{\mathrm { {-1~dB}}}$ of 20.5 dBm, an rms phase error less than 3.7°, and an rms gain error less than 0.55 dB with 1.587-W dc power from 30 to 40 GHz. The whole front-end occupies $3.2 \times 2.2$ mm2 including the testing pads. By choosing inductors and capacitors with reasonable values, designing a well-matched SPDT switch with high isolation, and optimum ordering of phase shift cells and LCAs in the phase shifter design, this TX/RX front-end achieves excellent rms phase/gain error performance without any trimming in a system-level measurement at Ka-band.


title: The control of flight force by a flapping wing: lift and drag production

abstract: We used a dynamically scaled mechanical model of the fruit fly Drosophila melanogaster to study how changes in wing kinematics influence the production of unsteady aerodynamic forces in insect flight. We examined 191 separate sets of kinematic patterns that differed with respect to stroke amplitude, angle of attack, flip timing, flip duration and the shape and magnitude of stroke deviation. Instantaneous aerodynamic forces were measured using a two-dimensional force sensor mounted at the base of the wing. The influence of unsteady rotational effects was assessed by comparing the time course of measured forces with that of corresponding translational quasi-steady estimates. For each pattern, we also calculated mean stroke-averaged values of the force coefficients and an estimate of profile power. The results of this analysis may be divided into four main points. ::: ::: (i) For a short, symmetrical wing flip, mean lift was optimized by a stroke amplitude of 180° and an angle of attack of 50°. At all stroke amplitudes, mean drag increased monotonically with increasing angle of attack. Translational quasi-steady predictions better matched the measured values at high stroke amplitude than at low stroke amplitude. This discrepancy was due to the increasing importance of rotational mechanisms in kinematic patterns with low stroke amplitude. ::: ::: (ii) For a 180° stroke amplitude and a 45° angle of attack, lift was maximized by short-duration flips occurring just slightly in advance of stroke reversal. Symmetrical rotations produced similarly high performance. Wing rotation that occurred after stroke reversal, however, produced very low mean lift. ::: ::: (iii) The production of aerodynamic forces was sensitive to changes in the magnitude of the wing’s deviation from the mean stroke plane (stroke deviation) as well as to the actual shape of the wing tip trajectory. However, in all examples, stroke deviation lowered aerodynamic performance relative to the no deviation case. This attenuation was due, in part, to a trade-off between lift and a radially directed component of total aerodynamic force. Thus, while we found no evidence that stroke deviation can augment lift, it nevertheless may be used to modulate forces on the two wings. Thus, insects might use such changes in wing kinematics during steering maneuvers to generate appropriate force moments. ::: ::: (iv) While quasi-steady estimates failed to capture the time course of measured lift for nearly all kinematic patterns, they did predict with reasonable accuracy stroke-averaged values for the mean lift coefficient. However, quasi-steady estimates grossly underestimated the magnitude of the mean drag coefficient under all conditions. This discrepancy was due to the contribution of rotational effects that steady-state estimates do not capture. This result suggests that many prior estimates of mechanical power based on wing kinematics may have been grossly underestimated.


title: Evaluating contributions of natural language parsers to protein–protein interaction extraction

abstract: Motivation: While text mining technologies for biomedical research have gained popularity as a way to take advantage of the explosive growth of information in text form in biomedical papers, selecting appropriate natural language processing (NLP) tools is still difficult for researchers who are not familiar with recent advances in NLP. This article provides a comparative evaluation of several state-of-the-art natural language parsers, focusing on the task of extracting protein–protein interaction (PPI) from biomedical papers. We measure how each parser, and its output representation, contributes to accuracy improvement when the parser is used as a component in a PPI system. ::: ::: Results: All the parsers attained improvements in accuracy of PPI extraction. The levels of accuracy obtained with these different parsers vary slightly, while differences in parsing speed are larger. The best accuracy in this work was obtained when we combined Miyao and Tsujii's Enju parser and Charniak and Johnson's reranking parser, and the accuracy is better than the state-of-the-art results on the same data. ::: ::: Availability: The PPI extraction system used in this work (AkanePPI) is available online at http://www-tsujii.is.s.u-tokyo.ac.jp/-100downloads/downloads.cgi. The evaluated parsers are also available online from each developer's site. ::: ::: Contact: yusuke@is.s.u-tokyo.ac.jp


title: 1000 Fps Highly Accurate Eye Detection with Stacked Denoising Autoencoder

abstract: Eye detection is an important step for a range of applications such as iris and face recognition. For eye detection in practice, speed is as equally important as accuracy. In this paper, we propose a super-fast (1000 fps on a general PC) eye detection method based on the label map of the raw image without face detection. We firstly produce the label map of a raw image according to the coordinates of its bounding box . Then we train a stacked denoising autoencoder (SDAE) which is specifically designed to learn the mapping from the raw image to the label map. Finally, through an effective post-processing step, we obtain the bounding boxes of two eyes. Experimental results show that our method is about 2,500 times faster than the deformable part-based model (DPM) while maintaining a comparable accuracy. Also, our method is much better than the popular LBP+Cascade model in terms of both accuracy and speed.


title: Force and focal adhesion assembly: a close relationship studied using elastic micropatterned substrates

abstract: Mechanical forces play a major role in the regulation of cell adhesion and cytoskeletal organization. In order to explore the molecular mechanism underlying this regulation, we have investigated the relationship between local force applied by the cell to the substrate and the assembly of focal adhesions. A novel approach was developed for real-time, high-resolution measurements of forces applied by cells at single adhesion sites. This method combines micropatterning of elastomer substrates and fluorescence imaging of focal adhesions in live cells expressing GFP-tagged vinculin. Local forces are correlated with the orientation, total fluorescence intensity and area of the focal adhesions, indicating a constant stress of 5.5 +/- 2 nNmicrom(-2). The dynamics of the force-dependent modulation of focal adhesions were characterized by blocking actomyosin contractility and were found to be on a time scale of seconds. The results put clear constraints on the possible molecular mechanisms for the mechanosensory response of focal adhesions to applied force.


title: A novel hybrid powered RFID sensor tag

abstract: In recent years we have seen a tremendous growth in applications of passive sensor-enabled RFID technology by researchers; however, their usability in applications such as activity recognition is limited by a key issue associated with their incapability to handle unintentional brownout events leading to missing significant sensed events such as a fall from a chair. Furthermore, due to the need to power and sample a sensor the practical operating range of passive-sensor enabled RFID tags are also limited with respect to passive RFID tags. Although using active or semi-passive tags can provide alternative solutions, they are not without the often undesirable maintenance and limited lifespan issues due to the need for batteries. In this article we propose a new hybrid powered sensor-enabled RFID tag concept which can sustain the supply voltage to the tag circuitry during brownouts and increase the operating range of the tag by combining the concepts from passive RFID tags and semipassive RFID tags, while potentially eliminating shortcomings of electric batteries. We have designed and built our concept, evaluated its desirable properties through extensive experiments and demonstrate its significance in the context of a human activity recognition application.


title: Physiological inverse tone mapping based on retina response

abstract: The mismatch between the Low Dynamic Range (LDR) content and the High Dynamic Range (HDR) display arouses the research on inverse tone mapping algorithms. In this paper, we present a physiological inverse tone mapping algorithm inspired by the property of the Human Visual System (HVS). It first imitates the retina response and deduce it to be local adaptive; then estimates local adaptation luminance at each point in the image; finally, the LDR image and local luminance are applied to the inversed local retina response to reconstruct the dynamic range of the original scene. The good performance and high-visual quality were validated by operating on 40 test images. Comparison results with several existing inverse tone mapping methods prove the conciseness and efficiency of the proposed algorithm.


title: Human Pose Regression by Combining Indirect Part Detection and Contextual Information

abstract: In this paper, we tackle the problem of human pose estimation from still images, which is a very active topic, specially due to its several applications, from image annotation to human-machine interface. We use the soft-argmax function to convert feature maps directly to body joint coordinates, resulting in a fully differentiable framework. Our method is able to learn heat maps representations indirectly, without additional steps of artificial ground truth generation. Consequently, contextual information can be included to the pose predictions in a seamless way. We evaluated our method on two challenging datasets, the Leeds Sports Poses (LSP) and the MPII Human Pose datasets, reaching the best performance among all the existing regression methods. Source code available at: https://github.com/dluvizon/pose-regression.


title: The difference between ''giving a rose'' and ''giving a kiss'': Sustained neural activity to the light verb construction

abstract: We used event-related potentials (ERPs) to investigate the neurocognitive mechanisms associated with processing light verb constructions such as “give a kiss”. These constructions consist of a semantically underspecified light verb (“give”) and an event nominal that contributes most of the meaning and also activates an argument structure of its own (“kiss”). This creates a mismatch between the syntactic constituents and the semantic roles of a sentence. Native speakers read German verb-final sentences that contained light verb constructions (e.g., “Julius gave Anne a kiss”), non-light constructions (e.g., “Julius gave Anne a rose”), and semantically anomalous constructions (e.g., *“Julius gave Anne a conversation”). ERPs were measured at the critical verb, which appeared after all its arguments. Compared to non-light constructions, the light verb constructions evoked a widely distributed, frontally focused, sustained negative-going effect between 500 and 900 ms after verb onset. We interpret this effect as reflecting working memory costs associated with complex semantic processes that establish a shared argument structure in the light verb constructions.


title: Music segmentation and summarization based on self-similarity matrix

abstract: In this paper, we propose a new method for segmenting and summarizing music based on its structure analysis. To do that, we first extract timbre feature from acoustic music signal and construct a self-similarity matrix that shows similarities among the features within music clip. We then determine candidate boundaries for music segmentation by tracking standard deviation in the matrix. Similar segments such as repetition in music clip are clustered and merged. In this way, each music clip can be represented by a sequence of states where each state represents a music segment with similar feature. We assume that the longest segment of a music clip represents the music and hence use it as a summary of the music clip. We show the performance of our proposed method through experiments.


title: Incremental Algorithms for Hierarchical Classification

abstract: We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchical loss function, the H-loss, implementing the simple intuition that additional mistakes in the subtree of a mistaken class should not be charged for. Based on a probabilistic data model introduced in earlier work, we derive the Bayes-optimal classifier for the H-loss. We then empirically compare two incremental approximations of the Bayes-optimal classifier with a flat SVM classifier and with classifiers obtained by using hierarchical versions of the Perceptron and SVM algorithms. The experiments show that our simplest incremental approximation of the Bayes-optimal classifier performs, after just one training epoch, nearly as well as the hierarchical SVM classifier (which performs best). For the same incremental algorithm we also derive an H-loss bound showing, when data are generated by our probabilistic data model, exponentially fast convergence to the H-loss of the hierarchical classifier based on the true model parameters.


title: Verified by Visa and MasterCard SecureCode: or, How Not to Design Authentication

abstract: Banks worldwide are starting to authenticate online card transactions using the ‘3-D Secure’ protocol, which is branded as Verified by Visa and MasterCard SecureCode. This has been partly driven by the sharp increase in online fraud that followed the deployment of EMV smart cards for cardholder-present payments in Europe and elsewhere. 3-D Secure has so far escaped academic scrutiny; yet it might be a textbook example of how not to design an authentication protocol. It ignores good design principles and has significant vulnerabilities, some of which are already being exploited. Also, it provides a fascinating lesson in security economics. While other single sign-on schemes such as OpenID, InfoCard and Liberty came up with decent technology they got the economics wrong, and their schemes have not been adopted. 3-D Secure has lousy technology, but got the economics right (at least for banks and merchants); it now boasts hundreds of millions of accounts. We suggest a path towards more robust authentication that is technologically sound and where the economics would work for banks, merchants and customers – given a gentle regulatory nudge.


title: Cross cultural adaptation and validation of the Chinese Health Assessment Questionnaire for use in rheumatoid arthritis.

abstract: OBJECTIVE ::: The Health Assessment Questionnaire - Disability Index (HAQ), used as a disability and outcome measurement in rheumatoid arthritis (RA), has been validated in several languages, but not in Chinese. Our aim was to validate the Chinese version of HAQ (Chinese-HAQ) to suit the needs of Chinese speaking patients with RA in an Asian setting. ::: ::: ::: METHODS ::: The original HAQ was modified in the context of Chinese culture and translated into Chinese by 2 translators aware of the objective of the questionnaire. The Chinese HAQ was self-administered by 42 patients with RA during their routine followup visit and one week later. ::: ::: ::: RESULTS ::: The test-retest reliability assessed using Spearman's correlation coefficient was 0.84. Between dimensions measured in the HAQ, the highest test-retest reliability was observed for walking (Spearman correlation coefficient rs=0.80) and the lowest was for eating (rs=0.54). The internal consistency of the scale using Cronbach's alpha was high at 0.86. In terms of criterion validity, the Chinese-HAQ score was found to correlate well with American College of Rheumatology functional status (rs=0.501, p=0.01). The Chinese-HAQ scores also correlated well with markers of disease activity such as patient's perception of pain measured on a visual analog scale (rs=0.55, p < 0.001), grip strength in mm Hg (rs=-0.55. p < 0.001 ), and physician's assessment of disease activity (rs=0.59, p < 0.001). ::: ::: ::: CONCLUSION ::: The Chinese HAQ is a reliable and valid instrument for studies measuring disability of patients with RA in Singapore.


title: Coherent inverse scattering via transmission matrices: Efficient phase retrieval algorithms and a public dataset

abstract: A transmission matrix describes the input-output relationship of a complex wavefront as it passes through/reflects off a multiple-scattering medium, such as frosted glass or a painted wall. Knowing a medium's transmission matrix enables one to image through the medium, send signals through the medium, or even use the medium as a lens. The double phase retrieval method is a recently proposed technique to learn a medium's transmission matrix that avoids difficult-to-capture interferometric measurements. Unfortunately, to perform high resolution imaging, existing double phase retrieval methods require (1) a large number of measurements and (2) an unreasonable amount of computation. In this work we focus on the latter of these two problems and reduce computation times with two distinct methods: First, we develop a new phase retrieval algorithm that is significantly faster than existing methods, especially when used with an amplitude-only spatial light modulator (SLM). Second, we calibrate the system using a phase-only SLM, rather than an amplitude-only SLM which was used in previous double phase retrieval experiments. This seemingly trivial change enables us to use a far faster class of phase retrieval algorithms. As a result of these advances, we achieve a 100x reduction in computation times, thereby allowing us to image through scattering media at state-of-the-art resolutions. In addition to these advances, we also release the first publicly available transmission matrix dataset. This contribution will enable phase retrieval researchers to apply their algorithms to real data. Of particular interest to this community, our measurement vectors are naturally i.i.d. subgaussian, i.e., no coded diffraction pattern is required.


title: Using Sessions from Clickstream Data Analysis to Uncover Different Types of Twitter Behaviour

abstract: While much is known about how Twitter is used for specific tasks or by particular groups of users, we understand surprisingly little about how the service is used generally on a daily basis. To learn more about general Twitter behaviour we perform a cluster analysis on a rich set of longitudinal interaction log data describing interactions 44 users had with the Twitter website over a 5 month period. We report on and interpret 5 clusters representing common usage patterns with the service.


title: SIGNAL RECOVERY BY PROXIMAL FORWARD-BACKWARD SPLITTING

abstract: We show that various inverse problems in signal recovery can be formulated as the generic problem of minimizing the sum of two convex functions with certain regularity properties. This formulation makes it possible to derive existence, uniqueness, characterization, and stability results in a unified and standardized fashion for a large class of apparently disparate problems. Recent results on monotone operator splitting methods are applied to establish the convergence of a forward-backward algorithm to solve the generic problem. In turn, we recover, extend, and provide a simplified analysis for a variety of existing iterative methods. Applications to geometry/texture image decomposition schemes are also discussed. A novelty of our framework is to use extensively the notion of a proximity operator, which was introduced by Moreau in the 1960s.


title: Anonymity Properties of the Bitcoin P2P Network

abstract: Bitcoin is a popular alternative to fiat money, widely used for its perceived anonymity properties. However, recent attacks on Bitcoin's peer-to-peer (P2P) network demonstrated that its gossip-based flooding protocols, which are used to ensure global network consistency, may enable user deanonymization---the linkage of a user's IP address with her pseudonym in the Bitcoin network. In 2015, the Bitcoin community responded to these attacks by changing the network's flooding mechanism to a different protocol, known as diffusion. However, no systematic justification was provided for the change, and it is unclear if diffusion actually improves the system's anonymity. In this paper, we model the Bitcoin networking stack and analyze its anonymity properties, both pre- and post-2015. In doing so, we consider new adversarial models and spreading mechanisms that have not been previously studied in the source-finding literature. We theoretically prove that Bitcoin's networking protocols (both pre- and post-2015) offer poor anonymity properties on networks with a regular-tree topology. We validate this claim in simulation on a 2015 snapshot of the real Bitcoin P2P network topology.


title: A 12.5-Gb/s near-GND transceiver for wire-line UHD video interfaces

abstract: This paper demonstrates a 12.5-Gb/s complete transceiver with near-ground and voltage-mode signaling. The output stage of the transmitter has two types of equalization methods: 2-tap FIR, AC-coupled equalization. The receiver equalizes the attenuation of the cable with a continuous time linear equalizer which includes a level-up shifter for receiving near-ground input signal. A dual-loop clock and data recovery circuit using a bang-bang phase detector is implemented for a receiver clock generation. The test-chips are fabricated in a 45-nm CMOS technology, and successfully perform data communication through -27 dB loss video cables. The entire transmitter and receiver test-chips respectively consume 92 mW and 138 mW from a 1.2 V supply while operating at 12.5 Gb/s.


The complete manual of corporate and industrial security


title: Association of host genome with intestinal microbial composition in a large healthy cohort

abstract: Intestinal microbiota is known to be important in health and disease. Its composition is influenced by both environmental and host factors. Few large-scale studies have evaluated the association between host genetic variation and the composition of microbiota. We recruited a cohort of 1,561 healthy individuals, of whom 270 belong in 123 families, and found that almost one-third of fecal bacterial taxa were heritable. In addition, we identified 58 SNPs associated with the relative abundance of 33 taxa in 1,098 discovery subjects. Among these, four loci were replicated in a second cohort of 463 subjects: rs62171178 (nearest gene UBR3) associated with Rikenellaceae, rs1394174 (CNTN6) associated with Faecalibacterium, rs59846192 (DMRTB1) associated with Lachnospira, and rs28473221 (SALL3) associated with Eubacterium. After correction for multiple testing, 6 of the 58 associations remained significant, one of which replicated. These results identify associations between specific genetic variants and the gut microbiome.


title: Protecting Location Privacy for Task Allocation in Ad Hoc Mobile Cloud Computing

abstract: Mobile cloud computing (MCC) is an emerging cloud-computing paradigm that integrates cloud computing and mobile computing to enable many useful mobile applications. However, the large-scale deployment of MCC is hindered by the concerns on possible privacy leakage. In this paper, we investigate the privacy issues in the ad hoc MCC, and propose a framework that can protect the location privacy when allocating tasks to mobile devices. Our mechanism is based on differential privacy and geocast, and allows mobile devices to contribute their resources to the ad hoc mobile cloud without leaking their location information. We develop analytical models and task allocation strategies that balance privacy, utility, and system overhead in an ad hoc mobile cloud. We also conduct extensive experiments based on real-world data sets, and the results show that our framework can protect location privacy for mobile devices while providing effective services with low system overhead.


title: Achieving Intelligent Traffic-Aware Consolidation of Virtual Machines in a Data Center Using Learning Automata

abstract: Cloud Computing (CC) is becoming increasingly pertinent and popular. A natural consequence of this is that many modern-day data centers experience very high internal traffic within the data centers themselves. The VMs with high mutual traffic often end up being far apart in the data center network, forcing them to communicate over unnecessarily long distances. The consequent traffic bottlenecks negatively affect both the performance of the application and the network in its entirety, posing non-trivial challenges for the administrators of these cloud- based data centers. The problem can, quite naturally, be compartmentalized into two phases which follow each other. First of all, the VMs are consolidated with a VM clustering algorithm, and this is achieved by utilizing the toolbox involving Learning Automata (LA). By mapping the clustering problem onto the Graph Partitioning (GP) problem, our LA-based solution successfully reduces the total communication cost by amounts that range between 34% to 85%. Thereafter, the resulting clusters are assigned to the server racks using a cluster placement algorithm that involves a completely different intelligent strategy, i.e., one that invokes Simulated Annealing (SA). This phase further reduces the total cost of communication by amounts that range between 89% to 99%. The analysis and results for different models and topologies demonstrate that the optimization is done in a fast and computationally-efficient way. Indeed, as far as we know, this paper pioneers the application of LA in the traffic-aware consolidation of virtual machines in data centers, and also pioneers a strategy which serializes the tools in LA and SA to optimize CC.


title: High-Breakdown Enhancement-Mode AlGaN/GaN HEMTs with Integrated Slant Field-Plate

abstract: Enhancement-mode (E-mode) AlGaN/GaN high-electron mobility transistors (HEMTs) with integrated slant field-plates were developed for high breakdown voltage (VBD) and low on-resistance (RON). Combination of the self-aligned slant field-plate technology for high VBD and self-aligned CF4 plasma treatment for E-mode operation yielded high-performance device with a VBD of 1400V, which is one of the highest reported VBD value among GaN-based E-mode HEMTs. Using the active area of the device, the RON was calculated to be below 3mOmegamiddotcm2


title: The TPTP Problem Library and Associated Infrastructure

abstract: This paper describes the First-Order Form (FOF) and Clause Normal Form (CNF) parts of the TPTP problem library, and the associated infrastructure. TPTP v3.5.0 was the last release containing only FOF and CNF problems, and thus serves as the exemplar. This paper summarizes the history and development of the TPTP, describes the structure and contents of the TPTP, and gives an overview of TPTP related projects and tools.


title: Emergent Hyperbolic Network Geometry

abstract: A large variety of interacting complex systems are characterized by interactions occurring between more than two nodes. These systems are described by simplicial complexes. Simplicial complexes are formed by simplices (nodes, links, triangles, tetrahedra etc.) that have a natural geometric interpretation. As such simplicial complexes are widely used in quantum gravity approaches that involve a discretization of spacetime. Here, by extending our knowledge of growing complex networks to growing simplicial complexes we investigate the nature of the emergent geometry of complex networks and explore whether this geometry is hyperbolic. Specifically we show that an hyperbolic network geometry emerges spontaneously from models of growing simplicial complexes that are purely combinatorial. The statistical and geometrical properties of the growing simplicial complexes strongly depend on their dimensionality and display the major universal properties of real complex networks (scale-free degree distribution, small-world and communities) at the same time. Interestingly, when the network dynamics includes an heterogeneous fitness of the faces, the growing simplicial complex can undergo phase transitions that are reflected by relevant changes in the network geometry.


title: SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity

abstract: Verbs play a critical role in the meaning of sentences, but these ubiquitous words have received little attention in recent distributional semantics research. We introduce SimVerb-3500, an evaluation resource that provides human ratings for the similarity of 3,500 verb pairs. SimVerb-3500 covers all normed verb types from the USF free-association database, providing at least three examples for every VerbNet class. This broad coverage facilitates detailed analyses of how syntactic and semantic phenomena together influence human understanding of verb meaning. Further, with significantly larger development and test sets than existing benchmarks, SimVerb-3500 enables more robust evaluation of representation learning architectures and promotes the development of methods tailored to verbs. We hope that SimVerb-3500 will enable a richer understanding of the diversity and complexity of verb semantics and guide the development of systems that can effectively represent and interpret this meaning.


title: Infinite-Label Learning with Semantic Output Codes

abstract: We develop a new statistical machine learning paradigm, named infinite-label learning, to annotate a data point with more than one relevant labels from a candidate set, which pools both the finite labels observed at training and a potentially infinite number of previously unseen labels. The infinite-label learning fundamentally expands the scope of conventional multi-label learning, and better models the practical requirements in various real-world applications, such as image tagging, ads-query association, and article categorization. However, how can we learn a labeling function that is capable of assigning to a data point the labels omitted from the training set? To answer the question, we seek some clues from the recent work on zero-shot learning, where the key is to represent a class/label by a vector of semantic codes, as opposed to treating them as atomic labels. We validate the infinite-label learning by a PAC bound in theory and some empirical studies on both synthetic and real data.


title: A personalized recommender system for pervasive social networks

abstract: Abstract The current availability of interconnected portable devices, and the advent of the Web 2.0, raise the problem of supporting anywhere and anytime access to a huge amount of content, generated and shared by mobile users. On the one hand, users tend to be always connected for sharing experiences and conducting their social interactions with friends and acquaintances, through so-called Mobile Social Networks, further improving their social inclusion. On the other hand, the pervasiveness of communication infrastructures spreading data (cellular networks, direct device-to-device contacts, interactions with ambient devices as in the Internet-of-Things) makes compulsory the deployment of solutions able to filter off undesired information and to select what content should be addressed to which users, for both ( i ) better user experience, and ( i i ) resource saving of both devices and network. In this work, we propose a novel framework for pervasive social networks, called Pervasive PLIERS (p-PLIERS), able to discover and select, in a highly personalized way, contents of interest for single mobile users. p-PLIERS exploits the recently proposed PLIERS tag-based recommender system (Arnaboldi et al., 2016) as a context reasoning tool able to adapt recommendations to heterogeneous interest profiles of different users. p-PLIERS effectively operates also when limited knowledge about the network is maintained. It is implemented in a completely decentralized environment, in which new contents are continuously generated and diffused through the network, and it relies only on the exchange of single nodes’ knowledge during proximity contacts and through device-to-device communications. We evaluated p-PLIERS by simulating its behavior in three different scenarios: a big event (Expo 2015), a conference venue (ACM KDD’15), and a working day in the city of Helsinki. For each scenario, we used real or synthetic mobility traces and we extracted real datasets from Twitter interactions to characterize the generation and sharing of user contents.


title: Automatic Defect Recognition in X-Ray Testing Using Computer Vision

abstract: To ensure safety in the construction of important metallic components for roadworthiness, it is necessary to check every component thoroughly using non-destructive testing. In last decades, X-ray testing has been adopted as the principal non-destructive testing method to identify defects within a component which are undetectable to the naked eye. Nowadays, modern computer vision techniques, such as deep learning and sparse representations, are opening new avenues in automatic object recognition in optical images. These techniques have been broadly used in object and texture recognition by the computer vision community with promising results in optical images. However, a comprehensive evaluation in X-ray testing is required. In this paper, we release a new dataset containing around 47.500 cropped X-ray images of 32 32 pixels with defects and no-defects in automotive components. Using this dataset, we evaluate and compare 24 computer vision techniques including deep learning, sparse representations, local descriptors and texture features, among others. We show in our experiments that the best performance was achieved by a simple LBP descriptor with a SVM-linear classifier obtaining 97% precision and 94% recall. We believe that the methodology presented could be used in similar projects that have to deal with automated detection of defects.


title: Ignorance to Awareness: Towards an Information Security Awareness Process

abstract: With most employees in small and medium enterprise (SME) engineering firms now having access to their own personal workstations, the need for information security management to safeguard against loss/alteration or theft of the firms’ important information has increased. These SMEs tend to be more concerned with vulnerabilities from external threats, although industry research suggests that a substantial proportion of security incidents originate from insiders within the firm. Hence, physical preventative measures such as antivirus software and firewalls are proving to solve only part of the problem as the employees using them lack adequate information security knowledge. This tends to expose a firm to risks and costly mistakes made by naive/uninformed employees. This paper presents an information security awareness process that seeks to cultivate positive security behaviours using a behavioural intention model based on the Theory of Reasoned Action, the Protection Motivation Theory and the Behaviourism Theory. The process and model have been refined, tested through action research at an SME engineering firm in South Africa, and the findings are presented and discussed in this paper.


title: VCAMS: Viterbi-Based Context Aware Mobile Sensing to Trade-Off Energy and Delay

abstract: Monitoring context depends on continuous collection of raw data from sensors which are either embedded in smart mobile devices or worn by the user. However, continuous sensing constitutes a major source of energy consumption; on the other hand, lowering the sensing rate may lead to missing the detection of critical contextual events. In this paper, we propose VCAMS: a Viterbi-based Context Aware Mobile Sensing mechanism that adaptively finds an optimized sensing schedule to decide when to trigger the sensors for data collection while trading off the sensing energy and the delay to detect a state change. The sensing schedule is adaptive from two aspects: 1) the decision rules are learned from the user’s past behavior, and 2) these rules are updated over real time whenever there is a significant change in the user’s behavior. VCAMS is validated using multiple experiments, which include evaluation of model success when considering binary and multi-user states. We also implemented VCAMS on an Android-based device to estimate its computational costs under realistic operational conditions. Test results show that our proposed strategy provides better trade-off than previous state-of-the-art methods under comparable conditions. Furthermore, the method provides 78 percent energy saving when compared to continuous sensing.


title: A High-Performance Single-Phase Bridgeless Interleaved PFC Converter for Plug-in Hybrid Electric Vehicle Battery Chargers

abstract: In this paper, a new front end ac-dc bridgeless interleaved power factor correction topology is proposed for level II plug-in hybrid electric vehicle (PHEV) battery charging. The topology can achieve high efficiency, which is critical for minimizing the charger size, PHEV charging time and the amount and cost of electricity drawn from the utility. In addition, a detailed analytical model for this topology is presented, enabling the calculation of the converter power losses and efficiency. Experimental and simulation results are included for a prototype boost converter converting universal ac input voltage (85-265 V) to 400 V dc output at up to 3.4 kW load. The experimental results demonstrate a power factor greater than 0.99 from 750 W to 3.4 kW, THD less than 5% from half load to full load and a peak efficiency of 98.9% at 70 kHz switching frequency, 265 V input and 1.2 kW load.


title: Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos

abstract: Learning depth and optical flow via deep neural networks by watching videos has made significant progress recently. In this paper, we jointly solve the two tasks by exploiting the underlying geometric rules within stereo videos. Specifically, given two consecutive stereo image pairs from a video, we first estimate depth, camera ego-motion and optical flow from three neural networks. Then the whole scene is decomposed into moving foreground and static background by compar- ing the estimated optical flow and rigid flow derived from the depth and ego-motion. We propose a novel consistency loss to let the optical flow learn from the more accurate rigid flow in static regions. We also design a rigid alignment module which helps refine ego-motion estimation by using the estimated depth and optical flow. Experiments on the KITTI dataset show that our results significantly outperform other state-of- the-art algorithms. Source codes can be found at https: //github.com/baidu-research/UnDepthflow


abstract: Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple human objects are segmented and their global motions are tracked in 3D using ellipsoid human shape models. Experiments show that it successfully applies to the cases where a small number of people move together, have occlusion, and cast shadow or reflection. In the second part, we estimate the modes (e.g., walking, running, standing) of the locomotion and 3D body postures by making inference in a prior locomotion model. Camera model and ground plane assumptions provide geometric constraints in both parts. Robust results are shown on some difficult sequences.


title: Distributed barcode tracking in visual sensor networks

abstract: A novel approach for robot tracking and identification based on barcodes is proposed in this paper. The proposed system tracks robots fitted with barcodes identifying them. The system performs distributed visual processing and collaborative barcode tracking, whereby the nodes exchange processed visual information with appropriate neighboring nodes, informing them about incoming targets. The proposed tracking system is deployed on a visual sensor network (VSN) based on the Raspberry Pi. Its practical realization includes various components that handle real-time collaborative tracking, streaming, communication, configuration, management, monitoring, and deployment. The proposed system is able to efficiently and accurately track barcodes in real time. Complexity modeling demonstrates that the proposed distributed system brings substantial complexity reductions compared to a system of independently operating cameras. Experimental results demonstrate that the proposed system can track multiple barcodes in real time with an accuracy of less than 1 centimeter.


title: CPW-Fed Wideband Circularly Polarized Printed Monopole Antenna With Open Loop and Asymmetric Ground Plane

abstract: A wideband circularly polarized (CP) printed monopole antenna with coplanar waveguide feeding is proposed. The antenna is composed of a rectangular monopole, an asymmetric ground plane, and a square-ring with an open gap at the bottom. By utilizing the asymmetric ground plane, CP radiation could be achieved at the upper band first. Then, a rectangular open loop is introduced to obtain wide-impedance bandwidth and broadband CP characteristic. The measured results demonstrate that the 10-dB impedance bandwidth reaches 96.5% (2.76 GHz, 1.48–4.24 GHz) and the 3-dB axial-ratio bandwidth is 63.3% (1.9 GHz, 2.05–3.95 GHz). In addition, parameter studies are performed, and surface current analysis is also given to show the operating mechanism of CP operation.


title: Curb detection and tracking using 3D-LIDAR scanner

abstract: This paper presents a novel road curb detection method using 3D-LIDAR scanner. To detect the curbs, the ground points are separated from the pointcloud first. Then the candidate curb points are selected using three spatial cues: the elevation difference, gradient value and normal orientation. Afterwards the false curb points caused by obstacles are removed using the short-term memory technique. Next the curbs are fitted using the parabola model. Finally, the particle filter is used to smooth the curb detection result. The proposed approach was evaluated on a dataset collected by an autonomous ground vehicle driving around the Ford Research campus and downtown Dearborn. Our curb detection results are accurate and robust despite variations introduced by moving vehicles and pedestrians, static obstacles, road curvature changes, etc.


title: Development environments for autonomous mobile robots: A survey

abstract: Robotic Development Environments (RDEs) have come to play an increasingly important role in robotics research in general, and for the development of architectures for mobile robots in particular. Yet, no systematic evaluation of available RDEs has been performed; establishing a comprehensive list of evaluation criteria targeted at robotics applications is desirable that can subsequently be used to compare their strengths and weaknesses. Moreover, there are no practical evaluations of the usability and impact of a large selection of RDEs that provides researchers with the information necessary to select an RDE most suited to their needs, nor identifies trends in RDE research that suggest directions for future RDE development. ::: ::: This survey addresses the above by selecting and describing nine open source, freely available RDEs for mobile robots, evaluating and comparing them from various points of view. First, based on previous work concerning agent systems, a conceptual framework of four broad categories is established, encompassing the characteristics and capabilities that an RDE supports. Then, a practical evaluation of RDE usability in designing, implementing, and executing robot architectures is presented. Finally, the impact of specific RDEs on the field of robotics is addressed by providing a list of published applications and research projects that give concrete examples of areas in which systems have been used. The comprehensive evaluation and comparison of the nine RDEs concludes with suggestions of how to use the results of this survey and a brief discussion of future trends in RDE design.


title: On using spheres of meaning to define and dignify the IS discipline

abstract: The question, ''Is information systems a real discipline?'' has taxed IS scholars for over 20 years, and they debate its identity at length. Many other disciplines are facing similar crises. This article suggests a new approach, which views disciplines in terms of what is meaningful to those who work within them. Making use of the philosophical notion of 'spheres of meaning', and one well-thought-out suite of such spheres, the core contribution of the IS discipline may be understood, giving it confidence among its neighbours, so as to maintain dialogue with them. Questions about identity are replaced by questions about dignity, destiny and responsibility, and strategic suggestions are offered.


title: Robot Mobility Systems for Planetary Surface Exploration – State-of-the-Art and Future Outlook: A Literature Survey

abstract: Mobile robots have been an essential element of space exploration to perform science on distant lunar and planetary objects. Past orbiter missions to Moon have shown evidence on the presence of ice inside the permanently shadowed polar areas. Similarly, Mars holds clues of life in the distant past. While data sent by orbiters provides a wealth of information and gives rise to new speculations and theories, in-situ science data from mobile rovers and landers is essential to validate or confirm them. “Mobility” is a vital element for a space missions due to valuable science return potential from different sites as opposed to static landers. Over the years, technology development has given rise to numerous mobile systems. Some of the systems are spin-offs from terrestrial applications like automobiles that use wheels, military tanks that use tracks and aerial balloons. Others have been developed purely for space application like hoppers and hybrid systems. Since 1970s, twelve surface missions have reported using mobile robots. Most of them used wheels as their mobility element for locomotion. Since surface space-exploration of planetary objects across the solar system has gained increasing importance in recent years, it is important to understand the state-of-the-art in mobile robotics and the technology development of the mobility system in particular. This need will be addressed in this chapter.


title: The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance

abstract: The expectation-confirmation model (ECM) of IT continuance is a model for investigating continued information technology (IT) usage behavior. This paper reports on a study that attempts to expand the set of post-adoption beliefs in the ECM, in order to extend the application of the ECM beyond an instrumental focus. The expanded ECM, incorporating the post-adoption beliefs of perceived usefulness, perceived enjoyment and perceived ease of use, was empirically validated with data collected from an on-line survey of 811 existing users of mobile Internet services. The data analysis showed that the expanded ECM has good explanatory power (R 2 = 57.6% of continued IT usage intention and R 2 = 67.8% of satisfaction), with all paths supported. Hence, the expanded ECM can provide supplementary information that is relevant for understanding continued IT usage. The significant effects of post-adoption perceived ease of use and perceived enjoyment signify that the nature of the IT can be an important boundary condition in understanding the continued IT usage behavior. At a practical level, the expanded ECM presents IT product/service providers with deeper insights into how to address IT users' satisfaction and continued patronage.


title: NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction

abstract: This paper describes our system used in the Aspect Based Sentiment Analysis Task 12 of SemEval-2015. Our system is based on two supervised machine learning algorithms: sigmoidal feedforward network to train binary classifiers for aspect category classification (Slot 1), and Conditional Random Fields to train classifiers for opinion target extraction (Slot 2). We extract a variety of lexicon and syntactic features, as well as cluster features induced from unlabeled data. Our system achieves state-of-the-art performances, ranking 1st for three of the evaluations (Slot 1 for both restaurant and laptop domains, and Slot 1 & 2) and 2nd for Slot 2 evaluation.


title: Adaptive synchronization of leader-follower networked systems against communication attenuation and actuators faults

abstract: This paper addresses the adaptive asymptotic synchronization problem of a class of leader-follower large scale networked systems against communicated signal attenuation and actuator bias faults. Adaptive mechanisms are constructed to estimate each unknown faulty factor of communications and actuators on-line. Based on the estimations, an adaptive compensation controller is designed to automatically remove the unexpected impacts of communication and actuator faults. Through the adaptive adjustment parameters and Lyapunov functions, the achievement of asymptotic synchronization of the leader-follower large-scale networked system with the improper actuator and faulty transmitted signals is obtained. Finally, the synchronization results are validated by a multiple vehicle large-scale device.


title: Producing radiologist-quality reports for interpretable artificial intelligence

abstract: Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision. We argue that such approaches are not enough to "open the black box" of medical decision making systems because they are missing a key component that has been used as a standard communication tool between doctors for centuries: language. We propose a model-agnostic interpretability method that involves training a simple recurrent neural network model to produce descriptive sentences to clarify the decision of deep learning classifiers. ::: We test our method on the task of detecting hip fractures from frontal pelvic x-rays. This process requires minimal additional labelling despite producing text containing elements that the original deep learning classification model was not specifically trained to detect. ::: The experimental results show that: 1) the sentences produced by our method consistently contain the desired information, 2) the generated sentences are preferred by doctors compared to current tools that create saliency maps, and 3) the combination of visualisations and generated text is better than either alone.


title: Optimizing the Cost-Performance Tradeoff for Coflows Across Geo-Distributed Datacenters

abstract: For data analytics jobs running across geographically distributed datacenters, coflows have to go through the inter-datacenter network over relatively low bandwidth and high cost links. In this case, optimizing cost-performance tradeoffs for such coflows becomes crucial. Ideally, decreasing the coflow completion time (CCT) can significantly improve the network performance, meanwhile, reducing the transmission cost introduced by these coflows is another fundamental goal for datacenter operators. Unfortunately, minimizing both the CCT and the transmission cost are conflicting objectives that cannot be achieved concurrently. Prior methods have significant limitations when exploring such tradeoffs, because they either merely decrease the average CCT or reduce the transmission cost independently. In this paper, we focus on a cost-performance tradeoff problem for coflows running across the inter-datacenter network. Specifically, we formulate an optimization problem, so as to minimize a combination of both the average CCT and the average transmission cost. This problem is inherently hard to solve due to the unknown information of future coflows. Therefore, we present Lever , an online coflow-aware optimization framework, to balance these two conflicting objectives. Without any prior knowledge of future coflows, Lever has been proved to have a non-trivial competitive ratio in solving this cost-performance tradeoff problem. Results from large-scale simulations demonstrate that Lever can significantly reduce the average transmission cost, and at the same time, speed up the completion of these coflows, compared with the state-of-the-art solutions.


title: Tweets Are Not Created Equal: investigating Twitter's client ecosystem

abstract: This article offers an investigation into the developer ecosystem of platforms drawing on the specific case of Twitter and explores how third-party clients enable different “ways of being” on Twitter. It suggests that researchers need to consider digital data as traces of distributed accomplishments between platforms, users, interfaces, and developers. The argument follows three main steps: We discuss how Twitter’s bounded openness enables and structures distributed data production through grammatization of action. We then suggest ways to explore and qualify sources by drawing on a weeklong data set of nearly 32 million tweets, retrieved from Twitter’s 1% random sample. We explore how clients show considerable differences in tweet characteristics and degrees of automation, and outline methodological steps to deploy the source variable to further investigate the heterogeneous practices common metrics risk flattening into singular counts. We conclude by returning to the question about the measures of the medium, suggesting how they might be revisited in the context of increasingly distributed platform ecosystems, and how platform data challenge key ideas of digital methods research.