title: IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT

abstract: With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IoT Sentinel, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IoT Sentinel is effective in identifying device types and has minimal performance overhead.


title: Cognitive enhancement: Cognitive enhancement

abstract: Cognitive enhancement refers to the improvement of cognitive ability in normal healthy individuals. In this article, we focus on the use of pharmaceutical agents and brain stimulation for cognitive enhancement, reviewing the most common methods of pharmacologic and electronic cognitive enhancement, and the mechanisms by which they are believed to work, the effectiveness of these methods and their prevalence. We note the many gaps in our knowledge of these matters, including open questions about the size, reliability and nature of the enhancing effects, and we conclude with recommendations for further research. WIREs Cogn Sci 2014, 5:95-103. doi: 10.1002/wcs.1250 CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.


title: A Multiethnic Cohort in Hawaii and Los Angeles: Baseline Characteristics

abstract: The authors describe the design and implementation of a large multiethnic cohort established to study diet and cancer in the United States. They detail the source of the subjects, sample size, questionnaire development, pilot work, and approaches to future analyses. The cohort consists of 215,251 adult men and women (age 45–75 years at baseline) living in Hawaii and in California (primarily Los Angeles County) with the following ethnic distribution: African-American (16.3%), Latino (22.0%), Japanese-American (26.4%), Native Hawaiian (6.5%), White (22.9%), and other ancestry (5.8%). From 1993 to 1996, participants entered the cohort by completing a 26-page, self-administered mail questionnaire that elicited a quantitative food frequency history, along with demographic and other information. Response rates ranged from 20% in Latinos to 49% in Japanese-Americans. As expected, both within and among ethnic groups, the questionnaire data show substantial variations in dietary intakes (nutrients as well as foods) and in the distributions of non-dietary risk factors (including smoking, alcohol consumption, obesity, and physical activity). When compared with corresponding ethnic-specific cancer incidence rates, the findings provide tentative support for several current dietary hypotheses. As sufficient numbers of cancer cases are identified through surveillance of the cohort, dietary and other hypotheses will be tested in prospective analyses.


title: The Programmable Permanent Magnet Actuator: A Paradigm Shift in Efficiency for Low-Speed Torque-Holding Robotic Applications

abstract: In this letter, we propose a new type of electromagnetic actuator called the “programmable permanent magnet” (PPM) actuator. The PPM actuator is designed to tackle low-speed high-torque applications where classic electromagnetic actuators are highly inefficient. The PPM actuator is based on the application of pulsed current to magnetize a hard ferromagnetic stator that is made of individual SmCo (custom grade) magnets placed in a Halbach array configuration. The motor creates a springlike torque function that generates up to 0.2 N ⋅m. Unlike electromagnetic actuators, by design, the PPM actuator does not draw current while exerting torque. Instead, the motor's power consumption depends on its rotational speed or torque variations. The PPM actuator is, by consequence, most efficient, whereas classical electromagnetic motors are most inefficient, and is a new alternative to actuators for robotic applications, which tend to operate in low-speed, high-torque conditions. The prototyped PPM actuator measures 47 mm in diameter, with a height of 35 mm and weight of 200 g, and has been successfully integrated in a commercially available 2-finger gripper from Robotiq, Inc.


title: LTSC-128: Stream Cipher Based on the Intractable Shortest Vector Problem in Lattice

abstract: LTSC-128 is a high secure stream cipher based on the hardness of the Shortest Vector Problem in Lattice space. The cipher is based on vectors multiplication over finite field, where these vectors are represented by polynomials to enhance the per formance of keystream generation. The key size is 128 bits and there is no attack faster than exhaustive key search has been identified. The experimental results showed that the encryption rate of LTSC-128 is about 50Mbit/second. LTSC-128 cipher has additional feature that its performance can be increased by applying multithreading technique on multi-core processors. The multithreaded version of LTSC-128 has achieved better en- cryption rate of 71Mbit/second on dual-core processor.


title: Effects of Video Game Training on Behavioral and Electrophysiological Measures of Attention and Memory: Protocol for a Randomized Controlled Trial

abstract: Background: Neuroplasticity-based approaches seem to offer promising ways of maintaining cognitive health in older adults and postponing the onset of cognitive decline symptoms. Although previous research suggests that training can produce transfer effects, this study was designed to overcome some limitations of previous studies by incorporating an active control group and the assessment of training expectations. Objective: The main objectives of this study are (1) to evaluate the effects of a randomized computer-based intervention consisting of training older adults with nonaction video games on brain and cognitive functions that decline with age, including attention and spatial working memory, using behavioral measures and electrophysiological recordings (event-related potentials [ERPs]) just after training and after a 6-month no-contact period; (2) to explore whether motivation, engagement, or expectations might account for possible training-related improvements; and (3) to examine whether inflammatory mechanisms assessed with noninvasive measurement of C-reactive protein in saliva impair cognitive training-induced effects. A better understanding of these mechanisms could elucidate pathways that could be targeted in the future by either behavioral or neuropsychological interventions. Methods: A single-blinded randomized controlled trial with an experimental group and an active control group, pretest, posttest, and 6-month follow-up repeated measures design is used in this study. A total of 75 cognitively healthy older adults were randomly distributed into experimental and active control groups. Participants in the experimental group received 16 1-hour training sessions with cognitive nonaction video games selected from Lumosity, a commercial brain training package. The active control group received the same number of training sessions with The Sims and SimCity, a simulation strategy game. Results: We have recruited participants, have conducted the training protocol and pretest assessments, and are currently conducting posttest evaluations. The study will conclude in the first semester of 2017. Data analysis will take place during 2017. The primary outcome is transfer of benefit from training to attention and working memory functions and the neural mechanisms underlying possible cognitive improvements. Conclusions: We expect that mental stimulation with video games will improve attention and memory both at the behavioral level and in ERP components promoting brain and mental health and extending independence among elderly people by avoiding the negative personal and economic consequences of long-term care. Trial Registration: Clinicaltrials.gov NCT02796508; https://clinicaltrials.gov/ct2/show/NCT02796508 (archived by WebCite at http://www.webcitation.org/6nFeKeFNB) [JMIR Res Protoc 2017;6(1):e8]


title: Burst Mode Elimination in High-Power $LLC$ Resonant Battery Charger for Electric Vehicles

abstract: In order to recover and fully charge batteries in electric vehicles, smart battery chargers should not only work under different loading conditions and output voltage regulations (close to zero to 1.5 times the nominal output voltage), but also provide a ripple-free charging current for battery packs and a noise-free environment for the battery management system (BMS). In this paper, an advanced $LLC$ design procedure is investigated to provide advantageous extreme regulation and eliminate detrimental burst mode operation. A modified, special $LLC$ tank driven by both variable frequency and phase shift proves to be a successful solution to achieve all the regulation requirements for battery charging (from recovery, bulk, equalization, to finish). The proposed solution can eliminate the negative impact of burst mode noises on the BMS, provide a ripple-free charging current for batteries in different states of charge, reduce the switching frequency variation, and facilitate the EMI filter and magnetic components designs procedure. In order to fully consider the characteristics of the full bridge $LLC$ resonant converter, especially the output voltage regulation range and soft transitions of the MOSFETs in the fixed frequency phase shift mode, a new set of analytical equations is obtained for the $LLC$ resonant converter with consideration of separated primary and secondary leakage inductances of the high frequency transformer. Based on the proposed strategy and analytical equations, multivariate statistical design methodology is employed to design and optimize a 120 VDC, 3-kW battery charger. The experimental results exhibit the excellent performance of the resulting converter, which has a peak efficiency of 96.5% with extreme regulation capability.


title: A flash-memory based file system

abstract: A flash memory device driver that supports a conventional UNIX file system transparently was designed. To avoid the limitations due to flash memory's restricted number of write cycles and its inability to be overwritten, this driver writes data to the flash memory system sequentially as a Log-structured File System (LFS) does and uses a cleaner to collect valid data blocks and reclaim invalid ones by erasing the corresponding flash memory regions. Measurements showed that the overhead of the cleaner has little effect on the performance of the prototype when utilization is low but that the effect becomes critical as the utilization gets higher, reducing the random write throughput from 222 Kbytes/s at 30% utilization to 40 Kbytes/s at 90% utilization. The performance of the prototype in the Andrew Benchmark test is roughly equivalent to that of the 4.4BSD Pageable Memory based File System (MFS).


title: Single-Antenna FMCW Radar CMOS Transceiver IC

abstract: This paper presents a CMOS transceiver IC for a single-antenna frequency-modulated continuous wave (FMCW) radar. Since transmitter (Tx) leakage is critical in a single antenna radar with CMOS technology, a comprehensive leakage canceling technique is proposed. It is able to cancel all the leakages caused by antenna reflection, asymmetry of a balanced structure, and lossy substrate without additional power or area. Even-order harmonic leakages from the power amplifier (PA) are also reduced by an even-harmonic filter, which is implemented simply by removing the real ground from the symmetrical point of the PA output transformer. Matching networks are simplified by using a modified coupler structure. A low-noise combining amplifier is used to make the combining circuit compact. As a result, the transceiver achieves the output power of −1.6 dBm, the phase noise of −105.44 dBc/Hz at 1MHz offset, the receiver (Rx) gain of 15.3 dB, and the noise figure of 11.6 dB. Tx leakages are canceled so that the isolation between Tx and Rx is 47.3 dB. The chip consumes 74.1 mA from a 1.5-V power supply. Despite the high integration level, the chip area including pads is 1.7 mm $\times $ 0.9 mm. A $ {K}$ -band FMCW radar module with a single antenna is implemented with this chip.


title: Archie: a tool for detecting, monitoring, and preserving architecturally significant code

abstract: The quality of a software architecture is largely dependent upon the underlying architectural decisions at the framework, tactic, and pattern levels. Decisions to adopt certain solutions determine the extent to which desired qualities such as security, availability, and performance are achieved in the delivered system. In this tool demo, we present our Eclipse plug-in named Archie as a solution for maintaining architectural qualities in the design and code despite long-term maintenance and evolution activities. Archie detects architectural tactics such as heartbeat, resource pooling, and role-based access control (RBAC) in the source code of a project; constructs traceability links between the tactics, design models, rationales and source code; and then uses these to monitor the environment for architecturally significant changes and to keep developers informed of underlying design decisions and their associated rationales.


title: Transistor analogs of emergent iono-neuronal dynamics

abstract: Neuromorphic analog metal-oxide-silicon (MOS) transistor circuits promise compact, low-power, and high-speed emulations of iono-neuronal dynamics orders-of-magnitude faster than digital simulation. However, their inherently limited input voltage dynamic range vs power consumption and silicon die area tradeoffs makes them highly sensitive to transistor mismatch due to fabrication inaccuracy, device noise, and other nonidealities. This limitation precludes robust analog very-large-scale-integration (aVLSI) circuits implementation of emergent iono-neuronal dynamics computations beyond simple spiking with limited ion channel dynamics. Here we present versatile neuromorphic analog building-block circuits that afford near-maximum voltage dynamic range operating within the low-power MOS transistor weak-inversion regime which is ideal for aVLSI implementation or implantable biomimetic device applications. The fabricated microchip allowed robust realization of dynamic iono-neuronal computations such as coincidence detection of presynaptic spikes or pre- and postsynaptic activities. As a critical performance benchmark, the high-speed and highly interactive iono-neuronal simulation capability on-chip enabled our prompt discovery of a minimal model of chaotic pacemaker bursting, an emergent iono-neuronal behavior of fundamental biological significance which has hitherto defied experimental testing or computational exploration via conventional digital or analog simulations. These compact and power-efficient transistor analogs of emergent iono-neuronal dynamics open new avenues for next-generation neuromorphic, neuroprosthetic, and brain-machine interface applications.


title: Bitcoin Block Withholding Attack: Analysis and Mitigation

abstract: We address two problems: first, we study a variant of block withholding (BWH) attack in Bitcoins and second, we propose solutions to prevent all existing types of BWH attacks in Bitcoins. We analyze the strategies of a selfish Bitcoin miner who in connivance with one pool attacks another pool and receives reward from the former mining pool for attacking the latter. We name this attack as “sponsored block withholding attack.” We present detailed quantitative analysis of the monetary incentive that a selfish miner can earn by adopting this strategy under different scenarios. We prove that under certain conditions, the attacker can maximize her revenue by adopting some strategies and by utilizing her computing power wisely. We also show that an attacker may use this strategy for attacking both the pools for earning higher amount of incentives. More importantly, we present a strategy that can effectively counter block withholding attack in any mining pool. First, we propose a generic scheme that uses cryptographic commitment schemes to counter BWH attack. Then, we suggest an alternative implementation of the same scheme using hash function. Our scheme protects a pool from rogue miners as well as rogue pool administrators. The scheme and its variant defend against BWH attack by making it impossible for the miners to distinguish between a partial proof of work and a complete proof of work. The scheme is so designed that the administrator cannot cheat on the entire pool. The scheme can be implemented by making minor changes to existing Bitcoin protocol. We also analyze the security of the scheme.


title: Object categorization by learned universal visual dictionary

abstract: This paper presents a new algorithm for the automatic recognition of object classes from images (categorization). Compact and yet discriminative appearance-based object class models are automatically learned from a set of training images. The method is simple and extremely fast, making it suitable for many applications such as semantic image retrieval, Web search, and interactive image editing. It classifies a region according to the proportions of different visual words (clusters in feature space). The specific visual words and the typical proportions in each object are learned from a segmented training set. The main contribution of this paper is twofold: i) an optimally compact visual dictionary is learned by pair-wise merging of visual words from an initially large dictionary. The final visual words are described by GMMs. ii) A novel statistical measure of discrimination is proposed which is optimized by each merge operation. High classification accuracy is demonstrated for nine object classes on photographs of real objects viewed under general lighting conditions, poses and viewpoints. The set of test images used for validation comprise: i) photographs acquired by us, ii) images from the Web and iii) images from the recently released Pascal dataset. The proposed algorithm performs well on both texture-rich objects (e.g. grass, sky, trees) and structure-rich ones (e.g. cars, bikes, planes)


title: Empirical Analysis of Denial-of-Service Attacks in the Bitcoin Ecosystem

abstract: We present an empirical investigation into the prevalence and impact of distributed denial-of-service (DDoS) attacks on operators in the Bitcoin economy. To that end, we gather and analyze posts mentioning “DDoS” on the popular Bitcoin forum bitcointalk.org. Starting from around 3 000 different posts made between May 2011 and October 2013, we document 142 unique DDoS attacks on 40 Bitcoin services. We find that 7 % of all known operators have been attacked, but that currency exchanges, mining pools, gambling operators, eWallets, and financial services are much more likely to be attacked than other services. Not coincidentally, we find currency exchanges and mining pools are much more likely to have DDoS protection such as CloudFlare, Incapsula, or Amazon Cloud. We show that those services that have been attacked are more than three times as likely to buy anti-DDoS services than operators who have not been attacked. We find that big mining pools (those with historical hashrate shares of at least 5 %) are much more likely to be DDoSed than small pools. We investigate Mt. Gox as a case study for DDoS attacks on currency exchanges and find a disproportionate amount of DDoS reports made during the large spike in trading volume and exchange rates in spring 2013. We conclude by outlining future opportunities for researching DDoS attacks on Bitcoin.


title: A cognitive-behavioral model of problematic online gaming in adolescents aged 12-22 years

abstract: The aim of this study was to apply the cognitive behavioral model of problematic Internet use to the context of online game use to obtain a better understanding of problematic use of online games and its negative consequences. In total, 597 online game playing adolescents aged 12-22years participated in this study. Results showed that the cognitive behavioral model of problematic Internet use can also be used in the context of online game use. More specifically, preference for online social interaction, mood regulation and deficient self-regulation appeared to play an important role in predicting negative outcomes from problematic online game use. Together, these cognitions and behaviors explained 79% of the variance of negative outcomes scores. These findings can be used to develop strategies that aim at reducing problematic online game behavior and its negative consequences.


title: Predictive learning via rule ensembles

abstract: General regression and classification models are constructed as linear combinations of simple rules derived from the data. Each rule consists of a conjunction of a small number of simple statements concerning the values of individual input variables. These rule ensembles are shown to produce predictive accuracy comparable to the best methods. However, their principal advantage lies in interpretation. Because of its simple form, each rule is easy to understand, as is its influence on individual predictions, selected subsets of predictions, or globally over the entire space of joint input variable values. Similarly, the degree of relevance of the respective input variables can be assessed globally, locally in different regions of the input space, or at individual prediction points. Techniques are presented for automatically identifying those variables that are involved in interactions with other variables, the strength and degree of those interactions, as well as the identities of the other variables with which they interact. Graphical representations are used to visualize both main and interaction effects.


title: Speech enhancement via ensemble modeling NMF adaptation

abstract: Nonnegative matrix factorization (NMF)-based speech enhancement algorithm has been proven to provide satisfactory performance when the prior information about speaker and noise types are given. In most real-world scenarios, however, such prior information may not always be accessible. Therefore, an adaptation technique is favorable to adapt the NMF matrices to match the testing condition for a better enhancement performance. In this study, we proposed a novel ensemble modeling (ESM) algorithm for NMF adaptation. The algorithm effectively uses the local information of the entire training data to facilitate an effective and efficient online NMF adaptation. The adapted NMF matrices were used to perform speech enhancement. Experimental results on the perceptual evaluation of speech quality (PESQ) confirmed the effectiveness of the ESM algorithm in various signal-to-noise ratio conditions.


title: Computing the Semantic Similarity of Resources in DBpedia for Recommendation Purposes

abstract: The Linked Open Data cloud has been increasing in popularity, with DBpedia as a first-class citizen in this cloud that has been widely adopted across many applications. Measuring similarity between resources and identifying their relatedness could be used for various applications such as item-based recommender systems. To this end, several similarity measures such as LDSD (Linked Data Semantic Distance) were proposed. However, some fundamental axioms for similarity measures such as “equal self-similarity”, “symmetry” or “minimality” are violated, and property similarities have been ignored. Moreover, none of the previous studies have provided a comparative study on other similarity measures. In this paper, we present a similarity measure, called Resim (Resource Similarity), based on top of a revised LDSD similarity measure. Resim aims to calculate the similarity of any resources in DBpedia by taking into account the similarity of the properties of these resources as well as satisfying the fundamental axioms. In addition, we evaluate our similarity measure with two state-of-the-art similarity measures (LDSD and Shakti) in terms of calculating the similarities for general resources (i.e., any resources without a domain restriction) in DBpedia and resources for music artist recommendations. Results show that our similarity measure can resolve some of the limitations of state-of-the-art similarity measures and performs better than them for calculating the similarities between general resources and music artist recommendations.


title: A Methodology and Framework to Simplify Usability Analysis of Mobile Applications

abstract: Usability analysis is an important step in software development in order to improve certain aspects of the system. However, it is often a challenge especially when it comes to evaluating applications running on mobile devices because of the restrictions posed by the device and the lack of supporting tools and software available to collect the necessary usability data. This paper proposes a methodology and framework to aid developers in preparing the mobile system for usability analysis. The focus is on the simplification of the developer's task in preparing the system for evaluation and the processing of the collected usability data by automating some of the tasks involved in the process.


title: Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment

abstract: Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem. Despite the success of cascaded regression for problems such as face alignment and head pose estimation, there are several shortcomings arising in the strategies proposed thus far. Specifically, (a) the regressors are learnt independently, (b) the descent directions may cancel one another out and (c) handcrafted features (e.g., HoGs, SIFT etc.) are mainly used to drive the cascade, which may be sub-optimal for the task at hand. In this paper, we propose a combined and jointly trained convolutional recurrent neural network architecture that allows the training of an end-to-end to system that attempts to alleviate the aforementioned drawbacks. The recurrent module facilitates the joint optimisation of the regressors by assuming the cascades form a nonlinear dynamical system, in effect fully utilising the information between all cascade levels by introducing a memory unit that shares information across all levels. The convolutional module allows the network to extract features that are specialised for the task at hand and are experimentally shown to outperform hand-crafted features. We show that the application of the proposed architecture for the problem of face alignment results in a strong improvement over the current state-of-the-art.


title: A Gaussian noise generator for hardware-based simulations

abstract: Hardware simulation offers the potential of improving code evaluation speed by orders of magnitude over workstation or PC-based simulation. We describe a hardware-based Gaussian noise generator used as a key component in a hardware simulation system, for exploring channel code behavior at very low bit error rates (BERs) in the range of 10/sup -9/ to 10/sup -10/. The main novelty is the design and use of nonuniform piecewise linear approximations in computing trigonometric and logarithmic functions. The parameters of the approximation are chosen carefully to enable rapid computation of coefficients from the inputs while still retaining high fidelity to the modeled functions. The output of the noise generator accurately models a true Gaussian Probability Density Function (PDF) even at very high /spl sigma/ values. Its properties are explored using: 1) several different statistical tests, including the chi-square test and the Anderson-Darling test, and 2) an application for decoding of low-density parity-check (LDPC) codes. An implementation at 133 MHz on a Xilinx Virtex-II XC2V4000-6 FPGA produces 133 million samples per second, which is seven times faster than a 2.6 GHz Pentium-IV PC; another implementation on a Xilinx Spartan-IIE XC2S300E-7 FPGA at 62 MHz is capable of a three times speedup. The performance can be improved by exploiting parallelism: an XC2V4000-6 FPGA with nine parallel instances of the noise generator at 105 MHz can run 50 times faster than a 2.6 GHz Pentium-IV PC. We illustrate the deterioration of clock speed with the increase in the number of instances.


title: Patellofemoral instability: surgical treatment of soft tissues.

abstract: Instability of the patella is a relatively frequent occurrence in adolescents. Its pathogenesis, which is multi-factorial, is still much debated. Stability of the patella is guaranteed by a delicate balance of a series of factors (osteo-cartilaginous, ligamentous and muscular), and it is not surprising that alteration of one or more of these can lead to pathological conditions that can range from simple anterior pain associated with a hypermobile patella to recurrent dislocation. The aim of surgical treatment is to correct these anatomical abnormalities. Surgical procedures on the soft tissues comprise reefing, realignment and reconstruction of the medial stabilizing structures, and release of the lateral structures. These procedures, although having precise indications, provide the surgeon with the instruments necessary to deal with almost all these anatomo-pathological conditions. Furthermore, preserving the osteo-cartilaginous components results in less morbidity than is associated with traditional surgical procedures, such as trochleoplasty and transposition of the anterior tibial tuberosity.


title: Simultaneous linear estimation of multiple view geometry and lens distortion

abstract: A problem in uncalibrated stereo reconstruction is that cameras which deviate from the pinhole model have to be pre-calibrated in order to correct for nonlinear lens distortion. If they are not, and point correspondence is attempted using the uncorrected images, the matching constraints provided by the fundamental matrix must be set so loose that point matching is significantly hampered. This paper shows how linear estimation of the fundamental matrix from two-view point correspondences may be augmented to include one term of radial lens distortion. This is achieved by (1) changing from the standard radial-lens model to another which (as we show) has equivalent power, but which takes a simpler form in homogeneous coordinates, and (2) expressing fundamental matrix estimation as a quadratic eigenvalue problem (QEP), for which efficient algorithms are well known. I derive the new estimator, and compare its performance against bundle-adjusted calibration-grid data. The new estimator is fast enough to be included in a RANSAC-based matching loop, and we show cases of matching being rendered possible by its use. I show how the same lens can be calibrated in a natural scene where the lack of straight lines precludes most previous techniques. The modification when the multi-view relation is a planar homography or trifocal tensor is described.


title: Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection

abstract: Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is underexplored. In this work, we present Amulet, a generic aggregating multi-level convolutional feature framework for salient object detection. Our framework first integrates multi-level feature maps into multiple resolutions, which simultaneously incorporate coarse semantics and fine details. Then it adaptively learns to combine these feature maps at each resolution and predict saliency maps with the combined features. Finally, the predicted results are efficiently fused to generate the final saliency map. In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework. By aggregating multi-level convolutional features in this efficient and flexible manner, the proposed saliency model provides accurate salient object labeling. Comprehensive experiments demonstrate that our method performs favorably against state-of-the art approaches in terms of near all compared evaluation metrics.


title: Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling

abstract: With the rapid growth of social tagging systems, many efforts have been put on tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping both the tag-based user and item profiles to an abstract deep feature space, where the deep-semantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized). Due to huge numbers of online items, the training of this model is usually computationally expensive in the real-world context. Therefore, we introduce negative sampling, which significantly increases the model's training efficiency (109.6 times quicker) and ensures the scalability in practice. Experimental results show that our model can significantly outperform the state-of-the-art baselines in tag-aware personalized recommendation: e.g., its mean reciprocal rank is between 5.7 and 16.5 times better than the baselines.


title: Body MRI artifacts in clinical practice: A physicist's and radiologist's perspective

abstract: The high information content of MRI exams brings with it unintended effects, which we call artifacts. The purpose of this review is to promote understanding of these artifacts, so they can be prevented or properly interpreted to optimize diagnostic effectiveness. We begin by addressing static magnetic field uniformity, which is essential for many techniques, such as fat saturation. Eddy currents, resulting from imperfect gradient pulses, are especially problematic for new techniques that depend on high performance gradient switching. Nonuniformity of the transmit radiofrequency system constitutes another source of artifacts, which are increasingly important as magnetic field strength increases. Defects in the receive portion of the radiofrequency system have become a more complex source of problems as the number of radiofrequency coils, and the sophistication of the analysis of their received signals, has increased. Unwanted signals and noise spikes have many causes, often manifesting as zipper or banding artifacts. These image alterations become particularly severe and complex when they are combined with aliasing effects. Aliasing is one of several phenomena addressed in our final section, on artifacts that derive from encoding the MR signals to produce images, also including those related to parallel imaging, chemical shift, motion, and image subtraction. J. Magn. Reson. Imaging 2013;38:269–287. © 2013 Wiley Periodicals, Inc.


title: Voice Controlled Home Automation System for the Elderly or Disabled People

abstract: Home automation is one of the major growing industries that can change the way people live. Some of these home automation systems target those seeking luxury and sophisticated home automation platforms; others target those with special needs like the elderly and the disabled. Typical wireless home automation system allows one to control house hold appliances from a centralized control unit which is wireless. These appliances usually have to be specially designed to be compatible with each other and with the control unit for most commercially available home automation systems. The developed system can be integrated as a single portable unit and allows one to wirelessly control lights, fans, air conditioners, television sets, security cameras, electronic doors, computer systems, audio/visual equipment’s etc. and turn ON or OFF any appliance that is plugged into a wall outlet, get the status of different sensors and take decision accordingly. The system is portable and constructed in a way that is easy to install, configure, run, and maintain. The perfect user interface still does not exist at present and to build a good interface requires knowledge of both sociology and technology fields. According to major companies that are involved in speech recognition researches, voice will be the primary interface between humans and machines in the near future. The problem lies with the situation of the elderly or disabled people, who cannot usually help themselves to move around, and might require external assistance. People who live alone might also need a helping hand at home. Therefore a voice controlled home automation system is designed, so that the users can perform certain tasks by just the use of their voices, moreover, the system is designed to have a hand-held device(remote) so that the user can easily speak their commands, otherwise they would have to walk over to the microphone to speak. Having a remote will make the system


title: Cover Feature Chameleon: Hierarchical Clustering Using Dynamic Modeling

abstract: Clustering is a discovery process in data mining. It groups a set of data in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters. Many advanced algorithms have difficulty dealing with highly variable clusters that do not follow a preconceived model. By basing its selections on both interconnectivity and closeness, the Chameleon algorithm yields accurate results for these highly variable clusters. Existing algorithms use a static model of the clusters and do not use information about the nature of individual clusters as they are merged. Furthermore, one set of schemes (the CURE algorithm and related schemes) ignores the information about the aggregate interconnectivity of items in two clusters. Another set of schemes (the Rock algorithm, group averaging method, and related schemes) ignores information about the closeness of two clusters as defined by the similarity of the closest items across two clusters. By considering either interconnectivity or closeness only, these algorithms can select and merge the wrong pair of clusters. Chameleon's key feature is that it accounts for both interconnectivity and closeness in identifying the most similar pair of clusters. Chameleon finds the clusters in the data set by using a two-phase algorithm. During the first phase, Chameleon uses a graph partitioning algorithm to cluster the data items into several relatively small subclusters. During the second phase, it uses an algorithm to find the genuine clusters by repeatedly combining these subclusters.


Longkit - A Universal Framework for BIOS/UEFI Rootkits in System Management Mode


title: The power of simple tabulation hashing

abstract: Randomized algorithms are often enjoyed for their simplicity, but the hash functions used to yield the desired theoretical guarantees are often neither simple nor practical. Here we show that the simplest possible tabulation hashing provides unexpectedly strong guarantees. The scheme itself dates back to Carter and Wegman (STOC'77). Keys are viewed as consisting of c characters. We initialize c tables T_1, ..., T_c mapping characters to random hash codes. A key x=(x_1, ..., x_c) is hashed to T_1[x_1] xor ... xor T_c[x_c]. While this scheme is not even 4-independent, we show that it provides many of the guarantees that are normally obtained via higher independence, e.g., Chernoff-type concentration, min-wise hashing for estimating set intersection, and cuckoo hashing.


title: Which NoSQL database for IoT Applications?

abstract: A large amount of data are generated every moment by connected objects creating Internet of Things (IoT). These data are difficult to handle using traditional databases leading to use NoSQL databases. These latter have achieved a large popularity thanks to their high performance, flexibility in scaling, and high availability. But, which NoSQL database is the most suitable for IoT applications? In this paper, we discuss the main requirements of IoT data management, and we compare five of the most popular NoSQL databases namely, Redis, Cassandra, MongoDb, Couchbase and Neo4j, in accordance with IoT data management requirements, in order to find the most suitable NoSQL database for IoT applications.


title: A Personalized Collaborative Filtering Recommendation Using Association Rules Mining and Self-Organizing Map

abstract: With the development of the Internet, the problem of information overload is becoming increasing serious. People all have experienced the feeling of being overwhelmed by the number of new books, articles, and proceedings coming out each year. Many researchers pay more attention on building a proper tool which can help users obtain personalized resources. Personalized recommendation systems are one such software tool used to help users obtain recommendations for unseen items based on their preferences. The commonly used personalized recommendation system methods are content-based filtering, collaborative filtering, and association rules mining. Unfortunately, each method has its drawbacks. This paper presented a personalized collaborative filtering recommendation method combining the association rules mining and self-organizing map. It used the association rules mining to fill the vacant where necessary. Then, it employs clustering function of self-organizing map to form nearest neighbors of the target item and it produces prediction of the target user to the target item using item-based collaborative filtering. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems.


title: Approximate Arc Length Parametrization

abstract: Current approaches to compute the arc length of a parametric curve rely on table lookup schemes. We present an approximate closed-form solution to the problem of computing an arc length parametrization for any given parametric curve. Our solution outputs a one or two-span Bezier curve which relates the length of the curve to the parametric variable. The main advantage of our approach is that we obtain a simple continuous function relating the length of the curve and the parametric variable. This allows the length to be easily computed given the parametric values. Tests with our algorithm on several thousand curves show that the maximum error in our approximation is 8.7% and that the average of maximum errors is 1.9%. Our algorithm is fast enough to compute the closed-form solution in a fraction of a second. After that a user can interactively get an approximation of the arc length for an arbitrary parameter value.


title: A bidirectional flyback converter with cross-coupled non-dissipative snubber circuits

abstract: This paper proposed a novel isolated bidirectional flyback converter integrated with two non-dissipative LC snubbers. In the proposed topology, the main flyback transformer and the LC snubbers are crossed-coupled to reduce current circulation and recycle the leakage energy. The proposed isolated bidirectional flyback converter can step-up the voltage of the battery (Vbat = 12V) to a high voltage side (V HV = 200V) for the load demand and vice versa. The main goal of this paper is to demonstrate the performances of this topology to achieve high voltage gain with less switching losses and reduce components stresses. The circuit analysis conferred in detail for Continuous Conduction Mode (CCM). Lastly, a laboratory prototype constructed to compare with simulation result.


title: EFFICIENT IMPLEMENTATION OF THE CKY ALGORITHM

abstract: When the CKY algorithm is presented in Natural Language Processing literature, it is often is described in high-level pseudo code. The implementation details of the CKY algorithm, despite being critical to efficiency, are rarely (if ever) discussed. In this paper I discuss multiple implementation approaches, and optimizations on these approaches to increase parsing time an order of magnitude when parsing with large grammars.


title: Apache Tez: A Unifying Framework for Modeling and Building Data Processing Applications

abstract: The broad success of Hadoop has led to a fast-evolving and diverse ecosystem of application engines that are building upon the YARN resource management layer. The open-source implementation of MapReduce is being slowly replaced by a collection of engines dedicated to specific verticals. This has led to growing fragmentation and repeated efforts with each new vertical engine re-implementing fundamental features (e.g. fault-tolerance, security, stragglers mitigation, etc.) from scratch. In this paper, we introduce Apache Tez, an open-source framework designed to build data-flow driven processing runtimes. Tez provides a scaffolding and library components that can be used to quickly build scalable and efficient data-flow centric engines. Central to our design is fostering component re-use, without hindering customizability of the performance-critical data plane. This is in fact the key differentiator with respect to the previous generation of systems (e.g. Dryad, MapReduce) and even emerging ones (e.g. Spark), that provided and mandated a fixed data plane implementation. Furthermore, Tez provides native support to build runtime optimizations, such as dynamic partition pruning for Hive. Tez is deployed at Yahoo!, Microsoft Azure, LinkedIn and numerous Hortonworks customer sites, and a growing number of engines are being integrated with it. This confirms our intuition that most of the popular vertical engines can leverage a core set of building blocks. We complement qualitative accounts of real-world adoption with quantitative experimental evidence that Tez-based implementations of Hive, Pig, Spark, and Cascading on YARN outperform their original YARN implementation on popular benchmarks (TPC-DS, TPC-H) and production workloads.


title: Efficient and practical protocol for silylation of hydroxyl groups using reusable lithium perchlorate dispread in silica gel under neutral condition

abstract: Abstract A very efficient and mild procedure for the trimethylsilylation of a wide variety of alcohols, including primary, allylic, benzylic, secondary, hindered secondary, tertiary, and phenols with hexamethyldisilazane on the surface of silica gel dispersed with LiClO 4 in room temperature at few minutes in excellent yields under neutral conditions is reported. This procedure also allows the excellent selectivity under LP-SiO 2 system for silylation of alcohols in the presence of amine and phenolic hydroxy groups.


title: Malware Visualization for Fine-Grained Classification

abstract: Due to the rapid rise of automated tools, the number of malware variants has increased dramatically, which poses a tremendous threat to the security of the Internet. Recently, some methods for quick analysis of malware have been proposed, but these methods usually require a large computational overhead and cannot classify samples accurately for large-scale and complex malware data set. Therefore, in this paper, we propose a new visualization method for characterizing malware globally and locally to achieve fast and effective fine-grained classification. We take a new approach to visualize malware as RGB-colored images and extract global features from the images. Gray-level co-occurrence matrix and color moments are selected to describe the global texture features and color features, respectively, which produces low-dimensional feature data to reduce the complexity of training model. Moreover, a series of special byte sequences are extracted from code sections and data sections of malware and are processed into feature vectors by Simhash as the local features. Finally, we merge the global features and local features to perform malware classification using random forest, K-nearest neighbor, and support vector machine. Experimental results show that our approach obtains the highest accuracy of 97.47% and the highest F-measure of 96.85% of 7087 samples from 15 families. Color features and the local features effectively assist in the classification based on texture features and enhance the F-measure by 3.4% and 1%, respectively. Overall, the combination of global features and local features can realize fine-grained malware classification with low computational cost.


title: Improving ASR error detection with non-decoder based features.

abstract: This study reports error detection experiments in large vocabulary automatic speech recognition (ASR) systems, by using statistical classifiers. We explored new features gathered from other knowledge sources than the decoder itself: a binary feature that compares outputs from two different ASR systems (word by word), a feature based on the number of hits of the hypothesized bigrams, obtained by queries entered into a very popular Web search engine, and finally a feature related to automatically infered topics at sentence and word levels. Experiments were conducted on a European Portuguese broadcast news corpus. The combination of baseline decoder-based features and two of these additional features led to significant improvements, from 13.87% to 12.16% classification error rate (CER) with a maximum entropy model, and from 14.01% to 12.39% CER with linear-chain conditional random fields, comparing to a baseline using only decoder-based features.


title: Taste, olfactory, and food reward value processing in the brain

abstract: Complementary neuronal recordings in primates, and functional neuroimaging in humans, show that the primary taste cortex in the anterior insula provides separate and combined representations of the taste, temperature, and texture (including fat texture) of food in the mouth independently of hunger and thus of reward value and pleasantness. One synapse on, in a second tier of processing, in the orbitofrontal cortex, these sensory inputs are for some neurons combined by associative learning with olfactory and visual inputs, and these neurons encode food reward value on a continuous scale in that they only respond to food when hungry, and in that activations correlate linearly with subjective pleasantness. Cognitive factors, including word-level descriptions, and selective attention to affective value, modulate the representation of the reward value of taste and olfactory stimuli in the orbitofrontal cortex and a region to which it projects, the anterior cingulate cortex, a tertiary taste cortical area. The food reward representations formed in this way play an important role in the control of appetite, and food intake. Individual differences in these reward representations may contribute to obesity, and there are age-related differences in these value representations that shape the foods that people in different age groups find palatable. In a third tier of processing in medial prefrontal cortex area 10, decisions between stimuli of different reward value are taken, by attractor decision-making networks.


title: The enhancement of gate-induced-drain-leakage (GIDL) current in short-channel SOI MOSFET and its application in measuring lateral bipolar current gain beta

abstract: An off-state leakage current unique for short-channel SOI MOSFETs is reported. This off-state leakage is the amplification of gate-induced-drain-leakage current by the lateral bipolar transistor in an SOI device due to the floating body. The leakage current can be enhanced by as much as 100 times for 1/4 mu m SOI devices. This can pose severe constraints in future 0.1 mu m SOI device design. A novel technique was developed based on this mechanism to measure the lateral bipolar transistor current gain beta of SOI devices without using a body contact. >


title: Robot therapy for elders affected by dementia

abstract: We used the seal robot, Paro, for therapy of patients suffering from dementia at a cortical neuron clinic. The efficacy of the robot therapy was evaluated by DIMENSION, which detects patient's cortical neuron activity from a 21-channel EEG. The results from preliminary experiments show that robot therapy has a high potential to improve the condition of brain activity in patients suffering from dementia. It is concluded that Paro will be widely used and provide help to people with dementia.


title: A High-Isolation, Wideband and Dual-Linear Polarization Patch Antenna

abstract: The design of a dual-polarization stacked patch antenna to be used in GSM-UMTS base station arrays is presented. The patch shows a high matching level in a broadband and isolation between elements that make it a suitable radiating element for base station arrays. Moreover, the most relevant achievement of this element is the isolation between the two polarization ports of the same element in the antenna operating bandwidth. A prototype has been manufactured and measured. The measurements, that match the design objectives, are also presented.


title: Mutual Coupling Calibration for Multiuser Massive MIMO Systems

abstract: Massive multiple-input multiple-output (MIMO) is a promising technique to greatly increase the spectral efficiency and may be adopted by the next generation mobile communication systems. Base stations (BSs) equipped with large-scale antennas can serve multiple users simultaneously by exploiting the downlink precoding in time division duplex (TDD) mode. However, channel state information (CSI) of uplink transmissions cannot be simply used for downlink precoding, because the gain mismatches of the transceiver radio frequency (RF) circuits disable the channel reciprocity. In this paper, we focus on antenna calibration for massive MIMO systems with maximal ratio transmit (MRT) precoding to solve the channel nonreciprocity problem. A new calibration method, called mutual coupling calibration, is proposed by using the effect of mutual coupling between adjacent antennas. By exploiting this method, the BS can perform the calibration without extra hardware circuit and users’ involvement. We also build up the model of calibration error and derive the closed-form expressions of the ergodic sum-rates for evaluating the impact of calibration error on system performance. Simulation results verify the high calibration accuracy of the proposed method and show the significant improvement of system performance by performing antenna calibration.


title: Knowledge Extraction by Using an Ontology Based Annotation Tool

abstract: This paper describes a Semantic Annotation Tool for extraction of knowledge structures from web pages through the use of simple user-defined knowledge extraction patterns. The semantic annotation tool contains: an ontology-based mark-up component which allows the user to browse and to mark-up relevant pieces of information; a learning component (Crystal from the University of Massachusetts at Amherst) which learns rules from examples and an information extraction component which extracts the objects and relation between these objects. Our final aim is to provide support for ontology population by using the information extraction component. Our system uses as domain of study “KMi Planet”, a Webbased news server that helps to communicate relevant information between members in our institute.


title: Fast Leaf Vein Extraction Using Hue and Intensity Information

abstract: Leaf vein contains significant physiological information of plant. Leaf vein extraction is one of the key steps in plant modeling and plant recognition. Several methods have been presented on leaf vein extraction but all of them are limited in narrow application fields. In this paper a universal method is proposed using hue and intensity information. According to the color difference between leaf vein and mesophyll, leaves are classified as the contrasting color leaf and the concolorous leaf. Leaf vein of the contrasting color leaf can be extracted using only hue information. Leaf vein extraction of the concolorous leaf needs both hue and intensity information. The method has been programmed under Visual C++6.0 and many leaves from different plants have been tested. The results show a good applicability. Compared with other method, new method is simple, fast and universal. It can be embedded in the leaf feature extraction system and work in human-Computer interaction way.


title: Measuring Similarity Similarly: LDA and Human Perception

abstract: Several intelligent technologies designed to improve navigability in and digestibility of text corpora use topic modeling such as the state-of-the-art Latent Dirichlet Allocation (LDA). This model and variants on it provide lower-dimensional document representations used in visualizations and in computing similarity between documents. This article contributes a method for validating such algorithms against human perceptions of similarity, especially applicable to contexts in which the algorithm is intended to support navigability between similar documents via dynamically generated hyperlinks. Such validation enables researchers to ground their methods in context of intended use instead of relying on assumptions of fit. In addition to the methodology, this article presents the results of an evaluation using a corpus of short documents and the LDA algorithm. We also present some analysis of potential causes of differences between cases in which this model matches human perceptions of similarity more or less well.


title: From theory to practice: an overview of MIMO space-time coded wireless systems

abstract: This paper presents an overview of progress in the area of multiple input multiple output (MIMO) space-time coded wireless systems. After some background on the research leading to the discovery of the enormous potential of MIMO wireless links, we highlight the different classes of techniques and algorithms proposed which attempt to realize the various benefits of MIMO including spatial multiplexing and space-time coding schemes. These algorithms are often derived and analyzed under ideal independent fading conditions. We present the state of the art in channel modeling and measurements, leading to a better understanding of actual MIMO gains. Finally, the paper addresses current questions regarding the integration of MIMO links in practical wireless systems and standards.


title: Synthetic Aperture Radar Imaging Using Stepped Frequency Waveform

abstract: This paper presents a synthetic aperture radar (SAR) imaging system using a stepped frequency waveform. The main problem in stepped frequency SAR is the range difference in one sequence of pulses. This paper analyzes the influence of range difference in one sequence of pulses in detail and proposes a method to compensate this influence in the Doppler domain. A Stolt interpolation for the stepped frequency signal is used to focus the data accurately. The parameters for the stepped frequency SAR are analyzed, and some criteria are proposed for system design. Finally, simulation and experimental results are presented to demonstrate the validity of the proposed method. The experimental data are collected by using a stepped frequency radar mounted on a rail.


Discovering Malware with Time Series Shapelets


title: Calibration and test of DIFAR sonobuoys

abstract: In this paper, a method for calibrating and testing DIFAR sonobuoy multiplexer without sea or water is proposed. For this purpose; the architecture of DIFAR sonobuoy is introduced, calibration factors are expressed, calibration and test environment in terms of hardware and software are described, calibration and test procedure is explained, and finally, advantages and disadvantages of the proposed calibration and test method is discussed.


title: A Novel Electric Insulation String Structure With High-Voltage Insulation and Wireless Power Transfer Capabilities

abstract: High-voltage insulation (HVI) strings are commonly used to hold high-voltage electric cables and electrically isolate them from the grounded transmission tower. In this paper, a novel concept of an electric insulation string with HVI and wireless power transfer (WPT) capabilities is presented. Based on the concept of the domino-resonator WPT system, this new structure consists of coil resonators embedded inside totally sealed insulation discs, which are then connected in series to form the new insulation string structure with the simultaneous HVI and WPT functions. This structure allows energy harvested from the ac magnetic field around the high-voltage cable to be transmitted wirelessly to power an online monitoring system in high-voltage transmission tower continuously, thereby reducing the storage requirements of the battery. The design and analysis of this new WPT structure based on the dimensions of commercially available HVI rod are included. Practical measurements obtained from a hardware prototype of about 25 W have been obtained to confirm the WPT capability of the proposal. An energy efficiency of more than 60% has been achieved for a transmission distance of 1.1 m over a wide range of load.


title: Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition

abstract: Face alignment and 3D face reconstruction are traditionally accomplished as separated tasks. By exploring the strong correlation between 2D landmarks and 3D shapes, in contrast, we propose a joint face alignment and 3D face reconstruction method to simultaneously solve these two problems for 2D face images of arbitrary poses and expressions. This method, based on a summation model of 3D faces and cascaded regression in 2D and 3D shape spaces, iteratively and alternately applies two cascaded regressors, one for updating 2D landmarks and the other for 3D shape. The 3D shape and the landmarks are correlated via a 3D-to-2D mapping matrix, which is updated in each iteration to refine the location and visibility of 2D landmarks. Unlike existing methods, the proposed method can fully automatically generate both pose-and-expression-normalized (PEN) and expressive 3D faces and localize both visible and invisible 2D landmarks. Based on the PEN 3D faces, we devise a method to enhance face recognition accuracy across poses and expressions. Both linear and nonlinear implementations of the proposed method are presented and evaluated in this paper. Extensive experiments show that the proposed method can achieve the state-of-the-art accuracy in both face alignment and 3D face reconstruction, and benefit face recognition owing to its reconstructed PEN 3D face.


title: Understanding Rating Behaviour and Predicting Ratings by Identifying Representative Users

abstract: Online user reviews describing various products and services are now abundant on the web. While the information conveyed through review texts and ratings is easily comprehensible, there is a wealth of hidden information in them that is not immediately obvious. In this study, we unlock this hidden value behind user reviews to understand the various dimensions along which users rate products. We learn a set of users that represent each of these dimensions and use their ratings to predict product ratings. Specifically, we work with restaurant reviews to identify users whose ratings are influenced by dimensions like 'Service', 'Atmosphere' etc. in order to predict restaurant ratings and understand the variation in rating behaviour across different cuisines. While previous approaches to obtaining product ratings require either a large number of user ratings or a few review texts, we show that it is possible to predict ratings with few user ratings and no review text. Our experiments show that our approach outperforms other conventional methods by 16-27% in terms of RMSE.


title: Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

abstract: The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications. However, the state-of-art deep neural networks are known to be highly overconfident in their predictions, i.e., do not distinguish in- and out-of-distributions. Recently, to handle this issue, several threshold-based detectors have been proposed given pre-trained neural classifiers. However, the performance of prior works highly depends on how to train the classifiers since they only focus on improving inference procedures. In this paper, we develop a novel training method for classifiers so that such inference algorithms can work better. In particular, we suggest two additional terms added to the original loss (e.g., cross entropy). The first one forces samples from out-of-distribution less confident by the classifier and the second one is for (implicitly) generating most effective training samples for the first one. In essence, our method jointly trains both classification and generative neural networks for out-of-distribution. We demonstrate its effectiveness using deep convolutional neural networks on various popular image datasets.


title: A Tool for Offline and Live Testing of Evasion Resilience in Network Intrusion Detection Systems

abstract: In this work we undertake the creation of a framework for testing the degree to which network intrusion detection systems (NIDS) detect and handle evasion attacks. Our prototype system, idsprobe , takes as input a packet trace and from it constructs a configurable set of variant traces that introduce different forms of ambiguities that can lead to evasions. Our test harness then uses these variant traces in either an offline configuration, in which the NIDS under test reads traffic from the traces directly, or a livesetup, in which we employ replay technology to feed traffic over a physical network past a NIDS reading directly from a network interface, and to potentially live victim machines. Summary reports of the differences in NIDS output tell the analyst to what degree the NIDS's results vary, reflecting sensitivities to (and possible detections of) different evasions. We demonstrate idsprobe using two popular open-source NIDSs and report on their respective abilities in dealing with evasive traffic.


Social Attractiveness in Dialogs.


title: Do RSSI values reliably map to RSS in a localization system?

abstract: In recent years, research into localization systems has become more popular as the proliferation of Wireless Sensor Networks (WSNs) grows. Wireless Localization can refer to either an “Active” system which tracks a mobile transceiver, or “Passive” localization which tracks a transceiver free entity by measuring the changes it makes to the surrounding wireless environment. Recent work has seen both of these systems implemented with Received Signal Strength Indication (RSSI) values from transceivers. Many algorithms and channel models have been presented to increase the accuracy of a Received Signal Strength (RSS) based system. In this paper we experimentally check whether RSSI values map to the expected RSS values within an IEEE 802.15.4 network. Indoor experiments are repeated within an ideal outdoor environment, with multiple device platforms, to eliminate indoor multipath propagation as the cause for inconsistent behavior of RSSI. We identify 3 key issues with raw RSSI values and present either a possible solution or a mitigation strategy to reduce their effect. We conclude that using raw RSSI values is flawed, as the premise that they uniquely map to the distance between transceivers is incorrect. However they may be calibrated to increase their accuracy, and therefore viability.


title: Web-scale information extraction with vertex

abstract: Vertex is a Wrapper Induction system developed at Yahoo! for extracting structured records from template-based Web pages. To operate at Web scale, Vertex employs a host of novel algorithms for (1) Grouping similar structured pages in a Web site, (2) Picking the appropriate sample pages for wrapper inference, (3) Learning XPath-based extraction rules that are robust to variations in site structure, (4) Detecting site changes by monitoring sample pages, and (5) Optimizing editorial costs by reusing rules, etc. The system is deployed in production and currently extracts more than 250 million records from more than 200 Web sites. To the best of our knowledge, Vertex is the first system to do high-precision information extraction at Web scale.


"Chin-sternum-heart syndrome": cardiac injury associated with parachuting mishaps.


title: Characteristics of Optical Channel for Underwater Optical Wireless Communication System

abstract: To understand an underwater optical communication, it is necessary to describe the optical path of the light underwater and the refractive index of water. In the present work the receiver signal power underwater has been calculated using free space optical wireless communication formula. This procedure has been used to calculate the link margin, data rate as a function of the distance link. Types of water have been taken in the consideration. Our results show that the signal to noise ratio most parameter in communication systems in the distance link, particularly for the refractive index n ≤ 1.4. The data rate, signal to noise ratio decreases as the refractive index of water and distance link increases.


title: Construction of Plasmonic Ag and Nitrogen-Doped Graphene Quantum Dots Codecorated Ultrathin Graphitic Carbon Nitride Nanosheet Composites with Enhanced Photocatalytic Activity: Full-Spectrum Response Ability and Mechanism Insight

abstract: The full utilization of solar energy has attracted great attention in the photocatalysis and environmental pollutant control. In this study, the local surface plasmon resonance effect of Ag nanoparticles (Ag NPs) with the upconversion property of nitrogen-doped graphene quantum dots (N-GQDs) was first combined for the formation of ternary Ag/N-GQDs/g-C3N4 nanocomposites. The prepared material presents enhanced full-spectrum light response ability, even in near-infrared (NIR) light. The experiment results disclosed that the 0.5% N-GQDs and 2.0% Ag NPs co-doped g-C3N4 show the highest photocatalytic activity, achieving 92.8 and 31.3% removal efficiency under full-spectrum light and NIR light irradiation, respectively, which was three-fold than that of pristine g-C3N4. The boosted photocatalytic activity can be attributed to the synergistic effect among the g-C3N4, N-GQDs, and Ag NPs. The g-C3N4 nanosheets can serve as the reaction matrix and support for the dispersion of N-GQDs and Ag NPs, inhibiting their ...


A Lightweight Method for Building Reliable Operating Systems Despite Unreliable Device Drivers Technical Report IRCS-018 , January 2006


Role of Emotional Maturity in Subjective Well-Being of Co-Education and Unisex-Education College Students


title: Artificial Roughness Encoding with a Bio-inspired MEMS- based Tactile Sensor Array

abstract: Abstract: A compliant 2x2 tactile sensor array was developed and investigated for roughness encoding. State of the art cross shape 3D MEMS sensors were integrated with polymeric packaging providing in total 16 sensitive elements to external mechanical stimuli in an area of about 20 mm 2 , similarly to the SA1 innervation density in humans. Experimental analysis of the bio-inspired tactile sensor array was performed by using ridged surfaces, with spatial periods from 2.6 mm to 4.1 mm, which were indented with regulated 1N normal force and stroked at constant sliding velocity from 15 mm/s to 48 mm/s. A repeatable and expected frequency shift of the sensor outputs depending on the applied stimulus and on its scanning velocity was observed between 3.66 Hz and 18.46 Hz with an overall maximum error of 1.7%. The tactile sensor could also perform contact imaging during static stimulus indentation. The experiments demonstrated the suitability of this approach for the design of a roughness encoding tactile sensor for an artificial fingerpad.


title: A Low-Profile Reconfigurable Cavity-Backed Slot Antenna With Frequency, Polarization, and Radiation Pattern Agility

abstract: A novel cavity-backed slot antenna, with the ability of reconfiguring the frequency, polarization, and radiation pattern, is presented. The reconfigurability is realized by electronically controlling the state of switches between two crossed slots etched on the surfaces of a substrate integrated waveguide cavity. The antenna is capable of simultaneously changing the radiation patterns between forward and backward directions, switching the polarization among two orthogonal linearly polarized (LP) and two orthogonal circularly polarized (CP) states, tuning between three frequency bands for LP states and between two frequency bands for CP states. A fully functional prototype is developed and tested, demonstrating the antenna with measured gains of approximately 4 dBi and stable unidirectional radiation patterns for all 20 states. In addition, the proposed design possesses a low profile of 0.01 free-space wavelength.


title: Do Images of ‘Watching Eyes’ Induce Behaviour That Is More Pro-Social or More Normative? A Field Experiment on Littering

abstract: Displaying images of eyes causes people to behave more pro-socially in a variety of contexts. However, it is unclear whether eyes work by making people universally more pro-social, or by making them more likely to conform to local norms. If the latter, images of eyes could sometimes make people less pro-social if pro-social behaviour is not the local norm. To separate these hypotheses we conducted a field experiment in which we explored whether manipulating a local descriptive norm altered the eyes effect. We recorded litter dropping decisions on a university campus in a 2 x 2 design, comparing situations with and without litter already on the ground (a manipulation of the local descriptive norm) and with and without large signs displaying images of watching eyes. We additionally recorded the number of potential human observers in the vicinity at the time of each littering decision. We observed a norm effect: the presence of litter on the ground increased littering, replicating previous findings. We also found that images of watching eyes reduced littering, although contrary to previous findings this was only when there were larger numbers of people around. With regard to our central aim, we found no evidence that litter on the ground interacted non-additively with images of eyes to induce increased littering behaviour. Our data therefore support the hypothesis that images of eyes induce more pro-social behaviour, independent of local norms. This finding has positive implications for the application of eye images in combating anti-social behaviour.


title: Implicit Queries for Email

abstract: Implicit query systems examine a document and automatically conduct searches for the most relevant information. In this paper, we offer three contributions to implicit query research. First, we show how to use query logs from a search engine: by constraining results to commonly issued queries, we can get dramatic improvements. Second, we describe a method for optimizing parameters for an implicit query system, by using logistic regression training. The method is designed to estimate the probability that any particular suggested query is a good one. Third, we show which features beyond standard TF-IDF features are most helpful in our logistic regression model: query frequency information, capitalization information, subject line information, and message length information. Using the optimization method and the additional features, we are able to produce a system with up to 6 times better results on top-1 score than a simple TF-IDF system.


title: Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation

abstract: Deep reinforcement learning has shown great potential in improving system performance autonomously, by learning from iterations with the environment. However, traditional reinforcement learning approaches are designed to work in static environments. In many real-world problems, the environments are commonly dynamic, in which the performance of reinforcement learning approaches can degrade drastically. A direct cause of the performance degradation is the high-variance and biased estimation of the reward, due to the distribution shifting in dynamic environments. In this paper, we propose two techniques to alleviate the unstable reward estimation problem in dynamic environments, the stratified sampling replay strategy and the approximate regretted reward, which address the problem from the sample aspect and the reward aspect, respectively. Integrating the two techniques with Double DQN, we propose the Robust DQN method. We apply Robust DQN in the tip recommendation system in Taobao online retail trading platform. We firstly disclose the highly dynamic property of the recommendation application. We then carried out online A/B test to examine Robust DQN. The results show that Robust DQN can effectively stabilize the value estimation and, therefore, improves the performance in this real-world dynamic environment.


title: The promises and perils of mining GitHub

abstract: We are now witnessing the rapid growth of decentralized source code management (DSCM) systems, in which every developer has her own repository. DSCMs facilitate a style of collaboration in which work output can flow sideways (and privately) between collaborators, rather than always up and down (and publicly) via a central repository. Decentralization comes with both the promise of new data and the peril of its misinterpretation. We focus on git, a very popular DSCM used in high-profile projects. Decentralization, and other features of git, such as automatically recorded contributor attribution, lead to richer content histories, giving rise to new questions such as “How do contributions flow between developers to the official project repository?” However, there are pitfalls. Commits may be reordered, deleted, or edited as they move between repositories. The semantics of terms common to SCMs and DSCMs sometimes differ markedly, potentially creating confusion. For example, a commit is immediately visible to all developers in centralized SCMs, but not in DSCMs. Our goal is to help researchers interested in DSCMs avoid these and other perils when mining and analyzing git data.


title: A New High Resolution Depth Map Estimation System Using Stereo Vision and Kinect Depth Sensing

abstract: Depth map estimation is an active and long standing problem in image/video processing and computer vision. Conventional depth estimation algorithms which rely on stereo/multi-view vision or depth sensing devices alone are limited by complicated scenes or imperfections of the depth sensing devices. On the other hand, the depth maps obtained from the stereo/multi-view vision and depth sensing devices are de facto complementary to each other. This motivates us to develop in this paper a new system for high resolution and high quality depth estimation by joint fusion of stereo and Kinect data. We modeled the observations using Markov random field (MRF) and formulated the fusion problem as a maximum a posteriori probability (MAP) estimation problem. The reliability and the probability density functions for describing the observations from the two devices are also derived. The MAP problem is solved using a multiscale belief propagation (BP) algorithm. To suppress possible estimation noise, the depth map estimated is further refined by color image guided depth matting and a 2D polynomial regression (LPR)-based filtering. Experimental results and numerical comparisons show that our system can provide high quality and high resolution depth maps, thanks to the complementary strengths of both stereo vision and Kinect depth sensors.


title: Enhancing the Searchability of Page-Image PDF Documents Using an Aligned Hidden Layer from a Truth Text

abstract: The search accuracy achieved in a PDF image-plus-hidden-text (PDF-IT) document depends upon the accuracy of the optical character recognition (OCR) process that produced the searchable hidden text layer. In many cases recognising words in a blurred area of a PDF page image may exceed the capabilities of an OCR engine. This paper describes a project to replace an inadequate hidden textual layer of a PDF-IT file with a more accurate hidden layer produced from a 'truth text'. The alignment of the truth text with the image is guided by using OCR-provided page-image co-ordinates, for those glyphs that are correctly recognised, as a set of fixed location points between which other truth-text words can be inserted and aligned with blurred glyphs in the image. Results are presented to show the much enhanced searchability of this new file when compared to that of the original file, which had an OCR-produced hidden layer with no truth-text enhancement.


title: Higher rates of neuropsychiatric adverse events leading to dolutegravir discontinuation in women and older patients

abstract: Objectives ::: Dolutegravir (DTG), a second-generation integrase strand transfer inhibitor (INSTI), is now among the most frequently used antiretroviral agents. However, recent reports have raised concerns about potential neurotoxicity. ::: ::: Methods ::: We performed a retrospective analysis of a cohort of HIV-infected patients who had initiated an INSTI in two large German out-patient clinics between 2007 and 2016. We compared discontinuation rates because of adverse events (AEs) within 2 years of starting treatment with dolutegravir, raltegravir or elvitegravir/cobicistat. We also evaluated factors associated with dolutegravir discontinuation. ::: ::: Results ::: A total of 1950 INSTI-based therapies were initiated in 1704 patients eligible for analysis within the observation period. The estimated rates of any AE and of neuropsychiatric AEs leading to discontinuation within 12 months were 7.6% and 5.6%, respectively, for dolutegravir (n = 985), 7.6% and 0.7%, respectively, for elvitegravir (n = 287), and 3.3% and 1.9%, respectively, for raltegravir (n = 678). Neuropsychiatric AEs leading to dolutegravir discontinuation were observed more frequently in women [hazard ratio (HR) 2.64; 95% confidence interval (CI) 1.23–5.65; P = 0.012], in patients older than 60 years (HR: 2.86; 95% CI: 1.42–5.77; P = 0.003) and in human leucocyte antigen (HLA)-B*5701-negative patients who initiated abacavir at the same time (HR: 2.42; 95% CI: 1.38–4.24; P = 0.002). ::: ::: Conclusions ::: In this large cohort, the rate of discontinuation of dolutegravir because of neuropsychiatric adverse events was significantly higher than for other INSTIs, at almost 6% within 12 months. Despite the limitations of this retrospective study, the almost three-fold higher discontinuation rates observed amongst women and older patients underscore the need for further investigation, especially in patient populations usually underrepresented in clinical trials.


title: Texture Segmentation Based Video Compression Using Convolutional Neural Networks

abstract: There has been a growing interest in using different approaches to improve the coding efficiency of modern video codec in recent years as demand for web-based video consumption increases. In this paper, we propose a model-based approach that uses texture analysis/synthesis to reconstruct blocks in texture regions of a video to achieve potential coding gains using the AV1 codec developed by the Alliance for Open Media (AOM). The proposed method uses convolutional neural networks to extract texture regions in a frame, which are then reconstructed using a global motion model. Our preliminary results show an increase in coding efficiency while maintaining satisfactory visual quality.


title: Vehicles of the Future: A Survey of Research on Safety Issues

abstract: Information and communication technologies (ICTs) have a profound impact on the current state and envisioned future of automobiles. This paper presents an overview of research on ICT-based support and assistance services for the safety of future connected vehicles. A general classification and a brief description of the focus areas for research and development in this direction are given under the titles of vehicle detection, road detection, lane detection, pedestrian detection, drowsiness detection, and collision avoidance. Following an overview and taxonomy of the reviewed research articles, a categorized literature survey of safety critical applications is presented in detail. Future research directions are also highlighted.


Selection of a Relevant In Vitro Blood-Brain Barrier Model to Investigate Pro-Metastatic Features of Human Breast Cancer Cell Lines


title: The path planning of cleaner robot for coverage region using Genetic Algorithms

abstract: Abstract The vacuum cleaner robot should have a mechanism such as the artificial intelligence to solve the problem of cleaning the entire environment areas taking into account some factors such as the number of turns and the length of the trajectory. This robot’s mechanism or task is known as the path planning of coverage region (PPCR). In this paper, to resolve the problem of PPCR in a room environment, we propose an evolutionary approach. The latter is based on Genetic Algorithms (GA) which, consist of several steps to get the solutions. Each gene represents the robot position and some of chromosomes represent also the mini-path. In addition, this algorithm helps the robot to pass through every part of the environment by avoiding obstacles using different sensors. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach.


An Unusual Cause of Stridor and Progressive Shortness of Breath


title: Product Matching through Ontology Mapping in Comparison Shopping

abstract: on ontology merging and mapping is one of the most important issues in the Semantic Web because ontologies are developed and used by various sites and organizations respectively. Electronic commerce is the area that require ontology mapping on product comparison over different product classification taxonomies of various shopping malls. But, strict mapping strategy may lead a customer's configuration to search failure. Therefore we suggest a mapping algorithm for product matching that can provide more products by increasing sensitivity with reasonable decrease of specificity. We compared our algorithm with PROMPT based on experiments with 6 sets of source ontology and target ontology.


title: A CONSOLIDATED UNDERSTANDING OF TECHNICAL DEBT

abstract: Technical debt utilises financial debt as a metaphor to describe the phenomenon of increasing software development costs over time. Whilst this phenomenon is evidently detrimental to the long-term success of software development, it appears to be poorly understood in the academic literature. The absence of a clear definition and model for technical debt means that the notion of technical debt remains metaphorical, thus preventing the realisation of technical debt’s utility as a conceptual and technical communication device. This exploratory study reconciles the high-level, abstracted view of technical debt presented in academic literature. It establishes the boundaries of the technical debt phenomenon and develops a comprehensive theoretical framework to facilitate future research. The resulting theoretical framework portrays a holistic view of technical debt that incorporates a set of precedents and outcomes, as well as the phenomenon itself.


PARAMETERS OF LIGHTNING CURRENT GIVEN IN IEC 62305 – BACKGROUND , EXPERIENCE AND OUTLOOK


title: Rheotaxis facilitates upstream navigation of mammalian sperm cells

abstract: A major puzzle in biology is how mammalian sperm maintain the correct swimming direction during various phases of the sexual reproduction process. Whilst chemotaxis may dominate near the ovum, it is unclear which cues guide spermatozoa on their long journey towards the egg. Hypothesized mechanisms range from peristaltic pumping to temperature sensing and response to fluid flow variations (rheotaxis), but little is known quantitatively about them. We report the first quantitative study of mammalian sperm rheotaxis, using microfluidic devices to investigate systematically swimming of human and bull sperm over a range of physiologically relevant shear rates and viscosities. Our measurements show that the interplay of fluid shear, steric surface-interactions, and chirality of the flagellar beat leads to stable upstream spiralling motion of sperm cells, thus providing a generic and robust rectification mechanism to support mammalian fertilisation. A minimal mathematical model is presented that accounts quantitatively for the experimental observations. ::: ::: DOI: http://dx.doi.org/10.7554/eLife.02403.001


title: DeepPano: Deep Panoramic Representation for 3-D Shape Recognition

abstract: This letter introduces a robust representation of 3-D shapes, named DeepPano, learned with deep convolutional neural networks (CNN). Firstly, each 3-D shape is converted into a panoramic view, namely a cylinder projection around its principle axis. Then, a variant of CNN is specifically designed for learning the deep representations directly from such views. Different from typical CNN, a row-wise max-pooling layer is inserted between the convolution and fully-connected layers, making the learned representations invariant to the rotation around a principle axis. Our approach achieves state-of-the-art retrieval/classification results on two large-scale 3-D model datasets (ModelNet-10 and ModelNet-40), outperforming typical methods by a large margin.


title: Fusion of MRI and CT images using guided image filter and image statistics

abstract: In medical imaging using different modalities such as MRI and CT, complementary information of a targeted organ will be captured. All the necessary information from these two modalities has to be integrated into a single image for better diagnosis and treatment of a patient. Image fusion is a process of combining useful or complementary information from multiple images into a single image. In this article, we present a new weighted average fusion algorithm to fuse MRI and CT images of a brain based on guided image filter and the image statistics. The proposed algorithm is as follows: detail layers are extracted from each source image by using guided image filter. Weights corresponding to each source image are calculated from the detail layers with help of image statistics. Then a weighted average fusion strategy is implemented to integrate source image information into a single image. Fusion performance is assessed both qualitatively and quantitatively. Proposed method is compared with the traditional and recent image fusion methods. Results showed that our algorithm yields superior performance.


title: Data acquisition and signal processing on FMCW navigation radar system

abstract: Data acquisition and Signal Processing is an important part of frequency modulated continuous wave (FMCW) radar system. In recent year P2ET-LIPI was developing FMCW navigation radar system in portable size. The main component of data acquisition for navigation radar is highspeed Analog to Digital Conversion (ADC) USB-2020 board and signal processing algorithm has been developed using Visual C# 2015 Community Edition. USB-2020 board has two channel ADC on board with 12 bits resolution and operated in a burst-io mode to get maximum sampling rate at 20 MS/s. A timing generator for synchronization between ADC and linear frequency modulation (LFM) chirp generator is developed using Complex Programmable Logic Device (CPLD) Max II series based on VHSIC hardware description language (VHDL). Signal processing unit consists of windowing function to reduce side- lobe suppression, Fast Fourier Transform (FFT) function to get range information and adaptive threshold using three algorithms of Constant False Alarm Rate (CFAR): Cell Averaging (CA), Order Statistic (OS) and Variable Index (VI) CFAR. The experiment shows that the data acquisition module can capture In-phase and Quadrature (I/Q) beat signal and signal processing algorithm can detect object/target from the noise.


title: Visualizing concept drift

abstract: We describe a visualization technique that uses brushed, parallel histograms to aid in understanding concept drift in multidimensional problem spaces. This technique illustrates the relationship between changes in distributions of multiple antecedent feature values and the outcome distribution. We can also observe effects on the relative utilization of predictive rules. Our parallel histogram technique solves the over-plotting difficulty of parallel coordinate graphs and the difficulty of comparing distributions of brushed and original data. We demonstrate our technique's usefulness in understanding concept drifts in power demand and stock investment returns.


title: Social Control of the Brain

abstract: In the course of evolution, social behavior has been a strikingly potent selective force in shaping brains to control action. Physiological, cellular, and molecular processes reflect this evolutionary force, particularly in the regulation of reproductive behavior and its neural circuitry. Typically, experimental analysis is directed at how the brain controls behavior, but the brain is also changed by behavior over evolution, during development, and through its ongoing function. Understanding how the brain is influenced by behavior offers unusual experimental challenges. General principles governing the social regulation of the brain are most evident in the control of reproductive behavior. This is most likely because reproduction is arguably the most important event in an animal's life and has been a powerful and essential selective force over evolution. Here I describe the mechanisms through which behavior changes the brain in the service of reproduction using a teleost fish model system.


title: SEARCHING FOR MEANING ON DEFUZZIFICATION

abstract: Defuzzification is an essential problem in fuzzy systems that it is always solved in a heuristic way. The aim of this work is to give a semantic interpretation to this process with the help of indistinguishability operators


title: Realtime 3D position detection of human pupil

abstract: A real time 3D pupil position detection system necessary for non-contact and remote eye gaze detection allowing head displacement was developed. So far, methodology had been proposed that estimates 3D positions of eyes from their 2D positions detected from the face images captured by two stereo cameras. However, using eye position detection is not prospective for precise and stable eye gaze detection. In the method proposed in the present study, several near-infrared LEDs were arranged around the apertures of two cameras. These two sets of light sources were alternately switched on and off synchronously with the even/odd signal. After differentiating the bright and dark pupil images obtained from each of the two cameras, the center coordinates of the two pupils were detected. An image processing algorithm for precise and stable pupil detection, including the pupil detection method utilizing a blink, is described. In one of the experiments, the 3D position of the pupil of one eye was measured by the two stereo cameras. Another narrow camera, which detects the centers of both pupil and corneal reflection images necessary for eye gaze direction determination, placed on a pan-tile unit could be automatically directed toward the 3D pupil position.


title: Quincy: fair scheduling for distributed computing clusters

abstract: This paper addresses the problem of scheduling concurrent jobs on clusters where application data is stored on the computing nodes. This setting, in which scheduling computations close to their data is crucial for performance, is increasingly common and arises in systems such as MapReduce, Hadoop, and Dryad as well as many grid-computing environments. We argue that data-intensive computation benefits from a fine-grain resource sharing model that differs from the coarser semi-static resource allocations implemented by most existing cluster computing architectures. The problem of scheduling with locality and fairness constraints has not previously been extensively studied under this resource-sharing model. We introduce a powerful and flexible new framework for scheduling concurrent distributed jobs with fine-grain resource sharing. The scheduling problem is mapped to a graph datastructure, where edge weights and capacities encode the competing demands of data locality, fairness, and starvation-freedom, and a standard solver computes the optimal online schedule according to a global cost model. We evaluate our implementation of this framework, which we call Quincy, on a cluster of a few hundred computers using a varied workload of data-and CPU-intensive jobs. We evaluate Quincy against an existing queue-based algorithm and implement several policies for each scheduler, with and without fairness constraints. Quincy gets better fairness when fairness is requested, while substantially improving data locality. The volume of data transferred across the cluster is reduced by up to a factor of 3.9 in our experiments, leading to a throughput increase of up to 40%.