title: iPACKMAN: high-quality, low-complexity texture compression for mobile phones

abstract: We present a novel texture compression scheme, called iPACKMAN, targeted for hardware implementation. In terms of image quality, it outperforms the previous de facto standard texture compression algorithms in the majority of all cases that we have tested. Our new algorithm is an extension of the PACKMAN texture compression system, and while it is a bit more complex than PACKMAN, it is still very low in terms of hardware complexity.


title: Divide and conquer: neuroevolution for multiclass classification

abstract: Neuroevolution is a powerful and general technique for evolving the structure and weights of artificial neural networks. Though neuroevolutionary approaches such as NeuroEvolution of Augmenting Topologies (NEAT) have been successfully applied to various problems including classification, regression, and reinforcement learning problems, little work has explored application of these techniques to larger-scale multiclass classification problems. In this paper, NEAT is evaluated in several multiclass classification problems, and then extended via two ensemble approaches: One-vs-All and One-vs-One. These approaches decompose multiclass classification problems into a set of binary classification problems, in which each binary problem is solved by an instance of NEAT. These ensemble models exhibit reduced variance and increasingly superior accuracy as the number of classes increases. Additionally, higher accuracy is achieved early in training, even when artificially constrained for the sake of fair comparison with standard NEAT. However, because the approach can be trivially distributed, it can be applied quickly at large scale to solve real problems. In fact, these approaches are incorporated into DarwinTM, an enterprise automatic machine learning solution that also incorporates various other algorithmic enhancements to NEAT. The resulting complete system has proven robust to a wide variety of client datasets.


Critical Digital Humanities and Machine-Learning


title: Dynamic adaptation of byzantine consensus protocols

abstract: The problem of distributed consensus in the presence of Byzantine faults has received particular attention in recent decades. Today a variety of solution to this problem exist, each optimized for particular execution conditions. Given that, in most cases, real systems operate under dynamic conditions, it is important to develop mechanisms that allow the algorithms to be adapted or changed at runtime to optimize the system to the current conditions. The problem of dynamic adaptation of consensus algorithms is not new, but the literature is scarce for the Byzantine case and there is no comprehensive comparison of existing solutions. This work has two complementary objectives. First, it studies how the different dynamic adaptation techniques proposed for the crash failure model can be applied in the presence of Byzantine faults. Second, it presents a comparative study of the performance of these switching algorithms in practice. For that purpose, we have implemented the switching algorithms in a common software framework, based on the open source BFT-SMaRt library. Using this framework we have performed an extensive evaluation that offers useful insights on the practical effects of different mechanisms used to support the run-time switching among Byzantine protocols.


title: Privacy Regulations for Cloud Computing: Compliance and Implementation in Theory and Practice

abstract: Cloud Computing is a new paradigm in the world of IT. In traditional IT environments, clients connected to a number of servers located on company premises. In Cloud Computing, users connect to the ’Cloud’, appearing as a single entity as opposed to multiple servers. Outsourcing data to the Cloud Service Provider (CSP), an external party involves giving the CSP some form of control over the data. Privacy regulations put requirements on organizations regarding storage, processing and transmission of data. Outsourcing this data to a CSP involves outsourcing partial control over the storage, processing and transmission of data and privacy regulations become relevant. This paper addresses the questions as to how existing regulations in the area of privacy affect the implementation of Cloud Computing technologies and how the implementation of Cloud Computing technologies affect compliance with these regulations.


Recurrent Neural Network Language Model with Incremental Updated Context Information Generated Using Bag-of-Words Representation.


RAPID EXTRACTING PILLARS BY SLICING POINT CLOUDS


Exosomes and breast cancer: a comprehensive review of novel therapeutic strategies from diagnosis to treatment


title: An Augmented Reality System Validation for the Treatment of Cockroach Phobia

abstract: Abstract Augmented reality (AR) is a new technology in which various virtual elements are incorporated into the user's perception of the real world. The most significant aspect of AR is that the virtual elements add relevant and helpful information to the real scene. AR shares some important characteristics with virtual reality as applied in clinical psychology. However, AR offers additional features that might be crucial for treating certain problems. An AR system designed to treat insect phobia has been used for treating phobia of small animals, and positive preliminary data about the global efficacy of the system have been obtained. However, it is necessary to determine the capacity of similar AR systems and their elements that are designed to evoke anxiety in participants; this is achieved by testing the correspondence between the inclusion of feared stimuli and the induction of anxiety. The objective of the present work is to validate whether the stimuli included in the AR-Insect Phobia system are ca...


title: A Requirements-Driven Methodology for the Proper Selection and Configuration of Blockchains

abstract: In recent years, the interest in blockchain has grown exponentially, and nowadays it is foreseen as a technology with the potential to revolutionize the way data is maintained and transferred around the globe. The reason of this excitement is ascribable to the ability of enabling new forms of transactions and interactions between mistrusting and decentralized entities. Indeed, it has attracted interests and huge investments from enterprises, and it is predictable that in a near future many industries will adopt it. However, it is not a panacea and in some cases may even become useless or not convenient. Moreover, even when it can really constitute an added value, selecting the proper blockchain and configuring it may not be trivial. Trying to go beyond the hype and to address this problem, this paper proposes a methodology addressing: i) whether, given a specific problem requirements, the blockchain is a proper solution for it ii) in such a case which is the blockchain category more suitable, and finally iii) guiding the designer throughout its configuration.


title: Implementation of high resolution unipolar PWM inverter using Xilinx System Generator

abstract: This paper presents an implementation technique of a high resolution Unipolar Pulse Width Modulator (UPWM) for power inverter applications. Unipolar switched inverter has the advantage of a higher efficiency and lower harmonic distortion. The technique was tested in a 2kW inverter with a typical LC filter and it was achieved a Total Harmonic Distortion below 2%. Simulations and experimental results were performed. The technique was developed with the Xilinx System Generator in MATLAB/Simulink and it was implemented on a System on Chip XC70Z020CLG484-1 Zynq device.


title: Smokers’ and drinkers’ choice of smartphone applications and expectations of engagement: a think aloud and interview study

abstract: BACKGROUND ::: Public health organisations such as the National Health Service in the United Kingdom and the National Institutes of Health in the United States provide access to online libraries of publicly endorsed smartphone applications (apps); however, there is little evidence that users rely on this guidance. Rather, one of the most common methods of finding new apps is to search an online store. As hundreds of smoking cessation and alcohol-related apps are currently available on the market, smokers and drinkers must actively choose which app to download prior to engaging with it. The influences on this choice are yet to be identified. This study aimed to investigate 1) design features that shape users' choice of smoking cessation or alcohol reduction apps, and 2) design features judged to be important for engagement. ::: ::: ::: METHODS ::: Adult smokers (n = 10) and drinkers (n = 10) interested in using an app to quit/cut down were asked to search an online store to identify and explore a smoking cessation or alcohol reduction app of their choice whilst thinking aloud. Semi-structured interview techniques were used to allow participants to elaborate on their statements. An interpretivist theoretical framework informed the analysis. Verbal reports were audio recorded, transcribed verbatim and analysed using inductive thematic analysis. ::: ::: ::: RESULTS ::: Participants chose apps based on their immediate look and feel, quality as judged by others' ratings and brand recognition ('social proof'), and titles judged to be realistic and relevant. Monitoring and feedback, goal setting, rewards and prompts were identified as important for engagement, fostering motivation and autonomy. Tailoring of content, a non-judgmental communication style, privacy and accuracy were viewed as important for engagement, fostering a sense of personal relevance and trust. Sharing progress on social media and the use of craving management techniques in social settings were judged not to be engaging because of concerns about others' negative reactions. ::: ::: ::: CONCLUSIONS ::: Choice of a smoking cessation or alcohol reduction app may be influenced by its immediate look and feel, 'social proof' and titles that appear realistic. Design features that enhance motivation, autonomy, personal relevance and credibility may be important for engagement.


title: HDM-Net: Monocular Non-Rigid 3D Reconstruction with Learned Deformation Model

abstract: Monocular dense 3D reconstruction of deformable objects is a hard ill-posed problem in computer vision. Current techniques either require dense correspondences and rely on motion and deformation cues, or assume a highly accurate reconstruction (referred to as a template) of at least a single frame given in advance and operate in the manner of non-rigid tracking. Accurate computation of dense point tracks often requires multiple frames and might be computationally expensive. Availability of a template is a very strong prior which restricts system operation to a pre-defined environment and scenarios. In this work, we propose a new hybrid approach for monocular non-rigid reconstruction which we call Hybrid Deformation Model Network (HDM-Net). In our approach, deformation model is learned by a deep neural network, with a combination of domain-specific loss functions. We train the network with multiple states of a non-rigidly deforming structure with a known shape at rest. HDM-Net learns different reconstruction cues including texture-dependent surface deformations, shading and contours. We show generalisability of HDM-Net to states not presented in the training dataset, with unseen textures and under new illumination conditions. Experiments with noisy data and a comparison with other methods demonstrate robustness and accuracy of the proposed approach and suggest possible application scenarios of the new technique in interventional diagnostics and augmented reality.


title: Blockclique: scaling blockchains through transaction sharding in a multithreaded block graph

abstract: Decentralized crypto-currencies based on the blockchain architecture under-utilize available network bandwidth, making them unable to scale to thousands of transactions per second. We define the Blockclique architecture, that addresses this limitation by sharding transactions in a block graph with a fixed number of threads. The architecture allows the creation of intrinsically compatible blocks in parallel, where each block references one previous block of each thread. The consistency of the Blockclique protocol is formally established in presence of attackers. An experimental evaluation of the architecture's performance in large realistic networks demonstrates an efficient use of available bandwidth and a throughput of thousands of transactions per second.


title: A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization

abstract: A variety of feature selection methods based on sparsity regularization have been developed with different loss functions and sparse regularization functions. Capitalizing on the existing sparsity regularized feature selection methods, we propose a general sparsity feature selection (GSR-FS) algorithm that optimizes a l 2, r (0 < r ≤ 2) based loss function with a l 2, p -norm (0 < p ≤ 2) sparse regularization. The l 2, r - norm (0 < 𝑟 ≤ 2) based loss function brings flexibility to balance data-fitting and robustness to outliers by tuning its parameter, and the l 2, p -norm (0 < p ≤ 1) based regularization function is able to boost the sparsity for feature selection. To solve the optimization problem with multiple non-smooth and non-convex functions when , we develop an efficient solver under the general umbrella of Iterative Reweighted Least Square (IRLS) algorithms. Our algorithm has been proved to converge with a theoretical convergence order of min(2 – r, 2 – p ) at least . The experimental results have demonstrated that our method could achieve competitive feature selection performance on publicly available datasets compared with state-of-the-art feature selection methods, with reduced computational cost.


title: Shape Display Shader Language (SDSL): A New Programming Model for Shape Changing Displays

abstract: Shape-changing displays' dynamic physical affordances have inspired a range of novel hardware designs to support new types of interaction. Despite rapid technological progress, the community lacks a common programming model for developing applications for these visually and physically-dynamic display surfaces. This results in complex, hardware-specific, custom-code that requires significant development effort and prevents researchers from easily building on and sharing their applications across hardware platforms. As a first attempt to address these issues we introduce SDSL, a Shape-Display Shader Language for easily programming shape-changing displays in a hardware-independent manner. We introduce the (graphics-derived) pipeline model of SDSL, an open-source implementation that includes a compiler, runtime, IDE, debugger, and simulator, and show demonstrator applications running on two shape-changing hardware setups.


VITAL-The Virtual Environment Interaction Technique Abstraction Layer


title: The German Traffic Sign Recognition Benchmark: A multi-class classification competition

abstract: The “German Traffic Sign Recognition Benchmark” is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in image processing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.


title: Comparison of non-linear activation functions for deep neural networks on MNIST classification task

abstract: Activation functions play a key role in neural networks so it becomes fundamental to understand their advantages and disadvantages in order to achieve better performances. This paper will first introduce common types of non linear activation functions that are alternative to the well known sigmoid function and then evaluate their characteristics. Moreover deeper neural networks will be analysed because they positively influence the final performances compared to shallower networks. They also strictly depend on the weight initialisation hence the effect of drawing weights from Gaussian and uniform distribution will be analysed making particular attention on how the number of incoming and outgoing connection to a node influence the whole network.


The Impact of Energy, Agriculture, Macroeconomic and Human-Induced Indicators on Environmental Pollution from 1971 to 2011


title: LCM: A Link-Aware Clustering Mechanism for Energy-Efficient Routing in Wireless Sensor Networks

abstract: In wireless sensor networks, nodes in the area of interest must report sensing readings to the sink, and this report always satisfies the report frequency required by the sink. This paper proposes a link-aware clustering mechanism, called LCM, to determine an energy-efficient and reliable routing path. The LCM primarily considers node status and link condition, and uses a novel clustering metric called the predicted transmission count (PTX), to evaluate the qualification of nodes for clusterheads and gateways to construct clusters. Each clusterhead or gateway candidate depends on the PTX to derive its priority, and the candidate with the highest priority becomes the clusterhead or gateway. Simulation results validate that the proposed LCM significantly outperforms the clustering mechanisms using random selection and by considering only link quality and residual energy in the packet delivery ratio, energy consumption, and delivery latency.


title: The neural basis of the hedonic quality of aesthetic experience

abstract: In this review, I provide an overall description of the findings obtained from neuroimaging studies aimed at investigating the neural substrates underpinning aesthetic experience when viewing masterpieces of Classical art. In particular, I here argue the idea that aesthetic experience is hallmarked by a hedonic response to the stimuli. This response would be triggered, according to our proposal, by integration of emotional and cognitive processes at the level of the right insular cortex and, more specifically, of its anterodorsal sector. I then discuss the recent results suggesting that the hedonic quality characterizing aesthetic experience for artworks is absent when aesthetically appraising biological non-artist stimuli.


title: Optical Pixel Sensor of Hydrogenated Amorphous Silicon Thin-Film Transistor Free of Variations in Ambient Illumination

abstract: This paper presents a primary color optical pixel sensor circuit that utilizes hydrogenated amorphous silicon thin-film transistors (TFTs). To minimize the effect of ambient light on the sensing result of optical sensor circuit, the proposed sensor circuit combines photo TFTs with color filters to sense a primary color optical input signal. A readout circuit, which also uses thin-film transistors, is integrated into the sensor circuit for sampling the stored charges in the pixel sensor circuit. Measurements demonstrate that the signal-to-noise ratio of the proposed sensor circuit is unaffected by ambient light under illumination up to 12 000 lux by white LEDs. Thus, the proposed optical pixel sensor circuit is suitable for receiving primary color optical input signals in large TFT-LCD panels.


title: Lateral velocity estimation based on automotive radar sensors

abstract: Automotive radar sensors are applied to measure the target range, azimuth angle and radial velocity simultaneously even in multiple target situations. The single target measured data are necessary for target tracking in advanced driver assistance systems (ADAS) e.g. in highway scenarios. In typical city traffic situations the radar measurement is also important but additionally even the lateral velocity component of each detected target such as a vehicle is of large interest in this case. It is shown in this paper that the lateral velocity of an extended target can be measured even in a mono observation situation. For an automotive radar sensor a high spectral resolution is required in this case which means the time on target should be sufficiently large.


title: Vector Processors for Energy-Efficient Embedded Systems

abstract: High-performance embedded processors are frequently designed as arrays of small, in-order scalar cores, even when their workloads exhibit high degrees of data-level parallelism (DLP). We show that these multiple instruction, multiple data (MIMD) systems can be made more efficient by instead directly exploiting DLP using a modern vector architecture. In our study, we compare arrays of scalar cores to vector machines of comparable silicon area and power consumption. Since vectors provide greater performance across the board - in some cases even with better programmability - we believe that embedded system designers should increasingly pursue vector architectures for machines at this scale.


title: Power/Performance Exploration of Single-core and Multi-core Processor Approaches for Biomedical Signal Processing

abstract: This study presents a single-core and a multi-core processor architecture for health monitoring systems where slow biosignal events and highly parallel computations exist. The single-core architecture is composed of a processing core (PC), an instruction memory (IM) and a data memory (DM), while the multi-core architecture consists of PCs, individual IMs for each core, a shared DM and an interconnection crossbar between the cores and the DM. These architectures are compared with respect to power vs performance trade-offs for a multi-lead electrocardiogram signal conditioning application exploiting near threshold computing. The results show that the multi-core solution consumes 66% less power for high computation requirements (50.1 MOps/s), whereas 10.4% more power for low computation needs (681 kOps/s).


title: DNA-guided crystallization of colloidal nanoparticles

abstract: Many nanometre-sized building blocks will readily assemble into macroscopic structures. If the process is accompanied by effective control over the interactions between the blocks and all entropic effects, then the resultant structures will be ordered with a precision hard to achieve with other fabrication methods. But it remains challenging to use self-assembly to design systems comprised of different types of building blocks-to realize novel magnetic, plasmonic and photonic metamaterials, for example. A conceptually simple idea for overcoming this problem is the use of 'encodable' interactions between building blocks; this can in principle be straightforwardly implemented using biomolecules. Strategies that use DNA programmability to control the placement of nanoparticles in one and two dimensions have indeed been demonstrated. However, our theoretical understanding of how to extend this approach to three dimensions is limited, and most experiments have yielded amorphous aggregates and only occasionally crystallites of close-packed micrometre-sized particles. Here, we report the formation of three-dimensional crystalline assemblies of gold nanoparticles mediated by interactions between complementary DNA molecules attached to the nanoparticles' surface. We find that the nanoparticle crystals form reversibly during heating and cooling cycles. Moreover, the body-centred-cubic lattice structure is temperature-tuneable and structurally open, with particles occupying only approximately 4% of the unit cell volume. We expect that our DNA-mediated crystallization approach, and the insight into DNA design requirements it has provided, will facilitate both the creation of new classes of ordered multicomponent metamaterials and the exploration of the phase behaviour of hybrid systems with addressable interactions.


title: A novel substrate integrated waveguide circular cavity bandpass filter loaded with complementary Split Ring Resonator

abstract: A novel substrate integrated waveguide bandpass filter with circular cavity is proposed in this paper. Two circular cavities based on Substrate Integrated Waveguide are connected via connect hole. The width of the hole can control the coupling between resonators. Two different complementary Split Ring Resonators are loaded at input and output feed line to produce transmission zeros and improve the characteristic of the stopband. All the design procedure are given and verified by the experimental results.


title: Design and Measurement of Integrated Converters for Belt-Driven Starter-Generator in 48 V Micro/Mild Hybrid Vehicles

abstract: With reference to a 48 V belt-driven starter-generator used in micro/mild hybrid vehicles, this paper shows the design and measurement of an integrated H-bridge and of a compact dc/dc converter, both fabricated in low-cost high voltage-metal oxide semiconductor (HV-MOS) technology. The H-bridge is in charge of rotor excitation and, thanks to a direct copper bonding of the HV-MOS devices on a ceramic substrate, it ensures a full-integrated solution with low on -resistance values. The compact dc/dc converter interfaces the 48 V power domain with the lower voltage domain of sensing and control electronics, such as 5 and 1.65 V in this case study, without using cumbersome inductors and transformers. The latter are difficult to integrate into silicon technology. The converter has a multistage architecture where each stage implements a switched capacitor regulation. Multiple voltage outputs are supported with a configurable regulation factor sustaining an input voltage variation from 6 V (in case of cranking) up to 60 V. Specific design techniques have been implemented to reduce electromagnetic interference (EMI), typical of switching converters. Experimental measurements on fabricated prototype chipsets confirm the suitability of the presented designs for low-EMI 48 V applications.


title: 28-Gbaud PAM4 and 56-Gb/s NRZ Performance Comparison Using 1310-nm Al-BH DFB Laser

abstract: We have developed a high-speed 1310-nm Al-MQW buried-hetero laser having 29-GHz bandwidth (BW). The laser was used to compare 28-Gbaud four-level pulse-amplitude-modulation (PAM4) and 56-Gb/s nonreturn to zero (NRZ) transmission performance. In both cases, it was possible to meet the 10-km link budget, however, 56-Gb/s NRZ operation achieved a 2-dB better sensitivity, attributable to the wide BW of the directly modulated laser and the larger eye amplitude for the NRZ format. On the other hand, the advantages for 28-Gbaud PAM4 were the reduced BW requirement for both the transmitter and the receiver PIN diode, which enabled us to use a lower bias to the laser and a PIN with a higher responsivity, or conversely enable the possibility of high temperature operation with lower power consumption. Both formats showed a negative dispersion penalty compared to back-to-back sensitivity using a negative fiber dispersion of −60 ps/nm, which was expected from the observed chirp characteristics of the laser. The reliability study up to 11 600 h at 85 °C under accelerated conditions showed no decrease in the output power at a constant bias of 60 mA.


title: Color contrast enhancement for visually impaired people

abstract: We propose an automatic color contrast enhancement algorithm that improves the visual quality of static images for both normally sighted people and for low-vision patients. Existing methods have been shown to work on people either with normal vision or with low vision. Due to the framework of this approach, an enhanced visual experience can be simultaneously provided to normally sighted people and low vision patients.


title: LDspider: An Open-source Crawling Framework for the Web of Linked Data

abstract: The Web of Linked Data is growing and currently consists of several hundred interconnected data sources altogether serving over 25 billion RDF triples to the Web. What has hampered the exploitation of this global dataspace up till now is the lack of an open-source Linked Data crawler which can be employed by Linked Data applications to localize (parts of) the dataspace for further processing. With LDSpider, we are closing this gap in the landscape of publicly available Linked Data tools. LDSpider traverses the Web of Linked Data by following RDF links between data items, it supports different crawling strategies and allows crawled data to be stored either in files or in an RDF store.


title: Learning to Extract Keyphrases from Text

abstract: Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft?s Word 97 and Verity?s Search 97, includes algorithms that automatically extract keyphrases from documents. In this paper, we approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for this task. The third set of experiments examines the performance of GenEx on the task of metadata generation, relative to the performance of Microsoft?s Word 97. The fourth and final set of experiments investigates the performance of GenEx on the task of highlighting, relative to Verity?s Search 97. The experimental results support the claim that a specialized learning algorithm (GenEx) can generate better keyphrases than a general-purpose learning algorithm (C4.5) and the non-learning algorithms that are used in commercial software (Word 97 and Search 97).


title: Genome engineering empowers the diatom Phaeodactylum tricornutum for biotechnology

abstract: Diatoms, a major group of photosynthetic microalgae, have a high biotechnological potential that has not been fully exploited because of the paucity of available genetic tools. Here we demonstrate targeted and stable modifications of the genome of the marine diatom Phaeodactylum tricornutum, using both meganucleases and TALE nucleases. When nuclease-encoding constructs are co-transformed with a selectable marker, high frequencies of genome modifications are readily attained with 56 and 27% of the colonies exhibiting targeted mutagenesis or targeted gene insertion, respectively. The generation of an enhanced lipid-producing strain (45-fold increase in triacylglycerol accumulation) through the disruption of the UDP-glucose pyrophosphorylase gene exemplifies the power of genome engineering to harness diatoms for biofuel production.


title: The effect of patient education and home monitoring on medication compliance, hypertension management, healthy lifestyle behaviours and BMI in a primary health care setting

abstract: Aim. The aim of this study was to determine the effect of anti-hypertensive patient-oriented education and in-home monitoring for medication adherence and management of hypertension in a primary care setting, by providing education on healthy lifestyle behaviours and medication adherence. ::: ::: Background. Hypertension is the third most common cause of death worldwide. Prevalence of hypertension in Turkey is approximately 30% in the general population and 45–50% in population over 50. ::: ::: Design. Randomised controlled study. ::: ::: Methods. This study conducted in Turkey used a study group comprised of 120 subjects (40 Group A, 40 Group B, 40 controls), all previously diagnosed with hypertension and who started medication therapy at least one year prior to start of study. The study was conducted between February–November 2006 at public primary health care facilities and homes of the study participants. Participants in Group A and B received a total of six monthly education sessions, four times during clinic visits and two home visits. Medication adherence education for Groups A and B and education about healthy lifestyle behaviours for Group B were administered in a structured and individualised format. The control group was routinely monitored in health care facilities. ::: ::: Results. Healthy lifestyle behaviours and perception of self-efficacy regarding medication adherence showed improvement after education sessions in Groups A and B. Systolic and diastolic blood pressures of subjects in Group A and B showed a significant decrease compared with those of the control group; the blood pressure decrease in Group B was greater than in Group A. ::: ::: Conclusion. Nurses play an important role in uncontrolled hypertension detection and can improve medication adherence and healthy lifestyle behaviours. ::: ::: Relevance to clinical practice. Patient education medication adherence alone and in combination with healthy lifestyle behaviour teaching is an effective tool for blood pressure reduction in the hypertensive population in primary health care settings.


title: ESPRESSO: Explaining Relationships between Entity Sets

abstract: Analyzing and explaining relationships between entities in a knowledge graph is a fundamental problem with many applications. Prior work has been limited to extracting the most informative subgraph connecting two entities of interest. This paper extends and generalizes the state of the art by considering the relationships between two sets of entities given at query time. Our method, coined ESPRESSO, explains the connection between these sets in terms of a small number of relatedness cores: dense sub-graphs that have strong relations with both query sets. The intuition for this model is that the cores correspond to key events in which entities from both sets play a major role. For example, to explain the relationships between US politicians and European politicians, our method identifies events like the PRISM scandal and the Syrian Civil War as relatedness cores. Computing cores of bounded size is NP-hard. This paper presents efficient approximation algorithms. Our experiments with real-life knowledge graphs demonstrate the practical viability of our approach and, through user studies, the superior output quality compared to state-of-the-art baselines.


Problem‐solving in Comparative Education


title: Brain Computer Interfaces, a Review

abstract: A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.


title: Dynamic range independent image quality assessment

abstract: The diversity of display technologies and introduction of high dynamic range imagery introduces the necessity of comparing images of radically different dynamic ranges. Current quality assessment metrics are not suitable for this task, as they assume that both reference and test images have the same dynamic range. Image fidelity measures employed by a majority of current metrics, based on the difference of pixel intensity or contrast values between test and reference images, result in meaningless predictions if this assumption does not hold. We present a novel image quality metric capable of operating on an image pair where both images have arbitrary dynamic ranges. Our metric utilizes a model of the human visual system, and its central idea is a new definition of visible distortion based on the detection and classification of visible changes in the image structure. Our metric is carefully calibrated and its performance is validated through perceptual experiments. We demonstrate possible applications of our metric to the evaluation of direct and inverse tone mapping operators as well as the analysis of the image appearance on displays with various characteristics.


title: DuraCap: a supercapacitor-based, power-bootstrapping, maximum power point tracking energy-harvesting system

abstract: DuraCap is a solar-powered energy harvesting system that stores harvested energy in supercapacitors and is voltage-compatible with lithium-ion batteries. The use of supercapacitors instead of batteries enables DuraCap to extend the operational life time from tens of months to tens of years. DuraCap addresses two additional problems with micro-solar systems: inefficient operation of superca-pacitors during cold booting, and maximum power point tracking (MPPT) over a variety of solar panels. Our approach is to dedicate a smaller supercapacitor to cold booting before handing over to the array of larger-value supercapacitors. For MPPT, we designed a bound-control circuit for PFM regulator switching and an I-V tracer to enable self-configuring over the panel's aging process and replacement. Experimental results show the DuraCap system to achieve high conversion efficiency and minimal downtime.


title: FingerScanner: Embedding a Fingerprint Scanner in a Raspberry Pi

abstract: Nowadays, researchers are paying increasing attention to embedding systems. Cost reduction has lead to an increase in the number of platforms supporting the operating system Linux, jointly with the Raspberry Pi motherboard. Thus, embedding devices on Raspberry-Linux systems is a goal in order to make competitive commercial products. This paper presents a low-cost fingerprint recognition system embedded into a Raspberry Pi with Linux.


title: Testing the circumplex model of emotions in a consumer setting

abstract: There is widespread belief that more positive emotional reactions to consumption situations will lead to positive business outcomes such as increased market share through the combination of increased loyalty, repeat purchase and strengthened reputation. However, most of the psychological work on emotions has not dealt directly with consumption experiences, but rather broader everyday experiences. In this study, psychological models of emotion were tested using magazine subscribers, specifically looking at their emotional responses to the magazine and the overall subscription package. The aim was to determine whether one of the major theories on emotional structure, the circumplex model, is relevant and consistent when applied specifically to a consumption experience. The results are positive, with the model being supported, and they provide insight into the structure and relations of different emotional responses (e.g., satisfaction, delight) consumers might have to a consumption experience.


title: New Algorithms for Learning Incoherent and Overcomplete Dictionaries

abstract: In sparse recovery we are given a matrix $A$ (the dictionary) and a vector of the form $A X$ where $X$ is sparse, and the goal is to recover $X$. This is a central notion in signal processing, statistics and machine learning. But in applications such as sparse coding, edge detection, compression and super resolution, the dictionary $A$ is unknown and has to be learned from random examples of the form $Y = AX$ where $X$ is drawn from an appropriate distribution --- this is the dictionary learning problem. In most settings, $A$ is overcomplete: it has more columns than rows. This paper presents a polynomial-time algorithm for learning overcomplete dictionaries; the only previously known algorithm with provable guarantees is the recent work of Spielman, Wang and Wright who gave an algorithm for the full-rank case, which is rarely the case in applications. Our algorithm applies to incoherent dictionaries which have been a central object of study since they were introduced in seminal work of Donoho and Huo. In particular, a dictionary is $\mu$-incoherent if each pair of columns has inner product at most $\mu / \sqrt{n}$. ::: The algorithm makes natural stochastic assumptions about the unknown sparse vector $X$, which can contain $k \leq c \min(\sqrt{n}/\mu \log n, m^{1/2 -\eta})$ non-zero entries (for any $\eta > 0$). This is close to the best $k$ allowable by the best sparse recovery algorithms even if one knows the dictionary $A$ exactly. Moreover, both the running time and sample complexity depend on $\log 1/\epsilon$, where $\epsilon$ is the target accuracy, and so our algorithms converge very quickly to the true dictionary. Our algorithm can also tolerate substantial amounts of noise provided it is incoherent with respect to the dictionary (e.g., Gaussian). In the noisy setting, our running time and sample complexity depend polynomially on $1/\epsilon$, and this is necessary.


title: A convolutional approach to reflection symmetry

abstract: We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. Code and a new database for 2D symmetry detection is available.


title: Integrated Billing Solutions in the Internet of Things

abstract: The Internet of Things is one of the most promising technological developments in information technology. It promises huge financial and nonfinancial benefits across supply chains, in product life cycle and customer relationship applications as well as in smart environments. However, the adoption process of the Internet of Things has been slower than expected. One of the main reasons for this is the missing profitability for each individual stakeholder. Costs and benefits are not equally distributed. Cost benefit sharing models have been proposed to overcome this problem and to enable new areas of application. However, these cost benefit sharing approaches are complex, time consuming, and have failed to achieve broad usage. In this chapter, an alternative concept, suggesting flexible pricing and trading of information, is proposed. On the basis of a beverage supply chain scenario, a prototype installation, based on an open source billing solution and the Electronic Product Code Information Service (EPCIS), is shown as a proof of concept and an introduction to different pricing options. This approach allows a more flexible and scalable solution for cost benefit sharing and may enable new business models for the Internet of Things.


title: The qualitative interview in IS research: Examining the craft

abstract: The qualitative interview is one of the most important data gathering tools in qualitative research, yet it has remained an unexamined craft in IS research. This paper discusses the potential difficulties, pitfalls and problems of the qualitative interview in IS research. Building on Goffman's seminal work on social life, the paper proposes a dramaturgical model as a useful way of conceptualizing the qualitative interview. Based on this model the authors suggest guidelines for the conduct of qualitative interviews.


title: Approaching process mining with sequence clustering: Experiments and findings

abstract: Sequence clustering is a technique of bioinformatics that is used todiscover the properties of sequences by grouping them into clusters and assigningeach sequence to one of those clusters. In business process mining, the goal is alsoto extract sequence behaviour from an event log but the problem is oftensimplified by assuming that each event is already known to belong to a givenprocess and process instance. In this paper, we describe two experiments wherethis information is not available. One is based on a real-world case study ofobserving a software development team for three weeks. The other is based onsimulation and shows that it is possible to recover the original behaviour in a fullyautomated way. In both experiments, sequence clustering plays a central role.


title: Abstract Image thresholding using Tsallis entropy

abstract: Image analysis usually refers to processing of images with the goal of finding objects presented in the image. Image segmentation is one of the most critical tasks in automatic image analysis. The nonextensive entropy is a recent development in statistical mechanics and it is a new formalism in which a real quantity q was introduced as parameter for physical systems that present long range interactions, long time memories and fractal-type structures. In image processing, one of the most efficient techniques for image segmentation is entropy-based thresholding. This approach uses the Shannon entropy originated from the information theory considering the gray level image histogram as a probability distribution. In this paper, Tsallis entropy is applied as a general entropy formalism for information theory. For the first time image thresholding by nonextensive entropy is proposed regarding the presence of nonadditive information content in some image classes. Some typical results are presented to illustrate the influence of the parameter q in the thresholding.


title: Accelerating search-based program synthesis using learned probabilistic models

abstract: A key challenge in program synthesis concerns how to efficiently search for the desired program in the space of possible programs. We propose a general approach to accelerate search-based program synthesis by biasing the search towards likely programs. Our approach targets a standard formulation, syntax-guided synthesis (SyGuS), by extending the grammar of possible programs with a probabilistic model dictating the likelihood of each program. We develop a weighted search algorithm to efficiently enumerate programs in order of their likelihood. We also propose a method based on transfer learning that enables to effectively learn a powerful model, called probabilistic higher-order grammar, from known solutions in a domain. We have implemented our approach in a tool called Euphony and evaluate it on SyGuS benchmark problems from a variety of domains. We show that Euphony can learn good models using easily obtainable solutions, and achieves significant performance gains over existing general-purpose as well as domain-specific synthesizers.


title: Bridging the Gap Between Politics and Techniques: On the next practices of participatory design

abstract: This paper discusses how we in the participatory design (PD) research community may contribute to the evolution of ICT design 1 practices into something that is much more attuned to people using ICT and to their interests. The main idea is that to do so we need to focus more on issues in the gap between politics and techniques, e.g., project funding, types of users and of use settings, the role of companies and of Intellectual Property Rights and the types of projects we work on. The paper presents material illustrating that impor - tant changes are going on in the dimensions outlined by these issues and argues that these changes create important, new opportunities for PD to contribute to the 'next practices' of ICT design—as well as serious problems. Thus to exploit these new opportunities we need to improve our understanding of the issues involved and to develop new ways of taking them into account when we design and do research projects.


title: BLITHE: Behavior Rule-Based Insider Threat Detection for Smart Grid

abstract: In this paper, we propose a behavior rule-based methodology for insider threat (BLITHE) detection of data monitor devices in smart grid, where the continuity and accuracy of operations are of vital importance. Based on the dc power flow model and state estimation model, three behavior rules are extracted to depict the behavior norms of each device, such that a device (trustee) that is being monitored on its behavior can be easily checked on the deviation from the behavior specification. Specifically, a rule-weight and compliance-distance-based grading strategy is designed, which greatly improves the effectiveness of the traditional grading strategy for evaluation of trustees. The statistical property, i.e., the mathematical expectation of compliance degree of each trustee, is particularly analyzed from both theoretical and practical perspectives, which achieves satisfactory tradeoff between detection accuracy and false alarms to detect more sophisticated and hidden attackers. In addition, based on real data run in POWER WORLD for IEEE benchmark power systems, and through comparative analysis, we demonstrate that BLITHE outperforms the state of arts for detecting abnormal behaviors in pervasive smart grid applications.


title: Active Learning of Pareto Fronts

abstract: This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built. The computational effort in generating the supervised information is reduced by an active learning strategy. In particular, the model is learned from a set of informative training objective vectors. The training objective vectors are approximated Pareto-optimal vectors obtained by solving different scalarized problem instances. The experimental results show that ALP achieves an accurate Pareto front approximation with a lower computational effort than state-of-the-art estimation of distribution algorithms and widely known genetic techniques.


title: A Thoughtful Approach to Instruction: Course Transformation for the Rest of Us

abstract: in their own courses. As a community, we must ask ourselves: How successfully are we educating all students in science? (Wieman and Perkins 2005). The data indicate that we are not where we want to be; too many undergraduates in our courses are not learning the science (Handelsman et al. 2004). We are fortunate, however, in that we have access to a growing body of research on effective ways to teach science (e.g., Bransford, Brown, and Cocking 2000; Cummings et al. 1999; Hake 1998; McDermott and Redish 1999; Redish 2003). This research tells us how we can improve student learning— through student-centered activities such as inquiry, peer instruction, and group work, plus an added focus on problem-solving ability, PH O TO G RA PH C O U RT ES Y O F TH E A U TH O RS


title: A secure multi-hop routing for IoT communication

abstract: This paper introduces a multi-hop routing protocol that enables secured IoT devices' communication. The routing protocol enables the IoT devices to authenticate before forming a new network or joining an existing network. The authentication uses multi-layer parameters to enhance the security of the communication. The proposed routing protocol embeds the multi-layer parameters into the routing algorithm, thus combining the authentication and routing processes without incurring significant overheads. The multi-layer parameters include a unique User-Controllable Identification, users' pre-agreed application(s), and a list of permitted devices, thus saving resources by maintaining smaller routing information. Experimental and field tests were conducted with results showing that our secure multi-hop routing is suitable to be deployed for IoT communication.


title: Vasodilatory prostaglandins in perinatal hypoxic brain damage.

abstract: Prostaglandin (PGE2 and PGI2) synthesis was determined in the cerebrospinal fluid (CSF) and serum of 19 hypoxic neonates at the age of 5-96 hours by using Enzyme Linked Immunosorbent Assay (ELISA) method. Control group consisted of 8 children of the same age whose samples were taken due to initial suspicion of neonatal meningitis. The prostaglandin concentrations in CSF were correlated with initial hypoxic-ischemic encephalopathy (HIE) stage and neurological findings of patients at the age of 12 months. The values of PGE2 and PGI2 in the CSF of children with perinatal hypoxia (PNH) were significantly higher than in the children from the control group. The values of PGI2 in serum were significantly higher than in "CSF" of patients with PNH. Although average values of PGE2 and PGI2 in the liquor were higher in children with advanced stage of HIE, the differences between different stages were not statistically significant. We did not find any significant correlation between average concentrations ofprostaglandins and neurological findings of the 12-month-old children.


title: Language-specific effects of pitch range on the perception of universal intonational meaning

abstract: Two groups of listeners, with Dutch and British English as their native language judged stimuli in Dutch and British English, respectively, on the scales CONFIDENT vs. NOT CONFIDENT and FRIENDLY vs. NOT FRIENDLY, two meanings derived from Ohala’s universal Frequency Code. The stimuli, which were lexically equivalent, were varied in pitch contour and pitch range. In both languages, the perceived degree of confidence decreases and that of friendliness increases when the pitch range is raised, as predicted by the Frequency Code. However, at identical pitch ranges, British English is perceived as more confident and more friendly than Dutch. We argue that this difference in degree of the use of the Frequency Code is due to the difference in the standard pitch ranges of Dutch and British English.


title: Entanglement entropy and quantum field theory

abstract: We carry out a systematic study of entanglement entropy in relativistic quantum field theory. This is defined as the von Neumann entropy SA = −Tr ρAlogρA corresponding to the reduced density matrix ρA of a subsystem A. For the case of a 1+1-dimensional critical system, whose continuum limit is a conformal field theory with central charge c, we re-derive the result of Holzhey et al when A is a finite interval of length in an infinite system, and extend it to many other cases: finite systems, finite temperatures, and when A consists of an arbitrary number of disjoint intervals. For such a system away from its critical point, when the correlation length ξ is large but finite, we show that , where is the number of boundary points of A. These results are verified for a free massive field theory, which is also used to confirm a scaling ansatz for the case of finite size off-critical systems, and for integrable lattice models, such as the Ising and XXZ models, which are solvable by corner transfer matrix methods. Finally the free field results are extended to higher dimensions, and used to motivate a scaling form for the singular part of the entanglement entropy near a quantum phase transition.


title: Dynamic channel assignment in mobile communications based on genetic algorithms

abstract: We investigate dynamic channel assignment (DCA) in mobile communications systems using genetic algorithms (GA). Two new strategies using GA are proposed. In the first strategy, GAL, channels previously assigned are kept locked during the call holding time. In the second strategy, GAS, calls can be switched to different channels during the connection time. We evaluate the performance of the proposed GAs in a 49 hexagonal cell arrangement operating under uniform and nonuniform traffic distributions. Numerical results show that the average call blocking probability of the GAS strategy is lower than that of fixed channel assignment with a borrowing directional channel-locked(BDCL) scheme and of DCA based on Q-learning. The performance of the GAL strategy is better than Q-learning-based DCA for all the investigated cases.


title: Unusual cell organelles during spermiogenesis in two species of gobies (Gobiidae, Teleostei)

abstract: Testes of Lesuerigobius friesii and Gobius bucchichi were studied in adult reproductive fish. During the onset of spermatid development, a peculiar system of alternating rough (RER) and smooth (SER) endoplasmic reticular tubules form rings distally to the cell nucleus. The RER tubules are seen to possess up to 12 ribosomes in cross-section, whereas the SER are strongly electron-dense. Nanotubules connect these stacks of tubules to the developing head and tail of the sperm. With ripening of the sperm these tubules disintegrate within the excessive cytoplasm. It seems likely that these are special forms of Macro-Golgi System that possibly provide protamines for the developing sperm.


title: A novel compact wide-band planar antenna for mobile handsets

abstract: A novel printed compact wide-band planar antenna for mobile handsets is proposed and analyzed in this paper. The radiating patch of the proposed antenna is designed jointly with the shape of the ground plane. A prototype of the proposed antenna with 30 mm in height and 50 mm in width has been fabricated and tested. Its operating bandwidth with voltage standing wave ratio (VSWR) lower than 3:1 is 870-2450 MHz, which covers the global system for mobile communication (GSM, 890-960 MHz), the global positioning system (GPS, 1575.42 MHz), digital communication system (DCS, 1710-1880 MHz), personal communication system (PCS, 1850-1990 MHz), universal mobile telecommunication system (UMTS, 1920-2170 MHz), and wireless local area network (WLAN, 2400-2484 MHz) bands. Therefore, it could be applicable for the existing and future mobile communication systems. Design details and experimental results are also presented and discussed.


title: Recommended Steps for Thematic Synthesis in Software Engineering

abstract: Thematic analysis is an approach that is often used for identifying, analyzing, and reporting patterns (themes) within data in primary qualitative research. 'Thematic synthesis' draws on the principles of thematic analysis and identifies the recurring themes or issues from multiple studies, interprets and explains these themes, and draws conclusions in systematic reviews. This paper conceptualizes the thematic synthesis approach in software engineering as a scientific inquiry involving five steps that parallel those of primary research. The process and outcome associated with each step are described and illustrated with examples from systematic reviews in software engineering.


title: A diagnostic approach for electro-mechanical actuators in aerospace systems

abstract: Electro-mechanical actuators (EMA) are finding increasing use in aerospace applications, especially with the trend towards all all-electric aircraft and spacecraft designs. However, electro-mechanical actuators still lack the knowledge base accumulated for other fielded actuator types, particularly with regard to fault detection and characterization. This paper presents a thorough analysis of some of the critical failure modes documented for EMAs and describes experiments conducted on detecting and isolating a subset of them. The list of failures has been prepared through an extensive Failure Modes and Criticality Analysis (FMECA) reference, literature review, and accessible industry experience. Methods for data acquisition and validation of algorithms on EMA test stands are described. A variety of condition indicators were developed that enabled detection, identification, and isolation among the various fault modes. A diagnostic algorithm based on an artificial neural network is shown to operate successfully using these condition indicators and furthermore, robustness of these diagnostic routines to sensor faults is demonstrated by showing their ability to distinguish between them and component failures. The paper concludes with a roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators.


title: Acquisition and Analysis of Forensic Data Artefacts of Some Popular Apps in Android Smartphone

abstract: Technology is trying to compete with itself (with condition in the past) today to achieve better popularity and make the humans more comfortable in their daily work. Of the latest ventures in this effort is the multitude of apps for the growing users of smartphones. But, like everything has a brighter as well as a darker side, there are a few drawbacks to the usage of these apps. In this paper, an exhaustive assessment and analysis has been done about the expanse of users' private information being stored by some popular & needful apps, both in the smartphones' local storage and the app server. Another factor highlighted is the proportion of information that can be retrieved in a user readable form, from the app-related artifacts, through forensic tools. Lastly, few recommendations have been proposed as a conclusion of the analysis and observations through a systematic methodology.


title: Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests

abstract: This paper presents an automated and accurate computer-aided diagnosis (CAD) system for brain magnetic resonance (MR) image classification. The system first utilizes two-dimensional discrete wavelet transform (2D DWT) for extracting features from the images. After feature vector normalization, probabilistic principal component analysis (PPCA) is employed to reduce the dimensionality of the feature vector. The reduced features are applied to the classifier to categorize MR images into normal and abnormal. This scheme uses an AdaBoost algorithm with random forests as its base classifier. Three benchmark MR image datasets, Dataset-66, Dataset-160, and Dataset-255, have been used to validate the proposed system. A 5×5-fold stratified cross validation scheme is used to enhance the generalization capability of the proposed scheme. Simulation results are compared with the existing schemes and it is observed that the proposed scheme outperforms others in all the three datasets.


title: Taiwanese Vegetarians and Omnivores: Dietary Composition, Prevalence of Diabetes and IFG

abstract: INTRODUCTION ::: Vegetarian diets have been shown to improve glucose metabolism and reduce risk for diabetes in Westerners but whether Chinese vegetarian diets have the same benefits is unknown. ::: ::: ::: METHODS ::: We evaluated the association between diet and diabetes/impaired fasting glucose (IFG) among 4384 Taiwanese Buddhist volunteers and identified diabetes/IFG cases from a comprehensive review of medical history and fasting plasma glucose. ::: ::: ::: RESULTS ::: Vegetarians had higher intakes of carbohydrates, fiber, calcium, magnesium, total and non-heme iron, folate, vitamin A, and lower intakes of saturated fat, cholesterol, and vitamin B12. Besides avoiding meat and fish, vegetarians had higher intakes of soy products, vegetables, whole grains, but similar intakes of dairy and fruits, compared with omnivores. The crude prevalence of diabetes in vegetarians versus omnivores is 0.6% versus 2.3% in pre-menopausal women, 2.8% versus 10% in menopausal women, and 4.3% versus 8.1% in men. Polytomous logistic regression adjusting for age, body mass index, family history of diabetes, education, leisure time physical activity, smoking and alcohol, showed that this vegetarian diet was negatively associated with diabetes and IFG in men (OR for diabetes: 0.49, 95% CI: 0.28-0.89; OR for IFG: 0.66, 95% CI: 0.46-0.95); in pre-menopausal women (OR for diabetes: 0.26, 95% CI: 0.06-1.21; OR for IFG: 0.60, 95% CI: 0.35-1.04); and in menopausal women (OR for diabetes: 0.25, 95% CI: 0.15-0.42; OR for IFG: 0.73, 95% CI: 0.56-0.95). ::: ::: ::: CONCLUSION ::: We found a strong protective association between Taiwanese vegetarian diet and diabetes/IFG, after controlling for various potential confounders and risk factors.


title: CRL: Class Representative Learning for Image Classification

abstract: Building robust and real-time classifiers with diverse datasets are one of the most significant challenges to deep learning researchers. It is because there is a considerable gap between a model built with training (seen) data and real (unseen) data in applications. Recent works including Zero-Shot Learning (ZSL), have attempted to deal with this problem of overcoming the apparent gap through transfer learning. In this paper, we propose a novel model, called Class Representative Learning Model (CRL), that can be especially effective in image classification influenced by ZSL. In the CRL model, first, the learning step is to build class representatives to represent classes in datasets by aggregating prominent features extracted from a Convolutional Neural Network (CNN). Second, the inferencing step in CRL is to match between the class representatives and new data. The proposed CRL model demonstrated superior performance compared to the current state-of-the-art research in ZSL and mobile deep learning. The proposed CRL model has been implemented and evaluated in a parallel environment, using Apache Spark, for both distributed learning and recognition. An extensive experimental study on the benchmark datasets, ImageNet-1K, CalTech-101, CalTech-256, CIFAR-100, shows that CRL can build a class distribution model with drastic improvement in learning and recognition performance without sacrificing accuracy compared to the state-of-the-art performances in image classification.


title: A weighted similarity measure approach to predict intensive care unit transfers

abstract: Classification models have proven useful for predicting clinical interventions and patient outcomes. One of the key issues that affect the predictive ability of supervised learning frameworks in the healthcare scenario is imbalance in data sets. In addition, non-uniform data collection processes in clinical scenarios lead to poor quality data sets. We designed a novel approach to predict Intensive Care Unit (ICU) transfers based on a weighted-similarity measure for patients outside of ICUs. The approach uses similarity between patient vital signs as input features for training the model. To address the data quality issues, we demonstrate the use of various up-sampling and down-sampling techniques to handle imbalanced data sets and train a classifier on a re-sampled data set. The data set used for testing the approach is derived from the MIMIC III database. We compare our results with the clinically accepted methodology to capture patient's health state, assisting in clinical decision making. Our model outperforms the standard methodology used in clinical decision making in standard scoring metrics such as F1-score, False Positive Rate and Mathew's Correlation Coefficient [MCC].


Using Recurrent Neural Networks for Time Series Forecasting


OHT : Hierarchical Distributed Hash Tables


title: Mining Partially-Ordered Sequential Rules Common to Multiple Sequences

abstract: Sequential rule mining is an important data mining problem with multiple applications. An important limitation of algorithms for mining sequential rules common to multiple sequences is that rules are very specific and therefore many similar rules may represent the same situation. This can cause three major problems: (1) similar rules can be rated quite differently, (2) rules may not be found because they are individually considered uninteresting, and (3) rules that are too specific are less likely to be used for making predictions. To address these issues, we explore the idea of mining “partially-ordered sequential rules” (POSR), a more general form of sequential rules such that items in the antecedent and the consequent of each rule are unordered. To mine POSR, we propose the RuleGrowth algorithm, which is efficient and easily extendable. In particular, we present an extension (TRuleGrowth) that accepts a sliding-window constraint to find rules occurring within a maximum amount of time. A performance study with four real-life datasets show that RuleGrowth and TRuleGrowth have excellent performance and scalability compared to baseline algorithms and that the number of rules discovered can be several orders of magnitude smaller when the sliding-window constraint is applied. Furthermore, we also report results from a real application showing that POSR can provide a much higher prediction accuracy than regular sequential rules for sequence prediction.


title: Best-Arm Identification in Linear Bandits

abstract: We study the best-arm identification problem in linear bandit, where the rewards of the arms depend linearly on an unknown parameter θ* and the objective is to return the arm with the largest reward. We characterize the complexity of the problem and introduce sample allocation strategies that pull arms to identify the best arm with a fixed confidence, while minimizing the sample budget. In particular, we show the importance of exploiting the global linear structure to improve the estimate of the reward of near-optimal arms. We analyze the proposed strategies and compare their empirical performance. Finally, as a by-product of our analysis, we point out the connection to the G-optimality criterion used in optimal experimental design.


title: A Systematic Review of Software Requirements Selection and Prioritization Using SBSE Approaches

abstract: Selection and prioritization of software requirements represents an area of interest in Search-Based Software Engineering SBSE and its main focus is finding and selecting a set of requirements that may be part of a software release. This paper uses a systematic review to investigate which SBSE approaches have been proposed to address software requirement selection and prioritization problems. The search strategy identified 30 articles in this area and they were analyzed for 18 previously established quality criteria. The results of this systematic review show which aspects of the requirements selection and prioritization problems were addressed by researchers, the methods approaches and search techniques currently adopted to address these problems, and the strengths and weaknesses of each of these techniques. The review provides a map showing the gaps and trends in the field, which can be useful to guide further research.


title: The Technical Efficiency of Specialised Milk Farms: A Regional View

abstract: The aim of the article is to evaluate production efficiency and its determinants of specialised dairy farming among the EU regions. In the most of European regions, there is a relatively high significance of small specialised farms including dairy farms. The DEAVRS method (data envelopment analysis with variable returns to scale) reveals efficient and inefficient regions including the scale efficiency. In the next step, the two-sample t-test determines differences of economic and structural indicators between efficient and inefficient regions. The research reveals that substitution of labour by capital/contract work explains the variability of the farm net value added per AWU (annual work unit) income indicator by more than 30%. The significant economic determinants of production efficiency in specialised dairy farming are farm size, herd size, crop output per hectare, productivity of energy, and capital (at α = 0.01). Specialised dairy farms in efficient regions have significantly higher farm net value added per AWU than inefficient regions. Agricultural enterprises in inefficient regions have a more extensive structure and produce more noncommodity output (public goods). Specialised dairy farms in efficient regions have a slightly higher milk yield, specific livestock costs of feed, bedding, and veterinary services per livestock unit.


title: Dangers of a fixed mindset: implications of self-theories research for computer science education

abstract: Psychology studies have shown that students' beliefs about their own intelligence--whether they view intelligence as fixed or malleable-have an important influence on student development and achievement. Yet the impact of these theories on success in Computer Science (CS) has not been directly investigated. Self-theories research has shown that students with a fixed mindset are more likely to exhibit a helpless response to substantial challenges and to experience decreases in self-esteem during college. Those with a growth mindset welcome challenges, displaying a mastery-oriented response, and maintaining self-esteem, primarily because they attribute failure to a lack of effort rather than a lack of intellectual ability. This paper introduces self-theories research, and relates this research to several issues in CS Education. We then make suggestions for how CS educators can consider self-theories in their teaching and research.


title: Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition

abstract: In this paper, we propose a convolutional neural network-based method to automatically retrieve missing or noisy cardiac acquisition plane information from magnetic resonance imaging and predict the five most common cardiac views. We fine-tune a convolutional neural network (CNN) initially trained on a large natural image recognition data-set (Imagenet ILSVRC2012) and transfer the learnt feature representations to cardiac view recognition. We contrast this approach with a previously introduced method using classification forests and an augmented set of image miniatures, with prediction using off the shelf CNN features, and with CNNs learnt from scratch. We validate this algorithm on two different cardiac studies with 200 patients and 15 healthy volunteers, respectively. We show that there is value in fine-tuning a model trained for natural images to transfer it to medical images. Our approach achieves an average F1 score of 97.66% and significantly improves the state-of-the-art of image-based cardiac view recognition. This is an important building block to organise and filter large collections of cardiac data prior to further analysis. It allows us to merge studies from multiple centres, to perform smarter image filtering, to select the most appropriate image processing algorithm, and to enhance visualisation of cardiac data-sets in content-based image retrieval.


2P. A conceptual framework to develop Green IT – going beyond the idea of environmental sustainability


title: Cache coherence protocols: evaluation using a multiprocessor simulation model

abstract: Using simulation, we examine the efficiency of several distributed, hardware-based solutions to the cache coherence problem in shared-bus multiprocessors. For each of the approaches, the associated protocol is outlined. The simulation model is described, and results from that model are presented. The magnitude of the potential performance difference between the various approaches indicates that the choice of coherence solution is very important in the design of an efficient shared-bus multiprocessor, since it may limit the number of processors in the system.


title: A local approach based on a Local Binary Patterns variant texture descriptor for classifying pain states

abstract: This paper focuses on the use of image-based techniques for classifying pain states, in particular we compare several texture descriptors based on Local Binary Patterns (LBP), and we proposed some novel solutions based on the combination of new texture descriptors: the Elongated Ternary Pattern (ELTP) and the Elongated Binary Pattern (ELBP). ELTP is the best performing descriptor in our experiments. The ELBP descriptor combines characteristics of the Local Ternary Pattern (LTP) and ELTP. These two variants of the standard LBP are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. The resulting extracted features are used to train a support vector machine classifier. Extensive experiments are conducted using the Infant COPE database of neonatal facial images. Our results show that a local approach based on the ELTP feature extractor produces a reliable system for classifying pain states.


title: FeedVis: A Path for Exploring News Feed Curation Algorithms

abstract: Social media feeds, personalized search results and recommendations are examples of algorithmically curated content in our daily digital Life. While the algorithms that curated this content have great power to shape users' experiences, they are mostly hidden behind the interface, leaving users unaware of their presence. Whether it is helpful to give users knowledge of the algorithms' existence and if this knowledge affects interaction behavior are open questions. To assist us in addressing these questions, we developed a system, FeedVis, that exposes Facebook users to comparisons between algorithmically curated and unadulterated News Feeds. We used the tools visualizations as concrete artifacts to study users' perceptions of the algorithms governing their social media feeds.


title: Graph Classification with Imbalanced Class Distributions and Noise ∗

abstract: Recent years have witnessed an increasing number of applications involving data with structural dependency and graph representations. For these applications, it is very common that their class distribution is imbalanced with minority samples being only a small portion of the population. Such imbalanced class distributions impose significant challenges to the learning algorithms. This problem is further complicated with the presence of noise or outliers in the graph data. In this paper, we propose an imbalanced graph boosting algorithm, igBoost, that progressively selects informative subgraph patterns from imbalanced graph data for learning. To handle class imbalance, we take class distributions into consideration to assign different weight values to graphs. The distance of each graph to its class center is also considered to adjust the weight to reduce the impact of noisy graph data. The weight values are integrated into the iterative subgraph feature selection and margin learning process to achieve maximum benefits. Experiments on real-world graph data with different degrees of class imbalance and noise demonstrate the algorithm performance.


title: Using supervised learning to classify authentic and fake online reviews

abstract: Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and fake reviews are not easy to differentiate. Hence, this paper uses supervised learning algorithms to analyze the extent to which authentic and fake reviews could be distinguished based on four linguistic clues, namely, understandability, level of details, writing style, and cognition indicators. The model performance was compared with two baselines. The results were generally promising.


title: KNUTSON, SELECTIVE Am J Psychiatry WOLKOWITZ, ALTERATION 155:3, March OF COLE, PERSONALITY 1998 ET AL. Selective Alteration of Personality and Social Behavior by Serotonergic Intervention

abstract: Objective: The authors sought to test the causal hypothesis that serotonergic function modulates aspects of the normal spectrum of individual differences in affective experience and social behavior in humans. Method: A selective serotonin reuptake inhibitor (SSRI), paroxetine, 20 mg/day (N=26), or placebo (N=25) was administered to normal volunteers in a double-blind manner for 4 weeks, and personality variables and social behavior were assessed at baseline and at weeks 1 and 4 of treatment. Results: Relative to placebo, SSRI administration reduced focal indices of hostility through a more general decrease in negative affect, yet did not alter indices of positive affect. In addition, SSRI administration increased a behavioral index of social affiliation. Changes in both negative affect and affiliative behavior were significantly related to volunteers’ plasma SSRI levels at the end of the experiment. Conclusions: Central serotonergic function may modulate a dimension of normal personality characterized by reduced negative affective experience and increased affiliative behavior. SSRI administration has significant and detectable effects on these measures even in the absence of baseline clinical depression or other psychopathology. (Am J Psychiatry 1998; 155:373‐379)


title: A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing

abstract: Algorithmic reductions are one of the corner stones of theoretical computer science. Surprisingly, to-date, they have only played a limited role in machine learning. In this paper we introduce a formal and practical reduction between two of the most widely used machine learning algorithms: from the Elastic Net (and the Lasso as a special case) to the Support Vector Machine. First, we derive the reduction and summarize it in only 11 lines of MATLABTM. Then, we demonstrate its high impact potential by translating recent advances in parallelizing SVM solvers directly to the Elastic Net. The resulting algorithm is a parallel solver for the Elastic Net (and Lasso) that naturally utilizes GPU and multi-core CPUs. We evaluate it on twelve real world data sets, and show that it yields identical results as the popular (and highly optimized) glmnet implementation but is up-to two orders of magnitude faster.


title: Adaptive Fault-Tolerant Synchronization Control of a Class of Complex Dynamical Networks With General Input Distribution Matrices and Actuator Faults

abstract: This paper is concerned with the problem of adaptive fault-tolerant synchronization control of a class of complex dynamical networks (CDNs) with actuator faults and unknown coupling weights. The considered input distribution matrix is assumed to be an arbitrary matrix, instead of a unit one. Within this framework, an adaptive fault-tolerant controller is designed to achieve synchronization for the CDN. Moreover, a convex combination technique and an important graph theory result are developed, such that the rigorous convergence analysis of synchronization errors can be conducted. In particular, it is shown that the proposed fault-tolerant synchronization control approach is valid for the CDN with both time-invariant and time-varying coupling weights. Finally, two simulation examples are provided to validate the effectiveness of the theoretical results.


title: Ajax Asynchronous Database Refresh

abstract: For century, This paper discuss the new era of Internet application and user experience, Ajax is a new technology and this paper address the Software system complexity and Algorithms for better feature and performance.


title: Methodology of forensic neurotoxicity evaluation: PCB case.

abstract: Many scientists and doctors are not aware that the level of certainty for litigation purposes differs from that usually associated with scientific studies. Although the 0.05 probability level is commonly used in scientific studies ("beyond a reasonable doubt"), in U.S. civil litigation the diagnostic criteria is "more probable than not" (greater than 0.50 or chance probability level). The following case of PCB neurotoxicity will be discussed within the context of a forensic evaluation. A number of workers were employed by a metal salvage company to strip copper coils from used electrical transformers. Symptoms of neurotoxicity developed in the men, which were evaluated by questionnaire, interview and psychological examination. Neuropsychological and nerve conduction tests were administered. Results showed deficits in nervous system function related to PCB exposure. An individual case is described in detail.


title: A question-entailment approach to question answering

abstract: Background ::: One of the challenges in large-scale information retrieval (IR) is developing fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is mapping new questions to formerly answered questions that are “similar”.


title: Gaussian process factorization machines for context-aware recommendations

abstract: Context-aware recommendation (CAR) can lead to significant improvements in the relevance of the recommended items by modeling the nuanced ways in which context influences preferences. The dominant approach in context-aware recommendation has been the multidimensional latent factors approach in which users, items, and context variables are represented as latent features in low-dimensional space. An interaction between a user, item, and a context variable is typically modeled as some linear combination of their latent features. However, given the many possible types of interactions between user, items and contextual variables, it may seem unrealistic to restrict the interactions among them to linearity. To address this limitation, we develop a novel and powerful non-linear probabilistic algorithm for context-aware recommendation using Gaussian processes. The method which we call Gaussian Process Factorization Machines (GPFM) is applicable to both the explicit feedback setting (e.g. numerical ratings as in the Netflix dataset) and the implicit feedback setting (i.e. purchases, clicks). We derive stochastic gradient descent optimization to allow scalability of the model. We test GPFM on five different benchmark contextual datasets. Experimental results demonstrate that GPFM outperforms state-of-the-art context-aware recommendation methods.


title: Involvement of lncRNA dysregulation in gastric cancer.

abstract: Benefiting from the fast development of sequencing technique and bioinformatics methods, more and more new long non-coding RNAs (lncRNAs) are discovered and identified. lncRNAs were firstly thought to be transcription noise that from genome desert without biological function; however, as the discovery of lncRNA XIST and HOTAIR uncovers the emerging roles of lncRNAs in development and tumorigenesis. In the past decades, accumulating evidence have indicated that lncRNAs involve in a wide range of biological functions, such as X-chromosome inactivation, reprogramming stem cell pluripotency, regulation of the immune response and carcinogenesis. Although lots of studies have demonstrated that dysregulation of lncRNAs involve in diverse diseases including cancers, the underlying molecular mechanisms of lncRNAs are not well documented. Interestingly, our previous studies and others' have shown that numerous of lncRNAs expression was misregulated in gastric cancer. In this review, we will focus on the dysregulated lncRNAs and their biological function and underlying pathways or mechanisms in GC. Finally, we will discuss the potential roles of lncRNAs acting as biomarkers or therapeutic targets in GC patients.


Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation


Am I a Baller? Basketball Skill Assessment using First-Person Cameras.


title: Wideband power amplifier MMICs utilizing GaN on SiC

abstract: The application of GaN on SiC technology to wideband power amplifier MMICs is explored. The unique characteristics of GaN on SiC applied to reactively matched and distributed wideband circuit topologies are discussed, including comparison to GaAs technology. A 2 – 18 GHz 11W power amplifier MMIC is presented as an example.


title: High-resolution computer meshes of the lower body bones of an adult human female derived from CT images

abstract: Background Computer-based geometrical meshes of bones are important for applications in computational biomechanics as well as clinical software. There is however a lack of freely available detailed bone meshes, especially related to the human female morphology. Methods&Results We provide high resolution bone meshes of the lower body, derived from CT images of a 59 year old female cadaver that were sourced from the Visible Human Data Set, Visible Human Project (NIH, USA). Important bone landmarks and joint rotation axes are identified from the extracted meshes. A script-based framework is developed to provide a graphical user interface that can visualize, resample and modify the meshes to fit different subject scales. Conclusion This open-data resource fills a gap in available data and is provided for free usage in research and other applications. The associated scripts allows users to easily transform the meshes to different laboratory and software setups. This resource may be accessed through the following web link: https://github.com/manishsreenivasa/BMFToolkit This document is the author's version of this article.


title: Life in the"Matrix": Human Mobility Patterns in the Cyber Space

abstract: With the wide adoption of the multi-community setting in many popular social media platforms, the increasing user engagements across multiple online communities warrant research attention. In this paper, we introduce a novel analogy between the movements in the cyber space and the physical space. This analogy implies a new way of studying human online activities by modelling the activities across online communities in a similar fashion as the movements among locations. First, we quantitatively validate the analogy by comparing several important properties of human online activities and physical movements. Our experiments reveal striking similarities between the cyber space and the physical space. Next, inspired by the established methodology on human mobility in the physical space, we propose a framework to study human"mobility"across online platforms. We discover three interesting patterns of user engagements in online communities. Furthermore, our experiments indicate that people with different mobility patterns also exhibit divergent preferences to online communities. This work not only attempts to achieve a better understanding of human online activities, but also intends to open a promising research direction with rich implications and applications.


title: Localization from visual landmarks on a free-flying robot

abstract: We present the localization approach for Astrobee, a new free-flying robot designed to navigate autonomously on the International Space Station (ISS). Astrobee will accommodate a variety of payloads and enable guest scientists to run experiments in zero-g, as well as assist astronauts and ground controllers. Astrobee will replace the SPHERES robots which currently operate on the ISS, whose use of fixed ultrasonic beacons for localization limits them to work in a 2 meter cube. Astrobee localizes with monocular vision and an IMU, without any environmental modifications. Visual features detected on a pre-built map, optical flow information, and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise, and extensively evaluate the localization algorithm.


Mechanical properties and fracture analysis of additively manufactured EH 36 steel parts by laser engineered net shaping process


title: Narrative and active video game in separate and additive effects of physical activity and cognitive function among young adults

abstract: Physically active video games (AVGs) have been found to positively impact physical activity behaviors, especially when a narrative is added. However, the motivational and cognitive benefits of adding narrative to AVG are unclear. We examined the separate and additive effects of narrative and AVG on physical activity and cognitive function versus an active comparator, such as a sedentary video game (SVG). We randomly assigned young adults to one of four groups (narrative-AVG, AVG, narrative-SVG, or SVG) and had them complete sustained attention and working memory tasks before and after a 30-min experimental condition. Participants in both narrative-AVG and AVG groups achieved a moderate-intensity physical activity, while adding narrative to AVG resulted in higher step counts and more time spent in moderate-to-vigorous physical activity than AVG without narrative. Regardless of the narrative effect, participants in both AVG groups performed better on overall working memory than both SVG groups, while both AVG and SVG groups similarly achieved maximal performance in sustained attention. Working memory enhancement was positively correlated with increased heart rate. Participants in narrative-SVG group had a better response accuracy in working memory than those who played SVG without narrative. Taken together, adding narrative to AVG as a motivational component increased physical activity, which was the primary factor in the improvement of overall working memory.


title: MSE-Based Transceiver Designs for the MIMO Interference Channel

abstract: Interference alignment (IA) has evolved as a powerful technique in the information theoretic framework for achieving the optimal degrees of freedom of interference channel. In practical systems, the design of specific interference alignment schemes is subject to various criteria and constraints. In this paper, we propose novel transceiver schemes for the MIMO interference channel based on the mean square error (MSE) criterion. Our objective is to optimize the system performance under a given and feasible degree of freedom. Both the total MSE and the maximum per-user MSE are chosen to be the objective functions to minimize. We show that the joint design of transmit precoding matrices and receiving filter matrices with both objectives can be realized through efficient iterative algorithms. The convergence of the proposed algorithms is proven as well. Simulation results show that the proposed schemes outperform the existing IA schemes in terms of BER performance. Considering the imperfection of channel state information (CSI), we also extend the MSE-based transceiver schemes for the MIMO interference channel with CSI estimation error. The robustness of the proposed algorithms is confirmed by simulations.


title: Optic Disc Detection in Retinal Images using Tensor Voting and Adaptive Mean-Shift

abstract: In this paper, we present a new approach to identify the position of an optic disc in retinal fundus images. The proposed algorithm is based on a tensor voting to analyze vessel structures, and the position of the optic disc is identified by mean-shift based mode detection. The core technique consists of three preprocessing stages that enhance local contrasts through illumination equalization, and we extract vessel patterns by tensor voting in equalized images. Lastly, the position of the optic disc is identified by mode detection that is based on mean-shift procedure. Our approach is evaluated against a dataset including healthy retinal images and diseased retinal images. The experimental results show that the proposed method can exactly identify the position of the optic disc from retinal fundus images.


title: CUNY-BLENDER TAC-KBP2010 Entity Linking and Slot Filling System Description

abstract: The CUNY-BLENDER team participated in the following tasks in TAC-KBP2010: Regular Entity Linking, Regular Slot Filling and Surprise Slot Filling task (per:disease slot). In the TAC-KBP program, the entity linking task is considered as independent from or a pre-processing step of the slot filling task. Previous efforts on this task mainly focus on utilizing the entity surface information and the sentence/document-level contextual information of the entity. Very little work has attempted using the slot filling results as feedback features to enhance entity linking. In the KBP2010 evaluation, the CUNY-BLENDER entity linking system explored the slot filling attributes that may potentially help disambiguate entity mentions. Evaluation results show that this feedback approach can achieve 9.1% absolute improvement on micro-average accuracy over the baseline using vector space model.