title: Novel battery charging regulation system for photovoltaic applications

abstract: A battery-charging system for stand-alone photovoltaic (PV) applications is presented. Advantages of the proposed method are: better exploitation of the available PV energy by means of a maximum power point tracking (MPPT) technique employed in the control algorithm, increased battery lifetime due to higher level state of charge operation, and the charging control process does not depend on accurate battery current measurements, reducing the effect of the current sensor sensitivity on the battery final state of charge. Also, since it is based on the battery current regulation principle, it can be effectively used with large battery strings. The experimental results verify that, using the proposed method, a better exploitation of the available PV energy, compared to a commercial battery charger based on the on/off principle, is achieved, and simultaneously a 100% battery state of charge is reached in shorter time.


title: An 8–11 Gb/s Reference-Less Bang-Bang CDR Enabled by “Phase Reset”

abstract: This paper embeds a “phase-reset” scheme into a bang-bang clock and data recovery (CDR) to periodically realign the clock phase to the data rising edge using a gated-VCO. This reduces both the CDR lock time and bit errors during pull-in, while increasing the CDR capture range. The CDR is fabricated in 65-nm CMOS, operates at 8-11 Gb/s, and demonstrates a 9 × increase in capture range. The CDR consumes 84 mW during lock, and 48 mW in steady state.


title: Optimal bit allocation for subband video coding

abstract: An optimal subband bit allocation technique for optimal distribution of bits among the subbands is presented. The proposed technique is based on a new rate-distortion model, which switches between two types of models at lower and higher bit rates. Based on this model, it is possible to select the best R-D strategy among the lower bit rate model, the higher bit rate model and the average between them. The Lagrangian optimisation method is then used to minimise this distortion model for a given bit budget and to obtain an optimum bit distribution among the subbands. The proposed optimal bit allocation framework is implemented on the motion-compensated 2D subband/DCT scalable video codec, which provides quality signal-to-noise ratio (SNR) scalability. The results show that the proposed bit allocation method outperforms the uniform allocation of bits over a wide range of target bit rates. Moreover, simulation results show that the performance of the presented scalable video coding is slightly inferior to the single layer H.264/AVC but outperforms the SNR scalability supported in joint scalable video model (JSVM), the state-of-the-art scalable video coding standard, that achieves its gain through the hierarchical B-pictures.


title: Deep learning for wind vector determination

abstract: Numerical Weather Prediction (NWP) is a process of using numerical simulation to track and predict weather patterns over time. NWP is presented with a number of challenges in that it aims for precision in a situation that is by its nature uncertain and influenced by factors that are very difficult to enumerate. As an alternative, Empirical Weather Prediction (EWP) attempts to use observational data to construct models of weather phenomena. We evaluate a deep learning method applied to EWP to make wind vector determinations from radiometric data using data collected from Hurricane Sandy. Our approach uses unsupervised pre-training of stacked autoencoders to construct multilayer perceptrons. We then discuss the role of our approach as an important step in positioning EWP as a viable alternative to NWP.


title: Low-Energy Power-ON-Reset Circuit for Dual Supply SRAM

abstract: Design of a low-energy power-ON reset (POR) circuit is proposed to reduce the energy consumed by the stable supply of the dual supply static random access memory (SRAM), as the other supply is ramping up. The proposed POR circuit, when embedded inside dual supply SRAM, removes its ramp-up constraints related to voltage sequencing and pin states. The circuit consumes negligible energy during ramp-up, does not consume dynamic power during operations, and includes hysteresis to improve noise immunity against voltage fluctuations on the power supply. The POR circuit, designed in the 40-nm CMOS technology within 10.6- $\mu \text{m}^{2}$ area, enabled $27\times $ reduction in the energy consumed by the SRAM array supply during periphery power-up in typical conditions.


title: Attribute-Based Access Control

abstract: Attribute-based access control (ABAC) is a flexible approach that can implement AC policies limited only by the computational language and the richness of the available attributes, making it ideal for many distributed or rapidly changing environments.


title: Laissez-Faire: Fully Asymmetric Backscatter Communication

abstract: Backscatter provides dual-benefits of energy harvesting and low-power communication, making it attractive to a broad class of wireless sensors. But the design of a protocol that enables extremely power-efficient radios for harvesting-based sensors as well as high-rate data transfer for data-rich sensors presents a conundrum. In this paper, we present a new {\em fully asymmetric} backscatter communication protocol where nodes blindly transmit data as and when they sense. This model enables fully flexible node designs, from extraordinarily power-efficient backscatter radios that consume barely a few micro-watts to high-throughput radios that can stream at hundreds of Kbps while consuming a paltry tens of micro-watts. The challenge, however, lies in decoding concurrent streams at the reader, which we achieve using a novel combination of time-domain separation of interleaved signal edges, and phase-domain separation of colliding transmissions. We provide an implementation of our protocol, LF-Backscatter, and show that it can achieve an order of magnitude or more improvement in throughput, latency and power over state-of-art alternatives.


title: A Better Method to Analyze Blockchain Consistency

abstract: The celebrated Nakamoto consensus protocol [16] ushered in several new consensus applications including cryptocurrencies. A few recent works [7, 17] have analyzed important properties of blockchains, including most significantly, consistency, which is a guarantee that all honest parties output the same sequence of blocks throughout the execution of the protocol. To establish consistency, the prior analysis of Pass, Seeman and Shelat [17] required a careful counting of certain combinatorial events that was difficult to apply to variations of Nakamoto. The work of Garay, Kiayas, and Leonardas [7] provides another method of analyzing the blockchain under the simplifying assumption that the network was synchronous. The contribution of this paper is the development of a simple Markov-chain based method for analyzing consistency properties of blockchain protocols. The method includes a formal way of stating strong concentration bounds as well as easy ways to concretely compute the bounds. We use our new method to answer a number of basic questions about consistency of blockchains: Our new analysis provides a tighter guarantee on the consistency property of Nakamoto's protocol, including for parameter regimes which [17] could not consider; We analyze a family of delaying attacks first presented in [17], and extend them to other protocols; We analyze how long a participant should wait before considering a high-value transaction "confirmed"; We analyze the consistency of CliqueChain, a variation of the Chainweb [14] system; We provide the first rigorous consistency analysis of GHOST [20] and also analyze a folklore "balancing"-attack. In each case, we use our framework to experimentally analyze the consensus bounds for various network delay parameters and adversarial computing percentages. We hope our techniques enable authors of future blockchain proposals to provide a more rigorous analysis of their schemes.


title: Overview and open issues on penetration test

abstract: Several studies regarding security testing for corporate environments, networks, and systems were developed in the past years. Therefore, to understand how methodologies and tools for security testing have evolved is an important task. One of the reasons for this evolution is due to penetration test, also known as Pentest. The main objective of this work is to provide an overview on Pentest, showing its application scenarios, models, methodologies, and tools from published papers. Thereby, this work may help researchers and people that work with security to understand the aspects and existing solutions related to Pentest. A systematic mapping study was conducted, with an initial gathering of 1145 papers, represented by 1090 distinct papers that have been evaluated. At the end, 54 primary studies were selected to be analyzed in a quantitative and qualitative way. As a result, we classified the tools and models that are used on Pentest. We also show the main scenarios in which these tools and methodologies are applied to. Finally, we present some open issues and research opportunities on Pentest.


title: Smart office energy management system using bluetooth low energy based beacons and a mobile app

abstract: This paper describes a smart office energy management system (SOEMS) that can reduce the energy consumption of PCs, monitors, and lights in an office environment through the use of a mobile app and Bluetooth Low Energy based Beacons (BLE Beacons). BLE Beacons placed at several places in an office and a mobile app are used to determine whether a user enters or exits the office to change the power saving mode of the user's PCs, monitors, and lights. The proposed system can reduce energy consumption in the office without causing user inconvenience.


title: Analytical Model of Slotted Air-Gap Surface Mounted Permanent-Magnet Synchronous Motor With Magnet Bars Magnetized in the Shifting Direction

abstract: An analytical model is presented, which uses two-dimensional field theory in polar coordinates to determine the flux density distribution, cogging torque, back EMF and electromagnetic torque in the slotted air gap of permanent-magnet motors with surface mounted magnet bars which are magnetized in shifting direction. The magnet arc to pole pitch ratio in the motor is not necessarily equal to unity like in the case of Halbach array magnetization. The effect of stator slots is introduced by modulating the magnetic field distribution in the slotless stator by the complex relative air-gap permeance. With this complex permeance, the radial and tangential components of flux density are calculated. In the analytical and numerical study a finite number of magnet bars, which is considered sufficient to get a sinusoidal magnetization, is used. The influence of the number of magnet bars on magnetization is also investigated. The accuracy of the developed model is verified by comparing its results with those obtained from experimental measurement and previously validated linear and nonlinear numerical finite element code.


title: DGFIndex for Smart Grid: Enhancing Hive with a Cost-Effective Multidimensional Range Index

abstract: In Smart Grid applications, as the number of deployed electric smart meters increases, massive amounts of valuable meter data is generated and collected every day. To enable reliable data collection and make business decisions fast, high throughput storage and high-performance analysis of massive meter data become crucial for grid companies. Considering the advantage of high efficiency, fault tolerance, and price-performance of Hadoop and Hive systems, they are frequently deployed as underlying platform for big data processing. However, in real business use cases, these data analysis applications typically involve multidimensional range queries (MDRQ) as well as batch reading and statistics on the meter data. While Hive is high-performance at complex data batch reading and analysis, it lacks efficient indexing techniques for MDRQ. ::: In this paper, we propose DGFIndex, an index structure for Hive that efficiently supports MDRQ for massive meter data. DGFIndex divides the data space into cubes using the grid file technique. Unlike the existing indexes in Hive, which stores all combinations of multiple dimensions, DGFIndex only stores the information of cubes. This leads to smaller index size and faster query processing. Furthermore, with pre-computing user-defined aggregations of each cube, DGFIndex only needs to access the boundary region for aggregation query. Our comprehensive experiments show that DGFIndex can save significant disk space in comparison with the existing indexes in Hive and the query performance with DGFIndex is 2-50 times faster than existing indexes in Hive and HadoopDB for aggregation query, 2-5 times faster than both for non-aggregation query, 2-75 times faster than scanning the whole table in different query selectivity.


title: Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications

abstract: Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers. In many low-level vision problems, not only it is known that the underlying structure of clean data is low-rank, but the exact rank of clean data is also known. Yet, when applying conventional rank minimization for those problems, the objective function is formulated in a way that does not fully utilize a priori target rank information about the problems. This observation motivates us to investigate whether there is a better alternative solution when using rank minimization. In this paper, instead of minimizing the nuclear norm, we propose to minimize the partial sum of singular values, which implicitly encourages the target rank constraint. Our experimental analyses show that, when the number of samples is deficient, our approach leads to a higher success rate than conventional rank minimization, while the solutions obtained by the two approaches are almost identical when the number of samples is more than sufficient. We apply our approach to various low-level vision problems, e.g., high dynamic range imaging, motion edge detection, photometric stereo, image alignment and recovery, and show that our results outperform those obtained by the conventional nuclear norm rank minimization method.


title: Wnt signaling controls the specification of definitive and primitive hematopoiesis from human pluripotent stem cells

abstract: Hematopoietic differentiation of human pluripotent stem cells is directed to either the definitive or primitive program, a step toward the generation of hematopoietic stem cells.


title: Multi-topic based query-oriented summarization

abstract: Query-oriented summarization aims at extracting an informative summary from a document collection for a given query. It is very useful to help users grasp the main information related to a query. Existing work can be mainly classified into two categories: supervised method and unsupervised method. The former requires training examples, which makes the method limited to predefined domains. While the latter usually utilizes clustering algorithms to find ‘centered’ sentences as the summary. However, the method does not consider the query information, thus the summarization is general about the document collection itself. Moreover, most of existing work assumes that documents related to the query only talks about one topic. Unfortunately, statistics show that a large portion of summarization tasks talk about multiple topics. In this paper, we try to break limitations of the existing methods and study a new setup of the problem of multi-topic based query-oriented summarization. We propose using a probabilistic approach to solve this problem. More specifically, we propose two strategies to incorporate the query information into a probabilistic model. Experimental results on two different genres of data show that our proposed approach can effectively extract a multi-topic summary from a document collection and the summarization performance is better than baseline methods. The approach is quite general and can be applied to many other mining tasks, for example product opinion analysis and question answering.


title: Interword distance changes represented by sine waves for watermarking text images

abstract: Digital watermarking is widely believed to be a valid means to discourage illicit distribution of information content. Digital watermarking methods for text documents are limited because of the binary nature of text documents. A distinct feature of a text document is its space patterning. We propose a new approach in text watermarking in which interword spaces of different text lines are slightly modified. After the modification, the average spaces of various lines have the characteristics of a sine wave and the wave constitutes a mark. Both nonblind and blind watermarking algorithms are discussed. Preliminary experiments have shown promising results. Our experiments suggest that space patterning of text documents can be a useful tool in digital watermarking.


title: Wideband coplanar waveguide-to-rectangular waveguide transition using fin-line taper

abstract: This letter introduces a new wideband coplanar waveguide-to-rectangular waveguide transition. The transition uses a uniplanar circuit in line with the waveguide, which eases the design and fabrication. The design does not require airbridges. Simulations and measurements of X-band (8.2-12.4 GHz) transitions based on both a low- and high-permittivity material (/spl epsiv//sub r/= 2.33 and 10.8) show that the transition works fine over the full frequency band. For /spl epsiv//sub r/= 2.33 the measured return and insertion loss of a back-to-back transition are more than 16 dB and less than 0.4dB, respectively. The corresponding values for /spl epsiv//sub r/= 10.8 are more than 10 dB and less than 1.0 dB, respectively, over 90% of the frequency band. The measured insertion loss values indicate losses of less than 0.14 dB and 0.36 dB at the center frequency for a single transition on a substrate with /spl epsiv//sub r/= 2.33 and 10.8, respectively.


title: Jupyter Notebooks—a publishing format for reproducible computational workflows

abstract: It is increasingly necessary for researchers in all fields to write computer code, and in order to reproduce research results, it is important that this code is published. We present Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable. We discuss various tools and use cases for notebook documents.


title: Self-organization patterns in wasp and open source communities

abstract: In this paper, we conducted a comparative study of how social organization takes place in a wasp colony and OSS developer communities. Both these systems display similar global organization patterns, such as hierarchies and clear labor divisions. As our analysis shows, both systems also define interacting agent networks with similar common features that reflect limited information sharing among agents. As far as we know, this is the first research study analyzing the patterns and functional significance of these systems' weighted-interaction networks. By illuminating the extent to which self-organization is responsible for patterns such as hierarchical structure, we can gain insight into the origins of organization in OSS communities.


title: Understanding the Computational Requirements of Virtualized Baseband Units Using a Programmable Cloud Radio Access Network Testbed

abstract: Cloud Radio Access Network (C-RAN) is emerging as a transformative architecture for the next generation of mobile cellular networks. In C-RAN, the Baseband Unit (BBU) is decoupled from the Base Station (BS) and consolidated in a centralized processing center. While the potential benefits of C-RAN have been studied extensively from the theoretical perspective, there are only a few works that address the system implementation issues and characterize the computational requirements of the virtualized BBU. In this paper, a programmable C-RAN testbed is presented where the BBU is virtualized using the OpenAirInterface (OAI) software platform, and the eNodeB and User Equipment (UEs) are implemented using USRP boards. Extensive experiments have been performed in a FDD downlink LTE emulation system to characterize the performance and computing resource consumption of the BBU under various conditions. It is shown that the processing time and CPU utilization of the BBU increase with the channel resources and with the Modulation and Coding Scheme (MCS) index, and that the CPU utilization percentage can be well approximated as a linear increasing function of the maximum downlink data rate. These results provide real-world insights into the characteristics of the BBU in terms of computing resource and power consumption, which may serve as inputs for the design of efficient resource-provisioning and allocation strategies in C-RAN systems.


title: SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity

abstract: This paper introduces a new task on Multilingual ::: and Cross-lingual Semantic Word ::: Similarity which measures the semantic ::: similarity of word pairs within and across ::: five languages: English, Farsi, German, ::: Italian and Spanish. High quality datasets ::: were manually curated for the five languages ::: with high inter-annotator agreements ::: (consistently in the 0.9 ballpark). ::: These were used for semi-automatic construction ::: of ten cross-lingual datasets. 17 ::: teams participated in the task, submitting ::: 24 systems in subtask 1 and 14 systems in ::: subtask 2. Results show that systems that ::: combine statistical knowledge from text ::: corpora, in the form of word embeddings, ::: and external knowledge from lexical resources ::: are best performers in both subtasks. ::: More information can be found on ::: the task website: http://alt.qcri. ::: org/semeval2017/task2/ .


title: Redesigning Commercial Floating-Gate Memory for Analog Computing Applications

abstract: We have modified a commercial NOR flash memory array to enable high-precision tuning of individual floating-gate cells for analog computing applications. The modified array area per cell in a 180 nm process is about 1.5 μm2. While this area is approximately twice the original cell size, it is still at least an order of magnitude smaller than in state-of-the-art analog circuit implementations. The new memory cell arrays have been successfully tested, in particular confirming that each cell may be automatically tuned, with ∼1% precision, to any desired subthreshold readout current value within an almost three-orders-of-magnitude dynamic range, even using an unoptimized tuning algorithm. Preliminary results for a four-quadrant vector-by-matrix multiplier, implemented with the modified memory array, gate-coupled with additional peripheral floating-gate transistors, show highly linear transfer characteristics over a broad range of input currents.


title: Personal information retrieval: smartphones vs. computers, emails vs. files

abstract: Millions of people retrieve their emails and files using their smartphones, yet smartphone retrieval of such personal information has never been studied or compared to retrievals from PCs. In our within-subjects study, we compared the retrievals of our 57 participants in four conditions: files using PCs, emails using PCs, files using smartphones, and emails using smartphones. Our results indicate that when using smartphones, retrievals were significantly less successful and efficient than when using PCs, casting doubt on the implicit assumption that the use of these devices is equivalent. Our results also indicate that participants used the search facility for emails about seven times more than for files, which can encourage vendors to invest more efforts in improving email search engines and file navigation systems. Finally, we found that the tendency to search shows interpersonal differences but consistency across different situations for the same individual and therefore can be regarded as a personal trait. Future research can attempt to explain the search tendency trait in terms of cognitive abilities and personality traits, incorporating it to well-established theories. This may pave the way to a new trait-related theory in the field of information science.


title: Deep Reinforcement Learning Attention Selection for Person Re-Identification

abstract: Existing person re-identification (re-id) methods assume the provision of accurately cropped person bounding boxes with minimum background noise, mostly by manually cropping. This is significantly breached in practice when person bounding boxes must be detected automatically given a very large number of images and/or videos processed. Compared to carefully cropped manually, auto-detected bounding boxes are far less accurate with random amount of background clutter which can degrade notably person re-id matching accuracy. In this work, we develop a joint learning deep model that optimises person re-id attention selection within any auto-detected person bounding boxes by reinforcement learning of background clutter minimisation subject to re-id label pairwise constraints. Specifically, we formulate a novel unified re-id architecture called Identity DiscriminativE Attention reinforcement Learning (IDEAL) to accurately select re-id attention in auto-detected bounding boxes for optimising re-id performance. Our model can improve re-id accuracy comparable to that from exhaustive human manual cropping of bounding boxes with additional advantages from identity discriminative attention selection that specially benefits re-id tasks beyond human knowledge. Extensive comparative evaluations demonstrate the re-id advantages of the proposed IDEAL model over a wide range of state-of-the-art re-id methods on two auto-detected re-id benchmarks CUHK03 and Market-1501.


title: An 8–16 Gb/s, 0.65–1.05 pJ/b, Voltage-Mode Transmitter With Analog Impedance Modulation Equalization and Sub-3 ns Power-State Transitioning

abstract: Serial link transmitters which efficiently incorporate equalization, while also enabling fast power-state transitioning to leverage dynamic power scaling, are necessary to meet future systems' I/O requirements. This paper presents a scalable voltage-mode transmitter which offers low static power dissipation and adopts an impedance-modulated 2-tap equalizer with analog tap control, thereby obviating driver segmentation and reducing pre-driver complexity and dynamic power. Topologies that allow for rapid power-up/down, including a replica-biased voltage regulator to power the output stages of multiple transmit channels and per-channel quadrature clock generation with injection-locked oscillators (ILO), enable fast power-state transitioning. Energy efficiency is further improved with capacitively driven low-swing global clock distribution and supply scaling at lower data rates, while output eye quality is maintained at low voltages with automatic phase calibration of the local ILO-generated quarter-rate clocks. A prototype fabricated in a general purpose 65 nm CMOS process includes a 2 mm global clock distribution network and two transmitters that support an output swing range of 100-300 mV with up to 12 dB of equalization. The transmitters achieve 8-16 Gb/s operation at 0.65-1.05 pJ/b energy efficiency and sub-3 ns power-up/down times.


title: Hierarchical disruption in the Bayesian brain: Focal epilepsy and brain networks

abstract: In this opinion paper, we describe a combined view of functional and effective brain connectivity along with the free-energy principle for investigating persistent disruptions in brain networks of patients with focal epilepsy. These changes are likely reflected in effective connectivity along the cortical hierarchy and construct the basis of increased local functional connectivity in focal epilepsy. We propose a testable framework based on dynamic causal modelling and functional connectivity analysis with the capacity of explaining commonly observed connectivity changes during interictal periods. We then hypothesise their possible relation with disrupted free-energy minimisation in the Bayesian brain. This may offer a new approach for neuroimaging to specifically develop and address hypotheses regarding the network pathomechanisms underlying epileptic phenotypes.


title: Synthesis of the siderophore pyoverdine in Pseudomonas aeruginosa involves a periplasmic maturation

abstract: Pyoverdines, the main siderophores produced by fluorescent Pseudomonads, comprise a fluorescent dihydroxyquinoline chromophore attached to a strain-specific peptide. These molecules are thought to be synthesized as non-fluorescent precursor peptides that are then modified to give functional pyoverdines. Using the fluorescent properties of PVDI, the pyoverdine produced by Pseudomonas aeruginosa PAO1, we were able to show that PVDI was not present in the cytoplasm of the bacteria, but large amounts of a fluorescent PVDI precursor PVDIp were stored in the periplasm. Like PVDI, PVDIp is able to transport iron into P. aeruginosa cells. Mutation of genes encoding the periplasmic PvdN, PvdO and PvdP proteins prevented accumulation of PVDIp in the periplasm and secretion of PVDI into the growth medium, indicating that these three enzymes are involved in PVDI synthesis. Mutation of the gene encoding PvdQ resulted in the presence of fluorescent PVDI precursor in the periplasm and secretion of a functional fluorescent siderophore that had different isoelectric properties to PVDI, suggesting a role for PvdQ in the periplasmic maturation of PVDI. Mutation of the gene encoding the export ABC transporter PvdE prevented PVDI production and accumulation of PVDIp in the periplasm. These data are consistent with a model in which a PVDI precursor peptide is synthesized in the cytoplasm and exported to the periplasm by PvdE where siderophore maturation, including formation of the chromophore moiety, occurs in a process involving the PvdN, PvdO, PvdP and PvdQ proteins.


title: MagPIE: A dataset for indoor positioning with magnetic anomalies

abstract: In this paper, we present a publicly available dataset for the evaluation of indoor positioning algorithms that use magnetic anomalies. Our dataset contains IMU and magnetometer measurements along with ground truth position measurements that have centimeter-level accuracy. To produce this dataset, we collected over 13 hours of data (51 kilometers of total distance traveled) from three different buildings, with sensors both handheld and mounted on a wheeled robot, in environments with and without changes in the placement of objects that affect magnetometer measurements ("live loads”). We conclude the paper with a discussion of why these characteristics of our dataset are important when evaluating positioning algorithms.


title: Confidence estimation in Deep Neural networks via density modelling

abstract: State-of-the-art Deep Neural Networks can be easily fooled into providing incorrect high-confidence predictions for images with small amounts of adversarial noise. Does this expose a flaw with deep neural networks, or do we simply need a better way to estimate confidence? In this paper we consider the problem of accurately estimating predictive confidence. We formulate this problem as that of density modelling, and show how traditional methods such as softmax produce poor estimates. To address this issue, we propose a novel confidence measure based on density modelling approaches. We test these measures on images distorted by blur, JPEG compression, random noise and adversarial noise. Experiments show that our confidence measure consistently shows reduced confidence scores in the presence of such distortions - a property which softmax often lacks.


title: A particle filtering method for wireless sensor network localization with an aerial robot beacon

abstract: This paper presents a new method for the 3D localization of an outdoor wireless sensor network (WSN) by using a single flying beacon-node on-board an autonomous helicopter, which is aware of its position thanks to a GPS device. The technique is based on particle filtering and does not require any prior information about the position of the nodes to be estimated. Its structure and stochastic nature allows a distributed computation of the position of the nodes. The paper shows how the method is very suitable for outdoor applications with robotic data-mule systems. The paper includes a section with experiments.


title: Speed control of stepper motor for collimator jaws positioning based on FPGA implementation

abstract: Stepper motor is widely used for precise position and speed control. In this paper speed control of stepper motor is used for collimator jaw positioning based on Field Programmable Gate Array (FPGA) implementation. Collimator consists of Symmetric and Asymmetric movement and can be used in dynamic mode also. In dynamic mode there is variation in the speed of jaw motion during the beam is ON. To provide speed variation stepper motor attached to jaws is controlled using FPGA. The proposed control mechanism uses speed variation technique to rotate motor through desired angle, speed and direction.


title: A Vision-Based Hierarchical Framework for Autonomous Front-Vehicle Taillights Detection and Signal Recognition

abstract: Automatically recognizing rear light signals of front vehicles can significantly improve driving safety by automatic alarm and taking actions proactively to prevent rear-end collisions and accidents. Much previous research only focuses on detecting brake signals at night. In this paper, we present the design and implementation of a robust hierarchical framework for detecting taillights of vehicles and estimating alert signals (turning and braking) in the daytime. The three-layer structure of the vision-based framework can obviously reduce both false positives and false negatives of taillight detection. Comparing to other existing work addressing nighttime detection, the proposed method is capable of recognizing taillight signals under different illumination circumstances. By carrying out contrast experiments with existing state-of-the-art methods, the results show the high detection rate of the framework in different weather conditions during the daytime.


title: Structural optimization research on hemispherical resonator gyro based on finite element analysis

abstract: Hemispherical shell resonator is the key component of hemispherical resonator gyro (HRG). The finite element model of the resonator of HRG is established based on the dynamic equation with ANSYS. The natural frequency and correlative vibration mode of resonator is calculated by the proposed model. On the theoretical analysis, the influence law of structural parameters on the vibration characteristic is obtained. The structural optimization model is established to guide the structural optimization of parameters. Theoretical and simulative results show that the optimization model can be applied to conduct the optimal structure design of resonator. It provides the theoretical basis and parametric reference for the design of hemispherical shell resonator.


title: A Survey on Visual Place Recognition for Mobile Robots Localization

abstract: Visual place recognition is an active research field in the robotic navigation and localization, which means the ability to recognize a known place in the environment using vision as the main sensor modality. Despite significant progress in computer vision and machine learning techniques, challenges remain especially in dynamic environments such as illumination change, viewpoint change and so on. In this paper, a survey and comparative study on existing approaches of visual place recognition is presented, including place feature extraction methods, image similarity metrics and searching algorithms, as well as some benchmark datasets and evaluation metrics. Experimental results show that the methods combining feature extraction using convolutional neural networks and sequential image searching achieve higher precision in large scale dynamic environment.


title: EHF Rotman lens for electronic scanning antennas

abstract: In this paper, we present a two-dimensional azimuth beam steerable antenna design by applying microwave lens beam former. The antenna array aperture is realized with uniformly spaced microstrip patches. The beam former utilizes each beam port to produce corresponding phase shift across the array aperture so as to generate the azimuth scan. The demonstrated antenna operates at the M frequency band, capable of producing 13 independent beams. Feasibility of such design is demonstrated with MoM simulations in FEKO.


title: A runtime cloud efficiency software quality metric

abstract: This paper introduces the Cloud Efficiency (CE) metric, a novel runtime metric which assesses how effectively an application uses software-defined infrastructure. The CE metric is computed as the ratio of two functions: i) a benefit function which captures the current set of benefits derived from the application, and ii) a cost function which describes the current charges incurred by the application's resources. We motivate the need for the CE metric, describe in further detail how to compute it, and present experimental results demonstrating its calculation.


title: Pricing on the Internet

abstract: Conventional theories suggest that the Internet will drive down prices and lead to perfectly competitive prices. However, there is contradictory evidence indicating that online prices are not absolutely lower than offline stores. Regardless, the Internet gives rise to many opportunities for leveraging pricing strategies, in research and testing capabilities, customer segmentation, dynamic pricing, product differentiation, developing brand loyalty, including shipping and handling in the profitability analysis, offering multiple versions, and creating or participating in electronic marketplaces. The trading platform of eBay, Priceline’s reverse auction, and price comparison Web sites are examples of novel Internet pricing models that are helping create a new pricing paradigm.


title: Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

abstract: Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.


title: Forecasting hotel arrivals and occupancy using Monte Carlo simulation

abstract: Forecasting hotel arrivals and occupancy is an important component in hotel revenue management systems. In this article, we propose a new Monte Carlo simulation approach for the arrivals and occupancy forecasting problem. In this approach, we simulate the hotel reservations process forward in time, and these future Monte Carlo paths will yield forecast densities. A key step for the faithful emulation of the reservations process is the accurate estimation of its parameters. We propose an approach for the estimation of these parameters from the historical data. Then, the reservations process will be simulated forward with all its constituent processes such as reservation arrivals, cancellations, length of stay, no shows, group reservations, seasonality, trend and so on. We considered as a case study the problem of forecasting room demand for Plaza Hotel, Alexandria, Egypt. The proposed model gives superior results compared to existing approaches.


title: Multi-class least squares classification at binary-classification complexity

abstract: This paper deals with multi-class classification problems. Many methods extend binary classifiers to operate a multi-class task, with strategies such as the one-vs-one and the one-vs-all schemes. However, the computational cost of such techniques is highly dependent on the number of available classes. We present a method for multi-class classification, with a computational complexity essentially independent of the number of classes. To this end, we exploit recent developments in multifunctional optimization in machine learning. We show that in the proposed algorithm, labels only appear in terms of inner products, in the same way as input data emerge as inner products in kernel machines via the so-called the kernel trick. Experimental results on real data show that the proposed method reduces efficiently the computational time of the classification task without sacrificing its generalization ability.


title: How mental stress affects endothelial function

abstract: Mental stress is an important factor contributing to recognized mechanisms underlying cardiovascular events. Among these, stress-related endothelial dysfunction is an early risk factor that predicts future development of severe cardiovascular disorders. Acute mental stress by a variety of tests impairs endothelial function in humans, although the opposite results have been reported by some investigators. Chronic stress always deteriorates endothelial function in humans and experimental animals. Stress hormones, such as glucocorticoids and pro-inflammatory cytokines, and endothelin-1 liberated in response to mental stress participate in endothelial dysfunction possibly via downregulation of endothelial nitric oxide synthase (eNOS) expression, eNOS inactivation, decreased nitric oxide (NO) actions, and increased NO degradation, together with vasoconstriction counteracting against NO-induced vasodilatation. Catecholamines do not directly affect endothelial function but impair its function when blood pressure elevation by the amines is sustained. Endogenous opioids favorably affect endothelial function, which counteract deteriorating effects of other stress hormones and mediators. Inhibition of cortisol and endothelin-1 production, prevention of pro-inflammatory mediator accumulation, hypnotics, mirthful laughter, humor orientation, and lifestyle modification would contribute to the prevention and treatment for stress-related endothelial dysfunction and future serious cardiovascular disease.


title: Enhancement of Web Proxy Caching Using Random Forest Machine Learning Technique

abstract: The Random Forest Tree is an ensemble learning method for Web data classification. In this study, we attempt to improve the performance of the traditional Web proxy cache replacement policies such as LRU and GDSF by integrating machine learning technique for enhancing the performance of the Web proxy cache. Web proxy caches are used to improve performance of the web. Web proxy cache reduces both network traffic and response time. In the first part of this paper, a supervised learning method as Random Forest Tree classifier (RFT) to learn from proxy log data and predict the classes of objects to be revisited or not. In the second part, a Random Forest Tree classifier (RFT) is incorporated with traditional Web proxy caching policies to form novel caching approaches known as RFT-LRU and RFT-GDSF. These proposed RFT-LRU and RFT-GDSF significantly improve the performances of LRU and GDSF respectively.


title: Pressure ulcers: prevention, evaluation, and management.

abstract: A pressure ulcer is a localized injury to the skin or underlying tissue, usually over a bony prominence, as a result of unrelieved pressure. Predisposing factors are classified as intrinsic (e.g., limited mobility, poor nutrition, comorbidities, aging skin) or extrinsic (e.g., pressure, friction, shear, moisture). Prevention includes identifying at-risk persons and implementing specific prevention measures, such as following a patient repositioning schedule; keeping the head of the bed at the lowest safe elevation to prevent shear; using pressure-reducing surfaces; and assessing nutrition and providing supplementation, if needed. When an ulcer occurs, documentation of each ulcer (i.e., size, location, eschar and granulation tissue, exudate, odor, sinus tracts, undermining, and infection) and appropriate staging (I through IV) are essential to the wound assessment. Treatment involves management of local and distant infections, removal of necrotic tissue, maintenance of a moist environment for wound healing, and possibly surgery. Debridement is indicated when necrotic tissue is present. Urgent sharp debridement should be performed if advancing cellulitis or sepsis occurs. Mechanical, enzymatic, and autolytic debridement methods are nonurgent treatments. Wound cleansing, preferably with normal saline and appropriate dressings, is a mainstay of treatment for clean ulcers and after debridement. Bacterial load can be managed with cleansing. Topical antibiotics should be considered if there is no improvement in healing after 14 days. Systemic antibiotics are used in patients with advancing cellulitis, osteomyelitis, or systemic infection.


title: Convolutional Neural Networks Based Fire Detection in Surveillance Videos

abstract: The recent advances in embedded processing have enabled the vision based systems to detect fire during surveillance using convolutional neural networks (CNNs). However, such methods generally need more computational time and memory, restricting its implementation in surveillance networks. In this research paper, we propose a cost-effective fire detection CNN architecture for surveillance videos. The model is inspired from GoogleNet architecture, considering its reasonable computational complexity and suitability for the intended problem compared to other computationally expensive networks such as AlexNet. To balance the efficiency and accuracy, the model is fine-tuned considering the nature of the target problem and fire data. Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in CCTV surveillance systems compared to state-of-the-art methods.


title: Automatic morphological analysis of learner Hungarian

abstract: In this paper, we describe a morphological analyzer for learner Hungarian, built upon limited grammatical knowledge of Hungarian. The rule-based analyzer requires very few resources and is flexible enough to do both morphological analysis and error detection, in addition to some unknown word handling. As this is work-in-progress, we demonstrate its current capabilities, some areas where analysis needs to be improved, and an initial foray into how the system output can support the analysis of interlanguage grammars.


title: A reusable 3D visualization component for the semantic web

abstract: Ontology visualization and exploration is not a trivial task as many issues can affect the effectiveness of interactions. As ontologies are, in the general case, quite connected graphs where concepts are the nodes and semantic relationships the edges, the problems include space allocation, edge superposition, scene over-crowding, etc. In this paper we propose a solution for the visualization and the exploration of ontologies using a 3-dimensional space, where information is represented on a 3D view-port enriched by visual cues. Our visualization tool aims at tackling representation issues of ontology models (as space allocation or the completeness and read-ability of displayed information) by adopting different views, at different granularities, in order to grant a constant navigability of the rendered model. Each provided view represents semantic information according to a different, task-based visualization paradigm, at a suitable level of detail. Besides being primarily implemented as a Protege plug-in, the proposed solution (named OntoSphere3D) is designed to be a reusable visualization component within Semantic Web applications; in fact, every scene can be exploited as a standalone facility that provides access to ontological data through an intuitive and appealing 3D interface. A case-study, is presented, where re-usability is demonstrated by integrating the OntoSphere3D visualization inside an Eclipse-based tool for Web Service design (called Web Services Design Tool) developed by some of the authors in the context of another research project.


title: Daemo: A Self-Governed Crowdsourcing Marketplace

abstract: Crowdsourcing marketplaces provide opportunities for autonomous and collaborative professional work as well as social engagement. However, in these marketplaces, workers feel disrespected due to unreasonable rejections and low payments, whereas requesters do not trust the results they receive. The lack of trust and uneven distribution of power among workers and requesters have raised serious concerns about sustainability of these marketplaces. To address the challenges of trust and power, this paper introduces Daemo, a self-governed crowdsourcing marketplace. We propose a prototype task to improve the work quality and open-governance model to achieve equitable representation. We envisage Daemo will enable workers to build sustainable careers and provide requesters with timely, quality labor for their businesses.


title: A framework for policy based secure intra vehicle communication

abstract: Over the past two decades, significant developments were introduced within the vehicular domain, evolving the modern vehicle into a network of dozens of embedded systems each hosting one or more applications. Communications within this distributed environment while adhering to safety-critical and secure systems guidelines implies the formulation of a comprehensive and consistent communications policy. Creating this policy is a complex, error-prone and labor-intensive task, requiring detailed knowledge of possible communication paths between all possible components of the system. For this reason, it is often skipped, trusting that each task will behave as intended and interact only with its peers. Traditional testing provides sufficient confidence to allow certification. Nevertheless, the existing process ignores malicious interference, whereby an adversary compromises a low-criticality process or subsystem and uses that to attack other subsystems, effectively taking over the vehicle. In this paper, we propose a framework to build a secure communications policy gradually by integrating it through the design and life cycle of vehicle's software components. We also propose a security module which acts as a connection policy checker vetting the incoming and outgoing communications and enforcing the distributed security policy.


title: An Empirical Study of the I2P Anonymity Network and its Censorship Resistance

abstract: Tor and I2P are well-known anonymity networks used by many individuals to protect their online privacy and anonymity. Tor's centralized directory services facilitate the understanding of the Tor network, as well as the measurement and visualization of its structure through the Tor Metrics project. In contrast, I2P does not rely on centralized directory servers, and thus obtaining a complete view of the network is challenging. In this work, we conduct an empirical study of the I2P network, in which we measure properties including population, churn rate, router type, and the geographic distribution of I2P peers. We find that there are currently around 32K active I2P peers in the network on a daily basis. Of these peers, 14K are located behind NAT or firewalls. Using the collected network data, we examine the blocking resistance of I2P against a censor that wants to prevent access to I2P using address-based blocking techniques. Despite the decentralized characteristics of I2P, we discover that a censor can block more than 95% of peer IP addresses known by a stable I2P client by operating only 10 routers in the network. This amounts to severe network impairment: a blocking rate of more than 70% is enough to cause significant latency in web browsing activities, while blocking more than 90% of peer IP addresses can make the network unusable. Finally, we discuss the security consequences of the network being blocked, and directions for potential approaches to make I2P more resistant to blocking.


title: Deep Semantic Role Labeling with Self-Attention

abstract: Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied. Recent years, end-to-end SRL with recurrent neural networks (RNN) has gained increasing attention. However, it remains a major challenge for RNNs to handle structural information and long range dependencies. In this paper, we present a simple and effective architecture for SRL which aims to address these problems. Our model is based on self-attention which can directly capture the relationships between two tokens regardless of their distance. Our single model achieves F$_1=83.4$ on the CoNLL-2005 shared task dataset and F$_1=82.7$ on the CoNLL-2012 shared task dataset, which outperforms the previous state-of-the-art results by $1.8$ and $1.0$ F$_1$ score respectively. Besides, our model is computationally efficient, and the parsing speed is 50K tokens per second on a single Titan X GPU.


Movies recommendation system using collaborative filtering and k-means


title: Intelligent Portrait Composition Assistance: Integrating Deep-learned Models and Photography Idea Retrieval

abstract: Retrieving photography ideas corresponding to a given location facilitates the usage of smart cameras, where there is a high interest among amateurs and enthusiasts to take astonishing photos at anytime and in any location. Existing research captures some aesthetic techniques and retrieves useful feedbacks based on one technique. However, they are restricted to a particular technique and the retrieved results have room to improve as they are confined to the quality of the query. There is a lack of a holistic framework to capture important aspects of a given scene and help a novice photographer by informative feedback to take a better shot in his/her photography adventure. This work proposes an intelligent framework of portrait composition using our deep-learned models and image retrieval methods. A highly-rated web-crawled portrait dataset is exploited for retrieval purposes. Our framework detects and extracts ingredients of a given scene representing as a correlated semantic model. It then matches extracted semantics with the dataset of aesthetically composed photos to investigate a ranked list of photography ideas, and gradually optimizes the human pose and other artistic aspects of the composed scene supposed to be captured. The conducted user study demonstrates that our approach is more helpful than other feedback retrieval systems.


A Unifying Framework for Animated and Interactive Unit Visualizations


title: On compressing social networks

abstract: Motivated by structural properties of the Web graph that support efficient data structures for in memory adjacency queries, we study the extent to which a large network can be compressed. Boldi and Vigna (WWW 2004), showed that Web graphs can be compressed down to three bits of storage per edge; we study the compressibility of social networks where again adjacency queries are a fundamental primitive. To this end, we propose simple combinatorial formulations that encapsulate efficient compressibility of graphs. We show that some of the problems are NP-hard yet admit effective heuristics, some of which can exploit properties of social networks such as link reciprocity. Our extensive experiments show that social networks and the Web graph exhibit vastly different compressibility characteristics.


title: Neural networks for the prediction and forecasting of water resources variables : a review of modelling issues and applications

abstract: Abstract Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables. In this paper, the steps that should be followed in the development of such models are outlined. These include the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation. The options available to modellers at each of these steps are discussed and the issues that should be considered are highlighted. A review of 43 papers dealing with the use of neural network models for the prediction and forecasting of water resources variables is undertaken in terms of the modelling process adopted. In all but two of the papers reviewed, feedforward networks are used. The vast majority of these networks are trained using the backpropagation algorithm. Issues in relation to the optimal division of the available data, data pre-processing and the choice of appropriate model inputs are seldom considered. In addition, the process of choosing appropriate stopping criteria and optimising network geometry and internal network parameters is generally described poorly or carried out inadequately. All of the above factors can result in non-optimal model performance and an inability to draw meaningful comparisons between different models. Future research efforts should be directed towards the development of guidelines which assist with the development of ANN models and the choice of when ANNs should be used in preference to alternative approaches, the assessment of methods for extracting the knowledge that is contained in the connection weights of trained ANNs and the incorporation of uncertainty into ANN models.


title: Automatic speech emotion recognition using recurrent neural networks with local attention

abstract: Automatic emotion recognition from speech is a challenging task which relies heavily on the effectiveness of the speech features used for classification. In this work, we study the use of deep learning to automatically discover emotionally relevant features from speech. It is shown that using a deep recurrent neural network, we can learn both the short-time frame-level acoustic features that are emotionally relevant, as well as an appropriate temporal aggregation of those features into a compact utterance-level representation. Moreover, we propose a novel strategy for feature pooling over time which uses local attention in order to focus on specific regions of a speech signal that are more emotionally salient. The proposed solution is evaluated on the IEMOCAP corpus, and is shown to provide more accurate predictions compared to existing emotion recognition algorithms.


title: Smart wheelchair based on eye tracking

abstract: This project is a smart wheelchair based on eye tracking which is designed for people with locomotor disabilities. The add-on controlled module can be used with any electrical wheelchair. The smart wheel chair consists of four modules including imaging processing module, wheelchair-controlled module, SMS manager module and appliance-controlled module. The image processing module comprises of a webcam installed on the eyeglass and C++ customized image processing software. The captured image which is transmitted to raspberry Pi microcontroller will be processed using OpenCV to derive the 2D direction of eye ball. The coordinate of eyeball movement is then wirelessly transmitted to wheelchair-controlled module to control the movement of wheel chair. The wheelchair-controlled module is two dimensional rotating stages that installed to the joystick of the electrical wheelchair to replace the manual control of the wheelchair. The motion of eyeball is also used as the cursor control on the raspberry Pi screen to control the operation of some equipped appliance and send message to smart phone.


title: Conceptual foundations for understanding smart tourism ecosystems

abstract: Using digital ecosystems and smart business networks as conceptual building blocks, this paper defines, describes and illustrates the idea of a smart tourism ecosystem (STE). It further draws on conceptualizations of smart technologies, smart cities and smart tourism to envision new ways in which value is created, exchanged and consumed in the STE. Technologies essential to the functioning of an STE are described and it is argued that data emerging from these technologies are the driver for new business models, interaction paradigms and even new species. Critical questions regarding the need for regulatory intervention and innovative research are raised.


title: A Survey on Reinforcement Learning Models and Algorithms for Traffic Signal Control

abstract: Traffic congestion has become a vexing and complex issue in many urban areas. Of particular interest are the intersections where traffic bottlenecks are known to occur despite being traditionally signalized. Reinforcement learning (RL), which is an artificial intelligence approach, has been adopted in traffic signal control for monitoring and ameliorating traffic congestion. RL enables autonomous decision makers (e.g., traffic signal controllers) to observe, learn, and select the optimal action (e.g., determining the appropriate traffic phase and its timing) to manage traffic such that system performance is improved. This article reviews various RL models and algorithms applied to traffic signal control in the aspects of the representations of the RL model (i.e., state, action, and reward), performance measures, and complexity to establish a foundation for further investigation in this research field. Open issues are presented toward the end of this article to discover new research areas with the objective to spark new interest in this research field.


title: Transfer Learning Based Visual Tracking with Gaussian Processes Regression

abstract: Modeling the target appearance is critical in many modern visual tracking algorithms. Many tracking-by-detection algorithms formulate the probability of target appearance as exponentially related to the confidence of a classifier output. By contrast, in this paper we directly analyze this probability using Gaussian Processes Regression (GPR), and introduce a latent variable to assist the tracking decision. Our observation model for regression is learnt in a semi-supervised fashion by using both labeled samples from previous frames and the unlabeled samples that are tracking candidates extracted from the current frame. We further divide the labeled samples into two categories: auxiliary samples collected from the very early frames and target samples from most recent frames. The auxiliary samples are dynamically re-weighted by the regression, and the final tracking result is determined by fusing decisions from two individual trackers, one derived from the auxiliary samples and the other from the target samples. All these ingredients together enable our tracker, denoted as TGPR, to alleviate the drifting issue from various aspects. The effectiveness of TGPR is clearly demonstrated by its excellent performances on three recently proposed public benchmarks, involving 161 sequences in total, in comparison with state-of-the-arts.


title: Flipped Classroom Strategies for CS Education

abstract: The basic principles of a flipped classroom teaching method are to deliver content outside of the class and to move active learning into the classroom. There are many strategies for delivering the content online, such as having instructors prepare online lectures, wrapping the course around a MOOC, and curating online videos from various sources. There are also many strategies for including active learning in the classroom that go beyond providing programming labs, and can include various forms of peer instruction. In this paper we describe our experiences flipping four different computer science classes across multiple semesters over two years. This breadth of experience with classroom flipping has enabled us to compare strategies and approaches and develop an understanding of which approaches appear to work under which circumstances. We discuss how we structured out-of-class preparatory work, how we created or sourced online videos, how we used active learning activities in-class to scaffold skills development and identify students' misconceptions, and how we structured teams for in class activities. This paper contributes a set of flexible strategies to consider for provision of curricular content out-of-class, structuring students' preparatory work, applying active learning of skills and concepts, and leveraging social interaction and peer instruction for CS education. We present the impact of our approaches based upon leading indicators of course evaluations and student surveys. We discuss lessons learned and students' responses to our strategies.


title: Thermoelectric Power Generation Using Waste-Heat Energy as an Alternative Green Technology

abstract: In recent years, an increasing concern of environmental issues of emissions, in particular global warming and the limitations of energy resources has resulted in extensive research into novel technologies of generating electrical power. Thermoelectric power generators have emerged as a promising alternative green technology due to their distinct advantages. Thermoelectric power generation offer a potential application in the direct conversion of waste-heat energy into electrical power where it is unnecessary to consider the cost of the thermal energy input. The application of this alternative green technology in converting waste-heat energy directly into electrical power can also improve the overall efficiencies of energy conversion systems. In this paper, a background on the basic concepts of thermoelectric power generation is presented and recent patents of thermoelectric power generation with their important and relevant applications to waste-heat energy are reviewed and discussed.


title: FPGA implementation of image steganography using Haar DWT and modified LSB techniques

abstract: Security places an important role in communication applications for secure data transfers. Image Steganography is one of the most reliable technique in encryption and decryption of an image (hidden) inside other image (cover) such way that only cover image is visible. In this paper frequency domain Image Steganography using DWT and Modified LSB technique is proposed. The proposed approach uses DWT to convert spatial domain information to frequency domain information. The LL band is used for further Image Steganographic process. The image is decoded using inverse LSB. Since the LL band is used for encoding and decoding purpose, memory requirement of the design is less for hardware implementation. Also this will increase the operating frequency of the architecture. The proposed technique obtains high PSNR for both stegano and recovered hidden image.


title: Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT

abstract: Neural machine translation (NMT), a new approach to machine translation, has been proved to outperform conventional statistical machine translation (SMT) across a variety of language pairs. Translation is an open-vocabulary problem, but most existing NMT systems operate with a fixed vocabulary, which causes the incapability of translating rare words. This problem can be alleviated by using different translation granularities, such as character, subword and hybrid word-character. Translation involving Chinese is one of the most difficult tasks in machine translation, however, to the best of our knowledge, there has not been any other work exploring which translation granularity is most suitable for Chinese in NMT. In this paper, we conduct an extensive comparison using Chinese-English NMT as a case study. Furthermore, we discuss the advantages and disadvantages of various translation granularities in detail. Our experiments show that subword model performs best for Chinese-to-English translation with the vocabulary which is not so big while hybrid word-character model is most suitable for English-to-Chinese translation. Moreover, experiments of different granularities show that Hybrid_BPE method can achieve best result on Chinese-to-English translation task.


title: Combining Health Data Uses to Ignite Health System Learning

abstract: Objectives: In this paper we aim to characterise the critical mass of linked data, methods and expertise required for health systems to adapt to the needs of the populations they serve – more recently known as learning health systems. The objectives are to: 1) identify opportunities to combine separate uses of common data sources in order to reduce duplication of data processing and improve information quality; 2) identify challenges in scaling-up the reuse of health data sufficiently to support health system learning. Methods: The challenges and opportunities were identified through a series of e-health stakeholder consultations and workshops in Northern England from 2011 to 2014. From 2013 the concepts presented here have been refined through feedback to collaborators, including patient/citizen representatives, in a regional health informatics research network (www.herc.ac.uk). Results: Health systems typically have separate information pipelines for: 1) commissioning services; 2) auditing service performance; 3) managing finances; 4) monitoring public health; and 5) research. These pipelines share common data sources but usually duplicate data extraction, aggregation, cleaning/preparation and analytics. Suboptimal analyses may be performed due to a lack of expertise, which may exist elsewhere in the health system but is fully committed to a different pipeline. Contextual knowledge that is essential for proper data analysis and interpretation may be needed in one pipeline but accessible only in another. The lack of capable health and care intelligence systems for populations can be attributed to a legacy of three flawed assumptions: 1) universality: the generalizability of evidence across populations; 2) time-invariance: the stability of evidence over time; and 3) reducibility: the reduction of evidence into specialised sub-systems that may be recombined. Conclusions: We conceptualize a population health and care intelligence system capable of supporting health system learning and we put forward a set of maturity tests of progress toward such a system. A factor common to each test is data-action latency; a mature system spawns timely actions proportionate to the information that can be derived from the data, and in doing so creates meaningful measurement about system learning. We illustrate, using future scenarios, some major opportunities to improve health systems by exchanging conventional intelligence pipelines for networked critical masses of data, methods and expertise that minimise data-action latency and ignite system-learning.


title: An experiment to assess different defect detection methods for software requirements inspections

abstract: Software requirements specifications (SRS) are usually validated by inspections, in which several reviewers read all or part of the specification and search for defects. We hypothesize that different methods for conducting these searches may have significantly different rates of success. Using a controlled experiment, we show that a scenario-based detection method, in which each reviewer executes a specific procedure to discover a particular class of defects has a higher defect detection rate than either ad hoc or checklist methods. We describe the design, execution and analysis of the experiment so others may reproduce it and test our results for different kinds of software developments and different populations of software engineers. >


title: Auditory processing of interaural timing information: new insights.

abstract: Differences in the time-of-arrival of sounds at the two ears, or interaural temporal disparities (ITDs), constitute one of the major binaural cues that underlie our ability to localize sounds in space. In addition, ITDs contribute to our ability to detect and to discriminate sounds, such as speech, in noisy environments. For low-frequency signals, ITDs are conveyed primarily by "cycle-by-cycle" disparities present in the fine-structure of the waveform. For high-frequency signals, ITDs are conveyed by disparities within the time-varying amplitude, or envelope, of the waveform. The results of laboratory studies conducted over the past few decades indicate that ITDs within the envelopes of high-frequency are less potent than those within the fine-structure of low-frequency stimuli. This is true for both measures of sensitivity to changes in ITD and for measures of the extent of the perceived lateral displacement of sounds containing ITDs. Colburn and Esquissaud (1976) hypothesized that it is differences in the specific aspects of the waveform that are coded neurally within each monaural (single ear) channel that account for the greater potency of ITDs at low frequencies rather than any differences in the more central binaural mechanisms that serve these different frequency regions. In this review, the results of new studies are reported that employed special high-frequency "transposed" stimuli that were designed to provide the high-frequency channels of the binaural processor with envelope-based information that mimics waveform-based information normally available only in low-frequency channels. The results demonstrate that these high-frequency transposed stimuli (1) yield sensitivity to ITDs that approaches, or is equivalent to, that obtained with "conventional" low-frequency stimuli and (2) yield large extents of laterality that are similar to those measured with conventional low-frequency stimuli. These findings suggest that by providing the high-frequency channels of the binaural processor with information that mimics that normally available only at low frequencies, the potency of ITDs in the two frequency regions can be made to be similar, if not identical. These outcomes provide strong support for Colburn and Esquissaud's (1976) hypothesis. The use of high-frequency transposed stimuli, in both behavioral and physiological investigations offers the promise of new and important insights into the nature of binaural processing.


The Lalita Vistara


title: Online Multi-Task Learning Using Active Sampling

abstract: One of the long-standing challenges in Artificial Intelligence for goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential tasks has been in the form of distillation based learning wherein a single student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks. While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large task-specific (expert) networks which require extensive training. We propose a simple yet efficient multi-task learning framework which solves multiple goal-directed tasks in an online or active learning setup without the need for expert supervision.


title: Fully homomorphic encryption with relatively small key and ciphertext sizes

abstract: We present a fully homomorphic encryption scheme which has both relatively small key and ciphertext size. Our construction follows that of Gentry by producing a fully homomorphic scheme from a “somewhat” homomorphic scheme. For the somewhat homomorphic scheme the public and private keys consist of two large integers (one of which is shared by both the public and private key) and the ciphertext consists of one large integer. As such, our scheme has smaller message expansion and key size than Gentry’s original scheme. In addition, our proposal allows efficient fully homomorphic encryption over any field of characteristic two.


title: A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network

abstract: Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. However, contemporary detection methods always take hundreds of milliseconds to pursue feeble improvements on the detection precession. In this paper, we tackle this problem by proposing a fast and compact salient score regression network which employs deep convolutional neural networks (CNN) to estimate the saliency of objects in images. It operates (including training and testing) in an end-to-end manner (image-to-image prediction) and also directly produces whole saliency maps from original images without any pre-processings and post-processings. Comparing with contemporary CNN-based saliency detection methods, the proposed method extremely simplifies the detection procedure and further promotes the representation ability of CNN for the saliency detection. Our method is evaluated on six public datasets, and experimental results show that the precision can be comparable to the published state-of-the-art methods while the speed gets a significant improvement (35 FPS, processing in real time).


title: CUDA Implementation of Deformable Pattern Recognition and its Application to MNIST Handwritten Digit Database

abstract: In this study we propose a deformable pattern recognition method with CUDA implementation. In order to achieve the proper correspondence between foreground pixels of input and prototype images, a pair of distance maps are generated from input and prototype images, whose pixel values are given based on the distance to the nearest foreground pixel. Then a regularization technique computes the horizontal and vertical displacements based on these distance maps. The dissimilarity is measured based on the eight-directional derivative of input and prototype images in order to leverage characteristic information on the curvature of line segments that might be lost after the deformation. The prototype-parallel displacement computation on CUDA and the gradual prototype elimination technique are employed for reducing the computational time without sacrificing the accuracy. A simulation shows that the proposed method with the k-nearest neighbor classifier gives the error rate of 0.57% for the MNIST handwritten digit database.


title: MIMO antenna system for LTE (4G)

abstract: Multiple-input-multiple-output (MIMO) technology will be used by fourth generation mobile networks (also called Long Term Evolution-LTE) to achieve very high data rates in both the uplink and downlink channels. MIMO is based on the use of multiple antenna systems within the mobile terminal as well as the base station. Such antenna systems are required to fit within the hand-held (mobile) terminal which occupies a small size (typically not more than 60 100 mm2). Antenna integration and miniaturization are two major challenges. We have worked on an electrically small antenna (ESA), based on the meander antenna structure that operates in the 850 MHz band of LTE and 3G cellular standards. The antenna has a measured center frequency of 850 MHz, bandwidth of 54.6 MHz and total size of 34*22 mm2. In addition, We present the design of a dual element MIMO antenna system based on the ESA antenna designed. The dual element MIMO antenna system covers the bands from 760 - 900 MHz and occupies an area of 30 30 mm2. Based on simulation results, antennas are fabricated. experiment results and Simulation results are found comparable and are presented.


title: Dytan: a generic dynamic taint analysis framework

abstract: Dynamic taint analysis is gaining momentum. Techniques based on dynamic tainting have been successfully used in the context of application security, and now their use is also being explored in different areas, such as program understanding, software testing, and debugging. Unfortunately, most existing approaches for dynamic tainting are defined in an ad-hoc manner, which makes it difficult to extend them, experiment with them, and adapt them to new contexts. Moreover, most existing approaches are focused on data-flow based tainting only and do not consider tainting due to control flow, which limits their applicability outside the security domain. To address these limitations and foster experimentation with dynamic tainting techniques, we defined and developed a general framework for dynamic tainting that (1) is highly flexible and customizable, (2) allows for performing both data-flow and control-flow based tainting conservatively, and (3) does not rely on any customized run-time system. We also present DYTAN, an implementation of our framework that works on x86 executables, and a set of preliminary studies that show how DYTAN can be used to implement different tainting-based approaches with limited effort. In the studies, we also show that DYTAN can be used on real software, by using FIREFOX as one of our subjects, and illustrate how the specific characteristics of the tainting approach used can affect efficiency and accuracy of the taint analysis, which further justifies the use of our framework to experiment with different variants of an approach.


◆ 4 ◆ EMOTION IN HUMAN – COMPUTER INTERACTION


title: Context adaptation in interactive recommender systems

abstract: Contextual factors can greatly influence the utility of recommendations for users. In many recommendation and personalization applications, particularly in domains where user context changes dynamically, it is difficult to represent and model contextual factors directly, but it is often possible to observe their impact on user preferences during the course of users' interactions with the system. In this paper, we introduce an interactive recommender system that can detect and adapt to changes in context based on the user's ongoing behavior. The system, then, dynamically tailors its recommendations to match the user's most recent preferences. We formulate this problem as a multi-armed bandit problem and use Thompson sampling heuristic to learn a model for the user. Following the Thompson sampling approach, the user model is updated after each interaction as the system observes the corresponding rewards for the recommendations provided during that interaction. To generate contextual recommendations, the user's preference model is monitored for changes at each step of interaction with the user and is updated incrementally. We will introduce a mechanism for detecting significant changes in the user's preferences and will describe how it can be used to improve the performance of the recommender system.


title: Learning from other subjects helps reducing Brain-Computer Interface calibration time

abstract: A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. In this paper, we propose a new method to reduce this calibration time by using data from other subjects. More precisely, we propose an algorithm to regularize the Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) algorithms based on the data from a subset of automatically selected subjects. An evaluation of our approach showed that our method significantly outperformed the standard BCI design especially when the amount of data from the target user is small. Thus, our approach helps in reducing the amount of data needed to achieve a given performance level.


title: MUCM: Multilevel User Cluster Mining Based on Behavior Profiles for Network Monitoring

abstract: Mastering user's behavior character is important for efficient network management and security monitoring. In this paper, we develop a novel framework named as multilevel user cluster mining (MUCM) to measure user's behavior similarity under different network prefix levels. Focusing on aggregated traffic behavior under different network prefixes cannot only reduce the number of traffic flows but also reveal detailed patterns for a group of users sharing similar behaviors. First, we employ the bidirectional flow and bipartite graphs to model network traffic characteristics in large-scale networks. Four traffic features are then extracted to characterize the user's behavior profiles. Second, an efficient method with adjustable weight factors is employed to calculate the user's behavior similarity, and entropy gain is applied to select the weight factor adaptively. Using the behavior similarity metrics, a simple clustering algorithm based on $k$ -means is employed to perform user clustering based on behavior profiles. Finally, we examine the applications of behavior clustering in profiling network traffic patterns and detecting anomalous behaviors. The efficiency of our methods is verified with extensive experiments using actual traffic traces collected from the northwest region center of China Education and Research Network (CERNET), and the cluster results can be used for flow control and traffic security monitoring.


title: Twitter versus Facebook: Exploring the role of narcissism in the motives and usage of different social media platforms

abstract: Tweeting is the preferred method of active SNS usage for college age narcissists.No relationship between active Facebook usage and narcissism in college sample.Platform differences explain the differences in active usage on Facebook versus Twitter.Narcissistic motives mediate the relationship between narcissism and active usage. The amount of research on social networking sites (SNS) and narcissism is accumulating quickly requiring greater levels of variable specification and more fine-tuned hypothesis testing to clearly determine the relationships among key variables. The current investigation examines two of the most popular SNS, Facebook and Twitter, formulating hypotheses around the specific features of each site within college and adult samples. Unlike previous research that has focused almost exclusively on SNS usage, we focused on active usage (i.e., SNS content generation) as opposed to passive usage (i.e., SNS consumption) and included reasons for usage as a potential black box in the narcissism to SNS usage relationship. Results suggest that the features of Twitter make tweeting the preferred means of active usage among narcissists in the college sample, but not the adult sample, who prefer Facebook. In fact, we found no significant direct or indirect relationship with active usage on Facebook for the college sample, calling into question popular press articles linking Millennial narcissism with Facebook use. Additionally platform differences (i.e., microblogging versus profile-based) may explain the importance of active usage on Twitter relative to Facebook. That is, with Twitter, narcissistic motives for usage all manifest through tweeting while Facebook provides other mechanisms to achieve narcissistic motives.


title: Learning-based Query Performance Modeling and Prediction

abstract: Accurate query performance prediction (QPP) is central to effective resource management, query optimization and query scheduling. Analytical cost models, used in current generation of query optimizers, have been successful in comparing the costs of alternative query plans, but they are poor predictors of execution latency. As a more promising approach to QPP, this paper studies the practicality and utility of sophisticated learning-based models, which have recently been applied to a variety of predictive tasks with great success, in both static (i.e., fixed) and dynamic query workloads. We propose and evaluate predictive modeling techniques that learn query execution behavior at different granularities, ranging from coarse-grained plan-level models to fine-grained operator-level models. We demonstrate that these two extremes offer a tradeoff between high accuracy for static workload queries and generality to unforeseen queries in dynamic workloads, respectively, and introduce a hybrid approach that combines their respective strengths by selectively composing them in the process of QPP. We discuss how we can use a training workload to (i) pre-build and materialize such models offline, so that they are readily available for future predictions, and (ii) build new models online as new predictions are needed. All prediction models are built using only static features (available prior to query execution) and the performance values obtained from the offline execution of the training workload. We fully implemented all these techniques and extensions on top of Postgre SQL and evaluated them experimentally by quantifying their effectiveness over analytical workloads, represented by well-established TPC-H data and queries. The results provide quantitative evidence that learning-based modeling for QPP is both feasible and effective for both static and dynamic workload scenarios.


title: SPRINT: A Scalable Parallel Classifier for Data Mining

abstract: Classification is an important data mining problem. Although classification is a wellstudied problem, most of the current classification algorithms require that all or a portion of the the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. We present a new decision-tree-based classification algorithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model. This parallelization, also presented here, exhibits excellent scalability as well. The combination of these characteristics makes the proposed algorithm an ideal tool for data mining.


title: Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition

abstract: Good old online backpropagation for plain multilayer perceptrons yields a very low 0.35% error rate on the MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images to avoid overfitting, and graphics cards to greatly speed up learning.


title: Analysis of non-orthogonal multiple access for 5G

abstract: The major challenge faced by the fifth generation (5G) mobile network is higher spectral efficiency and massive connectivity, i.e., the target spectrum efficiency is 3 times over 4G, and the target connection density is one million devices per square kilometer. These requirements are difficult to be satisfied with orthogonal multiple access (OMA) schemes. Non-orthogonal multiple access (NOMA) has thus been proposed as a promising candidate to address some of the challenges for 5G. In this paper, a comprehensive survey of different candidate NOMA schemes for 5G is presented, where the usage scenarios of 5G and the application requirements for NOMA are firstly discussed. A general framework of NOMA scheme is established and the features of typical NOMA schemes are analyzed and compared. We focus on the recent progress and challenge of NOMA in standardization of international telecommunication union (ITU), and 3rd generation partnership project (3GPP). In addition, prototype development and future research directions are also provided respectively.


title: A directional anemometer based on MEMS differential pressure sensors

abstract: This paper presents a solid-state 2-D anemometer for measuring the wind speed and direction simultaneously. The device consists of some MEMS differential pressure sensors and a cylinder with channels connecting the internal sensors with the lateral surface. The pressure difference across a diameter of the cylinder depends on the pressure distribution developed by the wind on the cylinder's surface. By detecting pressure differences across 4 diameters that have intervals of 45 degrees, the wind direction and speed can be determined. Prototypes were designed to estimate performance of this approach. The test results show that the maximum relative wind speed error is 5% and the angle difference is no more than 5 degrees in a wind velocity range of 5-40m/s.


title: Trps1 is necessary for normal temporomandibular joint development

abstract: Mutation of the human TRPS1 gene leads to trichorhinophalangeal syndrome (TRPS), which is characterized by an abnormal development of various organs including the craniofacial skeleton. Trps1 has recently been shown to be expressed in the jaw joints of zebrafish; however, whether Trps1 is expressed in the mammalian temporomandibular joint (TMJ), or whether it is necessary for TMJ development is unknown. We have analyzed (1) the expression pattern of Trps1 during TMJ development in mice and (2) TMJ development in Trps1 knockout animals. Trps1 is expressed in the maxillo-mandibular junction at embryonic day (E) 11.5. At E15.5, expression is restricted to the developing condylar cartilage and to the surrounding joint disc progenitor cells. In Trps1 knockout mice, the glenoid fossa of the temporal bone forms relatively normally but the condylar process is extremely small and the joint disc and cavities do not develop. The initiation of condyle formation is slightly delayed in the mutants at E14.5; however, at E18.5, the flattened chondrocyte layer is narrowed and most of the condylar chondrocytes exhibit precocious chondrocyte maturation. Expression of Runx2 and its target genes is expanded toward the condylar apex in the mutants. These observations underscore the indispensable role played by Trps1 in normal TMJ development in supporting the differentiation of disc and synoviocyte progenitor cells and in coordinating condylar chondrocyte differentiation.


Supervised Elman Neural Network Architecture for Solving Double Dummy Bridge Problem in Contract Bridge


title: Intelligent Agent for Computer Games

abstract: In modern computer games — like action, adventure, role-playing, strategy, simulation and sports games — artificial intelligence (AI) techniques play an important role. However, the requirements of such games are very different from those of the games normally studied in AI. This article discusses which approaches and fields of research are relevant to achieve a sophisticated goal-directed behavior for modern computer games’ nonplayer characters. It also presents a classification of approaches for autonomous agents and gives an overview of a solution developed in the EXCALIBUR project.


title: MalGene: Automatic Extraction of Malware Analysis Evasion Signature

abstract: Automated dynamic malware analysis is a common approach for detecting malicious software. However, many malware samples identify the presence of the analysis environment and evade detection by not performing any malicious activity. Recently, an approach to the automated detection of such evasive malware was proposed. In this approach, a malware sample is analyzed in multiple analysis environments, including a bare-metal environment, and its various behaviors are compared. Malware whose behavior deviates substantially is identified as evasive malware. However, a malware analyst still needs to re-analyze the identified evasive sample to understand the technique used for evasion. Different tools are available to help malware analysts in this process. However, these tools in practice require considerable manual input along with auxiliary information. This manual process is resource-intensive and not scalable. In this paper, we present MalGene, an automated technique for extracting analysis evasion signatures. MalGene leverages algorithms borrowed from bioinformatics to automatically locate evasive behavior in system call sequences. Data flow analysis and data mining techniques are used to identify call events and data comparison events used to perform the evasion. These events are used to construct a succinct evasion signature, which can be used by an analyst to quickly understand evasions. Finally, evasive malware samples are clustered based on their underlying evasive techniques. We evaluated our techniques on 2810 evasive samples. We were able to automatically extract their analysis evasion signatures and group them into 78 similar evasion techniques.


title: Context-aware recommender systems

abstract: The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.