title: Dynamic hot data stream prefetching for general-purpose programs

abstract: Prefetching data ahead of use has the potential to tolerate the grow ing processor-memory performance gap by overlapping long latency memory accesses with useful computation. While sophisti cated prefetching techniques have been automated for limited domains, such as scientific codes that access dense arrays in loop nests, a similar level of success has eluded general-purpose pro grams, especially pointer-chasing codes written in languages such as C and C++. We address this problem by describing, implementing and evaluating a dynamic prefetching scheme. Our technique runs on stock hardware, is completely automatic, and works for general-purpose programs, including pointer-chasing codes written in weakly-typed languages, such as C and C++. It operates in three phases. First, the profiling phase gathers a temporal data reference profile from a running program with low-overhead. Next, the profiling is turned off and a fast analysis algorithm extracts hot data streams, which are data reference sequences that frequently repeat in the same order, from the temporal profile. Then, the system dynamically injects code at appropriate program points to detect and prefetch these hot data streams. Finally, the process enters the hibernation phase where no profiling or analysis is performed, and the program continues to execute with the added prefetch instructions. At the end of the hibernation phase, the program is de-optimized to remove the inserted checks and prefetch instructions, and control returns to the profiling phase. For long-running programs, this profile, analyze and optimize, hibernate, cycle will repeat multiple times. Our initial results from applying dynamic prefetching are promising, indicating overall execution time improvements of 5.19% for several memory-performance-limited SPECint2000 benchmarks running their largest (ref) inputs.


title: Age Estimation by Multi-scale Convolutional Network

abstract: In the last five years, biologically inspired features (BIF) always held the state-of-the-art results for human age estimation from face images. Recently, researchers mainly put their focuses on the regression step after feature extraction, such as support vector regression (SVR), partial least squares (PLS), canonical correlation analysis (CCA) and so on. In this paper, we apply convolutional neural network (CNN) to the age estimation problem, which leads to a fully learned end-to-end system can estimate age from image pixels directly. Compared with BIF, the proposed method has deeper structure and the parameters are learned instead of hand-crafted. The multi-scale analysis strategy is also introduced from traditional methods to the CNN, which improves the performance significantly. Furthermore, we train an efficient network in a multi-task way which can do age estimation, gender classification and ethnicity classification well simultaneously. The experiments on MORPH Album 2 illustrate the superiorities of the proposed multi-scale CNN over other state-of-the-art methods.


title: Oil Accumulation via Heterotrophic/Mixotrophic Chlorella protothecoides

abstract: Microalgal oil is a potential energy source because it can be easily converted to fatty acid methyl ester or hydrocarbon type of diesel, and it is produced with relatively higher productivity compared with oil from plants and animals. Heterotrophic growth of microalgae is superior due to its high final product concentration; however, the cost of the raw materials is unacceptable if sugar is utilized as the carbon source. The aim of this study is to optimize the lipid accumulation of Chlorella protothecoides by using carbon sources other than glucose in heterotrophic and mixotrophic cultures. Different factors such as different carbon sources, carbon to nitrogen ratio, initial pH level, salinity, and rotational speed are studied in affecting the cell growth and the oil accumulation. Our experiments revealed that the heterotrophic and mixotrophic cultures of C. protothecoides grew better than autotrophic cultures. C. protothecoides can grow on glycerol or acetate, as well as on glucose. Several stress factors were confirmed or discovered to significantly increase the lipid content of microalgae cells. The replacement of glycerol and acetate as carbon sources for microalgae cultivations provides potential for waste utilization: glycerol from biodiesel industry and acetate from biohydrogen production.


title: Convolutional Sparse Coding for Trajectory Reconstruction

abstract: Trajectory basis Non-Rigid Structure from Motion (NRSfM) refers to the process of reconstructing the 3D trajectory of each point of a non-rigid object from just their 2D projected trajectories. Reconstruction relies on two factors: (i) the condition of the composed camera & trajectory basis matrix, and (ii) whether the trajectory basis has enough degrees of freedom to model the 3D point trajectory. These two factors are inherently conflicting. Employing a trajectory basis with small capacity has the positive characteristic of reducing the likelihood of an ill-conditioned system (when composed with the camera) during reconstruction. However, this has the negative characteristic of increasing the likelihood that the basis will not be able to fully model the object’s “true” 3D point trajectories. In this paper we draw upon a well known result centering around the Reduced Isometry Property (RIP) condition for sparse signal reconstruction. RIP allow us to relax the requirement that the full trajectory basis composed with the camera matrix must be well conditioned. Further, we propose a strategy for learning an over-complete basis using convolutional sparse coding from naturally occurring point trajectory corpora to increase the likelihood that the RIP condition holds for a broad class of point trajectories and camera motions. Finally, we propose an $\ell_{1}$ inspired objective for trajectory reconstruction that is able to “adaptively” select the smallest sub-matrix from an over-complete trajectory basis that balances (i) and (ii). We present more practical 3D reconstruction results compared to current state of the art in trajectory basis NRSfM.


title: Crowd Counting and Profiling: Methodology and Evaluation

abstract: Video imagery based crowd analysis for population profiling and density estimation in public spaces can be a highly effective tool for establishing global situational awareness. Different strategies such as counting by detection and counting by clustering have been proposed, and more recently counting by regression has also gained considerable interest due to its feasibility in handling relatively more crowded environments. However, the scenarios studied by existing regression-based techniques are rather diverse in terms of both evaluation data and experimental settings. It can be difficult to compare them in order to draw general conclusions on their effectiveness. In addition, contributions of individual components in the processing pipeline such as feature extraction and perspective normalization remain unclear and less well studied. This study describes and compares the state-of-the-art methods for video imagery based crowd counting, and provides a systematic evaluation of different methods using the same protocol. Moreover, we evaluate critically each processing component to identify potential bottlenecks encountered by existing techniques. Extensive evaluation is conducted on three public scene datasets, including a new shopping center environment with labelled ground truth for validation. Our study reveals new insights into solving the problem of crowd analysis for population profiling and density estimation, and considers open questions for future studies.


title: 'Hard to Reach' Hispanics Get Health News via Physician's Radio, TV Shows

abstract: IN HIS PRACTICE of preventive medicine, earphones and a microphone are as basic to Elmer E. Huerta, MD, MPH, as his stethoscope and tongue depressor. Huerta has been dispensing daily doses of health news and advice in Spanish to a rapidly growing audience, many of whom are among the most medically underserved in the United States. Up to 4 times every weekday, 29 Spanish-language radio stations across the United States broadcast Huerta's program " Cuidando su Salud " ("Taking Care of Your Health"). The 2-minute reports cover virtually every area of health and medicine—from research published in the current issues of leading medical journals to advice on dealing with domestic violence or obtaining health insurance, from emergency contraception to the importance of wearing car seat belts. And that's not all: On Saturdays, Huerta cohosts a live 1-hour cable television health program in Spanish, and on Sundays he conducts a 1-hour radio callin


title: Laser labioplasty of labia minora.

abstract: OBJECTIVE ::: To evaluate laser labioplasty for the correction of hypertrophy and asymmetry of labia minora. ::: ::: ::: METHODS ::: Between October 2003 and November 2004, 55 labioplasties were performed at the Clínica Las Condes, Santiago, Chile. On the 60th postoperative day a questionnaire was presented to the patients, who were aged between 10 and 55 years, to assess their acceptance of and satisfaction with the intervention. ::: ::: ::: RESULTS ::: The primary indications for surgery were moderate hypertrophy with aesthetic and/or functional or impairment, or labial asymmetry (37 cases [67%]). Four patients (7%) experienced minimal suture dehiscence during the early postoperative period. Of the 55 patients, 50 (91%) were very satisfied, 5 (9%) were satisfied, and none were dissatisfied. ::: ::: ::: CONCLUSIONS ::: Patients expressed a high degree of satisfaction with laser labioplasty, which can be combined with other surgical gynecologic interventions without increased complications.


title: Design and construction of a novel quad tilt-wing UAV

abstract: Abstract This paper presents aerodynamic and mechanical design, prototyping and flight control system design of a new unmanned aerial vehicle SUAVI ( S abanci University U nmanned A erial V eh I cle). SUAVI is an electric powered quad tilt-wing UAV that is capable of vertical takeoff and landing (VTOL) like a helicopter and long duration horizontal flight like an airplane. Aerodynamic and mechanical designs are optimized to enhance the operational performance of the aerial vehicle. Both of them have great importance for increasing efficiency, reaching the flight duration goals and achieving the desired tasks. A full dynamical model is derived by utilizing Newton–Euler formulation for the development of the flight control system. The prototype is constructed from carbon composite material. A hierarchical control system is designed where a high level controller (supervisor) is responsible for task decision, monitoring states of the vehicle, generating references for low level controllers, etc. and several low level controllers are responsible for attitude and altitude stabilization. Results of several simulations and real flight tests are provided along with flight data to show performance of the developed UAV.


title: Adaptive indexing in modern database kernels

abstract: Physical design represents one of the hardest problems for database management systems. Without proper tuning, systems cannot achieve good performance. Offline indexing creates indexes a priori assuming good workload knowledge and idle time. More recently, online indexing monitors the workload trends and creates or drops indexes online. Adaptive indexing takes another step towards completely automating the tuning process of a database system, by enabling incremental and partial online indexing. The main idea is that physical design changes continuously, adaptively, partially, incrementally and on demand while processing queries as part of the execution operators. As such it brings a plethora of opportunities for rethinking and improving every single corner of database system design. We will analyze the indexing space between offline, online and adaptive indexing through several state of the art indexing techniques, e. g., what-if analysis and soft indexes. We will discuss in detail adaptive indexing techniques such as database cracking, adaptive merging, sideways cracking and various hybrids that try to balance the online tuning overhead with the convergence speed to optimal performance. In addition, we will discuss how various aspects of modern techniques for database architectures, such as vectorization, bulk processing, column-store execution and storage affect adaptive indexing. Finally, we will discuss several open research topics towards fully automomous database kernels.


title: Drugs for bad bugs: confronting the challenges of antibacterial discovery

abstract: The sequencing of the first complete bacterial genome in 1995 heralded a new era of hope for antibacterial drug discoverers, who now had the tools to search entire genomes for new antibacterial targets. Several companies, including GlaxoSmithKline, moved back into the antibacterials area and embraced a genomics-derived, target-based approach to screen for new classes of drugs with novel modes of action. Here, we share our experience of evaluating more than 300 genes and 70 high-throughput screening campaigns over a period of 7 years, and look at what we learned and how that has influenced GlaxoSmithKline's antibacterials strategy going forward.


Guaranteed Tensor PCA with Optimality in Statistics and Computation


title: Comparative analysis of piezoelectric power harvesting circuits for rechargeable batteries

abstract: Using piezoelectric materials to harvest energy from ambient vibrations to power wireless sensors has been of great interest over the past few years. Due to the power output of the piezoelectric materials is relatively low, rechargeable battery is considered as one kind of energy storage to accumulate the harvested energy for intermittent use. Piezoelectric harvesting circuits for rechargeable batteries have two schemes: non-adaptive and adaptive ones. A non-adaptive harvesting scheme includes a conventional diode bridge rectifier and a passive circuit. In recent years, several researchers have developed adaptive schemes for the harvesting circuit. Among them, the adaptive harvesting scheme by Ottman et al. is the most promising. This paper is aimed to quantify the performances of adaptive and non-adaptive schemes and to discuss their performance characteristics.


title: An interactive graph cut method for brain tumor segmentation

abstract: Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and, in many cases, similarity between tumor and normal tissue. We propose a semi-automatic interactive brain tumor segmentation system that incorporates 2D interactive and 3D automatic tools with the ability to adjust operator control. The provided methods are based on an energy that incorporates region statistics computed on available MRI modalities and the usual regularization term. The energy is efficiently minimized on-line using graph cut. Experiments with radiation oncologists testing the semi-automatic tool vs. a manual tool show that the proposed system improves both segmentation time and repeatability.


title: Participation strategies and student performance:An undergraduate health science retrospective study

abstract: OBJECTIVE ::: This research explores participatory evidence-based teaching methods in a health science course to see if a relationship emerged between the level of student participation and course performance, the type of participation and course performance, or the amount of participation and course performance and level of demonstrated learning. ::: ::: ::: METHODS ::: Level of student participation was dichotomous (100% or <100%), and differences between groups on a knowledge test were compared using an unpaired t test. Type of participation was also dichotomous (in class or out), and differences in course performance on the knowledge test were compared using the unpaired t test. Amount of participation and course performance and level of demonstrated learning were also tested after the knowledge test was measured using a matrix based upon Bloom's taxonomy. ::: ::: ::: RESULTS ::: Students who participated 100% of the time scored 6% higher on average than students with less than 100% participation (t[183] = 3.55, p = .0005, d = 0.52). There was no difference between groups when assessing for differences in course performance by type of participation. Students with 100% participation scored higher on the short answer question section of the examination (t[183] = 4.58, p = .0001, d = 0.68), but there was no difference on the multiple choice question part of the examination. ::: ::: ::: CONCLUSION ::: Full participation in the course was related to higher examination scores and higher scores on examination questions assessing higher levels in the cognitive domain.


title: Least Squares Support Vector Machine Classifiers

abstract: In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM‘s. The approach is illustrated on a two-spiral benchmark classification problem.


title: A solitary pink lesion: dermoscopy and RCM features of lichen planus

abstract: We present an unusual onset of cutaneous lichen planus (LP) in a middle-aged patient. The initial presentation as solitary, indolent pink lesion required further investigations to rule out malignancy, especially amelanotic melanoma. Dermoscopy and reflectance confocal microscopy findings were found to be helpful in our case in addressing the correct diagnosis.


title: Wideband Dipole Array Loaded Substrate Integrated H-Plane Horn Antenna for Millimeter Waves

abstract: A novel dipole array loaded substrate integrated H-plane sectoral horn antenna with a simple three-layered geometry is proposed. The dipole array integrated into two extra dielectric strips is connected to the top and bottom edges of the horn aperture. Both the horn aperture and the dipole array are effectively radiated together. A wide impedance bandwidth of 44% for $\vert S_{11}\vert dB that can cover the entire Ka-band is verified by a fabricated prototype. Additionally, stable symmetrical radiation pattern with low cross polarizations, low backward radiation, and gain up to 12 dBi with a variation of 3 dB can also be obtained throughout the whole operating band. The proposed antenna provides a new mean to design wideband substrate integrated horns with excellent endfire radiation, which would be attractive for future wideband and multiband millimeter-wave wireless applications, including the fifth generation (5G) mobile communications and the WiGig system.


iJournaling: Fine-Grained Journaling for Improving the Latency of Fsync System Call


title: Reinforcement Learning from Imperfect Demonstrations

abstract: Robust real-world learning should benefit from both demonstrations and interaction with the environment. Current approaches to learning from demonstration and reward perform supervised learning on expert demonstration data and use reinforcement learning to further improve performance based on reward from the environment. These tasks have divergent losses which are difficult to jointly optimize; further, such methods can be very sensitive to noisy demonstrations. We propose a unified reinforcement learning algorithm that effectively normalizes the Q-function, reducing the Q-values of actions unseen in the demonstration data. Our Normalized Actor-Critic (NAC) method can learn from demonstration data of arbitrary quality and also leverages rewards from an interactive environment. NAC learns an initial policy network from demonstration and refines the policy in a real environment. Crucially, both learning from demonstration and interactive refinement use exactly the same objective, unlike prior approaches that combine distinct supervised and reinforcement losses. This makes NAC robust to suboptimal demonstration data, since the method is not forced to mimic all of the examples in the dataset. We show that our unified reinforcement learning algorithm can learn robustly and outperform existing baselines when evaluated on several realistic driving games.


title: Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making

abstract: The basal ganglia (BG) coordinate decision making processes by facilitating adaptive frontal motor commands while suppressing others. In previouswork, neuralnetworksimulationsaccounted for responseselectiondeficitsassociated withBGdopaminedepletion inParkinson’sdisease. Novel predictions from this model have been subsequently confirmed in Parkinson patients and in healthy participants under pharmacological challenge. Nevertheless, one clear limitation of that model is in its omission of the subthalamic nucleus (STN), a key BG structure that participates in both motor and cognitive processes. The present model incorporates the STN and shows that by modulating when a response is executed, the STN reduces premature responding and therefore has substantial effects on which response is ultimately selected, particularly when there are multiple competing responses. Increased cortical response conflict leads to dynamic adjustments in response thresholds via cortico-subthalamicpallidal pathways. The model accurately captures the dynamics of activity in various BG areas during response selection. Simulated dopamine depletion results in emergent oscillatory activity in BG structures, which has been linked with Parkinson’s tremor. Finally, the model accounts for the beneficial effects of STN lesions on these oscillations, but suggests that this benefit may come at the expense of impaired decision making. c 2006 Elsevier Ltd. All rights reserved.


title: MiRoR—Miniaturized Robotic Systems for Holistic In-Situ Repair and Maintenance Works in Restrained and Hazardous Environments

abstract: This letter presents the novel concept of a miniaturized robotized machine tool, Mini-RoboMach, consisting of a walking hexapod robot and a Slender Continuum Arm. By combining the mobility of the walking robot with the positioning accuracy of the machine tool, with its 24 + 25 degrees of freedom, camera-based calibration system, laser scanner, and two end-effectors of opposed orientations, the proposed system can provide a versatile tool for in-situ work (e.g., repair) in hazardous/unreachable locations in large installations.


A survey on approaches to anonymity in bitcoin and other cryptocurrencies.


title: High yield exfoliation of two-dimensional chalcogenides using sodium naphthalenide

abstract: Transition-metal dichalcogenides like molybdenum disulphide have attracted great interest as two-dimensional materials beyond graphene due to their unique electronic and optical properties. Solution-phase processes can be a viable method for producing printable single-layer chalcogenides. Molybdenum disulphide can be exfoliated into monolayer flakes using organolithium reduction chemistry; unfortunately, the method is hampered by low yield, submicron flake size and long lithiation time. Here we report a high-yield exfoliation process using lithium, potassium and sodium naphthalenide where an intermediate ternary Li(x)MX(n) crystalline phase (X=selenium, sulphur, and so on) is produced. Using a two-step expansion and intercalation method, we produce high-quality single-layer molybdenum disulphide sheets with unprecedentedly large flake size, that is up to 400 μm(2). Single-layer dichalcogenide inks prepared by this method may be directly inkjet-printed on a wide range of substrates.


title: Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy

abstract: We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both morbidity and mortality. RCM is an in-vivo non-invasive screening tool that produces virtual biopsies of skin lesions but its proficient and safe use requires hard to obtain expertise. Therefore, it may be useful to have an additional tool to aid diagnosis. The proposed network is based on the ResNet architecture. The dataset consists of 429 RCM mosaics and is divided into 3 classes: melanoma, basal cell carcinoma, and benign naevi with the ground-truth confirmed by a histopathological examination. The test set classification accuracy was 87%, higher than the accuracy achieved by medical, confocal users. The results show that the proposed classification system can be a useful tool to aid in early, noninvasive melanoma detection.


title: Smart irrigation using low-cost moisture sensors and XBee-based communication

abstract: Deficiency in fresh water resources globally has raised serious alarms in the last decade. Efficient management of water resources play an important role in the agriculture sector. Unfortunately, this is not given prime importance in the third world countries because of adhering to traditional practices. This paper presents a smart system that uses a bespoke, low cost soil moisture sensor to control water supply in water deficient areas. The sensor, which works on the principle of moisture dependent resistance change between two points in the soil, is fabricated using affordable materials and methods. Moisture data acquired from a sensor node is sent through XBEE wireless communication modules to a centralized server that controls water supply. A user-friendly interface is developed to visualize the daily moisture data. The low-cost and wireless nature of the sensing hardware presents the possibility to monitor the moisture levels of large agricultural fields. Moreover, the proposed moisture sensing method has the ability to be incorporated into an automated drip-irrigation scheme and perform automated, precision agriculture in conjunction with de-centralized water control.


title: Concealed Weapon Detection Using Image Processing

abstract: We have recently witnessed the series of bomb blasts in Dilshuknagar, Hyderabad. Bombs killed many and left many injured. On Feb 22nd two explosions took place with in one hour. And left the world in shell shock and the Indians in terror. This situation is not limited to Hyderabad but it can happen any where and any time in the world. People think bomb blasts can’t be predicted before handled. Here we show you the technology which predicts the suicide bombers and explosion of weapons through Image Processing for Conclead Weapon Detection. The detection of weapons concealed underneath a person’s clothing is very much important to the improvement of the security of the general public as well as the safety of public assets like airports, buildings, and railway stations etc. Manual screening procedures for detecting concealed weapons such as handguns, knives, and explosives are common in controlled access settings like airports, entrances to sensitive buildings and public events. It is desirable sometimes to be able to detect concealed weapons from a standoff distance, especially when it is impossible to arrange the flow of people through a controlled procedure in the present paper we describe the concepts of the technology ‘Concealead Weapon Detection’ the sensor improvements, how the imaging takes place and the challenges. And we also describe techniques for simultaneous noise suppression, object extraction.


title: Artificial dielectric material for lowering resonant frequency of microstrip circular patch antenna

abstract: In this paper, the implementation of artificial dielectric material is proposed to lower resonant frequency of conventional microstrip circular patch antenna. The artificial dielectric material is applied to replace the dielectric material of conventional microstrip circular patch antenna which is designed based on TM11 resonance mode. The development of artificial dielectric material is implemented by incorporating cylindrical conductors into FR4 Epoxy dielectric substrate used as host material. Here, the conventional microstrip circular patch antenna is designed to resonate at frequency of 2.4GHz. From characterization result, it shows that the resonant frequency of proposed antenna with artificial dielectric material can be lowered up to 1.648GHz. Hence there is an increase of effective permittivity of artificial dielectric material around 212.2%.


title: Network Information Flow

abstract: We introduce a new class of problems called network information flow which is inspired by computer network applications. Consider a point-to-point communication network on which a number of information sources are to be multicast to certain sets of destinations. We assume that the information sources are mutually independent. The problem is to characterize the admissible coding rate region. This model subsumes all previously studied models along the same line. We study the problem with one information source, and we have obtained a simple characterization of the admissible coding rate region. Our result can be regarded as the max-flow min-cut theorem for network information flow. Contrary to one's intuition, our work reveals that it is in general not optimal to regard the information to be multicast as a "fluid" which can simply be routed or replicated. Rather, by employing coding at the nodes, which we refer to as network coding, bandwidth can in general be saved. This finding may have significant impact on future design of switching systems.


title: Collapsed Variational Dirichlet Process Mixture Models

abstract: Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of today's datasets, computational efficiency becomes an essential ingredient in the applicability of these techniques to real world data. We study and experimentally compare a number of variational Bayesian (VB) approximations to the DP mixture model. In particular we consider the standard VB approximation where parameters are assumed to be independent from cluster assignment variables, and a novel collapsed VB approximation where mixture weights are marginalized out. For both VB approximations we consider two different ways to approximate the DP, by truncating the stick-breaking construction, and by using a finite mixture model with a symmetric Dirichlet prior.


title: Babelsberg/JS - A Browser-Based Implementation of an Object Constraint Language.

abstract: �� � � � � � �� � ��� �� � � � � � � � � � � � � � � � � � � � � � � � � � � �� � � � � � � � � � � � � � � � � �� � � � � � � � � � � Constraints provide a useful technique for ensuring that desired properties hold in an application. As a result, they have been used in a wide range of applications, including graphical layout, simulation, scheduling, and problem-solving. We describe the design and implementation of an Object Constraint Programming language, an object-oriented language that cleanly integrates constraints with the underlying language in a way that respects encapsulation and standard object-oriented programming techniques, and that runs in browser-based applications. Prior work on Object Constraint Programming languages has relied on modifying the underlying Virtual Machine, but that is not an option for web-based applications, which have become increasingly prominent. In this paper, we present an approach to implementing Object Constraint Programming without Virtual Machine support, along with an implementation as a JavaScript extension. We demonstrate the resulting language, Babelsberg/JS, on a number of applications and provide performance measurements. Programs without constraints in Babelsberg/JS run at the same speed as pure JavaScript versions, while programs that do have constraints can still be run efficiently. Our design and implementation also incorporate incremental re-solving to support interaction, as well as a cooperating solvers architecture that allows multiple solvers to work together to solve more difficult problems.


title: MockDroid: trading privacy for application functionality on smartphones

abstract: MockDroid is a modified version of the Android operating system which allows a user to 'mock' an application's access to a resource. This resource is subsequently reported as empty or unavailable whenever the application requests access. This approach allows users to revoke access to particular resources at run-time, encouraging users to consider the trade-off between functionality and the disclosure of personal information whilst they use an application. Existing applications continue to work on MockDroid, possibly with reduced functionality, since existing applications are already written to tolerate resource failure, such as network unavailability or lack of a GPS signal. We demonstrate the practicality of our approach by successfully running a random sample of twenty-three popular applications from the Android Market.


Certified Bitcoins


title: Hyperloglog: The analysis of a near-optimal cardinality estimation algorithm

abstract: This extended abstract describes and analyses a near-optimal probabilistic algorithm, HYPERLOGLOG, dedicated to estimating the number of \emphdistinct elements (the cardinality) of very large data ensembles. Using an auxiliary memory of m units (typically, "short bytes''), HYPERLOGLOG performs a single pass over the data and produces an estimate of the cardinality such that the relative accuracy (the standard error) is typically about $1.04/\sqrt{m}$. This improves on the best previously known cardinality estimator, LOGLOG, whose accuracy can be matched by consuming only 64% of the original memory. For instance, the new algorithm makes it possible to estimate cardinalities well beyond $10^9$ with a typical accuracy of 2% while using a memory of only 1.5 kilobytes. The algorithm parallelizes optimally and adapts to the sliding window model.


title: IOS Press Enabling the Geospatial Semantic Web with

abstract: As the amount of Linked Open Data on the web increases, so does the amount of data with an inherent spatial context. Without spatial reasoning, however, the value of this spatial context is limited. Over the past decade there have been several vocabularies and query languages that attempt to exploit this knowledge and enable spatial reasoning. These attempts provide varying levels of support for fundamental geospatial concepts. GeoSPARQL, a forthcoming OGC standard, attempts to unify data access for the geospatial Semantic Web. As authors of the Parliament triple store and contributors to the GeoSPARQL specification, we are particularly interested in the issues of geospatial data access and indexing. In this paper, we look at the overall state of geospatial data in the Semantic Web, with a focus on GeoSPARQL. We first describe the motivation for GeoSPARQL, then the current state of the art in industry and research, followed by an example use case, and finally our implementation of GeoSPARQL in the Parliament triple store.


title: Prevalence and sociodemographic patterns of physical activity among Bangladeshi young adults

abstract: BACKGROUND ::: Physical activity offers physical and psychosocial health benefits that are important during young adulthood and later in life. However, little is known about the physical activity of young adults in low- and middle-income countries. The purpose of this study was to estimate the participation of physical activity in Bangladeshi young adults and to assess differences by gender, age and family income. ::: ::: ::: METHODS ::: This cross-sectional study with a self-administered survey used a convenience sample of 573 young adults aged 18-24 years from six purposively selected universities in Dhaka City, Bangladesh. Data were collected during September-November 2015. Medians and their interquartile ranges of weekly time spent in total physical activity, and in different domains of physical activity, were computed. Non-parametric equality of medians test was used to examine gender differences in the median values. Chi-square test and Fisher's exact test were used to examine gender differences in the prevalence of meeting physical activity recommendations and frequency of participation in different leisure-time physical activities, and differences in meeting the activity recommendations by age and family income. ::: ::: ::: RESULTS ::: Seventeen percent of the participants were meeting moderate-to-vigorous physical activity (MVPA) recommendations with a significantly higher proportion of males than females (27 vs. 6%, p < .0001). Median duration of MVPA was significantly higher (p < .0001) for males [120 min/week (80, 190)] than females [90 min/week (50, 120)]. Jogging/running was the most commonly reported leisure-time physical activity, with 20% of males and 12% of females doing this at least once a week. Age and family income were not significantly associated with meeting MVPA recommendations. ::: ::: ::: CONCLUSIONS ::: Four out of five young adults in Dhaka City did not meet the physical activity recommendations. Additional population-based studies, including regional and metropolitan areas, and using objective measurement, are needed to understand the physical activity patterns of Bangladeshi young adults.


title: The ideal romantic partner personality

abstract: Abstract Most contemporary social psychological studies on inter-personal attraction have independently explored the competing concepts of similarity and complementarity. Incorporating evolutionary principles associated with assortative mating, two studies were conducted that examined individual difference preferences using the five-factor model (FFM) of human personality as assessed by the NEO-FFI. The first study (N = 104) examined the degree to which individuals showed an absolute or relative preference in an “ideal romantic partner” when compared to self-rated personality. The second study (N = 161) extended this by incorporating personality ratings for self, “ideal” romantic partner, and “actual” romantic partner, and perceptions of mate value for each. Overall, findings supported both evolutionary and social psychological theories of inter-personal attraction and support both relative and absolute preferences in romantic partners. Individuals sought mates that were matches of themselves to some degree (a concept that we termed aspirational positive assortative mating) but also sought mates that were somewhat higher in Conscientiousness, Extraversion, Agreeableness, and Mate Value, but lower in Neuroticism than themselves.


title: Demographic and psychosocial predictors of major depression and generalised anxiety disorder in Australian university students

abstract: Few studies have examined modifiable psychosocial risk factors for mental disorders among university students, and of these, none have employed measures that correspond to clinical diagnostic criteria. The aim of this study was to examine psychosocial and demographic risk factors for major depression and generalised anxiety disorder (GAD) in a sample of Australian university students. An anonymous web-based survey was distributed to undergraduate and postgraduate students at a mid-sized Australian university. A range of psychosocial and demographic risk factors were measured, and logistic regression models were used to examine significant predictors of major depression and GAD. A total of 611 students completed the survey. The prevalence of major depression and GAD in the sample was 7.9 and 17.5 %, respectively. In terms of demographic factors, the risk of depression was higher for students in their first year of undergraduate study, and the risk of GAD was higher for female students, those who moved to attend university, and students experiencing financial stress. In terms of psychosocial factors, students with experience of body image issues and lack of confidence were at significantly greater risk of major depression, and feeling too much pressure to succeed, lack of confidence, and difficulty coping with study was significantly associated with risk of GAD. University students experience a range of unique psychosocial stressors that increase their risk of major depression and GAD, in addition to sociodemographic risk factors. It is important to examine psychosocial factors, as these are potentially modifiable and could be the focus of university-specific mental health interventions.


title: A methodological approach to the classification of dermoscopy images

abstract: In this paper a methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented. First, automatic border detection is performed to separate the lesion from the background skin. Shape features are then extracted from this border. For the extraction of color and texture related features, the image is divided into various clinically significant regions using the Euclidean distance transform. This feature data is fed into an optimization framework, which ranks the features using various feature selection algorithms and determines the optimal feature subset size according to the area under the ROC curve measure obtained from support vector machine classification. The issue of class imbalance is addressed using various sampling strategies, and the classifier generalization error is estimated using Monte Carlo cross validation. Experiments on a set of 564 images yielded a specificity of 92.34% and a sensitivity of 93.33%.


title: Grouping dominant orientations for ill-structured road following

abstract: Many rural roads lack sharp, smoothly curving edges and a homogeneous surface appearance, hampering traditional vision-based road-following methods. However, they often have strong texture cues parallel to the road direction in the form of ruts and tracks left by other vehicles. In this paper, we describe an algorithm for following ill-structured roads in which dominant texture orientations computed with multi-scale Gabor wavelet filters vote for a consensus road vanishing point location. In-plane road curvature and out-of-plane undulation are estimated in each image by tracking the vanishing point indicated by a horizontal image strip as it moves up toward the putative vanishing line. Particle filtering is also used to track the vanishing point sequence induced by road curvature from image to image. Results are shown for vanishing point localization on a variety of road scenes ranging from gravel roads to dirt trails to highways.


title: A Comparative Usability Evaluation of Traditional Password Managers

abstract: Proposed in response to the growing number of passwords users have to memorize, password managers allow to store one's credentials, either on a third-party server (online password manager), or on a portable device (portable password manager) such as a mobile phone or a USB key. In this paper, we present a comparative usability study of three popular password managers: an online manager (LastPass), a phone manager (KeePassMobile) and a USB manager (Roboform2Go). Our study provides valuable insights on average users' perception of security and usability of the three password management approaches. We find, contrary to our intuition, that users overall prefer the two portable managers over the online manager, despite the better usability of the latter. Also, surprisingly, our non-technical pool of users shows a strong inclination towards the phone manager. These findings can generally be credited to the fact that the users were not comfortable giving control of their passwords to an online entity and preferred to manage their passwords themselves on their own portable devices. Our results prompt the need for research on developing user-friendly and secure phone managers, owing to the ubiquity of mobile phones.


title: Approximate Convex Hull of Data Streams

abstract: Given a finite set of points $P \subseteq \mathbb{R}^d$, we would like to find a small subset $S \subseteq P$ such that the convex hull of $S$ approximately contains $P$. More formally, every point in $P$ is within distance $\epsilon$ from the convex hull of $S$. Such a subset $S$ is called an $\epsilon$-hull. Computing an $\epsilon$-hull is an important problem in computational geometry, machine learning, and approximation algorithms. ::: In many real world applications, the set $P$ is too large to fit in memory. We consider the streaming model where the algorithm receives the points of $P$ sequentially and strives to use a minimal amount of memory. Existing streaming algorithms for computing an $\epsilon$-hull require $O(\epsilon^{-(d-1)/2})$ space, which is optimal for a worst-case input. However, this ignores the structure of the data. The minimal size of an $\epsilon$-hull of $P$, which we denote by $\text{OPT}$, can be much smaller. A natural question is whether a streaming algorithm can compute an $\epsilon$-hull using only $O(\text{OPT})$ space. ::: We begin with lower bounds that show that it is not possible to have a single-pass streaming algorithm that computes an $\epsilon$-hull with $O(\text{OPT})$ space. We instead propose three relaxations of the problem for which we can compute $\epsilon$-hulls using space near-linear to the optimal size. Our first algorithm for points in $\mathbb{R}^2$ that arrive in random-order uses $O(\log n\cdot \text{OPT})$ space. Our second algorithm for points in $\mathbb{R}^2$ makes $O(\log(\frac{1}{\epsilon}))$ passes before outputting the $\epsilon$-hull and requires $O(\text{OPT})$ space. Our third algorithm for points in $\mathbb{R}^d$ for any fixed dimension $d$ outputs an $\epsilon$-hull for all but $\delta$-fraction of directions and requires $O(\text{OPT} \cdot \log \text{OPT})$ space.


title: BACH: Grand Challenge on Breast Cancer Histology Images

abstract: Abstract Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.


title: Fast Video Shot Transition Localization with Deep Structured Models

abstract: Detection of video shot transition is a crucial pre-processing step in video analysis. Previous studies are restricted on detecting sudden content changes between frames through similarity measurement and multi-scale operations are widely utilized to deal with transitions of various lengths. However, localization of gradual transitions are still under-explored due to the high visual similarity between adjacent frames. Cut shot transitions are abrupt semantic breaks while gradual shot transitions contain low-level spatial-temporal patterns caused by video effects in addition to the gradual semantic breaks, e.g. dissolve. In order to address the problem, we propose a structured network which is able to detect these two shot transitions using targeted models separately. Considering speed performance trade-offs, we design a smart framework. With one TITAN GPU, the proposed method can achieve a 30\(\times\) real-time speed. Experiments on public TRECVID07 and RAI databases show that our method outperforms the state-of-the-art methods. In order to train a high-performance shot transition detector, we contribute a new database ClipShots, which contains 128636 cut transitions and 38120 gradual transitions from 4039 online videos. ClipShots intentionally collect short videos for more hard cases caused by hand-held camera vibrations, large object motions, and occlusion.


title: Counterfeit Integrated Circuits: Detection, Avoidance, and the Challenges Ahead

abstract: The counterfeiting of electronic components has become a major challenge in the 21st century. The electronic component supply chain has been greatly affected by widespread counterfeit incidents. A specialized service of testing, detection, and avoidance must be created to tackle the worldwide outbreak of counterfeit integrated circuits (ICs). So far, there are standards and programs in place for outlining the testing, documenting, and reporting procedures. However, there is not yet enough research addressing the detection and avoidance of such counterfeit parts. In this paper we will present, in detail, all types of counterfeits, the defects present in them, and their detection methods. We will then describe the challenges to implementing these test methods and to their effectiveness. We will present several anti-counterfeit measures to prevent this widespread counterfeiting, and we also consider the effectiveness and limitations of these anti-counterfeiting techniques.


title: Association between internal carotid artery dissection and arterial tortuosity

abstract: Introduction ::: Carotid artery dissection is an important cause of ischemic stroke in all age groups, particularly in young patients. The purpose of this work was to assess whether there is an association between the presence of an internal carotid artery dissection (ICAD) and the arterial tortuosity.


title: Learning Category-Specific Mesh Reconstruction from Image Collections

abstract: We present a learning framework for recovering the 3D shape, camera, and texture of an object from a single image. The shape is represented as a deformable 3D mesh model of an object category where a shape is parameterized by a learned mean shape and per-instance predicted deformation. Our approach allows leveraging an annotated image collection for training, where the deformable model and the 3D prediction mechanism are learned without relying on ground-truth 3D or multi-view supervision. Our representation enables us to go beyond existing 3D prediction approaches by incorporating texture inference as prediction of an image in a canonical appearance space. Additionally, we show that semantic keypoints can be easily associated with the predicted shapes. We present qualitative and quantitative results of our approach on CUB and PASCAL3D datasets and show that we can learn to predict diverse shapes and textures across objects using only annotated image collections. The project website can be found at https://akanazawa.github.io/cmr/.


title: Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks

abstract: Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer's disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer's disease diagnosis in clinical research. Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer's disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer's disease and obtains superior performance for early-stage diagnosis. We conducted ample experiments to demonstrate that our proposed model outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.


title: Women Who Prefer Longer Penises Are More Likely to Have Vaginal Orgasms (but Not Clitoral Orgasms): Implications for an Evolutionary Theory of Vaginal Orgasm

abstract: Introduction. Research indicates that (i) women's orgasm during penile-vaginal intercourse (PVI) is influenced by fitness-related male partner characteristics, (ii) penis size is important for many women, and (iii) preference for a longer penis is associated with greater vaginal orgasm consistency (triggered by PVI without concurrent clitoral masturbation). Aims. To test the hypothesis that vaginal orgasm frequency is associated with women's reporting that a longer than average penis is more likely to provoke their PVI orgasm. Method. Three hundred twenty-three women reported in an online survey their past month frequency of various sexual behaviors (including PVI, vaginal orgasm, and clitoral orgasm), the effects of a longer than average penis on likelihood of orgasm from PVI, and the importance they attributed to PVI and to noncoital sex. Main Outcome Measures. Univariate analyses of covariance with dependent variables being frequencies of various sexual behaviors and types of orgasm and with independent variable being women reporting vs. not reporting that a longer than average penis is important for their orgasm from PVI. Results. Likelihood of orgasm with a longer penis was related to greater vaginal orgasm frequency but unrelated to frequencies of other sexual behaviors, including clitoral orgasm. In binary logistic regression, likelihood of orgasm with a longer penis was related to greater importance attributed to PVI and lesser importance attributed to noncoital sex. Conclusions. Women who prefer deeper penile-vaginal stimulation are more likely to have vaginal orgasm, consis- tent with vaginal orgasm evolving as part of a female mate choice system favoring somewhat larger than average penises. Future research could extend the findings by overcoming limitations related to more precise measurement of penis length (to the pubis and pressed close to the pubic bone) and girth, and large representative samples. Future experimental research might assess to what extent different penis sizes influence women's satisfaction and likelihood of vaginal orgasm. Costa RM, Miller GF, and Brody B. Women who prefer longer penises are more likely to have vaginal orgasms (but not clitoral orgasms): Implications for an evolutionary theory of vaginal orgasm. J Sex Med 2012;9:3079-3088.


title: A 12-b, 1-GS/s 6.1 mW current-steering DAC in 14 nm FinFET with 80 dB SFDR for 2G/3G/4G cellular application

abstract: A 14nm FinFET CMOS 12-b current-steering digital-to-analog (DAC) for 2G/3G/4G cellular applications is presented. A bit segmentation of 6-bit thermometer and 6-bit binary is adopted, and it utilizes the dynamic element matching (DEM) technique to suppress the spurious tones caused by the current source mismatches in 3-D FinFETs. In addition, to keep the voltage drop across each transistor within long-term reliability limit, output switches are designed with shielding transistors while achieving make-before-break operation with the proposed low crossing point level shifter. The active area of a single DAC is 0.036 mm2, and its power consumption is 6.1 mW with SFDR of 80 dBc.


title: 3d reconstruction of a moving point from a series of 2d projections

abstract: This paper presents a linear solution for reconstructing the 3D trajectory of a moving point from its correspondence in a collection of 2D perspective images, given the 3D spatial pose and time of capture of the cameras that produced each image. Triangulation-based solutions do not apply, as multiple views of the point may not exist at each instant in time. A geometric analysis of the problem is presented and a criterion, called reconstructibility, is defined to precisely characterize the cases when reconstruction is possible, and how accurate it can be. We apply the linear reconstruction algorithm to reconstruct the time evolving 3D structure of several real-world scenes, given a collection of non-coincidental 2D images.


title: NEON crypto

abstract: NEON is a vector instruction set included in a large fraction of new ARM-based tablets and smartphones. This paper shows that NEON supports high-security cryptography at surprisingly high speeds; normally data arrives at lower speeds, giving the CPU time to handle tasks other than cryptography. In particular, this paper explains how to use a single 800MHz Cortex A8 core to compute the existing NaCl suite of high-security cryptographic primitives at the following speeds: 5.60 cycles per byte (1.14 Gbps) to encrypt using a shared secret key, 2.30 cycles per byte (2.78 Gbps) to authenticate using a shared secret key, 527102 cycles (1517/second) to compute a shared secret key for a new public key, 624846 cycles (1280/second) to verify a signature, and 244655 cycles (3269/second) to sign a message. These speeds make no use of secret branches and no use of secret memory addresses.


title: New solid-state Marx topology for bipolar repetitive high-voltage pulses

abstract: A novel bipolar high-voltage modulator topology, based on the Marx generator concept, is proposed for high-voltage repetitive pulsed power applications. The proposed topology is a generalized version of the negative and positive all-solid-state Marx modulator concepts, which takes advantage of the intensive use of power semiconductor switches to increase the performance of the classical circuit, strongly reducing losses and increasing the pulse repetition frequency. Additionally, the proposed topology enables the use of typical half-bridge semiconductor structures while ensuring that the maximum voltage blocked by the semiconductors is the voltage of the capacitor in each stage. Due to semiconductor topology used the output voltage is very flexible. Hence, it is possible to change from negative to positive unipolar to bipolar pulse, with different duty cycles and different switching patterns. Experimental results are presented and discussed. A laboratory prototype with 10 kW peak power, of this bipolar solid-state modulator circuit, was assembled 1200 V IGBTs and diodes, operating with 1000 V d-c input voltage and 10 kHz frequency, giving 2 kV bipolar pulses, 5 A, with 5 mus into a resistive load.


title: Completely Lazy Learning

abstract: Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not completely lazy because the neighborhood size k (or other locality parameter) is usually chosen by cross validation on the training set, which can require significant preprocessing and risks overfitting. We propose a simple alternative to cross validation of the neighborhood size that requires no preprocessing: instead of committing to one neighborhood size, average the discriminants for multiple neighborhoods. We show that this forms an expected estimated posterior that minimizes the expected Bregman loss with respect to the uncertainty about the neighborhood choice. We analyze this approach for six standard and state-of-the-art local classifiers, including discriminative adaptive metric kNN (DANN), a local support vector machine (SVM-KNN), hyperplane distance nearest neighbor (HKNN), and a new local Bayesian quadratic discriminant analysis (local BDA). The empirical effectiveness of this technique versus cross validation is confirmed with experiments on seven benchmark data sets, showing that similar classification performance can be attained without any training.


title: Autonomous Stair Climbing for Tracked Vehicles

abstract: In this paper, an algorithm for autonomous stair climbing with a tracked vehicle is presented. The proposed method achieves robust performance under real-world conditions, without assuming prior knowledge of the stair geometry, the dynamics of the vehicle's interaction with the stair surface, or lighting conditions. The approach relies on fast and accurate estimation of the robot's heading and its position relative to the stair boundaries. An extended Kalman filter is used for quaternion-based attitude estimation, fusing rotational velocity measurements from a 3-axial gyroscope, and measurements of the stair edges acquired with an onboard camera. A two-tiered controller, comprised of a centering- and a heading-control module, utilizes the estimates to guide the robot rapidly, safely, and accurately upstairs. Both the theoretical analysis and implementation of the algorithm are presented in detail, and extensive experimental results demonstrating the algorithm's performance are described.


title: 1-D Combline Leaky-Wave Antenna With the Open-Stopband Suppressed: Design Considerations and Comparisons With Measurements

abstract: The design of a 1-D planar periodic combline leaky-wave antenna that avoids the open-stopband effects as the beam is scanned through broadside is discussed and verified experimentally. This antenna radiates from nearly resonant stubs, which ensures a small leakage constant and hence a high directivity, and shows a single beam scanning through its n = -1 space harmonic in the X-band frequency range. The open-stopband suppression is obtained by applying a recently proposed matching technique within the unit cell that is experimentally validated here for the first time. Measurements are made of the phase and leakage constants, the S-parameters, and the realized gain patterns. Comparisons are made with numerical values obtained from specialized and commercial software and very good agreement is found in all cases.


title: ShareVR: Enabling Co-Located Experiences for Virtual Reality between HMD and Non-HMD Users

abstract: Virtual reality (VR) head-mounted displays (HMD) allow for a highly immersive experience and are currently becoming part of the living room entertainment. Current VR systems focus mainly on increasing the immersion and enjoyment for the user wearing the HMD (HMD user), resulting in all the bystanders (Non-HMD users) being excluded from the experience. We propose ShareVR, a proof-of-concept prototype using floor projection and mobile displays in combination with positional tracking to visualize the virtual world for the Non-HMD user, enabling them to interact with the HMD user and become part of the VR experience. We designed and implemented ShareVR based on the insights of an initial online survey (n=48) with early adopters of VR HMDs. We ran a user study (n=16) comparing ShareVRto a baseline condition showing how the interaction using ShareVR led to an increase of enjoyment, presence and social interaction. In a last step we implemented several experiences for ShareVR, exploring its design space and giving insights for designers of co-located asymmetric VR experiences.


title: A survey on combine approach of low level features extraction in CBIR

abstract: Nowadays, digital story is for the most part around the place. Images noticeably are such of the virtually important categories bounded by the accessible data. Image processing is a way of doing thing to interpret an thought into digital comprise and plow some operations on it, in term to win an enhanced brain wave or to dig in to the past some enjoyable reference from it. Image Processing forms essential probe area within engineering and personal digital assistant science disciplines. Digital Processing techniques bolster in back rub of the digital images by for computers. The common three phases that all types of story interim using digital course are preprocessing, enhancement and prove, impression extraction. Improvements in data storage and thought acquisition skills have enabled the mint of enormous image datasets. In this how things stack up, it is unavoidable to when push comes to shove appropriate information systems to efficiently did a bang up job these collections. The mean approaches manage it called CBIR which serve Content-Based Image Retrieval systems.


title: Flexible Filters: Reconfigurable-Bandwidth Bandpass Planar Filters with Ultralarge Tuning Ratio

abstract: The objective of this overview article is to report the latest research findings in the research into RF/ microwave reconfigurable-bandwidth bandpass planar filters with ultralarge passbandwidth tuning ratio. This means filtering devices with much higher flexibility, showing reconfigurable bandwidths between narrow/moderate-band and ultrawideband states. Specifically, two different solutions we recently proposed are described, with emphasis on their operating principles and achieved electrical performances. They consist of 1) transversal signal-interference switchable-bandwidth bandpass filters and 2) tune-all bandpass filters simultaneously exploiting MMRs and quality-factor control to achieve unprecedented reconfiguration levels in terms of center frequency and instantaneous passbandwidth. Some other modern filtering topologies proposed by other authors, which have clear interest to attain very high levels of bandwidth variation, are also expounded.


title: Rigorous benchmarking in reasonable time

abstract: Experimental evaluation is key to systems research. Because modern systems are complex and non-deterministic, good experimental methodology demands that researchers account for uncertainty. To obtain valid results, they are expected to run many iterations of benchmarks, invoke virtual machines (VMs) several times, or even rebuild VM or benchmark binaries more than once. All this repetition costs time to complete experiments. Currently, many evaluations give up on sufficient repetition or rigorous statistical methods, or even run benchmarks only in training sizes. The results reported often lack proper variation estimates and, when a small difference between two systems is reported, some are simply unreliable. In contrast, we provide a statistically rigorous methodology for repetition and summarising results that makes efficient use of experimentation time. Time efficiency comes from two key observations. First, a given benchmark on a given platform is typically prone to much less non-determinism than the common worst-case of published corner-case studies. Second, repetition is most needed where most uncertainty arises (whether between builds, between executions or between iterations). We capture experimentation cost with a novel mathematical model, which we use to identify the number of repetitions at each level of an experiment necessary and sufficient to obtain a given level of precision. We present our methodology as a cookbook that guides researchers on the number of repetitions they should run to obtain reliable results. We also show how to present results with an effect size confidence interval. As an example, we show how to use our methodology to conduct throughput experiments with the DaCapo and SPEC CPU benchmarks on three recent platforms.


title: Lung nodule detection based on 3D convolutional neural networks

abstract: With the increasing number of lung cancer patients, the CAD system is playing an increasingly important rule in the field of automatic identification for medical images. Since the 3D characteristics of low-dose CT images make the 3D convolution more suitable than 2D convolution, in this paper, we propose a method to detect lung nodule of lung CT images using 3D convolutional neural networks. Combined with the traditional morphological preprocessing methods, 3D convolutional neural networks are applied to lung CT images. The experimental accuracy indicates that this method is suitable for the problem of lung nodule detection and has great room to improve. The experimental results also demonstrate that the application of deep learning in the medical field will bring great progress for medical development.


title: Efficient Safety Proofs for Industry-Scale Code Using Abstractions and Bounded Model Checking

abstract: Loop Abstraction followed by Bounded Model Checking, or LABMC in short, is a promising recent technique for proving safety of large programs. In an experimental setup proposed last year [14], LABMC was combined with slicing and Iterative Context Extension (ICE) with the aim of achieving scalability over industrial code. In this paper, we address two major limitations of that set-up, namely i) the inability of ICE to prune redundant code in a verification context, and ii) the unavailability of a tool that implements the set-up. We propose an improvement over ICE called Iterative Function Level Slicing (IFLS) and incorporate it in our tool called ELABMC, to offer an efficient implementation of [14]. We substantiate our claim with two sets of experiments over industrial applications as well as academic benchmarks. Quantifying the benefits of IFLS over traditional ICE in one, our results report that IFLS leads to 34.9% increase in efficiency, 17.7% improvement in precision, and scales in 14.2% more cases. With the second experiment, we show that ELABMC outperforms state-of-the-art verification techniques in the task of identifying static analysis warnings as false alarms.


title: Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

abstract: Battery energy storage systems have gained increasing interest for serving grid support in various application tasks. In particular, systems based on lithium-ion batteries have evolved rapidly with a wide range of cell technologies and system architectures available on the market. On the application side, different tasks for storage deployment demand distinct properties of the storage system. This review aims to serve as a guideline for best choice of battery technology, system design and operation for lithium-ion based storage systems to match a specific system application. Starting with an overview to lithium-ion battery technologies and their characteristics with respect to performance and aging, the storage system design is analyzed in detail based on an evaluation of real-world projects. Typical storage system applications are grouped and classified with respect to the challenges posed to the battery system. Publicly available modeling tools for technical and economic analysis are presented. A brief analysis of optimization approaches aims to point out challenges and potential solution techniques for system sizing, positioning and dispatch operation. For all areas reviewed herein, expected improvements and possible future developments are highlighted. In order to extract the full potential of stationary battery storage systems and to enable increased profitability of systems, future research should aim to a holistic system level approach combining not only performance tuning on a battery cell level and careful analysis of the application requirements, but also consider a proper selection of storage sub-components as well as an optimized system operation strategy.


title: SVELTE: Real-time intrusion detection in the Internet of Things

abstract: In the Internet of Things (IoT), resource-constrained things are connected to the unreliable and untrusted Internet via IPv6 and 6LoWPAN networks. Even when they are secured with encryption and authentication, these things are exposed both to wireless attacks from inside the 6LoWPAN network and from the Internet. Since these attacks may succeed, Intrusion Detection Systems (IDS) are necessary. Currently, there are no IDSs that meet the requirements of the IPv6-connected IoT since the available approaches are either customized for Wireless Sensor Networks (WSN) or for the conventional Internet. In this paper we design, implement, and evaluate a novel intrusion detection system for the IoT that we call SVELTE. In our implementation and evaluation we primarily target routing attacks such as spoofed or altered information, sinkhole, and selective-forwarding. However, our approach can be extended to detect other attacks. We implement SVELTE in the Contiki OS and thoroughly evaluate it. Our evaluation shows that in the simulated scenarios, SVELTE detects all malicious nodes that launch our implemented sinkhole and/or selective forwarding attacks. However, the true positive rate is not 100%, i.e., we have some false alarms during the detection of malicious nodes. Also, SVELTE's overhead is small enough to deploy it on constrained nodes with limited energy and memory capacity.


title: A Multi-Indicator Approach for Geolocalization of Tweets

abstract: Real-time information from microblogs like Twitter is useful for different applications such as market research, opinion mining, and crisis management. For many of those messages, location information is required to derive useful insights. Today, however, only around 1% of all tweets are explicitly geotagged. We propose the first multi-indicator method for determining (1) the location where a tweet was created as well as (2) the location of the user's residence. Our method is based on various weighted indicators, including the names of places that appear in the text message, dedicated location entries, and additional information from the user profile. An evaluation shows that our method is capable of locating 92% of all tweets with a median accuracy of below 30km, as well as predicting the user's residence with a median accuracy of below 5.1km. With that level of accuracy, our approach significantly outperforms existing work.


title: 2-2D differential gear mechanism for robot moving inside pipelines

abstract: This paper presents a mechanism for the robot moving inside of pipelines. The mechanism is composed of the 2-2D differential gear mechanism, the active adhesion mechanism, and the rescue mechanism. The 2-2D differential gear mechanism is designed to be driven with single actuator at any elbow type pipelines without any additional control efforts. The active adhesion mechanism is for providing sufficient adhesion forces of the robot on climbing over the pipelines against the gravity. In addition the rescue mechanism is designed for retrieving the robot safely in emergency situation. In this paper, three proposed mechanisms are explained in details and their effectiveness is experimentally demonstrated.


title: Learning compound multi-step controllers under unknown dynamics

abstract: Applications of reinforcement learning for robotic manipulation often assume an episodic setting. However, controllers trained with reinforcement learning are often situated in the context of a more complex compound task, where multiple controllers might be invoked in sequence to accomplish a higher-level goal. Furthermore, training such controllers typically requires resetting the environment between episodes, which is typically handled manually. We describe an approach for training chains of controllers with reinforcement learning. This requires taking into account the state distributions induced by preceding controllers in the chain, as well as automatically training reset controllers that can reset the task between episodes. The initial state of each controller is determined by the controller that precedes it, resulting in a non-stationary learning problem. We demonstrate that a recently developed method that optimizes linear-Gaussian controllers under learned local linear models can tackle this sort of non-stationary problem, and that training controllers concurrently with a corresponding reset controller only minimally increases training time. We also demonstrate this method on a complex tool use task that consists of seven stages and requires using a toy wrench to screw in a bolt. This compound task requires grasping and handling complex contact dynamics. After training, the controllers can execute the entire task quickly and efficiently. Finally, we show that this method can be combined with guided policy search to automatically train nonlinear neural network controllers for a grasping task with considerable variation in target position.


title: Ranking, Boosting, and Model Adaptation

abstract: We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaR ank, which has been shown to be empirically optimal for a widely used information retrieval measure. The algorithm is based on boosted regression trees, although the ideas apply to any weak learners, and it is significantly fast er in both train and test phases than the state of the art, for comparable accuracy. We also show how to find the optimal linear combination for any two ran kers, and we use this method to solve the line search problem exactly during boosting. In addition, we show that starting with a previously tra ined model, and boosting using its residuals, furnishes an effective techn ique for model adaptation, and we give results for a particularly pressing prob lem in Web Search - training rankers for markets for which only small amounts of labeled data are available, given a ranker trained on much more data from a larger market.


Featureless Domain-Specific Term Extraction with Minimal Labelled Data


title: Learning Fine-Scaled Depth Maps from Single RGB Images

abstract: Inferring the underlying depth map from a single image is an ill-posed and inherently ambiguous problem. State-of-the-art deep learning methods can now estimate accurate depth maps, but when projected into 3D, still lack local detail and are often highly distorted. We propose a multi-scale convolution neural network to learn from single RGB images fine-scaled depth maps that result in realistic 3D reconstructions. To encourage spatial coherency, we introduce spatial coordinate feature maps and a local relative depth constraint. In our network, the three scales are closely integrated with skip fusion layers, making it highly efficient to train with large-scale data. Experiments on the NYU Depth v2 dataset shows that our depth predictions are not only competitive with state-of-the-art but also leading to 3D reconstructions that are accurate and rich with detail.


title: Multi-scale Temporal Modeling for Dimensional Emotion Recognition in Video

abstract: Understanding nonverbal behaviors in human machine interaction is a complex and challenge task. One of the key aspects is to recognize human emotion states accurately. This paper presents our effort to the Audio/Visual Emotion Challenge (AVEC'14), whose goal is to predict the continuous values of the emotion dimensions arousal, valence and dominance at each moment in time. The proposed method utilizes deep belief network based models to recognize emotion states from audio and visual modalities. Firstly, we employ temporal pooling functions in the deep neutral network to encode dynamic information in the features, which achieves the first time scale temporal modeling. Secondly, we combine the predicted results from different modalities and emotion temporal context information simultaneously. The proposed multimodal-temporal fusion achieves temporal modeling for the emotion states in the second time scale. Experiments results show the efficiency of each key point of the proposed method and competitive results are obtained


title: Automated diagnosis of epileptic EEG using entropies

abstract: Abstract Epilepsy is a neurological disorder characterized by the presence of recurring seizures. Like many other neurological disorders, epilepsy can be assessed by the electroencephalogram (EEG). The EEG signal is highly non-linear and non-stationary, and hence, it is difficult to characterize and interpret it. However, it is a well-established clinical technique with low associated costs. In this work, we propose a methodology for the automatic detection of normal , pre-ictal , and ictal conditions from recorded EEG signals. Four entropy features namely Approximate Entropy ( ApEn ), Sample Entropy ( SampEn ), Phase Entropy 1 ( S1 ), and Phase Entropy 2 ( S2 ) were extracted from the collected EEG signals. These features were fed to seven different classifiers: Fuzzy Sugeno Classifier (FSC), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Probabilistic Neural Network (PNN), Decision Tree (DT), Gaussian Mixture Model (GMM), and Naive Bayes Classifier (NBC). Our results show that the Fuzzy classifier was able to differentiate the three classes with a high accuracy of 98.1%. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of epilepsy with higher accuracy.


title: Failure risk management of buried infrastructure using fuzzy-based techniques

abstract: The effective management of failure risk of buried infrastructure assets requires knowledge of their current condition, their rate of deterioration, the expected consequences of their failure and the owner's (decision-maker) risk tolerance. Fuzzy-based techniques seem to be particularly suited to modeling the deterioration of buried infrastructure assets, for which data are scarce, cause-effect knowledge is imprecise and observations and criteria are often expressed in vague (linguistic) terms (e.g., ‘good’, ‘fair’ ‘poor’ condition, and so on). The use of fuzzy sets and fuzzy-based techniques helps to incorporate inherent imprecision, uncertainties and subjectivity of available data, as well as to propagate these attributes throughout the model, yielding more realistic results. ::: ::: This paper is the second of two companion papers that describe an entire method of managing risk of large buried infrastructure assets. The first companion paper describes the deterioration modeling of buried infrastructure assets, using a fuzzy rule-based, non-homogeneous Markov process. This paper describes how the fuzzy condition rating of the asset is translated into a possibility of failure. This possibility of failure is combined with the fuzzy failure consequences to obtain fuzzy risk of failure throughout the life of the pipe. This life-risk curve can be used to make effective decisions on pipe renewal. These decisions include when to schedule the next inspection and condition assessment or alternatively, when to renew a deteriorated pipe, and what renewal alternative should be selected.


title: Focus cues affect perceived depth

abstract: Depth information from focus cuesVaccommodation and the gradient of retinal blurVis typically incorrect in threedimensional (3-D) displays because the light comes from a planar display surface. If the visual system incorporates information from focus cues into its calculation of 3-D scene parameters, this could cause distortions in perceived depth even when the 2-D retinal images are geometrically correct. In Experiment 1 we measured the direct contribution of focus cues to perceived slant by varying independently the physical slant of the display surface and the slant of a simulated surface specified by binocular disparity (binocular viewing) or perspective/texture (monocular viewing). In the binocular condition, slant estimates were unaffected by display slant. In the monocular condition, display slant had a systematic effect on slant estimates. Estimates were consistent with a weighted average of slant from focus cues and slant from disparity/texture, where the cue weights are determined by the reliability of each cue. In Experiment 2, we examined whether focus cues also have an indirect effect on perceived slant via the distance estimate used in disparity scaling. We varied independently the simulated distance and the focal distance to a disparity-defined 3-D stimulus. Perceived slant was systematically affected by changes in focal distance. Accordingly, depth constancy (with respect to simulated distance) was significantly reduced when focal distance was held constant compared to when it varied appropriately with the simulated distance to the stimulus. The results of both experiments show that focus cues can contribute to estimates of 3-D scene parameters. Inappropriate focus cues in typical 3-D displays may therefore contribute to distortions in perceived space.


title: Recent Trends in Lower-Limb Robotic Rehabilitation Orthosis: Control Scheme and Strategy for Pneumatic Muscle Actuated Gait Trainers

abstract: It is a general assumption that pneumatic muscle-type actuators will play an important role in the development of an assistive rehabilitation robotics system. In the last decade, the development of a pneumatic muscle actuated lower-limb leg orthosis has been rather slow compared to other types of actuated leg orthoses that use AC motors, DC motors, pneumatic cylinders, linear actuators, series elastic actuators (SEA) and brushless servomotors. However, recent years have shown that the interest in this field has grown exponentially, mainly due to the demand for a more compliant and interactive human-robotics system. This paper presents a survey of existing lower-limb leg orthoses for rehabilitation, which implement pneumatic muscle-type actuators, such as McKibben artificial muscles, rubbertuators, air muscles, pneumatic artificial muscles (PAM) or pneumatic muscle actuators (PMA). It reviews all the currently existing lower-limb rehabilitation orthosis systems in terms of comparison and evaluation of the design, as well as the control scheme and strategy, with the aim of clarifying the current and on-going research in the lower-limb robotic rehabilitation field.


Addendum to “Dominator Tree Certification and Divergent Spanning Trees”


title: Artificial Intelligence and Economic Theories

abstract: The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that are considered are: demand and supply, asymmetrical information, pricing, rational choice, rational expectation, game theory, efficient market hypotheses, mechanism design, prospect, bounded rationality, portfolio theory, rational counterfactual and causality. The benefit of this book is that it evaluates existing theories of economics and update them based on the developments in artificial intelligence field.


title: Feeding Preferences of the Drywood Termite, Cryptotermes brevis

abstract: Feeding preference order of micropterous Cryptotermes brevis Walker nymphs in homogeneous laminates of paired sapwood pieces was balsa > western redcedar > Douglas-fir > southern yellow pine by volume consumed, and cedar > fir > balsa > pine by weight consumed. Only pine was consumed in significantly less quantities both by volume and weight. In laminates with combinations of 2 wood species, significantly more volumes of balsa were consumed than of any other wood, significantly more volumes of fir and cedar than pine were consumed when they were paired with pine, but there was no significant difference in volume consumption of fir and cedar when they were paired. Almost no pine was consumed regardless of the wood species with which it was paired. The responses based on weight were similar, except that significantly more fir than cedar was consumed in combination with cedar. ::: ::: Significantly more volume or weight of balsa than “normal” (average consumption of balsa in homogeneous laminates) was consumed in combination with cedar or fir, and significantly more fir than normal was consumed in combination with pine; significantly less cedar or fir than normal was consumed in combinations with balsa. ::: ::: Nymphal development was accelerated in balsa, whether the balsa was present in homogeneous or combination laminates.


title: A multi-scale fusion convolutional neural network for face detection

abstract: Nowadays, more and more methods have been proposed to solve the problem of face detection based on computer implementation. Due to the variations in background, illumination, pose and facial expressions, the problem of machine face detection is complex. Recently, deep learning approaches achieve an impressive performance on face detection. In this paper, a model named Multi-Scale Fusion Convolutional Neural Network (MSF-CNN) is proposed to train the face detector. The model is trained by Convolutional Neural Network and detecting is based on the Viola & Jones detector's sliding windows structure. Particularly, in the process of feature extraction, we adopt the design of multi-scale feature fusion with different scale convolution kernels. The results are as follows: First, the fusion of multi-scale features are rich in the characteristics of learning, and the classification accuracy is higher than the single-scale. Second, we decrease the model of complexity compared with existed methods of the cascaded CNN. Third, we achieve end-to-end learning compared with cascaded separate training. Meanwhile, the proposed model has showed that the performance of results outperforms the previous methods in some well-known face detection benchmark datasets.


title: Predicting credit risk in Peer-to-Peer lending with survival analysis

abstract: Peer-to-peer lending is an online micro financing, where lenders and borrowers meet virtually for loan transactions. Along with the high growth, there is high risk in peer-to-peer lending due to uncollaterized loans, information asymmetry and lack of expertise on borrowers' creditworthiness. Therefore, it is highly necessary to analyze the credit risk of borrowers in peer-to-peer lending. Conventional credit scoring models can only classify loans into good and bad groups, but they fail to identify the timing of default. This paper proposes survival analysis approach to predict survival probabilities of loans in peer-to-peer lending at different time periods. The results from the survival analysis are effective in predicting the survival periods of the loans. Furthermore, the results from survival analysis are modeled for classification with neural networks.


title: Query suggestion for E-commerce sites

abstract: Query suggestion module is an integral part of every search engine. It helps search engine users narrow or broaden their searches. Published work on query suggestion methods has mainly focused on the web domain. But, the module is also popular in the domain of e-commerce for product search. In this paper, we discuss query suggestion and its methodologies in the context of e-commerce search engines. We show that dynamic inventory combined with long and sparse tail of query distribution poses unique challenges to build a query suggestion method for an e-commerce marketplace. We compare and contrast the design of a query suggestion system for web search engines and e-commerce search engines. Further, we discuss interesting measures to quantify the effectiveness of our query suggestion methodologies. We also describe the learning gained from exposing our query suggestion module to a vibrant community of millions of users.


title: Machine Learning Methods for Spamdexing Detection

abstract: In this paper, we present recent contributions for the battle against one of the main problems faced by search engines: the spamdexing or web spamming. They are malicious techniques used in web pages with the purpose of circumvent the search engines in order to achieve good visibility in search results. To better understand the problem and finding the best setup and methods to avoid such virtual plague, in this paper we present a comprehensive performance evaluation of several established machine learning techniques. In our experiments, we employed two real, public and large datasets: the WEBSPAM-UK2006 and the WEBSPAM-UK2007 collections. The samples are represented by content-based, link-based, transformed link-based features and their combinations. The found results indicate that bagging of decision trees, multilayer perceptron neural networks, random forest and adaptive boosting of decision trees are promising in the task of web spam classification.


title: A Photoplethysmograph Based Practical Heart Rate Estimation Algorithm for Wearable Platforms

abstract: We propose a practical Heart Rate Estimation algorithm utilizing wrist-based photoplethysmography (PPG) signals for continuous health monitoring of crane workers who spend long hours in an isolated cabin in the harsh factory environment. Our novelty lies in devising a low footprint algorithm that can reliably estimate Heart Rate in presence of motion artefact as well as offers the feasibility of deploying on a wearable platform. More particularly, our solution addresses two fundamental issues: a) correcting weak wrist PPG signal from frequent motion artefacts and b) identifying signal processing techniques that can be practically implemented on an embedded platform with limited resources in terms of memory and CPU. Experimental results demonstrate the validity of such algorithm and exhibit a great potential to be employed in the real field.


title: On Predicting Sociodemographic Traits and Emotions from Communications in Social Networks and Their Implications to Online Self-Disclosure

abstract: We study 1 the relation between user demographic attributes, opinions and emotions on a large sample of Twitter users. Our methodology is based on building models for inferring coarse-grained emotions and attributes from user generated content. We examine various user attributes, including age, gender, ethnicity, income, political preference, intelligence and relationship status. We correlate these demographics with the emotional profile emanating from the users’ tweets, as captured by Ekman’s emotion classification. We find that some users tend to express significantly more joy and significantly less sadness in their tweets, such as those in a relationship, with children, or with higher than average annual income or educational level. Female users tend to be more opinionated whereas male users tend to be more neutral. Finally, users who are younger, liberal and with a below average intelligence tend to project more negative opinions and emotions.


title: Minimally Needed Evidence for Complex Event Recognition in Unconstrained Videos

abstract: This paper addresses the fundamental question -- How do humans recognize complex events in videos? Normally, humans view videos in a sequential manner. We hypothesize that humans can make high-level inference such as an event is present or not in a video, by looking at a very small number of frames not necessarily in a linear order. We attempt to verify this cognitive capability of humans and to discover the Minimally Needed Evidence (MNE) for each event. To this end, we introduce an online game based event quiz facilitating selection of minimal evidence required by humans to judge the presence or absence of a complex event in an open source video. Each video is divided into a set of temporally coherent microshots (1.5 secs in length) which are revealed only on player request. The player's task is to identify the positive and negative occurrences of the given target event with minimal number of requests to reveal evidence. Incentives are given to players for correct identification with the minimal number of requests. Our extensive human study using the game quiz validates our hypothesis - 55% of videos need only one microshot for correct human judgment and events of varying complexity require different amounts of evidence for human judgment. In addition, the proposed notion of MNE enables us to select discriminative features, drastically improving speed and accuracy of a video retrieval system.


title: Design and Deployment of Aqua Monitoring System Using Wireless Sensor Networks and IAR-Kick

abstract: In Aquaculture, the yields (shrimp, fish etc.) depend on the water characteristics of the aquaculture pond. For maximizing fish yields, the parameters which are to be kept at certain optimal levels in water are dissolved oxygen, temperature, salinity, turbidity, pH level, alkalinity and hardness, ammonia and nutrient levels. These parameters can vary a lot during the period of a day and can rapidly change depending on the external environmental conditions. Hence it is necessary to monitor these parameters with high frequency, if not continuously, for timely analysis and action. This need accurate real- time information system and performance in order to maximize their potential. Wireless sensor networks are used to monitor aqua farms for relevant parameters, such as pH levels, humidity, dissolved oxygen levels, water temperature, ammonia levels etc. This system consists of two modules which are transmitter station and receiver station. The transmitter station consists of sensor nodes such as pH, humidity, and temperature inside and outside of water, and also microcontrollers, GSM, analog/digital converters. The receiver station consists of GSM module for receiving the sensing data from transmitter through GSM network. The receiver station receives data through the com port and stores in PC in order to achieve human-computer interface. The graphical user interface was designed, so that farmers and investigators can observe, investigate and analyze the related data. The user interface allows us to convey the analyzed data in the form of a message to the farmers in their respective local languages to their Mobile Phones and alerts them in unhygienic environmental conditions. With this even semi-literate farmers can interact with the system and can understand the information in order to take suitable actions.