title: Control of a self-balancing robot driven by DC motors via IDA-PBC

abstract: The self-balancing robot is an example of underactuated mechanical systems; a class of mechanisms which have been very popular as benchmarks for control methods in the last years. The Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) is a control methodology designed to solve the stabilization problem of underactuated systems. In this paper, we present an application of this control methodology to a self-balancing robot, by using an extension of the IDA-PBC method, which includes actuator dynamics. Based on the Lyapunov direct method we carry out stability analysis and, by invoking the theorem of Barbashin---Krasovskii, we arrive to asymptotic stability conditions. Experimental results are shown to illustrate the performance and viability of the proposed control.


title: The Spanish Adaptation of the Sport Motivation Scale-II in Adolescent Athletes

abstract: The aim of this study was to adapt and validate the Spanish version of the Sport Motivation Scale-II (S-SMS-II) in adolescent athletes. The sample included 766 Spanish adolescents (263 females and ...


title: Comparison of primary physician patterns of and attitudes on emergency department use.

abstract: OBJECTIVE ::: This study aimed to compare physician viewpoints and clinic patterns between primary care providers (PCPs) with high patient emergency department (ED) use (HU) and PCPs with low patient ED use (LU). ::: ::: ::: METHODS ::: We conducted a mixed methods descriptive study of quantitative and qualitative data of 22 practices. We compared admission rates, American Academy of Pediatrics guideline adherence, efficiency, medical complexity, and patient satisfaction. Primary care provider interviews regarding ED use practices and perspectives were coded and inductively analyzed using Atlas 6.0 for themes. ::: ::: ::: RESULTS ::: Compared with LU, the HU group had a higher admission rate (92 vs 41 admissions per 1000 members, P = 0.005), lower scores in adherence to American Academy of Pediatrics guidelines, and higher scores in satisfaction overall. There were no significant differences in efficiency, medical complexity, PCP communications, timeliness for appointment, satisfaction with after-hour care or likelihood of PCP referral. All PCPs described the EDs' purpose as for things they "cannot handle." The LU group was more likely to identify the ED for emergencies, whereas the HU group had a broader, more ambiguous definition of what they "cannot handle," with parental anxiety identified as a significant factor. In addition, the LU group recognized the need for more parental education about ED use. ::: ::: ::: CONCLUSIONS ::: Primary care providers with low patient ED use were more likely to describe the EDs' purpose as being for emergencies and to recognize a need for more parental education about the use of the ED. All physicians struggled with reassuring parents.


title: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection

abstract: Big data has had a great share in the success of deep learning in computer vision. Recent works suggest that there is significant further potential to increase object detection performance by utilizing even bigger datasets. In this paper, we introduce the EuroCity Persons dataset, which provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving vehicle in 31 cities of 12 European countries. With over 238200 person instances manually labeled in over 47300 images, EuroCity Persons is nearly one order of magnitude larger than person datasets used previously for benchmarking. The dataset furthermore contains a large number of person orientation annotations (over 211200). We optimize four state-of-the-art deep learning approaches (Faster R-CNN, R-FCN, SSD and YOLOv3) to serve as baselines for the new object detection benchmark. In experiments with previous datasets we analyze the generalization capabilities of these detectors when trained with the new dataset. We furthermore study the effect of the training set size, the dataset diversity (day- vs. night-time, geographical region), the dataset detail (i.e. availability of object orientation information) and the annotation quality on the detector performance. Finally, we analyze error sources and discuss the road ahead.


title: Occurrence, characterisation and fate of (nano)particulate Ti and Ag in two Norwegian wastewater treatment plants

abstract: Abstract Due to their widespread application in consumer products, elemental titanium (e.g., titanium dioxide, TiO2) and silver (Ag), also in nanoparticulate form, are increasingly released from households and industrial facilities to urban wastewater treatment plants (WWTPs). A seven-day sampling campaign was conducted in two full-scale WWTPs in Trondheim (Norway) employing only primary treatment. We assessed the occurrence and elimination of Ti and Ag, and conducted size-based fractionation using sequential filtration of influent samples to separate particulate, colloidal and dissolved fractions. Eight-hour composite influent samples were collected to assess diurnal variations in total Ti and Ag influx. Measured influent Ti concentrations (up to 290 μg L−1) were significantly higher than Ag ( 0.7 μm). Removal efficiencies ≥70% were observed for both elements, requiring for one WWTP to account for the high Ti content (∼2 g L−1) in the flocculant. Nano- and micron-sized Ti particles were observed with scanning transmission electron microscopy (STEM) in influent, effluent and biosolids, while Ag nanoparticles were detected in biosolids only. Diurnal profiles of influent Ti were correlated to flow and pollutant concentration patterns (especially total suspended solids), with peaks during the morning and/or evening and minima at night, indicating household discharges as predominant source. Irregular profiles were exhibited by influent Ag, with periodic concentration spikes suggesting short-term discharges from one or few point sources (e.g., industry). Influent Ti and Ag dynamics were reproduced using a disturbance scenario generator model, and we estimated per capita loads of Ti (42–45 mg cap−1 d−1) and Ag (0.11 mg cap−1 d−1) from households as well as additional Ag load (14–22 g d−1) from point discharge. This is the first study to experimentally and mathematically describe short-term release dynamics and dry-weather sources of emissions of Ti and Ag in municipal WWTPs and receiving environments.


title: Scalable Graph Embedding for Asymmetric Proximity.

abstract: Graph Embedding methods are aimed at mapping each vertex into a low dimensional vector space, which preserves certain structural relationships among the vertices in the original graph. Recently, several works have been proposed to learn embeddings based on sampled paths from the graph, e.g., DeepWalk, Line, Node2Vec. However, their methods only preserve symmetric proximities, which could be insufficient in many applications, even the underlying graph is undirected. Besides, they lack of theoretical analysis of what exactly the relationships they preserve in their embedding space. In this paper, we propose an asymmetric proximity preserving (APP) graph embedding method via random walk with restart, which captures both asymmetric and high-order similarities between node pairs. We give theoretical analysis that our method implicitly preserves the Rooted PageRank score for any two vertices. We conduct extensive experiments on tasks of link prediction and node recommendation on open source datasets, as well as online recommendation services in Alibaba Group, in which the training graph has over 290 million vertices and 18 billion edges, showing our method to be highly scalable and effective.


Deterministic Attention for Sequence-to-Sequence Constituent Parsing.


title: Treatment of acute infectious diarrhoea in infants and children with a mixture of three Lactobacillus rhamnosus strains--a randomized, double-blind, placebo-controlled trial.

abstract: BACKGROUND ::: Multiple studies document that probiotics are effective in treating infectious diarrhoea in children. Lactobacillus rhamnosus GG is the most extensively studied but effectiveness of other strains has been poorly examined. ::: ::: ::: AIM ::: To determine whether L. rhamnosus strains (573L/1; 573L/2; 573L/3) (Lakcid L, Biomed, Lublin, Poland) would be effective in shortening infectious diarrhoea. ::: ::: ::: METHODS ::: In a randomized, double-blind, placebo-controlled trial, 87 children (age range: 2 months to 6 years) with infectious diarrhoea were administered Lakcid L at a dose 1.2 x 10(10) CFU or placebo, twice daily, for 5 days. Primary outcome measure was the duration of diarrhoea. Secondary measures were duration of parenteral rehydration, adverse events, and gastrointestinal tract colonization by administered strains. ::: ::: ::: RESULTS ::: In an intention to treat analysis of 87 children, the mean duration of diarrhoea in the treated group: 84 +/- 56 h; placebo: 96 +/- 72 h (P = 0.36). In rotavirus infection: 76 +/- 35 h vs. 115 +/- 67 h (P = 0.03), respectively. Duration of parenteral rehydration: 15 +/- 14 h vs. 38 +/- 33 h (P = 0.006). Gut colonization by administered strains was 80% and 41% at five and 14 days, respectively. No adverse events were noted. ::: ::: ::: CONCLUSIONS ::: Administration of L. rhamnosus strains shortens the duration of rotaviral diarrhoea in children but not of diarrhoea of any aetiology. Intervention shortens the time of intravenous rehydration.


title: Social media analytics and research test-bed (SMART dashboard)

abstract: We developed a social media analytics and research testbed (SMART) dashboard for monitoring Twitter messages and tracking the diffusion of information in different cities. SMART dashboard is an online geo-targeted search and analytics tool, including an automatic data processing procedure to help researchers to 1) search tweets in different cities; 2) filter noise (such as removing redundant retweets and using machine learning methods to improve precision); 3) analyze social media data from a spatiotemporal perspective, and 4) visualize social media data in various ways (such as weekly and monthly trends, top URLs, top retweets, top mentions, or top hashtags). By monitoring social messages in geo-targeted cities, we hope that SMART dashboard can assist researchers investigate and monitor various topics, such as flu outbreaks, drug abuse, and Ebola epidemics at the municipal level.


title: The Wound Healing Process

abstract: Wound healing, long neglected, has for a number of years begun to receive the attention it deserves in both scientific and clinical circles. This review of the wound healing process is divided into two sections: basic science aspects of wound healing and practical aspects of wound healing. The first section deals with the current knowledge of the three phases of wound healing. The second section is subdivided into acute and chronic wounds. Methods to speed healing of acute wounds and a problem-oriented approach to the management of chronic wounds is discussed, including the potential treatment options based on the causes of wounds.


title: An outdoor high-accuracy local positioning system for an autonomous robotic golf greens mower

abstract: This paper presents a high-accuracy local positioning system (LPS) for an autonomous robotic greens mower. The LPS uses a sensor tower mounted on top of the robot and four active beacons surrounding a target area. The proposed LPS concurrently determines robot location using a lateration technique and calculates orientation using angle measurements. To perform localization, the sensor tower emits an ultrasonic pulse that is received by the beacons. The time of arrival is measured by each beacon and transmitted back to the sensor tower. To determine the robot's orientation, the sensor tower has a circular receiver array that detects infrared signals emitted by each beacon. Using the direction and strength of the received infrared signals, the relative angles to each beacon are obtained and the robot orientation can be determined. Experimental data show that the LPS achieves a position accuracy of 3.1 cm RMS, and an orientation accuracy of 0.23° RMS. Several prototype robotic mowers utilizing the proposed LPS have been deployed for field testing, and the mowing results are comparable to an experienced professional human worker.


title: Scalable Topical Phrase Mining from Text Corpora

abstract: While most topic modeling algorithms model text corpora with unigrams, human interpretation often relies on inherent grouping of terms into phrases. As such, we consider the problem of discovering topical phrases of mixed lengths. Existing work either performs post processing to the results of unigram-based topic models, or utilizes complex n-gram-discovery topic models. These methods generally produce low-quality topical phrases or suffer from poor scalability on even moderately-sized datasets. We propose a different approach that is both computationally efficient and effective. Our solution combines a novel phrase mining framework to segment a document into single and multi-word phrases, and a new topic model that operates on the induced document partition. Our approach discovers high quality topical phrases with negligible extra cost to the bag-of-words topic model in a variety of datasets including research publication titles, abstracts, reviews, and news articles.


Robust Speech Recognition using Long Short-Term Memory Recurrent Neural Networks for Hybrid Acoustic Modelling


title: Automatic classification of movie trailers using data mining techniques: A review

abstract: Nowadays huge amount of data is generated through various activities. To manage the data in proper format and the way it is to be maintained. There are so many ways to maintain the data. Classification of data is done so as to identify and put that data in different classes. It is nothing but to group that data in different classes and retrieve as and when it is required with ease. The classification of data is to make the user aware the class of that data on the basis of certain attributes. In this paper we basically focus on the movie trailers using different data mining approaches to classify the videos. The amount of video that a viewer has to choose from is now so large that it is infeasible for a human to go through it all to find movie of interest. Here various methods and techniques are discussed for classification of video and movie trailers.


title: Reinforcement Learning with Unsupervised Auxiliary Tasks

abstract: Deep reinforcement learning agents have achieved state-of-the-art results by directly maximising cumulative reward. However, environments contain a much wider variety of possible training signals. In this paper, we introduce an agent that also maximises many other pseudo-reward functions simultaneously by reinforcement learning. All of these tasks share a common representation that, like unsupervised learning, continues to develop in the absence of extrinsic rewards. We also introduce a novel mechanism for focusing this representation upon extrinsic rewards, so that learning can rapidly adapt to the most relevant aspects of the actual task. Our agent significantly outperforms the previous state-of-the-art on Atari, averaging 880\% expert human performance, and a challenging suite of first-person, three-dimensional \emph{Labyrinth} tasks leading to a mean speedup in learning of 10$\times$ and averaging 87\% expert human performance on Labyrinth.


title: An ontology-based data integration approach for web analytics in e-commerce

abstract: A semantic approach to represent and consolidate web analytic data is proposed.An OWL ontology for web analytics in e-commerce is designed and proposed.The proposed approach is validated with tracking data of 15 real-world e-shops.Obtained semantized data successfully train advanced data mining algorithms.We provide actual e-shops with tools to enhance their commercial activities. Web analytics has emerged as one of the most important activities in e-commerce, since it allows companies and e-merchants to track the behavior of customers when visiting their web sites. There exist a series of tools for web analytics that are used not only for tracking and measuring web traffic, but also for analyzing the commercial activity. However, most of these tools focus on low level web attributes and metrics, making other sophisticated functionalities and analyses only available for commercial (non-free) versions.In this context, the SME-Ecompass European initiative aims at providing e-commerce SMEs with accessible tools for high level web analytics. These software facilities should use different sources of data coming from digital footprints allocated in e-shops, to fuse them together in a coherent way, and to make them available for advanced data mining procedures. This motivated us to propose in this work an ontology-based approach to collect, integrate and store web analytics data, from many sources of popular and commercial digital footprints. As article's main impact, we obtain enriched and semantically annotated data that is used to properly train an intelligent system, involving data mining procedures, for the analysis of customer behavior in real e-commerce sites. In concrete, for the validation of our semantic approach, we have captured and integrated data from Google Analytics and Piwik digital footprints allocated in 15 e-shops of different commercial sectors and countries (UK, Spain, Greece and Germany), throughout several months of activity. The obtained results show different perspectives in customer's behavior analysis that go one step beyond the most popular web analytics tools in the current market.


title: Optimising 5G infrastructure markets: The business of network slicing

abstract: In addition to providing substantial performance enhancements, future 5G networks will also change the mobile network ecosystem. Building on the network slicing concept, 5G allows to “slice” the network infrastructure into separate logical networks that may be operated independently and targeted at specific services. This opens the market to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, we need new algorithms for the allocation of network resources that consider these new players. In this paper, we address this issue by designing an algorithm for the admission and allocation of network slices requests that (i) maximises the infrastructure provider's revenue and (ii) ensures that the service guarantees provided to tenants are satisfied. Our key contributions include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modelled as a Semi-Markov Decision Process) and the optimisation of the infrastructure provider's revenue, and (iii) the design of an adaptive algorithm (based on Q-learning) that achieves close to optimal performance.


title: A fast high utility itemsets mining algorithm

abstract: Association rule mining (ARM) identifies frequent itemsets from databases and generates association rules by considering each item in equal value. However, items are actually different in many aspects in a number of real applications, such as retail marketing, network log, etc. The difference between items makes a strong impact on the decision making in these applications. Therefore, traditional ARM cannot meet the demands arising from these applications. By considering the different values of individual items as utilities, utility mining focuses on identifying the itemsets with high utilities. As "downward closure property" doesn't apply to utility mining, the generation of candidate itemsets is the most costly in terms of time and memory space. In this paper, we present a Two-Phase algorithm to efficiently prune down the number of candidates and can precisely obtain the complete set of high utility itemsets. In the first phase, we propose a model that applies the "transaction-weighted downward closure property" on the search space to expedite the identification of candidates. In the second phase, one extra database scan is performed to identify the high utility itemsets. We also parallelize our algorithm on shared memory multi-process architecture using Common Count Partitioned Database (CCPD) strategy. We verify our algorithm by applying it to both synthetic and real databases. It performs very efficiently in terms of speed and memory cost, and shows good scalability on multiple processors, even on large databases that are difficult for existing algorithms to handle.


title: Stochastic modeling of a power-managed system: construction and optimization

abstract: The goal of a dynamic power management policy is to reduce the power consumption of an electronic system by putting system components into different states, each representing certain performance and power consumption level. The policy determines the type and timing of these transitions based on the system history, workload and performance constraints. In this paper we propose a new abstract model of a power-managed electronic system. We formulate the problem of system-level power management as a controlled optimization problem based on the theories of continuous-time Markov decision processes and stochastic networks. This problem is solved exactly and efficiently using a "policy iteration" approach. Our method is compared with existing heuristic approaches for different workload statistics. Experimental results show that power management method based on Markov decision process outperforms heuristic approaches in terms of power dissipation savings for a given level of system performance.


title: Capturing Individual and Group Behavior with Wearable Sensors

abstract: We show how to obtain high level descriptions of human behavior in terms of physical activity, speech activity, face-to-face interaction (f2f), physical proximity, and social network attributes from sensor data. We present experimental results that showthat it is possible to identify individual personality traits as well as subjective and objective group performance metrics from low level data collected using wearable sensors.


title: Stadtpilot: First fully autonomous test drives in urban traffic

abstract: The Stadtpilot project aims at autonomous driving on Braunschweig's inner city ring road. For this purpose, an autonomous vehicle called “Leonie” has been developed. In October 2010, after two years of research, “Leonie's” abilities were presented in a public demonstration. This vehicle is one of the first worldwide to show the ability of driving autonomously in real urban traffic scenarios. This paper describes the legal issues and the homologation process for driving autonomously in public traffic in Braunschweig, Germany. It also dwells on the Safety Concept, the system architecture and current research activities.


title: The Effect of the Process Writing Approach on Writing Success and Anxiety.

abstract: AbstractThe process writing approach treats writing not as a completed product but as a process. Writing studies are carried out as a part of the process before the written text is completed. This approach focuses on the student in writing lessons, and the teacher only acts as a guide. The process writing approach involves activities occurring during the production of a written text. This study investigated the effect of the process writing approach on writing success and anxiety. The participants in this study were first-year preschool teaching students; a pretestposttest control group quasi-experimental design was employed. Experimental procedures for the study lasted 10 weeks. Data regarding the dimension of written expression were obtained through evaluation of academic writings generated by students at the beginning and end of experimental procedures. Data associated with writing anxiety were collected through the Writing Apprehension Test. Analysis of covariance (ANCOVA) was used for statistical analysis of the data. As a result of the statistical analysis, the study found that the process writing approach had a significant effect on writing success and anxiety. Based on this finding, the use of process writing approach is recommended for written expression studies.Key WordsProcess Writing Approach, Writing Anxiety, Writing Expression, Language Skills, Preschool Teacher Candidates.Written expression is a difficult language skill for students. The fact that writing activities in schools are mostly oriented toward formal features (Gocer, 2011; Temizkan, 2007; Ulper, 2012) is among the reasons for this difficulty. It is preferable to give priority to content characteristics in the act of writing that aims at transferring a message. Moreover, receiving only negative feedback related to errors leads to writing anxiety in students (Barnett, 1992; Madigan, Linton, & Johnson, 1996). This can result in unsuccessful acts of writing and consequently, unsuccessful texts. However, this problem can be solved if evaluation of the written text is conducted as part of the writing process. This study aims to determine the effect of Flower and Hayes's (1981) Process Writing Approach, which allows the written product to be checked and evaluated during the writing process, on students' writing success and anxiety.The Process Writing ApproachFour different approaches have been proposed for writing: the Schmidt model, the Van Galen model, the Hayes and Flower model, and the Hayes model (Gune?, 2007). In the approach developed by Flower and Hayes (1981), the points to be realized throughout the writing process are focused on improvement of the written product. In this approach, three dimensions of the act of writing are emphaa sized: the task environment, long-term memory, and the writing process. Two important units of the task environment are the topic and the text. The writer's ability to determine and present the topic is a major part of the writing process. The writer's long-term memory is important for retrieving information from memory or activating hints that will realize it. The retrieved information is expected to be integrated into the topic. Although correct information about a specific topic can be retrieved, sometimes it cannot be organized in a way the reader can understand (McCurdy, Schmitz, & Albertson, 2010). The writing process is the third unit of the cognitive process approach. This unit is composed of the stages of planning, translating, and reviewing. Planning refers to content production and organization (Graham, 2006). During the translation stage, opinions are transformed into written language, and the writer addresses numerous details of written language such as syntax, grammar, and spelling. During review, the writer reads to develop the text or correct any errors. Constant monitoring during the writing process serves to determine the transitions from one phase to another (Flower & Hayes, 1981). …


title: Dolphin Shows and Interaction Programs: Benefits for Conservation Education?

abstract: Dolphin shows and dolphin interaction programs are two types of education programs within zoological institutions used to educate visitors about dolphins and the marine environment. The current study examined the short- and long-term effects of these programs on visitors' conservation-related knowledge, attitude, and behavior. Participants of both dolphin shows and interaction programs demonstrated a significant short-term increase in knowledge, attitudes, and behavioral intentions. Three months following the experience, participants of both dolphin shows and interaction programs retained the knowledge learned during their experience and reported engaging in more conservation-related behaviors. Additionally, the number of dolphin shows attended in the past was a significant predictor of recent conservation-related behavior suggesting that repetition of these types of experiences may be important in inspiring people to conservation action. These results suggest that both dolphin shows and dolphin interaction programs can be an important part of a conservation education program for visitors of zoological facilities. Zoo Biol. 32:45-53, 2013. © 2012 Wiley Periodicals, Inc.


title: On the (Statistical) Detection of Adversarial Examples

abstract: Machine Learning (ML) models are applied in a variety of tasks such as network intrusion detection or Malware classification. Yet, these models are vulnerable to a class of malicious inputs known as adversarial examples. These are slightly perturbed inputs that are classified incorrectly by the ML model. The mitigation of these adversarial inputs remains an open problem. As a step towards understanding adversarial examples, we show that they are not drawn from the same distribution than the original data, and can thus be detected using statistical tests. Using thus knowledge, we introduce a complimentary approach to identify specific inputs that are adversarial. Specifically, we augment our ML model with an additional output, in which the model is trained to classify all adversarial inputs. We evaluate our approach on multiple adversarial example crafting methods (including the fast gradient sign and saliency map methods) with several datasets. The statistical test flags sample sets containing adversarial inputs confidently at sample sizes between 10 and 100 data points. Furthermore, our augmented model either detects adversarial examples as outliers with high accuracy (> 80%) or increases the adversary's cost - the perturbation added - by more than 150%. In this way, we show that statistical properties of adversarial examples are essential to their detection.


title: Evaluation of gold nanoparticle based lateral flow assays for diagnosis of enterobacteriaceae members in food and water.

abstract: Abstract Lateral flow immunoassays (LFIAs) are advantageous over conventional detection methods in terms of their simplicity and rapidity. These assays have been reported using various types of labels but colloidal gold nanoparticles are still the preferred choice as a label because of their easy synthesis, visual detection and stability. Bacterial contamination of food and drinking water is a major threat and hindrance towards ensuring food and water safety. Enterobacteriaceae family members are mainly transmitted by the consumption of contaminated water and food and implicated in various food or water borne infections. The LFIAs have been popularly used for detection of bacterial cells in different matrices. Therefore, this review intends to provide an analysis of the gold nanoparticle based lateral flow assays developed for detecting enterobacteriaceae family members in food and water samples. The review includes detailed data and discusses the factors that influence the performance of LFIAs and their shortcomings.


- 1-2012 State of the art satellite and airborne marine oil spill remote sensing : Application to the BP Deepwater Horizon oil spill


title: A Universal-Input Single-Stage High-Power-Factor Power Supply for HB-LEDs Based on Integrated Buck-Flyback Converter

abstract: Due to the high rise in luminous efficiency that HB-LEDs have experienced in the last recent years, many new applications have been researched. In this paper, a streetlight LED application will be covered, using the Integrated Buck-Flyback Converter developed in previous works, which performs power factor correction (PFC) from a universal ac source, as well as a control loop using the LM3524 IC, which allows PWM dimming operation mode. Firstly, the LED load will be linearized and modeled in order to calculate the IBFC topology properly. Afterwards, the converter will be calculated, presenting the one built in the lab. Secondly, the converter will be modeled in order to build the closed loop system, modeling the current sensor as well in order to develop an adequate controller. Finally, experimental results obtained from the lab tests will be presented.


Including an Autistic Middle School Child in General Physical Education: A CASE STUDY


title: FPGA implementation of moving object detection in frames by using background subtraction algorithm

abstract: In this paper a new background subtraction algorithm was developed to detect moving objects from a stable system in which visual surveillance plays a major role. Among all existing algorithms it was choosen because of low computational complexity which is the major parameter of time in VLSI. The concept of the background subtraction is to subtract the current image with respect to the reference image and compare it with to the certain threshold values. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. The test results are seen to be satisfactory. The area taken and the speed of the algorithm are also evaluated.


title: FashionBrain Project: A Vision for Understanding Europe's Fashion Data Universe

abstract: A core business in the fashion industry is the understanding and ::: prediction of customer needs and trends. Search engines and social ::: networks are at the same time a fundamental bridge and a costly ::: middleman between the customer’s purchase intention and the ::: retailer. To better exploit Europe’s distinctive characteristics e.g., ::: multiple languages, fashion and cultural differences, it is pivotal to ::: reduce retailers’ dependence to search engines. This goal can be ::: achieved by harnessing various data channels (manufacturers and ::: distribution networks, online shops, large retailers, social media, ::: market observers, call centers, press/magazines etc.) that retailers ::: can leverage in order to gain more insight about potential buyers, ::: and on the industry trends as a whole. This can enable the creation ::: of novel on-line shopping experiences, the detection of influencers, ::: and the prediction of upcoming fashion trends. ::: In this paper, we provide an overview of the main research ::: challenges and an analysis of the most promising technological ::: solutions that we are investigating in the FashionBrain project.


title: Exploring sub-20nm FinFET design with predictive technology models

abstract: Predictive MOSFET models are critical for early stage design-technology co-optimization and circuit design research. In this work, Predictive Technology Model files for sub-20nm multi-gate transistors have been developed (PTM-MG). Based on MOSFET scaling theory, the 2011 ITRS roadmap and early stage silicon data from published results, PTM for FinFET devices are generated for 5 technology nodes corresponding to the years 2012-2020 on the ITRS roadmap.


title: Learning analytics and educational data mining: towards communication and collaboration

abstract: Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.


title: Brain abnormalities in murderers indicated by positron emission tomography

abstract: Murderers pleading not guilty by reason of insanity (NGRI) are thought to have brain dysfunction, but there have been no previous studies reporting direct measures of both cortical and subcortical brain functioning in this specific group. Positron emission tomography brain imaging using a continuous performance challenge task was conducted on 41 murderer's pleading not guilty by reason of insanity and 41 age- and sex-matched controls. Murderer's were characterized by reduced glucose metabolism in the prefrontal cortex, superior parietal gyrus, left angular gyrus, and the corpus callosum, while abnormal asymmetries of activity (left hemisphere lower than right) were also found in the amygdala, thalamus, and medial temporal lobe. These preliminary findings provide initial indications of a network of abnormal cortical and subcortical brain processes that may predispose to violence in murderers pleading NGRI.


title: A Review of Ultra-Low-Power and Low-Cost Transceiver Design

abstract: This paper reviews trends for ultra-low-power wireless transceiver system and integrated circuit design under the perspective of using cost-effective CMOS technology nodes. These efficient transceiver structures typically find application in devices such as fitness monitors and other wearable healthcare devices, Internet of Things (IoT) devices and generic sensor nodes. A brief overview of State-of-the-Art (SoA) architectures, techniques, and performance metrics of ultra-low-power transmitter and receiver structures is presented. The often conflicting demands of communication range, data rate, reliability, and energy consumption are examined and a typical case study of a transceiver for medical sensor nodes is presented.


title: Increasing the voltage of PFN impulse generator capacitors by converting it to boost converter

abstract: This paper presents a new method to increase the magnitude of impulse square wave of Pulse Forming Grid (PFN) and capability of producing the repetitive impulse waves by temporary converting it to Boost Converter (BC). For achieving to this goal the PFN inductors and capacitors are connected in series and parallel respectively using solid state switches and during this procedure the PFN impulse generator converts to BC and increases the voltage of capacitors uniformly to the considerable value. This technique causes to increase the discharging voltage level to the load many times more than its DC voltage source without using additional converter or transformer. The yielding results from numeric simulations in MATLAB prove this technique.


title: Introduction to Recommender Systems Handbook

abstract: Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.


title: Novel selection policies for container-based cloud deployment models

abstract: Application deployment models have been introduced over the last decades. These models are characterized by low resource utilization efficiencies which are improved by the appearance of virtualization technologies. Currently, a more efficient utilization model called containers type virtualization has appeared. This allows different applications to share the same operating system kernel resulting in a superior application density. However, the developed infrastructures and frameworks contribute to the energy consumption and violation rates of the service level agreement. In this paper, a novel container and host selection policies for cloud deployment models are proposed. Experimental results show that the proposed policies achieve better energy and services level agreement commitment compared to the other competitors. This paper, demonstrate the existence of an association between container and host selection policies.


title: Smart and pervasive ICU based-IoT for improving intensive health care

abstract: Setting up a smart and pervasive environment is one of the current challenges being investigated in several research topics. Among the panoply of applications enabled by the Internet of Things (IoT), smart and connected health care is a particularly important one. Networked sensors, either worn on the body or embedded in our living environments, make possible the gathering of rich information indicative of our physical and mental health. Design a smart intensive care units is an original idea and a recent research topic which is tackled in this work. First, in this paper, we highlight the opportunities and challenges for IoT in realizing this vision of the future of health care and then, it is devoted to attainment of new patient monitoring intelligent system in ICUs in order to improve medical care service performance. We offer through this work, an hybrid architecture over a single platform for a visual patient monitoring system for Automatic Detection of risk Situations and Alert (ADSA) using a multi-camera system and collaborative medical sensors network.


title: Cleaning structured event logs: A graph repair approach

abstract: Event data are often dirty owing to various recording conventions or simply system errors. These errors may cause many serious damages to real applications, such as inaccurate provenance answers, poor profiling results or concealing interesting patterns from event data. Cleaning dirty event data is strongly demanded. While existing event data cleaning techniques view event logs as sequences, structural information do exist among events. We argue that such structural information enhances not only the accuracy of repairing inconsistent events but also the computation efficiency. It is notable that both the structure and the names (labeling) of events could be inconsistent. In real applications, while unsound structure is not repaired automatically (which needs manual effort from business actors to handle the structure error), it is highly desirable to repair the inconsistent event names introduced by recording mistakes. In this paper, we propose a graph repair approach for 1) detecting unsound structure, and 2) repairing inconsistent event name.


title: Query-Focused Multidocument Summarization Based on Hybrid Relevance Analysis and Surface Feature Salience

abstract: Query-focused multidocument summarization is to synthesize from a set of topic-related documents a brief, well-organized, fluent summary for the purpose of answering an information need that cannot be met by just stating a name, date, quantity, etc. In this paper, the task is essentially treated as a sentence retrieval task. We propose a hybrid relevance analysis to evaluate the relevance of a sentence to the query. This is achieved by combining similarities computed from the vector space model and latent semantic analysis. Surface features are also examined to discern the impact of low-level features for query-focused multidocument summarization. In addition, a modified Maximal Marginal Relevance is proposed to reduce redundancy by taking into account shallow feature salience. The experimental results show the proposed method obtained competitive results when evaluated with the DUC 2005 corpus.


title: Similarity Learning with Spatial Constraints for Person Re-identification

abstract: Pose variation remains one of the major factors that adversely affect the accuracy of person re-identification. Such variation is not arbitrary as body parts (e.g. head, torso, legs) have relative stable spatial distribution. Breaking down the variability of global appearance regarding the spatial distribution potentially benefits the person matching. We therefore learn a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion. In particular, we take advantage of the recently proposed polynomial feature map to describe the matching within each subregion, and inject all the feature maps into a unified framework. The framework not only outputs similarity measurements for different regions, but also makes a better consistency among them. Our framework can collaborate local similarities as well as global similarity to exploit their complementary strength. It is flexible to incorporate multiple visual cues to further elevate the performance. In experiments, we analyze the effectiveness of the major components. The results on four datasets show significant and consistent improvements over the state-of-the-art methods.


title: User-based meta-search with the co-citation graph

abstract: Although there are numerous search engines in the Web environment, no one could claim producing reliable results in all conditions. This problem is becoming more serious considering the exponential growth of the number of Web resources. In the response to these challenges, the meta-search engines are introduced to enhance the search process by devoting some outstanding search engines as their information resources. In recent years, some approaches are proposed to handle the result combination problem which is the fundamental problem in the meta-search environment. In this paper, a new merging/re-ranking method is introduced which uses the characteristics of the Web co-citation graph that is constructed from search engines and returned lists. The information extracted from the co-citation graph, is combined and enriched by the userspsila click-through data as their implicit feedback in an adaptive framework. Experimental results show a noticeable improvement against the basic method as well as some well-known meta-search engines.


title: Edge-Coloring Partialk-Trees

abstract: Many combinatorial problems can be efficiently solved for partialk-trees (graphs of treewidth bounded byk). The edge-coloring problem is one of the well-known combinatorial problems for which no efficient algorithms were previously known, except a polynomial-time algorithm of very high complexity. This paper gives a linear-time sequential algorithm and an optimal parallel algorithm which find an edge-coloring of a given partialk-tree with the minimum number of colors for fixedk.


title: Hybrid multilevel inverter with single DC source

abstract: A hybrid multilevel inverter model based on PSIM and MATLAB/SIMULINK is presented in this paper. It consists of a standard 3-leg inverter (one leg for each phase) and H-bridge in series with each inverter leg. The inverter can be used in hybrid electric vehicles (HEV) and electric vehicles (EV). The co-simulation model is employed in order to take full advantage of different power electronics simulation software. Specifically, the main circuit model is developed using PSIM, and the control model is developed using MATLAB/SIMULINK. An experimental 5-level hybrid inverter is tested, which is controlled by multilevel carrier-based PWM signals. The simulation yields a good estimation for the test results of the inverter.


title: Roentgenographic Aspects of Tracheal Tumors

abstract: Tumors of the trachea are rare clinical entities. This relative immunity to tumor formation is difficult to understand, since the proximal and distal structures, i.e., larynx and bronchi, are so frequently the sites of neoplastic disease. Holinger et al. (14) reported that the frequency of tumors of the trachea, as compared to laryngeal tumors, ranged from 1∶300 to 1∶800. Clegg (6) found 5 tracheal carcinomas and 1,623 cases of bronchial cancer during the same five-year period. Because of the rarity of tracheal tumors, they are seldom considered in the differential diagnosis of respiratory disease and, for that reason, often remain undetected during life. Whitby (33) reported a case of a large carcinosarcoma of the trachea which was discovered only at autopsy. Bronchoscopy failed to reveal the tumor on the posterior wall of the subglottis and upper trachea, and the author suggested that x-ray studies might have been diagnostic. Andren (1), in 1937, published a case of a posterior-wall tracheal tumor detec...


title: Carbohydrate terminology and classification

abstract: Dietary carbohydrates are a group of chemically defined substances with a range of physical and physiological properties and health benefits. As with other macronutrients, the primary classification of dietary carbohydrate is based on chemistry, that is character of individual monomers, degree of polymerization (DP) and type of linkage (alpha or beta), as agreed at the Food and Agriculture Organization/World Health Organization Expert Consultation in 1997. This divides carbohydrates into three main groups, sugars (DP 1-2), oligosaccharides (short-chain carbohydrates) (DP 3-9) and polysaccharides (DP> or =10). Within this classification, a number of terms are used such as mono- and disaccharides, polyols, oligosaccharides, starch, modified starch, non-starch polysaccharides, total carbohydrate, sugars, etc. While effects of carbohydrates are ultimately related to their primary chemistry, they are modified by their physical properties. These include water solubility, hydration, gel formation, crystalline state, association with other molecules such as protein, lipid and divalent cations and aggregation into complex structures in cell walls and other specialized plant tissues. A classification based on chemistry is essential for a system of measurement, predication of properties and estimation of intakes, but does not allow a simple translation into nutritional effects since each class of carbohydrate has overlapping physiological properties and effects on health. This dichotomy has led to the use of a number of terms to describe carbohydrate in foods, for example intrinsic and extrinsic sugars, prebiotic, resistant starch, dietary fibre, available and unavailable carbohydrate, complex carbohydrate, glycaemic and whole grain. This paper reviews these terms and suggests that some are more useful than others. A clearer understanding of what is meant by any particular word used to describe carbohydrate is essential to progress in translating the growing knowledge of the physiological properties of carbohydrate into public health messages.


title: Bubble-Up: increasing utilization in modern warehouse scale computers via sensible co-locations

abstract: As much of the world's computing continues to move into the cloud, the overprovisioning of computing resources to ensure the performance isolation of latency-sensitive tasks, such as web search, in modern datacenters is a major contributor to low machine utilization. Being unable to accurately predict performance degradation due to contention for shared resources on multicore systems has led to the heavy handed approach of simply disallowing the co-location of high-priority, latency-sensitive tasks with other tasks. Performing this precise prediction has been a challenging and unsolved problem. In this paper, we present Bubble-Up, a characterization methodology that enables the accurate prediction of the performance degradation that results from contention for shared resources in the memory subsystem. By using a bubble to apply a tunable amount of "pressure" to the memory subsystem on processors in production datacenters, our methodology can predict the performance interference between co-locate applications with an accuracy within 1% to 2% of the actual performance degradation. Using this methodology to arrive at "sensible" co-locations in Google's production datacenters with real-world large-scale applications, we can improve the utilization of a 500-machine cluster by 50% to 90% while guaranteeing a high quality of service of latency-sensitive applications.


title: TaxoGen: Constructing Topical Concept Taxonomy by Adaptive Term Embedding and Clustering

abstract: Taxonomy construction is not only a fundamental task for semantic analysis of text corpora, but also an important step for applications such as information filtering, recommendation, and Web search. Existing pattern-based methods extract hypernym-hyponym term pairs and then organize these pairs into a taxonomy. However, by considering each term as an independent concept node, they overlook the topical proximity and the semantic correlations among terms. In this paper, we propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms. Our method, TaxoGen, uses term embeddings and hierarchical clustering to construct a topic taxonomy in a recursive fashion. To ensure the quality of the recursive process, it consists of: (1) an adaptive spherical clustering module for allocating terms to proper levels when splitting a coarse topic into fine-grained ones; (2) a local embedding module for learning term embeddings that maintain strong discriminative power at different levels of the taxonomy. Our experiments on two real datasets demonstrate the effectiveness of TaxoGen compared with baseline methods.


title: DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling

abstract: We consider networked control systems consisting of multiple independent closed-loop control subsystems, operating over a shared communication network. Such systems are ubiquitous in cyber-physical systems, Internet of Things, and large-scale industrial systems. In many large-scale settings, the size of the communication network is smaller than the size of the system. In consequence, scheduling issues arise. The main contribution of this paper is to develop a deep reinforcement learning-based \emph{control-aware} scheduling (\textsc{DeepCAS}) algorithm to tackle these issues. We use the following (optimal) design strategy: First, we synthesize an optimal controller for each subsystem; next, we design learning algorithm that adapts to the chosen subsystem (plant) and controller. As a consequence of this adaptation, our algorithm finds a schedule that minimizes the \emph{control loss}. We present empirical results to show that \textsc{DeepCAS} finds schedules with better performance than periodic ones. Finally, we illustrate that our algorithm can be used for \emph{scheduling and resource allocation in more general networked control settings than the above-mentioned one}.


title: Music-syntactic processing and auditory memory: Similarities and differences between ERAN and MMN

abstract: The early right anterior negativity (ERAN) is an event-related potential (ERP) reflecting processing of music-syntactic information, that is, of acoustic information structured according to abstract and complex regularities. The ERAN is usually maximal between 150 and 250 ms, has anterior scalp distribution (and often right-hemispheric weighting), can be modified by short- and long-term musical experience, can be elicited under ignore conditions, and emerges in early childhood. Main generators of the ERAN appear to be located in inferior fronto-lateral cortex. The ERAN resembles both the physical MMN and the abstract feature MMN in a number of properties, but the cognitive mechanisms underlying ERAN and MMN partly differ: Whereas the generation of the MMN is based on representations of regularities of intersound relationships that are extracted online from the acoustic environment, the generation of the ERAN relies on representations of music-syntactic regularities that already exist in a long-term memory format. Other processes, such as predicting subsequent acoustic events and comparing new acoustic information with the predicted sound, presumably overlap strongly for MMN and ERAN.


title: Concept for an Algorithm Testing and Evaluation Program at NIST

abstract: F i g u re 1 Coord ina t e measu r ing system—data analysis software analyzes coordinate data, producing surface fits. "any piece of equipment which collects three dimensional coordinates (points), calculates and displays additional information using the measured points." [1]. Figure 1 displays a high level view of how a CMS works. A CMS—such as a coordinate measurement machine, vision system, or theodolite—works by measuring points on the part surface and then analyzing the points to evaluate the part. The coordinate data are evaluated by data analysis software embedded in the CMS. The data analysis software is what produces the dimensional measurements—surface fits. CMSs are widely used by the dimensional metrology community during the inspection of manufactured parts. The data analysis software within CMSs is becoming increasingly important. During the last few years research [2] shows that data analysis software contributes significantly to the total measurement error (measurement uncertainty) of a CMS. Hence, the overall performance of CMSs is affected. The performance of data analysis software is affected by the choice of analysis method, quality of the software, and characteristics of the specific measurement tasks. There are no standards for evaluating these and other effects of software on the overall uncertainty of measurement. Through standard efforts by the dimensional metrology community, industry has identified the need for a formal mechanism to test and evaluate data analysis software within CMSs. This report presents a concept for a formal mechanism, the NIST Algorithm Testing and Evaluation Program (ATEP). Section 2 describes the current problem with CMS data analysis software. Section 3 presents a proposed solution to the problem. Section 4 explains the technical approach to the proposed solution. Section 5 presents a summary.


title: A support vector method for multivariate performance measures

abstract: This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the F 1 -score. Taking a multivariate prediction approach, we give an algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially non-linear performance measures, in particular ROCArea and all measures that can be computed from the contingency table. The conventional classification SVM arises as a special case of our method.


title: Cache-Conscious Wavefront Scheduling

abstract: This paper studies the effects of hardware thread scheduling on cache management in GPUs. We propose Cache-Conscious Wave front Scheduling (CCWS), an adaptive hardware mechanism that makes use of a novel intra-wave front locality detector to capture locality that is lost by other schedulers due to excessive contention for cache capacity. In contrast to improvements in the replacement policy that can better tolerate difficult access patterns, CCWS shapes the access pattern to avoid thrashing the shared L1. We show that CCWS can outperform any replacement scheme by evaluating against the Belady-optimal policy. Our evaluation demonstrates that cache efficiency and preservation of intra-wave front locality become more important as GPU computing expands beyond use in high performance computing. At an estimated cost of 0.17% total chip area, CCWS reduces the number of threads actively issued on a core when appropriate. This leads to an average 25% fewer L1 data cache misses which results in a harmonic mean 24% performance improvement over previously proposed scheduling policies across a diverse selection of cache-sensitive workloads.


title: Towards a Self-Organized Agent-Based Simulation Model for Exploration of Human Synaptic Connections

abstract: In this paper, the early design of our self-organized agent-based simulation model for exploration of synaptic connections that faithfully generates what is observed in natural situation is given. While we take inspiration from neuroscience, our intent is not to create a veridical model of processes in neurodevelopmental biology, nor to represent a real biological system. Instead, our goal is to design a simulation model that learns acting in the same way of human nervous system by using findings on human subjects using reflex methodologies in order to estimate unknown connections.


title: Vehicle Detection Techniques for Collision Avoidance Systems: A Review

abstract: Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.


title: Random access in millimeter-wave beamforming cellular networks: issues and approaches

abstract: With the formidable growth of various booming wireless communication services that require ever increasing data throughputs, the conventional microwave band below 10 GHz, which is currently used by almost all mobile communication systems, is going to reach its saturation point within just a few years. Therefore, the attention of radio system designers has been pushed toward ever higher segments of the frequency spectrum in a quest for increased capacity. In this article we investigate the feasibility, advantages, and challenges of future wireless communications over the Eband frequencies. We start with a brief review of the history of the E-band spectrum and its light licensing policy as well as benefits/challenges. Then we introduce the propagation characteristics of E-band signals, based on which some potential fixed and mobile applications at the E-band are investigated. In particular, we analyze the achievability of a nontrivial multiplexing gain in fixed point-to-point E-band links, and propose an E-band mobile broadband (EMB) system as a candidate for the next generation mobile communication networks. The channelization and frame structure of the EMB system are discussed in detail.


title: Multilabel Subject-Based Classification of Poetry

abstract: Oftentimes, the question "what is this poem about?" has no trivial answer, regardless of length, style, author, or context in which the poem is found. We propose a simple system of multi-label classification of poems based on their subjects following the categories and subcategories as laid out by the Poetry Foundation. We make use of a model that combines the methodologies of tf-idf and Latent Dirichlet Allocation for feature extraction, and a Support Vector Machine model for the classification task. We determine how likely it is for our models to correctly classify each poem they read into one or more main categories and subcategories. Our contribution is, thus, a new method to automatically classify poetry given a set and various subsets of categories.


title: Relation Classification via Multi-Level Attention CNNs

abstract: Relation classification is a crucial ingredient ::: in numerous information extraction systems ::: seeking to mine structured facts from ::: text. We propose a novel convolutional ::: neural network architecture for this task, ::: relying on two levels of attention in order ::: to better discern patterns in heterogeneous ::: contexts. This architecture enables endto-end ::: learning from task-specific labeled ::: data, forgoing the need for external knowledge ::: such as explicit dependency structures. ::: Experiments show that our model outperforms ::: previous state-of-the-art methods, including ::: those relying on much richer forms ::: of prior knowledge.


title: The Netflix Prize

abstract: Netflix released a dataset containing 100 million anonymous movie ratings and challenged the data mining, machine learning and computer science communities to develop systems that could beat the accuracy of its recommendation system, Cinematch. We briefly describe the challenge itself, review related work and efforts, and summarize visible progress to date. Other potential uses of the data are outlined, including its application to the KDD Cup 2007.


title: Practical Analysis and Design of a Battery Management System for a Grid-Connected DC Microgrid for the Reduction of the Tariff Cost and Battery Life Maximization

abstract: This study is focused on two areas: the design of a Battery Energy Storage System (BESS) for a grid-connected DC Microgrid and the power management of that microgrid. The power management is performed by a Microgrid Central Controller (MGCC). A Microgrid operator provides daily information to the MGCC about the photovoltaic generation profile, the load demand profile, and the real-time prices of the electricity in order to plan the power interchange between the BESS and the main grid, establishing the desired state of charge (SOC) of the batteries at any time. The main goals of the power management strategy under study are to minimize the cost of the electricity that is imported from the grid and to maximize battery life by means of an adequate charging procedure, which sets the charging rate as a function of the MG state. Experimental and simulation results in many realistic scenarios demonstrate that the proposed methodology achieves a proper power management of the DC microgrid.


title: Digital color halftoning with generalized error diffusion and multichannel green-noise masks

abstract: In this paper, we introduce two novel techniques for digital color halftoning with green-noise-stochastic dither patterns generated by homogeneously distributing minority pixel clusters. The first technique employs error diffusion with output-dependent feedback where, unlike monochrome image halftoning, an interference term is added such that the overlapping of pixels of different colors can be regulated for increased color control. The second technique uses a green-noise mask, a dither array designed to create green-noise halftone patterns, which has been constructed to also regulate the overlapping of different colored pixels. As is the case with monochrome image halftoning, both techniques are tunable, allowing for large clusters in printers with high dot-gain characteristics, and small clusters in printers with low dot-gain characteristics.


title: On the impact of text similarity functions on hashtag recommendations in microblogging environments

abstract: Microblogging applications such as Twitter are experiencing tremendous success. Microblog users utilize hashtags to categorize posted messages which aim at bringing order to the myriads of microblog messages. However, the percentage of messages incorporating hashtags is small and the used hashtags are very heterogeneous as hashtags may be chosen freely and may consist of any arbitrary combination of characters. This heterogeneity and the lack of use of hashtags lead to significant drawbacks in regards to the search functionality as messages are not categorized in a homogeneous way. In this paper, we present an approach for the recommendation of hashtags suitable for the message the user currently enters which aims at creating a more homogeneous set of hashtags. Furthermore, we present a detailed study on how the similarity measures used for the computation of recommendations influence the final set of recommended hashtags.


title: Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

abstract: Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).


title: Intrinsically Motivated Reinforcement Learning: A Promising Framework for Developmental Robot Learning

abstract: Abstract : One of the primary challenges of developmental robotics is the question of how to learn and represent increasingly complex behavior in a self-motivated, open-ended way Barto, Singh, and Chentanez (Barto, Singh, & Chentanez 2004; Singh, Barto, & Chentanez 2004) have recently presented an algorithm for intrinsically motivated reinforcement learning that strives to achieve broad competence in an environment in a task-nonspecific manner by incorporating internal reward to build a hierarchical collection of skills. This paper suggests that with its emphasis on task-general, self-motivated, and hierarchical learning, intrinsically motivated reinforcement learning is an obvious choice for organizing behavior in developmental robotics. We present additional preliminary results from a gridworld abstraction of a robot environment and advocate a layered learning architecture for applying the algorithm on a physically embodied system.


title: Volumetric Modeling with Implicit Functions

abstract: [1] David Ebert, Ken Musgrave, Darwyn Peachey, Ken Perlin, and Steve Worley. Texturing andModeling: A Procedural Approach. Academic Press, October 1994. ISBN 0-12-228760-6.[2] David S. Ebert. Advanced modeling techniques for computer graphics. In CRC Handbook ofComputer Science and Engineering, chapter 56. CRC, 1997.[3] Andrew Guy and Brian Wyvil. Controlled blending for implicit surfaces. In Implicit Surfaces’95, April 1995.[4] Ken Perlin. An image synthesizer. In B. A. Barsky, editor, Computer Graphics (SIGGRAPH’85 Proceedings), volume 19, pages 287–296,July 1985.


title: Space-Variant Restoration of Images Degraded by Camera Motion Blur

abstract: We examine the problem of restoration from multiple images degraded by camera motion blur. We consider scenes with significant depth variations resulting in space-variant blur. The proposed algorithm can be applied if the camera moves along an arbitrary curve parallel to the image plane, without any rotations. The knowledge of camera trajectory and camera parameters is not necessary. At the input, the user selects a region where depth variations are negligible. The algorithm belongs to the group of variational methods that estimate simultaneously a sharp image and a depth map, based on the minimization of a cost functional. To initialize the minimization, it uses an auxiliary window-based depth estimation algorithm. Feasibility of the algorithm is demonstrated by three experiments with real images.


title: Microwave metamaterials made by fused deposition 3D printing of a highly conductive copper-based filament

abstract: This work reports a method for fabricating three-dimensional microwave metamaterials by fused deposition modeling 3D printing of a highly conductive polymer composite filament. The conductivity of such a filament is shown to be nearly equivalent to that of a perfect conductor for microwave metamaterial applications. The expanded degrees-of-freedom made available by 3D metamaterial designs are demonstrated by designing, fabricating, and testing a 3D-printed unit cell with a broadband permittivity as high as 14.4. The measured and simulated S-parameters agree well with a mean squared error smaller than 0.1. The presented method not only allows reliable and convenient fabrication of microwave metamaterials with high conductivity but also opens the door to exploiting the third dimension of the unit cell design space to achieve enhanced electromagnetic properties.


title: Integrating StockTwits with sentiment analysis for better prediction of stock price movement

abstract: Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization, person or any other entity. Sentiment Analysis can be used to predict the mood of people that have impact on stock prices, therefore it can help in prediction of actual stock movement. In order to exploit the benefits of sentiment analysis in stock market industry we have performed sentiment analysis on tweets related to Apple products, which are extracted from StockTwits (a social networking site) from 2010 to 2017. Along with tweets, we have also used market index data which is extracted from Yahoo Finance for the same period. The sentiment score of a tweet is calculated by sentiment analysis of tweets through SVM. As a result each tweet is categorized as bullish or bearish. Then sentiment score and market data is used to build a SVM model to predict next day's stock movement. Results show that there is positive relation between people opinion and market data and proposed work has an accuracy of 76.65% in stock prediction.


title: Sensor data fusion of LIDAR with stereo RGB-D camera for object tracking

abstract: In Industry 4.0 scenarios, autonomously navigating robots will have to perform dedicated tasks in controlled environments, such as production halls or storage facilities. In the presence of pedestrians and other dynamic objects, robust collision detection is imperative in order to avoid harm of human or material. Supplementary sensors as part of the infrastructure may provide additional real-time overview. In this paper, a concept for dynamic object tracking by sensor data fusion, using a stationary stereo camera and a laser range finder on a mobile platform, is presented and analyzed. The proposed approach consists of two modules that involve frame-to-frame detection of targets, as well as subsequent data association, fusion and tracking. Object detection is carried out by 3D-processing techniques on point clouds. Data association for multi-target tracking is achieved, using the Joint Probabilistic Data Association Filter (JPDAF). Combination of sensor information is done by a hierarchical data fusion approach. Experiments show that this improves robustness to occlusions or sensor failure significantly.


title: DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

abstract: A key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources. Prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. In this work, we present DenseFusion, a generic framework for estimating 6D pose of a set of known objects from RGB-D images. DenseFusion is a heterogeneous architecture that processes the two data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated. Furthermore, we integrate an end-to-end iterative pose refinement procedure that further improves the pose estimation while achieving near real-time inference. Our experiments show that our method outperforms state-of-the-art approaches in two datasets, YCB-Video and LineMOD. We also deploy our proposed method to a real robot to grasp and manipulate objects based on the estimated pose.


title: A hybrid algorithm for modeling ice formation

abstract: We present a novel algorithm that simulates ice formation. Motivated by the physical process of ice growth, we develop a novel hybrid algorithm by synthesizing three techniques: diffusion limited aggregation, phase field methods, and stable fluid solvers. Each technique maps to one of the three stages of solidification. The visual realism of the resulting algorithm appears to surpass that of each technique alone, particularly in animations of freezing. In addition, we present a faster, simplified phase field method, as well as a unified parameterization that enables artistic manipulation of the simulation. We illustrate the results on arbitrary 3D surfaces.


title: Teaching a Digital Performing Agent: Artificial Neural Network and Hidden Markov Model for recognising and performing dance movement

abstract: For a Digital Performing Agent to be able to perform live with a human dancer, it would be useful for the agent to be able to contextualize the movement the dancer is performing and to have a suitable movement vocabulary with which to contribute to the performance. In this paper we will discuss our research into the use of Artificial Neural Networks (ANN) as a means of allowing a software agent to learn a shared vocabulary of movement from a dancer. The agent is able to use the learnt movements to form an internal representation of what the dancer is performing, allowing it to follow the dancer, generate movement sequences based on the dancer's current movement and dance independently of the dancer using a shared movement vocabulary. By combining the ANN with a Hidden Markov Model (HMM) the agent is able to recognize short full body movement phrases and respond when the dancer performs these phrases. We consider the relationship between the dancer and agent as a means of supporting the agent's learning and performance, rather than developing the agent's capability in a self-contained fashion.


title: JouleGuard: energy guarantees for approximate applications

abstract: Energy consumption limits battery life in mobile devices and increases costs for servers and data centers. Approximate computing addresses energy concerns by allowing applications to trade accuracy for decreased energy consumption. Approximation frameworks can guarantee accuracy or performance and generally reduce energy usage; however, they provide no energy guarantees. Such guarantees would be beneficial for users who have a fixed energy budget and want to maximize application accuracy within that budget. We address this need by presenting JouleGuard: a runtime control system that coordinates approximate applications with system resource usage to provide control theoretic formal guarantees of energy consumption, while maximizing accuracy. We implement JouleGuard and test it on three different platforms (a mobile, tablet, and server) with eight different approximate applications created from two different frameworks. We find that JouleGuard respects energy budgets, provides near optimal accuracy, adapts to phases in application workload, and provides better outcomes than application approximation or system resource adaptation alone. JouleGuard is general with respect to the applications and systems it controls, making it a suitable runtime for a number of approximate computing frameworks.


title: Application of CRISPR/Cas9 in plant biology

abstract: The CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated proteins) system was first identified in bacteria and archaea and can degrade exogenous substrates. It was developed as a gene editing technology in 2013. Over the subsequent years, it has received extensive attention owing to its easy manipulation, high efficiency, and wide application in gene mutation and transcriptional regulation in mammals and plants. The process of CRISPR/Cas is optimized constantly and its application has also expanded dramatically. Therefore, CRISPR/Cas is considered a revolutionary technology in plant biology. Here, we introduce the mechanism of the type II CRISPR/Cas called CRISPR/Cas9, update its recent advances in various applications in plants, and discuss its future prospects to provide an argument for its use in the study of medicinal plants.


title: Predicting student placements using Bayesian classification

abstract: Rapidly increasing educational institutions, are imposing a great demand of quality education and placements for the benefit of the students as well as of the Institutions. In this paper application of Bayesian theorem on educational dataset has been proposed to predict placement results. Results are evaluated using Naive Bayes Classifier in WEKA and RapidMiner tool. Results generated will help instructors, educators as well as students to focus on those aspects which help in improving student placements.


title: A corpus for mining drug-related knowledge from Twitter chatter: Language models and their utilities

abstract: Abstract In this data article, we present to the data science, natural language processing and public heath communities an unlabeled corpus and a set of language models. We collected the data from Twitter using drug names as keywords, including their common misspelled forms. Using this data, which is rich in drug-related chatter, we developed language models to aid the development of data mining tools and methods in this domain. We generated several models that capture (i) distributed word representations and (ii) probabilities of n-gram sequences. The data set we are releasing consists of 267,215 Twitter posts made during the four-month period—November, 2014 to February, 2015. The posts mention over 250 drug-related keywords. The language models encapsulate semantic and sequential properties of the texts.


title: Analysis of Different Text Steganography Techniques: A Survey

abstract: Steganography is helping individual to send confidential data between two parties. It enables user to hide data in different digital mediums. Steganography is of many types such as image steganography, text steganography, audio/video steganography etc. Text Steganography is quite difficult than other techniques because of less amount of redundancy and changes can be detected quite easily. Some of the techniques of text steganography has been discussed along with characteristics and working.


title: Automated procedure clustering for reverse engineering PL/SQL programs

abstract: PL/SQL programs are mainly composed of procedures that interact with a database. These procedures are grouped into packages at the architecture design level for increasing modularity and maintainability. However, the intended modular structure is subject to architectural drift especially for large scale programs that are maintained for a long duration. Existing reverse engineering and refactoring techniques cannot be directly applied for these programs. Unlike other programs, PL/SQL programs involve procedures that are in most cases indirectly coupled due to their database operations. In this paper, we provide an approach and a tool that support reverse engineering and refactoring of PL/SQL programs. We analyze dependencies among PL/SQL procedures based on the accessed database elements. We represent these dependencies in the form of a design structure matrix. Then, we cluster the procedures and propose a packaging structure to the designer. We performed an industrial case study from the telecommunications domain. We observed promising results in which several unexisted packages were proposed by our tool and the accuracy of the results were approved by domain experts.


Ragdoll Physics


title: Snap-Through and Pull-In Instabilities of an Arch-Shaped Beam Under an Electrostatic Loading

abstract: The snap-through and pull-in instabilities of the micromachined arch-shaped beams under an electrostatic loading are studied both theoretically and experimentally. The pull-in instability that results in a system collision with an electrode substrate may lead to a system failure and, thus, limits the system maximum displacement. The beam/plate structure with a flat initial configuration under an electrostatic loading can only experience the pull-in instability. With the different arch configurations, the structure may experience either only the pull-in instability or the snap-through and pull-in instabilities together. As shown in our computation and experiment, those arch-shaped beams with the snap-through instability have the larger maximum displacement compared with the arch-shaped beams with only the pull-in stability and those with the flat initial configuration. The snap-through occurs by exerting a fixed load, and the structure experiences a discontinuous displacement jump without consuming power. Furthermore, after the snap-through jump, the structures are demonstrated to have the capacity to withstand further electrostatic loading without pull-in. Those properties of consuming no power and increasing the structure deflection range without pull-in is very useful in microelectromechanical systems design, which can offer better sensitivity and tuning range.


title: Why Does Large Batch Training Result in Poor Generalization? A Comprehensive Explanation and a Better Strategy from the Viewpoint of Stochastic Optimization

abstract: We present a comprehensive framework of search methods, such as simulated annealing and batch training, for solving nonconvex optimization problems. These methods search a wider range by gradually decreasing the randomness added to the standard gradient descent method. The formulation that we define on the basis of this framework can be directly applied to neural network training. This produces an effective approach that gradually increases batch size during training. We also explain why large batch training degrades generalization performance, which previous studies have not clarified.


title: 3D-LIDAR based branch estimation and intersection location for autonomous vehicles

abstract: It is crucial for autonomous vehicles to navigate at intersections. The accurate location of intersections and the orientation of each branches are necessary for decision making and path planning. In this paper, a unified method is proposed to estimate orientations of each branch at an intersection and to locate the position of the intersection. First, based on vehicle dynamics, a densifying method is used to obtain dense point-cloud data using 3D-LIDAR sensor. Then, according to the data of Open Street Map, the regions-of-interest are extracted and the points are interpolated to transform into an elevation image. Finally, a support vector regression model is employed to estimate the position and orientation of each branch and a fusion method is used to locate the intersection. The experimental results demonstrate the accuracy and robustness of the proposed algorithm.


title: Global network alignment using multiscale spectral signatures

abstract: Motivation: Protein interaction networks provide an important system-level view of biological processes. One of the fundamental problems in biological network analysis is the global alignment of a pair of networks, which puts the proteins of one network into correspondence with the proteins of another network in a manner that conserves their interactions while respecting other evidence of their homology. By providing a mapping between the networks of different species, alignments can be used to inform hypotheses about the functions of unannotated proteins, the existence of unobserved interactions, the evolutionary divergence between the two species and the evolution of complexes and pathways. ::: ::: Results: We introduce GHOST, a global pairwise network aligner that uses a novel spectral signature to measure topological similarity between subnetworks. It combines a seed-and-extend global alignment phase with a local search procedure and exceeds state-of-the-art performance on several network alignment tasks. We show that the spectral signature used by GHOST is highly discriminative, whereas the alignments it produces are also robust to experimental noise. When compared with other recent approaches, we find that GHOST is able to recover larger and more biologically significant, shared subnetworks between species. ::: ::: Availability: An efficient and parallelized implementation of GHOST, released under the Apache 2.0 license, is available at http://cbcb.umd.edu/kingsford_group/ghost ::: ::: Contact: rob@cs.umd.edu


title: A generic demand-side management model for smart grid

abstract: The demand-side management (DSM) is one of the most important aspects in future smart grids: towards electricity generation cost by minimizing the expensive thermal peak power plants. The DSM greatly affects the individual users’ cost and per unit cost. The main objective of this research article is to develop a generic demand-side management (G-DSM) model for residential users to reduce peak-to-average ratio (PAR), total energy cost, and waiting time of appliances (WTA) along with fast execution of the proposed algorithm. We propose a system architecture and mathematical formulation for total energy cost minimization, PAR reduction, and WTA. The G-DSM model is based on genetic algorithm (GA) for appliances scheduling and considers 20 users having a combination of appliances with different operational characteristics. Simulation results show the effectiveness of G-DSM model for both single and multiple user scenarios. Copyright © 2015 John Wiley & Sons, Ltd.


title: A Multi-Tiered Storage Structure for Cloud Computing

abstract: During recent decades, cloud-based shared storage has become a popular storage solution. Nevertheless, current cloud-based I/O subsystem assumes that the storage devices are homogeneous. Recently, the multi-tiered storage system extends the storage hierarchy by using SSDs to cache data from the hard disks has received significant attention. Thus, in this paper, we design an I/O framework for multi-tiered storages in multi-tenant cloud-based shared storage environments. Specifically, we propose a two-tier per-user queuing structure to solve the user interference and device interference problem. Furthermore, on the basis of the credit-based request scheduling algorithm, we introduce the concept of device credits, instead user credits in previous schemes, to proper charge users in different types of storage devices. The experimental results show our proposed framework is effective in realizing the multi-tiered-based shared storage systems in cloud computing environments.


title: Using hidden Markov models for topic segmentation of meeting transcripts

abstract: In this paper, we present a hidden Markov model (HMM) approach to segment meeting transcripts into topics. To learn the model, we use unsupervised learning to cluster the text segments obtained from topic boundary information. Using modified WinDiff and Pk metrics, we demonstrate that an HMM outperforms LCSeg, a state-of-the-art lexical chain based method for topic segmentation using the ICSI meeting corpus. We evaluate the effect of language model order, the number of hidden states, and the use of stop words. Our experimental results show that a unigram LM is better than a trigram LM, using too many hidden states degrades topic segmentation performance, and that removing the stop words from the transcripts does not improve segmentation performance.


title: Real-time bidding algorithms for performance-based display ad allocation

abstract: We describe a real-time bidding algorithm for performance-based display ad allocation. A central issue in performance display advertising is matching campaigns to ad impressions, which can be formulated as a constrained optimization problem that maximizes revenue subject to constraints such as budget limits and inventory availability. The current practice is to solve the optimization problem offline at a tractable level of impression granularity (e.g., the page level), and to serve ads online based on the precomputed static delivery scheme. Although this offline approach takes a global view to achieve optimality, it fails to scale to ad allocation at the individual impression level. Therefore, we propose a real-time bidding algorithm that enables fine-grained impression valuation (e.g., targeting users with real-time conversion data), and adjusts value-based bids according to real-time constraint snapshots (e.g., budget consumption levels). Theoretically, we show that under a linear programming (LP) primal-dual formulation, the simple real-time bidding algorithm is indeed an online solver to the original primal problem by taking the optimal solution to the dual problem as input. In other words, the online algorithm guarantees the offline optimality given the same level of knowledge an offline optimization would have. Empirically, we develop and experiment with two real-time bid adjustment approaches to adapting to the non-stationary nature of the marketplace: one adjusts bids against real-time constraint satisfaction levels using control-theoretic methods, and the other adjusts bids also based on the statistically modeled historical bidding landscape. Finally, we show experimental results with real-world ad delivery data that support our theoretical conclusions.


title: El Farol Bar problem, Potluck problem and electric energy balancing - on the importance of communication

abstract: Power balancing is an important issue when micro-grids in island mode are considered. It requires active, real-time decision making to minimise the imbalances. El Farol Bar and Potluck problems are artificial problems designed to challenge the decision making process in a situation of limited information. They are theoretical, ill-defined problems designed to show that rational decision making in a situation of scarce information can give worse results than non-rational behavior. Potluck problem can be compared to electric power balancing in microgrids. But due to reduced complexity and constraints of theoretical problems and differences in goal functions the approach has to be in each case different. The study of these differences in this paper shows the importance of exchange of information between participants (or agents); it also suggest what type of information are necessary in what conditions. The amount of information that needs to be exchanged depends on the relation between participants (agents): the less cooperating their relation is, the less information they are willing to exchange.


title: UML CLASS DIAGRAM OR ENTITY RELATIONSHIP DIAGRAM? AN OBJECT-RELATIONAL CONCEPTUAL IMPEDANCE MISMATCH

abstract: It is now nearly 30 years since Peter Chen’s watershed paper “The Entity-Relationship Model ‐ towards a Unified View of Data”. [1] The entity relationship model and variations and extensions to it have been taught in colleges and universities for many years. In his original paper Peter Chen looked at converting his new ER model to the then existing data structure diagrams for the Network model. In recent years there has been a tendency to use a Unified Modelling Language (UML) class diagram for conceptual modeling for relational databases, and several popular course text books use UML notation to some degree [2] [3]. However Object and Relational technology are based on different paradigms. In the paper we argue that the UML class diagram is more of a logical model (implementation specific). ER Diagrams on the other hand, are at a conceptual level of database design dealing with the main items and their relationships and not with implementation specific detail. UML focuses on OOAD (Object Oriented Analysis and Design) and is navigational and program dependent whereas the relational model is set based and exhibits data independence. The ER model provides a well-established set of mapping rules for mapping to a relational model. In this paper we look specifically at the areas which can cause problems for the novice database designer due to this conceptual mismatch of two different paradigms. Firstly, transferring the mapping of a weak entity from an Entity Relationship model to UML and secondly the representation of structural constraints between objects. We look at the mixture of notations which students mistakenly use when modeling. This is often the result of different notations being used on different courses throughout their degree. Several of the popular text books at the moment use either a variation of ER, UML, or both for teaching database modeling. At the moment if a student picks up a text book they could be faced with either; one of the many ER variations, UML, UML and a variation of ER both covered separately, or UML and ER merged together. We regard this problem as a conceptual impedance mismatch. This problem is documented in [21] who have produced a catalogue of impedance mismatch problems between object-relational and relational paradigms. We regard the problems of using UML class diagrams for relational database design as a conceptual impedance mismatch as the Entity Relationship model does not have the structures in the model to deal with Object Oriented concepts