title: No "power" struggles: coordinated multi-level power management for the data center

abstract: Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and software, and at local and global levels. Unfortunately, there has been no corresponding work on coordinating all these solutions. In the absence of such coordination, these solutions are likely to interfere with one another, in unpredictable (and potentially dangerous) ways. This paper seeks to address this problem. We make two key contributions. First, we propose and validate a power management solution that coordinates different individual approaches. Using simulations based on 180 server traces from nine different real-world enterprises, we demonstrate the correctness, stability, and efficiency advantages of our solution. Second, using our unified architecture as the base, we perform a detailed quantitative sensitivity analysis and draw conclusions about the impact of different architectures, implementations, workloads, and system design choices.


title: Idea expander: supporting group brainstorming with conversationally triggered visual thinking stimuli

abstract: Creativity is central to much human problem solving and innovation. Brainstorming processes attempt to leverage group creativity, but group dynamics sometimes limit their utility. We present IdeaExpander, a tool to support group brainstorming by intelligently selecting pictorial stimuli based on the group's conversation The design is based on theories of how perception, thinking, and communication interact; a pilot study (N=16) suggests that it increases individuals' idea production and that people value it.


title: Algorithms for the vehicle routing and scheduling problems with time window constraints

abstract: This paper considers the design and analysis of algorithms for vehicle routing and scheduling problems with time window constraints. Given the intrinsic difficulty of this problem class, approximation methods seem to offer the most promise for practical size problems. After describing a variety of heuristics, we conduct an extensive computational study of their performance. The problem set includes routing and scheduling environments that differ in terms of the type of data used to generate the problems, the percentage of customers with time windows, their tightness and positioning, and the scheduling horizon. We found that several heuristics performed well in different problem environments; in particular an insertion-type heuristic consistently gave very good results.


title: Evaluating Knowledge Representation and Reasoning Capabilities of Ontology Specification Languages

abstract: The interchange of ontologies across the World Wide Web (WWW) and the cooperation among heterogeneous agents placed on it is the main reason for the development of a new set of ontology specification languages, based on new web standards such as XML or RDF. These languages (SHOE, XOL, RDF, OIL, etc) aim to represent the knowledge contained in an ontology in a simple and human-readable way, as well as allow for the interchange of ontologies across the web. In this paper, we establish a common framework to compare the expressiveness of "traditional" ontology languages (Ontolingua, OKBC, OCML, FLogic, LOOM) and "web-based" ontology languages. As a result of this study, we conclude that different needs in KR and reasoning may exist in the building of an ontology-based application, and these needs must be evaluated in order to choose the most suitable ontology language(s).


title: Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI

abstract: The human brain processes information via multiple distributed networks. An accurate model of the brain's functional connectome is critical for understanding both normal brain function as well as the dysfunction present in neuropsychiatric illnesses. Current methodologies that attempt to discover the organization of the functional connectome typically assume spatial or temporal separation of the underlying networks. This assumption deviates from an intuitive understanding of brain function, which is that of multiple, inter-dependent spatially overlapping brain networks that efficiently integrate information pertinent to diverse brain functions. It is now increasingly evident that neural systems use parsimonious formations and functional representations to efficiently process information while minimizing redundancy. Hence we exploit recent advances in the mathematics of sparse modeling to develop a methodological framework aiming to understand complex resting-state fMRI connectivity data. By favoring networks that explain the data via a relatively small number of participating brain regions, we obtain a parsimonious representation of brain function in terms of multiple “Sparse Connectivity Patterns” (SCPs), such that differential presence of these SCPs explains inter-subject variability. In this manner the sparsity-based framework can effectively capture the heterogeneity of functional activity patterns across individuals while potentially highlighting multiple sub-populations within the data that display similar patterns. Our results from simulated as well as real resting state fMRI data show that SCPs are accurate and reproducible between sub-samples as well as across datasets. These findings substantiate existing knowledge of intrinsic functional connectivity and provide novel insights into the functional organization of the human brain.


title: Sustainable recycling of municipal solid waste in developing countries

abstract: Abstract This research focuses on recycling in developing countries as one form of sustainable municipal solid waste management (MSWM). Twenty-three case studies provided municipal solid waste (MSW) generation and recovery rates and composition for compilation and assessment. The average MSW generation rate was 0.77 kg/person/day, with recovery rates from 5–40%. The waste streams of 19 of these case studies consisted of 0–70% recyclables and 17–80% organics. Qualitative analysis of all 23 case studies identified barriers or incentives to recycling, which resulted in the development of factors influencing recycling of MSW in developing countries. The factors are government policy, government finances, waste characterization, waste collection and segregation, household education, household economics, MSWM (municipal solid waste management) administration, MSWM personnel education, MSWM plan, local recycled-material market, technological and human resources, and land availability. Necessary and beneficial relationships drawn among these factors revealed the collaborative nature of sustainable MSWM. The functionality of the factor relationships greatly influenced the success of sustainable MSWM. A correlation existed between stakeholder involvement and the three dimensions of sustainability: environment, society, and economy. The only factors driven by all three dimensions (waste collection and segregation, MSWM plan, and local recycled-material market) were those requiring the greatest collaboration with other factors.


title: Open Set Logo Detection and Retrieval

abstract: Current logo retrieval research focuses on closed set scenarios. We argue that the logo domain is too large for this strategy and requires an open set approach. To foster research in this direction, a large-scale logo dataset, called Logos in the Wild, is collected and released to the public. A typical open set logo retrieval application is, for example, assessing the effectiveness of advertisement in sports event broadcasts. Given a query sample in shape of a logo image, the task is to find all further occurrences of this logo in a set of images or videos. Currently, common logo retrieval approaches are unsuitable for this task because of their closed world assumption. Thus, an open set logo retrieval method is proposed in this work which allows searching for previously unseen logos by a single query sample. A two stage concept with separate logo detection and comparison is proposed where both modules are based on task specific CNNs. If trained with the Logos in the Wild data, significant performance improvements are observed, especially compared with state-of-the-art closed set approaches.


title: Neural Baby Talk

abstract: We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image. Our approach reconciles classical slot filling approaches (that are generally better grounded in images) with modern neural captioning approaches (that are generally more natural sounding and accurate). Our approach first generates a sentence `template' with slot locations explicitly tied to specific image regions. These slots are then filled in by visual concepts identified in the regions by object detectors. The entire architecture (sentence template generation and slot filling with object detectors) is end-to-end differentiable. We verify the effectiveness of our proposed model on different image captioning tasks. On standard image captioning and novel object captioning, our model reaches state-of-the-art on both COCO and Flickr30k datasets. We also demonstrate that our model has unique advantages when the train and test distributions of scene compositions -- and hence language priors of associated captions -- are different. Code has been made available at: https://github.com/jiasenlu/NeuralBabyTalk


Convolutional Neural Net and Bearing Fault Analysis


title: A single-feeding circularly polarized microstrip antenna with the effect of hybrid feeding

abstract: A single series feed cross-aperture coupled microstrip antenna with the effect of hybrid feeding is proposed and demonstrated. To understand this antenna better, the characteristics according to the variation of parameters are shown. This proposed antenna has the following advantages of the effect of hybrid feeding: improved axial ratio bandwidth (4.6%); high gain (8 dBi); flat 3 dB gain bandwidth (above 16.7%). In measured radiation patterns, we have 3 dB beamwidth of /spl plusmn/30/spl deg/ and good F/B (front to back ratio) of 20 dB.


title: Profit maximization for multiple products in online social networks

abstract: Information propagation in online social networks (OSNs), which helps shaping consumers' purchasing decisions, has received a lot of attention. The ultimate goal of marketing and advertising in OSNs is to massively influence audiences and enlarge the number of product adoptions. Most of existing works focus on maximizing the influence of a single product or promoting the adoption of one product in competing campaigns. However, in reality, the majority of companies produce various products for supplying customers with different needs. Therefore, it is truly significant and also challenging to wisely distribute limited budget across multiple products in viral marketing. In this paper, we investigate a Profit Maximization with Multiple Adoptions (PM2A) problem, which aims at maximizing the overall profit across all products. The natural greedy fails to provide a bounded result. In order to select high quality seeds for information propagation, we first proposed the PMCE algorithm, which has a ratio 1/2 (1 − 1/e2). Moreover, we further improve this ratio to (1−1/e) by proposing the PMIS algorithm. Comprehensive experiments on three real social networks are conducted. And results show that our algorithms outperform other heuristics, and better distribute the budget in terms of profit maximization.


title: NCAA Tournament Games: The Real Nitty-Gritty

abstract: The NCAA Division I Men's Basketball Committee annually selects its national championship tournament's at-large invitees, and assigns seeds to all participants. As part of its deliberations, the Committee is provided a so-called "nitty-gritty report" for each team, containing numerous team performance statistics. Many elements of this report receive a great deal of attention by the media and fans as the tournament nears, including a team's Ratings Percentage Index (or RPI), overall record, conference record, non-conference record, strength of schedule, record in its last 10 games, etc. However, few previous studies have evaluated the degree to which these factors are related to whether a team actually wins games once the tournament begins. Using nitty-gritty information for the participants in the 638 tournament games during the 10 seasons from 1999 through 2008, we use stepwise binary logit regression to build a model that includes only eight of the 32 nitty-gritty factors we examined. We find that in some cases factors that receive a great deal of attention are not related to game results, at least in the presence of the more highly related set of factors included in the model.


title: Temporal Relational Reasoning in Videos

abstract: Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets - Something-Something, Jester, and Charades - which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.


Is the figural aftereffect an aftereffect? A review of its intensity, onset, decay, and transfer characteristics.


title: Multiple Spatio-temporal Feature Learning for Video-based Emotion Recognition in the Wild

abstract: The difficulty of emotion recognition in the wild (EmotiW) is how to train a robust model to deal with diverse scenarios and anomalies. The Audio-video Sub-challenge in EmotiW contains audio-video short clips with several emotional labels and the task is to distinguish which label the video belongs to. For the better emotion recognition in videos, we propose a multiple spatio-temporal feature fusion (MSFF) framework, which can more accurately depict emotional information in spatial and temporal dimensions by two mutually complementary sources, including the facial image and audio. The framework is consisted of two parts: the facial image model and the audio model. With respect to the facial image model, three different architectures of spatial-temporal neural networks are employed to extract discriminative features about different emotions in facial expression images. Firstly, the high-level spatial features are obtained by the pre-trained convolutional neural networks (CNN), including VGG-Face and ResNet-50 which are all fed with the images generated by each video. Then, the features of all frames are sequentially input to the Bi-directional Long Short-Term Memory (BLSTM) so as to capture dynamic variations of facial appearance textures in a video. In addition to the structure of CNN-RNN, another spatio-temporal network, namely deep 3-Dimensional Convolutional Neural Networks (3D CNN) by extending the 2D convolution kernel to 3D, is also applied to attain evolving emotional information encoded in multiple adjacent frames. For the audio model, the spectrogram images of speech generated by preprocessing audio, are also modeled in a VGG-BLSTM framework to characterize the affective fluctuation more efficiently. Finally, a fusion strategy with the score matrices of different spatio-temporal networks gained from the above framework is proposed to boost the performance of emotion recognition complementally. Extensive experiments show that the overall accuracy of our proposed MSFF is 60.64%, which achieves a large improvement compared with the baseline and outperform the result of champion team in 2017.


title: Machine learning for real-time prediction of complications in critical care: a retrospective study.

abstract: BACKGROUND ::: The large amount of clinical signals in intensive care units can easily overwhelm health-care personnel and can lead to treatment delays, suboptimal care, or clinical errors. The aim of this study was to apply deep machine learning methods to predict severe complications during critical care in real time after cardiothoracic surgery. ::: ::: ::: METHODS ::: We used deep learning methods (recurrent neural networks) to predict several severe complications (mortality, renal failure with a need for renal replacement therapy, and postoperative bleeding leading to operative revision) in post cardiosurgical care in real time. Adult patients who underwent major open heart surgery from Jan 1, 2000, to Dec 31, 2016, in a German tertiary care centre for cardiovascular diseases formed the main derivation dataset. We measured the accuracy and timeliness of the deep learning model's forecasts and compared predictive quality to that of established standard-of-care clinical reference tools (clinical rule for postoperative bleeding, Simplified Acute Physiology Score II for mortality, and the Kidney Disease: Improving Global Outcomes staging criteria for acute renal failure) using positive predictive value (PPV), negative predictive value, sensitivity, specificity, area under the curve (AUC), and the F1 measure (which computes a harmonic mean of sensitivity and PPV). Results were externally retrospectively validated with 5898 cases from the published MIMIC-III dataset. ::: ::: ::: FINDINGS ::: Of 47 559 intensive care admissions (corresponding to 42 007 patients), we included 11 492 (corresponding to 9269 patients). The deep learning models yielded accurate predictions with the following PPV and sensitivity scores: PPV 0·90 and sensitivity 0·85 for mortality, 0·87 and 0·94 for renal failure, and 0·84 and 0·74 for bleeding. The predictions significantly outperformed the standard clinical reference tools, improving the absolute complication prediction AUC by 0·29 (95% CI 0·23-0·35) for bleeding, by 0·24 (0·19-0·29) for mortality, and by 0·24 (0·13-0·35) for renal failure (p<0·0001 for all three analyses). The deep learning methods showed accurate predictions immediately after patient admission to the intensive care unit. We also observed an increase in performance in our validation cohort when the machine learning approach was tested against clinical reference tools, with absolute improvements in AUC of 0·09 (95% CI 0·03-0·15; p=0·0026) for bleeding, of 0·18 (0·07-0·29; p=0·0013) for mortality, and of 0·25 (0·18-0·32; p<0·0001) for renal failure. ::: ::: ::: INTERPRETATION ::: The observed improvements in prediction for all three investigated clinical outcomes have the potential to improve critical care. These findings are noteworthy in that they use routinely collected clinical data exclusively, without the need for any manual processing. The deep machine learning method showed AUC scores that significantly surpass those of clinical reference tools, especially soon after admission. Taken together, these properties are encouraging for prospective deployment in critical care settings to direct the staff's attention towards patients who are most at risk. ::: ::: ::: FUNDING ::: No specific funding.


title: IO Performance Interference among Consolidated n-Tier Applications: Sharing Is Better Than Isolation for Disks

abstract: The performance unpredictability associated with migrating applications into cloud computing infrastructures has impeded this migration. For example, CPU contention between co-located applications has been shown to exhibit counter-intuitive behavior. In this paper, we investigate IO performance interference through the experimental study of consolidated n-tier applications leveraging the same disk. Surprisingly, we found that specifying a specific disk allocation, e.g., limiting the number of Input/Output Operations Per Second (IOPs) per VM, results in significantly lower performance than fully sharing disk across VMs. Moreover, we observe severe performance interference among VMs can not be totally eliminated even with a sharing strategy (e.g., response times for constant workloads still increase over 1,100%). By using a micro-benchmark (Filebench) and an n-tier application benchmark systems (RUBBoS), we demonstrate the existence of disk contention in consolidated environments, and how performance loss occurs when co-located database systems in order to maintain database consistency flush their logs from memory to disk. Potential solutions to these isolation issues are (1) to increase the log buffer size to amortize the disk IO cost (2) to decrease the number of write threads to alleviate disk contention. We validate these methods experimentally and find a 64% and 57% reduction in response time (or more generally, a reduction in performance interference) for constant and increasing workloads respectively.


abstract: Traveling salesman problems with profits (TSPs with profits) are a generalization of the traveling salesman problem (TSP), where it is not necessary to visit all vertices. A profit is associated with each vertex. The overall goal is the simultaneous optimization of the collected profit and the travel costs. These two optimization criteria appear either in the objective function or as a constraint. In this paper, a classification of TSPs with profits is proposed, and the existing literature is surveyed. Different classes of applications, modeling approaches, and exact or heuristic solution techniques are identified and compared. Conclusions emphasize the interest of this class of problems, with respect to applications as well as theoretical results.


title: Keyword extraction from a single document using word co-occurrence statistical information

abstract: We present a new keyword extraction algorithm that applies to a single document without using a corpus. Frequent terms are extracted first, then a set of cooccurrence between each term and the frequent terms, i.e., occurrences in the same sentences, is generated. Co-occurrence distribution shows importance of a term in the documentas follows. If probability distribution of co-occurrence between term a and the frequent terms is biased to a particular subset of frequent terms, then term a is likely to be a keyword. The degree of biases of distribution is measured by the χ 2 -measure. Our algorithm shows comparable performance to tfidf without using a corpus.


title: A GENERAL APPROACH TO TEMPORARY EMIGRATION IN MARK–RECAPTURE ANALYSIS

abstract: During mark-recapture studies of open populations, animals often tempo- rarily emigrate from study areas. Such temporary emigration can cause biased estimates of survival probabilities. We present a new statistical method to estimate survival probability from the capture histories of marked individuals in the presence of temporary emigration. This method uses stage-structured models that include one or more stages representing the individuals that have temporarily emigrated. Although not all parameters can be estimated in such stage structures, some important parameters are still estimable. Here, we determined the estimability of parameter values from the rank of the Jacobian of the likelihood function. We applied the temporary-emigration mark-recapture method to artificial data, representing various life histories and demonstrated consistency between actual and estimated values. As an example, we used the method to analyze data on reproductive female North Atlantic right whales. The method presented in this paper will be especially useful for studies of seabird, sea turtle, and marine-mammal populations where individuals are sampled only on their breeding grounds.


title: Sampling-based event-triggered consensus for multi-agent systems

abstract: Abstract This work considers the event-based consensus algorithms for multi-agent systems with periodic sampling schemes. An event-triggered consensus algorithm using sampled states are proposed. The measurement error in the event design is defined based on the state of local neighborhood center in order to reduce the number of events. Based on this algorithm, a self-triggered consensus protocol is proposed to further reduce the amount of communication and state sampling, as well as the number of events and thus the controller updates. It is noted that by introducing the periodic sampling scheme, Zeno behaviors can be naturally avoided in both of the algorithms. The effectiveness of the proposed algorithms is illustrated by simulation examples.


title: Real-time Approximation of Clothoids With Bounded Error for Path Planning Applications

abstract: We present a method for real-time computation of clothoid coordinates that guarantees bounded approximation error over a wide range of clothoid parameters provided that the clothoid's orientation change and length areReal-time approximation bounded. It is shown that coordinates of clothoid with any parameters can be computed from those of a single clothoid (with fixed parameters), using appropriate geometrical transformations. A comprehensive analysis is given on how to determine a required set of clothoids and, based on this, how to sample a clothoid in a lookup table in order to achieve required approximation precision. The algorithm is computationally very efficient and therefore suitable for real-time path planning, as well as for other applications that benefit from fast clothoid computation.


title: Chemotherapy-induced lung disease.

abstract: The lung has significant susceptibility to injury from a variety of chemotherapeutic agents. The clinician must be familiar with classic chemotherapeutic agents with well-described pulmonary toxicities and must also be vigilant about a host of new agents that may exert adverse effects on lung function. The diagnosis of chemotherapy-associated lung disease remains an exclusionary process, particularly with respect to considering usual and atypical infections, as well as recurrence of the underlying neoplastic process in these immune compromised patients. In many instances, chemotherapy-associated lung disease may respond to withdrawal of the offending agent and to the judicious application of corticosteroid therapy.


title: Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring

abstract: Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.


title: Dispersing Instant Social Video Service Across Multiple Clouds

abstract: Instant social video sharing which combines the online social network and user-generated short video streaming services, has become popular in today’s Internet. Cloud-based hosting of such instant social video contents has become a norm to serve the increasing users with user-generated contents. A fundamental problem of cloud-based social video sharing service is that users are located globally, who cannot be served with good service quality with a single cloud provider. In this paper, we investigate the feasibility of dispersing instant social video contents to multiple cloud providers. The challenge is that inter-cloud social propagation is indispensable with such multi-cloud social video hosting, yet such inter-cloud traffic incurs substantial operational cost. We analyze and formulate the multi-cloud hosting of an instant social video system as an optimization problem. We conduct large-scale measurement studies to show the characteristics of instant social video deployment, and demonstrate the trade-off between satisfying users with their ideal cloud providers, and reducing the inter-cloud data propagation. Our measurement insights of the social propagation allow us to propose a heuristic algorithm with acceptable complexity to solve the optimization problem, by partitioning a propagation-weighted social graph in two phases: a preference-aware initial cloud provider selection and a propagation-aware re-hosting. Our simulation experiments driven by real-world social network traces show the superiority of our design.


title: New Directions in Professional Higher Education.

abstract: Part 1 Setting the scene: life long learning and professional higher education issues of professionalism in higher education university education for developing professional practice. Part 2 Case studies: partnership in higher education independent learning and reflective practice using the new learning technologies using the Internet and the World Wide Web distance learning and the international dimension preparation for professional practice assessment and standards. Part 3 New directions: a framework for personal and professional development professional doctorates - the development of researching professionals practitioner centred research.


title: Open Palm Menu: A Virtual Menu Placed in Front of the Palm

abstract: Virtual and mixed realities make it possible to view and interact with virtual objects in a three-dimensional space. However, the location to display menus in three-dimensional space and the means of manipulating them are often problems. Existing studies developed methods of displaying a menu in the air or on the body. In this paper, a menu system is proposed that appears in front of the user's palm (of the non-dominant hand) when he/she opens that hand. The user employs the other hand (dominant hand) to then interact with the menu. Using the space around the body as opposed to projections onto actual limbs makes it possible to display more items in more varied layouts. Additionally, the user can control rendering of the menu by opening or closing the hand. Being adjacent to the palm of the open hand also enables the user to adjust the hand position to operate the menu more comfortably or to move the menu to an area where it is easier to view. In this study, we conducted an experiment to develop the menu design guidelines to ensure smooth menu operation, designed an optimal menu, and tested that menu by determining the ease of use in interacting with our modeling application.


title: Guideline and treatment algorithm for burn injuries.

abstract: As in many other countries, burn injuries are a challenging healthcare problem in Turkey. Initial management of burn patients is very important for future morbidity and mortality. Therefore, the Turkish Ministry of Health prepared "National Burns Treatment Algorithm" aided by the Scientific Burns Council. The basic aim of this algorithm is to guide physicians in the treatment of burn victims until they reach an experienced burns center. The content of this algorithm is first aid, initial management, resuscitation, and transfer policy. The Council started to work on this algorithm in 2011. Various consultants, including general surgeons, pediatric surgeons, aesthetic, plastic and reconstructive surgeons, anesthesiologists, and intensive care physicians, revised the first draft and it was sent to eight education and research hospitals of the Ministry of Health, four universities, and seven non-governmental organizations. In the last quarter of 2012, the algorithm was finalized and approved by the Scientific Council, after which, it was approved by the Ministry of Health and published.


title: Statistical techniques for online personalized advertising: a survey

abstract: Online advertising is the major source of revenue for most web service providers. Displaying advertisements that match user interests will not only lead to user satisfaction, but it will also maximize the revenues of both advertisers and web publishers. Online Advertisement systems use web mining and machine learning techniques to personalize advertisement selection to a particular user based on certain features such as his browsing behavior or demographic data. This paper presents an overview of online advertisement selection and summarizes the main technical challenges and open issues in this field. The paper investigates most of the relevant existing approaches carried out towards this perspective and provides a comparison and classification of these approaches.


title: Designing Smule's Ocarina: The iPhone's Magic Flute

abstract: The Smule Ocarina is a wind instrument designed for the iPhone, fully leveraging its wide array of technologies: microphone input (for breath input), multitouch (for fingering), accelerometer, real-time sound synthesis, highperformance graphics, GPS/location, and persistent data connection. In this mobile musical artifact, the interactions of the ancient flute-like instrument are both preserved and transformed via breath-control and multitouch finger-holes, while the onboard global positioning and persistent data connection provide the opportunity to create a new social experience, allowing the users of Ocarina to listen to one another. In this way, Ocarina is also a type of social instrument that enables a different, perhaps even magical, sense of global connectivity.


title: Combining kernel estimators in the uniform deconvolution problem

abstract: We construct a density estimator and an estimator of the distribution function in the uniform deconvolution model. The estimators are based on inversion formulas and kernel estimators of the density of the observations and its derivative. Initially the inversions yield two different estimators of the density and two estimators of the distribution function. We construct asymptotically optimal convex combinations of these two estimators. We also derive pointwise asymptotic normality of the resulting estimators, the pointwise asymptotic biases and an expansion of the mean integrated squared error of the density estimator. It turns out that the pointwise limit distribution of the density estimator is the same as the pointwise limit distribution of the density estimator introduced by Groeneboom and Jongbloed (Neerlandica, 57, 2003, 136), a kernel smoothed nonparametric maximum likelihood estimator of the distribution function.


title: Application of Fuzzy-PID Controller in Heating Ventilating and Air-Conditioning System

abstract: The controlled object in heating, ventilating and air-conditioning (HVAC) system has large inertia, pure lag and nonlinear characteristic. The uncertain disturbance factors affect the control performance. Combined the fuzzy control and general PID control, the temperature and humidity of room are controlled in HVAC system of a TV building. In winter, the experiments were done in a big studio. The experiments included the change of the operation set point in the studio and the 24 hours' control performance. The temperature of the studio was controlled through PID control, fuzzy control and fuzzy-PID control. Compared the three control algorithm, it is found that hybrid fuzzy-PID control has better adaptability and stability, less overshoot, faster response and higher precision than PID control and fuzzy control. The algorithm of fuzzy-PID control can reach the goal of comfort and energy saving. In addition, the threshold quantity in fuzzy-PID control affects the control performance so that the choice of valve is important.


title: Context-aware service discovery, selection and usage

abstract: Ongoing advances in technology and proceeding miniaturization have led to growing popularity and widespread use of mobile devices. Apart from communication features and basic functionality like address books or organizers users expect of today’s portable and handheld devices to provide more advanced features such as presenting relevant timetable information, a location plan or information about nearby shopping facilities. In spite of the advanced technological opportunities mobile devices are still restricted in terms of computing power, storage space, battery life, available network bandwidth and display size. One solution to overcome these shortcomings is by utilizing external functionality provided by services together with appropriate filtering mechanisms to obtain only relevant information. Since available and required services may vary due to the device’s mobility, the process of service discovery, selection and comparison has to be context-aware. In this paper we present our general idea of a framework for context-aware service discovery, selection and usage. We analyze problems and challenges in conjunction with our approach and discuss related works in this area of research.


title: Over-representation of Extreme Events in Decision-Making: A Rational Metacognitive Account

abstract: The Availability bias, manifested in the over-representation of extreme eventualities in decision-making, is a well-known cognitive bias, and is generally taken as evidence of human irrationality. In this work, we present the first rational, metacognitive account of the Availability bias, formally articulated at Marr's algorithmic level of analysis. Concretely, we present a normative, metacognitive model of how a cognitive system should over-represent extreme eventualities, depending on the amount of time available at its disposal for decision-making. Our model also accounts for two well-known framing effects in human decision-making under risk---the fourfold pattern of risk preferences in outcome probability (Tversky&Kahneman, 1992) and in outcome magnitude (Markovitz, 1952)---thereby providing the first metacognitively-rational basis for those effects. Empirical evidence, furthermore, confirms an important prediction of our model. Surprisingly, our model is unimaginably robust with respect to its focal parameter. We discuss the implications of our work for studies on human decision-making, and conclude by presenting a counterintuitive prediction of our model, which, if confirmed, would have intriguing implications for human decision-making under risk. To our knowledge, our model is the first metacognitive, resource-rational process model of cognitive biases in decision-making.


Assistive Technology and Universal Design for Learning : Two Sides of the Same Coin


title: Sanzu: A data science benchmark

abstract: The volume of data that is generated each day is rising rapidly. There is a need to analyze this data efficiently and produce results quickly. Data science offers a formal methodology for processing and analyzing data. It involves a work-flow with multiple stages, such as, data collection, data wrangling, statistical analysis and machine learning. In this paper, we look at data analytics systems that support the data science work-flow. The variety of current commercial and open-source data analytics systems differ significantly in terms of available features, functionality, and scalability. A benchmark can be used to evaluate the functionality and performance of a system. However, there is no standard benchmark for evaluating or comparing these data systems for doing data science. In this paper, we introduce a data science benchmark, Sanzu, to evaluate systems with data processing and analytics tasks. Our benchmark includes a micro and macro benchmark. The micro benchmark tests basic operations in isolation. It consists of task suites for reading and writing, data wrangling, statistical analysis, machine learning and time series analysis. Each macro workload evaluates an analytics application where a series of analysis or functions are based on a real world application. The macro benchmark focuses on sports and smart grid analytics. We evaluate these tasks on five different popular data science frameworks and systems: R, Anaconda Python, Dask, PostgreSQL (MADlib) and PySpark. For micro benchmark we generate synthetic datasets with 3 scale factors: 1, 10 and 100 (scale factor 1=1 million). The macro benchmark uses data generated from real-world data sources.


title: Path planning algorithm development for autonomous vacuum cleaner robots

abstract: A vacuum cleaner robot, generally called a robovac, is an autonomous robot that is controlled by intelligent program. Autonomous vacuum cleaning robot will perform task like sweeping and vacuuming in a single pass. The DVR-1 vacuum cleaning robot consists of two DC motor operated wheels that allow 360 degree rotation, a castor wheel, side spinning brushes, a front bumper and a miniature vacuum pump. Sensors in the bumper are used for generating binary information of obstacle detection then they are processed by some controlling algorithms. These algorithms are used for path planning and navigation. The robot's bumper prevents them from bumping into walls and furniture by reversing or changing path accordingly.


title: An Online Automated Scoring System for Java Programming Assignments

abstract:  Abstract—This paper proposes a web-based system that automatically scores programming assignments for students. The system receives a Java application program submitted by a student and returns the test result immediately. The test consists of compiler check, JUnit test, and result test. The result test is very useful for students in elementary programming courses, because a typical program is composed of only a main method that reads/writes data from/to the standard input/output devices. The system is used experimentally in an actual course of our university. We confirmed that the accuracy of scoring is very high and the response time of the system is satisfactory.


title: Bidirectional recurrent neural network language models for automatic speech recognition

abstract: Recurrent neural network language models have enjoyed great success in speech recognition, partially due to their ability to model longer-distance context than word n-gram models. In recurrent neural networks (RNNs), contextual information from past inputs is modeled with the help of recurrent connections at the hidden layer, while Long Short-Term Memory (LSTM) neural networks are RNNs that contain units that can store values for arbitrary amounts of time. While conventional unidirectional networks predict outputs from only past inputs, one can build bidirectional networks that also condition on future inputs. In this paper, we propose applying bidirectional RNNs and LSTM neural networks to language modeling for speech recognition. We discuss issues that arise when utilizing bidirectional models for speech, and compare unidirectional and bidirectional models on an English Broadcast News transcription task. We find that bidirectional RNNs significantly outperform unidirectional RNNs, but bidirectional LSTMs do not provide any further gain over their unidirectional counterparts.


title: Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction

abstract: We propose a novel ensemble model for bankruptcy prediction.We use Extreme Gradient Boosting as an ensemble of decision trees.We propose a new approach for generating synthetic features to improve prediction.The presented method is evaluated on real-life data of Polish companies. Bankruptcy prediction has been a subject of interests for almost a century and it still ranks high among hottest topics in economics. The aim of predicting financial distress is to develop a predictive model that combines various econometric measures and allows to foresee a financial condition of a firm. In this domain various methods were proposed that were based on statistical hypothesis testing, statistical modeling (e.g., generalized linear models), and recently artificial intelligence (e.g., neural networks, Support Vector Machines, decision tress). In this paper, we propose a novel approach for bankruptcy prediction that utilizes Extreme Gradient Boosting for learning an ensemble of decision trees. Additionally, in order to reflect higher-order statistics in data and impose a prior knowledge about data representation, we introduce a new concept that we refer as to synthetic features. A synthetic feature is a combination of the econometric measures using arithmetic operations (addition, subtraction, multiplication, division). Each synthetic feature can be seen as a single regression model that is developed in an evolutionary manner. We evaluate our solution using the collected data about Polish companies in five tasks corresponding to the bankruptcy prediction in the 1st, 2nd, 3rd, 4th, and 5th year. We compare our approach with the reference methods.


title: Substrate integrated suspended line to air-filled SIW transition for high-performance millimeter-wave multilayer integration

abstract: Substrate integrated suspended line (SISL) and air-filled substrate integrated waveguide (AFSIW) technological platforms have been recently reported. They are both of high interest in the design of high-performance integrated millimeter-wave systems based on low-cost multilayer printed circuit board (PCB) technologies. This has been confirmed through simulations and experiments when comparing the insertion loss at Ka-band of the SISL and AFSIW with other conventional transmission lines. To take advantage of both platforms and interconnect SISL and AFSIW structures and circuits, a broadband SISL to AFSIW transition is reported. For demonstration purpose, a back-to-back transition operating over the Ka-band has been designed and fabricated. It achieves a matching of better than −15 dB and an insertion loss of 0.27 ±0.22 dB (0.11 ±0.06 dB for the transition) over the Ka-band.


title: Incorporation of Flipped Learning into EFL Classrooms Performance and Perception

abstract: Innovative mobile-based tools have made new opportunities for learners to spend more quality time inside the classrooms. Keeping this in mind, the present study sought to examine the effect of the flipped classroom on developing EFL learners’ speaking and listening skills. To do so, 60 EFL students from two universities in Iran were assigned into one of the classes of flipped and conventional groups. Telegram app was used as the online platform for the flipped group's participants through which the listening materials, including Ted-talk videos and students' book's videos were posted prior to the class, and the speaking activities were done via open discussion forums in the Telegram group. To elicit the required data, multiple sources of data collection, including an Oxford Placement Test (OPT), a listening test, a speaking test, a perception questionnaire, and a written self-report survey were used. Both quantitative and qualitative analyses were used to analyze the data. The findings revealed that the flipped group's participants significantly outperformed those in the conventional group in the post-test. The results of the questionnaire also indicated that most participants of the flipped group were satisfied with learning English in the flipped classroom, and found this modern technology improving their listening and speaking skills. Furthermore, they found Telegram a suitable platform for learning language.


title: Effective resource management for enhancing performance of 2D and 3D stencils on GPUs

abstract: GPUs are an attractive target for data parallel stencil computations prevalent in scientific computing and image processing applications. Many tiling schemes, such as overlapped tiling and split tiling, have been proposed in past to improve the performance of stencil computations. While effective for 2D stencils, these techniques do not achieve the desired improvements for 3D stencils due to the hardware constraints of GPU. A major challenge in optimizing stencil computations is to effectively utilize all resources available on the GPU. In this paper we develop a tiling strategy that makes better use of resources like shared memory and register file available on the hardware. We present a systematic methodology to reason about which strategy should be employed for a given stencil and also discuss implementation choices that have a significant effect on the achieved performance. Applying these techniques to various 2D and 3D stencils gives a performance improvement of 200-400% over existing tools that target such computations.


title: A larger audience, please!: encouraging people to listen to a guide robot

abstract: Tour guidance is a common task of social robots. Such a robot must be able to encourage the participation of people who are not directly interacting with it. We are particularly interested in encouraging people to overhear its interaction with others, since it has often been observed that even people who hesitate to interact with a robot are willing to observe its activity. To encourage such participation as bystanders, we developed a robot that walks backwards based on observations of human tour guides. Our developed system uses a robust human tracking system that enables a robot to guide people by walking forward/backward and allows us to scrutinize people's behavior after the experiment. We conducted a field experiment to compare the ratios of overhearing in "walking forward" and "walking backward." The experimental results revealed that in fact people do more often overhear the robot's conversation in the "walking backward" condition.


title: Distributed road surface condition monitoring using mobile phones

abstract: The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.


title: Behavioral malware detection approaches for Android

abstract: Android, the fastest growing mobile operating system released in November 2007, boasts of a staggering 1.4 billion active users. Android users are susceptible to malicious applications that can hack into their personal data due to the lack of careful monitoring of their in-device security. There have been numerous works on devising malware detection methods. However, none of earlier works are conclusive enough for direct application and lack experimental validation. In this paper, we have investigated the natures and identities of malicious applications and devised two novel detection approaches for detection: network-based detection and system call based detection approaches. To evaluate our proposed approaches, we performed experiments on a subset of 1260 malwares, acquired from Android Malware Genome Project, a malware database created by Y. Zhou et al. [1] and 227 non-malware (benign) applications. Results show that our system call based approach is able to detect malwares with an accuracy of 87% which is quite significant in general malware detection context. Our proposed detection approaches along with the experimental results will provide security professionals with more precise and quantitative approaches in their investigations of mobile malwares on Android systems.


title: Adolescent idiopathic scoliosis: natural history and long term treatment effects

abstract: Adolescent idiopathic scoliosis is a lifetime, probably systemic condition of unknown cause, resulting in a spinal curve or curves of ten degrees or more in about 2.5% of most populations. However, in only about 0.25% does the curve progress to the point that treatment is warranted.Untreated, adolescent idiopathic scoliosis does not increase mortality rate, even though on rare occasions it can progress to the >100° range and cause premature death. The rate of shortness of breath is not increased, although patients with 50° curves at maturity or 80° curves during adulthood are at increased risk of developing shortness of breath. Compared to non-scoliotic controls, most patients with untreated adolescent idiopathic scoliosis function at or near normal levels. They do have increased pain prevalence and may or may not have increased pain severity. Self-image is often decreased. Mental health is usually not affected. Social function, including marriage and childbearing may be affected, but only at the threshold of relatively larger curves.Non-operative treatment consists of bracing for curves of 25° to 35° or 40° in patients with one to two years or more of growth remaining. Curve progression of ≥ 6° is 20 to 40% more likely with observation than with bracing. Operative treatment consists of instrumentation and arthrodesis to realign and stabilize the most affected portion of the spine. Lasting curve improvement of approximately 40% is usually achieved.In the most completely studied series to date, at 20 to 28 years follow-up both braced and operated patients had similar, significant, and clinically meaningful reduced function and increased pain compared to non-scoliotic controls. However, their function and pain scores were much closer to normal than patient groups with other, more serious conditions.Risks associated with treatment include temporary decrease in self-image in braced patients. Operated patients face the usual risks of major surgery, a 6 to 29% chance of requiring re-operation, and the remote possibility of developing a pain management problem.Knowledge of adolescent idiopathic scoliosis natural history and long-term treatment effects is and will always remain somewhat incomplete. However, enough is know to provide patients and parents the information needed to make informed decisions about management options.


title: Ultrawideband Synthetic Aperture Radar Landmine Detection

abstract: In this paper, we consider landmine detection using ultrawideband synthetic aperture radar, where the two main challenges are feature extraction and discriminator design. The space-wavenumber processing is proposed to retrieve the frequency-and aspect-angle-dependent scattering features of suspected objects. In order to reduce the dimensionality of the input feature vector for a discriminator, the sequential forward floating selection method is used to choose efficient features. Based on the obtained feature vector, a fuzzy hypersphere support vector machine is designed to deal with the problem of detecting landmines in an unconstrained environment. The experimental results show that the proposed method can achieve a significant improvement in detection performance for antitank mines.


title: Linked Data Indexing of Distributed Ledgers

abstract: Searching for information in distributed ledgers is currently not an easy task, as information relating to an entity may be scattered throughout the ledger with no index. As distributed ledger technologies become more established, they will increasingly be used to represent real world transactions involving many parties and the search requirements will grow. An index providing the ability to search using domain specific terms across multiple ledgers will greatly enhance to power, usability and scope of these systems. ::: ::: We have implemented a semantic index to the Ethereum blockchain platform, to expose distributed ledger data as Linked Data. As well as indexing block- and transaction-level data according to the BLONDiE ontology, we have mapped smart contracts to the Minimal Service Model ontology, to take the first steps towards connecting smart contracts with Semantic Web Services.


Document Level Sentiment Analysis: A survey


title: Accelerating convolutional neural network with FFT on tiny cores

abstract: FueXeA by ILSVRC and COCO competitions, Convolutional Neural Network (CNN) has become important in computer vision, and natural language processing. However state-of-the-art CNNs are computationally and memory intensive, thus energy efficient implementation on embedded platform is challenging. Recently VGGNet and ResNet showed that deep neural networks with more convolution layers (CV) and few fully connected layer (FC) can achieve lower error rates, thus reducing the complexity of convolution layers is of utmost importance. To reduce computations and shared memory usage in convolution layers, in this paper we evaluate the performance of direct convolution (Direct-Conv), Fast Fourier Transform (FFT) based convolution (FFT-Conv), and Overlap and Add FFT convolution (FFT-OVA-Conv) in embedded architecture including a low power domain specific many-core architecture called Power Efficient Nano Clusters (PENC) and ARM Cortex A53 CPU. To demonstrate the efficiency of FFT-Conv and FFT-OVA-Conv, we map ResNet-20 for the CIFAR-10 dataset on PENC as well as in ARM Cortex A53 CPU. Results are evaluated and compared with respect to throughput per watt, energy delay product, and execution time for three methods. Using built-in FFT instruction in PENC, the FFT-OVA-Conv performs 2.9× and 1.65× faster and achieves 6.7× and 2.3× better throughput per watt than Direct-Conv and FFT-Conv respectively. In ARM A53 CPU, the FFT-OVA-Conv achieves 3.36× and 1.38× improvement in execution time and 2.72× and 1.32× better throughput than Direct-Conv and FFT-Conv.


title: Analyzing and Detecting Review Spam

abstract: Mining of opinions from product reviews, forum posts and blogs is an important research topic with many applications. However, existing research has been focused on extraction, classification and summarization of opinions from these sources. An important issue that has not been studied so far is the opinion spam or the trustworthiness of online opinions. In this paper, we study this issue in the context of product reviews. To our knowledge, there is still no published study on this topic, although Web page spam and email spam have been investigated extensively. We will see that review spam is quite different from Web page spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that review spam is widespread. In this paper, we first present a categorization of spam reviews and then propose several techniques to detect them.


title: A Flexible, Micro-Lens-Coupled LED Stimulator for Optical Neuromodulation

abstract: Optogenetics is a fast growing neuromodulation method, which can remotely manipulate the specific activities of genetically-targeted neural cells and associated biological behaviors with millisecond temporal precision through light illumination. Application of optogenetics in neuroscience studies has created an increased need for the development of light sources and the instruments for light delivery. This paper presents a micro-lens-coupled LED neural stimulator which includes a backside reflector and a frontside microlens for light collection and collimation. The device structure has been optimized using optical simulation and the optimized device is able to increase the volume of excitable tissues by 70.4%. Device prototypes have been fabricated and integrated based on an optimization of the device structure. The measurement results show that the light power increases by 99% at an effective penetration depth of 5 000 $\mu\text{m}$ by the fabricated device under various voltages of 2.4–3.2 V.


title: Wide-Range 5.0/3.3/1.8-V I/O Buffer Using 0.35-m 3.3-V CMOS Technology

abstract: A 5.0/3.3/1.8-V tolerant I/O buffer implemented using typical CMOS 2P4M 0.35-mum process is proposed in this paper. Unlike traditional mixed-voltage-tolerant I/O buffers, the proposed I/O buffer can transmit and receive signals with voltage levels of 5.0/3.3/1.8 V. By using a stacked PMOS and a stacked NMOS at the output stage and a dynamic gate bias generator providing appropriate control voltages for the gates of the stacked PMOS, gate-oxide overstress and hot-carrier degradation are avoided. Moreover, gate-tracking and floating n-well circuits are used to remove undesirable leakage current paths. The proposed topology can be applied to any technologies with the constraint of VDD < VDDH < 2 times, which should be considered carefully in sub-100-nm technologies. Measurement results on silicon verify the function and the gate-oxide reliability of the proposed I/O buffer. The maximum transmitting speed of the proposed I/O buffer is measured to be 80/120/84 Mb/s for the supply voltage of I/O buffer at 5.0/3.3/1.8 V, respectively, given the load of 29 pF.


title: Comparative Analysis of Different Single-Diode PV Modeling Methods

abstract: Modeling of photovoltaic (PV) systems is essential for the designers of solar generation plants to do a yield analysis that accurately predicts the expected power output under changing environmental conditions. This paper presents a comparative analysis of PV module modeling methods based on the single-diode model with series and shunt resistances. Parameter estimation techniques within a modeling method are used to estimate the five unknown parameters in the single diode model. Two sets of estimated parameters were used to plot the I–V characteristics of two PV modules, i.e., SQ80 and KC200GT, for the different sets of modeling equations, which are classified into models 1 to 5 in this study. Each model is based on the different combinations of diode saturation current and photogenerated current plotted under varying irradiance and temperature. Modeling was done using MATLAB/Simulink software, and the results from each model were first verified for correctness against the results produced by their respective authors. Then, a comparison was made among the different models (models 1 to 5) with respect to experimentally measured and datasheet I–V curves. The resultant plots were used to draw conclusions on which combination of parameter estimation technique and modeling method best emulates the manufacturer specified characteristics.


title: Malware classification with LSTM and GRU language models and a character-level CNN

abstract: Malicious software, or malware, continues to be a problem for computer users, corporations, and governments. Previous research [1] has explored training file-based, malware classifiers using a two-stage approach. In the first stage, a malware language model is used to learn the feature representation which is then input to a second stage malware classifier. In Pascanu et al. [1], the language model is either a standard recurrent neural network (RNN) or an echo state network (ESN). In this work, we propose several new malware classification architectures which include a long short-term memory (LSTM) language model and a gated recurrent unit (GRU) language model. We also propose using an attention mechanism similar to [12] from the machine translation literature, in addition to temporal max pooling used in [1], as an alternative way to construct the file representation from neural features. Finally, we propose a new single-stage malware classifier based on a character-level convolutional neural network (CNN). Results show that the LSTM with temporal max pooling and logistic regression offers a 31.3% improvement in the true positive rate compared to the best system in [1] at a false positive rate of 1%.


title: Urbanity: A System for Interactive Exploration of Urban Dynamics from Streaming Human Sensing Data

abstract: With the urbanization process worldwide, modeling the dynamics of people's activities in urban environments has become a crucial socioeconomic task. We present Urbanity, a novel system that leverages geo-tagged social media streams for modeling urban dynamics. Urbanity automatically discovers the spatial and temporal hotspots where people's activities concentrate; and captures the cross-modal correlations among location, time, and text by jointly mapping different units into the same latent space. With Urbanity, the end users are able to use flexible query schemes to retrieve different resources (e.g., POIs, hotspots, hours, activities) that meet their needs. Furthermore, Urbanity can handle continuous streams to update the learned model, thus revealing up-to-date patterns of urban activities.


title: AC/DC converter with medium frequency link for railway traction application. Evaluation of semiconductor losses and operating limits.

abstract: Nowadays, the typical railway traction chain, which operates under AC power supplies, includes a bulky and heavy transformer. In order to reduce the weight and the size of the input transformer, a medium frequency topology, which involves switches in soft commutation mode, is proposed. A test bench, based on one elementary stage of the complete structure, was built. It allows characterizing 6.5 kV IGBT modules in soft switching conditions. Switching losses measurements are performed to determine the optimal switching frequency and the operating limits of the converter.


title: Barriers to Pediatric Pain Management: A Nursing Perspective

abstract: Abstract This study describes strategies used by the Joint Clinical Practice Council of Children's Hospital of Wisconsin to identify barriers perceived as interfering with nurses' (RNs) ability to provide optimal pain management. A survey was used to ascertain how nurses described optimal pain management and how much nurses perceived potential barriers as interfering with their ability to provide that level of care. The survey, "Barriers to Optimal Pain management" (adapted from Van Hulle Vincent & Denyes, 2004), was distributed to all RNs working in all patient care settings. Two hundred seventy-two surveys were returned. The five most significant barriers identified were insufficient physician (MD) orders, insufficient MD orders before procedures, insufficient time to premedicate patients before procedures, the perception of a low priority given to pain management by medical staff, and parents' reluctance to have patients receive pain medication. Additional barriers were identified through narrative comments. Information regarding the impact of the Acute Pain Service on patient care, RNs' ability to overcome barriers, and RNs' perception of current pain management practices is included, as are several specific interventions aimed at improving or ultimately eliminating identified barriers.


title: IOS Press A Scalable Approach for Statistical Learning in Semantic Graphs

abstract: Increasingly, data is published in the form of semantic graphs. The most notable example is the Linked Open Data LOD initiative where an increasing number of data sources are published in the Semantic Web's Resource Description Framework and where the various data sources are linked to reference one another. In this paper we apply machine learning to semantic graph data and argue that scalability and robustness can be achieved via an urn-based statistical sampling scheme. We apply the urn model to the SUNS framework which is based on multivariate prediction. We argue that multivariate prediction approaches are most suitable for dealing with the resulting high-dimensional sparse data matrix. Within the statistical framework, the approach scales up to large domains and is able to deal with highly sparse relationship data. We summarize experimental results using a friend-of-a-friend data set and a data set derived from DBpedia. In more detail, we describe novel experiments on disease gene prioritization using LOD data sources. The experiments confirm the ease-of-use, the scalability and the good performance of the approach.


title: Capacitive-Sensor-Induced Losses in 900-, 1800-, and 1900-MHz Antennas

abstract: Capacitive sensors have been designed for use as user proximity sensors in mobile-phone antennas. Sensor-induced losses were measured in 900- and 1800-MHz capacitive coupling element antennas and a 1900-MHz planar inverted-F antenna (PIFA) and compared to the RF losses caused by an impedance-matching sensor. The lengths of the capacitance sensors varied from 1 to 43 mm, which induced antenna losses from 0.05 to 1.35 dB at 1 GHz. A pair of pad sensors induced 0.1-0.25-dB losses at 0.8-1.85 GHz. The loss of capacitive sensors in sizes below 5 mm2 was comparable to that of antenna-matching sensors. Total RF insertion losses due to the compared proximity sensors and loss compensation systems were 0.4 and 0.55 dB, respectively, across the 900- and 1900-MHz frequency bands.


title: Recommender System for News Articles using Supervised Learning

abstract: In the last decade we have observed a mass increase of information, in particular information that is shared through smartphones. Consequently, the amount of information that is available does not allow the average user to be aware of all his options. In this context, recommender systems use a number of techniques to help a user find the desired product. Hence, nowadays recommender systems play an important role. Recommender Systems' aim to identify products that best fits user preferences. These techniques are advantageous to both users and vendors, as it enables the user to rapidly find what he needs and the vendors to promote their products and sales. As the industry became aware of the gains that could be accomplished by using these algorithms, also a very interesting problem for many researchers, recommender systems became a very active area since the mid 90's. Having in mind that this is an ongoing problem the present thesis intends to observe the value of using a recommender algorithm to find users likes by observing her domain preferences. In a balanced probabilistic method, this thesis will show how news topics can be used to recommend news articles. In this thesis, we used different machine learning methods to determine the user ratings for an article. To tackle this problem, supervised learning methods such as linear regression, Naive Bayes and logistic regression are used. All the aforementioned models have a different nature which has an impact on the solution of the given problem. Furthermore, number of experiments are presented and discussed to identify the feature set that fits best to the problem.


A Case of Attempted Bilateral Self-Enucleation in a Patient with Bipolar Disorder


title: Twitter Data Analytics

abstract: This brief provides methods for harnessing Twitter data to discover solutions to complex inquiries. The brief introduces the process of collecting data through Twitters APIs and offers strategies for curating large datasets. The text gives examples of Twitter data with real-world examples, the present challenges and complexities of building visual analytic tools, and the best strategies to address these issues. Examples demonstrate how powerful measures can be computed using various Twitter data sources. Due to its openness in sharing data, Twitter is a prime example of social media in which researchers can verify their hypotheses, and practitioners can mine interesting patterns and build their own applications. This brief is designed to provide researchers, practitioners, project managers, as well as graduate students with an entry point to jump start their Twitter endeavors. It also serves as a convenient reference for readers seasoned in Twitter data analysis.


title: Multi-Person Pose Estimation with Local Joint-to-Person Associations

abstract: Despite of the recent success of neural networks for human pose estimation, current approaches are limited to pose estimation of a single person and cannot handle humans in groups or crowds. In this work, we propose a method that estimates the poses of multiple persons in an image in which a person can be occluded by another person or might be truncated. To this end, we consider multi-person pose estimation as a joint-to-person association problem. We construct a fully connected graph from a set of detected joint candidates in an image and resolve the joint-to-person association and outlier detection using integer linear programming. Since solving joint-to-person association jointly for all persons in an image is an NP-hard problem and even approximations are expensive, we solve the problem locally for each person. On the challenging MPII Human Pose Dataset for multiple persons, our approach achieves the accuracy of a state-of-the-art method, but it is 6,000 to 19,000 times faster.


title: Arabic script based language character recognition: Nasta'liq vs Naskh analysis

abstract: Arabic and various Indic scripts received researcher attention after a long time of focus on East Asian and Western whereas this script is used by 1/4th of the world population by many languages in several countries. Arabic script is more complex as compared to Latin script. One of such difficulty with Arabic script is multiple writing styles. Naskh and Nastaliq are the two most commonly style adopted by Arabic script based languages. This paper compares both writing style and concludes that why work done for Naskh cannot be applied for Nasta'liq writing style.


title: Dynamic Movement Primitives–A Framework for Motor Control in Humans and Humanoid Robotics

abstract: Given the continuous stream of movements that biological systems exhibit in their daily activities, an account for such versatility and creativity has to assume that movement sequences consist of segments, executed either in sequence or with partial or complete overlap. Therefore, a fundamental question that has pervaded research in motor control both in artificial and biological systems revolves around identifying movement primitives (a.k.a. units of actions, basis behaviors, motor schemas, etc.). What are the fundamental building blocks that are strung together, adapted to, and created for ever new behaviors? This paper summarizes results that led to the hypothesis of Dynamic Movement Primitives (DMP). DMPs are units of action that are formalized as stable nonlinear attractor systems. They are useful for autonomous robotics as they are highly flexible in creating complex rhythmic (e.g., locomotion) and discrete (e.g., a tennis swing) behaviors that can quickly be adapted to the inevitable perturbations of a dynamically changing, stochastic environment. Moreover, DMPs provide a formal framework that also lends itself to investigations in computational neuroscience. A recent finding that allows creating DMPs with the help of well-understood statistical learning methods has elevated DMPs from a more heuristic to a principled modeling approach. Theoretical insights, evaluations on a humanoid robot, and behavioral and brain imaging data will serve to outline the framework of DMPs for a general approach to motor control in robotics and biology.


title: Code coverage based technique for prioritizing test cases for regression testing

abstract: Test case prioritization involves scheduling test cases in an order that increases their effectiveness in meeting some performance goals. One of the common performance goals is to run those test cases that achieve total code coverage at the earliest. In this work we propose a model that achieves 100% code coverage optimally during version specific regression testing.


title: Sequential pattern mining -- approaches and algorithms

abstract: Sequences of events, items, or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyze frequent subsequences is a common problem. Sequential Pattern Mining arose as a subfield of data mining to focus on this field. This article surveys the approaches and algorithms proposed to date.


title: Comparison of IEEE C37.118.2-2011 standard and GB/T 26865.2-2011 standard based on synchrophasor data transfer

abstract: With the development of synchronous phasor measurement technology and the improvement in the application of power systems, the relevant international organizations and country have issued and revised the synchronous phasor related standards. In this paper, we discuss the synchronous phasor data tracansmission according to IEEE C37.118.2-2011 and GB/T 26865.2-2011, including the format of the message frame, the support of the data transmission protocol and the transmission of the offline file and so on, analyzes the causes of the differences between China and international standards, and provides a reference for the international synchronization phasor measurement standard and the further improvement of our domestic standard.


title: Information Retrieval Evaluation

abstract: Evaluation has always played a major role in information retrieval, with the early pioneers such as Cyril Cleverdon and Gerard Salton laying the foundations for most of the evaluation methodologies in use today. The retrieval community has been extremely fortunate to have such a well-grounded evaluation paradigm during a period when most of the human language technologies were just developing. This lecture has the goal of explaining where these evaluation methodologies came from and how they have continued to adapt to the vastly changed environment in the search engine world today. The lecture starts with a discussion of the early evaluation of information retrieval systems, starting with the Cranfield testing in the early 1960s, continuing with the Lancaster "user" study for MEDLARS, and presenting the various test collection investigations by the SMART project and by groups in Britain. The emphasis in this chapter is on the how and the why of the various methodologies developed. The second chapter covers the more recent "batch" evaluations, examining the methodologies used in the various open evaluation campaigns such as TREC, NTCIR (emphasis on Asian languages), CLEF (emphasis on European languages), INEX (emphasis on semi-structured data), etc. Here again the focus is on the how and why, and in particular on the evolving of the older evaluation methodologies to handle new information access techniques. This includes how the test collection techniques were modified and how the metrics were changed to better reflect operational environments. The final chapters look at evaluation issues in user studies -- the interactive part of information retrieval, including a look at the search log studies mainly done by the commercial search engines. Here the goal is to show, via case studies, how the high-level issues of experimental design affect the final evaluations. Table of Contents: Introduction and Early History / "Batch" Evaluation Since 1992 / Interactive Evaluation / Conclusion


title: Digital Doherty Amplifier With Enhanced Efficiency and Extended Range

abstract: In this paper, a digital Doherty power amplifier (PA) with improved efficiency performance over a wide power range is proposed. The efficiency enhancement is achieved by using a digitally controlled dynamic input power distribution scheme implemented to improve the active load modulation mechanism and to minimize the drive power waste into the peaking branch at backed-off power levels. Furthermore, the proposed distribution scheme causes the premature saturation of the carrier amplifier of the proposed Doherty PA and results in an extended range of high back-off efficiency. A comprehensive study of the operational principle of the proposed efficiency-extended digital Doherty PA is provided to demonstrate its merits and to enlighten its operation. In particular, the current and power profiles of the proposed digital Doherty PA are exposed and its efficiency characteristics analyzed. For experimental validation, the proposed Doherty PA is implemented within the dual-input digitally driven architecture based on a 10-W gallium-nitride transistor. Using a one-carrier Worldwide Interoperability for Microwave Access signal with a 9-dB peak-to-average power ratio and 10-MHz bandwidth, the digitally linearized efficiency-extended Doherty PA exhibited an excellent drain efficiency of 50% along with - 38 dB of relative constellation error. The efficiency enhancement is 7% in comparison to a conventional fully analog Doherty PA.


title: An empirical analysis and comparison of apriori and FP- growth algorithm for frequent pattern mining

abstract: In this paper, we determine the empirical comparison of Apriori and FP-growth algorithm for frequent item set sequences for Web Usage data. We define the data structure, its implementation and algorithmic features mainly focusing on those that also arise in frequent item set mining. Web usage mining itself can be defined further depending on the type of usage data is considered like web server data, application server data and application level data. User logs that are collected at web server are also known as web server data. Some of the characteristic data collected at a web server include IP addresses of users, page references, and access time of the users and these are the main input to the present research. The comparison of algorithm concentrates on web usage mining and particularly focuses on determining the web usage patterns of websites from the server log files. In our analysis we take into empirical comparison for properties like memory size, input data, pre-fetching, scalability and processing efficiency etc, in order to better understand the results of the evaluation.


title: Examination of Artificial MiRNA Mimics with Centered–Site Complementarity for Gene Targeting

abstract: BACKGROUND ::: MiRNA primarily acts to repress gene expression at the post-transcriptional level through imperfect complementarity of its 5' region to the "seed site" in the 3' untranslated region of target mRNAs, with its "3'-supplementary site" and "center site" also playing important roles under certain circumstances. The aim of this study was to test if artificial miRNA mimics (miR-Mimics) that are designed to target the "centered sites" without "seed sites" complementarity are able to repress gene expression as natural miRNAs. ::: ::: ::: METHODS ::: We designed miR-Mimics carrying centered-site matches (CS-miR-Mimics) or seed-site matches (SS-miR-Mimics) and siRNA to two antiapoptotic genes BCL2 and AKT1. We tested the gene targeting of these constructs using real-time RT-PCR and Western blot to quantify mRNA and protein levels of BCL2 and AKT1, respectively, luciferase reporter gene assay to investigate the interaction between miR-Mimics and their target sites, and cell survival assay to study the functional outcomes of the miR-Mimics. ::: ::: ::: RESULTS ::: We found that CS-miR-Mimic, SS-miR-Mimic and siRNA, all down regulated the mRNA and protein levels of their cognate target BCL2 or AKT1 in a concentration-dependent manner. Luciferase reporter gene assay further confirmed the functional interactions of CS-miR-Mimic, SS-miR-Mimic and siRNA with their target sites. We then observed that the miR-Mimics and siRNAs were all able to induce cell death, as indicated by the reduced survival rate of cells. ::: ::: ::: CONCLUSIONS ::: We have provided evidence for the feasibility of CS-miR-Mimics for post-transcriptional repression of genes, which can be designed to have reduced numbers of seed type off-target sites compared to the number of target sites from an average endogenous seed-site miRNA. CS-miR-Mimics may be a novel approach for miRNA research requiring miRNA gain-of-function.


title: Monotony of road environment and driver fatigue: a simulator study

abstract: Studies have shown that drowsiness and hypovigilance frequently occur during highway driving and that they may have serious implications in terms of accident causation. This paper focuses on the task induced factors that are involved in the development of these phenomena. A driving simulator study was conducted in order to evaluate the impact of the monotony of roadside visual stimulation using a steering wheel movement (SWM) analysis procedure. Fifty-six male subjects each drove during two different 40-min periods. In one case, roadside visual stimuli were essentially repetitive and monotonous, while in the other one, the environment contained disparate visual elements aiming to disrupt monotony without changing road geometry. Subject's driving performance was compared across these conditions in order to determine whether disruptions of monotony can have a positive effect and help alleviate driver fatigue. Results reveal an early time-on-task effect on driving performance for both driving periods and more frequent large SWM when driving in the more monotonous road environment, which implies greater fatigue and vigilance decrements. Implications in terms of environmental countermeasures for driver fatigue are discussed.


title: Semantic Clustering for Robust Fine-Grained Scene Recognition

abstract: In domain generalization, the knowledge learnt from one or multiple source domains is transferred to an unseen target domain. In this work, we propose a novel domain generalization approach for fine-grained scene recognition. We first propose a semantic scene descriptor that jointly captures the subtle differences between fine-grained scenes, while being robust to varying object configurations across domains. We model the occurrence patterns of objects in scenes, capturing the informativeness and discriminability of each object for each scene. We then transform such occurrences into scene probabilities for each scene image. Second, we argue that scene images belong to hidden semantic topics that can be discovered by clustering our semantic descriptors. To evaluate the proposed method, we propose a new fine-grained scene dataset in cross-domain settings. Extensive experiments on the proposed dataset and three benchmark scene datasets show the effectiveness of the proposed approach for fine-grained scene transfer, where we outperform state-of-the-art scene recognition and domain generalization methods.


title: Received signal strength indication (RSSI) code assessment for wireless sensors network (WSN) deployed Raspberry-Pi

abstract: Wireless sensor network (WSN) is commonly used for localization applications. Through sniffing receive signal strength indicator (RSSI) in WSN system, localization and connection to access point highest RSSI can be done automatically. In this paper, we propose Raspberry-Pi (RasPi), based best access point selection method utilizing RSSI metric. The RasPi brings the advantages of a personal computer (PC) to the domain of sensor network, which makes it the perfect platform for interfacing with a wide variety of external peripherals. This work aims to investigate various source codes deployed on RasPi for localization purpose by sniffing the RSSI metric. Consequently, comparative analysis of its key elements and performances with some of the currently available wireless sensor nodes have shown that despite few disadvantages, RasPi remains an inexpensive single board computer (SBC) which has been used very successfully in sensor network domain and diverse range of research applications.


title: Balancing agility and discipline: evaluating and integrating agile and plan-driven methods

abstract: Rapid change and increasing software criticality drive successful development and acquisition organizations to balance the agility and discipline of their key processes. The emergence of agile methods in the software community is raising the expectations of customers and management, but the methods have shortfalls and their compatibility with traditional plan-driven methods such as those represented by CMMI, ISO-15288, and UK-DefStan-00-55 is largely unexplored.This tutorial pragmatically examines the aspects of agile and plan-driven methods and provides an approach to balancing through examples and case studies.


title: Darrieus Wind Turbine Performance Prediction: Computational Modeling

abstract: The Vertical Axis Wind Turbines Darrieus is facing a rapid installations growth due to the interest for a decentralizing generation. The Darrieus aerodynamic behavior analysis is a hard computational task. This paper starts by offering an insight into the Darrieus wind turbines performance prediction and proposes an algorithm based on the Multiple Streamtube modeling as a prediction approach in order to have an admissible handing in an Internet of things environment.


title: Vision-based method for detecting driver drowsiness and distraction in driver monitoring system

abstract: Most driver-monitoring systems have attempted to detect either driver drowsiness or distraction, although both factors should be considered for accident prevention. Therefore, we propose a new driver-monitoring method considering both factors. We make the following contributions. First, if the driver is looking ahead, drowsiness detection is performed; otherwise, distraction detection is performed. Thus, the computational cost and eye-detection error can be reduced. Second, we propose a new eye-detection algorithm that combines adaptive boosting, adaptive template matching, and blob detection with eye validation, thereby reducing the eye-detection error and processing time significantly, which is hardly achievable using a single method. Third, to enhance eye-detection accuracy, eye validation is applied after initial eye detection, using a support vector machine based on appearance features obtained by principal component analysis (PCA) and linear discriminant analysis (LDA). Fourth, we propose a novel eye state-detection algorithm that combines appearance features obtained using PCA and LDA, with statistical features such as the sparseness and kurtosis of the histogram from the horizontal edge image of the eye. Experimental results showed that the detection accuracies of the eye region and eye states were 99 and 97%, respectively. Both driver drowsiness and distraction were detected with a success rate of 98%.


title: Randomized trial of safinamide add-on to levodopa in Parkinson's disease with motor fluctuations

abstract: Levodopa is effective for the motor symptoms of Parkinson's disease (PD), but is associated with motor fluctuations and dyskinesia. Many patients require add-on therapy to improve motor fluctuations without exacerbating dyskinesia. The objective of this Phase III, multicenter, double-blind, placebo-controlled, parallel-group study was to evaluate the efficacy and safety of safinamide, an α-aminoamide with dopaminergic and nondopaminergic mechanisms, as add-on to l-dopa in the treatment of patients with PD and motor fluctuations. Patients were randomized to oral safinamide 100 mg/day (n = 224), 50 mg/day (n = 223), or placebo (n = 222) for 24 weeks. The primary endpoint was total on time with no or nontroublesome dyskinesia (assessed using the Hauser patient diaries). Secondary endpoints included off time, Unified Parkinson's Disease Rating Scale (UPDRS) Part III (motor) scores, and Clinical Global Impression-Change (CGI-C). At week 24, mean ± SD increases in total on time with no or nontroublesome dyskinesia were 1.36 ± 2.625 hours for safinamide 100 mg/day, 1.37 ± 2.745 hours for safinamide 50 mg/day, and 0.97 ± 2.375 hours for placebo. Least squares means differences in both safinamide groups were significantly higher versus placebo. Improvements in off time, UPDRS Part III, and CGI-C were significantly greater in both safinamide groups versus placebo. There were no significant between-group differences for incidences of treatment-emergent adverse events (TEAEs) or TEAEs leading to discontinuation. The addition of safinamide 50 mg/day or 100 mg/day to l-dopa in patients with PD and motor fluctuations significantly increased total on time with no or nontroublesome dyskinesia, decreased off time, and improved parkinsonism, indicating that safinamide improves motor symptoms and parkinsonism without worsening dyskinesia.


title: The design of public participation: who participates, when and how? Insights in climate adaptation planning from the Netherlands

abstract: The planning and implementation of climate adaptation measures requires the participation of citizens. The design of public participation is often determined by local government. Yet, it remains la...


title: The Affordances of Business Analytics for Strategic Decision-Making and Their Impact on Organisational Performance

abstract: Increasingly, business analytics is seen to provide the possibilities for businesses to effectively support strategic decision-making, thereby to become a source of strategic business value. However, little research exists regarding the mechanism through which business analytics supports strategic decisionmaking and ultimately organisational performance. This paper draws upon literature on IT affordances and strategic decision-making to (1) understand the decision-making affordances provided by business analytics, and (2) develop a research model linking business analytics, data-driven culture, decision-making affordances, strategic decision-making, and organisational performance. The model is empirically tested using structural equation modelling based on 296 survey responses collected from UK businesses. The study produces four main findings: (1) business analytics has a positive effect on decision-making affordances both directly and indirectly through the mediation of a data-driven culture; (2) decision-making affordances significantly influence strategic decision comprehensiveness positively and intuitive decision-making negatively; (3) data-driven culture has a significant and positive effect on strategic decision comprehensiveness; and (4) strategic decision comprehensiveness has a positive effect on organisational performance but a negative effect on intuitive decision-making.


title: Medical Image Segmentation using an Extended Active Shape Model

abstract: In this paper, an approach for improving active shape segmentation of medical images using machine learning techniques which can achieve high segmentation accuracy is described. A statistical shape model is created from a training dataset which is used to search for an object of interest in an image. Active shape model has shown over time to be a reliable image segmentation methodology, but its segmentation accuracy is hindered especially by poor initialization which can‟t be guaranteed to always be perfect. In our methodology, we extract features for each landmark using haarfilters[9]. We train a classifier with these features and use the classifier to classify points around the final points of an Active shape model search. The aim of this approach is to better place points that might have been wrongly placed from the ASM search. We have used the simple, yet effective K-Nearest Neighbour machine learning algorithm, and have demonstrated the ability of this method to improve segmentation accuracy by segmenting 2d images of the lateral ventricles of the brain. General Terms


Calibration Method for an Augmented Reality System


title: Compact tunable Ku-band Schiffman phase shifter for beam-steering applications

abstract: This study focuses on the design of a tunable compact phase shifter intended for Ku applications. The adopted topology is based on the basic Schiffman model where the coupled microstrip transmission lines have been modified and loaded by Varactor diodes. Various models of phase shifters can be obtained by moving the location of these diodes and optimized using ADS software from Agilent Technologies. The modified Schiffman phase shifter is able to create a continuously tunable phase shift by varying the voltage of the diodes while ensuring good performance over the entire operating band.


title: An Analysis of Monte Carlo Tree Search.

abstract: Monte Carlo Tree Search (MCTS) is a family of directed search algorithms that has gained widespread attention in recent years. Despite the vast amount of research into MCTS, the effect of modifications on the algorithm, as well as the manner in which it performs in various domains, is still not yet fully known. In particular, the effect of using knowledge-heavy rollouts in MCTS still remains poorly understood, with surprising results demonstrating that better-informed rollouts often result in worse-performing agents. We present experimental evidence suggesting that, under certain smoothness conditions, uniformly random simulation policies preserve the ordering over action preferences. This explains the success of MCTS despite its common use of these rollouts to evaluate states. We further analyse non-uniformly random rollout policies and describe conditions under which they offer improved performance.


title: Hydrogenated Amorphous Silicon Gate Driver With Low Leakage for Thin-Film Transistor Liquid Crystal Display Applications

abstract: This paper presents a new low-leakage gate driver circuit using hydrogenated amorphous silicon (a-Si:H) thin-film transistors (TFTs) for active-matrix liquid crystal display (AMLCD) applications. The pull-down TFTs are turned OFF to maintain the high driving capability of the proposed circuit. Four phase clock signals with a duty ratio of 33% are utilized to turn ON the pull-down TFTs in advance to improve the stability of the row line at ${V}_{L}$ . The electrical characteristics of a fabricated a-Si:H TFT are measured to establish the model for HSPICE simulation to elucidate the effect of charge loss on the proposed circuit. Measurements confirm that the output waveforms of the proposed gate driver circuit remain identical to the initial waveforms and can be stabilized at ${V}_{L}$ at 85 °C over 720 h, ensuring the feasibility of the use of the proposed gate driver circuit in AMLCDs.