Query: Enhanced Fraud Miner : Credit Card Fraud Detection using Clustering Data Mining Techniques

Relevant passages:
FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining <=SEP=> This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.

A decision support based on data mining in e-banking <=SEP=> The use of data mining techniques in banking domain is suitable due to the nature and sensitivity of bank data and due to the real time complex decision processes. The main concern for a bank's manager is to take good decisions in order to minimize the risks level associated to bank's activities. It is very important for a bank to have knowledge of causes which generate the financial crises or imbalances. Lending is one of the most risky activities in banking area and adequate methods to support the decision making process are necessary. In this paper the authors present a prototype decision support system based on data mining techniques used in lending process. The proposed system was designed to assist a customer who applies for a credit and it may represent an extension for e-banking activities.

A Survey on Outlier Detection Techniques for Credit Card Fraud Detection <=SEP=> Credit card fraud detection is an important application of outlier detection. Due to drastic increase in digital frauds, there is a loss of billions dollars and therefore various techniques are evolved for fraud detection and applied to diverse business fields. The traditional fraud detection schemes use data analysis methods that require knowledge about different domains such as financial, economics, law and business practices. The current fraud detection techniques may be offline or online, and may use neural networks, clustering, genetic algorithm, decision tree etc. There are various outlier detection techniques are available such as statistical based, density based, clustering based and so on. This paper projected to find credit card fraud by using appropriate outlier detection technique, which is suitable for online applications where large scale data is involved. The method should also work efficiently for applications where memory and computation limitations are present. Here we have discussed one such unsupervised method Principal Component Analysis(PCA) to detect an outlier.

Anomaly detection: A survey <=SEP=> Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

Survey of fraud detection techniques <=SEP=> Due to the dramatic increase of fraud which results in loss of billions of dollars worldwide each year, several modern techniques in detecting fraud are continually developed and applied to many business fields. Fraud detection involves monitoring the behavior of populations of users in order to estimate, detect, or avoid undesirable behavior. Undesirable behavior is a broad term including delinquency, fraud, intrusion, and account defaulting. This paper presents a survey of current techniques used in credit card fraud detection, telecommunication fraud detection, and computer intrusion detection. The goal of this paper is to provide a comprehensive review of different techniques to detect frauds.

Smart Home Mobile RFID-Based Internet-of-Things Systems and Services <=SEP=> Smart homes can apply new Internet-Of-Things concepts along with RFID technologies for creating ubiquitous services. This paper introduces a novel read-out method for a hierarchical wireless master-slave RFID reader architecture of multi standard NFC (Near Field Communication) and UHF (Ultra High Frequency) technologies to build a smart home service system that benefits in terms of cost, energy consumption and complexity. Various smart home service use cases such as washing programs, cooking, shopping and elderly health care are described as examples that make use of this system.

Handwritten document image segmentation into text lines and words <=SEP=> Article history: Received 22 July 2008 Received in revised form 23 February 2009 Accepted 14 May 2009

A hybrid dragonfly algorithm with extreme learning machine for prediction <=SEP=> In this work, a proposed hybrid dragonfly algorithm (DA) with extreme learning machine (ELM) system for prediction problem is presented. ELM model is considered a promising method for data regression and classification problems. It has fast training advantage, but it always requires a huge number of nodes in the hidden layer. The usage of a large number of nodes in the hidden layer increases the test/evaluation time of ELM. Also, there is no guarantee of optimality of weights and biases settings on the hidden layer. DA is a recently promising optimization algorithm that mimics the moving behavior of moths. DA is exploited here to select less number of nodes in the hidden layer to speed up the performance of the ELM. It also is used to choose the optimal hidden layer weights and biases. A set of assessment indicators is used to evaluate the proposed and compared methods over ten regression data sets from the UCI repository. Results prove the capability of the proposed DA-ELM model in searching for optimal feature combinations in feature space to enhance ELM generalization ability and prediction accuracy. The proposed model was compared against the set of commonly used optimizers and regression systems. These optimizers are namely, particle swarm optimization (PSO) and genetic algorithm (GA). The proposed DA-ELM model proved an advance overall compared methods in both accuracy and generalization ability.

A comparative study of vein pattern recognition for biometrie authentication <=SEP=> In informational age there are many information crucial to a individual. To protect the data from the unwanted use we proposed the biometric system of vein recognition as today's world demand for a easy and accurate biometric system to protect our data. The biometric system is accurate and easy to use as every individual has a unique vein pattern. In this paper we have studied various method used by different people to achieve the goal to secure world and give the reader a brief prospective of this technology.

The 2009 Mario AI Competition <=SEP=> This paper describes the 2009 Mario AI Competition, which was run in association with the IEEE Games Innovation Conference and the IEEE Symposium on Computational Intelligence and Games. The focus of the competition was on developing controllers that could play a version of Super Mario Bros as well as possible. We describe the motivations for holding this competition, the challenges associated with developing artificial intelligence for platform games, the software and API developed for the competition, the competition rules and organization, the submitted controllers and the results. We conclude the paper by discussing what the outcomes of the competition can teach us both about developing platform game AI and about organizing game AI competitions. The first two authors are the organizers of the competition, while the third author is the winner of the competition.

Commutation torque ripple reduction in brushless DC motor drives using a single DC current sensor <=SEP=> This paper presents a comprehensive study on reducing commutation torque ripples generated in brushless DC motor drives with only a single DC-link current sensor provided. In such drives, commutation torque ripple suppression techniques that are practically effective in low speed as well as high speed regions are scarcely found. The commutation compensation technique proposed here is based on a strategy that the current slopes of the incoming and the outgoing phases during the commutation interval can be equalized by a proper duty-ratio control. Being directly linked with deadbeat current control scheme, the proposed control method accomplishes suppression of the spikes and dips superimposed on the current and torque responses during the commutation intervals of the inverter. Effectiveness of the proposed control method is verified through simulations and experiments.

Characterization of evoked tactile sensation in forearm amputees with transcutaneous electrical nerve stimulation. <=SEP=> OBJECTIVE
The goal of this study is to characterize the phenomenon of evoked tactile sensation (ETS) on the stump skin of forearm amputees using transcutaneous electrical nerve stimulation (TENS).


APPROACH
We identified the projected finger map (PFM) of ETS on the stump skin in 11 forearm amputees, and compared perceptual attributes of the ETS in nine forearm amputees and eight able-bodied subjects using TENS. The profile of perceptual thresholds at the most sensitive points (MSPs) in each finger-projected area was obtained by modulating current amplitude, pulse width, and frequency of the biphasic, rectangular current stimulus. The long-term stability of the PFM and the perceptual threshold of the ETS were monitored in five forearm amputees for a period of 11 months.


MAIN RESULTS
Five finger-specific projection areas can be independently identified on the stump skin of forearm amputees with a relatively long residual stump length. The shape of the PFM was progressively similar to that of the hand with more distal amputation. Similar sensory modalities of touch, pressure, buzz, vibration, and numb below pain sensation could be evoked both in the PFM of the stump skin of amputees and in the normal skin of able-bodied subjects. Sensory thresholds in the normal skin of able-bodied subjects were generally lower than those in the stump skin of forearm amputees, however, both were linearly modulated by current amplitude and pulse width. The variation of the MSPs in the PFM was confined to a small elliptical area with 95% confidence. The perceptual thresholds of thumb-projected areas were found to vary less than 0.99 × 10(-2) mA cm(-2).


SIGNIFICANCE
The stable PFM and sensory thresholds of ETS are desirable for a non-invasive neural interface that can feed back finger-specific tactile information from the prosthetic hand to forearm amputees.

Evaluation of Text Summarization in a Cross-lingual Information Retrieval Framework <=SEP=> We report on research in multi-document summarization and on evaluation of summarization in the framework of cross-lingual information retrieval. This work was carried out during a summer workshop on Language Engineering held at Johns Hopkins University by a team of nine researchers from seven universities. The goals of the research were as follows: (1) to develop a toolkit for evaluation of single-document and multi-document summarizers, (2) to develop a modular multi-document summarizer, called MEAD, that works in both English and Chinese, and (3) to perform a meta-evaluation of four automatic summarizers, including MEAD, using several types of evaluation measures: some currently used by summarization researchers and a couple of novel techniques. Central to the experiments in this workshop was the cross-lingual experimental setup based on a large-scale Chinese and English parallel corpus. An extensive set of human judgments were specifically prepared by the Linguistic Data Consortium for our research. These human judgments include a) which documents are relevant to a certain query and b) which sentences in the relevant documents are most relevant to the query and which therefore constitute a good summary of the cluster. These judgments were used to construct variable-length multiand single document summaries as model summaries. Since one of the novel evaluation metrics that we used, Relevance Correlation, is based on the premise that good summaries preserve query relevance both within a language and across languages, we made use of a cross-lingual Information Retrieval (IR) engine. We evaluated the quality of the automatic summaries using co-selection and content-based evaluation, two established techniques. A relatively new metric, relative utility, was also extensively tested. Part of the new scientific contribution is the measurement of relevance correlation, which we introduced and systematically examined in this workshop. Relevance correlation measures the quality of summaries in comparison to the entire documents as a function of how much document relevance drops if summaries are indexed instead of documents. Our results show that this measure is sensible, in that it correlates with more established evaluation measures. Another contribution is the cross-lingual setup which allows us to automatically translate English queries into Chinese, perform Chinese IR with or without summarization. This allows us to calculate relevance correlation for English and for Chinese in parallel (i.e., for the same queries) and to take direct cross-lingual comparisons of evaluations. Additionally, an alternative way of constructing Chinese model summaries from English ones was implemented which relies on the sentence alignment of English and Chinese documents. The results of our large-scale meta-evaluation are numerous, but some of the highlights are the following: (1) All evaluation measures rank human summaries first, which is an appropriate and expected property of such measures, (2) Both relevance correlation and the content-based measures place leading sentence extracts ahead of the more sophisticated summarizers, (3) Relative utility ranks our system, MEAD, as the best summarizer for shorter summaries, although for longer summaries, lead-based summaries outperform MEAD, (4) Co-selection measurements show overall low agreement amongst humans (above chance), whereas relative utility reports higher numbers on the same data (but does not normalize for chance). The deliverable resources and software include: (1) a turn-key extractive multi-document summarizer, MEAD, which allows users to add their own features based on single sentences or pairs of sentences, (2) a large corpus of summaries produced by several automatic methods, including baseline and random summaries, (3) a collection of manual summaries produced by the Linguistic Data Consortium (LDC), (4) a battery of evaluation routines, (5) a collection of IR queries in English and Chinese and the corresponding relevance judgments from the Hong Kong news collection, (6) SMART relevance outputs for both full documents and summaries, (7) XML tools for processing of documents and summaries. JHU 2001 Summer workshop final report Evaluation of Text Summarization

The use of Social Networking Sites (SNSs) by the faculty members of the School of Library & Information Science, PAAET, Kuwait <=SEP=> Purpose – The purpose of this study is to describe the usage of Social Networking Sites (SNSs) by the faculty members of the School of Library and Information Science (SLIS), at the College of Basic Education, the Public Authority for Applied Education and Training (PAAET), Kuwait. Design/methodology/approach – A survey conducted to collect data from 33 faculty members of whom only 21 members were using SNSs, representing 63.6 per cent of the total sample, and 12 members were not using SNSs, representing 36.4 per cent of the total sample. This study revealed that SNSs are used moderately by the faculty members. Findings – This study showed that faculty members who were using SNSs tend to be males, aged between 41 and 50 years, PhD holders, ranked as assistant professors, full-time members, specialized in information technologies with a relatively new experience of teaching ranged from one to five years, and most of the faculty members who were not using SNSs tended to be also males, aged between 41 and 60 years, PhD holders, ranked as lecturers, full-time members specialized in organization of information with a teaching experience ranged from 16 to 20 years. More than half of the faculty members were using SNSs for three years to less than six years, and a large number of them were using SNSs several times a week and were accessing these sites more from their school office, home and school laboratory. There are no any statistical significant differences between the demographic data of participants (gender, age and education level) and either their use or non-use of SNSs. There are no significant differences between the academic rank, teaching status and teaching experience of faculty and their use of SNSs. However, there is a significant relation between the faculty’s area of teaching and their use of SNSs. Faculty members were interested in the use of SNSs. YouTube, Twitter, Facebook and blogs respectively were used mostly by faculty members, but Twitter, Facebook and YouTube were the most famous SNSs they have profiles on. Faculty members have adopted SNSs mainly for the purpose of communicating with others, finding and sharing information with peers and students as well. Tasks on SNSs made by faculty members were mostly to make communication, send/receive messages and find general and specific information. Faculty members’ profiles on SNSs were mostly on Twitter, Facebook, YouTube, blogs, wikis and podcasting respectively. Faculty members confirmed that the use of YouTube, Facebook, blogs, Twitter, wikis and podcasting respectively was at least effective and the use of YouTube, Facebook, Twitter, Blogs and Wikis respectively was at least fairly useful fairly easy to them. Faculty members are in general agreement about the effectiveness of SNSs especially for disseminating and sharing information, communication and informal collaboration. The study showed The researcher would like to thank the Public Authority for Applied Education and Training (PAAET), the state of Kuwait, for supporting this paper (BE-11-10). The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0264-0473.htm

AutoHair: fully automatic hair modeling from a single image <=SEP=> We introduce AutoHair, the first fully automatic method for 3D hair modeling from a single portrait image, with no user interaction or parameter tuning. Our method efficiently generates complete and high-quality hair geometries, which are comparable to those generated by the state-of-the-art methods, where user interaction is required. The core components of our method are: a novel hierarchical deep neural network for automatic hair segmentation and hair growth direction estimation, trained over an annotated hair image database; and an efficient and automatic data-driven hair matching and modeling algorithm, based on a large set of 3D hair exemplars. We demonstrate the efficacy and robustness of our method on Internet photos, resulting in a database of around 50K 3D hair models and a corresponding hairstyle space that covers a wide variety of real-world hairstyles. We also show novel applications enabled by our method, including 3D hairstyle space navigation and hair-aware image retrieval.

A Dual-Loop Antenna Design for Hepta-Band WWAN/LTE Metal-Rimmed Smartphone Applications <=SEP=> A simple direct-fed dual-loop antenna capable of providing hepta-band WWAN/LTE operation under surroundings of an unbroken metal rim in smartphone applications is proposed. The greatest highlight of this proposed antenna is that it provides a simple and effective multiband antenna solution for an unbroken metal-rimmed smartphone. The unbroken metal rim with 5 mm in height embraces the system circuit board of 130 × 70 mm2. Two no-ground portions of 10 × 70 mm2 and 5 × 70 mm2 are set on the top and bottom edge of the system circuit board, respectively. In-between the two separate no-ground portions, there is a system ground of 115 × 70 mm2 connected with the unbroken metal rim via a small grounded patch which divides the unbroken metal rim into two strips. Finally, a dual-loop antenna is formed by combining the inner system ground and two strips. This proposed dual-loop antenna is capable of covering GSM850/900/DCS/PCS/UMTS2100/LTE 2300/2500 operating bands. Detailed design considerations of the proposed antenna are described and both experimental and simulation results are also presented and discussed.

Kalman filtering with state constraints : a survey of linear and nonlinear algorithms <=SEP=> The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results.

The Tale of e-Government: A Review of the Stories that Have Been Told So Far and What is Yet to Come <=SEP=> Since its first appearance, the concept of eGovernment has evolved into a recognized means that has helped the public sector to increase its efficiency and effectiveness. A lot of research has therefore been done in this area to elaborate on the different aspects encompassing this concept. However, when looking at the existing e-Government literature, research mostly focuses on one specific aspect of e-Government and there are few generic publications that provide an overview of the diversity of this interdisciplinary research field over a longer term period. This study analyzes the abstracts of eight e-Government journals from 2000 to 2016 by means of a quantitative text mining analysis, backed by a qualitative Delphi approach. The article concludes with a discussion on the findings and implications as well as directions for future research.

Feature line extraction from unorganized noisy point clouds using truncated Fourier series <=SEP=> The detection of feature lines is important for representing and understanding geometric features of 3D models. In this paper, we introduce a new and robust method for extracting feature lines from unorganized point clouds. We use a one-dimensional truncated Fourier series for detecting feature points. Each point and its neighbors are approximated along the principal directions by using the truncated Fourier series, and the curvature of the point is computed from the approximated curves. The Fourier coefficients are computed by Fast Fourier Transform (FFT). We apply low-pass filtering to remove noise and to compute the curvature of the point robustly. For extracting feature points from the detected potential feature points, the potential feature points are thinned using a curvature weighted Laplacian-like smoothing method. The feature lines are constructed through growing extracted points and then projected onto the original point cloud. The efficiency and robustness of our approach is illustrated by several experimental results.

An analysis of facial expression recognition under partial facial image occlusion <=SEP=> In this paper, an analysis of the effect of partial occlusion on facial expression recognition is investigated. The classification from partially occluded images in one of the six basic facial expressions is performed using a method based on Gabor wavelets texture information extraction, a supervised image decomposition method based on Discriminant Non-negative Matrix Factorization and a shape-based method that exploits the geometrical displacement of certain facial features. We demonstrate how partial occlusion affects the above mentioned methods in the classification of the six basic facial expressions, and indicate the way partial occlusion affects human observers when recognizing facial expressions. An attempt to specify which part of the face (left, right, lower or upper region) contains more discriminant information for each facial expression, is also made and conclusions regarding the pairs of facial expressions misclassifications that each type of occlusion introduces, are drawn. 2008 Published by Elsevier B.V.

Otologic and Audiology Concerns of Microtia Repair. <=SEP=> Microtia is a congenital auricular deformity that commonly presents with associated congenital aural atresia. The most acute concern in these patients is concomitant hearing loss at birth. A team-based approach by plastic surgeons and otologists is necessary to address both the otologic and audiologic concerns of microtia and atresia. Hearing rehabilitation is imperative; yet it should not compromise the aesthetic components of reconstruction and vice versa. Here, the authors propose a framework to evaluate and manage patients with microtia and atresia with the goal of optimizing functional and cosmetic outcomes.

KeyGraph: Automatic Indexing by Co-Occurrence Graph Based on Building Construction Metaphor <=SEP=> In this paper, we present an algorithm for extracting keywords representing the asserted main point in a document, without relying on external devices such as natural language processing tools or a document corpus. Our algorithm KeyGraph is based on the segmentation of a graph, representing the co-occurrence between terms in a document, into clusters. Each cluster corresponds to a concept on which author's idea is based, and top ranked terms by a statistic based on each term's relationship to these clusters are selected as keywords. This strategy comes from considering that a document is constructed like a building for expressing new ideas based on traditional concepts. The experimental results show that thus extracted terms match author's point quite accurately, even though KeyGraph does not use each term's average frequency in a corpus, i.e., KeyGraph is a contentsensitive, domain independent device of indexing.

Building a smart home environment for service robots based on RFID and sensor networks <=SEP=> This paper is concerned with constructing a prototype smart home environment which has been built in the research building of Korea Institute of Industrial Technology (KITECH) to demonstrate the practicability of a robot-assisted future home environment. Key functionalities that a home service robot must provide are localization, navigation, object recognition and object handling. A considerable amount of research has been conducted to make the service robot perform these operations with its own sensors, actuators and a knowledge database. With all heavy sensors, actuators and a database, the robot could have performed the given tasks in a limited environment or showed the limited capabilities in a natural environment. We initiated a smart home environment project for light-weight service robots to provide reliable services by interacting with the environment through the wireless sensor networks. This environment consists of the following three main components: smart objects with an radio frequency identification (RFID) tag and smart appliances with sensor network functionality; the home server that connects smart devices as well as maintains information for reliable services; and the service robots that perform tasks in collaboration with the environment. In this paper, we introduce various types of smart devices which are developed for assisting the robot by providing sensing and actuation, and present our approach on the integration of these devices to construct the smart home environment. Finally, we discuss the future directions of our project.

Natural Language Interface to Relational Database (NLI-RDB) Through Object Relational Mapping (ORM) <=SEP=> This paper proposes a novel approach for building a Natural Language Interface to a Relational Database (NLI-RDB) using Conversational Agent (CA), Information Extraction (IE) and Object Relational Mapping (ORM) framework. The CA will help in disambiguating the user’s queries and guiding the user interaction. IE will play an important role in named entities extraction in order to map Natural Language queries into database queries. The ORM framework i.e. the Hibernate framework resolves the impedance mismatch between the Object Oriented Paradigms (OOP) and Relational Databases (RDBs) i.e. OOP concepts differ from RDB concepts, thus it reduces the complexity in generating SQL statements. Also, by utilizing ORM framework, the RDBs entities are mapped into real world objects, which bring the RDBs a step closer to the user. In addition, the ORM framework simplify the interaction between OOP and RDBs. The developed NLI-RDB system allows the user to interact with objects directly in natural language and through navigation, rather than by using SQL statements. This direct interaction tends to be easier and more acceptable for humans whom are nor technically orientated and have no SQL knowledge. The NLI-RDB system also offers friendly and interactive user interface in order to refine the query generated automatically. The NLI-RDB system has been evaluated by a group of participants through a combination of qualitative and quantitative measures. The experimental results show good performance of the prototype and excellent user’s satisfaction.

Artificial intelligence applications in the intensive care unit. <=SEP=> OBJECTIVE
To review the history and current applications of artificial intelligence in the intensive care unit.


DATA SOURCES
The MEDLINE database, bibliographies of selected articles, and current texts on the subject.


STUDY SELECTION
The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis.


DATA EXTRACTION
All literature relevant to the topic was reviewed.


DATA SYNTHESIS
Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.


CONCLUSIONS
The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.

Design of a Wideband Waveguide Slot Array Antenna and its Decoupling Method for Synthetic Aperture Radar <=SEP=> For the synthetic aperture radar systems, there is an increasing demand for achieving resolutions of 0.2 mtimes0.2 m. As the range resolution and system bandwidth are inversely proportional, the system bandwidth is expected to be greater than 1 GHz. Thus an antenna with wider band needs to be developed. Waveguide slot antennas have been implemented on several SAR satellites due to its inherent advantages such as high efficiency and power handling capacity, but its bandwidth is quite limited. To avoid the manufacture difficulties of the ridge waveguide, which is capable of broadening the bandwidth of slot antennas, a novel antenna element with conventional waveguide is designed. The bandwidth of VSWR les1.5 is greater than 1 GHz at X-band. To reduce the mutual coupling of closely placed antenna elements, a decoupling method with cavity-like walls inserted between adjacent elements is adopted and their effects on the performance of the antenna are summarized.

Identification of a DNA-binding site for the transcription factor Haa1, required for Saccharomyces cerevisiae response to acetic acid stress <=SEP=> The transcription factor Haa1 is the main player in reprogramming yeast genomic expression in response to acetic acid stress. Mapping of the promoter region of one of the Haa1-activated genes, TPO3, allowed the identification of an acetic acid responsive element (ACRE) to which Haa1 binds in vivo. The in silico analysis of the promoter regions of the genes of the Haa1-regulon led to the identification of an Haa1-responsive element (HRE) 5'-GNN(G/C)(A/C)(A/G)G(A/G/C)G-3'. Using surface plasmon resonance experiments and electrophoretic mobility shift assays it is demonstrated that Haa1 interacts with high affinity (K(D) of 2 nM) with the HRE motif present in the ACRE region of TPO3 promoter. No significant interaction was found between Haa1 and HRE motifs having adenine nucleotides at positions 6 and 8 (K(D) of 396 and 6780 nM, respectively) suggesting that Haa1p does not recognize these motifs in vivo. A lower affinity of Haa1 toward HRE motifs having mutations in the guanine nucleotides at position 7 and 9 (K(D) of 21 and 119 nM, respectively) was also observed. Altogether, the results obtained indicate that the minimal functional binding site of Haa1 is 5'-(G/C)(A/C)GG(G/C)G-3'. The Haa1-dependent transcriptional regulatory network active in yeast response to acetic acid stress is proposed.

Analyzing spatial user behavior in computer games using geographic information systems <=SEP=> An important aspect of the production of digital games is user-oriented testing. A central problem facing practitioners is however the increasing complexity of user-game interaction in modern games, which places challenges on the evaluation of interaction using traditional user-oriented approaches. Gameplay metrics are instrumentation data which detail user behavior within the virtual environment of digital games, forming accurate and detailed datasets about user behavior that supplement existing user-testing methods such as playtesting and usability testing. In this paper existing work on gameplay metrics is reviewed, and spatial analysis of gameplay metrics introduced as a new approach in the toolbox of user-experience testing and research. Furthermore, Geographic Information Systems (GIS) are introduced as a tool for performing spatial analysis. A case study is presented with Tomb Raider: Underworld, showcasing the merger of GIS with gameplay metrics analysis and its application to game testing and design.

Nanoseconds Switching for High Voltage Circuit Using Avalanche Transistors <=SEP=> The avalanche transistor is suitable for switching high voltage in kilovolts region. In this paper, a simple switching circuit consists of avalanche transistor is demonstrated. Sixteen of ZTX 415 avalanche transistors were used to switch a high voltage circuit up to 4.5 kV. A PIC16F84 microcontroller was utilized as control unit to generate input trigger. The result shows that the developed circuit was able to switch applied voltage up-to 4.5 kV with an average falling time is 2.89 ns.

Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data <=SEP=> Private companies, government entities, and institutions such as hospitals routinely gather vast amounts of digitized personal information about the individuals who are their customers, clients, or patients. Much of this information is private or sensitive, and a key technological challenge for the future is how to design systems and processing techniques for drawing inferences from this large-scale data while maintaining the privacy and security of the data and individual identities. Individuals are often willing to share data, especially for purposes such as public health, but they expect that their identity or the fact of their participation will not be disclosed. In recent years, there have been a number of privacy models and privacy-preserving data analysis algorithms to answer these challenges. In this article, we will describe the progress made on differentially private machine learning and signal processing.