title: Exploiting Eye Tracking for Smartphone Authentication

abstract: Traditional user authentication methods using passcode or finger movement on smartphones are vulnerable to shoulder surfing attack, smudge attack, and keylogger attack. These attacks are able to infer a passcode based on the information collection of user’s finger movement or tapping input. As an alternative user authentication approach, eye tracking can reduce the risk of suffering those attacks effectively because no hand input is required. However, most existing eye tracking techniques are designed for large screen devices. Many of them depend on special hardware like high resolution eye tracker and special process like calibration, which are not readily available for smartphone users. In this paper, we propose a new eye tracking method for user authentication on a smartphone. It utilizes the smartphone’s front camera to capture a user’s eye movement trajectories which are used as the input of user authentication. No special hardware or calibration process is needed. We develop a prototype and evaluate its effectiveness on an Android smartphone. We recruit a group of volunteers to participate in the user study. Our evaluation results show that the proposed eye tracking technique achieves very high accuracy in user authentication.


title: Analysis Operator Learning and its Application to Image Reconstruction

abstract: Exploiting a priori known structural information lies at the core of many image reconstruction methods that can be stated as inverse problems. The synthesis model, which assumes that images can be decomposed into a linear combination of very few atoms of some dictionary, is now a well established tool for the design of image reconstruction algorithms. An interesting alternative is the analysis model, where the signal is multiplied by an analysis operator and the outcome is assumed to be sparse. This approach has only recently gained increasing interest. The quality of reconstruction methods based on an analysis model severely depends on the right choice of the suitable operator. In this paper, we present an algorithm for learning an analysis operator from training images. Our method is based on lp-norm minimization on the set of full rank matrices with normalized columns. We carefully introduce the employed conjugate gradient method on manifolds, and explain the underlying geometry of the constraints. Moreover, we compare our approach to state-of-the-art methods for image denoising, inpainting, and single image super-resolution. Our numerical results show competitive performance of our general approach in all presented applications compared to the specialized state-of-the-art techniques.


title: Multi-Timescale Long Short-Term Memory Neural Network for Modelling Sentences and Documents

abstract: Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, it is still a challenge task to model long texts, such as sentences and documents. In this paper, we propose a multi-timescale long short-termmemory (MT-LSTM) neural network to model long texts. MTLSTM partitions the hidden states of the standard LSTM into several groups. Each group is activated at different time periods. Thus, MT-LSTM can model very long documents as well as short sentences. Experiments on four benchmark datasets show that our model outperforms the other neural models in text classification task.


title: Comparison of bundle adjustment software for camera calibration in close range photogrammetry

abstract: Camera calibration has always been an essential component of photogrammetric measurement. Nowadays, with self-calibration being an integral and routinely applied operation within photogrammetric triangulation, especially in high-accuracy close-range measurement. Photogrammetric camera calibration is usually carried out together with the calculation of object coordinates such as principal distance; principal point and lens distortion are usually determined by a self-calibrating bundle adjustment approach. There is a variety of bundle adjustment software for camera calibration that is available in the market nowadays. Basically, each of the software has their own capabilities to calibrate the camera. The user has to select appropriate and correct software to suite their needs. This paper discusses about the investigation and assessment of several bundle adjustment software used to calibrate digital camera. In this study, a test field was designed and fabricated. Then the digital camera is calibrated by using bundle adjustment software. Normally the camera calibration parameters comprise of the unknown parameters of the interior and exterior orientation and the 3D object coordinates. The quality of the result depends on many factors; however, the network configuration is among the most vital factor. After motion the differences of camera parameters determined by self-calibration bundle adjustment software are reported in this paper. In this study the result showed that the flexible and powerful tool for camera calibration using bundle block adjustment method is the Australis software.


title: Dynamic coordination of multi-robots by Bayesian modeling

abstract: Many of the tasks required of robotic agents can be done more effectively and efficiently if they are provided with a capability of making generalizations, drawing inferences and extracting patterns by observation. A generic framework for incorporating learning for the purposes of dynamic rescheduling in a multiple robotic system is proposed. The proposed method utilities a statistical model to make use of run-time observations to recover underlying dependencies in the task domain which is then used to make dynamically optimal plans. The mathematical implementation of the model and assessment issues are discussed, and the approach is illustrated by example of a foraging task.


title: A Recommender System for Web Mining using Neural Network and Fuzzy Algorithm

abstract: Web mining is, using data mining tools to discover knowledge from various sources in the web and according to sources type that mined, to be classified in various fields of research. One of the important tools in web mining is mining of web user’s behavior that is considered as a way to discover the potential knowledge of web user’s interaction. By identifying user’s behavior in some cases like: Targeted advertisement, e-commerce, and search engines, it would be possible to provide users with desired results. By providing information that users are interested to view it, users can be converted into permanent customers. In this article, by identifying user’s behavior and use of neural and fuzzy techniques it would present a system that will predict user’s interest and will propose them a list of pages based on their interests. So that it would enjoy fuzzy clustering method. Due to the user’s different interest and use of one or more interest in a time, their use may belong to several clusters. Fuzzy clusters provide a possible overlap. Then by resulting cluster it would extract fuzzy rules. After that, it will make user’s movement pattern and with the help of neural network it will propose a list of suggested pages to the users. The results show that the proposed algorithm is in a higher level of precision and recall compared with other algorithm.


title: Test-Driven Web Application Development in Java

abstract: Today, software availability as well as adaptability has a strong impact on application development. Together with the success of the Internet technology, it is often necessary to offer a web-based software solution. For Java development, J2EE builds a bridge from traditional towards web application development. Java Server Pages and Servlets allow the implementation of web sites with dynamic content. Anyway, it is still possible to apply well-tried programming paradigms, e.g. the Model-View-Controller (MVC) paradigm. With regard to software quality as well as adaptability, application development is affected more and more by advanced testing techniques. Especially unit testing provides a powerful technique to develop new functionality as well as to extend and reuse existing parts. This is also strengthened by the success of agile processes that force test-driven development. From there, application code grows up with unit tests from scratch on. For Java, JUnit has become the defacto standard unit test environment. Nevertheless, test-driven web application development requires broadening the unit test approach. Considering the special requirements of web application development, both the scope of the unit tests as well as the JUnit framework itself have to be extended to enable a test-driven development. In this paper, we present an approach to customize the unit test cycle as well as the JUnit framework towards a test-driven web application development.


title: Suitability of LTE for drone-to-infrastructure communications in very low level airspace

abstract: The increasing availability of cheap and powerful drones for various applications is likely to cause a heavy usage of the very low level airspace in metropolitan areas with hundreds of simultaneously airborne drones per square kilometer in the near future. Certainly, the predicted large number of drones presents a major challenge to future UTM and especially to supporting communications systems. However, a robust and reliable communications system for drone-to-infrastructure communications is inevitably needed to grant all drones access to various services provided by UTM. In previous works, it has already been shown that commercial LTE networks are capable of providing connectivity to drones flying at low altitudes in principle. However, airborne drones which transmit data to the UTM infrastructure produce severe inter-cell interference since they have a strong line-of-sight connection to multiple LTE base stations at a time. Hence, we investigate further the suitability of the LTE uplink for drone-to-infrastructure communications in very low level airspace by LTE system-level simulations in this work. In particular, we identify the maximum drone density that can be thoroughly monitored and safely coordinated by a UTM system with LTE communication links. Our simulations show that an LTE system with 5 Mhz uplink bandwidth can support a message delivery ratio of more than 95% for drone densities of up to 200 drones per square kilometer assuming that all drones have to periodically transmit messages of 300 bytes at a rate of 10 Hz. It is concluded that future research has to focus on the mitigation of inter-cell interference so even a larger number of drones can get reliable access to all UTM services.


title: Prognosis Risk of Urosepsis in Critical Care Medicine: A Prospective Observational Study

abstract: This study aimed to investigate the clinical features of urosepsis and to raise awareness of this problem. Of the 112 sepsis patients enrolled, 36 were identified as having urosepsis. The bacteria involved in the infection leading to urosepsis included Escherichia coli, Proteus species, Enterococcus species, Klebsiella species, other Gram-positive cocci, and Pseudomonas aeruginosa. Although the organ/system dysfunction appeared earlier in the urosepsis patients than in the other sepsis patients (4.7 ± 2.4 versus 7.2 ± 4.5 hours, P < 0.001), the urosepsis patients presented with a better prognosis and lower 28-day mortality rate than the others (6% versus 37%). In the multivariate analysis, the type of sepsis (urosepsis, OR = 0.019, 95% CI = 0.001, 0.335, P = 0.007) and SOFA score (OR = 1.896, 95% CI = 1.012, 3.554, P = 0.046) remained significantly associated with the survival. The time of admission to the intensive care unit of 17 patients transferred from the Department of Urinary Surgery was significantly prolonged compared with those transferred from other departments (11.6 ± 7.3 versus 7.2 ± 4.9 hours, P < 0.05). In conclusion, urosepsis suggested a better prognosis, but attention needs to be paid in clinical practice, especially in urinary surgery.


title: Sparse Learning-to-Rank via an Efficient Primal-Dual Algorithm

abstract: Learning-to-rank for information retrieval has gained increasing interest in recent years. Inspired by the success of sparse models, we consider the problem of sparse learning-to-rank, where the learned ranking models are constrained to be with only a few nonzero coefficients. We begin by formulating the sparse learning-to-rank problem as a convex optimization problem with a sparse-inducing $(\ell_1)$ constraint. Since the $(\ell_1)$ constraint is nondifferentiable, the critical issue arising here is how to efficiently solve the optimization problem. To address this issue, we propose a learning algorithm from the primal dual perspective. Furthermore, we prove that, after at most $(O({1\over \epsilon } ))$ iterations, the proposed algorithm can guarantee the obtainment of an $(\epsilon)$-accurate solution. This convergence rate is better than that of the popular subgradient descent algorithm. i.e., $(O({1\over \epsilon^2} ))$. Empirical evaluation on several public benchmark data sets demonstrates the effectiveness of the proposed algorithm: 1) Compared to the methods that learn dense models, learning a ranking model with sparsity constraints significantly improves the ranking accuracies. 2) Compared to other methods for sparse learning-to-rank, the proposed algorithm tends to obtain sparser models and has superior performance gain on both ranking accuracies and training time. 3) Compared to several state-of-the-art algorithms, the ranking accuracies of the proposed algorithm are very competitive and stable.


title: Semi-Supervised Learning with Deep Generative Models

abstract: The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.


Service Quality Dimensions: Key on Customer Satisfaction A Glance on Commercial Bank of Ethiopia


title: Multi-Band, Wide-Beam, Circularly Polarized, Crossed, Asymmetrically Barbed Dipole Antennas for GPS Applications

abstract: This communication presents a multi-band, circularly polarized (CP), wide beamwidth, highly efficient antenna for use in global positioning systems (GPS). The primary radiating elements are two crossed printed dipoles, which incorporate a 90° phase delay line realized with a vacant-quarter printed ring to produce the CP radiation and broadband impedance matching. To achieve multiple resonances, each dipole arm is divided into four branches with different lengths, and a printed inductor with a barbed end is inserted in each branch to reduce the radiator size. An inverted, pyramidal, cavity-backed reflector is incorporated with the crossed dipoles to produce a unidirectional radiation pattern with a wide 3-dB axial ratio (AR) beamwidth and a high front-to-back ratio. The multi-band antennas have broad impedance matching and 3-dB AR bandwidths, which cover the GPS L1-L5 bands.


title: Quasi Real-Time Summarization for Consumer Videos

abstract: With the widespread availability of video cameras, we are facing an ever-growing enormous collection of unedited and unstructured video data. Due to lack of an automatic way to generate summaries from this large collection of consumer videos, they can be tedious and time consuming to index or search. In this work, we propose online video highlighting, a principled way of generating short video summarizing the most important and interesting contents of an unedited and unstructured video, costly both time-wise and financially for manual processing. Specifically, our method learns a dictionary from given video using group sparse coding, and updates atoms in the dictionary on-the-fly. A summary video is then generated by combining segments that cannot be sparsely reconstructed using the learned dictionary. The online fashion of our proposed method enables it to process arbitrarily long videos and start generating summaries before seeing the end of the video. Moreover, the processing time required by our proposed method is close to the original video length, achieving quasi real-time summarization speed. Theoretical analysis, together with experimental results on more than 12 hours of surveillance and YouTube videos are provided, demonstrating the effectiveness of online video highlighting.


title: Novel RDL Design of Wafer-Level Packaging for Signal/Power Integrity in LPDDR4 Application

abstract: The emerging wafer-level packaging (WLP) technology suffers from serious signal integrity (SI) and power integrity (PI) issues due to its redistribution layer (RDL). There exhibit serious parasitic effects by the high-density RDL traces and less flexibility of decoupling capacitors, so the robust power distribution network is critical to design. This paper proposed a novel two-layered RDL design in low-power double data rate fourth generation (LPDDR4) application by proposing a novel power/ground meshed layout for superior PI performance. Besides, the second-order RLC simplified model and normalized resistance are derived to handle the process scaling issue for successful SI by adjusting the cross-sectional structure of RDL so that LPDDR4 4266 can work well on 2- $\mu \text{m}$ WLP.


title: Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models

abstract: The prediction accuracy has been the long-lasting and sole standard for comparing the performance of different image classification models, including the ImageNet competition. However, recent studies have highlighted the lack of robustness in well-trained deep neural networks to adversarial examples. Visually imperceptible perturbations to natural images can easily be crafted and mislead the image classifiers towards misclassification. To demystify the trade-offs between robustness and accuracy, in this paper we thoroughly benchmark 18 ImageNet models using multiple robustness metrics, including the distortion, success rate and transferability of adversarial examples between 306 pairs of models. Our extensive experimental results reveal several new insights: (1) linear scaling law - the empirical $\ell_2$ and $\ell_\infty$ distortion metrics scale linearly with the logarithm of classification error; (2) model architecture is a more critical factor to robustness than model size, and the disclosed accuracy-robustness Pareto frontier can be used as an evaluation criterion for ImageNet model designers; (3) for a similar network architecture, increasing network depth slightly improves robustness in $\ell_\infty$ distortion; (4) there exist models (in VGG family) that exhibit high adversarial transferability, while most adversarial examples crafted from one model can only be transferred within the same family. Experiment code is publicly available at \url{https://github.com/huanzhang12/Adversarial_Survey}.


title: MaxFlow Revisited: An Empirical Comparison of Maxflow Algorithms for Dense Vision Problems.

abstract: Algorithms for finding the maximum amount of flow possible in a network (or maxflow) play a central role in computer vision problems. We present an empirical comparison of different max-flow algorithms on modern problems. Our problem instances arise from energy minimization problems in Object Category Segmentation, Image Deconvolution, Super Resolution, Texture Restoration, Character Completion and 3D Segmentation. We compare 14 different implementations and find that the most popularly used implementation of Kolmogorov [5] is no longer the fastest algorithm available, especially for dense graphs.


title: Bacillus amyloliquefaciens, Bacillus velezensis, and Bacillus siamensis Form an “Operational Group B. amyloliquefaciens” within the B. subtilis Species Complex

abstract: The plant growth promoting model bacterium FZB42T was proposed as the type strain of Bacillus amyloliquefaciens subsp. plantarum (Borriss et al., 2011), but has been recently recognized as being synonymous to Bacillus velezensis due to phylogenomic analysis (Dunlap C. et al., 2016). However, until now, majority of publications consider plant-associated close relatives of FZB42 still as "B. amyloliquefaciens." Here, we reinvestigated the taxonomic status of FZB42 and related strains in its context to the free-living soil bacterium DSM7T, the type strain of B. amyloliquefaciens. We identified 66 bacterial genomes from the NCBI data bank with high similarity to DSM7T. Dendrograms based on complete rpoB nucleotide sequences and on core genome sequences, respectively, clustered into a clade consisting of three tightly linked branches: (1) B. amyloliquefaciens, (2) Bacillus siamensis, and (3) a conspecific group containing the type strains of B. velezensis, Bacillus methylotrophicus, and B. amyloliquefaciens subsp. plantarum. The three monophyletic clades shared a common mutation rate of 0.01 substitutions per nucleotide position, but were distantly related to Bacillus subtilis (0.1 substitutions per nucleotide position). The tight relatedness of the three clusters was corroborated by TETRA, dDDH, ANI, and AAI analysis of the core genomes, but dDDH and ANI values were found slightly below species level thresholds when B. amyloliquefaciens DSM7T genome sequence was used as query sequence. Due to these results, we propose that the B. amyloliquefaciens clade should be considered as a taxonomic unit above of species level, designated here as "operational group B. amyloliquefaciens" consisting of the soil borne B. amyloliquefaciens, and plant associated B. siamensis and B. velezensis, whose members are closely related and allow identifying changes on the genomic level due to developing the plant-associated life-style.


title: Dijkstra monads for free

abstract: Dijkstra monads enable a dependent type theory to be enhanced with support for specifying and verifying effectful code via weakest preconditions. Together with their closely related counterparts, Hoare monads, they provide the basis on which verification tools like F*, Hoare Type Theory (HTT), and Ynot are built. ::: We show that Dijkstra monads can be derived "for free" by applying a continuation-passing style (CPS) translation to the standard monadic definitions of the underlying computational effects. Automatically deriving Dijkstra monads in this way provides a correct-by-construction and efficient way of reasoning about user-defined effects in dependent type theories. ::: We demonstrate these ideas in EMF*, a new dependently typed calculus, validating it via both formal proof and a prototype implementation within F*. Besides equipping F* with a more uniform and extensible effect system, EMF* enables a novel mixture of intrinsic and extrinsic proofs within F*.


title: Urdu Nastaliq recognition using convolutionalrecursive deep learning

abstract: Recent developments in recognition of cursive scripts rely on implicit feature extraction methods that provide better results as compared to traditional hand-crafted feature extraction approaches. We present a hybrid approach based on explicit feature extraction by combining convolutional and recursive neural networks for feature learning and classification of cursive Urdu Nastaliq script. The first layer extracts low-level translational invariant features using Convolutional Neural Networks (CNN) which are then forwarded to Multi-dimensional Long Short-Term Memory Neural Networks (MDLSTM) for contextual feature extraction and learning. Experiments are carried out on the publicly available Urdu Printed Text-line Image (UPTI) dataset using the proposed hierarchical combination of CNN and MDLSTM. A recognition rate of up to 98.12% for 44-classes is achieved outperforming the state-of-the-art results on the UPTI dataset.


title: Wall street occupies the blockchain - Financial firms plan to move trillions in assets to blockchains in 2018

abstract: WHEN BLOCKCHAINS first appeared nearly a decade ago as the technical backbone of Bitcoin, the world's leading cryptocurrency, they seemed to offer the masses a way to cut out the financial middleman. But now the big banks and other industry players are finding ways to spin the new tool to their advantage. Their blockchains share a vision that is precisely the opposite of the one laid out in the Bitcoin white paper, published under the pseudonym Satoshi Nakamoto in 2009. Like Nakamoto himself (or herself), you can own bitcoins without even stating your real name; nobody is in charge; and anybody can check the history of any given transaction. The financial industry's blockchains, however, are closed or, in their jargon, permissioned; to join one you must reveal your identity to a system administrator, who must then approve you.


title: Operation of a Grid-Connected Lithium-Ion Battery Energy Storage System for Primary Frequency Regulation: A Battery Lifetime Perspective

abstract: Because of their characteristics, which have been continuously improved during the last years, Lithium-ion batteries have been proposed as an alternative viable solution to present fast-reacting conventional generating units to deliver the primary frequency regulation service. However, even though there are worldwide demonstration projects, where energy storage systems based on Lithium-ion batteries are evaluated for such applications, the field experience is still very limited. In consequence, at present, there are no very clear requirements on how the Lithium-ion battery energy storage systems should be operated, while providing frequency regulation service and how the system has to reestablish its state of charge (SOC) once the frequency event has passed. Therefore, this paper aims to investigate the effect on the lifetime of the Lithium-ion batteries energy storage system of various strategies for reestablishing the batteries’ SOC after the primary frequency regulation is successfully delivered.


title: Dynamic analysis of a 3-DOF 3-PUU parallel manipulator based on the principle of virtual work

abstract: This paper presents a straightforward and systematical methodology for conducting the inverse dynamic analysis of a 3-prismatic-universal-universal (PUU) parallel kinematic manipulator (PKM) through employing the principle of virtual work. The proposed 3-PUU PKM possesses three translational degree-of-freedom (DOF). The position, velocity and acceleration of joints are solved via the inverse kinematic analysis, the link Jacobian matrices which map the velocity of the moving platform into the velocity and angular velocity of struts are derived in the fixed coordinate system. Based on the d'Alembert's form of the principle of virtual work, the applied generalized forces of the whole mechanical system are divided into the applied forces of moving platform, struts, sliders, and motor-coupling-leadscrew, respectively. The joint actuated torques are obtained through eliminating the virtual displacement in the dynamic equations. Furthermore, the inertial term, centrifugal and Coriolis term, and gravitational term of the total joint torque are determined. Simulation for a conical spiral trajectory demonstrates that the total torque is dominated by the inertial term.


title: The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations

abstract: In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.


title: Comparison of 5-bit Thermometer-to-Binary Decoders in 1.8V, 0.18µm CMOS Technology for Flash ADCs

abstract: Thermometer-to-binary decoders have become the bottleneck in Flash analog-to-digital converters. This paper describes five different 5-bit thermometer-to-binary decoders which are suitable for flash analog-to-digital converter in 1.8V, 0.18µm CMOS technology. Comparative study between these thermometer-to-binary decoders is done based on power consumption, count of transistors and delay. The combination of medium power, low transistor count, more regular structure and shorter critical path makes the multiplexer-based decoder is a viable option for efficient Flash ADC design.


title: Contextual domain classification in spoken language understanding systems using recurrent neural network

abstract: In a multi-domain, multi-turn spoken language understanding session, information from the history often greatly reduces the ambiguity of the current turn. In this paper, we apply the recurrent neural network (RNN) to exploit contextual information for query domain classification. The Jordan-type RNN directly sends the vector of output distribution to the next query turn as additional input features to the convolutional neural network (CNN). We evaluate our approach against SVM with and without contextual features. On our contextually labeled dataset, we observe a 1.4% absolute (8.3% relative) improvement in classification error rate over the non-contextual SVM, and 0.9% absolute (5.5% relative) improvement over the contextual SVM.


title: The Effect of a Standardized Ginger Extract on Chemotherapy-Induced Nausea-Related Quality of Life in Patients Undergoing Moderately or Highly Emetogenic Chemotherapy: A Double Blind, Randomized, Placebo Controlled Trial

abstract: Ginger supplementation could be an effective adjuvant treatment for chemotherapy-induced nausea (CIN). The aim of this clinical trial was to address significant methodological limitations in previous trials. Patients (N = 51) were randomly allocated to receive either 1.2 g of standardised ginger extract or placebo per day, in addition to standard anti-emetic therapy, during the first three cycles of chemotherapy. The primary outcome was CIN-related quality of life (QoL) measured with the Functional Living Index- Emesis (FLIE) questionnaire. Secondary outcomes included acute and delayed nausea, vomiting, and retching as well as cancer-related fatigue, nutritional status, and CIN and vomiting-specific prognostic factors. Over three consecutive chemotherapy cycles, nausea was more prevalent than vomiting (47% vs. 12%). In chemotherapy Cycle 1, intervention participants reported significantly better QoL related to CIN (p = 0.029), chemotherapy-induced nausea and vomiting (CINV)-related QoL (p = 0.043), global QoL (p = 0.015) and less fatigue (p = 0.006) than placebo participants. There were no significant results in Cycle 2. In Cycle 3, global QoL (p = 0.040) and fatigue (p = 0.013) were significantly better in the intervention group compared to placebo. This trial suggests adjuvant ginger supplementation is associated with better chemotherapy-induced nausea-related quality of life and less cancer-related fatigue, with no difference in adverse effects compared to placebo.


title: Clinical Trials of Meditation Practices in Health Care: Characteristics and Quality

abstract: Abstract Objective: To provide a descriptive overview of the clinical trials assessing meditation practices for health care. Design: Systematic review of the literature. Comprehensive searches were conducted in 17 electronic bibliographic databases through September 2005. Other sources of potentially relevant studies included hand searches, reference tracking, contacting experts, and gray literature searches. Included studies were clinical trials with 10 or more adult participants using any meditation practice, providing quantitative data on health-related outcomes, and published in English. Two independent reviewers assessed study relevance, extracted the data, and assessed the methodological quality of the studies. Results: Four hundred clinical trials on meditation (72% described as randomized) were included in the review (publication years 1956–2005). Five broad categories of meditation practices were identified: mantra meditation, mindfulness meditation, yoga, t'ai chi, and qigong. The three most stu...


title: Model-Driven Development Patterns for Mobile Services in Cloud of Things

abstract: Cloud of Things (CoT) is an integration of Internet of Things (IoT) and cloud computing for intelligent and smart application especially in mobile environment. Model Driven Architecture (MDA) is used to develop Software as a Service (SaaS) so as to facilitate mobile application development by relieving developers from technical details. However, traditional service composition or mashup are somewhat unavailable due to complex relations and heterogeneous deployed environments. For the purpose of building cloud-enabled mobile applications in a configurable and adaptive way, Model-Driven Development Patterns based on semantic reasoning mechanism are provided towards CoT application development. Firstly, a meta-model covering both multi-view business elements and service components are provided for model transformation. Then, based on formal representation of models, three patterns from different tiers of Model-View-Controller (MVC) framework are used to transform business models into service component system so as to configure cloud services rapidly. Lastly, a related software platform is also provided for verification. The result shows that the platform is applicable for rapid system development by means of various service integration patterns.


Notes on the Rights of a Poor Woman in a Poor Country


title: Smart shopping cart with automatic billing system through RFID and ZigBee

abstract: Contemporary embedded systems are habitually based on microcontroller's i.e. CPUs in the company of integrated memory as well as peripheral interfaces but ordinary microprocessors by means of external chips for memory and peripheral interface circuits are also still common, especially in more complex systems. Radio frequency identification (RFID) technology may not only be useful for streamlining inventory and supply chains: it could also make shoppers swarm. ZigBee is based on an IEEE 802.15 standard. ZigBee devices often transmit data over longer distances by passing data through intermediate devices to reach more distant ones, creating a mesh network; i.e., a network with no centralized control or high-power transmitter/receiver able to reach all of the networked devices. This paper provides centralized and automated billing system using RFID and ZigBee communication. Each product of shopping mall, super markets will be provided with a RFID tag, to identify its type. Each shopping cart is designed or implemented with a Product Identification Device (PID) that contains microcontroller, LCD, an RFID reader, EEPROM, and ZigBee module. Purchasing product information will be read through a RFID reader on shopping cart, mean while product information will be stored into EEPROM attached to it and EEPROM data will be send to Central Billing System through ZigBee module. The central billing system gets the cart information and EEPROM data, it access the product database and calculates the total amount of purchasing for that particular cart. Main aim of this paper was to provide an automatic billing to avoid queue in malls and super markets.


title: Deactivation and regeneration of ZSM-5 zeolite in catalytic pyrolysis of plastic wastes

abstract: Highlights: > Pyrolysis transforms plastic wastes in valuable liquids and gases useful as fuels or source of chemicals. > The use of ZSM-5 zeolite in pyrolysis favours the production of gases and of lighter and more aromatic liquids. > ZSM-5 zeolite is almost completely deactivated after one plastics pyrolysis experiment. > ZSM-5 zeolite used in plastic wastes pyrolysis can be regenerated by burning the deposited coke in an air stream. > Regenerated ZSM-5 recovers its activity and produces liquids and gases equivalent to those obtained with fresh catalyst. - Abstract: In this work, a study of the regeneration and reuse of ZSM-5 zeolite in the pyrolysis of a plastic mixture has been carried out in a semi-batch reactor at 440 deg. C. The results have been compared with those obtained with fresh-catalyst and in non-catalytic experiments with the same conditions. The use of fresh catalyst produces a significant change in both the pyrolysis yields and the properties of the liquids and gases obtained. Gases more rich in C3-C4 and H{sub 2} are produced, as well as lower quantities of aromatic liquids if compared with those obtained in thermal decomposition. The authors have proved that after one pyrolysis experiment the zeolitemore » loses quite a lot of its activity, which is reflected in both the yields and the products quality; however, this deactivation was found to be reversible since after regeneration heating at 550 deg. C in oxygen atmosphere, this catalyst recovered its initial activity, generating similar products and in equivalent proportions as those obtained with fresh catalyst.« less


title: Emergence of Complex-Like Cells in a Temporal Product Network with Local Receptive Fields

abstract: We introduce a new neural architecture and an unsupervised algorithm for learning invariant representations from temporal sequence of images. The system uses two groups of complex cells whose outputs are combined multiplicatively: one that represents the content of the image, constrained to be constant over several consecutive frames, and one that represents the precise location of features, which is allowed to vary over time but constrained to be sparse. The architecture uses an encoder to extract features, and a decoder to reconstruct the input from the features. The method was applied to patches extracted from consecutive movie frames and produces orientation and frequency selective units analogous to the complex cells in V1. An extension of the method is proposed to train a network composed of units with local receptive field spread over a large image of arbitrary size. A layer of complex cells, subject to sparsity constraints, pool feature units over overlapping local neighborhoods, which causes the feature units to organize themselves into pinwheel patterns of orientation-selective receptive fields, similar to those observed in the mammalian visual cortex. A feed-forward encoder efficiently computes the feature representation of full images.


title: The cluster assessment of facial attractiveness using fuzzy neural network classifier based on 3D Moiré features

abstract: Facial attractiveness has long been argued upon varied emphases by philosophers, artists, psychologists and biologists. A number of studies empirically investigated how facial attractiveness was influenced by 2D facial characteristics, such as symmetry, averageness and golden ratio. However, few implementations of facial beauty assessment were based on 3D facial features. The purpose of this paper is to propose a novel cluster assessment system for facial attractiveness that is characterized by the incorporation of 3D geometric Moire features with an adjusted fuzzy neural network (FNN). We first extract 3D facial features from images acquired by a 3dMD scanner. Seven Moire features are employed to represent a 3D facial image. The FNN classifier, taking the Moire features as the parameters, is then trained and validated against independently conducted attractiveness ratings. A number of diverse referees were invited and offered their attractiveness ratings over a five-item Likert scale for 100 female facial images. The proposed assessment presents a high accuracy rate of 90%, and the area under curve (AUC) computed from the receiver operating characteristic (ROC) curve is 0.95. The results show that the perceptions of facial attractiveness are essentially consensus among raters, and can be mathematically modeled through supervised learning techniques. The high accuracy achieved proves that the proposed FNN classifier can serve as a general, automated and human-like judgment tool for objective classification of female facial attractiveness, and thus has potential applications to the entertainment industry, cosmetic industry, virtual media, and plastic surgery. An effective 3D feature extraction for facial attractiveness classification is applied.High accuracy rate of 90% and the AUC is 0.95 for classification is obtained.The facial attractiveness assessed can also be learned by machine learning techniques.An automated and objective judgment tool is presented.The potential applications, such as entertainment industry and plastic surgery.


title: Pitch, Eyebrows and the Perception of Focus

abstract: We report on an experiment with a Talking Head, aimed at finding out the relative contributions of pitch accents and rapid eyebrow movements for the perception of focus. For this purpose, we use a “dialogue reconstruction” experiment: subjects have to perform a perceptual task in which they have to determine on the basis of the distributions of pitch accent and eyebrow movements what the focus is of the current utterance. Our results reveal that both pitch accents and eyebrow movements can have a significant effect on the perception of focus, albeit that the effect of pitch is much larger than that of eyebrows.


title: Improvement Heuristics for Manufacturing System Design Using Complex Network Figures

abstract: Abstract Today's manufacturing systems are characterized by a high level of structural complexity, accompanied by a diverse, less uniform material flow. As this contribution will show, this development complicates the identification of work stations that limit the logistic target achievement, because the non-trivial network effects are hard to predict. However, this identification of key work stations is particularly important during the design and operation phase of a manufacturing system, as they allow a cost-optimal system improvement with limited inference on the operation. Our approach discusses the “bottleneck” oriented approach commonly applied today in the context of network effects in manufacturing systems and compares its performance to centrality measures from complex network theory.


title: Sleep-disordered breathing in heart failure.

abstract: Sleep-disordered breathing-comprising obstructive sleep apnoea (OSA), central sleep apnoea (CSA), or a combination of the two-is found in over half of heart failure (HF) patients and may have harmful effects on cardiac function, with swings in intrathoracic pressure (and therefore preload and afterload), blood pressure, sympathetic activity, and repetitive hypoxaemia. It is associated with reduced health-related quality of life, higher healthcare utilization, and a poor prognosis. Whilst continuous positive airway pressure (CPAP) is the treatment of choice for patients with daytime sleepiness due to OSA, the optimal management of CSA remains uncertain. There is much circumstantial evidence that the treatment of OSA in HF patients with CPAP can improve symptoms, cardiac function, biomarkers of cardiovascular disease, and quality of life, but the quality of evidence for an improvement in mortality is weak. For systolic HF patients with CSA, the CANPAP trial did not demonstrate an overall survival or hospitalization advantage for CPAP. A minute ventilation-targeted positive airway therapy, adaptive servoventilation (ASV), can control CSA and improves several surrogate markers of cardiovascular outcome, but in the recently published SERVE-HF randomized trial, ASV was associated with significantly increased mortality and no improvement in HF hospitalization or quality of life. Further research is needed to clarify the therapeutic rationale for the treatment of CSA in HF. Cardiologists should have a high index of suspicion for sleep-disordered breathing in those with HF, and work closely with sleep physicians to optimize patient management.


Small Fault Diagnosis of Front-end Speed Controlled Wind Generator Based on Deep Learning


title: Input-independent, scalable and fast string matching on the Cray XMT

abstract: String searching is at the core of many security and network applications like search engines, intrusion detection systems, virus scanners and spam filters. The growing size of on-line content and the increasing wire speeds push the need for fast, and often real-time, string searching solutions. For these conditions, many software implementations (if not all) targeting conventional cache-based microprocessors do not perform well. They either exhibit overall low performance or exhibit highly variable performance depending on the types of inputs. For this reason, real-time state of the art solutions rely on the use of either custom hardware or Field-Programmable Gate Arrays (FPGAs) at the expense of overall system flexibility and programmability. This paper presents a software based implementation of the Aho-Corasick string searching algorithm on the Cray XMT multithreaded shared memory machine. Our solution relies on the particular features of the XMT architecture and on several algorithmic strategies: it is fast, scalable and its performance is virtually content-independent. On a 128-processor Cray XMT, it reaches a scanning speed of ≈ 28 Gbps with a performance variability below 10%. In the 10 Gbps performance range, variability is below 2.5%. By comparison, an Intel dual-socket, 8-core system running at 2.66 GHz achieves a peak performance which varies from 500 Mbps to 10 Gbps depending on the type of input and dictionary size.


title: Compiling Comp Ling: Practical weighted dynamic programming and the Dyna language

abstract: Weighted deduction with aggregation is a powerful theoretical formalism that encompasses many NLP algorithms. This paper proposes a declarative specification language, Dyna; gives general agenda-based algorithms for computing weights and gradients; briefly discusses Dyna-to-Dyna program transformations; and shows that a first implementation of a Dyna-to-C++ compiler produces code that is efficient enough for real NLP research, though still several times slower than hand-crafted code.


title: Generative Adversarial Privacy

abstract: We present a data-driven framework called generative adversarial privacy (GAP). Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holder to learn the privatization mechanism directly from the data. Under GAP, finding the optimal privacy mechanism is formulated as a constrained minimax game between a privatizer and an adversary. We show that for appropriately chosen adversarial loss functions, GAP provides privacy guarantees against strong information-theoretic adversaries. We also evaluate the performance of GAP on multi-dimensional Gaussian mixture models and the GENKI face database.


title: Automated and Cooperative Vehicle Merging at Highway On-Ramps

abstract: Recognition of necessities of connected and automated vehicles (CAVs) is gaining momentum. CAVs can improve both transportation network efficiency and safety through control algorithms that can harmonically use all existing information to coordinate the vehicles. This paper addresses the problem of optimally coordinating CAVs at merging roadways to achieve smooth traffic flow without stop-and-go driving. We present an optimization framework and an analytical closed-form solution that allows online coordination of vehicles at merging zones. The effectiveness of the efficiency of the proposed solution is validated through a simulation, and it is shown that coordination of vehicles can significantly reduce both fuel consumption and travel time.


title: The Care of the Ultra-Orthodox Jewish Patient

abstract: The Ultra-Orthodox Jewish community embraces a system of values and a rigorous behavioral code that are deeply rooted in religious tradition and history. Here we describe some of the unique challenges that stem from the encounter between modern medical practice and the Ultra-Orthodox world. Through examples of clinical and ethical scenarios ranging from prenatal care to end-of-life decisions, we illustrate problems related to observance of age-old practices in a modern hospital setting, balancing acceptance of Divine will with standard risk assessment, reconciliation of patient autonomy with deference to rabbinic authority and fear of stigma associated with mental illness in a traditional society. We also offer a generalizable model where inquiry precedes pre-formulated judgment to help clinicians provide enhanced care for this population.


title: Self-Cannibalism: The Man Who Eats Himself

abstract: Self-mutilation is a general term for a variety of forms of intentional self-harm without the wish to die. Although there have been many reports of self-mutilation injuries in the literature, none have reported self-cannibalism after self-mutilation. In this article we present a patient with self-cannibalism following self-mutilation. ::: ::: A 34-year-old male patient was brought to the emergency department from the prison with a laceration on the right leg. Physical examination revealed a well-demarcated rectangular soft tissue defect on his right thigh. The prison authorities stated that the prisoner had cut his thigh with a knife and had eaten the flesh.


Efficient Graph-based Image Matching for Recognition and Retrieval


title: Testing as a Service (TaaS) on Clouds

abstract: Cloud computing leads an opportunity in offering testing as a service (TaaS) for SaaS, clouds, and cloud-based applications. This brings new business opportunities, challenges, and demands in innovative service models, testing techniques, QoS standards, and requirements. This paper provides a comprehensive tutorial on testing as a service in a cloud environment. It answers the common questions raised by engineers and managers, and provides clear conceptual discussions about testing as a service (TaaS), including its scope, objectives, motivations and values, distinct features, required techniques, as well as testing environments. It not only presents a classification of different types of testing services in TaaS, but also offers a clear comparative view and perspectives between conventional software testing service and cloud-based testing as a service. In addition, it examines underlying issues, challenges, and emergent needs.


title: Parallel Power Iteration Clustering for Big Data using MapReduce in Hadoop

abstract: In today’s life Distributed Data Mining is most popular topic in research area because as data are increasing in day to day life there are so many problems occurs to handle them and there are also a solutions for that but still they are not as per expectation, still there are some issue already there in the Distributed Data Mining, among them mainly we are focus in this papers that about reducing computational cost. PIC (Power Iteration Clustering) is simple, fast and relatively scalable and uses matrix vector Multiplication. PIC is the combination of two things one is PI that is Power Iteration and remaining C for Clustering. In Power Iteration it finds the largest Eigenvector and then after it apply K-mean algorithm for clustering. Here this algorithm is fast and capable of finding good clusters in a less time but it is not capable for handling large data, so here another is parallel approach for PIC called Parallel Power Iteration Clustering and implementations for minimizing communication cost. Due to its parallel approach different nodes are also their so for make our system more robust and avoid node failure we use Hadoop platform using Map Reduce. Hadoop provide the environment to implement application in distributed environment and it can capable of handling node failure. In Hadoop each DataNode contains the replica of other Datablocks of other DataNode and location of that Datablocks detail store into Namenode. Due to that when any node will goes down or fails at this moment we can use Replica of that Crash node from the other node. Keywords—Distributed Data Mining, Parallel Clustering, K-Mean Clustering, Threading, Hadoop, NameNode, DataNode,


Firmalice - Automatic Detection of Authentication Bypass Vulnerabilities in Binary Firmware.


title: Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis

abstract: As discussed in the previous statistical notes, although many statistical methods have been proposed to test normality of data in various ways, there is no current gold standard method. The eyeball test may be useful for medium to large sized (e.g., n > 50) samples, however may not useful for small samples. The formal normality tests including Shapiro-Wilk test and Kolmogorov-Smirnov test may be used from small to medium sized samples (e.g., n < 300), but may be unreliable for large samples. Moreover we may be confused because ‘eyeball test’ and ‘formal normality test’ may show incompatible results for the same data. To resolve the problem, another method of assessing normality using skewness and kurtosis of the distribution may be used, which may be relatively correct in both small samples and large samples. 1) Skewness and kurtosis Skewness is a measure of the asymmetry and kurtosis is a measure of ’peakedness’ of a distribution. Most statistical packages give you values of skewness and kurtosis as well as their standard errors.


title: Stability of miniplates and miniscrews used for orthodontic anchorage: experience with 492 temporary anchorage devices

abstract: OBJECTIVES ::: The aim of this retrospective study was to evaluate systematically the potential factors that influence failure rates of temporary anchorage devices (TADs) used for orthodontic anchorage. ::: ::: ::: MATERIALS AND METHODS ::: Data on 492 TADs (miniplates, pre-drilling miniscrews, and self-drilling miniscrews) in 194 patients were collected. The factors related to TAD failure were evaluated using univariate analysis and multivariate forward stepwise logistic regression analysis. ::: ::: ::: RESULTS ::: There were no significant differences in failure rates among the TADs for the following variables: gender, type of malocclusion, facial divergency, implantation site (buccal, lingual, or crestal/midpalatal), location (anterior or posterior), method of force application (power chain or Ni-Ti coil spring), arch (upper or lower), type of soft tissue (attached gingiva or removable mucosa), and most of the cephalometric measurements that reflect dento-cranio-facial characteristics. An increased failure rate was noted for the self-drilling miniscrew type of TAD, TADs used for tooth uprighting, those inserted on bone with lower density, those associated with local inflammation of the surrounding soft tissue, those loaded within 3 weeks after insertion, and those placed in patients with greater mandibular retrusion. Failure rates of the self-drilling miniscrews installed by an oral surgeon and by an orthodontist did not differ significantly. ::: ::: ::: CONCLUSIONS ::: Inflammation of soft tissue surrounding a TAD and early loading within 3 weeks after insertion were the most significant factors predicting TAD failure. Both orthodontists and oral surgeons who install orthodontic TADs must undergo sufficient training to achieve clinical excellence.


Grown , Printed , and Biologically Augmented : An Additively Manufactured Microfluidic Wearable , Functionally Templated for Synthetic Microbes


DESIGN AND PERFORMANCE ANALYSIS OF WALLACE TREE MULTIPLIER USING DIFFERENT COMPRESSORS


title: Scaling personalized web search

abstract: Recent web search techniques augment traditional text matching with a global notion of "importance" based on the linkage structure of the web, such as in Google's PageRank algorithm. For more refined searches, this global notion of importance can be specialized to create personalized views of importance--for example, importance scores can be biased according to a user-specified set of initially-interesting pages. Computing and storing all possible personalized views in advance is impractical, as is computing personalized views at query time, since the computation of each view requires an iterative computation over the web graph. We present new graph-theoretical results, and a new technique based on these results, that encode personalized views as partial vectors. Partial vectors are shared across multiple personalized views, and their computation and storage costs scale well with the number of views. Our approach enables incremental computation, so that the construction of personalized views from partial vectors is practical at query time. We present efficient dynamic programming algorithms for computing partial vectors, an algorithm for constructing personalized views from partial vectors, and experimental results demonstrating the effectiveness and scalability of our techniques.


title: Noise-enhanced convolutional neural networks

abstract: Injecting carefully chosen noise can speed convergence in the backpropagation training of a convolutional neural network (CNN). The Noisy CNN algorithm speeds training on average because the backpropagation algorithm is a special case of the generalized expectation-maximization (EM) algorithm and because such carefully chosen noise always speeds up the EM algorithm on average. The CNN framework gives a practical way to learn and recognize images because backpropagation scales with training data. It has only linear time complexity in the number of training samples. The Noisy CNN algorithm finds a special separating hyperplane in the network's noise space. The hyperplane arises from the likelihood-based positivity condition that noise-boosts the EM algorithm. The hyperplane cuts through a uniform-noise hypercube or Gaussian ball in the noise space depending on the type of noise used. Noise chosen from above the hyperplane speeds training on average. Noise chosen from below slows it on average. The algorithm can inject noise anywhere in the multilayered network. Adding noise to the output neurons reduced the average per-iteration training-set cross entropy by 39% on a standard MNIST image test set of handwritten digits. It also reduced the average per-iteration training-set classification error by 47%. Adding noise to the hidden layers can also reduce these performance measures. The noise benefit is most pronounced for smaller data sets because the largest EM hill-climbing gains tend to occur in the first few iterations. This noise effect can assist random sampling from large data sets because it allows a smaller random sample to give the same or better performance than a noiseless sample gives.


title: Using Mobile Instant Messaging to Leverage Learner Participation and Transform Pedagogy at a South African University of Technology.

abstract: One of the most complicated academic endeavours in transmission pedagogies is to generate democratic participation of all students and public expression of silenced voices. While the potential of mobile phones, particularly mobile instant messaging ( MIM), to trigger broadened academic participation is increasingly acknowledged in literature, integrating MIM into classrooms and out-of-the-classroom tasks has often been confronted with academic resistance. Academic uncertainty about MIM is often predicated on its perceived distractive nature and potential to trigger off-task social behaviours. This paper argues that MIM has potential to create alternative dialogic spaces for student collaborative engagements in informal contexts, which can gainfully transform teaching and learning. An instance of a MIM, Whats App, was adopted for an information technology course at a South African university with a view to heighten lecturer-student and peer-based participation, and enhance pedagogical delivery and inclusive learning in formal (lectures) and informal spaces. The findings suggest heightened student participation, the fostering of learning communities for knowledge creation and progressive shifts in the lecturer's mode of pedagogical delivery. However, the concomitant challenge of using MIM included mature adults' resentment of the merging of academic and family life occasioned by Whats App consultations after hours. Students also expressed ambivalence about MIM's wide-scale roll-out in different academic programmes. [ABSTRACT FROM AUTHOR]


title: A new algorithm for accurate and automatic chessboard corner detection

abstract: In this paper, we propose an accurate automatic chessboard corner detecting algorithm of sub-pixel precision. The algorithm is based on a novel hyperbolic tangent model. A set of corners are detected initially by Harris' algorithm. Then the hyperbolic tangent model for each corner point is optimized by parameter adjustment. Finally, we use the optimal model to obtain the sub-pixel position of each corner with fake corner elimination. Experiments on three datasets show that the proposed method achieves a detection rate of at least 98% and a reprojection error in camera calibration of at most 0.85 pixels. Our algorithm significantly outperforms the methods in three related works.


title: Adaptive and Energy-Efficient Architectures for Machine Learning: Challenges, Opportunities, and Research Roadmap

abstract: Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT) / Internet of Everything (IoE), and Cyber Physical Systems (CSP) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world under unpredictable, harsh, and energy-/power-constrained scenarios. Therefore, such systems need to support not only the high performance capabilities at tight power/energy envelop, but also need to be intelligent/cognitive, self-learning, and robust. As a result, a hype in the artificial intelligence research (e.g., deep learning and other machine learning techniques) has surfaced in numerous communities. This paper discusses the challenges and opportunities for building energy-efficient and adaptive architectures for machine learning. In particular, we focus on brain-inspired emerging computing paradigms, such as approximate computing; that can further reduce the energy requirements of the system. First, we guide through an approximate computing based methodology for development of energy-efficient accelerators, specifically for convolutional Deep Neural Networks (DNNs). We show that in-depth analysis of datapaths of a DNN allows better selection of Approximate Computing modules for energy-efficient accelerators. Further, we show that a multi-objective evolutionary algorithm can be used to develop an adaptive machine learning system in hardware. At the end, we summarize the challenges and the associated research roadmap that can aid in developing energy-efficient and adaptable hardware accelerators for machine learning.


title: Planar Ultra Wideband bandpass filter using Defected Ground Structure

abstract: The design of planar Ultra Wide Band (UWB) band pass filter using periodic Defected Ground Structure (DGS) is presented. The filter is realized with interdigital coupled lines, stepped impedance open stub and slotted DGS. Interdigital coupled lines are used to widen the band width of the filter. Stub is to provide transmission zeros in upper and lower stop band where as DGS improves the return loss of the filter. It provides an insertion loss of 0.3 dB with a wide pass band from 3.1 – 10.6 GHz. It also provides better return loss of 19 dB.


title: Leader election algorithms for mobile ad hoc networks

abstract: We present two new leader election algorithms for mobile ad hoc networks. The algorithms ensure that eventually each connected component of the topology graph has exactly one leader. The algorithms are based on a routing algorithm called TORA [5], which in turn is based on an algorithm by Gafni and Bertsekas [3]. The algorithm require nodes to communicate with only their current neighbors, making it well suited to the ad hoc environment. The first algorithm is for a single topology change and is provided with a proof of correctness. The second algorithm tolerates multiple concurrent topology changes.


title: Design considerations in manufacturing cost-effective robotic exoskeletons for upper extremity rehabilitation

abstract: The development of robotic exoskeletons is a young and active area of research in multidisciplinary fields such as biomedical engineering. In upper extremity rehabilitation, several developments have been made for robotic exoskeletons to help regain motor function by means of repetitive and task-oriented exercises. However, the economics and availability of such devices remain an issue to be addressed especially for those in the developing regions where access to healthcare options are limited. This article presents an analysis of design criteria and considerations in manufacturing cost-effective robotic exoskeletons for upper extremity rehabilitation. A review of recently developed concepts, prototypes and commercially-available devices was performed to establish a set of guidelines in developing products with high feasibility for adoption. Most of the design criteria discussed in this article focused on wearability, safety, and performance. Finally, a discussion of the trend towards low-cost manufacturing was done to challenge researchers in addressing the need for a reliable and accessible healthcare system for upper extremity rehabilitation.


title: Upright orientation of man-made objects

abstract: Humans usually associate an upright orientation with objects, placing them in a way that they are most commonly seen in our surroundings. While it is an open challenge to recover the functionality of a shape from its geometry alone, this paper shows that it is often possible to infer its upright orientation by analyzing its geometry. Our key idea is to reduce the two-dimensional (spherical) orientation space to a small set of orientation candidates using functionality-related geometric properties of the object, and then determine the best orientation using an assessment function of several functional geometric attributes defined with respect to each candidate. Specifically we focus on obtaining the upright orientation for man-made objects that typically stand on some flat surface (ground, floor, table, etc.), which include the vast majority of objects in our everyday surroundings. For these types of models orientation candidates can be defined according to static equilibrium. For each candidate, we introduce a set of discriminative attributes linking shape to function. We learn an assessment function of these attributes from a training set using a combination of Random Forest classifier and Support Vector Machine classifier. Experiments demonstrate that our method generalizes well and achieves about 90% prediction accuracy for both a 10-fold cross-validation over the training set and a validation with an independent test set.


title: ERP Implementation and change : towards a cross-functional view

abstract: ERP (Enterprise Resource Planning) systems are characterised by particular features such as functional coverage, interdependent relationships, single database and standard management and processing rules; all of which are capable of bringing about various degrees of change within the company and, potentially, encourage a more cross-functional overview of it. However, few quantitative studies have been conducted to measure these effects. This is the background to this paper, which studied 100 French companies to arrive at the following assessment of ERP adoption. It then goes on to test the relationships between the factors influencing the ERP lifecycle ((preparation (organizational vision, process re-engineering), engineering (specific developments), implementation strategy (functional coverage and speed)), the perception of a more cross-functional overview of the company and, more globally, the scope of the change this technology brings about within the company. All these factors play significant roles, with functional coverage appearing to be a particularly important consideration, which should be addressed in future research.


title: It’s Not the Way You Look, It’s How You Move: Validating a General Scheme for Robot Affective Behaviour

abstract: In the emerging world of human-robot interaction, people and robots will work together to achieve joint objectives. This paper discusses the design and validation of a general scheme for creating emotionally expressive behaviours for robots, in order that people might better interpret how a robot collaborator is succeeding or failing in its work. It exemplifies a unified approach to creating robot behaviours for two very different robot forms, based on combinations of four groups of design parameters (approach/avoidance, energy, intensity and frequency). 59 people rated video clips of robots performing expressive behaviours both for emotional expressivity on Valence-Arousal-Dominance dimensions, and their judgement of the successfulness of the robots’ work. Results are discussed in terms of the utility of expressive behaviour for facilitating human understanding of robot intentions and the design of cues for basic emotional states.


title: Design and Analysis of SIW Cavity Backed Dual-Band Antennas With a Dual-Mode Triangular-Ring Slot

abstract: Substrate integrated waveguide (SIW) cavity backed dual-band triangular-ring slot (TRS) antennas are proposed for planar integration. First, a low profile dual band 45 ::: ° ::: linearly polarized (LP) antenna is designed and analyzed. The dual-mode TRS is proposed and developed by simultaneously exciting the mode of the slot and the mode of the patch inside the slot. To minimize the antenna size, the lowest mode (TE110 mode) of the SIW cavity is adopted to excite the TRS. The -10 dB impedance bandwidth at the higher band is 5.9%, 58.3% wider than that of the conventional SIW cavity backed slot antenna. The interactions of the SIW cavity, the slot mode and the patch mode are analyzed with circuit models and verified by full wave simulations, which provides a new insight into the SIW cavity backed antennas. Then, by exciting a pair of the 45° LP antennas with an SIW coupler, a circularly polarized (CP) dual band antenna with a small frequency ratio of 1.26 is developed. The two antennas are fabricated and tested, and the measured and simulated results agree well.


title: Steps Toward Robust Artificial Intelligence

abstract: Recent advances in artificial intelligence are encouraging governments and corporations to deploy AI in high-stakes settings including driving cars autonomously, managing the power grid, trading on stock exchanges, and controlling autonomous weapons systems. Such applications require AI methods to be robust to both the known unknowns (those uncertain aspects of the world about which the computer can reason explicitly) and the unknown unknowns (those aspects of the world that are not captured by the system’s models). This article discusses recent progress in AI and then describes eight ideas related to robustness that are being pursued within the AI research community. While these ideas are a start, we need to devote more attention to the challenges of dealing with the known and unknown unknowns. These issues are fascinating, because they touch on the fundamental question of how finite systems can survive and thrive in a complex and dangerous world


title: Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

abstract: This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.


title: Urbanization, urban land expansion and environmental change in China

abstract: China’s economic reforms and unprecedented growth have generated many fascinating issues for scholarly research. An understanding of urbanization and land use change in China is required for appropriate strategies and policies to facilitate future sustainable development. This paper reviews the literature on urbanization, land use and sustainable development in China with a focus on land use change. We argue that land use and environmental research are embedded in the complex economic-geographical processes and multiple trajectories of development and urbanization in China. This paper highlights the important role of space–time modeling in a multi-disciplinary setting in the study of urbanization, land use and sustainable development. It also points out potential areas for future research.


title: Video Streaming : Concepts , Algorithms , and Systems

abstract: video streaming, video delivery, streaming media content delivery networks, video coding, errorresilient; multiple description coding Video has been an important media for communications and entertainment for many decades. Initially video was captured and transmitted in analog form. The advent of digital integrated circuits and computers led to the digitization of video, and digital video enabled a revolution in the compression and communication of video. Video compression became an important area of research in the late 1980’s and 1990’s and enabled a variety of applications including video storage on DVD’s and Video-CD’s, video broadcast over digital cable, satellite and terrestrial (over-the-air) digital television (DTV), and video conferencing and videophone over circuit-switched networks. The growth and popularity of the Internet in the mid-1990’s motivated video communication over best-effort packet networks. Video over best-effort packet networks is complicated by a number of factors including unknown and time-varying bandwidth, delay, and losses, as well as many additional issues such as how to fairly share the network resources amongst many flows and how to efficiently perform one-to-many communication for popular content. This article examines the challenges that make simultaneous delivery and playback, or streaming, of video difficult, and explores algorithms and systems that enable streaming of pre-encoded or live video over packet networks such as the Internet.


title: Dual polarized aperture-coupled microstrip patch antenna for X-band series-fed array

abstract: This paper discusses the design of a 4-port radiating element and a patch load radiating element (PL) that is used for a series feed linear array for CASA's student test-bed and the results obtained for their simulations.


title: GOLD: A Parallel Real-Time Stereo Vision System for Generic Obstacle and Lane Detection

abstract: This paper describes the generic obstacle and lane detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an on-board monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.


title: Partial discharge diagnostics and electrical equipment insulation condition assessment

abstract: Partial discharge (PD) measurement has long been used as a test to evaluate different insulation system designs, and as a quality control test for new equipment. However, in the past 20 years, PD measurement has been widely applied to diagnose the condition of the electrical insulation in operating apparatus such as switchgear, transformers, cables, as well as motor and generator stator windings. Improvements in the capabilities as well the lower cost of sensors, electronics and memory is partly the reason for the increased popularity of PD diagnostics. Another reason has been the development of methods-including the use of ultrawide band detection-to improve the reliability of the PD measurement in the presence of noise. In addition, rapid progress is being made in automated pattern recognition techniques that also helps to suppress noise. This paper reviews the various PD measurement technologies that have been specifically developed to improve PD diagnostic methods, and outlines how they have been implemented for stators, cables, transformers and switchgear. Areas for further research are also presented.


title: Energy-Efficient Approximate Multiplication for Digital Signal Processing and Classification Applications

abstract: The need to support various digital signal processing (DSP) and classification applications on energy-constrained devices has steadily grown. Such applications often extensively perform matrix multiplications using fixed-point arithmetic while exhibiting tolerance for some computational errors. Hence, improving the energy efficiency of multiplications is critical. In this brief, we propose multiplier architectures that can tradeoff computational accuracy with energy consumption at design time. Compared with a precise multiplier, the proposed multiplier can consume 58% less energy/op with average computational error of $\sim 1$ %. Finally, we demonstrate that such a small computational error does not notably impact the quality of DSP and the accuracy of classification applications.


title: RFID technology: Beyond cash-based methods in vending machine

abstract: This paper characterizes the design, implementation and employment of cashless and secure payment system in vending machine by using radio frequency identification technology, to improve the traditional cash-based payment system that involved lot of problems and risks i.e., hacking, auditing, storing, currency and material of coins and notes. RFID is achieving momentum in a multiple sectors like retail, security, transportation, pharmaceuticals, defense, healthcare etc., and a host of other fields, and now vending machines. Our proposed methodology consists of passive RFID identification cards and reader for consumers, Arduino Mega microcontroller, SPI protocol for RFID and Arduino interfacing, keypad for password protection, liquid crystal display (LCD) for displaying consumer name and current balance, and SMS is sent for notification using GSM module. Spiral coil architecture is supported by DC motors powered by relays in mechanical structure of vending machine. It also describes how such product-oriented RFID card based vending machine can maximum facilitates the system engineering.


title: Student performance prediction using Support Vector Machine and K-Nearest Neighbor

abstract: This work presented two prediction models for the estimation of student's performance in final examination. The work made use of the popular dataset provided by the University of Minho in Portugal, which relate to the performance in math subject and it consists of 395 data samples. Forecasting the performance of students can be useful in taking early precautions, instant actions, or selecting a student that is fit for a certain task. The need to explore better models to achieve better performance cannot be overemphasized. Most of earlier work on the same dataset used K-Nearest Neighbor algorithm and achieved low results, while Support Vector Machine algorithm was rarely used, which happens to be a very popular and powerful prediction technique. To ensure better comparison, we applied both Support Vector Machine algorithm and K-Nearest Neighbor algorithm on the dataset to predict the student's grade and then compared their accuracy. Empirical studies outcome indicated that Support Vector Machine achieved slightly better results with correlation coefficient of 0.96, while the K-Nearest Neighbor achieved correlation coefficient of 0.95.


Constructing Attack Scenario using Sequential Pattern Mining with Correlated Candidate Sequences *


title: Efficient and change-resilient test automation: an industrial case study

abstract: Test automation, which involves the conversion of manual test cases to executable test scripts, is necessary to carry out efficient regression testing of GUI-based applications. However, test automation takes significant investment of time and skilled effort. Moreover, it is not a one-time investment: as the application or its environment evolves, test scripts demand continuous patching. Thus, it is challenging to perform test automation in a cost-effective manner. At IBM, we developed a tool, called ATA [1], [2], to meet this challenge. ATA has novel features that are designed to lower the cost of initial test automation significantly. Moreover, ATA has the ability to patch scripts automatically for certain types of application or environment changes. How well does ATA meet its objectives in the real world? In this paper, we present a detailed case study in the context of a challenging production environment: an enterprise web application that has over 6500 manual test cases, comes in two variants, evolves frequently, and needs to be tested on multiple browsers in time-constrained and resource-constrained regression cycles. We measured how well ATA improved the efficiency in initial automation. We also evaluated the effectiveness of ATA's change-resilience along multiple dimensions: application versions, browsers, and browser versions. Our study highlights several lessons for test-automation practitioners as well as open research problems in test automation.


title: Natural Feature Tracking for Extendible Robust Augmented Realities

abstract: Vision-based tracking systems for augmented reality often require that artificial fiducials be placed in the scene. In this paper we utilize our approach for robust detection and tracking of natural features such as textures or corners. The tracked natural features are automatically calibrated to the fiducials that are used to initialize and facilitate normal tracking. Once calibrated, the natural features are used to extend the system’s tracking range and to stabilize the tracked pose against occlusions and noise. The emphasis of this paper is the integration of natural feature tracking with fiducial tracking to increase the range and robustness of vision-based augmented reality tracking.


title: Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

abstract: Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback-Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www.comp-sysbio.org/cmi2ni.


title: A reflection on call-by-value

abstract: A number of compilers exploit the following strategy: translate a term to continuation-passing style (CPS) and optimize the resulting term using a sequence of reductions. Recent work suggests that an alternative strategy is superior: optimize directly in an extended source calculus. We suggest that the appropriate relation between the source and target calculi may be captured by a special case of a Galois connection known as a reflection. Previous work has focused on the weaker notion of an equational correspondence, which is based on equality rather than reduction. We show that Moggi's monad translation and Plotkin's CPS translation can both be regarded as reflections, and thereby strengthen a number of results in the literature.


CoKE : Extending Contextualized Knowledge Embeddings for Word Sense Induction


title: Defining an evaluation framework for digital cities implementation

abstract: Digital cities are nowadays an important topic, both for social and for economic studies. However, the main papers about digital cities regard the technical implementation of infrastructures and networks, or the shaping of urban spaces. Few attention is paid to the socio-economic value of digital cities and to the concrete benefits deriving from their implementation, together with costs and burdens linked with the governance and maintenance of technical and legal platforms. The goal of this paper is to define an evaluation framework to assess the quality and value of a digital city environment, including all the actors involved both in its building and in its regulation, in its use and in its economic exploitation. The evaluation framework should support the choices regarding the prioritization of programs and technical solutions, considering the socio-economic impacts of the digital city on all the project stakeholders. Moreover, the same framework should also be able to measure ex-post benefits really gained by all the stakeholder categories, and to support improvements of the platform and further implementations. The framework design is based on a theoretical investigation on international literature about this topic; its realization will involve companies, local governments, infrastructure providers, users.


A Brief Note on the 2007 Excavation at Ille Cave, Palawan, the Philippines


title: A Deep Normalization and Convolutional Neural Network for Image Smoke Detection

abstract: It is a challenging task to recognize smoke from images due to large variance of smoke color, texture, and shapes. There are smoke detection methods that have been proposed, but most of them are based on hand-crafted features. To improve the performance of smoke detection, we propose a novel deep normalization and convolutional neural network (DNCNN) with 14 layers to implement automatic feature extraction and classification. In DNCNN, traditional convolutional layers are replaced with normalization and convolutional layers to accelerate the training process and boost the performance of smoke detection. To reduce overfitting caused by imbalanced and insufficient training samples, we generate more training samples from original training data sets by using a variety of data enhancement techniques. Experimental results show that our method achieved very low false alarm rates below 0.60% with detection rates above 96.37% on our smoke data sets.


title: Review of Proposed High Efficiency Video Coding (HEVC) Standard

abstract: The objective of this paper is to provide a review of the recent developments in HEVC standardization, particularly focusing on the key features of hybrid coding tools and gives theoretical analysis, summaries of the technological advancements, and compares its performance with the H.264/ MPEG-4 AVC high-profile standard. High Efficiency Video Coding (HEVC) is a proposed and under development new generation of video compression standard, which enables substantially higher compression capability than the existing state-of-the-art video coding standard and exhibit superior coding performance improvements over its predecessors. Some of the major contributors to the higher compression performance of HEVC are the introduction of quadtree structure, improved techniques to support parallel encoding/decoding, more directional intraprediction modes, support for several integer transforms with square as well as nonsquare transforms, merging of prediction blocks for improved motion information encoding, and extensive In-loop processing on reconstructed pictures. When completed, it unlocks future business not possible with today’s AVC notably suitable for resolutions up to Ultra High Definition (UHD) video coding in the future.


title: Engagement Capacity and Engaging Team Formation for Reach Maximization of Online Social Media Platforms

abstract: The challenges of assessing the "health" of online social media platforms and strategically growing them are recognized by many practitioners and researchers. For those platforms that primarily rely on user-generated content, the reach -- the degree of participation referring to the percentage and involvement of users -- is a key indicator of success. This paper lays a theoretical foundation for measuring engagement as a driver of reach that achieves growth via positive externality effects. The paper takes a game theoretic approach to quantifying engagement, viewing a platform's social capital as a cooperatively created value and finding a fair distribution of this value among the contributors. It introduces engagement capacity, a measure of the ability of users and user groups to engage peers, and formulates the Engaging Team Formation Problem (EngTFP) to identify the sets of users that "make a platform go". We show how engagement capacity can be useful in characterizing forum user behavior and in the reach maximization efforts. We also stress how engagement analysis differs from influence measurement. Computational investigations with Twitter and Health Forum data reveal the properties of engagement capacity and the utility of EngTFP.


title: Brand personality dimensions in the Brazilian context

abstract: Abstract Brands may be perceived as possessing a set of distinct traits or characteristics, i.e., a personality, similar to a person. Thus, the personality of a brand is relevant as a source of differentiation in an increasingly competitive market environment. In this study, the authors explore the dimensions of brand personality proposed by J. Aaker (1997), seeking to discover the particular evaluation dimensions of the Brazilian context. The study was conducted in exploratory stages, beginning with a preparatory stage that was carried out by professionals and academics from the fields of communication and marketing. This was followed by stages of conclusive research using an online survey with a sample of 1,302 Brazilian consumers. Two sub-samples were extracted: one calibration sample for exploratory factor analysis and a validation sample to perform confirmatory factor analysis in order to verify the convergent and discriminant validities of the final scale. Five dimensions of brand personality in Brazil were detected: credibility, joy, audacity, sophistication and sensitivity. These dimensions showed some differences in comparison with similar studies that had been carried out in other countries. The study led to a scale of 28 items for measuring brand personality and made it possible to compare competing brands in terms of brand personality. Key words : marketing; brands; brand personality.


title: Varactor-tuned combline bandpass filter using step-impedance microstrip lines

abstract: In this paper, a varactor-tuned combline bandpass filter using step-impedance microstrip lines is considered so that the absolute passband bandwidth can be maintained nearly constant within the tuning range. The difference between the odd- and even-mode characteristics of the coupled microstrip line makes it difficult to design a tunable bandpass filter with minimum degradation in passband performance. By using step-impedance microstrip lines, couplings between resonators can be controlled so that the constant bandwidth requirement could be satisfied with reasonable design parameter values. Lumped inductors are used for input and output coupling networks. Design equations are derived, and experimental results are compared with theoretical ones based on these equations.


Self-Folding Thermo-Magnetically Responsive Soft Microgrippers


Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition


title: Titian: Data Provenance Support in Spark

abstract: Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today's DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence (e.g., from log files) and performing trial and error debugging. To aid this effort, we built Titian, a library that enables data provenance---tracking data through transformations---in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds---orders-of-magnitude faster than alternative solutions---while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.


title: Source camera identification based on CFA interpolation

abstract: In this work, we focus our interest on blind source camera identification problem by extending our results in the direction of M. Kharrazi et al. (2004). The interpolation in the color surface of an image due to the use of a color filter array (CFA) forms the basis of the paper. We propose to identify the source camera of an image based on traces of the proprietary interpolation algorithm deployed by a digital camera. For this purpose, a set of image characteristics are defined and then used in conjunction with a support vector machine based multi-class classifier to determine the originating digital camera. We also provide initial results on identifying source among two and three digital cameras.


title: Hierarchical organization versus self-organization

abstract: In this paper we try to define the difference between hierarchical organization and self-organization. Organization is defined as a structure with a function. So we can define the difference between hierarchical organization and self-organization both on the structure as on the function. In the next two chapters these two definitions are given. For the structure we will use some existing definitions in graph theory, for the function we will use existing theory on (self-)organization. In the third chapter we will look how these two definitions agree. Finally we give a conclusion.


title: Data-Driven D iscovery of Quantitative Rules in Relational Databases

abstract: A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. An efficient induction method is developed for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. The method involves the learning of both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. It is shown that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases. >