title: RF modules (Tx-Rx) with multifunctional MMICs

abstract: Next generation RF sensor modules for multifunction active electronically steered antenna (AESA) systems will need a combination of different operating modes, such as radar, electronic warfare (EW) functionalities and communications/datalinks within the same antenna frontend. They typically operate in C-Band, X-Band and Ku-Band and imply a bandwidth requirement of more than 10 GHz. For the realisation of modern active electronically steered antennas, the transmit/receive (T/R) modules have to match strict geometry demands. A major challenge for these future multifunction RF sensor modules is dictated by the half-wavelength antenna grid spacing, that limits the physical channel width to < 12 mm or even less, depending on the highest frequency of operation with accordant beam pointing requirements. A promising solution to overcome these geometry demands is the reduction of the total monolithic microwave integrated circuit (MMIC) chip area, achieved by integrating individual RF functionalities, which are commonly achieved through individual integrated circuits (ICs), into new multifunctional (MFC) MMICs. Various concepts, some of them already implemented, towards next generation RF sensor modules will be discussed and explained in this work.


title: Microsatellite marker development by partial sequencing of the sour passion fruit genome (Passiflora edulis Sims)

abstract: BACKGROUND ::: The Passiflora genus comprises hundreds of wild and cultivated species of passion fruit used for food, industrial, ornamental and medicinal purposes. Efforts to develop genomic tools for genetic analysis of P. edulis, the most important commercial Passiflora species, are still incipient. In spite of many recognized applications of microsatellite markers in genetics and breeding, their availability for passion fruit research remains restricted. Microsatellite markers in P. edulis are usually limited in number, show reduced polymorphism, and are mostly based on compound or imperfect repeats. Furthermore, they are confined to only a few Passiflora species. We describe the use of NGS technology to partially assemble the P. edulis genome in order to develop hundreds of new microsatellite markers. ::: ::: ::: RESULTS ::: A total of 14.11 Gbp of Illumina paired-end sequence reads were analyzed to detect simple sequence repeat sites in the sour passion fruit genome. A sample of 1300 contigs containing perfect repeat microsatellite sequences was selected for PCR primer development. Panels of di- and tri-nucleotide repeat markers were then tested in P. edulis germplasm accessions for validation. DNA polymorphism was detected in 74% of the markers (PIC = 0.16 to 0.77; number of alleles/locus = 2 to 7). A core panel of highly polymorphic markers (PIC = 0.46 to 0.77) was used to cross-amplify PCR products in 79 species of Passiflora (including P. edulis), belonging to four subgenera (Astrophea, Decaloba, Distephana and Passiflora). Approximately 71% of the marker/species combinations resulted in positive amplicons in all species tested. DNA polymorphism was detected in germplasm accessions of six closely related Passiflora species (P. edulis, P. alata, P. maliformis, P. nitida, P. quadrangularis and P. setacea) and the data used for accession discrimination and species assignment. ::: ::: ::: CONCLUSION ::: A database of P. edulis DNA sequences obtained by NGS technology was examined to identify microsatellite repeats in the sour passion fruit genome. Markers were submitted to evaluation using accessions of cultivated and wild Passiflora species. The new microsatellite markers detected high levels of DNA polymorphism in sour passion fruit and can potentially be used in genetic analysis of P. edulis and other Passiflora species.


title: A Bayesian nonparametric approach to modeling motion patterns

abstract: The most difficult--and often most essential--aspect of many interception and tracking tasks is constructing motion models of the targets. Experts rarely can provide complete information about a target's expected motion pattern, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. ::: ::: In contrast, Bayesian nonparametric models of target motion are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides an adaptive representation for each individual motion pattern, while the DP prior allows us to represent an unknown number of motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area.


Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments


title: Securing vehicles against cyber attacks

abstract: The automobile industry has grown to become an integral part of our everyday life. As vehicles evolve, the primarily mechanical solutions for vehicle control are gradually replaced by electronics and software solutions forming in-vehicle computer networks. An emerging trend is to introduce wireless technology in the vehicle domain by attaching a wireless gateway to the in-vehicle network. By allowing wireless communication, real-time information exchange between vehicles and between infrastructure and vehicles become reality. This communication allows for road condition reporting, decision making, and remote diagnostics and firmware updates over-the-air. However, allowing external parties wireless access to the in-vehicle network creates a potential entry-point for cyber attacks. In this paper, we investigate the security issues of allowing external wireless communication. We use a defense-in-depth perspective and discuss security challenges for each of the prevention, detection, deflection, countermeasures, and recovery layers.


title: Gaze estimation from multimodal Kinect data

abstract: This paper addresses the problem of free gaze estimation under unrestricted head motion. More precisely, unlike previous approaches that mainly focus on estimating gaze towards a small planar screen, we propose a method to estimate the gaze direction in the 3D space. In this context the paper makes the following contributions: (i) leveraging on Kinect device, we propose a multimodal method that rely on depth sensing to obtain robust and accurate head pose tracking even under large head pose, and on the visual data to obtain the remaining eye-in-head gaze directional information from the eye image; (ii) a rectification scheme of the image that exploits the 3D mesh tracking, allowing to conduct a head pose free eye-in-head gaze directional estimation; (iii) a simple way of collecting ground truth data thanks to the Kinect device. Results on three users demonstrate the great potential of our approach.


title: Improving employees' compliance through information systems security training: an action research study

abstract: Employee noncompliance with information systems security policies is a key concern for organizations. If users do not comply with IS security policies, security solutions lose their efficacy. Of the different IS security policy compliance approaches, training is the most commonly suggested in the literature. Yet, few of the existing studies about training to promote IS policy compliance utilize theory to explain what learning principles affect user compliance with IS security policies, or offer empirical evidence of their practical effectiveness. Consequently, there is a need for IS security training approaches that are theory-based and empirically evaluated. Accordingly, we propose a training program based on two theories: the universal constructive instructional theory and the elaboration likelihood model. We then validate the training program for IS security policy compliance training through an action research project. The action research intervention suggests that the theory-based training achieved positive results and was practical to deploy. Moreover, the intervention suggests that information security training should utilize contents and methods that activate and motivate the learners to systematic cognitive processing of information they receive during the training. In addition, the action research study made clear that a continuous communication process was also required to improve user IS security policy compliance. The findings of this study offer new insights for scholars and practitioners involved in IS security policy compliance.


title: Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms

abstract: Quantitative prediction of quality properties (i.e. extra-functional properties such as performance, reliability, and cost) of software architectures during design supports a systematic software engineering approach. Designing architectures that exhibit a good trade-off between multiple quality criteria is hard, because even after a functional design has been created, many remaining degrees of freedom in the software architecture span a large, discontinuous design space. In current practice, software architects try to find solutions manually, which is time-consuming, can be error-prone and can lead to suboptimal designs. We propose an automated approach to search the design space for good solutions. Starting with a given initial architectural model, the approach iteratively modifies and evaluates architectural models. Our approach applies a multi-criteria genetic algorithm to software architectures modelled with the Palladio Component Model. It supports quantitative performance, reliability, and cost prediction and can be extended to other quantitative quality criteria of software architectures. We validate the applicability of our approach by applying it to an architecture model of a component-based business information system and analyse its quality criteria trade-offs by automatically investigating more than 1200 alternative design candidates.


title: Extractive Summarization by Maximizing Semantic Volume

abstract: The most successful approaches to extractive text summarization seek to maximize bigram coverage subject to a budget constraint. In this work, we propose instead to maximize semantic volume. We embed each sentence in a semantic space and construct a summary by choosing a subset of sentences whose convex hull maximizes volume in that space. We provide a greedy algorithm based on the GramSchmidt process to efficiently perform volume maximization. Our method outperforms the state-of-the-art summarization approaches on benchmark datasets.


title: Machine Vision-Based Positioning and Inspection Using Expectation–Maximization Technique

abstract: Precision positioning is very important for automatic assembly and inspection in the electronic manufacturing process. In this paper, we propose a fast image alignment method using the expectation–maximization (E–M) technique. The proposed algorithm is especially applied to positioning and defect inspection of printed circuit boards (PCBs). It can well handle deformed or incomplete object shapes with translation, rotation, and scale changes. The Canny edge detector is used to generate the edge maps of images. The E-step of the E–M procedure finds mutual edge points in both compared images by assigning weights to individual edge points. The mutual edge points give larger weights, while the foreign edge points in two images have smaller weights. The M-step then calculates the geometric transformation parameters using the weighted edge points in individual images. For an edge point in one image, a fast spiral search is proposed to find its corresponding edge point with the shortest distance in the other image. The spiral search is carried out by a predetermined lookup table, and no computation is involved in the search process. The weight of each edge point is inversely proportional to the neighboring distance. Experimental results indicate that the proposed E–M positioning method can achieve a translation error less than 1 pixel and a rotation error smaller than 1° for PCB positioning.


title: Physical Activity Improves Verbal and Spatial Memory in Older Adults with Probable Mild Cognitive Impairment: A 6-Month Randomized Controlled Trial

abstract: We report secondary findings from a randomized controlled trial on the effects of exercise on memory in older adults with probable MCI. We randomized 86 women aged 70–80 years with subjective memory complaints into one of three groups: resistance training, aerobic training, or balance and tone (control). All participants exercised twice per week for six months. We measured verbal memory and learning using the Rey Auditory Verbal Learning Test (RAVLT) and spatial memory using a computerized test, before and after trial completion. We found that the aerobic training group remembered significantly more items in the loss after interference condition of the RAVLT compared with the control group after six months of training. In addition, both experimental groups showed improved spatial memory performance in the most difficult condition where they were required to memorize the spatial location of three items, compared with the control group. Lastly, we found a significant correlation between spatial memory performance and overall physical capacity after intervention in the aerobic training group. Taken together, our results provide support for the prevailing notion that exercise can positively impact cognitive functioning and may represent an effective strategy to improve memory in those who have begun to experience cognitive decline.


Cryptographic Hashing Method using for Secure and Similarity Detection in Distributed Cloud Data


Controversies around the digital humanities: an agenda


title: THE EFFECTS OF SOCIAL NETWORKING SITES ON STUDENTS' ACADEMIC PERFORMANCE IN LYCEUM OF THE PHILIPPINES - LAGUNA

abstract: In the advent of internet technology, social communication using the internet became the frontier of daily communication, collaboration and networking. This frontier is delineated by many social networking websites and portals. This study was conducted to determine the effects of Social Networking Sites (SNSs) on students’ academic performance in Lyceum of the Philippines – Laguna. To determine the effects of SNSs on academic performance, the researcher used the descriptive correlational method of research. An online survey questionnaire designed using Google docs was distributed to 203 randomly-selected studentrespondents from the six colleges in Lyceum of the Philippines – Laguna. The study found out that all students in Lyceum of the Philippines – Laguna have Facebook accounts and were significantly registered to more than one SNSs account. The results of the study showed a statistically significant positive relationship between time spent by students on SNSs and their academic performance. The student-respondents tended to use SNSs to interact with their friends and not use for academic reasons. Specifically, the study found out that the higher the time spent on SNSs showed the lower is the time spent on studying. Hence, correlation analysis showed an inverse relationship between the GPA and the number of SNSs accounts being maintained by the students, the frequency of SNSs use, since when they have SNSs, length of SNSs use, and the number of study hours. The results revealed that as these mentioned variables increases, the GPA also increases, showing that SNSs had made a contribution in the attainment of higher grades. However, such relationships are not significant. Therefore, the study found that SNSs usage had no significant relationship on student academic performance. The improvement in the grade relies on other factors within the teaching-learning process and thus could not be attributed to the use of SNS alone.


Coverage-based Neural Machine Translation


title: : www.idealibrary.com on An Introduction to Model Selection

abstract: This paper is an introduction to model selection intended for nonspecialists who have knowledge of the statistical concepts covered in a typical first (occasionally second) statistics course. The intention is to explain the ideas that generate frequentist methodology for model selection, for example the Akaike information criterion, bootstrap criteria, and cross-validation criteria. Bayesian methods, including the Bayesian information criterion, are also mentioned in the context of the framework outlined in the paper. The ideas are illustrated using an example in which observations are available for the entire population of interest. This enables us to examine and to measure effects that are usually invisible, because in practical applications only a sample from the population is observed. The problem of selection bias, a hazard of which one needs to be aware in the context of model selection, is also discussed. Copyright 2000 Academic Press.


title: Fast and Scalable Structural SVM with Slack Rescaling

abstract: We present an efficient method for training slack-rescaled structural SVM. Although finding the most violating label in a margin-rescaled formulation is often easy since the target function decomposes with respect to the structure, this is not the case for a slack-rescaled formulation, and finding the most violated label might be very difficult. Our core contribution is an efficient method for finding the most-violating-label in a slack-rescaled formulation, given an oracle that returns the most-violating-label in a (slightly modified) margin-rescaled formulation. We show that our method enables accurate and scalable training for slack-rescaled SVMs, reducing runtime by an order of magnitude compared to previous approaches to slack-rescaled SVMs.


title: Detecting sweethearting in retail surveillance videos

abstract: A significant portion of retail shrink is attributed to employees and occurs around the point of sale (POS). In this paper, we target a major type of retail fraud in surveillance videos, known as sweethearting (or fake scan), where a cashier intentionally fails to enter one or more items into the transaction in an attempt to get free merchandise for the customer. We first develop a motion-based algorithm to identify video segments as candidates for primitive events at the POS. We then apply spatio-temporal features to recognize true primitive events from the candidates and prune those falsely alarmed. In particular, we learn location-aware event models by Multiple-Instance Learning to address the location-sensitive issues that appear in our problem. Finally, we validate the entire transaction by combining primitive events according to temporal ordering constraints. We demonstrate the effectiveness of our approach on data captured from a real grocery store.


Patch Antenna with Low Pass Filter Design to Supress Higher Order Harmonics in Efficient Rectenna Design


title: The nature of correlation perception in scatterplots

abstract: For scatterplots with gaussian distributions of dots, the perception of Pearson correlation r can be described by two simple laws: a linear one for discrimination, and a logarithmic one for perceived magnitude (Rensink & Baldridge, 2010). The underlying perceptual mechanisms, however, remain poorly understood. To cast light on these, four different distributions of datapoints were examined. The first had 100 points with equal variance in both dimensions. Consistent with earlier results, just noticeable difference (JND) was a linear function of the distance away from r = 1, and the magnitude of perceived correlation a logarithmic function of this quantity. In addition, these laws were linked, with the intercept of the JND line being the inverse of the bias in perceived magnitude. Three other conditions were also examined: a dot cloud with 25 points, a horizontal compression of the cloud, and a cloud with a uniform distribution of dots. Performance was found to be similar in all conditions. The generality and form of these laws suggest that what underlies correlation perception is not a geometric structure such as the shape of the dot cloud, but the shape of the probability distribution of the dots, likely inferred via a form of ensemble coding. It is suggested that this reflects the ability of observers to perceive the information entropy in an image, with this quantity used as a proxy for Pearson correlation.


title: Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition

abstract: Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.


title: A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis

abstract: with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. Automatic classification tools may reduce burden on doctors. This paper evaluates the selected classification algorithms for the classification of some liver patient datasets. The classification algorithms considered here are Naive Bayes classifier, C4.5, Back propagation Neural Network algorithm, and Support Vector Machines. These algorithms are evaluated based on four criteria: Accuracy, Precision, Sensitivity and Specificity.


title: Generating event causality hypotheses through semantic relations

abstract: Event causality knowledge is indispensable for intelligent natural language understanding. The problem is that any method for extracting event causalities from text is insufficient; it is likely that some event causalities that we can recognize in this world are not written in a corpus, no matter its size. We propose a method of hypothesizing unseen event causalities from known event causalities extracted from the web by the semantic relations between nouns. For example, our method can hypothesize deploy a security camera→avoid crimes from deploy a mosquito net→avoid malaria through semantic relation A PREVENTS B. Our experiments show that, from 2.4 million event causalities extracted from the web, our method generated more than 300,000 hypotheses, which were not in the input, with 70% precision. We also show that our method outperforms a state-of-the-art hypothesis generation method.


title: Can Deep Clinical Models Handle Real-World Domain Shifts?

abstract: The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare. Deep learning, in particular, has been a game changer in building predictive models, thereby leading to community-wide data curation efforts. However, due to the inherent variabilities in population characteristics and biological systems, these models are often biased to the training datasets. This can be limiting when models are deployed in new environments, particularly when there are systematic domain shifts not known a priori. In this paper, we formalize these challenges by emulating a large class of domain shifts that can occur in clinical settings, and argue that evaluating the behavior of predictive models in light of those shifts is an effective way of quantifying the reliability of clinical models. More specifically, we develop an approach for building challenging scenarios, based on analysis of \textit{disease landscapes}, and utilize unsupervised domain adaptation to compensate for the domain shifts. Using the openly available MIMIC-III EHR dataset for phenotyping, we generate a large class of scenarios and evaluate the ability of deep clinical models in those cases. For the first time, our work sheds light into data regimes where deep clinical models can fail to generalize, due to significant changes in the disease landscapes between the source and target landscapes. This study emphasizes the need for sophisticated evaluation mechanisms driven by real-world domain shifts to build effective AI solutions for healthcare.


An Investigation on IEEE 802.15.4 MAC Layer Attacks


title: A Boyer-Moore Type Algorithm for Compressed Pattern Matching

abstract: We apply the Boyer-Moore technique to compressed pattern matching for text string described in terms of collage system, which is a formal framework that captures various dictionary-based compression methods. For a subclass of collage systems that contain no truncation, our new algorithm runs in O(∥D∥ + n ċ m + m2 + r) time using O(∥D∥ + m2) space, where ∥D∥ is the size of dictionary D, n is the compressed text length, m is the pattern length, and r is the number of pattern occurrences. For a general collage system, the time complexity is O(height(D)ċ(∥D∥+n)+nċm+m2+r), where height(D) is the maximum dependency of tokens in D. We showed that the algorithm specialized for the so-called byte pair encoding (BPE) is very fast in practice. In fact it runs about 1.2 - 3.0 times faster than the exact match routine of the software package agrep, known as the fastest pattern matching tool.


title: An Alternative Prior Process for Nonparametric Bayesian Clustering

abstract: Prior distributions play a crucial role in Bayesian approaches to clustering. Two commonly-used prior distributions are the Dirichlet and Pitman-Yor processes. In this paper, we investigate the predictive probabilities that underlie these processes, and the implicit \rich-get-richer" characteristic of the resulting partitions. We explore an alternative prior for nonparametric Bayesian clustering|the uniform process|for applications where the \rich-get-richer" property is undesirable. We also explore the cost of this process: partitions are no longer exchangeable with respect to the ordering of variables. We present new asymptotic and simulation-based results for the clustering characteristics of the uniform process and compare these with known results for the Dirichlet and Pitman-Yor processes. We compare performance on a real document clustering task, demonstrating the practical advantage of the uniform process despite its lack of exchangeability over orderings.


title: A Standard Mutual Authentication Protocol for Cloud Computing Based Health Care System

abstract: Telecare Medical Information System (TMIS) supports a standard platform to the patient for getting necessary medical treatment from the doctor(s) via Internet communication. Security protection is important for medical records (data) of the patients because of very sensitive information. Besides, patient anonymity is another most important property, which must be protected. Most recently, Chiou et al. suggested an authentication protocol for TMIS by utilizing the concept of cloud environment. They claimed that their protocol is patient anonymous and well security protected. We reviewed their protocol and found that it is completely insecure against patient anonymity. Further, the same protocol is not protected against mobile device stolen attack. In order to improve security level and complexity, we design a light weight authentication protocol for the same environment. Our security analysis ensures resilience of all possible security attacks. The performance of our protocol is relatively standard in comparison with the related previous research.


title: Position-aware activity recognition with wearable devices

abstract: Reliable human activity recognition with wearable devices enables the development of human-centric pervasive applications. We aim to develop a robust wearable-based activity recognition system for real life situations where the device position is up to the user or where a user is unable to collect initial training data. Consequently, in this work we focus on the problem of recognizing the on-body position of the wearable device ensued by comprehensive experiments concerning subject-specific and cross-subjects activity recognition approaches that rely on acceleration data. We introduce a device localization method that predicts the on-body position with an F-measure of 89% and a cross-subjects activity recognition approach that considers common physical characteristics. In this context, we present a real world data set that has been collected from 15 participants for 8 common activities were they carried 7 wearable devices in different on-body positions. Our results show that the detection of the device position consistently improves the result of activity recognition for common activities. Regarding cross-subjects models, we identified the waist as the most suitable device location at which the acceleration patterns for the same activity across several people are most similar. In this context, our results provide evidence for the reliability of physical characteristics based cross-subjects models.


title: Semantic Signatures for Example-Based Linguistic Metaphor Detection

abstract: Metaphor is a pervasive feature of human language that enables us to conceptualize and communicate abstract concepts using more concrete terminology. Unfortunately, it is also a feature that serves to confound a computer’s ability to comprehend natural human language. We present a method to detect linguistic metaphors by inducing a domainaware semantic signature for a given text and compare this signature against a large index of known metaphors. By training a suite of binary classifiers using the results of several semantic signature-based rankings of the index, we are able to detect linguistic metaphors in unstructured text at a significantly higher precision as compared to several baseline approaches.


title: An approach to operational aircraft maintenance planning

abstract: This paper describes a short-term planning methodology of the line maintenance activities of an airline operator, at the airports, during turn-around time (TAT). The proposed methodology supports decision making for deferring maintenance actions that affect the dispatching of the aircrafts, aiming at high fleet operability and low maintenance cost. Based on health assessment and additional information regarding operational and economical constraints at the operator's fleet level, a multi-criteria mechanism evaluates a set of generated maintenance plan alternatives. An alternative is defined as the possible allocation of all deferred maintenance tasks to a set of suitable airport resources. The selected decision making criteria are cost, remaining useful life (RUL), operational risk and flight delay. A series of experiments is conducted in order to validate and test the approach.


title: Do cosmetics enhance female Caucasian facial attractiveness

abstract: Synopsis ::: This study sought to investigate whether cosmetics do improve female facial attractiveness, and to determine whether the contribution of different cosmetic products are separable, or whether they function synergistically to enhance female beauty. Ten volunteers were made up by a beautician under five cosmetics conditions: (i) no make-up; (ii) foundation only; (iii) eye make-up only; (iv) lip make-up only; and (v) full facial make-up. Male and female participants were asked to view the 10 sets of five photographs, and rank each set from most attractive to least attractive. As predicted, faces with full make-up were judged more attractive than the same faces with no make-up. Sex differences within the results were also apparent. Women judged eye make-up as contributing most to the attractiveness. Men rated eye make-up and foundation as having a significant impact on the attractiveness of a full facial makeover. Surprisingly, lipstick did not appear to contribute to attractiveness independently.


title: Actuation abilities of multiphasic electroactive polymeric systems

abstract: The need of low-cost, high-strain actuators has recently brought the attention on electroactive polymers. Specifically, a particular interest has been placed on dielectric-based electrostrictive polymers that are flexible, conformable, and with an easy manufacturing process. The purpose of this paper is to investigate the actuation abilities of such devices when several phases (i.e., dipolar and interfacial) contribute to the electroactive strain response. Based on a Debye-Langevin approach, the expression of each phase contribution will be investigated, and the response with several phase ratios discussed. In particular, it is shown that a trade-off exists between low electric-field response, where the main design parameter lies in the permittivity, and the high electric-field response, where a polarization saturation effect occurs that limits the electric-generated strain. In addition, it is shown both theoretically and experimentally that for a biphasic system, a particular crossing point appears in th...


title: The ambivalence of error: “scientific ideology” in the history of the life sciences and psychosomatic medicine

abstract: This paper discusses the concept of ‘‘scientiﬁc ideology’’ as it appears in the work of the historian and philosopher of ::: medicine Georges Canguilhem, whose work is becoming increasingly well known and used amongst anglophone social ::: scientists. Whilst addressing the problematic of legitimacy and illegitimacy in the history of science, the concept of ::: ‘‘scientiﬁc ideology’’ does something different and more complex than either the opposition between science and false ::: science, or the one between orthodoxy and heresy, allow for. On the one hand, it enables us to preserve a crucial ::: acknowledgment of the speciﬁcity of science in general, and of medical science in particular. On the other hand, it also ::: allows us to challenge the sharp contrast between science and non-science by setting that contrast in a diachronic ::: perspective. Drawing also on the work of Isabelle Stengers, the last part of the paper discusses an application of the ::: concept of scientiﬁc ideology in relation to the ﬁeld of psychosomatic medicine and psychoneuroimmunology.


The Case of the Disappearing Spindle Burst


title: ANTIFUNGAL INVESTIGATIONS ON PLANT ESSENTIAL OILS. A REVIEW

abstract: Plant essential oils are potential source of antimicrobials of natural origin. Essential oils and extracts obtained from many plants have recently gained a great popularity and scientific interest. Consumer demand for natural preservatives has increased, whereas the safety aspect of chemical additives has been questioned. The plant oil has been reported to have antibacterial, antifungal, antiviral, antiparasitic and antidermatophytic properties. It is now considered as a valuable source of natural products for development of medicines against various diseases and also for the development of industrial products. The present review is a compilation of updated information on plant essential oils with antifungal properties.


title: SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering

abstract: Recent advances in data acquisition produce volume data of very high resolution and large size, such as terabyte-sized microscopy volumes. These data often contain many fine and intricate structures, which pose huge challenges for volume rendering, and make it particularly important to efficiently skip empty space. This paper addresses two major challenges: (1) The complexity of large volumes containing fine structures often leads to highly fragmented space subdivisions that make empty regions hard to skip efficiently. (2) The classification of space into empty and non-empty regions changes frequently, because the user or the evaluation of an interactive query activate a different set of objects, which makes it unfeasible to pre-compute a well-adapted space subdivision. We describe the novel SparseLeap method for efficient empty space skipping in very large volumes, even around fine structures. The main performance characteristic of SparseLeap is that it moves the major cost of empty space skipping out of the ray-casting stage. We achieve this via a hybrid strategy that balances the computational load between determining empty ray segments in a rasterization (object-order) stage, and sampling non-empty volume data in the ray-casting (image-order) stage. Before ray-casting, we exploit the fast hardware rasterization of GPUs to create a ray segment list for each pixel, which identifies non-empty regions along the ray. The ray-casting stage then leaps over empty space without hierarchy traversal. Ray segment lists are created by rasterizing a set of fine-grained, view-independent bounding boxes. Frame coherence is exploited by re-using the same bounding boxes unless the set of active objects changes. We show that SparseLeap scales better to large, sparse data than standard octree empty space skipping.


title: Understanding the impact of precision quantization on the accuracy and energy of neural networks

abstract: Deep neural networks are gaining in popularity as they are used to generate state-of-the-art results for a variety of computer vision and machine learning applications. At the same time, these networks have grown in depth and complexity in order to solve harder problems. Given the limitations in power budgets dedicated to these networks, the importance of low-power, low-memory solutions has been stressed in recent years. While a large number of dedicated hardware using different precisions has recently been proposed, there exists no comprehensive study of different bit precisions and arithmetic in both inputs and network parameters. In this work, we address this issue and perform a study of different bit-precisions in neural networks (from floating-point to fixed-point, powers of two, and binary). In our evaluation, we consider and analyze the effect of precision scaling on both network accuracy and hardware metrics including memory footprint, power and energy consumption, and design area. We also investigate training-time methodologies to compensate for the reduction in accuracy due to limited bit precision and demonstrate that in most cases, precision scaling can deliver significant benefits in design metrics at the cost of very modest decreases in network accuracy. In addition, we propose that a small portion of the benefits achieved when using lower precisions can be forfeited to increase the network size and therefore the accuracy. We evaluate our experiments, using three well-recognized networks and datasets to show its generality. We investigate the trade-offs and highlight the benefits of using lower precisions in terms of energy and memory footprint.


title: DFA on AES

abstract: In this paper we describe two different DFA attacks on the AES. The first one uses a fault model that induces a fault on only one bit of an intermediate result, hence allowing us to obtain the key by using 50 faulty ciphertexts for an AES-128. The second attack uses a more realistic fault model: we assume that we may induce a fault on a whole byte. For an AES-128, this second attack provides the key by using less than 250 faulty ciphertexts. ::: ::: If we extend our hypothesis by supposing that the attacker can choose the byte affected by the fault, our bit-fault attack requires 35 faulty ciphertexts to obtain the secret key and our byte-fault attack requires only 31 faulty ciphertexts.


title: Towards real-time Speech Emotion Recognition using deep neural networks

abstract: Most existing Speech Emotion Recognition (SER) systems rely on turn-wise processing, which aims at recognizing emotions from complete utterances and an overly-complicated pipeline marred by many preprocessing steps and hand-engineered features. To overcome both drawbacks, we propose a real-time SER system based on end-to-end deep learning. Namely, a Deep Neural Network (DNN) that recognizes emotions from a one second frame of raw speech spectrograms is presented and investigated. This is achievable due to a deep hierarchical architecture, data augmentation, and sensible regularization. Promising results are reported on two databases which are the eNTERFACE database and the Surrey Audio-Visual Expressed Emotion (SAVEE) database.


title: Low self-esteem during adolescence predicts poor health, criminal behavior, and limited economic prospects during adulthood.

abstract: Using prospective data from the Dunedin Multidisciplinary Health and Development Study birth cohort, the authors found that adolescents with low self-esteem had poorer mental and physical health, worse economic prospects, and higher levels of criminal behavior during adulthood, compared with adolescents with high self-esteem. The long-term consequences of self-esteem could not be explained by adolescent depression, gender, or socioeconomic status. Moreover, the findings held when the outcome variables were assessed using objective measures and informant reports; therefore, the findings cannot be explained by shared method variance in self-report data. The findings suggest that low self-esteem during adolescence predicts negative real-world consequences during adulthood.


title: Smart drip irrigation system for sustainable agriculture

abstract: Agriculture is the back bone of India. To make the sustainable agriculture, this system is proposed. In this system ARM 9 processor is used to control and monitor the irrigation system. Different kinds of sensors are used. This paper presents a fully automated drip irrigation system which is controlled and monitored by using ARM9 processor. PH content and the nitrogen content of the soil are frequently monitored. For the purpose of monitoring and controlling, GSM module is implemented. The system informs user about any abnormal conditions like less moisture content and temperature rise, even concentration of CO2 via SMS through the GSM module.


title: Survey on Wireless Sensor Network Technologies for Industrial Automation: The Security and Quality of Service Perspectives

abstract: Wireless Sensor Networks (WSNs) are gradually adopted in the industrial world due to their advantages over wired networks. In addition to saving cabling costs, WSNs widen the realm of environments feasible for monitoring. They thus add sensing and acting capabilities to objects in the physical world and allow for communication among these objects or with services in the future Internet. However, the acceptance of WSNs by the industrial automation community is impeded by open issues, such as security guarantees and provision of Quality of Service (QoS). To examine both of these perspectives, we select and survey relevant WSN technologies dedicated to industrial automation. We determine QoS requirements and carry out a threat analysis, which act as basis of our evaluation of the current state-of-the-art. According to the results of this evaluation, we identify and discuss open research issues.


title: How Productive Is Workplace Health and Safety?

abstract: In this paper, we investigate the causal impact of workplace health and safety practices on firm performance, using Danish longitudinal matched employer–employee data merged with unique cross‐sectional representative firm survey data on work environment conditions. We estimate standard production functions, augmented with workplace environment indicators, addressing both time‐invariant and time‐varying potentially relevant unobservables in the production process. We find positive and large productivity effects of improved physical dimensions of the health and safety environment, specifically, “internal climate” and “monotonous repetitive work”.


title: Optimization of SVM MultiClass by Particle Swarm (PSO-SVM)

abstract: In many problems of classification, the performances of a classifier are often evaluated by a factor (rate of error).the factor is not well adapted for the complex real problems, in particular the problems multiclass. Our contribution consists in adapting an evolutionary method for optimization of this factor. Among the methods of optimization used we chose the method PSO (Particle Swarm Optimization) which makes it possible to optimize the performance of classifier SVM (Separating with Vast Margin). The experiments are carried out on corpus TIMIT. The results obtained show that approach PSO-SVM gives a better classification in terms of accuracy even though the execution time is increased.


title: A Bridge Between Hyperparameter Optimization and Larning-to-learn

abstract: We consider a class of a nested optimization problems involving inner and outer objectives. We observe that by taking into explicit account the optimization dynamics for the inner objective it is possible to derive a general framework that unifies gradient-based hyperparameter optimization and meta-learning (or learning-to-learn). Depending on the specific setting, the variables of the outer objective take either the meaning of hyperparameters in a supervised learning problem or parameters of a meta-learner. We show that some recently proposed methods in the latter setting can be instantiated in our framework and tackled with the same gradient-based algorithms. Finally, we discuss possible design patterns for learning-to-learn and present encouraging preliminary experiments for few-shot learning.


title: Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule

abstract: The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre-processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.


title: Functional and evolutionary aspects of chemoreceptors

abstract: The perception and processing of chemical signals from the environment is essential for any living systems and is most probably the first sense developed in life. This perspective discusses the physical limits of chemoreception and gives an overview on the receptor types developed during evolution to detect chemical signals from the outside world of an organism. It discusses the interaction of chemoreceptors with downstream signaling elements, especially the interaction between electrical and chemical signaling. It is further considered how the primary chemosignal is appropriately amplified. Three examples of chemosensory systems illustrate different strategies of such amplification.


title: A graphical password against spyware and shoulder-surfing attacks

abstract: Users may have various login ids that will be hard to remember their passwords and alphanumeric passwords are difficult to remember. One solution is to use graphical passwords that are more secure. Some threats of Internet security are spyware and shoulder-surfing attacks. This paper presents a new scheme for graphical password that uses images that are unexplainable and have larger password space. The proposed scheme is also resistant to spyware and shoulder-surfing attacks.


title: Min-cut based segmentation of point clouds

abstract: We present a min-cut based method of segmenting objects in point clouds. Given an object location, our method builds a k-nearest neighbors graph, assumes a background prior, adds hard foreground (and optionally background) constraints, and finds the min-cut to compute a foreground-background segmentation. Our method can be run fully automatically, or interactively with a user interface. We test our system on an outdoor urban scan, quantitatively evaluate our algorithm on a test set of about 1000 objects, and compare to several alternative approaches.


title: Quantifying ADHD classroom inattentiveness, its moderators, and variability

abstract: Background: Most classroom observation studies have documented significant deficiencies in the classroom attention of children with attention-deficit/hyperactivity disorder (ADHD) compared to their typically developing peers. The magnitude of these differences, however, varies considerably and may be influenced by contextual, sampling, diagnostic, and observational differences. Methods: Meta-analysis of 23 between-group classroom observation studies using weighted regression, publication bias, goodness of fit, best case, and original metric analyses. Results: Across studies, a large effect size (ES ¼ .73) was found prior to consideration of potential moderators. Weighted regression, best case, and original metric estimation indicate that this effect may be an underestimation of the classroom visual attention deficits of children with ADHD. Several methodological factors‐classroom environment, sample characteristics, diagnostic procedures, and observational coding schema‐differentially affect observed rates of classroom attentive behavior for children with ADHD and typically developing children. After accounting for these factors, children with ADHD were on-task approximately 75% of the time compared to 88% for their classroom peers (ES ¼ 1.40). Children with ADHD were also more variable in their attentive behavior across studies. Conclusions: The present study confirmed that children with ADHD exhibit deficient and more variable visual attending to required stimuli in classroom settings and provided an aggregate estimation of the magnitude of these deficits at the group level. It also demonstrated the impact of situational, sampling, diagnostic, and observational variables on observed rates of on-task behavior. Keywords: ADHD, classroom observation, attention, on-task, meta-analysis. Abbreviations: ADHD, attention-deficit/hyperactivity disorder; ES, effect size; MTA, Multimodal Treatment Study of ADHD. Attention-deficit/hyperactivity disorder (ADHD) is a chronic and disabling condition that affects an estimated 3‐7% of school-age children (APA, 2000). Inattention‐one of three hypothesized core deficits of ADHD‐has been studied extensively over the past several decades following the seminal work by Douglas and colleagues (Douglas, 1972; Sykes, Douglas, Weiss, & Minde, 1971).


Justified Recommendations based on Content and Rating Data


title: An effective Web page recommender using binary data clustering

abstract: Through growth of the Web, the amount of data on the net is growing in an uncontrolled way, that makes it hard for the users to find the relevant and required information- an issue which is usually referred to as information overload. Recommender systems are among the appealing methods that can handle this problem effectively. Theses systems are either based on collaborative filtering and content based approaches, or rely on rating of items and the behavior of the users to generate customized recommendations. In this paper we propose an efficient Web page recommender by exploiting session data of users. To this end, we propose a novel clustering algorithm to partition the binary session data into a fixed number of clusters and utilize the partitioned sessions to make recommendations. The proposed binary clustering algorithm is scalable and employs a novel method to find the representative of a set of binary vectors to represent the center of clusters--that might be interesting in its own right. In addition, the proposed clustering algorithm is integrated with the $$k$$k-means algorithm to achieve better clustering quality by combining its explorative power with fine-tuning power of the $$k$$k-means algorithm. We have performed extensive experiments on a real dataset to demonstrate the advantages of proposed binary data clustering methods and Web page recommendation algorithm. In particular, the proposed recommender system is compared to previously published works in terms of minimum frequency and based on the number of recommended pages to show its superiority in terms of accuracy, coverage and F-measure.


title: Technology-enhanced Learning for Students with Learning Disabilities

abstract: Today, Ministry of Education in Thailand realizes a significance of one disability among the student called Learning Disabilities or LD which is not caused by physical or intellectual impairment or retardation but rather resulted from various factors adversely affecting the brain e.g. genetic or inheritable factor or impact on brain development during pregnancy or toxic substances which precludes the brain from normally functioning or interpreting data [1]. The number of LD student is increasing whereas the use of assistive technology is limit. Assistive technology will help increasing ability or adjusting proficiency of LD student to learn effectively. Therefore Thai Word Prediction Program is designed and developed in order to enhance LD student in their writing.


title: Improving the training and evaluation efficiency of recurrent neural network language models

abstract: Recurrent neural network language models (RNNLMs) are becoming increasingly popular for speech recognition. Previously, we have shown that RNNLMs with a full (non-classed) output layer (F-RNNLMs) can be trained efficiently using a GPU giving a large reduction in training time over conventional class-based models (C-RNNLMs) on a standard CPU. However, since test-time RNNLM evaluation is often performed entirely on a CPU, standard F-RNNLMs are inefficient since the entire output layer needs to be calculated for normalisation. In this paper, it is demonstrated that C-RNNLMs can be efficiently trained on a GPU, using our spliced sentence bunch technique which allows good CPU test-time performance (42× speedup over F-RNNLM). Furthermore, the performance of different classing approaches is investigated. We also examine the use of variance regularisation of the softmax denominator for F-RNNLMs and show that it allows F-RNNLMs to be efficiently used in test (56× speedup on a CPU). Finally the use of two GPUs for F-RNNLM training using pipelining is described and shown to give a reduction in training time over a single GPU by a factor of 1.6×.


title: Effects of prone and supine positions on sleep state and stress responses in preterm infants

abstract: Aim: The purpose of the study is to assess the influence of prone or supine position on sleep states and on withdrawal and approach reactions of preterm infants. Methods: Thirty-two preterm infants from Meir Medical Center, Israel, mean post menstrual age 30.37 ± 2.57, mean birth weight 1250 g ± 313.86, participated in the study. Infants were studied during 48 h. Positions (prone and supine) were alternated every 3–4 h after feedings. Sleep states were assessed by Actigraph measurement and by two daily 30-min Naturalistic Observations of Newborn Behavior (NONB) to confirm sleep states and for recording the behavioral reactions (approach and withdrawal). Results: In the prone position there were more approach reactions as compared to withdrawal reactions (p < .001) while in the supine position, the approach and withdrawal reactions were comparable. In the prone position more sleep patterns (deep sleep, light sleep, drowsy) were observed as opposed to more awake patterns (quiet awake, active awake and agitated fussy) that were seen in the supine position. Conclusions: Clinical implications encourage placing the preterm infant in the prone position while in the NICU. This enables important achievements such as longer periods of quality sleep, and production of adaptive self-regulatory reactions.


title: A novel N491S mutation in the human SLC11A2 gene impairs protein trafficking and in association with the G212V mutation leads to microcytic anemia and liver iron overload.

abstract: BACKGROUND ::: DMT1 is a transmembrane iron transporter involved in iron duodenal absorption and cellular iron uptake. Mutations in the human SLC11A2 gene coding DMT1 lead to microcytic anemia and hepatic iron overload, with unexpectedly low levels of plasma ferritin in the presence of iron stores. ::: ::: ::: DESIGN AND METHODS ::: We report a patient with a similar phenotype due to two mutations in the SLC11A2 gene, the known p.Gly212Val (G212V) mutation and a novel one, p.Asn491Ser (N491S). To assess the expression of DMT1 in human liver, we studied the expression of the four DMT1 mRNA isoforms by real-time quantitative PCR in control human liver samples. We also studied the effect of G212V and N491S DMT1 mutations on RNA splicing in blood leukocytes and cellular trafficking of dsRed2-tagged-DMT1 protein in the human hepatic cell line HuH7. ::: ::: ::: RESULTS ::: Our results showed that i) only the isoforms 1B-IRE and 1B-nonIRE were significantly expressed in human liver; ii) the G212V mutation did not seem to affect mRNA splicing and the N491S mutation induced a splicing alteration leading to a truncated protein, which seemed quantitatively of low relevance; and iii) the N491S mutation, in contrast to the G212V mutation, led to abnormal protein trafficking. ::: ::: ::: CONCLUSIONS ::: Our data confirm the major role of DMT1 in the maintenance of iron homeostasis in humans and demonstrate that the N491S mutation, through its deleterious effect on protein trafficking, contributes together with the G212V mutation to the development of anemia and hepatic iron overload.


title: Magnesium, calcium and trace elements distribution in serum, erythrocytes, and hair of patients with chronic renal failure

abstract: Disturbance of mineral status in patients with chronic renal failure (CRF) is one of many complications of this disease. Trace elements analysis in hair is sometime used by clinicians for a diagnosis of mineral status. In the present study concentration of magnesium and other trace elements was determined in serum, erythrocytes, and hair of patients with CRF undergoing hemodialysis (n = 31) and with impaired renal function but non-dialyzed (n = 15). Measurements of mineral content were performed by the atomic absorption spectrometry method (AAS). In serum of hemodialyzed patients as well as in erythrocytes and hair we found significantly increased levels of almost all tested elements, especially for Mg, Al, and Cr, compared to the control group. No significant differences were observed between these groups only in the Cd content in the examined samples. However, a significant correlation between its concentration in serum and erythrocytes was only found in the case of this element,. Hair analysis reflected well the changes of mineral distribution in patients with CRF and may be used to diagnose these anomalies, in particular, with regard to Ca, Mg, Fe, and Cr. However, a strong variability of the concentration for these elements was found. In conclusion, our results confirm that renal failure as well as dialysis provoke imbalances of elemental status in physiological fluids and tissues, which should be monitored.


title: Cryptography Engineering: Design Principles and Practical Applications

abstract: The ultimate guide to cryptography, updated from an author team of the world's top cryptography experts. Cryptography is vital to keeping information safe, in an era when the formula to do so becomes more and more challenging. Written by a team of world-renowned cryptography experts, this essential guide is the definitive introduction to all major areas of cryptography: message security, key negotiation, and key management. You'll learn how to think like a cryptographer. You'll discover techniques for building cryptography into products from the start and you'll examine the many technical changes in the field. After a basic overview of cryptography and what it means today, this indispensable resource covers such topics as block ciphers, block modes, hash functions, encryption modes, message authentication codes, implementation issues, negotiation protocols, and more. Helpful examples and hands-on exercises enhance your understanding of the multi-faceted field of cryptography. An author team of internationally recognized cryptography experts updates you on vital topics in the field of cryptography Shows you how to build cryptography into products from the start Examines updates and changes to cryptography Includes coverage on key servers, message security, authentication codes, new standards, block ciphers, message authentication codes, and more Cryptography Engineering gets you up to speed in the ever-evolving field of cryptography.


title: Stereo Matching Techniques for High Dynamic Range Image Pairs

abstract: We investigate the stereo matching techniques for high dynamic range HDR image pairs. It is an emerging topic in computer vision and multimedia applications due to the availability of HDR image capture devices. The disparity computation will eventually take the stereo HDR input. In this work, three state-of-the-art stereo matching algorithms are modified and used to test the advantages of HDR stereo matching. By performing the HDR bit-plane slicing, it is found that only about 16 bits per channel is required for the HDR image format. We propose a 16-bit unsigned integer format to store the HDR image, which allows the available stereo matching algorithms to be adopted for disparity computation. Experiments and performance evaluation are carried out using Middlebury stereo datasets.


title: A comparative analysis of mmWave vs LTE technology for 5G Moving Networks

abstract: Mobile users in vehicular environments such as trains, buses and other public transportation vehicles suffer from the low quality signal from the outside wireless network. The rapid growth in the deployment of LTE femtocells for indoor use and their benefits have led many authors to propose using them even in vehicles, implementing the so-called Moving Networks. This paper shows that the use of pure LTE femtocells exhibits relevant issues in terms of interference and consequently performance in a realistic use. In order to overcome these issues, we propose to adopt the millimeter Wave technology in the Moving Networks, creating the Hybrid Mobile Femtocells. In the paper we discuss the concerns arising from applying mmWave communications at 60 GHz inside vehicles and we provide a new throughput analysis in order to benchmark our proposed architecture to the existing implementations.


Deep convolutional neural networks for accelerated dynamic magnetic resonance imaging


title: Learning styles and approaches: implications for medical education.

abstract: Summary. This paper reviews the recent literature on learning styles and approaches to learning. It identifies two separate streams of research, one originating from mainstream cognitive and psychometric psychology and one from research undertaken within the everyday learning environment. The latter is dealt with in greater detail as it seems to have more immediate practical relevance. ::: ::: A simple model of the teaching-learning process is presented showing how students learn in different ways which are partly attributable to their preferred learning style and partly to the context in which the learning takes place. Three basic approaches have been identified: surface, deep and strategic, each resulting in a different learning outcome. The most desirable and successful is the deep approach. ::: ::: The way in which the teaching and the policies of the department and school influence the students' approach to learning are reviewed in some detail. A consideration of these characteristics in medical schools suggested that many may hinder rather than assist in the development of the desired approach. The work reviewed here suggests that the remedy will require not only substantial changes in the teaching, curriculum and, particularly, assessment, but also a new strategy based on identifying and assisting individual students whose approaches to study are not those expected of a competent university-educated doctor.


title: MeUse: Recommending Internet Radio Stations

abstract: In this paper, we describe a novel Internet radio recommendation system called MeUse. We use the Shoutcast API to collect historical data about the artists that are played on a large set of Internet radio stations. This data is used to populate an artist-station index that is similar to the term-document matrix of a traditional text-based information retrieval system. When a user wants to find stations for a given seed artist, we check the index to determine a set of stations that are either currently playing or have recently played that artist. These stations are grouped into three clusters and one representative station is selected from each cluster. This promotes diversity among the stations that are returned to the user. In addition, we provide additional information such as relevant tags (e.g., genres, emotions) and similar artists to give the user more contextual information about the recommended stations. Finally, we describe a web-based user interface that provides an interactive experience that is more like a personalized Internet radio player (e.g., Pandora) and less like a search engine for Internet radio stations (e.g., Shoutcast). A smallscale user study suggests that the majority of users enjoyed using MeUse but that providing additional contextual information may be needed to help with recommendation transparency.


Avoiding the Undefined by Underspecification


title: Field measurements of the stiffness of jacked piles and pile groups

abstract: INTRODUCTION This Technical Note presents a back-analysis of the axial response of some field-scale tubular displacement piles installed by jacking. This study was motivated by the recent development of large pile-jacking rigs. These rigs are sufficiently strong to offer an alternative to bored piling in urban areas where conventional dynamic methods of displacement pile installation are not permitted (White et al., 2002). The most notable feature of the axial response was the very high stiffness. Some pile jacking machines ‘walk’ along the pile wall under construction, so all piles must necessarily be installed at close centres, or indeed touching. This geometry is in contrast to conventional design guidance, which suggests that a separation of typically two or three diameters is required between piles, to prevent harmful interaction effects (BSI, 1986; GEO, 1996). Load test results from jacked piles installed closer than this recommended minimum spacing are presented. The study comprised a series of field tests carried out in Kochi, Japan. Full details are provided by Yetginer et al. (2003), and brief details are repeated below. Test piles were installed singly and in groups forming a ‘cell foundation’, comprising jacked piles at close centres around an enclosed soil block. In this Technical Note, pile group efficiency is defined as the average performance per pile (strength or stiffness) when a group is loaded in unison, divided by the performance of a single pile tested alone. Using this definition, the behaviour of trial piles installed prior to the main group can be linked to the group response. This definition is used by Vesic (1969) and Fleming et al. (1992), and is shown in equation (1) for strength and stiffness. Group strength efficiency, GROUP, is defined in equation (1a), where QSINGLE and QGROUP represent the capacity of a single (isolated) pile and the capacity of a group of n piles respectively. Group stiffness efficiency, GROUP, is defined in equation (1b), where KSINGLE and KGROUP represent the stiffness of a single (isolated) pile and the stiffness of a group of n piles respectively.


title: Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis.

abstract: Mammographic features are associated with breast cancer risk, but estimates of the strength of the association vary markedly between studies, and it is uncertain whether the association is modified by other risk factors. We conducted a systematic review and meta-analysis of publications on mammographic patterns in relation to breast cancer risk. Random effects models were used to combine study-specific relative risks. Aggregate data for > 14,000 cases and 226,000 noncases from 42 studies were included. Associations were consistent in studies conducted in the general population but were highly heterogeneous in symptomatic populations. They were much stronger for percentage density than for Wolfe grade or Breast Imaging Reporting and Data System classification and were 20% to 30% stronger in studies of incident than of prevalent cancer. No differences were observed by age/menopausal status at mammography or by ethnicity. For percentage density measured using prediagnostic mammograms, combined relative risks of incident breast cancer in the general population were 1.79 (95% confidence interval, 1.48-2.16), 2.11 (1.70-2.63), 2.92 (2.49-3.42), and 4.64 (3.64-5.91) for categories 5% to 24%, 25% to 49%, 50% to 74%, and > or = 75% relative to < 5%. This association remained strong after excluding cancers diagnosed in the first-year postmammography. This review explains some of the heterogeneity in associations of breast density with breast cancer risk and shows that, in well-conducted studies, this is one of the strongest risk factors for breast cancer. It also refutes the suggestion that the association is an artifact of masking bias or that it is only present in a restricted age range.


An Optical Character Recognition


title: Co-Segmentation of 3D Shapes via Subspace Clustering

abstract: We present a novel algorithm for automatically co-segmenting a set of shapes from a common family into consistent parts. Starting from over-segmentations of shapes, our approach generates the segmentations by grouping the primitive patches of the shapes directly and obtains their correspondences simultaneously. The core of the algorithm is to compute an affinity matrix where each entry encodes the similarity between two patches, which is measured based on the geometric features of patches. Instead of concatenating the different features into one feature descriptor, we formulate co-segmentation into a subspace clustering problem in multiple feature spaces. Specifically, to fuse multiple features, we propose a new formulation of optimization with a consistent penalty, which facilitates both the identification of most similar patches and selection of master features for two similar patches. Therefore the affinity matrices for various features are sparsity-consistent and the similarity between a pair of patches may be determined by part of (instead of all) features. Experimental results have shown how our algorithm jointly extracts consistent parts across the collection in a good manner. © 2012 Wiley Periodicals, Inc.


title: For a Fee: The Impact of Information Pricing Strategy on the Pattern and Effectiveness of Word-of-Mouth via Social Media

abstract: With the new realities of the digital age, print newspapers are experimenting with different pricing models for their online content. Using NYT’s paywall rollout as a natural experiment, our study finds that a firm’s information pricing policy influences the pattern and effectiveness of online word of mouth (WOM) in social media. Using difference-in-difference-indifferences analysis, we find that implementing a paywall (i.e., charging for the content which was earlier available for free) has a disproportionate impact on WOM for popular and niche articles, creating a longer tail in the content sharing distribution. Further, we find that the impact of WOM on NYT’s website traffic weakens significantly after the introduction of NYT’s paywall. These results show that information pricing strategy has implications for product and promotion strategies. The study offers novel and important implications for the theory and practice of strategic use of social media and information pricing strategy.


title: Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models

abstract: Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach with the standard lasso and 10 other competitors: Akaike information criterion (AIC), corrected AIC, Bayesian information criterion (BIC), extended BIC, Hannan and Quinn information criterion (HQIC), risk information criterion (RIC), one-standard-error rule, adaptive lasso, stability selection, and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC, and the adaptive lasso, which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted. Copyright © 2016 John Wiley & Sons, Ltd.


title: The minimally effective dose of sucrose for procedural pain relief in neonates: a randomized controlled trial

abstract: BACKGROUND ::: Orally administered sucrose is effective and safe in reducing pain intensity during single, tissue-damaging procedures in neonates, and is commonly recommended in neonatal pain guidelines. However, there is wide variability in sucrose doses examined in research, and more than a 20-fold variation across neonatal care settings. The aim of this study was to determine the minimally effective dose of 24% sucrose for reducing pain in hospitalized neonates undergoing a single skin-breaking heel lance procedure. ::: ::: ::: METHODS ::: A total of 245 neonates from 4 Canadian tertiary neonatal intensive care units (NICUs), born between 24 and 42 weeks gestational age (GA), were prospectively randomized to receive one of three doses of 24% sucrose, plus non-nutritive sucking/pacifier, 2 min before a routine heel lance: 0.1 ml (Group 1; n = 81), 0.5 ml (Group 2; n = 81), or 1.0 ml (Group 3; n = 83). The primary outcome was pain intensity measured at 30 and 60 s following the heel lance, using the Premature Infant Pain Profile-Revised (PIPP-R). The secondary outcome was the incidence of adverse events. Analysis of covariance models, adjusting for GA and study site examined between group differences in pain intensity across intervention groups. ::: ::: ::: RESULTS ::: There was no difference in mean pain intensity PIPP-R scores between treatment groups at 30 s (P = .97) and 60 s (P = .93); however, pain was not fully eliminated during the heel lance procedure. There were 5 reported adverse events among 5/245 (2.0%) neonates, with no significant differences in the proportion of events by sucrose dose (P = .62). All events resolved spontaneously without medical intervention. ::: ::: ::: CONCLUSIONS ::: The minimally effective dose of 24% sucrose required to treat pain associated with a single heel lance in neonates was 0.1 ml. Further evaluation regarding the sustained effectiveness of this dose in reducing pain intensity in neonates for repeated painful procedures is warranted. ::: ::: ::: TRIAL REGISTRATION ::: ClinicalTrials.gov : NCT02134873. Date: May 5, 2014 (retrospectively registered).


title: Articulated pose estimation with flexible mixtures-of-parts

abstract: We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50% while being orders of magnitude faster.


title: Application Architecture Discovery - Towards Domain-driven, Easily-Extensible Code Structure

abstract: The architecture of a software system and its code structure have a strong impact on its maintainability -- the ability to fix problems, and make changes to the system efficiently. To ensure maintainability, software systems are usually organized as subsystems or modules, each with atomically defined responsibilities. However, as the system evolves, the structure of the system undergoes continuous modifications, drifting away from its original design, leading to functionally non-atomic modules and intertwined dependencies between the modules. In this paper, we propose an approach to improve the code structure and architecture by leveraging the domain knowledge of the system. Our approach exploits the knowledge about the functional architecture of the system to restructure the source code and align physically with the functional elements and the re-usable library layers. The approach is validated by applying to a case study which is an existing financial system. The preliminary analysis for the case-study reveals that the approach creates meaningful structure from the legacy code, which enables the developers to quickly identify the code that implements a given functionality.


title: Text-to-Speech Conversion

abstract: In the present time, text to speech conversions play a very important role in-order to understand and analyze the strength of the existing system and to get the appropriate and definite signature of the system. By using concatenated phoneme library techniques, we have implemented text to speech conversions. In this model we used phoneme audio library and developed a system which produces more human like speech. The system is an advantage over available text to speech conversion system in the way our system produces the sound which is very much human like. Aim of this approach to develop high-quality Text- to-Speech (TTS).


title: Temporal Pyramid Pooling-Based Convolutional Neural Network for Action Recognition

abstract: Encouraged by the success of convolutional neural networks (CNNs) in image classification, recently much effort is spent on applying the CNNs to the video-based action recognition problems. One challenge is that a video contains a varying number of frames, which is incompatible to the standard input format of the CNNs. Existing methods handle this issue either by directly sampling a fixed number of frames or bypassing this issue by introducing a 3D convolutional layer, which conducts convolution in spatial-temporal domain. In this paper, we propose a novel network structure, which allows an arbitrary number of frames as the network input. The key to our solution is to introduce a module consisting of an encoding layer and a temporal pyramid pooling layer. The encoding layer maps the activation from the previous layers to a feature vector suitable for pooling, whereas the temporal pyramid pooling layer converts multiple frame-level activations into a fixed-length video-level representation. In addition, we adopt a feature concatenation layer that combines the appearance and motion information. Compared with the frame sampling strategy, our method avoids the risk of missing any important frames. Compared with the 3D convolutional method, which requires a huge video data set for network training, our model can be learned on a small target data set because we can leverage the off-the-shelf image-level CNN for model parameter initialization. Experiments on three challenging data sets, Hollywood2, HMDB51, and UCF101 demonstrate the effectiveness of the proposed network.


title: Childhood Markers of Health Behavior Relate to Hippocampal Health, Memory, and Academic Performance

abstract: There has been an increasing body of evidence that a variety of factors, including physical activity, nutrition, and body composition, have a relationship with brain structure and function in school-aged children. Within the brain, the hippocampus is particularly sensitive to modulation by these lifestyle factors. This brain structure is known to be critical in learning and memory, and, we suggest, for progress in the classroom. Accordingly, the aims of this article include (1) examining the role of hippocampus and hippocampal-dependent memory in supporting academic performance; (2) reviewing the literature related to the associations between hippocampal-dependent memory and a number of lifestyle factors, including physical activity, nutrition, and body composition; and (3) discussing the implications of these findings in an educational setting. The findings discussed suggest that, through interventions that target these lifestyle factors, it may be possible to improve hippocampal function and academic performance in school-aged children.


title: Automatic Fall Monitoring: A Review

abstract: Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address.


title: Emotion Cause Detection with Linguistic Constructions

abstract: This paper proposes a multi-label approach to detect emotion causes. The multi-label model not only detects multi-clause causes, but also captures the long-distance information to facilitate emotion cause detection. In addition, based on the linguistic analysis, we create two sets of linguistic patterns during feature extraction. Both manually generalized patterns and automatically generalized patterns are designed to extract general cause expressions or specific constructions for emotion causes. Experiments show that our system achieves a performance much higher than a baseline model.


title: Comparison of five systems of classification of diabetic foot ulcers and predictive factors for amputation

abstract: Diabetes mellitus is a common metabolic disorder. Among various complications, diabetic neuropathy and peripheral vascular disorders are closely associated with diabetic foot ulcers (DFUs). Lower extremity ulcers and amputations are ongoing problems among individuals with diabetes. There are several classification systems for DFUs; however, no prognostic system has to date been accepted as the gold standard or the optimum prediction tool for amputations. A retrospective study was designed. Demographic data and baseline laboratory data were gathered and scored or evaluated using five representative DFU classification systems. These included (i) the diabetic ulcer severity score (DUSS); (ii) University of Texas (UT) diabetic wound classification; (iii) Meggitt-Wagner classification; (iv) depth of the ulcer, extent of bacterial colonisation, phase of ulcer and association aetiology (DEPA) scoring system; and (v) site, ischaemia, neuropathy, bacterial infection and depth (SINBAD) score. Finally, a statistical analysis was performed. A total of 137 patients were included in this study. During the follow-up, DFU had healed in 51·1% of subjects and 48·9% of the individuals underwent lower extremity amputations (LEAs). In a univariable logistic regression analysis, history of previous DFU, hypertension, neuropathy, haemoglobin, C-reactive protein (CRP) and ankle-brachial index (ABI) showed a statistically significant difference between the healed group and the LEA group. Moreover, the stages, grades or overall prognostic ability of all five classifications were highly associated with the overall occurrence of LEA. On multivariable logistic regression analysis of the risk of LEA, all classifications showed a significant positive trend with an increased number of amputations. All the five classification systems exhibited high sensitivity, specificity, classification accuracy, positive predictive, negative predictive and area under the curve (AUC) values. They showed substantial accuracy and their main variables were associated with LEA occurrence. The Wagner and UT systems, although they are relatively simple to assess, were better predictors of LEA.


title: A 55.1 mW 1.62-to-8.1 Gb/s Video Interface Receiver Generating up to 680 MHz Stream Clock Over 20 dB Loss Channel

abstract: A 1.62-to-8.1 Gb/s video interface receiver with an adaptive equalizer and a stream clock generator (SCG) is proposed. The adaptation logic is achieved by an edge-based adaptation and it controls the continuous-time linear equalizer ac boost. Using the adaptation logic, the minimum BER point is selected for several cables. The SCG consists of a phase-switching fractional divider and a delta–sigma modulator. The dividing factor is determined by the display resolution and the SCG operates up to 680 MHz which is the 4K UHD pixel frequency. The proposed receiver is fabricated in 65-nm CMOS technology and occupies an active area of 0.282 mm2. The measured BER is less than $10^{-12}$ with a 20-ft-long video cable, whose insertion loss at 4.05 GHz is 20 dB. The receiver consumes 55.1 mW at the data rate of 8.1 Gb/s.


An Unusual First Manifestation of Hodgkin Lymphoma: Epitrochlear Lymph Node İnvolvement—A Case Report and Brief Review of Literature


title: Real-time localization and navigation in an indoor environment using monocular camera for visually impaired

abstract: The aim of this research is to design an intelligent system that addresses the problem of real-time localization and navigation of visually impaired (VI) in an indoor environment using a monocular camera. Systems that have been developed so far for the VI use either many cameras (stereo and monocular) integrated with other sensors or use very complex algorithms that are computationally expensive. In this research work, a computationally less expensive integrated system has been proposed to combine imaging geometry, Visual Odometry (VO), Object Detection (OD) along with Distance-Depth (D-D) estimation algorithms for precise navigation and localization by utilizing a single monocular camera as the only sensor. The developed algorithm is tested for both standard Karlsruhe and indoor environment recorded datasets. Tests have been carried out in real-time using a smartphone camera that captures image data of the environment as the person moves and is sent over Wi-Fi for further processing to the MATLAB software model running on an Intel i7 processor. The algorithm provides accurate results on real-time navigation in the environment with an audio feedback about the person's location. The trajectory of the navigation is expressed in an arbitrary scale. Object detection based localization is accurate. The D-D estimation provides distance and depth measurements up to an accuracy of 94–98%.


title: A Dataset for Building Code-Mixed Goal Oriented Conversation Systems

abstract: There is an increasing demand for goal-oriented conversation systems which can assist users in various day-to-day activities such as booking tickets, restaurant reservations, shopping, etc. Most of the existing datasets for building such conversation systems focus on monolingual conversations and there is hardly any work on multilingual and/or code-mixed conversations. Such datasets and systems thus do not cater to the multilingual regions of the world, such as India, where it is very common for people to speak more than one language and seamlessly switch between them resulting in code-mixed conversations. For example, a Hindi speaking user looking to book a restaurant would typically ask,"Kya tum is restaurant mein ek table book karne mein meri help karoge?"("Can you help me in booking a table at this restaurant?"). To facilitate the development of such code-mixed conversation models, we build a goal-oriented dialog dataset containing code-mixed conversations. Specifically, we take the text from the DSTC2 restaurant reservation dataset and create code-mixed versions of it in Hindi-English, Bengali-English, Gujarati-English and Tamil-English. We also establish initial baselines on this dataset using existing state of the art models. This dataset along with our baseline implementations is made publicly available for research purposes.


title: BORIS -- An Experiment in In-Depth Understanding of Narratives.

abstract: Abstract boris is an experimental story understanding and question answering system which deals with the specification and interaction of many sources of knowledge. Unlike skimmers, which simply extract the “gist” of a story in a top-down manner and ignore everything else, boris attempts to understand everything that it reads to as great a depth as possible. This report focuses on how the boris program handles a complex story involving a divorce.


title: Incremental Multiset Hash Functions and Their Application to Memory Integrity Checking

abstract: We introduce a new cryptographic tool: multiset hash functions. Unlike standard hash functions which take strings as input, multiset hash functions operate on multisets (or sets). They map multisets of arbitrary finite size to strings (hashes) of fixed length. They are incremental in that, when new members are added to the multiset, the hash can be updated in time proportional to the change. The functions may be multiset-collision resistant in that it is difficult to find two multisets which produce the same hash, or just set-collision resistant in that it is difficult to find a set and a multiset which produce the same hash. We demonstrate how set-collision resistant multiset hash functions make an existing offline memory integrity checker secure against active adversaries. We improve on this checker such that it can use smaller time stamps without increasing the frequency of checks. The improved checker uses multiset-collision resistant multiset hash functions.


title: Comparison of CFL and LED lamp - harmonic disturbances, economics (cost and power quality) and maximum possible loading in a power system

abstract: The desire to reduce electrical loading by using energy efficient lighting has resulted in a high level of interest in replacing conventional incandescent lamp with Compact Fluorescent Lamps and LED lamps. However, their high harmonic content was always a problem for the power quality of the power system networks, especially the ones with a considerable share of nonlinear loads. The problem of harmonics cannot be neglected in cases of installations with high lighting load. This paper presents an analysis of harmonics in a network where lighting is one of the main loads. CFLs and LED lamps with electronic gear are characterized by extremely distorted current, with high total current harmonic distortions. Hence they cause a significant voltage distortion in electrical installations. The network is simulated using the impedance network model, in PSCAD®/EMTDC™ software. A comparative analysis is performed on the power quality, maximum loading and economics of CFL lamps and LED lamps.


title: Improving Handwritten Arabic Character Recognition by Modeling Human Handwriting Distortions

abstract: Handwritten Arabic character recognition systems face several challenges, including the unlimited variation in human handwriting and the unavailability of large public databases of handwritten characters and words. The use of synthetic data for training and testing handwritten character recognition systems is one of the possible solutions to provide several variations for these characters and to overcome the lack of large databases. While this can be using arbitrary distortions, such as image noise and randomized affine transformations, such distortions are not realistic. In this work, we model real distortions in handwriting using real handwritten Arabic character examples and then use these distortion models to synthesize handwritten examples that are more realistic. We show that the use of our proposed approach leads to significant improvements across different machine-learning classification algorithms.


title: An introduction to Support Vector Machines for data mining,” Keynote papers, young

abstract: With increasing amounts of data being generated by businesses and researchers there is a need for fast, accurate and robust algorithms for data analysis. Improvements in databases technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. The primary aim of data mining is to discover patterns in the data that lead to better understanding of the data generating process and to useful predictions. Examples of applications of data mining include detecting fraudulent credit card transactions, character recognition in automated zip code reading, and predicting compound activity in drug discovery. Real-world data sets are often characterized by having large numbers of examples, e.g. billions of credit card transactions and potential ‘drug-like’ compounds; being highly unbalanced, e.g. most transactions are not fraudulent, most compounds are not active against a given biological target; and, being corrupted by noise. The relationship between predictive variables, e.g. physical descriptors, and the target concept, e.g. compound activity, is often highly non-linear. One recent technique that has been developed to address these issues is the support vector machine. The support vector machine has been developed as robust tool for classification and regression in noisy, complex domains. The two key features of support vector machines are generalization theory, which leads to a principled way to choose an hypothesis; and, kernel functions, which introduce non-linearity in the hypothesis space without explicitly requiring a non-linear algorithm. In this tutorial I introduce support vector machines and highlight the advantages thereof over existing data analysis techniques, also are noted some important points for the data mining practitioner who wishes to use support vector machines.


Nodal sets of eigenfunctions on Riemann surfaces


title: Interpolation Methods for Curve Construction

abstract: This paper surveys a wide selection of the interpolation algorithms that are in use in financial markets for construction of curves such as forward curves, basis curves, and most importantly, yield curves. In the case of yield curves the issue of bootstrapping is reviewed and how the interpolation algorithm should be intimately connected to the bootstrap itself is discussed. The criterion for inclusion in this survey is that the method has been implemented by a software vendor (or indeed an inhouse developer) as a viable option for yield curve interpolation. As will be seen, many of these methods suffer from problems: they posit unreasonable expections, or are not even necessarily arbitrage free. Moreover, many methods lead one to derive hedging strategies that are not intuitively reasonable. In the last sections, two new interpolation methods (the monotone convex method and the minimal method) are introduced, which it is believed overcome many of the problems highlighted with the other methods discussed in the earlier sections.


title: Improved license plate localization

abstract: In this article, a new approach to explore the candidate car license plate scene is presented. First, the image is decomposed by using a Haar wavelet to get the HL sub-image with vertical edges. Next, a black top-hat algorithm is applied to minimize the effects of interfering large continuous features other than the license plate. Then, the horizontal and vertical projections of the HL sub-image provide an approximate location of the license plate. The experimental results show the high precision of the proposed technique, with a success rate of 98% for parked vehicles and 97% for moving vehicles.