title: Privacy Impacts of Data Encryption on the Efficiency of Digital Forensics Technology

abstract: Owing to a number of reasons, the deployment of encryption solutions are beginning to be ubiquitous at both organizational and individual levels. The most emphasized reason is the necessity to ensure confidentiality of privileged information. Unfortunately, it is also popular as cyber-criminals' escape route from the grasp of digital forensic investigations. The direct encryption of data or indirect encryption of storage devices, more often than not, prevents access to such information contained therein. This consequently leaves the forensics investigation team, and subsequently the prosecution, little or no evidence to work with, in sixty percent of such cases. However, it is unthinkable to jeopardize the successes brought by encryption technology to information security, in favour of digital forensics technology. This paper examines what data encryption contributes to information security, and then highlights its contributions to digital forensics of disk drives. The paper also discusses the available ways and tools, in digital forensics, to get around the problems constituted by encryption. A particular attention is paid to the Truecrypt encryption solution to illustrate ideas being discussed. It then compares encryption's contributions in both realms, to justify the need for introduction of new technologies to forensically defeat data encryption as the only solution, whilst maintaining the privacy goal of users.


title: Neural Program Lattices

abstract: We propose the Neural Program Lattice (NPL), a neural network that learns to perform complex tasks by composing low-level programs to express high-level programs. Our starting point is the recent work on Neural Programmer-Interpreters (NPI), which can only learn from strong supervision that contains the whole hierarchy of low-level and high-level programs. NPLs remove this limitation by providing the ability to learn from weak supervision consisting only of sequences of low-level operations. We demonstrate the capability of NPL to learn to perform long-hand addition and arrange blocks in a grid-world environment. Experiments show that it performs on par with NPI while using weak supervision in place of most of the strong supervision, thus indicating its ability to infer the high-level program structure from examples containing only the low-level operations.


title: Realistic modeling for facial animation

abstract: A major unsolved problem in computer graphics is the construction and animation of realistic human facial models. Traditionally, facial models have been built painstakingly by manual digitization and animated by ad hoc parametrically controlled facial mesh deformations or kinematic approximation of muscle actions. Fortunately, animators are now able to digitize facial geometries through the use of scanning range sensors and animate them through the dynamic simulation of facial tissues and muscles. However, these techniques require considerable user input to construct facial models of individuals suitable for animation. In this paper, we present a methodology for automating this challenging task. Starting with a structured facial mesh, we develop algorithms that automatically construct functional models of the heads of human subjects from laser-scanned range and reflectance data. These algorithms automatically insert contractile muscles at anatomically correct positions within a dynamic skin model and root them in an estimated skull structure with a hinged jaw. They also synthesize functional eyes, eyelids, teeth, and a neck and fit them to the final model. The constructed face may be animated via muscle actuations. In this way, we create the most authentic and functional facial models of individuals available to date and demonstrate their use in facial animation.


title: A Corpus and Model Integrating Multiword Expressions and Supersenses

abstract: This paper introduces a task of identifying and semantically classifying lexical expressions in running text. We investigate the online reviews genre, adding semantic supersense annotations to a 55,000 word English corpus that was previously annotated for multiword expressions. The noun and verb supersenses apply to full lexical expressions, whether single- or multiword. We then present a sequence tagging model that jointly infers lexical expressions and their supersenses. Results show that even with our relatively small training corpus in a noisy domain, the joint task can be performed to attain 70% class labeling F1.


title: Automatic recognition of serial numbers in bank notes

abstract: This paper presents a new topic of automatic recognition of bank note serial numbers, which will not only facilitate the prevention of forgery crimes, but also have a positive impact on the economy. Among all the different currencies, we focus on the study of RMB (renminbi bank note, the paper currency used in China) serial numbers. For evaluation, a new database NUST-RMB2013 has been collected from scanned RMB images, which contains the serial numbers of 35 categories with 17,262 training samples and 7000 testing samples in total. We comprehensively implement and compare two classic and one newly merged feature extraction methods (namely gradient direction feature, Gabor feature, and CNN trainable feature), four different types of well-known classifiers (SVM, LDF, MQDF, and CNN), and five multiple classifier combination strategies (including a specially designed novel cascade method). To further improve the recognition accuracy, the enhancements of three different kinds of distortions have been tested. Since high reliability is more important than accuracy in financial applications, we introduce three rejection schemes of first rank measurement (FRM), first two ranks measurement (FTRM) and linear discriminant analysis based measurement (LDAM). All the classifiers and classifier combination schemes are combined with different rejection criteria. A novel cascade rejection measurement achieves 100% reliability with less rejection rate compared with the existing methods. Experimental results show that MQDF reaches the accuracy of 99.59% using the gradient direction feature trained with gray level normalized data; the cascade classifier combination achieves the best performance of 99.67%. The distortions have been proved to be very helpful because the performances of CNNs boost at least 0.5% by training with transformed samples. With the cascade rejection method, 100% reliability has been obtained by rejecting 1.01% test samples. (C) 2014 Elsevier Ltd. All rights reserved.


Multiple criteria decision making in emergency management


title: Part Detector Discovery in Deep Convolutional Neural Networks

abstract: Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called “part detector discovery” (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training.


title: Comparison of 3.0-T MR vs 3.0-T MR arthrography of the hip for detection of acetabular labral tears and chondral defects in the same patient population

abstract: Objective:We report our experience in diagnostic sensitivity of 3.0-T conventional MR vs 3.0-T MR arthrography of the hip for detection of acetabular labral tears and chondral defects in the same patient population.Methods:43 consecutive patients had both conventional hip MR and MR arthrography examinations performed. These examinations were reviewed retrospectively by independent reading of two musculoskeletal radiologists who read the MR and MR arthrogram examinations in a randomized fashion (i.e. MR and MR arthrogram examinations were read at separate sittings and in a randomized fashion so as not to bias reviewers). Scans were assessed for acetabular labral tears and chondral defects. All patients went on to arthroscopy.Results:Of these 43 patients, 40 had acetabular labral tears read by Reader 1 and 39 had acetabular labral tears read by Reader 2 on MR arthrogram, 39 had acetabular labral tears read by Reader 1 and 38 had acetabular labral tears read by Reader 2 on conventional MR examination. There ...


Theory of Seepage Into Auger Holes1


title: Assessment of aflatoxins in peanuts grown in the Pothohar area of Pakistan

abstract: A survey was carried out to assess the mycotoxin (aflatoxins) contamination in locally grown peanuts. A total of 72 samples of raw, roasted and salty peanuts were collected randomly from the Pothohar Plateau of Pakistan. The samples were dried, ground and extracted by adding acetonitrile:water (84:16; v/v%). The filtered extracts were cleaned with MycoSep-226 columns and analysed by high-performance liquid chromatography with a fluorescence detector. The limit of quantification for aflatoxin B1 was 1 µg/kg with 70% recovery observed in spiked samples with a concentration range of 1–10 µg/kg. The results indicated that aflatoxins were present in almost 82% of the samples tested, with levels ranging from 14.3 to 98.8 μg/kg. This reflects that optimal conditions for fungal growth and mycotoxin contamination are frequent in peanut crop fields as well as in storehouses. Human exposure to such toxins can be controlled through appropriate measures, creating awareness and implementing regulations.


title: Comparison of think-aloud and constructive interaction in usability testing with children

abstract: Constructive interaction provides natural thinking-aloud as test subjects collaborate to solve tasks. Since children may face difficulties in following instructions for a standard think-aloud test, constructive interaction has been suggested as evaluation method when conducting usability testing with children. However, the relationship between think-aloud and constructive interaction is still poorly understood.We present an experiment that compares think-aloud and constructive interaction in usability testing. The experiment involves 60 children with three setups where children apply think-aloud, and constructive interaction in acquainted and non-acquainted pairs. Our results showed only minor significant differences between the setups, but the pairing of the children had impact on identification of usability problems as acquainted dyads identified more problems both in total and of the most severe than non-acquainted dyads and individual testers. Finally, the acquainted pairs reported that they had to put less effort into the testing than the think-aloud and non-acquainted children.


title: Predicting Information Credibility in Time-Sensitive Social Media

abstract: Purpose – Twitter is a popular microblogging service which has proven, in recent years, its potential for propagating news and information about developing events. The purpose of this paper is to focus on the analysis of information credibility on Twitter. The purpose of our research is to establish if an automatic discovery process of relevant and credible news events can be achieved. Design/methodology/approach – The paper follows a supervised learning approach for the task of automatic classification of credible news events. A first classifier decides if an information cascade corresponds to a newsworthy event. Then a second classifier decides if this cascade can be considered credible or not. The paper undertakes this effort training over a significant amount of labeled data, obtained using crowdsourcing tools. The paper validates these classifiers under two settings: the first, a sample of automatically detected Twitter “trends” in English, and second, the paper tests how well this model transfers to...


title: MUCK: A toolkit for extracting and visualizing semantic dimensions of large text collections

abstract: Users with large text collections are often faced with one of two problems; either they wish to retrieve a semanticallyrelevant subset of data from the collection for further scrutiny (needle-in-a-haystack) or they wish to glean a high-level understanding of how a subset compares to the parent corpus in the context of aforementioned semantic dimensions (forestfor-the-trees). In this paper, I describe MUCK 1 , an open-source toolkit that addresses both of these problems through a distributed text processing engine with an interactive visualization interface.


title: A high-power 200 kV power supply for capacitor charging applications

abstract: Development and testing of a compact 200-kV, 10-kJ/s industrial-grade power supply for capacitor charging applications is described. Pulse repetition rate (PRR) can be from single shot to 250 Hz, depending on the storage capacitance. Energy dosing (ED) topology enables high efficiency at switching frequency of up to 55 kHz using standard slow IGBTs. Circuit simulation examples are given. They clearly show zero-current switching at variable frequency during the charge set by the ED governing equations. Peak power drawn from the primary source is about only 60% higher than the average power, which lowers the stress on the input rectifier. Insulation design was assisted by electrostatic field analyses. Field plots of the main transformer insulation illustrate field distribution and stresses in it. Subsystem and system tests were performed including limited insulation life test. A precision, high-impedance, fast HV divider was developed for measuring voltages up to 250 kV with risetime down to 10 μs. The charger was successfully tested with stored energy of up to 550 J at discharge via a custom designed open-air spark gap at PRR up to 20 Hz (in bursts). Future work will include testing at customer sites.


title: Reward Systems in the Brain and Nutrition

abstract: The taste cortex in the anterior insula provides separate and combined representations of the taste, temperature, and texture of food in the mouth independently of hunger and thus of reward value and pleasantness. One synapse on, in the orbitofrontal cortex, these sensory inputs are combined by associative learning with olfactory and visual inputs for some neurons, and these neurons encode food reward value in that they respond to food only when hunger is present and in that activations correlate linearly with subjective pleasantness. Cognitive factors, including word-level descriptions and selective attention to affective value, modulate the representation of the reward value of taste, olfactory, and flavor stimuli in the orbitofrontal cortex and a region to which it projects, the anterior cingulate cortex. These food reward representations are important in the control of appetite and food intake. Individual differences in reward representations may contribute to obesity, and there are age-related differences in these reward representations. Implications of how reward systems in the brain operate for understanding, preventing, and treating obesity are described.


title: Predicting Hotel Bookings Cancellation with a Machine Learning Classification Model

abstract: Booking cancellations have significant impact on demand-management decisions in the hospitality industry. To mitigate the effect of cancellations, hotels implement rigid cancellation policies and overbooking tactics, which in turn can have a negative impact on revenue and on the hotel reputation. To reduce this impact, a machine learning based system prototype was developed. It makes use of the hotel’s Property Management Systems data and trains a classification model every day to predict which bookings are “likely to cancel” and with that calculate net demand. This prototype, deployed in a production environment in two hotels, by enforcing A/B testing, also enables the measurement of the impact of actions taken to act upon bookings predicted as “likely to cancel”. Results indicate good prototype performance and provide important indications for research progress whilst evidencing that bookings contacted by hotels cancel less than bookings not contacted.


title: Towards a multi-input model, method and tool for early design phases in innovation

abstract: This paper focuses on the early design phases of innovative projects. More precisely, the question of the innovation opportunities development and management is addressed here, starting from a theoretical model and methodology, until precise tool perspectives. The key elements of our approach are the potential technology concept (PTC) multi-input model and the C-K theory. Our PTC model is based on three dimensions (concept, technology and potential) and highlights the need of interactions between them regarding strategic and operational levels. Starting from the analysis of these three dimensions, different opportunities for the innovation are identified. In order to develop every identified opportunity, the three dimensions have to be explored with specific workshops and the C-K theory resulting in a tree diagram. The paper presents also tool perspectives dedicated to structure the preliminary exchanges among all stakeholders using criteria to consolidate new ideas. Two case studies are finally proposed.


title: Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

abstract: Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.


title: Alkaloidal Variation in Cissampelos Capensis (Menispermaceae)

abstract: Cissampelos capensis, commonly known by the Afrikaans name "dawidjies" or "dawidjieswortel", is the most important and best known medicinal plant of the family Menispermaceae used by the Khoisan and other rural people in the western region of South Africa. The main alkaloids in the leaves, stems and rhizomes were isolated and identified. Several of the main compounds were previously found in species of the related genus Antizoma and this similarity indicates that the two genera are closely related if not congeneric. Bulbocapnine (an aporphine alkaloid), dicentrine (an aporphine alkaloid) and salutaridine (a morphinane alkaloid) were the main alkaloids in the leaves, while bulbocapnine, cissacapine, cycleanine and insularine (the last three are bisbenzyltetrahydro-isoquinoline alkaloids) are the major compounds in the stems. The rhizome contains mostly bisbenzyltetrahydroisoquinoline alkaloids, with 12-O-methylcurine, cissacapine and cycleanine as the main ones. Alkaloids appear to be quite variable within different plant parts and different provenances, as confirmed by the difference in alkaloid patterns between coastal and inland forms of Cissampelos capensis (the morphinane alkaloid salutaridine, for example, is the major leaf alkaloid along the coast but is practically absent from the inland form of the species). The variety of alkaloids identified may contribute to the medicinal value of this species. The data on alkaloidal variation in the species has potential value and practical applications in chemotaxonomy, toxicology and pharmacognosy.


Introduction An attentive look at driver’s fixation durations (Draft)


title: Document selection methodologies for efficient and effective learning-to-rank

abstract: Learning-to-rank has attracted great attention in the IR community. Much thought and research has been placed on query-document feature extraction and development of sophisticated learning-to-rank algorithms. However, relatively little research has been conducted on selecting documents for learning-to-rank data sets nor on the effect of these choices on the efficiency and effectiveness of learning-to-rank algorithms. In this paper, we employ a number of document selection methodologies, widely used in the context of evaluation--depth-k pooling, sampling (infAP, statAP), active-learning (MTC), and on-line heuristics (hedge). Certain methodologies, e.g. sampling and active-learning, have been shown to lead to efficient and effective evaluation. We investigate whether they can also enable efficient and effective learning-to-rank. We compare them with the document selection methodology used to create the LETOR datasets. Further, all of the utilized methodologies are different in nature, and thus they construct training data sets with different properties, such as the proportion of relevant documents in the data or the similarity among them. We study how such properties affect the efficiency, effectiveness, and robustness of learning-to-rank collections.


NIH plans to enhance reproducibility


Metacognition: A Literature Review Research Report


Collaborative email-spam filtering with the hashing-trick


title: Lateral Control of Vehicle Platoons

abstract: Vehicle platoons are fully automated vehicles driving in close proximity of each other, where both distance keeping and steering is under automatic control. This paper is aiming at a variant of vehicle platoons, where the lateral control is using forward looking sensors, i.e. camera, radar. Such a system solution implies that the vehicle dynamics are coupled together laterally, in contrast to the classical look-down solutions. For such a platoon, lateral string stability is an important property that the controller needs to guarantee. This article proposes a method for designing such a distributed controller. It also examines the effect of model uncertainties on the lateral string stability of the platoon for the proposed method.


title: An embedded segmental K-means model for unsupervised segmentation and clustering of speech

abstract: Unsupervised segmentation and clustering of unlabelled speech are core problems in zero-resource speech processing. Most approaches lie at methodological extremes: some use probabilistic Bayesian models with convergence guarantees, while others opt for more efficient heuristic techniques. Despite competitive performance in previous work, the full Bayesian approach is difficult to scale to large speech corpora. We introduce an approximation to a recent Bayesian model that still has a clear objective function but improves efficiency by using hard clustering and segmentation rather than full Bayesian inference. Like its Bayesian counterpart, this embedded segmental K-means model (ES-KMeans) represents arbitrary-length word segments as fixed-dimensional acoustic word embeddings. We first compare ES-KMeans to previous approaches on common English and Xitsonga data sets (5 and 2.5 hours of speech): ES-KMeans outperforms a leading heuristic method in word segmentation, giving similar scores to the Bayesian model while being 5 times faster with fewer hyperparameters. However, its clusters are less pure than those of the other models. We then show that ES-KMeans scales to larger corpora by applying it to the 5 languages of the Zero Resource Speech Challenge 2017 (up to 45 hours), where it performs competitively compared to the challenge baseline.


title: Learning Symbolic Models of Stochastic Domains

abstract: In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.


title: Efficiently Enumerating all Maximal Cliques with Bit-Parallelism

abstract: Abstract The maximal clique enumeration (MCE) problem has numerous applications in biology, chemistry, sociology, and graph modeling. Though this problem is well studied, most current research focuses on finding solutions in large sparse graphs or very dense graphs, while sacrificing efficiency on the most difficult medium-density benchmark instances that are representative of data sets often encountered in practice. We show that techniques that have been successfully applied to the maximum clique problem give significant speed gains over the state-of-the-art MCE algorithms on these instances. Specifically, we show that a simple greedy pivot selection based on a fixed maximum-degree first ordering of vertices, when combined with bit-parallelism, performs consistently better than the theoretical worst-case optimal pivoting of the state-of-the-art algorithms of Tomita et al. [Theoretical Computer Science, 2006] and Naude [Theoretical Computer Science, 2016]. Experiments show that our algorithm is faster than the worst-case optimal algorithm of Tomita et al. on 60 out of 74 standard structured and random benchmark instances: we solve 48 instances 1.2 to 2.2 times faster, and solve the remaining 12 instances 3.6 to 47.6 times faster. We also see consistent speed improvements over the algorithm of Naude: solving 61 instances 1.2 to 2.4 times faster. To the best of our knowledge, we are the first to achieve such speed-ups compared to these state-of-the-art algorithms on these standard benchmarks.


title: Hookworm Detection in Wireless Capsule Endoscopy Images With Deep Learning

abstract: As one of the most common human helminths, hookworm is a leading cause of maternal and child morbidity, which seriously threatens human health. Recently, wireless capsule endoscopy (WCE) has been applied to automatic hookworm detection. Unfortunately, it remains a challenging task. In recent years, deep convolutional neural network (CNN) has demonstrated impressive performance in various image and video analysis tasks. In this paper, a novel deep hookworm detection framework is proposed for WCE images, which simultaneously models visual appearances and tubular patterns of hookworms. This is the first deep learning framework specifically designed for hookworm detection in WCE images. Two CNN networks, namely edge extraction network and hookworm classification network, are seamlessly integrated in the proposed framework, which avoid the edge feature caching and speed up the classification. Two edge pooling layers are introduced to integrate the tubular regions induced from edge extraction network and the feature maps from hookworm classification network, leading to enhanced feature maps emphasizing the tubular regions. Experiments have been conducted on one of the largest WCE datasets with WCE images, which demonstrate the effectiveness of the proposed hookworm detection framework. It significantly outperforms the state-of-the-art approaches. The high sensitivity and accuracy of the proposed method in detecting hookworms shows its potential for clinical application.


title: Addressing cold-start problem in recommendation systems

abstract: Recommender systems for automatically suggested items of interest to users have become increasingly essential in fields where mass personalization is highly valued. The popular core techniques of such systems are collaborative filtering, content-based filtering and combinations of these. In this paper, we discuss hybrid approaches, using collaborative and also content data to address cold-start - that is, giving recommendations to novel users who have no preference on any items, or recommending items that no user of the community has seen yet. While there have been lots of studies on solving the item-side problems, solution for user-side problems has not been seen public. So we develop a hybrid model based on the analysis of two probabilistic aspect models using pure collaborative filtering to combine with users' information. The experiments with MovieLen data indicate substantial and consistent improvements of this model in overcoming the cold-start user-side problem.


title: Question classification for medical domain Question Answering system

abstract: Question classification plays an important role in question answering system. It helps in finding or constructing accurate answers and hence improves the quality of Question Answering systems. The question classification approaches generally used are: Rule based, Machine learning and Hybrid. This paper presents our research work on question classification through rule based approach. The question processing module helps in assigning a suitable question category and identifying the keywords from the given input question. A prototype system based on the proposed method has been constructed and the experiment on 500 medical questions collected from patients and doctors has been carried out. Using the two layered taxonomy of 6 course grain and 50 fine grained categories developed by Li and Roth, we have classified the questions into various categories. We have also studied the syntactic structure of the question and suggest the syntactic patterns for particular category of questions. Using these question patterns we have classified the question into particular category. In this paper we have proposed a compact and effective method for question classification. The experimental output shows that even with small set of question categories we can classify the questions with more satisfactory and better result.


title: Tutorial on Remote Sensing Using GNSS Bistatic Radar of Opportunity

abstract: Abstract-In traditional GNSS applications, signals arriving at a receiver's antenna from nearby reflecting surfaces (multipath) interfere with the signals received directly from the satellites which can often result in a reduction of positioning accuracy. About two decades ago researchers produced an idea to use reflected GNSS signals for remote-sensing applications. In this new concept a GNSS transmitter together with a receiver capable of processing GNSS scattered signals of opportunity becomes bistatic radar. By properly processing the scattered signal, this system can be configured either as an altimeter, or a scatterometer allowing us to estimate such characteristics of land or ocean surface as height, roughness, or dielectric properties of the underlying media. From there, using various methods the geophysical parameters can be estimated such as mesoscale ocean topography, ocean surface winds, soil moisture, vegetation, snowpack, and sea ice. Depending on the platform of the GNSS receiver (stationary, airborne, or spaceborne), the capabilities of this technique and specific methods for processing of the reflected signals may vary. In this tutorial, we describe this new remotesensing technique, discuss some of the interesting results that have been already obtained, and give an overview of current and planned spacecraft missions.


title: The basic emotional circuits of mammalian brains: Do animals have affective lives?

abstract: The primal affects are intrinsic brain value systems that unconditionally and automatically inform animals how they are faring in survival. They serve an essential function in emotional learning. The positive affects index “comfort zones” that support survival, while negative affects inform animals of circumstances that may impair survival. Affective feelings come in several varieties, including sensory, homeostatic, and emotional (which I focus on here). Primary-process emotional feelings arise from ancient caudal and medial subcortical regions, and were among the first subjective experiences to exist on the face of the earth. Without them, higher forms of conscious “awareness” may not have emerged in primate brain evolution. Because of homologous “instinctual” neural infrastructures, we can utilize animal brain research to reveal the nature of primary-process human affects. Since all vertebrates appear to have some capacity for primal affective feelings, the implications for animal-welfare and how we ethically treat other animals are vast.


Feature Learning in Deep Neural Networks - A Study on Speech Recognition Tasks


title: Failure Mode and Effect Analysis Using Cloud Model Theory and PROMETHEE Method

abstract: Failure mode and effect analysis (FMEA) is a well-known engineering technique to recognize and reduce possible failures for quality and reliability improvement in products and services. It is a group-oriented method usually conducted by a multidisciplinary and cross-functional expert panel. In this paper, we explore two key issues inherent to the FMEA practice: the representation of diversified risk assessments of FMEA team members and the determination of priority ranking of failure modes. Specifically, a framework integrating cloud model, a new cognitive model for coping with fuzziness and randomness, and preference ranking organization method for enrichment evaluation (PROMETHEE) method, a powerful and flexible outranking decision making method, is developed for managing the group behaviors in FMEA. Moreover, FMEA team members’ weights are objectively derived taking advantage of the risk assessment information. Finally, we illustrate the new risk priority model with a healthcare risk analysis case, and further validate its effectiveness via sensitivity and comparison discussions.


title: The three-dimensional shapes of underground coal miners’ feet do not match the internal dimensions of their work boots

abstract: Mining work boots provide an interface between the foot and the ground, protecting and supporting miners' feet during lengthy coal mining shifts. Although underground coal miners report the fit of their work boots as reasonable to good, they frequently rate their boots as uncomfortable, suggesting that there is a mismatch between the shape of their feet and their boots. This study aimed to identify whether dimensions derived from the three-dimensional scans of 208 underground coal miners' feet (age 38.3 ± 9.8 years) differed from the internal dimensions of their work boots. The results revealed underground coal miners wore boots that were substantially longer than their feet, possibly because boots available in their correct length were too narrow. It is recommended boot manufacturers reassess the algorithms used to create boot lasts, focusing on adjusting boot circumference at the instep and heel relative to increases in foot length. Practitioner Summary: Fit and comfort ratings suggest a mismatch between the shape of underground coal miners' feet and their boots exists. This study examined whether three-dimensional scans of 208 miners' feet differed from their boot internal dimensions. Miners wore boots substantially longer than their feet, possibly due to inadequate width.


title: SSD-Assisted Backup and Recovery for Database Systems

abstract: Backup and recovery is an important feature of database systems since it protects data against unexpected hardware and software failures. Database systems can provide data safety and reliability by creating a backup and restoring the backup from a failure. Database administrators can use backup/recovery tools that are provided with database systems or backup/recovery methods with operating systems. However, the existing tools perform time-consuming jobs and the existing methods may negatively affect run-time performance during normal operation even though high-performance SSDs are used. In this paper, we present an SSD-assisted backup/recovery scheme for database systems. In our scheme, we extend the out-of-place update characteristics of flash-based SSDs for backup/recovery operations. To this end, we exploit the resources (e.g., flash translation layer and DRAM cache with supercapacitors) inside SSDs, and we call our SSD with new backup/recovery features BR-SSD. We design and implement the backup/recovery functionality in the Samsung enterprise-class SSD (i.e., SM843Tn) for more realistic systems. Furthermore, we conduct a case study of BR-SSDs in replicated database systems and modify MySQL with replication to integrate BR-SSDs. The experimental result demonstrates that our scheme provides fast recovery while it does not negatively affect the run-time performance during normal operation.


title: Missing Data Analysis: Making It Work in the Real World

abstract: This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descriptions of normalmodel multiple imputation (MI) and maximum likelihood methods. Practical missing data analysis issues are discussed, most notably the inclusion of auxiliary variables for improving power and reducing bias. Solutions are given for missing data challenges such as handling longitudinal, categorical, and clustered data with normal-model MI; including interactions in the missing data model; and handling large numbers of variables. The discussion of attrition and nonignorable missingness emphasizes the need for longitudinal diagnostics and for reducing the uncertainty about the missing data mechanism under attrition. Strategies suggested for reducing attrition bias include using auxiliary variables, collecting follow-up data on a sample of those initially missing, and collecting data on intent to drop out. Suggestions are given for moving forward with research on missing data and attrition.


title: Accidental displacement and migration of endosseous implants into adjacent craniofacial structures: A review and update

abstract: Objectives: Accidental displacement of endosseous implants into the maxillary sinus is an unusual but potential complication in implantology procedures due to the special features of the posterior aspect of the maxillary bone; there is also a possibility of migration throughout the upper paranasal sinuses and adjacent structures. The aim of this paper is to review the published literature about accidental displacement and migration of dental implants into the maxillary sinus and other adjacent structures. Study Design: A review has been done based on a search in the main on-line medical databases looking for papers about migration of dental implants published in major oral surgery, periodontal, dental implant and ear-nosethroat journals, using the keywords “implant,” “migration,” “complication,” “foreign body” and “sinus.” Results: 24 articles showing displacement or migration to maxillary, ethmoid and sphenoid sinuses, orbit and cranial fossae, with different degrees of associated symptoms, were identified. Techniques found to solve these clinical issues include Cadwell-Luc approach, transoral endoscopy approach via canine fossae and transnasal functional endoscopy surgery. Conclusion: Before removing the foreign body, a correct diagnosis should be done in order to evaluate the functional status of the ostiomeatal complex and the degree of affectation of paranasal sinuses and other involved structures, determining the size and the exact location of the foreign body. After a complete diagnosis, an indicated procedure for every case would be decided.


title: Virtual Program Counter (VPC) Prediction: Very Low Cost Indirect Branch Prediction Using Conditional Branch Prediction Hardware

abstract: Indirect branches have become increasingly common in modular programs written in modern object-oriented languages and virtual-machine-based runtime systems. Unfortunately, the prediction accuracy of indirect branches has not improved as much as that of conditional branches. Furthermore, previously proposed indirect branch predictors usually require a significant amount of extra hardware storage and complexity, which makes them less attractive to implement. This paper proposes a new technique for handling indirect branches, called Virtual Program Counter (VPC) prediction. The key idea of VPC prediction is to use the existing conditional branch prediction hardware to predict indirect branch targets, avoiding the need for a separate storage structure. Our comprehensive evaluation shows that VPC prediction improves average performance by 26.7 percent and reduces average energy consumption by 19 percent compared to a commonly used branch target buffer based predictor on 12 indirect branch intensive C/C++ applications. Moreover, VPC prediction improves the average performance of the full set of object-oriented Java DaCapo applications by 21.9 percent, while reducing their average energy consumption by 22 percent. We show that VPC prediction can be used with any existing conditional branch prediction mechanism and that the accuracy of VPC prediction improves when a more accurate conditional branch predictor is used.


title: A Miniaturized Absorptive Frequency Selective Surface

abstract: A miniaturized absorptive frequency selective surface (MAFSS) is presented in this letter, composed of a layer of miniaturized resistive surface placed above a metallic bandpass FSS. The MAFSS performs as a bandpass filter at operation band around 0.92 GHz, and acts as an absorber over a wide out-of-band 3–9 GHz. Moreover, due to its miniaturized elements, the MAFSS exhibits the property of eliminating the grating lobe in absorption band when illuminated by oblique incident wave. Numerical and experimental results have been given.


title: Vision of the unseen: Current trends and challenges in digital image and video forensics

abstract: Digital images are everywhere—from our cell phones to the pages of our online news sites. How we choose to use digital image processing raises a surprising host of legal and ethical questions that we must address. What are the ramifications of hiding data within an innocent imageq Is this an intentional security practice when used legitimately, or intentional deceptionq Is tampering with an image appropriate in cases where the image might affect public behaviorq Does an image represent a crime, or is it simply a representation of a scene that has never existedq Before action can even be taken on the basis of a questionable image, we must detect something about the image itself. Investigators from a diverse set of fields require the best possible tools to tackle the challenges presented by the malicious use of today's digital image processing techniques. In this survey, we introduce the emerging field of digital image forensics, including the main topic areas of source camera identification, forgery detection, and steganalysis. In source camera identification, we seek to identify the particular model of a camera, or the exact camera, that produced an image. Forgery detection's goal is to establish the authenticity of an image, or to expose any potential tampering the image might have undergone. With steganalysis, the detection of hidden data within an image is performed, with a possible attempt to recover any detected data. Each of these components of digital image forensics is described in detail, along with a critical analysis of the state of the art, and recommendations for the direction of future research.


title: Modeling human faces with multi-image photogrammetry

abstract: Modeling and measurement of the human face have been increasing by importance for various purposes. Laser scanning, coded light range digitizers, image-based approaches and digital stereo photogrammetry are the used methods currently employed in medical applications, computer animation, video surveillance, teleconferencing and virtual reality to produce three dimensional computer models of the human face. Depending on the application, different are the requirements. Ours are primarily high accuracy of the measurement and automation in the process. The method presented in this paper is based on multi-image photogrammetry. The equipment, the method and results achieved with this technique are here depicted. The process is composed of five steps: acquisition of multi-images, calibration of the system, establishment of corresponding points in the images, computation of their 3-D coordinates and generation of a surface model. The images captured by five CCD cameras arranged in front of the subject are digitized by a frame grabber. The complete system is calibrated using a reference object with coded target points, which can be measured fully automatically. To facilitate the establishment of correspondences in the images, texture in the form of random patterns can be projected from two directions onto the face. The multi-image matching process, based on a geometrical constrained least squares matching algorithm, produces a dense set of corresponding points in the five images. Neighborhood filters are then applied on the matching results to remove the errors. After filtering the data, the three-dimensional coordinates of the matched points are computed by forward intersection using the results of the calibration process; the achieved mean accuracy is about 0.2 mm in the sagittal direction and about 0.1 mm in the lateral direction. The last step of data processing is the generation of a surface model from the point cloud and the application of smooth filters. Moreover, a color texture image can be draped over the model to achieve a photorealistic visualization. The advantage of the presented method over laser scanning and coded light range digitizers is the acquisition of the source data in a fraction of a second, allowing the measurement of human faces with higher accuracy and the possibility to measure dynamic events like the speech of a person.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Seeing is believing: Trustworthiness as a dynamic belief

abstract: Recent efforts to understand the mechanisms underlying human cooperation have focused on the notion of trust, with research illustrating that both initial impressions and previous interactions impact the amount of trust people place in a partner. Less is known, however, about how these two types of information interact in iterated exchanges. The present study examined how implicit initial trustworthiness information interacts with experienced trustworthiness in a repeated Trust Game. Consistent with our hypotheses, these two factors reliably influence behavior both independently and synergistically, in terms of how much money players were willing to entrust to their partner and also in their post-game subjective ratings of trustworthiness. To further understand this interaction, we used Reinforcement Learning models to test several distinct processing hypotheses. These results suggest that trustworthiness is a belief about probability of reciprocation based initially on implicit judgments, and then dynamically updated based on experiences. This study provides a novel quantitative framework to conceptualize the notion of trustworthiness.


title: A floor cleaning robot using Swedish wheels

abstract: This paper presents a floor cleaning robot equipped with Swedish wheels. It can be used in crowded places such as houses, train station, airport etc. the robot can perform its work in autonomous and teleoperated mode. Moreover the robot can pivot around without turning, can avoid obstacles and is provided with automatic power management ability. And meanwhile, the kinematics for its control and controlling methods are studied and demonstrated. This new structure, smooth locomotion capability and high working efficiency are verified by experimentation.


title: Representing Knowledge in A-Prolog

abstract: In this paper, we review some recent work on declarative logic programming languages based on stable models/answer sets semantics of logic programs. These languages, gathered together under the name of A-Prolog, can be used to represent various types of knowledge about the world. By way of example we demonstrate how the corresponding representations together with inference mechanisms associated with A-Prolog can be used to solve various programming tasks.


title: HDTrans: an open source, low-level dynamic instrumentation system

abstract: Dynamic translation is a general purpose tool used for instrumenting programs at run time. Performance of translated execution relies on balancing the cost of translation against the benefits of any optimizations achieved, and many current translators perform substantial rewriting during translation in an attempt to reduce execution time. Our results show that these optimizations offer no significant benefit even when the translated program has a small, hot working set. When used in a broader range of applications, such as ubiquitous policy enforcement or penetration detection, translator performance cannot rely on the presence of a hot working set to amortize the cost of translation. A simpler, more maintainable, adaptable, and smaller translator appears preferable to more complicated designs in most cases.HDTrans is a light-weight dynamic instrumentation system for the IA-32 architecture that uses some simple and effective translation techniques in combination with established trace linearization and code caching optimizations. We present an evaluation of translation overhead under both benchmark and less idealized conditions, showing that conventional benchmarks do not provide a good prediction of translation overhead when used pervasively.A further contribution of this paper is an analysis of the effectiveness of post-link static pre-translation techniques for overhead reduction. Our results indicate that static pre-translation is effective only when expensive instrumentation or optimization is performed.


title: Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding

abstract: Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment, a significant number of attributes remain largely unexplored. In this paper, we propose a joint attribute-preserving embedding model for cross-lingual entity alignment. It jointly embeds the structures of two KBs into a unified vector space and further refines it by leveraging attribute correlations in the KBs. Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.


title: Performance Evaluation of NoSQL Databases: A Case Study

abstract: The choice of a particular NoSQL database imposes a specific distributed software architecture and data model, and is a major determinant of the overall system throughput. NoSQL database performance is in turn strongly influenced by how well the data model and query capabilities fit the application use cases, and so system-specific testing and characterization is required. This paper presents a method and the results of a study that selected among three NoSQL databases for a large, distributed healthcare organization. While the method and study considered consistency, availability, and partition tolerance (CAP) tradeoffs, and other quality attributes that influence the selection decision, this paper reports on the performance evaluation method and results. In our testing, a typical workload and configuration produced throughput that varied from 225 to 3200 operations per second between database products, while read operation latency varied by a factor of 5 and write latency by a factor of 4 (with the highest throughput product delivering the highest latency). We also found that achieving strong consistency reduced throughput by 10-25% compared to eventual consistency.


AUTOMATIC DELINEATION OF SMALL HOLDER AGRICULTURAL FIELD BOUNDARIES USING FULLY CONVOLUTIONAL NETWORKS


title: Searching trajectories by locations: an efficiency study

abstract: Trajectory search has long been an attractive and challenging topic which blooms various interesting applications in spatial-temporal databases. In this work, we study a new problem of searching trajectories by locations, in which context the query is only a small set of locations with or without an order specified, while the target is to find the k Best-Connected Trajectories (k-BCT) from a database such that the k-BCT best connect the designated locations geographically. Different from the conventional trajectory search that looks for similar trajectories w.r.t. shape or other criteria by using a sample query trajectory, we focus on the goodness of connection provided by a trajectory to the specified query locations. This new query can benefit users in many novel applications such as trip planning. In our work, we firstly define a new similarity function for measuring how well a trajectory connects the query locations, with both spatial distance and order constraint being considered. Upon the observation that the number of query locations is normally small (e.g. 10 or less) since it is impractical for a user to input too many locations, we analyze the feasibility of using a general-purpose spatial index to achieve efficient k-BCT search, based on a simple Incremental k-NN based Algorithm (IKNN). The IKNN effectively prunes and refines trajectories by using the devised lower bound and upper bound of similarity. Our contributions mainly lie in adapting the best-first and depth-first k-NN algorithms to the basic IKNN properly, and more importantly ensuring the efficiency in both search effort and memory usage. An in-depth study on the adaption and its efficiency is provided. Further optimization is also presented to accelerate the IKNN algorithm. Finally, we verify the efficiency of the algorithm by extensive experiments.


title: User guided entity similarity search using meta-path selection in heterogeneous information networks

abstract: With the emergence of web-based social and information applications, entity similarity search in information networks, aiming to find entities with high similarity to a given query entity, has gained wide attention. However, due to the diverse semantic meanings in heterogeneous information networks, which contain multi-typed entities and relationships, similarity measurement can be ambiguous without context. In this paper, we investigate entity similarity search and the resulting ambiguity problems in heterogeneous information networks. We propose to use a meta-path-based ranking model ensemble to represent semantic meanings for similarity queries, exploit the possibility of using using user-guidance to understand users query. Experiments on real-world datasets show that our framework significantly outperforms competitor methods.


title: Local Contrast Learning

abstract: Learning a deep model from small data is yet an opening and challenging problem. We focus on one-shot classification by deep learning approach based on a small quantity of training samples. We proposed a novel deep learning approach named Local Contrast Learning (LCL) based on the key insight about a human cognitive behavior that human recognizes the objects in a specific context by contrasting the objects in the context or in her/his memory. LCL is used to train a deep model that can contrast the recognizing sample with a couple of contrastive samples randomly drawn and shuffled. On one-shot classification task on Omniglot, the deep model based LCL with 122 layers and 1.94 millions of parameters, which was trained on a tiny dataset with only 60 classes and 20 samples per class, achieved the accuracy 97.99% that outperforms human and state-of-the-art established by Bayesian Program Learning (BPL) trained on 964 classes. LCL is a fundamental idea which can be applied to alleviate parametric model's overfitting resulted by lack of training samples.


title: User recruitment for mobile crowdsensing over opportunistic networks

abstract: We look into the realization of mobile crowdsensing campaigns that draw on the opportunistic networking paradigm, as practised in delay-tolerant networks but also in the emerging device-to-device communication mode in cellular networks. In particular, we ask how mobile users can be optimally selected in order to generate the required space-time paths across the network for collecting data from a set of fixed locations. The users hold different roles in these paths, from collecting data with their sensing-enabled devices to relaying them across the network and uploading them to data collection points with Internet connectivity. We first consider scenarios with deterministic node mobility and formulate the selection of users as a minimum-cost set cover problem with a submodular objective function. We then generalize to more realistic settings with uncertainty about the user mobility. A methodology is devised for translating the statistics of individual user mobility to statistics of spacetime path formation and feeding them to the set cover problem formulation. We describe practical greedy heuristics for the resulting NP-hard problems and compute their approximation ratios. Our experimentation with real mobility datasets (a) illustrates the multiple tradeoffs between the campaign cost and duration, the bound on the hopcount of space-time paths, and the number of collection points; and (b) provides evidence that in realistic problem instances the heuristics perform much better than what their pessimistic worst-case bounds suggest.


title: A DC-Side Sensorless Cascaded H-Bridge Multilevel Converter-Based Photovoltaic System

abstract: In this paper, a cascaded H-Bridge (CHB)-multilevel converter (MC)-based photovoltaic (PV) system with no voltage or current sensors at the dc-side is proposed. Eliminating the dc-side sensors simplifies the hardware, leading to lower cost and higher reliability of the PV system. A novel scheme estimating the capacitors’ voltages from the ac output voltage of the inverter is developed. The scheme allows replacing all dc-side voltage sensors by a single-voltage sensor at the ac-side of the converter. Furthermore, the dc current sensors, conventionally required for the maximum power point tracking (MPPT), are also eliminated. Instead, the outputs from the capacitors’ voltage control systems are utilized for the MPPT. The effectiveness of the proposed dc-side sensorless system is experimentally demonstrated on a 2-kW single-phase seven-level CHB converter-based PV system.


Adapting a Commercial Role-Playing Game for Educational Computer Game Production


title: An 8.5-ps Two-Stage Vernier Delay-Line Loop Shrinking Time-to-Digital Converter in 130-nm Flash FPGA

abstract: A new time-to-digital converter (TDC) with high resolution and high precision is designed and tested in this paper. The converter is realized by combining coarse clock counter with a two-stage delay-line loop shrinking interpolator (DLLSI) based on Vernier configuration, and its prototype has been implemented in a low-cost flash field-programmable gate array device SmartFusion A2F200M3F (Actel). Delay-line loops are used to achieve differential Vernier delay unit and directly shrink the time interval. In order to improve the resolution, decrease measurement time, and diminish the jitter of the cyclic pulse, a two-stage DLLSI method is proposed. The first-stage interpolator rapidly shrinks the measured time interval with low resolution, and the second-stage interpolator determines the final fine resolution. The resolutions are dependent on the entire delay time differences between two delay-line loops of each interpolator. The optimal resolutions are theoretically calculated, and statistic code density test is used to estimate the resolution of the implemented TDC. The implemented two-stage DLLSI has achieved 8.5-ps resolution with 42.4-ps standard deviation and 10-ns dynamic range. The maximum integral and differential nonlinearity errors are less than 7.8 and 3.1 ps.


title: Planning for Robust Airline Operations: Optimizing Aircraft Routings and Flight Departure Times to Minimize Passenger Disruptions

abstract: Airlines typically construct their schedules assuming that every flight leg will depart and arrive as planned. Because this optimistic scenario rarely occurs, these plans are frequently disrupted and airlines often incur significant costs in addition to those originally planned. Flight delays and schedule disruptions also cause passenger delays and disruptions. A more robust plan can reduce the occurrence and impact of these delays, thereby reducing costs. In this paper, we present two new approaches to minimize passenger disruptions and achieve robust airline schedule plans. The first approach involves routing aircraft, and the second involves retiming flight departure times. ::: ::: Because each airplane usually flies a sequence of flight legs, delay of one flight leg might propagate along the aircraft route to downstream flight legs and cause further delays and disruptions. We propose a new approach to reduce delay propagation by intelligently routing aircraft. We formulate this problem as a mixed-integer programming problem with stochastically generated inputs. An algorithmic solution approach is presented. Computational results obtained using data from a major U.S. airline show that our approach can reduce delay propagation significantly, thus improving on-time performance and reducing the numbers of passengers disrupted. ::: ::: Our second area of research considers passengers who miss their flight legs due to insufficient connection time. We develop a new approach to minimize the number of passenger misconnections by retiming the departure times of flight legs within a small time window. We formulate the problem and an algorithmic solution approach is presented. Computational results obtained using data from a major U.S. airline show that this approach can substantially reduce the number of passenger misconnections without significantly increasing operational costs.


S UP WSD : A Flexible Toolkit for Supervised Word Sense Disambiguation


title: Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

abstract: Abstract Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging workflows. Current methods demonstrate good results on non-enhanced T1-weighted images, but struggle when confronted with other modalities and pathologically altered tissue. In this paper we present a 3D convolutional deep learning architecture to address these shortcomings. In contrast to existing methods, we are not limited to non-enhanced T1w images. When trained appropriately, our approach handles an arbitrary number of modalities including contrast-enhanced scans. Its applicability to MRI data, comprising four channels: non-enhanced and contrast-enhanced T1w, T2w and FLAIR contrasts, is demonstrated on a challenging clinical data set containing brain tumors (N = 53), where our approach significantly outperforms six commonly used tools with a mean Dice score of 95.19. Further, the proposed method at least matches state-of-the-art performance as demonstrated on three publicly available data sets: IBSR, LPBA40 and OASIS, totaling N = 135 volumes. For the IBSR (96.32) and LPBA40 (96.96) data set the convolutional neuronal network (CNN) obtains the highest average Dice scores, albeit not being significantly different from the second best performing method. For the OASIS data the second best Dice (95.02) results are achieved, with no statistical difference in comparison to the best performing tool. For all data sets the highest average specificity measures are evaluated, whereas the sensitivity displays about average results. Adjusting the cut-off threshold for generating the binary masks from the CNN's probability output can be used to increase the sensitivity of the method. Of course, this comes at the cost of a decreased specificity and has to be decided application specific. Using an optimized GPU implementation predictions can be achieved in less than one minute. The proposed method may prove useful for large-scale studies and clinical trials.


title: Automatic ADL classification using 3-axial accelerometers and RFID sensor

abstract: This paper presents a new method for recognizing the activities of daily living (ADL) based on the state-dependent motion analysis using three accelerometers and a glove type RFID reader. Two accelerometers are used for the classification of five body states based on the decision tree. Classification of the instrumental activities is performed based on the hand interaction with an object ID using an accelerometer and a RFID reader. Hand movements when using objects are classified into five classes in advance and final decision combines the body state and the instrumental activities. Experiment shows that the suggested hierarchical motion analysis provides accuracy rate of over 90% for all 20 ADLs.


title: From Algorithmic Black Boxes to Adaptive White Boxes: Declarative Decision-Theoretic Ethical Programs as Codes of Ethics

abstract: Ethics of algorithms is an emerging topic in various disciplines such as social science, law, and philosophy, but also artificial intelligence (AI). The value alignment problem expresses the challenge of (machine) learning values that are, in some way, aligned with human requirements or values. In this paper I argue for looking at how humans have formalized and communicated values, in professional codes of ethics, and for exploring declarative decision-theoretic ethical programs (DDTEP) to formalize codes of ethics. This renders machine ethical reasoning and decision-making, as well as learning, more transparent and hopefully more accountable. The paper includes proof-of-concept examples of known toy dilemmas and gatekeeping domains such as archives and libraries.


title: The Importance of Syntactic Parsing and Inference in Semantic Role Labeling

abstract: We present a general framework for semantic role labeling. The framework combines a machine-learning technique with an integer linear programming-based inference procedure, which incorporates linguistic and structural constraints into a global decision process. Within this framework, we study the role of syntactic parsing information in semantic role labeling. We show that full syntactic parsing information is, by far, most relevant in identifying the argument, especially, in the very first stage---the pruning stage. Surprisingly, the quality of the pruning stage cannot be solely determined based on its recall and precision. Instead, it depends on the characteristics of the output candidates that determine the difficulty of the downstream problems. Motivated by this observation, we propose an effective and simple approach of combining different semantic role labeling systems through joint inference, which significantly improves its performance. ::: ::: Our system has been evaluated in the CoNLL-2005 shared task on semantic role labeling, and achieves the highest F1 score among 19 participants.


title: A novel security mechanism using hybrid cryptography algorithms

abstract: Data security is a primary concern for every communication system. Communication becomes an essential tool for any business, education, defense services etc. It is essential to transfer data safe and secure. At present, various cryptography algorithms have been proposed and implemented. Those algorithms are classified into symmetric and asymmetric algorithms based on the number of keys used. Even though several algorithms are used for data security, they are compromise the security at the certain period. Now the idea is to combine the several secure algorithms to provide a highly secure environment for data transmission. The algorithms that are going to be combined are AES symmetric cryptographic algorithm, RSA asymmetric algorithm and MD5 hashing algorithm. With these three algorithms, we can ensure three cryptography primitives confidentiality, authentication and integrity of data.


title: A survey on wearable health monitoring systems

abstract: Wearable health monitoring systems are being used as a way of cost reduction, and extension of medical care outside medical facilities. But, despite considerable time and work of the research in the field, important challenges, such as power consumption or security issues, still are a major concern. In this paper we present a survey in the field, extended to systems not wearable, to give a broad perspective of the research in this area, and find some of the most relevant challenges that this technology presents nowadays.


title: Reproducibility of importance extraction methods in neural network based fMRI classification

abstract: Abstract Recent advances in machine learning allow faster training, improved performance and increased interpretability of classification techniques. Consequently, their application in neuroscience is rapidly increasing. While classification approaches have proved useful in functional magnetic resonance imaging (fMRI) studies, there are concerns regarding extraction, reproducibility and visualization of brain regions that contribute most significantly to the classification. We addressed these issues using an fMRI classification scheme based on neural networks and compared a set of methods for extraction of category-related voxel importances in three simulated and two empirical datasets. The simulation data revealed that the proposed scheme successfully detects spatially distributed and overlapping activation patterns upon successful classification. Application of the proposed classification scheme to two previously published empirical fMRI datasets revealed robust importance maps that extensively overlap with univariate maps but also provide complementary information. Our results demonstrate increased statistical power of importance maps compared to univariate approaches for both detection of overlapping patterns and patterns with weak univariate information.


title: A wheelchair integrated lower limb exercise/ rehabilitation system: Design and experimental results on the knee joint

abstract: The disabled people suffer from spinal cord injuries (SCI) or post stroke hemiplegia are easily exposed to secondary problems due to limited mobility. We have developed the wheelchair integrated lower limb exercise/rehabilitation system which is to have an easy access to automated physical training programs. The system consists of three main modules: 1) an electric wheelchair, 2) a lifter which raises and supports the subject's body weight, and 3) a lower limb exoskeleton. This paper introduces the concept and configuration of the system. The details of the mechanism design and experimental results on the knee joint are presented.


title: Business process management, social network analysis and knowledge management : a triangulation of sorts?

abstract: As its name suggests, Business Process Management seeks to manage the processes companies typically undertake on a day to day basis. In line with many management techniques, improvements can made through analysing at varying granularity how processes are actually undertaken compared to how management may consider they are being accomplished and vice versa. One innovative way Business Process Management may be improved is through the use of Social Network Analysis to observe actual working relationships among employees. This latter technique permits the workflow manager specifically to consider how well matched employees are to their workflow and as a result of this, we have a means of either reconstructing workflows or alternatively employee practices. A small research-in-progress case study is presented illustrating how these principles may be applied in practice. Overall one may consider such improvements as aiding in the knowledge management of the organization as a whole.


title: Efficient hardware hashing functions for high performance computers

abstract: Hashing is critical for high performance computer architecture. Hashing is used extensively in hardware applications, such as page tables, for address translation. Bit extraction and exclusive ORing hashing "methods" are two commonly used hashing functions for hardware applications. There is no study of the performance of these functions and no mention anywhere of the practical performance of the hashing functions in comparison with the theoretical performance prediction of hashing schemes. In this paper, we show that, by choosing hashing functions at random from a particular class, called H/sub 3/, of hashing functions, the analytical performance of hashing can be achieved in practice on real-life data. Our results about the expected worst case performance of hashing are of special significance, as they provide evidence for earlier theoretical predictions.


title: Using Stories to Teach Human Values to Artificial Agents

abstract: Value alignment is a property of an intelligent agent indicating that it can only pursue goals that are beneficial to humans. Successful value alignment should ensure that an artificial general intelligence cannot intentionally or unintentionally perform behaviors that adversely affect humans. This is problematic in practice since it is difficult to exhaustively enumerated by human programmers. In order for successful value alignment, we argue that values should be learned. In this paper, we hypothesize that an artificial intelligence that can read and understand stories can learn the values tacitly held by the culture from which the stories originate.We describe preliminary work on using stories to generate a value-aligned reward signal for reinforcement learning agents that prevents psychotic-appearing behavior.


title: Clustering event logs using iterative partitioning

abstract: The importance of event logs, as a source of information in systems and network management cannot be overemphasized. With the ever increasing size and complexity of today's event logs, the task of analyzing event logs has become cumbersome to carry out manually. For this reason recent research has focused on the automatic analysis of these log files. In this paper we present IPLoM (Iterative Partitioning Log Mining), a novel algorithm for the mining of clusters from event logs. Through a 3-Step hierarchical partitioning process IPLoM partitions log data into its respective clusters. In its 4th and final stage IPLoM produces cluster descriptions or line formats for each of the clusters produced. Unlike other similar algorithms IPLoM is not based on the Apriori algorithm and it is able to find clusters in data whether or not its instances appear frequently. Evaluations show that IPLoM outperforms the other algorithms statistically significantly, and it is also able to achieve an average F-Measure performance 78% when the closest other algorithm achieves an F-Measure performance of 10%.


title: The LRU-K page replacement algorithm for database disk buffering

abstract: This paper introduces a new approach to database disk buffering, called the LRU-K method. The basic idea of LRU-K is to keep track of the times of the last K references to popular database pages, using this information to statistically estimate the interarrival times of references on a page by page basis. Although the LRU-K approach performs optimal statistical inference under relatively standard assumptions, it is fairly simple and incurs little bookkeeping overhead. As we demonstrate with simulation experiments, the LRU-K algorithm surpasses conventional buffering algorithms in discriminating between frequently and infrequently referenced pages. In fact, LRU-K can approach the behavior of buffering algorithms in which page sets with known access frequencies are manually assigned to different buffer pools of specifically tuned sizes. Unlike such customized buffering algorithms however, the LRU-K method is self-tuning, and does not rely on external hints about workload characteristics. Furthermore, the LRU-K algorithm adapts in real time to changing patterns of access.


title: Modeling of a forward osmosis and a pressure-retarded osmosis spiral wound module using the Spiegler-Kedem model and experimental validation

abstract: Abstract A mathematical model is developed for a spiral wound module for both forward osmosis (FO) and pressure-retarded osmosis (PRO) applications. This model is obtained by combining the membrane transport, external concentration polarization, and internal concentration polarization models with the pressure drop and mass balance equations for both the feed as well as the draw solutions. The Spiegler-Kedem (SK) model is used for describing the local mass transport in the active layer of the membrane. In addition, a new expression is established for determining the internal concentration polarization in the support layer of the membrane. A nonlinear constrained optimization technique is then applied, along with the experimental data for FO and PRO, to predict the unknown parameters of the mathematical model for the spiral wound module. To ensure the validity of the proposed model and predicted parameters, the model is again used to predict and compare the performance of the module at a variety of operating conditions. Apart from this, the product recovery and the energy extraction efficiency of the module at a variety of operating conditions are also analyzed. Lastly, we have tried to answer the question, whether the Solution Diffusion (SD) based model or the SK based model is better for design and analysis, and for obtaining the optimal operating conditions for both FO as well as PRO applications.


title: Real-time object tracking via CamShift-based robust framework

abstract: In recent years, lots of object tracking methods have been presented for better tracking accuracies. However, few of them can be applied to the real-time applications due to high computational cost. Aiming at achieving better realtime tracking performance, we propose an adaptive robust framework for object tracking based on the CamShift approach, which is notable for its simplicity and high processing efficiency. An adaptive local search method is presented to search for the best object candidate to avoid that the CamShift tracker gets confused by the surrounding background and erroneously incorporates it into the object region. A Kalman filter is also incorporated into our framework for prediction of the object's movement, so as to reduce the search effort and possible tracking failure caused by fast object motion. The experimental results demonstrate that the proposed tracking framework is robust and computationally effective.


title: Pivoted Document Length Normalization

abstract: Automatic information retrieval systems have to deal with documents of varying lengths in a text collection. Document length normalization is used to fairly retrieve documents of all lengths. In this study, we ohserve that a normalization scheme that retrieves documents of all lengths with similar chances as their likelihood of relevance will outperform another scheme which retrieves documents with chances very different from their likelihood of relevance. We show that the retrievaf probabilities for a particular normalization method deviate systematically from the relevance probabilities across different collections. We present pivoted normalization, a technique that can be used to modify any normalization function thereby reducing the gap between the relevance and the retrieval probabilities. Training pivoted normalization on one collection, we can successfully use it on other (new) text collections, yielding a robust, collectzorz independent normalization technique. We use the idea of pivoting with the well known cosine normalization function. We point out some shortcomings of the cosine function andpresent two new normalization functions--pivoted unique normalization and piuotert byte size normalization.


title: A hybrid dynamic forecast model for analyzing celebrity endorsement effects on consumer attitudes

abstract: This study investigates the time-varying effects of celebrity endorsements on consumer purchase attitudes toward promoted products using a novel dynamic hierarchical multi-attribute attitude forecast model. The induced direct and indirect effects via constructs of product attributes and net product value are then incorporated into the proposed conceptual model, which is formulated with a discrete-time nonlinear stochastic system. An empirical study of product categories of sport shoes and notebook computers demonstrates the feasibility of the proposed methodology. The analytical results demonstrate the capability of the proposed model to forecast consumer attitudes toward a promoted product and reveal the potential heterogeneity in patterns of attitude changes characterized by product attributes, price, and endorser performance as perceived by consumers. Furthermore, we infer that celebrity endorsement can significantly influence consumer purchase attitudes via both direct and indirect effects through product-attribute construct.


title: Natural Language is a Programming Language: Applying Natural Language Processing to Software Development

abstract: A powerful, but limited, way to view software is as source code alone. Treating a program as a sequence of instructions enables it to be formalized and makes it amenable to mathematical techniques such as abstract interpretation and model checking. ::: ::: A program consists of much more than a sequence of instructions. Developers make use of test cases, documentation, variable names, program structure, the version control repository, and more. I argue that it is time to take the blinders off of software analysis tools: tools should use all these artifacts to deduce more powerful and useful information about the program. ::: ::: Researchers are beginning to make progress towards this vision. This paper gives, as examples, four results that find bugs and generate code by applying natural language processing techniques to software artifacts. The four techniques use as input error messages, variable names, procedure documentation, and user questions. They use four different NLP techniques: document similarity, word semantics, parse trees, and neural networks. ::: ::: The initial results suggest that this is a promising avenue for future work.


title: A Novel Approximation for Multi-hop Connected Clustering Problem in Wireless Sensor Networks

abstract: Wireless sensor networks (WSNs) have been widely used in plenty of applications. To achieve higher efficiency for data collection, WSNs are often partitioned into several disjointed clusters, each with a representative cluster head in charge of the data gathering and routing process. Such a partition is balanced and effective if the distance between each node and its cluster head can be bounded within a constant number of hops, and any two cluster heads are connected. Finding such a cluster partition with minimum number of clusters and connectors between cluster heads is defined as minimum connected d-hop dominating set (d-MCDS) problem, which is proved to be NP-complete. In this paper, we propose a distributed approximation algorithm, named CS-Cluster, to address the d-MCDS problem. CS-Cluster constructs a sparser d-hop maximal independent set (d-MIS), connects the d-MIS and finally checks and removes redundant nodes. We prove the approximation ratio of CS-Cluster is (2d + l)λ, where λ is a parameter related with d but is no more than 18.4. Compared with the previous best result O(d2), our approximation ratio is a great improvement. Our evaluation results demonstrate the outstanding performance of our algorithm compared with previous works.


title: Configuration Tracking for Continuum Manipulators With Coupled Tendon Drive

abstract: Robotic control of flexible devices can enhance and simplify many medical procedures. We present a method for controlling a tendon-driven continuum manipulator by means of specifying the shape configuration. The basis for control is a linear beam configuration model that transforms beam configuration to tendon displacement by modeling internal loads of the compliant system. An essential aspect of this model is the inclusion of both the mechanical and geometrical coupling among serial articulating sections. Important capabilities of this model are the general forward kinematics and the decoupled inverse kinematics that allow for independent control of multiple sections. Tracking results are presented for a cardiac catheter with two articulating sections.


Peculiarities of export structure in Lithuania: synthesis and analysis


Introduction to CNNs and LSTMs for NLP


title: Boosting Adversarial Attacks with Momentum

abstract: Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks serve as an important surrogate to evaluate the robustness of deep learning models before they are deployed. However, most of existing adversarial attacks can only fool a black-box model with a low success rate. To address this issue, we propose a broad class of momentum-based iterative algorithms to boost adversarial attacks. By integrating the momentum term into the iterative process for attacks, our methods can stabilize update directions and escape from poor local maxima during the iterations, resulting in more transferable adversarial examples. To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks. We hope that the proposed methods will serve as a benchmark for evaluating the robustness of various deep models and defense methods. With this method, we won the first places in NIPS 2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack competitions.


title: On sparse reconstruction from Fourier and Gaussian measurements

abstract: This paper improves upon best-known guarantees for exact reconstruction of a sparse signal f from a small universal sample of Fourier measurements. The method for reconstruction that has recently gained momentum in the sparse approximation theory is to relax this highly nonconvex problem to a convex problem and then solve it as a linear program. We show that there exists a set of frequencies Ω such that one can exactly reconstruct every r-sparse signal f of length n from its frequencies in Ω, using the convex relaxation, and Ω has size k(r, n) = O(r log(n)·log 2 (r) log(r logn)) = O(r log 4 n ). A random set Ω satisfies this with high probability. This estimate is optimal within the log log n and log 3 r factors. We also give a relatively short argument for a similar problem with k(r, n) ≈ r[12 + 8 log(n/r)] Gaussian measurements. We use methods of geometric functional analysis and probability theory in Banach spaces, which makes our arguments quite short.


title: Phialemoniopsis endophytica sp. nov., a new species of endophytic fungi from Luffa cylindrica in Henan, China

abstract: During a survey of endophytic fungi in the cucurbit plants collected from Henan, China, a new species, Phialemoniopsis endophytica was isolated from the lower stem of Luffa cylindrica. It differs from other Phialemoniopsis species by its cylindrical to flask-shaped phialides, falcate conidia with blunt ends, ostiolate pycnidium-like conidiomata without marginal setae and ellipsoidal chlamydospores. Multi-locus (ITS, LSU, ACT, and TUB) phylogenetic analysis confirmed that P. endophytica is distinct from other species. A synopsis of the morphological characters of the new species is provided.


title: A review of gas sensors employed in electronic nose applications

abstract: This paper reviews the range of sensors used in electronic nose (e‐nose) systems to date. It outlines the operating principles and fabrication methods of each sensor type as well as the applications in which the different sensors have been utilised. It also outlines the advantages and disadvantages of each sensor for application in a cost‐effective low‐power handheld e‐nose system.


title: Structure and Levels of Meaning in Life and Its Relationship With Mental Health in Chinese Students Aged 10 to 25

abstract: This study examines the usage of the Meaning in Life Questionnaire in Chinese students aged from 10 to 25 within four age groups ( N = 5,510): early adolescence (10–13 years old, n = 1,258), middle adolescence (14–17 years old, n = 1,987), late adolescence (18–21 years old, n = 1,950) and early adulthood (22–25 years old, n = 315); and analyses the structure and levels of meaning in life, as well as the relationship between meaning in life and mental health. Results showed that: (1) the Meaning in Life Questionnaire in the four age groups of Chinese students had good construct validity and internal consistency reliability; (2) the average levels of the presence of meaning and search for meaning of Chinese students were moderate or above, and had obvious differences according to gender and family location (i.e., urban vs. rural); (3) the level of presence of meaning showed a trend of rising rapidly in middle adolescence and the level of search for meaning continued to rise in early adolescence and fell rapidly towards the end of adolescence; (4) presence of meaning was positively related to life satisfaction and positive affect and negatively related to depression and negative affect, and the same correlations were found with search for meaning.


Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems.


title: A fast incremental extreme learning machine algorithm for data streams classification

abstract: Data streams classification is an important approach to get useful knowledge from massive and dynamic data. Because of concept drift, traditional data mining techniques cannot be directly applied in data streams environment. Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN), comparing with the traditional neural network (e.g. BP network), ELM has a faster speed, so it is very suitable for real-time data processing. In order to deal with the challenge in data streams classification, a new approach based on extreme learning machine is proposed in this paper. The approach utilizes ELMs as base classifiers and adaptively decides the number of the neurons in hidden layer, in addition, activation functions are also randomly selected from a series of functions to improve the performance of the approach. Finally, the algorithm trains a series of classifiers and the decision results for unlabeled data are made by weighted voting strategy. When the concept in data streams keeps stable, every classifier is incrementally updated by using new data; if concept drift is detected, the classifiers with weak performance will be cleared away. In the experiment, we used 7 artificial data sets and 9 real data sets from UCI repository to evaluate the performance of the proposed approach. The testing results showed, comparing with the conventional classification methods for data streams such as ELM, BP, AUE2 and Learn++.MF, on most data sets, the new approach could not only be simplest in the structure, but also get a higher and more stable accuracy with lower time consuming.


title: Unintended Consequences of Information Technologies in Health Care--An Interactive Sociotechnical Analysis

abstract: Many unintended and undesired consequences of Healthcare Information Technologies (HIT) flow from interactions between the HIT and the healthcare organization's sociotechnical system—its workflows, culture, social interactions, and technologies. This paper develops and illustrates a conceptual model of these processes that we call Interactive Sociotechnical Analysis (ISTA). ISTA captures common types of interaction with special emphasis on recursive processes, i.e., feedback loops that alter the newly introduced HIT and promote second-level changes in the social system. ISTA draws on prior studies of unintended consequences, along with research in sociotechnical systems, ergonomics, social informatics, technology-in-practice, and social construction of technology. We present five types of sociotechnical interaction and illustrate each with cases from published research. The ISTA model should further research on emergent and recursive processes in HIT implementation and their unintended consequences. Familiarity with the model can also foster practitioners' awareness of unanticipated consequences that only become evident during HIT implementation.


title: A Weighted Frequent Itemset Mining Algorithm for Intelligent Decision in Smart Systems

abstract: Intelligent decision is the key technology of smart systems. Data mining technology has been playing an increasingly important role in decision-making activities. Frequent itemset mining (FIM), as an important step of association rule analysis, is becoming one of the most important research fields in data mining. Weighted FIM in uncertain databases should take both existential probability and importance of items into account in order to find frequent itemsets of great importance to users. However, the introduction of weight makes the weighted frequent itemsets not satisfy the downward closure property any longer. As a result, the search space of frequent itemsets cannot be narrowed according to downward closure property which leads to a poor time efficiency. In this paper, the weight judgment downward closure property for the weighted frequent itemsets and the existence property of weighted frequent subsets are introduced and proved first. Based on these two properties, the Weight judgment downward closure property-based FIM (WD-FIM) algorithm is proposed to narrow the searching space of the weighted frequent itemsets and improve the time efficiency. Moreover, the completeness and time efficiency of WD-FIM algorithm are analyzed theoretically. Finally, the performance of the proposed WD-FIM algorithm is verified on both synthetic and real-life data sets.


title: Facebook use and adolescents' emotional states of depression, anxiety, and stress.

abstract: Background: Facebook (FB) is becoming an increasingly salient feature in peoples’ lives and has grown into a bastion in our current society with over 1 billion users worldwide –the majority of which are college students. However, recent studies conducted suggest that the use of Facebook may impacts individuals’ well being. Thus, this paper aimed to explore the effects of Facebook usage on adolescents’ emotional states of depression, anxiety, and stress. Method and Material: A cross - sectional design was utilized in this investigation. The study population included 76 students enrolled in the Bachelor of Science in Nursing program from a government university in Samar, Philippines. Facebook Intensity Scale (FIS) and the Depression Anxiety and Stress Scale (DASS) were the primary instruments used in this study. Results: Findings indicated correlation coefficients of 0.11 (p=0.336), 0.07 (p=0.536), and 0.10 (p=0.377) between Facebook Intensity Scale (FIS) and Depression, Anxiety, and Stress scales in the DASS. Time spent on FBcorrelated significantly with depression (r=0.233, p=0.041) and anxiety (r=0.259, p=0.023). Similarly, the three emotional states (depression, anxiety, and stress) correlated significantly. Conclusions: Intensity of Facebook use is not directly related to negative emotional states. However, time spent on Facebooking increases depression and anxiety scores. Implications of the findings to the fields of counseling and psychology are discussed.


title: An algorithmic framework for frequent intraday pattern recognition and exploitation in forex market

abstract: We present a knowledge discovery-based framework that is capable of discovering, analyzing and exploiting new intraday price patterns in forex markets, beyond the well-known chart formations of technical analysis. We present a novel pattern recognition algorithm for Pattern Matching, that we successfully used to construct more than 16,000 new intraday price patterns. After processing and analysis, we extracted 3518 chart formations that are capable of predicting the short-term direction of prices. In our experiments, we used forex time series from 8 paired-currencies in various time frames. The system computes the probabilities of events such as "within next 5 periods, price will increase more than 20 pips". Results show that the system is capable of finding patterns whose output signals (tested on unseen data) have predictive accuracy which varies between 60 and 85% depending on the type of pattern. We test the usefulness of the discovered patterns, via implementation of an expert system using a straightforward strategy based on the direction and the accuracy of the pattern predictions. We compare our method against three standard trading techniques plus a "random trader," and we also test against the results presented in two recently published studies. Our framework performs very well against all systems we directly compare , and also, against all other published results.


Costas Loop Implementation for Synchronous Detection of Carrier in Radio Receiver