title: Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans

abstract: A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans.In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers.


title: Considerations for Incorporating “Well-Being” in Public Policy for Workers and Workplaces

abstract: Action to address workforce functioning and productivity requires a broader approach than the traditional scope of occupational safety and health. Focus on “well-being” may be one way to develop a more encompassing objective. Well-being is widely cited in public policy pronouncements, but often as “. . . and well-being” (e.g., health and well-being). It is generally not defined in policy and rarely operationalized for functional use. Many definitions of well-being exist in the occupational realm. Generally, it is a synonym for health and a summative term to describe a flourishing worker who benefits from a safe, supportive workplace, engages in satisfying work, and enjoys a fulfilling work life. We identified issues for considering well-being in public policy related to workers and the workplace.


Dancing with Decision Diagrams: A Combined Approach to Exact Cover.


title: Heart Disease Diagnosis System Using Fuzzy Logic

abstract: Treating people with ill health is a major problem in developed and underdeveloped countries. Most of these countries allocate a considerable portion of their budgets to ensuring that their citizens are healthy. However, countries remain unable to meet the demand for ideal medical services of their citizens because of the shortage of medical expertise in various hospitals. Medical diagnosis systems have been widely applied to diagnosing the symptoms of diseases such as cancer and diabetes. However, the analysis tools and methods are insufficient for identifying hidden relationships in the symptoms of coronary heart disease (CHD). Consequently, the ratio of people who suffer from this disease is growing rapidly; 12 million deaths each year are attributed to CHD. Meanwhile, the complex interdependency on various symptoms of this ailment indicates the difficulties in diagnosing CHD at an early stage. Furthermore, the diagnosis of CHD is a complex task that requires precision and effectiveness. Doctors do not have adequate time to devote to each case and encounter difficulties in keeping abreast of the newest application developments. Many alternative methods have been suggested for medical diagnosis in the healthcare domain. However, evaluating the functionality of CHD diagnosis systems remains challenging. Therefore, this study aims to develop a system that diagnoses CHD via fuzzy logic and evaluate the functionality of the proposed diagnostic CHD system. This study contributes to the healthcare domain as the developed system can assist doctors in accurately diagnosing when CHD symptoms have an ambiguous relationship. Therefore, the developed system will decrease doctors' workloads during consultations.


title: Attribute-Based Publishing with Hidden Credentials and Hidden Policies

abstract: With Hidden Credentials Alice can send policyencrypted data to Bob in such a way that he can decrypt the data only with the right combination of credentials. Alice gains no knowledge of Bob’s credentials in the process, and hence the name “Hidden Credentials.” Research on Hidden Credential systems has focused on messages sent to single recipients, where the sender needs to know the recipient’s pseudonym beforehand, and on Hidden Policies, where Bob learns as little information as possible about Alice’s policy for decrypting the message. Current schemes provide weak policy privacy — with non-interactive schemes, the recipient can learn parts of the policy, and with interactive schemes based on secure multiparty computation, a user can try different sets of credentials as input to gain knowledge of the policy after repeated decryption attempts. Furthermore, existing schemes do not support policies with negations efficiently. For example, a policy stating “Bob is not a student” is hard to enforce since Bob can simply withhold, or not use, his student credential. We propose a system called PEAPOD (PrivacyEnhanced Attribute-based Publishing Of Data) that provides the following properties: (1) Users can securely publish data protected by attribute-based policies to multiple possible recipients without requiring interaction between senders and receivers. This is achieved by using a semitrusted server. (2) The plaintext message and the policy are completely hidden from the server. (3) Any recipient, intended or not, learns no other information about a message’s policy beyond the number of clauses in policy that were satisfied. Furthermore the recipient is forced to use all of his or her issued credentials for decryption, and therefore cannot mount inference attacks by trying to decrypt the ∗This research was supported in part by the NSF, under grant CNS0524695, and the Bureau of Justice Assistance, under grant 2005-DD-BX1091. The views and conclusions do not necessarily reflect the views of the sponsors. message with different subsets of credentials. (4) Lastly, since recipients are forced to use all their credentials for decryption, PEAPOD efficiently supports non-monotonic boolean policies by allowing senders to include negations


title: Octave Bandwidth Orthomode Transducers for the Expanded Very Large Array

abstract: Quadruple-ridge orthomode transducers (OMTs) have been designed to operate over a full octave bandwidth for the expanded very large array (EVLA) project. The OMT separates linearly polarized signal components by matching a circular waveguide input to two orthogonal coaxial outputs. The OMT is used in conjunction with a quadrature hybrid to detect circularly polarized signal components. This paper focuses on the 1 GHz-2 GHz L-Band OMT design, which has better than 18.8 dB measured return loss across the band, with no evidence of trapped-mode resonances. The OMT is designed with an emphasis on performance, ease of tuning and manufacturability since a large number of units are needed for the array application. Extensive parametric analyses were carried out, and nominal dimensions have been set to ensure the devices exceed RF specifications provided the parts are machined to within specified tolerances. With excellent wideband performance and a simplified manufacturing process, the proposed OMT would be amenable to much larger future array projects.


abstract: farrell g.a., shafiei t. & salmon p. (2010) Facing up to ‘challenging behaviour’: a model for training in staff–client interaction. Journal of Advanced Nursing 66(7), 1422–1434. ::: ::: Abstract ::: Title. Facing up to ‘challenging behaviour’: a model for training in staff-client interaction ::: ::: Aim. This paper draws on theory and evidence to develop a conceptual staff training model for the management of ‘challenging behaviour’. ::: ::: Background. Staff working with clients who are experienced as challenging commonly report negative feelings such as anxiety, anger, guilt, fear, self-blame and powerlessness, as well as dissatisfaction with their jobs. Current training programmes in challenging behaviour offer a ‘smorgasbord’ of content, without a clearly defined conceptual framework. ::: ::: Data sources. Medline and PsychInfo were searched for papers in English from 1998 to 2008, linking ‘nurs*’ to ‘challenging behavio*’ and its related terms. Additional hand-searching identified informative papers from disciplines outside nursing older than the search period. ::: ::: Discussion. We developed an applied model for training educators in respect of challenging behaviours. The model directs educators to consider: the influence of the nurse, including their values, emotional processes and behavioural skills; features of the client; and features of the situation in which the behaviour occurs, including its culture and working practices and physical environment. ::: ::: Implications for nursing. The most striking implication of the model is that it explicitly recognizes the importance of domains of learning other than skill. This enables educators to find educationally appropriate responses to resource limitations that inevitably constrain training. ::: ::: Conclusion. Challenging behaviour should be considered as a product of several intertwined factors: the actors involved – nurses, clients and others – and the situation in which the behaviour occurs, including its culture and working practices and physical environment.


title: A block cipher using key-dependent S-box and P-boxes

abstract: Block ciphers based on key-dependent cipher structures have been investigated for years, however, their overall performance in terms of security and speed has not been sufficiently addressed. In this paper, we propose a 128-bit Feistel block cipher, which simultaneously engages key-dependent S-box and key-dependent P-box. With these two key-dependent transformations, the internal structure of this cipher algorithm is secured, so as to resist the linear and differential cryptanalysis in a few round encryptions. Hence, the encryption and decryption functions are quite efficiency. We named this key-dependent structure the DSDP structure, and the cipher DSDP. A fast permutation algorithm is used to generate both the key-dependent S-box and key-dependent P-boxes. This greatly compensates the performance penalty of complex key schedule. The basic operations used in DSDP are all efficient bytewise operations, so the algorithm will have a reasonable fast speed on recent processors, 16-bit processors and smart cards as well as 8-bit processors. We implement the algorithm with C and Java respectively on several PCs with different processors, and estimate the optimized assembly performance on Pentium. The experimental results and the estimation show that DSDP has a very fast encryption/decryption speed and a reasonable fast key scheduling implementation.


title: Scanning Electron Microscope Imaging of Onychomycosis

abstract: Although scanning electron microscope technology has been used for more than 60 years in many fields of medical research, no studies have focused on obtaining high-resolution microscopic images of onychomycosis of the toenail caused by Trichophyton rubrum in a geriatric population. To provide new insight into the intricate structure and behavior of chronic toenail onychomycosis, we produced three-dimensional images of onychomycosis obtained from two geriatric patients with confirmed growth of T rubrum. The photomicrographs illustrate the pervasive integration and penetration of the fungus hyphal elements, underscoring the clinical difficulty of obtaining rapid treatment of fungal infections in the distal and lateral subungual space of the human toenail. Although the scanning electron microscope may not be a practical diagnostic tool for most physicians, it remains invaluable for the researcher to obtain insight into the spatial orientation, behavior, and appearance of onychomycosis. (J Am Podiatr Med Asso...


title: A Novel Analysis of a $Ku$-Band Planar p-i-n Diode Limiter

abstract: Two new analyses for a single-stage limiter were presented; one is an analytic approach based on the approximation of the voltage waveforms of a p-i-n diode in the limiter, while the other models the p-i-n diode by a p-n diode. The analytic expressions resulting from the analytic approach explicitly show the role of the diode parameters on the limiter performances, which can be used as a guide to select adequate p-i-n diodes for the limiter. The two analyses were verified using the large-signal p-i-n diode model developed by measurements. Our analysis was then extended to multistage limiters. Based on our analysis, we designed and fabricated a limiter with an insertion loss of 1.3 dB and a flat leakage of 11.7 dBm for a Ku-band input power of 20 W.


title: Design of a tendon-driven robotic hand with an embedded camera

abstract: This paper designs a new five-fingered robotic hand with a camera. Several morphological features of the human hand are integrated to improve the appearance of the hand. The drive system of this hand is under-actuated to eliminate the weight of the hand and to embed all the actuators inside the palm. Despite of this under-actuation, this hand can grasp objects in several different ways. In addition, the two different transmissions are adopted to drive the fingers according to their roles. These transmissions help not only to improve drive efficiency but also to secure the space of the embedded camera.


title: Decentralized and Distributed Temperature Control via HVAC Systems in Energy Efficient Buildings

abstract: In this paper, we design real-time decentralized and distributed control schemes for Heating Ventilation and Air Conditioning (HVAC) systems in energy efficient buildings. The control schemes balance user comfort and energy saving, and are implemented without measuring or predicting exogenous disturbances. Firstly, we introduce a thermal dynamic model of building systems and formulate a steady-state resource allocation problem, which aims to minimize the aggregate deviation between zone temperatures and their set points, as well as the building energy consumption, subject to practical operating constraints, by adjusting zone flow rates. Because this problem is nonconvex, we propose two methods to (approximately) solve it and to design the real-time control. In the first method, we present a convex relaxation approach to solve an approximate version of the steady-state optimization problem, where the heat transfer between neighboring zones is ignored. We prove the tightness of the relaxation and develop a real-time decentralized algorithm to regulate the zone flow rate. In the second method, we introduce a mild assumption under which the original optimization problem becomes convex, and then a real-time distributed algorithm is developed to regulate the zone flow rate. In both cases, the thermal dynamics can be driven to equilibria which are optimal solutions to those associated steady-state optimization problems. Finally, numerical examples are provided to illustrate the effectiveness of the designed control schemes.


title: Neonatal exposure to estradiol in rats influences neuroactive steroid concentrations, GABAA receptor expression, and behavioral sensitivity to anxiolytic drugs

abstract: J. Neurochem. (2010) 113, 1285–1295. ::: ::: ::: ::: Abstract ::: ::: Gonadal steroids, in particular estradiol, exert important actions during pre- and perinatal periods in the regulation of sexual dimorphism and development of the nervous system. We have now examined the effects of neonatal estradiol administration in female rats on brain concentrations of the neuroactive steroids allopregnanolone and tetrahydrodeoxycorticosterone, expression of GABAA receptor subunits, and behavioral sensitivity to benzodiazepines and allopregnanolone. Administration of β-estradiol 3-benzoate on the day of birth resulted in marked decreases in the concentrations of progesterone and allopregnanolone in the cerebral cortex at 21, 60, and 180 days after birth. The concentrations of tetrahydrodeoxycorticosterone, 17β-estradiol, and dehydroepiandrosterone in the brain at 60 days were not affected by such treatment. Neonatal administration of β-estradiol 3-benzoate also increased the cerebrocortical abundance of α1, α2, and γ2 subunits of the GABAA receptor without affecting that of α3, α4, α5, or δ subunits. Diazepam induced a greater reduction in locomotor activity as well as a more pronounced anxiolytic-like effect in the elevated plus-maze test in rats subjected to neonatal treatment with β-estradiol 3-benzoate than in vehicle-treated controls, while allopregnanolone induced a similar effect in both groups. These effects of estradiol suggest that it plays a major role in regulation both of GABAergic transmission and of the abundance of endogenous modulators of such transmission during development of the central nervous system.


title: Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis

abstract: Emotion is expressed in multiple modalities, yet most research has considered at most one or two. This stems in part from the lack of large, diverse, well-annotated, multimodal databases with which to develop and test algorithms. We present a well-annotated, multimodal, multidimensional spontaneous emotion corpus of 140 participants. Emotion inductions were highly varied. Data were acquired from a variety of sensors of the face that included high-resolution 3D dynamic imaging, high-resolution 2D video, and thermal (infrared) sensing, and contact physiological sensors that included electrical conductivity of the skin, respiration, blood pressure, and heart rate. Facial expression was annotated for both the occurrence and intensity of facial action units from 2D video by experts in the Facial Action Coding System (FACS). The corpus further includes derived features from 3D, 2D, and IR (infrared) sensors and baseline results for facial expression and action unit detection. The entire corpus will be made available to the research community.


title: Survey of Wireless Geolocation Techniques

abstract: Ubiquitous and pervasive networks and applications include a growing number of research themes. In most use cases, applications require location information to interpret their environment and behave accordingly. In this paper, location algorithms and positioning methods that can be used for wireless geolocation are presented.


title: Investigating how well contextual features are captured by bi-directional recurrent neural network models

abstract: Learning algorithms for natural language processing (NLP) tasks traditionally rely on manually defined relevant contextual features. On the other hand, neural network models using an only distributional representation of words have been successfully applied for several NLP tasks. Such models learn features automatically and avoid explicit feature engineering. Across several domains, neural models become a natural choice specifically when limited characteristics of data are known. However, this flexibility comes at the cost of interpretability. In this paper, we define three different methods to investigate ability of bi-directional recurrent neural networks (RNNs) in capturing contextual features. In particular, we analyze RNNs for sequence tagging tasks. We perform a comprehensive analysis on general as well as biomedical domain datasets. Our experiments focus on important contextual words as features, which can easily be extended to analyze various other feature types. We also investigate positional effects of context words and show how the developed methods can be used for error analysis.


title: Internet of Hybrid Energy Harvesting Things

abstract: Internet of Things (IoT) is a perfect candidate to realize efficient observation and management for Smart City concept. This requires deployment of large number of wireless devices. However, replenishing batteries of thousands, maybe millions of devices may be hard or even impossible. In order to solve this problem, Internet of Energy Harvesting Things (IoEHT) is proposed. Although the first studies on IoEHT focused on energy harvesting (EH) as an auxiliary power provisioning method, now completely battery-free and self-sufficient systems are envisioned. Taking advantage of diverse sources that the concept of Smart City offers helps us to fully appreciate the capacity of EH. In this way, we address the primary shortcomings of IoEHT; availability, unreliability, and insufficiency by the Internet of Hybrid EH Things (IoHEHT). In this paper, we survey the various EH opportunities, propose an hybrid EH system, and discuss energy and data management issues for battery-free operation. We mathematically prove advantages of hybrid EH compared to single source harvesting as well. We also point out to hardware requirements and present the open research directions for different network layers specific to IoHEHT for Smart City concept.


title: High-gain SIW-based antipodal linearly tapered slot antenna for 60-GHz applications

abstract: A substrate integrated waveguide (SIW)-fed antipodal linear tapered slot antenna (ALTSA) with tapered microstrip-to-SIW transition for millimeter-wave (MMW) applications are presented. The antenna gain is 14.7 ± 0.5 dBi over the band of interest (50-70 GHz), while a radiation efficiency of 95% is achieved. Rectangular corrugation is added to the antenna, resulting in very low cross-polarization levels and relatively low sidelobe levels. The SIW, ALTSA and transition all designed in a single layer substrate. The given design parameters are obtained using both CST and HFSS simulators.


abstract: A CMOS passive mixer-first receiver front-end for UHF RFID Reader is presented in this paper. Instead of LNA input structure, we choose single-balanced passive mixer as the input stage to improve compression point, and can handle the large jammer noise from the leakage of the transmitter. The transimpedance amplifiers are followed by the mixers to provide enough gain and suppress the noise behind. We also use complex impedance match at baseband to control the real and imaginary part of the antenna equivalent impedance. This can leave out the matching network off chip. The receiver front-end circuit is designed in SMIC 0.13µm CMOS process.


title: Stochastic Optimization for Unit Commitment—A Review

abstract: Optimization models have been widely used in the power industry to aid the decision-making process of scheduling and dispatching electric power generation resources, a process known as unit commitment (UC). Since UC’s birth, there have been two major waves of revolution on UC research and real life practice. The first wave has made mixed integer programming stand out from the early solution and modeling approaches for deterministic UC, such as priority list, dynamic programming, and Lagrangian relaxation. With the high penetration of renewable energy, increasing deregulation of the electricity industry, and growing demands on system reliability, the next wave is focused on transitioning from traditional deterministic approaches to stochastic optimization for unit commitment. Since the literature has grown rapidly in the past several years, this paper is to review the works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC. Relevant lines of future research are also discussed to help transform research advances into real-world applications.


title: Construction and Evaluation of a User Experience Questionnaire

abstract: An end-user questionnaire to measure user experience quickly in a simple and immediate way while covering a preferably comprehensive impression of the product user experience was the goal of the reported construction process. An empirical approach for the item selection was used to ensure practical relevance of items. Usability experts collected terms and statements on user experience and usability, including `hard' as well as `soft' aspects. These statements were consolidated and transformed into a first questionnaire version containing 80 bipolar items. It was used to measure the user experience of software products in several empirical studies. Data were subjected to a factor analysis which resulted in the construction of a 26 item questionnaire including the six factors Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. Studies conducted for the original German questionnaire and an English version indicate a satisfactory level of reliability and construct validity.


title: Learning-based symmetry detection in natural images

abstract: In this work we propose a learning-based approach to symmetry detection in natural images. We focus on ribbon-like structures, i.e. contours marking local and approximate reflection symmetry and make three contributions to improve their detection. First, we create and make publicly available a ground-truth dataset for this task by building on the Berkeley Segmentation Dataset. Second, we extract features representing multiple complementary cues, such as grayscale structure, color, texture, and spectral clustering information. Third, we use supervised learning to learn how to combine these cues, and employ MIL to accommodate the unknown scale and orientation of the symmetric structures. We systematically evaluate the performance contribution of each individual component in our pipeline, and demonstrate that overall we consistently improve upon results obtained using existing alternatives.


title: Virtual reality using games for improving physical functioning in older adults: a systematic review

abstract: The use of virtual reality through exergames or active video game, i.e. a new form of interactive gaming, as a complementary tool in rehabilitation has been a frequent focus in research and clinical practice in the last few years. However, evidence of their effectiveness is scarce in the older population. This review aim to provide a summary of the effects of exergames in improving physical functioning in older adults. A search for randomized controlled trials was performed in the databases EMBASE, MEDLINE, PsyInfo, Cochrane data base, PEDro and ISI Web of Knowledge. Results from the included studies were analyzed through a critical review and methodological quality by the PEDro scale. Thirteen studies were included in the review. The most common apparatus for exergames intervention was the Nintendo Wii gaming console (8 studies), followed by computers games, Dance video game with pad (two studies each) and only one study with the Balance Rehabilitation Unit. The Timed Up and Go was the most frequently used instrument to assess physical functioning (7 studies). According to the PEDro scale, most of the studies presented methodological problems, with a high proportion of scores below 5 points (8 studies). The exergames protocols and their duration varied widely, and the benefits for physical function in older people remain inconclusive. However, a consensus between studies is the positive motivational aspect that the use of exergames provides. Further studies are needed in order to achieve better methodological quality, external validity and provide stronger scientific evidence.


Neural networks in microwave circuit design?beyond black-box models (invited article)


title: Spatio-Temporal Matching for Urban Transportation Applications

abstract: In this article, we present a search problem in which mobile agents are searching for static resources. Each agent wants to obtain exactly one resource. Both agents and resources are spatially located on a road network and the movement of the agents is constrained to the road network. This problem applies to various transportation applications including: vehicles (agents) searching for parking (resources) and taxicabs (agents) searching for clients to pick up (resources). In this work, we design search algorithms for such scenarios. We model the problem in different scenarios that vary based on the level of information that is available to the agents. These scenarios vary from scenarios in which agents have complete information about other agents and resources, to scenarios in which agents only have access to a fraction of the data about the availability of resources (uncertain data). We also propose pricing schemes that incentivize vehicles to search for resources in a way that benefits the system and the environment. Our proposed algorithms were tested in a simulation environment that uses real-world data. We were able to attain up to 40% improvements over other approaches that were tested against our algorithms.


title: J-MOD$^{2}$: Joint Monocular Obstacle Detection and Depth Estimation

abstract: In this letter, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approaches rely either on Visual simultaneous localization and mapping (SLAM) systems or on depth estimation models to build three-dimensional maps and detect obstacles. However, for the task of avoiding obstacles this level of complexity is not required. Recent works have proposed multitask at its first ocurrence in the text."?> architectures to perform both scene understanding and depth estimation. We follow their path and propose a specific architecture to jointly estimate depth and obstacles, without the need to compute a global map, but maintaining compatibility with a global SLAM system if needed. The network architecture is devised to jointly exploit the information learned from the obstacle detection task, which produces reliable bounding boxes, and the depth estimation one, increasing the robustness of both to scenario changes. We call this architecture J-MOD2. We test the effectiveness of our approach with experiments on sequences with different appearance and focal lengths and compare it to SotA multitask methods that perform both semantic segmentation and depth estimation. In addition, we show the integration in a full system using a set of simulated navigation experiments, where a micro aerial vehicle explores an unknown scenario and plans safe trajectories by using our detection model.


IT Project Management: Infamous Failures, Classic Mistakes, and Best Practices


title: Immune Regulatory Effect of Newly Isolated Lactobacillus delbrueckii from Indian Traditional Yogurt

abstract: Lactic acid bacteria (LAB) are microorganisms that are believed to provide health benefits. Here, we isolated LAB from Indian fermented foods, such as traditional Yogurt and Dosa. LAB from Yogurt most significantly induced TNF-α and IL-1β production, whereas LAB from Dosa induced mild cytokine production. After 16S rRNA gene sequencing and phylogenetic analysis, a Yogurt-borne lactic acid bacterium was identified and classified as Lactobacillus delbrueckii subsp. bulgaricus, and it was renamed L. delbrueckii K552 for the further studies. Our data suggest that the newly isolated L. delbrueckii can be used for the treatment of immune deficiency disorders.


Grid-based Bayesian Estimation Exploiting Sparsity for systems with nongaussian uncertainty


title: Insect succession on a decomposing piglet carcass placed in a man-made freshwater pond in Malaysia.

abstract: This entomological study was conducted in a man-made freshwater pond in a palm oil plantation in Tanjung Sepat, Selangor from 23 July 2007 by using pig (Sus scrofa) as a carcass model. A 1.5 month old piglet (5 kg), which died of asphyxia after being accidentally crushed by its mother, was thrown into a pond. Observation was made for ten days; one visit per day and climatological data were recorded. On the first two days, the piglet carcass sunk to the bottom of the pond. The carcass floated to the surface on the third day but no fly activities were seen. The blow fly, Chrysomya megacephala and Chrysomya rufifacies started to oviposit on the fourth day. Other than adult flies, a spider (Arachnida) was also observed on the carcass. Bubbles accumulated at the mouthpart, and the abdomen was greenish black. A lot of blow fly eggs were seen on the body surface on the fifth day (floating decay), along with first and second instars C. megacephala crawling under the piglet's skin. On the sixth day, adult blow fly, C. megacephala,and C. rufifacies,and muscid flies, Ophyra spinigera and Musca domestica were observed on to the carcass. High numbers of first and second instars of flies were observed wandering around the body surface with C. megacephala larvae being the predominant species. Two prominent maggot masses occurred on seventh and eighth days. Bloated deterioration stage began on day eighth exposing rib bones, humerus bones and intestines. Carcass was partially sinking and the maggot masses were at the water level. On day ninth, the carcass was partially sinking and three maggot masses were observed on the exposed surface. There were very few adult flies, including a scarab beetle was sighted on the carcass at this stage. The carcass along with the maggots sunk on day tenth, leaving an oily layer on the water surface.


title: Learning to Recommend Accurate and Diverse Items

abstract: In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as ground-truth for each user. We then represent each user or item as a vector resulted from the factorization of the user-item rating matrix. In our paper, we try to discover a factorization for matching the following supervised learning task. In doing this, we define two coupled optimization problems, parameterized matrix factorization and structural learning, to formulate our task. And we propose a diversified collaborative filtering algorithm (DCF) to solve the coupled problems. We also introduce a new pairwise accuracy metric and a normalized topic coverage diversity metric to measure the performance of accuracy and diversity respectively. Extensive experiments on benchmark datasets show the performance gains of DCF in comparison with the state-of-the-art algorithms.


title: Robust multilinear principal component analysis

abstract: We propose two methods for robustifying multilinear principal component analysis (MPCA) which is an extension of the conventional PCA for reducing the dimensions of vectors to higher-order tensors. For two kinds of outliers, i.e., sample outliers and intra-sample outliers, we derive iterative algorithms on the basis of the Lagrange multipliers. We also demonstrate that the proposed methods outperform the original MPCA when datasets contain such outliers experimentally.


title: Cervical radiculopathy: nonoperative management of neck pain and radicular symptoms.

abstract: Cervical radiculopathy is a disease process marked by nerve compression from herniated disk material or arthritic bone spurs. This impingement typically produces neck and radiating arm pain or numbness, sensory deficits, or motor dysfunction in the neck and upper extremities. Magnetic resonance imaging or computed tomographic myelography can confirm neurologic compression. The overall prognosis of persons with cervical radiculopathy is favorable. Most patients improve over time with a focused, nonoperative treatment course. There is little high-quality evidence on the best nonoperative therapy for cervical radiculopathy. Cervical collars may be used for a short period of immobilization, and traction may temporarily decompress nerve impingement. Medications may help alleviate pain and neuropathic symptoms. Physical therapy and manipulation may improve neck discomfort, and selective nerve blocks target nerve root pain. Although the effectiveness of individual treatments is controversial, a multimodal approach may benefit patients with cervical radiculopathy and associated neck pain.


title: A developmental study of English vowel production and perception by native Korean adults and children

abstract: Abstract This study examined the production and perception of English vowels by native Korean (NK) learners of English on two occasions separated by about 1 year. A preliminary experiment revealed that NK adults classified some pairs of contrastive English vowels using two different Korean vowels whereas other pairs showed classification overlap, implying they would be difficult for Korean learners of English to discriminate. In two subsequent experiments, NK adults and children differing in length of residence in North America (3 vs. 5 years; 4 groups of 18 each) were compared to age-matched native English (NE) speakers. In Experiment 2, NK children were found to discriminate English vowels more accurately than NK adults but less accurately than NE children. In Experiment 3, English words containing /i ɪ eɪ e ae ɑ ʌ/ were elicited using a picture-naming task. Some vowels produced by NK children were heard as intended significantly more often than vowels produced by NK adults. Acoustic analyses revealed that NK children produced significantly larger between-vowel contrasts than NK adults but did not differ from NE children. Taken together, the results suggested that although children are more successful than adults in learning the phonetic properties of second-language vowels, they might continue to differ from age-matched native speakers in certain respects as uncovered by the vowel discrimination test in Experiment 2.


title: Simultaneous two-photon imaging and two-photon optogenetics of cortical circuits in three dimensions

abstract: The simultaneous imaging and manipulating of neural activity could enable the functional dissection of neural circuits. Here we have combined two-photon optogenetics with simultaneous volumetric two-photon calcium imaging to measure and manipulate neural activity in mouse neocortex in vivo in three-dimensions (3D) with cellular resolution. Using a hybrid holographic approach, we simultaneously photostimulate more than 80 neurons over 150 μm in depth in layer 2/3 of the mouse visual cortex, while simultaneously imaging the activity of the surrounding neurons. We validate the usefulness of the method by photoactivating in 3D selected groups of interneurons, suppressing the response of nearby pyramidal neurons to visual stimuli in awake animals. Our all-optical approach could be used as a general platform to read and write neuronal activity.


title: Improving Interpretability and Regularization in Deep Learning

abstract: Deep learning approaches yield state-of-the-art performance in a range of tasks, including automatic speech recognition. However, the highly distributed representation in a deep neural network (DNN) or other network variations is difficult to analyze, making further parameter interpretation and regularization challenging. This paper presents a regularization scheme acting on the activation function output to improve the network interpretability and regularization. The proposed approach, referred to as activation regularization , encourages activation function outputs to satisfy a target pattern. By defining appropriate target patterns, different learning concepts can be imposed on the network. This method can aid network interpretability and also has the potential to reduce overfitting. The scheme is evaluated on several continuous speech recognition tasks: the Wall Street Journal continuous speech recognition task, eight conversational telephone speech tasks from the IARPA Babel program and a U.S. English broadcast news task. On all the tasks, the activation regularization achieved consistent performance gains over the standard DNN baselines.


title: Matching Natural Language Sentences with Hierarchical Sentence Factorization

abstract: Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Prior research has proposed both unsupervised distance-based schemes, when training data is not available, and deep learning schemes for sentence matching, given training data. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interaction between them. In this paper, we propose \textit{Hierarchical Sentence Factorization}—a technique that is able to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a “predicate-argument” form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).


title: Materials and Manufacturing Technologies Available for Production of a Pediatric Bioabsorbable Stent

abstract: Transcatheter treatment of children with congenital heart disease such as coarctation of the aorta and pulmonary artery stenosis currently involves the use of metal stents. While these provide good short term results, there are long term complications with their use. Children outgrow metal stents, obligating them to future transcatheter dilations and eventual surgical removal. A bioabsorbable stent, or a stent that goes away with time, would solve this problem. Bioabsorbable stents are being developed for use in coronary arteries, however these are too small for use in pediatric congenital heart disease. A bioabsorbable stent for use in pediatric congenital heart disease needs to be low profile, expandable to a diameter 8 mm, provide sufficient radial strength, and absorb quickly enough to allow vessel growth. Development of absorbable coronary stents has led to a great understanding of the available production techniques and materials such as bioabsorbable polymers and biocorrodable metals. Children with congenital heart disease will hopefully soon benefit from the current generation of bioabsorbable and biocorrodable materials and devices.


title: A Closed Form Solution to Multi-View Low-Rank Regression

abstract: Real life data often includes information from different channels. For example, in computer vision, we can describe an image using different image features, such as pixel intensity, color, HOG, GIST feature, SIFT features, etc.. These different aspects of the same objects are often called multi-view (or multi-modal) data. Low-rank regression model has been proved to be an effective learning mechanism by exploring the low-rank structure of real life data. But previous low-rank regression model only works on single view data. In this paper, we propose a multi-view low-rank regression model by imposing low-rank constraints on multi-view regression model. Most importantly, we provide a closed-form solution to the multi-view low-rank regression model. Extensive experiments on 4 multi-view datasets show that the multi-view low-rank regression model outperforms single-view regression model and reveals that multi-view low-rank structure is very helpful.


title: Simplified Reactive Power Control for Single-Phase Grid-Connected Photovoltaic Inverters

abstract: The objective of this paper is to propose a simplified reactive power control (SRPC) strategy for single-phase grid-tied photovoltaic (PV) inverters. With the proposed SRPC strategy, a cost-effective microcontroller can be adopted to achieve an effectively reactive power control. Moreover, the current-mode asynchronous sigma-delta modulation (CASDM) is adopted to enhance the current control's dynamic response and reduce both the current harmonic distortion and electromagnetic interference. In this paper, the operational principle of the proposed SRPC is introduced. Then, the small signal analysis for the PV inverter with the CASDM is presented. Finally, a 1-kVA single-phase PV inverter was built to verify the performance of the proposed control strategy.


title: Ensemble Learning in Recommender Systems: Combining Multiple User Interactions for Ranking Personalization

abstract: In this paper, we propose a technique that uses multimodal interactions of users to generate a more accurate list of recommendations optimized for the user . Our approach is a response to the actual scenario on the Web which allows users to interact with the content in different ways, and thus, more information about his preferences can be obtained to improve recommendation. The proposal consists of an ensemble learning technique that combines rankings generated by unimodal recommenders based on particular interaction types. By using a combination of different types of feedback from users, we are able to provide better recommendations, as shown by our experimental evaluation.


title: Antivirus security: naked during updates

abstract: The security of modern computer systems heavily depends on security tools, especially on antivirus software solutions. In the anti-malware research community, development of techniques for evading detection by antivirus software is an active research area. This has led to malware that can bypass or subvert antivirus software. The common strategies deployed include the use of obfuscated code and staged malware whose first instance usually installer such as dropper and downloader is not detected by the antivirus software. Increasingly, most of the modern malware are staged ones in order for them to be not detected by antivirus solutions at the early stage of intrusion. The installers then determine the method for further intrusion including antivirus bypassing techniques. Some malware target boot and/or shutdown time when antivirus software may be inactive so that they can perform their malicious activities. However, there can be another time frame where antivirus solutions may be inactive, namely, during the time of update. All antivirus software share a unique characteristic that they must be updated at a very high frequency to provide up-to-date protection of their system. In this paper, we suggest a novel attack vector that targets antivirus updates and show practical examples of how a system and antivirus software itself can be compromised during the update of antivirus software. Local privilege escalation using this vulnerability is also described. We have investigated this design vulnerability with several of the major antivirus software products such as Avira, AVG, McAfee, Microsoft, and Symantec and found that they are vulnerable to this new attack vector. The paper also discusses possible solutions that can be used to mitigate the attack in the existing versions of the antivirus software as well as in the future ones. Copyright © 2013 John Wiley & Sons, Ltd.


title: Deep Sparse Rectifier Neural Networks

abstract: While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for training multi-layer neural networks. This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-dierentiabil ity


title: The Ripple Effect: Emotional Contagion and Its Influence on Group Behavior

abstract: Group emotional contagion, the transfer of moods among people in a group, and its influence on work group dynamics was examined in a laboratory study of managerial decision making using multiple, convergent measures of mood, individual attitudes, behavior, and group-level dynamics. Using a 2 times 2 experimental design, with a trained confederate enacting mood conditions, the predicted effect of emotional contagion was found among group members, using both outside coders' ratings of participants' mood and participants' self-reported mood. No hypothesized differences in contagion effects due to the degree of pleasantness of the mood expressed and the energy level with which it was conveyed were found. There was a significant influence of emotional contagion on individual-level attitudes and group processes. As predicted, the positive emotional contagion group members experienced improved cooperation, decreased conflict, and increased perceived task performance. Theoretical implications and practical ramifi...


title: A Survey of Incentive Mechanisms for Participatory Sensing

abstract: Participatory sensing is now becoming more popular and has shown its great potential in various applications. It was originally proposed to recruit ordinary citizens to collect and share massive amounts of sensory data using their portable smart devices. By attracting participants and paying rewards as a return, incentive mechanisms play an important role to guarantee a stable scale of participants and to improve the accuracy/coverage/timeliness of the sensing results. Along this direction, a considerable amount of research activities have been conducted recently, ranging from experimental studies to theoretical solutions and practical applications, aiming at providing more comprehensive incentive procedures and/or protecting benefits of different system stakeholders. To this end, this paper surveys the literature over the period of 2004–2014 from the state of the art of theoretical frameworks, applications and system implementations, and experimental studies of the incentive strategies used in participatory sensing by providing up-to-date research in the literature. We also point out future directions of incentive strategies used in participatory sensing.


title: Cluster-based page segmentation-a fast and precise method for web page pre-processing

abstract: Segmenting a web page may be one of initial steps of information retrieval or content classification performed on that page. While there has been an extensive research in this area, the approaches usually focus either on performance or quality of the results. Vision based segmentation is one of the quality focused methods, which are considerably slow. This paper proposes an approach for boosting the performance of vision based algorithms. Our approach is based on concepts of modern web and a very common scenario in which an entire web site is processed at once. In this scenario, a great amount of performance boost can be gained by isomorphic mapping of previous results gathered from pages within the site to other pages on the same site. We provide the results of experiments performed on VIPS, the most common algorithm for page segmentation.


Chinese Grammatical Error Diagnosis with Long Short-Term Memory Networks.


title: Shape-Based Retrieval: A Case Study with Trademark Image Databases

abstract: Abstract Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval system. We propose a method for trademark image database retrieval based on object shape information that would supplement traditional text-based retrieval systems. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals when a query is presented; in the second stage, the candidates from the first stage are screened using a deformable template matching process to discard spurious matches. We have tested the algorithm using hand drawn queries on a trademark database containing 1100 images. Each retrieval takes a reasonable amount of computation time (∼4–5 s on a Sun Space 20 workstation). The topmost image retrieved by the system agrees with that obtained by human subjects, but there are significant differences between the ranking of the top-10 images retrieved by our system and the ranking of those selected by the human subjects. This demonstrates the need for developing shape features that are better able to capture human perceptual similarity of shapes. An improved heuristic has been suggested for more accurate retrievals. The proposed scheme matches filled-in query images against filled-in images from the database, thus using only the gross details in the image. Experiments with database images used as query images have shown that matching on the filled-in database extracts more images within the top-20 retrievals that have similar content. We believe that developing an automatic retrieval algorithm which matches human performance is an extremely difficult and challenging task. However, considering the substantial amount of time and effort needed for a manual retrieval from a large image database, an automatic shape-based retrieval technique can significantly simplify the retrieval task.


title: Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian

abstract: Automated blood vessel segmentation of retinal images offers huge potential benefits for medical diagnosis of different ocular diseases. In this paper, 2D Matched Filters (MF) are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Due to the Gaussian nature of blood vessel profile, the MF with Gaussian kernel often misclassifies non-vascular structures (e.g., step, ramp or other transients) as vessels. To avoid such false detection, this paper introduces Laplacian of Gaussian (LoG) filters in the vessel segmentation process. The inherent zero-crossing property of LoG filter is used in the algorithm, along with the MF, in order to extract vessels reliably from the retinal images. The proposed method is validated against three publicly available databases, STARE, DRIVE and HRF. Simulation results show that the proposed method is able to segment vessels accurately from the three database images with an average accuracy that is competitive to the existing methodologies.


title: Recognizing Insufficiently Supported Arguments in Argumentative Essays

abstract: In this paper, we propose a new task for assessing the quality of natural language arguments. The premises of a well-reasoned argument should provide enough evidence for accepting or rejecting its claim. Although this criterion, known as sufficiency, is widely adopted in argumentation theory, there are no empirical studies on its applicability to real arguments. In this work, we show that human annotators substantially agree on the sufficiency criterion and introduce a novel annotated corpus. Furthermore, we experiment with feature-rich SVMs and convolutional neural networks and achieve 84% accuracy for automatically identifying insufficiently supported arguments. The final corpus as well as the annotation guideline are freely available for encouraging future research on argument quality.


title: Evaluation of manpower scheduling strategies at outpatient pharmacy with discrete-event simulation

abstract: An outpatient pharmacy of a tertiary hospital in Singapore had uncertainty over the impact of different manpower scheduling strategies on the length of time (that is, cycle time) that their patients needed to spend during their visits. This article illustrated how this uncertainty could be addressed via application of discrete-event simulation (DES). Recent service rates of pharmacy staff and manpower allocation schedules were used to represent the process characteristics of the pharmacy in a DES model. On the basis of different new manpower scheduling strategies, the DES model projected quantitatively their respective impact on patient cycle times and manpower resource requirements. In this study, a new manpower scheduling plan, which matched manpower availability with patient arrival pattern, was recommended. On the basis of DES model projections, this recommendation could reduce both median and 95th percentile cycle times (39.7–45.7 per cent) with less than 7.5 per cent increase (or two new hires) in overall manpower requirement.


title: Text-Independent Speaker Verification Using 3D Convolutional Neural Networks

abstract: In this paper, a novel method using 3D Convolutional Neural Network (3D-CNN) architecture has been proposed for speaker verification in the text-independent setting. At the development phase, a CNN is trained to classify speakers at the utterance-level. In the enrollment stage, the trained network is utilized to directly create a speaker model for each speaker based on the extracted features. Finally, in the evaluation phase, the extracted features from the test utterance will be compared to the stored speaker model to verify the claimed identity. One of the main challenges is the creation of the speaker models. Previously-reported approaches create speaker models based on averaging the extracted features from utterances of the speaker, which is known as a d-vector system. In our paper, we propose an adaptive feature learning by utilizing the 3D-CNNs for direct speaker model creation in which, for both development and enrollment phases, an identical number of spoken utterances per speaker is fed to the network for representing the speaker utterances and creation of the speaker model. This leads to simultaneously capturing the speaker-related information and building a more robust system to cope with within-speaker variation. We demonstrate that the proposed method significantly outperforms the traditional d-vector verification system. Moreover, we empirically show that the proposed method is more effective than the end-to-end learning method.


Subword-level Word Vector Representations for Korean


An Investigation of Performance Analysis of Anomaly Detection Techniques for Big Data in SCADA Systems


title: Exploiting Machine Learning to Subvert Your Spam Filter

abstract: Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless--even if the adversary's access is limited to only 1% of the training messages. We further demonstrate a new class of focused attacks that successfully prevent victims from receiving specific email messages. Finally, we introduce two new types of defenses against these attacks.


title: NoSQL vs relational database: A comparative study about the generation of the most frequent N-grams

abstract: This study intends to help data mining developers to get better performance when obtaining the most frequent N-grams in Text Mining projects. The process of building new variables is one of the oldest and still challenging problems in Data Mining projects. The most frequent N-grams are commonly used as input variables in Text Mining projects. The N-grams represent the occurrence of N items in sequence in a given text. The items can be letters or words. This paper presents a performance comparison between the two main approaches of data storage, relational and NoSQL databases in the task of obtaining the most frequent N-grams. Validation of the study was executed using a database from a known benchmark from an international competition organized by PAN@CLEF 2013. The one-tailed paired t-test showed that NoSQL approach is statistically superior to the relational approach with a confidence level of 95%.


title: Fingerprint image enhancement method using directional median filter,” in Biometric Technology for Human Identification

abstract: The performance of any fingerprint recognizer highly depends on the ::: fingerprint image quality. Different types of noises in the fingerprint images pose greater difficulty for recognizers. ::: Most Automatic Fingerprint Identification Systems (AFIS) use some form of image enhancement. Although several methods have been described in the literature, there is still scope for improvement. In particular, effective methodology of cleaning the valleys between the ridge contours are lacking. We observe that noisy valley pixels and the pixels in the interrupted ridge flow gap are "impulse noises". Therefore, this paper describes a new approach to fingerprint image enhancement, which is based on integration of Anisotropic Filter and directional median filter(DMF). Gaussian-distributed noises are reduced effectively by Anisotropic Filter, "impulse noises" are ::: reduced efficiently by DMF. Usually, traditional median filter is the most effective method to remove pepper-and-salt noise and other small artifacts, the proposed DMF can not only finish its original tasks, it can also join broken fingerprint ridges, fill out the holes of fingerprint images, smooth irregular ridges as well as remove some annoying small artifacts between ridges. The enhancement algorithm has been implemented and tested on fingerprint images from FVC2002. Images of varying quality have been used to evaluate the performance of our approach. We have compared our method with other methods described in the literature in terms of matched minutiae, missed ::: minutiae, spurious minutiae, and flipped minutiae(between end points and bifurcation points). Experimental results show our method to be superior to those described in the literature.


title: Political conservatism as motivated social cognition

abstract: Analyzing political conservatism as motivated social cognition integrates theories of personality (authoritarianism, dogmatism-intolerance of ambiguity), epistemic and existential needs (for closure, regulatory focus, terror management), and ideological rationalization (social dominance, system justification). A meta-analysis (88 samples, 12 countries, 22,818 cases) confirms that several psychological variables predict political conservatism: death anxiety (weighted mean r = .50); system instability (.47); dogmatism-intolerance of ambiguity (.34); openness to experience (-.32); uncertainty tolerance (-.27); needs for order, structure, and closure (.26); integrative complexity (-.20); fear of threat and loss (.18); and self-esteem (-.09). The core ideology of conservatism stresses resistance to change and justification of inequality and is motivated by needs that vary situationally and dispositionally to manage uncertainty and threat.


title: Conformance Testing: Measuring the Alignment Between Event Logs and Process Models

abstract: Many companies have adopted Process Aware Information Systems (PAIS) for supporting their business processes in some form. On the one hand these systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. On the other hand explicit process models describing how the business process should (or is expected to) be executed are frequently available. Together with the data recorded in the log, this raises the interesting question "Do the model and the log conform to each other?". Conformance testing, also referred to as conformance analysis, aims at the detection of inconsistencies between a process model and its corresponding execution log, and their quantification by the formation of metrics. This paper proposes an incremental approach to check the conformance of a process model and an event log. At first, the fitness between the log and the model is ensured (i.e., "Does the observed process comply with the control flow specified by the process model?"). At second, the appropriateness of the model can be analyzed with respect to the log (i.e., "Does the model describe the observed process in a suitable way?"). Furthermore, this suitability must be evaluated from both a structural and a behavioral perspective. To verify the presented ideas a Conformance Checker has been implemented within the ProM framework.


title: Neural networks and rational functions

abstract: Neural networks and rational functions efficiently approximate each other. In more detail, it is shown here that for any ReLU network, there exists a rational function of degree $O(\text{polylog}(1/\epsilon))$ which is $\epsilon$-close, and similarly for any rational function there exists a ReLU network of size $O(\text{polylog}(1/\epsilon))$ which is $\epsilon$-close. By contrast, polynomials need degree $\Omega(\text{poly}(1/\epsilon))$ to approximate even a single ReLU. When converting a ReLU network to a rational function as above, the hidden constants depend exponentially on the number of layers, which is shown to be tight; in other words, a compositional representation can be beneficial even for rational functions.


title: A Morphological-Based License Plate Location

abstract: License plate location is an important phase in vehicle license plate recognition for intelligent transport systems. This paper presents a robust and real time method of license plate location. The proposed algorithm consists of some stages. In the first stage, we extract vertical edges of the input image using Sobel mask. In the next stage, histogram analysis is used for finding the candidate regions of license plate. Candidate regions are also verified by defined compact factor. In the last stage, we locate the license plate exactly with some morphological operators. Experiments have been conducted for this algorithm. 400 images taken from various scenes were employed, including diverse angles, different lightening conditions. The algorithm can quickly and correctly detect the region of license plate. The license plate detecting rate of success is 83.50%.


title: Approximation Algorithms for Min-Sum k-Clustering and Balanced k-Median

abstract: We consider two closely related fundamental clustering problems in this paper. In Min-Sumk-Clustering, one is given n points in a metric space and has to partition them into k clusters while minimizing the sum of pairwise distances between points in the same cluster. In the Balancedk-Median problem, the instance is the same and the objective is to obtain a partitioning into k clusters $$C_1,\ldots ,C_k$$ ::: , where each cluster $$C_i$$ ::: is centered at a point $$c_i$$ ::: , while minimizing the total assignment cost of the points in the metric; the cost of assigning a point j to a cluster $$C_i$$ ::: is equal to $$|C_i|$$ ::: times the distance between j and $$c_i$$ ::: in the metric. In this article, we present an $$O(\log n)$$ ::: -approximation for both these problems. This is an improvement over the $$O(\epsilon ^{-1}\log ^{1 + \epsilon } n)$$ ::: -approximation (for any constant $$\epsilon > 0$$ ::: ) obtained by Bartal, Charikar, and Raz [STOC ’01]. We also obtain a quasi-PTAS for Balanced k-Median in metrics with constant doubling dimension. As in the work of Bartal et al., our approximation for general metrics uses embeddings into tree metrics. The main technical contribution in this paper is an O(1)-approximation for Balanced k-Median in hierarchically separated trees (HSTs). Our improvement comes from a more direct dynamic programming approach that heavily exploits the properties of standard HSTs. In this way, we avoid the reduction to special types of HSTs that were considered by Bartal et al., thereby avoiding an additional $$O(\epsilon ^{-1} \log ^\epsilon n)$$ ::: loss.


title: Discriminative Transfer Learning with Tree-based Priors

abstract: High capacity classifiers, such as deep neural networks, often struggle on classes that have very few training examples. We propose a method for improving classification performance for such classes by discovering similar classes and transferring knowledge among them. Our method learns to organize the classes into a tree hierarchy. This tree structure imposes a prior over the classifier's parameters. We show that the performance of deep neural networks can be improved by applying these priors to the weights in the last layer. Our method combines the strength of discriminatively trained deep neural networks, which typically require large amounts of training data, with tree-based priors, making deep neural networks work well on infrequent classes as well. We also propose an algorithm for learning the underlying tree structure. Starting from an initial pre-specified tree, this algorithm modifies the tree to make it more pertinent to the task being solved, for example, removing semantic relationships in favour of visual ones for an image classification task. Our method achieves state-of-the-art classification results on the CIFAR-100 image data set and the MIR Flickr image-text data set.


title: Closeness centrality for networks with overlapping community structure

abstract: Certain real-life networks have a community structure in which communities overlap. For example, a typical bus network includes bus stops (nodes), which belong to one or more bus lines (communities) that often overlap. Clearly, it is important to take this information into account when measuring the centrality of a bus stop—how important it is to the functioning of the network. For example, if a certain stop becomes inaccessible, the impact will depend in part on the bus lines that visit it. However, existing centrality measures do not take such information into account. Our aim is to bridge this gap. We begin by developing a new game-theoretic solution concept, which we call the Configuration semivalue, in order to have greater flexibility in modelling the community structure compared to previous solution concepts from cooperative game theory. We then use the new concept as a building block to construct the first extension of Closeness centrality to networks with community structure (overlapping or otherwise). Despite the computational complexity inherited from the Configuration semivalue, we show that the corresponding extension of Closeness centrality can be computed in polynomial time. We empirically evaluate this measure and our algorithm that computes it by analysing the Warsaw public transportation network.


Securing Cloud Data


title: Knowledge Engineering: Principles and Methods

abstract: This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.


title: A novel 3-D printed loop antenna using flexible NinjaFlex material for wearable and IoT applications

abstract: A stretchable, flexible loop antenna working at 2.4GHz ISM band was fabricated by the additive manufacturing (AM) 3-D printing technology. NinjaFlex, a flexible 3-D printable material was utilized for the first time as a 3-D hemi-sphere substrate for the loop antenna. A 3-D printer based on the Fused Diffusion Modelling (FDM) technology was employed to 3-D print the substrate material. The resonance frequency of the antenna shifts in response to the applied force which makes the configuration suitable for the wireless strain sensing application. The proposed antenna was designed for wearable electronics application such as health monitoring earrings. Hence it was designed in such a way that it maintains the Specific Absorption Rate (SAR) of the human head tissues within the assigned standard limits when placed near human replicating head. The proposed antenna system could be useful in the additively manufactured wearable packaging and IoT applications.


title: Mobile-Assisted Language Learning: A Selected Annotated Bibliography of Implementation Studies 1994-2012.

abstract: To enhance the information contained in these references, over 90% of the entries are complemented by a brief (~80 word) summary. To the extent that the publication provides such information, each annotation identifies the country of origin of the study, native language (L1) and/or the second or foreign language (L2) involved, the mobile technology used, the learning area(s) targeted, the type of learners, their numbers, the duration of the study, and a summary of the results (i.e., learning outcomes and survey opinions). Since nearly 60% of MALL implementation studies appear outside of professional journals, in conference proceedings, project reports, academic dissertations, and so forth, locating copies of these publications poses a major challenge in itself. For this reason, where possible, links are included to copies of the works cited.


title: Active Appearance Models Revisited

abstract: Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to an image consists of minimising the error between the input image and the closest model instances i.e. solving a nonlinear optimisation problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show that the effects of appearance variation during fitting can be precomputed (“projected out”) using this algorithm and how it can be extended to include a global shape normalising warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.


title: Personality predictors of artistic preferences as a function of the emotional valence and perceived complexity of paintings.

abstract: This study explored associations among the Big Five personality factors, unconventionality, selected demographics, and preference for 4 distinct visual art genres (portraiture, abstract art, geometric art, and impressionism). In total, 3,254 participants completed an online survey assessing individual difference and preference ratings for different paintings. Participants were also asked to rate each observed painting for emotional liking and perceived complexity, which enabled examination of whether personality could predict artistic preferences when the latter was classified on the basis of consensual, rather than researcher-led or art historical, taxonomies. Correlations and structural equation models showed that the correlates and predictors of artistic preferences were stronger when art was classified using consensual ratings (particularly in the case of complex art) than according to researcher-led or art historical taxonomies. Although these findings are somewhat exploratory and more comprehensive measures of individual differences and art preferences could be employed, they suggest that trait-congruent classifications of aesthetic stimuli may improve prediction and understanding of individual differences in artistic preferences.


title: Fuzzy set theory

abstract: Seit Beginn der neunziger Jahre wird unter dem Schlagwort źFuzzy Logic" von einer Vielzahl von Anwendungen der Theorie unscharfer Mengen vor allem in Japan berichtet. Dies hat in Europa und hier insbesondere in Deutschland zu einem Umdenken bei Wissenschaftlern und Praktikern gefuhrt, die lange Zeit die Potentiale der neuen Technologie verkannten. Nun ist schlagartig eine sehr groβe Nachfrage nach Informationen zu diesem Thema entstanden. Dieser Artikel gibt einen Uberblick uber die wesentlichen Grundlagen der Theorie unscharfer Mengen und zeigt potentielle Anwendungen auf. ::: ::: Since the beginning nineties, entitled with the catchword "Fuzzy Logic" reports have been given on numerous, predominantly Japanese applications of fuzzy set theory. This had brought about a change in thinking for scientists and practitioners in Europe and, in particular, in Germany. Had the potentials of this new technology for a long time been underestimated, there now suddenly springs up a very high demand for information on this topic. This article gives a survey of the fundamentals of fuzzy set theory and describes potential applications.


title: User Identity Verification via Mouse Dynamics

abstract: Identity theft is a crime in which hackers perpetrate fraudulent activity under stolen identities by using credentials, such as passwords and smartcards, unlawfully obtained from legitimate users or by using logged-on computers that are left unattended. User verification methods provide a security layer in addition to the username and password by continuously validating the identity of logged-on users based on their physiological and behavioral characteristics. We introduce a novel method that continuously verifies users according to characteristics of their interaction with the mouse. The contribution of this work is threefold: first, user verification is derived based on the classification results of each individual mouse action, in contrast to methods which aggregate mouse actions. Second, we propose a hierarchy of mouse actions from which the features are extracted. Third, we introduce new features to characterize the mouse activity which are used in conjunction with features proposed in previous work. The proposed algorithm outperforms current state-of-the-art methods by achieving higher verification accuracy while reducing the response time of the system.


title: A Horst-Type Power Divider With Wide Frequency Tuning Range Using Varactors

abstract: This paper presents a novel power divider with a wide frequency tuning range. In our previous work, a pair of capacitors were connected in parallel with the front transmission lines of a Trantanella-type power divider introducing an additional reflection minimum, together with the original reflection zero to broaden the bandwidth. In this design, the latter transmission lines are removed. The added capacitors generate a reflection minimum, which can be easily controlled by varactors. Thus, the frequency band of the power divider can be flexibly tuned by altering the varactors. Design parameters are carefully chosen to eliminate the effect of the original reflection zero. By doing so, the power divider will have a tunable center frequency instead of a tunable bandwidth. Theoretical formulas for the characteristic impedances and electrical lengths of the transmission lines of the power divider are derived and analyzed. A power divider has been designed and fabricated to demonstrate the validity of the proposed design. The measured results indicate that the power divider can achieve a frequency tuning range of 0.9–4.2 GHz ( ::: ${f}_{H}/{f}_{L}= 4.67$ ::: :1), with in-band input and output return losses, both better than 22 dB, and an insertion loss of 3.2–4 dB. The measured in-band isolation is better than 15 dB. The power divider has a simple layout and a compact size of $0.2\,\,\lambda _{g} \times 0.16\,\,\lambda _{g}$ ::: , which demonstrates the excellent potential of the proposed power divider for modern communication systems.


title: Why People Fail to Recognize Their Own Incompetence

abstract: Successful negotiation of everyday life would seem to require people to possess insight about deficiencies in their intellectual and social skills. However, people tend to be blissfully unaware of their incompetence. This lack of awareness arises because poor performers are doubly cursed: Their lack of skill deprives them not only of the ability to produce correct responses, but also of the expertise necessary to surmise that they are not producing them. People base their perceptions of performance, in part, on their preconceived notions about their skills. Because these notions often do not correlate with objective performance, they can lead people to make judgments about their performance that have little to do with actual accomplishment.


title: Text Chunking By Combining Hand-Crafted Rules And Memory-Based Learning

abstract: This paper proposes a hybrid of hand-crafted rules and a machine learning method for chunking Korean. In the partially free word-order languages such as Korean and Japanese, a small number of rules dominate the performance due to their well-developed postpositions and endings. Thus, the proposed method is primarily based on the rules, and then the residual errors are corrected by adopting a memory-based machine learning method. Since the memory-based learning is an efficient method to handle exceptions in natural language processing, it is good at checking whether the estimates are exceptional cases of the rules and revising them. An evaluation of the method yields the improvement in F-score over the rules or various machine learning methods alone.


title: Knowledge Sharing Behaviour in the Public Sector : the Business Process Management Perspectives

abstract: The purpose of this paper is to demonstrate the preliminary analysis on the knowledge sharing behaviour of the public sectors in terms of individual attitudes, organisational environment, reward system and information and communication technology (ICT) within the system. The paper looks into the perspective of business process management which embedded in a knowledge sharing behaviour. This research-inprogress intends to use both quantitative and ::: qualitative methods (triangulation) because we believe ::: that it will be appropriate to answer the research ::: questions better. In the Phase One, consists of the ::: questionnaire that uses to measure perceptions, beliefs, ::: reactions, and attitudes related to knowledge sharing. ::: The unit of analysis using middle to senior management in the public sector as knowledge is said to be captured more systematically at this level. Phase Two will be carried out after analysis of data from the questionnaire survey is completed. The researcher intends to undertake an in-depth exploration of issues arising from Phase One. Hence a semi-structured interview will be carried out with small group of ::: people. Top management officials will be selected from within the same sample for this exercise. The research design in this study hopefully will answer all the research questions significantly. The preliminary findings will provide a basis for proposing a structured framework for further data collection and analysis.


title: The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?

abstract: Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.


title: Comparative Analysis of Semiconductor Device Architectures for 5-nm Node and Beyond

abstract: This letter, for the first time, investigates interactive logic cell schemes and transistor architecture scaling options for 5-nm technology node (N5) and beyond. The proposed novel transistors, such as Hexagonal NanoWire (NW) and NanoRing (NR) architectures, are introduced having higher current drivability and lower parasitic capacitance than conventional NW or NanoSlab devices. The standard cell sizing options, including a 1-fin-per-device version and a 2-fin-per-device design, are systematically evaluated. Each device flavor has multiple vertical stacks when wire-like or slab-like structure is used. Comprehensive transistor and logic cell studies demonstrate that the novel NR is the optimal structure for N5 and beyond.


title: The Origins of Structural Operational Semantics

abstract: Abstract We review the origins of structural operational semantics. The main publication `A Structural Approach to Operational Semantics,' also known as the `Aarhus Notes,' appeared in 1981 [G.D. Plotkin, A structural approach to operational semantics, DAIMI FN-19, Computer Science Department, Aarhus University, 1981]. The development of the ideas dates back to the early 1970s, involving many people and building on previous work on programming languages and logic. The former included abstract syntax, the SECD machine, and the abstract interpreting machines of the Vienna school; the latter included the λ -calculus and formal systems. The initial development of structural operational semantics was for simple functional languages, more or less variations of the λ -calculus; after that the ideas were gradually extended to include languages with parallel features, such as Milner's CCS. This experience set the ground for a more systematic exposition, the subject of an invited course of lectures at Aarhus University; some of these appeared in print as the 1981 Notes. We discuss the content of these lectures and some related considerations such as `small state' versus `grand state,' structural versus compositional semantics, the influence of the Scott–Strachey approach to denotational semantics, the treatment of recursion and jumps, and static semantics. We next discuss relations with other work and some immediate further development. We conclude with an account of an old, previously unpublished, idea: an alternative, perhaps more readable, graphical presentation of systems of rules for operational semantics.


title: Quality Aware Network for Set to Set Recognition

abstract: This paper targets on the problem of set to set recognition, which learns the metric between two image sets. Images in each set belong to the same identity. Since images in a set can be complementary, they hopefully lead to higher accuracy in practical applications. However, the quality of each sample cannot be guaranteed, and samples with poor quality will hurt the metric. In this paper, the quality aware network (QAN) is proposed to confront this problem, where the quality of each sample can be automatically learned although such information is not explicitly provided in the training stage. The network has two branches, where the first branch extracts appearance feature embedding for each sample and the other branch predicts quality score for each sample. Features and quality scores of all samples in a set are then aggregated to generate the final feature embedding. We show that the two branches can be trained in an end-to-end manner given only the set-level identity annotation. Analysis on gradient spread of this mechanism indicates that the quality learned by the network is beneficial to set-to-set recognition and simplifies the distribution that the network needs to fit. Experiments on both face verification and person re-identification show advantages of the proposed QAN. The source code and network structure can be downloaded at this https URL


title: A defect prediction method for software versioning

abstract: New methodologies and tools have gradually made the life cycle for software development more human-independent. Much of the research in this field focuses on defect reduction, defect identification and defect prediction. Defect prediction is a relatively new research area that involves using various methods from artificial intelligence to data mining. Identifying and locating defects in software projects is a difficult task. Measuring software in a continuous and disciplined manner provides many advantages such as the accurate estimation of project costs and schedules as well as improving product and process qualities. This study aims to propose a model to predict the number of defects in the new version of a software product with respect to the previous stable version. The new version may contain changes related to a new feature or a modification in the algorithm or bug fixes. Our proposed model aims to predict the new defects introduced into the new version by analyzing the types of changes in an objective and formal manner as well as considering the lines of code (LOC) change. Defect predictors are helpful tools for both project managers and developers. Accurate predictors may help reducing test times and guide developers towards implementing higher quality codes. Our proposed model can aid software engineers in determining the stability of software before it goes on production. Furthermore, such a model may provide useful insight for understanding the effects of a feature, bug fix or change in the process of defect detection.


title: Detection and Resolution of Data Inconsistencies, and Data Integration using Data Quality Criteria

abstract: In the processes and optimization of information integration, such as query processing, query planning and hierarchical structuring of results to the user, we argue that user quality priorities, data inconsistencies and data quality differences among the participating sources have not been fully addressed. We propose the development of a Data Quality Manager (DQM) to establish communication between the process of integration of information, the user and the application, to deal with semantic heterogeneity and data quality. DQM will contain a Reference Model, a Measurement Model, and an Assessment Model to define the quality criteria, the metrics and the assessment methods. DQM will also help in query planning by considering data quality estimations to find the best combination for the execution plan. After query execution, and detection of inconsistent data, data quality might also be used to perform data inconsistency resolution. Integration and ranking of query results using quality criteria defined by the user will be an outcome of this process. Index Terms — Data Quality, Heterogeneous Databases, Information Integration, Information Quality, Semantic Integration. —————————— ——————————


title: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

abstract: We propose mS2GD: a method incorporating a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent (S2GD). We consider the problem of minimizing a strongly convex function represented as the sum of an average of a large number of smooth convex functions, and a simple nonsmooth convex regularizer. Our method first performs a deterministic step (computation of the gradient of the objective function at the starting point), followed by a large number of stochastic steps. The process is repeated a few times with the last iterate becoming the new starting point. The novelty of our method is in introduction of mini-batching into the computation of stochastic steps. In each step, instead of choosing a single function, we sample $b$ functions, compute their gradients, and compute the direction based on this. We analyze the complexity of the method and show that it benefits from two speedup effects. First, we prove that as long as $b$ is below a certain threshold, we can reach any predefined accuracy with less overall work than without mini-batching. Second, our mini-batching scheme admits a simple parallel implementation, and hence is suitable for further acceleration by parallelization.


title: Spatial Uncertainty Sampling for End-to-End Control

abstract: End-to-end trained neural networks (NNs) are a compelling approach to autonomous vehicle control because of their ability to learn complex tasks without manual engineering of rule-based decisions. However, challenging road conditions, ambiguous navigation situations, and safety considerations require reliable uncertainty estimation for the eventual adoption of full-scale autonomous vehicles. Bayesian deep learning approaches provide a way to estimate uncertainty by approximating the posterior distribution of weights given a set of training data. Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty. In this paper, we propose a Bayesian NN for end-to-end control that estimates uncertainty by exploiting feature map correlation during training. This approach achieves improved model fits, as well as tighter uncertainty estimates, than traditional element-wise dropout. We evaluate our algorithms on a challenging dataset collected over many different road types, times of day, and weather conditions, and demonstrate how uncertainties can be used in conjunction with a human controller in a parallel autonomous setting.


title: Factors That Relate to Good and Poor Handwriting

abstract: OBJECTIVE ::: This study investigated the relationships between specific performance components, eye-hand coordination, visuomotor integration, in-hand manipulation, and handwriting skill. ::: ::: ::: METHOD ::: A sample of 48 typical first grade students were identified as good and poor handwriters by their teachers. Each child completed the Motor Accuracy Test; the Developmental Test of Visual-Motor Integration (VMI); two tests of in-hand manipulation, including a rotation and a translation task; and the Minnesota Handwriting Test (MHT). ::: ::: ::: RESULTS ::: All test scores for the subjects with good handwriting were significantly higher than those of the subjects with poor handwriting. Each performance component test was significantly correlated to MHT scores. Translation, VMI, and rotation scores were significant predictors of MHT scores, accounting for almost 73% of variance. A discriminant analysis using the performance components correctly classified 98% of the students as good or poor handwriters. ::: ::: ::: CONCLUSION ::: In-hand manipulation has significant association to handwriting skill.


title: Caspase-9: structure, mechanisms and clinical application

abstract: // Ping Li 1,2,3 , Libin Zhou 1 , Ting Zhao 1,2,3 , Xiongxiong Liu 1,2,3 , Pengcheng Zhang 1,2,3,4 , Yan Liu 1,2,3,4 , Xiaogang Zheng 1,2,3,4 and Qiang Li 1,2,3 1 Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, People’s Republic of China 2 Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, People’s Republic of China 3 Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou, People’s Republic of China 4 University of Chinese Academy of Sciences, Beijing, People’s Republic of China Correspondence to: Qiang Li, email: // Keywords : caspase-9, apoptosis, phosphorylation, alternative splicing, iCasp9 Received : October 12, 2016 Accepted : January 22, 2017 Published : February 04, 2017 Abstract As the most intensively studied initiator caspase, caspase-9 is a key player in the intrinsic or mitochondrial pathway which is involved in various stimuli, including chemotherapies, stress agents and radiation. Caspase-9 is activated on the apoptosome complex to remain catalytic status and is thought of involving homo-dimerization monomeric zymogens. Failing to activate caspase-9 has profound physiological and pathophysiological outcomes, leading to degenerative and developmental disorders even cancer. To govern the apoptotic commitment process appropriately, plenty of proteins and small molecules involved in regulating caspase-9. Therefore, this review is to summarize recent pertinent literature on the comprehensive description of the molecular events implicated in caspase-9 activation and inhibition, as well as the clinical trials in progress to give deep insight into caspase-9 for suppressing cancer. We hope that our concerns will be helpful for further clinical studies addressing the roles of caspase-9 and its regulators demanded to identify more effective solutions to overcome intrinsic apoptosis-related diseases especially cancer.


title: ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems

abstract: We propose an efficient, hybrid Fourier-wavelet regularized deconvolution (ForWaRD) algorithm that performs noise regularization via scalar shrinkage in both the Fourier and wavelet domains. The Fourier shrinkage exploits the Fourier transform's economical representation of the colored noise inherent in deconvolution, whereas the wavelet shrinkage exploits the wavelet domain's economical representation of piecewise smooth signals and images. We derive the optimal balance between the amount of Fourier and wavelet regularization by optimizing an approximate mean-squared error (MSE) metric and find that signals with more economical wavelet representations require less Fourier shrinkage. ForWaRD is applicable to all ill-conditioned deconvolution problems, unlike the purely wavelet-based wavelet-vaguelette deconvolution (WVD); moreover, its estimate features minimal ringing, unlike the purely Fourier-based Wiener deconvolution. Even in problems for which the WVD was designed, we prove that ForWaRD's MSE decays with the optimal WVD rate as the number of samples increases. Further, we demonstrate that over a wide range of practical sample-lengths, ForWaRD improves on WVD's performance.