Feeding ecology and social organization of wildcats (Felis silvestris) and domestic cats (Felis catus) in Scotland


title: Match Makers and Deal Breakers: Analyses of Assortative Mating in Newlywed Couples

abstract: We conducted a comprehensive analysis of assortative mating (i.e., the similarity between wives and husbands on a given char- acteristic) in a newlywed sample. These newlyweds showed (a) strong similarity in age, religiousness, and political orientation; (b) moderate similarity in education and verbal intelligence; (c) modest similarity in values; and (d) little similarity in matrix reasoning, self- and spouse-rated personality, emotional experience and expression, and attachment. Fur- ther analyses established that similarity was not simply due to back- ground variables such as age and education and reflected initial assortment (i.e., similarity at the time of marriage) rather than conver- gence (i.e., increasing similarity with time). Finally, marital satisfaction primarily was a function of the rater's own traits and showed little rela- tion to spousal similarity. What qualities do people find desirable in a potential life partner? This seemingly simple question actually can be answered in a number


Advanced Data Mining and Applications


title: Smart Cars on Smart Roads: An IEEE Intelligent Transportation Systems Society Update

abstract: To promote tighter collaboration between the IEEE Intelligent Transportation Systems Society and the pervasive computing research community, the authors introduce the ITS Society and present several pervasive computing-related research topics that ITS Society researchers are working on. This department is part of a special issue on Intelligent Transportation.


title: Towards a Near Universal Time Series Data Mining Tool: Introducing the Matrix Profile

abstract: Author(s): Yeh, Michael Chin-Chia | Advisor(s): Keogh, Eamonn | Abstract: The last decade has seen a flurry of research on all-pairs-similarity-search (or, self-join) for text, DNA, and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. Surprisingly, however, little progress has been made on addressing this problem for time series subsequences. In this thesis, we have introduced a near universal time series data mining tool called matrix profile which solves the all-pairs-similarity-search problem and caches the output in an easy-to-access fashion. The proposed algorithm is not only parameter-free, exact and scalable, but also applicable for both single and multidimensional time series. By building time series data mining methods on top of matrix profile, many time series data mining tasks (e.g., motif discovery, discord discovery, shapelet discovery, semantic segmentation, and clustering) can be efficiently solved. Because the same matrix profile can be shared by a diverse set of time series data mining methods, matrix profile is versatile and computed-once-use-many-times data structure. We demonstrate the utility of matrix profile for many time series data mining problems, including motif discovery, discord discovery, weakly labeled time series classification, and representation learning on domains as diverse as seismology, entomology, music processing, bioinformatics, human activity monitoring, electrical power-demand monitoring, and medicine. We hope the matrix profile is not the end but the beginning of many more time series data mining projects.


title: Gaze fixation and the neural circuitry of face processing in autism

abstract: Diminished gaze fixation is one of the core features of autism and has been proposed to be associated with abnormalities in the neural circuitry of affect. We tested this hypothesis in two separate studies using eye tracking while measuring functional brain activity during facial discrimination tasks in individuals with autism and in typically developing individuals. Activation in the fusiform gyrus and amygdala was strongly and positively correlated with the time spent fixating the eyes in the autistic group in both studies, suggesting that diminished gaze fixation may account for the fusiform hypoactivation to faces commonly reported in autism. In addition, variation in eye fixation within autistic individuals was strongly and positively associated with amygdala activation across both studies, suggesting a heightened emotional response associated with gaze fixation in autism. Autism is a pervasive developmental disorder associated with a unique profile of social and emotional behavior. The core symptomatology of autism highlights these deficits and includes diminished gaze fixation, lack of social or emotional reciprocity, and failure to develop ageappropriate peer relationships 1,2 . Recent studies have focused on attention to faces and face processing abilities in children with autism, because of the crucial importance of faces as a medium of social communication among humans 3‐8 . These studies demonstrate that inattention to faces is an early developmental sign of autism that is apparent as early as 1 year of age 9,10 . In addition, many children with autism are delayed in early, face-related social milestones, such as looking to another person’s face to reference that person’s reactions or to share their own experience of objects and events 11,12 . These findings raise the possibility that abnormalities in the perception of faces and their communicative signals may contribute to the social impairment that characterizes autism. Studies on brain function also show atypical patterns of brain activation in individuals with autism when processing faces. The most consistently reported and largest effect is in the fusiform gyrus, an area that is activated strongly during face processing in typically developing individuals but much less activated during these tasks in individuals with autism-spectrum disorders 13‐16 . Relatively little attention has been devoted to circuitry that is more highly activated in individuals with autism than in typically developing controls when processing faces. We predicted that individuals with autism would show hyperactivation in brain regions responsible for processing threatening social and emotional cues. We thus expected the autistic individuals to show hypoactivation in the fusiform gyrus along with hyperactivation in the amygdala in response to faces. For both of these brain regions, we also predicted that time spent fixating the eye region of the face would predict the magnitude of activation for the individuals with autism. Notably, to date, no study has yet reported on the relation between gaze fixation and brain activation patterns during processing of human faces in individuals with autism. We hypothesized that diminished gaze fixation is the proximal cause of the fusiform gyrus hypoactivation commonly reported for individuals with autism when processing faces. We thus predicted that fusiform gyrus hypoactivation would be associated with the lesser time spent in fixating on the eye region of faces by autistic individuals as compared with typically developing controls. Moreover, we also predicted that variations in time spent fixating on the eye region of the face would strongly predict amygdala activation in individuals with autism. To test these hypotheses, we conducted two separate studies, which tested emotion discrimination (Study I) and facial recognition (Study II). In each of these studies, we presented photographs of human faces to individuals with autism and typically developing controls while they were in a magnetic resonance imaging (MRI) scanner, using both standardized and naturalistic unfamiliar faces and naturalistic familiar faces (Fig. 1). In both studies, activation in the fusiform gyrus was strongly and positively correlated with the amount of time spent fixating the eye region in the autistic group, suggesting that diminished gaze fixation may account for the fusiform hypoactivation in response to faces commonly reported in autism. In addition, variation in gaze fixation among autistic individuals was strongly and positively associated with amygdala activation across both studies. This suggests that gaze fixation is associated with a heightened emotional response in autism.


title: Multi-channel real time active noise control system for infant incubators

abstract: Excessive noise levels inside infants incubators in neonatal intensive care units (NICU) contribute to number of harmful effects on the infant's health. This paper develops and implements practical active noise control (ANC) systems for the infant incubators. The filtered-X least mean square (FXLMS) algorithm is used to cancel the noise inside the incubator. The multi-channel and pseudo multi-channel ANC systems are proposed to enhance the noise cancellation performance in terms of cancellation gain and quiet zone. An experimental setup based on real Giraffe incubator from GE Healthcare is used for real-time experiments. The results show that the ANC system can dramatically reduce the noise and cost effective.


title: Segmentation and detection of first and second heart sounds (Si and S2) using variational mode decomposition

abstract: The segmentation algorithm, that separates the first heart sound Si and the second heart sound S2, is developed. The segmentation of phonocardiogram (PCG) signal is the first and the most important step for analyzing the PCG signal in the automatic diagnosis of heart sounds. The phonocardiogram signal is analyzed by employing variational mode decomposition (VMD) combined with Shannon Energy (SE) feature to locate the presence of heart sounds, and detect them from the processed data, forming the proposed Heart Sound Segmentation (HSS) scheme, namely HSS-VMD/SE. The performance of the algorithm has been evaluated using 53 cardiac periods obtained from 10 randomly selected phonocardiograph records. The simulation results have shown nearly 90 percent correct result (for less than 5 ms error), paving the way for further investigation of the diagnostic aspects of cardiac sounds in the daily clinical practice.


title: Static and dynamic characteristics of McKibben pneumatic artificial muscles

abstract: This paper reports mechanical testing and modeling results for the McKibben artificial muscle pneumatic actuator. This device first developed in the 1950's, contains an expanding tube surrounded by braided cords. The authors report static and dynamic length-tension testing results and derive a linearized model of these properties for three different models. The results are briefly compared with human muscle properties to evaluate the suitability of McKibben actuators for human muscle emulation in biologically based robot arms.<<ETX>>


title: Self-assembling machine

abstract: The design of a machine which is composed of homogeneous mechanical units is described. We show the design of both hardware and control software of the unit. Each unit can connect with other units and change the connection by itself. In spite of its simple mechanism, a set of these units realizes various mechanical functions. We developed the control software of the unit which realizes "self-assembly," one of the basic functions of this machine. A set of these units can form a given shape of the whole system by themselves. The units exchange information about local geometric relation by communication, and cooperate to form the whole shape through a diffusion-like process. There is no upper level controller to supervise these units, and the software of each unit is completely the same. Three actual units have been built to test the basic movements, and the function of self-assembly has been verified by computer simulation. >


title: Traditional Anishinabe Healing in a Clinical Setting: The Development of an Aboriginal Interdisciplinary Approach to Community-based Aboriginal Mental Health Care

abstract: Traditional medicine has been practiced by Aboriginal people for thousands of years at the community level. It is still practiced today outside of the mainstream health system by many Aboriginal people. However, providing this type of care in a clinical, health centre setting and in co-operation with western treatment methods is new, and requires a merging of traditional Aboriginal and western medical world views in order to develop protocols for service delivery that ensure the integrity of both systems. The groundwork required to ensure the safety of clients, providers, and organizations within the new integrated system is still largely undocumented. To address this gap, we studied factors that support the successful integration of traditional Aboriginal healing and western mental health care approaches, and document the experiences of clients and providers. To accomplish this we contextualize 10 years of experience of traditional healing services development with in-depth interviews and focus groups with 17 community service providers and 23 clients. We found that the development of traditional healing protocols, inter-professional education for providers and community members and a focus on client access to traditional Anishinabe health services provide the basis for the integration of western and traditional healing practices in the model under study. Our findings show integrated care resulted in positive experiences for clients and providers. We conclude that traditional healing approaches can be successfully integrated with clinical mental health services. Further research is necessary to improve our understanding of client experiences with this integrated approach and the impact on wholistic health and well-being.


title: The electronic stethoscope

abstract: Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.


title: Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources

abstract: We present a new approach to automatic summarization based on neural nets, called NetSum. We extract a set of features from each sentence that helps identify its importance in the document. We apply novel features based on news search query logs and Wikipedia entities. Using the RankNet learning algorithm, we train a pair-based sentence ranker to score every sentence in the document and identify the most important sentences. We apply our system to documents gathered from CNN.com, where each document includes highlights and an article. Our system significantly outperforms the standard baseline in the ROUGE-1 measure on over 70% of our document set.


2 Water Desalination by Membrane Distillation


title: Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression

abstract: Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, according to China’s 11th Five-Year Plan (2006-2010). In this paper, LTLF with an economic factor, GDP, is implemented. A support vector regression (SVR) is applied as the training algorithm to obtain the nonlinear relationship between load and the economic factor GDP to improve the accuracy of forecasting.


title: Pick and Place Without Geometric Object Models

abstract: We propose a novel formulation of robotic pick and place as a deep reinforcement learning (RL) problem. Whereas most deep RL approaches to robotic manipulation frame the problem in terms of low level states and actions, we propose a more abstract formulation. In this formulation, actions are target reach poses for the hand and states are a history of such reaches. We show this approach can solve a challenging class of pick-place and regrasping problems where the exact geometry of the objects to be handled is unknown. The only information our method requires is: 1) the sensor perception available to the robot at test time; 2) prior knowledge of the general class of objects for which the system was trained. We evaluate our method using objects belonging to two different categories, mugs and bottles, both in simulation and on real hardware. Results show a major improvement relative to a shape primitives baseline.


title: The Stanford Typed Dependencies Representation

abstract: This paper examines the Stanford typed dependencies representation, which was designed to provide a straightforward description of grammatical relations for any user who could benefit from automatic text understanding. For such purposes, we argue that dependency schemes must follow a simple design and provide semantically contentful information, as well as offer an automatic procedure to extract the relations. We consider the underlying design principles of the Stanford scheme from this perspective, and compare it to the GR and PARC representations. Finally, we address the question of the suitability of the Stanford scheme for parser evaluation.


title: Integrating State Representation Learning Into Deep Reinforcement Learning

abstract: Most deep reinforcement learning techniques are unsuitable for robotics, as they require too much interaction time to learn useful, general control policies. This problem can be largely attributed to the fact that a state representation needs to be learned as a part of learning control policies, which can only be done through fitting expected returns based on observed rewards. While the reward function provides information on the desirability of the state of the world, it does not necessarily provide information on how to distill a good, general representation of that state from the sensory observations. State representation learning objectives can be used to help learn such a representation. While many of these objectives have been proposed, they are typically not directly combined with reinforcement learning algorithms. We investigate several methods for integrating state representation learning into reinforcement learning. In these methods, the state representation learning objectives help regularize the state representation during the reinforcement learning, and the reinforcement learning itself is viewed as a crucial state representation learning objective and allowed to help shape the representation. Using autonomous racing tests in the TORCS simulator, we show how the integrated methods quickly learn policies that generalize to new environments much better than deep reinforcement learning without state representation learning.


title: Opposition-Based Differential Evolution

abstract: Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. In this work, opposite numbers have been utilized to improve the convergence rate of DE. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influence of dimensionality, population size, jumping rate, and various mutation strategies are also investigated. Additionally, the contribution of opposite numbers is empirically verified. We also provide a comparison of ODE to fuzzy adaptive DE (FADE). Experimental results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.


title: Surveillance Face Recognition Challenge

abstract: Face recognition (FR) is one of the most extensively investigated problems in computer vision. Significant progress in FR has been made due to the recent introduction of the larger scale FR challenges, particularly with constrained social media web images, e.g. high-resolution photos of celebrity faces taken by professional photo-journalists. However, the more challenging FR in unconstrained and low-resolution surveillance images remains largely under-studied. To facilitate more studies on developing FR models that are effective and robust for low-resolution surveillance facial images, we introduce a new Surveillance Face Recognition Challenge, which we call the QMUL-SurvFace benchmark. This new benchmark is the largest and more importantly the only true surveillance FR benchmark to our best knowledge, where low-resolution images are not synthesised by artificial down-sampling of native high-resolution images. This challenge contains 463,507 face images of 15,573 distinct identities captured in real-world uncooperative surveillance scenes over wide space and time. As a consequence, it presents an extremely challenging FR benchmark. We benchmark the FR performance on this challenge using five representative deep learning face recognition models, in comparison to existing benchmarks. We show that the current state of the arts are still far from being satisfactory to tackle the under-investigated surveillance FR problem in practical forensic scenarios. Face recognition is generally more difficult in an open-set setting which is typical for surveillance scenarios, owing to a large number of non-target people (distractors) appearing open spaced scenes. This is evidently so that on the new Surveillance FR Challenge, the top-performing CentreFace deep learning FR model on the MegaFace benchmark can now only achieve 13.2% success rate (at Rank-20) at a 10% false alarm rate.


title: The Discrete Weibull Distribution

abstract: The discrete Weibull distribution is defined to correspond with the Weibull distribution in continuous time. A few properties of the discrete Weibull distribution are discussed.


title: Next Basket Prediction using Recurring Sequential Patterns

abstract: Nowadays, a hot challenge for supermarket chains is to offer personalized services for their customers. Next basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable to capture at the same time the different factors influencing the customer's decision process: co-occurrency, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.


title: A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients

abstract: Regular physical activity is known to be beneficial to people suffering from diabetes type 2. Nevertheless, most such people are sedentary. Smartphones create new possibilities for helping people to adhere to their physical activity goals, through continuous monitoring and communication, coupled with personalized feedback. We provided 27 sedentary diabetes type 2 patients with a smartphone-based pedometer and a personal plan for physical activity. Patients were sent SMS messages to encourage physical activity between once a day and once per week. Messages were personalized through a Reinforcement Learning (RL) algorithm which optimized messages to improve each participant's compliance with the activity regimen. The RL algorithm was compared to a static policy for sending messages and to weekly reminders. Our results show that participants who received messages generated by the RL algorithm increased the amount of activity and pace of walking, while the control group patients did not. Patients assigned to the RL algorithm group experienced a superior reduction in blood glucose levels (HbA1c) compared to control policies, and longer participation caused greater reductions in blood glucose levels. The learning algorithm improved gradually in predicting which messages would lead participants to exercise. Our results suggest that a mobile phone application coupled with a learning algorithm can improve adherence to exercise in diabetic patients. As a learning algorithm is automated, and delivers personalized messages, it could be used in large populations of diabetic patients to improve health and glycemic control. Our results can be expanded to other areas where computer-led health coaching of humans may have a positive impact.


title: Evaluation of the ARTutor augmented reality educational platform in tertiary education

abstract: Augmented Reality (AR) has seen a significant increase in applications in a number of fields in recent years. One of the areas in which AR has been applied to is education, most commonly by means of augmenting educational books. The present paper builds on previous work by using an AR authoring environment and a mobile application, developed by the authors, in undergraduate courses at the Eastern Macedonia & Thrace Institute of Technology. Using the authoring tool, the students were able to enhance existing secondary education textbooks by adding digital content to them, and, using the mobile application, view the digital content and retrieve information from the textual content of the books by asking questions in natural language form. At the end of the semester, the students were asked to evaluate the ARTutor platform by means of questionnaires and the SECTIONS framework. This study presents and discusses the results of this evaluation exercise and proposes new directions of the research.


title: Approximate Analytical Solutions of Power Flow Equations Based on Multi-Dimensional Holomorphic Embedding Method

abstract: It is well known that closed-form analytical solutions for AC power flow equations do not exist in general. This paper proposes a multi-dimensional holomorphic embedding method (MDHEM) to obtain an explicit approximate analytical AC power-flow solution by finding a physical germ solution and arbitrarily embedding each power, each load or groups of loads with respective scales. Based on the MDHEM, the complete approximate analytical solutions to the power flow equations in the high-dimensional space become achievable, since the voltage vector of each bus can be explicitly expressed by a convergent multivariate power series of all the loads. Unlike the traditional iterative methods for power flow calculation and inaccurate sensitivity analysis method for voltage control, the algebraic variables of a power system in all operating conditions can be prepared offline and evaluated online by only plugging in the values of any operating conditions into the scales of the non-linear multivariate power series. Case studies implemented on the 4-bus test system and the IEEE 14-bus standard system confirm the effectiveness of the proposed method.


title: Some physical layer issues of wide-band cognitive radio systems

abstract: Recent study by FCC spectrum policy task force (SPTF) found that while the available spectrum becomes increasingly scarce, the assigned spectrum is significantly underutilized. Cognitive radio technology holds the key promise to solve such problems. In this paper, we investigate some physical layer issues of wide-band cognitive radio systems. Specifically, we discuss the physical layer signal structure to support flexible operations required by wide-band cognitive radios. We analyze the performances of generalized power sensing and waveform sensing. And we study the adjacent channel interference mechanisms focusing on the interference caused by time-domain signal truncation


title: Towards meaning change: experience goals driving design space expansion

abstract: Experience design is a relatively new approach to product design. While there are several possible starting points in designing for positive experiences, we start with experience goals that state a profound source for a meaningful experience. In this paper, we investigate three design cases that used experience goals as the starting point for both incremental and radical design, and analyse them from the perspective of their potential for design space expansion. Our work addresses the recent call for design research directed toward new interpretations of what could be meaningful to people, which is seen as the source for creating new meanings for products, and thereby, possibly leading to radical innovations. Based on this idea, we think about the design space as a set of possible concepts derived from deep meanings that experience goals help to communicate. According to our initial results from the small-scale touchpoint design cases, the type of experience goals we use seem to have the potential to generate not only incremental but also radical design ideas.


title: BAG: Managing GPU as Buffer Cache in Operating Systems

abstract: This paper presents the design, implementation and evaluation of BAG, a system that manages GPU as the buffer cache in operating systems. Unlike previous uses of GPUs, which have focused on the computational capabilities of GPUs, BAG is designed to explore a new dimension in managing GPUs in heterogeneous systems where the GPU memory is an exploitable but always ignored resource. With the carefully designed data structures and algorithms, such as concurrent hashtable, log-structured data store for the management of GPU memory, and highly-parallel GPU kernels for garbage collection, BAG achieves good performance under various workloads. In addition, leveraging the existing abstraction of the operating system not only makes the implementation of BAG non-intrusive, but also facilitates the system deployment.


title: Multiple primer pairs for polymerase chain reaction (PCR) amplification of human parvovirus B19 DNA

abstract: Abstract Human parvovirus B19 is the etiologic agent of erythema infectiosum and transient aplastic crisis in patients with hemolytic anemias and has been associated with fetal death, arthritis, and chronic anemia. Acute B19 infection is best diagnosed by detection of IgM antibodies, whereas the diagnosis of chronic infection often requires the sensitivity of PCR to demonstrate presence of virus over time. To improve our ability to detect B19 DNA by polymerase chain reaction (PCR), we evaluated 19 primers combined into 16 different primer pairs for their ability to detect temporally and geographically diverse B19 isolates. All 16 pairs reacted with all isolates tested but with different sensitivity. Sequence analysis showed few nucleotide changes compared with published sequences. These changes did not explain observed differences in sensitivity between primer pairs. The most sensitive primer pairs detected 350 to 3500 DNA copies after 35 cycles. A second amplification cycle with nested primers improved the sensitivity 100-fold. These 16 primer pairs provide the diagnostic virologist with multiple options for B19 PCR assays.


title: Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation

abstract: Most existing zero-shot learning approaches exploit transfer learning via an intermediate-level semantic representation such as visual attributes or semantic word vectors. Such a semantic representation is shared between an annotated auxiliary dataset and a target dataset with no annotation. A projection from a low-level feature space to the semantic space is learned from the auxiliary dataset and is applied without adaptation to the target dataset. In this paper we identify an inherent limitation with this approach. That is, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding, to solve it. It is ‘transductive’ in that unlabelled target data points are explored for projection adaptation, and ‘multi-view’ in that both low-level feature (view) and multiple semantic representations (views) are embedded to rectify the projection shift. We demonstrate through extensive experiments that our framework (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) achieves state-of-the-art recognition results on image and video benchmark datasets, and (4) enables novel cross-view annotation tasks.


title: Montreal Cognitive Assessment (MoCA)-Italian version: regression based norms and equivalent scores

abstract: The Montreal Cognitive Assessment (MoCA) is a brief cognitive screening instrument developed by Nasreddine et al. to detect mild cognitive impairment, a high-risk condition for Alzheimer’s disease and other forms of dementia. In this study we report normative data on the MoCA-Italian version, collected on a sample of 225 Italian healthy subjects ranged in age between 60 and 80 years, and in formal education from 5 to 23 years. The global normal cognition was established in accordance with the Mini–Mental State Examination score and with the Prose Memory Test score (Spinnler and Tognoni, Ital J Neurol Sci 6:25–27, 1987). None of the participants had a history of psychiatric, neurological, cerebrovascular disorders or brain injury or took drugs affecting cognition. Linear regression analysis was performed to evaluate the potential effect of age, education and sex on the MoCA total performance score. We provide correction grids to adjust raw scores and equivalent scores with cut-off value to allow comparison between MoCA performance and others neuropsychological test scores that can be administered on the same subject.


title: Unsupervised Joint Alignment of Complex Images

abstract: Many recognition algorithms depend on careful positioning of an object into a canonical pose, so the position of features relative to a fixed coordinate system can be examined. Currently, this positioning is done either manually or by training a class-specialized learning algorithm with samples of the class that have been hand-labeled with parts or poses. In this paper, we describe a novel method to achieve this positioning using poorly aligned examples of a class with no additional labeling. Given a set of unaligned examplars of a class, such as faces, we automatically build an alignment mechanism, without any additional labeling of parts or poses in the data set. Using this alignment mechanism, new members of the class, such as faces resulting from a face detector, can be precisely aligned for the recognition process. Our alignment method improves performance on a face recognition task, both over unaligned images and over images aligned with a face alignment algorithm specifically developed for and trained on hand-labeled face images. We also demonstrate its use on an entirely different class of objects (cars), again without providing any information about parts or pose to the learning algorithm.


title: Unilateral winged scapula: Clinical and electrodiagnostic experience with 128 cases, with special attention to long thoracic nerve palsy

abstract: INTRODUCTION ::: In this study we report a large series of patients with unilateral winged scapula (WS), with special attention to long thoracic nerve (LTN) palsy. ::: ::: ::: METHODS ::: Clinical and electrodiagnostic data were collected from 128 patients over a 25-year period. ::: ::: ::: RESULTS ::: Causes of unilateral WS were LTN palsy (n = 70), spinal accessory nerve (SAN) palsy (n = 39), both LTN and SAN palsy (n = 5), facioscapulohumeral dystrophy (FSH) (n = 5), orthopedic causes (n = 11), voluntary WS (n = 6), and no definite cause (n = 2). LTN palsy was related to neuralgic amyotrophy (NA) in 61 patients and involved the right side in 62 patients. ::: ::: ::: DISCUSSION ::: Clinical data allow for identifying 2 main clinical patterns for LTN and SAN palsy. Electrodiagnostic examination should consider bilateral nerve conduction studies of the LTN and SAN, and needle electromyography of their target muscles. LTN palsy is the most frequent cause of unilateral WS and is usually related to NA. Voluntary WS and FSH must be considered in young patients. Muscle Nerve 57: 913-920, 2018.


title: One year outcomes and costs following a vertebral fracture

abstract: Vertebral fractures are believed to be important predictors for future vertebral and other fractures, leading to at least a 4- to 5-fold increase in the risk of subsequent fractures. However, little is known about their associated near-term costs. The purpose of this study was to quantify the subsequent fracture and cost outcomes emanating from patients with an incident vertebral fracture. A probabilistic decision analysis model was developed to estimate the expected cost of all subsequent fractures. We ran Kaplan-Meier time-to-event models on placebo patients in risedronate’s pivotal phase III clinical trial data to determine the cumulative incidence or probabilities of all fractures within one year of an incident vertebral fracture. Unit costs for health care payers in the USA and Sweden for vertebral, hip, other, and forearm/wrist fractures were multiplied by fracture probabilities to generate the expected costs of new fractures within one year of incident vertebral fractures. Our analysis found that that 26.1% of vertebral fracture patients with a mean age of 74 years refractured within 1 year (vertebral 17.4%; hip 3.6%; “other” 3.5%; forearm/wrist 1.6%). The calculated medical costs for those patients who refracture within 1 year was $5906 and €3670 for the USA and Sweden, respectively, while the weighted average cost across all patients (refracture and non-fracture) within a year of their incident fracture was $1541 (USA) and €958 (Sweden). These results suggest that therapies with proven, rapid efficacy may offer important economic value to healthcare payers, providers and patients.


title: Knowledge modeling fore-agriculture using ontology

abstract: Agriculture is considered to be the backbone of the country's economy. The emergence of ICT in the last couple of years has opened new challenges in the field of knowledge management concerning to distribution, sharing and integration. It is true that a lot of good practices related to agriculture are transferred without being well documented so most part of these practices remain tacit rather than explicit. Therefore, it is necessary to effectively manage knowledge for future generations before it disappears as a result of technological advancement. Knowledge management is considered as a challenge. In this paper, we have presented the knowledge management approach in the domain of agriculture, its results are presented by prototype implementation and its effectiveness has been checked by evaluation. This knowledge management approach assists the stakeholders to share and understand the domain and instance level knowledge, removes ambiguities as well as integration with other sources of knowledge compared to the traditional approaches of information management.


title: A Case Study of the Health of an Augmented Reality Software Ecosystem: Vuforia

abstract: Augmented Reality is becoming increasingly popular. The success of a platform is typically observed by measuring the health of the software ecosystem surrounding it. In this paper, we take a closer look at the Vuforia ecosystem’s health by mining the Vuforia platform application repository. It is observed that the developer ecosystem is the strength of the platform. We also determine that Vuforia could be the biggest player in the market if they lay its focus on specific types of app development.


title: Consumer trust in an Internet store

abstract: The study reported here raises some questions about the conventional wisdom that the Internet creates a “level playing field” for large and small retailers and for retailers with and without an established reputation. In our study, consumers recognized differences in size and reputation among Internet stores, and those differences influenced their assessments of store trustworthiness and their perception of risk, as well as their willingness to patronize the store. After describing our research methods and results, we draw some implications for Internet merchants.


title: Understanding the Decision-Making Process for Personal Investments

abstract: Contingent upon the circumstances, “six dominant pathways” are part of, or all of, the major forms of decision making, that is perception, information, judgment, and decision choice. These circumstances entail the degree of an individual’s expertise, completeness of information sources, stableness of the environment, and time pressures.


GUTI Reallocation Demystified: Cellular Location Tracking with Changing Temporary Identifier


The Neurohistochemistry of Mammalian Ureter: A New Combination of Histochemical Procedures to Demonstrate Adrenergic, Cholinergic and Chromaffin Structures in Ureter


title: Clustering Voices in The Waste Land

abstract: T.S. Eliot’s modernist poem The Waste Land is often interpreted as collection of voices which appear multiple times throughout the text. Here, we investigate whether we can automatically cluster existing segmentations of the text into coherent, expert-identified characters. We show that clustering The Waste Land is a fairly difficult task, though we can do much better than random baselines, particularly if we begin with a good initial segmentation.


title: Trachea: anatomy and physiology.

abstract: The windpipe (trachea) is a tube of 12 cm length connecting the larynx to the principal bronchi that lead to the lungs. The main functions of the trachea comprise air flow into the lungs, mucociliary clearance, and humidification and warming of air. Mucociliary clearance is achieved by kinocilia and goblet cells in the mucosa, and by tracheal glands. The trachea develops from the endodermal lining of the foregut in interaction with the visceral mesoderm. During adult life, different types of stem cells reside in the mucosal epithelium and glandular ducts. Recently, cholinergic chemosensory cells have been described in the trachea.


title: Energy-Aware Fault Tolerant Task offloading of Mobile Cloud Computing

abstract: With all the hardware advances that have beenachieved lately relating to hand-held mobile devices, stillresource-intensive applications consider an important issue. Theheavy computational tasks of these applications cannot beprocessed in the mobile device itself because of their limitedprocessing and storage capabilities. Recently, many attemptshave been achieved to handle this issue. Most of these attemptsare depending on utilizing remote servers of the cloudenvironment. This process which takes advantageous of cloudservices allows mobile users offloading their computationallycomplicated tasks to be processed in remote servers of cloudenvironment, giving the birth to what is called mobile cloudcomputing model. Despite the benefits that outcome from taskoffloading process, challenges of energy efficiency (e.g. energyconsumption for task processing), reliability (e.g. node failure),and time management (e.g. task deadline and execution time) stillneed to be significantly addressed. In this paper, we propose anovel scheduling technique based on DNA combinations andgenetic algorithm processing under the precedence level. Thistechnique is suggested to decrease the ratio of energyconsumption, minimize the processing time of the task executionwithout exceeding the task deadline, and provide reliability byretrieving the processed data successfully by the mobile deviceuser and avoid task failure in mobile cloud computing model.


title: A Comparative Study of Learning-to-Rank Techniques for Tag Recommendation

abstract: Tags have become very popular on the Web 2.0 as they facilitate and encourage users to create and share their own content. In this context, there is a large interest in developing strategies to recommend relevant and useful tags for a target object, improving the quality of the generated tags and of the Information Retrieval (IR) services that use them as data source. Several existing tag recommendation strategies treat the problem as a multiple candidate tag ranking problem, recommending tags that are in top positions of the generated ranking. This motivates the use of Learning-to- Rank (L2R) based strategies to automatically “learn” good tag ranking functions. However, previous work has explored only three diﬀerent L2R techniques, namely, Genetic Programming (GP), RankSV M and RankBoost, comparing at most two of them with respect to eﬀectiveness. In contrast, we here perform a much more comprehensive comparative study of the use of L2R techniques for tag recommendation. Speciﬁcally, we compare eight diﬀerent L2R techniques, namely, Random Forest (RF), MART, λ-MART, ListNet, AdaRank and the three aforementioned techniques, with respect to both eﬀectiveness (i.e., precision, NDCG) and eﬃciency (i.e., time complexity). We perform experiments using real data collected from ﬁve popular Web 2.0 applications, namely, Bibsonomy, LastFM, MovieLens, YahooVideo and YouTube. Our results show that the best L2R based strategy signiﬁcantly outperforms the best state-of-the-art unsupervised technique (by up to 29% in NDCG). Moreover, unlike existing comparisons of diﬀerent L2R techniques in other domains, we ﬁnd that, for tag recommendation, there is a clear winning group of methods (RF, MART and λ-MART) with a slight advantage of two (RF and λ-MART) over the other, with gains in NDCG ranging from 4% to 12% over the best of the remaining alternatives considered. We also ﬁnd that recommendation time, despite some variation among the diﬀerent methods, is under 1.3 seconds, on average (in the worst case scenario), for all L2R methods, which conﬁrms the feasibility of the L2R approach for tag recommendation.


Voiceprint analysis using Perceptual Linear Prediction and Support Vector Machines for detecting persons with Parkinson ’ s disease


title: Policy Optimization by Genetic Distillation

abstract: Genetic algorithms have been widely used in many practical optimization problems. Inspired by natural selection, operators, including mutation, crossover and selection, provide effective heuristics for search and black-box optimization. However, they have not been shown useful for deep reinforcement learning, possibly due to the catastrophic consequence of parameter crossovers of neural networks. Here, we present Genetic Policy Optimization (GPO), a new genetic algorithm for sample-efficient deep policy optimization. GPO uses imitation learning for policy crossover in the state space and applies policy gradient methods for mutation. Our experiments on MuJoCo tasks show that GPO as a genetic algorithm is able to provide superior performance over the state-of-the-art policy gradient methods and achieves comparable or higher sample efficiency.


title: Ptychnet: CNN based fourier ptychography

abstract: Fourier ptychography is an imaging technique that overcomes the diffraction limit of conventional cameras with applications in microscopy and long range imaging. Diffraction blur causes resolution loss in both cases. In Fourier ptychography, a coherent light source illuminates an object, which is then imaged from multiple viewpoints. The reconstruction of the object from these set of recordings can be obtained by an iterative phase retrieval algorithm. However, the retrieval process is slow and does not work well under certain conditions. In this paper, we propose a new reconstruction algorithm that is based on convolutional neural networks and demonstrate its advantages in terms of speed and performance.


title: Auditable $\sigma $ -Time Outsourced Attribute-Based Encryption for Access Control in Cloud Computing

abstract: As a sophisticated mechanism for secure fine-grained access control over encrypted data, ciphertext-policy attribute-based encryption (CP-ABE) is one of the highly promising candidates for cloud computing applications. However, there exist two main long-lasting open problems of CP-ABE that may limit its wide deployment in commercial applications. One is that decryption yields expensive pairing cost which often grows with the increase of access policy size. The other is that one is granted access privilege for unlimited times as long as his attribute set satisfies the access policy of a given ciphertext. Such powerful access rights, which are provided by CP-ABE, may be undesirable in real-world applications (e.g., pay-as-you-use). To address the above drawbacks, in this paper, we propose a new notion called auditable $\sigma $ -time outsourced CP-ABE , which is believed to be applicable to cloud computing. In our notion, expensive pairing operation incurred by decryption is offloaded to cloud and meanwhile, the correctness of the operation can be audited efficiently. Moreover, the notion provides $\sigma $ -time fine-grained access control . The cloud service provider may limit a particular set of users to enjoy access privilege for at most $\sigma $ times within a specified period. As of independent interest, the notion also captures key-leakage resistance . The leakage of a user’s decryption key does not help a malicious third party in decrypting the ciphertexts belonging to the user. We design a concrete construction (satisfying our notion) in the key encapsulation mechanism setting based on Rouselakis and Waters (prime order) CP-ABE, and further present security and extensive experimental analysis to highlight the scalability and efficiency of our construction.


title: Test-enhanced learning in the classroom: Long-term improvements from quizzing.

abstract: Three experiments examined whether quizzing promotes learning and retention of material from a social studies course with sixth grade students from a suburban middle school. The material used in the experiments was the course material students were to learn and some of the dependent measures were the actual tests on which students received grades. In within-subject designs, students received three low-stakes multiple-choice quizzes in Experiments 1 and 2 and performance on quizzed items was compared to that on items that were presented twice (Experiment 2) or items that were not presented on the initial quizzes (Experiments 1 and 2). We found that students’ performance on both chapter exams and semester exams improved following quizzing relative to either not being quizzed or relative to the twice-presented items. In Experiment 3, students were given one multiple-choice quiz in class and encouraged to quiz themselves outside of class using a Web-based system. The assessment in this experiment was a short answer test in which students had to produce answers, but we also used multiple-choice tests. Once again, we found that quizzing of material produced a positive effect on chapter and semester exams. These results show the robustness of retrieval practice via testing as a learning mechanism in a classroom setting using the subject matter of the course and (in most cases) the tests on which students received grades as the dependent measures. Our results add to a growing body of evidence that retrieval practice in the classroom can boost academic performance.


title: On Using Twitter to Monitor Political Sentiment and Predict Election Results

abstract: The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an ::: electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General ::: Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using ::: supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using ::: both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during ::: an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content.


title: Reassembleable Disassembling

abstract: Reverse engineering has many important applications in computer security, one of which is retrofitting software for safety and security hardening when source code is not available. By surveying available commercial and academic reverse engineering tools, we surprisingly found that no existing tool is able to disassemble executable binaries into assembly code that can be correctly assembled back in a fully automated manner, even for simple programs. Actually in many cases, the resulted disassembled code is far from a state that an assembler accepts, which is hard to fix even by manual effort. This has become a severe obstacle. People have tried to overcome it by patching or duplicating new code sections for retrofitting of executables, which is not only inefficient but also cumbersome and restrictive on what retrofitting techniques can be applied to. ::: ::: In this paper, we present UROBOROS, a tool that can disassemble executables to the extent that the generated code can be assembled back to working binaries without manual effort. By empirically studying 244 binaries, we summarize a set of rules that can make the disassembled code relocatable, which is the key to reassembleable disassembling. With UROBOROS, the disassembly-reassembly process can be repeated thousands of times. We have implemented a prototype of UROBOROS and tested over the whole set of GNU Coreutils, SPEC2006, and a set of other real-world application and server programs. The experiment results show that our tool is effective with a very modest cost.


title: Comparative biochemistry of CO2 fixation and the evolution of autotrophy

abstract: Carbon dioxide fixation is a polyphyletic trait that has evolved in widely separated prokaryotic branches. The three principal CO2-assimilation pathways are (i) the reductive pentose-phosphate cycle, i. e. the Calvin-Benson cycle; (ii) the reductive citric acid (or Arnon) cycle; and (iii) the net synthesis of acetyl-CoA from CO/CO2, or Wood pathway. Sequence analysis and the comparative biochemistry of these routes suggest that all of them were shaped to a considerable extent by the evolutionary recruitment of enzymes. Molecular phylogenetic trees show that the Calvin-Benson cycle was a relatively late development in the (eu)bacterial branch, suggesting that some form(s) of carbon assimilation may have been operative before chlorophyll-based photosynthesis. On the other hand, the ample phylogenetic distribution of both the Arnon and the Wood pathways does not allow us to infer which one of them is older. However, different lines of evidence, including experimental reports on the NiS/FeS-mediated C–C bond formation from CO and CH3SH are used here to argue that the first CO2-fixation route may have been a semienzymatic Wood-like pathway.


title: Adaptive image-based intersection volume

abstract: A method for image-based contact detection and modeling, with guaranteed precision on the intersection volume, is presented. Unlike previous image-based methods, our method optimizes a nonuniform ray sampling resolution and allows precise control of the volume error. By cumulatively projecting all mesh edges into a generalized 2D texture, we construct a novel data structure, the Error Bound Polynomial Image (EBPI), which allows efficient computation of the maximum volume error as a function of ray density. Based on a precision criterion, EBPI pixels are subdivided or clustered. The rays are then cast in the projection direction according to the non-uniform resolution. The EBPI data, combined with ray-surface intersection points and normals, is also used to detect transient edges at surface intersections. This allows us to model intersection volumes at arbitrary resolution, while avoiding the geometric computation of mesh intersections. Moreover, the ray casting acceleration data structures can be reused for the generation of high quality images.


title: Equal-loudness-level contours for pure tones

abstract: Equal-loudness-level contours provide the foundation for theoretical and practical analyses of intensity-frequency characteristics of auditory systems. Since 1956 equal-loudness-level contours based on the free-field measurements of Robinson and Dadson [Br. J. Appl. Phys. 7, 166–181 (1956)] have been widely accepted. However, in 1987 some questions about the general applicability of these contours were published [H. Fastl and E. Zwicker, Fortschritte der Akustik, DAGA ’87, pp. 189–193 (1987)]. As a result, a new international effort to measure equal-loudness-level contours was undertaken. The present paper brings together the results of 12 studies starting in the mid-1980s to arrive at a new set of contours. The new contours estimated in this study are compared with four sets of classic contours taken from the available literature. The contours described by Fletcher and Munson [J. Acoust. Soc. Am. 5, 82–108 (1933)] exhibit some overall similarity to our proposed estimated contours in the mid-frequency ran...


title: Rule-based OWL Modeling with ROWLTab Protege Plugin

abstract: It has been argued that it is much easier to convey logical statements using rules rather than OWL (or description logic (DL)) axioms. Based on recent theoretical developments on transformations between rules and DLs, we have developed ROWLTab, a Protege plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL 2 DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule. In this paper, we present ROWLTab, together with a user evaluation of its effectiveness compared to entering axioms using the standard Protege interface. Our evaluation shows that modeling with ROWLTab is much quicker than the standard interface, while at the same time, also less prone to errors for hard modeling tasks.


title: Fast Nearest Neighbor Search on Road Networks

abstract: Nearest neighbor (NN) queries have been extended from Euclidean spaces to road networks. Existing approaches are either based on Dijkstra-like network expansion or NN/distance precomputation. The former may cause an explosive number of node accesses for sparse datasets because all nodes closer than the NN to the query must be visited. The latter, e.g., the Voronoi Network Nearest Neighbor (VN3) approach, can handle sparse datasets but is inappropriate for medium and dense datasets due to its high precomputation and storage overhead. In this paper, we propose a new approach that indexes the network topology based on a novel network reduction technique. It simplifies the network by replacing the graph topology with a set of interconnected tree-based structures called SPIE’s. An nd index is developed for each SPIE and our new (k)NN search algorithms on an SPIE follow a predetermined tree path to avoid costly network expansion. By mathematical analysis and experimental results, our new approach is shown to be efficient and robust for various network topologies and data distributions.


title: On the Character of Phishing URLs: Accurate and Robust Statistical Learning Classifiers

abstract: Phishing attacks resulted in an estimated $3.2 billion dollars worth of stolen property in 2007, and the success rate for phishing attacks is increasing each year [17]. Phishing attacks are becoming harder to detect and more elusive by using short time windows to launch attacks. In order to combat the increasing effectiveness of phishing attacks, we propose that combining statistical analysis of website URLs with machine learning techniques will give a more accurate classification of phishing URLs. Using a two-sample Kolmogorov-Smirnov test along with other features we were able to accurately classify 99.3% of our dataset, with a false positive rate of less than 0.4%. Thus, accuracy of phishing URL classification can be greatly increased through the use of these statistical measures.


title: The origin of Korotkoff sounds and the accuracy of auscultatory blood pressure measurements

abstract: Abstract This study explores the hypothesis that the sharper, high frequency Korotkoff sounds come from resonant motion of the arterial wall, which begins after the artery transitions from a buckled state to an expanding state. The motions of one mass, two nonlinear springs, and one damper, driven by transmural pressure under the cuff, are used to model and compute the Korotkoff sounds according to principles of classical Newtonian physics. The natural resonance of this spring-mass-damper system provides a concise, yet rigorous, explanation for the origin of Korotkoff sounds. Fundamentally, wall stretching in expansion requires more force than wall bending in buckling. At cuff pressures between systolic and diastolic arterial pressure, audible vibrations (> 40 Hz) occur during early expansion of the artery wall beyond its zero pressure radius after the outward moving mass of tissue experiences sudden deceleration, caused by the discontinuity in stiffness between bucked and expanded states. The idealized spring-mass-damper model faithfully reproduces the time-domain waveforms of actual Korotkoff sounds in humans. Appearance of arterial sounds occurs at or just above the level of systolic pressure. Disappearance of arterial sounds occurs at or just above the level of diastolic pressure. Muffling of the sounds is explained by increased resistance of the artery to collapse, caused by downstream venous engorgement. A simple analytical model can define the physical origin of Korotkoff sounds, suggesting improved mechanical or electronic filters for their selective detection and confirming the disappearance of the Korotkoff sounds as the optimal diastolic end point.


Chatbot Evaluation and Database Expansion via Crowdsourcing


title: A proximity language model for information retrieval

abstract: The proximity of query terms in a document is a very important information to enable ranking models go beyond the "bag of word" assumption in information retrieval. This paper studies the integration of term proximity information into the unigram language modeling. A new proximity language model (PLM) is proposed which views query terms' proximity centrality as the Dirichlet hyper-parameter that weights the parameters of the unigram document language model. Several forms of proximity measure are developed to be used in PLM which could compute a query term's proximate centrality in a specific document. In experiments, the proximity language model is compared with the basic language model and previous works that combine the proximity information with language model using linear score combination. The experiment results show that the proposed model performs better in both top precision and average precision.


title: AoD-adaptive subspace codebook for channel feedback in FDD massive MIMO systems

abstract: Channel feedback is essential for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems to realize precoding and power allocation. Traditional codebooks for channel feedback, where the required number of feedback bits is proportional to the number of base station (BS) antennas, can not scale up with massive MIMO due to the large number of BS antennas. To solve this problem, in this paper, we propose an angle-of-departure (AoD) adaptive subspace codebook to reduce the codebook size and feedback overhead. Specifically, by leveraging the concept of angle coherence time, which implies that the path AoDs vary much slower than path gains, we propose an AoD-adaptive subspace codebook to quantize the channel vector in a more accurate way. We also provide performance analysis of the proposed AoD-adaptive subspace codebook, where we prove that the required number of feedback bits only scales linearly with the number of resolvable AoDs, which is much smaller than the number of BS antennas. This quantitative result is also verified by simulations.


title: Investigating Examination Behavior of Image Search Users

abstract: Image search engines show results differently from general Web search engines in three key ways: (1) most Web-based image search engines adopt the two-dimensional result placement instead of the linear result list; (2) image searches show snapshots instead of snippets (query-dependent abstracts of landing pages) on search engine result pages (SERPs); and (3) pagination is usually not (explicitly) supported on image search SERPs, and users can view results without having to click on the "next page'' button. Compared with the extensive study of user behavior in general Web search scenarios, there exists no thorough investigation how the different interaction mechanism of image search engines affects users' examination behavior. To shed light on this research question, we conducted an eye-tracking study to investigate users' examination behavior in image searches. We focus on the impacts of factors in examination including position, visual saliency, edge density, the existence of textual information, and human faces in result images. Three interesting findings indicate users' behavior biases: (1) instead of the traditional "Golden Triangle'' phenomena in the user examination patterns of general Web search, we observe a middle-position bias, (2) besides the position factor, the content of image results (e.g., visual saliency) affects examination behavior, and (3) some popular behavior assumptions in general Web search (e.g., examination hypothesis) do not hold in image search scenarios. We predict users' examination behavior with different impact factors. Results show that combining position and visual content features can improve prediction in image searches.


Recent Advances in Pediatric Use of Oral Paracetamol in Fever and Pain Management


Updates in lichen sclerosis: British Association of Dermatologists guidelines for the management of lichen sclerosus 2018


Human Papillomavirus Infection and Vaccination: Awareness and Knowledge of HPV and Acceptability of HPV Vaccine among Mothers of Teenage Daughters in Weihai, Shandong, China


title: Gamification of M-learning Mandarin as second language

abstract: This paper presents some empiric results on teaching basic Mandarin as second language to college students using gamification approach. This study shows some evidences that gamification outperform non-gamification teaching method in related to learning concentration, skills, feedback, and immersion. However, gamification method has some drawbacks in related to compliance of quiz difficulty with the level/learning progress, clarity of overall learning goal, and clarity of goal at each level. These strength and drawbacks of gamification design are expected to give valuable information for teachers in delivering Mandarin as second language.


title: Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints

abstract: We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (nonnegativity-constrained autoencoder), that learns features that show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposing data into parts and its prediction performance is tested on three standard image data sets and one text data set. The results indicate that the nonnegativity constraint forces the autoencoder to learn features that amount to a part-based representation of data, while improving sparsity and reconstruction quality in comparison with the traditional sparse autoencoder and nonnegative matrix factorization. It is also shown that this newly acquired representation improves the prediction performance of a deep neural network.


title: Rigor in Health-Related Research: Toward an Expanded Conceptualization

abstract: When the research process is viewed not merely as a set of methods for data collection and analysis but, instead, as an integrated series of arbitrary choices made by the researcher, it quickly becomes apparent that the prevailing conceptualization of rigor is much too narrow; indeed, the way rigor is currently conceptualized may well be responsible for the many errors that are commonly made in the research process. An expanded conceptualization of rigor is presented, and its implications for research into some critical health issues are discussed in some detail.


title: Inhibition of central angiotensin I conversion by oral captopril.

abstract: 1. The central action of captopril (D-3-mercapto-methylpropanoyl-L-proline, SQ 14225) in the rat was evaluated by measuring the effect of the orally administered drug on responses to angiotensin I given intracerebroventricularly. 2. The pressor and dipsogenic effects of angiotensin I (20 ng) injected into the third ventricle were reduced by about 50% for 4 h following the oral administration of captopril, 20mg/kg. Both responses returned to pretreatment control values 8 h after captopril administration. 3. Orally administered captopril affects the action of angiotensin I on the central nervous system thus providing an alternative explanation for the hypotensive effect of captopril. This effect if probably due to the direct passage of captopril through the blood brain barrier.


title: Emotion in games

abstract: Computer games are unique elicitors of emotion. Recognition of player emotion, dynamic construction of affective player models, and modelling emotions in non-playing characters, represent challenging areas of research and practice at the crossroads of cognitive and affective science, psychology, artificial intelligence and human-computer interaction. Techniques from AI and HCI can be used to recognize player affective states and to model emotion in non-playing characters. Multiple input modalities provide novel means for measuring player satisfaction and engagement. These data can then be used to adapt the gameplay to the player's state, to maximize player engagement and to close the affective game loop. ::: ::: The Emotion in Games workshop (EmoGames 2011 http://sirenproject. eu/content/acii-2011-workshop-emotion-games) will bring together researchers and practitioners in affective computing, user experience research, social psychology and cognition, machine learning, and AI and HCI, to explore topics in player experience research, affect induction, sensing and modelling and affect-driven game adaptation, and modelling of emotion in nonplaying characters. It will also provide new insights on how gaming can be used as a research platform, to induce and capture affective interactions with single and multiple users, and to model affect- and behaviour-related concepts, helping to operationalize concepts such as flow and engagement. ::: ::: The workshop will include a keynote, paper and poster presentations, and panel discussions. Selected papers will appear in a special issue of the IEEE Transactions on Affective Computing, "Emotion in Games", in mid-2013. ::: ::: The EmoGames2011 workshop is organized in coordination with the newly formed 'Emotion in Games' Special Interest Group (SIG) of the Humaine Association and the IEEE Computational Intelligence Society (CIS) Task Force on Player Satisfaction Modelling. We would like to thank all participants, as well as the members of the Program Committee, for their reviews of the workshop submissions: Elisabeth Andre, Ruth Aylett, Nadia Bianchi-Berthouze, Antonio Camurri, Marc Cavazza, Jonathan Gratch, Hatice Gunes, Dirk Heylen, Katherine Isbister, Stefanos Kollias, Maurizio Mancini, Anton Nijholt, Julian Togelius, Asimina Vasalou, Gualtiero Volpe, Catherine Pelachaud, and Tom Ziemke.


title: Smoking and Hormesis as Confounding Factors in Radiation Pulmonary Carcinogenesis

abstract: Confounding factors in radiation pulmonary carcinogenesis are passive and active cigarette smoke exposures and radiation hormesis. Significantly increased lung cancer risk from ionizing radiation at lung doses < 1 Gy is not observed in never smokers exposed to ionizing radiations. Residential radon is not a cause of lung cancer in never smokers and may protect against lung cancer in smokers. The risk of lung cancer found in many epidemiological studies was less than the expected risk (hormetic effect) for nuclear weapons and power plant workers, shipyard workers, fluoroscopy patients, and inhabitants of high-dose background radiation. The protective effect was noted for low- and mixed high- and low-linear energy transfer (LET) radiations in both genders. Many studies showed a protection factor (PROFAC) > 0.40 (40% avoided) against the occurrence of lung cancer. The ubiquitous nature of the radiation hormesis response in cellular, animal, and epidemiological studies negates the healthy worker effect as an explanation for radiation hormesis. Low-dose radiation may stimulate DNA repair/apoptosis and immunity to suppress and eliminate cigarette-smoke-induced transformed cells in the lung, reducing lung cancer occurrence in smokers.


title: The Empathy Quotient: An Investigation of Adults with Asperger Syndrome or High Functioning Autism, and Normal Sex Differences

abstract: Empathy is an essential part of normal social functioning, yet there are precious few instruments for measuring individual differences in this domain. In this article we review psychological theories of empathy and its measurement. Previous instruments that purport to measure this have not always focused purely on empathy. We report a new self-report questionnaire, the Empathy Quotient (EQ), for use with adults of normal intelligence. It contains 40 empathy items and 20 filler/control items. On each empathy item a person can score 2, 1, or 0, so the EQ has a maximum score of 80 and a minimum of zero. In Study I we employed the EQ with n = 90 adults (65 males, 25 females) with Asperger Syndrome (AS) or high-functioning autism (HFA), who are reported clinically to have difficulties in empathy. The adults with AS/HFA scored significantly lower on the EQ than n = 90 (65 males, 25 females) age-matched controls. Of the adults with AS/HFA, 81% scored equal to or fewer than 30 points out of 80, compared with only 12% of controls. In Study 2 we carried out a study of n = 197 adults from a general population, to test for previously reported sex differences (female superiority) in empathy. This confirmed that women scored significantly higher than men. The EQ reveals both a sex difference in empathy in the general population and an empathy deficit in AS/HFA.


title: Election campaigning on social media: Politicians, audiences and the mediation of political communication on Facebook and Twitter

abstract: Although considerable research has concentrated on online campaigning, it is still unclear how politicians use different social media platforms in political communication. Focusing on the German federal election campaign 2013, this article investigates whether election candidates address the topics most important to the mass audience and to which extent their communication is shaped by the characteristics of Facebook and Twitter. Based on open-ended responses from a representative survey conducted during the election campaign, we train a human-interpretable Bayesian language model to identify political topics. Applying the model to social media messages of candidates and their direct audiences, we find that both prioritize different topics than the mass audience. The analysis also shows that politicians use Facebook and Twitter for different purposes. We relate the various findings to the mediation of political communication on social media induced by the particular characteristics of audiences and sociot...


React Native application development – A comparison between native Android and React Native


title: The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions.

abstract: OBJECTIVE ::: To test the feasibility of creating a valid and reliable checklist with the following features: appropriate for assessing both randomised and non-randomised studies; provision of both an overall score for study quality and a profile of scores not only for the quality of reporting, internal validity (bias and confounding) and power, but also for external validity. ::: ::: ::: DESIGN ::: A pilot version was first developed, based on epidemiological principles, reviews, and existing checklists for randomised studies. Face and content validity were assessed by three experienced reviewers and reliability was determined using two raters assessing 10 randomised and 10 non-randomised studies. Using different raters, the checklist was revised and tested for internal consistency (Kuder-Richardson 20), test-retest and inter-rater reliability (Spearman correlation coefficient and sign rank test; kappa statistics), criterion validity, and respondent burden. ::: ::: ::: MAIN RESULTS ::: The performance of the checklist improved considerably after revision of a pilot version. The Quality Index had high internal consistency (KR-20: 0.89) as did the subscales apart from external validity (KR-20: 0.54). Test-retest (r 0.88) and inter-rater (r 0.75) reliability of the Quality Index were good. Reliability of the subscales varied from good (bias) to poor (external validity). The Quality Index correlated highly with an existing, established instrument for assessing randomised studies (r 0.90). There was little difference between its performance with non-randomised and with randomised studies. Raters took about 20 minutes to assess each paper (range 10 to 45 minutes). ::: ::: ::: CONCLUSIONS ::: This study has shown that it is feasible to develop a checklist that can be used to assess the methodological quality not only of randomised controlled trials but also non-randomised studies. It has also shown that it is possible to produce a checklist that provides a profile of the paper, alerting reviewers to its particular methodological strengths and weaknesses. Further work is required to improve the checklist and the training of raters in the assessment of external validity.


title: Graph Clustering with Dynamic Embedding

abstract: Graph clustering (or community detection) has long drawn enormous attention from the research on web mining and information networks. Recent literature on this topic has reached a consensus that node contents and link structures should be integrated for reliable graph clustering, especially in an unsupervised setting. However, existing methods based on shallow models often suffer from content noise and sparsity. In this work, we propose to utilize deep embedding for graph clustering, motivated by the well-recognized power of neural networks in learning intrinsic content representations. Upon that, we capture the dynamic nature of networks through the principle of influence propagation and calculate the dynamic network embedding. Network clusters are then detected based on the stable state of such an embedding. Unlike most existing embedding methods that are task-agnostic, we simultaneously solve for the underlying node representations and the optimal clustering assignments in an end-to-end manner. To provide more insight, we theoretically analyze our interpretation of network clusters and find its underlying connections with two widely applied approaches for network modeling. Extensive experimental results on six real-world datasets including both social networks and citation networks demonstrate the superiority of our proposed model over the state-of-the-art.


title: Aquaporin based biomimetic membrane in forward osmosis: Chemical cleaning resistance and practical operation

abstract: Abstract Aquaporin plays a promising role in fabricating high performance biomimetic forward osmosis (FO) membranes. However, aquaporin as a protein also has a risk of denaturation caused by various chemicals, resulting in a possible decay of membrane performance. The present study tested a novel aquaporin based biomimetic membrane in simulated membrane cleaning processes. The effects of cleaning agents on water flux and salt rejection were evaluated. The membrane showed a good resistance to the chemical agents. The water flux after chemical cleaning showed significant increases, particularly after cleaning with NaOCl and Alconox. Changes in the membrane structure and increased hydrophilicity in the surrounding areas of the aquaporin may be accountable for the increase in water permeability. The membrane shows stable salt rejection up to 99% after all cleaning agents were tested. A 15-day experiment with secondary wastewater effluent as the feed solution and seawater as the draw solution showed a stable flux and high salt rejection. The average rejection of the dissolved organic carbon from wastewater after the 15-day test was 90%. The results demonstrated that the aquaporin based biomimetic FO membrane exhibits chemical resistance for most agents used in membrane cleaning procedures, maintaining a stable flux and high salt rejection.


title: 2GHz band quadruple mode dielectric resonator filter for cellular base station

abstract: This paper describes a novel 2GHz bandpass filter using a novel quadruple mode dielectric resonator for compact cellular base station. We propose small high Q dielectric resonator technique using TM/sub 11//spl delta/-x, TM/sub 11//spl delta/-y, TE/sub 01//spl delta/-x and TE/sub 01//spl delta/-y modes. It has very high unloaded Q factor value of about 15000 at 2GHz band. We have designed and manufactured a 4-pole band filter using this resonator. Insertion loss is very low of less than 0.23 dB and high attenuation of 90dB. Outer dimensions are 48 /spl times/ 48 /spl times/ 42 mm.


title: All-but-the-Top: Simple and Effective Postprocessing for Word Representations

abstract: Real-valued word representations have transformed NLP applications; popular examples are word2vec and GloVe, recognized for their ability to capture linguistic regularities. In this paper, we demonstrate a {\em very simple}, and yet counter-intuitive, postprocessing technique -- eliminate the common mean vector and a few top dominating directions from the word vectors -- that renders off-the-shelf representations {\em even stronger}. The postprocessing is empirically validated on a variety of lexical-level intrinsic tasks (word similarity, concept categorization, word analogy) and sentence-level tasks (semantic textural similarity and { text classification}) on multiple datasets and with a variety of representation methods and hyperparameter choices in multiple languages; in each case, the processed representations are consistently better than the original ones.


title: Forward link capacity for integrated voice/data traffic in CDMA wireless local loops

abstract: A high speed data service will be provided by the next generation wireless local loops for Internet access and data file download. Consequently, the impact of high speed data service on the system capacity becomes an important issue for ongoing research and system design of wireless local loops, especially on the forward link due to asymmetrical traffic patterns. Due to their fixed nature, directional antennas can be used on wireless local loop terminals to enhance the forward link performance. This paper investigates the capacity of such a wide band CDMA wireless local loop system for data download applications.


title: How brains beware: neural mechanisms of emotional attention

abstract: Emotional processes not only serve to record the value of sensory events, but also to elicit adaptive responses and modify perception. Recent research using functional brain imaging in human subjects has begun to reveal neural substrates by which sensory processing and attention can be modulated by the affective significance of stimuli. The amygdala plays a crucial role in providing both direct and indirect top-down signals on sensory pathways, which can influence the representation of emotional events, especially when related to threat. These modulatory effects implement specialized mechanisms of 'emotional attention' that might supplement but also compete with other sources of top-down control on perception. This work should help to elucidate the neural processes and temporal dynamics governing the integration of cognitive and affective influences in attention and behaviour.


title: Social Politics: Agenda Setting and Political Communication on Social Media

abstract: Social media play an increasingly important role in political communication. Various studies investigated how individuals adopt social media for political discussion, to share their views about politics and policy, or to mobilize and protest against social issues. Yet, little attention has been devoted to the main actors of political discussions: the politicians. In this paper, we explore the topics of discussion of U.S. President Obama and the 50 U.S. State Governors using Twitter data and agenda-setting theory as a tool to describe the patterns of daily political discussion, uncovering the main topics of attention and interest of these actors. We examine over one hundred thousand tweets produced by these politicians and identify seven macro-topics of conversation, finding that Twitter represents a particularly appealing vehicle of conversation for American opposition politicians. We highlight the main motifs of political conversation of the two parties, discovering that Republican and Democrat Governors are more or less similarly active on Twitter but exhibit different styles of communication. Finally, by reconstructing the networks of occurrences of Governors’ hashtags and keywords related to political issues, we observe that Republicans and Democrats form two tight yet polarized cores, with a strongly different shared agenda on many issues of discussion.


title: PCA versus LDA

abstract: In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (linear discriminant analysis) are superior to those based on PCA (principal components analysis). In this communication, we show that this is not always the case. We present our case first by using intuitively plausible arguments and, then, by showing actual results on a face database. Our overall conclusion is that when the training data set is small, PCA can outperform LDA and, also, that PCA is less sensitive to different training data sets.


title: Scientific Article Summarization Using Citation-Context and Article's Discourse Structure

abstract: We propose a summarization approach for scientific articles which takes advantage of citation-context and the document discourse model. While citations have been previously used in generating scientific summaries, they lack the related context from the referenced article and therefore do not accurately reflect the article’s content. Our method overcomes the problem of inconsistency between the citation summary and the article’s content by providing context for each citation. We also leverage the inherent scientific article’s discourse for producing better summaries. We show that our proposed method effectively improves over existing summarization approaches (greater than 30% improvement over the best performing baseline) in terms of ROUGE scores on TAC2014 scientific summarization dataset. While the dataset we use for evaluation is in the biomedical domain, most of our approaches are general and therefore adaptable to other domains.


Towards Continuous Sign Language Recognition with Deep Learning


title: Attack chain detection

abstract: A targeted network intrusion typically evolves through multiple phases, termed the attack chain. When appropriate data are monitored, these phases will generate multiple events across the attack chain on a compromised host. It is shown empirically that events in different parts of the attack chain are largely independent under nonattack conditions. This suggests that a powerful detector can be constructed by combining across events spanning the attack. This article describes the development of such a detector for a larger network. To construct events that span the attack chain, multiple data sources are used, and the detector combines across events observed on the same machine, across local neighborhoods of machines linked by network communications, as well as across events observed on multiple computers. A probabilistic approach for evaluating the combined events is developed, and empirical investigations support the underlying assumptions. The detection power of the approach is studied by inserting plausible attack scenarios into observed network and host data, and an application to a real-world intrusion is given.


title: Failures of Gradient-Based Deep Learning

abstract: In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. However, it is important, for both theoreticians and practitioners, to gain a deeper understanding of the difficulties and limitations associated with common approaches and algorithms. We describe four types of simple problems, for which the gradient-based algorithms commonly used in deep learning either fail or suffer from significant difficulties. We illustrate the failures through practical experiments, and provide theoretical insights explaining their source, and how they might be remedied.


An Exploration into Improving Examinees' Acceptance of Participation in an Online Exam


title: Controlling and monitoring process in industrial automation using Zigbee

abstract: WSN based Zigbee network has been extensively used in monitoring and controlling purpose in industries. In this paper, deployment of the Zigbee based WSN for monitoring and controlling the power at each work station in the weaving industry is discussed. Here the traditional working of the weaving industry is discussed and the suitability of Zigbee in monitoring and direct load controlling for the efficient power utilization is proposed. The CC2431 module is considered for wireless transmission. The controlling action is made possible with the microcontroller present in the CC2431 module. The main aim of this work is used to monitor the different workstations at industries using Zigbee devices and to correspondingly cut off the power to the particular workstation when it is in idle state without distracting the other workstations. The issues related with power and transmission range of the proposed work are discussed.


title: A new synchronous frame current reference generation for hybrid active filter and system stability analysis

abstract: In this paper, the authors present a new synchronous frame current reference generation method for hybrid active power filter (HAPF), which can extract both harmonic and reactive current contained in the nonlinear load under the condition of unsymmetrical and non-sinusoidal source voltages. Furthermore, this method can extract selective order of harmonics, which enables the active filter to detect and compensate the lower order of harmonics while the passive counterpart compensates the residual harmonics. The stability of the system is also studied. Finally simulation and experimental results are presented to verify the effectiveness of the proposed method.


title: Proprioceptive Actuator Design in the MIT Cheetah: Impact Mitigation and High-Bandwidth Physical Interaction for Dynamic Legged Robots

abstract: Designing an actuator system for highly dynamic legged robots has been one of the grand challenges in robotics research. Conventional actuators for manufacturing applications have difficulty satisfying design requirements for high-speed locomotion, such as the need for high torque density and the ability to manage dynamic physical interactions. To address this challenge, this paper suggests a proprioceptive actuation paradigm that enables highly dynamic performance in legged machines. Proprioceptive actuation uses collocated force control at the joints to effectively control contact interactions at the feet under dynamic conditions. Modal analysis of a reduced leg model and dimensional analysis of DC motors address the main principles for implementation of this paradigm. In the realm of legged machines, this paradigm provides a unique combination of high torque density, high-bandwidth force control, and the ability to mitigate impacts through backdrivability. We introduce a new metric named the “impact mitigation factor” (IMF) to quantify backdrivability at impact, which enables design comparison across a wide class of robots. The MIT Cheetah leg is presented, and is shown to have an IMF that is comparable to other quadrupeds with series springs to handle impact. The design enables the Cheetah to control contact forces during dynamic bounding, with contact times down to 85 ms and peak forces over 450 N. The unique capabilities of the MIT Cheetah, achieving impact-robust force-controlled operation in high-speed three-dimensional running and jumping, suggest wider implementation of this holistic actuation approach.


title: Log Your Car: The Non-invasive Vehicle Forensics

abstract: Digital forensics is becoming an important feature for many embedded devices. In automotive systems, digital forensics involves multiple electronic control units (ECUs) used to support the connected and intelligent vehicle's technology. Digital evidence from these ECUs can be used in forensics investigation and analysis. Such a mechanism can potentially facilitate crash investigation, insurance claims and crime investigation. Issues related to forensics include the authenticity, integrity and privacy of the data. In this paper, the security of the forensic process and data in automotive systems is analysed. We propose an efficient, secure, privacy-preserving and reliable mechanism to provide a forensics data collection and storage process. A diagnostic application for smart phones, DiaLOG, is incorporated in the proposed process that uses a secure protocol to communicate the collected forensic data to a secure cloud storage. The proposed protocol for communicating forensic data is implemented to measure performance results and formally analysed using Scyther and CasperFDR with no known attack found.


title: Unsupervised Textual Grounding: Linking Words to Image Concepts

abstract: Textual grounding, i.e., linking words to objects in images, is a challenging but important task for robotics and human-computer interaction. Existing techniques benefit from recent progress in deep learning and generally formulate the task as a supervised learning problem, selecting a bounding box from a set of possible options. To train these deep net based approaches, access to a large-scale datasets is required, however, constructing such a dataset is time-consuming and expensive. Therefore, we develop a completely unsupervised mechanism for textual grounding using hypothesis testing as a mechanism to link words to detected image concepts. We demonstrate our approach on the ReferIt Game dataset and the Flickr30k data, outperforming baselines by 7.98% and 6.96% respectively.


title: SIN2: Stealth infection on neural network — A low-cost agile neural Trojan attack methodology

abstract: Deep Neural Network (DNN) has recently become the “de facto” technique to drive the artificial intelligence (AI) industry. However, there also emerges many security issues as the DNN based intelligent systems are being increasingly prevalent. Existing DNN security studies, such as adversarial attacks and poisoning attacks, are usually narrowly conducted at the software algorithm level, with the misclassification as their primary goal. The more realistic system-level attacks introduced by the emerging intelligent service supply chain, e.g. the third-party cloud based machine learning as a service (MLaaS) along with the portable DNN computing engine, have never been discussed. In this work, we propose a low-cost modular methodology-Stealth Infection on Neural Network, namely “SIN2”, to demonstrate the novel and practical intelligent supply chain triggered neural Trojan attacks. Our “SIN2” well leverages the attacking opportunities built upon the static neural network model and the underlying dynamic runtime system of neural computing framework through a bunch of neural Trojaning techniques. We implement a variety of neural Trojan attacks in Linux sandbox by following proposed “SIN2”. Experimental results show that our modular design can rapidly produce and trigger various Trojan attacks that can easily evade the existing defenses.