title: Adapting the right measures for K-means clustering

abstract: Clustering validation is a long standing challenge in the clustering literature. While many validation measures have been developed for evaluating the performance of clustering algorithms, these measures often provide inconsistent information about the clustering performance and the best suitable measures to use in practice remain unknown. This paper thus fills this crucial void by giving an organized study of 16 external validation measures for K-means clustering. Specifically, we first introduce the importance of measure normalization in the evaluation of the clustering performance on data with imbalanced class distributions. We also provide normalization solutions for several measures. In addition, we summarize the major properties of these external measures. These properties can serve as the guidance for the selection of validation measures in different application scenarios. Finally, we reveal the interrelationships among these external measures. By mathematical transformation, we show that some validation measures are equivalent. Also, some measures have consistent validation performances. Most importantly, we provide a guide line to select the most suitable validation measures for K-means clustering.


title: Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation

abstract: Despite the effectiveness of levodopa for treatment of Parkinson’s disease (PD), prolonged usage leads to development of motor complications, most notably levodopa-induced dyskinesia (LID). Persons with PD and their physicians must regularly modify treatment regimens and timing for optimal relief of symptoms. While standardized clinical rating scales exist for assessing the severity of PD symptoms, they must be administered by a trained medical professional and are inherently subjective. Computer vision is an attractive, non-contact, potential solution for automated assessment of PD, made possible by recent advances in computational power and deep learning algorithms. The objective of this paper was to evaluate the feasibility of vision-based assessment of parkinsonism and LID using pose estimation. Nine participants with PD and LID completed a levodopa infusion protocol, where symptoms were assessed at regular intervals using the Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson’s Disease Rating Scale (UPDRS). Movement trajectories of individual joints were extracted from videos of PD assessment using Convolutional Pose Machines, a pose estimation algorithm built with deep learning. Features of the movement trajectories (e.g. kinematic, frequency) were used to train random forests to detect and estimate the severity of parkinsonism and LID. Communication and drinking tasks were used to assess LID, while leg agility and toe tapping tasks were used to assess parkinsonism. Feature sets from tasks were also combined to predict total UDysRS and UPDRS Part III scores. For LID, the communication task yielded the best results (detection: AUC = 0.930, severity estimation: r = 0.661). For parkinsonism, leg agility had better results for severity estimation (r = 0.618), while toe tapping was better for detection (AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r = 0.741 and 0.530, respectively. The proposed system provides insight into the potential of computer vision and deep learning for clinical application in PD and demonstrates promising performance for the future translation of deep learning to PD clinical practices. Convenient and objective assessment of PD symptoms will facilitate more frequent touchpoints between patients and clinicians, leading to better tailoring of treatment and quality of care.


title: JSEL: Jamming Skin Enabled Locomotion

abstract: A soft, mobile, morphing robot is a desirable platform for traversing rough terrain and navigating into small holes. In this work, a new paradigm in soft robots is presented that utilizes jamming of a granular medium. The concept of activators (as opposed to actuators) is presented to jam and unjam cells that then modulate the direction and amount of work done by a single central actuator. A prototype jamming soft robot utilizing JSEL (Jamming Skin Enabled Locomotion) with external power and control is discussed and both morphing results and mobility (rolling) results are presented. Future directions for the design of a soft, hole traversing robot are discussed, as is the role and promises of jamming as an enabling technology for soft robotics.


title: The visual perception of 3D shape

abstract: A fundamental problem for the visual perception of 3D shape is that patterns of optical stimulation are inherently ambiguous. Recent mathematical analyses have shown, however, that these ambiguities can be highly constrained, so that many aspects of 3D structure are uniquely specified even though others might be underdetermined. Empirical results with human observers reveal a similar pattern of performance. Judgments about 3D shape are often systematically distorted relative to the actual structure of an observed scene, but these distortions are typically constrained to a limited class of transformations. These findings suggest that the perceptual representation of 3D shape involves a relatively abstract data structure that is based primarily on qualitative properties that can be reliably determined from visual information.


title: Succinct: enabling queries on compressed data

abstract: Succinct is a data store that enables efficient queries directly on a compressed representation of the input data. Succinct uses a compression technique that allows random access into the input, thus enabling efficient storage and retrieval of data. In addition, Succinct natively supports a wide range of queries including count and search of arbitrary strings, range and wildcard queries. What differentiates Succinct from previous techniques is that Succinct supports these queries without storing indexes -- all the required information is embedded within the compressed representation. ::: ::: Evaluation on real-world datasets show that Succinct requires an order of magnitude lower memory than systems with similar functionality. Succinct thus pushes more data in memory, and provides low query latency for a larger range of input sizes than existing systems.


title: Art and brain: insights from neuropsychology, biology and evolution

abstract: Art is a uniquely human activity associated fundamentally with symbolic and abstract cognition. Its practice in human societies throughout the world, coupled with seeming non-functionality, has led to three major brain theories of art. (1) The localized brain regions and pathways theory links art to multiple neural regions. (2) The display of art and its aesthetics theory is tied to the biological motivation of courtship signals and mate selection strategies in animals. (3) The evolutionary theory links the symbolic nature of art to critical pivotal brain changes in Homo sapiens supporting increased development of language and hierarchical social grouping. Collectively, these theories point to art as a multi-process cognition dependent on diverse brain regions and on redundancy in art-related functional representation.


title: Singular Values and Eigenvalues of Tensors: A Variational Approach

abstract: We propose a theory of eigenvalues, eigenvectors, singular values, and singular vectors for tensors based on a constrained variational approach much like the Rayleigh quotient for symmetric matrix eigenvalues. These notions are particularly useful in generalizing certain areas where the spectral theory of matrices has traditionally played an important role. For illustration, we will discuss a multilinear generalization of the Perron-Frobenius theorem.


title: Latent Topic Models for Hypertext

abstract: Latent topic models have been successfully applied as an unsupervised topic discovery technique in large document collections. With the proliferation of hypertext document collection such as the Internet, there has also been great interest in extending these approaches to hypertext [6, 9]. These approaches typically model links in an analogous fashion to how they model words - the document-link co-occurrence matrix is modeled in the same way that the document-word co-occurrence matrix is modeled in standard topic models. In this paper we present a probabilistic generative model for hypertext document collections that explicitly models the generation of links. Specifically, links from a word w to a document d depend directly on how frequent the topic of w is in d, in addition to the in-degree of d. We show how to perform EM learning on this model efficiently. By not modeling links as analogous to words, we end up using far fewer free parameters and obtain better link prediction results.


title: Improve SAT-solving with Machine Learning

abstract: In this project, we aimed to improve the runtime of Minisat, a Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional Boolean Satisfiability (SAT) problem. We first used a logistic regression model to predict the satisfiability of propositional boolean formulae after fixing the values of a certain fraction of the variables in each formula. We then applied the logistic model and added a preprocessing period to Minisat to determine the preferable initial value (either true or false) of each boolean variable using a Monte-Carlo approach. Concretely, for each Monte-Carlo trial, we fixed the values of a certain ratio of randomly selected variables, and calculated the confidence that the resulting sub-formula is satisfiable with our logistic regression model. The initial value of each variable was set based on the mean confidence scores of the trials that started from the literals of that variable. We were particularly interested in setting the initial values of the backbone variables correctly, which are variables that have the same value in all solutions of a SAT formula. Our Monte-Carlo method was able to set 78% of the backbones correctly. Excluding the preprocessing time, compared with the default setting of Minisat, the runtime of Minisat for satisfiable formulae decreased by 23%. However, our method did not outperform vanilla Minisat in runtime, as the decrease in the conflicts was outweighed by the long runtime of the preprocessing period.


title: Estimating a time-dependent concordance index for survival prediction models with covariate dependent censoring

abstract: Given a predictive marker and a time-to-event response variable, the proportion of concordant pairs in a data set is called concordance index. A specifically useful marker is the risk predicted by a survival regression model. This article extends the existing methodology for applications where the length of the follow-up period depends on the predictor variables. A class of inverse probability of censoring weighted estimators is discussed in which the estimates rely on a working model for the conditional censoring distribution. The estimators are consistent for a truncated concordance index if the working model is correctly specified and if the probability of being uncensored at the truncation time is positive. In this framework, all kinds of prediction models can be assessed, and time trends in the discrimination ability of a model can be captured by varying the truncation time point. For illustration, we re-analyze a study on risk prediction for prostate cancer patients. The effects of misspecification of the censoring model are studied in simulated data. Copyright © 2012 John Wiley & Sons, Ltd.


title: Maxillary gap at 11-13 weeks' gestation: marker of cleft lip and palate

abstract: OBJECTIVE ::: To describe a new sign of cleft lip and palate (CLP), the maxillary gap, which is visible in the mid-sagittal plane of the fetal face used routinely for measurement of nuchal translucency thickness. ::: ::: ::: METHODS ::: This was a retrospective study of stored images of the mid-sagittal view of the fetal face at 11-13 weeks' gestation in 86 cases of CLP and 86 normal controls. The images were examined to determine if a maxillary gap was present, in which case its size was measured. ::: ::: ::: RESULTS ::: In 37 (43.0%) cases of CLP the defect was isolated and in 49 (57.0%) there were additional fetal defects. In the isolated CLP group, the diagnosis of facial cleft was made in the first trimester in nine (24.3%) cases and in the second trimester in 28 (75.7%). In the group with additional defects, the diagnosis of facial cleft was made in the first trimester in 46 (93.9%) cases and in the second trimester in three (6.1%). A maxillary gap was observed in 96% of cases of CLP with additional defects, in 65% of those with isolated CLP and in 7% of normal fetuses. There was a large gap (>1.5 mm) or complete absence of signals from the maxilla in the midline in 69% of cases of CLP with additional defects, in 35% of those with isolated CLP and in none of the normal controls. ::: ::: ::: CONCLUSIONS ::: The maxillary gap is a new simple marker of possible CLP, which could increase the detection rate of CLP, especially in isolated cases.


title: A 1kb 9T subthreshold SRAM with bit-interleaving scheme in 65nm CMOS

abstract: Subthreshold SRAM is a significant approach to reduce power consumption in energy-constrained SoC design. For the ultra-low power consideration, the primary concerns of SRAM are stability and reliability instead of performance. In this paper, the proposed 9T bit-cell enhances write ability by cutting off the positive feedback loop of inverter pair. In the read mode, the isolated read path and storage node enlarge the read SNM. Besides, a 9T subthreshold SRAM is proposed to enable implementation of bit-interleaving structure which achieves soft-error tolerance. The proposed SRAM is able to operate at a voltage as low as 0.3V. One extra virtual ground (VVSS) line is used to reduce the bit-line leakage to ensure the data can be read successfully. A 1kb bit-interleaved 9T SRAM is implemented in UMC 65nm 1P10M CMOS technology to verify the proposed scheme, which operates at the minimum energy point (0.3V) with 5.824pJ energy consumption for one write and one read operation.


title: Development and Validation of the Childhood Narcissism Scale

abstract: In this article, we describe the development and validation of a short (10 item) but comprehensive self-report measure of childhood narcissism. The Childhood Narcissism Scale (CNS) is a 1-dimensional measure of stable individual differences in childhood narcissism with strong internal consistency reliability (Studies 1–4). The CNS is virtually unrelated to conventional measures of self-esteem but is positively related to self-appraised superiority, social evaluative concern and self-esteem contingency, agentic interpersonal goals, and emotional extremity (Study 5). Furthermore, the CNS is negatively related to empathic concern and positively related to aggression following ego threat (Study 6). These results suggest that childhood narcissism has similar psychological and interpersonal correlates as adult narcissism. The CNS provides researchers a convenient tool for measuring narcissism in children and young adolescents with strong preliminary psychometric characteristics. “So may he himself love, and not gain the thing he loves!” According to the Greek myth, it was this prayer by a nymph that destined handsome Narcissus to fall in love with his own reflection in the water. In modern times, the trait of narcissism involves grandiose yet ultimately vulnerable views of self. More often than not, these inflated self-views are associated with an adversarial orientation toward others. Narcissism should not be confused with self-esteem (e.g., Brown & Zeigler-Hill, 2004). Self-esteem (or high self-esteem) involves a positive appraisal of worth as a person, the feeling that one can be satisfied with the person one is. In current adult literature, much interest has revolved around the trait of narcissism and its distinctness from self-esteem. Unfortunately, the child literature still lags behind because a tool to assess narcissism in children is lacking. The purpose of this article is to develop and provide preliminary validity data on a short self-report measure of childhood narcissism. In doing so, we hope to provide researchers a tool to study an important dimension of children’s self-views that has largely been overlooked. ADULT NARCISSISM Having high self-esteem feels good, and having low selfesteem feels bad. Perhaps spurred by this experiential fact of life, generations of psychologists have studied the benefits of high self-esteem. Unfortunately, the data have shown that these benefits are much less powerful and straightforward than once assumed (Baumeister, Campbell, Krueger, & Vohs, 2003; DuBois & Tevendale, 1999). Based on the conviction that the self should somehow play a central role in psychological and interpersonal functioning, researchers have argued that one should stray beyond the narrow focus on level of self-esteem. They have shown


title: A model for team-based access control (TMAC 2004)

abstract: Role based access control (RBAC) has been proved to be effective for defining access control. However, in an environment where collaborative work is needed, additional features should be added on top of RBAC to accommodate the new requirements. In this paper we describe a team access control extension model called TMAC04 (Team-Based Access Control 2004), which is built on the well-known RBAC. The TMAC04 model efficiently represents teamwork in the real world. It allows certain users to join a team based on their existing roles in an organization within limited contexts and new permissions to perform the required work.


title: Step-Up DC–DC Converters: A Comprehensive Review of Voltage-Boosting Techniques, Topologies, and Applications

abstract: DC–DC converters with voltage boost capability are widely used in a large number of power conversion applications, from fraction-of-volt to tens of thousands of volts at power levels from milliwatts to megawatts. The literature has reported on various voltage-boosting techniques, in which fundamental energy storing elements (inductors and capacitors) and/or transformers in conjunction with switch(es) and diode(s) are utilized in the circuit. These techniques include switched capacitor (charge pump), voltage multiplier, switched inductor/voltage lift, magnetic coupling, and multistage/-level, and each has its own merits and demerits depending on application, in terms of cost, complexity, power density, reliability, and efficiency. To meet the growing demand for such applications, new power converter topologies that use the above voltage-boosting techniques, as well as some active and passive components, are continuously being proposed. The permutations and combinations of the various voltage-boosting techniques with additional components in a circuit allow for numerous new topologies and configurations, which are often confusing and difficult to follow. Therefore, to present a clear picture on the general law and framework of the development of next-generation step-up dc–dc converters, this paper aims to comprehensively review and classify various step-up dc–dc converters based on their characteristics and voltage-boosting techniques. In addition, the advantages and disadvantages of these voltage-boosting techniques and associated converters are discussed in detail. Finally, broad applications of dc–dc converters are presented and summarized with comparative study of different voltage-boosting techniques.


title: Design of a role-based trust-management framework

abstract: We introduce the RT framework, a family of role-based trust management languages for representing policies and credentials in distributed authorization. RT combines the strengths of role-based access control and trust-management systems and is especially suitable for attribute-based access control. Using a few simple credential forms, RT provides localized authority over roles, delegation in role definition, linked roles, and parameterized roles. RT also introduces manifold roles, which can be used to express threshold and separation-of-duty policies, and delegation of role activations. We formally define the semantics of credentials in the RT framework by presenting a translation from credentials to Datalog rules. This translation also shows that this semantics is algorithmically tractable.


title: Representing and Querying Correlated Tuples in Probabilistic Databases

abstract: Probabilistic databases have received considerable attention recently due to the need for storing uncertain data produced by many real world applications. The widespread use of probabilistic databases is hampered by two limitations: (1) current probabilistic databases make simplistic assumptions about the data (e.g., complete independence among tuples) that make it difficult to use them in applications that naturally produce correlated data, and (2) most probabilistic databases can only answer a restricted subset of the queries that can be expressed using traditional query languages. We address both these limitations by proposing a framework that can represent not only probabilistic tuples, but also correlations that may be present among them. Our proposed framework naturally lends itself to the possible world semantics thus preserving the precise query semantics extant in current probabilistic databases. We develop an efficient strategy for query evaluation over such probabilistic databases by casting the query processing problem as an inference problem in an appropriately constructed probabilistic graphical model. We present several optimizations specific to probabilistic databases that enable efficient query evaluation. We validate our approach by presenting an experimental evaluation that illustrates the effectiveness of our techniques at answering various queries using real and synthetic datasets.


title: Learning cloud computing and security through CloudSim simulation

abstract: Cloud computing is an emerging parallel and distributed service-oriented computing paradigm that provides platform service, software service, and infrastructure service through computing resource virtualization. Cloud computing is fast evolving and growing and is rapidly transforming the IT service industry. Many universities are offering or planning to offer cloud computing courses to meet the growing demand for cloud computing professionals. Some of them focus on big data processing programming using MapReduce with service charges from public cloud service, while many others teach cloud computing as a seminar type class without any hands-on lab exercises. A hands-on labware will be a big plus for teaching and learning cloud computing. This paper proposes a cloud computing labware with the open source CloudSim (Cloud modeling and simulation toolkit) and its extension projects to enhance computer science and information technology student learning. The proposed labware will support learning not only on MapReduce programming model but also on important topics including cloud principles, concepts, architecture, loadbalancing, and task scheduling. The proposed labware has been implemented in a cross-listed course (as both an undergraduate course and a graduate course) in two sections (hybrid section and fully-online section). Survey results show that participating students highly appreciate the proposed labware in promoting their interest and academic progress in studying Cloud computing/security.


title: Evaluating regular expression matching engines on network and general purpose processors

abstract: In recent years we have witnessed a proliferation of data structure and algorithm proposals for efficient deep packet inspection on memory based architectures. In parallel, we have observed an increasing interest in network processors as target architectures for high performance networking applications. In this paper we explore design alternatives in the implementation of regular expression matching architectures on network processors (NPs) and general purpose processors (GPPs). Specifically, we present a performance evaluation on an Intel IXP2800 NP, on an Intel Xeon GPP and on a multiprocessor system consisting of four AMD Opteron 850 cores. Our study shows how to exploit the Intel IXP2800 architectural features in order to maximize system throughput, identifies and evaluates algorithmic and architectural trade-offs and limitations, and highlights how the presence of caches affects the overall performances. We provide an implementation of our NP designs within the Open Network Laboratory (http://www.onl.wustl.edu).


title: A model for piloting pathways for computational thinking in a general education curriculum

abstract: Computational thinking has been identified as a necessary fundamental skill for all students. University curricula, however, are currently not designed to provide such knowledge to a broad student population. In this paper, we report on our experiences in the development of a model for incorporating computational thinking into the undergraduate, general education curriculum at Towson University. We discuss the model in terms of eliciting faculty interest, institutional support, and positive student response. In the first two years of this NSF-funded three-year project, we have developed, piloted and assessed five computational thinking general education courses - an Everyday Computational Thinking course, and four discipline-specific computational thinking general education courses. Initial assessments show promising and significant student, instructor and administration interest in computational thinking as a basis in courses covering multiple disciplines within the general education curriculum.


title: ActiveTrust: Secure and Trustable Routing in Wireless Sensor Networks

abstract: Wireless sensor networks (WSNs) are increasingly being deployed in security-critical applications. Because of their inherent resource-constrained characteristics, they are prone to various security attacks, and a black hole attack is a type of attack that seriously affects data collection. To conquer that challenge, an active detection-based security and trust routing scheme named ActiveTrust is proposed for WSNs. The most important innovation of ActiveTrust is that it avoids black holes through the active creation of a number of detection routes to quickly detect and obtain nodal trust and thus improve the data route security. More importantly, the generation and the distribution of detection routes are given in the ActiveTrust scheme, which can fully use the energy in non-hotspots to create as many detection routes as needed to achieve the desired security and energy efficiency. Both comprehensive theoretical analysis and experimental results indicate that the performance of the ActiveTrust scheme is better than that of the previous studies. ActiveTrust can significantly improve the data route success probability and ability against black hole attacks and can optimize network lifetime.


title: Liveness Detection Using Gaze Collinearity

abstract: This paper presents a liveness detection method based on tracking the gaze of the user of a face recognition system using a single camera. The user is required to follow a visual animation of a moving object on a display screen while his/her gaze is measured. The visual stimulus is designed to direct the gaze of the user to sets of collinear points on the screen. Features based on the measured collinearity of the observed gaze are then used to discriminate between live attempts at responding to this challenge and those conducted by âimpostorsâ holding photographs and attempting to follow the stimulus. An initial set of experiments is reported that indicates the effectiveness of the proposed method in detecting this class of spoofing attacks.


title: Fona: Quantitative Metric to Measure Focus Navigation on Rich Internet Applications

abstract: The Web 2.0 brought new requirements to the architecture of web systems. Web applications’ interfaces are becoming more and more interactive. However, these changes are severely impacting how disabled users interact through assistive technologies with the web. In order to deploy an accessible web application, developers can use WAI-ARIA to design an accessible web application, which manually implements focus and keyboard navigation mechanisms. This article presents a quantitative metric, named Fona, which measures how the Focus Navigation WAI-ARIA requirement has been implemented on the web. Fona counts JavaScript mouse event listeners, HTML elements with role attributes, and TabIndex attributes in the DOM structure of webpages. Fona’s evaluation approach provides a narrow analysis of one single accessibility requirement. But it enables monitoring this accessibility requirement in a large number of webpages. This monitoring activity might be used to give insights about how Focus Navigation and ARIA requirements have been considered by web development teams. Fona is validated comparing the results of a set of WAI-ARIA conformant implementations and a set of webpages formed by Alexa’s 349 top most popular websites. The analysis of Fona’s value for Alexa’s websites highlights that many websites still lack the implementation of Focus Navigation through their JavaScript interactive content.


title: Optimizing Semantic Coherence in Topic Models

abstract: Latent variable models have the potential to add value to large document collections by discovering interpretable, low-dimensional subspaces. In order for people to use such models, however, they must trust them. Unfortunately, typical dimensionality reduction methods for text, such as latent Dirichlet allocation, often produce low-dimensional subspaces (topics) that are obviously flawed to human domain experts. The contributions of this paper are threefold: (1) An analysis of the ways in which topics can be flawed; (2) an automated evaluation metric for identifying such topics that does not rely on human annotators or reference collections outside the training data; (3) a novel statistical topic model based on this metric that significantly improves topic quality in a large-scale document collection from the National Institutes of Health (NIH).


title: Sequential Acral Lentiginous Melanomas of the Foot

abstract: A 64-year-old Japanese woman had a lightly brown-blackish pigmented macule (1.2 cm in diameter) on the left sole of her foot. She received surgical excision following a diagnosis of acral lentiginous melanoma (ALM), which was confirmed histopathologically. One month after the operation, a second melanoma lesion was noticed adjacent to the grafted site. Histopathologically, the two lesions had no continuity, but HMB-45 and cyclin D1 double-positive cells were detected not only on aggregates of atypical melanocytes but also on single cells near the cutting edge of the first lesion. The unique occurrence of a sequential lesion of a primary melanoma might be caused by stimulated subclinical field cells during the wound healing process following the initial operation. This case warrants further investigation to establish the appropriate surgical margin of ALM lesions.


title: How do you feel? Interoception: the sense of the physiological condition of the body

abstract: As humans, we perceive feelings from our bodies that relate our state of well-being, our energy and stress levels, our mood and disposition. How do we have these feelings? What neural processes do they represent? Recent functional anatomical work has detailed an afferent neural system in primates and in humans that represents all aspects of the physiological condition of the physical body. This system constitutes a representation of 'the material me', and might provide a foundation for subjective feelings, emotion and self-awareness.


title: User-oriented document summarization through vision-based eye-tracking

abstract: We propose a new document summarization algorithm which is personalized. The key idea is to rely on the attention (reading) time of individual users spent on single words in a document as the essential clue. The prediction of user attention over every word in a document is based on the user's attention during his previous reads, which is acquired via a vision-based commodity eye-tracking mechanism. Once the user's attentions over a small collection of words are known, our algorithm can predict the user's attention over every word in the document through word semantics analysis. Our algorithm then summarizes the document according to user attention on every individual word in the document. With our algorithm, we have developed a document summarization prototype system. Experiment results produced by our algorithm are compared with the ones manually summarized by users as well as by commercial summarization software, which clearly demonstrates the advantages of our new algorithm for user-oriented document summarization.


title: Automatic Vehicle Parallel Parking Design Using Fifth Degree Polynomial Path Planning

abstract: Automatic vehicle parallel parking design and its related concerns about safety improvement remain some of the heated problems for automatic land vehicular control. This paper presents the calculation process of a parallel parking car's path planning and the algorithm development for its motion design based on a fifth-degree polynomial curve. In addition to the proposed algorithm for automatic vehicle parking, the minimum horizontal distance allowed for parking between a car and a parking spot is also investigated. The preliminary results show that the fifth degree polynomial path planning and the algorithm are well applied to the automatic parallel parking problem.


title: Dividends: New evidence on the catering theory

abstract: This paper is built upon the predictions of the catering theory of dividends, and examines whether investors? sentiments exert significant influence on corporate dividend policy. To achieve this aim, we propose a dividend model that incorporates a variable at a firm-level proxing for the catering effect. The estimation of the model by using the Generalized Method of Moments provides interesting results. Consistent with the predictions of the catering theory, we find that companies in eurozone countries cater to their investors? sentiments. Additionally, other relevant findings show an interaction effect between catering and firm characteristics; particularly, liquid assets, investment opportunities and free cash flow. First, we find a catering effect only in firms with high liquid assets. Second, the positive effect of catering is only found in firms with valuable investment opportunities. Third, companies with higher levels of free cash flow cater more strongly to their investors? sentiments.


title: Cuffless blood pressure estimation by error-correcting output coding method based on an aggregation of AdaBoost with a photoplethysmograph sensor

abstract: This paper presented a novel cuffless and non-invasive technique of Blood Pressure (BP) estimation with a pattern recognition method by using a Photoplethysmograph (PPG) sensor instead of a cuff. Error-Correcting Output Coding (ECOC) method was adopted as a multi-classifier machine based on an aggregation of general binary classifiers. AdaBoost was applied as binary classifier machine. 368 volunteers participated in the experiment. The estimated Systolic Blood Pressure (SBP) was calculated from their individual information and several features of their Pulse Wave (PW). As a result of the comparison between measured SBP, estimated SBP, MD = 3D-1.2 [mmHg] and SD = 3D11.7 [mmHg] were obtained. Hence, this technique would be helpful to the advance the development of continuous BP monitoring system, since the only one device to monitor BP is smaller than traditional measurements.


title: The impact of reinforcement contingencies on AD/HD: A review and theoretical appraisal

abstract: Abstract One of the core deficits in attention deficit/hyperactivity disorder (AD/HD) is thought to be an aberrant sensitivity to reinforcement, such as reward and response cost. Twenty-two studies ( N =1181 children) employing AD/HD and reinforcement contingencies are reviewed from vantage points: task performance, motivation, and psychophysiology. Results indicate that reinforcement contingencies have a positive impact on task performance and levels of motivation for both children with AD/HD and normal controls. There is evidence that the effect related to task performance is somewhat more prominent in AD/HD. There is some evidence that a high intensity of reinforcement is highly effective in AD/HD. Children with AD/HD prefer immediate over delayed reward. From a psychophysiological point of view, children with AD/HD seem less sensitive to reinforcement compared to controls. While comorbid disorders are suggested to be confounders of the dependent variables, many studies do not examine the effect of oppositional defiant disorder (ODD) and conduct disorder (CD). We discuss the implications of the findings for five theoretical frameworks, including the model by Haenlein and Caul (1987) , Douglas (1999) , the cognitive-energetic model (CEM) ( Sergeant et al., 1999 ), the dual-pathway model ( Sonuga-Barke, 2003 ) and the BIS/BAS model ( Quay, 1988a,b,c ). Results show a discrepancy between the theoretical models and the behavioural findings.


Assessment of Morphogenetic Inherited Traits ; Earlobe Attachment , Bent Little Finger and Hitchhiker ’ s Thumb in Quetta , Pakistan


title: A new initialization method for categorical data clustering

abstract: In clustering algorithms, choosing a subset of representative examples is very important in data set. Such ''exemplars'' can be found by randomly choosing an initial subset of data objects and then iteratively refining it, but this works well only if that initial choice is close to a good solution. In this paper, based on the frequency of attribute values, the average density of an object is defined. Furthermore, a novel initialization method for categorical data is proposed, in which the distance between objects and the density of the object is considered. We also apply the proposed initialization method to k-modes algorithm and fuzzy k-modes algorithm. Experimental results illustrate that the proposed initialization method is superior to random initialization method and can be applied to large data sets for its linear time complexity with respect to the number of data objects.


title: HBIM TO VR. SEMANTIC AWARENESS AND DATA ENRICHMENT INTEROPERABILITY FOR PARAMETRIC LIBRARIES OF HISTORICAL ARCHITECTURE

abstract: Recently we assist to an increasing availability of HBIM models rich in geometric and informative terms. Instead, there is still a lack of researches implementing dedicated libraries, based on parametric intelligence and semantically aware, related to the architectural heritage. Additional challenges became from their portability in non-desktop environment (such as VR). The research article demonstrates the validity of a workflow applied to the architectural heritage, which starting from the semantic modeling reaches the visualization in a virtual reality environment, passing through the necessary phases of export, data migration and management. The three-dimensional modeling of the classical Doric order takes place in the BIM work environment and is configured as a necessary starting point for the implementation of data, parametric intelligences and definition of ontologies that exclusively qualify the model. The study also enables an effective method for data migration from the BIM model to databases integrated into VR technologies for AH. Furthermore, the process intends to propose a methodology, applicable in a return path, suited to the achievement of an appropriate data enrichment of each model and to the possibility of interaction in VR environment with the model.


title: A Double-Blind Placebo Controlled Study of a 5% Lidocaine/Prilocaine Cream (EMLA) for Topical Anesthesia During Thermolysis

abstract: background. Electrolysis and thermolysis are commonly used electrosurgical methods for the permanent destruction of unwanted hair. However, patient fear of pain during treatment may interfere with or even prevent desired treatment of hirsutism on sensitive areas such as the upper lip. ::: ::: ::: ::: objective. This study was conducted to assess the efficacy of EMLA (Eutectic Mixture of Local Anesthetics), a 5% lidocaine/prilocaine cream, as a topically applied local anesthetic during thermolysis on the upper lip. ::: ::: ::: ::: methods. A double-blind placebo control study was conducted by the University of Texas Medical Branch thermolysis clinic. Thirty women with idiopathic hirsutism of the upper lip were studied. Pain was measured with a 100 mm visual analog scale (VAS). ::: ::: ::: ::: results. Total paired VAS score differences comparing the placebo VAS scores to the EMLA VAS scores were significant by paired t-test (P = .0001). Adverse reactions were mild and transient. ::: ::: ::: ::: conclusion. EMLA cream may have multiple applications to the practice of dermatologic surgery, including topically achieved local anesthesia during thermolysis and electrolysis.


title: Protein-protein interactions in AQP regulation - biophysical characterization of AQP0-CaM and AQP2-LIP5 complex formation.

abstract: Protein-protein interactions play important roles in regulating human aquaporins (AQP) by gating as well as trafficking. While structural and functional studies have provided detailed knowledge of AQP transport mechanisms, selectivity as well as gating by conformational changes of loops or termini, the mechanism behind how protein-protein interactions control AQP-mediated water transport through cellular membranes remains poorly characterized. Here we explore the interaction between two human AQPs and regulatory proteins: the interaction between AQP0 and calmodulin, which mediates AQP0 gating, as well as the interaction between AQP2 and LIP5, which is involved in trafficking. Using microscale thermophoresis (MST) and fluorescence anisotropy, two methods that have the advantage of low sample consumption and detergent compatibility, we show that the interactions can be studied using both full-length AQPs and AQP peptides corresponding to the regulatory protein binding sites. However, full-length AQPs gave better reproducibility between methods and for the first time revealed that AQP0 binds CaM in a cooperative manner, which was not seen in experiments using peptides. Our study highlights that, while peptides are great tools for locating binding sites and pinpointing interacting residues, full-length proteins may give additional insights, such as binding mechanism, allostery and cooperativity, important parameters for understanding protein-protein mediated regulation in the cellular context. Our work provides a platform for further studies of AQP regulation that may be of interest for designing drugs that target AQP complexes as well as the development of artificial bio-mimetic water channels for water-purification purposes.


title: Gait support for complete spinal cord injury patient by synchronized leg-swing with HAL

abstract: Biped walking improves the circulation of blood as well as bone density of the lower limbs, thereby enhancing the quality of life (QOL). It is significant not only to healthy people but also to physically challenged persons such as complete spinal cord injury (SCI) patients. The purpose of this paper is to propose an estimation algorithm that infers the intention related to the forward leg-swing in order to support the gait for complete SCI patients wearing an exoskeleton system called a Hybrid Assistive Limb (HAL), and to verify the effectiveness of the proposed algorithm through a clinical trial. The proposed algorithm infers the patient's intention in synchronization with the deviation of the center of the ground reaction force (CoGRF) that is observed immediately before a person starts walking. The patient conveys this intention by inducing the deviation of the CoGRF, using crutches or handrails with both of his/her arms. In the clinical trial, we confirmed that the algorithm inferred the patient's intention to swing the leg forward, and achieved a smooth gait in synchronization with it. As a result, the gait speed and cadence of the SCI patient with HAL during the 10-meter walking test increased to 6.67 [m/min] and 20 [steps/min], respectively after several trials.


title: Numerical Synthesis of Six-Bar Linkages for Mechanical Computation

abstract: © 2014 by ASME. This paper presents a design procedure for six-bar linkages that use eight accuracy points to approximate a specified input-output function. In the kinematic synthesis of linkages, this is known as the synthesis of a function generator to perform mechanical computation. Our formulation uses isotropic coordinates to define the loop equations of the Watt II, Stephenson II, and Stephenson III six-bar linkages. The result is 22 polynomial equations in 22 unknowns that are solved using the polynomial homotopy software BERTINI. The bilinear structure of the system yields a polynomial degree of 705,432. Our first run of BERTINI generated 92,736 nonsingular solutions, which were used as the basis of a parameter homotopy solution. The algorithm was tested on the design of the Watt II logarithmic function generator patented by Svoboda in 1944. Our algorithm yielded his linkage and 64 others in 129 min of parallel computation on a Mac Pro with 12±2.93 GHz processors. Three additional examples are provided as well.


title: Self-Esteem, Daily Internet Use and Social Media Addiction as Predictors of Depression among Turkish Adolescents.

abstract: In this study, direct and indirect effects of self-esteem, daily internet use and social media addiction to depression levels of adolescents have been investigated by testing a model. This descriptive study was conducted with 1130 students aged between 12 and 18 who are enrolled at different schools in southern region of Aegean. In order to collect data, “Children's Depression Inventory”, “Rosenberg Self-esteem Scale” and “Social Media Addiction Scale” have been used. In order to test the hypotheses Pearson's correlation and structural equation modeling were performed. The findings revealed that self-esteem and social media addiction predict %20 of the daily internet use. Furthermore, while depression was associated with self-esteem and daily internet use directly, social media addiction was affecting depression indirectly. Tested model was able to predict %28 of the depression among adolescents. Keywords: depression, self-esteem, daily internet use, social media addiction, structural equation modeling


title: State-aware Anti-drift Robust Correlation Tracking

abstract: Correlation filter (CF)-based trackers have aroused increasing attention in the visual tracking field due to the superior performance on several datasets while maintaining high running speed. For each frame, an ideal filter is trained in order to discriminate the target from its surrounding background. Considering that the target always undergoes external and internal interference during the tracking procedure, the trained tracker should not only have the ability to judge the current state when failure occurs, but also to resist the model drift caused by challenging distractions. To this end, we present a state-aware anti-drift tracker (SAT) in this paper, which jointly models the discrimination and reliability information in filter learning. Specifically, global context patches are incorporated into the filter training stage to better distinguish the target from backgrounds. Meanwhile, a color-based reliable mask is learned to encourage the filter to focus on more reliable regions suitable for tracking. We show that the proposed optimization problem could be efficiently solved using alternative direction method of multipliers and fully carried out in the Fourier domain. Furthermore, a kurtosis-based updating scheme is advocated to reveal the tracking condition as well as guarantee a high-confidence template updating. Extensive experiments are conducted on OTB-100 and UAV-20L datasets to compare the SAT tracker with other relevant state-of-the-art methods. In this paper, both quantitative and qualitative evaluations further demonstrate the effectiveness and robustness.


A Domain-independent Framework for building Conversational Recommender Systems


title: Why Buy When You Can Rent ? Bribery Attacks on Bitcoin-Style Consensus

abstract: The Bitcoin cryptocurrency introduced a novel distributed consensus mechanism relying on economic incentives. While a coalition controlling a majority of computational power may undermine the system, for example by double-spending funds, it is often assumed it would be incentivized not to attack to protect its long-term stake in the health of the currency. We show how an attacker might purchase mining power (perhaps at a cost premium) for a short duration via bribery. Indeed, bribery can even be performed in-band with the system itself enforcing the bribe. A bribing attacker would not have the same concerns about the long-term health of the system, as their majority control is inherently short-lived. New modeling assumptions are needed to explain why such attacks have not been observed in practice. The need for all miners to avoid short-term profits by accepting bribes further suggests a potential tragedy of the commons which has not yet been analyzed.


title: [Solid-state stability and preformulation study of a new parenteral cephalosporin antibiotics (E1040)].

abstract: In designing the dosage form, one major factor controlling their physicochemical properties is solid forms of the drug powder. The method for improving the physicochemical stability of unstable beta-lactam antibiotics is very important. E1040 is a novel parenteral 3-betaine type cephalosporin which has a broad antibacterial spectrum and potent activities against Citrobacter, freundii, Enterobacter cloacase, and glucose-non-fermentative bacteria, including P. aeruginosa. The present study was intended to provide the solid-state chemical stability of perenteral steril dry dosage form of E1040. The chemical stability differences among the various solid forms, dry amorphous, additive freeze dried amorphous solid and crystalline powder, were evaluated as a function of temperature by thermo stress tests. Freeze dried anhydrous amorphous form was the first steril dry dosage form investigated during the preformulation study. However, this compound is chemically unstable, in the titer of them, reduction are observed in the freeze dried amorphous solid. In order to select the most suitable solid form of E1040, two methods were used. One was crystalline solid and the other was NaCl additive freeze-dried formulation. Through our experiments, the solid-state chemical stabilization can be achieved by these two methods (effect of crystal structure and effect of NaCl additive).


title: Human Body Pose Interpretation and Classification for Social Human-Robot Interaction

abstract: A novel breed of robots known as socially assistive robots is emerging. These robots are capable of providing assistance to individuals through social and cognitive interaction. However, there are a number of research issues that need to be addressed in order to design such robots. In this paper, we address one main challenge in the development of intelligent socially assistive robots: The robot’s ability to identify human non-verbal communication during assistive interactions. In particular, we present a unique non-contact and non-restricting automated sensor-based approach for identification and categorization of human upper body language in determining how accessible a person is to the robot during natural real-time human-robot interaction (HRI). This classification will allow a robot to effectively determine its own reactive task-driven behavior during assistive interactions. Human body language is an important aspect of communicative nonverbal behavior. Body pose and position can play a vital role in conveying human intent, moods, attitudes and affect. Preliminary experiments show the potential of integrating the proposed body language recognition and classification technique into socially assistive robotic systems partaking in HRI scenarios.


A sound analysis/synthesis system based on a deterministic plus stochastic decomposition


title: Online Consumer Reviews: Does Negative Electronic Word-of-Mouth Hurt More?

abstract: Web-based technologies have created numerous opportunities for electronic word-of-mouth (eWOM) communication. This phenomenon impacts retailers as this easily accessible information could greatly affect consumer purchasing decision. The connectivity of the Web allows one-to-many and many-to-many communications among users that make information spread much faster and broader, including the spread of negative information. In this paper, we investigate the impact of online consumer reviews on consumer online purchasing decision. Specifically, we compare the impact of both negative and positive eWOM using a laboratory experiment. Our results show that the mean values of emotional trust and intention to shop online are significantly lower in the negative treatment group. We expect the result of this paper will generate researchers and marketers’ interest in this important area of research and practice.


title: The effects of a 6 week plyometric training programme on explosive strength and agility in professional basketball players

abstract: BACKGROUND: Explosive strength of the lower extremities and agility are important parts of game performance in basketball. Although numerous studies have focused on the assessment of the training effect of plyometric training, studies focusing on elite players are missing. OBJECTIVE: The aim of the study was to find out what changes in explosive strength of the lower extremities take place after a 6 week plyometric training applied in training units during the pre-season in elite basketball players. METHODS: Elite basketball players (N = 12, age 24.36 ± 3.9 years, height 196.2 ± 9.6 cm, weight 92.9 ± 13.9 kg) performed a 6 week plyometric training (PT) programme predominantly focused on explosive strength of the lower body and upper body and was conducted in sixteen training units during pre-season. The changes in explosive strength were measured by the Counter Movement Jump Free Arms test and Two Step Run Up Jump test; agility was measured using the “T” Drill test and Hexagonal Obstacle test. The players participated in three measurements. The 1st (pretesting) was performed on the first day of pre-season, the 2nd (post-testing) was done two days after completing the PT programme and the 3rd (post-testing) six weeks after completing the PT programme. Friedman’s ANOVA for repeated measurements was used to determine the significance of differences between the measurement sessions (p ＜ .05). RESULTS: A significant effect of the training programme was observed only for the Hexagonal Obstacle test (p = .01). A post hoc analysis revealed a significant increase in test performance between the 1st and 3rd measurement (p ＜ .01) and between the 2nd and 3rd measurement (p ＜ .01). CONCLUSIONS: The results of the study of elite basketball players did not positively support the assumption that plyometric exercises can be an effective tool for the improvement of explosive strength and agility. However, in some players the improvements corresponded to average improvements after training programmes presented in literature.


title: Prometheus design tool

abstract: The Prometheus Design Tool is a graphical editor which supports the design tasks specified within the Prometheus methodology for designing agent systems. The tool propagates information where possible and ensures consistency between various parts of the design.


title: Self-Regulation of Amygdala Activation Using Real-Time fMRI Neurofeedback

abstract: Real-time functional magnetic resonance imaging (rtfMRI) with neurofeedback allows investigation of human brain neuroplastic changes that arise as subjects learn to modulate neurophysiological function using real-time feedback regarding their own hemodynamic responses to stimuli. We investigated the feasibility of training healthy humans to self-regulate the hemodynamic activity of the amygdala, which plays major roles in emotional processing. Participants in the experimental group were provided with ongoing information about the blood oxygen level dependent (BOLD) activity in the left amygdala (LA) and were instructed to raise the BOLD rtfMRI signal by contemplating positive autobiographical memories. A control group was assigned the same task but was instead provided with sham feedback from the left horizontal segment of the intraparietal sulcus (HIPS) region. In the LA, we found a significant BOLD signal increase due to rtfMRI neurofeedback training in the experimental group versus the control group. This effect persisted during the Transfer run without neurofeedback. For the individual subjects in the experimental group the training effect on the LA BOLD activity correlated inversely with scores on the Difficulty Identifying Feelings subscale of the Toronto Alexithymia Scale. The whole brain data analysis revealed significant differences for Happy Memories versus Rest condition between the experimental and control groups. Functional connectivity analysis of the amygdala network revealed significant widespread correlations in a fronto-temporo-limbic network. Additionally, we identified six regions — right medial frontal polar cortex, bilateral dorsomedial prefrontal cortex, left anterior cingulate cortex, and bilateral superior frontal gyrus — where the functional connectivity with the LA increased significantly across the rtfMRI neurofeedback runs and the Transfer run. The findings demonstrate that healthy subjects can learn to regulate their amygdala activation using rtfMRI neurofeedback, suggesting possible applications of rtfMRI neurofeedback training in the treatment of patients with neuropsychiatric disorders.


title: Convex optimization for fast image dehazing

abstract: This paper presents a new convex optimization method for the image dehazing problem. It is based on reformulation of the hazed image model to solve the bilinearly coupled hazed image and light transmission distribution term as a single optimization variable. This resolves the nonconvex difficulty of the original problem and is sufficient for straightforward reconstruction of the haze free image. The Haar wavelet transform is further applied to derive a subband hazed image model for the convex optimization, which leads to great reduction of the computational workload.


title: A Tutorial on Machine Learning and Data Science Tools with Python

abstract: In this tutorial, we will provide an introduction to the main Python software tools used for applying machine learning techniques to medical data. The focus will be on open-source software that is freely available and is cross platform. To aid the learning experience, a companion GitHub repository is available so that you can follow the examples contained in this paper interactively using Jupyter notebooks. The notebooks will be more exhaustive than what is contained in this chapter, and will focus on medical datasets and healthcare problems. Briefly, this tutorial will first introduce Python as a language, and then describe some of the lower level, general matrix and data structure packages that are popular in the machine learning and data science communities, such as NumPy and Pandas. From there, we will move to dedicated machine learning software, such as SciKit-Learn. Finally we will introduce the Keras deep learning and neural networks library. The emphasis of this paper is readability, with as little jargon used as possible. No previous experience with machine learning is assumed. We will use openly available medical datasets throughout.


title: SLIQ: A fast scalable classifier for data mining

abstract: Classification is an important problem in the emerging field of data mining. Although classification has been studied extensively in the past, most of the classification algorithms are designed only for memory-resident data, thus limiting their suitability for data mining large data sets. This paper discusses issues in building a scalable classifier and presents the design of SLIQ, a new classifier. SLIQ is a decision tree classifier that can handle both numeric and categorical attributes. It uses a novel pre-sorting technique in the tree-growth phase. This sorting procedure is integrated with a breadth-first tree growing strategy to enable classification of disk-resident datasets. SLIQ also uses a new tree-pruning algorithm that is inexpensive, and results in compact and accurate trees. The combination of these techniques enables SLIQ to scale for large data sets and classify data sets irrespective of the number of classes, attributes, and examples (records), thus making it an attractive tool for data mining.


title: A Conceptual Framework for the Promotion of Trusted Online Retailing Environment in Saudi Arabia

abstract: This paper presents a model conceptual framework that is aimed at promoting trust in the online retailing environment in the Kingdom of Saudi Arabia (KSA). Despite rapid Internet growth, the development of online retailing in Saudi Arabia continues to progress very slowly compared to that of the developed and leading developing countries. To determine the reason behind the sluggish growth of online retailing in the KSA, a mixed methods study involving retailers and customers was conducted in four stages. The outcomes of the study point to distrust in the online retailing environment in Saudi Arabia as a key inhibitory factor for growth. As such, a five-part model is proposed to promote trust in the online shopping environment in the KSA.


title: High spatial resolution impedance measurement of EIS sensors for light addressable cell adhesion monitoring.

abstract: Abstract In this paper, impedance measurement of electrolyte–insulator–semiconductor (EIS) structure with high spatial resolution was proposed to monitor cell adhesion. The light addressing ability of this work overcomes the geometrical restrict of cell culture on conventional impedance detection devices such as interdigitated electrode (IDE) and electric cell-substrate impedance sensing (ECIS). Instead of studying cells on predetermined sites of IDE and ECIS, cells cultured anywhere on EIS sensor surface can be addressed and selected as target cells. Principle and primary models for high resolution impedance detection were described and tested by experiments. The EIS sensor was investigated in terms of its intrinsic characteristics, like impedance behavior, voltage characteristic, frequency dependency and photovoltaic effect. Optimized working condition was studied for cell experiments. Cell adhesion under treatment of 0.1% Triton X-100 was monitored using rat kidney cells as the source. Results showed good sensitivity (10% change of impedance) and resolution (40 μm) for cell adhesion impedance detection and suggested this work should be suitable for monitoring cell impedance. Further improvements on sensitivity, spatial resolution were discussed as well as the further applications for single cell monitoring and cell adhesion imaging.


title: Bootstrapping LCF Declarative Proofs

abstract: Suppose we have been sold on the idea that formalised proofs in an LCF system should resemble their written counterparts, and so consist of formulas that only provide signposts for a fully verified proof. To be practical, most of the fully elaborated verification must then be done by way of general purpose proof procedures. Now if these are the only procedures we implement outside the kernel of logical rules, what does the theorem prover look like? We give our account, working from scratch in the ProofPeer theorem prover, making observations about this new setting along the way.


' s personal copy Influencing behaviour : The mindspace way


title: Functional-link net computing: theory, system architecture, and functionalities

abstract: A system architecture and a network computational approach compatible with the goal of devising a general-purpose artificial neural network computer are described. The functionalities of supervised learning and optimization are illustrated, and cluster analysis and associative recall are briefly mentioned. >


title: Automatic Recognition of Deceptive Facial Expressions of Emotion

abstract: Humans modify facial expressions in order to mislead observers regarding their true emotional states. Being able to recognize the authenticity of emotional displays is notoriously difficult for human observers. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of genuine and deceptive facial expressions of emotions for automatic recognition. We show that overall the problem of recognizing deceptive facial expressions can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt feature space. Interesting additional results show that on average it is easier to distinguish among genuine expressions than deceptive ones and that certain emotion pairs are more difficult to distinguish than others.


title: Screen Dependency Disorders: a new challenge for child neurology

abstract: Children’s neurological development is influenced by what they do and do not experience. Early experiences and the environments in which they occur can alter gene expression and affect long-term neural development. Today, discretionary screen time (DST), often involving multiple devices, is the single main experience and environment of children. Various screen activities are reported to induce structural and functional brain plasticity in adults. However, childhood is a time of significantly greater changes in brain anatomical structure and connectivity. Digital natives exhibit a higher prevalence of screen-related ‘addictive’ behaviours that reflect impaired neurological reward-processing and impulse-control mechanisms. Associations are emerging between screen dependency disorders (SDD) such as Internet Addiction Disorder and specific neurogenetic polymorphisms, abnormal neural tissue and neural function. Although abnormal neural structural and functional characteristics may be a precondition rather than a consequence of addiction, there may also be a bidirectional relationship. As is the case with substance addictions, it is possible that intensive routine exposure to certain screen activities during critical stages of neural development may alter gene expression resulting in structural, synaptic and functional changes in the developing brain leading to SDD, particularly in children with predisposing neurogenetic profiles. There may also be compound/secondary effects on paediatric neural development. Screen dependency disorders, even at subclinical levels, involve high levels of discretionary screen time, inducing greater child sedentary behaviour thereby reducing vital aerobic fitness, which plays an important role in the neurological health of children, particularly in brain structure and function. Child health policy must therefore adhere to the principle of precaution as a prudent approach to protecting child neurological integrity and well-being. This paper explains the basis of current paediatric neurological concerns surrounding SDD and proposes preventive strategies for child neurology and allied professions.


title: Implant occlusion: biomechanical considerations for implant-supported prostheses

abstract: Clinically, natural occlusal concepts can be applied to implant prostheses. However, a natural tooth has a support design that reduces the forces to the surrounding crest of bone compared to the same region around an implant. If a clinical condition is likely to increase biomechanical stresses, dentists should implement occlusal mechanisms to decrease the stresses and develop an occlusal scheme that minimizes risk factors and allows the restoration to function in harmony with the rest of the stomatognathic system. By avoiding initial and long-term loss of crestal bone surrounding implant fixtures, implant-protected occlusion is proposed as a way to overcome mechanical stresses and strain from the oral musculature and occlusion. Implant-protected occlusion can be accomplished by increasing the surface area of implants, decreasing the width of the occlusal table, improving the force direction, and reducing the magnification of the force. By doing these things, we can minimize overload on bone-implant interfaces and implant prostheses, to maintain an implant load within the physiological limits of individualized occlusion, and ultimately provide long-term stability of implants and implant


title: Electrostatic-motor-driven electroadhesive robot

abstract: This paper presents a new kind of climbing robots whose locomotion and adhesion are both realized by electrostatic force. Two crawler-type robots were prototyped. For both of the prototypes, high-power electrostatic film motors are installed. The slider of the electrostatic motor works as a crawling belt as well as an electrode for electroadhesion. The first prototype that has a rigid mechanical structure is light in weight (49 g) and thin in thickness (14 mm). It can climb on a vertical aluminum board with a speed up to 104 mm/s and horizontally pull a payload of about 3.2 N. The other prototype is equipped with a flexible mechanical structure that consists of a series of passive joints. By smoothly transitioning from a horizontal floor up to a slope, the robot demonstrated its capability to comply with complicated surfaces.


title: Enhancing Root Extractors Using Light Stemmers

abstract: The rise of Natural Language Processing (NLP) opened new possibilities for various applications that were not applicable before. A morphological-rich language such as Arabic introduces a set of features, such as roots, that would assist the progress of NLP. Many tools were developed to capture the process of root extraction (stemming). Stemmers have improved many NLP tasks without explicit knowledge about its stemming accuracy. In this paper, a study is conducted to evaluate various Arabic stemmers. The study is done as a series of comparisons using a manually annotated dataset, which shows the efficiency of Arabic stemmers, and points out potential improvements to existing stemmers. The paper also presents enhanced root extractors by using light stemmers as a preprocessing phase.


title: Learning to walk with a robotic ankle exoskeleton.

abstract: We used a lower limb robotic exoskeleton controlled by the wearer's muscle activity to study human locomotor adaptation to disrupted muscular coordination. Ten healthy subjects walked while wearing a pneumatically powered ankle exoskeleton on one limb that effectively increased plantar flexor strength of the soleus muscle. Soleus electromyography amplitude controlled plantar flexion assistance from the exoskeleton in real time. We hypothesized that subjects' gait kinematics would be initially distorted by the added exoskeleton power, but that subjects would reduce soleus muscle recruitment with practice to return to gait kinematics more similar to normal. We also examined the ability of subjects to recall their adapted motor pattern for exoskeleton walking by testing subjects on two separate sessions, 3 days apart. The mechanical power added by the exoskeleton greatly perturbed ankle joint movements at first, causing subjects to walk with significantly increased plantar flexion during stance. With practice, subjects reduced soleus recruitment by approximately 35% and learned to use the exoskeleton to perform almost exclusively positive work about the ankle. Subjects demonstrated the ability to retain the adapted locomotor pattern between testing sessions as evidenced by similar muscle activity, kinematic and kinetic patterns between the end of the first test day and the beginning of the second. These results demonstrate that robotic exoskeletons controlled by muscle activity could be useful tools for testing neural mechanisms of human locomotor adaptation.


title: Being present in the face of existential threat: The role of trait mindfulness in reducing defensive responses to mortality salience

abstract: Terror management theory posits that people tend to respond defensively to reminders of death, including worldview defense, self-esteem striving, and suppression of death thoughts. Seven experiments examined whether trait mindfulness—a disposition characterized by receptive attention to present experience—reduced defensive responses to mortality salience (MS). Under MS, less mindful individuals showed higher worldview defense (Studies 1–3) and self-esteem striving (Study 5), yet more mindful individuals did not defend a constellation of values theoretically associated with mindfulness (Study 4). To explain these findings through proximal defense processes, Study 6 showed that more mindful individuals wrote about their death for a longer period of time, which partially mediated the inverse association between trait mindfulness and worldview defense. Study 7 demonstrated that trait mindfulness predicted less suppression of death thoughts immediately following MS. The discussion highlights the relevance of mindfulness to theories that emphasize the nature of conscious processing in understanding responses to threat.


title: Intelligent churn prediction in telecom: employing mRMR feature selection and RotBoost based ensemble classification

abstract: Churn prediction in telecom has recently gained substantial interest of stakeholders because of associated revenue losses. ::: ::: Predicting telecom churners, is a challenging problem due to the enormous nature of the telecom datasets. In this regard, we propose an intelligent churn prediction system for telecom by employing efficient feature extraction technique and ensemble method. We have used Random Forest, Rotation Forest, RotBoost and DECORATE ensembles in combination with minimum redundancy and maximum relevance (mRMR), Fisher's ratio and F-score methods to model the telecom churn prediction problem. We have observed that mRMR method returns most explanatory features compared to Fisher's ratio and F-score, which significantly reduces the computations and help ensembles in attaining improved performance. In comparison to Random Forest, Rotation Forest and DECORATE, RotBoost in combination with mRMR features attains better prediction performance on the standard telecom datasets. The better performance of RotBoost ensemble is largely attributed to the rotation of feature space, which enables the base classifier to learn different aspects of the churners and non-churners. Moreover, the Adaboosting process in RotBoost also contributes in achieving higher prediction accuracy by handling hard instances. The performance evaluation is conducted on standard telecom datasets using AUC, sensitivity and specificity based measures. Simulation results reveal that the proposed approach based on RotBoost in combination with mRMR features (CP-MRB) is effective in handling high dimensionality of the telecom datasets. CP-MRB offers higher accuracy in predicting churners and thus is quite prospective in modeling the challenging problems of customer churn prediction in telecom.


title: The WHOI micro-modem: an acoustic communications and navigation system for multiple platforms

abstract: The micro-modem is a compact, low-power, underwater acoustic communications and navigation subsystem. It has the capability to perform low-rate frequency-hopping frequency-shift keying (FH-FSK), variable rate phase-coherent keying (PSK), and two different types of long base line navigation, narrow-band and broadband. The system can be configured to transmit in four different bands from 3 to 30 kHz, with a larger board required for the lowest frequency. The user interface is based on the NMEA standard, which is a serial port specification. The modem also includes a simple built-in networking capability which supports up to 16 units in a polled or random-access mode and has an acknowledgement capability which supports guaranteed delivery transactions. The paper contains a detailed system description and results from several tests are also presented


title: A novel actuation concept for a multi rotor UAV

abstract: This paper proposes a novel strategy to improve the performance and fault tolerance of multi-rotor vehicles. The proposed strategy uses dual axis tilting propellers and thus enables three different actuation mechanisms, namely, gyroscopic torques, thrust vectoring and differential thrusting. Unlike the conventional quadrotor, the proposed strategy offers a wider range of control torques by combining the three actuation mechanisms. Conventional quadrotors cannot be reconfigured if one of rotors fails. However, the proposed strategy is still able to reconfigure the vehicle with complete failure of one rotor and a pair of adverse motors. In order to prove this concept, a dual axis tilting UAV is first designed and prototyped. Next, a mathematical representation of the prototyped vehicle is modelled and verified using experiments. Then, a control system is developed based on a PD controller and pseudoinverse control allocator and validated through tests on a rig and flight tests. The tests show that the vehicle is faster than a conventional counterpart and that it can resist the failure of two rotors. Finally, this paper suggests how to lead further substantial improvements in performance.


title: Predicting retweet count using visual cues

abstract: Social media platforms allow rapid information diffusion, and serve as a source of information to many of the users. Particularly, in Twitter information provided by tweets diffuses over the users through retweets. Hence, being able to predict the retweet count of a given tweet is important for understanding and controlling information diffusion on Twitter. Since the length of a tweet is limited to 140 characters, extracting relevant features to predict the retweet count is a challenging task. However, visual features of images linked in tweets may provide predictive features. In this study, we focus on predicting the expected retweet count of a tweet by using visual cues of an image linked in that tweet in addition to content and structure-based features.


title: Polarization for arbitrary discrete memoryless channels

abstract: Channel polarization, originally proposed for binary-input channels, is generalized to arbitrary discrete memoryless channels. Specifically, it is shown that when the input alphabet size is a prime number, a similar construction to that for the binary case leads to polarization. This method can be extended to channels of composite input alphabet sizes by decomposing such channels into a set of channels with prime input alphabet sizes. It is also shown that all discrete memoryless channels can be polarized by randomized constructions. The introduction of randomness does not change the order of complexity of polar code construction, encoding, and decoding. A previous result on the error probability behavior of polar codes is also extended to the case of arbitrary discrete memoryless channels. The generalization of polarization to channels with arbitrary finite input alphabet sizes leads to polar-coding methods for approaching the true (as opposed to symmetric) channel capacity of arbitrary channels with discrete or continuous input alphabets.


title: Topic model allocation of conversational dialogue records by Latent Dirichlet Allocation

abstract: The topic information of conversational content is important for continuation with communication, so topic detection and tracking is one of important research. Due to there are many topic transform occurring frequently in long time communication, and the conversation maybe have many topics, so it's important to detect different topics in conversational content. This paper detects topic information by using agglomerative clustering of utterances and Dynamic Latent Dirichlet Allocation topic model, uses proportion of verb and noun to analyze similarity between utterances and cluster all utterances in conversational content by agglomerative clustering algorithm. The topic structure of conversational content is friability, so we use speech act information and gets the hypernym information by E-HowNet that obtains robustness of word categories. Latent Dirichlet Allocation topic model is used to detect topic in file units, it just can detect only one topic if uses it in conversational content, because of there are many topics in conversational content frequently, and also uses speech act information and hypernym information to train the latent Dirichlet allocation models, then uses trained models to detect different topic information in conversational content. For evaluating the proposed method, support vector machine is developed for comparison. According to the experimental results, we can find the proposed method outperforms the approach based on support vector machine in topic detection and tracking in spoken dialogue.


title: Extrinsic Evaluation of Sentence Alignment Systems

abstract: Parallel corpora are usually a collection of documents which are translations of each other. To be useful in NLP applications such as word alignment or machine translation, they first have to be aligned at the sentence level. This paper is a user study briefly reviewing several sentence aligners and evaluating them based on the performance achieved by the SMT systems trained on their output. We conducted experiments on two language pairs and showed that using a more advanced sentence alignment algorithm may yield gains of 0.5 to 1 BLEU points.


title: Landmine detection and reporting using light weight zumo bot

abstract: The purpose of this research work is to propose a simple bot to detect landmines and mark their locations using autonomous navigation using a simple Zumo32U4 robot. Landmine detection is an important and yet challenging aspect in many of the countries which are still plagued my landmine issues for several decades now. It is not only the problem of the military, but it is also a humanitarian concern. So, in order to address this issue we came up with simple, light weight, autonomous wireless controlled robot which is capable of detecting the landmine at the depth of 5cm to 9cm under group. The paper describes about the design and development of the landmine detection mechanism. The results are very promising that this bot can be well used in fields for identifying and locating the landmines.


title: High loading of Pt–Ru nanocatalysts by pentagon defects introduced in a bamboo-shaped carbon nanotube support for high performance anode of direct methanol fuel cells

abstract: Abstract Bamboo-shaped carbon nanotubes (BCNTs), with a large amount of pentagon defects introduced in the walls, were explored as the support of high loaded Pt–Ru catalysts for the anode of direct methanol fuel cells (DMFCs) in comparison with conventional carbon nanotubes (CNTs) and Vulcan XC carbon black. By ethylene glycol reduction, Pt–Ru catalysts with a high loading (60 wt%) and uniform particle size of 2–3 nm were uniformly deposited on BCNTs; while 60 wt% Pt–Ru catalysts on CNTs resulted in significant agglomeration. The Pt–Ru/BCNT catalyst showed the highest activity on methanol oxidation in cyclic voltammetry and highest performance as the anode in a DMFC single cell. Such an enhancement was largely ascribed to an enhanced interaction of the introduced pentagon defects with Pt–Ru, which could promote a high loading and well dispersion of Pt–Ru catalysts and the charge transfer from Pt–Ru to the tubes.


title: Quantifying the security of graphical passwords: the case of android unlock patterns

abstract: Graphical passwords were proposed as an alternative to overcome the inherent limitations of text-based passwords, inspired by research that shows that the graphical memory of humans is particularly well developed. A graphical password scheme that has been widely adopted is the Android Unlock Pattern, a special case of the Pass-Go scheme with grid size restricted to 3x3 points and restricted stroke count. In this paper, we study the security of Android unlock patterns. By performing a large-scale user study, we measure actual user choices of patterns instead of theoretical considerations on password spaces. From this data we construct a model based on Markov chains that enables us to quantify the strength of Android unlock patterns. We found empirically that there is a high bias in the pattern selection process, e.g., the upper left corner and three-point long straight lines are very typical selection strategies. Consequently, the entropy of patterns is rather low, and our results indicate that the security offered by the scheme is less than the security of only three digit randomly-assigned PINs for guessing 20% of all passwords (i.e., we estimate a partial guessing entropy G_0.2 of 9.10 bit). Based on these insights, we systematically improve the scheme by finding a small, but still effective change in the pattern layout that makes graphical user logins substantially more secure. By means of another user study, we show that some changes improve the security by more than doubling the space of actually used passwords (i.e., increasing the partial guessing entropy G_0.2 to 10.81 bit).


title: Singularity Analysis of 3-DOF Planar Parallel Mechanisms via Screw Theory

abstract: This paper presents the results of a detailed study of the singular configurations of 3-DOF planar parallel mechanisms with three identical legs. Only prismatic and revolute joints are considered. From the point of view of singularity analysis, there are ten different architectures. All of them are examined in a compact and systematic manner using planar screw theory. The nature of each possible singular configuration is discussed and the singularity loci for a constant orientation of the mobile platform are obtained. For some architectures, simplified designs with easy to determine singularities are identified.


title: Fully Homomorphic SIMD Operations

abstract: At PKC 2010 Smart and Vercauteren presented a variant of Gentry’s fully homomorphic public key encryption scheme and mentioned that the scheme could support SIMD style operations. The slow key generation process of the Smart–Vercauteren system was then addressed in a paper by Gentry and Halevi, but their key generation method appears to exclude the SIMD style operation alluded to by Smart and Vercauteren. In this paper, we show how to select parameters to enable such SIMD operations. As such, we obtain a somewhat homomorphic scheme supporting both SIMD operations and operations on large finite fields of characteristic two. This somewhat homomorphic scheme can be made fully homomorphic in a naive way by recrypting all data elements separately. However, we show that the SIMD operations can be used to perform the recrypt procedure in parallel, resulting in a substantial speed-up. Finally, we demonstrate how such SIMD operations can be used to perform various tasks by studying two use cases: implementing AES homomorphically and encrypted database lookup.


title: CPW-fed Circular Fractal Slot Antenna for 2.38/5.35 GHz Dual-bands

abstract: Multiband/broadband antennas have received the increasing interest in wireless communications. Compared to typical microstrip antennas [1], coplanar waveguide (CPW) structures have several useful properties including wider bandwidth, better impedance matching, lower radiation loss and easy application with AIA (active integration antenna). Therefore, various techniques of broadband and dual-band [2]-[3] [9]-[10] responses were explored in this investigation. Fig. 1 depicts an innovative configuration of circular-shaped aperture with an appropriate inner conductor, which can be applied to broadband response and high gain antennas. Using a self-similar iteration design, a practical fractal antenna could be developed for multiband and broadband applications [3]-[4][11]. The typical Sierpinski gasket configuration was slotted in the triangle patch to form the multi-band antenna, broadband and ultra-wideband antennas [11]. Dependent on the Descartes circle theorem in the CPW-fed circular aperture has been proposed to present alternative broadband antennas [5]-[7]. By configuration synthesis and design map for accurate design and better performance, the CPW-fed circular fractal slot antennas are proposed for dual-band applications in this paper. The practical quarter-wavelength designs with wide-band response and available radiation gain are described herein. Simulations are verified by measurements with S11 spectrum, two-cut patterns, contour distribution patterns and current distributions.


title: BasisDetect: a model-based network event detection framework

abstract: The ability to detect unexpected events in large networks can be a significant benefit to daily network operations. A great deal of work has been done over the past decade to develop effective anomaly detection tools, but they remain virtually unused in live network operations due to an unacceptably high false alarm rate. In this paper, we seek to improve the ability to accurately detect unexpected network events through the use of BasisDetect, a flexible but precise modeling framework. Using a small dataset with labeled anomalies, the BasisDetect framework allows us to define large classes of anomalies and detect them in different types of network data, both from single sources and from multiple, potentially diverse sources. Network anomaly signal characteristics are learned via a novel basis pursuit based methodology. We demonstrate the feasibility of our BasisDetect framework method and compare it to previous detection methods using a combination of synthetic and real-world data. In comparison with previous anomaly detection methods, our BasisDetect methodology results show a 50% reduction in the number of false alarms in a single node dataset, and over 65% reduction in false alarms for synthetic network-wide data.


title: YouTube Science Channel Video Presenters and Comments: Female Friendly or Vestiges of Sexism?

abstract: Purpose This paper analyses popular YouTube science video channels for evidence of attractiveness to a female audience. Design/methodology/approach The influence of presenter gender and commenter sentiment towards males and females is investigated for 50 YouTube science channels with a combined view-count approaching ten billion. This is cross-referenced with commenter gender as a proxy for audience gender. Findings The ratio of male to female commenters varies between 1 and 39 to 1, but the low proportions of females seem to be due to the topic or presentation style rather than the gender of the presenter or the attitudes of the commenters. Although male commenters were more hostile to other males than to females, a few posted inappropriate sexual references that may alienate females. Research limitations/implications Comments reflect a tiny and biased sample of YouTube science channel viewers and so their analysis provides weak evidence. Practical implications Sexist behaviour in YouTube commenting need...


title: Combining classifiers: A theoretical framework

abstract: The problem of classifier combination is considered in the context of the two main fusion scenarios: fusion of opinions based on identical and on distinct representations. We develop a theoretical framework for classifier combination for these two scenarios. For multiple experts using distinct representations we argue that many existing schemes such as the product rule, sum rule, min rule, max rule, majority voting, and weighted combination, can be considered as special cases of compound classification. We then consider the effect of classifier combination in the case of multiple experts using a shared representation where the aim of fusion is to obtain a better estimate of the appropriatea posteriori class probabilities. We also show that the two theoretical frameworks can be used for devising fusion strategies when the individual experts use features some of which are shared and the remaining ones distinct. We show that in both cases (distinct and shared representations), the expert fusion involves the computation of a linear or nonlinear function of thea posteriori class probabilities estimated by the individual experts. Classifier combination can therefore be viewed as a multistage classification process whereby thea posteriori class probabilities generated by the individual classifiers are considered as features for a second stage classification scheme. Most importantly, when the linear or nonlinear combination functions are obtained by training, the distinctions between the two scenarios fade away, and one can view classifier fusion in a unified way.


title: Big data challenges and opportunities in the hype of Industry 4.0

abstract: The world of industrial automation technology is at the outset of a new era of innovation with the hype of Industry 4.0. Global modern industrial system converges the power of machines, computing, analytics, connectivity, cyber-physical systems, Internet of things, automation, cloud system and data exchange. Industry 4.0 is a revolution towards the digital world of digital factories and smart products. Big data is an integration of multi-disciplinary technologies and facilitates customer by bringing incredible services to a click. Internet of things connected the world of machines by adding communication capability in every device to connect to other devices or access the Internet. Big Data inflict a new horizon of opportunities in these systems. In this paper, challenges and opportunities of industrial big data are revealed in the context of Industry 4.0 with a different perspective. The current study helps the researchers to threshold these modern systems of Industry 4.0 in designing big data algorithms and techniques.


Final analysis of a phase II study of modified FOLFIRINOX in locally advanced and metastatic pancreatic cancer


title: Centered Weight Normalization in Accelerating Training of Deep Neural Networks

abstract: Training deep neural networks is difficult for the pathological curvature problem. Re-parameterization is an effective way to relieve the problem by learning the curvature approximately or constraining the solutions of weights with good properties for optimization. This paper proposes to reparameterize the input weight of each neuron in deep neural networks by normalizing it with zero-mean and unit-norm, followed by a learnable scalar parameter to adjust the norm of the weight. This technique effectively stabilizes the distribution implicitly. Besides, it improves the conditioning of the optimization problem and thus accelerates the training of deep neural networks. It can be wrapped as a linear module in practice and plugged in any architecture to replace the standard linear module. We highlight the benefits of our method on both multi-layer perceptrons and convolutional neural networks, and demonstrate its scalability and efficiency on SVHN, CIFAR-10, CIFAR-100 and ImageNet datasets.


title: Review of Literature for the Development of Indian Sign Language Recognition System

abstract: In recent years, sign language recognition has attracted much attention in computer vision . A sign language is a means of conveying the message by using hand, arm, body, and face to convey thoughts and meanings. Like spoken languages, sign languages emerge and evolve naturally within hearing-impaired communities. However, sign languages are not universal. There is no internationally recognized and standardized sign language for all deaf people. As is the case in spoken language, every country has got its own sign language with high degree of grammatical variations. The sign language used in India is commonly known as Indian Sign Language (henceforth called ISL).


title: Abstract Dialectical Frameworks for Legal Reasoning.

abstract: Dialectical Frameworks for Legal Reasoning Latifa AL-ABDULKARIM, Katie ATKINSON, Trevor BENCH-CAPON Department of Computer Science, The University of Liverpool, UK Abstract. In recent years a powerful generalisation of Dung’s abstract argumentation frameworks, Abstract Dialectical Frameworks (ADF), has been developed. ADFs generalise the abstract argumentation frameworks introduced by Dung by replacing Dung’s single acceptance condition (that all attackers be defeated) with acceptance conditions local to each particular node. Such local acceptance conditions allow structured argumentation to be straightforwardly incorporated. Related to ADFs are prioritised ADFs, which allow for reasons pro and con a node. In this paper we show how these structures provide an excellent framework for representing a leading approach to reasoning with legal cases. In recent years a powerful generalisation of Dung’s abstract argumentation frameworks, Abstract Dialectical Frameworks (ADF), has been developed. ADFs generalise the abstract argumentation frameworks introduced by Dung by replacing Dung’s single acceptance condition (that all attackers be defeated) with acceptance conditions local to each particular node. Such local acceptance conditions allow structured argumentation to be straightforwardly incorporated. Related to ADFs are prioritised ADFs, which allow for reasons pro and con a node. In this paper we show how these structures provide an excellent framework for representing a leading approach to reasoning with legal cases.