title: Reliable Multicast with Pipelined Network Coding Using Opportunistic Feeding and Routing

abstract: Multicast is an important mechanism in modern wireless networks and has attracted significant efforts to improve its performance with different metrics including throughput, delay, energy efficiency, etc. Traditionally, an ideal loss-free channel model is widely used to facilitate routing protocol design. However, the quality of wireless links is affected or even jeopardized resulting in transmission failures by many factors like collisions, fading or the noise of environment. In this paper, we propose a reliable multicast protocol, called CodePipe, with energy-efficiency, high throughput and fairness in lossy wireless networks. Building upon opportunistic routing and random linear network coding, CodePipe can not only eliminate coordination between nodes, but also improve the multicast throughput significantly by exploiting both intra-batch and inter-batch coding opportunities. In particular, four key techniques, namely, LP-based opportunistic routing structure, opportunistic feeding, fast batch moving and inter-batch coding, are proposed to offer significant improvement in throughput, energy-efficiency and fairness. Moreover, we design an efficient online extension of CodePipe such that it can work in a dynamic network where nodes join and leave the network as time progresses. We evaluate CodePipe on ns2 simulator by comparing with other two state-of-art multicast protocols, MORE and Pacifier. Simulation results show that CodePipe significantly outperforms both of them.


title: Geometrically consistent plane extraction for dense indoor 3D maps segmentation

abstract: Modern SLAM systems with a depth sensor are able to reliably reconstruct dense 3D geometric maps of indoor scenes. Representing these maps in terms of meaningful entities is a step towards building semantic maps for autonomous robots. One approach is to segment the 3D maps into semantic objects using Conditional Random Fields (CRF), which requires large 3D ground truth datasets to train the classification model. Additionally, the CRF inference is often computationally expensive. In this paper, we present an unsupervised geometric-based approach for the segmentation of 3D point clouds into objects and meaningful scene structures. We approximate an input point cloud by an adjacency graph over surface patches, whose edges are then classified as being either on or off. We devise an effective classifier which utilises both global planar surfaces and local surface convexities for edge classification. More importantly, we propose a novel global plane extraction algorithm for robustly discovering the underlying planes in the scene. Our algorithm is able to enforce the extracted planes to be mutually orthogonal or parallel which conforms usually with human-made indoor environments. We reconstruct 654 3D indoor scenes from NYUv2 sequences to validate the efficiency and effectiveness of our segmentation method.


title: Making Convolutional Networks Shift-Invariant Again

abstract: Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling. However, simply inserting this module into deep networks degrades performance; as a result, it is seldomly used today. We show that when integrated correctly, it is compatible with existing architectural components, such as max-pooling and strided-convolution. We observe \textit{increased accuracy} in ImageNet classification, across several commonly-used architectures, such as ResNet, DenseNet, and MobileNet, indicating effective regularization. Furthermore, we observe \textit{better generalization}, in terms of stability and robustness to input corruptions. Our results demonstrate that this classical signal processing technique has been undeservingly overlooked in modern deep networks. Code and anti-aliased versions of popular networks are available at this https URL .


title: Analysis of the Characteristics of Production Database Workloads and Comparison with the TPC Benchmarks

abstract: There has been very little empirical analysis of any real production database workloads. Although The Transaction Processing Performance Council benchmarks C (TPCC) and D (TPC-D) have become the standard benchmarks for online transaction processing and decision support systems respectively, there has also not been any major effort to systematically analyze their workload characteristics, especially in relation to those of real production database workloads. In this paper, we examine the characteristics of the production database workloads of ten of the world’s largest corporations and we also compare them to TPC-C and TPCD. We find that the production workloads exhibit a wide range of behavior; in some cases, the TPC benchmarks fall reasonably within the range of real workload behavior, and in other cases, the TPC benchmarks are not representative of the real workloads. While the two TPC benchmarks generally complement one another in reflecting the characteristics of the production workloads but there are still some aspects This work has been supported by the State of California under the MICRO program, and by IBM, Cisco Corporation, Fujitsu Microelectronics, Intel Corporation, Microsoft Corporation, Quantum Corporation, Sun Microsystems and Toshiba Corporation. This report is also available as Research Report RJ 10165, IBM Almaden Research Center, San Jose, CA. of the real workloads that are not represented by either of the benchmarks. Specifically, our analysis suggests that the TPC benchmarks tend to exercise the following aspects of the system differently than the production workloads: concurrency control mechanism (TPC-C tends to have longer transactions and fewer read-only transactions than the production workloads while some of TPC-D’s transactions are much longer but are read-only and are run serially), workload-adaptive techniques (the production workloads have I/O demands that are much more bursty), scheduling and resource allocation policies (unlike TPC-C whose transactions are very regular and TPC-D where the queries are run serially, the production workloads tend have many concurrent and diverse transactions), and I/O optimizations for temporary and index files (TPC-C has no I/O activity to temporary objects while most of TPC-D’s references are directed at index objects). In this paper, we also reexamine Amdahl’s rule of thumb for a typical data processing system (one bit of I/O for every instruction) and discover that both the TPC benchmarks and the production workloads generate on the order of 0.5 to 1.0 bit of logical I/O per instruction, surprisingly close to the much earlier figure.


title: Evolution of glutathione S-transferase subunits in culicidae and related nematocera: Electrophoretic and immunological evidence for conserved enzyme structure and expression

abstract: The antigenic relatedness and molecular weights of glutathione S-transferase (GST) protein subunits expressed by representative members of the Dipteran suborder Nematocera were analyzed by immunoblotting and affinity chromatography. Ten species within the genus Aedes expressed two subunits which cross-reacted with an antiserum developed against the GST-1b isozyme purified from Aedes aegypti. One of these two subunits showed no discernible molecular weight variability within the ten Aedes species. Its molecular weight (25,900 Da) was identical to the GST-1a subunit found in A. aegypti. The molecular weight of the other subunits varied among the ten species, but in all cases was larger than 25,900 Da. In ten species within the genus Anopheles (considered to be among the more primitive genera in the Culicidae), only one immunologically related GST subunit was expressed. Its molecular weight was also identical to the GST-1a subunit found in the Aedes species. Members of genera considered phylogenetically intermediate between Anopheles and Aedes had either one or two cross-reacting subunits. Representative species of families closely related to the Culicidae (the Chaoboridae, Chironomidae, Simuliidae, and Cecidomyiidae) also expressed immunologically related GST subunits. GST isozymes were partially purified from Anopheles freeborni and A aegypti adults, larvae, and ovaries using S-hexylglutathione affinity chromatography. Adult and larval Anopheles freeborni expressed not only the immunologically related subunit, but an additional, immunologically unrelated subunit. In fully developed ovaries of both species, however, only subunits immunologically related to GST-1b were expressed. These results suggest that there is an immunologically conserved domain shared among specific GST isozymes within the Nemotocea. The conserved nature of these GST subunits, their correlation with oocyte development, and their localization within ovary tissue, suggests that they share a conserved in vivo function during oocyte maturation. In addition, the conserved antibody binding domain within these subunits apparently has been duplicated in several species of Culicidae.


title: Deep Learning for Categorization of Lung Cancer CT Images

abstract: Lung cancer is a serious health problem. In the United States alone, approximately 225,000 people each year are diagnosed with lung cancer. Early detection is a crucial part of giving patients the best chance of recovery. Deep learning gives us an opportunity to increase the accuracy of the automated initial diagnosis. Here we present an ensemble of Convolution Neural Networks(CNN) using multiple preprocessing methods to increase the accuracy of the automated labeling of the scans. We have done this by implementing ensembles of CNNs along with a voting system to get the consensus of the two networks. The initial results of our best method show both a consistently high accuracy (97.5%) and a low percentage of false positives (


AI Methods for Algorithmic Composition: A Survey, a Critical View and Future Prospects


title: Understanding Danmaku's Potential in Online Video Learning

abstract: Danmaku is a video comment feature which is used to overlay comments onto videos of many types and is gaining popularity in China. In this paper, we explore Danmaku's potential in online video learning. We present our survey results of 213 Chinese online users regarding their usage of danmaku. We also propose a new design in which danmaku is modified and leveraged to better facilitate user engagement and interaction in an online learning environment. Our results show that participants perceived both designs to have many benefits in terms of social presence and cognitive presence. Most of the participants post danmaku themselves instead of simply watching others' danmaku. In addition, about 40% of the participants who did not prefer the original design showed more positive feedback in the new design.


title: A lightweight, high performance communication protocol for grid computing

abstract: This paper describes a lightweight, high-performance communication protocol for the high-bandwidth, high-delay networks typical of computational Grids. One unique feature of this protocol is that it incorporates an extremely accurate classification mechanism that is efficient enough to diagnose the cause of data loss in real time, providing to the controller the opportunity to respond to different causes of data loss in different ways. The simplest adaptive response, and the one discussed in this paper, is to trigger aggressive congestion control measures only when the data loss is diagnosed as network related. However, even this very simple adaptation can have a tremendous impact on performance in a Grid setting where the resources allocated to a long-running, data-intensive application can fluctuate significantly during the course of its execution. In fact, we provide results showing that the utilization of the information provided by the classifier increased performance by over two orders of magnitude depending on the dominant cause of data loss. In this paper, we discuss the Bayesian statistical framework upon which the classifier is based and the classification metrics that make this approach highly successful. We discuss the integration of the classifier into the congestion control structures of an existing high-performance communication protocol, and provide empirical results showing that it correctly diagnosed the cause of data loss in over 98% of the experimental trials.


title: On the Use of “ Deep ” Features for Online Image Sharing

abstract: Online image sharing in social networking sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. Despite that social networking sites allow users to set their privacy preferences, this can be cumbersome for the vast majority of users. In this paper, we explore privacy prediction models for social media that can automatically identify private (or sensitive) content from images, before they are shared online, in order to help protect users' privacy in social media. More precisely, we study "deep" visual features that are extracted from various layers of a pre-trained deep Convolutional Neural Network (CNN) as well as "deep" image tags generated from the CNN. Experimental results on a Flickr dataset of thousands of images show that the deep visual features and deep image tags can successfully identify images' private content and substantially outperform previous models for this task.


title: Toward Explainable Fashion Recommendation

abstract: Many studies have been conducted so far to build systems for recommending fashion items and outfits. Although they achieve good performances in their respective tasks, most of them cannot explain their judgments to the users, which compromises their usefulness. Toward explainable fashion recommendation, this study proposes a system that is able not only to provide a goodness score for an outfit but also to explain the score by providing reason behind it. For this purpose, we propose a method for quantifying how influential each feature of each item is to the score. Using this influence value, we can identify which item and what feature make the outfit good or bad. We represent the image of each item with a combination of human-interpretable features, and thereby the identification of the most influential item-feature pair gives useful explanation of the output score. To evaluate the performance of this approach, we design an experiment that can be performed without human annotation; we replace a single item-feature pair in an outfit so that the score will decrease, and then we test if the proposed method can detect the replaced item correctly using the above influence values. The experimental results show that the proposed method can accurately detect bad items in outfits lowering their scores.


title: Soft fluidic actuators of rotary type for safe physical human-machine interaction

abstract: Safe actuators with controllable compliance are indispensable for assistive robots working in human environment and especially for rehabilitation devices physically interacting with patient. The paper introduces new patent pending soft fluidic actuators with rotary elastic chambers (REC), which produces rotational motion without any additional transmission elements. The key actuator features as inherent compliance, high back-driveability and light-weight design build a foundation for safe physical human-machine interaction, although torques of several tens of Newton-meter can be generated. Due to their compact design the REC-actuators demonstrate better integration capabilities into complex kinematic structures than conventional fluidic muscles of linear type. The actuator compliance can be varied by pressure regulation in antagonistic acting chambers, which makes soft fluidic actuators very suitable for making the transition from continuous passive motion to active (assistive) behaviour during the therapy depending on a patient activity. Conceptual design of modular compact assistive motion therapy devices based on new “slim-line” REC-actuators is presented.


title: Distribution market clearing and settlement

abstract: There are various undergoing efforts by system operators to set up an electricity market at the distribution level to enable a rapid and widespread deployment of distributed energy resources (DERs) and microgrids. This paper follows the previous work of the authors in implementing the distribution market operator (DMO) concept, and focuses on investigating the clearing and settlement processes performed by the DMO. The DMO clears the market to assign the awarded power from the wholesale market to customers within its service territory based on their associated demand bids. The DMO accordingly settles the market to identify the distribution locational marginal prices (DLMPs) and calculate payments from each customer and the total payment to the system operator. Numerical simulations exhibit the merits and effectiveness of the proposed DMO clearing and settlement processes.


title: VFH/sup */: local obstacle avoidance with look-ahead verification

abstract: This paper presents an enhancement to the earlier developed vector field histogram (VFH) method for mobile robot obstacle avoidance. The enhanced method, called VFH/sup */ successfully deals with situations that are problematic for purely local obstacle avoidance algorithms. The VFH/sup */ method verifies that a particular candidate direction guides the robot around an obstacle. The verification is performed by using the A/sup */ search algorithm and appropriate cost and heuristic functions.


Forecasting for Adaptive Traffic Signal Control


title: A graph-based approach to API usage adaptation

abstract: Reusing existing library components is essential for reducing the cost of software development and maintenance. When library components evolve to accommodate new feature requests, to fix bugs, or to meet new standards, the clients of software libraries often need to make corresponding changes to correctly use the updated libraries. Existing API usage adaptation techniques support simple adaptation such as replacing the target of calls to a deprecated API, however, cannot handle complex adaptations such as creating a new object to be passed to a different API method, or adding an exception handling logic that surrounds the updated API method calls. This paper presents LIBSYNC that guides developers in adapting API usage code by learning complex API usage adaptation patterns from other clients that already migrated to a new library version (and also from the API usages within the library's test code). LIBSYNC uses several graph-based techniques (1) to identify changes to API declarations by comparing two library versions, (2) to extract associated API usage skeletons before and after library migration, and (3) to compare the extracted API usage skeletons to recover API usage adaptation patterns. Using the learned adaptation patterns, LIBSYNC recommends the locations and edit operations for adapting API usages. The evaluation of LIBSYNC on real-world software systems shows that it is highly correct and useful with a precision of 100% and a recall of 91%.


title: Class Noise Mitigation through Instance Weighting

abstract: We describe a novel framework for class noise mitigation that assigns a vector of class membership probabilities to each training instance, and uses the confidence on the current label as a weight during training. The probability vector should be calculated such that clean instances have a high confidence on its current label, while mislabeled instances have a low confidence on its current label and a high confidence on its correct label. Past research focuses on techniques that either discard or correct instances. This paper proposes that discarding and correcting are special cases of instance weighting, and thus, part of this framework. We propose a method that uses clustering to calculate a probability distribution over the class labels for each instance. We demonstrate that our method improves classifier accuracy over the original training set. We also demonstrate that instance weighting can outperform discarding.


title: The Effect of Probiotics on Childhood Constipation: A Randomized Controlled Double Blind Clinical Trial

abstract: Background. Inconsistent data exist about the role of probiotics in the treatment of constipated children. The aim of this study was to investigate the effectiveness of probiotics in childhood constipation. Materials and Methods. In this placebo controlled trial, fifty-six children aged 4–12 years with constipation received randomly lactulose plus Protexin or lactulose plus placebo daily for four weeks. Stool frequency and consistency, abdominal pain, fecal incontinence, and weight gain were studied at the beginning, after the first week, and at the end of the 4th week in both groups. Results. Forty-eight patients completed the study. At the end of the fourth week, the frequency and consistency of defecation improved significantly (P = 0.042 and P = 0.049, resp.). At the end of the first week, fecal incontinence and abdominal pain improved significantly in intervention group (P = 0.030 and P = 0.017, resp.) but, at the end of the fourth week, this difference was not significant (P = 0.125 and P = 0.161, resp.). A significant weight gain was observed at the end of the 1st week in the treatment group. Conclusion. This study showed that probiotics had a positive role in increasing the frequency and improving the consistency at the end of 4th week.


title: Creativity and mental illness: prevalence rates in writers and their first-degree relatives

abstract: Rates of mental illness were examined in 30 creative writers, 30 matched control subjects, and the first-degree relatives of both groups. The writers had a substantially higher rate of mental illness, predominantly affective disorder, with a tendency toward the bipolar subtype. There was also a higher prevalence of affective disorder and creativity in the writers' first-degree relatives, suggesting that these traits run together in families and could be genetically mediated. Both writers and control subjects had IQs in the superior range; the writers excelled only on the WAIS vocabulary subtest, confirming previous observations that intelligence and creativity are independent mental abilities.


title: Over 10W C-Ku band GaN MMIC non-uniform distributed power amplifier with broadband couplers

abstract: A 6–18 GHz monolithic microwave integrated circuit (MMIC) power amplifier (PA) was successfully developed using a quarter wavelength short stub and a monolithic broadband coupler for a non-uniform distributed power amplifier (NDPA) topology. This topology improved the output power at 18 GHz and attained a flat output power profile over 6–18 GHz. It also achieved filtering characteristics for both lower and higher cut-off frequencies. A fabricated MMIC PA with 0.25µm GaN HEMTs delivered an output power of more than 10 W with average power added efficiency (PAE) of 18% over 6 to 18 GHz. To the best of our knowledge, this is the best combination of output power and bandwidth for any solid-state MMIC amplifier operating up to the full Ku-band.


title: Gendered Media: The Influence of Media on Views of Gender

abstract: Of the many influences on how we view men and women, media are the most pervasive and one of the most powerful. Woven throughout our daily lives, media insinuate their messages into our consciousness at every turn. All forms of media communicate images of the sexes, many of which perpetuate unrealistic, stereotypical, and limiting perceptions. Three themes describe how media represent gender. First, women are underrepresented which falsely implies that men are the cultural standard and women are unimportant or invisible. Second, men and women are portrayed in stereotypical ways that reflect and sustain socially endorsed views of gender. Third, depictions of relationships between men and women emphasize traditional roles and normalize violence against women. We will consider each of these themes in this section.


title: Statistics and Causal Inference

abstract: Abstract Problems involving causal inference have dogged at the heels of statistics since its earliest days. Correlation does not imply causation, and yet causal conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about causation? This question is addressed by using a particular model for causal inference (Holland and Rubin 1983; Rubin 1974) to critique the discussions of other writers on causation and causal inference. These include selected philosophers, medical researchers, statisticians, econometricians, and proponents of causal modeling.


title: Word Translation Without Parallel Data

abstract: State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or English-Chinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation.


title: Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge

abstract: Deep convolutional neural networks (DCNNs) have achieved great success in various computer vision and pattern recognition applications, including those for handwritten Chinese character recognition (HCCR). However, most current DCNN-based HCCR approaches treat the handwritten sample simply as an image bitmap, ignoring some vital domain-specific information that may be useful but that cannot be learnt by traditional networks. In this paper, we propose an enhancement of the DCNN approach to online HCCR by incorporating a variety of domain-specific knowledge, including deformation, non-linear normalization, imaginary strokes, path signature, and 8-directional features. Our contribution is twofold. First, these domain-specific technologies are investigated and integrated with a DCNN to form a composite network to achieve improved performance. Second, the resulting DCNNs with diversity in their domain knowledge are combined using a hybrid serial-parallel (HSP) strategy. Consequently, we achieve a promising accuracy of 97.20% and 96.87% on CASIA-OLHWDB1.0 and CASIA-OLHWDB1.1, respectively, outperforming the best results previously reported in the literature.


abstract: Due to their potential to deliver increased performance over single-core processors, multi-core processors have become mainstream in processor design. Computation-intensive real-time systems must exploit intra-task parallelism to take full advantage of multi-core processing. However, existing results in real-time scheduling of parallel tasks focus on restrictive task models such as the synchronous model where a task is a sequence of alternating parallel and sequential segments, and parallel segments have threads of execution that are of equal length. In this paper, we address a general model for deterministic parallel tasks, where a task is represented as a DAG with different nodes having different execution requirements. We make several key contributions towards both preemptive and non-preemptive realtime scheduling of DAG tasks on multi-core processors. First, we propose a task decomposition that splits a DAG into sequential tasks. Second, we prove that parallel tasks, upon decomposition, can be scheduled using preemptive global EDF with a resource augmentation bound of 4. This bound is as good as the best known bound for more restrictive models, and is the first for a general DAG model. Third, we prove that the decomposition has a resource augmentation bound of 4 plus a non-preemption overhead for non-preemptive global EDF scheduling. To our knowledge, this is the first resource augmentation bound for nonpreemptive scheduling of parallel tasks. Through simulations, Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Real-Time Scheduling of Parallel Tasks under a General DAG Model Abusayeed Saifullah, David Ferry, Kunal Agrawal, Chenyang Lu, and Christopher Gill Department of Computer Science and Engineering Washington University in St. Louis Abstract—Due to their potential to deliver increased performance over single-core processors, multi-core processors have become mainstream in processor design. Computation-intensive real-time systems must exploit intra-task parallelism to take full advantage of multi-core processing. However, existing results in real-time scheduling of parallel tasks focus on restrictive task models such as the synchronous model where a task is a sequence of alternating parallel and sequential segments, and parallel segments have threads of execution that are of equal length. In this paper, we address a general model for deterministic parallel tasks, where a task is represented as a DAG with different nodes having different execution requirements. We make several key contributions towards both preemptive and non-preemptive realtime scheduling of DAG tasks on multi-core processors. First, we propose a task decomposition that splits a DAG into sequential tasks. Second, we prove that parallel tasks, upon decomposition, can be scheduled using preemptive global EDF with a resource augmentation bound of 4. This bound is as good as the best known bound for more restrictive models, and is the first for a general DAG model. Third, we prove that the decomposition has a resource augmentation bound of 4 plus a non-preemption overhead for non-preemptive global EDF scheduling. To our knowledge, this is the first resource augmentation bound for nonpreemptive scheduling of parallel tasks. Through simulations, we demonstrate that the achieved bounds are safe and sufficient.Due to their potential to deliver increased performance over single-core processors, multi-core processors have become mainstream in processor design. Computation-intensive real-time systems must exploit intra-task parallelism to take full advantage of multi-core processing. However, existing results in real-time scheduling of parallel tasks focus on restrictive task models such as the synchronous model where a task is a sequence of alternating parallel and sequential segments, and parallel segments have threads of execution that are of equal length. In this paper, we address a general model for deterministic parallel tasks, where a task is represented as a DAG with different nodes having different execution requirements. We make several key contributions towards both preemptive and non-preemptive realtime scheduling of DAG tasks on multi-core processors. First, we propose a task decomposition that splits a DAG into sequential tasks. Second, we prove that parallel tasks, upon decomposition, can be scheduled using preemptive global EDF with a resource augmentation bound of 4. This bound is as good as the best known bound for more restrictive models, and is the first for a general DAG model. Third, we prove that the decomposition has a resource augmentation bound of 4 plus a non-preemption overhead for non-preemptive global EDF scheduling. To our knowledge, this is the first resource augmentation bound for nonpreemptive scheduling of parallel tasks. Through simulations, we demonstrate that the achieved bounds are safe and sufficient.


title: Approximate Mean Delay Analysis for a Signalized Intersection With Indisciplined Traffic

abstract: Mixed vehicular traffic comprising small cars and two-wheeled vehicles (called motorcycles in this paper) arrive at a lane of a signalized road intersection. The traffic does not follow lane-discipline, in that the arriving vehicles do not necessarily queue up one behind the other. The motorcycles are small enough to stand side-by-side with cars or other motorcycles, so as to fill up the width of the lane. With such queue joining behavior, the waiting vehicles form batches, comprising motorcycles, and at most one car. During the green signal period the vehicles in the head-of-the-line batch exit the intersection together. In this paper, assuming a Poisson point process model for vehicle arrivals, we have provided an approximate analysis of such a queueing system. Our approach is to use an assembly queue model for the batching process. The batches generated by the assembly queue enter an interrupted M/SemiMarkov/1 (or M/SM/1) queue. By analyzing the assembly queue we characterize the batch input process for the interrupted M/SM/1 queue. We then develop an extension of the Webster mean delay formula for obtaining the approximate mean delay in the interrupted M/SM/1 queue. Numerical results from the analysis are compared with simulation results. The analysis is shown to be accurate in predicting the increase in the system capacity due to the batching behavior.


title: Fair assignment of indivisible objects under ordinal preferences

abstract: We consider the discrete assignment problem in which agents express ordinal preferences over objects and these objects are allocated to the agents in a fair manner. We use the stochastic dominance relation between fractional or randomized allocations to systematically define varying notions of proportionality and envy-freeness for discrete assignments. The computational complexity of checking whether a fair assignment exists is studied for these fairness notions. We also characterize the conditions under which a fair assignment is guaranteed to exist. For a number of fairness concepts, polynomial-time algorithms are presented to check whether a fair assignment exists. Our algorithmic results also extend to the case of unequal entitlements of agents. Our NP-hardness result, which holds for several variants of envy-freeness, answers an open question posed by Bouveret, Endriss, and Lang (ECAI 2010). We also propose fairness concepts that always suggest a non-empty set of assignments with meaningful fairness properties. Among these concepts, optimal proportionality and optimal weak proportionality appear to be desirable fairness concepts.


title: Effect of health on knowledge, self-efficacy and health behaviours of women with urinary tract infection

abstract: Urinary tract infection (UTI) is one of the most common bacterial infections affecting about 40% of women in some points in their lives. UTI can cause serious potential consequences such as life-threatening sepsis, if it is not treated by primary health care providers. Health behaviours play an important role in causing UTI. Self-efficacy, as an important moderator of the relationship between knowledge and behaviour, is an important prerequisite for behaviour change. The aim of this study is to investigate the effect of health education on knowledge, self-efficacy and health behaviours of women with UTI. This is a quasi-experimental study of the intervention and comparison groups, with pre-test and post-test design, conducted with 170 married women with UTI; referred to selected hospital laboratories in Tehran. The data collection tool was a self-made questionnaire which was answered by both groups, prior to the intervention and 12 weeks thereafter. The intervention was performed on the intervention group samples. On the basis of the study results, after the intervention the average score of the knowledge (p = 0·002), self-efficacy (p < 0·001) and health behaviours (p < 0·001) of the intervention group had significantly increased compared to the comparison group. The findings showed that health education was effective in promotion of the knowledge, self-efficacy and health behaviours of women with UTI.


title: Adopting an Agile Approach for the Development of Mobile Applications

abstract: With the increasing popularity and demand for mobile applications, there has been a significant increase in the number of mobile application development projects. Highly volatile requirements of mobile applications require adaptive software development methods. The Agile approach is seen as a natural fit for mobile application and there is a need to explore various Agile methodologies for the development of mobile applications. This paper evaluates how adopting various Agile approaches improves the development of mobile applications and if they can be used in order to provide more tailor-made process improvements within an organization. A survey related to mobile application development process improvement was developed. The use of various Agile approaches for success in mobile application development were evaluated by determining the significance of the most used Agile engineering paradigms such as XP, Scrum, and Lean. The findings of the study show that these Agile methods have the potential to help deliver enhanced speed and quality for mobile application development.


title: Predicting Prevalence of Influenza-Like Illness From Geo-Tagged Tweets

abstract: Modeling disease spread and distribution using social media data has become an increasingly popular research area. While Twitter data has recently been investigated for estimating disease spread, the extent to which it is representative of disease spread and distribution in a macro perspective is still an open question. In this paper, we focus on macro-scale modeling of influenza-like illnesses (ILI) using a large dataset containing 8,961,932 tweets from Australia collected in 2015. We first propose modifications of the state-of-the-art ILI-related tweet detection approaches to acquire a more refined dataset. We normalize the number of detected ILI-related tweets with Internet access and Twitter penetration rates in each state. Then, we establish a state-level linear regression model between the number of ILI-related tweets and the number of real influenza notifications. The Pearson correlation coefficient of the model is 0.93. Our results indicate that: 1) a strong positive linear correlation exists between the number of ILI-related tweets and the number of recorded influenza notifications at state scale; 2) Twitter data has promising ability in helping detect influenza outbreaks; 3) taking into account the population, Internet access and Twitter penetration rates in each state enhances the prevalence modeling analysis.


title: Environmental sound recognition: A survey

abstract: Although research in audio recognition has traditionally focused on speech and music signals, the problem of environmental sound recognition (ESR) has received more attention in recent years. Research on ESR has significantly increased in the past decade. Recent work has focused on the appraisal of non-stationary aspects of environmental sounds, and several new features predicated on non-stationary characteristics have been proposed. These features strive to maximize their information content pertaining to signal's temporal and spectral characteristics. Furthermore, sequential learning methods have been used to capture the long-term variation of environmental sounds. In this survey, we will offer a qualitative and elucidatory survey on recent developments. It includes three parts: i) basic environmental sound processing schemes, ii) stationary ESR techniques and iii) non-stationary ESR techniques. Finally, concluding remarks and future research and development trends in the ESR field will be given.


title: Large scale metric learning from equivalence constraints

abstract: In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. Further, if one considers the constantly growing amount of data it is often infeasible to specify fully supervised labels for all data points. Instead, it is easier to specify labels in form of equivalence constraints. We introduce a simple though effective strategy to learn a distance metric from equivalence constraints, based on a statistical inference perspective. In contrast to existing methods we do not rely on complex optimization problems requiring computationally expensive iterations. Hence, our method is orders of magnitudes faster than comparable methods. Results on a variety of challenging benchmarks with rather diverse nature demonstrate the power of our method. These include faces in unconstrained environments, matching before unseen object instances and person re-identification across spatially disjoint cameras. In the latter two benchmarks we clearly outperform the state-of-the-art.


title: OPRM1 and EGFR contribute to skin pigmentation differences between Indigenous Americans and Europeans

abstract: Contemporary variation in skin pigmentation is the result of hundreds of thousands years of human evolution in new and changing environments. Previous studies have identified several genes involved in skin pigmentation differences among African, Asian, and European populations. However, none have examined skin pigmentation variation among Indigenous American populations, creating a critical gap in our understanding of skin pigmentation variation. This study investigates signatures of selection at 76 pigmentation candidate genes that may contribute to skin pigmentation differences between Indigenous Americans and Europeans. Analysis was performed on two samples of Indigenous Americans genotyped on genome-wide SNP arrays. Using four tests for natural selection—locus-specific branch length (LSBL), ratio of heterozygosities (lnRH), Tajima’s D difference, and extended haplotype homozygosity (EHH)—we identified 14 selection-nominated candidate genes (SNCGs). SNPs in each of the SNCGs were tested for association with skin pigmentation in 515 admixed Indigenous American and European individuals from regions of the Americas with high ground-level ultraviolet radiation. In addition to SLC24A5 and SLC45A2, genes previously associated with European/non-European differences in skin pigmentation, OPRM1 and EGFR were associated with variation in skin pigmentation in New World populations for the first time.


title: Machine learning approches and pattern recognition for spectral data

abstract: The adaptive and automated analysis of spectral data plays an important role in many areas of research such as physics, astronomy and geophysics, chemistry, bioinformatics, biochemistry, engineering, and others. The amount of data may range from several billion samples in geophysics to only a few in medical applications. Further, a vectorial rep- resentation of spectra typically leads to huge-dimensional problems. This scenario gives the background for particular requirements of respective machine learning approaches which will be the focus of this overview.


title: CARD9 Signaling in the Innate Immune Response

abstract: CARD9 is a caspase recruitment domain-containing signaling protein that is highly expressed in dendritic cells and in macrophages. Work over the last two years has identified CARD9 as a central regulator of innate immunity. Best characterized is CARD9's function downstream of ITAM-bearing or ITAM-coupled receptors in myeloid cells, including its essential role downstream of the antifungal pattern-recognition receptor Dectin-1. In the ITAM receptor pathway, CARD9 couples receptor proximal splenic tyrosine kinase SYK activation to the canonical NF-κB pathway. In addition, CARD9 is involved in the activation of p38 and JNK kinases and it can function downstream of cytosolic pattern-recognition receptors. CARD9 signaling mediates mammalian innate immune responses against selected fungi, bacteria, and viruses and can prime and shape adaptive immunity. This review will summarize current knowledge on CARD9 signaling and its function in the innate immune response.


title: EYEDIAP: a database for the development and evaluation of gaze estimation algorithms from RGB and RGB-D cameras

abstract: The lack of a common benchmark for the evaluation of the gaze estimation task from RGB and RGB-D data is a serious limitation for distinguishing the advantages and disadvantages of the many proposed algorithms found in the literature. This paper intends to overcome this limitation by introducing a novel database along with a common framework for the training and evaluation of gaze estimation approaches. In particular, we have designed this database to enable the evaluation of the robustness of algorithms with respect to the main challenges associated to this task: i) Head pose variations; ii) Person variation; iii) Changes in ambient and sensing conditions and iv) Types of target: screen or 3D object.


title: Design optimization for a 2-DOF cable-driven joint with large stiffness range

abstract: Cable-driven manipulator has the advantages of lightweight structure and adjustable stiffness. As such, it is intrinsically safe for human-robot interactions as a service robot. This paper proposed a 2-DOF cable-driven joint module with variable stiffness. To increase the stiffness range, a variable stiffness device (VSD) with a compact configuration is proposed and optimized. A design optimization method for the 2-DOF cable-driven joint module is proposed to obtain a large stiffness adjustable range. The average stiffness adjustable range in the workspace is employed as an objective function to optimize the dimensions of the joint module with VSDs. The optimization result shows that the stiffness range and the workspace have been increased significantly.


Should we add stem cells to the code cart in resuscitation of heatstroke?


title: Cultural Problem Being Faced By International Students in the University of the Punjab

abstract: AbstractsThe aim of this research is to identify the "cultural problems being faced by international students studying in the University of the Punjab." It was descriptive in nature and survey research technique was use to identify the Cultural problems being faced by the said students. All international students of university of the Punjab were population of the study, random sampling strategy was used for sample selection. The sample size of the research was 40 international students, which were 50% of total population. The quantitative approaches were used for data collection. Questionnaire was used as research instrument. After the data collection, the data were entered in SPSS for study and analyzed using ANOVA test, sample t-test, frequency analysis, graphic investigation.Key words: International students, University of the Punjab, Cultural problem.IntroductionIt is a relationship of consequence of behavior and realized behaviors those are transmitted and shared by the components of a specific culture. (Linton, 1945).However, international universities is to teach the international graduates to develop more capability, communicative, competitiveness in the international professional market and competence for cross-cultural knowledge (Stacy, 1999). Due to diverse culture and educational environment of the international students, the advanced education is becoming more diverse (Hewitt, 2002). To achieve the international student's needs, expectations and goals having difference societies/ cultural and academic backgrounds is a responsibility of education providers. (Stacy, 1999). When taking the decision to study abroad, many students come with problems in their academic mission to be rationally successful in their new educational setting (Carol and Ryan, 2005). They faced several cultural norms, social, ethics from the ones they have to familiar, performance, conventions about participation, different modes of teaching, learning and different expectations etc. Generally these students are normally a week group w.r.t leaving their country, leaving all their social underpinnings, language, family, all sort of available assistance and references, doctors, religious people and relatives etc. (Morton, 2007)Culture of PakistanPakistan has miscellaneous and distinct many cultures. There are different nations in the society of Pakistan and all these nations have left their symbol in the culture of Pakistan in some or other way around.The study surveys that the Pakistan is a 6th more populated in the world after Indonesia.The Punjab is a big province of Pakistan; and it was founded/ settled by Sikh and perform an important role in the development of Pakistan. Pakistani culture has a very highly-flavored and long history w.r.t. Karachi and Lahore. The contribution of Pakistan is incredibly rich and wide varieties of social actions. These areas make some new trends and safeguard our cultural backgrounds. These need a solid and self-sufficient tool to systematize the communication of cultural events and leisure interests in order to launch an essential information and linkage b/w daily life of the individual and culture. (Ali , 2012)Culture Layers"Having 3 levels or layers of culture i.e. part of your learned perceptions and behavior patterns. The majority, is the body of cultural background that differentiates your specific culture. When persons speak the languages e.g. Samoan, Japanese or Italian culture, pointed out shared traditions, beliefs, and languages; these persons are away from each other.2nd layer of this society may be part of your uniqueness. The subculture, the various societies in which public have come from diverse parts of this world. They often keep hold of their innovative cultural backgrounds. Resultantly, a part of a particular subculture in their new culture. The joint cultural attributes of subcultures set away from each other society. …


title: MEMO_Calendring: A smart reminder for Alzheimer's disease patients

abstract: From the earliest stages, Alzheimer's causes memory impairments leading to perturbations on everyday life functioning. To improve autonomy, we introduce MEMO_Calendring, a smart reminder to help Alzheimer's disease patients organize their daily life e.g., reminding the user to take the correct medicines on time. This intervention is proposed in the context of the ongoing CAPTAIN MEMO memory aid. Compared to related work, this intervention has four main advantages. (1) It automatically generates some additional helpful information about the scheduled events e.g., displaying the address of a place, the user needs to go to. (2) It is designed to be facile and pleasant to use. (3) It supports natural language inputs. (4) It supports multilingualism: French, Arabic and English. We evaluated the accessibility of the user interfaces of the CAPTAIN MEMO memory aid, including the MEMO_Calendring smart reminder, with 24 Alzheimer's disease patients. The preliminary results confirmed that the presented user interfaces are accessible and user-friendly.


title: PROTEIN-PROTEIN INTERACTION DATABASES: AN OVERALL VIEW ON INTERACTOME ORGANIZATION

abstract: Protein-protein interaction (PPI) maps or "interactome" represent a global view of cellular functions and biological processes. There are more than 130 public databases which have been already established to store the sizeable amount of PPI data. The valuable data is available for a wide group of researchers via adopting common data standards and coordinated curation practices between the databases. Three major types of databases have deposited PPI data; primary databases capture experimentally proven protein- interactions from literatures according to different curation polices; metadatabases integrate the work of other databases; prediction databases combine both predicted and experimental PPIs data from different approaches. Several lines of evidence indicate that primary databases report PPI data according to their different organizing strategies; as a result, this issue may affect the structure of derived PPI networks. Since PPI network's structure is useful to get biologically meaningful information, quality of PPI data needs more attention. In this sense,


title: Ideas of Constructed Market in Late Imperial Russia: Constructivist Liberalism of Peter Struve (1870 – 1944)

abstract: This paper defines constructivist liberalism as based on the belief that disadvantageous initial conditions for a natural commencing of an unfettered market order can be overcome through new institutional arrangements, initiated and nurtured by the highest political authority, usually the state. Within this context, the present study examines the economic scholarship of Peter Struve (1870 – 1944), the only Russian academic economist who openly advocated deliberate construction of a free market economy in Imperial Russia, and situates his views on the nature of the liberal market order within the intellectual landscape of 20th century liberal economics. Currently, Struve's contribution to the liberal economic doctrine remains practically unknown not only in North America but also in Russia due to the lack of professional economic studies of his works. The paper presents the theoretical core of Struve's economic doctrine and then introduces Struve's concept of “constitutional” liberalism, which this study labels constructivist liberalism. This study concludes that Struve's views on the informational role of market price in an open-ended competitive economic system in many ways anticipated Hayekian analysis of the problem, while his concept of constructivist liberalism, despite having been presented in rather embryonic form, was similar to what in late 1940s became known as a neoliberal constructivism of the Mont Pelerin Society and post-WWII Chicago school of economics.


title: CUSTOMER LIFETIME VALUE (CLV) MEASUREMENT BASED ON RFM MODEL

abstract: Nowadays companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. A frequently-encountered difficulty for companies wishing to measure customer profitability is that management accounting and reporting systems have tended to reflect product profitability rather than customer profitability. But in spite of these difficulties, Companies looking for methods to know how calculate their customers's CLV. In this paper, we used K-Mean clustering approach to determine customers's CLV and segment them based on recency, frequency and monetary (RFM) measures. We also used Discriminant analysis to approve clustering results. Data required applying this method gathered from one branch of an Iranian private bank which is established newly. Finally, in terms of this segmentation, we proposed customer retention strategies for treating with the bank customers.


title: Cognitive Cellular Networks: A Q-Learning Framework for Self-Organizing Networks

abstract: Self-organizing networks (SON) aim at simplifying network management (NM) and optimizing network capital and operational expenditure through automation. Most SON functions (SFs) are rule-based control structures, which evaluate metrics and decide actions based on a set of rules. These rigid structures are, however, very complex to design since rules must be derived for each SF in each possible scenario. In practice, rules only support generic behavior, which cannot respond to the specific scenarios in each network or cell. Moreover, SON coordination becomes very complicated with such varied control structures. In this paper, we propose to advance SON toward cognitive cellular networks (CCN) by adding cognition that enables the SFs to independently learn the required optimal configurations. We propose a generalized Q-learning framework for the CCN functions and show how the framework fits to a general SF control loop. We then apply this framework to two functions on mobility robustness optimization (MRO) and mobility load balancing (MLB). Our results show that the MRO function learns to optimize handover performance while the MLB function learns to distribute instantaneous load among cells.


title: Dynamic face recognition: From human to machine vision

abstract: As confirmed by recent neurophysiological studies, the use of dynamic information is extremely important for humans in visual perception of biological forms and motion. Apart from the mere computation of the visual motion of the viewed objects, the motion itself conveys far more information, which helps understanding the scene. This paper provides an overview and some new insights on the use of dynamic visual information for face recognition. In this context, not only physical features emerge in the face representation, but also behavioral features should be accounted. While physical features are obtained from the subject's face appearance, behavioral features are obtained from the individual motion and articulation of the face. In order to capture both the face appearance and the face dynamics, a dynamical face model based on a combination of Hidden Markov Models is presented. The number of states (or facial expressions) are automatically determined from the data by unsupervised clustering of expressions of faces in the video. The underlying architecture closely recalls the neural patterns activated in the perception of moving faces. Experimental results obtained from real video image data show the feasibility of the proposed approach.


title: A Survey On: Content Based Image Retrieval Systems

abstract: network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. so nowadays the content based image retrieval are becoming a source of exact and fast retrieval. In this paper the techniques of content based image retrieval are discussed, analysed and compared. It also introduced the feature like neuro fuzzy technique, color histogram, texture and edge density for accurate and effective Content Based Image Retrieval System.


title: A faster algorithm computing string edit distances

abstract: The edit distance between two character strings can be defined as the minimum cost of a sequence of editing operations which transforms one string into the other. The operations we admit are deleting, inserting and replacing one symbol at a time, with possibly different costs for each of these operations. The problem of finding the longest common subsequence of two strings is a special case of the problem of computing edit distances. We describe an algorithm for computing the edit distance between two strings of length n and m, n ⪖ m, which requires O(n · max(1, mlog n)) steps whenever the costs of edit operations are integral multiples of a single positive real number and the alphabet for the strings is finite. These conditions are necessary for the algorithm to achieve the time bound.


title: No place to hide: a study of privacy concerns due to location sharing on geo-social networks

abstract: User location data collected on Geo-Social Networking ::: applications (GeoSNs) can be used to enhance the services ::: provided by such applications. However, personal location information ::: can potentially be utilised for undesirable purposes ::: that can compromise users’ privacy. This paper presents a study ::: of privacy implications of location-based information provision ::: and collection on user awareness and behaviour when using ::: GeoSNs. The dimensions of the problem are analysed and ::: used to guide an analytical study of some representative data ::: sets from such applications. The results of the data analysis ::: demonstrate the extent of potential personal information that may ::: be derived from the location information. In addition, a survey is ::: undertaken to examine user awareness, concerns and subsequent ::: attitude and behaviour given knowledge of the possible derived ::: information. The results clearly demonstrate users’ needs for ::: improving their knowledge, access and visibility of their data sets ::: as well as for means to control and manage their location data. ::: Future work needs to investigate the current state of personal ::: data management on GeoSNs and how their interfaces may be ::: improved to satisfy the highlighted users’ needs and to protect ::: their privacy.


title: AP Association Optimization and CCA Threshold Adjustment in Dense WLANs

abstract: Dense deployment of wireless local area networks (WLANs) is part of the next generation Wi-Fi and standardization (802.11ax) efforts are underway. However, dense deployment of WLAN access points (APs) faces increased interference and uncoordinated association of user stations (STAs) with APs, which degrade network throughput. To assess the potential of improving uplink throughput in the presence of interference using AP association coordination, we propose an association optimization algorithm that matches STAs to APs in dense WLAN (DWLAN). While existing cell breathing approaches suggest tuning of APs' beacon powers for association control, the proposed approach utilizes uplink signal- interference-noise ratio (SINR) of stations (STAs) to coordinate STA-AP association. In order to further coordinate interference and increase spatial reuse, an algorithm is proposed to adjust the clear channel assessment (CCA) threshold of the 802.11 MAC protocol in each AP cell to address the problem of overlapped basic service set (OBSS) that degrades overall network throughput. Performance evaluation reveals that our SINR-based AP association coordination and CCA threshold adjustment schemes achieve significant increase in per-user throughput in a DWLAN.


title: Contact-Free Heart Rate Measurement Using a Camera

abstract: In this paper, a contact-free heart rate measurement method using an ambient light camera is proposed. The color features of skin-like pixels are used for time domain frequency analysis, in which a skin color classifier is applied. A novel data adjustment scheme is further integrated to automatically expand sampling time length and increase measurement precision. Five people of different age, six cameras of different type, and four different body parts have been tested in this paper. Experimental results show the effectiveness of the proposed method comparing with a pulse oximeter device. The advantages of the proposed method include: 1) it uses a low-cost ambient light camera, 2) it eases the discomfort of people based on contact-free methodology, 3) it measures multiple persons' heart rates fully automatic, and 4) it can be applied in multiple parts of a human body such as head and neck, arm, and palm regions.


title: Multiagent systems for cardiac pacing simulation and control

abstract: Simulating and controlling physiological phenomena are complex tasks to tackle. This is due to the fact that physiological processes are usually described by a set of partial models representing specific aspects of the phenomena and their adoption does not allow the achievement of an effective simulation/control system. A current open issue is the development of techniques able to comprehensively describe a phenomenon exploiting partial models. Simulation and control heavily rely on accurate modelling of physiological systems. In addition, since a large number of partial models of a single physiological phenomenon have been proposed over the years, the evaluation of their effectiveness and of their combinations is a fundamental task. In this paper we propose a multiagent paradigm, called anthropic agency, as a flexible tool to support and evaluate the combination of partial models embedded in agents. We present an agent negotiation paradigm, that improves the one we employed in our previous applications, as a flexible approach to combine optimally the partial models. We formally describe the negotiation protocol and we embed it in a FIPA agent interaction protocol. Furthermore, as an example of practical application, we describe how our paradigm can be a potential solution to the problem of adaptive cardiac pacing. Finally, we experimentally evaluate our approach and we discuss its properties and peculiarities.


title: Viewpoint Invariant Texture Description Using Fractal Analysis

abstract: Image texture provides a rich visual description of the surfaces in the scene. Many texture signatures based on various statistical descriptions and various local measurements have been developed. Existing signatures, in general, are not invariant to 3D geometric transformations, which is a serious limitation for many applications. In this paper we introduce a new texture signature, called the multifractal spectrum (MFS). The MFS is invariant under the bi-Lipschitz map, which includes view-point changes and non-rigid deformations of the texture surface, as well as local affine illumination changes. It provides an efficient framework combining global spatial invariance and local robust measurements. Intuitively, the MFS could be viewed as a "better histogram" with greater robustness to various environmental changes and the advantage of capturing some geometrical distribution information encoded in the texture. Experiments demonstrate that the MFS codes the essential structure of textures with very low dimension, and thus represents an useful tool for texture classification.


Simulations of Bubble Collapse in a Non-Linear Viscoelastic Medium


title: Distilling a Neural Network Into a Soft Decision Tree

abstract: Deep neural networks have proved to be a very effective way to perform classification tasks. They excel when the input data is high dimensional, the relationship between the input and the output is complicated, and the number of labeled training examples is large. But it is hard to explain why a learned network makes a particular classification decision on a particular test case. This is due to their reliance on distributed hierarchical representations. If we could take the knowledge acquired by the neural net and express the same knowledge in a model that relies on hierarchical decisions instead, explaining a particular decision would be much easier. We describe a way of using a trained neural net to create a type of soft decision tree that generalizes better than one learned directly from the training data.


title: Open Problems in Spectral Dimensionality Reduction

abstract: The last few years have seen a great increase in the amount of data available to scientists, yet many of the techniques used to analyse this data cannot cope with such large datasets. Therefore, strategies need to be employed as a pre-processing step to reduce the number of objects or measurements whilst retaining important information. Spectral dimensionality reduction is one such tool for the data processing pipeline. Numerous algorithms and improvements have been proposed for the purpose of performing spectral dimensionality reduction, yet there is still no gold standard technique. This book provides a survey and reference aimed at advanced undergraduate and postgraduate students as well as researchers, scientists, and engineers in a wide range of disciplines. Dimensionality reduction has proven useful in a wide range of problem domains and so this book will be applicable to anyone with a solid grounding in statistics and computer science seeking to apply spectral dimensionality to their work.


title: Cognitive radar: A knowledge-aided fully adaptive approach

abstract: A confluence of recent breakthroughs in knowledge-aided (KA) processing and adaptive transmit (ATx) radar technologies has enabled a new generation of cognitive radar architectures that affords unprecedented levels of intelligent adaptivity. In this paper one such architecture is introduced by the integration of KA and ATx methods to produce a new cognitive radar that encompasses an environmental dynamic database (EDDB), a “look-ahead” high performance embedded computing (HPEC) architecture pioneered under the DARPA/AFRL KASSPER project, and adaptive transmit (spatio-temporal) functionality.


title: 3D Reconstruction of Head MRI Based on One Class Support Vector Machine with Immune Algorithm

abstract: Due to complexity and irregulation of each encephalic tissue boundary, three-dimensional (3D) reconstruction for MRI image has been a hot area. Support vector machine (SVM) based on statistical learning theory is mainly utilized in classification and regression. One Class SVM (OCSVM) was originally proposed for solving some special classification problems. In this paper, OCSVM, which tries to find the smallest hypersphere enclosing target data in high dimensional space by kernel function, is firstly explored into the application to 3D reconstruction. However, selecting parameters for OCSVM is a complicated problem. In order to reduce the blindness of parameter selection and perfect SVM theory, Immune Algorithm (IA) and K-fold cross validation are introduced to intelligently search optimal parameter. The experimental results demonstrate OCSVM is effective with high reconstruction accuracy.


Are Robots Embodied?


title: Medium-Voltage Multilevel Converters—State of the Art, Challenges, and Requirements in Industrial Applications

abstract: This paper gives an overview of medium-voltage (MV) multilevel converters with a focus on achieving minimum harmonic distortion and high efficiency at low switching frequency operation. Increasing the power rating by minimizing switching frequency while still maintaining reasonable power quality is an important requirement and a persistent challenge for the industry. Existing solutions are discussed and analyzed based on their topologies, limitations, and control techniques. As a preferred option for future research and application, an inverter configuration based on three-level building blocks to generate five-level voltage waveforms is suggested. This paper shows that such an inverter may be operated at a very low switching frequency to achieve minimum on-state and dynamic device losses for highly efficient MV drive applications while maintaining low harmonic distortion.


title: Incremental Common Spatial Pattern algorithm for BCI

abstract: A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the on-line non-stationarity of the data blocks. An effective BCI system should be adaptive to and robust against the dynamic variations in brain signals. One solution to it is to adapt the model parameters of BCI system online. However, CSP is poor at adaptability since it is a batch type algorithm. To overcome this, in this paper, we propose the Incremental Common Spatial Pattern (ICSP) algorithm which performs the adaptive feature extraction on-line. This method allows us to perform the online adjustment of spatial filter. This procedure helps the BCI system robust to possible non-stationarity of the EEG data. We test our method to data from BCI motor imagery experiments, and the results demonstrate the good performance of adaptation of the proposed algorithm.


title: Testing of Advanced Driver Assistance Towards Automated Driving: A Survey and Taxonomy on Existing Approaches and Open Questions

abstract: In this work, we propose a novel taxonomy to partition the problem of testing advanced driver assistance systems (ADAS) into three basic dimensions. These dimensions are detailed and confirmed with recent research. Our framework permits the consideration of open research questions which have to be answered to pave the way for future highly automated driving. Despite the importance of this problem, a similarly comprehensive and structured survey has to the best of the authors' knowledge not been developed before.


Malware Classification Using N-grams Sequential Pattern Features


title: Hierarchical beta processes and the Indian buffet process. This volume

abstract: We show that the beta process is the de Finetti mixing distribution underlying the Indian buffet process of [2]. This result shows that the beta process plays the role for the Indian buffet process that the Dirichlet process plays for the Chinese restaurant process, a parallel that guides us in deriving analogs for the beta process of the many known extensions of the Dirichlet process. In particular we define Bayesian hierarchies of beta processes and use the connection to the beta process to develop posterior inference algorithms for the Indian buffet process. We also present an application to document classification, exploring a relationship between the hierarchical beta process and smoothed naive Bayes models.


title: Memory shapes time perception and intertemporal choices

abstract: There is a consensus that human and non-human subjects experience temporal distortions in many stages of their perceptual and decision-making systems. Similarly, intertemporal choice research has shown that decision-makers undervalue future outcomes relative to immediate ones. Here we combine techniques from information theory and artificial intelligence to show how both temporal distortions and intertemporal choice preferences can be explained as a consequence of the coding efficiency of sensorimotor representation. In particular, the model implies that interactions that constrain future behavior are perceived as being both longer in duration and more valuable. Furthermore, using simulations of artificial agents, we investigate how memory constraints enforce a renormalization of the perceived timescales. Our results show that qualitatively different discount functions, such as exponential and hyperbolic discounting, arise as a consequence of an agent's probabilistic model of the world.


Employee Turnover and Retention Strategies : An Empirical Study of Public Sector Organizations of Pakistan


title: Deep Density Clustering of Unconstrained Faces

abstract: In this paper, we consider the problem of grouping a collection of unconstrained face images in which the number of subjects is not known. We propose an unsupervised clustering algorithm called Deep Density Clustering (DDC) which is based on measuring density affinities between local neighborhoods in the feature space. By learning the minimal covering sphere for each neighborhood, information about the underlying structure is encapsulated. The encapsulation is also capable of locating high-density region of the neighborhood, which aids in measuring the neighborhood similarity. We theoretically show that the encapsulation asymptotically converges to a Parzen window density estimator. Our experiments show that DDC is a superior candidate for clustering unconstrained faces when the number of subjects is unknown. Unlike conventional linkage and density-based methods that are sensitive to the selection operating points, DDC attains more consistent and improved performance. Furthermore, the density-aware property reduces the difficulty in finding appropriate operating points.


title: Sectional neuroanatomy of the pelvic floor.

abstract: This is the sixth in a series of articles on the spine. The first 5 reviewed the sectional anatomy of the cervical, thoracic, and lumbosacral spines. This paper will review both the male and female pelves. Procedures performed in the pelvis include electromyography of the anal sphincter, pudendal and sacral nerve stimulator implants, and botulinum toxin type A injections into the prostate, the bladder, the urethra, and the anus. Complications from these procedures are rare. Electromyography in this region is particularly uncomfortable. Botulinum toxin type A denervation may result in local effects such as incontinence or urinary retention or rarely remote effects such as limb weakness. Neurostimulators may get infected or may fail. This article provides anatomically accurate schematics of innervations of the pelvis that can be used to interpret magnetic resonance images of muscles and nerves in the pelvic floor region. Cross-sectional schematics of the male and female pelves were drawn as they appear on imaging projections. The relevant nerves were color coded. The muscles and the skin surfaces were labeled and assigned the color of the appropriate nerves. An organized comprehensive map of the motor innervation of both the male and female pelves allows the physician to increase the accuracy and efficacy of interventional procedures. This anatomic map could also assist the electromyographer in correlating the clinical and electrophysiologic findings on magnetic resonance images.


title: Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

abstract: Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.


title: The Design Space of 3D Printable Interactivity

abstract: The capabilities of 3D printers are rapidly progressing towards fabrication of fully interactive products. For designers to reason about the best way to achieve their interaction design goals, it is helpful to not only know what exists in the literature, but to also understand the design space of options. Such an understanding can help in comparison, analysis, selection of suitable technology, and also in the generation of new ideas. In this article, we survey the state of the art in 3D printing fully functional sensors and actuators to support explicit interaction techniques. We classify and organize the surveyed works around the following parameters: mechanism, designed affordances, interaction primitives, and output modality. The design space is presented in the form of a multidimensional matrix known as a Zwicky box. Using the tables, we can make observations about the existing literature and also identify gaps in this design space. Many such gaps can potentially lead to exciting new research or engineering opportunities.


B-link-trees for DB/OS Co-Design


title: Distal femur reconstruction with modular tumour prostheses: a single Institution analysis of implant survival comparing fixed versus rotating hinge knee prostheses

abstract: Purpose ::: To evaluate the incidence of complications in distal femur reconstructions with modular prostheses, comparing fixed vs rotating hinge knee.


title: Unsupervised Learning of Finite Mixture Models

abstract: This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective "unsupervised" is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectation-maximization (EM) algorithm, it does not require careful initialization. The proposed method also avoids another drawback of EM for mixture fitting: the possibility of convergence toward a singular estimate at the boundary of the parameter space. The novelty of our approach is that we do not use a model selection criterion to choose one among a set of preestimated candidate models; instead, we seamlessly integrate estimation and model selection in a single algorithm. Our technique can be applied to any type of parametric mixture model for which it is possible to write an EM algorithm; in this paper, we illustrate it with experiments involving Gaussian mixtures. These experiments testify for the good performance of our approach.


title: Benchmarking Replication and Consistency Strategies in Cloud Serving Databases: HBase and Cassandra

abstract: Databases serving OLTP operations generated by cloud applications have been widely researched and deployed nowadays. Such cloud serving databases like BigTable, HBase, Cassandra, Azure and many others are designed to handle a large number of concurrent requests performed on the cloud end. Such systems can elastically scale out to thousands of commodity hardware by using a shared nothing distributed architecture. This implies a strong need of data replication to guarantee service availability and data access performance. Data replication can improve system availability by redirecting operations against failed data blocks to their replicas and improve performance by rebalancing load across multiple replicas. However, according to the PACELC model, as soon as a distributed database replicates data, another tradeoff between consistency and latency arises. This tradeoff motivates us to figure out how the latency changes when we adjust the replication factor and the consistency level. The replication factor determines how many replicas a data block should maintain, and the consistency level specifies how to deal with read and write requests performed on replicas. We use YCSB to conduct several benchmarking efforts to do this job. We report benchmark results for two widely used systems: HBase and Cassandra.


Review of PHM Data Competitions from 2008 to 2017: Methodologies and Analytics


title: Modeling and simulating electronic textile applications

abstract: This paper describes our design of a simulation environment for electronic textiles (e-textiles) and our experiences with that environment. This simulation environment, based upon Ptolemy II, enables us to model a diverse range of areas related to the design of electronic textiles, including the physical environment they will be used in, the behavior of the sensors incorporated into the fabric, the on-fabric network, the power consumption of the system, and the execution of the application and system software. This paper focuses on two aspects of the system, modeling the motion of a person wearing the e-textile and modeling the effect of faults in the e-textile system. To partially validate this environment, we compare simulation results against results from two different physical prototypes, a large-scale acoustic beamformer and a pair of shape-sensing pants.


title: Fundamental noise limit of piezoelectric accelerometer

abstract: Since significant progress is achieved in the development of piezoelectric (PE) accelerometers for small signal applications nowadays (for example, piezoelectric seismic vibration sensors), the question about the fundamental noise limit of these sensors becomes vital. The noise of the PE transducer is the fundamental noise limit of the PE accelerometer and should be taken into account if the noise of the electronics is small enough. The two noise sources of PE transducer, the mechanical-thermal noise of the damped mechanical harmonic oscillator and the electrical-thermal noise of the PE element's material, are analyzed in this paper. The equation of the total fundamental noise limit of the PE accelerometer is presented. This equation can be used for the calculation of the fundamental noise limit of PE accelerometers if their parameters are known or can be obtained by measurement.


title: Comparing the Conceptual Graphs Extracted from Patent Claims

abstract: A patent claim defines the protection of the invention. It is usually very time consuming and laborious to manually conduct analysis on patents in any domain of interest. A maximal common edge subgraph (MCES) is a subgraph consisting of the largest number of edges common between two graphs G and G'. This paper automatically compares conceptual graphs, extracted from patent claims by an NLP parser, using anchored relaxation labeling.


title: TraPlan: An Effective Three-in-One Trajectory-Prediction Model in Transportation Networks

abstract: The existing approaches for trajectory prediction (TP) are primarily concerned with discovering frequent trajectory patterns (FTPs) from historical movement data. Moreover, most of these approaches work by using a linear TP model to depict the positions of objects, which does not lend itself to the complexities of most real-world applications. In this research, the authors propose a three-in-one TP model in road-constrained transportation networks called TraPlan. TraPlan contains three essential techniques: 1) constrained network R-tree (CNR-tree), which is a two-tiered dynamic index structure of moving objects based on transportation networks; 2) a region-of-interest (RoI) discovery algorithm is employed to partition a large number of trajectory points into distinct clusters; and 3) a FTP-tree-based TP approach, called FTP-mining, is proposed to discover FTPs to infer future locations of objects moving within RoIs. In order to evaluate the results of the proposed CNR-tree index structure, the authors conducted experiments on synthetically generated data sets taken from real-world transportation networks. The results show that the CNR-tree can reduce the time cost of index maintenance by an average gap of about 40% when compared with the traditional NDTR-tree, as well as reduce the time cost of trajectory queries. Moreover, compared with fixed network R-Tree (FNR-trees), the accuracy of range queries has shown an on average improvement of about 32%. Furthermore, the experimental results show that the TraPlan demonstrates accurate and efficient prediction of possible motion curves of objects in distinct trajectory data sets by over 80% on average. Finally, the authors evaluate these results and the performance of the TraPlan model in regard to TP by comparing it with other TP algorithms.


title: DEHUMANIZING AND REHUMANIZING RESPONSES TO SEPTEMBER 11

abstract: Abstract This article explores Americans' reactions to the national trauma of September 11. It examines individual and collective responses in relation to their potential for escalating the conflict, or for leading to a more permanent peace. The article presents conflict resolution models used in other nations struggling with political violence as alternative pathways for our own country. It discusses the psychosocial dangers involved in the transformation of fear into retaliatory anger, of neglecting unhealed trauma, and of delegating the peace process only to political hierarchies. Finally, it suggests ways in which systemic therapists can apply these approaches to clinical and community practice.


title: Brains and brands: developing mutually informative research in neuroscience and marketing

abstract: � Advances in neuroimaging technology have led to an explosion in the number of studies investigating the living human brain, and thereby our understanding of its structure and function.Withthe proliferation of dazzling imagesfrom brain scansin both scientific and popular media, researchers from other fields in the social and behavioral sciences have naturally become interested in the application of neuroimaging to their own research. Commercial enterprises have long been interested in the prospects of literally ‘‘getting inside the heads’’ of customers and partners, with a variety of goals in mind. Here we consider the ways in which scholars of consumer behavior may draw upon neuroscientific advances to inform their own research. We describe the motivation of neuroscientific inquiry from the point of view of neuroscientists, including an introduction to the technologies and methodologies available; correspondingly, we consider major questions in consumer behavior that are likely to be of interest to neuroscientists and why. Recent key discoveries in neuroscienceare presented which will likely have a direct impact on the development of a neuromarketing subdiscipline and for neuroimaging as a marketing research technique. We discuss where and how neuroscience methodologies may reasonably be added to the research inventory of marketers. In sum, we aim to show not only that a neuromarketing subdiscipline may fruitfully contribute to our understanding of the biological bases of human behavior, but also that developing this as a productive research field will rest largely in framing marketing research questions in the braincentric mindset of neuroscientists. Copyright # 2008 John Wiley & Sons, Ltd.


title: A Survey on Feature Extraction Techniques

abstract: Data Mining (DM) technique is able to process the high volume of data. The data mining applications contain dataset with high dimensionality. Due to this high dimensionality, the performance of the machine learning algorithms get degraded and this problem is resolved using a technique called Dimensionality Reduction (DR). DR is an essential preprocessing technique in DM to reduce the high dimensionality. Feature Extraction is one of the important techniques in DR to extract the most important features. The goal of this survey is to provide a comprehensive review of various feature extraction approaches to improve the classification accuracy. This paper gives an over view of various feature extraction techniques which are used to the budding researchers.


title: Analysis of Three IoT-Based Wireless Sensors for Environmental Monitoring

abstract: The recent changes in climate have increased the importance of environmental monitoring, making it a topical and highly active research area. This field is based on remote sensing and on wireless sensor networks for gathering data about the environment. Recent advancements, such as the vision of the Internet of Things (IoT), the cloud computing model, and cyber-physical systems, provide support for the transmission and management of huge amounts of data regarding the trends observed in environmental parameters. In this context, the current work presents three different IoT-based wireless sensors for environmental and ambient monitoring: one employing User Datagram Protocol (UDP)-based Wi-Fi communication, one communicating through Wi-Fi and Hypertext Transfer Protocol (HTTP), and a third one using Bluetooth Smart. All of the presented systems provide the possibility of recording data at remote locations and of visualizing them from every device with an Internet connection, enabling the monitoring of geographically large areas. The development details of these systems are described, along with the major differences and similarities between them. The feasibility of the three developed systems for implementing monitoring applications, taking into account their energy autonomy, ease of use, solution complexity, and Internet connectivity facility, was analyzed, and revealed that they make good candidates for IoT-based solutions.


title: DeepSheet: A sheet music generator based on deep learning

abstract: Sheet music has long been regarded as one of the most effective medias for musicians, music players, and amateurs to communicate with each other. It is also an intuitive way for non-professionals to learn how to play a musical instrument or sing a song. However, not all composers have willingness to share their own sheet music, especially those protected by strict copyright regulations. For amateurs or novice musicians without the capability to distinguish the chords by their own ears, it would be difficult for them to enjoy the happiness of playing other's music, or singing songs. In order to provide the beginners with an effective way to play music or sing songs without a given sheet music, we developed a sheet music generator - “DeepSheet”, which was engineered based on deep learning techniques. Basically, the DeepSheet system is mainly composed of three distinctive components as follows: 1. voice separation of the audio song, 2. chord estimation of the background music, and 3. alignment of lyrics and music beats. The experimental results drawn from more than 150 songs of the Beatles and Queen indicated that the newly developed DeepSheet system has the capability and sensibility to generate sheet music with accuracy of approximately 76%.


title: A Zernike moment based method for classification of Alzheimer's disease from structural MRI

abstract: This paper proposed a method based on Zernike moments to classify the various stages of Alzheimer's Disease(AD) from structural MRIs. The proposed method is benefited from all three orthogonal directions of MRIs i.e. Axial, Sagittal and Coronal images. Three back-propagation algorithms had been used to train the neural network with seven neurons in hidden layer to reach the best accuracy. We experimented this method with 232 MRIs from OASIS database. 70 percent of the subjects had been used for training and the other 30 percent was used to evaluate the trained network. We achieved accuracy of 86.46 percent in multiclass mode and 96.67 percent of accuracy in two class mode between HC and AD.


title: 3D Image Acquisition System Based on Shape from Focus Technique

abstract: This paper describes the design of a 3D image acquisition system dedicated to natural complex scenes composed of randomly distributed objects with spatial discontinuities. In agronomic sciences, the 3D acquisition of natural scene is difficult due to the complex nature of the scenes. Our system is based on the Shape from Focus technique initially used in the microscopic domain. We propose to adapt this technique to the macroscopic domain and we detail the system as well as the image processing used to perform such technique. The Shape from Focus technique is a monocular and passive 3D acquisition method that resolves the occlusion problem affecting the multi-cameras systems. Indeed, this problem occurs frequently in natural complex scenes like agronomic scenes. The depth information is obtained by acting on optical parameters and mainly the depth of field. A focus measure is applied on a 2D image stack previously acquired by the system. When this focus measure is performed, we can create the depth map of the scene.


title: Synchronized Cell-Balancing Charging of Supercapacitors: A Consensus-Based Approach

abstract: The switched resistor circuit has been a popular cell balancing scheme in low-power supercapacitor applications. However, the cell balancing performance of the circuit is vulnerable to current variations and resistance deviations with the existing asynchronous cell-balancing (ACB) method. To address this challenge, in this paper, we propose a synchronized cell-balancing (SCB) method based on the consensus theory, with the aim of synchronizing cell voltages during the charging process. First, the cyber-physical modeling of the cell balancing system is presented, where the physical layer models the circuit components and the cyber layer represents the communication topology among cells. Second, a distributed voltage tracker is proposed using a consensus tracking protocol, which benefits from a good scalability. A compensator is designed to stabilize the closed-loop multiple-in–multiple-out (MIMO) system and frequency-domain methods are applied to determine the compensator parameters. A laboratory testbed has been built to verify the effectiveness of the proposed method. Experiment results show that the SCB method has a better tolerance of current variations and resistance deviations than the ACB method.


title: Chinese Dialect Identification Using Tone Features Based on Pitch Flux

abstract: This paper presents a method to extract tone relevant features based on pitch flux from continuous speech signal. The autocorrelations of two adjacent frames are calculated and the covariance between them is estimated to extract multi-dimensional pitch flux features. These features, together with MFCCs, are modeled in a 2-stream GMM models, and are tested in a 3-dialect identification task for Chinese. The pitch flux features have shown to be very effective in identifying tonal languages with short speech segments. For the test speech segments of 3 seconds, 2-stream model achieves more than 30% error reduction over MFCC-based model.


title: Learning To Count Objects in Images

abstract: We propose a new supervised learning framework for visual object counting tasks, such as estimating the number of cells in a microscopic image or the number of humans in surveillance video frames. We focus on the practically-attractive case when the training images are annotated with dots (one dot per object). ::: ::: Our goal is to accurately estimate the count. However, we evade the hard task of learning to detect and localize individual object instances. Instead, we cast the problem as that of estimating an image density whose integral over any image region gives the count of objects within that region. Learning to infer such density can be formulated as a minimization of a regularized risk quadratic cost function. We introduce a new loss function, which is well-suited for such learning, and at the same time can be computed efficiently via a maximum subarray algorithm. The learning can then be posed as a convex quadratic program solvable with cutting-plane optimization. ::: ::: The proposed framework is very flexible as it can accept any domain-specific visual features. Once trained, our system provides accurate object counts and requires a very small time overhead over the feature extraction step, making it a good candidate for applications involving real-time processing or dealing with huge amount of visual data.


The analysis and ranking of effective factors of supplier selection based on Analytic Hierarchy Process ( AHP )