title: Geometric regularity of direct-sum decompositions in some classes of modules

abstract: In this paper, we show that modules with semilocal endomorphism rings appear in abundance in applications, that their direct-sum decompositions are described by the so-called Krull monoids, and that this implies a geometric regularity of the direct-sum decompositions of these modules. Their direct-sum decompositions into indecomposables are not necessarily unique in the sense of the Krull-Schmidt theorem. The application of the theory of Krull monoids to the study of direct-sum decompositions of modules has been developed during the last five years. After a quick survey of the results obtained in this direction, we concentrate in particular on the abundance of examples. At present, these examples are scattered in the literature, and we try to collect them in a systematic way.


On the Move to Meaningful Internet Systems: OTM 2013 Workshops


title: Risk indicators and psychopathology in traumatised children and adolescents with a history of sexual abuse

abstract: Purpose ::: Childhood sexual abuse (CSA) is widespread amongst South African (SA) children, yet data on risk factors and psychiatric consequences are limited and mixed.


title: Robust camera calibration for sport videos using court models

abstract: We propose an automatic camera calibration algorithm for court sports. The obtained camera calibration parameters are required for applications that need to convert positions in the video frame to real-world coordinates or vice versa. Our algorithm uses a model of the arrangement of court lines for calibration. Since the court model can be specified by the user, the algorithm can be applied to a variety of different sports. The algorithm starts with a model initialization step which locates the court in the image without any user assistance or a-priori knowledge about the most probable position. Image pixels are classified as court line pixels if they pass several tests including color and local texture constraints. A Hough transform is applied to extract line elements, forming a set of court line candidates. The subsequent combinatorial search establishes correspondences between lines in the input image and lines from the court model. For the succeeding input frames, an abbreviated calibration algorithm is used, which predicts the camera parameters for the new image and optimizes the parameters using a gradient-descent algorithm. We have conducted experiments on a variety of sport videos (tennis, volleyball, and goal area sequences of soccer games). Video scenes with considerable difficulties were selected to test the robustness of the algorithm. Results show that the algorithm is very robust to occlusions, partial court views, bad lighting conditions, or shadows.


title: Fast and simple scheduling algorithm for PROFINET IRT networks

abstract: Scheduling of the communication in industrial networks takes time. Since it has to be carried out every time a new network is brought to work, the time necessary to perform scheduling becomes a prominent factor. The approach presented in this paper, simplifies the scheduling problem of PROFINET IRT by resolving communication conflicts by utilization of the dynamic frame packing (DFP) mechanism.


title: Three Expansions of Prehistoric Humans towards Northeast Margin of Qinghai-Tibet Plateau and Environmental Change

abstract: Prehistoric human had moved forward northeast margin of Tibetan Plateau three times since Late Pleistocene,these activities are closely related to environmental evolution.The first expansion took place in 40-30 kaBP under warm-humid climate background;later,owing to the hard natural environment of Last Glacial Maximum,human activities have greatly shrunk at the plateau,and the cultural gap emerged.The rapid improvement of the natural environment driven the second expansion moved forward to the northeast margin of the Tibetan Plateau in the last deglaciation,then,persistent warm-humid climate is the guarantee of human survival and diffusion at the plateau.Majiayao culture at the east neighbourhood in 6-4 kaBP,under Holocene megathermal period background,came into the northeast of the Tibetan Plateau again,promoted the residents living on the Plateau before the end of Paleolithic and entered Neolithic Age,the social nature generated a huge progress.


A tuneable metamaterial design using microelectromechanical system ( MEMS ) based split ring resonator ( SRR )


title: Towards Extracting Faithful and Descriptive Representations of Latent Variable Models

abstract: Methods that use latent representations of data, such as matrix and tensor factorization or deep neural methods, are becoming increasingly popular for applications such as knowledge base population and recommendation systems. These approaches have been shown to be very robust and scalable but, in contrast to more symbolic approaches, lack interpretability. This makes debugging such models difficult, and might result in users not trusting the predictions of such systems. To overcome this issue we propose to extract an interpretable proxy model from a predictive latent variable model. We use a socalled pedagogical method, where we query our predictive model to obtain observations needed for learning a descriptive model. We describe two families of (presumably more) descriptive models, simple logic rules and Bayesian networks, and show how members of these families provide descriptive representations of matrix factorization models. Preliminary experiments on knowledge extraction from text indicate that even though Bayesian networks may be more faithful to a matrix factorization model than the logic rules, the latter are possibly more useful for interpretation and debugging.


title: Visualizing Cross-Lingual/Cross-Cultural Differences in Concerns in Multilingual Blogs

abstract: The goal of this paper is to cross-lingually analyze multilingual blogs collected with a topic keyword. The framework of collecting multilingual blogs with a topic keyword is designed as the blog feed retrieval procedure. Multilingual queries for retrieving blog feeds are created from Wikipedia entries. Finally, we present an interface for visualizing crosslingual/cross-cultural differences in concerns and opinions that are closely related to a given topic. Preliminary evaluation results support the effectiveness of the proposed framework.


title: Life cycle analysis of distributed recycling of post-consumer high density polyethylene for 3-D printing filament

abstract: The growth of desktop 3-D printers is driving an interest in recycled 3-D printer filament to reduce costs of distributed production. Life cycle analysis studies were performed on the recycling of high density polyethylene into filament suitable for additive layer manufacturing with 3-D printers. The conventional centralized recycling system for high population density and low population density rural locations was compared to the proposed in home, distributed recycling system. This system would involve shredding and then producing filament with an open-source plastic extruder from post-consumer plastics and then printing the extruded filament into usable, value-added parts and products with 3-D printers such as the open-source self replicating rapid prototyper, or RepRap. The embodied energy and carbon dioxide emissions were calculated for high density polyethylene recycling using SimaPro 7.2 and the database EcoInvent v2.0. The results showed that distributed recycling uses less embodied energy than the best-case scenario used for centralized recycling. For centralized recycling in a low-density population case study involving substantial embodied energy use for transportation and collection these savings for distributed recycling were found to extend to over 80%. If the distributed process is applied to the U.S. high density polyethylene currently recycled, more than 100 million MJ of energy could be conserved per annum along with the concomitant significant reductions in greenhouse gas emissions. It is concluded that with the open-source 3-D printing network expanding rapidly the potential for widespread adoption of in-home recycling of post-consumer plastic represents a novel path to a future of distributed manufacturing appropriate for both the developed and developing world with lower environmental impacts than the current system.


title: Decentralized configuration of embedded web services for smart home applications

abstract: Web Services can be used as a communication structure for embedded devices. Out of numerous Web Service specifications there is a subset (Profile) to implement Web Services on resource constrained devices, called Devices Profile for Web Services (DPWS). The resulting service oriented architecture enables the user to discover new devices, e.g., in the local smart home network. The open standards for Web Services further define a metadata format for service description. Besides the simple invocation of service operations, an eventing mechanism is specified. A client which subscribes to an event will receive notifications. Those features and the inherent Plug&Play capability provided by Web Services are suitable to connect smart home devices in a user-friendly manner. However, DPWS specifies no standard procedure to combine multiple devices with each other to build more complex applications. Therefore, new concepts for embedded Web Service orchestration are needed. Some commercial solutions use a central hub, which represents a Single Point of Failure (SPoF). Hence, a failure would lead to a breakdown of all smart Internet of Things (IoT) applications and decrease the user acceptance. We propose a concept where no central broker is needed by defining a Configuration Service that runs on multiple devices. Based on that service a smartphone app is used to establish trigger-action rules between devices. A prototype is implemented as a proof-of-concept for different smart home devices. Furthermore, a mobile Android application to find and orchestrate the devices is presented.


title: Capturing Geographical Influence in POI Recommendations

abstract: Point-of-Interest POI recommendation is a significant service for location-based social networks LBSNs. It recommends new places such as clubs, restaurants, and coffee bars to users. Whether recommended locations meet users' interests depends on three factors: user preference, social influence, and geographical influence. Hence extracting the information from users' check-in records is the key to POI recommendation in LBSNs. Capturing user preference and social influence is relatively easy since it is analogical to the methods in a movie recommender system. However, it is a new topic to capture geographical influence. Previous studies indicate that check-in locations disperse around several centers and we are able to employ Gaussian distribution based models to approximate users' check-in behaviors. Yet centers discovering methods are dissatisfactory. In this paper, we propose two models--Gaussian mixture model GMM and genetic algorithm based Gaussian mixture model GA-GMM to capture geographical influence. More specifically, we exploit GMM to automatically learn users' activity centers; further we utilize GA-GMM to improve GMM by eliminating outliers. Experimental results on a real-world LBSN dataset show that GMM beats several popular geographical capturing models in terms of POI recommendation, while GA-GMM excludes the effect of outliers and enhances GMM.


title: Modified self-organizing mixture network for probability density estimation and classification

abstract: In this paper, a modified algorithm based on the Self-organizing Mixture Network (SOMN) is proposed to learn arbitrarily complex density functions accurately and effectively. The algorithm is derived based on the criterion of minimizing the Kullback-Leibler divergence, maximum likelihood approach and self-organizing principle. It has the advantages of stochastic approximation method such as fewer local optima and faster convergence speed and the prominent properties of the neural networks such as good generalization ability, and overcomes the limitations of the SOMN. These greatly improve its stability, applicability and computation performance. This algorithm also simplifies the competitive and cooperative mechanism used in the self-organizing map (SOM). This lets it has a well-defined objective function and helps to provide a general proof of convergence. Experiments show that this modified algorithm outperforms the Expectation-Maximization (EM) algorithm, the SOMN and the joint entropy maximization algorithm in estimation accuracy. It is far superior to the EM algorithm in terms of learning speed and computational cost. Experimental results show that when used to estimate large datasets, this algorithm is 30-80 times faster than the EM algorithm at least. Owing to its outstanding density estimation performance, this algorithm is very helpful to the construction of optimal classifiers. The effectiveness of the algorithm is demonstrated in several real-world applications.


title: Rule-based layout solving and its application to procedural interior generation

abstract: Due to the recent advancement in procedural generation techniques, games are presenting players with ever growing cities and terrains to explore. However most sandbox-style games situated in cities, do not allow players to wander into buildings. In past research, space planning techniques have already been utilized to generate suitable layouts for both building floor plans and room layouts. We introduce a novel rule-based layout solving approach, especially suited for use in conjunction with procedural generation methods. We show how this solving approach can be used for procedural generation by providing the solver with a user-defined plan. In this plan, users can specify objects to be placed as instances of classes, which in turn contain rules about how instances should be placed. This approach gives us the opportunity to use our generic solver in different procedural generation scenarios. In this paper, we will illustrate mainly with interior generation examples


Reasoning based on property propagation on CIDOC-CRM and CRMdig based repositories


title: It's only a computer: Virtual humans increase willingness to disclose

abstract: We provide empirical evidence that virtual humans increase willingness to disclose.We frame a virtual human interviewer as computer or human during health screenings.Framing the interviewer as computer lowers evaluation fears and impression management.Framing as a computer increases expressed sadness and objectively-rated disclosure. Research has begun to explore the use of virtual humans (VHs) in clinical interviews (Bickmore, Gruber, & Picard, 2005). When designed as supportive and "safe" interaction partners, VHs may improve such screenings by increasing willingness to disclose information (Gratch, Wang, Gerten, & Fast, 2007). In health and mental health contexts, patients are often reluctant to respond honestly. In the context of health-screening interviews, we report a study in which participants interacted with a VH interviewer and were led to believe that the VH was controlled by either humans or automation. As predicted, compared to those who believed they were interacting with a human operator, participants who believed they were interacting with a computer reported lower fear of self-disclosure, lower impression management, displayed their sadness more intensely, and were rated by observers as more willing to disclose. These results suggest that automated VHs can help overcome a significant barrier to obtaining truthful patient information.


title: A Grey Wolf-Assisted Perturb & Observe MPPT Algorithm for a PV System

abstract: This paper proposes a new hybrid maximum power point tracking (MPPT) algorithm combining grey wolf optimization (GWO) and perturb & observe (P&O) technique for efficient extraction of maximum power from a photovoltaic system subjected to rapid variation of solar irradiance and partial shading conditions. GWO handles the initial stages of MPPT followed by application of the P&O algorithm at the final stage in view of achieving faster convergence to the global peak (GP). This MPPT thus overcomes the computational overhead as encountered in the case of a GWO-based MPPT algorithm reported earlier by Mohanty et al. The idea behind using the hybrid technique is to scale down the search space of GWO which helps to speed up for achieving convergence toward the GP. The proposed MPPT algorithm is first implemented using MATLAB/Simulink and subsequently an experimental setup is prepared for its practical implementation. From the obtained results, it is confirmed that the proposed MPPT provides superior tracking performance in any weather conditions compared to both GWO and PSO+PO-based MPPT algorithms.


title: Entity resolution using inferred relationships and behavior

abstract: We present a method for entity resolution that infers relationships between observed identities and uses those relationships to aid in mapping identities to underlying entities. We also introduce the idea of using graphlets for entity resolution. Graphlets are collections of small graphs that can be used to characterize the “role” of a node in a graph. The idea is that graphlets can provide a richer set of features to characterize identities. We validate our method on standard author datasets, and we further evaluate our method using data collected from Twitter. We find that inferred relationships and graphlets are useful for entity resolution.


title: A case study of multi-threading in the embedded space

abstract: The continuing miniaturization of technology coupled with wireless networks has made it feasible to physically embed sensor network systems into the environment. Sensor net processors are tasked with the job of handling a disparate set of interrupt driven activity, from networks to timers to the sensors themselves.In this paper,we demonstrate the advantages of a tiny multi-threaded microcontroller design which targets embedded applications that need to respond to events at high speed. While multi-threading is typically used to improve resource utilization, in the embedded space it can provide zero-cycle context switching and interrupt service threads (IST), enabling complex programmable control in latency constrained environments. To explore the advantages of multi-threading on these embedded problems, we have implemented in hardware a family of controllers supporting eight dynamically interleaved threads and executing the AVR instruction set. This allows us to carefully quantify the effects of threading on interrupt latency, code size, overall processor throughput, cycle time, and design area for complete designs with different numbers of threads.


title: Algorithms and data structures for flash memories

abstract: Flash memory is a type of electrically-erasable programmable read-only memory (EEPROM). Because flash memories are nonvolatile and relatively dense, they are now used to store files and other persistent objects in handheld computers, mobile phones, digital cameras, portable music players, and many other computer systems in which magnetic disks are inappropriate. Flash, like earlier EEPROM devices, suffers from two limitations. First, bits can only be cleared by erasing a large block of memory. Second, each block can only sustain a limited number of erasures, after which it can no longer reliably store data. Due to these limitations, sophisticated data structures and algorithms are required to effectively use flash memories. These algorithms and data structures support efficient not-in-place updates of data, reduce the number of erasures, and level the wear of the blocks in the device. This survey presents these algorithms and data structures, many of which have only been described in patents until now.


title: Priming and human memory systems

abstract: Priming is a nonconscious form of human memory, which is concerned with perceptual identification of words and objects and which has only recently been recognized as separate from other forms of memory or memory systems. It is currently under intense experimental scrutiny. Evidence is converging for the proposition that priming is an expression of a perceptual representation system that operates at a pre-semantic level; it emerges early in development, and access to it lacks the kind of flexibility characteristic of other cognitive memory systems. Conceptual priming, however, seems to be based on the operations of semantic memory.


title: Human aggression

abstract: Research on human aggression has progressed to a point at which a unifying framework is needed. Major domain-limited theories of aggression include cognitive neoassociation, social learning, social interaction, script, and excitation transfer theories. Using the general aggression model (GAM), this review posits cognition, affect, and arousal to mediate the effects of situational and personological variables on aggression. The review also organizes recent theories of the development and persistence of aggressive personality. Personality is conceptualized as a set of stable knowledge structures that individuals use to interpret events in their social world and to guide their behavior. In addition to organizing what is already known about human aggression, this review, using the GAM framework, also serves the heuristic function of suggesting what research is needed to fill in theoretical gaps and can be used to create and test interventions for reducing aggression.


Representing local structure using tensors


title: Dynamic circular work-stealing deque

abstract: The non-blocking work-stealing algorithm of Arora, Blumofe, and Plaxton (henceforth ABP work-stealing) is on its way to becoming the multiprocessor load balancing technology of choice in both industry and academia. This highly efficient scheme is based on a collection of array-based double-ended queues (deques) with low cost synchronization among local and stealing processes. Unfortunately, the algorithm's synchronization protocol is strongly based on the use of fixed size arrays, which are prone to overflows, especially in the multiprogrammed environments for which they are designed. We present a work-stealing deque that does not have the overflow problem.The only ABP-style work-stealing algorithm that eliminates the overflow problem is the list-based one presented by Hendler, Lev and Shavit. Their algorithm indeed deals with the overflow problem, but it is complicated, and introduces a trade-off between the space and time complexity, due to the extra work required to maintain the list.Our new algorithm presents a simple lock-free work-stealing deque, which stores the elements in a cyclic array that can grow when it overflows. The algorithm has no limit other than integer overflow (and the system's memory size) on the number of elements that may be on the deque, and the total memory required is linear in the number of elements in the deque.


title: DataSeries: an efficient, flexible data format for structured serial data

abstract: Structured serial data is used in many scientific fields; such data sets consist of a series of records, and are typically written once, read many times, chronologically ordered, and read sequentially. In this paper we introduce DataSeries, an on-disk format, run-time library and set of tools for storing and analyzing structured serial data. We identify six key properties of a system to store and analyze this type of data, and describe how DataSeries was designed to provide these properties. We quantify the benefits of DataSeries through several experiments. In particular, we demonstrate that DataSeries exceeds the performance of common trace formats by at least a factor of two.


abstract: Poisson regression models the noisy output of a counting function as a Poisson random variable, with a log-mean parameter that is a linear function of the input vector. In this work, we analyze Poisson regression in a Bayesian setting, by introducing a prior distribution on the weights of the linear function. Since exact inference is analytically unobtainable, we derive a closed-form approximation to the predictive distribution of the model. We show that the predictive distribution can be kernelized, enabling the representation of non-linear log-mean functions. We also derive an approximate marginal likelihood that can be optimized to learn the hyperparameters of the kernel. We then relate the proposed approximate Bayesian Poisson regression to Gaussian processes. Finally, we present experimental results using Bayesian Poisson regression for crowd counting from low-level features.


title: DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

abstract: Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.


title: Searching for processes and threads in Microsoft Windows memory dumps

abstract: Current tools to analyze memory dumps of systems running Microsoft Windows usually build on the concept of enumerating lists maintained by the kernel to keep track of processes, threads and other objects. Therefore they will frequently fail to detect objects that are already terminated or which have been hidden by Direct Kernel Object Manipulation techniques. This article analyzes the in-memory structures which represent processes and threads. It develops search patterns which will then be used to scan the whole memory dump for traces of said objects, independent from the aforementioned lists. As demonstrated by a proof-of-concept implementation this approach could reveal hidden and terminated processes and threads, under some circumstances even after the system under examination has been rebooted.


title: Analyzing stability in wide-area network performance

abstract: The Internet is a very large scale, complex, dynamical system that is hard to model and analyze. In this paper, we develop and analyze statistical models for the observed end-to-end network performance based on extensive packet-level traces (consisting of approximately 1.5 billion packets) collected from the primary Web site for the Atlanta Summer Olympic Games in 1996. We find that observed mean throughputs for these transfers measured over 60 million complete connections vary widely as a function of end-host location and time of day, confirming that the Internet is characterized by a large degree of heterogeneity. Despite this heterogeneity, we find (using best-fit linear regression techniques) that we can express the throughput for Web transfers to most hosts as a random variable with a log-normal distribution. Then, using observed throughput as the control parameter, we attempt to quantify the spatial (statistical similarity across neighboring hosts) and temporal (persistence over time) stability of network performance. We find that Internet hosts that are close to each other often have almost identically distributed probability distributions of throughput. We also find that throughputs to individual hosts often do not change appreciably for several minutes. Overall, these results indicate that there is promise in protocol mechanisms that cache and share network characteristics both within a single host and amongst nearby hosts.


title: Modified Banker's algorithm for scheduling in multi-AGV systems

abstract: In today's highly complex multi-AGV systems key research objective is finding a scheduling and routing policy that avoids deadlock while assuring that vehicle utilization is as high as possible. It is well known that finding such an optimal policy is a NP-hard task in general case. Therefore, big part of the research is oriented towards finding various suboptimal policies that can be applied to real world plants. In this paper we propose modified Banker's algorithm for scheduling in multi-AGV systems. A predetermined mission's path is executed in a way that some non-safe states are allowed in order to achieve better utilization of vehicles. A graph-based method of polynomial complexity for verification of these states is given. Algorithm is tested on a layout of a real plant for packing and warehousing palettes. Results shown at the end of the paper demonstrate advantages of the proposed method compared with other methods based on Banker's algorithm.


title: On Conceptualizing Self-Control as More Than the Effortful Inhibition of Impulses:

abstract: The notion that self-control entails effortful inhibition of impulses dominates prevailing psychological models of self-control. This article describes some of the conceptual and empirical limitations of defining self-control as the effortful inhibition of impulses. The present article instead advocates for a dual-motive conceptualization, which describes self-control as the process of advancing distal rather than proximal motivations when the two compete. Effortful impulse inhibition in this model represents only one of many means by which people promote their self-control efforts. Adopting a dual-motive approach offers new insight and proposes several new research directions. This article discusses these implications and calls for psychologists to reconsider the way self-control is currently understood.


title: Semantic Information Extraction for Improved Word Embeddings

abstract: Word embeddings have recently proven useful in a number of different applications that deal with natural language. Such embeddings succinctly reflect semantic similarities between words based on their sentence-internal contexts in large corpora. In this paper, we show that information extraction techniques provide valuable additional evidence of semantic relationships that can be exploited when producing word embeddings. We propose a joint model to train word embeddings both on regular context information and on more explicit semantic extractions. The word vectors obtained from such an augmented joint training show improved results on word similarity tasks, suggesting that they can be useful in applications that involve word meanings.


title: Digital Forensics: Validation and Verification in a Dynamic Work Environment

abstract: Many forensic computing practitioners work in a high workload and low resource environment. With the move by the discipline to seek ISO 17025 laboratory accreditation, practitioners are finding it difficult to meet the demands of validation and verification of their tools and still meet the demands of the accreditation framework. Many agencies are ill-equipped to reproduce tests conducted by organizations such as NIST since they cannot verify the results with their equipment and in many cases rely solely on an independent validation study of other peoples' equipment. This creates the issue of tools in reality never being tested. Studies have shown that independent validation and verification of complex forensic tools is expensive and time consuming, and many practitioners also use tools that were not originally designed for forensic purposes. This paper explores the issues of validation and verification in the accreditation environment and proposes a paradigm that will reduce the time and expense required to validate and verify forensic software tools


Zeros of the Eisenstein series E(2)


title: Hardware acceleration of feature detection and description algorithms on low-power embedded platforms

abstract: Image features are broadly used in embedded computer vision applications, from object detection and tracking to motion estimation and 3D reconstruction. Efficient feature extraction and description are crucial due to the real-time requirements of such applications over a constant stream of input data. High-speed computation typically comes at the cost of high power dissipation, yet embedded systems are often highly power constrained, making discovery of power-aware solutions especially critical for these systems. In this paper, we present a power and performance evaluation of three low cost feature detection and description algorithms implemented on various embedded systems (embedded CPUs, GPUs and FPGAs). We show that FPGAs in particular offer attractive solutions for both performance and power and describe several design techniques utilized to accelerate feature extraction and description algorithms on low-cost Zynq SoC FPGAs.


title: Integrating time from experience in the lateral entorhinal cortex

abstract: The encoding of time and its binding to events are crucial for episodic memory, but how these processes are carried out in hippocampal–entorhinal circuits is unclear. Here we show in freely foraging rats that temporal information is robustly encoded across time scales from seconds to hours within the overall population state of the lateral entorhinal cortex. Similarly pronounced encoding of time was not present in the medial entorhinal cortex or in hippocampal areas CA3–CA1. When animals’ experiences were constrained by behavioural tasks to become similar across repeated trials, the encoding of temporal flow across trials was reduced, whereas the encoding of time relative to the start of trials was improved. The findings suggest that populations of lateral entorhinal cortex neurons represent time inherently through the encoding of experience. This representation of episodic time may be integrated with spatial inputs from the medial entorhinal cortex in the hippocampus, allowing the hippocampus to store a unified representation of what, where and when. Temporal information that is useful for episodic memory is encoded across a wide range of timescales in the lateral entorhinal cortex, arising inherently from its representation of ongoing experience.


title: Measurement of Nuchal Translucency Thickness for Detection of Chromosomal Abnormalities using First Trimester Ultrasound Fetal Images

abstract: The Nuchal Translucency thickness measurement is made to identify the Down Syndrome in screening first trimester fetus and presented in this paper. The mean shift analysis and canny operators are utilized for segmenting the nuchal translucency region and the exact thickness has been estimated using Blob analysis. It is observed from the results that the fetus in the 14th week of Gestation is expected to have a nuchal translucency thickness of 1.87 plus or minus 0.25mm.


title: New zero-voltage-switching phase-shift full-bridge converter with low conduction losses

abstract: A new zero-voltage-switching (ZVS) phase-shift full-bridge (PSFB) converter with low conduction losses is proposed. It is based on the PSFB converter with series-connected two transformers, which features wide ZVS ranges. By adding a capacitor, the proposed converter overcomes the disadvantage of the based converter, such as the high circulating energy. Furthermore, the turns ratio of the transformers can be increased as well. Therefore, high efficiency of the proposed converter can be achieved. Operational principles and experimental results for a 100-W (5 V, 20 A) prototype are presented to validate the proposed converter.


title: Tendon elasticity and muscle function

abstract: Vertebrate animals exploit the elastic properties of their tendons in several different ways. Firstly, metabolic energy can be saved in locomotion if tendons stretch and then recoil, storing and returning elastic strain energy, as the animal loses and regains kinetic energy. Leg tendons save energy in this way when birds and mammals run, and an aponeurosis in the back is also important in galloping mammals. Tendons may have similar energy-saving roles in other modes of locomotion, for example in cetacean swimming. Secondly, tendons can recoil elastically much faster than muscles can shorten, enabling animals to jump further than they otherwise could. Thirdly, tendon elasticity affects the control of muscles, enhancing force control at the expense of position control.


title: The emotional arcs of stories are dominated by six basic shapes

abstract: Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture’s evolution through its texts using a ‘big data’ lens. Our ability to communicate relies in part upon a shared emotional experience, with stories often following distinct emotional trajectories and forming patterns that are meaningful to us. Here, by classifying the emotional arcs for a filtered subset of 1,327 stories from Project Gutenberg’s fiction collection, we find a set of six core emotional arcs which form the essential building blocks of complex emotional trajectories. We strengthen our findings by separately applying matrix decomposition, supervised learning, and unsupervised learning. For each of these six core emotional arcs, we examine the closest characteristic stories in publication today and find that particular emotional arcs enjoy greater success, as measured by downloads.


title: Detection and identification of heart sounds using homomorphic envelogram and self-organizing probabilistic model

abstract: This work presents a novel method for automatic detection and identification of heart sounds. Homomorphic filtering is used to obtain a smooth envelogram of the phono cardiogram, which enables a robust detection of events of interest in heart sound signal. Sequences of features extracted from the detected events are used as observations of a hidden Markov model. It is demonstrated that the task of detection and identification of the major heart sounds can be learned from unlabelled phono cardiograms by an unsupervised training process and without the assistance of any additional synchronizing channels


title: Qualitative methods in research on teaching

abstract: This chapter reviews basic issues of theory and method in approaches to research on teaching that are alternatively called ethnographic, qualitative, participant observational, case study, symbolic interactionist, phenomenological, constructivist, or interpretive. These approaches are all slightly different, but each bears strong family resemblance to the others. The set of related approaches is relatively new in the field of research on teaching. The approaches have emerged as significant in the decade of the 1960s in England and in the 1970s in the United States, Australia, New Zealand, and Germany. Because interest in these approaches is so recent, the previous editions of the Handbook of Research on Teaching do not contain a chapter devoted to participant observational research. Accordingly, this chapter attempts to describe r~s and their theoretical _nresup~s in considerable detail and does not attempt an exhaustive review of the rapidly growing literature in the field. Such a review will be appropriate for the next edition of this handbook. From this point on I will use the term interpretive to refer to the whole family of approaches to participant observational research. I adopt this term for three reasons: (a) It is more inclusive than many of the others (e.g., ethnography, case study); (b) W Blake


title: Transfer learning via inter-task mappings for temporal difference learning

abstract: Temporal difference (TD) learning (Sutton and Barto, 1998) has become a popular reinforcement learning technique in recent years. TD methods, relying on function approximators to generalize learning to novel situations, have had some experimental successes and have been shown to exhibit some desirable properties in theory, but the most basic algorithms have often been found slow in practice. This empirical result has motivated the development of many methods that speed up reinforcement learning by modifying a task for the learner or helping the learner better generalize to novel situations. This article focuses on generalizing across tasks, thereby speeding up learning, via a novel form of transfer using handcoded task relationships. We compare learning on a complex task with three function approximators, a cerebellar model arithmetic computer (CMAC), an artificial neural network (ANN), and a radial basis function (RBF), and empirically demonstrate that directly transferring the action-value function can lead to a dramatic speedup in learning with all three. Using transfer via inter-task mapping (TVITM), agents are able to learn one task and then markedly reduce the time it takes to learn a more complex task. Our algorithms are fully implemented and tested in the RoboCup soccer Keepaway domain. ::: ::: This article contains and extends material published in two conference papers (Taylor and Stone, 2005; Taylor et al., 2005).


title: A self-calibrating 900-MHz CMOS image-reject receiver

abstract: This paper introduces an analog phase and gain calibration technique for image-reject receivers. The technique uniquely detects the phase and gain mismatches and drives their magnitudes toward zero through the use of a negative-feedback loop. An experimental CMOS prototype operating at 900 MHz achieves an image rejection ratio of 57 dB by applying phase calibration to a Weaver architecture. Fabricated in a 0.35-µm CMOS technology and running from a 3-V supply, the receiver dissipates 105 mW during the receive mode and 170 mW during the calibration mode.


title: Why interleaving enhances inductive learning: The roles of discrimination and retrieval

abstract: Kornell and Bjork (Psychological Science 19:585–592, 2008) found that interleaving exemplars of different categories enhanced inductive learning of the concepts based on those exemplars. They hypothesized that the benefit of mixing exemplars from different categories is that doing so highlights differences between the categories. Kang and Pashler (Applied Cognitive Psychology 26:97–103, 2012) obtained results consistent with this discriminative-contrast hypothesis: Interleaving enhanced inductive learning, but temporal spacing, which does not highlight category differences, did not. We further tested the discriminative-contrast hypothesis by examining the effects of interleaving and spacing, as well as their combined effects. In three experiments, using photographs of butterflies and birds as the stimuli, temporal spacing was harmful when it interrupted the juxtaposition of interleaved categories, even when total spacing was held constant, supporting the discriminative-contrast hypothesis. Temporal spacing also had value, however, when it did not interrupt discrimination processing.


title: Electronic Throttle Control System: Modeling, Identification and Model-Based Control Designs

abstract: Electronic throttle control (ETC) system has worked its way to becoming a standard subsystem in most of the current automobiles as it has contributed much to the improvement of fuel economy, emissions, drivability and safety. Precision control of the subsystem, which consists of a dc motor driving a throttle plate, a pre-loaded return spring and a set of gear train to regulate airflow into the engine, seems rather straightforward and yet complex. The difficulties lie in the unknown system parameters, hard nonlinearity of the pre-loaded spring that pulls the throttle plate to its default position, and friction, among others. In this paper, we extend our previous results obtained for the modeling, unknown system parameters identification and control of a commercially available Bosch’s DV-E5 ETC system. Details of modeling and parameters identification based on laboratory experiments, data analysis, and knowledge of the system are provided. The parameters identification results were verified and validated by a real-time PID control implemented with an xPC Target. A nonlinear control design was then proposed utilizing the input-output feedback linearization approach and technique. In view of a recent massive auto recalls due to the controversial uncontrollable engine accelerations, the results of this paper may inspire further research interest on the drive-by-wire technology.


An E-Commerce Recommender System using Complaint Data and Review Data


title: Emotional deficiency and psychopathy

abstract: Aside from an antisocial life-style, the concept of psychopathy is based on character features which can be described in terms of specific patterns of interpersonal, behavioral, and, in particular, affective characteristics. Concluding from studies which have dealt with the affective domain in psychopaths, emotional deficiency may predispose to violence in several ways. (1) Poor conditioning implicates a failure to review the harmful consequences of one's actions leading to a deficit of avoidance behavior. (2) Emotional detachment prevents experiencing feelings, which naturally inhibit the acting out of violent impulses. (3) Emotional deficiency is closely associated with a general underarousal, leading to sensation seeking. Current data suggest that there may be a close association between difficulties in emotional processing and poor prefrontal functioning. From a psychosocial perspective, psychopaths were shown to have been exposed to severe familial and societal difficulties. Whether biological or environmental factors dominate in the etiology of this personality disorder, psychopathy does not per se justify the assumption of decreased legal responsibility. Copyright © 2000 John Wiley & Sons, Ltd.


title: The interaction of architecture and operating system design

abstract: Today’s high-performance RISC microprocessors have been highly tuned for integer and floating point application performance. These architectures have paid less attention to operating system requirements. At the same time, new operating system designs often have overlooked modern architectural trends which may unavoidably change the relative cost of certain primitive operations. The result is that operating system performance is well below application code performance on contemporary RISCS. This paper examines recent directions in computer architecture and operating systems, and the implications of changes in each domain for the other. The requirements of three components of operating system design are discussed in detail: interprocess communication, virt ual memory, and thread management. For each component, we relate operating system functional and performance needs to the mechanisms available on commercial RISC architectures such as the MIPS R2000 and R3000, Sun SPARC, IBM RS6000, Motorola 88000, and Intel i860. Our analysis reveals a number of specific reasons why the performance of operating system primitives on RISCS has not scaled with integer performance. In addition, we identify areas in which architectures could better (and cost-effectively) accommodate operating system needs, and areas in which operating system design could accommodate certain necessary characteristics of cost-effective highperformance microprocessors. This work was supported in part by the National Science Foundation under Grants No. CCR-8703049, CCR-8619663, and CCR-8907666, by the Washington Technology Center, by the Digital Equipment Corporation Systems Research Center and External Research Program, and by IBM and AT&T Fellowships. Bershad is now with the School of Computer Science, Carnegie Mellon University. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. 01991 ACM 0-89791 -380 -9/91 /0003 -0108 . ..$1 .50


title: Effects of Thiamethoxam on Vespula Germanica (F.) (Hymenoptera: Vespidae)

abstract: Damage of pesticides used in agriculture on non-target organisms (except honey bees) is not sufficiently considered and neglected. Studies showed that wild bees, like honey bees, were also affected by pesticides. Wild bee species should also be protected because of their importance in pollination and biological control and also in order to protect biodiversity. Effects of pesticides on wild bees should be investigated without delay and precautions should be taken to protect the generations of wildlife. In addition, while increasing the agricultural areas, habitat for wild pollinators should be established. In this study, effect of Thiamethoxam, an agriculturally active agent in the neonicotinoid group, commonly used in agriculture fields, on the European wasp (Vespula germanica ) was investigated. For this purpose, 2 molar carnation-flavored syrup in a petri dish was placed in a field where wild bees are frequent, and wild bees had got accustomed there. Trial doses were prepared with thiamethoxam dose commonly used in agricultural areas (15 ml/100 L water) and 50% dilutions of this dose (15.00, 7.50, 3.75, 1.87, 0.93, 0.46, 0.23, 0.12, 0.06 ml/100 L water) and fed in 2 molar syrup. Those who returned to the carnation petri dish after feeding were recorded at the 1st, 4th and 24th hours. At the end of the study, bees fed with 15.00, 7.50, and 3.75 ml / 100 L doses of the pesticide all died. One hour after pesticide ingestion, 84.73% of the control group and 13.33% of the bees fed with 1.87 ml / 100L dose, were alive and returned to the syrup petri dish.


title: TiViPE - Tino's Visual Programming Environment

abstract: TiViPE is a component based visual programming environment (VPE) that enables users to build programs by construction of a network of components interactively. A single module (component), represented by a graphical icon, is a computational unit. Multiple icons can be connected to each other to yield a directed graph (a network) that represent a program. TiViPE is, in appearance similar to programs such as AVS, Vee, OpenDX, Khoros, LabVIEW, NeatVision, and ViPEr, but presents some fundamental differences. TiViPE integrates documentation with an existing routine call (that has been programmed in C++, C, Fortran, or Java), and automatically generates C++ code that is compiled to stand-alone program. This program is able to execute the specified routine, provide a graphical icon, or give HTML-formatted documentation about the routine. Hence, within TiViPE there is no textual programming for the user TiViPE strongly re-uses code, which is inherent to visual programming, and automatic code regeneration by compounding a network of modules to a single module, which leads to faster programming. TiViPE supports networking and parallel processing in a natural way, and allows the user to modify an activated network. TiViPE also aims at rapid prototyping which demands user friendliness, programming by existing modules for basic users, and focuses on the documentation of a module. TiViPE has been used in the field of computer vision, robotics, and computational neuroscience


Massive Multimodality, Deception, and Genetic Algorithms


title: Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima

abstract: We provide novel theoretical results regarding local optima of regularized M-estimators, allowing for nonconvexity in both loss and penalty functions. Under restricted strong convexity on the loss and suitable regularity conditions on the penalty, we prove that any stationary point of the composite objective function will lie within statistical precision of the underlying parameter vector. Our theory covers many nonconvex objective functions of interest, including the corrected Lasso for errors-in-variables linear models; regression for generalized linear models with nonconvex penalties such as SCAD, MCP, and capped-l 1; and high-dimensional graphical model estimation. We quantify statistical accuracy by providing bounds on the l1-, l2-, and prediction error between stationary points and the population-level optimum. We also propose a simple modification of composite gradient descent that may be used to obtain a near-global optimum within statistical precision estat in log(1/estat) steps, which is the fastest possible rate of any first-order method. We provide simulation studies illustrating the sharpness of our theoretical results.


title: Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation

abstract: The open source frameworks of deep learning including TensorFlow, Caffe, Torch, etc. are widely used all over the world and its acceleration have great meaning. In these frameworks, a lot of computation time is spent on convolution, and highly tuned libraries such as cuDNN play important role on accelerating convolution. In these libraries, however, a convolution computation is performed without approximating a dense matrices. In this research, we propose a method to introduce the low-rank approximation method, widely used in the field of scientific and technical computation, into the convolution computation. As a result of investigating the influence on the recognition accuracy of the existing model, it is possible to reduce up to about 90% of rank of data matrices while keeping recognition accuracy −2% of baseline.


title: Can Inner Peace be Improved by Mindfulness Training: A Randomized Controlled Trial.

abstract: This article reports a randomized controlled trial to investigate whether mindfulness training can successfully improve inner peace in participants with no known mental disorder. Fifty-seven participants were randomized to either mindfulness training (n = 29) or wait-list control (n = 28). The experience sampling method was used to measure the fleeting momentary experience of inner peace in participants. In addition, we used an experimental approach to assessing ability to focus attention: the Meditation Breath Attention Score, as well as the self-report Five-Facet Mindfulness Questionnaire (FFMQ). Compared with the wait-list control group, mindfulness training led to an increase in scores of inner peace, Meditation Breath Attention Score and FFMQ, using analysis of repeated measures analysis of variance. Change in inner peace was not, however, mediated by changes in self-rated mindfulness (FFMQ) nor by increased attentional focus. The findings provide first evidence suggesting that using mindfulness training improves the participants' inner peace. The focus here was on the immediate effects and future studies need to use follow-up. © 2013 The Authors. Stress and Health published by John Wiley & Sons Ltd.


title: Hyponatremia and Hepatorenal Syndrome

abstract: Hyponatremia and hepatorenal syndrome are severe complications in patients with cirrhosis and ascites resulting from circulatory abnormalities (splanchnic and systemic vasodilatation) that develop with portal hypertension. Both conditions are associated with an increased risk of death. Hyponatremia and renal failure may develop in patients with cirrhosis due to causes other than portal hypertension. Making an accurate differential diagnosis is important both therapeutically and prognostically. In this article, we discuss the pathophysiology, diagnosis, differential diagnosis, and management of hyponatremia and hepatorenal syndrome in patients with cirrhosis.


title: Passive Reconfigurable Triple-Wideband Antenna for LTE Tablet Computer

abstract: A passive reconfigurable triple-wideband tablet computer antenna for the LTE operation in the low band of $698\sim 960~{\rm MHz}$ , middle band of $1710\sim 2690~{\rm MHz}$ , and high band of $3400\sim 3800~{\rm MHz}$ is presented. The antenna mainly acts like an inverted-F antenna (IFA) at frequencies in the LTE low and middle bands and like a loop antenna at frequencies in the LTE high band. The reconfigurable IFA/loop antenna is achieved without active switch circuits and occupies a small volume of $10 \times 35 \times 3~{\rm mm}^{3}$ . The antenna is obtained by first configuring the radiating arm of an IFA to be folded and coupled to the device ground plane through a distributed capacitor. Then, the IFA's shorting and feeding strips are respectively loaded with an inductor and a capacitor. This leads to good excitation of a resonant mode generated in the LTE low band with the radiating arm length of the IFA only about 0.16 wavelength at 800 MHz. By further aided using a high-pass matching circuit integrated within the antenna, a wide low band is obtained for the antenna. The IFA's first two higher-order modes can also be excited in the middle band. Owing to the distributed capacitor between the IFA's radiating arm and the device ground plane, the two higher-order modes can lead to a wide middle band for the antenna. At frequencies in the LTE high band, the antenna mainly acts like a loop antenna operated at its one-wavelength loop mode, which contributes to a wide high band for the antenna. Triple-wideband operation covering the LTE operation is hence obtained. Details of the proposed antenna are described, and experimental results are presented. Working principle of the antenna acting like an IFA and a loop antenna at different frequencies is discussed.


title: Re-engineering the ISO 15926 Data Model: A Multi-level Metamodel Perspective

abstract: The ISO 15926 standard was developed to facilitate the integration of life-cycle data of process plants. The core of the standard is a highly generic and extensible data model trying to capture a holistic view of the world. We investigated the standard from a software modelling point of view and identified some challenges in terminology, circular definitions and inconsistencies in relationships during the mapping from concepts specified in the standard to an object-oriented model. This makes the standard difficult to understand and more challenging to implement. In this paper we look at mapping the ISO 15926 data model to a multilevel metamodel, and aim to formalise critical aspects of the data model which will simplify the model and ease the adoption process.


title: Well-Definedness and Efficient Inference for Probabilistic Logic Programming under the Distribution Semantics

abstract: The distribution semantics is one of the most prominent approaches for the combination of logic programming and probability theory. Many languages follow this semantics, such as Independent Choice Logic, PRISM, pD, Logic Programs with Annotated Disjunctions (LPADs) and ProbLog. When a program contains functions symbols, the distribution semantics is well-defined only if the set of explanations for a query is finite and so is each explanation. Well-definedness is usually either explicitly imposed or is achieved by severely limiting the class of allowed programs. In this paper we identify a larger class of programs for which the semantics is well-defined together with an efficient procedure for computing the probability of queries. Since LPADs offer the most general syntax, we present our results for them, but our results are applicable to all languages under the distribution semantics. We present the algorithm "Probabilistic Inference with Tabling and Answer subsumption" (PITA) that computes the probability of queries by transforming a probabilistic program into a normal program and then applying SLG resolution with answer subsumption. PITA has been implemented in XSB and tested on six domains: two with function symbols and four without. The execution times are compared with those of ProbLog, cplint and CVE, PITA was almost always able to solve larger problems in a shorter time, on domains with and without function symbols.


title: Handwriting Arabic Character Recognition LeNet Using Neural Network

abstract: Character recognition has served as one of the principal proving grounds for neural network methods and has emerged as one of the most successful applications of this technology. In this paper, a new network is designed to recognize a set of handwritten arabic characters. This new network consists of two stages. The first is to recognize the main shape of the character, and the second stage is for dots recognition. Also, the characteristics, structure, and the training algorithm for the network are presented.


title: Robust double D topology for roadway IPT applications

abstract: Roadway Inductive Power Transfer depends on robust and reliable primary magnetic couplers (pads), but existing pads use large quantities of ferrite, a brittle magnetic material. This paper presents two variations on the Double D topology to reduce or remove the ferrite thereby making the system more robust, using an additional coil to help shape the field instead. Simulations and laboratory validations are presented. Results show a ferrite-less Double D could allow a higher power to be transferred for a given leakage at the side of the roadway, at the expense of a higher primary current and system efficiency. In addition, a reduced ferrite pad with an additional coil allows a significant power transfer for a lower leakage profile than a full ferrite pad, enabling a less expensive and more robust system at the expense of end to end efficiency.


title: NASA Faked the Moon Landing—Therefore, (Climate) Science Is a Hoax: An Anatomy of the Motivated Rejection of Science

abstract: Although nearly all domain experts agree that carbon dioxide emissions are altering the world’s climate, segments of the public remain unconvinced by the scientific evidence. Internet blogs have become a platform for denial of climate change, and bloggers have taken a prominent role in questioning climate science. We report a survey of climate-blog visitors to identify the variables underlying acceptance and rejection of climate science. Our findings parallel those of previous work and show that endorsement of free-market economics predicted rejection of climate science. Endorsement of free markets also predicted the rejection of other established scientific findings, such as the facts that HIV causes AIDS and that smoking causes lung cancer. We additionally show that, above and beyond endorsement of free markets, endorsement of a cluster of conspiracy theories (e.g., that the Federal Bureau of Investigation killed Martin Luther King, Jr.) predicted rejection of climate science as well as other scientific...


Efficiently Summarising Event Sequences with Rich Interleaving Patterns


title: The effect of asset and ownership structure on political risk: Some evidence from Mitterrand's election in France

abstract: Abstract Previous research suggests that political risk varies considerably from firm to firm. Yet few empirical tests of the cross-sectional variation in political risk exist. French assets were massively revalued the day after Mitterrand was elected in 1981, providing a unique opportunity to test for the effect of firm-specific factors on political risk. Although foreign ownership tends to increase political risk, other factors can reduce it. A simple model of political behavior is presented in which politicians value, among other things, economic growth. A foreign firm with significant future growth options is likely to experience less political risk than other foreign firms and, under certain circumstances, less risk than even other domestic firms. Tests of these hypotheses are conducted using data from the French and U.S. capital markets. The results are generally consistent with the hypotheses.


title: Error norm estimation and stopping criteria in preconditioned conjugate gradient iterations

abstract: Some techniques suitable to the control of the solution error in the Preconditioned Conjugate Method are considered and compared. The estimation can be performed both in the course ot the iterations and after their termination. The importance of such techniques follows from the non-existence of some reasonable a priori error estimate for very ill-conditioned linear systems when information about the right-hand side vector is lacking. Hence, some a posteriori estimates are required, which make it possible to verify the quality of the solution obtained for a prescribed right hand side. The performance of the considered error control procedures is demonstrated using real-world large-scale linear systems arising in computational mechanics.


title: Speech parameter generation algorithms for HMM-based speech synthesis

abstract: This paper derives a speech parameter generation algorithm for HMM-based speech synthesis, in which the speech parameter sequence is generated from HMMs whose observation vector consists of a spectral parameter vector and its dynamic feature vectors. In the algorithm, we assume that the state sequence (state and mixture sequence for the multi-mixture case) or a part of the state sequence is unobservable (i.e., hidden or latent). As a result, the algorithm iterates the forward-backward algorithm and the parameter generation algorithm for the case where the state sequence is given. Experimental results show that by using the algorithm, we can reproduce clear formant structure from multi-mixture HMMs as compared with that produced from single-mixture HMMs.


title: Image watercolorization based on visual weight-map

abstract: This paper presents a watercolor rendering technique that recreates the artistic effects. Visual features make focused object apply different simulation value. The method is organized in two steps: Image abstraction based on the weight-map; Detail verisimilitude of watercolor features. Verisimilitude includes color space conversion, boundary region diffusion, morphological smoothing, and adding texture. In experiments, amount of color images were processed for promoting reality. The result indicates this method is more efficient and suitable for image watercolorization.


title: An analysis of the YouNow live streaming platform

abstract: Video streaming platforms like Twitch.tv or YouNow have attracted the attention of both users and researchers in the last few years. Users increasingly adopt these platforms to share user-generated videos while researchers study their usage patterns to learn how to provide better and new services.


title: FPGA-based resonant-frequency-tracking power amplifier for ultrasonic transducer

abstract: An FPGA-based class-D power amplifier is designed for driving an ultrasonic transducer. The amplifier is capable of automatically detecting and tracking the transducer's resonant frequency, driving the transducer right at its resonance for maximizing vibration efficiency. The design focuses on devising resonant frequency tracking and optimal modulation algorithms that are suitable for FPGA implementation.


Universal Dependency Parser : A Single Parser for Many Languages on Arc-Swift


title: RFID-based production data analysis in an IoT-enabled smart job-shop

abstract: Under industry 4.0, internet of things U+0028 IoT U+0029, especially radio frequency identification U+0028 RFID U+0029 technology, has been widely applied in manufacturing environment. This technology can bring convenience to production control and production transparency. Meanwhile, it generates increasing production data that are sometimes discrete, uncorrelated, and hard-to-use. Thus, an efficient analysis method is needed to utilize the invaluable data. This work provides an RFID-based production data analysis method for production control in IoT-enabled smart job-shops. The physical configuration and operation logic of IoT-enabled smart job-shop production are firstly described. Based on that, an RFID-based production data model is built to formalize and correlate the heterogeneous production data. Then, an eventdriven RFID-based production data analysis method is proposed to construct the RFID events and judge the process command execution. Furthermore, a near big data approach is used to excavate hidden information and knowledge from the historical production data. A demonstrative case is studied to verify the feasibility of the proposed model and methods. It is expected that our work will provide a different insight into the RFIDbased production data analysis.


title: Ganoderma Lucidum(Reishi) in Cancer Treatment

abstract: The popular edible mushroom Ganoderma lucidum (Reishi) has been widely used for the general promotion of health and longevity in Asian countries. The dried powder of Ganoderma lucidum was popular as a cancer chemotherapy agent in ancient China. The authors recently demonstrated that Ganoderma lucidum inhibits constitutively active tran- scription factors nuclear factor kappa B (NF-κB) and AP-1, which resulted in the inhibition of expression of urokinase- type plasminogen activator (uPA) and its receptor uPAR. Ganoderma lucidum also suppressed cell adhesion and cell migration of highly invasive breast and prostate cancer cells, suggesting its potency to reduce tumor invasiveness. Thus, Ganoderma lucidum clearly demonstrates anticancer activity in experiments with cancer cells and has possible therapeu- tic potential as a dietary supplement for an alternative ther- apy for breast and prostate cancer. However, because of the availability of Ganoderma lucidum from different sources, it is advisable to test its biologic activity. Traditional Chinese medicine (TCM) has been prac- ticed in many Asian countries during the past 2 millen- nia. In TCM, foods play an important role for main- taining and improving health and for preventing and treating disease. 1 Even with the progress of modern Western medicine in the fields of surgery, radiation therapy, and chemotherapy for cancer treatment, many malignancies can be prevented or treated with proper nutrition. The general use of dietary supple- ments in the United States significantly increased dur- ing the past 10 years, and the use of dietary supple- ments and herbal therapies by cancer patients continues to increase. 2 By tracing the multistep pro- cess of carcinogenesis at the cellular levels, it is pos- sible to understand the molecular mechanisms through which the components of foods and botani- cal dietary supplements affect the development of cancer. 3 Therefore, we can identify specific molecular targets, which can eventually be modulated in the pre- vention and/or treatment of cancers by the dietary supplements. Ganoderma lucidum Ganoderma lucidum (Fr.) Karst. (Ganodermata- ceae), basidiomycetous fungi, has been used as a med- ical remedy in China, Korea, and Japan for centuries. 4


title: Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring

abstract: Trends in bird population sizes are an important indicator in nature conservation but measuring such sizes is a very difficult, labour intensive process. Enormous progress in audio signal processing and pattern recognition in recent years makes it possible to incorporate automated methods into the detection of bird vocalisations. These methods can be employed to support the census of population sizes. We report about a study testing the feasibility of bird monitoring supported by automatic bird song detection. In particular, we describe novel algorithms for the detection of the vocalisations of two endangered bird species and show how these can be used in automatic habitat mapping. These methods are based on detecting temporal patterns in a given frequency band typical for the species. Special effort is put into the suppression of the noise present in real-world audio scenes. Our results show that even in real-world recording conditions high recognition rates with a tolerable rate of false positive detections are possible.


title: Ranking Outliers Using Symmetric Neighborhood Relationship

abstract: Mining outliers in database is to find exceptional objects that deviate from the rest of the data set. Besides classical outlier analysis algorithms, recent studies have focused on mining local outliers, i.e., the outliers that have density distribution significantly different from their neighborhood. The estimation of density distribution at the location of an object has so far been based on the density distribution of its k-nearest neighbors [2,11]. However, when outliers are in the location where the density distributions in the neighborhood are significantly different, for example, in the case of objects from a sparse cluster close to a denser cluster, this may result in wrong estimation. To avoid this problem, here we propose a simple but effective measure on local outliers based on a symmetric neighborhood relationship. The proposed measure considers both neighbors and reverse neighbors of an object when estimating its density distribution. As a result, outliers so discovered are more meaningful. To compute such local outliers efficiently, several mining algorithms are developed that detects top-n outliers based on our definition. A comprehensive performance evaluation and analysis shows that our methods are not only efficient in the computation but also more effective in ranking outliers.


title: Multi-Label Zero-Shot Human Action Recognition via Joint Latent Embedding

abstract: Human action recognition refers to automatic recognizing human actions from a video clip. In reality, there often exist multiple human actions in a video stream. Such a video stream is often weakly-annotated with a set of relevant human action labels at a global level rather than assigning each label to a specific video episode corresponding to a single action, which leads to a multi-label learning problem. Furthermore, there are many meaningful human actions in reality but it would be extremely difficult to collect/annotate video clips regarding all of various human actions, which leads to a zero-shot learning scenario. To the best of our knowledge, there is no work that has addressed all the above issues together in human action recognition. In this paper, we formulate a real-world human action recognition task as a multi-label zero-shot learning problem and propose a framework to tackle this problem in a holistic way. Our framework holistically tackles the issue of unknown temporal boundaries between different actions for multi-label learning and exploits the side information regarding the semantic relationship between different human actions for knowledge transfer. Consequently, our framework leads to a joint latent ranking embedding for multi-label zero-shot human action recognition. A novel neural architecture of two component models and an alternate learning algorithm are proposed to carry out the joint latent ranking embedding learning. Thus, multi-label zero-shot recognition is done by measuring relatedness scores of action labels to a test video clip in the joint latent visual and semantic embedding spaces. We evaluate our framework with different settings, including a novel data split scheme designed especially for evaluating multi-label zero-shot learning, on two datasets: Breakfast and Charades. The experimental results demonstrate the effectiveness of our framework.


title: CrowdDB: answering queries with crowdsourcing

abstract: Some queries cannot be answered by machines only. Processing such queries requires human input for providing information that is missing from the database, for performing computationally difficult functions, and for matching, ranking, or aggregating results based on fuzzy criteria. CrowdDB uses human input via crowdsourcing to process queries that neither database systems nor search engines can adequately answer. It uses SQL both as a language for posing complex queries and as a way to model data. While CrowdDB leverages many aspects of traditional database systems, there are also important differences. Conceptually, a major change is that the traditional closed-world assumption for query processing does not hold for human input. From an implementation perspective, human-oriented query operators are needed to solicit, integrate and cleanse crowdsourced data. Furthermore, performance and cost depend on a number of new factors including worker affinity, training, fatigue, motivation and location. We describe the design of CrowdDB, report on an initial set of experiments using Amazon Mechanical Turk, and outline important avenues for future work in the development of crowdsourced query processing systems.


title: A high-throughput reconfigurable processing array for neural networks

abstract: FPGA-based neural-networks typically leave performance on the table because the DSP resources run at less than a third of the peak clock rate. This paper presents a processing array architected to consistently achieve timing closure at 100% of the peak DSP clock rate with standard FPGA tools. In the HDL design environment, our processing array operates at the peak DSP clock rates on Xilinx UltraScale (741 MHz) and UltraScale+ (891 MHz) devices. To enhance portability and consistency of timing closure, this array operates at a high clock rate while data SRAMs run at a fraction of this rate. As a proof of concept, this paper outlines a processing array for matrix multiplication and convolution, the most compute-intensive operations of a convolutional neural network (CNN).


title: A Read–Write Circuit for STT-MRAM With Stochastic Switchings

abstract: Spin-transfer torque magnetic random memory (STT-MRAM) is a promising candidate for universal memory due to its non-volatility, fast access speed, high density, and low power consumption. In this paper, we propose a read-and-write circuit for STT-MRAMs, which decreases energy consumption and improves the endurance of STT-MRAM cells. The proposed circuit is capable of terminating write operations when the state of the cell has been switched to the desired one. In the circuit, the read and write operations share a single current driver. The proposed circuit including all the digital logic parts has been implemented using a CMOS process and validated using SPICE-level simulations.


title: Towards playful learning and computational thinking — Developing the educational robot BRICKO

abstract: Educational Robotics has proven a feasible way of supporting and exemplifying Computational Thinking. With this paper, we describe the user-centered iterative and incremental development of a new educational robotic system, BRICKO, to support tangible, social and playful interaction while educating children in 1st–3rd grade in Computational Thinking. We develop the system through seven main iterations including a total of 108 participant pupils and their teachers. The methodology is a mixture of observation and interviews using Wizard of OZ testing with the early pilot prototypes as well as usability and user experience testing with the following incrementally improved digital prototypes. Our results for development and evaluation are presented for each iteration leading onto the next and describes the evolution of both the BRICKO driving robot as well as the BRICKO programming-board and its different categories of command-bricks. We discuss the methodologies used for assuring a playful and social educational robotic system and conclude that we achieved a useful prototype for supporting education in Computational Thinking.


title: Optimized Parallelization of Binary Classification Algorithms Based on Spark

abstract: In recent years, parallel computing and machine learning have gradually became a research hotspot in big data field. Now users who are familiar with the R language can easily complete the data analysis on the platform of Apache Spark with the programming interface called SparkR provided by Apache Spark. There are several realized classification algorithms (including multiple classification and binary classification) in SparkR, in order to speed up the training process in binary-class classification, this paper proposes a theory of adopting an iterative calculation model for the local optimization on the basis of conventional parallelization. The experimental result shows that the design of parallelization of binary classification algorithm based on spark is much better and faster comparing with the MapReduce on Hadoop.


title: C-Flash: An Ultra-Low Power Single Poly Logic NVM

abstract: An ultra-low power logic NVM has currents <10 nA/cell in all operating regimes, high programming/erase speeds, excellent endurance/retention and allows strong Vdd fluctuations. The memory uses CMOS inverter read-out principle (C-Flash) and F-N injection for programming and erase with voltages below +5 V. The memory is intended for RFID and advanced mobile applications requiring small/middle sized embedded memory modules.


title: Cause-effect knowledge acquisition and neural association model for solving a set of winograd schema problems

abstract: This paper focuses on the investigations in Winograd Schema (WS), a challenging problem which has been proposed for measuring progress in commonsense reasoning. Due to the lack of commonsense knowledge and training data, very little work has been reported on the WS problems. This paper addresses a set of WS problems by proposing a knowledge acquisition method and a general neural association model. To avoid the sparseness issue, the knowledge we aim to collect is the cause-effect relationships between a collection of commonly used words. The knowledge acquisition method supports us to extract hundreds of thousands of cause-effect pairs from text corpora automatically. Meanwhile, a neural association model (NAM) is proposed to encode the association relationships between any two discrete events. Based on the extracted knowledge and the NAM models, we successfully build a system for solving a causal subset of WS problems from scratch and achieve 70% accuracy. Most importantly, this paper provides a flexible framework to solve WS problems based on event association and neural network methods.


title: Incremental effects of reward on experienced performance pressure: positive outcomes for intrinsic interest and creativity

abstract: Two field studies and a laboratory study examined the influence of reward for high performance on experienced performance pressure, intrinsic interest and creativity. Study 1 found that employees' expected reward for high performance was positively related to performance pressure which, in turn, was positively associated with the employees' interest in their jobs. Study 2 replicated this finding and showed that intrinsic interest, produced by performance pressure, was positively related to supervisors' ratings of creative performance. Study 3 found that college students' receipt of reward for high performance increased their experienced performance pressure which, in turn, was positively related to intrinsic interest and creativity. Copyright © 2008 John Wiley & Sons, Ltd.


title: SteerFit: automated parameter fitting for steering algorithms

abstract: In the context of crowd simulation, there is a diverse set of algorithms that model steering. The performance of steering approaches, both in terms of quality of results and computational efficiency, depends on internal parameters that are manually tuned to satisfy application-specific requirements. This paper investigates the effect that these parameters have on an algorithm's performance. Using three representative steering algorithms and a set of established performance criteria, we perform a number of large scale optimization experiments that optimize an algorithm's parameters for a range of objectives. For example, our method automatically finds optimal parameters to minimize turbulence at bottlenecks, reduce building evacuation times, produce emergent patterns, and increase the computational efficiency of an algorithm. We also propose using the Pareto Optimal front as an efficient way of modelling optimal relationships between multiple objectives, and demonstrate its effectiveness by estimating optimal parameters for interactively defined combinations of the associated objectives. The proposed methodologies are general and can be applied to any steering algorithm using any set of performance criteria.


title: A theory of object recognition: Computations and circuits in the feedforward path of the ventral stream in primate visual cortex

abstract: Abstract : We describe a quantitative theory to account for the computations performed by the feedforward path of the ventral stream of visual cortex and the local circuits implementing them. We show that a model instantiating the theory is capable of performing recognition on datasets of complex images at the level of human observers in rapid categorization tasks. We also show that the theory is consistent with (and in some case has predicted) several properties of neurons in V1, V4, IT and PFC. The theory seems sufficiently comprehensive, detailed and satisfactory to represent an interesting challenge for physiologists and modelers: either disprove its basic features or propose alternative theories of equivalent scope. The theory suggests a number of open questions for visual physiology and psychophysics.


title: Consensus Attention-based Neural Networks for Chinese Reading Comprehension

abstract: Reading comprehension has embraced a booming in recent NLP research. Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension. In this work, we firstly present Chinese reading comprehension datasets, which consist of People Daily news dataset and Children's Fairy Tale (CFT) dataset. Also, we propose a consensus attention-based neural network architecture to tackle the Cloze-style reading comprehension problem, which aims to induce a consensus attention over every words in the query. Experimental results show that the proposed neural network significantly outperforms the state-of-the-art baselines in several public datasets. Furthermore, we setup a baseline for Chinese reading comprehension task, and hopefully this would speed up the process for future research.


title: Game Theoretic Approaches for Cooperative Spectrum Sensing in Energy-Harvesting Cognitive Radio Networks

abstract: In this paper, we propose game-theoretic approaches to model the cooperative spectrum sensing in cognitive radio environment with cognitive secondary users (SU) capable of energy harvesting. An evolutionary game model is considered in the first scenario, where distributed SUs are allowed to choose between two strategies, cooperate to sense the spectrum, or deny to be a free rider. The sensed data is sent to a fusion center (FC) to have a final decision about the existence of the primary user. In the second scenario, the Stackelberg game is applied to model the case when the FC participates as a leader in the game, with carefully designed incentives to SUs to encourage cooperation. Simulation results show the behavior of the two proposed game models and quantify the improvement due to the FC intervention in the Stackelberg game model.


How Does Corporate Social Responsibility Promote Innovation? The Sequential Mediating Mechanism of Employees’ Meaningfulness of Work and Intrinsic Motivation


title: A Bridgeless Single-Stage Half-Bridge AC/DC Converter

abstract: This paper proposes a new bridgeless single-stage half-bridge ac-dc converter for power factor correction. The proposed converter integrates the bridgeless boost rectifier with the asymmetrical pulse-width modulation half-bridge dc-dc converter. The proposed converter provides an isolated dc output voltage without using any full-bridge diode rectifier. Conduction losses are lowered by eliminating the full-bridge diode rectifier. Zero-voltage switching of the power switches reduces the switching power losses. The proposed converter gives a high efficiency, high power factor, and low cost. The effectiveness of the proposed converter is verified on a 250 W (48 V/5.2 A) experimental prototype. The proposed converter achieves a high efficiency of 93.0% and an almost unity power factor for 250 W output power at 90 Vrms line voltage.


title: Multi-agent motion planning and coordination in polygonal environments using vector fields and model predictive control

abstract: In this paper, we extend earlier work on motion planning and coordination of multiple agents, to environments of arbitrary polygonal obstacles, using non-gradient vector fields to steer each agent towards their goal configurations while avoiding collisions. We formulate the vector fields so that the pattern of their integral curves depends on a parameter λ. By manipulating the value of λ, we obtain a set of vector fields whose integral curves define the flow lines for an a priori known obstacle environment. We use the vector field design in tandem with model predictive control to compute safe trajectories for multi-agent systems. The competence of the proposed methodology is demonstrated for both static and dynamic environment via simulation results. The efficacy of model predictive control in achieving control trajectories, free of chattering, for multi-agent coordination is validated through comparison to a state feedback coordination and control protocol.


title: Fire models and methods to map fuel types: The role of remote sensing

abstract: Understanding fire is essential to improving forest management strategies. More specifically, an accurate knowledge of the spatial distribution of fuels is critical when analyzing, modelling and predicting fire behaviour. First, we review the main concepts and terminology associated with forest fuels and a number of fuel type classifications. Second, we summarize the main techniques employed to map fuel types starting with the most traditional approaches, such as field work, aerial photo interpretation or ecological modelling. We pay special attention to more contemporary techniques, which involve the use of remote sensing systems. In general, remote sensing systems are low-priced, can be regularly updated and are less time-consuming than traditional methods, but they are still facing important limitations. Recent work has shown that the integration of different sources of information and methods in a complementary way helps to overcome most of these limitations. Further research is encouraged to develop novel and enhanced remote sensing techniques.


title: AN IMPROVED DEFECT CLASSIFICATION ALGORITHM FOR SIX PRINTING DEFECTS AND ITS IMPLEMENTATION ON REAL PRINTED CIRCUIT BOARD IMAGES

abstract: Because decisions made by human inspectors often involve subjective judgment, in addition to being intensive and therefore costly, an automated approach for printed circuit board (PCB) inspection is preferred to eliminate subjective discrimination and thus provide fast, quantitative, and dimensional assessments. In this study, defect classification is essential to the identification of defect sources. Therefore, an algorithm for PCB defect classification is presented that consists of well-known conventional operations, including image difference, image subtraction, image addition, counted image comparator, flood-fill, and labeling for the classification of six different defects, namely, missing hole, pinhole, underetch, short-circuit, open-circuit, and mousebite. The defect classification algorithm is improved by incorporating proper image registration and thresholding techniques to solve the alignment and uneven illumination problem. The improved PCB defect classification algorithm has been applied to real PCB images to successfully classify all of the defects.


title: Nurses' response time to call lights and fall occurrences.

abstract: Nurses respond to fallers' call lights more quickly than they do to lights initiated by non-fallers. The nurses' responsiveness to call lights could be a compensatory mechanism in responding to the fall prevalence on the unit.


title: Using Citations to Generate surveys of Scientific Paradigms

abstract: The number of research publications in various disciplines is growing exponentially. Researchers and scientists are increasingly finding themselves in the position of having to quickly understand large amounts of technical material. In this paper we present the first steps in producing an automatically generated, readily consumable, technical survey. Specifically we explore the combination of citation information and summarization techniques. Even though prior work (Teufel et al., 2006) argues that citation text is unsuitable for summarization, we show that in the framework of multi-document survey creation, citation texts can play a crucial role.


title: Activity, short-term stability (poisoning tolerance) and durability of carbon supported Pt–Pr catalysts for ethanol oxidation

abstract: Abstract Pt–Pr/C electrocatalysts were prepared by a modified formic acid method, and their activity for carbon monoxide and ethanol oxidation, their short term stability and durability were compared to that of commercial Pt/C and Pt–Sn/C (3:1) catalysts. By derivative voltammetry (DV) it was found that ethanol electro-oxidation takes place by two main pathways at different potentials. It was observed that, in the presence of Pr, ethanol electro-oxidation takes place mostly through the pathway at lower potential, which is the most interesting for fuel cell application. The Pt–Pr/C catalysts were less tolerant to poisoning by ethanol oxidation intermediate species than Pt/C. Durability test by a repetitive potential cycling under Ar atmosphere revealed a good structural stability of Pt–Pr/C catalysts. A repetitive potential cycling under CO atmosphere carried out on the Pt–Pr/C (1:1) catalyst, instead, indicated a structural change, likely by formation of a core–shell structure.


1 8-2015 A Conceptual Framework for Understanding Crowdfunding


title: A social gamification framework for a K-6 learning platform

abstract: As video games, particularly, social games are growing in popularity and number of users, there has been an increasing interest in its potential as innovative teaching tools. Gamification is a new concept intending to use elements from video games in non-game applications. Education is an area with high potential for application of this concept since it seeks to promote people's motivation and engagement. The research in progress will try to find how to apply social gamification in education, testing and validating the results of that application. To fulfil these objectives, this paper presents the guidelines and main features of a social gamification framework to be applied in an existent K-6 social learning environment.


title: A novel curve lane detection based on Improved River Flow and RANSA

abstract: Accurate and robust lane detection, especially the curve lane detection, is the premise of a Lane Departure Warning System (LDWS) and a Forward Collision Warning System (FCWS). Lane detection on the structural roads under challenging scenarios such as the dashed lane markings and vehicle occlusion is a difficult task because of unreliable lane feature points. In this paper, a robust curve lane detection method based on the Improved River Flow (IRF) and the Random Sample Consensus (RANSAC) method is proposed to detect a curve lane under challenging conditions. The lane markings are grouped into a near vision field of a straight line and a far vision field of a curve line. The curve lanes are based on a Hyperbola-pair model. To determine the coefficient of curvature, a novel method is proposed based on the Improved River Flow method and the RANSAC method. In the new method, the Improved River Flow method is employed to search feature points in the far vision field guided by the results of detected straight lines in the near vision field or the curve lines from the last frame, which can connect dashed lane markings or obscured lane markings. So, it is robust on dashed lane markings and vehicle occlusion. Then, the RANSAC is utilized to calculate the curvature, which can eliminate noisy feature points obtained from the Improved River Flow. The experimental results show that the proposed method can robustly and accurately detect some challenging markings, such as the dashed lane markings and vehicle occlusion.


title: RAPID-A Video Rate Object Tracker

abstract: Three-dimensional (3D) Model-Based Vision enables observed image features to be used to determine the pose (ie. position and attitude) of a known 3D object with respect to the camera (or alternatively, the viewpoint of the camera with respect to the model) [1]. The knowledge concerning the object that is used to perform Model-Based Vision is the 3D locations of salient and easily observed object features, such as corners and high-contrast edges (eg. surface marking sand crease edges).


title: Dynamic Partitioning of Shared Cache Memory

abstract: This paper proposes dynamic cache partitioning amongst simultaneously executing processes/threads. We present a general partitioning scheme that can be applied to set-associative caches. ::: ::: Since memory reference characteristics of processes/threads can change over time, our method collects the cache miss characteristics of processes/threads at run-time. Also, the workload is determined at run-time by the operating system scheduler. Our scheme combines the information, and partitions the cache amongst the executing processes/threads. Partition sizes are varied dynamically to reduce the total number of misses. ::: ::: The partitioning scheme has been evaluated using a processor simulator modeling a two-processor CMP system. The results show that the scheme can improve the total IPC significantly over the standard least recently used (LRU) replacement policy. In a certain case, partitioning doubles the total IPC over standard LRU. Our results show that smart cache management and scheduling is essential to achieve high performance with shared cache memory.