Query: DPICO: a high speed deep packet inspection engine using compact finite automata

Relevant passages:
Space/Time Trade-offs in Hash Coding with Allowable Errors <=SEP=> In this paper trade-offs among certain computational factors in hash coding are analyzed. The paradigm problem considered is that of testing a series of messages one-by-one for membership in a given set of messages. Two new hash-coding methods are examined and compared with a particular conventional hash-coding method. The computational factors considered are the size of the hash area (space), the time required to identify a message as a nonmember of the given set (reject time), and an allowable error frequency.
The new methods are intended to reduce the amount of space required to contain the hash-coded information from that associated with conventional methods. The reduction in space is accomplished by exploiting the possibility that a small fraction of errors of commission may be tolerable in some applications, in particular, applications in which a large amount of data is involved and a core resident hash area is consequently not feasible using conventional methods.
In such applications, it is envisaged that overall performance could be improved by using a smaller core resident hash area in conjunction with the new methods and, when necessary, by using some secondary and perhaps time-consuming test to “catch” the small fraction of errors associated with the new methods. An example is discussed which illustrates possible areas of application for the new methods.
Analysis of the paradigm problem demonstrates that allowing a small number of test messages to be falsely identified as members of the given set will permit a much smaller hash area to be used without increasing reject time.

Fast Text Searching Allowing Errors <=SEP=> T h e string-matching problem is a very c o m m o n problem. We are searching for a string P = PtP2. . "Pro i n s i d e a la rge t ex t f i le T = t l t2. . . t . , b o t h sequences of characters from a f i n i t e character set Z. T h e characters may be English characters in a text file, DNA base pairs, lines of source code, angles between edges in polygons, machines or machine parts in a production schedule, music notes and tempo in a musical score, and so fo r th . We w a n t to f i n d a l l occurrences of P i n T; n a m e l y , we are searching for the set of starting posit ions F = {i[1 --i--n m + 1 s u c h t h a t titi+ l " " t i + m 1 = P } " T h e two most famous algorithms for this problem are t h e B o y e r M o o r e algorithm [3] and t h e K n u t h Morris Pratt algorithm [10]. There are many extensions to t h i s problem; for example, we may be looking for a set of patterns, a pattern w i t h "wi ld cards," or a regular expression. String-matching tools are included in every reasonable text editor, word processor, and many other applications.

Introduction to Automata Theory, Languages and Computation <=SEP=> Let n be the pumping-lemma constant (note this n is unrelated to the n that is a local variable in the definition of the language L). Pick w = 0^n10^n. Then when we write w = xyz, we know that |xy| <= n, and therefore y consists of only 0's. Thus, xz, which must be in L if L is regular, consists of fewer than n 0's, followed by a 1 and exactly n 0's. That string is not in L, so we contradict the assumption that L is regular.

A Fast String Searching Algorithm <=SEP=> An algorithm is presented that searches for the location, “<italic>i</italic>l” of the first occurrence of a character string, “<italic>pat</italic>,” in another string, “<italic>string</italic>.” During the search operation, the characters of <italic>pat</italic> are matched starting with the last character of <italic>pat</italic>. The information gained by starting the match at the end of the pattern often allows the algorithm to proceed in large jumps through the text being searched. Thus the algorithm has the unusual property that, in most cases, not all of the first <italic>i</italic> characters of <italic>string</italic> are inspected. The number of characters actually inspected (on the average) decreases as a function of the length of <italic>pat</italic>. For a random English pattern of length 5, the algorithm will typically inspect <italic>i</italic>/4 characters of <italic>string</italic> before finding a match at <italic>i</italic>. Furthermore, the algorithm has been implemented so that (on the average) fewer than <italic>i</italic> + <italic>patlen</italic> machine instructions are executed. These conclusions are supported with empirical evidence and a theoretical analysis of the average behavior of the algorithm. The worst case behavior of the algorithm is linear in <italic>i</italic> + <italic>patlen</italic>, assuming the availability of array space for tables linear in <italic>patlen</italic> plus the size of the alphabet.
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Binary codes capable of correcting deletions, insertions and reversals <=SEP=> 

Black-Box Test Data Generation for GUI Testing <=SEP=> Effective system testing of applications with a Graphical User Interface (GUI) front-end demands careful generation of event sequences as well as providing relevant test data for parameterized widgets, i.e., widgets that accept input values such as textboxes and textareas. Current GUI testing techniques either manipulate the source code of the application under test (AUT) to generate the test data, or blindly use a set of random string values. In this paper, we propose a third novel way to generate relevant test data for GUI testing. We exploit the information provided in the GUI structure to extract a set of key identifiers for each parameterized widget. These identifiers are used to compose appropriate search phrases and collect relevant test data from the Internet. The results of an empirical study on five GUI-based applications show that the proposed approach is applicable and can get some hard-to-cover branches in the subject programs to execute. The proposed technique works from the black-box perspective and is entirely independent from GUI modeling and event sequence generation, thus it does not need access to the source code of AUT and provides an opportunity to be integrated with the existing GUI testing frameworks.

Trend Following Trading under a Regime Switching Model <=SEP=> This paper is concerned with the optimality of a trend following trading rule. The idea is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. We characterize the bull and bear phases of the markets mathematically using the conditional probabilities of the bull market given the up to date stock prices. The optimal buying and selling times are given in terms of a sequence of stopping times determined by two threshold curves. Numerical experiments are conducted to validate the theoretical results and demonstrate how they perform in a marketplace.

TASK Channels on Basal Forebrain Cholinergic Neurons Modulate Electrocortical Signatures of Arousal by Histamine. <=SEP=> UNLABELLED
Basal forebrain cholinergic neurons are the main source of cortical acetylcholine, and their activation by histamine elicits cortical arousal. TWIK-like acid-sensitive K(+) (TASK) channels modulate neuronal excitability and are expressed on basal forebrain cholinergic neurons, but the role of TASK channels in the histamine-basal forebrain cholinergic arousal circuit is unknown. We first expressed TASK channel subunits and histamine Type 1 receptors in HEK cells. Application of histamine in vitro inhibited the acid-sensitive K(+) current, indicating a functionally coupled signaling mechanism. We then studied the role of TASK channels in modulating electrocortical activity in vivo using freely behaving wild-type (n = 12) and ChAT-Cre:TASK(f/f) mice (n = 12), the latter lacking TASK-1/3 channels on cholinergic neurons. TASK channel deletion on cholinergic neurons significantly altered endogenous electroencephalogram oscillations in multiple frequency bands. We then identified the effect of TASK channel deletion during microperfusion of histamine into the basal forebrain. In non-rapid eye movement sleep, TASK channel deletion on cholinergic neurons significantly attenuated the histamine-induced increase in 30-50 Hz activity, consistent with TASK channels contributing to histamine action on basal forebrain cholinergic neurons. In contrast, during active wakefulness, histamine significantly increased 30-50 Hz activity in ChAT-Cre:TASK(f/f) mice but not wild-type mice, showing that the histamine response depended upon the prevailing cortical arousal state. In summary, we identify TASK channel modulation in response to histamine receptor activation in vitro, as well as a role of TASK channels on cholinergic neurons in modulating endogenous oscillations in the electroencephalogram and the electrocortical response to histamine at the basal forebrain in vivo.


SIGNIFICANCE STATEMENT
Attentive states and cognitive function are associated with the generation of γ EEG activity. Basal forebrain cholinergic neurons are important modulators of cortical arousal and γ activity, and in this study we investigated the mechanism by which these neurons are activated by the wake-active neurotransmitter histamine. We found that histamine inhibited a class of K(+) leak channels called TASK channels and that deletion of TASK channels selectively on cholinergic neurons modulated baseline EEG activity as well as histamine-induced changes in γ activity. By identifying a discrete brain circuit where TASK channels can influence γ activity, these results represent new knowledge that enhances our understanding of how subcortical arousal systems may contribute to the generation of attentive states.

Separate face and body selectivity on the fusiform gyrus. <=SEP=> Recent reports of a high response to bodies in the fusiform face area (FFA) challenge the idea that the FFA is exclusively selective for face stimuli. We examined this claim by conducting a functional magnetic resonance imaging experiment at both standard (3.125 x 3.125 x 4.0 mm) and high resolution (1.4 x 1.4 x 2.0 mm). In both experiments, regions of interest (ROIs) were defined using data from blocked localizer runs. Within each ROI, we measured the mean peak response to a variety of stimulus types in independent data from a subsequent event-related experiment. Our localizer scans identified a fusiform body area (FBA), a body-selective region reported recently by Peelen and Downing (2005) that is anatomically distinct from the extrastriate body area. The FBA overlapped with and was adjacent to the FFA in all but two participants. Selectivity of the FFA to faces and FBA to bodies was stronger for the high-resolution scans, as expected from the reduction in partial volume effects. When new ROIs were constructed for the high-resolution experiment by omitting the voxels showing overlapping selectivity for both bodies and faces in the localizer scans, the resulting FFA* ROI showed no response above control objects for body stimuli, and the FBA* ROI showed no response above control objects for face stimuli. These results demonstrate strong selectivities in distinct but adjacent regions in the fusiform gyrus for only faces in one region (the FFA*) and only bodies in the other (the FBA*).

Impedance-based fault location in transmission networks: theory and application <=SEP=> A number of impedance-based fault location algorithms have been developed for estimating the distance to faults in a transmission network. Each algorithm has specific input data requirements and makes certain assumptions that may or may not hold true in a particular fault location scenario. Without a detailed understanding of the principle of each fault-locating method, choosing the most suitable fault location algorithm can be a challenging task. This paper, therefore, presents the theory of one-ended (simple reactance, Takagi, modified Takagi, Eriksson, and Novosel et al.) and two-ended (synchronized, unsynchronized, and current-only) impedance-based fault location algorithms and demonstrates their application in locating real-world faults. The theory details the formulation and input data requirement of each fault-locating algorithm and evaluates the sensitivity of each to the following error sources: 1) load; 2) remote infeed; 3) fault resistance; 4) mutual coupling; 5) inaccurate line impedances; 6) DC offset and CT saturation; 7) three-terminal lines; and 8) tapped radial lines. From the theoretical analysis and field data testing, the following criteria are recommended for choosing the most suitable fault-locating algorithm: 1) data availability and 2) fault location application scenario. Another objective of this paper is to assess what additional information can be gleaned from waveforms recorded by intelligent electronic devices (IEDs) during a fault. Actual fault event data captured in utility networks is exploited to gain valuable feedback about the transmission network upstream from the IED device, and estimate the value of fault resistance.

Promoting physical activity and health literacy: study protocol for a longitudinal, mixed methods evaluation of a cross-provider workplace-related intervention in Germany (The AtRisk study) <=SEP=> BACKGROUND
Physical activity and health literacy are topics of utmost importance in the prevention of chronic diseases. The present article describes the study protocol for evaluating a cross-provider workplace-related intervention promoting physical activity and health literacy.


METHODS
The RE-AIM Framework will be the conceptual framework of the AtRisk study. A controlled natural experiment and a qualitative study will be conducted. The cross-provider intervention is based on the cooperation of the German Pension Fund Rhineland and cooperating German Statutory Health Insurances. It combines two components: a behavior-oriented lifestyle intervention and the assignment of a health coach. The single-provider intervention only includes the behavior-oriented lifestyle intervention. The quantitative study (natural experiment) encompasses three measuring points (T0 = start of the behavior-oriented lifestyle intervention (baseline); T1 = end of the behavior-oriented lifestyle intervention (16 weeks); T2 = 6 month follow-up) and will compare the effectiveness of the cross-provider workplace-related intervention compared with the single provider intervention. Participants are employees with health related risk factors. ANCOVA will be used to evaluate the effect of the intervention on the outcome variables leisure time physical (primary outcome) activity and health literacy (secondary outcome). The qualitative study comprises semi-structured interviews, systematic field notes of stakeholder meetings and document analyses.


DISCUSSION
The AtRisk study will contribute towards the claim for cross-provider interventions and workplace-related approaches described in the new Preventive Health Care Act. The results of this study will inform providers, payers and policy makers about the effectiveness of a cross-provider workplace-related lifestyle intervention compared to a single-provider intervention. Beyond, the study will identify challenges for implementing cross-provider preventive interventions. With respect to the sustainability of preventive interventions the AtRisk study will give insight in the expectations and needs on health coaching from the perspective of different stakeholders.


TRIAL REGISTRATION
DRKS00010693 .

Radio resource allocation for non-orthogonal multiple access (NOMA) relay network using matching game <=SEP=> In this paper, we study the resource allocation problem for a single-cell non-orthogonal multiple access (NOMA) relay network where an OFDM amplify-and-forward (AF) relay allocates the spectrum and power resources to the source-destination (SD) pairs. We aim to optimize the spectrum and power resource allocation to maximize the total sum-rate. This is a very complicated problem and the optimal approach requires an exhaustive search, leading to a NP hard problem. To solve this problem, we propose an efficient many-to-many two sided SD pair-subchannel matching algorithm in which the SD pairs and sub-channels are considered as two sets of rational and selfish players chasing their own interests. The algorithm converges to a pair-wise stable matching after a limited number of iterations with a low complexity compared with the optimal solution. Simulation results show that the sum-rate of the proposed algorithm approaches the performance of the optimal exhaustive search and significantly outperforms the conventional orthogonal multiple access scheme, in terms of the total sum-rate and number of accessed SD pairs.

CA-LSTM: Search Task Identification with Context Attention based LSTM <=SEP=> Search task identification aims to understand a user's information needs to improve search quality for applications such as query suggestion, personalized search, and advertisement retrieval. To properly identify the search task within long query sessions, it is important to partition these sessions into segments before further processing. In this paper, we present the first search session segmentation model that uses a long short-term memory (LSTM) network with an attention mechanism. This model considers sequential context knowledge, including temporal information, word and character, and essentially learns which parts of the input query sequence are relevant to the current segmentation task and determines the influence of these parts. This segmentation technique is also combined with an efficient clustering method using a novel query relevance metric for end-to-end search task identification. Using real-world datasets, we demonstrate that our segmentation technique improves task identification accuracy of existing clustering-based algorithms.

StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks <=SEP=> Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto- image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256.256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.

Digital Games in Education: The Design of Games-Based Learning Environments. <=SEP=> In recentyears, electronicgames have assumed an importantplace in the lives of children and adolescents. Children acquire digital literacy informally, through plaj and neither schools nor other educational institutions take suficient account of this important aspect. We consider that multimedia design for training and education should combine the mostpowerfulfedtures of interactive multimedia design with the most effective principles of technologically-mediated learning. An examination of the evolution of the design of videogames is a good way to analyze the main contributions and characteristics ofgames-ba~edlearnin~ environments. At the same time, we will discuss the main obstacles and challenges to the use ofgames for learning. (

Efficient OFDMA for LiFi Downlink <=SEP=> Light-fidelity (LiFi) relies on visible light communication to provide multiuser access in a small cell. For higher throughput, this paper studies the resource allocation problem for an orthogonal frequency-division multiplexing access scheme for LiFi downlink transmissions and presents a joint design of the bias level, power and subcarrier allocation. Decomposing the original optimization into subproblems, we first develop an efficient algorithm for the bias optimization, along with its performance analysis in terms of convergence properties and the global optimality. We then propose several power and subcarrier allocation strategies, including an optimal algorithm and a near-optimal simplified method, to satisfy different complexity and accuracy requirements. Finally, we present two algorithms to jointly optimize the bias, power, and subcarrier, and analyze the convergence properties in terms of both the objective and the solution. Comprehensive simulation results under practical LiFi system setups illustrate the effectiveness of the proposed algorithms.

Advances in deep neural network approaches to speaker recognition <=SEP=> The recent application of deep neural networks (DNN) to speaker identification (SID) has resulted in significant improvements over current state-of-the-art on telephone speech. In this work, we report a similar achievement in DNN-based SID performance on microphone speech. We consider two approaches to DNN-based SID: one that uses the DNN to extract features, and another that uses the DNN during feature modeling. Modeling is conducted using the DNN/i-vector framework, in which the traditional universal background model is replaced with a DNN. The recently proposed use of bottleneck features extracted from a DNN is also evaluated. Systems are first compared with a conventional universal background model (UBM) Gaussian mixture model (GMM) i-vector system on the clean conditions of the NIST 2012 speaker recognition evaluation corpus, where a lack of robustness to microphone speech is found. Several methods of DNN feature processing are then applied to bring significantly greater robustness to microphone speech. To direct future research, the DNN-based systems are also evaluated in the context of audio degradations including noise and reverberation.

Developmental deficits in social perception in autism: the role of the amygdala and fusiform face area <=SEP=> Autism is a severe developmental disorder marked by a triad of deficits, including impairments in reciprocal social interaction, delays in early language and communication, and the presence of restrictive, repetitive and stereotyped behaviors. In this review, it is argued that the search for the neurobiological bases of the autism spectrum disorders should focus on the social deficits, as they alone are specific to autism and they are likely to be most informative with respect to modeling the pathophysiology of the disorder. Many recent studies have documented the difficulties persons with an autism spectrum disorder have accurately perceiving facial identity and facial expressions. This behavioral literature on face perception abnormalities in autism is reviewed and integrated with the functional magnetic resonance imaging (fMRI) literature in this area, and a heuristic model of the pathophysiology of autism is presented. This model posits an early developmental failure in autism involving the amygdala, with a cascading influence on the development of cortical areas that mediate social perception in the visual domain, specifically the fusiform "face area" of the ventral temporal lobe. Moreover, there are now some provocative data to suggest that visual perceptual areas of the ventral temporal pathway are also involved in important ways in representations of the semantic attributes of people, social knowledge and social cognition. Social perception and social cognition are postulated as normally linked during development such that growth in social perceptual skills during childhood provides important scaffolding for social skill development. It is argued that the development of face perception and social cognitive skills are supported by the amygdala-fusiform system, and that deficits in this network are instrumental in causing autism.

Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences <=SEP=> With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to answer questions like 'What outfit goes well with this pair of shoes?' To answer these types of questions, one has to go beyond learning visual similarity and learn a visual notion of compatibility across categories. In this paper, we propose a novel learning framework to help answer these types of questions. The main idea of this framework is to learn a feature transformation from images of items into a latent space that expresses compatibility. For the feature transformation, we use a Siamese Convolutional Neural Network (CNN) architecture, where training examples are pairs of items that are either compatible or incompatible. We model compatibility based on co-occurrence in large-scale user behavior data, in particular co-purchase data from Amazon.com. To learn cross-category fit, we introduce a strategic method to sample training data, where pairs of items are heterogeneous dyads, i.e., the two elements of a pair belong to different high-level categories. While this approach is applicable to a wide variety of settings, we focus on the representative problem of learning compatible clothing style. Our results indicate that the proposed framework is capable of learning semantic information about visual style and is able to generate outfits of clothes, with items from different categories, that go well together.

ERP Event Log Preprocessing: Timestamps vs. Accounting Logic <=SEP=> Process mining has been gaining significant attention in academia and practice. A promising first step to apply process mining in the audit domain was taken with the mining of process instances from accounting data. However, the resulting process instances constitute graphs. Commonly, timestamp oriented event log formats require a sequential list of activities and do not support graph structures. Thus, event log based process mining techniques cannot readily be applied to accounting data. To close this gap, we present an algorithm that determines an activity sequence from accounting data. With this algorithm, mined process instance graphs can be decomposed in a way they fit into sequential event log formats. Event log based process mining techniques can then be used to construct process models. A case study demonstrates the effectiveness of the presented approach. Results reveal that the preprocessing of the event logs considerably improves the derived process models.

The activation of attentional networks <=SEP=> Alerting, orienting, and executive control are widely thought to be relatively independent aspects of attention that are linked to separable brain regions. However, neuroimaging studies have yet to examine evidence for the anatomical separability of these three aspects of attention in the same subjects performing the same task. The attention network test (ANT) examines the effects of cues and targets within a single reaction time task to provide a means of exploring the efficiency of the alerting, orienting, and executive control networks involved in attention. It also provides an opportunity to examine the brain activity of these three networks as they operate in a single integrated task. We used event-related functional magnetic resonance imaging (fMRI) to explore the brain areas involved in the three attention systems targeted by the ANT. The alerting contrast showed strong thalamic involvement and activation of anterior and posterior cortical sites. As expected, the orienting contrast activated parietal sites and frontal eye fields. The executive control network contrast showed activation of the anterior cingulate along with several other brain areas. With some exceptions, activation patterns of these three networks within this single task are consistent with previous fMRI studies that have been studied in separate tasks. Overall, the fMRI results suggest that the functional contrasts within this single task differentially activate three separable anatomical networks related to the components of attention.

A new 2D static hand gesture colour image dataset for ASL gestures <=SEP=> It usually takes a fusion of image processing and machine learning algorithms in order to build a fully-functioning computer vision system for hand gesture recognition. Fortunately, the complexity of developing such a system could be alleviated by treating the system as a collection of multiple sub-systems working together, in such a way that they can be dealt with in isolation. Machine learning need to feed on thousands of exemplars (e.g. images, features) to automatically establish some recognisable patterns for all possible classes (e.g. hand gestures) that applies to the problem domain. A good number of exemplars helps, but it is also important to note that the efficacy of these exemplars depends on the variability of illumination conditions, hand postures, angles of rotation, scaling and on the number of volunteers from whom the hand gesture images were taken. These exemplars are usually subjected to image processing first, to reduce the presence of noise and extract the important features from the images. These features serve as inputs to the machine learning system. Different sub-systems are integrated together to form a complete computer vision system for gesture recognition. The main contribution of this work is on the production of the exemplars. We discuss how a dataset of standard American Sign Language (ASL) hand gestures containing 2425 images from 5 individuals, with variations in lighting conditions and hand postures is generated with the aid of image processing techniques. A minor contribution is given in the form of a specific feature extraction method called moment invariants, for which the computation method and the values are furnished with the dataset.

TechMiner: Extracting Technologies from Academic Publications <=SEP=> In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision.

Explicit Modelling of the Implicit Short Term User Preferences for Music Recommendation <=SEP=> Recommender systems are a key component of music sharing platforms, which suggest musical recordings a user might like. People often have implicit preferences while listening to music, though these preferences might not always be the same while they listen to music at different times. For example, a user might be interested in listening to songs of only a particular artist at some time, and the same user might be interested in the top-rated songs of a genre at another time. In this paper we try to explicitly model the short term preferences of the user with the help of Last.fm tags of the songs the user has listened to. With a session defined as a period of activity surrounded by periods of inactivity, we introduce the concept of a subsession, which is that part of the session wherein the preference of the user does not change much. We assume the user preference might change within a session and a session might have multiple subsessions. We use our modelling of the user preferences to generate recommendations for the next song the user might listen to. Experiments on the user listening histories taken from Last.fm indicate that this approach beats the present methodologies in predicting the next recording a user might listen to.

Improving Relation Extraction by Pre-trained Language Representations <=SEP=> Current state-of-the-art relation extraction methods typically rely on a set of lexical, syntactic, and semantic features, explicitly computed in a pre-processing step. Training feature extraction models requires additional annotated language resources, which severely restricts the applicability and portability of relation extraction to novel languages. Similarly, pre-processing introduces an additional source of error. To address these limitations, we introduce TRE, a Transformer for Relation Extraction. Unlike previous relation extraction models, TRE uses pre-trained deep language representations instead of explicit linguistic features to inform the relation classification and combines it with the self-attentive Transformer architecture to effectively model long-range dependencies between entity mentions. TRE allows us to learn implicit linguistic features solely from plain text corpora by unsupervised pre-training, before fine-tuning the learned language representations on the relation extraction task. TRE obtains a new state-of-the-art result on the TACRED and SemEval 2010 Task 8 datasets, achieving a test F1 of 67.4 and 87.1, respectively. Furthermore, we observe a significant increase in sample efficiency. With only 20% of the training examples, TRE matches the performance of our baselines and our model trained from scratch on 100% of the TACRED dataset. We open-source our trained models, experiments, and source code.

Ring-Shaped Substrate Integrated Waveguide Wilkinson Power Dividers/Combiners <=SEP=> This paper proposes and presents a class of substrate integrated waveguide (SIW) power dividers/combiners based on a modified Wilkinson architecture. The proposed design consists of a ring-type circuit of 1.5 λg length with three waveguide ports. Impedance variation without affecting cutoff frequency and integration of isolation resistance are the two major limitations in connection with the design of SIW Wilkinson structures. Central ring part with different thickness ensures different impedance in the first proposed scheme while the half-mode SIW in the second option. As for the second limitation, the optimal configuration of integrating the isolation resistance is studied. A version without resistance is also examined using a broad-wall series slot. To validate the proposed concept, a bilayered circuit with standard Printed circuit board prototype was fabricated and measured. A table summarizing performances of all the proposed circuits shows a bandwidth of around 25% at the 10 dB reference. A negligible additional insertion loss is observed. The proposed design techniques are found useful at millimeter-wave frequencies where reduced physical dimensions make a circuit configuration suitable for low-cost implementation. This concept is also valid for rectangular waveguide counterparts.

Municipal Solid Waste Management Problems in Nigeria : Evolving Knowledge Management Solution <=SEP=> The paper attempts a synthesis of problems relating to municipal waste management in Nigeria and proposes a conceptual knowledge management approach for tackling municipal waste problems in cities across Nigeria. The application of knowledge management approach and strategy is crucial for inculcating a change of attitude towards improving the management of waste. The paper is a review of existing literatures, information, policies and data on municipal waste management in Nigeria. The inefficient management of waste by individuals, households, consumers and waste management companies can be attributed to inadequate information on waste management benefits, lack of producers’ involvement in waste management as well as poor implementation of government policies. The paper presents an alternative approach providing solutions promoting efficient municipal waste management. Keywords—Environment, Knowledge management, Municipal waste management, Nigeria.

Variable-On-Time-Controlled Critical-Conduction-Mode Flyback PFC Converter <=SEP=> A variable on-time (VOT) control strategy for a critical conduction mode (CRM) flyback power factor correction (PFC) converter with low total harmonic distortion (THD) and high power factor (PF) is proposed in this paper. By using input voltage and the voltage across the auxiliary winding of the flyback transformer to modulate the turn-on time of the switch of the CRM flyback PFC converter, the PF and THD of the converter can be improved. The operation principles of the traditional constant on-time (COT)-controlled CRM flyback PFC converter and VOT-controlled CRM flyback PFC converter are analyzed. The experimental results show that the PF and THD of the VOT-controlled CRM flyback PFC converter are better than those of the COT-controlled CRM flyback PFC converter.

A framework of traveling companion discovery on trajectory data streams <=SEP=> The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data streams. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management, and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from trajectory streams. The traveling buddies are microgroups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along the trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. In addition, we extend the proposed framework to discover companions on more complicated scenarios with spatial and temporal constraints, such as on the road network and battlefield. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. Experimental results show that our proposed buddy-based approach is an order of magnitude faster than the baselines and achieves higher accuracy in companion discovery.

Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities <=SEP=> Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imaging modalities. In this paper, we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks. A single CNN is trained to segment six tissues in MR brain images, the pectoral muscle in MR breast images, and the coronary arteries in cardiac CTA. The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes. For each of the three tasks (brain MRI, breast MRI and cardiac CTA), this combined training procedure resulted in a segmentation performance equivalent to that of a CNN trained specifically for that task, demonstrating the high capacity of CNN architectures. Hence, a single system could be used in clinical practice to automatically perform diverse segmentation tasks without task-specific training.