title: The hydrodynamics of water strider locomotion

abstract: Water striders Gerridae are insects of characteristic length 1 cm and weight 10 dynes that reside on the surface of ponds, rivers, and the open ocean. Their weight is supported by the surface tension force generated by curvature of the free surface, and they propel themselves by driving their central pair of hydrophobic legs in a sculling motion. Previous investigators have assumed that the hydrodynamic propulsion of the water strider relies on momentum transfer by surface waves. This assumption leads to Denny's paradox: infant water striders, whose legs are too slow to generate waves, should be incapable of propelling themselves along the surface. We here resolve this paradox through reporting the results of high-speed video and particle-tracking studies. Experiments reveal that the strider transfers momentum to the underlying fluid not primarily through capillary waves, but rather through hemispherical vortices shed by its driving legs. This insight guided us in constructing a self-contained mechanical water strider whose means of propulsion is analogous to that of its natural counterpart.


title: Low Rank Regularization : a review

abstract: Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many fields including machine learning, data mining and computer version. Over the last decade, much progress has been made in theories and practical applications. Nevertheless, the intersection between them is very slight. In order to construct a bridge between practical applications and theoretical research, in this paper we provide a comprehensive survey for low rank regularization. We first review several traditional machine learning models using low rank regularization, and then show their (or their variants) applications in solving practical issues, such as non-rigid structure from motion and image denoising. Subsequently, we summarize the regularizers and optimization methods that achieve great success in traditional machine learning tasks but are rarely seen in solving practical issues. Finally, we provide a discussion and comparison for some representative regularizers including convex and non-convex relaxations. Extensive experimental results demonstrate that non-convex regularizers can provide a large advantage over the nuclear norm, the regularizer widely used in solving practical issues.


title: Similarity-based prediction of travel times for vehicles traveling on known routes

abstract: The use of centralized, real-time position tracking is proliferating in the areas of logistics and public transportation. Real-time positions can be used to provide up-to-date information to a variety of users, and they can also be accumulated for uses in subsequent data analyses. In particular, historical data in combination with real-time data may be used to predict the future travel times of vehicles more accurately, thus improving the experience of the users who rely on such information. We propose a Nearest-Neighbor Trajectory (NNT) technique that identifies the historical trajectory that is the most similar to the current, partial trajectory of a vehicle. The historical trajectory is then used for predicting the future movement of the vehicle. The paper's specific contributions are two-fold. First, we define distance measures and a notion of nearest neighbor that are specific to trajectories of vehicles that travel along known routes. In empirical studies with real data from buses, we evaluate how well the proposed distance functions are capable of predicting future vehicle movements. Second, we propose a main-memory index structure that enables incremental similarity search and that is capable of supporting varying-length nearest neighbor queries.


title: When to stop?: towards universal instructional policies

abstract: The adaptivity of intelligent tutoring systems relies on the accuracy of the student model and the design of the instructional policy. Recently an instructional policy has been presented that is compatible with all common student models. In this work we present the next step towards a universal instructional policy. We introduce a new policy that is applicable to an even wider range of student models including DBNs modeling skill topologies and forgetting. We theoretically and empirically compare our policy to previous policies. Using synthetic and real world data sets we show that our policy can effectively handle wheel-spinning students as well as forgetting across a wide range of student models.


ModelHub: Lifecycle Management for Deep Learning


title: CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling

abstract: In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing language generation techniques are usually based on recurrent neural networks (RNNs). However, it is non-trivial to impose constraints on RNNs while maintaining generation quality, since RNNs generate sentences sequentially (or with beam search) from the first word to the last. In this paper, we propose CGMH, a novel approach using Metropolis-Hastings sampling for constrained sentence generation. CGMH allows complicated constraints such as the occurrence of multiple keywords in the target sentences, which cannot be handled in traditional RNN-based approaches. Moreover, CGMH works in the inference stage, and does not require parallel corpora for training. We evaluate our method on a variety of tasks, including keywords-to-sentence generation, unsupervised sentence paraphrasing, and unsupervised sentence error correction. CGMH achieves high performance compared with previous supervised methods for sentence generation. Our code is released at https://github.com/NingMiao/CGMH


title: Multi-Agent Imitation Learning for Driving Simulation

abstract: Simulation is an appealing option for validating the safety of autonomous vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative human driver models. These human driver models were learned through training in single-agent environments, but they have difficulty in generalizing to multi-agent driving scenarios. We argue these difficulties arise because observations at training and test time are sampled from different distributions. This difference makes such models unsuitable for the simulation of driving scenes, where multiple agents must interact realistically over long time horizons. We extend GAIL to address these shortcomings through a parameter-sharing approach grounded in curriculum learning. Compared with single-agent GAIL policies, policies generated by our PS-GAIL method prove superior at interacting stably in a multi-agent setting and capturing the emergent behavior of human drivers.


title: Light intensity and photoperiod influence the growth and development of hydroponically grown leaf lettuce in a closed-type plant factory system

abstract: Effect of light provided by various light intensities combined with different photoperiods on the growth and morphogenesis of lettuce (Lactuca sativa L.) ‘Hongyeom Jeockchukmyeon’ in a closed-type plant factory system were evaluated in this study. Four light intensity treatments, i.e., 200, 230, 260, and 290 μmol·m−2·s−1 PPFD, provided from light-emitting diodes (LEDs), with a combination of three different photoperiods 18/6 (1 cycle), 9/3 (2 cycles) or 6/2 (3 cycles) (light/dark) were used. The combination of 290-9/3 (light intensity-photoperiod) showed the highest plant height and fresh shoot weight, while plants grown at 290-18/6 exhibited the greatest root fresh weight, leaf dry weight, and longest root length. The greatest leaf width, maximum number of leaves, and greatest root dry weight were observed in the treatment combination of 290-6/2. Anthocyanin content was found to be highest in the 290-6/2 and lowest in the 200-6/2 treatment, whereas chlorophyll fluorescence was observed to be highest in the 260-6/2 and the lowest in the 290-9/3 treatment. Our data showed that providing a high light intensity of 290 μmol·m−2·s−1 PPFD with a shorter photoperiod of 6/2 (light/dark) resulted in good plant growth and development of lettuce, whereas growth at light intensities of 230 or 260 μmol·m−2·s−1 PPFD with longer photoperiods of 18/6 and 9/3 (light/dark) resulted in good growth as well as higher photosynthetic capacity.


title: Supporting Comment Moderators in Identifying High Quality Online News Comments

abstract: Online comments submitted by readers of news articles can provide valuable feedback and critique, personal views and perspectives, and opportunities for discussion. The varying quality of these comments necessitates that publishers remove the low quality ones, but there is also a growing awareness that by identifying and highlighting high quality contributions this can promote the general quality of the community. In this paper we take a user-centered design approach towards developing a system, CommentIQ, which supports comment moderators in interactively identifying high quality comments using a combination of comment analytic scores as well as visualizations and flexible UI components. We evaluated this system with professional comment moderators working at local and national news outlets and provide insights into the utility and appropriateness of features for journalistic tasks, as well as how the system may enable or transform journalistic practices around online comments.


title: Real-Time Processing of Contrast Limited Adaptive Histogram Equalization on FPGA

abstract: Contrast limited adaptive histogram equalization (CLAHE) is a technique to enhance the visibility of local details of an image by increasing the contrast in local regions. In CLAHE, the enhancement is controlled to avoid excess amplification of the noises in the local regions. CLAHE computes several histograms of intensity values, each corresponding to a distinct region of the image, distributes the histograms to avoid the excess amplification, and remaps the intensity values using the distributed histograms. In this paper, we propose an approach for real-time computation of CLAHE using an FPGA. In our system, (1) a histogram is generated for each pixel in a image, and the intensity of only the pixel is remapped using the histogram to generate smooth images, and (2) each histogram is speculatively distributed without iterations by feeding back the distribution result of its previous pixel. With this approach, we can achieve real-time processing of HD images.


title: Bias , optimal linear estimation , and the differences between open-loop simulation and closed-loop performance of spiking-based brain – computer interface algorithms

abstract: The activity of dozens of simultaneously recorded neurons can be used to control the movement of a robotic arm or a cursor on a computer screen. This motor neural prosthetic technology has spurred an increased interest in the algorithms by which motor intention can be inferred. The simplest of these algorithms is the population vector algorithm (PVA), where the activity of each cell is used to weight a vector pointing in that neuron's preferred direction. Off-line, it is possible to show that more complicated algorithms, such as the optimal linear estimator (OLE), can yield substantial improvements in the accuracy of reconstructed hand movements over the PVA. We call this open-loop performance. In contrast, this performance difference may not be present in closed-loop, on-line control. The obvious difference between open and closed-loop control is the ability to adapt to the specifics of the decoder in use at the time. In order to predict performance gains that an algorithm may yield in closed-loop control, it is necessary to build a model that captures aspects of this adaptation process. Here we present a framework for modeling the closed-loop performance of the PVA and the OLE. Using both simulations and experiments, we show that (1) the performance gain with certain decoders can be far less extreme than predicted by off-line results, (2) that subjects are able to compensate for certain types of bias in decoders, and (3) that care must be taken to ensure that estimation error does not degrade the performance of theoretically optimal decoders.


title: Employees Motivation in Organizations: An integrative literature review

abstract: Aim: The purpose of this paper is to present findings of an integrative literature review related to employees' motivational practices in organizations. Method: A broad search of computerized databases focusing on articles published in English during 1999- 2010 was completed. Extensive screening sought to determine current literature themes and empirical research evidence completed in employees' focused specifically on motivation in organization. Results: 40 articles are included in this integrative literature review. The literature focuses on how job characteristics, employee characteristic, management practices and broader environmental factors influence employees' motivation. Research that links employee's motivation is both based on qualitative and quantitative studies. Conclusion: This literature reveals widespread support of motivation concepts in organizations. Theoretical and editorial literature confirms motivation concepts are central to employees. Job characteristics, management practices, employee characteristics and broader environmental factors are the key variables influence employees' motivation in organization.


title: "Picture the scene...";: Visually Summarising Social Media Events

abstract: Due to the advent of social media and web 2.0, we are faced with a deluge of information; recently, research efforts have focused on filtering out noisy, irrelevant information items from social media streams and in particular have attempted to automatically identify and summarise events. However, due to the heterogeneous nature of such social media streams, these efforts have not reached fruition. In this paper, we investigate how images can be used as a source for summarising events. Existing approaches have considered only textual summaries which are often poorly written, in a different language and slow to digest. Alternatively, images are "worth 1,000 words" and are able to quickly and easily convey an idea or scene. Since images in social media can also be noisy, irrelevant and repetitive, we propose new techniques for their automatic selection, ranking and presentation. We evaluate our approach on a recently created social media event data set containing 365k tweets and 50 events, for which we extend by collecting 625k related images. By conducting two crowdsourced evaluations, we firstly show how our approach overcomes the problems of automatically collecting relevant and diverse images from noisy microblog data, before highlighting the advantages of multimedia summarisation over text based approaches.


title: Freight Transport Restructuring in the Baltic States

abstract: A brief outline of the restructuring of the freight transport sector in the three Baltic States (Estonia, Latvia, and Lithuania) is provided that focuses on the provision of logistics and freight-forwarding services in international transport and trade. The three countries have made significant progress in all areas of the transport sector in the past 10 years. During this period the private sector has taken over a large part of the transport sector. Despite the very positive development, continued efforts are needed to improve the transport sector further. Issues pertaining to national legislation are recognized to be of high priority in all three countries, but cooperation in customs issues between countries of the Commonwealth of Independent States is problematic. Also delays in border crossings, especially to and from Russia and Belarus, appear to be a common and persistent problem in the Baltic States. The general transport-supporting services and the performance of customs have improved substantially during the past decade. Entry of foreign operators, especially in international road-based transport and logistics services, has restructured the logistics market profoundly. It can be argued that the efficiency of the industry and the overall quality of services have improved substantially.


title: Bilateral congenital choanal atresia and osteoma of ethmoid sinus with supernumerary nostril: a case report and review of the literature

abstract: IntroductionCongenital choanal atresia is a relatively rare deformity, especially bilateral congenital choanal atresia. We report a case of bilateral congenital choanal atresia in a 22-year-old Chinese man, who was also diagnosed with congenital right accessory nasal deformity, osteoma of his left ethmoid sinus and congenital keratoleukoma of his right eye.Case presentationA 22-year-old Chinese man presented with mouth breathing, sleep snoring and difficult feeding after birth, with no olfactory sensation. Three-dimensional computed tomography revealed bilateral choanal atresia and a high density bony shadow in his left ethmoid sinus that extended to his left frontal sinus.ConclusionsChoanal atresia is often accompanied by other congenital abnormalities. To the best of our knowledge, this is the first report of choanal atresia accompanied by congenital accessory nasal deformity and congenital keratoleukoma.


title: Analysis of rotor eddy current losses in slotless high-speed permanent magnet machines

abstract: Rotor eddy current losses are one of the main reasons of permanent magnet demagnetization in high-speed permanent magnet machines. In this paper the rotor eddy current losses of high-speed permanent magnet machines with different slotless windings have been analysed. The analysis of the losses was performed using 2D and 3D analytical models. In the study, test machines with different windings and the same torque production capability have been analysed. Presented paper shows the dependency of rotor eddy current losses on sine- and square-wave PWM supply voltages and rotor sleeve properties. Several recommendations for reduction of rotor eddy current losses in high-speed permanent magnet machines are given.


title: Backdoor attacks against learning systems

abstract: Many of today's machine learning (ML) systems are composed by an array of primitive learning modules (PLMs). The heavy use of PLMs significantly simplifies and expedites the system development cycles. However, as most PLMs are contributed and maintained by third parties, their lack of standardization or regulation entails profound security implications. In this paper, for the first time, we demonstrate that potentially harmful PLMs incur immense threats to the security of ML-powered systems. We present a general class of backdoor attacks in which maliciously crafted PLMs trigger host systems to malfunction in a predictable manner once predefined conditions are present. We validate the feasibility of such attacks by empirically investigating a state-of-the-art skin cancer screening system. For example, it proves highly probable to force the system to misdiagnose a targeted victim, without any prior knowledge about how the system is built or trained. Further, we discuss the root causes behind the success of PLM-based attacks, which point to the characteristics of today's ML models: high dimensionality, non-linearity, and non-convexity. Therefore, the issue seems industry-wide.


title: The Noise Power Ratio - Theory and ADC Testing

abstract: This paper develops the theory behind the noise power ratio (NPR) testing of analog-to-digital converters (ADC's). A mid-riser formulation is used for mathematical simplicity. Both simulated and measured results, using digital-to-analog converter (DAC) generated signals, suggests that the uniformly distributed signal is more sensitive to amplitude dependent distortion.


title: Evolution of Swarm Robotics Systems with Novelty Search

abstract: Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a predefined objective. The lack of a predefined objective precludes premature convergence caused by a deceptive fitness function. In this paper, we apply novelty search combined with NEAT to the evolution of neural controllers for homogeneous swarms of robots. Our empirical study is conducted in simulation, and we use a common swarm robotics task—aggregation, and a more challenging task—sharing of an energy recharging station. Our results show that novelty search is unaffected by deception, is notably effective in bootstrapping evolution, can find solutions with lower complexity than fitness-based evolution, and can find a broad diversity of solutions for the same task. Even in non-deceptive setups, novelty search achieves solution qualities similar to those obtained in traditional fitness-based evolution. Our study also encompasses variants of novelty search that work in concert with fitness-based evolution to combine the exploratory character of novelty search with the exploitatory character of objective-based evolution. We show that these variants can further improve the performance of novelty search. Overall, our study shows that novelty search is a promising alternative for the evolution of controllers for robotic swarms.


title: Approximation techniques for spatial data

abstract: Spatial Database Management Systems (SDBMS), e.g., Geographical Information Systems, that manage spatial objects such as points, lines, and hyper-rectangles, often have very high query processing costs. Accurate selectivity estimation during query optimization therefore is crucially important for finding good query plans, especially when spatial joins are involved. Selectivity estimation has been studied for relational database systems, but to date has only received little attention in SDBMS. In this paper, we introduce novel methods that permit high-quality selectivity estimation for spatial joins and range queries. Our techniques can be constructed in a single scan over the input, handle inserts and deletes to the database incrementally, and hence they can also be used for processing of streaming spatial data. In contrast to previous approaches, our techniques return approximate results that come with provable probabilistic quality guarantees. We present a detailed analysis and experimentally demonstrate the efficacy of the proposed techniques.


title: Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network

abstract: Human sensing enables intelligent vehicles to provide driver-adaptive support by classifying perceived workload into multiple levels. Objective of this study is to classify driver workload associated with traffic complexity into five levels. We conducted driving experiments in systematically varied traffic complexity levels in a simulator. We recorded driver physiological signals including electrocardiography, electrodermal activity, and electroencephalography. In addition, we integrated driver performance and subjective workload measures. Deep learning based models outperform statistical machine learning methods when dealing with dynamic time-series data with variable sequence lengths. We show that our long short-term memory based recurrent neural network model can classify driver perceived-workload into five classes with an accuracy of 74.5%. Since perceived workload differ between individual drivers for the same traffic situation, our results further highlight the significance of including driver characteristics such as driving style and workload sensitivity to achieve higher classification accuracy.


title: Your Sentiment Precedes You: Using an author's historical tweets to predict sarcasm

abstract: Sarcasm understanding may require information beyond the text itself, as in the case of ‘I absolutely love this restaurant!’ which may be sarcastic, depending on the contextual situation. We present the first quantitative evidence to show that historical tweets by an author can provide additional context for sarcasm detection. Our sarcasm detection approach uses two components: a contrast-based predictor (that identifies if there is a sentiment contrast within a target tweet), and a historical tweet-based predictor (that identifies if the sentiment expressed towards an entity in the target tweet agrees with sentiment expressed by the author towards that entity in the past).


title: Color Balance and Fusion for Underwater Image Enhancement

abstract: We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It builds on the blending of two images that are directly derived from a color-compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, we also adapt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.


title: Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network

abstract: Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus on addressing audio information only. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. In the proposed AVDCNN SE model, audio and visual data are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at the output layer. The AVDCNN model is trained in an end-to-end manner, and parameters are jointly learned through back-propagation. We evaluate enhanced speech using five objective criteria. Results show that the AVDCNN yields notably better performance, compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual information into the SE process.


title: Design of 56 Gb/s NRZ and PAM4 SerDes Transceivers in CMOS Technologies

abstract: This paper presents two ultra-high-speed SerDes dedicated for PAM4 and NRZ data. The PAM4 TX incorporates an output driver with 3-tap FFE and adjustable weighting to deliver clean outputs at 4 levels, and the PAM4 RX employs a purely linear full-rate CDR and CTLE/1-tap DFE combination to recover and demultiplex the data. NRZ TX includes a tree-structure MUX with built-in PLL and phase aligner. NRZ RX adopts linear PD with special vernier technique to handle the 56 Gb/s input data. All chips have been verified in silicon with reasonable performance, providing prospective design examples for next-generation 400 GbE.


title: Multi-objective optimization of an isoflux antenna for LEO satellite down-handling link

abstract: A multi-objective optimization approach has been applied to the synthesis problem of the geometrical structure of antenna with complex set of requirement specification. This methodology is a promising tool for new development and application for several kind of microwave structure.


title: Relational active learning for link-based classification

abstract: Many information tasks involve objects that are explicitly or implicitly connected in a network (or graph), such as webpages connected by hyperlinks or people linked by “friendships” in a social network. Research on link-based classification (LBC) has shown how to leverage these connections to improve classification accuracy. Unfortunately, acquiring a sufficient number of labeled examples to enable accurate learning for LBC can often be expensive or impractical. In response, some recent work has proposed the use of active learning, where the LBC method can intelligently select a limited set of additional labels to acquire, so as to reduce the overall cost of learning a model with sufficient accuracy. This work, however, has produced conflicting results and has not considered recent progress for LBC inference and semi-supervised learning. In this paper, we evaluate multiple prior methods for active learning and demonstrate that none consistently improve upon random guessing. We then introduce two new methods that both seek to improve active learning by leveraging the link structure to identify nodes to acquire that are more representative of the underlying data. We show that both approaches have some merit, but that one method, by proactively acquiring nodes so as to produce a more representative distribution of known labels, often leads to significant accuracy increases with minimal computational cost.


title: Reconfigurable context-sensitive middleware for pervasive computing

abstract: Context-sensitive applications need data from sensors, devices, and user actions, and might need ad hoc communication support to dynamically discover new devices and engage in spontaneous information exchange. Reconfigurable Context-Sensitive Middleware facilitates the development and runtime operations of context-sensitive pervasive computing software.


Bayesian Grasp Planning


title: Natural Steganography: cover-source switching for better steganography

abstract: This paper proposes a new steganographic scheme relying on the principle of " cover-source switching " , the key idea being that the embedding should switch from one cover-source to another. The proposed implementation, called Natural Steganography, considers the sensor noise naturally present in the raw images and uses the principle that, by the addition of a specific noise the steganographic embedding tries to mimic a change of ISO sensitivity. The embedding methodology consists in 1) perturbing the image in the raw domain, 2) modeling the perturbation in the processed domain, 3) embedding the payload in the processed domain. We show that this methodology is easily tractable whenever the processes are known and enables to embed large and undetectable payloads. We also show that already used heuristics such as synchronization of embedding changes or detectability after rescaling can be respectively explained by operations such as color demosaicing and down-scaling kernels.


Job Performance


title: Hominins on Flores, Indonesia, by one million years ago

abstract: Previous excavations at Mata Menge and Boa Lesa in the Soa Basin of Flores, Indonesia, recovered stone artefacts in association with fossilized remains of the large-bodied Stegodon florensis florensis. Zircon fission-track ages from these sites indicated that hominins had colonized the island by 0.88 +/- 0.07 million years (Myr) ago. Here we describe the contents, context and age of Wolo Sege, a recently discovered archaeological site in the Soa Basin that has in situ stone artefacts and that lies stratigraphically below Mata Menge and immediately above the basement breccias of the basin. We show using (40)Ar/(39)Ar dating that an ignimbrite overlying the artefact layers at Wolo Sege was erupted 1.02 +/- 0.02 Myr ago, providing a new minimum age for hominins on Flores. This predates the disappearance from the Soa Basin of 'pygmy' Stegodon sondaari and Geochelone spp. (giant tortoise), as evident at the nearby site of Tangi Talo, which has been dated to 0.90 +/- 0.07 Myr ago. It now seems that this extirpation or possible extinction event and the associated faunal turnover were the result of natural processes rather than the arrival of hominins. It also appears that the volcanic and fluvio-lacustrine deposits infilling the Soa Basin may not be old enough to register the initial arrival of hominins on the island.


title: Early Detection of Sudden Pedestrian Crossing for Safe Driving During Summer Nights

abstract: Sudden pedestrian crossing (SPC) is the major reason for pedestrian-vehicle crashes. In this paper, we focus on detecting SPCs at night for supporting an advanced driver assistance system using a far-infrared (FIR) camera mounted on the front roof of a vehicle. Although the thermal temperature of the road is similar to or higher than that of the pedestrians during summer nights, many previous researches have focused on pedestrian detection during the winter, spring, or autumn seasons. However, our research concentrates on SPC during the hot summer season because the number of collisions between pedestrians and vehicles in Korea is higher at that time than during the other seasons. For real-time processing, we first decide the optimal levels of image scaling and search area. We then use our proposed method for detecting virtual reference lines that are associated with road segmentation without using color information and change these lines according to the turning direction of the vehicle. Pedestrian detection is conducted using a cascade random forest with low-dimensional Haar-like features and oriented center-symmetric local binary patterns. The SPC is predicted based on the likelihood and the spatiotemporal features of the pedestrians, such as their overlapping ratio with virtual reference lines, as well as the direction and magnitude of each pedestrian’s movement. The proposed algorithm was successfully applied to various pedestrian data sets captured by an FIR camera, and the results show that its SPC detection performance is better than those of other methods.


title: MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

abstract: While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving. Towards this goal, we present an approach to joint classification, detection and semantic segmentation using a unified architecture where the encoder is shared amongst the three tasks. Our approach is very simple, can be trained end-to-end and performs extremely well in the challenging KITTI dataset. Our approach is also very efficient, allowing us to perform inference at more then 23 frames per second. Training scripts and trained weights to reproduce our results can be found here: https://github.com/MarvinTeichmann/MultiNet


title: Are augmented reality picture books magic or real for preschool children aged five to six?

abstract: The aim of this study is to determine preschool children's attitudes towards augmented reality picture books (ARPB), their story comprehension performance (SCP) and the relationships between these variables. The sample consisted of 92 five- and six-year-olds (49 boys, 43 girls). An attitude form, story comprehension test and interview form were used as data collection tools. Most of the children reported feeling 'very happy' with the activity and enjoyed using the ARPB, which they found interesting and fun. The children also delivered strong story SCP. Their happiness was found to affect their SCP, while their enjoyment did not. ARPB are attractive and evocative for children, who perceive them as magic and more enjoyable than conventional books. This study demonstrates how they can be used as effective educational tools to improve preschool children's cognition and listening skills. [ABSTRACT FROM AUTHOR]


title: Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising

abstract: In real-world situations, speech is masked by both background noise and reverberation, which negatively affect perceptual quality and intelligibility. In this paper, we address monaural speech separation in reverberant and noisy environments. We perform dereverberation and denoising using supervised learning with a deep neural network. Specifically, we enhance the magnitude and phase by performing separation with an estimate of the complex ideal ratio mask. We define the complex ideal ratio mask so that direct speech results after the mask is applied to reverberant and noisy speech. Our approach is evaluated using simulated and real room impulse responses, and with background noises. The proposed approach improves objective speech quality and intelligibility significantly. Evaluations and comparisons show that it outperforms related methods in many reverberant and noisy environments.


Honeypots : The Need of Network Security


Diagnosis of Bacterial Gastroenteritis


title: Associations between TCF4 Gene Polymorphism and Cognitive Functions in Schizophrenia Patients and Healthy Controls

abstract: The SNP rs2958182 was reported to be significantly associated with schizophrenia (SCZ) in Han Chinese. This study examined this SNP's associations with cognitive functions in 580 SCZ patients and 498 controls. Cognitive functions were assessed using the Wechsler Adult Intelligence Scale-Revised (WAIS-RC), the Attention Network Task (ANT), the Stroop task, the dot pattern expectancy (DPX), task and the N-back working memory task. Results showed significant or marginally significant interaction effects between genotype and diagnosis status on IQ (P=0.011) and attention-related tasks (ie, the forward digit span of WAIS-RC, P=0.005; the ANT conflict effect; P=0.020, and its ratios over mean reaction time (RT), P=0.036; the Stroop conflict effect, P=0.032, and its ratios over mean RT, P=0.062; and the DPX task's error rate under the BX condition, P<0.001, and the error rate of BX minus the error rate of AY (BX-AY), P=0.002). There were no such interaction effects on the measures of working memory (all P-values >0.05). Further analysis of the significant genotype-by-diagnosis interactions showed that the risk (T) allele was associated with better performance on cognitive tasks in patients but with worse performance in controls. These results seem to indicate that the association between this SNP and selected cognitive functions may be of an inverted U-shaped pattern. Future research is needed to replicate these results and to explore the biochemical mechanisms behind this association.


title: Simplified 3.3V tolerance circuit for 2.5V I/O design in PCI-X signaling environment

abstract: A low voltage interface circuit with a high voltage tolerance enables devices with different power supply levels to be efficiently coupled together without significant leakage current or damage to the circuits. The interface circuit includes an impedance control circuit, an output buffer, an input buffer, an isolation circuit and a pull up protection circuit. When a high voltage is applied to the IO pad, the pull-up protection circuit drives the gate of the pull up transistor to the high IO pad voltage to ensure that no current flows to the positive supply voltage. Also the isolation circuit couples the high IO pad voltage to the body of the pull-up transistor to prevent leakage current through parasitic diodes formed by the pull-up transistor.


title: Convexity in scientific collaboration networks

abstract: Abstract Convexity in a network (graph) has been recently defined as a property of each of its subgraphs to include all shortest paths between the nodes of that subgraph. It can be measured on the scale [0, 1] with 1 being assigned to fully convex networks. The largest convex component of a graph that emerges after the removal of the least number of edges is called a convex skeleton. It is basically a tree of cliques, which has been shown to have many interesting features. In this article the notions of convexity and convex skeletons in the context of scientific collaboration networks are discussed. More specifically, we analyze the co-authorship networks of Slovenian researchers in computer science, physics, sociology, mathematics, and economics and extract convex skeletons from them. We then compare these convex skeletons with the residual graphs (remainders) in terms of collaboration frequency distributions by various parameters such as the publication year and type, co-authors’ birth year, status, gender, discipline, etc. We also show the top-ranked scientists by four basic centrality measures as calculated on the original networks and their skeletons and conclude that convex skeletons may help detect influential scholars that are hardly identifiable in the original collaboration network. As their inherent feature, convex skeletons retain the properties of collaboration networks. These include high-level structural properties but also the fact that the same authors are highlighted by centrality measures. Moreover, the most important ties and thus the most important collaborations are retained in the skeletons.


title: Cloudlet deployment in local wireless networks: Motivation, architectures, applications, and open challenges

abstract: In past few years, advancement in mobile applications and their integration with Cloud computing services has introduced a new computing paradigm known as Mobile Cloud Computing. Although Cloud services support a wide range of mobile applications, access to these services suffers from several performance issues such as WAN latency, jitter, and packet losses. Cloudlet frameworks are proposed to overcome these performance issues. More specifically, Cloudlets aim to bring the Cloud or a specific part of the Cloud closer to the mobile device by utilizing proximate computing resources to perform compute-intensive tasks. This paper presents a comprehensive survey on the state-of-the-art mobile Cloudlet architectures. We also classify the state-of-the-art Cloudlet solutions by presenting a hierarchical taxonomy. Moreover, the areas of Cloudlet applications are also identified and presented. Cloudlet discovery, resource management, data security, mobility, application offloading, and most importantly incentives to deploy a Cloudlet are areas that still needs to be investigated by the research community. The critical aspects of the current Cloudlet frameworks in Mobile Cloud Computing are analyzed to determine the strengths and weaknesses of the frameworks. The similarities and differences of the frameworks based on the important parameters, such as scalability, mobility support, Internet dependency, dynamic configuration, energy savings, and execution cost, are also investigated. The requirements for deploying the Cloudlet in a Local Wireless Network are also highlighted and presented. We also discuss open research challenges that Cloudlet deployments face in Local Wireless Networks.


title: The coverage of Microsoft Academic: analyzing the publication output of a university

abstract: Abstract This is the first detailed study on the coverage of Microsoft Academic (MA). Based on the complete and verified publication list of a university, the coverage of MA was assessed and compared with two benchmark databases, Scopus and Web of Science (WoS), on the level of individual publications. Citation counts were analyzed, and issues related to data retrieval and data quality were examined. A Perl script was written to retrieve metadata from MA based on publication titles. The script is freely available on GitHub. We find that MA covers journal articles, working papers, and conference items to a substantial extent and indexes more document types than the benchmark databases (e.g., working papers, dissertations). MA clearly surpasses Scopus and WoS in covering book-related document types and conference items but falls slightly behind Scopus in journal articles. The coverage of MA is favorable for evaluative bibliometrics in most research fields, including economics/business, computer/information sciences, and mathematics. However, MA shows biases similar to Scopus and WoS with regard to the coverage of the humanities, non-English publications, and open-access publications. Rank correlations of citation counts are high between MA and the benchmark databases. We find that the publication year is correct for 89.5% of all publications and the number of authors is correct for 95.1% of the journal articles. Given the fast and ongoing development of MA, we conclude that MA is on the verge of becoming a bibliometric superpower. However, comprehensive studies on the quality of MA metadata are still lacking.


title: Design and analysis of adaptive processor

abstract: A new computation model called CACHE (Cache Architecture for Configurable Hardware Engine) is proposed in this paper. This model does not require a dedicated host processor and its software to harness the reconfiguration. Autonomous reconfiguration is performed within a working-set of application datapaths. The CACHE model has lots of side effects; caching, resource allocation and assignment, placement and routing, and defragmentation, with a processing array itself and a special register called a working-set register file. The model aims to reduce three major workloads: (1) the processor and application design workload, (2) runtime resource management and scheduling workload, and (3) reconfiguration workload. In order to reduce these workloads, processor architecture is definitely different from traditional computing model and its microprocessor architecture. There are three major ideas to construct the computing system: (1) an on-chip working-set model mainly in order to control load and store of streams, namely to control traffics introducing overheads, (2) an on-chip deadlock properties model mainly in order to manage resources and to continuously configure datapaths corresponding to a working-set window, (3) a cache memory technique to work for these models, the mechanism is equivalent to the working-set window, and the cache memory's procedure is equivalent to resource request, acquirement, and release of deadlock properties. The first model focuses onto streaming applications, for example vector and matrix operations, filters, and so on, which takes coarser grained operations such as integer operations of C-language. Regarding performance compared with DSPs, that comes from constant throughput across different scale of the applications. In addition, extended model, we call Instant model that automatically generates instance of a datapath, outperforms the DSPs. This paper shows its computation model, architecture, low-level design, and analyses about basic characteristics of the execution.


title: PRIDE Inspector: a tool to visualize and validate MS proteomics data

abstract: This work was supported by the Wellcome Trust ::: (grant number WT085949MA) and EMBL core ::: funding. R.G.C. is supported by EU FP7 grant SLING ::: (grant number 226073). J.A.V. is supported by the EU ::: FP7 grants LipidomicNet (grant number 202272) and ::: ProteomeXchange (grant number 260558). A.F. was ::: partially supported by the Spanish network ::: COMBIOMED (RD07/0067/0006, ISCIII-FIS). ::: L.M. would like to acknowledge support from the EU ::: FP7 PRIME-XS grant (grant number 262067).


Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches: an Experimental Evaluation


title: Axial-Flux Permanent-Magnet Generator for Induction Heating Gensets

abstract: This paper presents a single-phase slotless axial-flux permanent-magnet synchronous machine for induction heating gensets. A full-scale prototype of the machine (110 kVA, 400 Hz, 690 A) has been designed and subsequently analyzed through finite element analysis (FEA). Induced current distributions in the permanent magnets and in the rotors have also been calculated through FEA, showing that the resulting losses are kept at bay due to the low armature reaction. An effective way of achieving regulation of the power transferred to the load has been analytically derived. The prototype has also been built, and experimental tests confirm the aforesaid analyses.


title: PathTrack: Fast Trajectory Annotation with Path Supervision

abstract: Progress in Multiple Object Tracking (MOT) has been historically limited by the size of the available datasets. We present an efficient framework to annotate trajectories and use it to produce a MOT dataset of unprecedented size. In our novel path supervision the annotator loosely follows the object with the cursor while watching the video, providing a path annotation for each object in the sequence. Our approach is able to turn such weak annotations into dense box trajectories. Our experiments on existing datasets prove that our framework produces more accurate annotations than the state of the art, in a fraction of the time. We further validate our approach by crowdsourcing the PathTrack dataset, with more than 15,000 person trajectories in 720 sequences. Tracking approaches can benefit training on such large-scale datasets, as did object recognition. We prove this by re-training an off-the-shelf person matching network, originally trained on the MOT15 dataset, almost halving the misclassification rate. Additionally, training on our data consistently improves tracking results, both on our dataset and on MOT15. On the latter, we improve the top-performing tracker (NOMT) dropping the number of IDSwitches by 18% and fragments by 5%.


title: Understanding disentangling in $\beta$-VAE

abstract: We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we show the circumstances under which representations aligned with the underlying generative factors of variation of data emerge when optimising the modified ELBO bound in $\beta$-VAE, as training progresses. From these insights, we propose a modification to the training regime of $\beta$-VAE, that progressively increases the information capacity of the latent code during training. This modification facilitates the robust learning of disentangled representations in $\beta$-VAE, without the previous trade-off in reconstruction accuracy.


Multi-Talker Speech Recognition Based on Blind Source Separation with ad hoc Microphone Array Using Smartphones and Cloud Storage


title: A miniature multi-joint wall-climbing robot based on new Vibration Suction Robotic Foot

abstract: Vibration suction method (VSM) is a new kind of suction strategy for wall-climbing robot. The first vibration suction robotic foot (VSRF) was built and tested to verify the applicability of VSM in former works. But the suction capability, safety, and wall adaptability of the first VSRF are not enough. A new generation of VSRF was designed, fabricated and tested. Experiment results show that the new robotic foot performs better than the former design. A miniature multi-joint wall-climbing robot was designed and implemented based on the new VSRF. Kinematics analysis and gait planning of the robot were made. Experiments proved that this robot can finish three kinds of walking function: walking in straight line, turning and making face-to-face transfer successfully.


title: Unsupervised Metaphor Identification Using Hierarchical Graph Factorization Clustering

abstract: We present a novel approach to automatic metaphor identification, that discovers both metaphorical associations and metaphorical expressions in unrestricted text. Our system first performs hierarchical graph factorization clustering (HGFC) of nouns and then searches the resulting graph for metaphorical connections between concepts. It then makes use of the salient features of the metaphorically connected clusters to identify the actual metaphorical expressions. In contrast to previous work, our method is fully unsupervised. Despite this fact, it operates with an encouraging precision (0.69) and recall (0.61). Our approach is also the first one in NLP to exploit the cognitive findings on the differences in organisation of abstract and concrete concepts in the human brain.


title: Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

abstract: We present a novel method for classifying emotions from static facial images. Our approach leverages on the recent success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike the settings often assumed there, far less labeled data is typically available for training emotion classification systems. Our method is therefore designed with the goal of simplifying the problem domain by removing confounding factors from the input images, with an emphasis on image illumination variations. This, in an effort to reduce the amount of data required to effectively train deep CNN models. To this end, we propose novel transformations of image intensities to 3D spaces, designed to be invariant to monotonic photometric transformations. These are applied to CASIA Webface images which are then used to train an ensemble of multiple architecture CNNs on multiple representations. Each model is then fine-tuned with limited emotion labeled training data to obtain final classification models. Our method was tested on the Emotion Recognition in the Wild Challenge (EmotiW 2015), Static Facial Expression Recognition sub-challenge (SFEW) and shown to provide a substantial, 15.36% improvement over baseline results (40% gain in performance).


title: Acute Effects of Cocaine on Human Brain Activity and Emotion

abstract: Abstract We investigated brain circuitry mediating cocaine- induced euphoria and craving using functional MRI (fMRI). During double-blind cocaine (0.6 mg/kg) and saline infusions in cocaine-dependent subjects, the entire brain was imaged for 5 min before and 13 min after infusion while subjects rated scales for rush, high, low, and craving. Cocaine induced focal signal increases in nucleus accumbens/subcallosal cortex (NAc/SCC), caudate, putamen, basal forebrain, thalamus, insula, hippocampus, parahippocampal gyrus, cingulate, lateral prefrontal and temporal cortices, parietal cortex, striate/extrastriate cortices, ventral tegmentum, and pons and produced signal decreases in amygdala, temporal pole, and medial frontal cortex. Saline produced few positive or negative activations, which were localized to lateral prefrontal cortex and temporo-occipital cortex. Subjects who underwent repeat studies showed good replication of the regional fMRI activation pattern following cocaine and saline infusions, with activations on saline retest that might reflect expectancy. Brain regions that exhibited early and short duration signal maxima showed a higher correlation with rush ratings. These included the ventral tegmentum, pons, basal forebrain, caudate, cingulate, and most regions of lateral prefrontal cortex. In contrast, regions that demonstrated early but sustained signal maxima were more correlated with craving than with rush ratings; such regions included the NAc/SCC, right parahippocampal gyrus, and some regions of lateral prefrontal cortex. Sustained negative signal change was noted in the amygdala, which correlated with craving ratings. Our data demonstrate the ability of fMRI to map dynamic patterns of brain activation following cocaine infusion in cocaine-dependent subjects and provide evidence of dynamically changing brain networks associated with cocaine-induced euphoria and cocaine-induced craving.


Ranking Automatically Generated Questions Using Common Human Queries


title: Navigation queries from triangular meshes

abstract: Navigation meshes are commonly employed as a practical representation for path planning and other navigation queries in animated virtual environments and computer games. This paper explores the use of triangulations as a navigation mesh, and discusses several useful triangulation-based algorithms and operations: environment modeling and validity, automatic agent placement, tracking moving obstacles, ray-obstacle intersection queries, path planning with arbitrary clearance, determination of corridors, etc. While several of the addressed queries and operations can be applied to generic triangular meshes, the efficient computation of paths with arbitrary clearance requires a new type of triangular mesh, called a Local Clearance Triangulation, which enables the efficient and correct determination if a disc of arbitrary size can pass through any narrow passages of the mesh. This paper shows that triangular meshes can support the efficient computation of several navigation procedures and an implementation of the presented methods is available.


title: Identifying Unknown Android Malware with Feature Extractions and Classification Techniques

abstract: Android malware unfortunately have little difficulty to sneak in marketplaces. While known malware and their variants are nowadays quite well detected by antivirus scanners, new unknown malware, which are fundamentally different from others (e.g. "0-day"), remain an issue. To discover such new malware, the SherlockDroid framework filters masses of applications and only keeps the most likely to be malicious for future inspection by antivirus teams. Apart from crawling applications from marketplaces, SherlockDroid extracts code-level features, and then classifies unknown applications with Alligator. Alligator is a classification tool that efficiently and automatically combines several classification algorithms. To demonstrate the efficiency of our approach, we have extracted properties and classified over 600,000 applications during two crawling campaigns in July 2014 and October 2014, with the detection of one new malware, Android/Odpa.A!tr.spy, and two new riskware. With other findings, this increases SherlockDroid's "Hall of Shame" to 9 totally unknown malware and potentially unwanted applications.


title: AES cryptography in color image steganography by genetic algorithms

abstract: This work incorporates the AES cryptography algorithm, to improve the hidden data security in two methodologies for steganography: the genetic algorithm and path relinking. It also combines them proposing a new hybrid approach that outperforms the LSB (least significant bits) substitution technique presented in works cited in the literature concerning the quality of a stego image. It improves the possibility of hiding data inside color images significantly, increasing the space available for information by more than three times when compared to the usual steganography approach used by grayscale images. Moreover all types of digital information from text and compressed files to even executable programs can be hidden inside the cover image. This considerably increases the scope of application of the technique for transmitting information inside a typical image, hiding the data from intruders.


Pulse granuloma of the rectum: an unusual entity


title: Security-control methods for statistical databases: a comparative study

abstract: This paper considers the problem of providing security to statistical databases against disclosure of confidential information. Security-control methods suggested in the literature are classified into four general approaches: conceptual, query restriction, data perturbation, and output perturbation. Criteria for evaluating the performance of the various security-control methods are identified. Security-control methods that are based on each of the four approaches are discussed, together with their performance with respect to the identified evaluation criteria. A detailed comparative analysis of the most promising methods for protecting dynamic-online statistical databases is also presented. To date no single security-control method prevents both exact and partial disclosures. There are, however, a few perturbation-based methods that prevent exact disclosure and enable the database administrator to exercise "statistical disclosure control." Some of these methods, however introduce bias into query responses or suffer from the 0/1 query-set-size problem (i.e., partial disclosure is possible in case of null query set or a query set of size 1). We recommend directing future research efforts toward developing new methods that prevent exact disclosure and provide statistical-disclosure control, while at the same time do not suffer from the bias problem and the 0/1 query-set-size problem. Furthermore, efforts directed toward developing a bias-correction mechanism and solving the general problem of small query-set-size would help salvage a few of the current perturbation-based methods.


title: Infusing NLU into Automatic Question Generation

abstract: This paper presents an approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems.


title: Receiver channel with resonance-based timing detection for a laser range finder

abstract: An integrated receiver channel for a pulsed time-of-flight laser range finder is presented. The receiver operates in a wide dynamic range without gain control. This is achieved by converting the received unipolar pulse to a bipolar waveform already after the optical detector before the signal is fed to amplifier blocks. Thus the nonlinearities of the amplifiers have the minimum effect on the timing point, which is located in the zero crossing of the bipolar pulse. A parallel resonant circuit is used to shape the pulse in the input of the channel, which consists of a cascade of limiting voltage amplifiers followed by a comparator. The measurements show that the receiver has a walk error of 74 ps in the dynamic range of 1:1280. This corresponds to 11 mm in distance. The minimum usable input signal is limited by the noise of the receiver and equals 1.9 muA


title: Empowering Truth Discovery with Multi-Truth Prediction

abstract: Truth discovery is the problem of detecting true values from the conflicting data provided by multiple sources on the same data items. Since sources' reliability is unknown a priori, a truth discovery method usually estimates sources' reliability along with the truth discovery process. A major limitation of existing truth discovery methods is that they commonly assume exactly one true value on each data item and therefore cannot deal with the more general case that a data item may have multiple true values (or multi-truth). Since the number of true values may vary from data item to data item, this requires truth discovery methods being able to detect varying numbers of truth values from the multi-source data. In this paper, we propose a multi-truth discovery approach, which addresses the above challenges by providing a generic framework for enhancing existing truth discovery methods. In particular, we redeem the numbers of true values as an important clue for facilitating multi-truth discovery. We present the procedure and components of our approach, and propose three models, namely the byproduct model, the joint model, and the synthesis model to implement our approach. We further propose two extensions to enhance our approach, by leveraging the implications of similar numerical values and values' co-occurrence information in sources' claims to improve the truth discovery accuracy. Experimental studies on real-world datasets demonstrate the effectiveness of our approach.


title: Engaging students through mobile game development

abstract: This paper describes using mobile game development as a motivational tool to engage students early in the curriculum. Mobile devices have become an integral part of everyday lives of modern students; using these devices as a part of the coursework may help them see the immediate connections between Computer Science and real-world technology. Compared to traditional game development, programming mobile games is less complex, which enables students with limited programming experience to create playable mobile games within the scope of a single course. Experience presented in this paper may be easily duplicated, but it may be especially useful in the first college-level course for students with CS AP credits.


title: An algorithm for robust fitting of autoregressive models

abstract: An algorithm for robust fitting of AR models is given, based on a linear regression idea. The new method appears to outperform the Yule-Walker estimator in a setting of data contaminated with outliers.


title: Compact Dual-Band WLAN Diversity Antennas on USB Dongle Platform

abstract: An antenna system comprising two closely spaced chip antennas is proposed for dual-band wireless LAN uses. The distance between two chips is 12 mm and the spacing between antenna feeds is 1 mm only. Yet, superior isolation performance is achieved in both operation bands. A novel isolation enhancing measure is implemented for the higher 5 GHz band, which is done by adding a pair of open-ended stubs next to antenna feeds. The stub pair functions as an all-stop filter and elevates the port isolation. Rigorous simulation works were conducted to associate antenna geometry with performance features. Results such as impedance matching, isolation, and radiation patterns were examined jointly to identify mechanisms of its decoupling and diversity features. Antenna diversity assessment measures including the envelope correlation coefficient and the effective diversity gain were derived. Results demonstrate that the proposed compact two-antenna configuration suits well to the diversity antenna need on portable devices.


title: Is classification necessary after Google

abstract: Purpose – The purpose of this paper is to examine challenges facing bibliographic classification at both the practical and theoretical levels. At the practical level, libraries are increasingly dispensing with classifying books. At the theoretical level, many researchers, managers, and users believe that the activity of “classification” is not worth the effort, as search engines can be improved without the heavy cost of providing metadata.Design/methodology/approach – The basic issue in classification is seen as providing criteria for deciding whether A should be classified as X. Such decisions are considered to be dependent on the purpose and values inherent in the specific classification process. These decisions are not independent of theories and values in the document being classified, but are dependent on an interpretation of the discourses within those documents.Findings – At the practical level, there is a need to provide high‐quality control mechanisms. At the theoretical level, there is a need to...


title: Peer-to-peer vs. client/server: Reliability and efficiency of a content distribution service

abstract: In this paper we evaluate the performance of a content distribution service with respect to reliability and efficiency. The considered technology for realizing such a service can either be a traditional client/server (CS) architecture or a peer-to-peer (P2P) network. In CS, the capacity of the server is the bottleneck and has to be dimensioned in such a way that all requests can be accommodated at any time, while a P2P system does not burden a single server since the content is distributed in the network among sharing peers. However, corrupted or fake files may diminish the reliability of the P2P service due to downloading of useless contents. We compare a CS system to P2P and evaluate the downloading time, success ratio, and fairness while considering flash crowd arrivals and corrupted contents.


title: A-DFA: A Time- and Space-Efficient DFA Compression Algorithm for Fast Regular Expression Evaluation

abstract: Modern network intrusion detection systems need to perform regular expression matching at line rate in order to detect the occurrence of critical patterns in packet payloads. While Deterministic Finite Automata (DFAs) allow this operation to be performed in linear time, they may exhibit prohibitive memory requirements. Kumar et al. [2006a] have proposed Delayed Input DFAs (D2FAs), which provide a trade-off between the memory requirements of the compressed DFA and the number of states visited for each character processed, which in turn affects the memory bandwidth required to evaluate regular expressions. In this article we introduce Amortized time − bandwidth overhead DFAs (A − DFAs), a general compression technique that results in at most N(k + 1)/k state traversals when processing a string of length N, k being a positive integer. In comparison to the D2FA approach, our technique achieves comparable levels of compression with lower provable bounds on memory bandwidth (or greater compression for a given bandwidth bound). Moreover, the A-DFA algorithm has lower complexity, can be applied during DFA creation, and is suitable for scenarios where a compressed DFA needs to be dynamically built or updated. Finally, we show how to combine A-DFA with alphabet reduction and multistride DFAs, two techniques aimed at reducing the memory space and bandwidth requirement of DFAs, and discuss memory encoding schemes suitable for A-DFAs.


title: Dynamic Modeling with Nonlinear Inputs and Backstepping Control for a Hexarotor Micro-Aerial Vehicle

abstract: In this paper the hexarotor dynamic model with nonlinear inputs and full state back stepping technique is presented. Dead zone and saturation nonlinear inputs are considered reflected to the rotors. The goal is to obtain a faithful mathematical representation of the mechanical system for analysis and control design, not only in hover, but also in motion when take-off, land and flight for aerial navigation tasks. The model was implemented in Matlab/Simulink to optimize the design of control system. Simulations of the hexarotor model shows the performance of the control law and stabilizes with good tracking.


title: Thermal performance modeling of foil wound concentrated coils in electric machines

abstract: While the use of foil windings in transformers and air core inductors is not uncommon, their use in electric machines is just emerging. Although prototype realizations have been reported in the literature, a definitive analysis of their thermal performance is necessary to evaluate their potential for broad applications. This paper is devoted to presenting a comprehensive model for evaluating their thermal performance including loss modeling and temperature rise prediction in realistic designs. An overview of foil windings in electric machines and their advantages in improved fill factor is presented, along with experimental results that compare the AC losses and temperature distribution under identical steady-state operating conditions with a prototype round conductor coil and foil conductor coil. A lumped parameter thermal model that predicts the heat transfer behavior of the foil conductor coil is presented along with supporting experimental results.


title: What online gamers really think of the Internet?

abstract: The Internet has provided a network infrastructure with global connectivity for the games industry to develop and deploy online games. However, unlike the document interface paradigm of the World Wide Web (WWW), these online games have more stringent requirements that are not fulfilled by the Internet's best effort service model.A key characteristic of online games is the possibility of having multiple participants share the same experience. Consequently, the volatile nature of the Internet can affect the enjoyment of all, or at the very least a few, of the users. To ameliorate the impact caused by network problems that may arise during game play, game developers have adopted adaptation techniques in the design and implementation of online games. However, little is known of how the user perceives these mechanisms.This paper presents the results of a questionnaire targeted at the online gaming community to provide insight into what users really think of the Internet and its impact on their playing experience. One of the main results is to demonstrate that the existing mechanisms fail to maintain the utility of the game at all times, leading to frustration on the part of the users. In spite of this, users are not willing to pay for any service guarantees.


title: Handling Named Entities and Compound Verbs in Phrase-Based Statistical Machine Translation

abstract: Data preprocessing plays a crucial role in phrase-based statistical machine translation (PB-SMT). In this paper, we show how single-tokenization of two types of multi-word expressions (MWE), namely named entities (NE) and compound verbs, as well as their prior alignment can boost the performance of PB-SMT. Single-tokenization of compound verbs and named entities (NE) provides significant gains over the baseline PB-SMT system. Automatic alignment of NEs substantially improves the overall MT performance, and thereby the word alignment quality indirectly. For establishing NE alignments, we transliterate source NEs into the target language and then compare them with the target NEs. Target language NEs are first converted into a canonical form before the comparison takes place. Our best system achieves statistically significant improvements (4.59 BLEU points absolute, 52.5% relative improvement) on an English—Bangla translation task.


title: Maternal depression and early childhood growth in developing countries: systematic review and meta-analysis

abstract: OBJECTIVE ::: To investigate the relationship between maternal depression and child growth in developing countries through a systematic literature review and meta-analysis. ::: ::: ::: METHODS ::: Six databases were searched for studies from developing countries on maternal depression and child growth published up until 2010. Standard meta-analytical methods were followed and pooled odds ratios (ORs) for underweight and stunting in the children of depressed mothers were calculated using random effects models for all studies and for subsets of studies that met strict criteria on study design, exposure to maternal depression and outcome variables. The population attributable risk (PAR) was estimated for selected studies. ::: ::: ::: FINDINGS ::: Seventeen studies including a total of 13,923 mother and child pairs from 11 countries met inclusion criteria. The children of mothers with depression or depressive symptoms were more likely to be underweight (OR: 1.5; 95% confidence interval, CI: 1.2-1.8) or stunted (OR: 1.4; 95% CI: 1.2-1.7). Subanalysis of three longitudinal studies showed a stronger effect: the OR for underweight was 2.2 (95% CI: 1.5-3.2) and for stunting, 2.0 (95% CI: 1.0-3.9). The PAR for selected studies indicated that if the infant population were entirely unexposed to maternal depressive symptoms 23% to 29% fewer children would be underweight or stunted. ::: ::: ::: CONCLUSION ::: Maternal depression was associated with early childhood underweight and stunting. Rigorous prospective studies are needed to identify mechanisms and causes. Early identification, treatment and prevention of maternal depression may help reduce child stunting and underweight in developing countries.


title: Fixation Precision in High-Speed Noncontact Eye-Gaze Tracking

abstract: The precision of point-of-gaze (POG) estimation during a fixation is an important factor in determining the usability of a noncontact eye-gaze tracking system for real-time applications. The objective of this paper is to define and measure POG fixation precision, propose methods for increasing the fixation precision, and examine the improvements when the methods are applied to two POG estimation approaches. To achieve these objectives, techniques for high-speed image processing that allow POG sampling rates of over 400 Hz are presented. With these high-speed POG sampling rates, the fixation precision can be improved by filtering while maintaining an acceptable real-time latency. The high-speed sampling and digital filtering techniques developed were applied to two POG estimation techniques, i.e., the highspeed pupil-corneal reflection (HS P-CR) vector method and a 3-D model-based method allowing free head motion. Evaluation on the subjects has shown that when operating at 407 frames per second (fps) with filtering, the fixation precision for the HS P-CR POG estimation method was improved by a factor of 5.8 to 0.035deg (1.6 screen pixels) compared to the unfiltered operation at 30 fps. For the 3-D POG estimation method, the fixation precision was improved by a factor of 11 to 0.050deg (2.3 screen pixels) compared to the unfiltered operation at 30 fps.


title: A Probabilistic Multi-Touch Attribution Model for Online Advertising

abstract: It is an important problem in computational advertising to study the effects of different advertising channels upon user conversions, as advertisers can use the discoveries to plan or optimize advertising campaigns. In this paper, we propose a novel Probabilistic Multi-Touch Attribution (PMTA) model which takes into account not only which ads have been viewed or clicked by the user but also when each such interaction occurred. Borrowing the techniques from survival analysis, we use the Weibull distribution to describe the observed conversion delay and use the hazard rate of conversion to measure the influence of an ad exposure. It has been shown by extensive experiments on a large real-world dataset that our proposed model is superior to state-of-the-art methods in both conversion prediction and attribution analysis. Furthermore, a surprising research finding obtained from this dataset is that search ads are often not the root cause of final conversions but just the consequence of previously viewed ads.


A Serendipity-Oriented Greedy Algorithm for Recommendations


Personality Recognition in Source Code Working Note: Team BESUMich


title: Clothes Size Prediction from Dressed-Human Silhouettes

abstract: We propose an effective and efficient way to automatically predict clothes size for users to buy clothes online. We take human height and dressed-human silhouettes in front and side views as input, and estimate 3D body sizes with a data-driven method. We adopt 20 body sizes which are closely related to clothes size, and use such 3D body sizes to get clothes size by searching corresponding size chart. Previous image-based methods need to calibrate camera to estimate 3D information from 2D images, because the same person has different appearances of silhouettes (e.g. size and shape) when the camera configuration (intrinsic and extrinsic parameters) is different. Our method avoids camera calibration, which is much more convenient. We set up our virtual camera and train the relationship between human height and silhouette size under this camera configuration. After estimating silhouette size, we regress the positions of 2D body landmarks. We define 2D body sizes as the distances between corresponding 2D body landmarks. Finally, we learn the relationship between 2D body sizes and 3D body sizes. The training samples for each regression process come from a database of 3D naked and dressed bodies created by previous work. We evaluate the whole procedure and each process of our framework. We also compare the performance with several regression models. The total time-consumption for clothes size prediction is less than 0.1 s and the average estimation error of body sizes is 0.824 cm, which can satisfy the tolerance for customers to shop clothes online.


title: Jointless structure and under-actuation mechanism for compact hand exoskeleton

abstract: It is important for a wearable robot to be compact and sufficiently light for use as an assistive device. Since human fingers are arranged in a row in dense space, the concept of traditional wearable robots using a rigid frame and a pin joint result in size and complexity problems. A structure without a conventional pin joint, called a jointless structure, has the potential to be used as a wearable robotic hand because the human skeleton and joint can replace the robot's conventional structure. Another way to reduce the weight of the system is to use under-actuation. Under-actuation enables adaptive grasping with less number of actuators for robotic hands. Differential mechanisms are widely used for multi-finger under-actuation; however, they require additional working space. We propose a design with a jointless structure and a novel under-actuation mechanism to reduce the size and weight of a hand exoskeleton. Using these concepts, we developed a prototype that weighs only 80 grams. To evaluate the prototype, fingertip force and blocked force are measured. Fingertip force is the force that can be applied by the finger of the hand exoskeleton on the object surface. The fingertip force is about 18 N when actuated by a tension force of 35 N from the motor. 18 N is sufficient for simple pinch motion in daily activities. Another factor related to performance of the under-actuation mechanism is blocked force, which is a force required to stop one finger while the other finger keeps on moving. It is measured to be 0.5 N, which is sufficiently small. With these experiments, the feasibility of the new hand exoskeleton has been shown.


Donation Behavior in Online Micro Charities: An Investigation of Charitable Crowdfunding Projects


title: Design of Hybrid Folded Rectangular Waveguide Filters With Transmission Zeros Below the Passband

abstract: The design and physical implementation of hybrid folded rectangular waveguide filters providing multiple transmission zeros (TZs) below the passband are considered in this paper. These structures offer great flexibility to locate the TZs in a wide frequency range. Different implementations have been considered, each one offering certain advantages in terms of ease of manufacture, number of TZs, and their separation from the passband. A simple design procedure is also described. Measurements of a manufactured five-pole filter prototype and simulations fully validate the novel configurations proposed in this study.


title: Compact Antenna for Small Satellite Applications [Antenna Applications Corner]

abstract: i»?The combined effort of the Universiti Kebangsaan Malaysia and the Kyushu Institute of Technology (KYUTECH) to develop a small-satellite antenna is presented in this paper. Microstrip antennas offer an ideal solution to satellite communication requirements due to their light weight and low profile. In this paper, a compact single-layer coaxial-probe-fed circularly polarized high-gain patch antenna designed for HORYU-IV nanosatellite S-band communication is presented. HORYU-IV aims to acquire data about high-voltage discharge phenomena in low Earth orbit (LEO). This will enhance the understanding of satellite charging, and overall satellite reliability can be improved for future high-power space programs. The proposed antenna consists of four asymmetric V-shaped slits, i.e., one at each corner of a rectangular patch, and a parasitic rectangular strip. The proposed antenna achieves a sufficient beamwidth for LEO satellite application and less than 3-dB axial ratio for the entire field of view. A prototype of the antenna was developed with a 1.57-mm-thick singlelayer Rogers substrate with a relative permittivity of 2.2, and the measured results are consistent with the simulation.


Predicting Song Popularity


title: Kernel sparse representation for time series classification

abstract: Abstract In recent years there has been growing interests in mining time series data. To overcome the adverse influence of time shift, a number of effective elastic matching approaches such as dynamic time warp (DTW), edit distance with real penalty (ERP), and time warp edit distance (TWED) have been developed based on the nearest neighbor classification (NNC) framework, where the distance d ( x , C i ) between a test sample x and one specific class C i is simply defined as the minimum distance between x and the training samples in this class. In many applications, the sparse representation classifier (SRC) was applied by defining d ( x , C i ) as the distance of x to a linear combination of the samples in class C i , and it usually outperformed NNC in terms of classification accuracy. However, due to time shift, a linear combination of several time series is generally meaningless and may result in poor classification performance. In this paper, a family of Gaussian elastic matching kernels was introduced to deal with the problems of time shift and nonlinear representation. In this way, a linear combination of time series can be conducted in the implicit kernel space. Then a kernel sparse representation learning framework for time series classification was proposed. To improve computational efficiency and classification performance, both unsupervised and supervised dictionary learning techniques were developed by extending KSVD and label consistent KSVD algorithms. Experimental results showed that the proposed methods generally outperformed state-of-the-arts methods in terms of classification accuracy.


title: Parallel operation of IGBTs modular converter system for high power high frequency induction heating applications

abstract: The aim of this work is to develop a modular converter system for induction heating application, which considerably reduces the expenditure, while covering the whole range of power at high frequency (400 KHz). Such a system shall consist of identical converter-modules, which are combined to produce the desired power. To increase the power, parallel connection of modules was employed. At high power high frequency, the IGBT losses are very important and the IGBT losses are totally dominated by the turn-off losses. The maximum output power rating of the inverter is manly limited by the IGBT losses. For parallel operation of IGBTs modules, which used to increase the output power, there are two ways used to distressing of IGBTs, normal parallel operation method and cycle control parallel operation method. This paper presents the influence of these two different methods which used for IGBTs modules operate at 400 KHz and 100 KW for one bridge on the maximum output power.


title: Relation Classification via Convolutional Deep Neural Network

abstract: The state-of-the-art methods used for relation classification are primarily based on statistical machine learning, and their performance strongly depends on the quality of the extracted features. The extracted features are often derived from the output of pre-existing natural language processing (NLP) systems, which leads to the propagation of the errors in the existing tools and hinders the performance of these systems. In this paper, we exploit a convolutional deep neural network (DNN) to extract lexical and sentence level features. Our method takes all of the word tokens as input without complicated pre-processing. First, the word tokens are transformed to vectors by looking up word embeddings 1 . Then, lexical level features are extracted according to the given nouns. Meanwhile, sentence level features are learned using a convolutional approach. These two level features are concatenated to form the final extracted feature vector. Finally, the features are fed into a softmax classifier to predict the relationship between two marked nouns. The experimental results demonstrate that our approach significantly outperforms the state-of-the-art methods.


title: Power-constrained contrast enhancement for OLED displays based on histogram equalization

abstract: A novel power-constrained contrast enhancement algorithm for organic light-emitting diode (OLED) displays is proposed in this work. We first develop the log-modified histogram equalization (LMHE) scheme, which reduces overstretching artifacts of the conventional histogram equalization technique. Then, we model the power consumption in OLED displays, and incorporate it into LMHE to achieve the optimal tradeoff between contrast enhancement and power saving. Simulation results demonstrate that the proposed algorithm can reduce the power consumption significantly, while preserving image qualities.


title: Role of nanostructures in reducing thermal conductivity below alloy limit in crystalline solids

abstract: Atomic substitution in alloys can efficiently scatter phonons, thereby significantly reducing the thermal conductivity in crystalline solids to the "alloy limit". It has been difficult to beat the alloy limit without creating defects, dislocations, and voids, which also reduce electrical conductivity, making it ineffective for increasing the material's thermoelectric figure of merit. Using In/sub 0.53/Ga/sub 0.47/As containing epitaxially embedded ErAs nanoislands a few nm in size, we demonstrate thermal conductivity reduction by almost a factor of two below the alloy limit, and corresponding increase in thermoelectric figure of merit by more than a factor of two. A theoretical model suggests that while point defects in alloys efficiently scatter short wavelength phonons, the ErAs nanoislands provides additional scattering mechanism for the mid to long wavelength phonon - the combination reduces the thermal conductivity below the alloy limit.


title: Distributed Mutual Exclusion Algorithms for Intersection Traffic Control

abstract: Traffic control at intersections is a key issue and hot research topic in intelligent transportation systems. Existing approaches, including traffic light scheduling and trajectory maneuver, are either inaccurate and inflexible or complicated and costly. More importantly, due to the dynamics of traffic, it is really difficult to obtain the optimal solution in a real-time way. Inspired by the emergence of vehicular ad hoc network, we propose a novel approach to traffic control at intersections. Via vehicle to vehicle or vehicle to infrastructure communications, vehicles can compete for the privilege of passing the intersection, i.e., traffic is controlled via coordination among vehicles. Such an approach is flexible and efficient. To realize the coordination among vehicles, we first model the problem as a new variant of the classic mutual exclusion problem, and then design algorithms to solve new problem. Both centralized and distributed algorithms are. We conduct extensive simulations to evaluate the performance of our proposed algorithms. The results show that, our approach is efficient and outperforms a reference algorithm based on optimal traffic light scheduling. Moreover, our approach does not rely on traffic light or intersection controller facilities, which makes it flexible and applicable to various kinds of intersections.


title: Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

abstract: This paper demonstrates theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over previous results, which require O(m2) measurements. The new results for OMP are comparable with recent results for another approach called basis pursuit (BP). In some settings, the OMP algorithm is faster and easier to implement, so it is an attractive alternative to BP for signal recovery problems.


title: Forecast and control of epidemics in a globalized world

abstract: The rapid worldwide spread of the severe acute respiratory syndrome (SARS) demonstrated the potential threat an infectious disease poses in a closely interconnected and interdependent world. Here we introduce a probabilistic model which describes the worldwide spreading of infectious diseases and demonstrate that a forecast of the geographical spread of epidemics is indeed possible. It combines a stochastic local infection dynamics between individuals with stochastic transport in a worldwide network which takes into account the national and international civil aviation traffic. Our simulations of the SARS outbreak are in suprisingly good agreement with published case reports. We show that the high degree of predictability is caused by the strong heterogeneity of the network. Our model can be used to predict the worldwide spreading of future infectious diseases and to identify endangered regions in advance. The performance of different control strategies is analyzed and our simulations show that a quick and focused reaction is essential to inhibit the global spreading of epidemics.


title: Cross-scenario clothing retrieval and fine-grained style recognition

abstract: In this paper, we propose a new approach for cross-scenario clothing retrieval and fine-grained clothing style recognition. The query clothing photos captured by cameras or other mobile devices are filled with noisy background while the product clothing images online for shopping are usually presented in a pure environment. We tackle this problem by two steps. Firstly, a hierarchical super-pixel merging algorithm based on semantic segmentation is proposed to obtain the intact query clothing item. Secondly, aiming at solving the problem of clothing style recognition in different scenarios, we propose sparse coding based on domain-adaptive dictionary learning to improve the accuracy of the classifier and adaptability of the dictionary. In this way, we obtain fine-grained attributes of the clothing items and use the attributes matching score to re-rank the retrieval results further. The experiment results show that our method outperforms the state-of-the-art approaches. Furthermore, we build a well labeled clothing dataset, where the images are selected from 1.5 billion product clothing images.


title: Compiling Database Queries into Machine Code

abstract: On modern servers the working set of database management systems becomes more and more main memory resident. Slow disk accesses are largely avoided, and thus the in-memory processing speed of databases becomes an important factor. One very attractive approach for fast query processing is justin-time compilation of incoming queries. By producing machine code at runtime we avoid the overhead of traditional interpretation systems, and by carefully organizing the code around register usage we minimize memory traffic and get excellent performance. In this paper we show how queries can be brought into a form suitable for efficient translation, and how the underlying code generation can be orchestrated. By carefully abstracting away the necessary plumbing infrastructure we can build a query compiler that is both maintainable and efficient. The effectiveness of the approach is demonstrated by the HyPer system, that uses query compilation as its execution strategy, and that achieves excellent performance.


Erythromelanosis follicularis faciei et colli with reticulated hyperpigmentation of the extremities


title: GPU Versus FPGA for High Productivity Computing

abstract: Heterogeneous or co-processor architectures are becoming an important component of high productivity computing systems (HPCS). In this work the performance of a GPU based HPCS is compared with the performance of a commercially available FPGA based HPC. Contrary to previous approaches that focussed on specific examples, a broader analysis is performed by considering processes at an architectural level. A set of benchmarks is employed that use different process architectures in order to exploit the benefits of each technology. These include the asynchronous pipelines common to "map" tasks, a partially synchronous tree common to "reduce" tasks and a fully synchronous, fully connected mesh. We show that the GPU is more productive than the FPGA architecture for most of the benchmarks and conclude that FPGA-based HPCS is being marginalised by GPUs.


title: Social-Aware Video Recommendation for Online Social Groups

abstract: Group recommendation plays a significant role in today's social media systems, where users form social groups to receive multimedia content together and interact with each other, instead of consuming the online content individually. Limitations of traditional group recommendation approaches are as follows. First, they usually infer group members’ preferences by their historical behaviors, failing to capture inactive users’ preferences from the sparse historical data. Second, relationships between group members are not studied by these approaches, which fail to capture the inherent personality of members in a group. To address these issues, we propose a social-aware group recommendation framework that jointly utilizes both social relationships and social behaviors to not only infer a group's preference, but also model the tolerance and altruism characteristics of group members. Based on the observation that the following relationship in the online social network reflects common interests of users, we propose a group preference model based on external experts of group members. Furthermore, we model users’ tolerance (willingness to receive content not preferred) and altruism (willingness to receive content preferred by friends). Finally, based on the group preference model, we design recommendation algorithms for users under different social contexts. Experimental results demonstrate the effectiveness of our approach, which significantly improves the recommendation accuracy against traditional approaches, especially in the cases of inactive group members.


title: Diverse Routing in Networks With Probabilistic Failures

abstract: We develop diverse routing schemes for dealing with multiple, possibly correlated, failures. While disjoint path protection can effectively deal with isolated single link failures, recovering from multiple failures is not guaranteed. In particular, events such as natural disasters or intentional attacks can lead to multiple correlated failures, for which recovery mechanisms are not well understood. We take a probabilistic view of network failures where multiple failure events can occur simultaneously, and develop algorithms for finding diverse routes with minimum joint failure probability. Moreover, we develop a novel Probabilistic Shared Risk Link Group (PSRLG) framework for modeling correlated failures. In this context, we formulate the problem of finding two paths with minimum joint failure probability as an integer nonlinear program (INLP) and develop approximations and linear relaxations that can find nearly optimal solutions in most cases.