title: Reporting of sample size calculation in randomised controlled trials: review

abstract: Objectives To assess quality of reporting of sample size calculation, ascertain accuracy of calculations, and determine the relevance of assumptions made when calculating sample size in randomised controlled trials. ::: ::: Design Review. ::: ::: Data sources We searched MEDLINE for all primary reports of two arm parallel group randomised controlled trials of superiority with a single primary outcome published in six high impact factor general medical journals between 1 January 2005 and 31 December 2006. All extra material related to design of trials (other articles, online material, online trial registration) was systematically assessed. Data extracted by use of a standardised form included parameters required for sample size calculation and corresponding data reported in results sections of articles. We checked completeness of reporting of the sample size calculation, systematically replicated the sample size calculation to assess its accuracy, then quantified discrepancies between a priori hypothesised parameters necessary for calculation and a posteriori estimates. ::: ::: Results Of the 215 selected articles, 10 (5%) did not report any sample size calculation and 92 (43%) did not report all the required parameters. The difference between the sample size reported in the article and the replicated sample size calculation was greater than 10% in 47 (30%) of the 157 reports that gave enough data to recalculate the sample size. The difference between the assumptions for the control group and the observed data was greater than 30% in 31% (n=45) of articles and greater than 50% in 17% (n=24). Only 73 trials (34%) reported all data required to calculate the sample size, had an accurate calculation, and used accurate assumptions for the control group. ::: ::: Conclusions Sample size calculation is still inadequately reported, often erroneous, and based on assumptions that are frequently inaccurate. Such a situation raises questions about how sample size is calculated in randomised controlled trials.


title: Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer

abstract: Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can "interpolate": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce an output which semantically mixes characteristics from the datapoints. In this paper, we propose a regularization procedure which encourages interpolated outputs to appear more realistic by fooling a critic network which has been trained to recover the mixing coefficient from interpolated data. We then develop a simple benchmark task where we can quantitatively measure the extent to which various autoencoders can interpolate and show that our regularizer dramatically improves interpolation in this setting. We also demonstrate empirically that our regularizer produces latent codes which are more effective on downstream tasks, suggesting a possible link between interpolation abilities and learning useful representations.


title: Bounded Information Rate Variational Autoencoders

abstract: This paper introduces a new member of the family of Variational Autoencoders (VAE) that constrains the rate of information transferred by the latent layer. The latent layer is interpreted as a communication channel, the information rate of which is bound by imposing a pre-set signal-to-noise ratio. The new constraint subsumes the mutual information between the input and latent variables, combining naturally with the likelihood objective of the observed data as used in a conventional VAE. The resulting Bounded-Information-Rate Variational Autoencoder (BIR-VAE) provides a meaningful latent representation with an information resolution that can be specified directly in bits by the system designer. The rate constraint can be used to prevent overtraining, and the method naturally facilitates quantisation of the latent variables at the set rate. Our experiments confirm that the BIR-VAE has a meaningful latent representation and that its performance is at least as good as state-of-the-art competing algorithms, but with lower computational complexity.


title: Imparting Interpretability to Word Embeddings

abstract: As an ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation. They capture semantic and syntactic relations among words but the vector corresponding to the words are only meaningful relative to each other. Neither the vector nor its dimensions have any absolute, interpretable meaning. We introduce an additive modification to the objective function of the embedding learning algorithm that encourages the embedding vectors of words that are semantically related a predefined concept to take larger values along a specified dimension, while leaving the original semantic learning mechanism mostly unaffected. In other words, we align words that are already determined to be related, along predefined concepts. Therefore, we impart interpretability to the word embedding by assigning meaning to its vector dimensions. The predefined concepts are derived from an external lexical resource, which in this paper is chosen as Roget's Thesaurus. We observe that alignment along the chosen concepts is not limited to words in the Thesaurus and extends to other related words as well. We quantify the extent of interpretability and assignment of meaning from our experimental results. We also demonstrate the preservation of semantic coherence of the resulting vector space by using word-analogy and word-similarity tests. These tests show that the interpretability-imparted word embeddings that are obtained by the proposed framework do not sacrifice performances in common benchmark tests.


title: Static Friction in a Robot Joint—Modeling and Identification of Load and Temperature Effects

abstract: Friction is the result of complex interactions between contacting surfaces in down to a nanoscale perspective. Depending on the application, the different models available are more or less suitable ...


title: Automatic Fado Music Classification

abstract: In late 2011, Fado was elevated to the oral and intangible heritage of humanity by UNESCO. This study aims to develop a tool for automatic detection of Fado music based on the audio signal. To do this, frequency spectrum-related characteristics were captured form the audio signal: in addition to the Mel Frequency Cepstral Coefficients (MFCCs) and the energy of the signal, the signal was further analysed in two frequency ranges, providing additional information. Tests were run both in a 10-fold cross-validation setup (97.6% accuracy), and in a traditional train/test setup (95.8% accuracy). The good results reflect the fact that Fado is a very distinctive musical style.


title: A multi-center, randomized, clinical trial comparing adhesive polyurethane foam dressing and adhesive hydrocolloid dressing in patients with grade II pressure ulcers in primary care and nursing homes

abstract: Background ::: Pressure ulcers (PrUs) are ischemic wounds in the skin and underlying tissues caused by long-standing pressure force over an external bone or cartilaginous surface. PrUs are an important challenge for the overall health system because can prolong patient hospitalization and reduce quality of life. Moreover, 95% of PrUs are avoidable, suggesting they are caused by poor quality care assistance. PrUs are also costly, increasing national costs. For example, they represent about 5% of overall annual health expenses in Spain. Stages I and II PrUs have a combined prevalence of 65%. According main clinical guidelines, stage II PrUs (PrU-IIs) are usually treated by applying special dressings (polyurethane or hydrocolloid). However, little scientific evidence regarding their efficacy has been identified in scientific literature. Our aim is to assess the comparative efficacy of adhesive polyurethane foam and hydrocolloid dressings in the treatment of PrU-IIs in terms of healed ulcer after 8 weeks of follow-up.


title: Learning style, learning patterns, and learning performance in a WebCT-based MIS course

abstract: Web-based learning has been suggested to be the future of all types of distance learning. This study was undertaken to identify the impact of student learning styles, learning patterns, and other selected factors on their learning performance in a Web Course Tools (WebCT) MIS graduate course. Six specific research questions were developed and 76 graduate students participated in this study. It found that none of the factors, except ethnic groups, showed any significant impact on students' learning performance. The results suggest that, at the graduate level, students are able to learn equally well in WebCT online courses despite their different learning styles, WebCT learning patterns, and background in terms of gender, age, job status, year of admission, previous Web-based learning experiences, and MIS preparation.


title: Opportunities for Price Manipulation by Aggregators in Electricity Markets

abstract: Aggregators are playing an increasingly crucial role for integrating renewable generation into power systems. However, the intermittent nature of renewable generation makes market interactions of aggregators di cult to monitor and regulate, raising concerns about potential market manipulations. In this paper, we address this issue by quantifying the profit an aggregator can obtain through strategic curtailment of generation in an electricity market. We show that, while the problem of maximizing the benefit from curtailment is hard in general, e cient algorithms exist when the topology of the network is radial (acyclic). Further, we highlight that significant increases in profit can be obtained through strategic curtailment in practical settings.


title: Effect and tolerability of combining lovastatin with nifedipine or lisinopril.

abstract: Single cardiovascular risk factor intervention is probably not sufficient to prevent atherosclerosis progression. There is a lack of data on concomitant use of hypocholesterolemic agents and antihypertensive drugs with respect to possible interactions and adverse experiences. We studied 293 patients (below 65 years of age) under treatment with either lisinopril (n = 144) or nifedipine (n = 149) for mild to moderate hypertension for 10 weeks, and with serum cholesterol above 6.5 mmol/L, who were randomized to either lovastatin 20 mg every day or placebo in a double-blind, double-dummy design for 6 weeks. Lovastatin effectively lowered cholesterol by 16% and 15% in the lisinopril and nifedipine group respectively (P < .01 compared to placebo for both groups) without any negative impact on the antihypertensive efficacy of either lisinopril or nifedipine. The drugs in combination were well tolerated and did not affect the well-being of the patients, and did not cause any more adverse effects than the antihypertensive agents alone. Liver enzymes increased slightly during lovastatin therapy, while no case of myopathy was reported. Combined therapy with lovastatin and antihypertensive therapy can be safely undertaken.


title: Exposure limits and dielectric contrast for breast cancer tissues: Experimental results up to 50 GHz

abstract: At microwave and mm-wave frequencies, the interactions of the human tissue with electromagnetic fields depend on the dielectric properties of the tissue itself. The knowledge of these properties of healthy and malignant tissues, beyond to be crucial in several biomedical applications, is necessary to fix the safety thresholds in policy-making and the exposure limits for the human body. In particular, breast tissues are of particular interest from the medical and social point of view due to the relatively large diffusion of breast cancer. In literature, experimental data are available up to 20 GHz, and consequently at mm-wave frequencies they are only derived from extrapolations. This paper presents a feasibility study based on the experimental results of a broadband dielectric characterization of breast tissues up to 50 GHz. In particular, two main aspects are addressed. First, the contrast achievable between normal and tumorous tissues, a key parameter for several biomedical applications. Second, the compliance of a mm-wave imaging system for breast cancer detection with the ICNIRP recommendations.


title: Keratosis pilaris and ulerythema ophryogenes in a woman with monosomy of the short arm of chromosome 18.

abstract: Partial monosomy of the short arm of chromosome 18 (18p- syndrome) is characterized mainly by speech delay, mild to moderate mental retardation and short stature. We describe a patient with the 18p- syndrome and widespread severe keratosis pilaris and ulerythema ophryogenes. This is the fourth case in which such an association has been reported. This association is of considerable interest because it may uncover a candidate genomic region and help to identify the gene responsible for follicular keratinization.


title: Toward standardized near-data processing with unrestricted data placement for GPUs

abstract: 3D-stacked memory devices with processing logic can help alleviate the memory bandwidth bottleneck in GPUs. However, in order for such Near-Data Processing (NDP) memory stacks to be used for different GPU architectures, it is desirable to standardize the NDP architecture. Our proposal enables this standardization by allowing data to be spread across multiple memory stacks as is the norm in high-performance systems without an MMU on the NDP stack. The keys to this architecture are the ability to move data between memory stacks as required for computation, and a partitioned execution mechanism that offloads memory-intensive application segments onto the NDP stack and decouples address translation from DRAM accesses. By enhancing this system with a smart offload selection mechanism that is cognizant of the compute capability of the NDP and cache locality on the host processor, system performance and energy are improved by up to 66.8% and 37.6%, respectively.


title: Improved GPU SIMD control flow efficiency via hybrid warp size mechanism

abstract: Abstract High single instruction multiple data (SIMD) efficiency and low power consumption have made graphic processing units (GPUs) an ideal platform for many complex computational applications. Thousands of threads can be created by programmers and grouped into fixed-size SIMD batches, known as warps. High throughput is then achieved by concurrently executing such warps with minimal control overhead. However, if a branch instruction occurs, which assigns different paths to different threads, one warp will be broken into multiple warps that have to be executed serially, consequently reducing the efficiency advantage of SIMD. In this paper, the contemporary fixed-size warp design is abandoned for a hybrid warp size (HWS) mechanism. Mixed-size warps are generated according to HWS and are scheduled and issued flexibly. The simulation results show that this mechanism yields an average speedup of 1.20 over the baseline architecture for a wide variety of general purpose GPU applications. The paper also integrates HWS with dynamic warp formation (DWF), which is a well-known branch handling mechanism used to improve SIMD utilization by forming new warps out of split warps in real time. The simulation results show that the combination of DWF and HWS generates an average speedup of 1.27 over the DWF-only platform with an estimated area increase of about 1% of DWF.


title: Agricultural crop monitoring using IOT - a study

abstract: The Internet of things (IOT) is remodeling the agriculture enabling the farmers with the wide range of techniques such as precision and sustainable agriculture to face challenges in the field. IOT technology helps in collecting information about conditions like weather, moisture, temperature and fertility of soil, Crop online monitoring enables detection of weed, level of water, pest detection, animal intrusion in to the field, crop growth, agriculture. IOT leverages farmers to get connected to his farm from anywhere and anytime. Wireless sensor networks are used for monitoring the farm conditions and micro controllers are used to control and automate the farm processes. To view remotely the conditions in the form of image and video, wireless cameras have been used. A smart phone empowers farmer to keep updated with the ongoing conditions of his agricultural land using IOT at any time and any part of the world. IOT technology can reduce the cost and enhance the productivity of traditional farming.


title: Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts

abstract: Named entity recognition (NER) is an important task in NLP, which is all the more challenging in conversational domain with their noisy facets. Moreover, conversational texts are often available in limited amount, making supervised tasks infeasible. To learn from small data, strong inductive biases are required. Previous work relied on hand-crafted features to encode these biases until transfer learning emerges. Here, we explore a transfer learning method, namely language model pretraining, on NER task in Indonesian conversational texts. We utilize large unlabeled data (generic domain) to be transferred to conversational texts, enabling supervised training on limited in-domain data. We report two transfer learning variants, namely supervised model fine-tuning and unsupervised pretrained LM fine-tuning. Our experiments show that both variants outperform baseline neural models when trained on small data (100 sentences), yielding an absolute improvement of 32 points of test F1 score. Furthermore, we find that the pretrained LM encodes part-of-speech information which is a strong predictor for NER.


title: Toward autonomous mapping and exploration for mobile robots through deep supervised learning

abstract: We consider an autonomous mapping and exploration problem in which a range-sensing mobile robot is guided by an information-based controller through an a priori unknown environment, choosing to collect its next measurement at the location estimated to yield the maximum information gain within its current field of view. We propose a novel and time-efficient approach to predict the most informative sensing action using a deep neural network. After training the deep neural network on a series of thousands of randomly-generated “dungeon maps”, the predicted optimal sensing action can be computed in constant time, with prospects for appealing scalability in the testing phase to higher dimensional systems. We evaluated the performance of deep neural networks on the autonomous exploration of two-dimensional workspaces, comparing several different neural networks that were selected due to their success in recent ImageNet challenges. Our computational results demonstrate that the proposed method provides high efficiency as well as accuracy in selecting informative sensing actions that support autonomous mobile robot exploration.


title: The Use of Contour, Shape and Form in an Integrated Neural Approach for Object Recognition

abstract: How objects are recognised by humans is still an open research field. But, in general there is an agreement that humans recognise objects as established by the similarity principle – among othersof the Gestalt theory of visual perception, which states that things which share visual characteristics such as contour, shape, form, size, colour, texture, value or orientation will be seen as belonging together (Ellis, 1950). This principle applies to human operators; for instance, when an operator is given the task to pick up a specific object from a set of similar objects; the first approaching action will probably be guided solely by visual information clues such as shape similarity. But, if further information is given (i.e. type of surface), then a finer clustering could be accomplished to identify the target object.


title: What drives knowledge sharing in software development teams: A literature review and classification framework

abstract: Abstract Although scholars have long studied knowledge sharing drivers within software development teams, our knowledge remains fragmented by the divergent efforts that are based on and contribute to theoretical perspectives. This study provides a review of the extant literature (1993–2011) on knowledge sharing drivers in software teams and establishes a classification framework using an organizational change perspective. A synthesis of the literature uncovers diverse themes and gaps in the existing body of knowledge, suggests several paths for advancing theory on knowledge sharing in software development contexts, and discusses implications for practitioners concerned with knowledge sharing in software development.


title: Employing a virtual reality tool to explicate tacit knowledge of machine operations

abstract: The quality and durability of asphalted roads strongly depends on the final step in the road construction process; the profiling and compaction of the fresh spread asphalt. During compaction machine operators continuously make decisions on how to proceed with the compaction accounting for projectspecific aspects as: ambient conditions, road geometry, roller type, asphalt mixture characteristics and behavior of other machines. ::: ::: In discussions over quality improvement in road construction it is often suggested to robotize rollers. To operate such robots would of course require operational strategies and routines. The reality of this moment is that these operational strategies and routines are not documented. To identify the existing best working practices and, ultimately, to proceed with developing automated robotized compactors, the knowledge of machine operators should at first be explored and described in a formalized way. Unfortunately operators have difficulty verbalizing their expertise. Observation of behavior, as machine movement patterns, could be helpful to extract the operational strategies, but such observations are time and labor consuming. To overcome this burden we developed a Virtual Reality (VR) environment. In this VR environment operators perform compaction virtually, and their operational choices are traced and analysed. ::: ::: This paper describes this VR development and explains how it helps in following machine operators and extracting their (tacit) professional knowledge. The road geometry and the working conditions are visually represented; the operators show - rather than explain - how they would proceed with the compaction process in the given conditions. Movements of virtual machines are documented, analysed, visualised and discussed. This VR approach is expected to contribute to learning, to more formal description of operational strategies; stepping stones towards compaction algorithms for roller robotisation.


title: Artificial Intelligence Approaches To UCAV Autonomy

abstract: This paper covers a number of approaches that leverage Artificial Intelligence algorithms and techniques to aid Unmanned Combat Aerial Vehicle (UCAV) autonomy. An analysis of current approaches to autonomous control is provided followed by an exploration of how these techniques can be extended and enriched with AI techniques including Artificial Neural Networks (ANN), Ensembling and Reinforcement Learning (RL) to evolve control strategies for UCAVs.


title: Robust support vector regression networks for function approximation with outliers

abstract: Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. When the parameters used in SVR are improperly selected, overfitting phenomena may still occur. However, the selection of various parameters is not straightforward. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. In this paper, a novel regression approach, termed as the robust support vector regression (RSVR) network, is proposed to enhance the robust capability of SVR. In the approach, traditional robust learning approaches are employed to improve the learning performance for any selected parameters. From the simulation results, our RSVR can always improve the performance of the learned systems for all cases. Besides, it can be found that even the training lasted for a long period, the testing errors would not go up. In other words, the overfitting phenomenon is indeed suppressed.


title: A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

abstract: We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a deep neural network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first letter that describes an approach to perceive forest trials, which is demonstrated on a quadrotor micro aerial vehicle.


title: Ultralow loss successive divisions using silicon-on-insulator microwaveguides

abstract: The low excess loss experimental demonstration of ten successive light divisions is presented. This distribution is realized by using shallow-etched rib silicon-on-insulator waveguides with compact beam splitters and 90° turns based on total internal reflection corner mirrors. The measured excess optical loss is only 0.7dB per division. This result is an important step in a 1 to 1024 optical distribution demonstration.


title: Stochastic Vehicle Routing Problem: A Literature Survey

abstract: The purpose of this paper is to develop structural classification of Stochastic Vehicle Routing Problem (SVRP) by different domains and attributes. This research used a systematic review and meta-analysis on SVRP literatures. This includes browsing relevant researches and publications, screening related articles, identifying domains, attributes and categorising the articles based on the identified domains and attributes. The findings of the study show clear differences on the number of studies under each domain and attribute. Most studied attributes are stochastic customer demand, capacitated vehicle, synthesis data and objective function with cost minimization. Whereas the least studied are maximisation objective function, stochastic service time, and an applied model using stochastic with recurs. The research helps to summarise and map a comprehensive survey on SVRP literatures so that various contributions in the field are organised in a manner that provide a clear view for the readers and identify future research directions. This paper is the first of its kind in the field of SVRP that develop a classification scheme for articles published since 1993 to enhances the development of this newly emerging discipline.


title: A Dynamical Approach to Gestural Patterning in Speech Production

abstract: In this article, we attempt to reconcile the linguistic hypothesis that speech involves an underlying sequencing of abstract, discrete, context-independent units, with the empirical observation of continuous, context-dependent interleaving of articulatory movements. To this end, we first review a previously proposed task-dynamic model for the coordination and control of the speech articulators. We then describe an extension of this model in which invariant speech units (gestural primitives) are identified with context-independent sets of parameters in a dynamical system having two functionally distinct but interacting levels. The intergestural level is defined according to a set of activation coordinates; the interarticulator level is defined according to both model articulator and tractvariable coordinates. In the framework of this extended model, coproduction effects in speech are described in terms of the blending dynamics defined among a set of temporally overlapping active units; the relative timing ...


title: Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data

abstract: Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals. The increased ability of accelerometers to store large quantities of high resolution data has prompted a need for automated behavioural classification. We assessed the performance of several machine learning (ML) classifiers to discern five behaviours performed by accelerometer-equipped juvenile lemon sharks (Negaprion brevirostris) at Bimini, Bahamas (25°44'N, 79°16'W). The sharks were observed to exhibit chafing, burst swimming, headshaking, resting and swimming in a semi-captive environment and these observations were used to ground-truth data for ML training and testing. ML methods included logistic regression, an artificial neural network, two random forest models, a gradient boosting model and a voting ensemble (VE) model, which combined the predictions of all other (base) models to improve classifier performance. The macro-averaged F-measure, an indicator of classifier performance, showed that the VE model improved overall classification (F-measure 0.88) above the strongest base learner model, gradient boosting (0.86). To test whether the VE model provided biologically meaningful results when applied to accelerometer data obtained from wild sharks, we investigated headshaking behaviour, as a proxy for prey capture, in relation to the variables: time of day, tidal phase and season. All variables were significant in predicting prey capture, with predations most likely to occur during early evening and less frequently during the dry season and high tides. These findings support previous hypotheses from sporadic visual observations.


title: Clustering with Noisy Queries

abstract: In this paper, we initiate a rigorous theoretical study of clustering with noisy queries (or a faulty oracle). Given a set of $n$ elements, our goal is to recover the true clustering by asking minimum number of pairwise queries to an oracle. Oracle can answer queries of the form : "do elements $u$ and $v$ belong to the same cluster?" -- the queries can be asked interactively (adaptive queries), or non-adaptively up-front, but its answer can be erroneous with probability $p$. In this paper, we provide the first information theoretic lower bound on the number of queries for clustering with noisy oracle in both situations. We design novel algorithms that closely match this query complexity lower bound, even when the number of clusters is unknown. Moreover, we design computationally efficient algorithms both for the adaptive and non-adaptive settings. The problem captures/generalizes multiple application scenarios. It is directly motivated by the growing body of work that use crowdsourcing for {\em entity resolution}, a fundamental and challenging data mining task aimed to identify all records in a database referring to the same entity. Here crowd represents the noisy oracle, and the number of queries directly relates to the cost of crowdsourcing. Another application comes from the problem of {\em sign edge prediction} in social network, where social interactions can be both positive and negative, and one must identify the sign of all pair-wise interactions by querying a few pairs. Furthermore, clustering with noisy oracle is intimately connected to correlation clustering, leading to improvement therein. Finally, it introduces a new direction of study in the popular {\em stochastic block model} where one has an incomplete stochastic block model matrix to recover the clusters.


title: A CNN-Based Method of Vehicle Detection from Aerial Images Using Hard Example Mining

abstract: Recently, deep learning techniques have had a practical role in vehicle detection. While much effort has been spent on applying deep learning to vehicle detection, the effective use of training data has not been thoroughly studied, although it has great potential for improving training results, especially in cases where the training data are sparse. In this paper, we proposed using hard example mining (HEM) in the training process of a convolutional neural network (CNN) for vehicle detection in aerial images. We applied HEM to stochastic gradient descent (SGD) to choose the most informative training data by calculating the loss values in each batch and employing the examples with the largest losses. We picked 100 out of both 500 and 1000 examples for training in one iteration, and we tested different ratios of positive to negative examples in the training data to evaluate how the balance of positive and negative examples would affect the performance. In any case, our method always outperformed the plain SGD. The experimental results for images from New York showed improved performance over a CNN trained in plain SGD where the F1 score of our method was 0.02 higher.


title: Divertible protocols and atomic proxy cryptography

abstract: First, we introduce the notion of divertibility as a protocol property as opposed to the existing notion as a language property (see Okamoto, Ohta [OO90]). We give a definition of protocol divertibility that applies to arbitrary 2-party protocols and is compatible with Okamoto and Ohta's definition in the case of interactive zero-knowledge proofs. Other important examples falling under the new definition are blind signature protocols. We propose a sufficiency criterion for divertibility that is satisfied by many existing protocols and which, surprisingly, generalizes to cover several protocols not normally associated with divertibility (e.g., Diffie-Hellman key exchange). Next, we introduce atomic proxy cryptography, in which an atomic proxy function, in conjunction with a public proxy key, converts ciphertexts (messages or signatures) for one key into ciphertexts for another. Proxy keys, once generated, may be made public and proxy functions applied in untrusted environments. We present atomic proxy functions for discrete-log-based encryption, identification, and signature schemes. It is not clear whether atomic proxy functions exist in general for all public-key cryptosystems. Finally, we discuss the relationship between divertibility and proxy cryptography.


title: Animations 25 Years Later: New Roles and Opportunities

abstract: Animations are commonplace in today's user interfaces. From bouncing icons that catch attention, to transitions helping with orientation, to tutorials, animations can serve numerous purposes. We revisit Baecker and Small's pioneering work Animation at the Interface, 25 years later. We reviewed academic publications and commercial systems, and interviewed 20 professionals of various backgrounds. Our insights led to an expanded set of roles played by animation in interfaces today for keeping in context, teaching, improving user experience, data encoding and visual discourse. We illustrate each role with examples from practice and research, discuss evaluation methods and point to opportunities for future research. This expanded description of roles aims at inspiring the HCI research community to find novel uses of animation, guide them towards evaluation and spark further research.


title: Face R-CNN

abstract: Faster R-CNN is one of the most representative and successful methods for object detection, and has been becoming increasingly popular in various objection detection applications. In this report, we propose a robust deep face detection approach based on Faster R-CNN. In our approach, we exploit several new techniques including new multi-task loss function design, online hard example mining, and multi-scale training strategy to improve Faster R-CNN in multiple aspects. The proposed approach is well suited for face detection, so we call it Face R-CNN. Extensive experiments are conducted on two most popular and challenging face detection benchmarks, FDDB and WIDER FACE, to demonstrate the superiority of the proposed approach over state-of-the-arts.


title: A Study of Prefixes in the Thorndike List to Establish a List of Prefixes that should be Taught in the Elementary School

abstract: * A limited number of copies of this article may be obtained through the Reading Clinic, Pennsylvania State College, State College, Pennsylvania, at a cost of ten cents per copy. ** A seminar discussion under Dr. E. A. Betts, Research Professor in Elementary Education, Pennsylvania State College, State College, Pa. *** A Teacher's Word Book of the Twenty Thousand Words Found Most Fre quently and Widely in General Reading for Children and Young People, prepared by Edward L. Thorndike, Professor of Education, Teachers College, Columbia University. 1Osburn, W. L. Remedial and Follow-up Work. Bloomington, 111: Public School Publishing Co., 1925. 453


title: Orthogonal Positive-Bevel Termination for Chip-Size SiC Reverse Blocking Devices

abstract: Symmetric blocking power semiconductor switches require positive-bevel edge terminations for the reverse blocking p-n junction. This technique has been extensively applied to silicon wafer-size devices with high current ratings. In this letter, we propose and experimentally demonstrate, for the first time, that an orthogonal positive-bevel termination can be used for the reverse blocking junction of chip-size SiC devices. The edge termination was formed by sawing the SiC wafer with a V-shaped dicing blade. For proof of concept, our experiment was done on a SiC wafer with a 15.8-μm 6.1 × 1015 cm-3 p-type epitaxial layer grown on an N+ substrate. The positive-bevel termination resulted in a breakdown voltage of over 1000 V as limited by reach-through breakdown even without removal of damage from the sawing. The leakage current was found to be reduced by two orders of magnitude after reactive ion etching of the SiC bevel surface to remove the sawing damage.


title: Omegabot : Biomimetic inchworm robot using SMA coil actuator and smart composite microstructures (SCM)

abstract: Many researchers have developed various robots with novel gaits that can travel on rough terrain where conventional vehicles or other robots cannot. Many of these robots are bio-inspired since there is lots of amazing locomotions in nature that enable movement through various obstacles. This paper presents a robot based on the motion of ascotis selenaria, a type of inchworm with a locomotion that has an omega shape bending motion in between the extension motions. This type of inchworm can travel approximately its body length per stroke on a rough surfaces, leaf edges and branches of trees. The robot is built with smart composite microstructures, a fabrication method that uses laser micromachining to cut composites and assemble them into micro structures. The robot is actuated with a single shape memory alloy coil actuator. This robot can be used for search and rescue or gathering useful information in an area where only small scale robots can penetrate.


title: Traditional and Digital Media Advertising Preference: A Descriptive Evaluation of Billings, Montana Mothers

abstract: Understanding consumers allows marketers to tailor specifically to a target market effectively. Digital advertising methods are relatively new compared to more established traditional advertising methods; the introduction and proliferation of the Internet and digital devices have steadily increased digital advertising use by marketers. Preferences within digital advertising and within traditional advertising have been explored; however, as digital media usage has increased, preference between the two methods has not been examined. Research indicates mother consumers in America conduct a majority of the household shopping across numerous product categories. An understanding of media channel preference for this demographic group could increase marketing campaign reception and indicate to marketers how marketing dollars should be budgeted for mother consumers. Marketing allocation success could be significantly improved with a better understanding of consumer preference between the traditional and digital media channels. Mothers in Billings, Montana with children living in the home under the age of 18 years old were asked to complete a mail survey to determine mother media preference between traditional and digital media advertising. There were 285 mother consumers’ responses to the mail survey request. A t-test and ANOVA analysis were used to show mothers' preference for traditional media channels over digital. A significant difference was found in mother media channel preference for traditional media channels (M= 2.63, SD= 0.71) over digital media channels (M= 1.68, SD= 0.57) conditions; t(285) = 1.97, p < .000. The ANOVA results were significant, F(5,1698) = 128.239, p = .000, indicating a statistically significant difference between channel preference within the pairs of the six media channels studied. This study provides marketers information about the media channel preferences for mother consumers that can be used to effectively target the consumer preference and behavior of this demographic group wielding significant purchase power.


title: A Survey on High Efficiency Wireless Local Area Networks: Next Generation WiFi

abstract: The emerging paradigm of the Internet of Everything, along with the increasing demand of Internet services everywhere, results in a remarkable and continuous growth of the global Internet traffic. As a cost-effective Internet access solution, WiFi networks currently generate a major portion of the global Internet traffic. Furthermore, the number of WiFi public hotspots worldwide is expected to increase by more than sevenfold by 2018. To face this huge increase in the number of densely deployed WiFi networks, and the massive amount of data to be supported by these networks in indoor and outdoor environments, it is necessary to improve the current WiFi standard and define specifications for high efficiency wireless local area networks (HEWs). This paper presents potential techniques that can be applied for HEWs, in order to achieve the required performance in dense HEW deployment scenarios, as expected in the near future. The HEW solutions under consideration includes physical layer techniques, medium access control layer strategies, spatial frequency reuse schemes, and power saving mechanisms. To accurately assess a newly proposed HEW scheme, we discuss suitable evaluation methodologies, by defining simulation scenarios that represent future HEW usage models, performance metrics that reflect HEW user experience, traffic models for dominant HEW applications, and channel models for indoor and outdoor HEW deployments. Finally, we highlight open issues for future HEW research and development.


title: CacheKit: Evading Memory Introspection Using Cache Incoherence

abstract: With the growing importance of networked embedded devices in the upcoming Internet of Things, new attacks targeting embedded OSes are emerging. ARM processors, which power over 60% of embedded devices, introduce a hardware security extension called TrustZone to protect secure applications in an isolated secure world that cannot be manipulated by a compromised OS in the normal world. Leveraging TrustZone technology, a number of memory integrity checking schemes have been proposed in the secure world to introspect malicious memory modification of the normal world. In this paper, we first discover and verify an ARM TrustZone cache incoherence behavior, which results in the cache contents of the two worlds, secure and non-secure, potentially being different even when they are mapped to the same physical address. Furthermore, code in one TrustZone world cannot access the cache content in the other world. Based on this observation, we develop a new rootkit called CacheKit that hides in the cache of the normal world and is able to evade memory introspection from the secure world. We implement a CacheKit prototype on Cortex-A8 processors after solving a number of challenges. First, we employ the Cache-as-RAM technique to ensure that the malicious code is only loaded into the CPU cache and not RAM. Thus, the secure world cannot detect the existence of the malicious code by examining the RAM. Second, we use the ARM processor's hardware support on cache settings to keep the malicious code persistent in the cache. Third, to evade introspection that flushes cache content back into RAM, we utilize physical addresses from the I/O address range that is not backed by any real I/O devices or RAM. The experimental results show that CacheKit can successfully evade memory introspection from the secure world and has small performance impacts on the rich OS. We discuss potential countermeasures to detect this type of rootkit attack.


title: Using graph cut segmentation for food calorie measurement

abstract: Calorie measurement systems that run on smart phones allow the user to take a picture of the food and measure the number of calories automatically. In order to identify the food accurately in such systems, image segmentation, which partitions an image into different regions, plays an important role. In this paper, we present the implementation of Graph cut segmentation as a means of improving the accuracy of our food classification and recognition system. Graph cut based method is well-known to be efficient, robust, and capable of finding the best contour of objects in an image, suggesting it to be a good method for separating food portions in a food image for calorie measurement. In this paper, we provide the analysis of the Graph cut algorithm as applied to food recognition. We also perform a number of experiments where we used results from the segmentation phase to the Support Vector Machine (SVM) classification model. The results show an improvement in the accuracy of food recognition, especially mixed food where accuracy increases by 15% compared to our previous work [10].


title: Resistance Spot Welding: Fundamentals and Applications for the Automotive Industry

abstract: Abstract The early chapters of this book provide thorough coverage of resistance spot welding fundamentals and principles. Topics covered include lobe and current range curves, contact resistance vs. electrode force, dynamic resistance, heat balance, nugget growth, etc. Equipment issues such as machine types, power supplies, and electrodes are addressed. Subsequent chapters focus on specific spot welding challenges to modern automotive manufacturing. Approaches to welding modern materials including advanced high-strength steels, coated steels, and aluminum alloys are covered in much detail. The final chapters focus on many common production and quality control issues, such as electrode wear, monitoring and testing, computational modeling, and welding codes. The overall goal of the book is to provide a comprehensive resource for automotive engineers and technicians who work with modern spot welding equipment and automotive materials. Table of Contents: Preface / Introduction to Resistance Spot Welding / Re...


title: Enhancing Expert Finding Using Organizational Hierarchies

abstract: The task in expert finding is to identify members of an organization with relevant expertise on a given topic. In existing expert finding systems, profiles are constructed from sources such as email or documents, and used as the basis for expert identification. In this paper, we leverage the organizational hierarchy (depicting relationships between managers, subordinates, and peers) to find members for whom we have little or no information. We propose an algorithm to improve expert finding performance by considering not only the expertise of the member, but also the expertise of his or her neighbors. We show that providing this additional information to an expert finding system improves its retrieval performance.


Modeling and Characterization of Piezoelectric Mems Energy Harvester


title: Dynamic Load Balancing for High-Performance Graph Processing on Hybrid CPU-GPU Platforms

abstract: Graph analysis is becoming increasingly important in many research fields - biology, social sciences, data mining - and daily applications - path finding, product recommendation. Many different large-scale graph-processing systems have been proposed for different platforms. However, little effort has been placed on designing systems for hybrid CPU-GPU platforms.In this work, we present HyGraph, a novel graph-processing systems for hybrid platforms which delivers performance by using CPUs and GPUs concurrently. Its core feature is a specialized data structure which enables dynamic scheduling of jobs onto both the CPU and the GPUs, thus (1) supersedes the need for static workload distribution, (2) provides load balancing, and (3) minimizes inter-process communication overhead by overlapping computation and communication.Our preliminary results demonstrate that HyGraph outperforms CPU-only and GPU-only solutions, delivering close-to-optimal performance on the hybrid system. Moreover, it supports large-scale graphs which do not fit into GPU memory, and it is competitive against state-of-the-art systems.


title: Laser speckle photography for surface tampering detection

abstract: It is often desirable to detect whether a surface has been touched, even when the changes made to that surface are too subtle to see in a pair of before and after images. To address this challenge, we introduce a new imaging technique that combines computational photography and laser speckle imaging. Without requiring controlled laboratory conditions, our method is able to detect surface changes that would be indistinguishable in regular photographs. It is also mobile and does not need to be present at the time of contact with the surface, making it well suited for applications where the surface of interest cannot be constantly monitored. Our approach takes advantage of the fact that tiny surface deformations cause phase changes in reflected coherent light which alter the speckle pattern visible under laser illumination. We take before and after images of the surface under laser light and can detect subtle contact by correlating the speckle patterns in these images. A key challenge we address is that speckle imaging is very sensitive to the location of the camera, so removing and reintroducing the camera requires high-accuracy viewpoint alignment. To this end, we use a combination of computational rephotog-raphy and correlation analysis of the speckle pattern as a function of camera translation. Our technique provides a reliable way of detecting subtle surface contact at a level that was previously only possible under laboratory conditions. With our system, the detection of these subtle surface changes can now be brought into the wild.


title: A comparative study of graphic symbol recognition methods

abstract: From the very beginning of written scripts, contents of documents generally comprise of text, images, figures, graphs and graphic symbols. A graphic recognition system involves representation of graphic symbols, description of features extracted from the symbol and classification of the unknown symbols. Due to the wide range of symbols, no generalize technique is available that can recognize the symbol for all the application domains. this paper, we present an overview of the many models and methodologies available to symbol recognition for representation, description and classification. We provide a general survey of symbol recognition process, beginning with the basic definition of symbol, which is further classified into their types based on application areas. distinctive part of the survey is categorization of different symbol recognition methods into four categories i.e. statistical, structural, syntactical and hybrid methods, which is aimed to help potential researchers in exploring areas of research in the field of graphic symbol recognition.


title: Minnorm training: an algorithm for training overcomplete deep neural networks

abstract: In this work, we propose a new training method for finding minimum weight norm solutions in over-parameterized neural networks (NNs). This method seeks to improve training speed and generalization performance by framing NN training as a constrained optimization problem wherein the sum of the norm of the weights in each layer of the network is minimized, under the constraint of exactly fitting training data. It draws inspiration from support vector machines (SVMs), which are able to generalize well, despite often having an infinite number of free parameters in their primal form, and from recent theoretical generalization bounds on NNs which suggest that lower norm solutions generalize better. To solve this constrained optimization problem, our method employs Lagrange multipliers that act as integrators of error over training and identify `support vector'-like examples. The method can be implemented as a wrapper around gradient based methods and uses standard back-propagation of gradients from the NN for both regression and classification versions of the algorithm. We provide theoretical justifications for the effectiveness of this algorithm in comparison to early stopping and $L_2$-regularization using simple, analytically tractable settings. In particular, we show faster convergence to the max-margin hyperplane in a shallow network (compared to vanilla gradient descent); faster convergence to the minimum-norm solution in a linear chain (compared to $L_2$-regularization); and initialization-independent generalization performance in a deep linear network. Finally, using the MNIST dataset, we demonstrate that this algorithm can boost test accuracy and identify difficult examples in real-world datasets.


title: Adversarial Machine Learning at Scale

abstract: Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model's parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet. Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than single-step attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a "label leaking" effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process.


title: Data mining for web vulnerability detection: A critical review

abstract: Developing software applications that are free of vulnerabilities is a necessity, especially if these applications are intended to operate over the World Wide Web. As the development of software applications is evolving in a quick manner the need to develop tools that can automatically detect vulnerabilities in a software has emerged. The main challenge in these tools is providing a high accuracy and low false positive or false negative rates. In this paper, we analyze how data mining techniques can be used to improve the quality of vulnerability detection. We start by discussing web Application vulnerabilities along with the static detection approach and its limitations. Then we explore data mining techniques and their potential to improve vulnerability detection. The results of using such techniques in the literature are reviewed and discussed. As a conclusion, we argue that with the growing body of research utilizing data mining for vulnerabilities detection we might get a better detection alternative, however, several aspects need to be further investigated including feature engineering, adaptive classifiers and feature selection.


title: Efficient Data Structures For Tamper-Evident Logging

abstract: Many real-world applications wish to collect tamperevident logs for forensic purposes. This paper considers the case of an untrusted logger, serving a number of clients who wish to store their events in the log, and kept honest by a number of auditors who will challenge the logger to prove its correct behavior. We propose semantics of tamper-evident logs in terms of this auditing process. The logger must be able to prove that individual logged events are still present, and that the log, as seen now, is consistent with how it was seen in the past. To accomplish this efficiently, we describe a tree-based data structure that can generate such proofs with logarithmic size and space, improving over previous linear constructions. Where a classic hash chain might require an 800 MB trace to prove that a randomly chosen event is in a log with 80 million events, our prototype returns a 3 KB proof with the same semantics. We also present a flexible mechanism for the log server to present authenticated and tamper-evident search results for all events matching a predicate. This can allow large-scale log servers to selectively delete old events, in an agreed-upon fashion, while generating efficient proofs that no inappropriate events were deleted. We describe a prototype implementation and measure its performance on an 80 million event syslog trace at 1,750 events per second using a single CPU core. Performance improves to 10,500 events per second if cryptographic signatures are offloaded, corresponding to 1.1 TB of logging throughput per week.


title: Scene classification via pLSA

abstract: Given a set of images of scenes containing multiple object categories (e.g. grass, roads, buildings) our objective is to discover these objects in each image in an unsupervised manner, and to use this object distribution to perform scene classification. We achieve this discovery using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature, here applied to a bag of visual words representation for each image. The scene classification on the object distribution is carried out by a k-nearest neighbour classifier. ::: ::: We investigate the classification performance under changes in the visual vocabulary and number of latent topics learnt, and develop a novel vocabulary using colour SIFT descriptors. Classification performance is compared to the supervised approaches of Vogel & Schiele [19] and Oliva & Torralba [11], and the semi-supervised approach of Fei Fei & Perona [3] using their own datasets and testing protocols. In all cases the combination of (unsupervised) pLSA followed by (supervised) nearest neighbour classification achieves superior results. We show applications of this method to image retrieval with relevance feedback and to scene classification in videos.


title: Older adults' attitudes towards and perceptions of ‘smart home’ technologies: a pilot study

abstract: PRIMARY OBJECTIVE ::: The study aim is to explore the perceptions and expectations of seniors in regard to "smart home" technology installed and operated in their homes with the purpose of improving their quality of life and/or monitoring their health status. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: Three focus group sessions were conducted within this pilot study to assess older adults' perceptions of the technology and ways they believe technology can improve their daily lives. Themes discussed in these groups included participants' perceptions of the usefulness of devices and sensors in health-related issues such as preventing or detecting falls, assisting with visual or hearing impairments, improving mobility, reducing isolation, managing medications, and monitoring of physiological parameters. The audiotapes were transcribed and a content analysis was performed. ::: ::: ::: RESULTS ::: A total of 15 older adults participated in three focus group sessions. Areas where advanced technologies would benefit older adult residents included emergency help, prevention and detection of falls, monitoring of physiological parameters, etc. Concerns were expressed about the user-friendliness of the devices, lack of human response and the need for training tailored to older learners. ::: ::: ::: CONCLUSIONS ::: All participants had an overall positive attitude towards devices and sensors that can be installed in their homes in order to enhance their lives.


title: Sports Specialization in Young Athletes

abstract: Context: Sports specialization is intense training in 1 sport while excluding others. Sports specialization in early to middle childhood has become increasingly common. While most experts agree that some degree of sports specialization is necessary to achieve elite levels, there is some debate as to whether such intense practice time must begin during early childhood and to the exclusion of other sports to maximize potential for success. There is a concern that sports specialization before adolescence may be deleterious to a young athlete. Evidence Acquisition: PubMed and OVID were searched for English-language articles from 1990 to 2011 discussing sports specialization, expert athletes, or elite versus novice athletes, including original research articles, consensus opinions, and position statements. Results: For most sports, there is no evidence that intense training and specialization before puberty are necessary to achieve elite status. Risks of early sports specialization include higher rates of injury, increased psychological stress, and quitting sports at a young age. Sports specialization occurs along a continuum. Survey tools are being developed to identify where athletes fall along the spectrum of specialization. Conclusion: Some degree of sports specialization is necessary to develop elite-level skill development. However, for most sports, such intense training in a single sport to the exclusion of others should be delayed until late adolescence to optimize success while minimizing injury, psychological stress, and burnout.


title: Parallel matrix factorization for recommender systems

abstract: Matrix factorization, when the matrix has missing values, has become one of the leading techniques for recommender systems. To handle web-scale datasets with millions of users and billions of ratings, scalability becomes an important issue. Alternating least squares (ALS) and stochastic gradient descent (SGD) are two popular approaches to compute matrix factorization, and there has been a recent flurry of activity to parallelize these algorithms. However, due to the cubic time complexity in the target rank, ALS is not scalable to large-scale datasets. On the other hand, SGD conducts efficient updates but usually suffers from slow convergence that is sensitive to the parameters. Coordinate descent, a classical optimization approach, has been used for many other large-scale problems, but its application to matrix factorization for recommender systems has not been thoroughly explored. In this paper, we show that coordinate descent-based methods have a more efficient update rule compared to ALS and have faster and more stable convergence than SGD. We study different update sequences and propose the CCD++ algorithm, which updates rank-one factors one by one. In addition, CCD++ can be easily parallelized on both multi-core and distributed systems. We empirically show that CCD++ is much faster than ALS and SGD in both settings. As an example, with a synthetic dataset containing 14.6 billion ratings, on a distributed memory cluster with 64 processors, to deliver the desired test RMSE, CCD++ is 49 times faster than SGD and 20 times faster than ALS. When the number of processors is increased to 256, CCD++ takes only 16 s and is still 40 times faster than SGD and 20 times faster than ALS.


title: Comparison of a bigram PLSA and a novel context-based PLSA language model for speech recognition

abstract: We propose a novel context-based probabilistic latent semantic analysis (PLSA) language model for speech recognition. In this model, the topic is conditioned on the immediate history context and the document in the original PLSA model. This allows computing all the possible bigram probabilities of the seen history context using the model. It properly computes the topic probability of an unseen document for each history context present in the document. We compare our approach with a recently proposed unsmoothed bigram PLSA model where only the seen bigram probabilities are calculated, which causes computing the incorrect topic probability for the present history context of the unseen document. The proposed model requires a significantly less amount of computation time and memory space requirements than the unsmoothed bigram PLSA model. We carried out experiments on a continuous speech recognition (CSR) task using theWall Street Journal (WSJ) corpus. The proposed approach shows significant reduction in both perplexity and word error rate (WER) measurements over the other approach.


title: 3D Kinematic Simulation for PA10-7C Robot Arm Based on VRML

abstract: In this paper, a graphical, flexible, interactive, and systematic 3D simulation helps facilitate analyzing and previewing kinematics of PA10-7C robot arm in terms of forward kinematics, inverse kinematics, and the Denavit-Hartenberg convention. Modeling and control are of critical importance when the robot arm is used for practical applications. In the paper, the D-H model of PA10-7C robot arm is given first to describe the relationship between two consecutive frames of joints. Based on this D-H model, forward kinematics is calculated efficiently. To describe the relationship between joint angular velocities in the joint space and the end effector's velocities in Cartesian space, Jacobian matrix for PA10-7C robot arm is derived. By Jacobian matrix, inverse kinematics approach is obtained in the paper. VRML-based model of PA10-7C is built in the paper for the simulation. Finally, simulation results are discussed and the paper is concluded.


title: Direct Synthesis of Complex Loaded Chebyshev Filters in a Complex Filtering Network

abstract: In this paper, a direct synthesis approach is proposed for designing a Chebyshev filter that matches the frequency variant complex loads at both the ports. The approach is based on the power wave renormalization theory and two practical assumptions: 1) the prescribed transmission zeros are stationary and 2) the reflection zeros are located along an imaginary axis. Three conditions are derived to stipulate the characteristic polynomials of the filter’s responses through the renormalization of reference load impedances. These conditions are sequentially applied to ensure that the filter is physically realizable and well matched to the complex loads. It has been shown that the optimally synthesized filtering network that consists of an ideal filter cascaded with a piece of transmission line of an optimal length at each port can match the complex loads over a given frequency band with the best effort. By incorporating with an adjustment of the junction parameters, the approach provides an analytical yet flexible way to synthesize advanced microwave circuits composed of multiple filters connected together through some generalized junctions. The effectiveness of the method is demonstrated through three synthesis examples.


title: Knowledge Base Completion: Baselines Strike Back

abstract: Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on standard datasets such as FB15k and WN18. This paper shows that the accuracy of almost all models published on the FB15k can be outperformed by an appropriately tuned baseline - our reimplementation of the DistMult model. Our findings cast doubt on the claim that the performance improvements of recent models are due to architectural changes as opposed to hyper-parameter tuning or different training objectives. This should prompt future research to re-consider how the performance of models is evaluated and reported.


title: Signal Reconstruction from Interferometric Measurements under Sensing Constraints

abstract: Abstract We develop a framework in which the problem of signal reconstruction from interferometric measurements amounts to one of basis analysis, with measurements that are linear in the basis coefficients. We leverage a generalized interferometry approach to enable the reconstruction of signals represented in different bases of arbitrary domains. Our framework is unifying in that it applies whether the sought-after information concerns the input signal or a sample object, as well as in settings where we have no control over the relative delays of the two paths of the interferometer. While the linear transformation underlying the measurement system has only a limited number of degrees of freedom set by the constrained sensing structure, we show that compressive signal recovery is achievable without introducing any additional randomization to the measurement setup. We establish performance guarantees under constrained sensing by proving that the transformation satisfies sufficient conditions for successful reconstruction. Also, we propose two control policies to guide the collection of informative measurements given prior knowledge about the constrained sensing structure. By minimizing the mutual coherence of the sampled measurements, the controlled approach is shown to yield further gains in sample size complexity.


title: Abstract Cartoon-Looking Rendering of 3D-Scenes

abstract: We present a rendering algorithm that produces images with the appearance of a traditional cartoon from a 3D description of the scene (a static or an animated scene). The 3D scene is rendered with techniques allowing to: outline the profiles and edges of objects in back, uniformly color the surface inside the outlines, render shadows (backface shadows and projected shadows) due to light sources.


title: Building a Multilingual Named Entity-Annotated Corpus Using Annotation Projection

abstract: As developers of a highly multilingual named entity recognition (NER) system, we face an evaluation resource bottleneck problem: we need evaluation data in many languages, the annotation should not be too time-consuming, and the evaluation results across languages should be comparable. We solve the problem by automatically annotating the English version of a multi-parallel corpus and by projecting the annotations into all the other language versions. For the translation of English entities, we use a phrase-based statistical machine translation system as well as a lookup of known names from a multilingual name database. For the projection, we incrementally apply different methods: perfect string matching, perfect consonant signature matching and edit distance similarity. The resulting annotated parallel corpus will be made available for reuse.


title: Automatic programming assessment and test data generation a review on its approaches

abstract: Automatic programming assessment has recently become an important method in assisting lecturers and instructors of programming courses to automatically mark and grade students' programming exercises as well as to provide useful feedbacks on students' programming solutions. As part of the method, test data generation process plays as an integral part to perform a dynamic testing on students' programs. To date, various automated methods for test data generation particularly in software testing field are available. Unfortunately, they are seldom used in the context of automatic programming assessment research area. Nevertheless, there have been limited studies taking a stab to integrate both of them due to more useful features and to include a better quality program testing coverage. Thus, this paper provides a review on approaches that have been implemented in various studies with regard to automatic programming assessment, test data generation and integration of both of them. This review is aimed at gathering different techniques that have been employed to forward an interested reader the starting points for finding further information regarding its trends. In addition, the result of the review reveals the main gap that exists within the context of the considered areas which contributes to our main research topic of interest.


title: Aggregating user input in ecology citizen science projects

abstract: Camera traps (remote, automatic cameras) are revolutionizing large-scale studies in ecology. The Serengeti Lion Project has used camera traps to produce over 1.5 million pictures of animals in the Serengeti. To analyze these pictures, the Project created Snapshot Serengeti, a citizen science website where volunteers can help classify animals. To increase accuracy, each photo is shown to multiple users and a critical step is aggregating individual classifications. In this paper, we present a new aggregation algorithm which achieves an accuracy of 98.6%, better than many human experts. Our algorithm also requires fewer users per photo than existing methods. The algorithm is intuitive and designed so that nonexperts can understand the end results.


title: Antisocial behavior from a developmental psychopathology perspective.

abstract: This paper reviews research on chronic patterns of antisocial behavior and places this research into a developmental psychopathology framework. Specifically, research suggests that there are at least three important pathways through which children and adolescents can develop severe antisocial behaviors. One group of youth shows antisocial behavior that begins in adolescence, and two groups show antisocial behavior that begins in childhood but differ on the presence or absence of callous-unemotional traits. In outlining these distinct pathways to antisocial behavior, we have tried to illustrate some key concepts from developmental psychopathology such as equifinality and multifinality, the importance of understanding the interface between normal and abnormal development, and the importance of using multiple levels of analyses to advance causal theories. Finally, we discuss how this development model can be used to enhance existing interventions for antisocial individuals.


title: Pattern of injuries to neck structures in hanging-an autopsy study.

abstract: One hundred eighty-nine cases of known dead bodies brought for medicolegal autopsy with alleged history of hanging were studied at the Department of Forensic Medicine, State Medico Legal Institute, Government Medical College, Thiruvananthapuram, Kerala, India. All the findings noticed during detailed external examination and flap dissection of the neck were analyzed with special emphasis on correlation between the external and internal injuries on neck.There was a preponderance of males in the study group (70.9%), majority of them being middle-aged (64.9%). Among females, the major group was composed of adolescents and young adults (72.8%). Easily available materials were used as ligature materials. Soft materials such as saree, shawl, lungie, and so on were used as ligature by 47% of victims and hard materials such as coir rope, plastic rope, telephone cable, and so on were used by 29% of victims. Oblique ligature mark was noted in 94.2% of cases. The ligature mark was noncontinuous in 78% of cases. Horizontal ligature marks were noted in cases of partial hanging.Rupture of muscle fibers at the lower attachment of sternomastoid was seen in 19.6%, and carotid intimal tear was seen in 1.1% of cases. Fracture of hyoid bone at their greater horns was seen in 2.7% and thyroid cartilage in 5.3% of cases. Vertebral fracture and dislocation were noted between third and fourth cervical vertebrae in 1.6% of cases. Neck skeleton injuries were noted in persons older than 38 years.


title: CPE virtualization by unifying NFV, SDN and Cloud technologies

abstract: This paper will describe CPE virtualization solution for home and enterprise environments by unifying Network Function Virtualization (NFV), Software Defined Networking (SDN) and Cloud technologies in Internet providers' networks and data centers. The goal of this solution approach is to reduce operational and capital costs, increase network and service flexibility for providers and to broaden service offering and experience for the end-users. This is achieved by leveraging state of the art technologies that provide automation, flexibility and simplify operations. Possible applications, implementation and potentials will be studied throughout this paper.


title: The Application of Petri Nets to Workflow Management

abstract: Workflow management promises a new solution to an age-old problem: controlling, monitoring, optimizing and supporting business processes. What is new about workflow management is the explicit representation of the business process logic which allows for computerized support. This paper discusses the use of Petri nets in the context of workflow management. Petri nets are an established tool for modeling and analyzing processes. On the one hand, Petri nets can be used as a design language for the specification of complex workflows. On the other hand, Petri net theory provides for powerful analysis techniques which can be used to verify the correctness of workflow procedures. This paper introduces workflow management as an application domain for Petri nets, presents state-of-the-art results with respect to the verification of workflows, and highlights some Petri-net-based workflow tools.


title: Human Computer Interaction

abstract: Contents Foreword Preface to the third edition Preface to the second edition Preface to the first edition Introduction Part 1 Foundations Chapter 1 The human 1.1 Introduction 1.2 Input-output channels Design Focus: Getting noticed Design Focus: Where's the middle? 1.3 Human memory Design Focus: Cashing in Design Focus: 7 +- 2 revisited 1.4 Thinking: reasoning and problem solving Design Focus: Human error and false memories 1.5 Emotion 1.6 Individual differences 1.7 Psychology and the design of interactive systems 1.8 Summary Exercises Recommended reading Chapter 2 The computer 2.1 Introduction Design Focus: Numeric keypads 2.2 Text entry devices 2.3 Positioning, pointing and drawing 2.4 Display devices Design Focus: Hermes: a situated display 2.5 Devices for virtual reality and 3D interaction 2.6 Physical controls, sensors and special devices Design Focus: Feeling the road Design Focus: Smart-Its - making sensors easy 2.7 Paper: printing and scanning Design Focus: Readability of text 2.8 Memory 2.9 Processing and networks Design Focus: The myth of the infinitely fast machine 2.10 Summary Exercises Recommended reading Chapter 3 The interaction 3.1 Introduction 3.2 Models of interaction Design Focus: Video recorder 3.3 Frameworks and HCI 3.4 Ergonomics Design Focus: Industrial interfaces 3.5 Interaction styles Design Focus: Navigation in 3D and 2D 3.6 Elements of the WIMP interface Design Focus: Learning toolbars 3.7 Interactivity 3.8 The context of the interaction Design Focus: Half the picture? 3.9 Experience, engagement and fun 3.10 Summary Exercises Recommended reading Chapter 4 Paradigms 4.1 Introduction 4.2 Paradigms for interaction 4.3 Summary Exercises Recommended reading Part 2 Design process Chapter 5 Interaction design basics 5.1 Introduction 5.2 What is design? 5.3 The process of design 5.4 User focus Design Focus: Cultural probes 5.5 Scenarios 5.6 Navigation design Design Focus: Beware the big button trap Design Focus: Modes 5.7 Screen design and layout Design Focus: Alignment and layout matter Design Focus: Checking screen colors 5.8 Iteration and prototyping 5.9 Summary Exercises Recommended reading Chapter 6 HCI in the software process 6.1 Introduction 6.2 The software life cycle 6.3 Usability engineering 6.4 Iterative design and prototyping Design Focus: Prototyping in practice 6.5 Design rationale 6.6 Summary Exercises Recommended reading Chapter 7 Design rules 7.1 Introduction 7.2 Principles to support usability 7.3 Standards 7.4 Guidelines 7.5 Golden rules and heuristics 7.6 HCI patterns 7.7 Summary Exercises Recommended reading Chapter 8 Implementation support 8.1 Introduction 8.2 Elements of windowing systems 8.3 Programming the application Design Focus: Going with the grain 8.4 Using toolkits Design Focus: Java and AWT 8.5 User interface management systems 8.6 Summary Exercises Recommended reading Chapter 9 Evaluation techniques 9.1 What is evaluation? 9.2 Goals of evaluation 9.3 Evaluation through expert analysis 9.4 Evaluation through user participation 9.5 Choosing an evaluation method 9.6 Summary Exercises Recommended reading Chapter 10 Universal design 10.1 Introduction 10.2 Universal design principles 10.3 Multi-modal interaction Design Focus: Designing websites for screen readers Design Focus: Choosing the right kind of speech Design Focus: Apple Newton 10.4 Designing for diversity Design Focus: Mathematics for the blind 10.5 Summary Exercises Recommended reading Chapter 11 User support 11.1 Introduction 11.2 Requirements of user support 11.3 Approaches to user support 11.4 Adaptive help systems Design Focus: It's good to talk - help from real people 11.5 Designing user support systems 11.6 Summary Exercises Recommended reading Part 3 Models and theories Chapter 12 Cognitive models 12.1 Introduction 12.2 Goal and task hierarchies Design Focus: GOMS saves money 12.3 Linguistic models 12.4 The challenge of display-based systems 12.5 Physical and device models 12.6 Cognitive architectures 12.7 Summary Exercises Recommended reading Chapter 13 Socio-organizational issues and stakeholder requirements 13.1 Introduction 13.2 Organizational issues Design Focus: Implementing workflow in Lotus Notes 13.3 Capturing requirements Design Focus: Tomorrow's hospital - using participatory design 13.4 Summary Exercises Recommended reading Chapter 14 Communication and collaboration models 14.1 Introduction 14.2 Face-to-face communication Design Focus: Looking real - Avatar Conference 14.3 Conversation 14.4 Text-based communication 14.5 Group working 14.6 Summary Exercises Recommended reading Chapter 15 Task analysis 15.1 Introduction 15.2 Differences between task analysis and other techniques 15.3 Task decomposition 15.4 Knowledge-based analysis 15.5 Entity-relationship-based techniques 15.6 Sources of information and data collection 15.7 Uses of task analysis 15.8 Summary Exercises Recommended reading Chapter 16 Dialog notations and design 16.1 What is dialog? 16.2 Dialog design notations 16.3 Diagrammatic notations Design Focus: Using STNs in prototyping Design Focus: Digital watch - documentation and analysis 16.4 Textual dialog notations 16.5 Dialog semantics 16.6 Dialog analysis and design 16.7 Summary Exercises Recommended reading Chapter 17 Models of the system 17.1 Introduction 17.2 Standard formalisms 17.3 Interaction models 17.4 Continuous behavior 17.5 Summary Exercises Recommended reading Chapter 18 Modeling rich interaction 18.1 Introduction 18.2 Status-event analysis 18.3 Rich contexts 18.4 Low intention and sensor-based interaction Design Focus: Designing a car courtesy light 18.5 Summary Exercises Recommended reading Part 4 Outside the box Chapter 19 Groupware 19.1 Introduction 19.2 Groupware systems 19.3 Computer-mediated communication Design Focus: SMS in action 19.4 Meeting and decision support systems 19.5 Shared applications and artifacts 19.6 Frameworks for groupware Design Focus: TOWER - workspace awareness Exercises Recommended reading Chapter 20 Ubiquitous computing and augmented realities 20.1 Introduction 20.2 Ubiquitous computing applications research Design Focus: Ambient Wood - augmenting the physical Design Focus: Classroom 2000/eClass - deploying and evaluating ubicomp 20.3 Virtual and augmented reality Design Focus: Shared experience Design Focus: Applications of augmented reality 20.4 Information and data visualization Design Focus: Getting the size right 20.5 Summary Exercises Recommended reading Chapter 21 Hypertext, multimedia and the world wide web 21.1 Introduction 21.2 Understanding hypertext 21.3 Finding things 21.4 Web technology and issues 21.5 Static web content 21.6 Dynamic web content 21.7 Summary Exercises Recommended reading References Index


title: Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry

abstract: Abstract E-commerce has provided new opportunities for both businesses and consumers to easily share information, find and buy a product, increasing the ease of movement from one company to another as well as to increase the risk of churn. In this study we develop a churn prediction model tailored for B2B e-commerce industry by testing the forecasting capability of a new model, the support vector machine (SVM) based on the AUC parameter-selection technique (SVMauc). The predictive performance of SVMauc is benchmarked to logistic regression, neural network and classic support vector machine. Our study shows that the parameter optimization procedure plays an important role in the predictive performance and the SVMauc points out good generalization performance when applied to noisy, imbalance and nonlinear marketing data outperforming the other methods. Thus, our findings confirm that the data-driven approach to churn prediction and the development of retention strategies outperforms commonly used managerial heuristics in B2B e-commerce industry.


title: AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling

abstract: Interpretability of the underlying AI representations is a key raison d'\^{e}tre for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring Systems (ITS) research. OLMs provide tools for 'opening' up the AI models of learners' cognition and emotions for the purpose of supporting human learning and teaching. Over thirty years of research in ITS (also known as AI in Education) produced important work, which informs about how AI can be used in Education to best effects and, through the OLM research, what are the necessary considerations to make it interpretable and explainable for the benefit of learning. We argue that this work can provide a valuable starting point for a framework of interpretable AI, and as such is of relevance to the application of both knowledge-based and machine learning systems in other high-stakes contexts, beyond education.


title: Micropayments for Decentralized Currencies

abstract: Electronic financial transactions in the US, even those enabled by Bitcoin, have relatively high transaction costs. As a result, it becomes infeasible to make micropayments, i.e. payments that are pennies or fractions of a penny. In order to circumvent the cost of recording all transactions, Wheeler (1996) and Rivest (1997) suggested the notion of a probabilistic payment, that is, one implements payments that have expected value on the order of micro pennies by running an appropriately biased lottery for a larger payment. While there have been quite a few proposed solutions to such lottery-based micropayment schemes, all these solutions rely on a trusted third party to coordinate the transactions; furthermore, to implement these systems in today's economy would require a a global change to how either banks or electronic payment companies (e.g., Visa and Mastercard) handle transactions. We put forth a new lottery-based micropayment scheme for any ledger-based transaction system, that can be used today without any change to the current infrastructure. We implement our scheme in a sample web application and show how a single server can handle thousands of micropayment requests per second. We provide an analysis for how the scheme can work at Internet scale.


title: Ontology construction for information selection

abstract: Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while ensuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user specified keywords. But many documents convey desired semantic information without containing these keywords. One can overcome this problem by indexing documents according to meanings rather than words, although this will entail a way of converting words to meanings and the creation of ontology. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontology. Ontology is a collection of concepts and their interrelationships, which provide an abstract view of an application domain. We propose a new mechanism that can generate ontology automatically in order to make our approach scalable. For this we modify the existing self-organizing tree algorithm (SOTA) that constructs a hierarchy from top to bottom. Furthermore, in order to find an appropriate concept for each node in the hierarchy we propose an automatic concept selection algorithm from WordNet called linguistic ontology. To illustrate the effectiveness of our automatic ontology construction method, we have explored our ontology construction in text documents. The Reuters21578 text document corpus has been used. We have observed that our modified SOTA outperforms hierarchical agglomerative clustering (HAC).


title: An ICP variant using a point-to-line metric

abstract: This paper describes PLICP, an ICP (iterative closest/corresponding point) variant that uses a point-to-line metric, and an exact closed-form for minimizing such metric. The resulting algorithm has some interesting properties: it converges quadratically, and in a finite number of steps. The method is validated against vanilla ICP, IDC (iterative dual correspondences), and MBICP (Metric-Based ICP) by reproducing the experiments performed in Minguez et al. (2006). The experiments suggest that PLICP is more precise, and requires less iterations. However, it is less robust to very large initial displacement errors. The last part of the paper is devoted to purely algorithmic optimization of the correspondence search; this allows for a significant speed-up of the computation. The source code is available for download.


title: Digital innovation as a fundamental and powerful concept in the information systems curriculum

abstract: The 50-year march of Moore's Law has led to the creation of a relatively cheap and increasingly easy-touse world-wide digital infrastructure of computers, mobile devices, broadband network connections, and advanced application platforms. This digital infrastructure has, in turn, accelerated the emergence of new technologies that enable transformations in how we live and work, how companies organize, and the structure of entire industries. ::: ::: As a result, it has become important for all business students to have a strong grounding in IT and digital innovation in order to manage, lead, and transform organizations that are increasingly dependent on digital innovation. Yet, at many schools, students do not get such grounding because the required information systems core class is stuck in the past. We present a vision for a redesigned IS core class that adopts digital innovation as a fundamental and powerful concept (FPC). A good FPC serves as both a foundational concept and an organizing principle for a course. ::: ::: We espouse a particularly broad conceptualization of digital innovation that allows for a variety of teaching styles and topical emphases for the IS core class. This conceptualization includes three types of innovation (i.e., process, product, and business model innovation), and four stages for the overall innovation process (i.e., discovery, development, diffusion, and impact). Based on this conceptualization, we examine the implications of adopting digital innovation as an FPC. We also briefly discuss broader implications relating to (1) the IS curriculum beyond the core class, (2) the research agenda for the IS field, and (3) the identity and legitimacy of IS in business schools.


title: RADAR: an in-building RF-based user location and tracking system

abstract: The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.


title: Amygdala damage impairs emotion recognition from scenes only when they contain facial expressions

abstract: Bilateral damage to the human amygdala impairs recognition of negatively valenced emotions from facial expressions, but it is unclear if this finding generalizes to richer visual stimuli that contain cues in addition to faces. We investigated this issue in 4 subjects with bilateral amygdala damage, 23 with unilateral amygdala damage, 22 brain-damaged controls and 16 normal individuals. Subjects were shown two blocks of complex social scenes; all stimuli in the two blocks were identical, except that the first block had all facial expressions in the image erased. While control subjects were more accurate in recognizing emotions when facial expressions were present, subjects with bilateral amygdala damage did not show the same benefit for negative emotions, often performing equivalently across the two conditions. Most striking, subjects with bilateral amygdala damage were more accurate in recognizing scenes showing anger with faces erased than with faces present, an effect resulting in part from highly abnormal recognition of certain angry facial expressions. All four subjects with bilateral amygdala damage were impaired in recognizing angry faces shown in isolation, and frequently mistook expressions of anger for smiles, a mistake never made by any control subject. Bilateral amygdala damage thus disproportionately impairs recognition of certain emotions from complex visual stimuli when subjects utilize information from facial expressions.


title: A cyber-physical system approach to smart city development

abstract: In the contemporary era, the world is confronting numerous challenges that should be dealt with in the near future like effective management of energy, necessity for economic development, reducing environmental challenges, quality and security of life of its inhabitants. The growing concentration of the global inhabitants into towns and cities puts the urban areas in the center of the concerns and make them vital stakeholders for the viable growth strategy of the world. Consequently, Cyber-physical systems (CPS) can play a significant role in their adaptation to smart cities applications and projects. This paper aims to give an overview of the smart city concept and emerging technologies in this area and a brief explanation of cyber-physical systems. It then highlights the potential role of CPS in the development of smart city applications and includes some actual examples of the adaptation of CPS to smart city projects. The paper then outlines some advantages of the CPS in this context and discusses potential challenges. The paper also offers a research agenda for the future.


title: Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers

abstract: Bibliographic reference parsing refers to extracting machine-readable metadata, such as the names of the authors, the title, or journal name, from bibliographic reference strings. Many approaches to this problem have been proposed so far, including regular expressions, knowledge bases and supervised machine learning. Many open source reference parsers based on various algorithms are also available. In this paper, we apply, evaluate and compare ten reference parsing tools in a specific business use case. The tools are Anystyle-Parser, Biblio, CERMINE, Citation, Citation-Parser, GROBID, ParsCit, PDFSSA4MET, Reference Tagger and Science Parse, and we compare them in both their out-of-the-box versions and versions tuned to the project-specific data. According to our evaluation, the best performing out-of-the-box tool is GROBID (F1 0.89), followed by CERMINE (F1 0.83) and ParsCit (F1 0.75). We also found that even though machine learning-based tools and tools based on rules or regular expressions achieve on average similar precision (0.77 for ML-based tools vs. 0.76 for non-ML-based tools), applying machine learning-based tools results in a recall three times higher than in the case of non-ML-based tools (0.66 vs. 0.22). Our study also confirms that tuning the models to the task-specific data results in the increase in the quality. The retrained versions of reference parsers are in all cases better than their out-of-the-box counterparts; for GROBID F1 increased by 3% (0.92 vs. 0.89), for CERMINE by 11% (0.92 vs. 0.83), and for ParsCit by 16% (0.87 vs. 0.75).