title: A Review of the Stern Review on the Economics of Climate Change

abstract: The Stern Review calls for immediate decisive action to stabilize greenhouse gases because "the benefits of strong, early action on climate change outweighs the costs." The economic analysis supporting this conclusion consists mostly of two basic strands. The first strand is a formal aggregative model that relies for its conclusions primari- ly upon imposing a very low discount rate. Concerning this discount-rate aspect, I am skeptical of the Review's formal analysis, but this essay points out that we are actual- ly a lot less sure about what interest rate should be used for discounting climate change than is commonly acknowledged. The Review's second basic strand is a more intuitive argument that it might be very important to avoid possibly large uncertain- ties that are difficult to quantify. Concerning this uncertainty aspect, I argue that it might be recast into sound analytical reasoning that might justify some of the Review's conclusions. The basic issue here is that spending money to slow global warming should perhaps not be conceptualized primarily as being about consump- tion smoothing as much as being about how much insurance to buy to offset the small change of a ruinous catastrophe that is difficult to compensate by ordinary savings.


title: A Neurally Controlled Computer Game Avatar With Humanlike Behavior

abstract: This paper describes the NeuroBot system, which uses a global workspace architecture, implemented in spiking neurons, to control an avatar within the Unreal Tournament 2004 (UT2004) computer game. This system is designed to display humanlike behavior within UT2004, which provides a good environment for comparing human and embodied AI behavior without the cost and difficulty of full humanoid robots. Using a biologically inspired approach, the architecture is loosely based on theories about the high-level control circuits in the brain, and it is the first neural implementation of a global workspace that has been embodied in a complex dynamic real-time environment. NeuroBot's humanlike behavior was tested by competing in the 2011 BotPrize competition, in which human judges play UT2004 and rate the humanness of other avatars that are controlled by a human or a bot. NeuroBot came a close second, achieving a humanness rating of 36%, while the most human human reached 67%. We also developed a humanness metric that combines a number of statistical measures of an avatar's behavior into a single number. In our experiments with this metric, NeuroBot was rated as 33% human, and the most human human achieved 73%.


title: Zinc Pyrithione Inhibits Yeast Growth through Copper Influx and Inactivation of Iron-Sulfur Proteins

abstract: Zinc pyrithione (ZPT) is an antimicrobial material with widespread use in antidandruff shampoos and antifouling paints. Despite decades of commercial use, there is little understanding of its antimicrobial mechanism of action. We used a combination of genome-wide approaches (yeast deletion mutants and microarrays) and traditional methods (gene constructs and atomic emission) to characterize the activity of ZPT against a model yeast, Saccharomyces cerevisiae. ZPT acts through an increase in cellular copper levels that leads to loss of activity of iron-sulfur cluster-containing proteins. ZPT was also found to mediate growth inhibition through an increase in copper in the scalp fungus Malassezia globosa. A model is presented in which pyrithione acts as a copper ionophore, enabling copper to enter cells and distribute across intracellular membranes. This is the first report of a metal-ligand complex that inhibits fungal growth by increasing the cellular level of a different metal.


title: Comparison of Four Chemotherapy Regimens for Advanced Non–Small-Cell Lung Cancer

abstract: BACKGROUND ::: We conducted a randomized study to determine whether any of three chemotherapy regimens was superior to cisplatin and paclitaxel in patients with advanced non-small-cell lung cancer. ::: ::: ::: METHODS ::: A total of 1207 patients with advanced non-small-cell lung cancer were randomly assigned to a reference regimen of cisplatin and paclitaxel or to one of three experimental regimens: cisplatin and gemcitabine, cisplatin and docetaxel, or carboplatin and paclitaxel. ::: ::: ::: RESULTS ::: The response rate for all 1155 eligible patients was 19 percent, with a median survival of 7.9 months (95 percent confidence interval, 7.3 to 8.5), a 1-year survival rate of 33 percent (95 percent confidence interval, 30 to 36 percent), and a 2-year survival rate of 11 percent (95 percent confidence interval, 8 to 12 percent). The response rate and survival did not differ significantly between patients assigned to receive cisplatin and paclitaxel and those assigned to receive any of the three experimental regimens. Treatment with cisplatin and gemcitabine was associated with a significantly longer time to the progression of disease than was treatment with cisplatin and paclitaxel but was more likely to cause grade 3, 4, or 5 renal toxicity (in 9 percent of patients, vs. 3 percent of those treated with cisplatin plus paclitaxel). Patients with a performance status of 2 had a significantly lower rate of survival than did those with a performance status of 0 or 1. ::: ::: ::: CONCLUSIONS ::: None of four chemotherapy regimens offered a significant advantage over the others in the treatment of advanced non-small-cell lung cancer.


title: Understanding Continuance Intention of Blog Users: A Perspective of Flow and Expectation Confirmation Theory

abstract: Blogs are currently very popular. Many website portals, such as Yahoo Blog, PChome Blog, and Sina, offer different functions and personal services to attract potential blog users, as this generates greater advertising income. Ensuring that users continue to use blog is important to the survival of these portals. Previous articles investigate the system functions and information quality of blogs, but these technologies are already mature. Relatively few studies discuss the flow experience of using blogs. This study investigates whether the users’ flow experience affects their satisfaction with, and intention to continue using, blogs. This study surveys 303 bloggers online. Research findings indicate that confirmation, perceived usefulness, flow, challenge, and arousal positively affected bloggers’ satisfaction with using blogs; perceived usefulness, satisfaction, and flow also positively influenced bloggers’ intentions to continue using blogs. In addition, the findings point out that the higher satisfaction users have, the more are continuance intention users get. This study provides recommendations on how service providers can increase blogger continuance intentions.


title: Paraphrase detection using semantic relatedness based on Synset Shortest Path in WordNet

abstract: This study presents a sentence-to-sentence semantic relatedness measures for paraphrase detection. The proposed measures adopt the shortest path between synsets in WordNet as the core to measure the relatedness between two sentences. The interlinked synsets in WordNet are based on the conceptual-semantic relation between two synsets. Thus the distance between two synsets in WordNet can be used to measure the semantic relatedness between two synsets. This study derived a sentence-level semantic relatedness using this feature to detect paraphrasing among sentences. The performance of the proposed semantic relatedness in paraphrasing is evaluated based on the accuracy and F-measures of the proposed measures in identifying paraphrase in Microsoft Research Paraphrase Corpus. The proposed method achieved 71.1% in accuracy and 81.8% in F-measures. The performance of the proposed method is compared with 6 paraphrase detection methods which include Salient Semantic Analysis and Second-order Co-occurrence Pointwise Mutual Information. In the comparison, the proposed method achieved the fourth highest accuracy and the second highest F-measure compare to other methods. This is a reasonable performance for the proposed semantic relatedness in paraphrase detection.


title: An Evaluation Model of New Product Launch Strategy

abstract: Abstract The objective of this article is to develop an empirically based framework for formulating and selecting a product launch strategy. Managers usually face Fuzzy decision scenarios. Traditional decision-making methods fail to satisfy a manager's need in this regard. Thus, a hierarchical fuzzy multi-criteria decision-making (Fuzzy MCDM) method for evaluating a new product launch strategy is proposed in this study. In order to show the practicality and usefulness of this model, an empirical study of the Taiwan IC industry is demonstrated. The results show that the fast follower strategy is the most applicable.


title: Mutations in Plasmodium falciparum K13 propeller gene from Bangladesh (2009–2013)

abstract: BackgroundBangladesh is a malaria hypo-endemic country sharing borders with India and Myanmar. Artemisinin combination therapy (ACT) remains successful in Bangladesh. An increase of artemisinin-resistant malaria parasites on the Thai-Cambodia and Thai-Myanmar borders is worrisome. K13 propeller gene (PF3D7_1343700 or PF13_0238) mutations have been linked to both in vitro artemisinin resistance and in vivo slow parasite clearance rates. This group undertook to evaluate if mutations seen in Cambodia have emerged in Bangladesh where ACT use is now standard for a decade.MethodsSamples were obtained from Plasmodium falciparum-infected malaria patients from Upazila health complexes (UHC) between 2009 and 2013 in seven endemic districts of Bangladesh. These districts included Khagrachari (Matiranga UHC), Rangamati (Rajasthali UHC), Cox’s Bazar (Ramu and Ukhia UHC), Bandarban (Lama UHC), Mymensingh (Haluaghat UHC), Netrokona (Durgapur and Kalmakanda UHC), and Moulvibazar (Sreemangal and Kamalganj UHC).ResultsOut of 296 microscopically positive P. falciparum samples, 271 (91.6%) were confirmed as mono-infections by both real-time PCR and nested PCR. The K13 propeller gene from 253 (93.4%) samples was sequenced bi-directionally. One non-synonymous mutation (A578S) was found in Bangladeshi clinical isolates. The A578S mutation was confirmed and lies adjacent to the C580Y mutation, the major mutation causing delayed parasite clearance in Cambodia. Based on computational modeling A578S should have a significant effect on tertiary structure of the protein.ConclusionThe data suggest that P. falciparum in Bangladesh remains free of the C580Y mutation linked to delayed parasite clearance. However, the mutation A578S is present and based on structural analysis could affect K13 gene function. Further in vivo clinical studies are required to validate the effect of this mutation.


title: MT-based sentence alignment for OCR-generated parallel texts

abstract: The performance of current sentence alignment tools varies according to the to-be-aligned texts. We have found existing tools unsuitable for hard-to-align parallel texts and describe an alternative alignment algorithm. The basic idea is to use machine translations of a text and BLEU as a similarity score to find reliable alignments which are used as anchor points. The gaps between these anchor points are then filled using BLEU-based and length-based heuristics. We show that this approach outperforms state-of-the-art algorithms in our alignment task, and that this improvement in alignment quality translates into better SMT performance. Furthermore, we show that even length-based alignment algorithms profit from having a machine translation as a point of comparison.


Clinical and oral microbiology


title: Hyperarticulation detection in repetitive voice queries using pairwise comparison for improved speech recognition

abstract: Automatic speech recognition systems can benefit from cues in user voice such as hyperarticulation. Traditional approaches typically attempt to define and detect an absolute state of hyperarticulation, which is very difficult, especially on short voice queries. We present a novel approach for hyperarticulation detection using pairwise comparisons and demonstrate its application in a real-world speech recognition system. Our approach uses delta features extracted from a pair of repetitive user utterances. Results show significant improvements in WER (word error rate) by using hyperarticulation information as a feature in a second pass N-best hypotheses rescoring setup.


title: Research of Dynamic Model of Knowledge Alliance Based on Knowledge Creation

abstract: Based on the Analyses of the process of knowledge transfer and creation in knowledge alliance, this paper gives a dynamic game model of knowledge alliance; the influence of absorptive capability and transparency of knowledge on the decision of participators is analyzed. Future study about the issue is given in the last.


title: Designing Secure Ethereum Smart Contracts: A Finite State Machine Based Approach

abstract: The adoption of blockchain-based distributed computation platforms is growing fast. Some of these platforms, such as Ethereum, provide support for implementing smart contracts, which are envisioned to have novel applications in a broad range of areas, including finance and the Internet-of-Things. However, a significant number of smart contracts deployed in practice suffer from security vulnerabilities, which enable malicious users to steal assets from a contract or to cause damage. Vulnerabilities present a serious issue since contracts may handle financial assets of considerable value, and contract bugs are non-fixable by design. To help developers create more secure smart contracts, we introduce FSolidM, a framework rooted in rigorous semantics for designing contracts as Finite State Machines (FSM). We present a tool for creating FSM on an easy-to-use graphical interface and for automatically generating Ethereum contracts. Further, we introduce a set of design patterns, which we implement as plugins that developers can easily add to their contracts to enhance security and functionality.


title: Prosthetic hand with a linkage finger mechanism for power grasping applications

abstract: The human hand is an exceptionally significant part of the human body which has a very complex biological system with bones, joints, and muscles. Among all hand functions, power grasping plays a crucial role in the activities of daily living. In this research a prosthetic terminal device is designed to assist the power grasping activities of amputees subjected to wrist disarticulation. The designed terminal device contains four identical fingers made of a novel linkage mechanism, which can accomplish flexion and extension. With the intention of verifying the effectiveness of the mechanism, kinematic analysis has been carried out. Furthermore, the motion simulation has demonstrated that the mechanism is capable of generating the appropriate finger movements to accomplish cylindrical and spherical power grasps. In addition, the work envelop of the proposed prosthetic finger has been determined. The 3D printed prototype of the finger was experimentally tested. The experimental results validate the effectiveness of the proposed mechanism to gain the expected motion patterns.


title: Benign and malignant breast tumors classification based on region growing and CNN segmentation

abstract: CNN templates are generated using a genetic algorithm to segment mammograms.An adaptive threshold is computed in region growing process by using ANN and intensity features.In tumor classification, CNN produces better results than region growing.MLP produces the highest classification accuracy among other classifiers.Results on DDSM images are more appropriate than those of MIAS. Breast cancer is regarded as one of the most frequent mortality causes among women. As early detection of breast cancer increases the survival chance, creation of a system to diagnose suspicious masses in mammograms is important. In this paper, two automated methods are presented to diagnose mass types of benign and malignant in mammograms. In the first proposed method, segmentation is done using an automated region growing whose threshold is obtained by a trained artificial neural network (ANN). In the second proposed method, segmentation is performed by a cellular neural network (CNN) whose parameters are determined by a genetic algorithm (GA). Intensity, textural, and shape features are extracted from segmented tumors. GA is used to select appropriate features from the set of extracted features. In the next stage, ANNs are used to classify the mammograms as benign or malignant. To evaluate the performance of the proposed methods different classifiers (such as random forest, naive Bayes, SVM, and KNN) are used. Results of the proposed techniques performed on MIAS and DDSM databases are promising. The obtained sensitivity, specificity, and accuracy rates are 96.87%, 95.94%, and 96.47%, respectively.


title: CrowdEstimator: Approximating Crowd Sizes with Multi-modal Data for Internet-of-Things Services

abstract: Crowd mobility has been paid attention for the Internet-of-things (IoT) applications. This paper addresses the crowd estimation problem and builds an IoT service to share the crowd estimation results across different systems. The crowd estimation problem is to approximate the crowd size in a targeted area using the observed information (e.g., Wi-Fi data). This paper exploits Wi-Fi probe request packets ("Wi-Fi probes" for short) broadcasted by mobile devices to solve this problem. However, using only Wi-Fi probes to estimate the crowd size may result in inaccurate results due to various environmental uncertainties which may lead to crowd overestimation or underestimation. Moreover, the ground-truth is unavailable because the coverage of Wi-Fi signals is time-varying and invisible. This paper introduces auxiliary sensors, stereoscopic cameras, to collect the near ground-truth at a specified calibration choke point. Two calibration algorithms are proposed to solve the crowd estimation problem. The key idea is to calibrate the Wi-Fi-only crowd estimation based on the correlations between the two types of data modalities. Then, to share the calibrated results across systems required by different stakeholders, our system is integrated with the FIWARE-based IoT platform. To verify the proposed system, we have launched an indoor pilot study in the Wellington Railway Station and an outdoor pilot study in the Christchurch Re:START Mall in New Zealand. The large-scale pilot studies show that stereoscopic cameras can reach minimum accuracy of 85% and high precision detection for providing the near ground-truth. The proposed calibration algorithms reduce estimation errors by 43.68% on average compared to the Wi-Fi-only approach.


title: Distinguishing Chinese Grain Safety from the Angle of Economy Ecologization

abstract: Through analyzing and evaluating the safety level of the grain industry in terms of system equilibrium, stability, adaptability, and fluency, problems of the grain safety were identified from the angle of economy ecologization and feasible suggestions were put forward to strengthen national resources security, promote coordination in dynamic mechanism, and improve comprehensive decision-making in grain development.


title: BER-based adaptive ADC-equalizer based receiver for communication links

abstract: This paper presents the architecture of a non-uniform reference level bit error-rate (BER)-optimal analog-to-digital converter (ADC) and equalizer, for high-speed communication links. Finite precision analysis demonstrates that the use of the BER-optimal ADC does not increase the equalizer complexity/power significantly. An adaptive algorithm referred to as the approximate minimum BER algorithm (AMBER) is proposed in order to determine the BER-optimal reference levels. Finite-precision analysis of AMBER indicates that reference levels represented with 9-bit precision is sufficient for a 3-bit BER-optimal ADC to achieve BER equal to that of a 4-bit conventional ADC. An architectural implementation of AMBER is also presented. The reference-level adaptation unit (RL-UD) has a full-adder (FA) complexity that is 76% over the conventional adaptive equalizer. The RL-UD block is clock-gated after convergence and hence does not present a power overhead. Thus, for high-speed links employing the flash ADC architecture, the proposed AMBER receiver represents a power savings of approximately 50% in the ADC.


title: Tongue Tracking in Ultrasound Images using EigenTongue Decomposition and Artificial Neural Networks

abstract: This paper describes a machine learning approach for extracting automatically the tongue contour in ultrasound images. This method is developed in the context of visual articulatory biofeedback for speech therapy. The goal is to provide a speaker with an intuitive visualization of his/her tongue movement, in real-time, and with minimum human intervention. Contrary to most widely used techniques based on active contours, the proposed method aims at exploiting the information of all image pixels to infer the tongue contour. For that purpose, a compact representation of each image is extracted using a PCA-based decomposition technique (named EigenTongue). Artificial neural networks are then used to convert the extracted visual features into control parameters of a PCA-based tongue contour model. The proposed method is evaluated on 9 speakers, using data recorded with the ultrasound probe hold manually (as in the targeted application). Speaker-dependent experiments demonstrated the effectiveness of the proposed method (with an average error of ~1.3 mm when training from 80 manually annotated images), even when the tongue contour is poorly imaged. The performance was significantly lower in speaker-independent experiments (i.e. when estimating contours on an unknown speaker), likely due to anatomical differences across speakers.


title: An end-to-end neural network for polyphonic piano music transcription

abstract: We present a supervised neural network model for polyphonic piano music transcription. The architecture of the proposed model is analogous to speech recognition systems and comprises an acoustic model and a music language model . The acoustic model is a neural network used for estimating the probabilities of pitches in a frame of audio. The language model is a recurrent neural network that models the correlations between pitch combinations over time. The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony. The acoustic and language model predictions are combined using a probabilistic graphical model. Inference over the output variables is performed using the beam search algorithm. We perform two sets of experiments. We investigate various neural network architectures for the acoustic models and also investigate the effect of combining acoustic and music language model predictions using the proposed architecture. We compare performance of the neural network-based acoustic models with two popular unsupervised acoustic models. Results show that convolutional neural network acoustic models yield the best performance across all evaluation metrics. We also observe improved performance with the application of the music language models. Finally, we present an efficient variant of beam search that improves performance and reduces run-times by an order of magnitude, making the model suitable for real-time applications.


title: Audio recapture detection using deep learning

abstract: Since the audio recapture can be used to assist audio splicing, it is important to identify whether a suspected audio recording is recaptured or not. However, few works on such detection have been reported. In this paper, we propose an method to detect the recaptured audio based on deep learning and we investigate two deep learning techniques, i.e., neural network with dropout method and stack auto-encoders (SAE). The waveform samples of audio frame is directly used as the input for the deep neural network. The experimental results show that error rate around 7.5% can be achieved, which indicates that our proposed method can successfully discriminate recaptured audio and original audio.


title: How does search behavior change as search becomes more difficult?

abstract: Search engines make it easy to check facts online, but finding some specific kinds of information sometimes proves to be difficult. We studied the behavioral signals that suggest that a user is having trouble in a search task. First, we ran a lab study with 23 users to gain a preliminary understanding on how users' behavior changes when they struggle finding the information they're looking for. The observations were then tested with 179 participants who all completed an average of 22.3 tasks from a pool of 100 tasks. The large-scale study provided quantitative support for our qualitative observations from the lab study. When having difficulty in finding information, users start to formulate more diverse queries, they use advanced operators more, and they spend a longer time on the search result page as compared to the successful tasks. The results complement the existing body of research focusing on successful search strategies.


title: Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

abstract: Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine-tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross-validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.


title: Making Contextual Decisions with Low Technical Debt

abstract: Applications and systems are constantly faced with decisions that require picking from a set of actions based on contextual information. Reinforcement-based learning algorithms such as contextual bandits can be very effective in these settings, but applying them in practice is fraught with technical debt, and no general system exists that supports them completely. We address this and create the first general system for contextual learning, called the Decision Service. ::: Existing systems often suffer from technical debt that arises from issues like incorrect data collection and weak debuggability, issues we systematically address through our ML methodology and system abstractions. The Decision Service enables all aspects of contextual bandit learning using four system abstractions which connect together in a loop: explore (the decision space), log, learn, and deploy. Notably, our new explore and log abstractions ensure the system produces correct, unbiased data, which our learner uses for online learning and to enable real-time safeguards, all in a fully reproducible manner. ::: The Decision Service has a simple user interface and works with a variety of applications: we present two live production deployments for content recommendation that achieved click-through improvements of 25-30%, another with 18% revenue lift in the landing page, and ongoing applications in tech support and machine failure handling. The service makes real-time decisions and learns continuously and scalably, while significantly lowering technical debt.


title: Numerical Simulation of Mutual Capacitance Touch Screens for Ungrounded Objects

abstract: Mutual capacitance touch screens translate billions of finger touches to digital signals around the world daily. By enabling these screens to detect ungrounded objects, it may be possible to extend their functionality by detecting tangible user interface objects on the screen surface. This paper presents results from finite-element method simulations, as well as experiments, showing that the mutual capacitance touch screen panel sensing principle can be used to detect ungrounded objects. Looking into how different design parameters of the touch panel affect grounded and ungrounded touch differently, we have found a film-glass-film stack-up to be 1.55 times more sensitive to ungrounded than grounded touch, compared with 0.65 and 0.20 for two different configurations of glass-film stack-up.


Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings


title: Mindfulness Meditation Training in Adults and Adolescents With ADHD A Feasibility Study

abstract: Objective: ADHD is a childhood-onset psychiatric condition that often continues into adulthood. Stimulant medications are the mainstay of treatment; however, additional approaches are frequently desired. In recent years, mindfulness meditation has been proposed to improve attention, reduce stress, and improve mood. This study tests the feasibility of an 8-week mindfulness training program for adults and adolescents with ADHD. Method: Twenty-four adults and eight adolescents with ADHD enrolled in a feasibility study of an 8-week mindfulness training program. Results: The majority of participants completed the training and reported high satisfaction with the training. Pre—post improvements in self-reported ADHD symptoms and test performance on tasks measuring attention and cognitive inhibition were noted. Improvements in anxiety and depressive symptoms were also observed. Conclusion: Mindfulness training is a feasible intervention in a subset of ADHD adults and adolescents and may improve behavioral and neu...


Machine Learning for Brain Image Segmentation


title: A survey of smoothing techniques for ME models

abstract: In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood (ML) training for exponential models, and like other ML methods is prone to overfitting of training data. Several smoothing methods for ME models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing methods for other types of related models. In this work, we survey previous work in ME smoothing and compare the performance of several of these algorithms with conventional techniques for smoothing n-gram language models. Because of the mature body of research in n-gram model smoothing and the close connection between ME and conventional n-gram models, this domain is well-suited to gauge the performance of ME smoothing methods. Over a large number of data sets, we find that fuzzy ME smoothing performs as well as or better than all other algorithms under consideration. We contrast this method with previous n-gram smoothing methods to explain its superior performance.


title: SAR target recognition based on deep learning

abstract: Deep learning algorithms such as convolutional neural networks (CNN) have been successfully applied in computer vision. This paper attempts to adapt the optical camera-oriented CNN to its microwave counterpart, i.e. synthetic aperture radar (SAR). As a preliminary study, a single layer of convolutional neural network is used to automatically learn features from SAR images. Instead of using the classical backpropagation algorithm, the convolution kernel is trained on randomly sampled image patches using unsupervised sparse auto-encoder. After convolution and pooling, an input SAR image is then transformed into a series of feature maps. These feature maps are then used to train a final softmax classifier. Initial experiments on MSTAR public data set show that an accuracy of 90.1% can be achieved on three types of targets classification task, and an accuracy of 84.7% is achievable on ten types of targets classification task.


title: The conceptual model to solve the problem of interoperability in health information systems

abstract: In the domain of e-health, there are diverse and heterogeneous health care systems with different brands on various platforms. One of the most important challenges in this field is the interoperability which plays a key role on information exchange and sharing. Achieving the interoperability is a difficult task because of complexity and diversity of systems, standards, and kinds of information. The lack of interoperability would lead to increase costs and errors of medical operation in hospitals. The purpose of this article is to present a conceptual model for solving interoperability in health information systems. A Health Service Bus (HSB) as an integrated infrastructure is suggested to facilitate Service Oriented Architecture. A scenario-based evaluation on the proposed conceptual model shows that adopting web service technology is an effective way for this task.


title: Epidemic algorithms for replicated database maintenance

abstract: Whru a dilt~lhSC is replicated at, many sites2 maintaining mutual consistrnry among t,he sites iu the fac:e of updat,es is a signitirant problem. This paper descrikrs several randomized algorit,hms for dist,rihut.ing updates and driving t,he replicas toward consist,c>nc,y. The algorit Inns are very simple and require few guarant,ees from the underlying conllllunicat.ioll system, yc+ they rnsutc t.hat. the off(~c~t, of (‘very update is evcnt,uwlly rf+irt-ted in a11 rq1ica.s. The cost, and parformancc of t,hr algorithms arc tuned I>? c%oosing appropriat,c dist,rilMions in t,hc randoinizat,ioii step. TIN> idgoritlmls ilr(’ c*los~*ly analogoIls t,o epidemics, and t,he epidcWliolog)litc\ratiirc, ilitlh iii Illld~~rsti4lldill~ tlicir bc*liavior. One of tlW i$,oritlims 11&S brc>n implrmcWrd in the Clraringhousr sprv(brs of thr Xerox C’orporat~c~ Iiitcrnc4, solviiig long-standing prol>lf~lns of high traffic and tlatirl>ilsr inconsistcllcp.


title: How Self-Esteem Shapes our Interactions with Play Technologies

abstract: The experience that results from video game play is shaped by the play environment, but also by various characteristics of the person playing. We investigated how player self-esteem predicts post-game motivation (enjoyment, effort, and tension), and conducted mediated regressions to show that players' self-esteem alters post-play motivation by affecting how needs are satisfied during play. We also explored how self-esteem predicts post-play positive and negative affect and conducted mediated regressions to show how motivation partially mediates those effects. Our work suggests that players with different levels of self-esteem experience games differently; but more importantly, we provide an explanation of how these differences form by examining the mechanisms during games that ultimately contribute to player experience. Situating our results within theories of self, we discuss the importance of self-esteem for understanding player experience, describe the implications for games research, and consider how self-esteem shapes our interactions with play technologies.


title: Buyers satisfaction in a virtual fitting room scenario based on realism of avatar

abstract: With the rapid development and wide-spread availability of hand-held market-level 3D scanner, character modeling has recently gained focus in both academia and industry. Virtual shopping applications have been widely-used in e-business. We present our parameter-based human avatar generation system and ongoing work on expanding the virtual shopping to the immersive virtual reality platforms employing natural user interfaces. We discuss ideas to evaluate buyer satisfaction using our system.


title: Compact Microstrip Dual-/Tri-/Quad-Band Bandpass Filter Using Open Stubs Loaded Shorted Stepped-Impedance Resonator

abstract: This paper presents a new class of dual-, tri- and quad-band BPF by using proposed open stub-loaded shorted stepped-impedance resonator (OSLSSIR). The OSLSSIR consists of a two-end-shorted three-section stepped-impedance resistor (SIR) with two identical open stubs loaded at its impedance junctions. Two 50- Ω tapped lines are directly connected to two shorted sections of the SIR to serve as I/O ports. As the electrical lengths of two identical open stubs increase, many more transmission poles (TPs) and transmission zeros (TZs) can be shifted or excited within the interested frequency range. The TZs introduced by open stubs divide the TPs into multiple groups, which can be applied to design a multiple-band bandpass filter (BPF). In order to increase many more design freedoms for tuning filter performance, a high-impedance open stub and the narrow/broad side coupling are introduced as perturbations in all filters design, which can tune the even- and odd-mode TPs separately. In addition, two branches of I/O coupling and open stub-loaded shorted microstrip line are employed in tri- and quad-band BPF design. As examples, two dual-wideband BPFs, one tri-band BPF, and one quad-band BPF have been successfully developed. The fabricated four BPFs have merits of compact sizes, low insertion losses, and high band-to-band isolations. The measured results are in good agreement with the full-wave simulated results.


title: A Survey of Petri Net Tools

abstract: Petri net is a mathematical modeling language used to describe a system graphically. It is a strong language that can be used to represent parallel or concurrent activities in a system. With a Petri net tool, users can view the overall system graphically and edit it with the editor. A Petri net tool can also be used to analyze the performance of the system, generate code, simulate the system and perform model checking on it. A review on twenty Petri net tools in this paper will give the readers an idea on what is a Petri net tool and the main functions of a Petri net tool. This paper can serve as an introduction of twenty Petri net tools to the reader. However, to date, there are many Petri net tools available to be downloaded online. This survey paper aims to compare twenty Petri net tools in different aspect. This is important as users will not have to read the reviews of Petri net tools online one by one. Just be having a look at the discussion provided, readers can determined the best recommended Petri net tools to be used based on their operating systems and the types of Petri net tool to be analyzed. The main purpose of this survey paper is to recommend Petri net tools based on the operating system and the types of Petri net to be analyzed.


The Graph Story of the SAP HANA Database


title: Glare suppression by coherence gated negation

abstract: Imaging of a weak target hidden behind a scattering medium can be significantly confounded by glare. We report a method, termed coherence gated negation (CGN), that uses destructive optical interference to suppress glare and allow improved imaging of a weak target. As a demonstration, we show that by permuting through a set range of amplitude and phase values for a reference beam interfering with the optical field from the glare and target reflection, we can suppress glare by an order of magnitude, even when the optical wavefront is highly disordered. This strategy significantly departs from conventional coherence gating methods in that CGN actively “gates out” the unwanted optical contributions while conventional methods “gate in” the target optical signal. We further show that the CGN method can outperform conventional coherence gating image quality in certain scenarios by more effectively rejecting unwanted optical contributions.


title: Online Parameter Identification for Model-Based Sensorless Control of Interior Permanent Magnet Synchronous Machine

abstract: This paper proposes an online identification method that can accurately estimate the stator resistance and dq -axis stator inductances for the effective model-based sensorless control of interior permanent magnet synchronous motors (IPMSMs). The proposed affine projection algorithms are uniquely designed in the estimated rotating γ-δ frame to precisely identify the parameters mentioned above. The two time-scale approaches are employed in the affine projection algorithms to estimate the three electrical parameters. Despite the electrical parameter variations due to the temperature change and magnetic saturation during operation, the rich enough data are provided to the affine projection algorithms in the discrete-time domain to accurately retrieve the updated parameters. These correctly estimated parameters are adapted to the extended back electromotive force observer for the sensorless control of IPMSM drives. Hence, the adaptation of online updated parameters makes the observer stable and robust to parameter variations as compared to the conventional observer without updated parameters. The MATLAB/Simulink-based simulation results and experimental results via a prototype IPMSM test-bed having TMS320F28335 DSP are given to verify the accurate convergence of the estimated parameters, which results into a stable sensorless control system under various operating conditions.


title: A Dual-Polarized 1-D Tightly Coupled Dipole Array Antenna

abstract: A dual-polarized 1-D tightly coupled dipole array (TCDA) antenna is proposed in this paper. To apply the TCDA design concept to a 1-D array, we place a conducting wall with slits and ferrite sheets along the 1-D dipole array. The simulated dual-polarized 1-D infinite TCDA antenna has a wide overlapped bandwidth of 2.83:1 (from 1 to 2.83 GHz) with VSWR 25 dB) between the horizontal and vertical polarizations (VPs), and low height of $1/5~\lambda $ at the lowest operating frequency. A prototype of the proposed array is fabricated and measured, which consists of nine horizontal polarization (HP) dipoles and eight VP dipoles interleaved between the HP dipoles. The measured results show high gains >7.2 dB when scanning up to 30° and a wide half-power beamwidth in the $yz$ plane >61° over the operating frequency band for both polarizations, consistent with the simulation result.


title: PlanetSense: a real-time streaming and spatio-temporal analytics platform for gathering geo-spatial intelligence from open source data

abstract: Geospatial intelligence has traditionally relied on the use of archived and unvarying data for planning and exploration purposes. In consequence, the tools and methods that are architected to provide insight and generate projections only rely on such datasets. Albeit, if this approach has proven effective in several cases, such as land use identification and route mapping, it has severely restricted the ability of researchers to inculcate current information in their work. This approach is inadequate in scenarios requiring real-time information to act and to adjust in ever changing dynamic environments, such as evacuation and rescue missions. In this work, we propose PlanetSense, a platform for geospatial intelligence that is built to harness the existing power of archived data and add to that, the dynamics of real-time streams, seamlessly integrated with sophisticated data mining algorithms and analytics tools for generating operational intelligence on the fly. The platform has four main components -- i) GeoData Cloud -- a data architecture for storing and managing disparate datasets; ii) Mechanism to harvest real-time streaming data; iii) Data analytics framework; iv) Presentation and visualization through web interface and RESTful services. Using two case studies, we underpin the necessity of our platform in modeling ambient population and building occupancy at scale.


title: Priority based dynamic resource allocation in Cloud computing with modified waiting queue

abstract: Today Cloud computing is on demand as it offers dynamic flexible resource allocation, for reliable and guaranteed services in pay-as-you-use manner, to Cloud service users. So there must be a provision that all resources are made available to requesting users in efficient manner to satisfy their needs. This resource provision is done by considering the Service Level Agreements (SLA) and with the help of parallel processing. Recent work considers various strategies with single SLA parameter. Hence by considering multiple SLA parameter and resource allocation by preemption mechanism for high priority task execution can improve the resource utilization in Cloud. In this paper we propose an algorithm which considered Preemptable task execution and multiple SLA parameters such as memory, network bandwidth, and required CPU time. An obtained experimental results show that in a situation where resource contention is fierce our algorithm provides better utilization of resources.


Social Proximity Effects on Galvanic Skin Responses in Adult Humans


title: Training Postgraduate Psychology Students to Deliver Psychological Services Online

abstract: Information and communication technologies are increasingly being used to remotely deliver psychological services. This delivery method confers clear advantages to both client and therapist, including the accessibility of services by otherwise unserved populations and cost-effective treatment. Remote services can be delivered in a real-time or delayed manner, providing clients with a wealth of therapy options not previously available. The proliferation of these services has outstripped the development and implementation of all but the most rudimentary of regulatory frameworks, potentially exposing clients to substandard psychological services. Integrating mandatory training on the delivery of online psychological services into accredited postgraduate psychology courses would aid in addressing this issue. The purpose of this article is to outline issues of consideration in the development and implementation of such a training programme. An online etherapy training programme developed by Swinburne University's National eTherapy Centre will be used as an example throughout.


title: Error repair in shift-reduce parsers

abstract: Local error repair of strings during CFG parsing requires the insertion and deletion of symbols in the region of a syntax error to produce a string that is error free. Rather than precalculating tables at parser generation time to assist in finding such repairs, this article shows how such repairs can be found during shift-reduce parsing by using the parsing tables themselves. This results in a substantial space saving over methods that require precalculated tables. Furthermore, the article shows how the method can be integrated with lookahead to avoid finding repairs that immediately result in further syntax errors. The article presents the results of experiments on a version of the LALR(1)-based parser generator Bison to which the algorithm was added.


title: Modeling and state equations of an aluminum rotor single side axial field induction motor

abstract: Because of the large airgap and main factors vary with diameter, traditional analysis method results in large error in analyzing axial field motor, and the skin effect can't be described accurately. A new disc induction motor electromagnetic model is proposed. Sub-loop calculation and multi-layer model are used to solve the key issues and improve the model accuracy. T model equivalent circuit of the model is achieved based on electromagnetic field distribution. Field inductance, rotor resistance and torque vary with slip ratio sr& analyzed. Test the equivalent circuit parameters by no-load test and short-circuit test, the results match well, which indicate the analyzing method is capable to estimate the single side axial field induction motor's parameters accurately. Calculât torque output varies with slip ratio, the calculation results are close to experimental results under high slip ratio working condition.


title: Latent Dirichlet Allocation complement in the vector space model for Multi-Label Text Classification

abstract: In text classification task one of the main problems is to choose which features give the best results. Various features can be used like words, n-grams, syntactic n-grams of various types (POS tags, dependency relations, mixed, etc.), or a combinations of these features can be considered. Also, algorithms for dimensionality reduction of these sets of features can be applied, like Latent Dirichlet Allocation (LDA). In this paper, we consider multi-label text classification task and apply various feature sets. We consider a subset of multi-labeled files from the Reuters-21578 corpus. We use traditional tf-IDF values of the features and tried both considering and ignoring stop words. We also tried several combinations of features, like bigrams and unigrams. We also experimented with adding LDA results into Vector Space Models as new features. These last experiments obtained the best results.


title: Road crack detection using deep convolutional neural network

abstract: Automatic detection of pavement cracks is an important task in transportation maintenance for driving safety assurance. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavement and possible shadows with similar intensity. Inspired by recent success on applying deep learning to computer vision and medical problems, a deep-learning based method for crack detection is proposed in this paper. A supervised deep convolutional neural network is trained to classify each image patch in the collected images. Quantitative evaluation conducted on a data set of 500 images of size 3264 χ 2448, collected by a low-cost smart phone, demonstrates that the learned deep features with the proposed deep learning framework provide superior crack detection performance when compared with features extracted with existing hand-craft methods.


title: Sentiment Classification using Images and Label Embeddings

abstract: In this project we analysed how much semantic information images carry, and how much value image data can add to sentiment analysis of the text associated with the images. To better understand the contribution from images, we compared models which only made use of image data, models which only made use of text data, and models which combined both data types. We also analysed if this approach could help sentiment classifiers generalize to unknown sentiments.


title: Performance Analysis of M-QAM Multihop Relaying over mmWave Weibull Fading Channels

abstract: The paper presents a comprehensive closed-form performance analysis framework of multihop cooperative communications as promising schemes in the next generation mmwave systems. As fading channel model, we adopt the advocated Weibull channel for its flexible ability to cover different channel conditions, and supported by many recent measurement campaigns in various emerging 5G scenarios. The analyzed scheme consists of multiple "Detect-and-Forward" relays with generalized high-order M-QAM transmissions. The end-to-end performance of the multihop communication is evaluated in terms of outage probability, ergodic capacity, bit error rate, and symbol error rate. For all these metrics, we present exact closed-form expressions and their asymptotic behavior, some in terms of generalized hypergeometric functions. The exactness of our study is set by numerical analysis, and Monte-Carlo simulations assess the accuracy of our obtained results for different system and channel parameters. Moreover, while Fox H and bivariate Fox H functions are not yet implemented in Matlab, we provide new codes for computing these functions in general contexts.


title: Generating timeline summaries with social media attention

abstract: Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of one given topic. Previous methods simply split the time span into fixed, equal time intervals without studying the role of the evolutionary patterns of the underlying topic in timeline generation. In addition, few of these methods take users' collective interests into considerations to generate timelines. ::: ::: We consider utilizing social media attention to address these two problems due to the facts: 1) social media is an important pool of real users' collective interests; 2) the information cascades generated in it might be good indicators for boundaries of topic phases. Employing Twitter as a basis, we propose to incorporate topic phases and user's collective interests which are learnt from social media into a unified timeline generation algorithm.We construct both one informativeness-oriented and three interestingness-oriented evaluation sets over five topics.We demonstrate that it is very effective to generate both informative and interesting timelines. In addition, our idea naturally leads to a novel presentation of timelines, i.e., phase based timelines, which can potentially improve user experience.


title: Generative adversarial models for people attribute recognition in surveillance

abstract: In this paper we propose a deep architecture for detecting people attributes (e.g. gender, race, clothing ...) in surveillance contexts. Our proposal explicitly deal with poor resolution and occlusion issues that often occur in surveillance footages by enhancing the images by means of Deep Convolutional Generative Adversarial Networks (DCGAN). Experiments show that by combining both our Generative Reconstruction and Deep Attribute Classification Network we can effectively extract attributes even when resolution is poor and in presence of strong occlusions up to 80\% of the whole person figure.


title: Semantic Information and the Syntax of Propositional Attitude Verbs

abstract: Propositional attitude verbs, such as think and want, have long held interest for both theoretical linguists and language acquisitionists because their syntactic, semantic, and pragmatic properties display complex interactions that have proven difficult to fully capture from either perspective. This paper explores the granularity with which these verbs' semantic and pragmatic properties are recoverable from their syntactic distributions, using three behavioral experiments aimed at explicitly quantifying the relationship between these two sets of properties. Experiment 1 gathers a measure of 30 propositional attitude verbs' syntactic distributions using an acceptability judgment task. Experiments 2a and 2b gather measures of semantic similarity between those same verbs using a generalized semantic discrimination (triad or "odd man out") task and an ordinal (Likert) scale task, respectively. Two kinds of analyses are conducted on the data from these experiments. The first compares both the acceptability judgments and the semantic similarity judgments to previous classifications derived from the syntax and semantics literature. The second kind compares the acceptability judgments to the semantic similarity judgments directly. Through these comparisons, we show that there is quite fine-grained information about propositional attitude verbs' semantics carried in their syntactic distributions-whether one considers the sorts of discrete qualitative classifications that linguists traditionally work with or the sorts of continuous quantitative classifications that can be derived experimentally.


title: Random texts exhibit Zipf's-law-like word frequency distribution

abstract: It is shown that the distribution of word frequencies for randomly generated texts is very similar to Zipf's law observed in natural languages such as English. The facts that the frequency of occurrence of a word is almost an inverse power law function of its rank and the exponent of this inverse power law is very close to 1 are largely due to the transformation from the word's length to its rank, which stretches an exponential function to a power law function. >


title: Market Basket Analysis of Crash Data From Large Jurisdictions and Its Potential As a Decision Support Tool

abstract: Data mining applications are becoming increasingly popular for many applications across a set of very divergent fields. Analysis of crash data is no exception. There are many data mining methodologies that have been applied to crash data in the recent past. However, one particular application conspicuously missing from the traffic safety literature until recently is association analysis or market basket analysis. The methodology is used by retailers all over the world to determine which items are purchased together. In this study, crashes are analyzed as supermarket transactions to detect interdependence among crash characteristics. The results from the analysis include simple rules that indicate which crash characteristics are associated with each other. The application is demonstrated using non-intersection crash data from the state of Florida for the year 2004. In the proposed methodology no variable needs to be assigned as dependent variable. Hence, it is useful in identifying previously unknown patterns in the data obtained from large jurisdictions (such as the State of Florida) as opposed to the data from a single roadway or intersection. Based on the association rules discovered from the analysis, it was concluded that there is a significant correlation between lack of illumination and high severity of crashes. Furthermore, it was found that under rainy conditions straight sections with vertical curves are particularly crash prone. Results are consistent with the understanding of crash characteristics and point to the potential of this methodology for the analysis of crash data collected by the state and federal agencies. The potential of this technique may be realized in the form of a decision support tool for the traffic safety administrators.


title: Firefly: illuminating future network-on-chip with nanophotonics

abstract: Future many-core processors will require high-performance yet energy-efficient on-chip networks to provide a communication substrate for the increasing number of cores. Recent advances in silicon nanophotonics create new opportunities for on-chip networks. To efficiently exploit the benefits of nanophotonics, we propose Firefly - a hybrid, hierarchical network architecture. Firefly consists of clusters of nodes that are connected using conventional, electrical signaling while the inter-cluster communication is done using nanophotonics - exploiting the benefits of electrical signaling for short, local communication while nanophotonics is used only for global communication to realize an efficient on-chip network. Crossbar architecture is used for inter-cluster communication. However, to avoid global arbitration, the crossbar is partitioned into multiple, logical crossbars and their arbitration is localized. Our evaluations show that Firefly improves the performance by up to 57% compared to an all-electrical concentrated mesh (CMESH) topology on adversarial traffic patterns and up to 54% compared to an all-optical crossbar (OP XBAR) on traffic patterns with locality. If the energy-delay-product is compared, Firefly improves the efficiency of the on-chip network by up to 51% and 38% compared to CMESH and OP XBAR, respectively.


title: 60-GHz Millimeter-Wave Life Detection System (MLDS) for Noncontact Human Vital-Signal Monitoring

abstract: A first reported experimental study of a 60 GHz millimeter-wave life detection system (MLDS) for noncontact human vital-signal monitoring is presented. This detection system is constructed by using V-band millimeter-wave waveguide components. A clutter canceller is implemented in the system with an adjustable attenuator and phase shifter. It performs clutter cancellation for the transmitting power leakage from the circulator and background reflection to enhance the detecting sensitivity of weak vital signals. The noncontact vital signal measurements have been conducted on a human subject in four different physical orientations from distances of 1 and 2 m. The time-domain and spectrum waveforms of the measured breathing and heartbeat are presented. This prototype system will be useful for the development of the 60-GHz CMOS MLDS detector chip design.


title: The Frequency of Nail Changes and Disorders in Iraqi People above 50 Years Old

abstract: Background: Nail changes associated with ageing are common in the elderly ::: and include characteristic modifications of color, contour, growth, surface, ::: thickness and histology. No cutaneous examination is complete without a careful ::: clinical evaluation of the nails. Objective: The aim of present work is to ::: assess the frequency of senile nail changes and disorders in Iraqi people above ::: 50 years old compared with control age group (20 - 30 years old). Patients and ::: methods: This case-controlled, cross-sectional study was performed in the ::: outpatient clinic of Dermatology and Venereology Department at the Kufa College ::: of Medicine Teaching Hospital in Najaf Province in the period from September ::: 2010 to April 2011. The study included 200 subjects, of which 100 patients were ::: above 50 years old (study group), and the other 100 subjects were from 20 - 30 ::: years old (control group). A detailed history from each subject was recorded to ::: detect the onset, duration and progression of nail changes and/or disorders, ::: their occupation and any environmental exposure. A careful examination of the nails was carried out to assess the type of ::: nail changes, site and symmetry. Any patient with systemic disease, ::: dermatological disease or with suspected drug intake was excluded from the ::: study. Results: The present work has shown that many nail changes were more ::: frequent among older age individuals than younger age group with significant ::: statistical difference (p = 0.043 - 0.000) like dull opaque appearance, rough ::: lusterlessness, longitudinal ridging, altered thickness, ragged cuticle, ::: altered contour, subungual hyperkeratosis and scaling nail folds. Chromonychia ::: was not statistically significant (14% of study group versus 16% of the ::: control) with p = 0.692. Longitudinal melanonychia was significantly higher in ::: the study group (6% versus 0% of the control) with p = 0.013, while punctate ::: leukonychia was significantly higher in the control group (16% versus 4% of ::: study group) with p = 0.005. Conclusion: Some changes of the nail are significantly ::: correlated with advanced age like dull opaque nails, rough lusterlessness, ::: longitudinal ridging. Therefore, these signs can be regarded as indicative of ::: ageing of healthy Iraqi people.


title: Natural Language-Based Software Analyses and Tools for Software Maintenance

abstract: Significant portions of software life cycle resources are devoted to program maintenance, which motivates the development of automated techniques and tools to support the tedious, error-prone tasks. Natural language clues from programmers’ naming in literals, identifiers, and comments can be leveraged to improve the effectiveness of many software tools. For example, they can be used to increase the accuracy of software search tools, improve the ability of program navigation tools to recommend related methods, and raise the accuracy of other program analyses by providing access to natural language information. This chapter focuses on how to capture, model, and apply the programmers’ conceptual knowledge expressed in both linguistic information as well as programming language structure and semantics. We call this kind of analysis Natural Language Program Analysis (NLPA) since it combines natural language processing techniques with program analysis to extract information for analysis of the source program.


title: TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints

abstract: Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.


Hierarchical Classification of Ten Skin Lesion Classes


title: Camera angle affects dominance in video-mediated communication

abstract: Physical proximity and appearance guide people to interact with each other in different ways [1,6]. However, in Video-Mediated Communications (VMC), these are distorted in various ways. Monitors and camera zooms make people look close or far, monitors and camera angles can be high or low making people look tall or short, volume can be loud or soft, making people sound assertive or submissive, --all independent of the true physical characteristics or intentions of the participants. Here we test the apparent height of a person on how dominant they are in a group decision-making task. We found that the artificially tall people had more influence in the group decision than the artifically short people.


title: CloudFridge: A Testbed for Smart Fridge Interactions

abstract: We present a testbed for exploring novel smart refrigerator interactions, and identify three key adoption-limiting interaction shortcomings of state-of-the-art smart fridges: lack of 1) user experience focus, 2) low-intrusion object recognition and 2) automatic item position detection. Our testbed system addresses these limitations by a combination of sensors, software filters, architectural components and a RESTful API to track interaction events in real-time, and retrieve current state and historical data to learn patterns and recommend user actions. We evaluate the accuracy and overhead of our system in a realistic interaction flow. The accuracy was measured to 83- 88% and the overhead compared to a representative state-of- the-art barcode scanner improved by 27%. We also showcase two applications built on top of our testbed, one for finding expired items and ingredients of dishes; and one to monitor your health. The pattern that these applications have in common is that they cast the interaction as an item-recommendation problem triggered when the user takes something out. Our testbed could help reveal further user-experience centric interaction patterns and new classes of applications for smart fridges that inherently, by relying on our testbed primitives, mitigate the issues with existing approaches.


title: Confidence fusion [sensor fusion]

abstract: The field of sensor fusion is well developed, but approaches to describing the confidence one ought to have in the fused result as a function of the confidence one has in the individual measurements is not. This speculative paper is intended to stimulate discussion in the robotics and instrumentation communities regarding how to approach "confidence fusion" in a way that can be practically applied in robotics applications and is intuitively satisfying with respect our feelings regarding how confidence in measurements ought to propagate. Preliminary ideas are developed in the context of a scenario wherein two one-bit-output sensors in which we have different confidence are used together. The results are compared with numerical experiments simulating a real-life experiment that we have described in the field of sensor fusion for context-aware computing.


title: Flip-flop clustering by weighted K-means algorithm

abstract: This paper presents a novel flip-flop clustering and relocation framework to help reduce the overall chip power consumption. Given an initial legalized placement, our goal is to reduce the wirelength of the clock network by reducing distance between flip-flops and their drivers, while minimize the disturbance of original placement result. The idea is to form flip-flops into clusters, such that all flip-flops within each cluster can be placed near a single clock buffer and connected by a simple routing structure. Therefore, overall clock network wirelength can be greatly reduced and significant power savings can be achieved. In particular, we propose a modified K-means algorithm which effectively assigns flops into clusters at the clustering step. Then, at the relocation step, flops are actually relocated and regularly structured clusters are formed. Our framework is evaluated on real industrial benchmarks. We compare our framework with a flow without flop clustering and an industrial window based flop clustering flow. Experimental results show our framework can achieve significant dynamic power savings while has less disturbance of the original placement.


title: Bangladeshi dialect recognition using Mel Frequency Cepstral Coefficient, Delta, Delta-delta and Gaussian Mixture Model

abstract: Automatic recognition systems are generally applied successfully in speech processing to categorize observed utterances by the speaker identity, dialect and linguistic communication. A lot of research has been performed to detect speeches, dialects and languages of different region throughout the world. But the work on dialects of Bangladesh is infrequent to our research. These dialects, in turn, differ quite a bit from each other. In this paper, we present a method to detect Bangladeshi different dialects which utilizes Mel Frequency Cepstral Coefficient (MFCC), its Delta and Delta-delta as main features and Gaussian Mixture Models (GMM) to classify characteristics of a specific dialect. Particularly we extract the MFCCs, Deltas and Delta-deltas from the speech signal. Then they are merged together to form a feature vector for a specific dialect. GMM is trained using the iterative Expectation Maximization (EM) algorithm where feature vectors are served as input. This scheme is tested on 5 databases of 30 speech samples each. Speech samples contain dialects of Borishal, Noakhali, Sylhet, Chittagong and Chapai Nawabganj regions of Bangladesh. Experiments show that GMM adaptation gives comparable good performance.


title: A Jitter and Power Analysis on DCO

abstract: A jitter and power analysis on a digitally controlled oscillator (DCO) is presented in this brief. By analyzing variable capacitance components on each switching node of the DCO, a simple jitter and power model was derived in a closed form. The proposed mathematical analysis can be effectively used for the accurate and faster estimation of the DCO jitter and power consumption; thus, the overall DCO design time can be significantly reduced. In order to validate our proposed mathematical modeling, the DCO has been designed and fabricated using a 0.13-μm 1.2-V CMOS process. The fabricated chip presents the root-mean-square and peak-to-peak jitters of 8.9 and 70 ps, respectively, at the output frequency of 600 MHz, under the operation range of 179-656 MHz with a 2.8-ps resolution, which clearly shows that our proposed modeling is well matched with the experimental results.


NimbRo Rescue: Solving Disaster-Response Tasks through Mobile Manipulation Robot Momaro


title: Exploring the importance of citizen participation and involvement in e‐government projects: Practice, incentives, and organization

abstract: Purpose – The purpose of this research is to investigate if, and in that case, how and what the e-government field can learn from user participation concepts and theories in general IS research. We ...


title: A new class of upper bounds on the log partition function

abstract: Bounds on the log partition function are important in a variety of contexts, including approximate inference, model fitting, decision theory, and large deviations analysis. We introduce a new class of upper bounds on the log partition function, based on convex combinations of distributions in the exponential domain, that is applicable to an arbitrary undirected graphical model. In the special case of convex combinations of tree-structured distributions, we obtain a family of variational problems, similar to the Bethe free energy, but distinguished by the following desirable properties: i. they are cnvex, and have a unique global minimum; and ii. the global minimum gives an upper bound on the log partition function. The global minimum is defined by stationary conditions very similar to those defining fixed points of belief propagation or tree-based reparameterization Wainwright et al., 2001. As with BP fixed points, the elements of the minimizing argument can be used as approximations to the marginals of the original model. The analysis described here can be extended to structures of higher treewidth e.g., hypertrees, thereby making connections with more advanced approximations e.g., Kikuchi and variants Yedidia et al., 2001; Minka, 2001.


title: Check my chart: a robust color chart tracker for colorimetric camera calibration

abstract: We present an algorithm to detect and track color calibration charts in images robustly. A model of the calibration chart describes the geometry of the chart regions by polygons along with the related reference colors. A homography maps the model into the image and an optimizer adapts the mapping parameters to align the model regions to the chart in the image. We propose a cost function that evaluates the quality of model alignment based on color statistics and an efficient method to extract color statistics in polygonal image regions using the integral image. The algorithm measures the colors of the chart in the image and determines correction parameters. Without loss of generality, we describe the method using the X-Rite ColorChecker Classic as a typical chart. Experiments show the robustness to noise and blur and the real-time capability of the system.


title: Advanced interest flooding attacks in named-data networking

abstract: The Named-Data Networking (NDN) has emerged as a clean-slate Internet proposal on the wave of Information-Centric Networking. Although the NDN's data-plane seems to offer many advantages, e.g., native support for multicast communications and flow balance, it also makes the network infrastructure vulnerable to a specific DDoS attack, the Interest Flooding Attack (IFA). In IFAs, a botnet issuing unsatisfiable content requests can be set up effortlessly to exhaust routers' resources and cause a severe performance drop to legitimate users. So far several countermeasures have addressed this security threat, however, their efficacy was proved by means of simplistic assumptions on the attack model. Therefore, we propose a more complete attack model and design an advanced IFA. We show the efficiency of our novel attack scheme by extensively assessing some of the state-of-the-art countermeasures. Further, we release the software to perform this attack as open source tool to help design future more robust defense mechanisms.


title: Active B12: A Rapid, Automated Assay for Holotranscobalamin on the Abbott AxSYM Analyzer

abstract: Background: Conventional tests for vitamin B12 deficiency measure total serum vitamin B12, whereas only that portion of vitamin B12 carried by transcobalamin (holotranscobalamin) is metabolically active. Measurement of holotranscobalamin (holoTC) may be more diagnostically accurate for detecting B12 deficiency that requires therapy. We developed an automated assay for holoTC that can be used on the Abbott AxSYM immunoassay analyzer. ::: ::: Methods: AxSYM Active B12 is a 2-step sandwich microparticle enzyme immunoassay. In step 1, a holoTC-specific antibody immobilized onto latex microparticles captures holoTC in samples of serum or plasma. In step 2, the captured holoTC is detected with a conjugate of alkaline phosphatase and antiTC antibody. ::: ::: Results: Neither apoTC nor haptocorrin exhibited detectable cross-reactivity. The detection limit was ≤0.1 pmol/L. Within-run and total imprecision (CV ranges) were 3.4%–5.1% and 6.3%–8.5%, respectively. Assay CVs were <20% from at least 3 pmol/L to 107 pmol/L. With diluted serum samples, measured concentrations were 104%–114% of the expected values in the working range of the assay. No interference from bilirubin, hemoglobin, triglycerides, erythrocytes, rheumatoid factor, or total protein was detected at expected (abnormal) concentrations. A comparison of the AxSYM Active B12 assay with a commercial RIA for holoTC yielded the regression equation: AxSYM = 0.98 RIA + 4.7 pmol/L ( S y x , 11.4 pmol/L; n = 204). Assay throughput was 45 tests/h. A 95% reference interval of 19–134 pmol/L holoTC was established with samples from 292 healthy individuals. ::: ::: Conclusions: The AxSYM Active B12 assay allows rapid, precise, sensitive, specific, and automated measurement of human holoTC in serum and plasma.


title: Development of Attendance Management System using Biometrics.

abstract: In this paper, the development of an attendance management system using biometrics is proposed. Managing student attendance during lecture periods has become a difficult challenge. The ability to compute the attendance percentage becomes a major task as manual computation produces errors, and also wastes a lot of time. For the stated reason, an efficient attendance management system using biometrics is designed. This system takes attendance electronically with the help of a finger print device and the records of the attendance are stored in a database. Attendance is marked after student identification.


title: Modifiable risk factors in the first 1000 days for subsequent risk of childhood overweight in an Asian cohort: significance of parental overweight status

abstract: BACKGROUND/OBJECTIVE ::: Many studies have identified early-life risk factors for subsequent childhood overweight/obesity, but few have evaluated how they combine to influence risk of childhood overweight/obesity. We examined associations, individually and in combination, of potentially modifiable risk factors in the first 1000 days after conception with childhood adiposity and risk of overweight/obesity in an Asian cohort. ::: ::: ::: METHODS ::: Six risk factors were examined: maternal pre-pregnancy overweight/obesity (body mass index (BMI) ⩾25 kg m-2), paternal overweight/obesity at 24 months post delivery, maternal excessive gestational weight gain, raised maternal fasting glucose during pregnancy (⩾5.1 mmol l-1), breastfeeding duration <4 months and early introduction of solid foods (<4 months). Associations between number of risk factors and adiposity measures (BMI, waist-to-height ratio (WHtR), sum of skinfolds (SSFs), fat mass index (FMI) and overweight/obesity) at 48 months were assessed using multivariable regression models. ::: ::: ::: RESULTS ::: Of 858 children followed up at 48 months, 172 (19%) had none, 274 (32%) had 1, 244 (29%) had 2, 126 (15%) had 3 and 42 (5%) had ⩾4 risk factors. Adjusting for confounders, significant graded positive associations were observed between number of risk factors and adiposity outcomes at 48 months. Compared with children with no risk factors, those with four or more risk factors had s.d. unit increases of 0.78 (95% confidence interval 0.41-1.15) for BMI, 0.79 (0.41-1.16) for WHtR, 0.46 (0.06-0.83) for SSF and 0.67 (0.07-1.27) for FMI. The adjusted relative risk of overweight/obesity in children with four or more risk factors was 11.1(2.5-49.1) compared with children with no risk factors. Children exposed to maternal pre-pregnancy (11.8(9.8-13.8)%) or paternal overweight status (10.6(9.6-11.6)%) had the largest individual predicted probability of child overweight/obesity. ::: ::: ::: CONCLUSIONS ::: Early-life risk factors added cumulatively to increase childhood adiposity and risk of overweight/obesity. Early-life and preconception intervention programmes may be more effective in preventing overweight/obesity if they concurrently address these multiple modifiable risk factors.


title: Secure Mobile Crowdsensing with Deep Learning

abstract: In order to stimulate secure sensing for Internet of Things (IoT) applications such as healthcare and traffic monitoring, mobile crowdsensing (MCS) systems have to address security threats, such as jamming, spoofing and faked sensing attacks, during both the sensing and the information exchange processes in large-scale dynamic and heterogenous networks. In this article, we investigate secure mobile crowdsensing and present how to use deep learning (DL) methods such as stacked autoencoder (SAE), deep neural network (DNN), and convolutional neural network (CNN) to improve the MCS security approaches including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS. We discuss the performance gain of these DL-based approaches compared with traditional security schemes and identify the challenges that need to be addressed to implement them in practical MCS systems.


title: Deep Reinforcement Learning for Playing 2.5D Fighting Games

abstract: Deep reinforcement learning has shown its success in game playing. However, 2.5D fighting games would be a challenging task to handle due to ambiguity in visual appearances like height or depth of the characters. Moreover, actions in such games typically involve particular sequential action orders, which also makes the network design very difficult. Based on the network of Asynchronous Advantage Actor-Critic (A3C), we create an OpenAI-gym-like gaming environment with the game of Little Fighter 2 (LF2), and present a novel A3C+ network for learning RL agents. The introduced model includes a Recurrent Info network, which utilizes game-related info features with recurrent layers to observe combo skills for fighting. In the experiments, we consider LF2 in different settings, which successfully demonstrates the use of our proposed model for learning 2.5D fighting games.


Demonstrating map++: A crowd-sensing system for automatic map semantics identification


title: Reduction of Cogging Torque in Double-Rotor Axial-Flux Permanent-Magnet Disk Motors: A Review of Cost-Effective Magnet-Skewing Techniques With Experimental Verification

abstract: Minimizing cogging torque in designing axial-flux permanent-magnet (AFPM) motors is one of the main issues which must be considered during the design process. This paper presents several cost-effective magnet-skewing techniques to minimize cogging torque components in double-rotor AFPM motors. Rotor-side cogging torque minimization methods are examined in detail with major focus on magnet-skewing approach, and several cost-effective alternative skewing techniques are proposed. A detailed comparison of magnet-skewing approaches is provided. A prototype AFPM motor with different rotor structures is built based on the analyses. Analyses are then validated with experimental results, and the influence of cogging torque component on torque quality of AFPM motors is explored. The results confirm that the proposed magnet-skewing approaches can significantly reduce the cogging component as opposed to reference AFPM motor with unskewed magnets and help to improve the torque quality of the disk motors.


title: A 0.1-1.8 GHz, 100 W GaN HEMT Power Amplifier Module

abstract: We have demonstrated a 0.1-1.8 GHz, 100 W GaN HEMT power amplifier module with 11 dB gain, 94-142 W CW output power and 40.6-74.0 % drain efficiency over the band. The power amplifier module uses broadband low loss coaxial baluns to combine four 30 W broadband lossy matched GaN HEMT PAs on a 2 x 2 inch compact PCB. The individual PAs are fully matched to 50 Ohms and obtains 23.6-30.9 W with 44.5-63.7 % drain efficiency over the band. The packaged amplifiers contain a GaN on SiC device operating at 50 V drain voltage with a GaAs integrated passive matching circuitry. These amplifiers are targeted for use in multi-band multi-standard communication systems and for instrumentation applications.


title: Digital facial dysmorphology for genetic screening: Hierarchical constrained local model using ICA

abstract: Abstract Down syndrome, the most common single cause of human birth defects, produces alterations in physical growth and mental retardation. If missed before birth, the early detection of Down syndrome is crucial for the management of patients and disease. However, the diagnostic accuracy for pediatricians prior to cytogenetic results is moderate and the access to specialists is limited in many social and low-economic areas. In this study, we propose a simple, non-invasive and automated framework for Down syndrome detection based on disease-specific facial patterns. Geometric and local texture features are extracted based on automatically detected anatomical landmarks to describe facial morphology and structure. To accurately locate the anatomical facial landmarks, a hierarchical constrained local model using independent component analysis (ICA) is proposed. We also introduce a data-driven ordering method for selecting dominant independent components in ICA. The hierarchical structure of the model increases the accuracy of landmark detection by fitting separate models to different groups. Then the most representative features are selected and we also demonstrate that they match clinical observations. Finally, a variety of classifiers are evaluated to discriminate between Down syndrome and healthy populations. The best performance achieved 0.967 accuracy and 0.956 F 1 score using combined features and linear discriminant analysis. The method was also validated on a dataset with mixed genetic syndromes and high performance (0.970 accuracy and 0.930 F 1 score) was also obtained. The promising results indicate that our method could assist in Down syndrome screening effectively in a simple, non-invasive way, and extensible to detection of other genetic syndromes.


title: Automatically Identifying the Source Words of Lexical Blends in English

abstract: Newly coined words pose problems for natural language processing systems because they are not in a system's lexicon, and therefore no lexical information is available for such words. A common way to form new words is lexical blending, as in cosmeceutical, a blend of cosmetic and pharmaceutical. We propose a statistical model for inferring a blend's source words drawing on observed linguistic properties of blends; these properties are largely based on the recognizability of the source words in a blend. We annotate a set of 1,186 recently coined expressions which includes 515 blends, and evaluate our methods on a 324-item subset. In this first study of novel blends we achieve an accuracy of 40% on the task of inferring a blend's source words, which corresponds to a reduction in error rate of 39% over an informed baseline. We also give preliminary results showing that our features for source word identification can be used to distinguish blends from other kinds of novel words.


title: Settling Control with Reference Redesign for Dual Actuator Hard Disk Drive Systems

abstract: Abstract This paper considers settling control for a dual actuator system for hard disk drives consisting of a coarse actuator(voice coil motor) and a fine actuator(peizoelectric transducer, PZT). The design method proposed in this paper is called the dual actuator reference trajectory re-design(RTRD). In the RTRD, the design is divided into three steps. In the first step, the coarse actuator loop is designed to be stable via active damping and achieve the basic performance. In the second step, the fine actuator path is designed by loop shaping for superior performance of the overall system. Finally the reference signals are generated for both VCM and PZT actuators. In the dual rate RTRD : the coarse actuator works at slow sampling frequency and the fine actuator works at fast sampling frequency. The “soft switching” technique is applied for gradually introducing the fine actuator(PZT actuator), which may shorten the settling time and achieve smoother settling response. The effectiveness of the proposed approach is evaluated by simulations.


title: Establishing a causal chain: Why experiments are often more effective than mediational analyses in examining psychological processes.

abstract: The authors propose that experiments that utilize mediational analyses as suggested by R. M. Baron and D. A. Kenny (1986) are overused and sometimes improperly held up as necessary for a good social psychological paper. The authors argue that when it is easy to manipulate and measure a proposed psychological process that a series of experiments that demonstrates the proposed causal chain is superior. They further argue that when it is easy to manipulate a proposed psychological process but difficult to measure it that designs that examine underlying process by utilizing moderation can be effective. It is only when measurement of a proposed psychological process is easy and manipulation of it is difficult that designs that rely on mediational analyses should be preferred, and even in these situations careful consideration should be given to the limiting factors of such designs.


title: Multiview projectors/cameras system for 3D reconstruction of dynamic scenes

abstract: Active vision systems are usually limited to either partial or static scene reconstructions. In this paper, we propose to acquire the entire 3D shape of a dynamic scene. This is performed using a multiple projectors and cameras system, that allows to recover the entire shape of the object within a single scan at each frame. Like previous approaches, a static and simple pattern is used to avoid interferences of multiple patterns projected on the same object. In this paper, we extend the technique to capture a dense entire shape of a moving object with accuracy and high video frame rate. To achieve this, we mainly propose two additional steps; one is checking the consistency between the multiple cameras and projectors, and the other is an algorithm for light sectioning based on a plane parameter optimization. In addition, we also propose efficient noise reduction and mesh generation algorithm which are necessary for practical applications. In the experiments, we show that we can successfully reconstruct dense entire shapes of moving objects. Results are illustrated on real data from a system composed of six projectors and six cameras that was actually built.


Overview of the CL-SciSumm 2016 Shared Task


title: A weighting k-modes algorithm for subspace clustering of categorical data

abstract: Traditional clustering algorithms consider all of the dimensions of an input data set equally. However, in the high dimensional data, a common property is that data points are highly clustered in subspaces, which means classes of objects are categorized in subspaces rather than the entire space. Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a data set. In this paper, a weighting k-modes algorithm is presented for subspace clustering of categorical data and its corresponding time complexity is analyzed as well. In the proposed algorithm, an additional step is added to the k-modes clustering process to automatically compute the weight of all dimensions in each cluster by using complement entropy. Furthermore, the attribute weight can be used to identify the subsets of important dimensions that categorize different clusters. The effectiveness of the proposed algorithm is demonstrated with real data sets and synthetic data sets.


title: Algorithm 864: General and robot-packable variants of the three-dimensional bin packing problem

abstract: We consider the problem of orthogonally packing a given set of rectangular-shaped boxes into the minimum number of three-dimensional rectangular bins. The problem is NP-hard in the strong sense and extremely difficult to solve in practice. We characterize relevant subclasses of packing and present an algorithm which is able to solve moderately large instances to optimality. Extensive computational experiments compare the algorithm for the three-dimensional bin packing when solving general orthogonal packings and when restricted to robot packings.


title: Spot noise texture synthesis for data visualization

abstract: The use of stochastic texture for the visualization of scalar and vector fields over surfaces is discussed. Current techniques for texture synthesis are not suitable, because they do not provide local control, and are not suited for the design of textures. A new technique, spot noise, is presented that does provide these features. Spot noise is synthesized by addition of randomly weighted and positioned spots. Local control of the texture is realized by variation of the spot. The spot is a useful primitive for texture design, because, in general, the relations between features of the spot and features of the texture are straightforward. Various examples and applications are shown. Spot noise lends itself well for the synthesis of texture over curved surfaces, and is therefore an alternative to solid texturing. The relations of spot noise with a variety of other techniques, such as radom faults, filtering, sparse convolution, and particle systems, are discussed. It appears that spot noise provides a new perspective on those techniques.


title: AURA: a programming language for authorization and audit

abstract: This paper presents AURA, a programming language for access control that treats ordinary programming constructs (e.g., integers and recursive functions) and authorization logic constructs (e.g., principals and access control policies) in a uniform way. AURA is based on polymorphic DCC and uses dependent types to permit assertions that refer directly to AURA values while keeping computation out of the assertion level to ensure tractability. The main technical results of this paper include fully mechanically verified proofs of the decidability and soundness for AURA's type system, and a prototype typechecker and interpreter.


title: Learning to reinforcement learn

abstract: In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.


title: Workflow Transformation for Real-Time Big Data Processing

abstract: With the explosion of big data, processing and analyzing large numbers of continuous data streams in real-time, such as social media stream, sensor data streams, log streams, stock exchanges streams, etc., has become a crucial requirement for many scientific and industrial applications in recent years. Increased volume of streaming data as well as the demand for more complex real-time analytics require for execution of processing pipelines among heterogeneous event processing engines as a workflow. In this paper, we propose a workflow transformation for cost minimization in real-time big data processing on the heterogeneous systems. We first give the definition of stream-based workflow, and then we define eight different patterns as rules for workflow transformation, next, we give our workflow transformation algorithm based on our designed rules. Finally, our experiment shows that our proposed workflow transformation method can reduce the communication and computation cost effectively.


title: Taming Google-Scale Continuous Testing

abstract: Growth in Google's code size and feature churn rate has seen increased reliance on continuous integration (CI) and testing to maintain quality. Even with enormous resources dedicated to testing, we are unable to regression test each code change individually, resulting in increased lag time between code check-ins and test result feedback to developers. We report results of a project that aims to reduce this time by: (1) controlling test workload without compromising quality, and (2) distilling test results data to inform developers, while they write code, of the impact of their latest changes on quality. We model, empirically understand, and leverage the correlations that exist between our code, test cases, developers, programming languages, and code-change and test-execution frequencies, to improve our CI and development processes. Our findings show: very few of our tests ever fail, but those that do are generally "closer" to the code they test, certain frequently modified code and certain users/tools cause more breakages, and code recently modified by multiple developers (more than 3) breaks more often.