title: Achieving Flexibility for ABE with Outsourcing via Proxy Re-Encryption

abstract: Outsourcing the decryption of attribute-based encryption (ABE) ciphertext is a promising way to tackle the question of how users can perform decryption efficiently. However, existing solutions require the type of the target ciphertext to be determined at the setup of the outsourcing scheme. As such, making the target cryptosystems (or the clients) to be versatile becomes an issue that warrants investigations. In this paper, the problem we wish to tackle is to transform an ABE ciphertext to any client who is using the same, or possibly different, public-key encryption (PKE) system with the sender. The problem is of practical interest since it is hard to require all clients to use the same PKE, especially in the case of remote and cross-system data sharing. In addition, we also consider whether robust client-side decryption scheme can be adopted. This feature is not supported in the existing ABE with outsourcing. We introduce cross-system proxy re-encryptions (CS-PRE), a new re-encryption paradigm in which a semi-trusted proxy converts a ciphertext of a source cryptosystem ($¶i_0$) into a ciphertext for a target cryptosystem ($¶i$). We formalize CS-PRE and present a construction that performs well in the following aspects. (1)Versatility: $¶i_0$ can be any attribute-based encryption (ABE) within Attrapadung's pair encoding framework. $¶i$ can be any public-key encryption. Furthermore, the keys and public parameters can be generated independently. (2) Compatibility: CS-PRE does not modify the public parameters and keys of $¶i_0$ and $¶i$. Besides, input for the conversion is an ordinary ciphertext of $¶i_0$. (3) Efficiency: The computational cost for re-encryption and decryption of the re-encrypted ciphertext are roughly the same as a decryption in $¶i_0$ and $¶i$ respectively. We prove that our construction is fully secure assuming $¶i_0$ is secure in Attrapadung's framework and $¶i$ is IND-CPA secure. Furthermore, it remains secure when there are multiple target cryptosystems. As with other proxy re-encryption, CS-PRE enables flexible sharing of cloud data, as the owner can instruct the cloud server to re-encrypt his ciphertext to those for the intended recipient. In addition, it allows lightweight devices to enjoy access to remote data encrypted under powerful but possibly costly encryption, such as functional encryption, by utilizing the server's power in converting the ciphertext to a simpler encryption, such as RSA. Finally, instances of CS-PRE can be viewed as new proxy re-encryption schemes, such as a PRE supporting ABE for regular language to Hierarchical IBE or Doubly Spatial Encryption to lattice-based encryptions (e.g. NTRUCCA).


title: “They Can’t Understand It”: Maternity Health and Care Needs of Immigrant Muslim Women in St. John’s, Newfoundland

abstract: Objectives ::: The purpose of this qualitative study was to document and explore the maternity health care needs and the barriers to accessing maternity health services from the perspective of immigrant Muslim women living in St. John’s, Canada.


title: Assessing Citizens’ Adoption of a Transactional E-Government System: Validation of the Extended Decomposed Theory of Planned Behavior (DTPB)

abstract: The study explores the adoption of an electronic government (e-government) system called online PAN card registration system (OPCRS) in context of India. The study validates the extended decomposed theory of planned behaviour (DTPB) to understand the impact of some of its factors on the citizen’s intention to adopt this system. The proposed research model is validated using the data collected from 377 respondents from six selected cities covering all different locations of India. The data is analysed using AMOS 22.0. The empirical findings of the proposed research model indicated the significant relationships of all proposed hypotheses. The study also provides its limitations, future research directions, and implications for theory and practice toward the end.


A novel approach for detection of copy move forgery using completed robust local binary pattern


Predicting Corporate Bankruptcy: Whither Do We Stand?


title: SceneSuggest: Context-driven 3D Scene Design

abstract: We present SceneSuggest: an interactive 3D scene design system providing context-driven suggestions for 3D model retrieval and placement. Using a point-and-click metaphor we specify regions in a scene in which to automatically place and orient relevant 3D models. Candidate models are ranked using a set of static support, position, and orientation priors learned from 3D scenes. We show that our suggestions enable rapid assembly of indoor scenes. We perform a user study comparing suggestions to manual search and selection, as well as to suggestions with no automatic orientation. We find that suggestions reduce total modeling time by 32%, that orientation priors reduce time spent re-orienting objects by 27%, and that context-driven suggestions reduce the number of text queries by 50%.


title: Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Software Evaluation

abstract: Software metric is a measure of some property of a piece of software or its specifications. The goal is to obtain reproducible and quantifiable measurements, which may have several valuable applications in schedule and budget planning, effort and cost evaluation, quality assurance testing, software debugging, software performance optimization, and optimal personnel task assignments. Software effort evaluation is one of the most essential and crucial part of software project planning for which efficient effort metrics is required. Software effort evaluation is followed by software cost evaluation which is helpful for both customers and developers. Thus, efficiency of effort component of software is very essential. The algorithmic models are weak in estimating early effort evaluation with regards to uncertainty and imprecision in software projects. To overcome this problem, there are various machine learning methods. One of the methods is soft computing in which there are various methodologies viz., Artificial Neural Network, Fuzzy Logic, Evolutionary computation based Genetic Algorithm and Metaheuristic based Particle Swarm Optimization. These methods are good at solving real-world ambiguities. This paper highlights the design of an efficient software effort evaluation model using Adaptive Neuro-Fuzzy Inference System (ANFIS) for uncertain datasets and it shows that this technique significantly outperforms with sufficient results.


title: A Bayesian Analysis of Some Nonparametric Problems

abstract: Bayesian approach remained rather unsuccessful in treating nonparametric problems. This is primarily due to the difficulty in finding workable prior distribution on the parameter space , which in nonparametric problems is taken to be a set of probability distributions on a given sample space. Two Desirable Properties of a Prior 1. The support of the prior should be large. 2. Posterior distribution given a sample of observation should be manageable analytically. These properties are antagonistic : One may be obtained at the expense of other.


title: Efficient and High-Performance Parallel Hardware Architectures for the AES-GCM

abstract: Since its acceptance as the adopted symmetric-key algorithm, the Advanced Encryption Standard (AES) and its recently standardized authentication Galois/Counter Mode (GCM) have been utilized in various security-constrained applications. Many of the AES-GCM applications are power and resource constrained and require efficient hardware implementations. In this paper, different application-specific integrated circuit (ASIC) architectures of building blocks of the AES-GCM algorithms are evaluated and optimized to identify the high-performance and low-power architectures for the AES-GCM. For the AES, we evaluate the performance of more than 40 S-boxes utilizing a fixed benchmark platform in 65-nm CMOS technology. To obtain the least complexity S-box, the formulations for the Galois Field (GF) subfield inversions in GF(24) are optimized. By conducting exhaustive simulations for the input transitions, we analyze the average and peak power consumptions of the AES S-boxes considering the switching activities, gate-level netlists, and parasitic information. Additionally, we present high-speed, parallel hardware architectures for reaching low-latency and high-throughput structures of the GCM. Finally, by investigating the high-performance GF(2128) multiplier architectures, we benchmark the proposed AES-GCM architectures using quadratic and subquadratic hardware complexity GF(2128) multipliers. It is shown that the performance of the presented AES-GCM architectures outperforms the previously reported ones in the utilized 65-nm CMOS technology.


title: Robust whole-brain segmentation: Application to traumatic brain injury

abstract: Abstract We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called “Multi-Atlas Label Propagation with Expectation–Maximisation based refinement” (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.


title: Multiband Printed Metamaterial Inverted-F Antenna (IFA) for USB Applications

abstract: A printed multiband coplanar waveguide (CPW)-fed inverted-F antenna (IFA) is presented in this letter. The concept of loading a printed-IFA with composite right/left-handed (CRLH) unit cells is theoretically and experimentally investigated by using electromagnetic simulators, Advanced Design System (ADS) and High Frequency Structure Simulator (HFSS), for universal serial bus (USB) applications. The proposed USB antenna structure consists of a CPW printed-IFA with two arms loaded by two CRLH unit cells to achieve two extra operating bands in addition to the two fundamental resonant frequencies of two IFA arms. The structure is designed to operate at GSM 1.8 GHz, Bluetooth 2.4 GHz, WiMAX 3.5 GHz, and WLAN 5.2 GHz. Measured and simulated results are in good agreement with the theoretical predictions.


title: Elevated admission INR strongly predicts mortality in victims of abusive head trauma.

abstract: BACKGROUND ::: Victims of abusive head trauma have poor outcomes compared to other injured children. There is often a delay in diagnosis as these young patients are unable to communicate with healthcare providers. These critically injured patients would benefit from early identification and therapy. ::: ::: ::: METHODS ::: We performed a retrospective review of our single hospital trauma registry from 2005-2014. All level 1 pediatric (age 0-17) trauma patients who sustained abusive head trauma were included. Exclusion criteria included: no admission coagulation studies, prehospital product transfusion, preexisting coagulation disorder or death upon arrival. Primary outcome was mortality; secondary outcomes were early blood transfusion and neurosurgical intervention.Univariate analysis included Fisher exact and Wilcoxon rank-sum testing; we then performed logistic regression modeling and calculated adjusted odds ratios (AOR) to control for known predictors of poor outcome including hypotension, hypothermia, acidosis, ISS, and Head AIS. ::: ::: ::: RESULTS ::: In 101 total subjects, 35% (n=35) had INR≥1.3 on admission. On univariate analysis, patients with coagulation dysregulation were more likely to have hypothermia, hypotension, acidosis, high ISS and low GCS (all p<0.05). There was no difference in age, anemia, and incidence of polytrauma. Overall mortality was 24.8% (n=25) which varied significantly based on admission INR (60% INR≥1.3 vs 6% INR>1.3, p<0.001). Patients with elevated INR were also more likely to have early PRBC transfusion (p=0.003) and neurosurgical intervention (p=0.011). In logistic regression analysis, admission INR was the strongest independent predictor of mortality with increased odds of 3.65 (p=0.045). Adjusted odds ratio after controlling specifically for hypotension, hypothermia and acidosis was 6.25 (p=0.006) and after controlling for head AIS and admission GCS the AOR=5.27 (p=0.007). ::: ::: ::: CONCLUSIONS ::: Admission INR≥1.3 strongly predicts mortality in abusive head trauma. These patients should be targeted for early aggressive interventions and monitoring with the goal of improving patient outcomes. Further study is warranted to investigate potential therapeutic targets in trauma-induced coagulation dysregulation. ::: ::: ::: LEVEL OF EVIDENCE ::: III, prognostic and epidemiological.


title: Rural wages in India: Recent trends and determinants

abstract: Abstract This paper looks into the overall trends in rural wages from 1983 to 2011–12, and attempts to analyse the factors that led to the unprecedented rise in rural wages (particularly since 2007–08) which attracted much policy debate. Did growth in agricultural productivity lead to the unprecedented rise in rural wages? Or was the uptrend determined by factors outside the agricultural sector, most importantly the implementation of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) since 2006–07, and the overall economic growth with the resultant structural changes in the labour market? Using panel data regression, the paper finds that the MGNREGA was not the sole factor responsible for this uptrend, as has been claimed by several researchers. Factors such as agricultural productivity, construction sector growth, urbanisation, and literacy also majorly contributed to the rise in rural wages.


title: Association between facial height development and mandibular growth rotation in low and high MP-SN angle faces: A longitudinal study

abstract: Abstract Two groups of children with low (n=29) and high (n=29) MP-SN angles were followed longitudinally from 6 to 15 years of age. The purpose was to thoroughly examine associations between vertical craniofacial growth and mandibular growth rotation. Correlations between dimensional and rotational variables occurred in different variable pairs in the two groups and changed with age. Increase in posterior lower facial height distinguished itself by being consistently positively correlated with forward matrix rotation irrespective of mandibular plane angle or age. The same applied to increase in ramus height. Increase in lower anterior facial height was, surprisingly, weakly correlated with mandibular rotation, but strongly and positively correlated with increase in corpus length. Overdevelopment of lower anterior facial height in high angle cases occurred because the steep mandibular plane directed corpus growth more downward than normal, not because the mandible rotated backward.


title: Comparing vector–host and SIR models for dengue transmission.

abstract: Various simple mathematical models have been used to investigate dengue transmission. Some of these models explicitly model the mosquito population, while others model the mosquitoes implicitly in the transmission term. We study the impact of modeling assumptions on the dynamics of dengue in Thailand by fitting dengue hemorrhagic fever (DHF) data to simple vector–host and SIR models using Bayesian Markov chain Monte Carlo estimation. The parameter estimates obtained for both models were consistent with previous studies. Most importantly, model selection found that the SIR model was substantially better than the vector–host model for the DHF data from Thailand. Therefore, explicitly incorporating the mosquito population may not be necessary in modeling dengue transmission for some populations.


Cointopia : Blockchain Analysis using Online Forums


title: 3-HBP: A Three-Level Hidden Bayesian Link Prediction Model in Social Networks

abstract: In social networks, link establishment among the users is affected by complex factors. In this paper, we try to investigate the internal and external factors that affect the formation of links and propose a three-level hidden Bayesian link prediction model by integrating the user behavior as well as user relationships to link prediction. First, based on the user multiple interest characteristics, a latent Dirichlet allocation (LDA) traditional text modeling method is applied into user behavior modeling. Taking the advantage of LDA topic model in dealing with the problem of polysemy and synonym, we can mine user latent interest distribution and analyze the effects of internal driving factors. Second, owing to the power-law characteristics of user behavior, LDA is improved by Gaussian weighting. In this way, the negative impact of the interest distribution to the high-frequency users can be reduced and the expression ability of interests can be enhanced. Furthermore, taking the impact of common neighbor dependencies in link establishment, the model can be extended with hidden naive Bayesian algorithm. By quantifying the dependencies between common neighbors, we can analyze the effects of external driving factors and combine internal driving factors to link prediction. Experimental results indicate that the model can not only mine user latent interest distribution but also can improve the performance of link prediction effectively.


title: Cortical mechanisms of human eating.

abstract: The hedonic component of eating is an underexplored topic within neuroscience, which is surprising given its importance for our survival and general well-being, as well as the obvious links to obesity


title: A review of the pathogenesis of ankylosing spondylitis

abstract: Ankylosing spondylitis (AS) is a chronic, progressive inflammatory rheumatic disease involving primarily the sacroiliac joints and the axial skeleton. The main clinical features are back pain and progressive stiffness of the spine. Oligoarthritis of the hips and shoulders, enthesopathy, and anterior uveitis are common, and involvement of the heart and lungs is rare. The current understanding of the pathogenesis of this disorder is limited. Despite the strong association between human leukocyte antigen B27 (HLA-B27) and susceptibility to AS reported over the past 30 years, the exact pathogenic role of HLA-B27 in AS and other spondyloarthropathies has yet to be determined. The authors present a review of the literature pertaining to the pathogenesis of AS over the past several decades. Ankylosing spondylitis is a polygenic disorder, with HLA-B27 playing a critical causative role in its pathogenesis. Animal studies of the immunobiology of HLA-B27 have provided significant insight into the pathogenic role of HLA-B27. The search for the antigenic peptide to support the "arthritogenic peptide" hypothesis has been disappointing. Over the past decade there has been increasing interest in the critical role of the misfolding and unfolded protein response of the heavy chain HLA-B27 in the modulation of the inflammatory response. Although there have been significant new findings in the understanding of the pathogenesis of AS, the exact mechanisms have yet to be identified. There is considerable optimism that additional susceptibility genes, predisposing factors, and regulators of the inflammatory process will be identified that will provide avenues for future treatment.


title: LSTM Recurrent Networks Learn Simple Context Free and Context Sensitive Languages

abstract: Previous work on learning regular languages from exemplary training sequences showed that long short-term memory (LSTM) outperforms traditional recurrent neural networks (RNNs). We demonstrate LSTMs superior performance on context-free language benchmarks for RNNs, and show that it works even better than previous hardwired or highly specialized architectures. To the best of our knowledge, LSTM variants are also the first RNNs to learn a simple context-sensitive language, namely a/sup n/b/sup n/c/sup n/.


title: Understanding pre-service teachers' computer attitudes: applying and extending the technology acceptance model

abstract: Computers are increasingly widespread, influencing many aspects of our social and work lives, As we move into a technology‐based society, it is important that classroom experiences with computers are made available for all students. The purpose of this study is to examine pre‐service teachers' attitudes towards computers. This study extends the technology acceptance model (TAM) framework, with subjective norm and facilitating conditions acting as external variables. Results shows that perceived usefulness, perceived ease of use and subjective norm were significant determinants of pre‐service computer attitudes. Facilitating conditions did not influence computer attitude directly but through perceived ease of use. These findings demonstrate that social norm and facilitating conditions are potential variables that may be used to extend the TAM for research on computer attitudes.


title: Detecting Spam Review through Sentiment Analysis

abstract: Online review can help people getting more information about store and product. The potential customers tend to make decision according to it. However, driven by profit, spammers post spurious reviews to mislead the customers by promoting or demoting target store. Previous studies mainly utilize rating as indicator for the detection. However, these studies ignore an important problem that the rating will not necessarily represent the sentiment accurately. In this paper, we first incorporate the sentiment analysis techniques into review spam detection. The proposed method compute sentiment score from the natural language text by a shallow dependency parser. We further discuss the relationship between sentiment score and spam reviews. A series of discriminative rules are established through intuitive observation. In the end, this paper establishes a time series combined with discriminative rules to detect the spam store and spam review efficiently. Experimental results show that the proposed methods in this paper have good detection result and outperform existing methods.


title: Introducing Time into RDF

abstract: The resource description framework (RDF) is a metadata model and language recommended by the W3C. This paper presents a framework to incorporate temporal reasoning into RDF, yielding temporal RDF graphs. We present a semantics for these kinds of graphs which includes the notion of temporal entailment and a syntax to incorporate this framework into standard RDF graphs, using the RDF vocabulary plus temporal labels. We give a characterization of temporal entailment in terms of RDF entailment and show that the former does not yield extra asymptotic complexity with respect to nontemporal RDF graphs. We also discuss temporal RDF graphs with anonymous timestamps, providing a theoretical framework for the study of temporal anonymity. Finally, we sketch a temporal query language for RDF, along with complexity results for query evaluation that show that the time dimension preserves the tractability of answers


title: Performance of Medium-Voltage DC-Bus PV System Architecture Utilizing High-Gain DC–DC Converter

abstract: The number of large-scale solar photovoltaic (PV) systems continues to increase, while the size of the largest systems has already reached several hundred megawatts. This trend will challenge existing PV system architectures and will require new PV system architectures with higher power ratings and higher voltage levels at the point of common coupling (PCC). This paper reports a medium-voltage (MV) dc-bus PV system architecture based on a high-gain soft-switched interleaved boost (SSIB) dc–dc converter. The interleaved characteristic increases the flexibility of the converter, allowing for either a higher voltage and/or current rating, thus increasing the power rating of the converter. The high-gain capability of the SSIB converter allows it to be connected directly to an MV dc bus. This will facilitate direct connection of a PV system to an MV ac grid (i.e., 20 kV) using only one step-up transformer. Simulation and experimental results are presented to verify the operation of the SSIB converter and to confirm the steady-state and dynamic performance of the proposed PV system architecture.


title: Excessive internet use: the role of personality, loneliness and social support networks in internet addiction

abstract: An online survey of 96 adults showed that, based on Young's (1998) criteria for the Internet Addiction Test, 40% of the sample could be classified as average internet users, 52% as problem over-users and 8% as pathologically addicted to the internet. The three groups differed on a range of factors, with over-users and addicts spending increasingly more time in online activities, being more neurotic and less extraverted, more socially anxious and emotionally lonely, and gaining greater support from internet social networks than average internet users. Further analysis revealed that only neuroticism and perceived support from online social networks were significant predictors of excessive internet use. In addition, over-users were found to be younger and less experienced in computer use than average or addicted users. Further research is needed to explicate the role of personality and track the possible pathways from novice over-use to eventual average use or pathological addiction.


title: Speed Control of DC Motor Using Fuzzy Logic Application

abstract: Direct current (DC) motors are controlled easily and have very high performance. The speed of the motors could be adjusted within a wide range. Today, classical control techniques (such as Proportional Integral Differential - PID) are very commonly used for speed control purposes. However, it is observed that the classical control techniques do not have an adequate performance in the case of nonlinear systems. Thus, instead, a modern technique is preferred: fuzzy logic. In this paper the control system is modelled using MATLAB/Simulink. Using both PID controller and fuzzy logic techniques, the results are compared for different speed values.


title: Heuristic versus systematic information processing and the use of source versus message cues in persuasion.

abstract: In Experiment 1, subjects read a persuasive message from a likable or unlikable communicator who presented six or two arguments concerning one of two topics. High response involvement subjects anticipated discussing the message topic at a future experimental session, whereas low involvement subjects anticipated discussing a different topic. For high involvement subjects, opinion change was significantly greater given six arguments but was unaffected by communicator likability. For low involvement subjects, opinion change was significantly greater given a likable communicator but was unaffected by the arguments manipulation. In Experiment 2, high issue involvement subjects showed slightly greater opinion change when exposed to five arguments from an unlikable (vs. one argument from a likable) communicator, whereas low involvement subjects exhibited significantly greater persuasion in response to one argument from a likable (vs. five arguments from an unlikable) communicator. These findings support the idea that high involvement leads message recipients to employ a systematic information processing strategy in which message-based cognitions mediate persuasion, whereas low involvement leads recipients to use a heuristic processing strategy in which simple decision rules mediate persuasion. Support was also obtained for the hypothesis that content-mediated (vs. source-mediated) opinion change would shower greater persistence.


Non-Financial Recognition The Most Effective of Rewards ?


title: Cerebral Cortex Advance Access published July 21, 2004 Alpha Phase Synchronization Predicts P1 and N1 Latency and Amplitude Size

abstract: The oscillatory phase-resetting model predicts that event-related potential (ERP) components are generated by a superposition of evoked oscillations with different frequencies. We investigate this question in a memory task in which human subjects had to retrieve a verbal label in response to the presentation of a picture. The results show that (i) evoked oscillations in the delta, theta, alpha and beta range undergo a significant phase resetting and (ii) become synchronized in absolute phase during small time windows that (iii) coincide with the latencies of the P1--N1 complex. Our conclusion is that the latencies of ERP components can be predicted at least in part by phase synchronization between frequencies.


title: Contribution mapping: a method for mapping the contribution of research to enhance its impact

abstract: BACKGROUND ::: At a time of growing emphasis on both the use of research and accountability, it is important for research funders, researchers and other stakeholders to monitor and evaluate the extent to which research contributes to better action for health, and find ways to enhance the likelihood that beneficial contributions are realized. Past attempts to assess research 'impact' struggle with operationalizing 'impact', identifying the users of research and attributing impact to research projects as source. In this article we describe Contribution Mapping, a novel approach to research monitoring and evaluation that aims to assess contributions instead of impacts. The approach focuses on processes and actors and systematically assesses anticipatory efforts that aim to enhance contributions, so-called alignment efforts. The approach is designed to be useful for both accountability purposes and for assisting in better employing research to contribute to better action for health. ::: ::: ::: METHODS ::: Contribution Mapping is inspired by a perspective from social studies of science on how research and knowledge utilization processes evolve. For each research project that is assessed, a three-phase process map is developed that includes the main actors, activities and alignment efforts during research formulation, production and knowledge extension (e.g. dissemination and utilization). The approach focuses on the actors involved in, or interacting with, a research project (the linked actors) and the most likely influential users, who are referred to as potential key users. In the first stage, the investigators of the assessed project are interviewed to develop a preliminary version of the process map and first estimation of research-related contributions. In the second stage, potential key-users and other informants are interviewed to trace, explore and triangulate possible contributions. In the third stage, the presence and role of alignment efforts is analyzed and the preliminary results are shared with relevant stakeholders for feedback and validation. After inconsistencies are clarified or described, the results are shared with stakeholders for learning, improvement and accountability purposes. ::: ::: ::: CONCLUSION ::: Contribution Mapping provides an interesting alternative to existing methods that aim to assess research impact. The method is expected to be useful for research monitoring, single case studies, comparing multiple cases and indicating how research can better be employed to contribute to better action for health.


title: 3-D Integration and Through-Silicon Vias in MEMS and Microsensors

abstract: After two decades of intensive development, 3-D integration has proven invaluable for allowing integrated circuits to adhere to Moore’s Law without needing to continuously shrink feature sizes. The 3-D integration is also an enabling technology for hetero-integration of microelectromechanical systems (MEMS)/microsensors with different technologies, such as CMOS and optoelectronics. This 3-D hetero-integration allows for the development of highly integrated multifunctional microsystems with small footprints, low cost, and high performance demanded by emerging applications. This paper reviews the following aspects of the MEMS/microsensor-centered 3-D integration: fabrication technologies and processes, processing considerations and strategies for 3-D integration, integrated device configurations and wafer-level packaging, and applications and commercial MEMS/microsensor products using 3-D integration technologies. Of particular interest throughout this paper is the hetero-integration of the MEMS and CMOS technologies. [2015-0158]


title: DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics

abstract: Deep learning shows great promise in providing more intelligence to augmented reality (AR) devices, but few AR apps use deep learning due to lack of infrastructure support. Deep learning algorithms are computationally intensive, and front-end devices cannot deliver sufficient compute power for real-time processing. In this work, we design a framework that ties together front-end devices with more powerful backend “helpers” (e.g., home servers) to allow deep learning to be executed locally or remotely in the cloud/edge. We consider the complex interaction between model accuracy, video quality, battery constraints, network data usage, and network conditions to determine an optimal offloading strategy. Our contributions are: (1) extensive measurements to understand the tradeoffs between video quality, network conditions, battery consumption, processing delay, and model accuracy; (2) a measurement-driven mathematical framework that efficiently solves the resulting combinatorial optimization problem; (3) an Android application that performs real-time object detection for AR applications, with experimental results that demonstrate the superiority of our approach.


title: Fast and Efficient Deep Sparse Multi-Strength Spiking Neural Networks with Dynamic Pruning

abstract: Deep convolutional neural networks (CNNs) have shown state-of-the-art accuracy for various computer vision and speech tasks. However, CNNs are computation-intensive and energy-inefficient which are difficult to be deployed in real-time systems. Event-driven Spiking Neural Networks (SNNs) are extremely power efficient, which provides an alternative for ultra-low power applications. But effective training methods for SNN are still lacking. Due to its spatio-temporal feature of SNN, conventional training method for CNN can not be employed in SNN. To address this problem, some researchers proposed to convert the corresponding weights of trained CNNs into the synapse weights of SNNs (CNNs-SNNs). Nevertheless, limited by the [0, 1] constraints on the SNN neuron outputs, the accuracy of the converted SNNs is impaired. Besides, as the SNN network becomes deeper, the convergence speed of SNN inference are unacceptably slow. In this work, we proposed an innovative deep multi-strength SNN (M-SNN) structure which relaxes the restriction of the neuron output spike strength while the event-driven feature for low-power implementations is maintained. Using this architecture, large scale SNN can be converted from CNN with comparable accuracy and fast inference speed. The evaluation results show 3.7 × convergence speedup. Moreover, with multi-strength spike, aggressive pruning strategies can be applied to reduce the computational operations by almost 85% while maintaining the same accuracy.


title: ACE: An Efficient Asynchronous Corner Tracker for Event Cameras

abstract: The emergence of bio-inspired event cameras has opened up new exciting possibilities in high-frequency tracking, overcoming some of the limitations of traditional frame-based vision (e.g. motion blur during high-speed motions or saturation in scenes with high dynamic range). As a result, research has been focusing on the processing of their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream into frame-like representations, we are yet to harness the true power of these cameras. In this paper, we propose the ACE tracker: a purely asynchronous framework to track corner-event features. Evaluation on benchmarking datasets reveals significant improvements in accuracy and computational efficiency in comparison to state-of-the-art event-based trackers. ACE achieves robust performance even in challenging scenarios, where traditional frame-based vision algorithms fail.


Students ' Attitudes Towards the Use of Social Networks for Learning the English Language


title: Compelling intelligent user interfaces—how much AI?

abstract: Efforts to incorporate intelligence into the user interface have been underway for decades, but the commercial impact of this work has not lived up to early expectations, and is not immediately apparent. This situation appears to be changing. However, so far the most interesting intelligent user interfaces (IUIS) have tended to use minimal or simplistic AI. In this panel we consider whether more or less AI is the key to the development of compelling IUIS. The panelists will present examples of compelling IUIS that use a selection of AI techniques, mostly simple, but some complex. Each panelist will then comment on the merits of different kinds and quantities of AI in the development of pragmatic interface technology.


A Statistical Framework for Product Description Generation


Stochastic Differential Equation Methods for Spatio-Temporal Gaussian Process Regression


Recognition Technology of Money Laundering Transactions Based on Data Mining


title: Sparse logistic regression for whole-brain classification of fMRI data

abstract: Abstract Multivariate pattern recognition methods are increasingly being used to identify multiregional brain activity patterns that collectively discriminate one cognitive condition or experimental group from another, using fMRI data. The performance of these methods is often limited because the number of regions considered in the analysis of fMRI data is large compared to the number of observations (trials or participants). Existing methods that aim to tackle this dimensionality problem are less than optimal because they either over-fit the data or are computationally intractable. Here, we describe a novel method based on logistic regression using a combination of L1 and L2 norm regularization that more accurately estimates discriminative brain regions across multiple conditions or groups. The L1 norm, computed using a fast estimation procedure, ensures a fast, sparse and generalizable solution; the L2 norm ensures that correlated brain regions are included in the resulting solution, a critical aspect of fMRI data analysis often overlooked by existing methods. We first evaluate the performance of our method on simulated data and then examine its effectiveness in discriminating between well-matched music and speech stimuli. We also compared our procedures with other methods which use either L1-norm regularization alone or support vector machine-based feature elimination. On simulated data, our methods performed significantly better than existing methods across a wide range of contrast-to-noise ratios and feature prevalence rates. On experimental fMRI data, our methods were more effective in selectively isolating a distributed fronto-temporal network that distinguished between brain regions known to be involved in speech and music processing. These findings suggest that our method is not only computationally efficient, but it also achieves the twin objectives of identifying relevant discriminative brain regions and accurately classifying fMRI data.


title: Deep Unsupervised Representation Learning for Remote Sensing Images

abstract: Scene classification plays a key role in interpreting the remotely sensed high-resolution images. With the development of deep learning, supervised learning in classification of Remote Sensing with convolutional networks (CNNs) has been frequently adopted. However, researchers paid less attention to unsupervised learning in remote sensing with CNNs. In order to filling the gap, this paper proposes a set of CNNs called \textbf{M}ultiple l\textbf{A}ye\textbf{R} fea\textbf{T}ure m\textbf{A}tching(MARTA) generative adversarial networks (GANs) to learn representation using only unlabeled data. There will be two models of MARTA GANs involved: (1) a generative model $G$ that captures the data distribution and provides more training data; (2) a discriminative model $D$ that estimates the possibility that a sample came from the training data rather than $G$ and in this way a well-formed representation of dataset can be learned. Therefore, MARTA GANs obtain the state-of-the-art results which outperform the results got from UC-Merced Land-use dataset and Brazilian Coffee Scenes dataset.


title: A Cross Cultural, Comparative Analysis of Teamwork to Embrace Diversity in Schools

abstract: Increasing diversity in schools today is prompting schools around the world to find effective means of addressing this diversity. Effective collaboration among educational professionals (teachers and other service providers) as a team is one key variable in effectively meeting the demands posed by diversity in classrooms. Responses (n =210) from special education and general education professionals in Spain and the United States to a survey, were analyzed to study how teachers and service providers in different schools from the two countries perceived team functioning. The study also compared team make-up and team functioning in schools in the two countries. The respondents expressed a need for continuous within-teams review of the goals ensuring parental input and parental satisfaction enabling the team to evaluate their success in achieving the goals set initially. Respondents from both countries did not perceive family input and involvement as being valued relative to their teaming. This paper discusses results, implications, and directions for schools, educators, and future research.


Robotics in Lower-Limb Rehabilitation after Stroke


title: A study of an autonomous mobile robot for a sewer inspection system

abstract: This article describes a prototype autonomous mobile robot, KANTARO, designed for inspecting sewer pipes. It is able to move autonomously in 200–300-mm-diameter sewer pipes, to turn smoothly through 90° at a junction, and to go down a 5-cm step. KANTARO carries all the resources required, such as a control unit, a camera, a 2D laser, and an IR sensor. Damage or abnormalities in sewer pipes are detected based on recorded sensory data. KANTARO has demonstrated its effectiveness in inspection and in autonomous navigation in a dry sewer test field at the FAIS–Robotics Development Support Office (FAIS–RDSO).


title: Cycles structure and local ordering in complex networks

abstract: We study the properties of metrics aimed at the characterization of grid-like ordering in complex networks. These metrics are based on the global and local behavior of cycles of order four, which are the minimal structures able to identify rectangular clustering. The analysis of data from real networks reveals the ubiquitous presence of a high level of grid-like ordering that is non-trivially correlated with the local degree properties. These observations provide new insights on the hierarchical structure of complex networks. Many networks arising in social, biological, and technological contexts are growing and self-organizing systems which are not modeled by any supervising entity, nor follow an externally defined blueprint [1, 2, 3, 4, 5]. Empirical evidences, indeed, have prompted that most of the times the network’s topology exhibits complex features which cannot be explained by merely extrapolating the local properties of their constituents. The most relevant among these features are the small-world property [6, 3] and a high level of heterogeneity, usually reflected in a scale-free behavior of the network’s connectivity


title: Combined Direct and Indirect CT Venography (Combined CTV) in Detecting Lower Extremity Deep Vein Thrombosis

abstract: AbstractThis study aimed to evaluate the diagnostic accuracy of combined direct and indirect CT venography (combined CTV) in the detection of lower extremity deep vein thrombosis (LEDVT).The institutional review board approved the study protocol, and patients or qualifying family members provided informed consent. A total of 96 consecutive patients undergoing combined CTV were prospectively enrolled. A combined examination with digital subtraction angiography (DSA) plus duplex ultrasonography (US) was used as the criterion standard. Three observers were blinded to clinical, DSA, and US results, and they independently analyzed all combined CTV datasets. Interobserver agreement was expressed in terms of the Cohen k value for categorical variables. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of combined CTV in the detection of LEDVT were determined by using patient- and location-based evaluations.Of the 96 patients, DSA plus US revealed LEDVT in 125 segmental veins in 63 patients. Patient-based evaluation with combined CTV yielded an accuracy of 96.9% to 97.9%, a sensitivity of 95.2% to 96.8%, a specificity of 100% to 100%, a PPV of 100% to 100%, and an NPV of 91.7% to 94.3% in the detection of LEDVT. Location-based evaluation yielded similar results. Through combined direct and indirect CTV, patients obtained a combined CT angiogram on the diseased limb and an indirect CT angiogram on the opposite side. The image quality of combined CTV was superior to an indirect venogram.Combined CTV shows promising diagnostic accuracy in the detection of LEDVT with 3-dimensional modeling of the lower limb venous system.


title: Unsupervised scene segmentation using sparse coding context

abstract: This paper presents an approach to image understanding on the aspect of unsupervised scene segmentation. With the goal of image understanding in mind, we consider ‘unsupervised scene segmentation’ a task of dividing a given image into semantically meaningful regions without using annotation or other human-labeled information. We seek to investigate how well an algorithm can achieve at partitioning an image with limited human-involved learning procedures. Specifically, we are interested in developing an unsupervised segmentation algorithm that only relies on the contextual prior learned from a set of images. Our algorithm incorporates a small set of images that are similar to the input image in their scene structures. We use the sparse coding technique to analyze the appearance of this set of images; the effectiveness of sparse coding allows us to derive a priori the context of the scene from the set of images. Gaussian mixture models can then be constructed for different parts of the input image based on the sparse-coding contextual prior, and can be combined into an Markov-random-field-based segmentation process. The experimental results show that our unsupervised segmentation algorithm is able to partition an image into semantic regions, such as buildings, roads, trees, and skies, without using human-annotated information. The semantic regions generated by our algorithm can be useful, as pre-processed inputs for subsequent classification-based labeling algorithms, in achieving automatic scene annotation and scene parsing.


title: Large-scale Hierarchical Alignment for Author Style Transfer

abstract: We propose a simple method for extracting pseudo-parallel monolingual sentence pairs from comparable corpora representative of two different author styles, such as scientific papers and Wikipedia articles. Our approach is to first hierarchically search for nearest document neighbours and then for sentences therein. We demonstrate the effectiveness of our method through automatic and extrinsic evaluation on two tasks: text simplification from Wikipedia to Simple Wikipedia and style transfer from scientific journal articles to press releases. We show that pseudo-parallel sentences extracted with our method not only improve existing parallel data, but can even lead to competitive performance on their own.


title: A neuro-inspired architecture for unsupervised continual learning based on online clustering and hierarchical predictive coding

abstract: We propose that the Continual Learning desiderata can be achieved through a neuro-inspired architecture, grounded on Mountcastle's cortical column hypothesis. The proposed architecture involves a single module, called Self-Taught Associative Memory (STAM), which models the function of a cortical column. STAMs are repeated in multi-level hierarchies involving feedforward, lateral and feedback connections. STAM networks learn in an unsupervised manner, based on a combination of online clustering and hierarchical predictive coding. This short paper only presents the architecture and its connections with neuroscience. A mathematical formulation and experimental results will be presented in an extended version of this paper.


title: Sequential Match Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots

abstract: We study response selection for multi-turn conversation in retrieval-based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among utterances or important contextual information. We propose a sequential matching network (SMN) to address both problems. SMN first matches a response with each utterance in the context on multiple levels of granularity, and distills important matching information from each pair as a vector with convolution and pooling operations. The vectors are then accumulated in a chronological order through a recurrent neural network (RNN) which models relationships among utterances. The final matching score is calculated with the hidden states of the RNN. An empirical study on two public data sets shows that SMN can significantly outperform state-of-the-art methods for response selection in multi-turn conversation.


title: Isolation and characterization of cancer stem cells from a human glioblastoma cell line U87

abstract: A variety of malignant cancers have been found to contain a subpopulation of stem cell-like tumor cells, or cancer stem cells (CSCs). However, the existence of CSCs in U87, a most commonly used glioma cell line, is still controversial. In this study, we demonstrate that U87 cell line contained a fraction of tumor cells that could form tumor spheres and were enriched by progressively increasing the concentration of serum-free neural stem cell medium with or without low dose vincristine. These cells possessed the ability of self-renewal and multipotency, the defined characteristics of CSCs. Moreover, the tumors formed by the secondary spheres displayed typical histological features of human glioblastoma, including cellular pleomorphism, pseudopalisades surrounding necrosis, hyperchromatic nuclei, high density of microvessels and invasion to the brain parenchyma. These results indicate that gradually increasing the concentration of serum-free neural stem cell culture medium with or without vincristine is a simple and effective method for isolation of CSCs to study the initiation and progression of human glioblastoma.


A Dataset for Telling the Stories of Social Media Videos


title: Multi-Head Attention with Disagreement Regularization

abstract: Multi-head attention is appealing for the ability to jointly attend to information from different representation subspaces at different positions. In this work, we introduce a disagreement regularization to explicitly encourage the diversity among multiple attention heads. Specifically, we propose three types of disagreement regularization, which respectively encourage the subspace, the attended positions, and the output representation associated with each attention head to be different from other heads. Experimental results on widely-used WMT14 English-German and WMT17 Chinese-English translation tasks demonstrate the effectiveness and universality of the proposed approach.


Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network


title: A 400GΩ Input-Impedance, 220MVpp Linear-Input-Range, 2.8Vpp CM-Interference-Tolerant Active Electrode for Non-Contact Capacitively Coupled ECG Acquisition

abstract: This paper presents a purely capacitively coupled active electrode (AE) for ECG acquisition for implementation in smart clothing or furniture. The AE has a DC input impedance of 400GΩ and 3.7µV rms (0.5-100Hz) input-referred noise. The whole design supports 220mV pp differential linear input range required in practical scenarios. An active driven-right-leg (DRL) loop is provided, achieving at least 70dB CMRR for up to 2.8V pp common-mode (CM) interference. Implemented in a 0.18µm CMOS process, the prototype occupies an area of 1.23mm2, and consumes 18µA and 13µA for the AE and DRL respectively. Through-clothing non-contact capacitively coupled ECG acquisition is demonstrated.


title: Resource-Constrained Simultaneous Detection and Labeling of Objects in High-Resolution Satellite Images

abstract: We describe a strategy for detection and classification of man-made objects in large high-resolution satellite photos under computational resource constraints. We detect and classify candidate objects by using five pipelines of convolutional neural network processing (CNN), run in parallel. Each pipeline has its own unique strategy for fine tunning parameters, proposal region filtering, and dealing with image scales. The conflicting region proposals are merged based on region confidence and not just based on overlap areas, which improves the quality of the final bounding-box regions selected. We demonstrate this strategy using the recent xView challenge, which is a complex benchmark with more than 1,100 high-resolution images, spanning 800,000 aerial objects around the world covering a total area of 1,400 square kilometers at 0.3 meter ground sample distance. To tackle the resource-constrained problem posed by the xView challenge, where inferences are restricted to be on CPU with 8GB memory limit, we used lightweight CNN's trained with the single shot detector algorithm. Our approach was competitive on sequestered sets; it was ranked third.


title: Papular epidermal nevus with "skyline" basal cell layer (PENS).

abstract: BACKGROUND ::: Several types of epidermal keratinocytic nevus are recognized. ::: ::: ::: OBJECTIVE ::: We sought to describe a previously unreported keratinocytic nevus with distinctive clinical and histopathologic features in 5 patients. ::: ::: ::: METHODS ::: We performed a clinical and photographic review, and obtained skin biopsy samples for histopathologic examination from each patient. Genetic analysis to screen for fibroblast growth factor receptor 3 and phosphatidylinositol 3-kinase, catalytic, alpha hotspot mutations was performed on lesional skin from two patients. ::: ::: ::: RESULTS ::: Five infants (2 male, 3 female) had from 1 to 11 lesions present since birth. These consisted of 1- to 7-mm hyperkeratotic papules with a rough, flat surface and a round, commalike, rectangular, or polygonal shape. Histopathologic examination showed acanthosis with broad and rectangular rete ridges, and strikingly arranged basal cells with palisaded nuclei. Genetic testing on paraffin-embedded specimens from two patients ruled out hotspot mutations in the fibroblast growth factor receptor 3 and phosphatidylinositol 3-kinase, catalytic, alpha genes. ::: ::: ::: LIMITATIONS ::: A small number of patients are presented. ::: ::: ::: CONCLUSION ::: We propose the name "papular epidermal nevus with 'skyline' basal cell layer" (PENS) for this newly recognized condition.


title: Survey on Aspect-Level Sentiment Analysis

abstract: The field of sentiment analysis, in which sentiment is gathered, analyzed, and aggregated from text, has seen a lot of attention in the last few years. The corresponding growth of the field has resulted in the emergence of various subareas, each addressing a different level of analysis or research question. This survey focuses on aspect-level sentiment analysis, where the goal is to find and aggregate sentiment on entities mentioned within documents or aspects of them. An in-depth overview of the current state-of-the-art is given, showing the tremendous progress that has already been made in finding both the target, which can be an entity as such, or some aspect of it, and the corresponding sentiment. Aspect-level sentiment analysis yields very fine-grained sentiment information which can be useful for applications in various domains. Current solutions are categorized based on whether they provide a method for aspect detection, sentiment analysis, or both. Furthermore, a breakdown based on the type of algorithm used is provided. For each discussed study, the reported performance is included. To facilitate the quantitative evaluation of the various proposed methods, a call is made for the standardization of the evaluation methodology that includes the use of shared data sets. Semantically-rich concept-centric aspect-level sentiment analysis is discussed and identified as one of the most promising future research direction.


title: Analysis of word embeddings and sequence featuresfor clinical information extraction

abstract: This study investigates the use of unsupervised features derived from word embedding approaches and novel sequence representation approaches for improving clinical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, we further determine the influence that the corpora used to generate such features have. We also demonstrate the promise of sequence-based unsupervised features for further improving concept extraction.


title: Learning Food Image Similarity for Food Image Retrieval

abstract: For food application, recipe retrieval is an important task. However, many of them rely on only text query. Food image retrieval has relation to recipe retrieval so that similar food images are expected that they have similar recipes. Rising image retrieval performance is desired for recipe retrieval. On the other hand, to learn similarity by Siamese Network or Triplet Network are known as an effective method for image retrieval. However, there are no research for food image retrieval using similarity learning with Convolutional Neural Network as far as we know. Food recognition is known as one of fine-grained recognition tasks. Therefore it is unclear that how effective similarity learning methods based on CNN in food images. In our work, we trained some networks for feature similarity, and evaluated their effectiveness in food image retrieval.


title: Image Reshaping for Efficient Compression of Plenoptic Content

abstract: With insertion of an additional microlens array in front of the imaging sensor, plenoptic camera, which acquires light field by a single exposure, attracts great interests both from academy and industry. A plenoptic image records both spatial and angular light radiance in a super high definition with macropixel structures, which results in a distinct intensity distribution. Several compression techniques have been proposed, however, most of them are suffered from the standard incompatibility and high compression complexity. In this paper, a novel and standard compatible plenoptic data reshaping algorithm is proposed to align the macropixel structures in the lenslet image with the coding unit grids of block-based image/video coding standards before compression. After encoding and decoding the reshaped lenslet image using the existing block-based image/video coding standards, the inverse image reshaping is used to convert the reconstructed image back to the original light-field data format for further applications. The experimental results reveal that the proposed method introduces 10.47% and 27.64% bitrate reduction to HEVC/H.265 format range extension profile and AVC/H.264 high 4:4:4 profile, respectively, by compressing the reshaped lenslet images. Also, it brings additional compression efficiency improvements to intraprediction tools, such as intrablock copy and locally linear embedding based coding method, by an average of 11.62% and 9.81% bitrate reduction as they compress the reshaped plenoptic images.


title: The Genetic Structure of Marijuana and Hemp

abstract: Despite its cultivation as a source of food, fibre and medicine, and its global status as the most used illicit drug, the genus Cannabis has an inconclusive taxonomic organization and evolutionary history. Drug types of Cannabis (marijuana), which contain high amounts of the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC), are used for medical purposes and as a recreational drug. Hemp types are grown for the production of seed and fibre, and contain low amounts of THC. Two species or gene pools (C. sativa and C. indica) are widely used in describing the pedigree or appearance of cultivated Cannabis plants. Using 14,031 single-nucleotide polymorphisms (SNPs) genotyped in 81 marijuana and 43 hemp samples, we show that marijuana and hemp are significantly differentiated at a genome-wide level, demonstrating that the distinction between these populations is not limited to genes underlying THC production. We find a moderate correlation between the genetic structure of marijuana strains and their reported C. sativa and C. indica ancestry and show that marijuana strain names often do not reflect a meaningful genetic identity. We also provide evidence that hemp is genetically more similar to C. indica type marijuana than to C. sativa strains.


title: A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory

abstract: In this paper, the problem of classifying an unseen pattern on the basis of its nearest neighbors in a recorded data set is addressed from the point of view of Dempster-Shafer theory. Each neighbor of a sample to be classified is considered as an item of evidence that supports certain hypotheses regarding the class membership of that pattern. The degree of support is defined as a function of the distance between the two vectors. The evidence of the k nearest neighbors is then pooled by means of Dempster's rule of combination. This approach provides a global treatment of such issues as ambiguity and distance rejection, and imperfect knowledge regarding the class membership of training patterns. The effectiveness of this classification scheme as compared to the voting and distance-weighted k-NN procedures is demonstrated using several sets of simulated and real-world data. >


title: Balancing precision and performance in structured merge

abstract: Software-merging techniques face the challenge of finding a balance between precision and performance. In practice, developers use unstructured-merge (i.e., line-based) tools, which are fast but imprecise. In academia, many approaches incorporate information on the structure of the artifacts being merged. While this increases precision in conflict detection and resolution, it can induce severe performance penalties. Striving for a proper balance between precision and performance, we propose a structured-merge approach with auto-tuning. In a nutshell, we tune the merge process on-line by switching between unstructured and structured merge, depending on the presence of conflicts. We implemented a corresponding merge tool for Java, called JDime. Our experiments with 50 real-world Java projects, involving 434 merge scenarios with over 51 million lines of code, demonstrate that our approach indeed hits a sweet spot: While largely maintaining a precision that is superior to that of unstructured merge, structured merge with auto-tuning is up to 92 times faster than purely structured merge, 10 times on average.


title: Improved methodology for lumbosacral nerve root stimulation

abstract: The site of S1-S2 root activation following percutaneous high-voltage electrical (ES) and magnetic stimulation were located by analyzing the variations of the time interval from M to H soleus responses elicited by moving the stimulus point from lumbar to low thoracic levels. ES was effective in activating S1-S2 roots at their origin. However supramaximal motor root stimulation required a dorsoventral montage, the anode being a large, circular surface electrode placed ventrally, midline between the apex of the xiphoid process and the umbilicus. Responses to magnetic stimuli always resulted from the activation of a fraction of the fiber pool, sometimes limited to the low-thresholds afferent component, near its exit from the intervertebral foramina, or even more distally. Normal values for conduction velocity in motor and 1a afferent fibers in the proximal nerve tract are provided.


title: Yubi-Toko: finger walking in snowy scene using pseudo-haptic technique on touchpad

abstract: "Yubi-Toko" is a touchpad system in which users can walk in a snowy scene using their fingers and feel the difficulty in moving forward caused as generated by a pseudo-haptic technique (Fig. 1).


title: Diagram interaction during intelligent tutoring in geometry: Support for knowledge retention and deep transfer

abstract: Prior research has shown that skilled problem solvers often demonstrate meaningful links between relevant visual and verbal knowledge components, but little is known about how to support novice learners in connecting visual and verbal information during learning or whether such support will improve learning outcomes. This work explored two methods to support meaningful connections between visual and verbal information in an intelligent tutor for geometry: 1) student interaction with diagrams during problem solving, and 2) student explanations that connected diagram features to geometry rules at each problem-solving step. Research was conducted in 10 grade classrooms using an experimental version of the Geometry Cognitive Tutor. Results demonstrated that interaction with diagrams promoted longterm retention of problem-solving skills and supported performance on transfer tasks; diagram-rule explanations did not significantly influence learning. Findings suggest that student focus on relevant visual information should be carefully integrated into problem-solving practice to support deep learning.


title: A Coalgebraic Decision Procedure for NetKAT

abstract: NetKAT is a domain-specific language and logic for specifying and verifying network packet-processing functions. It consists of Kleene algebra with tests (KAT) augmented with primitives for testing and modifying packet headers and encoding network topologies. Previous work developed the design of the language and its standard semantics, proved the soundness and completeness of the logic, defined a PSPACE algorithm for deciding equivalence, and presented several practical applications. This paper develops the coalgebraic theory of NetKAT, including a specialized version of the Brzozowski derivative, and presents a new efficient algorithm for deciding the equational theory using bisimulation. The coalgebraic structure admits an efficient sparse representation that results in a significant reduction in the size of the state space. We discuss the details of our implementation and optimizations that exploit NetKAT's equational axioms and coalgebraic structure to yield significantly improved performance. We present results from experiments demonstrating that our tool is competitive with state-of-the-art tools on several benchmarks including all-pairs connectivity, loop-freedom, and translation validation.


title: Stock Market Prediction with Multiple Regression, Fuzzy Type-2 Clustering and Neural Networks

abstract: Abstract Stock market forecasting research offers many challenges and opportunities, with the forecasting of individual stocks or indexes focusing on forecasting either the level (value) of future market prices, or the direction of market price movement. A three-stage stock market prediction system is introduced in this article. In the first phase, Multiple Regression Analysis is applied to define the economic and financial variables which have a strong relationship with the output. In the second phase, Differential Evolution-based type-2 Fuzzy Clustering is implemented to create a prediction model. For the third phase, a Fuzzy type-2 Neural Network is used to perform the reasoning for future stock price prediction. The results of the network simulation show that the suggested model outperforms traditional models for forecasting stock market prices.


title: How much can behavioral targeting help online advertising?

abstract: Behavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia when looking at how much BT can truly help online advertising in commercial search engines. To answer this question, in this paper we provide an empirical study on the click-through log of advertisements collected from a commercial search engine. From the comprehensively experiment results on the sponsored search log of the commercial search engine over a period of seven days, we can draw three important conclusions: (1) Users who clicked the same ad will truly have similar behaviors on the Web; (2) Click-Through Rate (CTR) of an ad can be averagely improved as high as 670% by properly segmenting users for behavioral targeted advertising in a sponsored search; (3) Using the short term user behaviors to represent users is more effective than using the long term user behaviors for BT. The statistical t-test verifies that all conclusions drawn in the paper are statistically significant. To the best of our knowledge, this work is the first empirical study for BT on the click-through log of real world ads.


title: Parametric Blur Estimation for Blind Restoration of Natural Images: Linear Motion and Out-of-Focus

abstract: This paper presents a new method to estimate the parameters of two types of blurs, linear uniform motion (approximated by a line characterized by angle and length) and out-of-focus (modeled as a uniform disk characterized by its radius), for blind restoration of natural images. The method is based on the spectrum of the blurred images and is supported on a weak assumption, which is valid for the most natural images: the power-spectrum is approximately isotropic and has a power-law decay with the spatial frequency. We introduce two modifications to the radon transform, which allow the identification of the blur spectrum pattern of the two types of blurs above mentioned. The blur parameters are identified by fitting an appropriate function that accounts separately for the natural image spectrum and the blur frequency response. The accuracy of the proposed method is validated by simulations, and the effectiveness of the proposed method is assessed by testing the algorithm on real natural blurred images and comparing it with state-of-the-art blind deconvolution methods.


title: A Multichannel Convolutional Neural Network for Hand Posture Recognition

abstract: Natural communication between humans involves hand gestures, which has an impact on research in human-robot interaction. In a real-world scenario, understanding human gestures by a robot is hard due to several challenges like hand segmentation. To recognize hand postures this paper proposes a novel convolutional implementation. The model is able to recognize hand postures recorded by a robot camera in real-time, in a real-world application scenario. The proposed model was also evaluated with a benchmark database and showed better results than the ones reported in the benchmark paper.


title: WCET Analysis for Multi-Core Processors with Shared L2 Instruction Caches

abstract: Multi-core chips have been increasingly adopted by microprocessor industry. For real-time systems to safely harness the potential of multi-core computing, designers must be able to accurately obtain the worst-case execution time (WCET) of applications running on multi-core platforms, which is very challenging due to the possible runtime inter-core interferences in using shared resources such as the shared L2 caches. As the first step toward time-predictable multi-core computing, this paper presents a novel approach to bounding the worst-case performance for threads running on multi-core processors with shared L2 instruction caches. The idea of our approach is to compute the worst-case instruction access interferences between different threads based on the program control flow information of each thread, which can be statically analyzed. Our experiments indicate that the proposed approach can reasonably estimate the worst- case shared L2 instruction cache misses by considering inter-thread instruction conflicts. Also, the WCET of applications running on multi-core processors estimated by our approach is much better than the estimation by simply assuming all L2 instruction accesses are misses.


title: High Heels Increase Women’s Attractiveness

abstract: Research has found that the appearance of women’s apparel helps increase their attractiveness as rated by men and that men care more about physical features in potential opposite-sex mates. However, the effect of sartorial appearance has received little interest from scientists. In a series of studies, the length of women’s shoe heels was examined. A woman confederate wearing black shoes with 0, 5, or 9 cm heels asked men for help in various circumstances. In Study 1, she asked men to respond to a short survey on gender equality. In Study 2, the confederate asked men and women to participate in a survey on local food habit consumption. In Study 3, men and women in the street were observed while walking in back of the female confederate who dropped a glove apparently unaware of her loss. It was found that men’s helping behavior increased as soon as heel length increased. However, heel length had no effect on women’s helping behavior. It was also found that men spontaneously approached women more quickly when they wore high-heeled shoes (Study 4). Change in gait, foot-size judgment, and misattribution of sexiness and sexual intent were used as possible explanations.


title: Workshop on Unsupervised and Transfer Learning Deep Learning of Representations for Unsupervised and Transfer Learning

abstract: Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features. The objective is to make these higher-level representations more abstract, with their individual features more invariant to most of the variations that are typically present in the training distribution, while collectively preserving as much as possible of the information in the input. Ideally, we would like these representations to disentangle the unknown factors of variation that underlie the training distribution. Such unsupervised learning of representations can be exploited usefully under the hypothesis that the input distribution P(x) is structurally related to some task of interest, say predicting P(y/x). This paper focuses on the context of the Unsupervised and Transfer Learning Challenge, on why unsupervised pre-training of representations can be useful, and how it can be exploited in the transfer learning scenario, where we care about predictions on examples that are not from the same distribution as the training distribution.


title: A Visual Analytics Approach for Detecting and Understanding Anomalous Resident Behaviors in Smart Healthcare

abstract: With the development of science and technology, it is possible to analyze residents’ daily behaviors for the purpose of smart healthcare in the smart home environment. Many researchers have begun to detect residents’ anomalous behaviors and assess their physical condition, but these approaches used by the researchers are often caught in plight caused by a lack of ground truth, one-sided analysis of behavior, and difficulty of understanding behaviors. In this paper, we put forward a smart home visual analysis system (SHVis) to help analysts detect and comprehend unusual behaviors of residents, and predict the health information intelligently. Firstly, the system classifies daily activities recorded by sensor devices in smart home environment into different categories, and discovers unusual behavior patterns of residents living in this environment by using various characteristics extracted from those activities and appropriate unsupervised anomaly detection algorithm. Secondly, on the basis of figuring out the residents’ anomaly degree of every date, we explore the daily behavior patterns and details with the help of several visualization views, and compare and analyze residents’ activities of various dates to find the reasons why residents act unusually. In the case study of this paper, we analyze residents’ behaviors that happened over two months and find unusual indoor behaviors and give health advice to the residents.


Supervised Classification of Multisensor Remotely Sensed Images Using a Deep Learning Framework


title: Jitter and Shimmer Measurements for Speaker Recognition

abstract: Comunicacio presentada a: 8th Annual Conference of the International Speech Communication Association a Antwerp (Belgium) celebrada del 27 al 31 d'agost de 2007.


title: The language faculty that wasn't: a usage-based account of natural language recursion

abstract: In the generative tradition, the language faculty has been shrinking—perhaps to include only the mechanism of recursion. This paper argues that even this view of the language faculty is too expansive. We first argue that a language faculty is difficult to reconcile with evolutionary considerations. We then focus on recursion as a detailed case study, arguing that our ability to process recursive structure does not rely on recursion as a property of the grammar, but instead emerges gradually by piggybacking on domain-general sequence learning abilities. Evidence from genetics, comparative work on non-human primates, and cognitive neuroscience suggests that humans have evolved complex sequence learning skills, which were subsequently pressed into service to accommodate language. Constraints on sequence learning therefore have played an important role in shaping the cultural evolution of linguistic structure, including our limited abilities for processing recursive structure. Finally, we re-evaluate some of the key considerations that have often been taken to require the postulation of a language faculty.


title: A circuit simulation model for V-groove SiC power MOSFET

abstract: In this paper, a novel circuit simulation model for V-groove SiC power MOSFET with buried P-layers is proposed. By considering the structure of the MOSFET, bias dependence of on-resistance and the kink in the terminal capacitance are represented in the proposed model. Through experiments using a V-groove SiC MOSFET, it is demonstrated that the proposed model successfully reproduces both I-V and C-V characteristics.


title: Factoring polynomials with rational coefficients

abstract: In this paper we present a polynomial-time algorithm to solve the following problem: given a non-zero polynomial fe Q(X) in one variable with rational coefficients, find the decomposition of f into irreducible factors in Q(X). It is well known that this is equivalent to factoring primitive polynomials feZ(X) into irreducible factors in Z(X). Here we call f~ Z(X) primitive if the greatest common divisor of its coefficients (the content of f) is 1. Our algorithm performs well in practice, cf. (8). Its running time, measured in bit operations, is O(nl2+n9(log(fD3).


title: Document clustering using locality preserving indexing

abstract: We propose a novel document clustering method which aims to cluster the documents into different semantic classes. The document space is generally of high dimensionality and clustering in such a high dimensional space is often infeasible due to the curse of dimensionality. By using locality preserving indexing (LPI), the documents can be projected into a lower-dimensional semantic space in which the documents related to the same semantics are close to each other. Different from previous document clustering methods based on latent semantic indexing (LSI) or nonnegative matrix factorization (NMF), our method tries to discover both the geometric and discriminating structures of the document space. Theoretical analysis of our method shows that LPI is an unsupervised approximation of the supervised linear discriminant analysis (LDA) method, which gives the intuitive motivation of our method. Extensive experimental evaluations are performed on the Reuters-21578 and TDT2 data sets.


title: An Ontology Based Natural Language Story Generation Approach: WELOTA

abstract: WELOTA is an automatic story generator which generates and narrates “Well Loved Tales” for kids between 5-7 years old. WELOTA implements a generic and a powerful approach to extract information from digital text files, represent that using a knowledge representation mechanism and later retrieve the knowledge to construct meaningful natural language sentences. Kid’s story domain is chosen to implement and prove this approach. Knowledge Extraction is handled through GATE NLP tool and WordNet 2.1. Extracted knowledge is then represented automatically in a domain specific ontology built in protege. Later the knowledge base is inferred on a selected story title using SPARQL query language. Then the retrieved knowledge is converted into simple English sentences through SimpleNLG API and a set of linguistic rules. WELOTA also embeds narrative terms and phrases to the story content which are constructed using its tacit knowledge.


title: Cognitive Linguistics in Critical Discourse Analysis: Application and Theory

abstract: In contemporary linguistics, both cognitive and critical approaches to language have been elaborated in some detail. Unfortunately, the two perspectives have seldom converged, despite the potential theoretical advances such collaboration offers. The contributions to this volume explore the convergence of cognitive and critical trends in the guise of cognitive linguistics and critical discourse analysis. The volume addresses a range of socio-political discourses in various international contexts, including discourses on nation, education, immigration, and war. One single integrated model is not presented, but rather, a number of methodologies are developed and assessed across the chapters. The application of established cognitive linguistic theories, including conceptual metaphor theory, conceptual blending theory and frame semantics, are discussed, as well as developing theories, such as metaphor power theory and discourse space theory. The book is of value to anyone interested in the interaction between language, mind, and society, including both students and scholars of cognitive linguistics and critical discourse analysis.


title: Early cortical processing of natural and artificial emotional faces differs between lower and higher socially anxious persons

abstract: Emotional facial expressions provide critical information for social interactions. Above all, angry faces are assumed to reflect potential social threat. We investigated event-related potentials (ERPs) triggered by natural and artificial faces expressing fear, anger, happiness or no emotion in participants with low and high levels of social anxiety. Overall, artificial faces elicited stronger P100 and N170 responses than natural faces. Additionally, the N170 component was larger for emotional compared to neutral facial expressions. Social anxiety was associated with an enhanced emotional modulation of the early posterior negativity (EPN) in response to fearful and angry facial expressions. Additionally, while the late positive potential (LPP) was larger for emotional than for neutral faces in low socially anxious participants, LPPs of higher socially anxious participants did not differ. LPPs might therefore be enhanced in higher socially anxious participants for both emotional and neutral faces. Furthermore, the modulations of the EPN and LPP were comparable between natural and artificial faces. These results indicate that social anxiety influences early perceptual processing of faces and that artificial faces are suitable for psychophysiological emotion research.


title: Fast Image Processing with Fully-Convolutional Networks

abstract: We present an approach to accelerating a wide variety of image processing operators. Our approach uses a fully-convolutional network that is trained on input-output pairs that demonstrate the operator's action. After training, the original operator need not be run at all. The trained network operates at full resolution and runs in constant time. We investigate the effect of network architecture on approximation accuracy, runtime, and memory footprint, and identify a specific architecture that balances these considerations. We evaluate the presented approach on ten advanced image processing operators, including multiple variational models, multiscale tone and detail manipulation, photographic style transfer, nonlocal dehazing, and nonphotorealistic stylization. All operators are approximated by the same model. Experiments demonstrate that the presented approach is significantly more accurate than prior approximation schemes. It increases approximation accuracy as measured by PSNR across the evaluated operators by 8.5 dB on the MIT-Adobe dataset (from 27.5 to 36 dB) and reduces DSSIM by a multiplicative factor of 3 compared to the most accurate prior approximation scheme, while being the fastest. We show that our models generalize across datasets and across resolutions, and investigate a number of extensions of the presented approach. The results are shown in the supplementary video at this https URL


title: Long-Term Time Series Prediction with the NARX Network: An Empirical Evaluation Abstract

abstract: The NARX network is a dynamical neural architecture commonly used for input-output modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward time delay neural network (TDNN), i.e., without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, we show that the original architecture of the NARX network can be easily and efficiently applied to long-term (multi-step-ahead) prediction of univariate time series. We evaluate the proposed approach using two real-world data sets, namely the well-known chaotic laser time series and a variable bit rate (VBR) video traffic time series. All the results show that the proposed approach consistently outperforms standard neural network based predictors, such as the TDNN and Elman architectures.


title: A General SIMD-based Approach to Accelerating Compression Algorithms

abstract: Compression algorithms are important for data-oriented tasks, especially in the era of “Big Data.” Modern processors equipped with powerful SIMD instruction sets provide us with an opportunity for achieving better compression performance. Previous research has shown that SIMD-based optimizations can multiply decoding speeds. Following these pioneering studies, we propose a general approach to accelerate compression algorithms. By instantiating the approach, we have developed several novel integer compression algorithms, called Group-Simple, Group-Scheme, Group-AFOR, and Group-PFD, and implemented their corresponding vectorized versions. We evaluate the proposed algorithms on two public TREC datasets, a Wikipedia dataset, and a Twitter dataset. With competitive compression ratios and encoding speeds, our SIMD-based algorithms outperform state-of-the-art nonvectorized algorithms with respect to decoding speeds.


title: Cloud computing security issues and challenges

abstract: In the last few years, cloud computing has grown from being a promising business concept to one of the fastest growing segments of the IT industry. Now, recession-hit companies are increasingly realizing that simply by tapping into the cloud they can gain fast access to best-of-breed business applications or drastically boost their infrastructure resources, all at negligible cost. But as more and more information on individuals and companies is placed in the cloud, concerns are beginning to grow about just how safe an environment it is. This paper discusses security issues, requirements and challenges that cloud service providers (CSP) face during cloud engineering. Recommended security standards and management models to address these are suggested for technical and business community.


title: Level-of-detail visualization of clustered graph layouts

abstract: The level-of-detail techniques presented in this paper enable a comprehensible interactive visualization of large and complex clustered graph layouts either in 2D or 3D. Implicit surfaces are used for the visually simplified representation of vertex clusters, and so-called edge bundles are formed for the simplification of edges. Additionally, dedicated transition techniques are provided for continuously adaptive and adjustable views of graphs that range from very abstract to very detailed representations.


title: A scalable multi-user uplink for Wi-Fi

abstract: Mobile devices have fewer antennas than APs due to size and energy constraints. This antenna asymmetry restricts uplink capacity to the client antenna array size rather than the AP's. To overcome antenna asymmetry, multiple clients can be grouped into a simultaneous multi-user transmission to achieve a full rank transmission that matches the number of antennas at the AP. In this paper, we design, implement, and experimentally evaluate MUSE, the first distributed and scalable system to achieve full-rank uplink multi-user capacity without control signaling for channel estimation, channel reporting, or user selection. Our experiments demonstrate full-rank multiplexing gains in the evaluated scenarios that show linear gains as the number of users increase while maintaining constant overhead.


title: Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

abstract: A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering.


title: Automatic Evaluation Of Students' Answers Using Syntactically Enhanced LSA

abstract: Latent semantic analysis (LSA) has been used in several intelligent tutoring systems(ITS's) for assessing students' learning by evaluating their answers to questions in the tutoring domain. It is based on word-document co-occurrence statistics in the training corpus and a dimensionality reduction technique. However, it doesn't consider the word-order or syntactic information, which can improve the knowledge representation and therefore lead to better performance of an ITS. We present here an approach called Syntactically Enhanced LSA (SELSA) which generalizes LSA by considering a word along with its syntactic neighborhood given by the part-of-speech tag of its preceding word, as a unit of knowledge representation. The experimental results on Auto-Tutor task to evaluate students' answers to basic computer science questions by SELSA and its comparison with LSA are presented in terms of several cognitive measures. SELSA is able to correctly evaluate a few more answers than LSA but is having less correlation with human evaluators than LSA has. It also provides better discrimination of syntactic-semantic knowledge representation than LSA.


title: Peer to Peer Anti-Money Laundering Resource Allocation Based on Semi-Markov Decision Process

abstract: The multimedia communication technologies have been widely used in the anti-money laundering (AML) field to improve the efficiency and security of the business transactions. To reduce the cost of massive multimedia processing and communications, it is desirable to allow multiple financial industries (FIs) to share the AML resources, including human, computation, communication, and storage resources, and cooperate with each other to complete the transaction tasks. In this paper, the optimal AML resource management among peer FIs towards the maximal AML rewards is studied. Specifically, an AML resource allocation model (AMLRAM) based on semi-Markov decision process(SMDP) is proposed, where the system state is represented by a tuple, i.e., the number of High-Risk Operation (HRO), the number of Low or Moderate Risk Operation (L/MRO), and the current event type (i.e., the arrival of HRO or L/MRO suspicious transaction report which needs to be further checked, and the departure of HRO or L/MRO suspicious transaction report which has been checked and releases the occupied AML resource) in the AML field. The maximal long-term rewards of the system is derived, and the optimal AML resource allocation decision among peer FIs is made to achieve the maximal system rewards. Extensive simulations validate our analysis.


title: Juror Punishment, Juror Guidance and the Criminal Justice and Courts Act 2015

abstract: The Criminal Justice and Courts Act 2015 creates several new offences relating to juror misconduct, which have generally been considered pragmatic responses to the immediate problem of jurors using the internet to find additional "evidence." Taking as its starting point the possibility of jury studies being written from an "interdisciplinary" perspective situated between mainstream legal scholarship and legal history, this article argues that after the practical abolition of juror punishment in 1670 the judge-jury relationship has usually been focused on juror guidance, not on juror punishment. This has had important consequences for the institution’s civic function, meaning any move in the direction of juror punishment has to be considered not simply as a pragmatic response to an immediate, isolated problem, but also as an important part of the jury’s continuing history as a political institution.


title: Improving Neural Machine Translation through Phrase-based Forced Decoding

abstract: Compared to traditional statistical machine translation (SMT), neural machine translation (NMT) often sacrifices adequacy for the sake of fluency. We propose a method to combine the advantages of traditional SMT and NMT by exploiting an existing phrase-based SMT model to compute the phrase-based decoding cost for an NMT output and then using this cost to rerank the n-best NMT outputs. The main challenge in implementing this approach is that NMT outputs may not be in the search space of the standard phrase-based decoding algorithm, because the search space of phrase-based SMT is limited by the phrase-based translation rule table. We propose a soft forced decoding algorithm, which can always successfully find a decoding path for any NMT output. We show that using the forced decoding cost to rerank the NMT outputs can successfully improve translation quality on four different language pairs.