title: Caring Behaviors Inventory: a reduction of the 42-item instrument.

abstract: BACKGROUND ::: Caring traditionally has been at the center of nursing. Effectively measuring the process of nurse caring is vital in nursing research. A short, less burdensome dimensional instrument for patients' use is needed for this purpose. ::: ::: ::: OBJECTIVES ::: To derive and validate a shorter Caring Behaviors Inventory (CBI) within the context of the 42-item CBI. ::: ::: ::: METHODS ::: The responses to the 42-item CBI from 362 hospitalized patients were used to develop a short form using factor analysis. A test-retest reliability study was conducted by administering the shortened CBI to new samples of patients (n = 64) and nurses (n = 42). ::: ::: ::: RESULTS ::: Factor analysis yielded a 24-item short form (CBI-24) that (a) covers the four major dimensions assessed by the 42-item CBI, (b) has internal consistency (alpha =.96) and convergent validity (r =.62) similar to the 42-item CBI, (c) reproduces at least 97% of the variance of the 42 items in patients and nurses, (d) provides statistical conclusions similar to the 42-item CBI on scoring for caring behaviors by patients and nurses, (e) has similar sensitivity in detecting between-patient difference in perceptions, (f) obtains good test-retest reliability (r = .88 for patients and r=.82 for nurses), and (g) confirms high internal consistency (alpha >.95) as a stand-alone instrument administered to the new samples. ::: ::: ::: CONCLUSION ::: CBI-24 appears to be equivalent to the 42-item CBI in psychometric properties, validity, reliability, and scoring for caring behaviors among patients and nurses. These results recommend the use of CBI-24 to reduce response burden and research costs.


title: Development and Current Functioning in Adolescents with Asperger Syndrome: A Comparative Study

abstract: Adolescents with Asperger syndrome (AS: without delay in speech development, diagnosed according to ICD-10 clinical criteria) were compared with a group with high-functioning autism (HFA: all with delayed speech development), and a group with conduct disorder (CD). Family and genetic studies suggest that Asperger syndrome and autism form part of the same spectrum, whereas the social impairments in conduct disorder are assumed to have different origins. The aims were to explore the relationships between early speech development and other aspects of functioning in autistic disorders, and to compare autistic and nonautistic social impairments. Early and current behaviour and IQ profiles were investigated. ::: ::: The CD group were clearly different from both the AS and HFA groups. The AS group tended to have less severe early behavioural abnormalities than the HFA group, and were unlikely to have speech abnormalities, but other communicative, social, and restricted/ stereotyped behavioural difficulties were largely of a similar pattern to the abnormalities in the HFA group. Eighty per cent of the AS group met criteria for autism on the diagnostic algorithm associated with the Autism Diagnostic Interview-Revised. By adolescence, the AS group were reported to be as abnormal as the HFA group but in structured 1: 1 interaction their conversation was better. IQ profile in the AS group showed relative strength on verbal measures, unlike the HFA group, but relatively good performance on the Block Design subtest of the WISC/WAIS was a feature of both the AS and HFA groups. The results indicate closely similar behavioural manifestations may arise by adolescence despite differences in speech development. Follow-up studies and further family investigations will be required to clarify the origins of these and other patterns of autistic development.


title: Pedagogical possibilities for the 2048 puzzle game

abstract: In this paper, we describe an engaging puzzle game called 2048 and outline a variety of exercises that can leverage the game's popularity to engage student interest, reinforce core CS concepts, and excite student curiosity towards undergraduate research. Exercises range in difficulty from CS1-level exercises suitable for exercising and assessing 1D and 2D array skills to empirical undergraduate research in Monte Carlo Tree Search methods and skilled heuristic evaluation design.


title: Filterbank learning for deep neural network based polyphonic sound event detection

abstract: Deep learning techniques such as deep feedforward neural networks and deep convolutional neural networks have recently been shown to improve the performance in sound event detection compared to traditional methods such as Gaussian mixture models. One of the key factors of this improvement is the capability of deep architectures to automatically learn higher levels of acoustic features in each layer. In this work, we aim to combine the feature learning capabilities of deep architectures with the empirical knowledge of human perception. We use the first layer of a deep neural network to learn a mapping from a high-resolution magnitude spectrum to smaller amount of frequency bands, which effectively learns a filterbank for the sound event detection task. We initialize the first hidden layer weights to match with the perceptually motivated mel filterbank magnitude response. We also integrate this initialization scheme with context windowing by using an appropriately constrained deep convolutional neural network. The proposed method does not only result with better detection accuracy, but also provides insight on the frequencies deemed essential for better discrimination of given sound events.


Neuroplasticity in the language system : Reorganization in post-stroke aphasia and in neuromodulation interventions


title: Series-Resonant Multiinverter for Multiple Induction Heaters

abstract: Multiple-load and multiple-source systems are widely present in the current technology. These systems require controlling either the supplied voltage or power to several loads with different requirements simultaneously. As a consequence, the cost and size of the power stage may increase beyond the admissible limits for certain applications. Considering multiple-inductor loads, a novel series-resonant multiinverter topology is proposed to obtain a cost-effective and high-power density solution. The converter is based on a common inverter block and a resonant-load block. The performed analysis includes the description of the operation modes and the control strategy analysis. Domestic induction heating has been considered for application due to its special cost and size requirements, and the extensive inductor use. The proposed converter has been designed and validated experimentally through a prototype, which includes the power converter and the field-programmable gate array-based control architecture.


title: Training Feedforward Neural Networks Using Genetic Algorithms

abstract: Multilayered feedforward neural networks possess a number of properties which make them particularly suited to complex pattern classification problems. However, their application to some realworld problems has been hampered by the lack of a training algonthm which reliably finds a nearly globally optimal set of weights in a relatively short time. Genetic algorithms are a class of optimization procedures which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum. Hence, they are well suited to the problem of training feedforward networks. In this paper, we describe a set of experiments performed on data from a sonar image classification problem. These experiments both 1) illustrate the improvements gained by using a genetic algorithm rather than backpropagation and 2) chronicle the evolution of the performance of the genetic algorithm as we added more and more domain-specific knowledge into it.


title: Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

abstract: Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.


title: Opinion mining over twitterspace: Classifying tweets programmatically using the R approach

abstract: Today the channels for expressing opinions seem to increase daily. When these opinions are relevant to a company, they are important sources of business insight, whether they represent critical intelligence about a customer's defection risk, the impact of an influential reviewer on other people's purchase decisions, or early feedback on product releases, company news or competitors. Capturing and analyzing these opinions is a necessity for proactive product planning, marketing and customer service and it is also critical in maintaining brand integrity. The importance of harnessing opinion is growing as consumers use technologies such as Twitter to express their views directly to other consumers. Tracking the disparate sources of opinion is hard - but even harder is quickly and accurately extracting the meaning so companies can analyze and act. Tweets' Language is complicated and contextual, especially when people are expressing opinions and requires reliable sentiment analysis based on parsing many linguistic shades of gray. This article argues that using the R programming platform for analyzing tweets programmatically simplifies the task of sentiment analysis and opinion mining. An R programming technique has been used for testing different sentiment lexicons as well as different scoring schemes. Experiments on analyzing the tweets of users over six NHL hockey teams reveals the effectively of using the opinion lexicon and the Latent Dirichlet Allocation (LDA) scoring scheme.


title: Comparison of NER Performance Using Word Embedding

abstract: Recent studies in NER use the supervised machine learning. This study used CRF as a learning algorithm, and applied word embedding to feature for NER training. Word embedding is helpful in many learning algorithms of NLP, indicating that words in a sentence are mapped by a real vector in a lowdimension space. As a result of comparing the performance of multiple techniques for word embedding to NER, it was found that CCA (85.96%) in Test A and Word2Vec (80.72%) in Test B exhibited the best performance.


title: A quantitative analysis of business process reengineering and organizational resistance: the case of Uganda

abstract: Many organizations in African countries need to reengineer their business processes to improve on efficiency. The general objective of this study was to identify the impact of different factors, including organizational resistance to change on Business Process Reengineering (BPR). The study showed that only 30.4% of BPR projects in Uganda have delivered the intended usable Information Systems. The researchers have identified the factors impacting on BPR and possible causes of BPR failures. The identified emotional response of the users towards the BPR implementation ranges from Acceptance to Testing, Indifference and Anger. Based upon the study findings, the researchers have formulated the set of recommendations for organizations implementing BPR. This paper will be of interest to the organizational managers, BPR implementers and the future researchers in a related area of study.


title: Non-invasive blood pressure estimation from photoplethysmography signals using artificial neural networks

abstract: This work focuses on the potential of artificial neural networks to classify biological signals in a healthcare setting, specifically in the estimation of blood pressure from photoplethysmography signal readings obtained via medical devices. This signal is known to have valuable cardiovascular information and has been related to heart rate and blood pressure pulsewave. Among the literature there have been attempts to correlate this signal directly to a single blood pressure value and/or classify it into one of the blood pressure clinical states (e.g. Hypotension, Normal, Pre Hypertension, Stage 1 Hypertension, Stage 2 Hypertension). We propose models based on artificial neural networks that achieve similar performance to those in previous works, without needing engineered nor demographic features. These models are capable of learning how to extract descriptive features from only the raw photoplethysmography signals, and use them for classification into a blood pressure class. Test results are promising and validate the usefulness of artificial neural network architectures for this task.


title: Autofocusing of inverse synthetic aperture radar images using contrast optimization

abstract: A number of autofocusing techniques are proposed for motion compensation in inverse synthetic aperture radar (ISAR) imaging of objects. These techniques are based on the optimization of a contrast function, which represents a measure of the degree of focus of the image. Three novel image contrast functions are defined and the main steps of the autofocusing algorithm are described. A comparison of the behavior of the proposed techniques is performed analyzing some results obtained from simulation of realistic targets.


title: Time-Varying Formation Controllers for Unmanned Aerial Vehicles Using Deep Reinforcement Learning

abstract: We consider the problem of designing scalable and portable controllers for unmanned aerial vehicles (UAVs) to reach time-varying formations as quickly as possible. This brief confirms that deep reinforcement learning can be used in a multi-agent fashion to drive UAVs to reach any formation while taking into account optimality and portability. We use a deep neural network to estimate how good a state is, so the agent can choose actions accordingly. The system is tested with different non-high-dimensional sensory inputs without any change in the neural network architecture, algorithm or hyperparameters, just with additional training.


title: A Hierarchical LSTM Model for Joint Tasks

abstract: Previous work has shown that joint modeling of two Natural Language Processing (NLP) tasks are effective for achieving better performances for both tasks. Lots of task-specific joint models are proposed. This paper proposes a Hierarchical Long Short-Term Memory (HLSTM) model and some its variants for modeling two tasks jointly. The models are flexible for modeling different types of combinations of tasks. It avoids task-specific feature engineering. Besides the enabling of correlation information between tasks, our models take the hierarchical relations between two tasks into consideration, which is not discussed in previous work. Experimental results show that our models outperform strong baselines in three different types of task combination. While both correlation information and hierarchical relations between two tasks are helpful to improve performances for both tasks, the models especially boost performance of tasks on the top of the hierarchical structures.


title: Direct Torque Control of induction machine using 3-level neutral point clamped inverter

abstract: This paper presents a Direct Torque Control (DTC) of induction motor drives utilizing 3-level neutral-point clamped (NPC) multilevel inverter. This research aims to provide a simple strategy to address capacitor voltage balancing problem of NPC inverter as well as produce better performance for DTC of induction motor in term of reducing torque ripple and high switching frequency. The proposed method uses 2-level hysteresis comparator to restrict the error of upper and lower capacitor voltages and then generate a signal which determine either to increase or decrease the particular capacitor voltage. Using that information, the appropriate selection of small voltage vectors is tabulated in look-up table (LUT) to stabilize the mismatched of capacitor voltage. Simulation results are presented to show the effectiveness of the proposed capacitor voltage balancing strategy.


title: Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition

abstract: This paper addresses the relative usefulness of Independent Component Analysis (ICA) for Face Recognition. Comparative assessments are made regarding (i) ICA sensitivity to the dimension of the space where it is carried out, and (ii) ICA discriminant performance alone or when combined with other discriminant criteria such as Bayesian framework or Fisher’s Linear Discriminant (FLD). Sensitivity analysis suggests that for enhanced performance ICA should be carried out in a compressed and whitened Principal Component Analysis (PCA) space where the small trailing eigenvalues are discarded. The reason for this finding is that during whitening the eigenvalues of the covariance matrix appear in the denominator and that the small trailing eigenvalues mostly encode noise. As a consequence the whitening component, if used in an uncompressed image space, would fit for misleading variations and thus generalize poorly to new data. Discriminant analysis shows that the ICA criterion, when carried out in the properly compressed and whitened space, performs better than the eigenfaces and Fisherfaces methods for face recognition, but its performance deteriorates when augmented by additional criteria such as the Maximum A Posteriori (MAP) rule of the Bayes classifier or the FLD. The reason for the last finding is that the Mahalanobis distance embedded in the MAP classifier duplicates to some extent the whitening component, while using FLD is counter to the independence criterion intrinsic to ICA.


title: Fusing Multiview and Photometric Stereo for 3D Reconstruction under Uncalibrated Illumination

abstract: We propose a method to obtain a complete and accurate 3D model from multiview images captured under a variety of unknown illuminations. Based on recent results showing that for Lambertian objects, general illumination can be approximated well using low-order spherical harmonics, we develop a robust alternating approach to recover surface normals. Surface normals are initialized using a multi-illumination multiview stereo algorithm, then refined using a robust alternating optimization method based on the l1 metric. Erroneous normal estimates are detected using a shape prior. Finally, the computed normals are used to improve the preliminary 3D model. The reconstruction system achieves watertight and robust 3D reconstruction while neither requiring manual interactions nor imposing any constraints on the illumination. Experimental results on both real world and synthetic data show that the technique can acquire accurate 3D models for Lambertian surfaces, and even tolerates small violations of the Lambertian assumption.


title: An investigation of model-free planning

abstract: The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity. For an RL agent to address these challenges, it is essential that it can plan effectively. Prior work has typically utilized an explicit model of the environment, combined with a specific planning algorithm (such as tree search). More recently, a new family of methods have been proposed that learn how to plan, by providing the structure for planning via an inductive bias in the function approximator (such as a tree structured neural network), trained end-to-end by a model-free RL algorithm. In this paper, we go even further, and demonstrate empirically that an entirely model-free approach, without special structure beyond standard neural network components such as convolutional networks and LSTMs, can learn to exhibit many of the characteristics typically associated with a model-based planner. We measure our agent's effectiveness at planning in terms of its ability to generalize across a combinatorial and irreversible state space, its data efficiency, and its ability to utilize additional thinking time. We find that our agent has many of the characteristics that one might expect to find in a planning algorithm. Furthermore, it exceeds the state-of-the-art in challenging combinatorial domains such as Sokoban and outperforms other model-free approaches that utilize strong inductive biases toward planning.


title: NONSMOOTH COORDINATION AND GEOMETRIC OPTIMIZATION VIA DISTRIBUTED DYNAMICAL SYSTEMS

abstract: This paper discusses dynamical systems for disk-covering and sphere-packing problems. We present facility location functions from geometric optimization and characterize their differentiable properties. We design and analyze a collection of distributed control laws that are related to nonsmooth gradient systems. The resulting dynamical systems promise to be of use in coordination problems for networked robots; in this setting the distributed control laws correspond to local interactions between the robots. The technical approach relies on concepts from computational geometry, nonsmooth analysis, and the dynamical system approach to algorithms.


title: AI and Music From Composition to Expressive Performance

abstract: In this article, we first survey the three major types of computer music systems based on AI techniques: (1) compositional, (2) improvisational, and (3) performance systems. Representative examples of each type are briefly described. Then, we look in more detail at the problem of endowing the resulting performances with the expressiveness that characterizes human-generated music. This is one of the most challenging aspects of computer music that has been addressed just recently. The main problem in modeling expressiveness is to grasp the performer's "touch," that is, the knowledge applied when performing a score. Humans acquire it through a long process of observation and imitation. For this reason, previous approaches, based on following musical rules trying to capture interpretation knowledge, had serious limitations. An alternative approach, much closer to the observation-imitation process observed in humans, is that of directly using the interpretation knowledge implicit in examples extracted from recordings of human performers instead of trying to make explicit such knowledge. In the last part of the article, we report on a performance system, SAXEX, based on this alternative approach, that is capable of generating high-quality expressive solo performances of jazz ballads based on examples of human performers within a case-based reasoning (CBR) system.


Data mining techniques for Fraud Detection


title: End to End Learning for Self-Driving Cars

abstract: We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with unclear visual guidance such as in parking lots and on unpaved roads. ::: The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal. We never explicitly trained it to detect, for example, the outline of roads. ::: Compared to explicit decomposition of the problem, such as lane marking detection, path planning, and control, our end-to-end system optimizes all processing steps simultaneously. We argue that this will eventually lead to better performance and smaller systems. Better performance will result because the internal components self-optimize to maximize overall system performance, instead of optimizing human-selected intermediate criteria, e.g., lane detection. Such criteria understandably are selected for ease of human interpretation which doesn't automatically guarantee maximum system performance. Smaller networks are possible because the system learns to solve the problem with the minimal number of processing steps. ::: We used an NVIDIA DevBox and Torch 7 for training and an NVIDIA DRIVE(TM) PX self-driving car computer also running Torch 7 for determining where to drive. The system operates at 30 frames per second (FPS).


title: A new paradigm for learning language : Connectionist artificial inteligence

abstract: Important improvements in the area of the connectionist paradigm were designed and executed in the last two decades. Connectionist modeling techniques have been used to help us understand how individuals acquire, maintain and, in some cases, lose mental functions. Connectionist models involve parallel distributed processing (PDP). Despite its successes, connectionism is far from presenting a final solution to all the problems of cognition. Our claim is that connectionism has better an explanatory power than constitutes a simulation of real brain processes.


Enterprise Modeling


title: LSTM Benchmarks for Deep Learning Frameworks

abstract: This study provides benchmarks for different implementations of LSTM units between the deep learning frameworks PyTorch, TensorFlow, Lasagne and Keras. The comparison includes cuDNN LSTMs, fused LSTM variants and less optimized, but more flexible LSTM implementations. The benchmarks reflect two typical scenarios for automatic speech recognition, notably continuous speech recognition and isolated digit recognition. These scenarios cover input sequences of fixed and variable length as well as the loss functions CTC and cross entropy. Additionally, a comparison between four different PyTorch versions is included. The code is available online this https URL.


title: Optimising engagement for stroke rehabilitation using serious games

abstract: Effective stroke rehabilitation must be early, intensive and repetitive, which can lead to problems with patient motivation and engagement. The design of video games, often associated with good user engagement, may offer insights into how more effective systems for stroke rehabilitation can be developed. In this paper we identify game design principles for upper limb stroke rehabilitation and present several games developed using these principles. The games use low-cost video-capture technology which may make them suitable for deployment at home. Results from evaluating the games with both healthy subjects and people with stroke in their home are encouraging.


title: Tracing Information Flow from Erk to Target Gene Induction Reveals Mechanisms of Dynamic and Combinatorial Control.

abstract: Cell signaling networks coordinate specific patterns of protein expression in response to external cues, yet the logic by which signaling pathway activity determines the eventual abundance of target proteins is complex and poorly understood. Here, we describe an approach for simultaneously controlling the Ras/Erk pathway and monitoring a target gene's transcription and protein accumulation in single live cells. We apply our approach to dissect how Erk activity is decoded by immediate early genes (IEGs). We find that IEG transcription decodes Erk dynamics through a shared band-pass filtering circuit; repeated Erk pulses transcribe IEGs more efficiently than sustained Erk inputs. However, despite highly similar transcriptional responses, each IEG exhibits dramatically different protein-level accumulation, demonstrating a high degree of post-transcriptional regulation by combinations of multiple pathways. Our results demonstrate that the Ras/Erk pathway is decoded by both dynamic filters and logic gates to shape target gene responses in a context-specific manner.


title: The joint student response analysis and recognizing textual entailment challenge: making sense of student responses in educational applications

abstract: We present the results of the joint student response analysis (SRA) and 8th recognizing textual entailment challenge. The goal of this challenge was to bring together researchers from the educational natural language processing and computational semantics communities. The goal of the SRA task is to assess student responses to questions in the science domain, focusing on correctness and completeness of the response content. Nine teams took part in the challenge, submitting a total of 18 runs using methods and features adapted from previous research on automated short answer grading, recognizing textual entailment and semantic textual similarity. We provide an extended analysis of the results focusing on the impact of evaluation metrics, application scenarios and the methods and features used by the participants. We conclude that additional research is required to be able to leverage syntactic dependency features and external semantic resources for this task, possibly due to limited coverage of scientific domains in existing resources. However, each of three approaches to using features and models adjusted to application scenarios achieved better system performance, meriting further investigation by the research community.


title: Efficient CORDIC Algorithms and Architectures for Low Area and High Throughput Implementation

abstract: This paper presents two area-efficient algorithms and their architectures based on CORDIC. While the first algorithm eliminates ROM and requires only low-complexity barrel shifters, the second eliminates barrel shifters completely. As a consequence, both the algorithms consume approximately 50% area in comparison with other CORDIC designs. Further, the proposed algorithms are applicable to the entire range of angles. The FPGA implementations consume approximately 8% LUTs of a Xilinx Spartan XC2S200E device and have a slice-delay product of about 3. Convergence proofs for the algorithms are presented. Experimental comparisons with prior CORDIC designs confirm the efficacy of the proposed designs.


title: Frostbite following cryolipolysis treatment in a beauty salon: a case study

abstract: Abstract: This case study describes frostbite, a previously unreported complication following cryolipolysis, which resulted in substantial necrosis of the flank. Medical attention was not sought until one week after treatment. On examination, two distinct areas of significant frostbite in the left flank with surrounding erythema were revealed. Surgical intervention was avoided, as is recommended in cases of frostbite, and conservative treatment resulted in recovery of the affected area. Here, the authors highlight the adverse effects related to cryolipolysis, analysing the pathogenesis, clinical manifestations and management of this injury. The necessity of regulation within the cosmetic sector and the challenges associated with its implementation are also described. The authors believe emphasis must be placed on increasing patient awareness on the potential hazards of seeking cosmetic treatment from unregulated providers.


title: A Survey on Continuous User Identity Verification Using Biometric Traits for Secure Internet Services

abstract: Security of the web based services is become serious concern now a days. Secure user authentication is very important and fundamental in most of the systems User authentication systems are traditionally based on pairs of username and password and verify the identity of the user only at login phase. No checks are performed during working sessions, which are terminated by an explicit logout or expire after an idle activity period of the user. Emerging biometric solutions provides substituting username and password with biometric data during session establishment, but in such an approach still a single shot verification is less sufficient, and the identity of a user is considered permanent during the entire session. A basic solution is to use very short session timeouts and periodically request the user to input his credentials over and over, but this is not a definitive solution and heavily penalizes the service usability and ultimately the satisfaction of users. This paper explores promising alternatives offered by applying biometrics in the management of sessions. A secure protocol is defined for perpetual authentication through continuous user verification. Finally, the use of biometric authentication allows credentials to be acquired transparently i.e. without explicitly notifying the user or requiring his interaction, which is essential to guarantee better service usability.


title: DCU-UVT: Word-Level Language Classification with Code-Mixed Data

abstract: This paper describes the DCU-UVT team’s participation in the Language Identification in Code-Switched Data shared task in the Workshop on Computational Approaches to Code Switching. Word-level classification experiments were carried out using a simple dictionary-based method, linear kernel support vector machines (SVMs) with and without contextual clues, and a k-nearest neighbour approach. Based on these experiments, we select our SVM-based system with contextual clues as our final system and present results for the Nepali-English and Spanish-English datasets.


title: Analysis of the contention access period of IEEE 802.15.4 MAC

abstract: The recent ratification of IEEE 802.15.4 PHY-MAC specifications for low-rate wireless personal area networks represents a significant milestone in promoting deployment of wireless sensor networks (WSNs) for a variety of commercial uses. The 15.4 specifications specifically target wireless networking among low-rate, low-power and low-cost devices that is expected to be a key market segment for a large number of WSN applications. In this article, we first analyze the performance of the contention access period specified in the IEEE 802.15.4 standard in terms of throughput and energy consumption. This analysis is facilitated by a modeling of the contention access period as nonpersistent CSMA with backoff. We show that, in certain applications in which having an inactive period in the superframe may not be desirable due to delay constraints, shutting down the radio between transmissions provides significant savings in power without significantly compromising the throughput. We also propose and analyze the performance of a modification to the specification which could be used for applications in which MAC-level acknowledgements are not used. Extensive ns-2 simulations are used to verify the analysis.


title: CRISPR-Cas9 Structures and Mechanisms.

abstract: Many bacterial clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated (Cas) systems employ the dual RNA-guided DNA endonuclease Cas9 to defend against invading phages and conjugative plasmids by introducing site-specific double-stranded breaks in target DNA. Target recognition strictly requires the presence of a short protospacer adjacent motif (PAM) flanking the target site, and subsequent R-loop formation and strand scission are driven by complementary base pairing between the guide RNA and target DNA, Cas9-DNA interactions, and associated conformational changes. The use of CRISPR-Cas9 as an RNA-programmable DNA targeting and editing platform is simplified by a synthetic single-guide RNA (sgRNA) mimicking the natural dual trans-activating CRISPR RNA (tracrRNA)-CRISPR RNA (crRNA) structure. This review aims to provide an in-depth mechanistic and structural understanding of Cas9-mediated RNA-guided DNA targeting and cleavage. Molecular insights from biochemical and structural studies provide a framework for rational engineering aimed at altering catalytic function, guide RNA specificity, and PAM requirements and reducing off-target activity for the development of Cas9-based therapies against genetic diseases.


title: Designing a Temperature Model to Understand the Thermal Challenges of Portable Computing Platforms

abstract: Modern high-performance processors are embedded in portable electronics, such as smartphones, self-driving automobiles, and augmented reality wearable. These computing platforms provide real-time direction navigation, high definition video entertainment, real-time sensing and control. One of the major performance limiting factors in these platforms is the poorly designed thermal solution to prevent overheating at the processor transistor junction and at the platform surface. The former limits the maximum operating temperature of transistors to guarantee reliability and lifetime whereas the latter limits the maximum platform surface temperature to ensure user ergonomic comfort. To design effective cooling solutions for high-performance, multi-layer, multi-processor platforms, an accurate and detailed platform level temperature model is needed. In this work, we present a detailed finite volume model to predict the temperature behavior of a tightly packaged, high-performance portable platform, from the system level down to the processor architecture level. We first characterize the thermal response of real-world, representative mobile workloads and perform parametric studies to predict the processor junction and platform surface temperature based on the properties of the platform. We observe that thermal hot spots are sensitive to workload computation characteristics. In particular, for mobile workloads, such as web browsing, computations often occur in short time burst, leading to instantaneous power spikes. This results in temperature spikes as thermal hot spots. To mitigate the thermal issue, modern systems implement dynamic voltage and frequency scaling during high performance and high power scenario to prevent cores from overheating. We validate the proposed temperature model used to predict the maximum temperature at the processor chip and at the platform surface, with measurements from the embedded temperature sensors and an infrared camera. The temperature prediction accuracy for hot spots on the processor chip and on the platform surface is demonstrated to be higher than 90%. With the tool, we show that the physical construction (such as air gaps and material properties) and the floor plan of the multi-layer platform have a profound effect on the system temperature behavior. As the air gap between the different layers of the computing platform increases, the processor temperature rises significantly. Also there exists an optimal thickness setting of the air gap for platform surface thermal spreading. We hope the detailed characterization results, the developed temperature prediction tool, and the insights presented in the paper can motivate advanced thermal management solution for high-performance embedded platforms in the years to come.


title: An Extension to iFogSim to Enable the Design of Data Placement Strategies

abstract: Fog computing consists in extending Cloud services down to the network edge by using resources such as base stations, routers and switches. It presents a dense, heterogeneous and geo-distributed infrastructure which pushes to investigate how data are placed within this infrastructure in order to minimize service latency, network utilization and energy consumption. iFogSim is a Fog and IoT environments simulator dedicated to manage IoT services in a Fog infrastructure. In this paper, we present an extension to iFogSim to be able to model and simulate scenarios with strategies aiming to optimize data placement in Fog and IoT contexts. Data placement problem is NP-Hard due to the large number of Fog nodes and the high amount of data to be placed. Thus, we added a support to divide and conquer strategies to subdivide the issued infrastructure into several parts hence reducing the data placement computing time. Moreover, the extension involves a generic smart city scenario with different workloads making it possible for the users to investigate the behavior of their strategies using various workloads. In order to optimize the execution time of the simulations, we parallelized the Floyd-Warshall algorithm. This algorithm is used in iFogSim to compute all shortest paths between nodes in order to simulate data transmission. We have evaluated this extension using the proposed smart city scenario with various infrastructure configurations. The experiments show that our extension has a small overhead in terms of simulation time and memory utilization.


title: A Deep Learning Approach for Classification of Reaching Targets from EEG Images

abstract: In this paper, we propose a new approach for the classification of reaching targets before movement onset, during visually-guided reaching in 3D space. Our approach combines the discriminant power of two-dimensional Electroencephalography (EEG) signals (i.e., EEG images) built from short epochs, with the feature extraction and classification capabilities of deep learning (DL) techniques, such as the Convolutional Neural Networks (CNN). In this work, reaching motions are performed into four directions: left, right, up and down. To allow more natural reaching movements, we explore the use of Virtual Reality (VR) to build an experimental setup that allows the subject to perform self-paced reaching in 3D space while standing. Our results reported an increase both in classification performance and early detection in the majority of our experiments. To our knowledge this is the first time that EEG images and CNN are combined for the classification of reaching targets before movement onset.


title: FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition

abstract: Relatively small data sets available for expression recognition research make the training of deep networks for expression recognition very challenging. Although fine-tuning can partially alleviate the issue, the performance is still below acceptable levels as the deep features probably contain redun- dant information from the pre-trained domain. In this paper, we present FaceNet2ExpNet, a novel idea to train an expression recognition network based on static images. We first propose a new distribution function to model the high-level neurons of the expression network. Based on this, a two-stage training algorithm is carefully designed. In the pre-training stage, we train the convolutional layers of the expression net, regularized by the face net; In the refining stage, we append fully- connected layers to the pre-trained convolutional layers and train the whole network jointly. Visualization shows that the model trained with our method captures improved high-level expression semantics. Evaluations on four public expression databases, CK+, Oulu-CASIA, TFD, and SFEW demonstrate that our method achieves better results than state-of-the-art.


title: INVITRO TOXICITY OF SILVER NANOPARTICLES SYNTHESIZED BY USING CLOVES OF SYZYGIUM AROMATICUM AGAINST HEp-2 CELLS DERIVED FROM LARYNX CARCINOMA

abstract: Present study reports sucessfull bio-synthesis of silver nanoparticles (AgNPs) using aqueous extract of cloves (Syzygium aromaticum) belonging to family Myrtaceae. The obtained nanoparticles were characterized by using UV–vis spectroscopy and transmission electron microscopy (TEM) and HR-TEM. The AgNPs were spherical, poly-dispersed and well scattered with a size ranging from 10 to 80 nm. The FTIR results confirmed the role of protiens liable for silver nitrate reduction into AgNPs. The biosynthesized silver nanoparticles were checked for their cytotoxic property against HEp-2 cell line derived from larynx carcinoma. The results of MTT measures of AgNPs analyzed at different concentrations extending from 10 to 100µg/ml, demonstrated a considerable dose dependent toxicity of silver nanoparticles for HEp-2 cell line. The outstanding morphology appropriately relevant for anticancer property of silver nanoparticles against HEp-2 cells will have a beneficial impact of utilizing biosynthesized nanoparticles for cancer therapy.


title: Increasing Fruit and Vegetable Intake and Decreasing Fat and Sugar Intake in Families at Risk for Childhood Obesity

abstract: Results: Changes over 1 year showed that treatment influenced targeted parent and child fruit and vegetable intake and high-fat/high-sugar intake, with the Increase Fruit and Vegetable group also decreasing their consumption of high-fat/high-sugar foods. Parents in the increased fruit and vegetable group showed significantly greater decreases in percentage of overweight than parents in the decreased high-fat/high-sugar group. Discussion: These results suggest that focusing on increasing intake of healthy foods may be a useful approach for nutritional change in obese parents and their children.


title: The Malar Septum: The Anatomic Basis of Malar Mounds and Malar Edema

abstract: Abstract The anatomy of malar mounds and malar edema is evaluated in a series of 18 fresh cadaver dissections. Dye injection, histologic evaluation, and gross anatomic dissection are used to identify a previously unrecognized fascial structure of the lower eyelid and cheek. The malar septum originates from orbital rim periosteum superiorly and inserts into cheek skin 2.5 to 3 cm inferior to the lateral canthus. This fascial structure acts as a relatively impermeable barrier that allows tissue edema and hemoglobin pigment to accumulate above its cutaneous insertion. The malar septum, which acts as both a functional and a structural barrier, defines the lower boundary of several clinical entities: malar mounds, malar edema, malar festoons, and periorbital ecchymosis. The permeability characteristics of the malar septum suggest that, at least in some persons, malar mounds may be accentuated by chronic lower eyelid edema, and these characteristics may imply a time course in the progressive development from malar edema to malar mounds and, ultimately, to malar festoons. The anatomy of the malar septum is clinically relevant because it defines the four anatomic compartments of the malar mound that should be considered during surgery: the superior compartment of suborbicularis oculi fat, orbicularis oculi muscle, and superficial cheek fat and cheek skin superior to the cutaneous insertion of the malar septum.


title: Combination 830-nm and 633-nm Light-Emitting Diode Phototherapy Shows Promise in the Treatment of Recalcitrant Psoriasis: Preliminary Findings

abstract: BACKGROUND AND OBJECTIVES ::: Psoriasis is one of the major problems facing dermatologists worldwide. Planar arrays of light-emitting diodes (LEDs) have recently attracted attention in the treatment of difficult dermatological entities, 830 nm in near infrared (near-IR) and 633 nm in visible red. This study was designed to assess the efficacy of combination 830-nm and 633-nm LED phototherapy in the treatment of recalcitrant psoriasis. ::: ::: ::: SUBJECTS AND METHODS ::: Nine informed and consenting patients with psoriasis were enrolled in this preliminary study, (3 men, 6 women, mean age 34.3, skin types I to IV). All had chronic psoriasis, which in most cases had proved resistant to conventional treatments. They were treated sequentially with LED arrays delivering continuous-wave 830 nm (near-IR) and 633 nm (red) in two 20-min sessions over 4 or 5 weeks, with 48 h between sessions (830 nm, 60 J/cm(2); 633 nm, 126 J/cm(2)). ::: ::: ::: RESULTS ::: All patients completed their LED regimens (4 requiring 1 regimen, 5 requiring a second). Follow-up periods were from 3 to 8 months, except in two patients who were lost to follow-up. Clearance rates at the end of the follow-up period ranged from 60% to 100%. Satisfaction was universally very high. ::: ::: ::: CONCLUSIONS ::: The antiinflammatory effects of LED energy at 830 nm and 633 nm have been well documented, as has their use in wound healing. LED phototherapy is easy to apply, pain free and side-effect free, and is well tolerated by patients of all skin types. The promising results of this preliminary study warrant a proper controlled double-blind study with a larger patient population.


title: Dynamic capabilities and the emergence of intraindustry differential firm performance: insights from a simulation study

abstract: This paper explores how the dynamic capabilities of firms may be linked to differential firm performance within an industry. A formal model is presented in which dynamic capabilities are treated as a set of routines guiding the evolution of a firm's resource configuration. The model centers on the endogenous choice firms make between resource deployment through imitation and experimentation in order to generate alternative resource configurations. Three performance-relevant attributes of dynamic capabilities are proposed: timing, cost, and learning of resource deployment. Theoretical propositions are developed that suggest how these attributes contribute to the emergence of differential intraindustry firm performance. Simulation analysis offers insights into the trajectories of evolutionary change engendered by dynamic capability, and serves to refine the theoretical propositions. It is found that timing, cost, and learning effects foster the emergence of robust performance differences among firms with strikingly similar dynamic capabilities. Moreover, the results show that even small initial differences among firms can generate significant intraindustry differential firm performance, especially when the effects of timing, cost and learning are combined. Copyright © 2002 John Wiley & Sons, Ltd.


title: Fitness functions in evolutionary robotics: A survey and analysis

abstract: This paper surveys fitness functions used in the field of evolutionary robotics (ER). Evolutionary robotics is a field of research that applies artificial evolution to generate control systems for autonomous robots. During evolution, robots attempt to perform a given task in a given environment. The controllers in the better performing robots are selected, altered and propagated to perform the task again in an iterative process that mimics some aspects of natural evolution. A key component of this process-one might argue, the key component-is the measurement of fitness in the evolving controllers. ER is one of a host of machine learning methods that rely on interaction with, and feedback from, a complex dynamic environment to drive synthesis of controllers for autonomous agents. These methods have the potential to lead to the development of robots that can adapt to uncharacterized environments and which may be able to perform tasks that human designers do not completely understand. In order to achieve this, issues regarding fitness evaluation must be addressed. In this paper we survey current ER research and focus on work that involved real robots. The surveyed research is organized according to the degree of a priori knowledge used to formulate the various fitness functions employed during evolution. The underlying motivation for this is to identify methods that allow the development of the greatest degree of novel control, while requiring the minimum amount of a priori task knowledge from the designer.


title: The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility

abstract: Researchers and practitioners regard information technology (IT) as a competitive tool. However, current knowledge on IT capability mechanisms that affect firm performance remains unclear. Based on the dynamic capabilities perspective and the view of a hierarchy of capabilities, this article proposes a model to examine how IT capabilities (i.e., flexible IT infrastructure and IT assimilation) affect firm performance through absorptive capacity and supply chain agility in the supply chain context. Survey data show that absorptive capacity and supply chain agility fully mediate the influences of IT capabilities on firm performance. In addition to the direct effects, absorptive capacity also has indirect effects on firm performance by shaping supply chain agility. We conclude with implications and suggestions for future research.


title: Stock market prediction using neural network through news on online social networks

abstract: Stock market prediction has attracted a lot of attention from both business and academia. In this paper, we implement a model based on Recurrent Neural Networks (RNN) with Gated Recurrent Units (GRU) to predict the stock volatility in the Chinese stock market. We also propose many price related features which are used as inputs for our model. Apart from that, we carefully select official accounts from Chinese largest online social networks - Sina Weibo and extract the content posted by these accounts to analyze the public moods. An influence feature is derived based on the public moods to further improve the prediction model. The experimental results show that our model outperforms the baseline method and can achieve a good prediction performance.


title: Demonstration of SDN-based orchestration for multi-domain Segment Routing networks

abstract: This work demonstrates a hierarchical control plane architecture for Software Defined Networking (SDN)-based Segment Routing (SR) in multi-domain networks. An orchestrator application, on top of multiple open source SDN controllers, creates a hierarchical control plane architecture using northbound RESTFul APIs of controllers. The orchestrator has control, visibility and traffic engineering capabilities to manage multi-domain SR service creation. Standard southbound interfaces with proper SR extensions are exploited to manage SR tunnels in the MPLS data plane.


title: SayWAT: Augmenting Face-to-Face Conversations for Adults with Autism

abstract: During face-to-face conversations, adults with autism frequently use atypical rhythms and sounds in their speech (prosody), which can result in misunderstandings and miscommunication. SayWAT is a Wearable Assistive Technology that provides feedback to wearers about their prosody during face-to-face conversations. In this paper, we describe the design process that led to five design guidelines that governed the development of SayWAT and present results from two studies involving our prototype solution. Our results indicate that wearable assistive technologies can automatically detect atypical prosody and deliver feedback in real time without disrupting the wearer or the conversation partner. Additionally, we provide suggestions for wearable assistive technologies for social support.


title: Deep Representation Learning for Domain Adaptation of Semantic Image Segmentation

abstract: Deep Convolutional Neural Networks have pushed the state-of-the art for semantic segmentation provided that a large amount of images together with pixel-wise annotations is available. Data collection is expensive and a solution to alleviate it is to use transfer learning. This reduces the amount of annotated data required for the network training but it does not get rid of this heavy processing step. We propose a method of transfer learning without annotations on the target task for datasets with redundant content and distinct pixel distributions. Our method takes advantage of the approximate content alignment of the images between two datasets when the approximation error prevents the reuse of annotation from one dataset to another. Given the annotations for only one dataset, we train a first network in a supervised manner. This network autonomously learns to generate deep data representations relevant to the semantic segmentation. Then the images in the new dataset, we train a new network to generate a deep data representation that matches the one from the first network on the previous dataset. The training consists in a regression between feature maps and does not require any annotations on the new dataset. We show that this method reaches performances similar to a classic transfer learning on the PASCAL VOC dataset with synthetic transformations.


title: Unsupervised Learning of Style-sensitive Word Vectors

abstract: This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continuous bag of words (CBOW) model (Mikolov et al., 2013) to learn style-sensitive word vectors using a wider context window under the assumption that the style of all the words in an utterance is consistent. In addition, we introduce a novel task to predict lexical stylistic similarity and to create a benchmark dataset for this task. Our experiment with this dataset supports our assumption and demonstrates that the proposed extensions contribute to the acquisition of style-sensitive word embeddings.


title: Handwritten Character Recognition Using Histograms of Oriented Gradient Features in Deep Learning of Artificial Neural Network

abstract: Feature extraction plays an essential role in hand written character recognition because of its effect on the capability of classifiers. This paper presents a framework for investigating and comparing the recognition ability of two classifiers: Deep-Learning Feedforward-Backpropagation Neural Network (DFBNN) and Extreme Learning Machine (ELM). Three data sets: Thai handwritten characters, Bangla handwritten numerals, and Devanagari handwritten numerals were studied. Each data set was divided into two categories: non-extracted and extracted features by Histograms of Oriented Gradients (HOG). The experimental results showed that using HOG to extract features can improve recognition rates of both of DFBNN and ELM. Furthermore, DFBNN provides higher slightly recognition rates than those of ELM.


Simulator Model for Training Cesarean Sections in Kampala , Uganda Group 20 Team Members


title: Fault Detection and Isolation in Low-Voltage DC-Bus Microgrid System

abstract: A fault detection and isolation scheme for low-voltage dc-bus microgrid systems is presented in this paper. Unlike traditional ac distribution systems, protection has been challenging for dc systems. The goals of the proposed scheme are to detect the fault in the bus between devices and to isolate the faulted section so that the system keeps operating without disabling the entire system. To achieve these goals, a loop-type dc-bus-based microgrid system, which has a segment controller between connected components, is proposed. The segment controller consists of master and slave controllers that monitor currents and control the segment separation, which include solid-state bidirectional switches and snubber circuits. The proposed system can detect faults on the bus regardless of fault current amplitude or the power supply's feeding capacity. The proposed concepts have been verified by OrCAD/PSpice simulations and experiments on hardware test bed.


title: Forskolin and protein kinase inhibitors differentially affect hair cell potassium currents and transmitter release at the cytoneural junction in the isolated frog labyrinth

abstract: The post-transductional elaboration of sensory input at the frog semicircular canal has been studied by correlating the effects of drugs that interfere with phosphorylation processes on: (i) potassium conductances in isolated hair cell and (ii) transmitter release at the cytoneural junction in the intact labyrinth. At hair cells, delayed potassium currents (IKD) undergo voltage- and time-dependent inactivation; inactivation removal requires ATP, is sensitive to kinase blockade, but is unaffected by exogenous application of cyclic nucleotides. We report here that forskolin, an activator of endogenous adenylyl cyclase, enhances IKD inactivation removal in isolated hair cells, but produces an overall decrease in IKD amplitude consistent with the direct blocking action of the drug on several families of K channels. In the intact labyrinth, forskolin enhances transmitter release, consistent with such depression of K conductances. Kinase blockers - H-89 and KT5823 - have been shown to reduce IKD inactivation removal and IKD amplitude at isolated hair cells. In the labyrinth, the effects of these drugs on junctional activity are quite variable, with predominant inhibition of transmitter release, rather than the enhancement expected from the impairment of K currents. The overall action of forskolin and kinase inhibitors on K conductances is similar (depression), but they have opposite effects on transmitter release: this indicates that some intermediate steps between the bioelectric control of hair cell membrane potential and transmitter release are affected in opposite ways and therefore are presumably regulated by protein phosphorylation.


title: A Novel Transient Wrinkle Detection Algorithm and Its Application for Expression Synthesis

abstract: Because facial wrinkle is a representative feature of facial expression, automatic wrinkle detection has been an important and challenging topic for expression simulation, recognition, and animation. Recently, most works about wrinkle detection have focused on permanent wrinkles (e.g., age wrinkles), which are usually linear shapes, whereas the detection of transient wrinkles (e.g., expression wrinkles) has not been sufficiently studied because of their shape diversity and complexity. In this work, a novel algorithm for automatic detection of transient wrinkles with linear, fixed, and chaotic shapes is proposed, which largely consists of edge pair matching, active-appearance-model-based wrinkle structure location, and support-vector-machine-based wrinkle classification. The proposed wrinkle detector is applied for expression synthesis and an improved Poisson wrinkle mapping approach is proposed. Experimental results illustrate the competitiveness of the proposed wrinkle detector in detecting different transient wrinkles. Compared with state-of-the-art algorithms, the proposed approach yields complete and accurate wrinkle centers. The expression synthesized by the improved wrinkle mapping is also much more realistic.


title: Current status and perspectives in translational biomarker research for PD-1/PD-L1 immune checkpoint blockade therapy

abstract: Modulating immune inhibitory pathways has been a major recent breakthrough in cancer treatment. Checkpoint blockade antibodies targeting cytotoxic T-lymphocyte antigen 4 (CTLA-4) and programed cell-death protein 1 (PD-1) have demonstrated acceptable toxicity, promising clinical responses, durable disease control, and improved survival in some patients with advanced melanoma, non-small cell lung cancer (NSCLC), and other tumor types. About 20 % of advanced NSCLC patients and 30 % of advanced melanoma patients experience tumor responses from checkpoint blockade monotherapy, with better clinical responses seen with the combination of anti-PD-1 and anti-CTLA-4 antibodies. Given the power of these new therapies, it is important to understand the complex and dynamic nature of host immune responses and the regulation of additional molecules in the tumor microenvironment and normal organs in response to the checkpoint blockade therapies. In this era of precision oncology, there remains a largely unmet need to identify the patients who are most likely to benefit from immunotherapy, to optimize the monitoring assays for tumor-specific immune responses, to develop strategies to improve clinical efficacy, and to identify biomarkers so that immune-related adverse events can be avoided. At this time, PD-L1 immunohistochemistry (IHC) staining using 22C3 antibody is the only FDA-approved companion diagnostic for patients with NSCLC-treated pembrolizumab, but more are expected to come to market. We here summarize the current knowledge, clinical efficacy, potential immune biomarkers, and associated assays for immune checkpoint blockade therapies in advanced solid tumors.


title: Attentive Filtering Networks for Audio Replay Attack Detection

abstract: An attacker may use a variety of techniques to fool an automatic speaker verification system into accepting them as a genuine user. Anti-spoofing methods meanwhile aim to make the system robust against such attacks. The ASVspoof 2017 Challenge focused specifically on replay attacks, with the intention of measuring the limits of replay attack detection as well as developing countermeasures against them. In this work, we propose our replay attacks detection system - Attentive Filtering Network, which is composed of an attention-based filtering mechanism that enhances feature representations in both the frequency and time domains, and a ResNet-based classifier. We show that the network enables us to visualize the automatically acquired feature representations that are helpful for spoofing detection. Attentive Filtering Network attains an evaluation EER of 8.99% on the ASVspoof 2017 Version 2.0 dataset. With system fusion, our best system further obtains a 30% relative improvement over the ASVspoof 2017 enhanced baseline system.


title: Keynote: Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities

abstract: Manual analysis of pedestrians and crowds is often impractical for massive datasets of surveillance videos. Automatic tracking of humans is one of the essential abilities for computerized analysis of such videos. In this keynote paper, we present two state of the art methods for automatic pedestrian tracking in videos with low and high crowd density. For videos with low density, first we detect each person using a part-based human detector. Then, we employ a global data association method based on Generalized Graphs for tracking each individual in the whole video. In videos with high crowd-density, we track individuals using a scene structured force model and crowd flow modeling. Additionally, we present an alternative approach which utilizes contextual information without the need to learn the structure of the scene. Performed evaluations show the presented methods outperform the currently available algorithms on several benchmarks.


title: eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys

abstract: For years security machine learning research has promised to obviate the need for signature based detection by automatically learning to detect indicators of attack. Unfortunately, this vision hasn't come to fruition: in fact, developing and maintaining today's security machine learning systems can require engineering resources that are comparable to that of signature-based detection systems, due in part to the need to develop and continuously tune the "features" these machine learning systems look at as attacks evolve. Deep learning, a subfield of machine learning, promises to change this by operating on raw input signals and automating the process of feature design and extraction. In this paper we propose the eXpose neural network, which uses a deep learning approach we have developed to take generic, raw short character strings as input (a common case for security inputs, which include artifacts like potentially malicious URLs, file paths, named pipes, named mutexes, and registry keys), and learns to simultaneously extract features and classify using character-level embeddings and convolutional neural network. In addition to completely automating the feature design and extraction process, eXpose outperforms manual feature extraction based baselines on all of the intrusion detection problems we tested it on, yielding a 5%-10% detection rate gain at 0.1% false positive rate compared to these baselines.


title: Analyzing Thermal and Visual Clues of Deception for a Non-Contact Deception Detection Approach

abstract: With increased levels of security threats and the long-term consequences of falsely accusing the innocent and freeing the guilty, there is a growing need for reliable and efficient deception detection systems. Polygraph tests are invasive and require elongated time and human expertise, which is subject to bias and error. In this paper, we analyze thermal and visual clues of deception using a dataset collected from 30 subjects and multiple scenarios. We analyze expressions and other visual features and provide the first comparison between thermal facial regions to identify areas with higher capability of indicating deceit. Our experimental results show that our non-contact feature fusion model outperforms traditional physiological measurements, paving the road for non-invasive deception detection methodologies.


title: Presence vs. flow in the context of computer games

abstract: Flow and presence share several similarities such as the immersive component and intense feelings of involvement. Until today, relatively little attention has been paid to the connection between the two concepts. Thus, the purpose of this study (N = 70) was to examine whether presence and flow are related in the context of computer games. Factor analysis proved presence and flow to be two distinct constructs. In addition, flow seems to mediate the relationship between presence and enjoyment.


title: Learning from mistakes: towards a correctable learning algorithm

abstract: Many learning algorithms generate complex models that are difficult for a human to interpret, debug, and extend. In this paper, we address this challenge by proposing a new learning paradigm called correctable learning, where the learning algorithm receives external feedback about which data examples are incorrectly learned. We define a set of metrics which measure the correctability of a learning algorithm. We then propose a simple and efficient correctable learning algorithm which learns local models for different regions of the data space. Given an incorrect example, our method samples data in the neighborhood of that example and learns a new, more correct local model over that region. Experiments over multiple classification and ranking datasets show that our correctable learning algorithm offers significant improvements over the state-of-the-art techniques.


title: Implicit user modeling for personalized search

abstract: Information retrieval systems (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a tourist and a programmer may use the same word "java" to search for different information, but the current search systems would return the same results. In this paper, we study how to infer a user's interest from the user's search context and use the inferred implicit user model for personalized search. We present a decision theoretic framework and develop techniques for implicit user modeling in information retrieval. We develop an intelligent client-side web search agent (UCAIR) that can perform eager implicit feedback, e.g., query expansion based on previous queries and immediate result reranking based on clickthrough information. Experiments on web search show that our search agent can improve search accuracy over the popular Google search engine.


title: Deep Learning approach for sentiment analysis of short texts

abstract: Unstructured text data produced on the internet grows rapidly, and sentiment analysis for short texts becomes a challenge because of the limit of the contextual information they usually contain. Learning good vector representations for sentences is a challenging task and an ongoing research area. Moreover, learning long-term dependencies with gradient descent is difficult in neural network language model because of the vanishing gradients problem. Natural Language Processing (NLP) systems traditionally treat words as discrete atomic symbols; the model can leverage small amounts of information regarding the relationship between the individual symbols. In this paper, we propose ConvLstm, neural network architecture that employs Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) on top of pre-trained word vectors. In our experiments, ConvLstm exploit LSTM as a substitute of pooling layer in CNN to reduce the loss of detailed local information and capture long term dependencies in sequence of sentences. We validate the proposed model on two sentiment datasets IMDB, and Stanford Sentiment Treebank (SSTb). Empirical results show that ConvLstm achieved comparable performances with less parameters on sentiment analysis tasks.


title: Mindfulness: A Dialogue between Buddhism and Clinical Psychology

abstract: Evidence for the effectiveness of mindfulness as a clinical intervention is quickly growing. Much of our current understanding and application of mindfulness within clinical psychology has arisen from dialogue with Buddhist traditions, with the notable exception of Acceptance and Commitment Therapy. We wrote this article with two purposes: (1) to provide a concise review of mindfulness within the Buddhist traditions for interested clinicians and researchers and (2) to explore whether further dialogue between Buddhism and clinical psychology could enhance mindfulness as it is used within clinical psychology. We concluded that mindfulness, as it is understood and applied in Buddhism, is a richer concept than thus far understood and applied in psychology. In addition, within Buddhism the development of mindfulness must be understood in tandem with the development of wisdom, compassion, and ethics. We suggest an operational definition of mindfulness within Buddhism. We also explore implications for clinical psychology and possible future directions for mindfulness research and practice.


title: New immunotherapies targeting the PD-1 pathway.

abstract: Ligands from the B7 family bind to receptors of the CD28 family, which regulate early T cell activation in lymphoid organs and control inflammation and autoimmunity in peripheral tissues. Programmed death-1 (PD-1), a member of the CD28 family, is an inhibitory receptor on T cells and is responsible for their dysfunction in infectious diseases and cancers. The complex mechanisms controlling the expression and signaling of PD-1 and programmed death ligand 1 (PD-L1) are emerging. Recently completed and ongoing clinical trials that target these molecules have shown remarkable success by generating durable clinical responses in some cancer patients. In chronic viral infections, preclinical data reveal that targeting PD-1 and its ligands can improve T cell responses and virus clearance. There is also promise in stimulating this pathway for the treatment of autoimmune and inflammatory disorders.


title: Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

abstract: Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 HPFs (400× magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Our approach is accurate, fast, and requires fewer computing resources compared to existent methods, making this feasible for clinical use.


title: Reinforcement Learning in Finite MDPs: PAC Analysis

abstract: We study the problem of learning near-optimal behavior in finite Markov Decision Processes (MDPs) with a polynomial number of samples. These "PAC-MDP" algorithms include the well-known E3 and R-MAX algorithms as well as the more recent Delayed Q-learning algorithm. We summarize the current state-of-the-art by presenting bounds for the problem in a unified theoretical framework. A more refined analysis for upper and lower bounds is presented to yield insight into the differences between the model-free Delayed Q-learning and the model-based R-MAX.


title: Enhancing participant selection through caching in mobile crowd sensing

abstract: With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of participants from the huge user pool to perform the tasks and achieve certain level of coverage. In this paper, we introduce a new MCS architecture which leverages the cached sensing data to fulfill partial sensing tasks in order to reduce the size of selected participant set. We present a newly designed participant selection algorithm with caching and evaluate it via extensive simulations with a real-world mobile dataset.


title: Detection of replay attack on smart grid with code signal and bargaining game

abstract: Ensuring cyber security of smart grid based on supervisory control and data acquisition using information and communications technology has been becoming a major challenge. Especially, a replay attack is a dangerous cyber attack affecting integrity and authorization. The goal of this paper is to detect replay attack that is one of cyber attacks on the sensors of a control system. We propose a detecting method adding intentional noise to not only sensors as code signal but also input. Replay attack can immediately be reflected by using fault diagnosis matrices that are composed of the estimator and observed values even if code signal is decrypted. Finally, we show simulation results to analyze effectiveness of the proposed method.


title: Gimli: open source and high-performance biomedical name recognition

abstract: BackgroundAutomatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research.ResultsWe present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions.ConclusionsGimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli.


title: Mumyo - evaluating and exploring the myo armband for musical interaction

abstract: The MYO armband from Thalmic Labs is a complete and wireless motion and muscle sensing platform. This paper evaluates the armband's sensors and its potential for NIME applications. This is followed by a presentation of the prototype instrument MuMYO. We conclude that, despite some shortcomings, the armband has potential of becoming a new "standard" controller in the NIME community.


title: Discovering Relations Between Named Entities from a Large Raw Corpus Using Tree Similarity-Based Clustering

abstract: We propose a tree-similarity-based unsupervised learning method to extract relations between Named Entities from a large raw corpus. Our method regards relation extraction as a clustering problem on shallow parse trees. First, we modify previous tree kernels on relation extraction to estimate the similarity between parse trees more efficiently. Then, the similarity between parse trees is used in a hierarchical clustering algorithm to group entity pairs into different clusters. Finally, each cluster is labeled by an indicative word and unreliable clusters are pruned out. Evaluation on the New York Times (1995) corpus shows that our method outperforms the only previous work by 5 in F-measure. It also shows that our method performs well on both high-frequent and less-frequent entity pairs. To the best of our knowledge, this is the first work to use a tree similarity metric in relation clustering.


title: Forecasting Seasonal Influenza Fusing Digital Indicators and a Mechanistic Disease Model

abstract: The availability of novel digital data streams that can be used as proxy for monitoring infectious disease incidence is ushering in a new era for real-time forecast approaches to disease spreading. Here, we propose the first seasonal influenza forecast framework based on a stochastic, spatially structured mechanistic model (individual level microsimulation) initialized with geo-localized microblogging data. The framework provides for more than 600 census areas in the United States, Italy and Spain, the initial conditions for a stochastic epidemic computational model that generates an ensemble of forecasts for the main indicators of the epidemic season: peak time and intensity. We evaluate the forecasts accuracy and reliability by comparing the results with the data from the official influenza surveillance systems in the US, Italy and Spain in the seasons 2014/15 and 2015/16. In all countries studied, the proposed framework provides reliable results with leads of up to 6 weeks that became more stable and accurate with progression of the season. The results for the United States have been generated in real-time in the context of the Centers for Disease Control and Prevention ``Forecasting the Influenza Season Challenge''. A characteristic feature of the mechanistic modeling approach is in the explicit estimate of key epidemiological parameters relevant for public health decision-making that cannot be achieved with statistical models that do not consider the disease dynamic. Furthermore, the presented framework allows the fusion of multiple data streams in the initialization stage and can be enriched with census, weather and socioeconomic data.


title: Triangulating mult iply-connected polygons : A simple , yet efficient algorithm

abstract: We present a new, simple, yet efficient algorithm for triangulating multiply-connected polygons. The algorithm requires sorting only local concave minima (sags). The order in which triangles are created mimics a flooding process of the interior of the polygon. At each stage, the algorithm analyses the positions and neighborhoods of two vertices only, and possibly checks for active sags, so as to determine which of five possible actions to take. Actions are based on a local decomposition of the polygon into monotonic regions, or gorges (raise the water level in the current gorge, spill into an adjacent gorge, jump to the other bank of a filled gorge, divide a gorge into two, and fill a gorge to its top). The implementation is extremely simple and numerically robust for a large class of polygons. It has been tested on millions of cases as a preprocessing step of a walkthrough and inspection program for complex mechanical and architectural scenes. Extensive experimental results indicate that the observed complexity in terms of the number of vertices, remains under in all cases.


title: Question Classification using Semantic, Syntactic and Lexical features

abstract: Question classification is very important for question answering. This paper present our research work on question classification through machine learning approach. In order to train th e learning model, we designed a rich set of features that are predictive of question categories. An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a question. In this work we combined lexical, syntactic and semantic f eatures which improve the accuracy of classification. Furthermore, we adopted three different classifiers: Nearest Neighbors (NN), Naive Bayes (NB), and Support Vector Machines (SVM) using two kinds of features: bag -of-words and bag-of n grams. Furthermore, we discovered that when we take SVM classifier and combine the semantic, syntactic, lexical feature we found that it will improve the accuracy of classification. We tested our proposed approaches on the well-known UIUC dataset and succeeded to achieve anew record on the accuracy of classification on this dataset.


title: A Comprehensive Analysis of Misclassified Handwritten Chinese Character Samples by Incorporating Human Recognition

abstract: The development of convolutional neural networks (CNN) has led to revolutionary progress in the resolution of the offline handwritten Chinese character recognition (HCCR) problem. As the recognition rate on a standard offline HCCR testbed is outstanding, a few samples that remain misclassified have kindled our interest. In this paper, with the help of human recognition results, we present a comprehensive analysis of the samples misclassified by a state-of-the-art CNN model. We performed the analysis based on the top-1-votes, which are obtained from the statistical analysis of human recognition results, and derived the following conclusions: (1) the majority of samples with high top-1-votes were mis-labeled. Besides, by comparing the results of human recognition with that of CNN, some limitations of CNN that provide scope for further improvement are presented; (2) in the samples with medium top- 1-votes, it is shown that the samples with different confidence level have different characteristics. Specifically, some samples could be regarded as multi-label samples; (3) the samples with low top-1- votes are either wrongly written or written extensively in cursive style, which are difficult to match their given ground-truths; (4)the relationship between writing styles and misclassifications are also introduced in the paper. We believe this work should provide some insights and brings new clues on designing new classification methods to deal with these challenging samples.


title: Modeling Missing Data in Clinical Time Series with RNNs

abstract: We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the pediatric intensive care unit (PICU) at Children's Hospital Los Angeles, our data consists of multivariate time series of observations. The measurements are irregularly spaced, leading to missingness patterns in temporally discretized sequences. While these artifacts are typically handled by imputation, we achieve superior predictive performance by treating the artifacts as features. Unlike linear models, recurrent neural networks can realize this improvement using only simple binary indicators of missingness. For linear models, we show an alternative strategy to capture this signal. Training models on missingness patterns only, we show that for some diseases, what tests are run can be as predictive as the results themselves.


title: Graph SLAM for AGV using geometrical arrangement based on lamp and SURF features in a factory environment

abstract: In this study, we propose a method to extract features for graph SLAM using an upward-looking camera mounted in an automated guided vehicle (AGV) in a factory environment. To this end, a novel feature detector and descriptor GALS (geometrical arrangement based on lamps and SURF) is proposed in this study. This algorithm consists of two parts: 1) geometrical arrangement of SURF features around a lamp that is often observed in a factory environment; and 2) a SURF descriptor. The use of the geometrical arrangement of several SURF features provides a more distinguishable feature than the use of a single SURF feature, and thus it can be robustly used for feature matching. As a result of GALS-based graph SLAM, a graph is created and optimized through a Georgia Tech Smoothing and Mapping Library (GTSAM) tool so that the accumulated error due to the slip is minimized. A series of experiments in indoor factory environments showed that the proposed scheme resulted in reliable navigation.


title: MAPP: a Scalable Multi-Agent Path Planning Algorithm with Tractability and Completeness Guarantees

abstract: Multi-agent path planning is a challenging problem with numerous real-life applications. Running a centralized search such as A* in the combined state space of all units is complete and cost-optimal, but scales poorly, as the state space size is exponential in the number of mobile units. Traditional decentralized approaches, such as FAR and WHCA*, are faster and more scalable, being based on problem decomposition. However, such methods are incomplete and provide no guarantees with respect to the running time or the solution quality. They are not necessarily able to tell in a reasonable time whether they would succeed in finding a solution to a given instance. ::: ::: We introduce MAPP, a tractable algorithm for multi-agent path planning on undirected graphs. We present a basic version and several extensions. They have low-polynomial worst-case upper bounds for the running time, the memory requirements, and the length of solutions. Even though all algorithmic versions are incomplete in the general case, each provides formal guarantees on problems it can solve. For each version, we discuss the algorithm's completeness with respect to clearly defined subclasses of instances. ::: ::: Experiments were run on realistic game grid maps. MAPP solved 99.86% of all mobile units, which is 18-22% better than the percentage of FAR and WHCA*. MAPP marked 98.82% of all units as provably solvable during the first stage of plan computation. Parts of MAPP's computation can be re-used across instances on the same map. Speed-wise, MAPP is competitive or significantly faster than WHCA*, depending on whether MAPP performs all computations from scratch. When data that MAPP can re-use are preprocessed offline and readily available, MAPP is slower than the very fast FAR algorithm by a factor of 2.18 on average. MAPP's solutions are on average 20% longer than FAR's solutions and 7-31% longer than WHCA*'s solutions.


title: Splatting lines: an efficient method for illustrating 3D surfaces and volumes

abstract: While 3D line drawing techniques have become well established in the past decade, most are only suitable for polygonal meshes. This paper presents splatting lines, a unified framework for generating line drawings from various types of 3D models, including point clouds, volumes, and polygonal meshes. In contrast to the existing mesh-based approaches, our method takes a densely sampled point cloud as input. It renders two diffuse shading images using splatting with different parameters and then generates line drawings by subtracting these images. Our point-based approach can be easily extended to polygonal meshes and volumes by an efficient (iso-)surface sampling method. Our method is highly efficient and it does not require any pre-computation. It is also more robust to the mesh tessellation than existing mesh-based techniques. Experimental results indicate the advantages of our method in terms of both performance and quality.


title: Automatic segmentation of lung lobes in ct images based on fissures, vessels, and bronchi

abstract: Lobewise analysis of the pulmonary parenchyma is of clinical relevance for diagnosing and monitoring pathologies. In this work, a fully automatic lobe segmentation approach is presented, which is based on a previously proposed watershed transformation approach. The proposed extension explicitly considers the pulmonary fissures by including them in the cost image for the watershed segmentation. The fissure structures are found through a tailored feature analysis of the Hessian matrix. The method is evaluated using 42 data sets, and a comparison with manual segmentations yields an average volumetric agreement of 96.8%. In comparison to the previously proposed approach, this method increases segmentation accuracy where the fissures are visible.


title: Multiple classifier systems for robust classifier design in adversarial environments

abstract: Pattern recognition systems are increasingly being used in adversarial environments like network intrusion detection, spam filtering and biometric authentication and verification systems, in which an adversary may adaptively manipulate data to make a classifier ineffective. Current theory and design methods of pattern recognition systems do not take into account the adversarial nature of such kind of applications. Their extension to adversarial settings is thus mandatory, to safeguard the security and reliability of pattern recognition systems in adversarial environments. In this paper we focus on a strategy recently proposed in the literature to improve the robustness of linear classifiers to adversarial data manipulation, and experimentally investigate whether it can be implemented using two well known techniques for the construction of multiple classifier systems, namely, bagging and the random subspace method. Our results provide some hints on the potential usefulness of classifier ensembles in adversarial classification tasks, which is different from the motivations suggested so far in the literature.


Autonomous Quadrotor Control with Reinforcement Learning


title: The Confluence of Perceiving and Thinking in Consciousness Phenomenology

abstract: The processual relation of thinking and perceiving shall be examined from a historical perspective as well as on the basis of methodically conducted first-person observation. Historically, these two psychological aspects of human knowledge and corresponding philosophical positions have predominant alternating phases. At certain historical points, thinking and perceiving tend to converge, while in the interim phases they seem to diverge with an emphasis on one of them. While at the birth of modern science, for instance, these two forms of mental life were deeply interlinked, today they seem to be separated more than ever before – as a number of scientific crises have shown. Turning from the outer to the inner aspect of this issue, a phenomenological view becomes relevant. In terms of the consciousness phenomenology developed by Rudolf Steiner (1861–1925) and Herbert Witzenmann’s (1905–1988) Structure Phenomenology, this article will show how a methodical integration of thinking and perceiving can be carried out on the basis of first-person observation. In the course of a skilled introspective or meditative self-observation the individual’s own mental micro-actions of separating and integrating come into view, jointly constituting what we usually call thinking and perceiving. Consequently, this approach includes a conceptual as well as a perceptual dimension the experimental confluence of which ties in with the methodological core principle of modern natural science. At the same time, making this principle explicit may open the way to a further development of human consciousness and its scientific delineation.


title: Implementing the Syntax of Japanese Numeral Classifiers

abstract: While the sortal constraints associated with Japanese numeral classifiers are well-studied, less attention has been paid to the details of their syntax. We describe an analysis implemented within a broad-coverage HPSG that handles an intricate set of numeral classifier construction types and compositionally relates each to an appropriate semantic representation, using Minimal Recursion Semantics.