title: The Human Phenotype Ontology: A Tool for Annotating and Analyzing Human Hereditary Disease

abstract: There are many thousands of hereditary diseases in humans, each of which has a specific combination of phenotypic features, but computational analysis of phenotypic data has been hampered by lack of adequate computational data structures. Therefore, we have developed a Human Phenotype Ontology (HPO) with over 8000 terms representing individual phenotypic anomalies and have annotated all clinical entries in Online Mendelian Inheritance in Man with the terms of the HPO. We show that the HPO is able to capture phenotypic similarities between diseases in a useful and highly significant fashion.


title: RML Glove—An Exoskeleton Glove Mechanism With Haptics Feedback

abstract: This paper presents the design, implementation, and experimental validation of a haptic glove mechanism: the RML glove (Robotics and Mechatronics Lab). The designed haptic interface is a lightweight, portable, and self-contained mechatronic system that fits on a bare hand and provides haptic force feedback to each finger of the hand without constraining their movement. In order to experimentally test the new design, teleportation with this glove for mobile robot navigation is also studied. By comparing teleportation experiments with and without force feedback, the results show that this new admittance (using force as input and position as output) glove with force feedback can provide effective force feedback to the user and augment telepresence.


title: A process study of computer-aided translation

abstract: We investigate novel types of assistance for human translators, based on statistical machine translation methods. We developed the computer-aided tool Caitra that makes suggestions for sentence completion, shows word and phrase translation options, and allows postediting of machine translation output. We carried out a study of the translation process that involved non-professional translators that were native in either French or English and recorded their interaction with the tool. Users translated 192 sentences from French news stories into English. Most translators were faster and better when assisted by our tool. A detailed examination of the logs also provides insight into the human translation process, such as time spent on different activities and length of pauses.


title: A Deep Q-Network for the Beer Game: A Reinforcement Learning algorithm to Solve Inventory Optimization Problems

abstract: The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate the bullwhip effect. The game is a decentralized, multi-agent, cooperative problem that can be modeled as a serial supply chain network in which agents cooperatively attempt to minimize the total cost of the network even though each agent can only observe its own local information. Each agent chooses order quantities to replenish its stock. Under some conditions, a base-stock replenishment policy is known to be optimal. However, in a decentralized supply chain in which some agents (stages) may act irrationally (as they do in the beer game), there is no known optimal policy for an agent wishing to act optimally. ::: We propose a machine learning algorithm, based on deep Q-networks, to optimize the replenishment decisions at a given stage. When playing alongside agents who follow a base-stock policy, our algorithm obtains near-optimal order quantities. It performs much better than a base-stock policy when the other agents use a more realistic model of human ordering behavior. Unlike most other algorithms in the literature, our algorithm does not have any limits on the beer game parameter values. Like any deep learning algorithm, training the algorithm can be computationally intensive, but this can be performed ahead of time; the algorithm executes in real time when the game is played. Moreover, we propose a transfer learning approach so that the training performed for one agent and one set of cost coefficients can be adapted quickly for other agents and costs. Our algorithm can be extended to other decentralized multi-agent cooperative games with partially observed information, which is a common type of situation in real-world supply chain problems.


title: Deep Dual Learning for Semantic Image Segmentation

abstract: Deep neural networks have advanced many computer vision tasks, because of their compelling capacities to learn from large amount of labeled data. However, their performances are not fully exploited in semantic image segmentation as the scale of training set is limited, where perpixel labelmaps are expensive to obtain. To reduce labeling efforts, a natural solution is to collect additional images from Internet that are associated with image-level tags. Unlike existing works that treated labelmaps and tags as independent supervisions, we present a novel learning setting, namely dual image segmentation (DIS), which consists of two complementary learning problems that are jointly solved. One predicts labelmaps and tags from images, and the other reconstructs the images using the predicted labelmaps. DIS has three appealing properties. 1) Given an image with tags only, its labelmap can be inferred by leveraging the images and tags as constraints. The estimated labelmaps that capture accurate object classes and boundaries are used as ground truths in training to boost performance. 2) DIS is able to clean tags that have noises. 3) DIS significantly reduces the number of perpixel annotations in training, while still achieves state-ofthe- art performance. Extensive experiments demonstrate the effectiveness of DIS, which outperforms an existing bestperforming baseline by 12.6% on Pascal VOC 2012 test set, without any post-processing such as CRF/MRF smoothing.


title: Infill Optimization for Additive Manufacturing -- Approaching Bone-like Porous Structures

abstract: Porous structures such as trabecular bone are widely seen in nature. These structures are lightweight and exhibit strong mechanical properties. In this paper, we present a method to generate bone-like porous structures as lightweight infill for additive manufacturing. Our method builds upon and extends voxel-wise topology optimization. In particular, for the purpose of generating sparse yet stable structures distributed in the interior of a given shape, we propose upper bounds on the localized material volume in the proximity of each voxel in the design domain. We then aggregate the local per-voxel constraints by their p -norm into an equivalent global constraint, in order to facilitate an efficient optimization process. Implemented on a high-resolution topology optimization framework, our results demonstrate mechanically optimized, detailed porous structures which mimic those found in nature. We further show variants of the optimized structures subject to different design specifications, and we analyze the optimality and robustness of the obtained structures.


title: Targeted patch-clamp recordings and single-cell electroporation of unlabeled neurons in vivo

abstract: Here we describe an approach for making targeted patch-clamp recordings from single neurons in vivo, visualized by two-photon microscopy. A patch electrode is used to perfuse the extracellular space surrounding the neuron of interest with a fluorescent dye, thus enabling the neuron to be visualized as a negative image ('shadow') and identified on the basis of its somatodendritic structure. The same electrode is then placed on the neuron under visual control to allow formation of a gigaseal ('shadowpatching'). We demonstrate the reliability and versatility of shadowpatching by performing whole-cell recordings from visually identified neurons in the neocortex and cerebellum of rat and mouse. We also show that the method can be used for targeted in vivo single-cell electroporation of plasmid DNA into identified cell types, leading to stable transgene expression. This approach facilitates the recording, labeling and genetic manipulation of single neurons in the intact native mammalian brain without the need to pre-label neuronal populations.


title: Advanced Bloom Filter Based Algorithms for Efficient Approximate Data De-Duplication in Streams

abstract: Applications involving telecommunication call data records, web pages, online transactions, medical records, stock markets, climate warning systems, etc., necessitate efficient management and processing of such massively exponential amount of data from diverse sources. De-duplication or Intelligent Compression in streaming scenarios for approximate identification and elimination of duplicates from such unbounded data stream is a greater challenge given the real-time nature of data arrival. Stable Bloom Filters (SBF) addresses this problem to a certain extent. . ::: In this work, we present several novel algorithms for the problem of approximate detection of duplicates in data streams. We propose the Reservoir Sampling based Bloom Filter (RSBF) combining the working principle of reservoir sampling and Bloom Filters. We also present variants of the novel Biased Sampling based Bloom Filter (BSBF) based on biased sampling concepts. We also propose a randomized load balanced variant of the sampling Bloom Filter approach to efficiently tackle the duplicate detection. In this work, we thus provide a generic framework for de-duplication using Bloom Filters. Using detailed theoretical analysis we prove analytical bounds on the false positive rate, false negative rate and convergence rate of the proposed structures. We exhibit that our models clearly outperform the existing methods. We also demonstrate empirical analysis of the structures using real-world datasets (3 million records) and also with synthetic datasets (1 billion records) capturing various input distributions.


title: Bone mineral density among postmenopausal Saudi women.

abstract: OBJECTIVE ::: Osteoporosis is reported to be common among postmenopausal Saudi women. The reported incidence varies between 50-60%. Different machines were used to reach these conclusions. At present it is believed that dual energy x-ray absorptiometry (DEXA) is the most accurate method to diagnose osteoporosis. This study was conducted to measure bone mineral density (BMD) measurement of lumbar spine and the upper femur of Saudi postmenopausal women attending orthopedic clinic with unrelated complaints. ::: ::: ::: METHODS ::: This study comprises of 256 patients attending orthopedic clinics at the King Fahd Hospital of the University, Al-Khobar, Kingdom of Saudi Arabia between January 2002 and June 2003. The data gathered was age, duration of menopause, height and weight for body mass index (BMI) calculation. Women with secondary osteoporosis were excluded from the study. Patients' orthopedic complaints were also recorded in the database. Bone mineral density measurements were carried out using Hologic total body DEXA machine. The data were analyzed using SPSS package. ::: ::: ::: RESULTS ::: The data of 256 patients was available for analysis. The average age of patients screened was 57.62 years (49-76) SD +/- 6.71. The BMI was 21.3-42.9 Kg/m2 (SD +/- 5.34). The BMD of the lumbar spine was 0.785 gm/cm2 (0.527-1.023) SD +/-0.142 and that of the hip region was 0.764 gm/cm2 (0. 500-1.069) SD +/- 0.149. As per the WHO classification 59 women (23%) were classified as normal with T score of -0.82, 78 (30.5%) as osteopenic with T score -2.5 and 119 (46.7%) as osteoporosis with T score -3.58. When the BMD of the hip was analyzed 62 (24.2%) were normal T score -1.0, 81 (31.6%) as osteopenic, T score -2.5 and 113 (44.1%) as osteoporotic, with a T score of -3.1. On the basis of analysis of the lumbar spine 190 (74.2%) had increased risk of fracture as compared to the analysis of hip 59% were at increased risk of fracture. ::: ::: ::: CONCLUSION ::: Our results indicate that postmenopausal Saudi women suffer from osteoporosis and osteopenia higher than those from other parts of the country. Necessary steps are needed so as to avoid osteoporosis and its complications which could end up in epidemic proportions.


title: Be conservative: enhancing failure diagnosis with proactive logging

abstract: When systems fail in the field, logged error or warning messages are frequently the only evidence available for assessing and diagnosing the underlying cause. Consequently, the efficacy of such logging--how often and how well error causes can be determined via postmortem log messages--is a matter of significant practical importance. However, there is little empirical data about how well existing logging practices work and how they can yet be improved. We describe a comprehensive study characterizing the efficacy of logging practices across five large and widely used software systems. Across 250 randomly sampled reported failures, we first identify that more than half of the failures could not be diagnosed well using existing log data. Surprisingly, we find that majority of these unreported failures are manifested via a common set of generic error patterns (e.g., system call return errors) that, if logged, can significantly ease the diagnosis of these unreported failure cases. We further mechanize this knowledge in a tool called Errlog, that proactively adds appropriate logging statements into source code while adding only 1.4% performance overhead. A controlled user study suggests that Errlog can reduce diagnosis time by 60.7%.


Efficient Locally Weighted Polynomial Regression Predictions


title: Rational Function Fitting of Electromagnetic Transfer Functions from Frequency-Domain and Time-Domain Data

abstract: This paper presents a new methodology for synthesis of broadband equivalent circuits for multi-port high speed interconnect systems from numerically obtained and/or measured frequency-domain and time-domain response data. The equivalent circuit synthesis is based on the rational function fitting of admittance matrix, which combines the frequency-domain vector fitting process, VECTFIT with its time-domain analog, TDVF to yield a robust and versatile fitting algorithm. The generated rational fit is directly converted into a SPICE-compatible circuit after passivity enforcement. The accuracy of the resulting algorithm is demonstrated through its application to the fitting of the admittance matrix of a power/ground plane structure


title: APPLICATIONS OF GRAPH THEORY IN COMPUTER SCIENCE AN OVERVIEW

abstract: The field of mathematics plays vital role in various fields. One of the important areas in mathematics is graph theory which is used in structural models. This structural arrangements of various objects or technologies lead to new inventions and modifications in the existing environment for enhancement in those fields. The field graph theory started its journey from the problem of Koinsberg bridge in 1735. This paper gives an overview of the applications of graph theory in heterogeneous fields to some extent but mainly focuses on the computer science applications that uses graph theoretical concepts. Various papers based on graph theory have been studied related to scheduling concepts, computer science applications and an overview has been presented here.


title: Archetype-Driven Character Dialogue Generation for Interactive Narrative

abstract: Recent years have seen a growing interest in creating virtual agents to populate the cast of characters for interactive narrative. A key challenge posed by interactive characters for narrative environments is devising expressive dialogue generators. To be effective, character dialogue generators must be able to simultaneously take into account multiple sources of information that bear on dialogue, including character attributes, plot development, and communicative goals. Building on the narrative theory of character archetypes, we propose an archetype-driven character dialogue generator that uses a probabilistic unification framework to generate dialogue motivated by character personality and narrative history to achieve communicative goals. The generator's behavior is illustrated with character dialogue generation in a narrative-centered learning environment, Crystal Island .


title: A compact micro Coriolis mass flow sensor with flow bypass for a monopropellant micro propulsion system

abstract: We have designed, fabricated and tested a micromachined Coriolis flow sensor for the measurement of hydrazine (N2H4) propellant flow in a micro chemical propulsion system (uCPS). The sensor will be used for measurement and characterization of the uCPS in a simulated space vacuum environment. To reach the required flow range a bypass system is integrated. Initial measurements demonstrate an increase of the flow range in accordance with the designed bypass ratio. The sensor is operated as a two-port resonator in an oscillator circuit to improve frequency stability.


title: On-Device Mobile Phone Security Exploits Machine Learning

abstract: The authors present a novel approach to protecting mobile devices from malware that might leak private information or exploit vulnerabilities. The approach, which can also keep devices from connecting to malicious access points, uses learning techniques to statically analyze apps, analyze the behavior of apps at runtime, and monitor the way devices associate with Wi-Fi access points.


title: Power-dense multilevel inverter module using interleaved GaN-based phases for electric aircraft propulsion

abstract: Realizing the electrification of aircraft necessitates the development of inverters and rectifiers with ultra-high efficiency and specific power simultaneously. Unconventional topologies such as the flying capacitor multilevel (FCML) inverter hold great promise for achieving transformational reductions in inverter power loss and weight due to drastically smaller filters and compact hybrid energy transfer mechanism. After describing methods to overcome obstacles in ultra-efficient and lightweight converter design, this work presents an example of interleaved FCML inverter operation. The successful, balanced interleaved operation was demonstrated in the laboratory with a dual-interleaved, 9-level converter prototype at 1 kV input bus voltage and 9.7 kW output power. At 98.6% peak efficiency, this converter achieves a gravimetric power density of 17.3 kW/kg and a volumetric power density of 35.3 kW/L including the contributions of the heat sink. Interestingly, observations from loss modeling and hardware efficiency measurements revealed the large impact of second-order loss mechanisms, such as dynamic on-state resistance and junction temperature. Insights gained from this new understanding can be incorporated into future modeling for more thorough optimization of the converter design.


title: Enhancing student's learning experience at middle east college by using blended learning

abstract: This research aims to provide a mechanism which can enhance student experience and confidence at Middle East College. The research is to find out the reason why students lack in experience and confidence in choosing Web Application Development module at Middle East College. By using a learning environment (blended learning), which can be suitable to increase both the experience and confidence level of the students.


Encoding Temporal Information for Time-Aware Link Prediction


title: Effects of upper limb robot-assisted therapy on motor recovery in subacute stroke patients

abstract: Background and purposeThere is little evidence available on the use of robot-assisted therapy in subacute stroke patients. A randomized controlled trial was carried out to evaluate the short-time efficacy of intensive robot-assisted therapy compared to usual physical therapy performed in the early phase after stroke onset.MethodsFifty-three subacute stroke patients at their first-ever stroke were enrolled 30 ± 7 days after the acute event and randomized into two groups, both exposed to standard therapy. Additional 30 sessions of robot-assisted therapy were provided to the Experimental Group. Additional 30 sessions of usual therapy were provided to the Control Group.The following impairment evaluations were performed at the beginning (T0), after 15 sessions (T1), and at the end of the treatment (T2): Fugl-Meyer Assessment Scale (FM), Modified Ashworth Scale-Shoulder (MAS-S), Modified Ashworth Scale-Elbow (MAS-E), Total Passive Range of Motion-Shoulder/Elbow (pROM), and Motricity Index (MI).ResultsEvidence of significant improvements in MAS-S (p = 0.004), MAS-E (p = 0.018) and pROM (p < 0.0001) was found in the Experimental Group. Significant improvement was demonstrated in both Experimental and Control Group in FM (EG: p < 0.0001, CG: p < 0.0001) and MI (EG: p < 0.0001, CG: p < 0.0001), with an higher improvement in the Experimental Group.ConclusionsRobot-assisted upper limb rehabilitation treatment can contribute to increasing motor recovery in subacute stroke patients. Focusing on the early phase of stroke recovery has a high potential impact in clinical practice.


title: Human RCS measurements and dummy requirements for the assessment of radar based active pedestrian safety systems

abstract: Based on EuroNCAP regulations the number of autonomous emergency braking systems for pedestrians (AEB-P) will increase over the next years. According to accident research a considerable amount of severe pedestrian accidents happen at artificial lighting, twilight or total darkness conditions. Because radar sensors are very robust in these situations, they will play an important role for future AEB-P systems. To assess and evaluate systems a pedestrian dummy with reflection characteristics as close as possible to real humans is indispensable. As an extension to existing measurements in literature this paper addresses open issues like the influence of different positions of the limbs or different clothing for both relevant automotive frequency bands. Additionally suggestions and requirements for specification of pedestrian dummies based on results of RCS measurements of humans and first experimental developed dummies are given.


title: Lie tracking: social presence, truth and deception in avatar-mediated telecommunication

abstract: The success of visual telecommunication systems depends on their ability to transmit and display users' natural nonverbal behavior. While video-mediated communication (VMC) is the most widely used form of interpersonal remote interaction, avatar-mediated communication (AMC) in shared virtual environments is increasingly common. This paper presents two experiments investigating eye tracking in AMC. The first experiment compares the degree of social presence experienced in AMC and VMC during truthful and deceptive discourse. Eye tracking data (gaze, blinking, and pupil size) demonstrates that oculesic behavior is similar in both mediation types, and uncovers systematic differences between truth telling and lying. Subjective measures show users' psychological arousal to be greater in VMC than AMC. The second experiment demonstrates that observers of AMC can more accurately detect truth and deception when viewing avatars with added oculesic behavior driven by eye tracking. We discuss implications for the design of future visual telecommunication media interfaces.


title: Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations

abstract: Information extraction (IE) holds the promise of generating a large-scale knowledge base from the Web's natural language text. Knowledge-based weak supervision, using structured data to heuristically label a training corpus, works towards this goal by enabling the automated learning of a potentially unbounded number of relation extractors. Recently, researchers have developed multi-instance learning algorithms to combat the noisy training data that can come from heuristic labeling, but their models assume relations are disjoint --- for example they cannot extract the pair Founded(Jobs, Apple) and CEO-of(Jobs, Apple). ::: ::: This paper presents a novel approach for multi-instance learning with overlapping relations that combines a sentence-level extraction model with a simple, corpus-level component for aggregating the individual facts. We apply our model to learn extractors for NY Times text using weak supervision from Free-base. Experiments show that the approach runs quickly and yields surprising gains in accuracy, at both the aggregate and sentence level.


title: Parallel mild hybrid equivalent to the Tata Safari

abstract: The vehicle range provided by a fully charged battery module in any electric vehicle is no match to the range provided by the internal combustion engine driven vehicles with full tank gasoline or diesel. This limitation of electric vehicles paved way for the evolution of hybrid electric vehicles. Among the various known architectures for hybrid electric vehicles, the parallel mild hybrid is expected to be the right candidate to be introduced in a developing country. This is because the size of the electric powertrain in such vehicles shall be relatively smaller compared to the other vehicle architectures and hence shall be less costly compared to the electric vehicle or other hybrid electric vehicle configurations. This paper aims at explaining the procedure of deriving a parallel mild hybrid vehicle equivalent to Tata Safari, a passenger vehicle already available in Indian market. The paper also compares the performance specifications of both vehicles and determines the losses in the proposed vehicle during the normal cruising mode and regenerative modes while traversing a typical Indian urban driving cycle and an Indian highway driving cycle.


title: Mining, indexing, and querying historical spatiotemporal data

abstract: In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data, apart from unveiling important information to the data analyst, can facilitate data management substantially. Based on this observation, we propose a framework that analyzes, manages, and queries object movements that follow such patterns. We define the spatiotemporal periodic pattern mining problem and propose an effective and fast mining algorithm for retrieving maximal periodic patterns. We also devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotemporal queries. We evaluate our methods experimentally using datasets with object trajectories that exhibit periodicity.


title: Model-Based Vector-Fitting Method for Circuit Model Extraction of Coupled-Resonator Diplexers

abstract: In this paper, a novel rational function approximation method, namely, model-based vector fitting (MVF), is proposed for accurate extraction of the characteristic functions of a coupled-resonator diplexer with a resonant type of junction from noise-contaminated measurement data. MVF inherits all the merits of the vector-fitting (VF) method and can also stipulate the order of the numerator of the model. Thus, MVF is suitable for the high-order diplexer system identification problem against measurement noise. With the extracted characteristic functions, a three-port transversal coupling matrix of a diplexer can be synthesized. A matrix orthogonal transformation strategy is also proposed to transform the obtained transversal matrix to a target coupling matrix configuration, whose entries have one-to-one relationship with the physical tuning elements. The whole model extraction procedure is analytical and robust, and can be used in a computer-aided tuning (CAT) program for coupled-resonator diplexers. A practical tuning example of a diplexer with a common resonator is given in detail to demonstrate the effectiveness and the practical value of the proposed method.


title: Examining the Reliability, Validity and Factor Structure of the DRS-15 with College Athletes

abstract: Hardiness, comprising feelings of commitment, control and challenge, is most frequently measured with the Dispositional Resiliency Scale (DRS), but little work has been done with the brief 15-item version. To examine the factor structure, reliability, validity, and item functioning for gender invariance of the 15-item DRS with 525 collegiate athletes from a wide range of sports. Convergent and divergent validity were examined through relationships with mental toughness, grit and competitive anxiety. Participants completed measures of mental toughness, optimism, grit, competitive anxiety, and the DRS-15. Confirmatory Factor Analysis revealed a poor fit for the three-factor hardiness model, and subsequent exploratory factor analysis yielded a four-factor model with better fit than the three-factor structure. Additionally, several items appear to be biased towards males or females. The fourth factor may be unique to the collegiate athlete population, and related to perceived lack of control in future life directions. Convergent and divergent validity were supported through correlations of DRS scores with related measures. The four-factor model should be tested with different samples to determine if these changes should be adapted when using the DRS-15 in collegiate athletics or other settings.


title: Knowledge and attitudes of theatre staff prior to the implementation of robotic-assisted surgery in the public sector

abstract: BACKGROUND ::: The use of robotic-assisted surgery (RAS) is becoming increasingly prevalent across a range of surgical specialties within public hospitals around Australia. As a result, it is critical that organisations consider workplace factors such as staff knowledge, attitudes and behaviours prior to the implementation of such new technology. This study aimed to describe the knowledge and attitudes of operating theatre staff from a large public tertiary referral hospital prior to the commencement of an RAS program. ::: ::: ::: METHODS ::: A cross-sectional survey of nursing, medical and support staff working in the operating theatre complex of a large public tertiary referral hospital was completed over a one-week period in June 2016. A 23-item questionnaire was utilised for data collection. ::: ::: ::: RESULTS ::: 164 (66%) theatre staff returned the surveys and were included in this study. The majority of medical staff reported being knowledgeable about RAS, whilst the majority of nursing and support staff did not. Overall the theatre staff were neutral about the potential benefits of RAS to patients. The majority of medical staff believed the implementation of RAS will increase the value of staff roles and job satisfaction, while nursing and support staff were uncertain about these benefits. All three staff groups were concerned about the impact of an RAS program on Workplace Health and Safety, and care and handling. ::: ::: ::: CONCLUSION ::: Operating theatre staff presented different knowledge and attitudes prior to the introduction of RAS. Whilst theatre staff were more favourable towards RAS than negative, they largely reserved their judgement about the new system prior to their own experiences. Collectively, these findings should be taken into consideration for training and support strategies prior to the implementation of a RAS program.


title: GeoSRS: A Hybrid Social Recommender System for Geolocated Data

abstract: All right sreserved. We present GeoSRS, a hybrid recommender system for a popular location-based social network (LBSN), in which users are able to write short reviews on the places of interest they visit. Using state-of-the-art text mining techniques, our system recommends locations to users using as source the whole set of text reviews in addition to their geographical location. To evaluate our system, we have collected our own data sets by crawling the social network Foursquare. To do this efficiently, we propose the use of a parallel version of the Quadtree technique, which may be applicable to crawling/exploring other spatially distributed sources. Finally, we study the performance of GeoSRS on our collected data set and conclude that by combining sentiment analysis and text modeling, GeoSRS generates more accurate recommendations. The performance of the system improves as more reviews are available, which further motivates the use of large-scale crawling techniques such as the Quadtree.


title: A fuzzy vault scheme

abstract: We describe a simple and novel cryptographic construction that we call a fuzzy vault. Alice may place a secret value /spl kappa/ in a fuzzy vault and "lock" it using an unordered set A of elements from some public universe U. If Bob tries to "unlock" the vault using an unordered set B, he obtains /spl kappa/ only if B is close to A, i.e., only if A and B overlap substantially.


title: Semi-supervised Seizure Prediction with Generative Adversarial Networks

abstract: Many outstanding studies have reported promising results in seizure prediction that is considered one of the most challenging predictive data analysis. This is mainly because electroencephalogram (EEG) bio-signal intensity is very small, in µV range, and there are significant sensing difficulties given physiological and non-physiological artifacts. In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which are more accessible. We also suggest the use of data fusion to further improve the seizure prediction accuracy. Data fusion in our vision includes EEG signals, cardiogram signals, body temperature and time. We use the short-time Fourier transform on 28-s EEG windows as a pre-processing step. A generative adversarial network (GAN) is trained in an unsupervised manner where information of seizure onset is disregarded. The trained Discriminator of the GAN is then used as feature extractor. Features generated by the feature extractor are classified by two fully-connected layers (can be replaced by any classifier) for the labeled EEG signals. This semi-supervised seizure prediction method achieves area under the operating characteristic curve (AUC) of 77.68% and 75.47% for the CHBMIT scalp EEG dataset and the Freiburg Hospital intracranial EEG dataset, respectively. Unsupervised training without the need of labeling is important because not only it can be performed in real-time during EEG signal recording, but also it does not require feature engineering effort for each patient.


title: Image Captioning for Ambient Awareness on a Sidewalk

abstract: Ambient awareness on a sidewalk is critical for safe navigation, especially for the people who are blind or visually impaired. An affordable and interactive system is necessary for this purpose. In this paper, we present the the outcome of the experiment of off-the-shelf image captioning systems. Design and implementation of a system embedded in a RPi3 is part of the experiment. The main component of the system include: a. generation of meaningful caption from images, b. implementation of personalized feedback mechanism for efficient communication with a minimal cognitive load, and c. an interactive user interface and energy efficient integration that account for multiple configurations to be more inclusive. In particular, the performance of off-the-shelf image captioning systems was compared e.g., Microsoft Cognitive Service, Clarifai, Google Vision API, and IBM BlueMix to determine best platform for meaningful caption generation. We implemented three different schemes, namely text-to-speech synthesis, haptics, and ring tone to provide personalized feedback. The implemented system interface is energy efficient and interactive to provide ambient awareness. The objective evaluation of the fully integrated system was performed on the sidewalk. In particular, we focus on the accuracy of the captioning system and the usage analytics to provide helpful tips on the spot and to understand long term system behavior.


title: Scenarios for the application of learning analytics and the flipped classroom

abstract: This work aims to define and present some scenarios to use learning analytics with flipped classroom models. We selected some important indicators and visualizations widely used in learning analytics and analyze how to apply them in different types of flipped classroom. Besides, we specify some activities that students and teachers can do in these types of flipped classroom based on the use of learning analytics in order to improve the learning process. We also discuss the advantages of the combination of learning analytics with the flipped classroom, in comparison with only using the flipped classroom.


title: Learn to Combine Modalities in Multimodal Deep Learning

abstract: Combining complementary information from multiple modalities is intuitively appealing for improving the performance of learning-based approaches. However, it is challenging to fully leverage different modalities due to practical challenges such as varying levels of noise and conflicts between modalities. Existing methods do not adopt a joint approach to capturing synergies between the modalities while simultaneously filtering noise and resolving conflicts on a per sample basis. In this work we propose a novel deep neural network based technique that multiplicatively combines information from different source modalities. Thus the model training process automatically focuses on information from more reliable modalities while reducing emphasis on the less reliable modalities. Furthermore, we propose an extension that multiplicatively combines not only the single-source modalities, but a set of mixtured source modalities to better capture cross-modal signal correlations. We demonstrate the effectiveness of our proposed technique by presenting empirical results on three multimodal classification tasks from different domains. The results show consistent accuracy improvements on all three tasks.


title: FPAP: A Folded Architecture for Energy-Quality Scalable Convolutional Neural Networks

abstract: Emerging convolutional neural networks (CNNs) tend to be designed with varied per-layer data widths and sparse representations. However, these two features, which bring many redundant computations, have not been exploited simultaneously in existing hardware architectures for CNNs. This paper proposes an energy-quality scalable architecture, namely folded precision-adjustable processor (FPAP), to eliminate all computational redundancies by using folding techniques. On one hand, FPAP decomposes the dominant multiply-accumulate (MAC) operations into multiple adds and folds them into single arithmetic unit. Only effective adds (or part of them) are then calculated serially. Thus, FPAP can adapt to different per-layer data widths and enable precision-adjustable approximate computing. Particularly, FPAP adaptively selects either activation or weight to be decomposed in every single MAC to minimize the total number of adds and clock cycles. On the other hand, a 1-D convolution is undertaken by a multi-tap transposed finite impulse response (FIR) filter, which is folded into one tap to skip MACs with zero weights or activations. Besides, a judicious delay element remapping scheme and a novel genetic algorithm-based kernel reallocation scheme, are developed to reduce the power consumption in a folded FIR filter and mitigate the load imbalance issue caused by irregular sparsity, respectively. With all these optimizations, FPAP is able to reach comparable or even faster processing speed over the corresponding unfolded design in sparse CNNs while consuming smaller area. Experimental results on real CNN models demonstrate that FPAP can scale its energy efficiency from 4.28 to 23.63 TOP/s/W, and area efficiency from 37.79 to 164.15GOP/s/mm2, respectively, under the TSMC 28-nm HPC CMOS technology.


title: Epidemiology of alcohol and other drug use among American college students.

abstract: OBJECTIVE ::: This article provides information on the extent of alcohol use and other drug use among American college students. ::: ::: ::: METHOD ::: Five different sources of data are examined for estimating recent levels of alcohol (and other drug) use among college students: Harvard School of Public Health College Alcohol Study (CAS), the Core Institute (CORE), Monitoring the Future (MTF), National College Health Risk Behavior Survey (NCHRBS) and National Household Survey on Drug Abuse (NHSDA). ::: ::: ::: RESULTS ::: Alcohol use rates are very high among college students. Approximately two of five American college students were heavy drinkers, defined as having had five or more drinks in a row in the past 2 weeks. Alcohol use is higher among male than female students. White students are highest in heavy drinking, black students are lowest and Hispanic students are intermediate. Use of alcohol--but not cigarettes, marijuana and cocaine--is higher among college students than among noncollege age-mates. Longitudinal data show that, while in high school, students who go on to attend college have lower rates of heavy drinking than do those who will not attend college. Both groups increase their heavy drinking after high school graduation, but the college students increase distinctly more and actually surpass their nonstudent age-mates. Trend data from 1980 to 1999 show some slight improvement in recent years. ::: ::: ::: CONCLUSIONS ::: Despite improvements in the past 20 years, colleges need to do more to reduce heavy alcohol use among students.


10Gb/s Limiting Amplifier and Laser/Modulator Driver


XGvis: Interactive Data Visualization with Multidimensional Scaling


title: Learning deep representations by mutual information estimation and maximization

abstract: In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of the input to the objective can greatly influence a representation's suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and competes with fully-supervised learning on several classification tasks. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation-learning objectives for specific end-goals.


title: Dual Low-Rank Decompositions for Robust Cross-View Learning

abstract: Cross-view data are very popular contemporarily, as different viewpoints or sensors attempt to richly represent data in various views. However, the cross-view data from different views present a significant divergence, that is, cross-view data from the same category have a lower similarity than those in different categories but within the same view. Considering that each cross-view sample is drawn from two intertwined manifold structures, i.e., class manifold and view manifold, in this paper, we propose a robust cross-view learning framework to seek a robust view-invariant low-dimensional space. Specifically, we develop a dual low-rank decomposition technique to unweave those intertwined manifold structures from one another in the learned space. Moreover, we design two discriminative graphs to constrain the dual low-rank decompositions by fully exploring the prior knowledge. Thus, our proposed algorithm is able to capture more within-class knowledge and mitigate the view divergence to obtain a more effective view-invariant feature extractor. Furthermore, our proposed method is very flexible in addressing such a challenging cross-view learning scenario that we only obtain the view information of the training data while with the view information of the evaluation data unknown. Experiments on face and object benchmarks demonstrate the effective performance of our designed model over the state-of-the-art algorithms.


title: Membrane curvature and mechanisms of dynamic cell membrane remodelling

abstract: Membrane curvature is no longer seen as a passive consequence of cellular activity but an active means to create membrane domains and to organize centres for membrane trafficking. Curvature can be dynamically modulated by changes in lipid composition, the oligomerization of curvature scaffolding proteins and the reversible insertion of protein regions that act like wedges in membranes. There is an interplay between curvature-generating and curvature-sensing proteins during vesicle budding. This is seen during vesicle budding and in the formation of microenvironments. On a larger scale, membrane curvature is a prime player in growth, division and movement.


Player Modeling


title: Ultra low power capless LDO with dynamic biasing of derivative feedback

abstract: In this paper, a low power, output-capacitor-free, low-dropout regulator (LDO) is proposed with a new dynamic biased, multiloop feedback strategy. Initially, a theoretical macromodel is presented based on the analogy between the capless LDO and the mechanical non-linear harmonic oscillator, enabling to shed new light on the non-linear amplification, dynamic and adaptive biasing techniques used in the multiloop feedback LDO control in the large signal context. To implement some of the unexplored abilities of this model the new capless LDO topology is proposed. The output class AB stage of the error amplifier and the non-linear derivative current amplifier of the LDO feedback loop ensure dynamical extended close-loop bandwidth gain and dynamical damping enhancement for fast load and line LDO transients. Using the new dynamic biasing of the derivative loop, an improvement is obtained in the derivative sensing of the fast output voltage variations, enabling a significant enhancement in the transient response of the capless LDO. The proposed LDO, designed for a maximum current of 50mA in UMC RF 1P8M 0.13µm, requires a quiescent current of only 4.1µA and presents excellent transient response when compared to the state-of-the-art.


title: Business psychographics revisited: from segmentation theory to successful marketing practice

abstract: Consumer marketers have realised that geodemographics seldom provide adequate perspectives of today's complex markets. Instead, psychographic segmentation has revealed more powerful target market insights while providing marketers a springboard for adapting selling propositions and tailoring the marketing mix. Although its application in business-to-business (B2B) markets is also well acknowledged, there is limited evidence that psychographics has merit. To illustrate its power, this study examines the body of literature and practitioner experience that supports the usefulness of B2B psychographics as an alternative or complement to firmographics. The study supports its specific relevance to micro-segmentation where insights into a buyer's motives, risk aversion and relationship style can help determine favourable or unfavourable predispositions to marketing initiatives. A case application of a B2B services firm demonstrates its value in sales management.


title: Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes

abstract: While visual tracking has been greatly improved over the recent years, crowd scenes remain particularly challenging for people tracking due to heavy occlusions, high crowd density, and significant appearance variation. To address these challenges, we first design a Sparse Kernelized Correlation Filter (S-KCF) to suppress target response variations caused by occlusions and illumination changes, and spurious responses due to similar distractor objects. We then propose a people tracking framework that fuses the S-KCF response map with an estimated crowd density map using a convolutional neural network (CNN), yielding a refined response map. To train the fusion CNN, we propose a two-stage strategy to gradually optimize the parameters. The first stage is to train a preliminary model in batch mode with image patches selected around the targets, and the second stage is to fine-tune the preliminary model using the real frame-by-frame tracking process. Our density fusion framework can significantly improves people tracking in crowd scenes, and can also be combined with other trackers to improve the tracking performance. We validate our framework on two crowd video datasets.


The parallel bgl: A generic library for distributed graph computations


TagLDA: Bringing document structure knowledge into topic models


title: Learning the Dynamics of Arterial Traffic From Probe Data Using a Dynamic Bayesian Network

abstract: Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. Sparse probe data represent the vast majority of the data available on arterial roads. This paper proposes a probabilistic modeling framework for estimating and predicting arterial travel-time distributions using sparsely observed probe vehicles. We introduce a model based on hydrodynamic traffic theory to learn the density of vehicles on arterial road segments, illustrating the distribution of delay within a road segment. The characterization of this distribution is essentially to use probe vehicles for traffic estimation: Probe vehicles report their location at random locations, and the travel times between location reports must be properly scaled to match the map discretization. A dynamic Bayesian network represents the spatiotemporal dependence on the network and provides a flexible framework to learn traffic dynamics from historical data and to perform real-time estimation with streaming data. The model is evaluated using data from a fleet of 500 probe vehicles in San Francisco, CA, which send Global Positioning System (GPS) data to our server every minute. The numerical experiments analyze the learning and estimation capabilities on a subnetwork with more than 800 links. The sampling rate of the probe vehicles does not provide detailed information about the location where vehicles encountered delay or the reason for any delay (i.e., signal delay, congestion delay, etc.). The model provides an increase in estimation accuracy of 35% when compared with a baseline approach to process probe-vehicle data.


title: Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection

abstract: As humans we possess an intuitive ability for navigation which we master through years of practice; however existing approaches to model this trait for diverse tasks including monitoring pedestrian flow and detecting abnormal events have been limited by using a variety of hand-crafted features. Recent research in the area of deep-learning has demonstrated the power of learning features directly from the data; and related research in recurrent neural networks has shown exemplary results in sequence-to-sequence problems such as neural machine translation and neural image caption generation. Motivated by these approaches, we propose a novel method to predict the future motion of a pedestrian given a short history of their, and their neighbours, past behaviour. The novelty of the proposed method is the combined attention model which utilises both "soft attention" as well as "hard-wired" attention in order to map the trajectory information from the local neighbourhood to the future positions of the pedestrian of interest. We illustrate how a simple approximation of attention weights (i.e. hard-wired) can be merged together with soft attention weights in order to make our model applicable for challenging real world scenarios with hundreds of neighbours. The navigational capability of the proposed method is tested on two challenging publicly available surveillance databases where our model outperforms the current-state-of-the-art methods. Additionally, we illustrate how the proposed architecture can be directly applied for the task of abnormal event detection without handcrafting the features.


Music and literature: are there shared empathy and predictive mechanisms underlying their affective impact?


title: Removing Rain from a Single Image via Discriminative Sparse Coding

abstract: Visual distortions on images caused by bad weather conditions can have a negative impact on the performance of many outdoor vision systems. One often seen bad weather is rain which causes significant yet complex local intensity fluctuations in images. The paper aims at developing an effective algorithm to remove visual effects of rain from a single rainy image, i.e. separate the rain layer and the de-rained image layer from an rainy image. Built upon a non-linear generative model of rainy image, namely screen blend mode, we proposed a dictionary learning based algorithm for single image de-raining. The basic idea is to sparsely approximate the patches of two layers by very high discriminative codes over a learned dictionary with strong mutual exclusivity property. Such discriminative sparse codes lead to accurate separation of two layers from their non-linear composite. The experiments showed that the proposed method outperformed the existing single image de-raining methods on tested rain images.


title: Don’t Let Me Be #Misunderstood: Linguistically Motivated Algorithm for Predicting the Popularity of Textual Memes

abstract: Prediction of the popularity of online textual snippets gained much attention in recent years. In this paper we investigate some of the factors that contribute to popularity of specific phrases such as Twitter hashtags. We define a new prediction task and propose a linguistically motivated algorithm for accurate prediction of hashtag popularity. Our prediction algorithm successfully models the interplay between various constraints such as the length restriction, typing effort and ease of comprehension. Controlling for network structure and social aspects we get a glimpse into the processes that shape the way we produce language and coin new words. In order to learn the interactions between the constraints we cast the problem as a ranking task. We adapt Gradient Boosted Trees for learning ranking functions in order to predict the hashtags/neologisms to be accepted. Our results outperform several baseline algorithms including SVM-rank, while maintaining higher interpretability, thus our model's prediction power can be used for better crafting of future hashtags.


title: The Influence of Natural Lipid Asymmetry upon the Conformation of a Membrane-inserted Protein (Perfringolysin O)*

abstract: Eukaryotic membrane proteins generally reside in membrane bilayers that have lipid asymmetry. However, in vitro studies of the impact of lipids upon membrane proteins are generally carried out in model membrane vesicles that lack lipid asymmetry. Our recently developed method to prepare lipid vesicles with asymmetry similar to that in plasma membranes and with controlled amounts of cholesterol was used to investigate the influence of lipid composition and lipid asymmetry upon the conformational behavior of the pore-forming, cholesterol-dependent cytolysin perfringolysin O (PFO). PFO conformational behavior in asymmetric vesicles was found to be distinct both from that in symmetric vesicles with the same lipid composition as the asymmetric vesicles and from that in vesicles containing either only the inner leaflet lipids from the asymmetric vesicles or only the outer leaflet lipids from the asymmetric vesicles. The presence of phosphatidylcholine in the outer leaflet increased the cholesterol concentration required to induce PFO binding, whereas phosphatidylethanolamine and phosphatidylserine in the inner leaflet of asymmetric vesicles stabilized the formation of a novel deeply inserted conformation that does not form pores, even though it contains transmembrane segments. This conformation may represent an important intermediate stage in PFO pore formation. These studies show that lipid asymmetry can strongly influence the behavior of membrane-inserted proteins.


title: Control optimization for a power-split hybrid vehicle

abstract: Toyota hybrid system (THS) is used in the current best-selling hybrid vehicle on the market - the Toyota Prius. This hybrid system contains a power split planetary gear system which combines the benefits of series and parallel hybrid vehicles. This paper first develops a dynamic model to investigate the control strategy of the THS power train. An equivalent consumption minimization strategy (ECMS) is developed which is based on instantaneous optimization concept. The dynamic programming (DP) technique is then utilized to obtain a performance benchmark and insight toward fine-tuning of the ECMS algorithm for better performance.


title: Fast Scans on Key-Value Stores

abstract: Key-Value Stores (KVS) are becoming increasingly popular because they scale up and down elastically, sustain high throughputs for get/put workloads and have low latencies. KVS owe these advantages to their simplicity. This simplicity, however, comes at a cost: It is expensive to process complex, analytical queries on top of a KVS because today's generation of KVS does not support an efficient way to scan the data. The problem is that there are conflicting goals when designing a KVS for analytical queries and for simple get/put workloads: Analytical queries require high locality and a compact representation of data whereas elastic get/put workloads require sparse indexes. This paper shows that it is possible to have it all, with reasonable compromises. We studied the KVS design space and built TellStore, a distributed KVS, that performs almost as well as state-of-the-art KVS for get/put workloads and orders of magnitude better for analytical and mixed workloads. This paper presents the results of comprehensive experiments with an extended version of the YCSB benchmark and a workload from the telecommunication industry.


title: A state diagram analysis of the multi-queue M/M/1 model with finite lengths

abstract: The M/M/1 model is the most frequently used model in queueing problems. The research on the M/M/1 model with single queue and finite/infinite buffer length is already well developed. However, combinations of multiple queues with limited buffer capacity are rarely discussed. The reason for this deficiency is the lack of queue selection probability with the involved polling strategies. In this article, we propose to apply the multi-dimensional state diagram in analyzing the queueing behaviors of the q-M/M/1/Ki /FCFS/QSR model, where q is the number of queues, Ki the limited length of the ith queue, and QSR the dedicated queue selection rule. The considered QSRs can be either first-come-first-served polling or priority polling in this study. The state probabilities are solved by a set of linear equations, based on steady-state distributions. In order to verify the accuracy and performance of the proposed approach, we conducted several experiments to estimate the state probabilities, queue lengths, and waitin...


title: The Effects of Prosocial Video Games on Prosocial Behaviors: International Evidence From Correlational, Longitudinal, and Experimental Studies

abstract: Although dozens of studies have documented a relation between violent video games and aggressive behaviors, very little attention has been paid to potential effects of prosocial games. Theoretically, games in which game characters help and support each other in nonviolent ways should increase both short-term and long-term prosocial behaviors. We report three studies conducted in three countries with three age groups to test this hypothesis. In the correlational study, Singaporean middle-school students who played more prosocial games behaved more prosocially. In the two longitudinal samples of Japanese children and adolescents, prosocial game play predicted later increases in prosocial behavior. In the experimental study, U.S. undergraduates randomly assigned to play prosocial games behaved more prosocially toward another student. These similar results across different methodologies, ages, and cultures provide robust evidence a prosocial game content effect, and provide support for the General Learning Model.


A Comparison of the Impact of Plant – Based and Meat – Based Diets On Overall General Well-Being


title: Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks

abstract: Traditional physics-based approaches to infer sub-surface properties such as full-waveform inversion or reflectivity inversion are time-consuming and computationally expensive. We present a deep-learning technique that eliminates the need for these computationally complex methods by posing the problem as one of domain transfer. Our solution is based on a deep convolutional generative adversarial network and dramatically reduces computation time. Training based on two different types of synthetic data produced a neural net that generates realistic velocity models when applied to a real data set. The system’s ability to generalize means it is robust against the inherent occurrence of velocity errors and artifacts in both training and test datasets.


title: AEAS - Towards High Energy-efficiency Design for OpenSSL Encryption Acceleration through HW/SW Co-design

abstract: Entering the Big Data Era leads to the rapid development of web applications which provide high performance sensitive access on large data centers. OpenSSL has been widely deployed as a freely available implementation of SSL/TLS protocol that secures transactions over the Internet. In order to accelerate the speed of OpenSSL, many alternative encryption approaches are designed. However, energy consumption has been ignored in the rush for performance. Energy efficiency becomes a challenge with the increasing demands for performance and energy saving in data centers. In this paper, we present the Adaptive Encryption Acceleration System (AEAS), an OpenSSL encryption acceleration scheme. It provides high energy-efficiency encryption through HW/SW co-design. The essential idea is exerting the superiorities of energy efficiency for different encryption approaches and making full use of system resource through the Dynamic Management Mechanism including RequestAllocation algorithm and DynamicScheduler algorithm. Specifically, this scheme supports instruction set and hardware to process the computation compatibly by the Adaptive Control Crypto (ac_crypto) engine. Experimental results show that AEAS can improve energy efficiency by up to 933.5%, 68.8%, and 483.7% comparing with software, AES-NI and QAT, respectively.


title: Stock market random forest-text mining system mining critical indicators of stock market movements

abstract: Stock Market (SM) is believed to be a significant sector of a free market economy as it plays a crucial role in the growth of commerce and industry of a country. The increasing importance of SMs and their direct influence on economy were the main reasons for analysing SM movements. The need to determine early warning indicators for SM crisis has been the focus of study by many economists and politicians. Whilst most research into the identification of these critical indicators applied data mining to uncover hidden knowledge, very few attempted to adopt a text mining approach. This paper demonstrates how text mining combined with Random Forest algorithm can offer a novel approach to the extraction of critical indicators, and classification of related news articles. The findings of this study extend the current classification of critical indicators from three to eight classes; it also show that Random Forest can outperform other classifiers and produce high accuracy.


title: Taking Psychedelics Seriously

abstract: BACKGROUND ::: Psychiatric research in the 1950s and 1960s showed potential for psychedelic medications to markedly alleviate depression and suffering associated with terminal illness. More recent published studies have demonstrated the safety and efficacy of psilocybin, MDMA, and ketamine when administered in a medically supervised and monitored approach. A single or brief series of sessions often results in substantial and sustained improvement among people with treatment-resistant depression and anxiety, including those with serious medical conditions. Need and Clinical Considerations: Palliative care clinicians occasionally encounter patients with emotional, existential, or spiritual suffering, which persists despite optimal existing treatments. Such suffering may rob people of a sense that life is worth living. Data from Oregon show that most terminally people who obtain prescriptions to intentionally end their lives are motivated by non-physical suffering. This paper overviews the history of this class of drugs and their therapeutic potential. Clinical cautions, adverse reactions, and important steps related to safe administration of psychedelics are presented, emphasizing careful patient screening, preparation, setting and supervision. ::: ::: ::: CONCLUSION ::: Even with an expanding evidence base confirming safety and benefits, political, regulatory, and industry issues impose challenges to the legitimate use of psychedelics. The federal expanded access program and right-to-try laws in multiple states provide precendents for giving terminally ill patients access to medications that have not yet earned FDA approval. Given the prevalence of persistent suffering and growing acceptance of physician-hastened death as a medical response, it is time to revisit the legitimate therapeutic use of psychedelics.


title: Parameterized unit tests

abstract: Parameterized unit tests extend the current industry practice of using closed unit tests defined as parameterless methods. Parameterized unit tests separate two concerns: 1) They specify the external behavior of the involved methods for all test arguments. 2) Test cases can be re-obtained as traditional closed unit tests by instantiating the parameterized unit tests. Symbolic execution and constraint solving can be used to automatically choose a minimal set of inputs that exercise a parameterized unit test with respect to possible code paths of the implementation. In addition, parameterized unit tests can be used as symbolic summaries which allows symbolic execution to scale for arbitrary abstraction levels. We have developed a prototype tool which computes test cases from parameterized unit tests. We report on its first use testing parts of the .NET base class library.


title: Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element

abstract: Using two phase-change memory devices per synapse, a three-layer perceptron network with 164 885 synapses is trained on a subset (5000 examples) of the MNIST database of handwritten digits using a backpropagation variant suitable for nonvolatile memory (NVM) + selector crossbar arrays, obtaining a training (generalization) accuracy of 82.2% (82.9%). Using a neural network simulator matched to the experimental demonstrator, extensive tolerancing is performed with respect to NVM variability, yield, and the stochasticity, linearity, and asymmetry of the NVM-conductance response. We show that a bidirectional NVM with a symmetric, linear conductance response of high dynamic range is capable of delivering the same high classification accuracies on this problem as a conventional, software-based implementation of this same network.


title: Explaining Predictions of Non-Linear Classifiers in NLP

abstract: Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional neural network (CNN) trained on a topic categorization task. Our analysis highlights which words are relevant for a specific prediction of the CNN. We compare our technique to standard sensitivity analysis, both qualitatively and quantitatively, using a "word deleting" perturbation experiment, a PCA analysis, and various visualizations. All experiments validate the suitability of LRP for explaining the CNN predictions, which is also in line with results reported in recent image classification studies.


title: A case study of computer gaming for math: Engaged learning from gameplay?

abstract: Employing mixed-method approach, this case study examined the in situ use of educational computer games in a summer math program to facilitate 4th and 5th graders' cognitive math achievement, metacognitive awareness, and positive attitudes toward math learning. The results indicated that students developed more positive attitudes toward math learning through five-week computer math gaming, but there was no significant effect of computer gaming on students' cognitive test performance or metacognitive awareness development. The in-field observation and students' think-aloud protocol informed that not every computer math drill game would engage children in committed learning. The study findings have highlighted the value of situating learning activities within the game story, making games pleasantly challenging, scaffolding reflections, and designing suitable off-computer activities.


title: A Wireless Capsule Endoscope System With Low-Power Controlling and Processing ASIC

abstract: This paper presents the design of a wireless capsule endoscope system. The proposed system is mainly composed of a CMOS image sensor, a RF transceiver and a low-power controlling and processing application specific integrated circuit (ASIC). Several design challenges involving system power reduction, system miniaturization and wireless wake-up method are resolved by employing optimized system architecture, integration of an area and power efficient image compression module, a power management unit (PMU) and a novel wireless wake-up subsystem with zero standby current in the ASIC design. The ASIC has been fabricated in 0.18-mum CMOS technology with a die area of 3.4 mm * 3.3 mm. The digital baseband can work under a power supply down to 0.95 V with a power dissipation of 1.3 mW. The prototype capsule based on the ASIC and a data recorder has been developed. Test result shows that proposed system architecture with local image compression lead to an average of 45% energy reduction for transmitting an image frame.


title: EFFECTS OF TWO TYPES OF ACTIVITY ON THE PERFORMANCE OF SECOND-, THIRD-, AND FOURTH-GRADE STUDENTS ON A TEST OF CONCENTRATION '

abstract: Sz~mmary.-The appropriateness of recess in the elementary program concinues to be questioned although generally it is believed to be useful by elementary principals despite a dearrh of supportive dara. This study was a developmental study of.the effects of physical activity on concentration. Comparison of passive and directed physical educarion activities on che concentration of second-, third-, and fourth-grade children was made. The Woodcock-Johnson Test of Concentration showed better performance by the fourth graders and within Grade 4 in favor of the physical activity group. A structured physical activity or a classroom activity immediately prior to a concentration task was not detrimental to children in Grades 2 and 3. Fourth-grade children performed significantly better on a test of concentration after engaging in a physical activity. Given the ever-increasing demands to assess children in the elementary school, the relative scheduhg of physical education class is a concern of school administrators and teachers. The time of day to schedule physical education to minimize the possible influence on testing or classroom learning has been a concern, but quantitative data are limited, and even theoretical discussion has often not involved the elementary school population. One area in which physical exercise has been viewed as increasingly beneficial and not causing educational interference is in the counsehg literature. According to Scherman (19891, stress interferes with school performance but physical activity reduces stress and enhances positive attitudes. The use of exercise to promote emotional health positively has been reported for children with dsruptive behavior and adults with moderate depression (Karper, 1981). Carlson (1982) wrote of the rnultimodal effect of counsehg and the benefits of physical exercise in enhancing emotional, cognitive, social, imaginative, and behavioral processes of the child. Raviv and Low (1990) gave the d2 Concentration Test (Brickenkamp, 1962), developed to measure concentration under stress induced by completion time criteria. It was administered twice to four groups of junior high school students as a repeated measure-at the beginning and end of each lesson. Two groups participated in a physical education lesson and two groups


title: 3D-MAM: 3D Morphable Appearance Model for Efficient Fine Head Pose Estimation from Still Images

abstract: Identity-invariant estimation of head pose from still images is a challenging task due to the high variability of facial appearance. We present a novel 3D head pose estimation approach, which utilizes the flexibility and expressibility of a dense generative 3D facial model in combination with a very fast fitting algorithm. The efficiency of the head pose estimation is obtained by a 2D synthesis of the facial input image. This optimization procedure drives the appearance and pose of the 3D facial model. In contrast to many other approaches we are specifically interested in the more difficult task of head pose estimation from still images, instead of tracking faces in image sequences. We evaluate our approach on two publicly available databases (FacePix and USF HumanID) and compare our method to the 3D morphable model and other state of the art approaches in terms of accuracy and speed.


title: Synchronous Electric Charge Extraction for low voltage Piezoelectric Energy Harvester array

abstract: This paper introduces a topology to combine multiple low voltage Piezoelectric Energy Harvesters with the goal of widening its harvestable frequency range and improving the power output. The architecture uses a shared inductor scheme and a rectifier-free approach. The proposed harvester is able to handle multiple harvesters efficiently by harvesting the peak energy from each harvester. The proposed topology and its circuit implementation has been validated using SPICE simulation. The circuit consumes about 2.6 µA @ 3V per energy harvester and is capable of harvesting energy from low voltage sources down to 0.4V pk .


title: Fast Feature Selection for Learning to Rank

abstract: An emerging research area named Learning-to-Rank (LtR) has shown that effective solutions to the ranking problem can leverage machine learning techniques applied to a large set of features capturing the relevance of a candidate document for the user query. Large-scale search systems must however answer user queries very fast, and the computation of the features for candidate documents must comply with strict back-end latency constraints. The number of features cannot thus grow beyond a given limit, and Feature Selection (FS) techniques have to be exploited to find a subset of features that both meets latency requirements and leads to high effectiveness of the trained models. In this paper, we propose three new algorithms for FS specifically designed for the LtR context where hundreds of continuous or categorical features can be involved. We present a comprehensive experimental analysis conducted on publicly available LtR datasets and we show that the proposed strategies outperform a well-known state-of-the-art competitor.


title: Ontology-enhanced automatic chief complaint classification for syndromic surveillance

abstract: Emergency department free-text chief complaints (CCs) are a major data source for syndromic surveillance. CCs need to be classified into syndromic categories for subsequent automatic analysis. However, the lack of a standard vocabulary and high-quality encodings of CCs hinder effective classification. This paper presents a new ontology-enhanced automatic CC classification approach. Exploiting semantic relations in a medical ontology, this approach is motivated to address the CC vocabulary variation problem in general and to meet the specific need for a classification approach capable of handling multiple sets of syndromic categories. We report an experimental study comparing our approach with two popular CC classification methods using a real-world dataset. This study indicates that our ontology-enhanced approach performs significantly better than the benchmark methods in terms of sensitivity, F measure, and F2 measure.


title: Scaling archetypal criminals

abstract: The current study introduces an exploratory postdiction scale whose elements are derived from Moffitt’s (2001) developmental taxonomy, specifically her idea of the life-course persistent offender. Using data obtained from a probability sample of 500 adult arrestees in the western United States, this 7-item additive scale postdicts such criminal behavior as violent and property Index arrests, felony convictions, and prison sentences with overall accuracy ranging from 85% to 92%. Unlike other efforts, this scale suggests that most criminals are benign, low-level offenders, a finding anticipated by Moffitt’s theory. Like prior criminological scales, the current measure has difficulty correctly identifying highrate offenders. The merits of using theory to influence scale construction are expressed and suggestions for refining this instrument are offered.


title: Hadoop Performance Tuning - A Pragmatic & Iterative Approach

abstract: Hadoop represents a Java-based distributed computing framework that is designed to support applications that are implemented via the MapReduce programming model. In general, workload dependent Hadoop performance optimization efforts have to focus on 3 major categories: the systems HW, the systems SW, and the configuration and tuning/optimization of the Hadoop infrastructure components. From a systems HW perspective, it is paramount to balance the appropriate HW components in regards to performance, scalability, and cost. It has to be pointed out that Hadoop is classified as a highly-scalable, but not necessarily as a high-performance cluster solution. From a SW perspective, the choice of the OS, the JVM, the specific Hadoop version, as well as other SW components necessary to run the Hadoop setup do have a profound impact on performance and stability of the environment. The design, setup, configuration, and tuning phase of any Hadoop project is paramount to fully benefit from the distributed Hadoop HW and SW solution stack.


title: A Blind Navigation System Using RFID for Indoor Environments

abstract: A location and tracking system becomes very important to our future world of pervasive computing, where information is all around us. Location is one of the most needed information for emerging and future applications. Since the public use of GPS satellite is allowed, several state-of-the-art devices become part of our life, e.g. a car navigator and a mobile phone with a built-in GPS receiver. However, location information for indoor environments is still very limited as mentioned in paper. Several techniques are proposed to get location information in buildings such as using a radio signal triangulation. Using Radio Frequency Identification (RFID) tags is a new way of giving location information to users. Due to its passive communication circuit, RFID tags can be embedded almost anywhere without an energy source. The tags stores location information and give it to any reader that is within a proximity range which can be up to 7-10 centimeters. In this paper RFID-based system for navigation in a building for blind people or visually impaired


title: Solving the 0–1 Knapsack problem using Genetic Algorithm and Rough Set Theory

abstract: This paper describes a hybrid algorithm to solve the 0–1 Knapsack Problem using the Genetic Algorithm combined with Rough Set Theory. The Knapsack problem is a combinatorial optimization problem where one has to maximize the benefit of objects in a knapsack without exceeding its capacity. There are other ways to solve this problem, namely Dynamic Programming and Greedy Method, but they are not very efficient. The complexity of Dynamic approach is of the order of O(n3) whereas the Greedy Method doesn't always converge to an optimum solution[2]. The Genetic Algorithm provides a way to solve the knapsack problem in linear time complexity[2]. The attribute reduction technique which incorporates Rough Set Theory finds the important genes, hence reducing the search space and ensures that the effective information will not be lost. The inclusion of Rough Set Theory in the Genetic Algorithm is able to improve its searching efficiency and quality.


title: A Gentle Introduction to Deep Learning in Medical Image Processing

abstract: Abstract This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep ()learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.


title: Using Deep Learning to Recommend Discussion Threads to Users in an Online Forum

abstract: Using comments made in discussion threads on the social media aggregation website Reddit, the topics of conversation were identified using the probabilistic topic model Latent Dirichlet Allocation (LDA). Employing these topics as features for a neural network, several different neural network frameworks were trained on the data to serve as models to identify which threads a given user would be interested in contributing to based on their previously shown interests. This was done on a sample set of 30 users using 10 different initial random weights for each framework. The ideal model for each user was identified as being the one that scored the highest F2-Score, the harmonic mean of precision and recall with a bias towards recall, on a development set. Testing these ideal models on a test set, they achieved an average F2-Score of 0.825, as well as an average precision of 0.542 and an average recall of 0.956 for a sample set of users.


title: Deep automatic license plate recognition system

abstract: Automatic License Plate Recognition (ALPR) has important applications in traffic surveillance. It is a challenging problem especially in countries like in India where the license plates have varying sizes, number of lines, fonts etc. The difficulty is all the more accentuated in traffic videos as the cameras are placed high and most plates appear skewed. This work aims to address ALPR using Deep CNN methods for real-time traffic videos. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall. These proposals are fed to a CNN classifier for License Plate detection obtaining high precision. We then use a CNN classifier trained for individual characters along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic videos with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition. We also make available the dataset for further research on this topic.


title: Differential diagnosis of pearly penile papules and penile condyloma acuminatum by dermoscopy

abstract: Auteur(s) : Tessin Watanabe, Yuichi Yoshida, Osamu Yamamoto Division of Dermatology, Department of Medicine of Sensory and Motor Organs, Faculty of Medicine, Tottori University, 86 Nishi-cho, Yonago 683-8503, Japan Pearly penile papules (PPP) are physiological changes of the penis and histologically similar to angiofibroma [1, 2]. Condyloma acuminatum (CA) is a sexually transmitted disease by human papilloma virus infection. It is sometimes difficult to distinguish these [...]


title: Analysis of single-phase-to-ground faults at the valve-side of HB-MMCs in bipolar HVDC systems

abstract: Half-bridge (HB) modular multi-level converter (MMC) based bipolar HVDC transmission systems can be employed to transfer bulk power over long distances with high reliability and flexibility. However, this configuration exhibits special fault behaviors that should be taken into account during its design stage. For instance, a single-phase-to-ground fault at the valve-side of the HB-MMCs will lead to overvoltages in the upper arm and high dc components in the fault currents. More importantly, such type of fault currents will bring difficulties for grid side ac circuit breakers to interrupt arcings within the contact parting time. In this paper, such issues are theoretically analyzed in detail. A hybrid MMC using both HB and full-bridge submodules is proposed to solve the aforementioned problems. The proposed solution is verified through simulations performed in PSCAD/EMTDC. The studies in this paper will contribute to the design and protection of MMCs in bipolar HVDC transmission systems for station internal ac grounding faults.


title: Fast Pixelwise Adaptive Visual Tracking of Non-Rigid Objects

abstract: In this paper, we present a new algorithm for real-time single-object tracking in videos in unconstrained environments. The algorithm comprises two different components that are trained "in one shot" at the first video frame: a detector that makes use of the generalized Hough transform with color and gradient descriptors and a probabilistic segmentation method based on global models for foreground and background color distributions. Both components work at pixel level and are used for tracking in a combined way adapting each other in a co-training manner. Moreover, we propose an adaptive shape model as well as a new probabilistic method for updating the scale of the tracker. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging benchmarks, and outperforms the state-of-the-art tracking methods designed for the same task. Finally, a very efficient implementation of the proposed models allows for extremely fast tracking.


title: Intercultural Sensitivity and Cross-cultural Adjustment among Malaysian Students Abroad

abstract: Effective socio-cultural integration has formed Malaysia into a nation with harmonious ethnic diversification and living. Cultural understanding is a foundation to sustain a respectful interethnic relationship among multi-ethnic populations in this country. However, Malaysian may experience cross-cultural difficulties when facing a monoculture or unusual cultural environment overseas. Thus, main purpose of the present study is to examine the extent of intercultural sensitivity support the effectiveness of cross-cultural adjustments among Malaysian students while studying and living abroad. Key finding found in the present study shows the magnitude of intercultural sensitivity is moderately facilitate cross-cultural adjustment among Malaysian students abroad. The implications towards the effectiveness of intercultural relations are also highlighted and discussed. DOI: 10.5901/jesr.2013.v3n7p693


title: Management of resource constrained devices in the internet of things

abstract: The embedded computing devices deployed within the Internet of Things are expected to be resource constrained. This resource constraint not only applies to memory and processing capabilities, but the low-power radio standards utilized further constrain the network interfaces. The IPv6 protocol provides a suitable basis for interoperability in the IoT, due to its large address space and a number of existing protocols that function over IP and its flexibility. We investigate how existing IP-based network management protocols can be implemented on resource-constrained devices. We present the resource requirements for SNMP and NETCONF on an 8-bit AVR based device.


title: Log-Euclidean Kernels for Sparse Representation and Dictionary Learning

abstract: The symmetric positive definite (SPD) matrices have been widely used in image and vision problems. Recently there are growing interests in studying sparse representation (SR) of SPD matrices, motivated by the great success of SR for vector data. Though the space of SPD matrices is well-known to form a Lie group that is a Riemannian manifold, existing work fails to take full advantage of its geometric structure. This paper attempts to tackle this problem by proposing a kernel based method for SR and dictionary learning (DL) of SPD matrices. We disclose that the space of SPD matrices, with the operations of logarithmic multiplication and scalar logarithmic multiplication defined in the Log-Euclidean framework, is a complete inner product space. We can thus develop a broad family of kernels that satisfies Mercer's condition. These kernels characterize the geodesic distance and can be computed efficiently. We also consider the geometric structure in the DL process by updating atom matrices in the Riemannian space instead of in the Euclidean space. The proposed method is evaluated with various vision problems and shows notable performance gains over state-of-the-arts.


title: SQL DOM: compile time checking of dynamic SQL statements

abstract: Most object oriented applications that involve persistent data interact with a relational database. The most common interaction mechanism is a call level interface (CLI) such as ODBC or JDBC. While there are many advantages to using a CLI -- expressive power and performance being two of the most key -- there are also drawbacks. Applications communicate through a CLI by constructing strings that contain SQL statements. These SQL statements are only checked for correctness at runtime, tend to be fragile and are vulnerable to SQL injection attacks. To solve these and other problems, we present the SQL DOM: a set of classes that are strongly-typed to a database schema. Instead of string manipulation, these classes are used to generate SQL statements. We show how to extract the SQL DOM automatically from an existing database schema, demonstrate its applicability to solve the mentioned problems, and evaluate its performance.


title: Semantic Parametric Reshaping of Human Body Models

abstract: We develop a novel approach to generate human body models in a variety of shapes and poses via tuning semantic parameters. Our approach is investigated with datasets of up to 3000 scanned body models which have been placed in point to point correspondence. Correspondence is established by nonrigid deformation of a template mesh. The large dataset allows a local model to be learned robustly, in which individual parts of the human body can be accurately reshaped according to semantic parameters. We evaluate performance on two datasets and find that our model outperforms existing methods.


title: Analysis of Sacrococcygeal Morphology in Koreans Using Computed Tomography

abstract: BACKGROUND ::: The sacrococcygeal morphology of Arabs and Europeans has been studied using computed tomography (CT) or magnetic resonance imaging to determine the cause of coccydynia. Studies have suggested differences in sacrococcygeal morphology among ethnic groups. However, there are no data on the sacrococcygeal anatomy of Koreans. ::: ::: ::: METHODS ::: We conducted a retrospective analysis of 606 pelvic CT scans that were taken at Cheju Halla General Hospital between 2008 and 2014. Fractures of the sacrum or coccyx were excluded. Differences in the sacrococcygeal morphology among age groups stratified by decade of life and between genders were analyzed using sagittal plane pelvic CT scans. The morphological parameters studied were the sacral and coccygeal curved indexes, sacrococcygeal angle, intercoccygeal angle, coccygeal type, coccygeal segmental number, and sacrococcygeal fusion. ::: ::: ::: RESULTS ::: The average sacral and coccygeal curved indexes were 6.15 and 7.41, respectively. The average sacrococcygeal and intercoccygeal angles were 110° and 49°, respectively. Type II coccyx was most common, and the rate of sacrococcygeal fusion was 34%. There was a moderate positive correlation between age and the sacral curved index (r = 0.493, p = 0.000) and a weak negative correlation between age and the coccyx curved index (r = -0.257, p = 0.000). There was a weak negative correlation between age and the intercoccygeal angle (r = -0.187, p = 0.000). The average intercoccygeal angle in males and females was 53.9° and 44.7°, respectively. ::: ::: ::: CONCLUSIONS ::: The sacrum tended to be more curved and the coccyx straighter with age. The coccyx was straighter in females than males. Knowledge of the sacrococcygeal anatomy of Koreans will promote better understanding of anatomical differences among ethnicities and future studies on coccydynia.


AI techniques for the game of Go


title: Information Requirements for National Level Cyber Situational Awareness

abstract: As modern societies become more dependent on IT services, the potential impact both of adversarial cyberattacks and non-adversarial service management mistakes grows. This calls for better cyber situational awareness-decision-makers need to know what is going on. The main focus of this paper is to examine the information elements that need to be collected and included in a common operational picture in order for stakeholders to acquire cyber situational awareness. This problem is addressed through a survey conducted among the participants of a national information assurance exercise conducted in Sweden. Most participants were government officials and employees of commercial companies that operate critical infrastructure. The results give insight into information elements that are perceived as useful, that can be contributed to and required from other organizations, which roles and stakeholders would benefit from certain information, and how the organizations work with creating cyber common operational pictures today. Among findings, it is noteworthy that adversarial behavior is not perceived as interesting, and that the respondents in general focus solely on their own organization.


title: Design of a Simple Gas Knob: An Application of IoT

abstract: Today, the revolting topic in the area of Information Technology is IoT. With IoT, one can convert the daily life real world objects into insightful virtual articles. IoT binds together all real-world objects in a typical framework and in turn provide us the control of these things also it let you know about the condition of things. The IoT means to bind together all the objects of daily life into a typical framework giving us command of items everywhere, as well as getting posted about condition of the objects. By getting help of investigation and ordered study of several academic research papers, corporate white papers and online materials, sufficient stuff and ideas about the current scenario of IoT is analyzed. Also, we have mentioned, how to implement the concept of IoT for checking the status of the LPG Stove whether it is on or off and the outcome is depicted with the help of an Android App. With the help of the proposed tools and techniques the end user could easily check the status of LPG at any place as it is connected to the internet via WiFi module. The proposed model is cost effective, simple that could be easily implemented in the real-time basis. As we know that LPG has become the part and parcel of every kitchen nowadays. But, along with its benefits., it comes with various risks. Nowadays people being multitasking everywhere., they need smart applications for their all basic needs. In case of the LPG stove., sometimes the unwilling ignorance may result in life threatening accidents. This proposed system will help to overcome such accidents up to an extent.


title: Toward Enabling Automated Cognition and Decision-Making in Complex Cyber-Physical Systems

abstract: This article presents a framework for empowering automated cognition and decision making in complex cyber-physical systems (CPS). The key idea is for each cyber-physical component in the system to be able to construct from observations and actions a reduced model of its behavior and respond to external stimuli in the form of a (timed) probabilistic automaton such as a semi-Markov decision process. The reduced order behavioral models of various cyber-physical components can then be integrated into an optimization and decision-making module to determine the best actions for the components under different operating scenarios and cost / payoff functions by solving a bounded-rationality global decision-making problem.1