title: Acoustic emotion recognition: A benchmark comparison of performances

abstract: In the light of the first challenge on emotion recognition from speech we provide the largest-to-date benchmark comparison under equal conditions on nine standard corpora in the field using the two pre-dominant paradigms: modeling on a frame-level by means of Hidden Markov Models and supra-segmental modeling by systematic feature brute-forcing. Investigated corpora are the ABC, AVIC, DES, EMO-DB, eNTERFACE, SAL, SmartKom, SUSAS, and VAM databases. To provide better comparability among sets, we additionally cluster each database's emotions into binary valence and arousal discrimination tasks. In the result large differences are found among corpora that mostly stem from naturalistic emotions and spontaneous speech vs. more prototypical events. Further, supra-segmental modeling proves significantly beneficial on average when several classes are addressed at a time.


title: Diverse Weighted Bipartite b-Matching

abstract: Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation. A practitioner's goal is typically to maximize a matching market's economic efficiency, possibly subject to some fairness requirements that promote equal access to resources. A natural balancing act exists between fairness and efficiency in matching markets, and has been the subject of much research. ::: In this paper, we study a complementary goal---balancing diversity and efficiency---in a generalization of bipartite matching where agents on one side of the market can be matched to sets of agents on the other. Adapting a classical definition of the diversity of a set, we propose a quadratic programming-based approach to solving a supermodular minimization problem that balances diversity and total weight of the solution. We also provide a scalable greedy algorithm with theoretical performance bounds. We then define the price of diversity, a measure of the efficiency loss due to enforcing diversity, and give a worst-case theoretical bound. Finally, we demonstrate the efficacy of our methods on three real-world datasets, and show that the price of diversity is not bad in practice.


2 . 2 um BSI CMOS image sensor with two layer photodetector


title: The Psychological Roots of Anti-Vaccination Attitudes: A 24-Nation Investigation

abstract: Strengthening of antivaccination movements in recent decades has coincided with unprecedented increases in the incidence of some communicable diseases. Many intervention programs work from a deficit model of science communication, presuming that vaccination skeptics lack the ability to access or understand evidence. However, interventions focusing on evidence and the debunking of vaccine-related myths have proven to be either nonproductive or counterproductive. Working from a motivated reasoning perspective, we examine the psychological factors that might motivate people to reject scientific consensus around vaccination. To assist with international generalizability, we examine this question in 24 countries. We sampled 5,323 participants in 24 countries, and measured their antivaccination attitudes. We also measured their belief in conspiracy theories, reactance (the tendency for people to have a low tolerance for impingements on their freedoms), disgust sensitivity toward blood and needles, and individualistic/hierarchical worldviews (i.e., people's beliefs about how much control society should have over individuals, and whether hierarchies are desirable). In order of magnitude, antivaccination attitudes were highest among those who (a) were high in conspiratorial thinking, (b) were high in reactance, (c) reported high levels of disgust toward blood and needles, and (d) had strong individualistic/hierarchical worldviews. In contrast, demographic variables (including education) accounted for nonsignificant or trivial levels of variance. These data help identify the "attitude roots" that may motivate and sustain vaccine skepticism. In so doing, they help shed light on why repetition of evidence can be nonproductive, and suggest communication solutions to that problem. (PsycINFO Database Record


title: Deep Co-Training for Semi-Supervised Image Recognition

abstract: In this paper, we study the problem of semi-supervised image recognition, which is to learn classifiers using both labeled and unlabeled images. We present Deep Co-Training, a deep learning based method inspired by the Co-Training framework. The original Co-Training learns two classifiers on two views which are data from different sources that describe the same instances. To extend this concept to deep learning, Deep Co-Training trains multiple deep neural networks to be the different views and exploits adversarial examples to encourage view difference, in order to prevent the networks from collapsing into each other. As a result, the co-trained networks provide different and complementary information about the data, which is necessary for the Co-Training framework to achieve good results. We test our method on SVHN, CIFAR-10/100 and ImageNet datasets, and our method outperforms the previous state-of-the-art methods by a large margin.


title: BELMiner: adapting a rule-based relation extraction system to extract biological expression language statements from bio-medical literature evidence sentences

abstract: Extracting meaningful relationships with semantic significance from biomedical literature is often a challenging task. BioCreative V track4 challenge for the first time has organized a comprehensive shared task to test the robustness of the text-mining algorithms in extracting semantically meaningful assertions from the evidence statement in biomedical text. In this work, we tested the ability of a rule-based semantic parser to extract Biological Expression Language (BEL) statements from evidence sentences culled out of biomedical literature as part of BioCreative V Track4 challenge. The system achieved an overall best F-measure of 21.29% in extracting the complete BEL statement. For relation extraction, the system achieved an F-measure of 65.13% on test data set. Our system achieved the best performance in five of the six criteria that was adopted for evaluation by the task organizers. Lack of ability to derive semantic inferences, limitation in the rule sets to map the textual extractions to BEL function were some of the reasons for low performance in extracting the complete BEL statement. Post shared task we also evaluated the impact of differential NER components on the ability to extract BEL statements on the test data sets besides making a single change in the rule sets that translate relation extractions into a BEL statement. There is a marked improvement by over 20% in the overall performance of the BELMiner’s capability to extract BEL statement on the test set. The system is available as a REST-API at http://54.146.11.205:8484/BELXtractor/finder/ ::: ::: Database URL: http://54.146.11.205:8484/BELXtractor/finder/


title: Socio-Economic and Legal Impact of Autonomous Robotics and AI Entities: The RAiLE Project

abstract: Singularity, or as some may call it - the specific point in time when advanced technological development, for instance artificial intelligence (AI), leads to the creation of machines that are smarter than human beings - will arrive roughly around the year 2045 according to author, computer scientist, inventor and futurist Ray Kurzweil. [1] The timeline presented by Kurzweil, can be argued, but not the fact that we are dealing with rapid advances in the fields of robotics and AI.


title: Synthesis of N3- and 2-NH2-substituted 6,7-diphenylpterins and their use as intermediates for the preparation of oligonucleotide conjugates designed to target photooxidative damage on single-stranded DNA representing the bcr–abl chimeric gene

abstract: Two 17-mer oligodeoxynucleotide-5′-linked-(6,7-diphenylpterin) conjugates, 2 and 3, were prepared as photosensitisers for targeting photooxidative damage to a 34-mer DNA oligodeoxynucleotide (ODN) fragment 1 representing the chimeric bcr–abl gene that is implicated in the pathogenesis of chronic myeloid leukaemia (CML). The base sequence in the 17-mer was 3′ G G T A G T T A T T C C T T C T T 5′. In the first of these ODN conjugates (2) the pterin was attached at its N3 atom, via a –(CH2)3OPO(OH)– linker, to the 5′-OH group of the ODN. Conjugate 2 was prepared from 2-amino-3-(3-hydroxypropyl)-6,7-diphenyl-4(3H)-pteridinone 10, using phosphoramidite methodology. Starting material 10 was prepared from 5-amino-7-methylthiofurazano[3,4-d]pyrimidine 4via an unusual highly resonance stabilised cation 8, incorporating the rare 2H,6H-pyrimido[6,1-b][1,3]oxazine ring system. In the characterisation of 10 two pteridine phosphazenes, 15 and 29, were obtained, as well as new products containing two uncommon tricyclic ring systems, namely pyrimido[2,1-b]pteridine (20 and 24) and pyrimido[1,2-c]pteridine (27). In the second ODN conjugate 3 the linker was –(CH2)5CONH(CH2)6OPO(OH)– and was attached to the 2-amino group of the pterin. In the preparation of 3, the N-hydroxysuccinimide ester 37 of 2-(5-carboxypentylamino)-6,7-diphenyl-4(3H)-pteridinone 36 was condensed with the hexylamino-modified 17-mer. Excitation of 36 with near UV light in the presence of the single-stranded target 34-mer, 5′T G A C C A T C A A T A A G14 G A A G18 A A G21 C C C T T C A G C G G C C 3′1 caused oxidative damage at guanine bases, leading to alkali-labile sites which were monitored by polyacrylamide gel electrophoresis. Cleavage was observed at all guanine sites with a marked preference for cleavage at G14. In contrast, excitation of ODN–pteridine conjugate 2 in the presence of 1 caused oxidation of the latter predominantly at G18, with a smaller extent of cleavage at G15 and G14 (in the double-stranded portion) and G21. These results contrast with our previous observation of specific cleavage at G21 with ruthenium polypyridyl sensitisers, and suggest that a different mechanism, probably one involving Type 1 photochemical electron transfer, is operative. Much lower yields were found with the ODN–pteridine conjugate 3, perhaps as a consequence of the longer linker between the ODN and the pteridine in this case.


title: 6-DOF Model Based Tracking via Object Coordinate Regression

abstract: This work investigates the problem of 6-Degrees-Of-Freedom (6-DOF) object tracking from RGB-D images, where the object is rigid and a 3D model of the object is known. As in many previous works, we utilize a Particle Filter (PF) framework. In order to have a fast tracker, the key aspect is to design a clever proposal distribution which works reliably even with a small number of particles. To achieve this we build on a recently developed state-of-the-art system for single image 6D pose estimation of known 3D objects, using the concept of so-called 3D object coordinates. The idea is to train a random forest that regresses the 3D object coordinates from the RGB-D image. Our key technical contribution is a two-way procedure to integrate the random forest predictions in the proposal distribution generation. This has many practical advantages, in particular better generalization ability with respect to occlusions, changes in lighting and fast-moving objects. We demonstrate experimentally that we exceed state-of-the-art on a given, public dataset. To raise the bar in terms of fast-moving objects and object occlusions, we also create a new dataset, which will be made publicly available.


title: DIKIW: Data, Information, Knowledge, Intelligence, Wisdom and their Interrelationships

abstract: DIKW hierarchy is the model used for discussion of data, information, knowledge, wisdom and their interrelationships. However, definitions of data, information, and knowledge are entrapped in a logical fallacy known as circular definition. There are some agreements in the definitions without the fallacy. The missing ‘Intelligence’ in DIKW plays a major role between knowledge and wisdom. This gives rise to a revised DIKW model here named DIKIW.


title: Development of an assist controller with robot suit HAL for hemiplegic patients using motion data on the unaffected side

abstract: Among several characteristics seen in gait of hemiplegic patients after stroke, symmetry is known to be an indicator of the degree of impairment of walking ability. This paper proposes a control method for a wearable type lower limb motion assist robot to realize spontaneous symmetric gait for these individuals. This control method stores the motion of the unaffected limb during swing and then provides motion support on the affected limb during the subsequent swing using the stored pattern to realize symmetric gait based on spontaneous limb swing. This method is implemented on the robot suit HAL (Hybrid Assistive Limbs). Clinical tests were conducted in order to assess the feasibility of the control method. Our case study involved participation of one chronic stroke patient who was not able to flex his right knee. As a result, the walking support for hemiplegic leg provided by the HAL improved the subject's gait symmetry. The feasibility study showed promising basis for the future clinical study.


Detecting Signals of Adverse Drug Reactions from Health Consumer Contributed Content in Social Media


title: An Empirical Analysis of Network Externalities in Peer-to-Peer Music-Sharing Networks

abstract: Peer-to-peer (P2P) file sharing networks are an important medium for the distribution of information goods. However, there is little empirical research into the optimal design of these networks under real-world conditions. Early speculation about the behavior of P2P networks has focused on the role that positive network externalities play in improving performance as the network grows. However, negative network externalities also arise in P2P networks because of the consumption of scarce network resources or an increased propensity of users to free ride in larger networks, and the impact of these negative network externalities--while potentially important--has received far less attention.Our research addresses this gap in understanding by measuring the impact of both positive and negative network externalities on the optimal size of P2P networks. Our research uses a unique dataset collected from the six most popular OpenNap P2P networks between December 19, 2000, and April 22, 2001. We find that users contribute additional value to the network at a decreasing rate and impose costs on the network at an increasing rate, while the network increases in size. Our results also suggest that users are less likely to contribute resources to the network as the network size increases. Together, these results suggest that the optimal size of these centralized P2P networks is bounded--At some point the costs that a marginal user imposes on the network will exceed the value they provide to the network. This finding is in contrast to early predictions that larger P2P networks would always provide more value to users than smaller networks. Finally, these results also highlight the importance of considering user incentives--an important determinant of resource sharing in P2P networks--in network design.


title: Organizational Learning and Absorptive Capacity in Managing ERP Implementation Projects

abstract: In this paper we focus on large-scale IS implementation using the lens of absorptive capacity. Our case study concentrates on the double loop learning process that occurs over the implementation phase of ERP which we depict as a series of learning cycles. From this perspective, ERP implementation is best viewed not as a one-time process but rather as a series of implementation and practical use cycles. Our results highlight that the learning process requires the accumulation of knowledge, a long-term perspective, and phases of explorative and exploitative learning that overlap. This learning process requires a) the development of specific organizational capabilities which allow organizational actors to “accept” and assimilate external knowledge, b) the understanding that such capabilities should be developed over time, and c) the capacity to explore and exploit knowledge simultaneously. Suggestions are provided for future field research on absorptive capacity in the realm of the qualitative research.


title: Tools and equipment modelling for interactive assembling operating in a virtual environment

abstract: Tools and equipment (T&E) play an important role in the simulation of virtual assembly, especially in the assembly process simulation and plan. The interactive operation simulation of T&E is also a challenge for interactive assembly operation because of variety in function and complexity in structure and manipulation. Based on detailed analysis of interactive operations, the functional requirement for T&E is given. Then, a unified modelling method for information expression and function realisation of general T&E is represented, and the handling methods of some typical manual, semi-automatic, automatic T&E are discussed. Finally, the rear suspension and the front suspension assembly workplace operation simulation in the RA-750 car assembly line are introduced. The result shows that the modelling and handling methods are applicable in the interactive simulation of various T&E.


title: Signalling and the Long-run Performance of Spanish Initial Public Offerings (IPOs)

abstract: Academic research into firms that have gone public has focused on the study of two anomalies: initial underpricing and long-run underperformance. We analyse Spanish Initial Public Offerings to provide additional evidence on the long-run performance of IPOs and its relationship with initial underpricing. Results reveal the existence of negative long-run abnormal stock returns, in line with the international literature. Long-run performance presents a positive relationship with underpricing and the volume of funds obtained in seasoned offerings, in consonance with the predictions of Allen and Faulhaber (1989) , Welch (1989) and Grinblatt and Hwang (1989) . Copyright Blackwell Publishers Ltd, 2005.


title: Is the DSM-5 Anxious Distress Specifier Interview a Valid Measure of Anxiety in Patients with Generalized Anxiety Disorder: A Comparison to the Hamilton Anxiety Scale

abstract: Abstract DSM-5 includes criteria for an anxious distress specifier for major depressive disorder, and measures have been developed to assess these criteria. The validity of measures of the severity of anxious distress has been established in depressed patients. In the present report from the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project, we examined whether a severity measure of anxious distress was as valid as the Hamilton Anxiety Scale (HAMA) as a measure of the severity of anxiety in patients with generalized anxiety disorder (GAD). Eighty-five patients with GAD were interviewed by trained raters who administered the DSM-5 Anxious Distress Specifier Interview (DADSI), HAMA, and Hamilton Depression Rating Scale (HAMD). The patients completed self-report measures of depression, anxiety, and irritability. The DADSI and HAMA were significantly correlated (r=.52, p


title: Learning From Graph Neighborhoods Using LSTMs

abstract: Many prediction problems can be phrased as inferences over local neighborhoods of graphs. The graph represents the interaction between entities, and the neighborhood of each entity contains information that allows the inferences or predictions. We present an approach for applying machine learning directly to such graph neighborhoods, yielding predicitons for graph nodes on the basis of the structure of their local neighborhood and the features of the nodes in it. Our approach allows predictions to be learned directly from examples, bypassing the step of creating and tuning an inference model or summarizing the neighborhoods via a fixed set of hand-crafted features. The approach is based on a multi-level architecture built from Long Short-Term Memory neural nets (LSTMs); the LSTMs learn how to summarize the neighborhood from data. We demonstrate the effectiveness of the proposed technique on a synthetic example and on real-world data related to crowdsourced grading, Bitcoin transactions, and Wikipedia edit reversions.


title: Improved direct torque control of induction motor with dither injection

abstract: In this paper, a three-level inverter-fed induction motor drive operating under Direct Torque Control (DTC) is presented. A triangular wave is used as dither signal of minute amplitude (for torque hysteresis band and flux hysteresis band respectively) in the error block. This method minimizes flux and torque ripple in a three-level inverter fed induction motor drive while the dynamic performance is not affected. The optimal value of dither frequency and magnitude is found out under free running condition. The proposed technique reduces torque ripple by 60% (peak to peak) compared to the case without dither injection, results in low acoustic noise and increases the switching frequency of the inverter. A laboratory prototype of the drive system has been developed and the simulation and experimental results are reported.


Using Change Context with Statistical Learning for API Code Recommendation


title: ‘Horses for Courses’ in demand forecasting

abstract: Forecasting as a scientific discipline has progressed a lot in the last 40years, with Nobel prizes being awarded for seminal work in the field, most notably to Engle, Granger and Kahneman. Despite these advances, even today we are unable to answer a very simple question, the one that is always the first tabled during discussions with practitioners: “what is the best method for my data?”. In essence, as there are horses for courses, there must also be forecasting methods that are more tailored to some types of data, and, therefore, enable practitioners to make informed method selection when facing new data. The current study attempts to shed light on this direction via identifying the main determinants of forecasting accuracy, through simulations and empirical investigations involving 14 popular forecasting methods (and combinations of them), seven time series features (seasonality, trend, cycle, randomness, number of observations, inter-demand interval and coefficient of variation) and one strategic decision (the forecasting horizon). Our main findings dictate that forecasting accuracy is influenced as follows: (a) for fast-moving data, cycle and randomness have the biggest (negative) effect and the longer the forecasting horizon, the more accuracy decreases; (b) for intermittent data, inter-demand interval has bigger (negative) impact than the coefficient of variation; and (c) for all types of data, increasing the length of a series has a small positive effect.


title: Inexact design: beyond fault-tolerance

abstract: In a new approach to making computers more efficient, called "inexact," "probabilistic," or "approximate" computing, errors are not avoided; they are welcomed. Some call it "living dangerously."


title: Anomaly Detection Using Replicator Neural Networks Trained on Examples of One Class

abstract: Anomaly detection aims to find patterns in data that are significantly different from what is defined as normal. One of the challenges of anomaly detection is the lack of labelled examples, especially for the anomalous classes. We describe a neural network based approach to detect anomalous instances using only examples of the normal class in training. In this work we train the net to build a model of the normal examples, which is then used to predict the class of previously unseen instances based on reconstruction error rate. The input to this network is also the desired output. We have tested the method on six benchmark data sets commonly used in the anomaly detection community. The results demonstrate that the proposed method is promising for anomaly detection. We achieve F-score of more than 90% on 3 data sets and outperform the original work of Hawkins et al. on the Wisconsin breast cancer set.


title: Medical Consequences of Obesity

abstract: Obesity is an epidemic disease that threatens to inundate health care resources by increasing the incidence of diabetes, heart disease, hypertension, and cancer. These effects of obesity result from two factors: the increased mass of adipose tissue and the increased secretion of pathogenetic products from enlarged fat cells. This concept of the pathogenesis of obesity as a disease allows an easy division of disadvantages of obesity into those produced by the mass of fat and those produced by the metabolic effects of fat cells. In the former category are the social disabilities resulting from the stigma associated with obesity, sleep apnea that results in part from increased parapharyngeal fat deposits, and osteoarthritis resulting from the wear and tear on joints from carrying an increased mass of fat. The second category includes the metabolic factors associated with distant effects of products released from enlarged fat cells. The insulin-resistant state that is so common in obesity probably reflects the effects of increased release of fatty acids from fat cells that are then stored in the liver or muscle. When the secretory capacity of the pancreas is overwhelmed by battling insulin resistance, diabetes develops. The strong association of increased fat, especially visceral fat, with diabetes makes this consequence particularly ominous for health care costs. The release of cytokines, particularly IL-6, from the fat cell may stimulate the proinflammatory state that characterizes obesity. The increased secretion of prothrombin activator inhibitor-1 from fat cells may play a role in the procoagulant state of obesity and, along with changes in endothelial function, may be responsible for the increased risk of cardiovascular disease and hypertension. For cancer, the production of estrogens by the enlarged stromal mass plays a role in the risk for breast cancer. Increased cytokine release may play a role in other forms of proliferative growth. The combined effect of these pathogenetic consequences of increased fat stores is an increased risk of shortened life expectancy.


title: Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not

abstract: Abstract The ‘Executive Functions’ (EFs) of inhibitory control, working memory, and cognitive flexibility enable us to think before we act, resist temptations or impulsive reactions, stay focused, reason, problem-solve, flexibly adjust to changed demands or priorities, and see things from new and different perspectives. These skills are critical for success in all life's aspects and are sometimes more predictive than even IQ or socioeconomic status. Understandably, there is great interest in improving EFs. It's now clear they can be improved at any age through training and practice, much as physical exercise hones physical fitness. However, despite claims to the contrary, wide transfer does not seem to occur and ‘mindless’ aerobic exercise does little to improve EFs. Important questions remain: How much can EFs be improved (are benefits only superficial) and how long can benefits be sustained? What are the best methods for improving EFs? What about an approach accounts for its success? Do the answers to these differ by individual characteristics such as age or gender? Since stress, sadness, loneliness, or poor health impair EFs, and the reverse enhances EFs, we predict that besides directly train EFs, the most successful approaches for improving EFs will also address emotional, social, and physical needs.


title: Debiasing crowdsourced quantitative characteristics in local businesses and services

abstract: Information about quantitative characteristics in local businesses and services, such as the number of people waiting in line in a cafe and the number of available fitness machines in a gym, is important for informed decision, crowd management and event detection. In this paper, we investigate the potential of leveraging crowds as sensors to report such quantitative characteristics and investigate how to recover the true quantity values from noisy crowdsourced information. Through experiments, we find that crowd sensors have both bias and variance in quantity sensing, and task difficulties impact the sensing accuracy. Based on these findings, we propose an unsupervised probabilistic model to jointly assess task difficulties, ability of crowd sensors and true quantity values. Our model differs from existing categorical truth finding models as ours is specifically designed to tackle quantitative truth. In addition to devising an efficient model inference algorithm in a batch mode, we also design an even faster online version for handling streaming data. Experimental results in various scenarios demonstrate the effectiveness of our model.


title: Design of Printed Chipless-RFID Tags With QR-Code Appearance Based on Genetic Algorithm

abstract: In this paper, the design of the chipless-Radio Frequency Identification (RFID) tags based on genetic algorithm (GA) optimization techniques is introduced. The GA is applied for the first time to create a family of frequency-domain chipless tags with a quick responselike appearance. The resultant tags have an area of 30 mm $\times $ 30 mm and consist of a grid of small metallic elements arranged arbitrarily over a $60 \times 60$ element array creating a variety of new structures. The frequency signature of the GA-based tags is optimized to fit with a frequency-shift keying-based coding methodology and a capacity of 8 b is achieved. The performance of the resultant tags is experimentally verified. Optimal tag samples are fabricated using silver-ink, low-cost flexible substrates, and by screen printing, which is a mass-compatible production technique. The feasibility of this optimization technique for the design of chipless-RFID tags is corroborated.


title: Venti: A New Approach to Archival Storage

abstract: This paper describes a network storage system, called Venti, intended for archival data. In this system, a unique hash of a block's contents acts as the block identifier for read and write operations. This approach enforces a write-once policy, preventing accidental or malicious destruction of data. In addition, duplicate copies of a block can be coalesced, reducing the consumption of storage and simplifying the implementation of clients. Venti is a building block for constructing a variety of storage applications such as logical backup, physical backup, and snapshot file systems. ::: ::: We have built a prototype of the system and present some preliminary performance results. The system uses magnetic disks as the storage technology, resulting in an access time for archival data that is comparable to non-archival data. The feasibility of the write-once model for storage is demonstrated using data from over a decade's use of two Plan 9 file systems.


title: Design of iMobot, an intelligent reconfigurable mobile robot with novel locomotion

abstract: The design and novel features of a reconfigurable modular robot, called iMobot, with four controllable degrees of freedom is presented in this paper. iMobot, which is designed for search and rescue operations as well as other applications such as research and teaching, has versatile locomotion, including a unique feature of driving as though with wheels and lifting itself into a camera platform. Future work is envisioned for using these modules in clusters to achieve advanced mobility. The accompanying video demonstrates the various locomotion of the modular robot.


title: Disentangled Representation Learning GAN for Pose-Invariant Face Recognition

abstract: The large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator allows DR-GAN to learn a generative and discriminative representation, in addition to image synthesis. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. Third, DR-GAN can take one or multiple images as the input, and generate one unified representation along with an arbitrary number of synthetic images. Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.


title: The Endonasal Lip Lift: Personal Technique

abstract: Given the shortcomings of current methods to shorten the cutaneous upper lip, the senior authors modified the traditional subnasal lip lift with endonasal flaps, performing 311 such lifts over a 15-year period. Ideal surgical candidates are adults with tall philtra, negligible dental show, and no maxillary abnormalities. Benefits of the procedure include improved scar appearance and pout, absence of tension in visible areas, increased dental and vermilion display, and minimal scar migration (ie, loss of lift). Moreover, caudal rhinoplasty procedures are facilitated via the same incision sites. Undercorrection, sill widening, and alar distortion are mostly avoidable (or at least corrigible) complications. Nasal sill disruption, however, remains an inevitable drawback that may be minimized through meticulous technique.


Investigation of Smart Parking Systems and their technologies


title: Malloapeltic acid, a new benzopyran derivative from Mallotus apelta

abstract: A new benzopyran derivative, malloapeltic acid (1), along with seven known compounds, was isolated from the roots of Mallotus apelta. The structure of the new compound 1 was elucidated by spectroscopic data analysis. The known compounds 2-8 were identified by comparison of their spectroscopic data with reported values in the literature. Compound 1 showed strong anti-HIV activity in vitro.


title: Feature selection via sensitivity analysis of SVM probabilistic outputs

abstract: Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ in their choices of the mapping function from standard SVM output to its probabilistic output: FSPP1 uses a simple threshold function while FSPP2 uses a sigmoid function. The second two directly approximate the criterion but differ in the smoothness assumptions of criterion with respect to the features. The performance of these approximations, used in an overall feature-selection scheme, is then evaluated on various artificial problems and real-world problems, including datasets from the recent Neural Information Processing Systems (NIPS) feature selection competition. FSPP1-3 show good performance consistently with FSPP2 being the best overall by a slight margin. The performance of FSPP2 is competitive with some of the best performing feature-selection methods in the literature on the datasets that we have tested. Its associated computations are modest and hence it is suitable as a feature-selection method for SVM applications.


title: On-Body Long-Range Wireless Backscattering Sensing System Using Inkjet-/3-D-Printed Flexible Ambient RF Energy Harvesters Capable of Simultaneous DC and Harmonics Generation

abstract: A novel wearable and flexible energy autonomous on-body sensing network is proposed featuring full operability through energy harvesting from a hand-held 464.5-MHz UHF two-way talk radio. Three different functions are provided utilizing the hand-held two-way talk radio as the only energy source for our proposed system. There are two types of energy harvesters (EHs) proposed for the presented system. The first EH that is mounted on the sensing capable backscattering RFID tags harvests the 464.5-MHz signal energy to drive the tags; the second EH that can be worn on hands harvests the same 464.5-MHz signal to produce both the dc power and the carrier signal. The second EH is more efficient than conventional ambient RF energy harvesting architectures because for the first time, both the dc and the second harmonics generated by the rectifier are utilized to enable two additional functions. The generated second harmonic is used to interrogate backscattering RFID tags for on-body sensing, while the dc power is used to power an RF amplifier in order to enhance the second harmonic to effectively extend the sensing and communication range. For the proof-of-concept demonstration, the measured dc and the second harmonic, 929 MHz, output power from the proposed EH are 17.5 and 1.43 dBm, respectively, while a two-way talk radio is 9 cm away. The measured second harmonic output power is increased to 13 dBm utilizing the harvester-powered RF amplifier, and the reading range of the custom backscattering sensor tag is extended to more than 70 m. Also, the interrogation of multiple sensor tags and the wireless detection of ammonia gas utilizing an inkjet printed flexible ammonia sensor are demonstrated showing the wide range of applications of the proposed approach.


title: The AGI Containment Problem

abstract: There is considerable uncertainty about what properties, capabilities and motivations future AGIs will have. In some plausible scenarios, AGIs may pose security risks arising from accidents and defects. In order to mitigate these risks, prudent early AGI research teams will perform significant testing on their creations before use. Unfortunately, if an AGI has human-level or greater intelligence, testing itself may not be safe; some natural AGI goal systems create emergent incentives for AGIs to tamper with their test environments, make copies of themselves on the internet, or convince developers and operators to do dangerous things. In this paper, we survey the AGI containment problem - the question of how to build a container in which tests can be conducted safely and reliably, even on AGIs with unknown motivations and capabilities that could be dangerous. We identify requirements for AGI containers, available mechanisms, and weaknesses that need to be addressed.


title: A carrier-based PWM with synchronous switching technique for a vienna rectifier

abstract: This paper proposes a carrier-based pulse width modulation (CB-PWM) method with synchronous switching technique for a Vienna rectifier. In this paper, a Vienna rectifier is one of the 3-level converter topologies. It is similar to a 3-level T-type topology the used back-to-back switches. When CB-PWM switching method is used, a Vienna rectifier is operated with six PWM signals. On the other hand, when the back-to-back switches are synchronized, PWM signals can be reduced to three from six. However, the synchronous switching method has a problem that the current distortion around zero-crossing point is worse than one of the conventional CB-PWM switching method. To improve current distortions, this paper proposes a reactive current injection technique. The performance and effectiveness of the proposed synchronous switching method are verified by simulation with a 5-kW Vienna rectifier.


title: Developmental basis for telencephalon expansion in waterfowl: enlargement prior to neurogenesis

abstract: Some altricial and some precocial species of birds have evolved enlarged telencephalons compared with other birds. Previous work has shown that finches and parakeets, two species that hatch in an immature (i.e. altricial) state, enlarged their telencephalon by delaying telencephalic neurogenesis. To determine whether species that hatch in a relatively mature (i.e. precocial) state also enlarged their telencephalon by delaying telencephalic neurogenesis, we examined brain development in geese, ducks, turkeys and chickens, which are all precocial. Whereas the telencephalon occupies less than 55 per cent of the brain in chickens and turkeys, it occupies more than 65 per cent in ducks and geese. To determine how these species differences in adult brain region proportions arise during development, we examined brain maturation (i.e. neurogenesis timing) and estimated telencephalon, tectum and medulla volumes from serial Nissl-stained sections in the four species. We found that incubation time predicts the timing of neurogenesis in all major brain regions and that the telencephalon is proportionally larger in ducks and geese before telencephalic neurogenesis begins. These findings demonstrate that the expansion of the telencephalon in ducks and geese is achieved by altering development prior to neurogenesis onset. Thus, precocial and altricial species evolved different developmental strategies to expand their telencephalon.


title: Adaptive Locks: Combining Transactions and Locks for Efficient Concurrency

abstract: Transactional memory is being advanced as an alternative to traditional lock-based synchronization for concurrent programming. Transactional memory simplifies the programming model and maximizes concurrency. At the same time, transactions can suffer from interference that causes them to often abort, from heavy overheads for memory accesses, and from expressiveness limitations (e.g., for I/O operations). In this paper we propose an adaptive locking technique that dynamically observes whether a critical section would be best executed transactionally or while holding a mutex lock. The critical new elements of our approach include the adaptivity logic and cost-benefit analysis, a low overhead implementation of statistics collection and adaptive locking in a full C compiler, and an exposition of the effects on the programming model. In experiments with both microand macro-benchmarks we found adaptive locks to consistently match or out perform the better of the two component mechanisms (mutexes or transactions). Compared to either mechanism alone, adaptive locks often provide 3-to-10x speedups. Additionally, adaptive locks simplify the programming model by reducing the need for fine-grained locking: with adaptive locks, the programmer can specify coarse-grained locking annotations and often achieve fine-grained locking performance due to the transactional memory mechanisms.


title: Where to find my next passenger

abstract: We present a recommender for taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers' mobility patterns and 2) taxi drivers' pick-up behaviors learned from the GPS trajectories of taxicabs. First, this recommender provides taxi drivers with some locations and the routes to these locations, towards which they are more likely to pick up passengers quickly (during the routes or at these locations) and maximize the profit. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we learn the above knowledge (represented by probabilities) from GPS trajectories of taxis. Then, we feed the knowledge into a probabilistic model which estimates the profit of the candidate locations for a particular driver based on where and when the driver requests for the recommendation. We validate our recommender using historical trajectories generated by over 12,000 taxis during 110 days.


title: Automatic subspace clustering of high dimensional data for data mining applications

abstract: Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehension. It produces identical results irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accurate cluster in large high dimensional datasets.


title: On Detecting Road Regions in a Single UAV Image

abstract: Automatic detection of road regions in aerial images remains a challenging research topic. Most existing approaches work well on the requirement of users to provide some seedlike points/strokes in the road area as the initial location of road regions, or detecting particular roads such as well-paved roads or straight roads. This paper presents a fully automatic approach that can detect generic roads from a single unmanned aerial vehicles (UAV) image. The proposed method consists of two major components: automatic generation of road/nonroad seeds and seeded segmentation of road areas. To know where roads probably are (i.e., road seeds), a distinct road feature is proposed based on the stroke width transformation (SWT) of road image. To the best of our knowledge, it is the first time to introduce SWT as road features, which show the effectiveness on capturing road areas in images in our experiments. Different road features, including the SWT-based geometry information, colors, and width, are then combined to classify road candidates. Based on the candidates, a Gaussian mixture model is built to produce road seeds and background seeds. Finally, starting from these road and background seeds, a convex active contour model segmentation is proposed to extract whole road regions. Experimental results on varieties of UAV images demonstrate the effectiveness of the proposed method. Comparison with existing techniques shows the robustness and accuracy of our method to different roads.


title: A $K$ -Band Portable FMCW Radar With Beamforming Array for Short-Range Localization and Vital-Doppler Targets Discrimination

abstract: This paper presents a printed circuit board realization of a $K$ -band portable frequency-modulated continuous-wave radar transceiver with beamforming array for short-range localization. The transmitter channel of the proposed radar consists of a free-running voltage-controlled oscillator (VCO) and a single patch antenna. A linear frequency-modulated chirp signal is generated by the VCO, which is controlled by an analog “sawtooth” voltage generator. The receiver channel has a four-element linear beamforming array, a six-port circuit, and a baseband circuit. The beam of the array can be continuously steered in a range of ±45° on the $H$ -plane through an array of vector controllers. Each vector controller is capable of simultaneously controlling the phase and the amplitude of the corresponding array element. The design principle of the binary-phase-shift attenuator, the vector controller, and the radar system are discussed. Calibration method of the array is introduced to minimize the errors caused by component variation and fabrication. The radiation patterns of the array with phase-only beam steering and phase-amplitude beamforming are measured and compared, demonstrating the advantage of the beamforming design. System-level experiments showed that the proposed solution is suitable for short-range localization. In addition, experiments with a human subject revealed the capability of the proposed radar system to discriminate a human target from other objects based on the vital-Doppler effect.


title: Bilateral Multi-Perspective Matching for Natural Language Sentences

abstract: Natural language sentence matching is a fundamental technology for a variety of tasks. Previous approaches either match sentences from a single direction or only apply single granular (word-by-word or sentence-by-sentence) matching. In this work, we propose a bilateral multi-perspective matching (BiMPM) model under the"matching-aggregation"framework. Given two sentences $P$ and $Q$, our model first encodes them with a BiLSTM encoder. Next, we match the two encoded sentences in two directions $P \rightarrow Q$ and $P \leftarrow Q$. In each matching direction, each time step of one sentence is matched against all time-steps of the other sentence from multiple perspectives. Then, another BiLSTM layer is utilized to aggregate the matching results into a fix-length matching vector. Finally, based on the matching vector, the decision is made through a fully connected layer. We evaluate our model on three tasks: paraphrase identification, natural language inference and answer sentence selection. Experimental results on standard benchmark datasets show that our model achieves the state-of-the-art performance on all tasks.


title: RUTICO: Recommending Successful Learning Paths Under Time Constraints

abstract: Nowadays using E-learning platforms such as Intelligent Tutoring Systems (ITS) that support users to learn subjects are quite common. Despite the availability and the advantages of these systems, they ignore the learners' time limitation for learning a subject. In this paper we propose RUTICO, that recommends successful learning paths with respect to a learner's knowledge background and under a time constraint. RUTICO, which is an example of Long Term goal Recommender Systems (LTRS), after locating a learner in the course graph, it utilizes a Depth-first search (DFS) algorithm to find all possible paths for a learner given a time restriction. RUTICO also estimates learning time and score for the paths and finally, it recommends a path with the maximum score that satisfies the learner time restriction. In order to evaluate the ability of RUTICO in estimating time and score for paths, we used the Mean Absolute Error and Error. Our results show that we are able to generate a learning path that maximizes a learner's score under a time restriction.


title: Estimating Taxi Demand-Supply Level Using Taxi Trajectory Data Stream

abstract: Taxis provide a flexible and indispensable service to satisfy the urban travel demand of public commuters. Understanding taxi supply and commuter demand, especially the imbalance between the supply and the demand, would directly help to improve the quality of taxi service and eventually increase a city's traffic system efficiency. In this paper, we consider the taxi demand from a region during a period of time to include two parts: satisfied demand, i.e., passengers successfully receive taxi service during this period of time, and unmet demand, i.e., passengers are still waiting for taxi service. To properly estimate the demand-supply level (short for "the level of the taxi demand vs. supply imbalance"), we propose a novel indicator that reflects how fast an available taxi is taken in any given region. Accordingly, we design and implement a taxi analytics system to provide such information in near real time. Finally, we use the passenger waiting time survey data and the taxi streaming data to validate the proposed indicator on the built taxi analytics system.


title: Enhancing Recommendation Diversity Through a Dual Recommendation Interface

abstract: The beyond-relevance objectives of recommender system are drawing more and more attention. For example, a diversity-enhanced interface has been shown to positively associate with overall levels of user satisfaction. However, little is known about how a diversity-enhanced interface can help users to accomplish various real-world tasks. In this paper, we present a visual diversity-enhanced interface that presents recommendations in a two-dimensional scatter plot. Our goal was to design a recommender system interface to explore the different relevance prospects of recommended items in parallel and to stress their diversity. A within-subject user study with real-life tasks was conducted to compare our visual interface to a standard ranked list interface. Our user study results show that the visual interface significantly reduced exploration efforts required for explored tasks. Also, the users' subjective evaluation shows significant improvement on many user-centric metrics. We show that the users explored a diverse set of recommended items while experiencing an improvement in overall user satisfaction.


title: Boolean symmetric searchable encryption

abstract: In this article we tackle the issue of searchable encryption with a generalized query model. Departing from many previous works that focused on queries consisting of a single keyword, we consider the the case of queries consisting of arbitrary boolean expressions on keywords, that is to say conjunctions and disjunctions of keywords and their complement. Our construction of boolean symmetric searchable encryption BSSE is mainly based on the orthogonalization of the keyword field according to the Gram-Schmidt process. Each document stored in an outsourced server is associated with a label which contains all the keywords corresponding to the document, and searches are performed by way of a simple inner product. Furthermore, the queries in the BSSE scheme are randomized. This randomization hides the search pattern of the user since the search results cannot be associated deterministically to queries. We formally define an adaptive security model for the BSSE scheme. In addition, the search complexity is in $O(n)$ where $n$ is the number of documents stored in the outsourced server.


title: FPGA implementation of digital modulation techniques

abstract: This paper presents the review of the different digital modulation techniques and the various methods and tools that are used to implement it on FPGA along with the design summary and logic utilization of the resources. It includes an approach for the implementation of three modulators mainly Amplitude Shift Keying modulator, Phase Shift Keying modulators and Frequency Shift Keying modulator in VHDL by means of Xilinx 13.1 and simulation in Modelsim. The same modulators has also been developed in MATLAB/SIMULINK environment. The various factors that affect the choice of a particular modulation technique have also been discussed and accordingly anyone of the techniques can be adopted taking into consideration the application.


title: Comparing Similarity Measures for Original WSD Lesk Algorithm

abstract: | @ | gmail.com, www.gelbukh.com Abstract. There are many similarity measures to determine the similarity relatedness between two words. Measures of similarity or relatedness are used in such applications as word sense disambiguation. One of the methods used to resolve WSD is the Lesk algorithm. The performance of this algorithm is connected with the similarity relatedness between all words in the text, i.e the success rate of WSD should increase as the similarity measure's performance gets better. This paper presents a comparison of several similarity measures applied to WSD using the original Lesk Algorithm.


title: Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo

abstract: Based on the probabilistic reformulation of principal component analysis (PCA), we consider the problem of determining the number of principal components as a model selection problem. We present a hierarchical model for probabilistic PCA and construct a Bayesian inference method for this model using reversible jump Markov chain Monte Carlo (MCMC). By regarding each principal component as a point in a one-dimensional space and employing only birth-death moves in our reversible jump methodology, our proposed method is simple and capable of automatically determining the number of principal components and estimating the parameters simultaneously under the same disciplined framework. Simulation experiments are performed to demonstrate the effectiveness of our MCMC method.


title: Improving Bayesian Reasoning: The Effects of Phrasing, Visualization, and Spatial Ability

abstract: Decades of research have repeatedly shown that people perform poorly at estimating and understanding conditional probabilities that are inherent in Bayesian reasoning problems. Yet in the medical domain, both physicians and patients make daily, life-critical judgments based on conditional probability. Although there have been a number of attempts to develop more effective ways to facilitate Bayesian reasoning, reports of these findings tend to be inconsistent and sometimes even contradictory. For instance, the reported accuracies for individuals being able to correctly estimate conditional probability range from 6% to 62%. In this work, we show that problem representation can significantly affect accuracies. By controlling the amount of information presented to the user, we demonstrate how text and visualization designs can increase overall accuracies to as high as 77%. Additionally, we found that for users with high spatial ability, our designs can further improve their accuracies to as high as 100%. By and large, our findings provide explanations for the inconsistent reports on accuracy in Bayesian reasoning tasks and show a significant improvement over existing methods. We believe that these findings can have immediate impact on risk communication in health-related fields.


title: Real-time garbage collection for flash-memory storage systems of real-time embedded systems

abstract: Flash-memory technology is becoming critical in building embedded systems applications because of its shock-resistant, power economic, and nonvolatile nature. With the recent technology breakthroughs in both capacity and reliability, flash-memory storage systems are now very popular in many types of embedded systems. However, because flash memory is a write-once and bulk-erase medium, we need a translation layer and a garbage-collection mechanism to provide applications a transparent storage service. In the past work, various techniques were introduced to improve the garbage-collection mechanism. These techniques aimed at both performance and endurance issues, but they all failed in providing applications a guaranteed performance. In this paper, we propose a real-time garbage-collection mechanism, which provides a guaranteed performance, for hard real-time systems. On the other hand, the proposed mechanism supports non-real-time tasks so that the potential bandwidth of the storage system can be fully utilized. A wear-leveling method, which is executed as a non-real-time service, is presented to resolve the endurance problem of flash memory. The capability of the proposed mechanism is demonstrated by a series of experiments over our system prototype.


title: The Evaluation on Flexible Working Hours Implemented in the Urban Areas of Wenzhou

abstract: The inhabitants of Wenzhou have powerful ability to buy private car. Presently, the average holders of private car have been on top in China. The government of Wenzhou pays equal attention to road construction and traffic management to alleviate the pungent contradiction between road construction and traffic demand increase. Flexible working hours was applied in Wenzhou to adjust traffic demand in 2002. Wenzhou is the first city to implement this measure in China. Both evident effect and universal applause have achieved presently.


title: Subglottic stenosis in the first year of life. Characteristics and treatment options

abstract: Subglottic stenosis of congenital origin or acquired within the first 12 months of life are challenging in many aspects. Surgical reconstruction is difficult due to the small anatomic dimensions. Tracheostomy is an additional risk factor attributing to mortality and should be avoided, if possible. In this paper, the most important types of subglottic stenosis in the first year of life are discussed. Conservative, endoscopic, and open surgical treatment options are presented and evaluated. Laryngotracheal reconstruction with autologous thyroid cartilage is the treatment of choice for the majority of significant subglottic stenosis cases in this age group. This technique is comparatively less invasive, versatile, and allows all options for open reconstruction using other techniques in case of recurrent stenosis. Subglottic stenosis in early infancy requires expertise and experience in diagnosis and treatment. Considering the limited incidence, these cases should be managed in a referral center.


CVBed : Structuring CVs using Word Embeddings Shweta Garg


title: Lightweight Mutual Authentication for IoT and Its Applications

abstract: The Internet of Things (IoT) provides transparent and seamless incorporation of heterogeneous and different end systems. It has been widely used in many applications including smart cities such as public water system, power grid, water management, and vehicle traffic control system. In these smart city applications, a large number of IoT devices are deployed that can sense, communicate, compute, and potentially actuate. The uninterrupted and accurate functioning of these devices are critical to smart city applications as crucial decisions will be made based on the data received. One of the challenging tasks is to assure the authenticity of the devices so that we can rely on the decision making process with a very high confidence. One of the characteristics of IoT devices deployed in such applications is that they have limited battery power. A challenge is to design a secure mutual authentication protocol which is affordable to resource constrained devices. In this paper, we propose a lightweight mutual authentication protocol based on a novel public key encryption scheme for smart city applications. The proposed protocol takes a balance between the efficiency and communication cost without sacrificing the security. We evaluate the performance of our protocol in software and hardware environments. On the same security level, our protocol performance is significantly better than existing RSA and ECC based protocols. We also provide security analysis of the proposed encryption scheme and the mutual authentication protocol.


title: A “K Hypotheses + Other ” Belief Updating Model

abstract: Spoken dialog systems typically rely on recognition confidence scores to guard against potential misunderstandings. While confidence scores can provide an initial assessment for the reliability of the information obtained from the user, ideally systems should leverage information that is available in subsequent user responses to update and improve the accuracy of their beliefs. We present a machine-learning based solution for this problem. We use a compressed representation of beliefs that tracks up to k hypotheses for each concept at any given time. We train a generalized linear model to perform the updates. Experimental results show that the proposed approach significantly outperforms heuristic rules used for this task in current systems. Furthermore, a user study with a mixed-initiative spoken dialog system shows that the approach leads to significant gains in task success and in the efficiency of the interaction, across a wide range of recognition error-rates.


title: Screening for transient biological interactions as applied to albumin ligands: a new concept for drug discovery.

abstract: The notion that many biological interactions are based on transient binding (dissociation constants (K(d)) in the range of 10-0.01 mM) is familiar, yet the implications for biological sciences have been realized only recently. An important area of biological sciences is drug design, where the traditional "lock and key" view of binding has prevailed and drug candidates are usually selected on their merits as being tight binders. However, the rationale that transient interactions are of importance for drug discovery is slowly gaining acceptance. These interactions may relate not only to the desired target interaction but also to unwanted interactions creating, for example, toxicity problems. Here we demonstrate, in a high-throughput screening format, affinity selection of weak binders to a model target of albumin by zonal retardation chromatography. It is perceived that this approach can define the "transient drug" as a complement to current drug discovery procedures.


title: The design of eco-feedback technology

abstract: Eco-feedback technology provides feedback on individual or group behaviors with a goal of reducing environmental impact. The history of eco-feedback extends back more than 40 years to the origins of environmental psychology. Despite its stated purpose, few HCI eco-feedback studies have attempted to measure behavior change. This leads to two overarching questions: (1) what can HCI learn from environmental psychology and (2) what role should HCI have in designing and evaluating eco-feedback technology? To help answer these questions, this paper conducts a comparative survey of eco-feedback technology, including 89 papers from environmental psychology and 44 papers from the HCI and UbiComp literature. We also provide an overview of predominant models of proenvironmental behaviors and a summary of key motivation techniques to promote this behavior.


title: Adaptive subspace learning: an iterative approach for document clustering

abstract: The performance of clustering in document space can be influenced by the high dimension of the vectors, because there exists a great deal of redundant information in the high-dimensional vectors, which may make the similarity between vectors inaccurate. Hence, it is very considerable to derive a low-dimensional subspace that contains less redundant information, so that document vectors can be grouped more reasonably. In general, learning a subspace and clustering vectors are treated as two independent steps; in this case, we cannot estimate whether the subspace is appropriate for the method of clustering or vice versa. To overcome this drawback, this paper combines subspace learning and clustering into an iterative procedure named adaptive subspace learning (ASL). Firstly, the intracluster similarity and the intercluster separability of vectors can be increased via the initial cluster indicators in the step of subspace learning, and then affinity propagation is adopted to partition the vectors into a specific number of clusters, so as to update the cluster indicators and repeat subspace learning. In ASL, the obtained subspace can become more suitable for the clustering with the iterative optimization. The proposed method is evaluated using NG20, Classic3 and K1b datasets, and the results are shown to be superior to the conventional methods of document clustering.


title: A Survey on User Modeling in Multi-application Environments

abstract: Sharing heterogeneous data among distributed environments in a user-centric way represents today the main challenge for personalization. In recent years several techniques have been proposed to support user modeling for cross-system personalization, following two main approaches. A first group of researchers base their solutions on the use of a top-down approach, involving standard ontologies or unified user models (standardization-based user modeling); in a second research direction, a bottom-up approach based on mappings between different user model representations is the envisaged solution (mediation-based user modeling). The aim of this paper is to discuss general problems connected to user modeling in multiapplication environments and to provide a short survey on current research in this field. Based on this, we briefly draw out some further research.


title: VTR 7.0: Next Generation Architecture and CAD System for FPGAs

abstract: Exploring architectures for large, modern FPGAs requires sophisticated software that can model and target hypothetical devices. Furthermore, research into new CAD algorithms often requires a complete and open source baseline CAD flow. This article describes recent advances in the open source Verilog-to-Routing (VTR) CAD flow that enable further research in these areas. VTR now supports designs with multiple clocks in both timing analysis and optimization. Hard adder/carry logic can be included in an architecture in various ways and significantly improves the performance of arithmetic circuits. The flow now models energy consumption, an increasingly important concern. The speed and quality of the packing algorithms have been significantly improved. VTR can now generate a netlist of the final post-routed circuit which enables detailed simulation of a design for a variety of purposes. We also release new FPGA architecture files and models that are much closer to modern commercial architectures, enabling more realistic experiments. Finally, we show that while this version of VTR supports new and complex features, it has a 1.5× compile time speed-up for simple architectures and a 6× speed-up for complex architectures compared to the previous release, with no degradation to timing or wire-length quality.


title: Towards tacit knowledge sharing over social web tools

abstract: Purpose – Researchers debate whether tacit knowledge sharing through information technology (IT) is actually possible. However, with the advent of social web tools, it has been argued that most shortcomings of tacit knowledge sharing are likely to disappear. The purpose of this paper is two‐fold: first, to demonstrate the existing debates in the literature regarding tacit knowledge sharing using IT; and second, to identify key research gaps that lay the foundations for future research into tacit knowledge sharing using the social web.Design/methodology/approach – This paper reviews current literature on IT‐mediated tacit knowledge sharing and opens a discussion on tacit knowledge sharing through the use of the social web.Findings – First, the existing schools of thought in regards to IT ability for tacit knowledge sharing are introduced. Next, difficulties of sharing tacit knowledge through the use of IT are discussed. Then, potentials and pitfalls of social web tools are presented. Finally, the paper con...


title: Automatic graph drawing and readability of diagrams

abstract: The state of the art in automatic graph drawing is reviewed, with special attention to the readability of information system diagrams. Existing results in the literature are compared, and a comprehensive algorithmic approach to the problem is proposed. The algorithm presented draws graphs on a grid and is suitable for both undirected graphs and mixed graphs that contain as subgraphs hierarchic structures. Several applications of GIOTTO, a graphic tool that embodies the aforementioned facility, are shown. >


title: Semantic Segmentation with Reverse Attention

abstract: Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation. Traditionally, the convolutional classifiers are taught to learn the representative semantic features of labeled semantic objects. In this work, we propose a reverse attention network (RAN) architecture that trains the network to capture the opposite concept (i.e., what are not associated with a target class) as well. The RAN is a three-branch network that performs the direct, reverse and reverse-attention learning processes simultaneously. Extensive experiments are conducted to show the effectiveness of the RAN in semantic segmentation. Being built upon the DeepLabv2-LargeFOV, the RAN achieves the state-of-the-art mIoU score (48.1%) for the challenging PASCAL-Context dataset. Significant performance improvements are also observed for the PASCAL-VOC, Person-Part, NYUDv2 and ADE20K datasets.


BRAIN TUMOR CLASSIFICATION USING NEURAL NETWORK BASED METHODS


title: Mother-child aspiration technique.

abstract: A 94-year-old woman underwent primary percutaneous coronary intervention for a total occlusion of the right coronary artery. A 6-Fr Ikari-left guiding catheter was inserted through the right radial artery. Initially, thrombectomy was performed with a conventional thrombus aspiration catheter. However, only small fragments of thrombus were aspirated and coronary blood flow was still obstructed. Subsequently, a 4.5-Fr straight guiding catheter was advanced through the 6-Fr guiding catheter into the coronary artery and aspiration was reinitiated. During the course of aspiration, the backflow of the blood was blocked. The inner catheter was pulled back slowly, maintaining suction. Just before it reached the tip of the 6-Fr guiding catheter, another vacuum syringe was attached to the side arm of the Y-connecter of the 6-Fr guiding catheter and additional aspiration with the outer guiding catheter was started. After complete retrieval of the 4.5-Fr catheter from the guiding catheter, thrombus was found in the vacuum syringes from both inner and outer guiding catheters. We consider that this double aspiration, with a mother-child catheter technique, is a simple and effective means of aspirating a larger thrombus, and may prevent the dislodgement of thrombus at the tip of the outer guiding catheter, which could cause systemic embolism.


title: Measuring Photoplethysmogram-Based Stress-Induced Vascular Response Index to Assess Cognitive Load and Stress

abstract: Quantitative assessment for cognitive load and mental stress is very important in optimizing human-computer system designs to improve performance and efficiency. Traditional physiological measures, such as heart rate variation (HRV), blood pressure and electrodermal activity (EDA), are widely used but still have limitations in sensitivity, reliability and usability. In this study, we propose a novel photoplethysmogram-based stress induced vascular index (sVRI) to measure cognitive load and stress. We also provide the basic methodology and detailed algorithm framework. We employed a classic experiment with three levels of task difficulty and three stages of testing period to verify the new measure. Compared with the blood pressure, heart rate and HRV components recorded simultaneously, the sVRI reached the same level of significance on the effect of task difficulty/period as the most significant other measure. Our findings showed sVRI's potential as a sensitive, reliable and usable parameter.


Improving SpikeProp : Enhancements to An Error-Backpropagation Rule for Spiking Neural Networks


title: The building blocks of economic complexity

abstract: For Adam Smith, wealth was related to the division of labor. As people and firms specialize in different activities, economic efficiency increases, suggesting that development is associated with an increase in the number of individual activities and with the complexity that emerges from the interactions between them. Here we develop a view of economic growth and development that gives a central role to the complexity of a country's economy by interpreting trade data as a bipartite network in which countries are connected to the products they export, and show that it is possible to quantify the complexity of a country's economy by characterizing the structure of this network. Furthermore, we show that the measures of complexity we derive are correlated with a country's level of income, and that deviations from this relationship are predictive of future growth. This suggests that countries tend to converge to the level of income dictated by the complexity of their productive structures, indicating that development efforts should focus on generating the conditions that would allow complexity to emerge to generate sustained growth and prosperity.


title: Designing for Deeper Learning in a Blended Computer Science Course for Middle School Students.

abstract: The focus of this research was to create and test an introductory computer science course for middle school. Titled “Foundations for Advancing Computational Thinking” (FACT), the course aims to prepare and motivate middle school learners for future engagement with algorithmic problem solving. FACT was also piloted as a seven-week course on Stanford’s OpenEdX MOOC platform for blended in-class learning. Unique aspects of FACT include balanced pedagogical designs that address the cognitive, interpersonal, and intrapersonal aspects of “deeper learning”; a focus on pedagogical strategies for mediating and assessing for transfer from block-based to text-based programming; curricular materials for remedying misperceptions of computing; and “systems of assessments” (including formative and summative quizzes and tests, directed as well as open-ended programming assignments, and a transfer test) to get a comprehensive picture of students’ deeper computational learning. Empirical investigations, accomplished over t...


Survey of Distributed Stream Processing


title: Role of Plant Growth Promoting Rhizobacteria in Agricultural Sustainability—A Review

abstract: Plant growth promoting rhizobacteria (PGPR) shows an important role in the sustainable agriculture industry. The increasing demand for crop production with a significant reduction of synthetic chemical fertilizers and pesticides use is a big challenge nowadays. The use of PGPR has been proven to be an environmentally sound way of increasing crop yields by facilitating plant growth through either a direct or indirect mechanism. The mechanisms of PGPR include regulating hormonal and nutritional balance, inducing resistance against plant pathogens, and solubilizing nutrients for easy uptake by plants. In addition, PGPR show synergistic and antagonistic interactions with microorganisms within the rhizosphere and beyond in bulk soil, which indirectly boosts plant growth rate. There are many bacteria species that act as PGPR, described in the literature as successful for improving plant growth. However, there is a gap between the mode of action (mechanism) of the PGPR for plant growth and the role of the PGPR as biofertilizer—thus the importance of nano-encapsulation technology in improving the efficacy of PGPR. Hence, this review bridges the gap mentioned and summarizes the mechanism of PGPR as a biofertilizer for agricultural sustainability.


title: Channel estimation and precoder design for millimeter-wave communications: The sparse way

abstract: We propose strategies for mmWave communications that exploit the inherent sparsity of mmWave channels in the angle and delay domains. In particular, we propose the use of aperture shaping to ensure a sparse virtual-domain MIMO channel representation; fast FFT-based modulation and demodulation schemes to expose the virtual-channel coefficients; a pilot design that facilitates fast LASSO-based sparse-channel estimation; and spectrally efficient precoding and decoding, via the Lanczos algorithm and waterfilling over both frequency and angle. Numerical experiments suggest that our approach comes close to achieving the perfect-CSI capacity of the mmWave channel.


title: The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research

abstract: The widespread use of ‘statistical significance’ as a license for making a claim of a scientific finding leads to considerable distortion of the scientific process (according to the American Statistical Association). We review why degrading p -values into ‘significant’ and ‘nonsignificant’ contributes to making studies irreproducible, or to making them seem irreproducible. A major problem is that we tend to take small p -values at face value, but mistrust results with larger p -values. In either case, p -values tell little about reliability of research, because they are hardly replicable even if an alternative hypothesis is true. Also significance ( p ≤ 0.05) is hardly replicable: at a good statistical power of 80%, two studies will be ‘conflicting’, meaning that one is significant and the other is not, in one third of the cases if there is a true effect. A replication can therefore not be interpreted as having failed only because it is nonsignificant. Many apparent replication failures may thus reflect faulty judgment based on significance thresholds rather than a crisis of unreplicable research. Reliable conclusions on replicability and practical importance of a finding can only be drawn using cumulative evidence from multiple independent studies. However, applying significance thresholds makes cumulative knowledge unreliable. One reason is that with anything but ideal statistical power, significant effect sizes will be biased upwards. Interpreting inflated significant results while ignoring nonsignificant results will thus lead to wrong conclusions. But current incentives to hunt for significance lead to selective reporting and to publication bias against nonsignificant findings. Data dredging, p -hacking, and publication bias should be addressed by removing fixed significance thresholds. Consistent with the recommendations of the late Ronald Fisher, p -values should be interpreted as graded measures of the strength of evidence against the null hypothesis. Also larger p -values offer some evidence against the null hypothesis, and they cannot be interpreted as supporting the null hypothesis, falsely concluding that ‘there is no effect’. Information on possible true effect sizes that are compatible with the data must be obtained from the point estimate, e.g., from a sample average, and from the interval estimate, such as a confidence interval. We review how confusion about interpretation of larger p -values can be traced back to historical disputes among the founders of modern statistics. We further discuss potential arguments against removing significance thresholds, for example that decision rules should rather be more stringent, that sample sizes could decrease, or that p -values should better be completely abandoned. We conclude that whatever method of statistical inference we use, dichotomous threshold thinking must give way to non-automated informed judgment.


title: Rigid Body Motion from Range Image Sequences

abstract: An algorithm is described for recovering the six degrees of freedom of motion of a vehicle from a sequence of range images of a static environment taken by a range camera rigidly attached to the vehicle. The technique utilizes a least-squares minimization of the difference between the measured rate of change of elevation at a point and the rate predicted by the so-called elevation rate constmint equation. It is assumed that most of the surface is smooth enough so that local tangent planes can be constructed, and that the motion between frames is smaller than the size of most features in the range image. This method does not depend on the determination of correspondences between isolated high-level features in the range images. The algorithm has been successfully applied to data obtained from the range imager on the Autonomous Land Vehicle (ALV). Other sensors on the ALV provide an initial approximation to the motion between frames. It was found that the outputs of the vehicle sensors themselves are not suitable for accurate motion recovery because of errors in dead reckoning resulting from such problems as wheel slippage. The sensor measurements are used only to approximately register range data. The algorithm described here then recovers the difference between the true motion and that estimated from the sensor outputs. s 1991 Academic Press. Inc.


title: Serum leptin: a marker of prostate cancer irrespective of obesity.

abstract: INTRODUCTION ::: High expression of leptin receptors have been observed in the prostate cancer in various clinical studies; however the association of serum leptin with carcinoma prostate remains unresolved. We studied association, between serum leptin and carcinoma prostate in Asian (Indian) population and its association with obesity. ::: ::: ::: MATERIAL AND METHODS ::: 30 prospective cases of cancer prostate and 30 age matched controls were included in this study. Body mass index (BMI) was estimated and categorized in 4 groups by WHO criteria. Waist hip ratio (WHR) was calculated and divided into three groups. Serum leptin was estimated by sandwich ELISA technique (DRG leptin ELISA kit, Marburg, Germany). ::: ::: ::: RESULTS ::: Both the groups were comparable for age, WHR and BMI. Serum leptin was significantly higher in patients with cancer prostate as compared to controls (median 14.18 ng/ml vs. 1.63 ng/ml; p< 0.001). The level of leptin was found to have positive correlation with BMI and WHR in controls (r=0.485, p=0.007; r=0.314, p=0.091, respectively) however, no correlation was observed in patients with cancer prostate (r=0.071, p=0.711; r=0.067, p=0.725, respectively). There was no correlation between leptin and PSA. The serum leptin level was not related to the Gleason's score and stage of the carcinoma. ::: ::: ::: CONCLUSIONS ::: This study shows that Prostate cancer is associated with raised serum leptin which is independent of obesity and serum PSA. It hints the role of leptin in pathogenesis of this tumor. It may not be a surrogate marker of aggressiveness. For validation, further studies including a large patient population is required.


title: Learning Hierarchical Information Flow with Recurrent Neural Modules

abstract: We propose ThalNet, a deep learning model inspired by neocortical communication via the thalamus. Our model consists of recurrent neural modules that send features through a routing center, endowing the modules with the flexibility to share features over multiple time steps. We show that our model learns to route information hierarchically, processing input data by a chain of modules. We observe common architectures, such as feed forward neural networks and skip connections, emerging as special cases of our architecture, while novel connectivity patterns are learned for the text8 compression task. Our model outperforms standard recurrent neural networks on several sequential benchmarks.


title: Towards instance-dependent label noise-tolerant classification: a probabilistic approach

abstract: Learning from labelled data is becoming more and more challenging due to inherent imperfection of training labels. Existing label noise-tolerant learning machines were primarily designed to tackle class-conditional noise which occurs at random, independently from input instances. However, relatively less attention was given to a more general type of label noise which is influenced by input features. In this paper, we try to address the problem of learning a classifier in the presence of instance-dependent label noise by developing a novel label noise model which is expected to capture the variation of label noise rate within a class. This is accomplished by adopting a probability density function of a mixture of Gaussians to approximate the label flipping probabilities. Experimental results demonstrate the effectiveness of the proposed method over existing approaches.


title: Finding best-fitted rectangle for regions using a bisection method

abstract: In this paper, we have presented a new method for computing the best-fitted rectangle for closed regions using their boundary points. The vertices of the best-fitted rectangle are computed using a bisection method starting with the upper-estimated rectangle and the under-estimated rectangle. The vertices of the upper- and under-estimated rectangles are directly computed using closed-form solutions by solving for pairs of straight lines. Starting with these two rectangles, we solve for the best-fitted rectangle iteratively using a bisection method. The algorithm stops when the areas of the fitted rectangles remain unchanged during consecutive iterations. Extensive evaluation of our algorithm demonstrates its effectiveness.


title: The role played by the concept of presence in validating the efficacy of a cybertherapy treatment: a literature review

abstract: The present paper considers the existing research in cybertherapy, which is a psychological therapy carried out with the use of a mediated environment, and examines the way in which the users’ sense of presence in the mediated environment can be of relevance for the validation of the intervention. With this purpose, a collection of 41 papers reporting the measurement of presence in the context of a cybertherapy treatment has been identified and examined. The general relevance of presence in cybertherapy and the measurement techniques adopted in the studies collected here are described and discussed. The way in which presence corresponds to establishing internal validity, convergent or predictive validity and external validity of a treatment is examined. In conclusion, a checklist to apply when planning a validation study is proposed, to improve the way in which presence is used.


Supporting Ontology-Based Semantic Annotation of Business Processes with Automated Suggestions


title: A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

abstract: In this study, we propose a novel nonlinear ensemble forecasting model integrating generalized linear auto-regression (GLAR) with artificial neural networks (ANN) in order to obtain accurate prediction results and ameliorate forecasting performances. We compare the new model's performance with the two individual forecasting models-GLAR and ANN-as well as with the hybrid model and the linear combination models. Empirical results obtained reveal that the prediction using the nonlinear ensemble model is generally better than those obtained using the other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonlinear ensemble model proposed here can be used as an alternative forecasting tool for exchange rates to achieve greater forecasting accuracy and improve prediction quality further.


title: Sex differences in the implications of partner physical attractiveness for the trajectory of marital satisfaction.

abstract: Do men value physical attractiveness in a mate more than women? Scientists in numerous disciplines believe that they do, but recent research using speed-dating paradigms suggests that males and females are equally influenced by physical attractiveness when choosing potential mates. Nevertheless, the premise of the current work is that sex differences in the importance of physical attractiveness are most likely to emerge in research on long-term relationships. Accordingly, the current work drew from 4 independent, longitudinal studies to examine sex differences in the implications of partner physical attractiveness for trajectories of marital satisfaction. In all 4 studies, both partners’ physical attractiveness was objectively rated at baseline, and both partners reported their marital satisfaction up to 8 times over the first 4 years of marriage. Whereas husbands were more satisfied at the beginning of the marriage and remained more satisfied over the next 4 years to the extent that they had an attractive wife, wives were no more or less satisfied initially or over the next 4 years to the extent that they had an attractive husband. Most importantly, a direct test indicated that partner physical attractiveness played a larger role in predicting husbands’ satisfaction than predicting wives’ satisfaction. These findings strengthen support for the idea that sex differences in self-reported preferences for physical attractiveness do have implications for long-term relationship outcomes.


title: Rapid emergence of quinolone resistance in cirrhotic patients treated with norfloxacin to prevent spontaneous bacterial peritonitis.

abstract: We carried out quantitative culturing of stools from 31 hospitalized alcoholic patients with cirrhosis and ascites, before treatment with 400 mg of norfloxacin per day, weekly for the first month, and then every 2 weeks thereafter for 15 to 229 days (median, 54 days). Members of the family Enterobacteriaceae virtually disappeared from the stools (< 10(2)/g), but treatment had little effect on enterococci. No selection of resistant organisms occurred in 15 patients, but the remaining 16 patients developed fecal organisms resistant to fluoroquinolones between days 14 and 43 of treatment (median, 25 days). Staphylococcus aureus was isolated four times, coagulase-negative Staphylococcus spp. were isolated six times, Citrobacter freundii was isolated four times, Enterobacter cloacae was isolated three times, Klebsiella oxytoca was isolated twice, Proteus rettgeri was isolated once, and untypeable streptococci were isolated six times. Some isolates persisted, while others were transient (one to seven consecutively positive cultures). The MICs of four quinolones (nalidixic acid, norfloxacin, ofloxacin, and ciprofloxacin) were determined by use of experimental microwell strips (ATB CMI; Biomerieux S.A.). All the strains isolated before treatment were susceptible to the four quinolones, with low MICs, whereas those isolated during norfloxacin treatment were highly resistant. Long-term norfloxacin administration thus carries a risk of disturbing the bacterial ecology in these patients, suggesting that digestive decontamination should no longer be prescribed routinely to cirrhotic patients with ascites.


title: A soft exoskeleton for hand assistive and rehabilitation application using pneumatic actuators with variable stiffness

abstract: In this paper, we present the design of a soft wearable exoskeleton that comprises of a glove embedded with pneumatic actuators of variable stiffness for hand assistive and rehabilitation application. The device is lightweight and easily wearable due to the usage of soft pneumatic actuators. A key feature of the device is the variable stiffness of the actuators at different localities that not only conform to the finger profile during actuation, but also provides customizability for different hand dimensions. The actuators can achieve different bending profiles with variable stiffness implemented at different localities. Therefore, the device is able to perform different hand therapy exercises such as full fist, straight fist, hook fist and table top. The device was characterized in terms of its range of motion and maximum force output. Experiments were conducted to examine the differences between active and passive actuation. The results showed that the device could achieve hand grasping and pinching with acceptable range of motion and force.


title: Simulation of Wave Effects Based on Ray Tracing

abstract: A novel wave model is presented in this paper for realistically rendering diffraction and interference effects in ray-based renderers. The model indirectly simulate multibounce interference effects by means of the Wigner distribution function, where the bidirectional reflectance distribution function is constructed to encapsulate the phase variations. The approach is extended to directly compute phase variations in outgoing rays to render diffraction effects using the diffraction grating equation. We also implement an acceleration algorithm based on the current graphic hardware to gain realtime performance of rendering. The proposed model can be easily loaded into any ray-based renderer, and be used for rendering wave effects from surfaces with size of the wavelength.


title: Assessing the understandability of UML statechart diagrams with composite states—A family of empirical studies

abstract: The main goal of this work is to present a family of empirical studies that we have carried out to investigate whether the use of composite states may improve the understandability of UML statechart diagrams derived from class diagrams. Our hypotheses derive from conventional wisdom, which says that hierarchical modeling mechanisms are helpful in mastering the complexity of a software system. In our research, we have carried out three empirical studies, consisting of five experiments in total. The studies differed somewhat as regards the size of the UML statechart models, though their size and the complexity of the models were chosen so that they could be analyzed by the subjects within a limited time period. The studies also differed with respect to the type of subjects (students vs. professionals), the familiarity of the subjects with the domains of the diagrams, and other factors. To integrate the results obtained from each of the five experiments, we performed a meta-analysis study which allowed us to take into account the differences between studies and to obtain the overall effect that the use of composite states has on the understandability of UML statechart diagrams throughout all the experiments. The results obtained are not completely conclusive. They cast doubts on the usefulness of composite states for a better understanding and memorizing of UML statechart diagrams. Composite states seem only to be helpful for acquiring knowledge from the diagrams. At any rate, it should be noted that these results are affected by the previous experience of the subjects on modeling, as well as by the size and complexity of the UML statechart diagrams we used, so care should be taken when generalizing our results.


title: A reinforcement learning scheme for a multi-agent card game

abstract: We formulate an automatic strategy acquisition problem for the multi-agent card game "hearts" as a reinforcement learning (RL) problem. Since there are often a lot of unobservable cards in this game, RL is approximately dealt with in the framework of a partially observable Markov decision process (POMDP). This article presents a POMDP-RL method based on estimation of unobservable state variables and prediction of actions of the opponent agents. Simulation results show our model-based POMDP-RL method is applicable to a realistic multi-agent problem.