title: Real time detection system for rail surface defects based on machine vision

abstract: The detection of rail surface defects is an important part of railway daily inspection, according to the requirements of modern railway automatic detection technology on real-time detection and adaptability. This paper presents a method for real-time detection of rail surface defects based on machine vision. According to the basic principle of machine vision, an image acquisition device equipped with LED auxiliary light source and shading box has been designed and the portable testing model is designed to carry on the field experiment. In view of the real-time requirement, the method of extracting the target area from the original image is carried out without image pre-processing. The surface defects of the rail are optimized based on morphological process and the characteristics of the defects are obtained by tracking the direction chain code. It is demonstrated that the maximum positioning time of this proposed method is 4.65 ms and its maximum positioning failure rate is 5%. The real-time detection speed of this proposed method can reach 2 m/s, which can carry out real-time detection of artificial hand walking. The time of processing each picture is up to 245.61 ms, which ensures the real-time performance of the portable track defect vision inspection system. To a certain extent, the system can replace manual inspection and carry out the digital management of track defects.


Face Recognition Using Active Near-IR Illumination


Synthetic Sub-flow Pairs for Timely and Stable IP Traffic Identification Abstract — Literature on the use of Machine Learning (ML)


Similarity Measure for Social Networks - A Brief Survey


title: Adaptive Input-Power Distribution in Doherty Power Amplifiers for Linearity and Efficiency Enhancement

abstract: A new technique based on an adaptive input-power distribution is introduced to overcome the limitations of practical Doherty power amplifiers. The proposed Doherty amplifier employs an extended-resonance power-divider at its input. By taking advantage of the auxiliary cell's nonlinear input impedance, the extended-resonance divider is designed such that it provides a proper power-dependent power-division between the main and auxiliary cells. Therefore, the two cells are efficiently driven and can generate output current and voltage characteristics similar to the ideal Doherty amplifier, resulting in both linearity and efficiency improvements. The performance of the new Doherty amplifier is compared with a conventional design through simulations and measurements. The proposed Doherty amplifier achieves a measured ACLR improvement of 5-7 dB over a wide range of output power levels, as well as an increased power-added-efficiency of up to 5% for WCDMA signals. The proposed Doherty design does not require complex circuitry and yields a compact circuit.


title: Tracking control for mobile robots with uncertain parameters based on Model Reference Adaptive Control

abstract: The paper presents a Model Reference Adaptive Control algorithm for mobile robots with parameter uncertainties and unstructured dynamics to guarantee that a robot can track to desired trajectory asymptotically and avoid jerky. The controller has been designed in two consecutive parts; one is a nonlinear kinematic controller and the other is the model reference adaptive controller using the feedback signal from the mobile robot's actuator. The simulation results show the efficiency and the application ability of the proposed controller.


title: Robust Ground Plane Detection with Normalized Homography in Monocular Sequences from a Robot Platform

abstract: We present a homography-based approach to detect the ground plane from monocular sequences captured by a robot platform. By assuming that the camera is fixed on the robot platform and can at most rotate horizontally, we derive the constraints that the homograph of the ground plane must satisfy and then use these constraints to design algorithms for detecting the ground plane. Due to the reduced degree of freedom, the resultant algorithm is not only more efficient and robust, but also able to avoid false detection due to virtual planes. We present experiments with real data from a robot platform to validate the proposed approaches.


title: Design of a distributed switched reluctance motor for a tip-driven fan

abstract: This paper presents a design of a distributed switched reluctance motor for an integrated motorfan system. Unlike a conventional compact motor structure, the rotor is distributed into the ends of the impeller blades. This distributed structure of motor makes more space for airflow to pass through so that the system efficiency is highly improved. Simultaneously, the distributed structure gives the motor a higher torque, better efficiency and heat dissipation. The paper first gives an initial design of a switched reluctance motor based on system structure constraints and output equations, then it predicts the machine performance and determines phase current and winding turns based on equivalent magnetic circuit analysis; finally it validates and refines the analytical design with 3D transient finite element analysis. It is found that the analytical performance prediction agrees well with finite element analysis results except for the weakness on core losses estimation. The results of the design shows that the distributed switched reluctance motor can produce a large torque of pretty high efficiency at specified speeds.


title: A 4Mb embedded SLC resistive-RAM macro with 7.2ns read-write random-access time and 160ns MLC-access capability

abstract: Several emerging nonvolatile memories (NVMs) including phase-change RAM (PCRAM) [1–3], MRAM [4–5], and resistive RAM (RRAM) [6–8] have achieved faster operating speeds than embedded Flash. Among those emerging NVMs, RRAM has advantages in faster write time, a larger resistance-ratio (R-ratio), and smaller write power consumption. However, RRAM cells have large cross-die and within-die resistance variations (R-variations) and require low read-mode bitline (BL) bias voltage (V BL-R ) to prevent read disturbance. This work proposes process/resistance variation-insensitive read schemes for embedded RRAM to achieve fast read speeds with high yields. An embedded mega-bit scale (4Mb), single-level-cell (SLC) RRAM macro with sub-8ns read-write random-access time is presented. Multi-level-cell (MLC) operation with 160ns write-ver-ify operation is demonstrated.


title: Behavioral Cloning from Observation

abstract: Humans often learn how to perform tasks via imitation: they observe others perform a task, and then very quickly infer the appropriate actions to take based on their observations. While extending this paradigm to autonomous agents is a well-studied problem in general, there are two particular aspects that have largely been overlooked: (1) that the learning is done from observation only (i.e., without explicit action information), and (2) that the learning is typically done very quickly. In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects. First, we allow the agent to acquire experience in a self-supervised fashion. This experience is used to develop a model which is then utilized to learn a particular task by observing an expert perform that task without the knowledge of the specific actions taken. We experimentally compare BCO to imitation learning methods, including the state-of-the-art, generative adversarial imitation learning (GAIL) technique, and we show comparable task performance in several different simulation domains while exhibiting increased learning speed after expert trajectories become available.


title: Violent Video Games: Specific Effects of Violent Content on Aggressive Thoughts and Behavior

abstract: Publisher Summary This chapter presents three experimental studies, one correlational study, and a meta-analysis tested key hypotheses concerning the short-term and long-term impact of exposure to violent video games. Experiment 1 found that violent video games in general increase the accessibility of aggressive thoughts. Experiments 2 and 3 found that playing violent video games increased aggression, even when arousal and affect were controlled. Experiments 2 and 3 also found that trait hostility and trait aggression were positively related to laboratory aggression. Furthermore, there was correlational evidence of a link between repeated exposure to violent video games and trait aggressiveness. Mediational analyses suggested that the trait effects and the violent video game effects on laboratory aggression were partially mediated by revenge motivation. The correlational study uncovered links among habitual exposure to violent video games, persistent aggressive cognitions, and self-reported aggressive behavior. The meta-analyses revealed significant effects of violent video games on aggressive behavior, affect, and cognition; on cardiovascular arousal; and on prosocial behavior.


title: Extraction Method of Handwritten Digit Recognition Tested on the MNIST Database

abstract: This paper deals with an optical character recognition (OCR) system of handwritten digit, with the use of neural networks (MLP multilayer perceptron). And a method of extraction of characteristics based on the digit form, this method is tested on the MNIST handwritten isolated digit database (60000 images in learning and 10000 images in test). This work has achieved approximately 80% of success rate for MNIST database identification.


title: Issues in Designing a Bitcoin-like Community Currency

abstract: The invention of the Bitcoin protocol has opened the door to new forms of financial interaction. One such form may be to adapt Bitcoin technology for use as a community currency. A community currency is a form of money issued by a non-government entity to serve the economic or social interests of a group of people, often in a small geographic area. We propose a model of a community cryptocurrency that includes a community fund from which community members may take out loans if the community votes to approve them. We consider possible vulnerabilities and mitigations to issues that would affect this community fund, including issues of identity, voting protocols and funds management. We conclude that these vulnerabilities are, in most cases, amenable to technological mitigations that must be adaptable to both community values and changing conditions, emphasizing the need for careful currency design.


title: Learning Interpretable Spatial Operations in a Rich 3D Blocks World

abstract: In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world. We first introduce a new dataset that pairs complex 3D spatial operations to rich natural language descriptions that require complex spatial and pragmatic interpretations such as "mirroring", "twisting", and "balancing". This dataset, built on the simulation environment of Bisk, Yuret, and Marcu (2016), attains language that is significantly richer and more complex, while also doubling the size of the original dataset in the 2D environment with 100 new world configurations and 250,000 tokens. In addition, we propose a new neural architecture that achieves competitive results while automatically discovering an inventory of interpretable spatial operations (Figure 5)


title: Power-Dependence Relations

abstract: A simple theory of power relations is developed in an effort to resolve some of the ambiguities surrounding "power," "authority," "legitimacy," and power "structures," through bringing them together in a coherent scheme. After defining a reciprocal power-dependence relation, attention is focused upon properties of balance and "balancing operations" in such relations. The theory dictates exactly four generic types of balancing process, and discussion of these leads directly into processes of group formation, including the emergence of group norms, role structure and status hierarchy, all presented as the outcome of balancing tendencies in power relations. Within the framework of this theory, authority appears quite naturally to be legitimized power, vested in roles, and "legitimation" is seen as a special case of the coalition process through which norms and role-prescriptions are formed. Finally, through treating both persons and groups as actors in a power-network (two or more connected powerdependence relations) the door is opened for meaningful analysis of complex power structures. Brief reference is made to findings from two experiments pertaining to hypotheses advanced in this theory.


title: Introduction To The Special Issue On Summarization

abstract: generation based on rhetorical structure extraction. In Proceedings of the International Conference on Computational Linguistics, Kyoto, Japan, pages 344–348. Otterbacher, Jahna, Dragomir R. Radev, and Airong Luo. 2002. Revisions that improve cohesion in multi-document summaries: A preliminary study. In ACL Workshop on Text Summarization, Philadelphia. Papineni, K., S. Roukos, T. Ward, and W-J. Zhu. 2001. BLEU: A method for automatic evaluation of machine translation. Research Report RC22176, IBM. Radev, Dragomir, Simone Teufel, Horacio Saggion, Wai Lam, John Blitzer, Arda Celebi, Hong Qi, Elliott Drabek, and Danyu Liu. 2002. Evaluation of text summarization in a cross-lingual information retrieval framework. Technical Report, Center for Language and Speech Processing, Johns Hopkins University, Baltimore, June. Radev, Dragomir R., Hongyan Jing, and Malgorzata Budzikowska. 2000. Centroid-based summarization of multiple documents: Sentence extraction, utility-based evaluation, and user studies. In ANLP/NAACL Workshop on Summarization, Seattle, April. Radev, Dragomir R. and Kathleen R. McKeown. 1998. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 24(3):469–500. Rau, Lisa and Paul Jacobs. 1991. Creating segmented databases from free text for text retrieval. In Proceedings of the 14th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, New York, pages 337–346. Saggion, Horacio and Guy Lapalme. 2002. Generating indicative-informative summaries with SumUM. Computational Linguistics, 28(4), 497–526. Salton, G., A. Singhal, M. Mitra, and C. Buckley. 1997. Automatic text structuring and summarization. Information Processing & Management, 33(2):193–207. Silber, H. Gregory and Kathleen McCoy. 2002. Efficiently computed lexical chains as an intermediate representation for automatic text summarization. Computational Linguistics, 28(4), 487–496. Sparck Jones, Karen. 1999. Automatic summarizing: Factors and directions. In I. Mani and M. T. Maybury, editors, Advances in Automatic Text Summarization. MIT Press, Cambridge, pages 1–13. Strzalkowski, Tomek, Gees Stein, J. Wang, and Bowden Wise. 1999. A robust practical text summarizer. In I. Mani and M. T. Maybury, editors, Advances in Automatic Text Summarization. MIT Press, Cambridge, pages 137–154. Teufel, Simone and Marc Moens. 2002. Summarizing scientific articles: Experiments with relevance and rhetorical status. Computational Linguistics, 28(4), 409–445. White, Michael and Claire Cardie. 2002. Selecting sentences for multidocument summaries using randomized local search. In Proceedings of the Workshop on Automatic Summarization (including DUC 2002), Philadelphia, July. Association for Computational Linguistics, New Brunswick, NJ, pages 9–18. Witbrock, Michael and Vibhu Mittal. 1999. Ultra-summarization: A statistical approach to generating highly condensed non-extractive summaries. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, pages 315–316. Zechner, Klaus. 2002. Automatic summarization of open-domain multiparty dialogues in diverse genres. Computational Linguistics, 28(4), 447–485.


title: The Conversation: Deep Audio-Visual Speech Enhancement

abstract: Our goal is to isolate individual speakers from multi-talker simultaneous speech in videos. Existing works in this area have focussed on trying to separate utterances from known speakers in controlled environments. In this paper, we propose a deep audio-visual speech enhancement network that is able to separate a speaker's voice given lip regions in the corresponding video, by predicting both the magnitude and the phase of the target signal. The method is applicable to speakers unheard and unseen during training, and for unconstrained environments. We demonstrate strong quantitative and qualitative results, isolating extremely challenging real-world examples.


title: Multi-Person Tracking by Multicut and Deep Matching

abstract: In Tang et al. (2015), we proposed a graph-based formulation that links and clusters person hypotheses over time by solving a minimum cost subgraph multicut problem. In this paper, we modify and extend Tang et al. (2015) in three ways: (1) We introduce a novel local pairwise feature based on local appearance matching that is robust to partial occlusion and camera motion. (2) We perform extensive experiments to compare different pairwise potentials and to analyze the robustness of the tracking formulation. (3) We consider a plain multicut problem and remove outlying clusters from its solution. This allows us to employ an efficient primal feasible optimization algorithm that is not applicable to the subgraph multicut problem of Tang et al. (2015). Unlike the branch-and-cut algorithm used there, this efficient algorithm used here is applicable to long videos and many detections. Together with the novel pairwise feature, it eliminates the need for the intermediate tracklet representation of Tang et al. (2015). We demonstrate the effectiveness of our overall approach on the MOT16 benchmark (Milan et al. 2016), achieving state-of-art performance.


Classification of graph metrics


title: On Machine Learning and Structure for Mobile Robots

abstract: Due to recent advances - compute, data, models - the role of learning in autonomous systems has expanded significantly, rendering new applications possible for the first time. While some of the most significant benefits are obtained in the perception modules of the software stack, other aspects continue to rely on known manual procedures based on prior knowledge on geometry, dynamics, kinematics etc. Nonetheless, learning gains relevance in these modules when data collection and curation become easier than manual rule design. Building on this coarse and broad survey of current research, the final sections aim to provide insights into future potentials and challenges as well as the necessity of structure in current practical applications.


title: From schemas to neural networks: A multi-level modelling approach to biologically-inspired autonomous robotic systems

abstract: Biology has been an important source of inspiration in building adaptive autonomous robotic systems. Due to the inherent complexity of these models, most biologically-inspired robotic systems tend to be ethological without linkage to underlying neural circuitry. Yet, neural mechanisms are crucial in modelling adaptation and learning. The work presented in this paper describes a schema and neural network multi-level modelling approach to biologically inspired autonomous robotic systems. A prey acquisition model with detour behaviour in frogs is presented to exemplify the modelling approach. The model is tested with simulated and physical robots using the ASL/NSL and MIRO robotic system.


title: Benchmark graphs for testing community detection algorithms

abstract: Community structure is one of the most important features of real networks and reveals the internal organization of the nodes. Many algorithms have been proposed but the crucial issue of testing, i.e., the question of how good an algorithm is, with respect to others, is still open. Standard tests include the analysis of simple artificial graphs with a built-in community structure, that the algorithm has to recover. However, the special graphs adopted in actual tests have a structure that does not reflect the real properties of nodes and communities found in real networks. Here we introduce a class of benchmark graphs, that account for the heterogeneity in the distributions of node degrees and of community sizes. We use this benchmark to test two popular methods of community detection, modularity optimization, and Potts model clustering. The results show that the benchmark poses a much more severe test to algorithms than standard benchmarks, revealing limits that may not be apparent at a first analysis.


title: TASK SCHEDULING OF AGV IN FMS USING NON-TRADITIONAL OPTIMIZATION TECHNIQUES

abstract: Flexible Manufacturing System (FMS), which is equipped with several CNC machines and Automated Guided Vehicle (AGV) based material handling system is designed and implemented to gain the flexibility and efficiency of production. After the implementation of FMS, in practice, the scheduling of the resources, such as frequent variation in the parts, tools, AGV routings, becomes a complex task. This is being done traditionally using various mathematical programming techniques. In recent years, random search algorithms have been attempted for scheduling. Most of the research has been emphasized only on single objective optimization. Multi objective problems in scheduling with conflicting objectives are more complex and combinatorial in nature and hardly have a unique solution. This paper addresses multi objective task scheduling of AGV in a flexible manufacturing environment using nontraditional optimization algorithms. In this paper the authors made an attempt to find the nearoptimum schedule for two AGVs based on the balanced workload and the minimum traveling time for maximum utilization. The proposed methods are exemplified with illustrations. (Received in March 2009, accepted in September 2009. This paper was with the authors 3 months for 1 revision.)


title: Synthesis of a molecularly imprinted polymer for the selective recognition of carmoisine (Azorubin E122) from pomegranate juice.

abstract: Since natural pigments are lost during the processing of beverages such as pomegranate juice, carmoisine, as an adulterant, is often added into the pure juice to improve color characteristics. In this study, molecularly imprinted polymers, as an adsorbent of carmoisine, were synthesized using acrylamide, methacrylic acid, and 4-vinylpyridine as functional monomers and then they were evaluated in terms of the separation and detection of carmoisine. Experiments on the batch adsorption of carmoisine 10 ppm stock solution revealed a better binding capacity for the 4-vinylpyridine-based polymer in comparison to methacrylic acid and acrylamide polymers. The complexation of carmoisine with the 4-vinylpyridine-based polymer was confirmed by Fourier transform infrared spectroscopy. The synthesized polymer exerted a high thermal degradation point and average diameter of polymer particles were obtained to be 0.2 μm by dynamic light scattering analysis. This work showed that detection of pomegranate juice adulteration with carmoisine is not necessarily difficult, time consuming or expensive with selective separation techniques such as molecularly imprinted polymers.


title: On the Min-cost Traveling Salesman Problem with Drone

abstract: Previously used exclusively in military domain, unmanned aerial vehicles (drones) have stepped up to become a part of new logistic method in commercial sector called"last-mile delivery". In this novel approach, small unmanned aerial vehicles (UAV), also known as drones, are deployed alongside with trucks to deliver goods to customers in order to improve the service quality and reduce the transportation cost. It gives rise to a new variant of the traveling salesman problem (TSP), called TSP with drone (TSP-D). A variant of the problem which aims to minimize the time at which the last customer is serviced (or to maximize the service quality in other words) was studied in the literature. This article considers a new variant of the TSP-D where the objective is to minimize the total transportation cost. The problem is first formulated mathematically and two algorithms are proposed for the solution. The first algorithm (TSP-LS) inspired from the idea of Murray and Chu in which an optimal TSP solution is converted to a feasible TSP-D solution by local searches. The second one, a Greedy Randomized Adaptive Search Procedure (GRASP), is based on a new split procedure which optimally splits any TSP tour into a TSP-D solution. Once a TSP-D solution is generated, it is then improved through local search operators. Numerical results obtained on various instances with different sizes and characteristics are presented.


title: Trading off prediction accuracy and power consumption for context-aware wearable computing

abstract: Context-aware mobile computing requires wearable sensors to acquire information about the user. Continuous sensing rapidly depletes the -wearable system's energy, which is a critically constrained resource. In this paper, we analyze the trade-off between power consumption and prediction accuracy of context classifiers working on dual-axis accelerometer data collected from the eWaich sensing and notification platform. We improve power consumption techniques by providing competitive classification performance even in the low frequency region of 1-10 Hz and for the highly erratic wrist based sensing location. Furthermore, we propose and analyze a collection of selective sampling strategies in order to reduce the number of required sensor readings and the computation cycles even further. Our results indicate that optimized sampling schemes can increase the deployment lifetime of a wearable computing platform by a factor of four without a significant loss in prediction accuracy.


title: Hierarchical Adaptive Structural SVM for Domain Adaptation

abstract: A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains. Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM; Xu et al., IEEE Trans Pattern Anal Mach Intell 36(12):2367---2380, 2014a), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM). As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable part-based model (DPM; Felzenszwalb et al., IEEE Trans Pattern Anal Mach Intell 32(9):1627---1645, 2010) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition.


title: SoftLearn: A Process Mining Platform for the Discovery of Learning Paths

abstract: One of the most challenging issues in learning analytics is the development of techniques and tools that facilitate the evaluation of the learning activities carried out by learners. In this paper, we faced this issue through a process mining-based platform, called Soft Learn, that is able to discover complete, precise and simple learning paths from event logs. This platform has a graphical interface that allows teachers to better understand the real learning paths undertaken by learners.


title: POS-Tagger For English-Vietnamese Bilingual Corpus

abstract: Corpus-based Natural Language Processing (NLP) tasks for such popular languages as English, French, etc. have been well studied with satisfactory achievements. In contrast, corpus-based NLP tasks for unpopular languages (e.g. Vietnamese) are at a deadlock due to absence of annotated training data for these languages. Furthermore, hand-annotation of even reasonably well-determined features such as part-of-speech (POS) tags has proved to be labor intensive and costly. In this paper, we suggest a solution to partially overcome the annotated resource shortage in Vietnamese by building a POS-tagger for an automatically word-aligned English-Vietnamese parallel Corpus (named EVC). This POS-tagger made use of the Transformation-Based Learning (or TBL) method to bootstrap the POS-annotation results of the English POS-tagger by exploiting the POS-information of the corresponding Vietnamese words via their word-alignments in EVC. Then, we directly project POS-annotations from English side to Vietnamese via available word alignments. This POS-annotated Vietnamese corpus will be manually corrected to become an annotated training data for Vietnamese NLP tasks such as POS-tagger, Phrase-Chunker, Parser, Word-Sense Disambiguator, etc.


title: Accuracy and Security Evaluation of Multi-Factor Biometric Authentication

abstract: This paper aims to evaluate the security and accuracy of Multi-Factor Biometric Authentication (MFBA) schemes that are based on applying UserBased Transformations (UBTs) on biometric features. Typically, UBTs employ transformation keys generated from passwords/PINs or retrieved from tokens. In this paper, we not only highlight the importance of simulating the scenario of compromised transformation keys rigorously, but also show that there has been misevaluation of this scenario as the results can be easily misinterpreted. In particular, we expose the falsehood of the widely reported claim in the literature that in the case of stolen keys, authentication accuracy drops but remains close to the authentication accuracy of biometric only system. We show that MFBA systems setup to operate at zero (%) Equal Error Rates (EER) can be undermined in the event of keys being compromised where the False Acceptance Rate reaches unacceptable levels. We demonstrate that for commonly used recognition schemes the FAR could be as high as 21%, 56%, and 66% for iris, fingerprint, and face biometrics respectively when using stolen transformation keys compared to near zero (%) EER when keys are assumed secure. We also discuss the trade off between improving accuracy of biometric systems using additional authentication factor(s) and compromising the security when the additional factor(s) are compromised. Finally, we propose mechanisms to enhance the security as well as the accuracy of MFBA schemes.


title: Configuration of an X-band FMCW radar targeted for drone detection

abstract: This paper discusses the detection of drone with Frequency-Modulated Continuous-Wave (FMCW) Radar system. The system employs 11GHz to 11.15GHz frequency bandwidth to generate sawtooth linear wave through bistatic antennas with the gain of 33.7dBi, and the output transmitter power is 35dBm. Metal baffles were placed between and each side of the antennas to minimize the direct path interference. The system also includes digital down converter to obtain IQ signal with minimum mismatch. In the actual performance measurement, the drone signal was detected up to more than 500m distance in real-time with the error of less than 0.1%. The data has been saved as bin file and then later processed with MATLAB.


title: BOES: Building Occupancy Estimation System using sparse ambient vibration monitoring

abstract: In this paper, we present a room-level building occupancy estimation system (BOES) utilizing low-resolution ::: vibration sensors that are sparsely distributed. Many ubiquitous computing and building maintenance systems ::: require fine-grained occupancy knowledge to enable occupant centric services and optimize space and energy ::: utilization. The sensing infrastructure support for current occupancy estimation systems often requires multiple ::: intrusive sensors per room, resulting in systems that are both costly to deploy and difficult to maintain. To ::: address these shortcomings, we developed BOES. BOES utilizes sparse vibration sensors to track occupancy ::: levels and activities. Our system has three major components. 1) It extracts features that distinguish occupant ::: activities from noise prone ambient vibrations and detects human footsteps. 2) Using a sequence of footsteps, the ::: system localizes and tracks individuals by observing changes in the sequences. It uses this tracking information to ::: identify when an occupant leaves or enters a room. 3) The entering and leaving room information are combined ::: with detected individual location information to update the room-level occupancy state of the building. Through ::: validation experiments in two different buildings, our system was able to achieve 99.55% accuracy for event ::: detection, less than three feet average error for localization, and 85% accuracy in occupancy counting.


Enabling Self-Service BI on Document Stores


title: Sleep Complaints Affecting School Performance at Different Educational Levels

abstract: The clear association between reports of sleep disturbance and poor school performance has been documented for sleepy adolescents. This study extends that research to students outside the adolescent age grouping in an associated school setting (98 middle school students, 67 high school students, and 64 college students). Reported restless legs and periodic limb movements are significantly associated with lower GPA's in junior high students. Consistent with previous studies, daytime sleepiness was the sleep variable most likely to negatively affects high school students. Sleep onset and maintenance insomnia were the reported sleep variables significantly correlated with poorer school performance in college students. This study indicates that different sleep disorder variables negatively affect performance at different age and educational levels.


title: 6.5 A 64×64-pixel digital silicon photomultiplier direct ToF sensor with 100Mphotons/s/pixel background rejection and imaging/altimeter mode with 0.14% precision up to 6km for spacecraft navigation and landing

abstract: Recent technology surveys identified flash light detection and ranging technology as the best choice for the navigation and landing of spacecrafts in extraplanetary missions, working from single-point altimeter to range-imaging camera mode. Among all available technologies for a 2D array of direct time-of-flight (DTOF) pixels, CMOS single-photon avalanche diodes (SPADs) represent the ideal candidate due to their rugged design and electronics integration. However, state-of-the-art SPAD imagers are not designed for operation over a wide variety of scenarios, including variable background light, very long to short range, or fast relative movement.


title: A 1.8-GHz CMOS Power Amplifier Using a Dual-Primary Transformer With Improved Efficiency in the Low Power Region

abstract: A 1.8-GHz CMOS power amplifier for a polar transmitter is implemented with a 0.18- RF CMOS process. The matching components, including the input and output transformers, were integrated. A dual-primary transformer is proposed in order to increase the efficiency in the low power region of the amplifier. The loss induced by the matching network for the low-output power region is minimized using the dual-primary transformer. The amplifier achieved a power-added efficiency of 40.7% at a maximum output power of 31.6 dBm. The dynamic range was 34 dB for a supply voltage that ranged from 0.5 to 3.3 V. The low power efficiency was 32% at the output power of 16 dBm.


title: A structured argumentation framework for detaching conditional obligations

abstract: We present a general formal argumentation system for dealing with the detachment of conditional obligations. Given a set of facts, constraints, and conditional obligations, we answer the question whether an unconditional obligation is detachable by considering reasons for and against its detachment. For the evaluation of arguments in favor of detaching obligations we use a Dung-style argumentation-theoretical semantics. We illustrate the modularity of the general framework by considering some extensions, and we compare the framework to some related approaches from the literature.


title: “When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

abstract: Background: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. Objectives: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid–related tweets. Methods: Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-scores. One-tailed t test was used to determine if differences in F-scores were statistically significant. Results: In multiclass source classification, the use of expanded URLs did not contribute to significant improvement in classifier performance (0.7972 vs 0.8102 for SVM, P=.19). In binary classification, the identification of all source categories improved significantly when unshortened URLs were used, with personal communication tweets benefiting the most (0.8736 vs 0.8200, P<.001). In multiclass sentiment classification Approach 1, SVM (0.6723) performed similarly to NB (0.6683) and LR (0.6703). In Approach 2, SVM (0.7062) did not differ from NB (0.6980, P=.13) or LR (F=0.6931, P=.05), but it was over 40% more accurate than VADER (F=0.5030, P<.001). In multiclass task, improvements in sentiment classification (Approach 2 vs Approach 1) did not reach statistical significance (eg, SVM: 0.7062 vs 0.6723, P=.052). In binary sentiment classification (positive vs negative), Approach 2 (focus on personal communication tweets only) improved classification results, compared with Approach 1, for LR (0.8752 vs 0.8516, P=.04) and SVM (0.8800 vs 0.8557, P=.045). Conclusions: The study provides an example of the use of supervised machine learning methods to categorize cannabis- and synthetic cannabinoid–related tweets with fairly high accuracy. Use of these content analysis tools along with geographic identification capabilities developed by the eDrugTrends platform will provide powerful methods for tracking regional changes in user opinions related to cannabis and synthetic cannabinoids use over time and across different regions. [JMIR Public Health Surveill 2016;2(2):e162]


title: Comparative Evaluation of Three-Phase AC–AC Matrix Converter and Voltage DC-Link Back-to-Back Converter Systems

abstract: This paper introduces the methodology and the results of a comprehensive comparison of a direct matrix converter (MC), an indirect MC, and a voltage dc-link back-to-back converter for a 15-kW permanent magnet synchronous motor drive. The comparison involves the investigation of the passive components, including the EMI input filter, the required silicon chip area for a defined maximum admissible thermal loading of the power semiconductors, the total losses and/or achievable efficiency, a prediction of the resulting volume and weight of the passive components, and, finally, a tradeoff study between the efficiency, volume, and weight of the converters. Different performance indicators that ultimately allow a systematic determination of the application area of each converter topology are provided with this comparative evaluation.


title: Speed Limitation Based on an Advanced Curve Warning System

abstract: This paper presents an integrated system to prevent driver from taking a curve with a dangerous speed. The integrated system underlines three functions developed as follows : the first function consists in knowing the geometry characteristics of the coming road; the second function has to compute a safe speed according to the road section, the vehicle and the driver; the last function must impose the speed control of the vehicle in order to achieve a safe speed in the case that the driver does not achieve it.


title: Space-Time Completion of Video

abstract: This paper presents a new framework for the completion of missing information based on local structures. It poses the task of completion as a global optimization problem with a well-defined objective function and derives a new algorithm to optimize it. Missing values are constrained to form coherent structures with respect to reference examples. We apply this method to space-time completion of large space-time "holes" in video sequences of complex dynamic scenes. The missing portions are filled in by sampling spatio-temporal patches from the available parts of the video, while enforcing global spatio-temporal consistency between all patches in and around the hole. The consistent completion of static scene parts simultaneously with dynamic behaviors leads to realistic looking video sequences and images. Space-time video completion is useful for a variety of tasks, including, but not limited to: 1) sophisticated video removal (of undesired static or dynamic objects) by completing the appropriate static or dynamic background information. 2) Correction of missing/corrupted video frames in old movies. 3) Modifying a visual story by replacing unwanted elements. 4) Creation of video textures by extending smaller ones. 5) Creation of complete field-of-view stabilized video. 6) As images are one-frame videos, we apply the method to this special case as well


title: A new approach for sparse Bayesian channel estimation in SCMA uplink systems

abstract: The rapid growth of traffic and number of simultaneously available devices leads to the new challenges in constructing fifth generation wireless networks (5G). To handle with them various schemes of non-orthogonal multiple access (NOMA) were proposed. One of these schemes is Sparse Code Multiple Access (SCMA), which is shown to achieve better link level performance. In order to support SCMA signal decoding channel estimation is needed and sparse Bayesian learning framework may be used to reduce the requirement of pilot overhead. In this paper we propose a modification of sparse Bayesian learning based channel estimation algorithm that is shown to achieve better accuracy of user detection and faster convergence in numerical simulations.


title: Enterprise architectural framework for supply‐chain integration

abstract: The concept of supply chain is about managing coordinated information and material flows, plant operations, and logistics. It provides flexibility and agility in responding to consumer demand shifts without cost overlays in resource utilization. The fundamental premise of this philosophy is; synchronization among multiple autonomous business entities represented in it. That is, improved coordination within and between various supply‐chain members. Increased coordination can lead to reduction in lead times and costs, alignment of interdependent decision‐making processes, and improvement in the overall performance of each member as well as the supply chain. Describes architecture to create the appropriate structure, install proper controls, and implement principles of optimization to synchronize the supply chain. A supply‐chain model based on a collaborative system approach is illustrated utilizing the example of the textile industry.


title: Superpixel segmentation using Linear Spectral Clustering

abstract: We present in this paper a superpixel segmentation algorithm called Linear Spectral Clustering (LSC), which produces compact and uniform superpixels with low computational costs. Basically, a normalized cuts formulation of the superpixel segmentation is adopted based on a similarity metric that measures the color similarity and space proximity between image pixels. However, instead of using the traditional eigen-based algorithm, we approximate the similarity metric using a kernel function leading to an explicitly mapping of pixel values and coordinates into a high dimensional feature space. We revisit the conclusion that by appropriately weighting each point in this feature space, the objective functions of weighted K-means and normalized cuts share the same optimum point. As such, it is possible to optimize the cost function of normalized cuts by iteratively applying simple K-means clustering in the proposed feature space. LSC is of linear computational complexity and high memory efficiency and is able to preserve global properties of images. Experimental results show that LSC performs equally well or better than state of the art superpixel segmentation algorithms in terms of several commonly used evaluation metrics in image segmentation.


title: VSA-II: a novel prototype of variable stiffness actuator for safe and performing robots interacting with humans

abstract: This paper presents design and performance of a novel joint based actuator for a robot run by variable stiffness actuation, meant for systems physically interacting with humans. This new actuator prototype (VSA-II) is developed as an improvement over our previously developed one reported in [9], where an optimal mechanical-control co-design principle established in [7] is followed as well. While the first version was built in a way to demonstrate effectiveness of variable impedance actuation (VIA), it had limitations in torque capacities, life cycle and implementability in a real robot. VSA-II overcomes the problem of implementability with higher capacities and robustness in design for longer life. The paper discusses design and stiffness behaviour of VSA-II in theory and experiments. A comparison of stiffness characteristics between the two actuator is discussed, highlighting the advantages of the new design. A simple, but effective PD scheme is employed to independently control joint-stiffness and joint-position of a 1-link arm. Finally, results from performed impact tests of 1- link arm are reported, showing the effectiveness of stiffness variation in controlling value of a safety metric.


SimiHash: Hash-based Similarity Detection


title: Between ontology and folksonomy: a study of collaborative and implicit ontology evolution

abstract: We present our first user study of CRAFT, a semantic prototype for collaborative investigation and analysis, which allows users to extend the system's ontology to capture new concepts as they conduct their work. We devised a paradigm in which multiple series of ontologies evolve in different trajectories from the same initial point. We analyze the ontology evolution quantitatively with several metrics, and user behavior qualitatively through interviews and observation. Based on our study, we propose a set of design suggestions for semantic applications with collaborative and implicit ontology development.


title: Data envelopment analysis: theory and applications

abstract: The common weights approach is one of the most prominent methods to further prioritize the subset of DEA efficient units. This approach can be modeled as a multiobjective problem, where one seeks for a common set of weights that locates the efficiency ratio of each unit as close as possible to the target score of 1. In such a setting, different metrics can be applied to measure the distance of the efficiency ratios from target, such as the L1, L2 and L∞. When L1 and L2 metrics are used the models derived are non-linear. In case of the L∞ metric, the problem can be heuristically solved by the bisection method and a series of linear programs. We investigate in this paper the ability of genetic algorithms to solve the problem for estimating efficiency scores, by using an evolutionary optimization method based on a variant of the Nondominated Sorted Genetic Algorithm.


title: Personalized tourist route generation

abstract: When tourists are at a destination, they typically search for information in the Local Tourist Organizations. There, the staff determines the profile of the tourists and their restrictions. Combining this information with their up-to-date knowledge about the local attractions and public transportation, they suggest a personalized route for the tourist agenda. Finally, they fine tune up this route to better fit tourists' needs. We present an intelligent routing system to fulfil the same task. We divide this process in three steps: recommendation, route generation and route customization. We focus on the last two steps and analyze them. We model the tourist planning problem, integrating public transportation, as the Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) and we present an heuristic able to solve it on real-time. Finally, we show the prototype which generates and customizes routes in real-time.


title: Predicting Student Grade based on Free-style Comments using Word2Vec and ANN by Considering Prediction Results Obtained in Consecutive Lessons

abstract: Continuously tracking students during a whole semester plays a vital role to enable a teacher to grasp their learning situation, attitude and motivation. It also helps to give correct assessment and useful feedback to them. To this end, we ask students to write their comments just after each lesson, because student comments re ect their learning attitude towards the lesson, understanding of course contents, and di culties of learning. In this paper, we propose a new method to predict nal student grades. The method employs Word2Vec and Arti cial Neural Network (ANN) to predict student grade in each lesson based on their comments freely written just after the lesson. In addition, we apply a window function to the predicted results obtained in consecutive lessons to keep track of each student's learning situation. The experiment results show that the prediction correct rate reached 80% by considering the predicted student grades from six consecutive lessons, and a nal rate became 94% from all 15 lessons. The results illustrate that our proposed method continuously tracked student learning situation and improved prediction performance of nal student grades as the lessons go by.


title: When Humanoid Robots Become Human-Like Interaction Partners: Corepresentation of Robotic Actions

abstract: In human‐human interactions, corepresenting a partner’s actions is crucial to successfully adjust and coordinate actions with others. Current research suggests that action corepresentation is restricted to interactions between human agents facilitating social interaction with conspecifics. In this study, we investigated whether action corepresentation, as measured by the social Simon effect (SSE), is present when we share a task with a real humanoid robot. Further, we tested whether the believed humanness of the robot’s functional principle modulates the extent to which robotic actions are corepresented. We described the robot to participants either as functioning in a biologically inspired human-like way or in a purely deterministic machine-like manner. The SSE was present in the human-like but not in the machine-like robot condition. These findings suggest that humans corepresent the actions of nonbiological robotic agents when they start to attribute human-like cognitive processes to the robot. Our findings provide novel evidence for top-down modulation effects on action corepresentation in human‐robot interaction situations.


title: Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild

abstract: This paper integrates techniques in natural language processing and computer vision to improve recognition and description of entities and activities in real-world videos. We propose a strategy for generating textual descriptions of videos by using a factor graph to combine visual detections with language statistics. We use state-of-the-art visual recognition systems to obtain confidences on entities, activities, and scenes present in the video. Our factor graph model combines these detection confidences with probabilistic knowledge mined from text corpora to estimate the most likely subject, verb, object, and place. Results on YouTube videos show that our approach improves both the joint detection of these latent, diverse sentence components and the detection of some individual components when compared to using the vision system alone, as well as over a previous n-gram language-modeling approach. The joint detection allows us to automatically generate more accurate, richer sentential descriptions of videos with a wide array of possible content.


APPLYING DATA MINING TECHNIQUES ON ACADAMIC INSTITUTIONAL SYSTEM USING WEKA S


title: A Directed Acyclic Graph Approach to Online Log Parsing

abstract: Logs are widely used in modern software system management because they are often the only data accessible that record system events at runtime. In recent years, because of the ever-increasing log size, data mining techniques are often utilized to help developers and operators conduct system reliability management. A typical log-based system reliability management procedure is to first parse log messages because of their unstructured format; and apply data mining techniques on the parsed logs to obtain critical system behavior information. Most of existing research studies focus on offline log parsing, which need to parse logs in batch mode. However, software systems, especially distributed systems, require online monitoring and maintenance. Thus, a log parser that can parse log messages in a streaming manner is highly in demand. To address this problem, we propose an online log parsing method, namely Drain, based on directed acyclic graph, which encodes specially designed rules for parsing. Drain can automatically generate a directed acyclic graph for a new system and update the graph according to the incoming log messages. Besides, Drain frees developers from the burden of parameter tuning by allowing them use Drain with no pre-defined parameters. To evaluate the performance of Drain, we collect 11 log datasets generated by real-world systems, ranging from distributed systems, Web applications, supercomputers, operating systems, to standalone software. The experimental results show that Drain has the highest accuracy on all 11 datasets. Moreover, Drain obtains 37.15\%$\sim$ 97.14\% improvement in the running time over the state-of-the-art online parsers. We also conduct a case study on a log-based anomaly detection task using Drain in the parsing step, which determines its effectiveness in system reliability management.


title: Bayesian indoor positioning systems

abstract: In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model eliminates the requirement for training data as compared with existing approaches, thereby introducing the notion of a fully adaptive zero profiling approach to location estimation.


Recent works on Parallel Sentence Extraction from Comparable Corpora


title: Linear Array CCD Image Sub-pixel Edge Detection Based on Wavelet Transform

abstract: In order to increase accuracy of the linear array CCD edge detection system, a wavelet-based sub-pixel edge detection method is proposed, the basic process is like this: firstly, according to the step gradient features, automatically calculate the pixel-level border of the CCD image. Then use the wavelet transform algorithm to devide the image’s edge location in sub-pixel level, thus detecting the sub-pixel edge. In this way we prove that the method has no principle error and at the same time possesses a good anti-noise performance. Experiments show that under the circumstance of no special requirements, the accuracy of the method is greater than 0.02 pixel, thus verifying the correctness of the theory.


title: Induction of lucid dreams: A systematic review of evidence

abstract: In lucid dreams the dreamer is aware of dreaming and often able to influence the ongoing dream content. Lucid dreaming is a learnable skill and a variety of techniques is suggested for lucid dreaming induction. This systematic review evaluated the evidence for the effectiveness of induction techniques. A comprehensive literature search was carried out in biomedical databases and specific resources. Thirty-five studies were included in the analysis (11 sleep laboratory and 24 field studies), of which 26 employed cognitive techniques, 11 external stimulation and one drug application. The methodological quality of the included studies was relatively low. None of the induction techniques were verified to induce lucid dreams reliably and consistently, although some of them look promising. On the basis of the reviewed studies, a taxonomy of lucid dream induction methods is presented. Several methodological issues are discussed and further directions for future studies are proposed.


title: Towards compliant and wearable robotic orthoses: A review of current and emerging actuator technologies

abstract: Abstract Robotic orthoses, or exoskeletons, have the potential to provide effective rehabilitation while overcoming the availability and cost constraints of therapists. However, current orthosis actuation systems use components designed for industrial applications, not specifically for interacting with humans. This can limit orthoses' capabilities and, if their users' needs are not adequately considered, contribute to their abandonment. Here, a user centered review is presented on: requirements for orthosis actuators; the electric, hydraulic, and pneumatic actuators currently used in orthoses and their advantages and limitations; the potential of new actuator technologies, including smart materials, to actuate orthoses; and the future of orthosis actuator research.


title: Poster: A VLC Solution for Smart Parking

abstract: With the rapid growth of vehicle ownership, parking has become an issue, especially in metropolitan areas -- the extra time for check-ins, check-outs and finding available parking spaces not only causes frustration and potential road rage on the driver side, but also increases the traffic congestion, gasoline waste and air pollution in consequence. In order to address these problems, the concept of "smart parking" is put forward. To make a parking lot "smart", we argue that three basic features, namely Vehicle Identification, Parking Space Detection and Indoor Localization are are critical and should be supported by the infrastructure. Herein, we present LightPark, a Visible Light Communication (VLC) solution to realize the vision of "smart parking". Building on top of the visible light backscatter communication primitive, LightPark is able to leverage the lighting infrastructure to perform scalable visible light communication and networking with the batter-free tag devices instrumented on the vehicles and parking spaces to manage the critical information such as identification and real-time location of vehicles, and status of parking spaces in a centralized and low-cost manner.


title: Pension reform and capital market development

abstract: Capital market development in Latin America has been driven largely by regulations imposed by the government on the pensions industry and other financial institutions, the state-sponsored modernization of the capital market infrastructure, and tax and bankruptcy reform. Unlike in some OECD countries, where pension funds have been an independent driving force behind important financial innovations, the role of pension funds in the development of capital markets in Latin American countries is largely determined by government instructions that touch every aspect of their operations, from the amount of contributions that the industry receives to the investment of pension assets The main beneficiary of this regulatory framework has been the government debt market. The illiquidity of pension investments, coupled with the conservatism in investment strategies has brought much needed stability to these markets. However, much of this stability is artificial, driven at least in part by portfolio rules that force pension funds to hold mainly domestic assets and in some countries oblige them to invest a minimum percentage of their assets in government bonds.


title: CSIsnoop: Attacker Inference of Channel State Information in Multi-User WLANs

abstract: Channel State Information (CSI) has been proposed to enhance physical layer security between a transmitter and a receiver because it decorrelates over half wavelength distances in rich scattering environments. Consequently, CSI was employed to generate passwords, to authenticate the source of packets, and to inject artificial noise to thwart eavesdroppers. However, in this paper, we present CSIsnoop, and show that an attacker can infer CSI in a multi-user WLAN, even if both channel sounding sequences from the access point and CSI measurement feedback from the clients are encrypted. The insights of CSIsnoop are that the CSI of clients can be computed based on transmit beamforming weights at the access point, and that the transmit beamforming weights can be estimated from downlink multi-user transmission. We implement CSIsnoop on a software defined radio and conduct experiments in various indoor environments. Our results show that on average CSIsnoop can infer CSI of the target client with an absolute normalized correlation of over 0.99, thereby urging reconsideration of the use of CSI as a tool to enhance physical layer security in multi-user WLANs.


title: Evaluation of real-time physics simulation systems

abstract: We present a qualitative evaluation of a number of free publicly available physics engines for simulation systems and game development. A brief overview of the aspects of a physics engine is presented accompanied by a comparison of the capabilities of each physics engine. Aspects that are investigated the accuracy and computational efficiency of the integrator properties, material properties, stacks, links, and collision detection system.


title: Relationship of Physical Activity to Fundamental Movement Skills Among Adolescents

abstract: OKELY, A. D., M. L. BOOTH, and J. W. PATTERSON. Relationship of physical activity to fundamental movement skills among adolescents. Med. Sci. Sports Exerc., Vol. 33, No. 11, 2001, pp. 1899–1904. Purpose:To determine the relationship of participation in organized and nonorganized physical activity wi


title: Miniature soft hand with curling rubber pneumatic actuators

abstract: In medical and biotechnology fields, soft devices are required because of their high safety from low mechanical impedance. FMA (Flexible Microactuator) is one of the typical soft actuators. It consists of fiber-reinforced rubber structure with multi air chambers and realizes bending motion pneumatically. It has been applied to robot hands, robot legs and so on. High potential of FMA has been confirmed by many experiments reported in several papers. However in fabrication process of the actuator, it is difficult to embed the reinforced fiber in the rubber structure. In this study, we aim at development of a fiber less FMA realizing quite large motion, which can be said curling motion, and a soft hand using the actuators. We design the actuator without fiber using nonlinear FEM (Finite Element Method) and derived efficient shape. The actuator is fabricated through micro rubber casting process including micro machining process for molds, micro vacuum rubber molding process and rubber bonding process with surface improvement by excimer light. Basic driving experiments of the actuator showed that it realized curling motion which agreed well with FEM results. And the actuator could grasp a fish egg without breaking. Additionally, we made a soft hand consisting of three curling actuators. This hand also could be manufactured by simple casting process. The developed hand works opening and closing motions well.


title: User Centred Rapid Application Development

abstract: This paper describes our experiences modifying the Rapid Application Development methodology for rapid system development to design a data gathering system for mobile fieldworkers using handheld computers in harsh environmental conditions. In our development process, we integrated User-Centred Design as an explicit stage in the Rapid Application Development (RAD) software engineering methodology. We describe our design process in detail and present a case study of its use in the development of a working system. Finally, we use the design of the working system to highlight some of the lessons learned, and provide guidelines for the design of software systems for mobile data collection. In pursuing this project, we worked with field ecologists monitoring the evolution of coastal wetlands in the San Francisco Bay Area. The overall goal of the ecology project was to provide accurate information on the impact development has on these wetland areas. While the architecture of our system is tuned to the specific needs of the ecologists with whom we worked, the design process and the lessons we learned during design are of interest to other software engineers designing for similar work practices.


title: Competitive Pricing for Spectrum Sharing in Cognitive Radio Networks: Dynamic Game, Inefficiency of Nash Equilibrium, and Collusion

abstract: We address the problem of spectrum pricing in a cognitive radio network where multiple primary service providers compete with each other to offer spectrum access opportunities to the secondary users. By using an equilibrium pricing scheme, each of the primary service providers aims to maximize its profit under quality of service (QoS) constraint for primary users. We formulate this situation as an oligopoly market consisting of a few firms and a consumer. The QoS degradation of the primary services is considered as the cost in offering spectrum access to the secondary users. For the secondary users, we adopt a utility function to obtain the demand function. With a Bertrand game model, we analyze the impacts of several system parameters such as spectrum substitutability and channel quality on the Nash equilibrium (i.e., equilibrium pricing adopted by the primary services). We present distributed algorithms to obtain the solution for this dynamic game. The stability of the proposed dynamic game algorithms in terms of convergence to the Nash equilibrium is studied. However, the Nash equilibrium is not efficient in the sense that the total profit of the primary service providers is not maximized. An optimal solution to gain the highest total profit can be obtained. A collusion can be established among the primary services so that they gain higher profit than that for the Nash equilibrium. However, since one or more of the primary service providers may deviate from the optimal solution, a punishment mechanism may be applied to the deviating primary service provider. A repeated game among primary service providers is formulated to show that the collusion can be maintained if all of the primary service providers are aware of this punishment mechanism, and therefore, properly weight their profits to be obtained in the future.


Data mining workflow templates for intelligent discovery assistance and auto-experimentation


Antidiabetic activity of the aqueous extract of Chlorophytum borivilianum L. in streptozotocin induced-hyperglycemic rats - a preliminary study.


title: Learning to Skim Text

abstract: Recurrent Neural Networks are showing much promise in many sub-areas of natural language processing, ranging from document classification to machine translation to automatic question answering. Despite their promise, many recurrent models have to read the whole text word by word, making it slow to handle long documents. For example, it is difficult to use a recurrent network to read a book and answer questions about it. In this paper, we present an approach of reading text while skipping irrelevant information if needed. The underlying model is a recurrent network that learns how far to jump after reading a few words of the input text. We employ a standard policy gradient method to train the model to make discrete jumping decisions. In our benchmarks on four different tasks, including number prediction, sentiment analysis, news article classification and automatic Q\&A, our proposed model, a modified LSTM with jumping, is up to 6 times faster than the standard sequential LSTM, while maintaining the same or even better accuracy.


title: Design of Broadband Dual-Band Dipole for Base Station Antenna

abstract: A novel dual-band unidirectional antenna composed of an irregular shorted patch and planar dipole elements is presented and studied. This proposed antenna employs an L-shaped feeding strip for exciting dual-band operations. For the lower frequency band, a V-slot is loaded on the planar dipole for acting as a capacitive loading for having wide impedance matching of the antenna. Then, two additional smaller planar dipoles are placed and connected to the irregular short patch for achieving another wideband performance at the higher frequency band. The proposed antenna has wide dual-band impedance bandwidths of 34% and 49.5% in the lower and the upper bands, ranged from 0.78 GHz to 1.1 GHz and from 1.58 GHz to 2.62 GHz, respectively. More importantly, the antenna has stable gains of 7 dBi and 8 dBi across each band, demonstrating the high stability of the radiation characteristic for the two radiation modes. The radiation patterns are symmetric, low back-lobe radiation and low cross-polarization. Analysis and parametric studies of the proposed antenna are provided.


title: Challenges of Social Business Process Management

abstract: Social BPM is the practice of actively involving all relevant stakeholders into a BPM endeavor through the use of social software and its underlying principles. This allows enhancing the correctness, completeness, acceptance, and usefulness of process models by leveraging the domain as well as method knowledge of an extensive community of actors. However, the large number and variety of contributors and contributions also gives rise to a number of challenges, which are examined in this paper.


AS SCALING THREATENS TO ERODE RELIABILITY STANDARDS, LIFETIME RELIABILITY MUST BECOME A FIRST-CLASS DESIGN CONSTRAINT. MICROARCHITECTURAL INTERVENTION OFFERS A NOVEL WAY TO MANAGE LIFETIME RELIABILITY WITHOUT SIGNIFICANTLY SACRIFICING COST AND


title: Predicting Mobile-Captured Document Images Sharpness Quality

abstract: Nowadays the number of mobile applications is fast growing. Among them, mobile applications based on Optical Character Recognition (OCR) play an important role. One of the main challenge of such applications to overcome is that the image acquisition procedure is in a manner unreliable and may contain many distortions. As a consequence, a suitable OCR output requires efforts to enhance the quality of the captured image. This leads to an increase of computation time and cost. In this perspective, we focus on the prediction of image's sharpness quality. We choose to concentrate on image's sharpness quality because blur distortions seriously alter readability for both human and computer. Our contribution consists of a method combining focus and sharpness measures with a Support Vector Machine to classify image's sharpness quality. This approach is fast, reliable, and can be easily implemented on mobile devices. Experimental results show that our method is efficient for OCR based mobile-captured document images.


title: Tourniquet pain during spinal anesthesia: a comparison of plain solutions of tetracaine and bupivacaine.

abstract: The incidence of tourniquet pain was evaluated in 40 patients having orthopedic surgery of the lower extremities during spinal anesthesia using 15 mg of a plain solution of either 0.5% tetracaine or 0.5% bupivacaine. The drugs were administered in a randomized fashion, and measurement of the levels of sensory anesthesia to pinprick and motor blockade as well as the occurrence of tourniquet pain were made by an independent blinded observer. The onset and maximum cephalad spread of sensory anesthesia and the onset and degree of motor block were similar in both groups of patients. However, the duration of sensory anesthesia was significantly longer in patients in whom tetracaine was used. The incidence of tourniquet pain was significantly greater in patients given tetracaine (60%) than in patients given bupivacaine (25%). The occurrence of tourniquet pain was not related to the level of sensory anesthesia, because patients in the tetracaine group had a higher level of sensory anesthesia (mean T6) than did patients in the bupivacaine group (mean T10) at the onset of tourniquet pain. It is speculated that during spinal anesthesia both A and C fibers (mediating fast and slow pain, respectively) are initially equally inhibited. However, as the concentration of local anesthetic in the cerebrospinal fluid declines, C fibers may become unblocked earlier with tetracaine than A fibers, resulting in tourniquet pain in the presence of an otherwise satisfactory spinal anesthetic.


title: Compressive Sequential Learning for Action Similarity Labeling

abstract: Human action recognition in videos has been extensively studied in recent years due to its wide range of applications. Instead of classifying video sequences into a number of action categories, in this paper, we focus on a particular problem of action similarity labeling (ASLAN), which aims at verifying whether a pair of videos contain the same type of action or not. To address this challenge, a novel approach called compressive sequential learning (CSL) is proposed by leveraging the compressive sensing theory and sequential learning. We first project data points to a low-dimensional space by effectively exploring an important property in compressive sensing: the restricted isometry property. In particular, a very sparse measurement matrix is adopted to reduce the dimensionality efficiently. We then learn an ensemble classifier for measuring similarities between pairwise videos by iteratively minimizing its empirical risk with the AdaBoost strategy on the training set. Unlike conventional AdaBoost, the weak learner for each iteration is not explicitly defined and its parameters are learned through greedy optimization. Furthermore, an alternative of CSL named compressive sequential encoding is developed as an encoding technique and followed by a linear classifier to address the similarity-labeling problem. Our method has been systematically evaluated on four action data sets: ASLAN, KTH, HMDB51, and Hollywood2, and the results show the effectiveness and superiority of our method for ASLAN.


Rush Hour is PSPACE-complete


title: Different measurement metrics to evaluate a chatbot system

abstract: A chatbot is a software system, which can interact or "chat" with a human user in natural language such as English. For the annual Loebner Prize contest, rival chatbots have been assessed in terms of ability to fool a judge in a restricted chat session. We are investigating methods to train and adapt a chatbot to a specific user's language use or application, via a user-supplied training corpus. We advocate open-ended trials by real users, such as an example Afrikaans chatbot for Afrikaans-speaking researchers and students in South Africa. This is evaluated in terms of "glass box" dialogue efficiency metrics, and "black box" dialogue quality metrics and user satisfaction feedback. The other examples presented in this paper are the Qur'an and the FAQchat prototypes. Our general conclusion is that evaluation should be adapted to the application and to user needs.


title: Learning Grammars for Architecture-Specific Facade Parsing

abstract: Parsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four different datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework.


title: Classification with Hybrid Generative/Discriminative Models

abstract: Although discriminatively trained classifiers are usually more accurate when labeled training data is abundant, previous work has shown that when training data is limited, generative classifiers can out-perform them. This paper describes a hybrid model in which a high-dimensional subset of the parameters are trained to maximize generative likelihood, and another, small, subset of parameters are discriminatively trained to maximize conditional likelihood. We give a sample complexity bound showing that in order to fit the discriminative parameters well, the number of training examples required depends only on the logarithm of the number of feature occurrences and feature set size. Experimental results show that hybrid models can provide lower test error and can produce better accuracy/coverage curves than either their purely generative or purely discriminative counterparts. We also discuss several advantages of hybrid models, and advocate further work in this area.


title: Towards a Model of Context-Aware Recommender System

abstract: Users often have difficulties to use large-scale information systems efficiently because of their complexity. Additionally, these systems might be context dependent. If these context dependencies are taken into account during the system’s run-time phase, the most appropriate functionality might be provided to users in the form of recommendations for each context situation. The paper proposes to account for the context dependencies by using context aware recommendations and outlines an approach for modeling such recommendations. This approach is based on methodological foundations of Capability Driven Development. The paper discusses a motivational case for developing context aware recommendations and presents the initial method for recommendation modelling.


title: Real time motion generation and control for biped robot -2nd report: Running gait pattern generation-

abstract: Bipedal running can easily result in a fall due to poor availability of the ground reaction force at the boundary of the flight and support phases. We propose methods to decompose and synthesize a running gait pattern into vertical, horizontal and rotational components so that time-dependent ground friction limits are satisfied. We also extend previously proposed boundary condition, the divergent component of motion, for switching walking gait patterns into running which involves vertical acceleration of the center of gravity. Using these techniques, running at 10 km/h is achieved on a real robot whose dimension are same as ASIMO.


title: Dict Layer: A Structured Dictionary Layer

abstract: Dictionary learning and deep learning both have played important roles in computer vision. Dictionary learning strives to learn best representative atoms to reconstruct the source images or signals. Alternating iterative methods are developed to solve such problems. On the other hand, deep learning performs in an end-to-end mode, where both feature extraction and classification are achieved simultaneously. Obviously, the scheme of deep learning is different from that of traditional dictionary learning which is shallow and focuses more on data reconstruction. However, studies on building a deep layer-stacked model imply that there could be a relationship between them. In this paper, the relationship between dictionary learning and deep learning is studied. Dictionary learning can be viewed as a special full connection layer (FC Layer) of deep learning. According to the relationship, we try to introduce those mature improvements from dictionary learning to deep learning. Hence, a new kind of layer named as Dict Layer is introduced in this paper, where the idea of structured dictionary is adopted. In Dict Layer, neural units (coefficients) are class specified, which means the activated neural units are encouraged to be the same class. The proposed method is evaluated on MNIST, CIFAR-10 and SVHN as an improvement of FC Layer. Experiments on AR and Extended YaleB are conducted where Dict Layer is viewed as a special form of dictionary learning method. Results show that the outputs of Dict Layer are more discriminative and class specific than that of the traditional FC Layer.


title: Effects of Group Music Intervention on Behavioral and Psychological Symptoms in Patients with Dementia: A Pilot-Controlled Trial

abstract: We investigated the effects of group music intervention on behavioral and psychological symptoms in patients with dementia. Twenty patients were nonrandomly allocated to either a music-intervention group, or an usual care group. The music-intervention group received 50 minutes of music intervention 3 times per week for 5 consecutive weeks. After 15 sessions, the music-intervention group showed significant in improvement with regard to agitation, and the total scores of both patients and caregivers were lower, compared with the control group. These findings suggest that music can improve behavioral and psychological symptoms, especially in patients with dementia and their caregivers.


title: Pyramid Attention Network for Semantic Segmentation

abstract: A Pyramid Attention Network(PAN) is proposed to exploit the impact of global contextual information in semantic segmentation. Different from most existing works, we combine attention mechanism and spatial pyramid to extract precise dense features for pixel labeling instead of complicated dilated convolution and artificially designed decoder networks. Specifically, we introduce a Feature Pyramid Attention module to perform spatial pyramid attention structure on high-level output and combining global pooling to learn a better feature representation, and a Global Attention Upsample module on each decoder layer to provide global context as a guidance of low-level features to select category localization details. The proposed approach achieves state-of-the-art performance on PASCAL VOC 2012 and Cityscapes benchmarks with a new record of mIoU accuracy 84.0% on PASCAL VOC 2012, while training without COCO dataset.


REGIONAL NECROSIS FOLLOWING NORADRENALINE ADMINISTRATION


Time Series Sales Forecasting


title: Mixed-mode fracture of human cortical bone

abstract: Although the mode I (tensile opening) fracture toughness has been the focus of most fracture mechanics studies of human cortical bone, bones in vivo are invariably loaded multiaxially. Consequently, an understanding of mixed-mode fracture is necessary to determine whether a mode I fracture toughness test provides the appropriate information to accurately quantify fracture risk. In this study, we examine the mixed-mode fracture of human cortical bone by characterizing the crack-initiation fracture toughness in the transverse (breaking) orientation under combined mode I (tensile opening) plus mode II (shear) loading using samples loaded in symmetric and asymmetric four-point bending. Whereas in most structural materials, the fracture toughness is increased with increasing mode-mixity (i.e., where the shear loading component gets larger), in the transverse orientation of bone the situation is quite different. Indeed, the competition between the maximum applied mechanical mixed-mode driving force and the weakest microstructural paths in bone results in a behavior that is distinctly different to most homogeneous brittle materials. Specifically, in this orientation, the fracture toughness of bone is markedly decreased with increasing mode-mixity.


title: CORFU: A Shared Log Design for Flash Clusters

abstract: CORFU organizes a cluster of flash devices as a single, shared log that can be accessed concurrently by multiple clients over the network. The CORFU shared log makes it easy to build distributed applications that require strong consistency at high speeds, such as databases, transactional key-value stores, replicated state machines, and metadata services. CORFU can be viewed as a distributed SSD, providing advantages over conventional SSDs such as distributed wear-leveling, network locality, fault tolerance, incremental scalability and geo-distribution. A single CORFU instance can support up to 200K appends/sec, while reads scale linearly with cluster size. Importantly, CORFU is designed to work directly over network-attached flash devices, slashing cost, power consumption and latency by eliminating storage servers.


title: Performance Analysis Of Scalable Sql And Nosql Databases : A Quantitative Approach

abstract: PERFORMANCE ANALYSIS OF SCALABLE SQL AND NOSQL DATABASES: A QUANTITATIVE APPROACH by HARISH BALASUBRAMANIAN May 2014 Advisor: Dr.Weisong Shi Major: Computer Science Degree: Master of Science Benchmarking is a common method in evaluating and choosing a NoSQL database. There are already lots of benchmarking reports available in internet and research papers. Most of the benchmark reports measure the database performance only by overall throughput and latency. This is an adequate performance analysis but need not to be the end. We define some new perspectives which also need to be considered during NoSQL performance analysis. We have demonstrated this approach by benchmarking HBase, MongoDB and sharded MySQL using YCSB. Based on the results we observe that NoSQL databases do not consider the capability of the data nodes while assigning data to it. And these databases’ performance is seriously affected by the bottleneck nodes and the databases are not attempting to resolve this bottleneck situation automatically.


W1389 Probiotics in Clinical Trials from 2004 to 2007: A Review of Current Literature


AUTOMATED ATTENDANCE CAPTURE AND TRACKING SYSTEM EU


title: Energy-Efficient Long-term Continuous Personal Health Monitoring

abstract: Continuous health monitoring using wireless body area networks of implantable and wearable medical devices (IWMDs) is envisioned as a transformative approach to healthcare. Rapid advances in biomedical sensors, low-power electronics, and wireless communications have brought this vision to the verge of reality. However, key challenges still remain to be addressed. The constrained sizes of IWMDs imply that they are designed with very limited processing, storage, and battery capacities. Therefore, there is a very strong need for efficiency in data collection, analysis, storage, and communication. In this paper, we first quantify the energy and storage requirements of a continuous personal health monitoring system that uses eight biomedical sensors: (1) heart rate, (2) blood pressure, (3) oxygen saturation, (4) body temperature, (5) blood glucose, (6) accelerometer, (7) electrocardiogram (ECG), and (8) electroencephalogram (EEG). Our analysis suggests that there exists a significant gap between the energy and storage requirements for long-term continuous monitoring and the capabilities of current devices. To enable energy-efficient continuous health monitoring, we propose schemes for sample aggregation, anomaly-driven transmission, and compressive sensing to reduce the overheads of wirelessly transmitting, storing, and encrypting/authenticating the data. We evaluate these techniques and demonstrate that they result in two to three orders-of-magnitude improvements in energy and storage requirements, and can help realize the potential of long-term continuous health monitoring.


title: An unsupervised content based news personalization using geolocation information

abstract: This paper presents content based recommendation system for news articles by filtering redundant news articles which describes the same event and happening from different sources by implementing unsupervised learning technique. A representative of similar articles is retained for further learning process. In this paper, a method for ranking news articles is also proposed based on similarity between user preference and the articles. Further the ranking is enhanced or improved by invoking location details of the user as they are more likely to be interested in their local news and event.


title: REST and Web Services: In Theory and in Practice

abstract: There are two competing architectural styles employed for building Web services: RESTful services and services based on the WS– ∗ standards (also known as “SOAP Web services”). These two styles have separate follower bases, but many differences between them are ideological rather than factual. In order to promote the healthy growth of Web services research and practice, it is important to distinguish arguments for implementation practices over abstract concepts represented by these styles, carefully evaluating the respective advantages of RESTful and WS– ∗ Web services. Understanding these distinctions is especially critical for the development of enterprise systems, because in this domain, tool vendors have preferred WS– ∗ services to the neglect of RESTful solutions. This chapter evaluates some of the key questions regarding the real and perceived distinctions between these two styles of Web services. It analyzes how the current tools for building RESTful Web services embody the principles of REST. Finally, it presents select open research questions to further the growth of RESTful Web services.


title: Qualitatively characterizing neural network optimization problems

abstract: Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles motivating a variety of schemes to improve optimization, such as unsupervised pretraining. However, modern neural networks are able to achieve negligible training error on complex tasks, using only direct training with stochastic gradient descent. We introduce a simple analysis technique to look for evidence that such networks are overcoming local optima. We find that, in fact, on a straight path from initialization to solution, a variety of state of the art neural networks never encounter any significant obstacles.


title: Risk-averse reinforcement learning for algorithmic trading

abstract: We propose a general framework of risk-averse reinforcement learning for algorithmic trading. Our approach is tested in an experiment based on 1.5 years of millisecond time-scale limit order data from NASDAQ, which contain the data around the 2010 flash crash. The results show that our algorithm outperforms the risk-neutral reinforcement learning algorithm by 1) keeping the trading cost at a substantially low level at the spot when the flash crash happened, and 2) significantly reducing the risk over the whole test period.


title: Efficacy of education, continuous monitoring and nutritional care on quality of life of cirrhotic patients and reducing of liver cirrhosis complications

abstract: Background and study aim: Liver cirrhosis is a complex chronic disease with a wide variety of morbidities and these complications directly affect the quality of life and result in significant morbidity and mortality. This study designed to determine the effects of an educational awareness program and continuous monitoring on quality of life (QOL) among cirrhotic patients and their complications. Methods: Overall 18 cirrhotic patients included. An educational awareness program including a face-to-face training session, providing a guide booklet about caring of the liver cirrhosis, consulting with dietician and continuous monitoring of patients every other week was conducted and the patients followed for 6 months. The QOL, awareness score, complications of liver disease, severity of disease by using MELD score and laboratory parameters were evaluated at baseline and at the end of intervention. Results: At the end of the intervention, the prevalence of complications, including edema, ascites, hepatic encephalopathy, variceal bleeding and hospitalization rate were significantly decreased compare to baseline (P 0.05). Conclusion: Using a simple educational program can significantly improve not only cirrhotics awareness and QOL but also reduce complications and duration of hospitalization which could be cost effective and worth to try among cirrhotics.


title: Designing pedagogical interventions to support student use of learning analytics

abstract: This article addresses a relatively unexplored area in the emerging field of learning analytics, the design of learning analytics interventions. A learning analytics intervention is defined as the surrounding frame of activity through which analytic tools, data, and reports are taken up and used. It is a soft technology that involves the orchestration of the human process of engaging with the analytics as part of the larger teaching and learning activity. This paper first makes the case for the overall importance of intervention design, situating it within the larger landscape of the learning analytics field, and then considers the specific issues of intervention design for student use of learning analytics. Four principles of pedagogical learning analytics intervention design that can be used by teachers and course developers to support the productive use of learning analytics by students are introduced: Integration, Agency, Reference Frame and Dialogue. In addition three core processes in which to engage students are described: Grounding, Goal-Setting and Reflection. These principles and processes are united in a preliminary model of pedagogical learning analytics intervention design for students, presented as a starting point for further inquiry.


title: VnCoreNLP: A Vietnamese Natural Language Processing Toolkit

abstract: We present an easy-to-use and fast toolkit, namely VnCoreNLP---a Java NLP annotation pipeline for Vietnamese. Our VnCoreNLP supports key natural language processing (NLP) tasks including word segmentation, part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing, and obtains state-of-the-art (SOTA) results for these tasks. We release VnCoreNLP to provide rich linguistic annotations to facilitate research work on Vietnamese NLP. Our VnCoreNLP is open-source under GPL v3, and available at: this https URL


title: Graphical fisheye views of graphs

abstract: A fisheye lens is a very wide angle lens that shows places nearby in detail while also showing remote regions in successively less detail. This paper describes a system for viewing and browsing planar graphs using a software analog of a fisheye lens. We first show how to implement such a view using solely geometric transformations. We then describe a more general transformation that allows hierarchical, structured information about the graph to modify the views. Our general transformation is a fundamental extension to the previous research in fisheye views.


title: Techniques for interpretable machine learning

abstract: Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. We provide a survey covering existing techniques to increase the interpretability of machine learning models. We also discuss crucial issues that the community should consider in future work such as designing user-friendly explanations and developing comprehensive evaluation metrics to further push forward the area of interpretable machine learning.