title: SemEval-2016 Task 8: Meaning Representation Parsing

abstract: In this report we summarize the results of the SemEval 2016 Task 8: Meaning Representation Parsing. Participants were asked to generate Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the news and discussion forum domains. Eleven sites submitted valid systems. The availability of state-of-the-art baseline systems was a key factor in lowering the bar to entry; many submissions relied on CAMR (Wang et al., 2015b; Wang et al., 2015a) as a baseline system and added extensions to it to improve scores. The evaluation set was quite difficult to parse, particularly due to creative approaches to word representation in the web forum portion. The top scoring systems scored 0.62 F1 according to the Smatch (Cai and Knight, 2013) evaluation heuristic. We show some sample sentences along with a comparison of system parses and perform quantitative ablative studies.


title: Design and Analysis of the Millimeter-Wave SPDT Switch for TDD Applications

abstract: This paper presents a low-loss and high Tx-to-Rx isolation single-pole double-throw (SPDT) millimeter-wave switch for true time delay applications. The switch is designed based on matching-network and double-shunt transistors with quarter-wavelength transmission lines. The insertion loss and isolation characteristics of the switches are analyzed revealing that optimization of the transistor size with a matching-network switch on the receiver side and a double-shunt switch on the transmitter side can enhance the isolation performance with low loss. Implemented in 90-nm CMOS, the switch achieves a measured insertion loss and Tx-to-Rx isolation of 1.9 and 39 dB at 60 GHz, respectively. The input 1-dB gain compression point is 10 dBm at 60 GHz, and the return loss of the SPDT switch ports is greater than 10 dB at 48-67 GHz.


title: Sentiment Analysis for Polish Using Transfer Learning Approach

abstract: A method for sentiment polarity assignment for textual content written in Polish using supervised machine learning approach with transfer learning scheme is proposed in the paper. It has been shown that performing simple natural language processing steps prior to classification, provides inspiring results without redundant computation overhead. The documents containing subjective opinions were classified using N-gram and Bi-gram language model that is able to encode some of complex word phrases. The experiments carried out on two real datasets taken from different domains proved that learning on one dataset and testing on another, which is commonly called transfer learning, can be effective and may result in very high classification quality.


title: Robust tracking-by-detection using a detector confidence particle filter

abstract: We propose a novel approach for multi-person tracking-by-detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. A main contribution of this paper is the exploration of how these unreliable information sources can be used for multi-person tracking. The resulting algorithm robustly tracks a large number of dynamically moving persons in complex scenes with occlusions, does not rely on background modeling, and operates entirely in 2D (requiring no camera or ground plane calibration). Our Markovian approach relies only on information from the past and is suitable for online applications. We evaluate the performance on a variety of datasets and show that it improves upon state-of-the-art methods.


title: The SALSA Corpus: a German Corpus Resource for Lexical Semantics

abstract: This paper describes the SALSA corpus, a large German corpus manually annotated with role-semantic information, based on the syntactically annotated TIGER newspaper corpus (Brants et al., 2002). The first release, comprising about 20,000 annotated predicate instances (about half the TIGER corpus), is scheduled for mid-2006. In this paper we discuss the frame-semantic annotation framework and its cross-lingual applicability, problems arising from exhaustive annotation, strategies for quality control, and possible applications.


title: Wire detection using synthetic data and dilated convolutional networks for unmanned aerial vehicles

abstract: Wire detection is a key capability for safe navigation of autonomous aerial vehicles and is a challenging problem as wires are generally only a few pixels wide, can appear at any orientation and location, and are hard to distinguish from other similar looking lines and edges. We leverage the recent advances in deep learning by treating wire detection as a semantic segmentation task, and investigate the effectiveness of convolutional neural networks for the same. To find an optimal model in terms of detection accuracy and real time performance on a portable GPU, we perform a grid search over a finite space of architectures. Further, to combat the issue of unavailability of a large public dataset with annotations, we render synthetic wires using a ray tracing engine, and overlay them on 67K images from flight videos available on the internet. We use this synthetic dataset for pretraining our models before finetuning on real data, and show that synthetic data alone can lead to pretty accurate detections qualitatively as well. We also verify if providing explicit information about local evidence of wiry-ness in the form of edge and line detection results from a traditional computer vision method, as additional channels to the network input, makes the task easier or not. We evaluate our best models from the grid search on a publicly available dataset and show that they outperform previous work using traditional computer vision and various deep net baselines of FCNs, SegNet and E-Net, on both standard edge detection metrics and inference speed. Our top models run at more than 3Hz on the NVIDIA Jetson TX2 with input resolution of 480×640, with an Average Precision score of 0.73 on our test split of the USF dataset.


title: Analyzing the Bitcoin Network: The First Four Years

abstract: In this explorative study, we examine the economy and transaction network of the decentralized digital currency Bitcoin during the first four years of its existence. The objective is to develop insights into the evolution of the Bitcoin economy during this period. For this, we establish and analyze a novel integrated dataset that enriches data from the Bitcoin blockchain with off-network data such as business categories and geo-locations. Our analyses reveal the major Bitcoin businesses and markets. Our results also give insights on the business distribution by countries and how businesses evolve over time. We also show that there is a gambling network that features many very small transactions. Furthermore, regional differences in the adoption and business distribution could be found. In the network analysis, the small world phenomenon is investigated and confirmed for several subgraphs of the Bitcoin network.


title: Hyperuricemia, Gout, and Autosomal Dominant Polycystic Kidney Disease

abstract: The relationship between hyperuricemia, gout, and autosomal dominant polycystic kidney disease (ADPKD) is not widely recognized. In an attempt to further clarify this relationship, the authors have studied 17 patients with ADPKD, 9 controls, 9 patients with proven gout and chronic renal failure, 11 patients with gout and normal renal function, and 11 patients with chronic renal failure. The mean serum uric acid concentration was higher in patients with ADPKD as a group than in controls (8.0 ±1.7 mg/dl vs. 6.4 ± 1.6 mg/dl, p < .02). Clinical gout was identified in 24% of patients with ADPKD; none of the patients with chronic renal failure of other etiologies had gout. Fractional excretion of uric acid and the activity of the enzyme hypoxanthine guanine phosphoribosyl transferase (HGPRT) were not different among the groups studied. From this study the authors conclude that ADPKD should be included among those diseases associated with hyperuricemia and gout. A partial deficiency in HGPRT or abnormal renal handling of uric acid do not appear to be responsible for the increased incidence of gout in patients with ADPKD.


title: Contour Detection and Hierarchical Image Segmentation

abstract: This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by user-specified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.


title: What is the best multi-stage architecture for object recognition?

abstract: In many recent object recognition systems, feature extraction stages are generally composed of a filter bank, a non-linear transformation, and some sort of feature pooling layer. Most systems use only one stage of feature extraction in which the filters are hard-wired, or two stages where the filters in one or both stages are learned in supervised or unsupervised mode. This paper addresses three questions: 1. How does the non-linearities that follow the filter banks influence the recognition accuracy? 2. does learning the filter banks in an unsupervised or supervised manner improve the performance over random filters or hardwired filters? 3. Is there any advantage to using an architecture with two stages of feature extraction, rather than one? We show that using non-linearities that include rectification and local contrast normalization is the single most important ingredient for good accuracy on object recognition benchmarks. We show that two stages of feature extraction yield better accuracy than one. Most surprisingly, we show that a two-stage system with random filters can yield almost 63% recognition rate on Caltech-101, provided that the proper non-linearities and pooling layers are used. Finally, we show that with supervised refinement, the system achieves state-of-the-art performance on NORB dataset (5.6%) and unsupervised pre-training followed by supervised refinement produces good accuracy on Caltech-101 (≫ 65%), and the lowest known error rate on the undistorted, unprocessed MNIST dataset (0.53%).


title: CityGML – 3D City Models and their Potential for Emergency Response

abstract: Virtual 3D city models provide important information for different aspects of disaster management. In order to ensure the unambiguous interpretation of the represented objects, an ontology in the sense of a common information model for urban and regional structures has to be defined. Furthermore, up-to-dateness of and flexible access to 3D city models are of utmost importance. Spatial Data Infrastructures (SDI) provide the appropriate framework to cover this aspect, integrating distributed data sources on demand. In this chapter we present CityGML, which is in the first place an ontology for the three-dimensional, multi-purpose, and multi-scale representation of cities, sites, and regions. The implementation of CityGML is based on the standard GML3 of the Open Geospatial Consortium and thus defines an exchange format for the storage of and interoperable access to 3D city models in SDIs. The class taxonomy distinguishes between buildings and other man-made artifacts, vegetation objects, water bodies, and transportation facilities like streets and railways. Spatial as well as semantic properties are structured in five consecutive levels of detail. Throughout this chapter, special focus is on the utilization of model concepts with respect to different tasks in the context of emergency response.


Long-term cell tracking using lightsheet microscopy in toto of the zebrafish


title: A novel high-speed and low-power negative voltage level shifter for low voltage applications

abstract: A novel high-speed and low-power negative level shifter suitable for low voltage applications is presented. To reduce the switching delay and leakage current, a novel bootstrapping technique is designed for the level shifter. Furthermore, a pull-down driver is proposed to have high driving capability under different operation modes. The circuit has been designed in 130nm 1.5V/5V triple-well CMOS technology with a nominal power supply V DD of 1.5V and a negative voltage of −4.5V. Simulation results show that the switching delay and power consumption have been significantly reduced by roughly 62% and 65%, respectively. In addition, the proposed level shifter realizes a wide operation margin with a lower V DD compared to conventional implementations.


Attitudes of applied clinicians toward roles, functions, and training in clinical psychology: A comparative survey.


title: Software Defined Radio: Challenges and Opportunities

abstract: Software Defined Radio (SDR) may provide flexible, upgradeable and longer lifetime radio equipment for the military and for civilian wireless communications infrastructure. SDR may also provide more flexible and possibly cheaper multi-standard-terminals for end users. It is also important as a convenient base technology for the future context-sensitive, adaptive and learning radio units referred to as cognitive radios. SDR also poses many challenges, however, some of them causing SDR to evolve slower than otherwise anticipated. Transceiver development challenges include size, weight and power issues such as the required computing capacity, but also SW architectural challenges such as waveform application portability. SDR has demanding implications for regulators, security organizations and business developers.


title: SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations

abstract: We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation. In SCDV, word embedding's are clustered to capture multiple semantic contexts in which words occur. They are then chained together to form document topic-vectors that can express complex, multi-topic documents. Through extensive experiments on multi-class and multi-label classification tasks, we outperform the previous state-of-the-art method, NTSG (Liu et al., 2015a). We also show that SCDV embedding's perform well on heterogeneous tasks like Topic Coherence, context-sensitive Learning and Information Retrieval. Moreover, we achieve significant reduction in training and prediction times compared to other representation methods. SCDV achieves best of both worlds - better performance with lower time and space complexity.


title: Control Plane Latency With SDN Network Hypervisors: The Cost of Virtualization

abstract: Software defined networking (SDN) network hypervisors provide the functionalities needed for virtualizing software-defined networks. Hypervisors sit logically between the multiple virtual SDN networks (vSDNs), which reside on the underlying physical SDN network infrastructure, and the corresponding tenant (vSDN) controllers. Different SDN network hypervisor architectures have mainly been explored through proof-of-concept implementations. We fundamentally advance SDN network hypervisor research by conducting a model-based analysis of SDN hypervisor architectures. Specifically, we introduce mixed integer programming formulations for four different SDN network hypervisor architectures. Our model formulations can also optimize the placement of multi-controller switches in virtualized OpenFlow-enabled SDN networks. We employ our models to quantitatively examine the optimal placement of the hypervisor instances. We compare the control plane latencies of the different SDN hypervisor architectures and quantify the cost of virtualization, i.e., the latency overhead due to virtualizing SDN networks via hypervisors. For generalization, we quantify how the hypervisor architectures behave for different network topologies. Our model formulations and the insights drawn from our evaluations inform network operators about the trade-offs of the different hypervisor architectures and help choosing an architecture according to operator demands.


title: SFCM: Learn a Pooling Kernel for Weakly Supervised Object Localization

abstract: The weakly supervised object localization (WSOL) is to locate the objects in an image while only image-level labels are available during the training procedure. In this work, the Selective Feature Category Mapping (SFCM) method is proposed, which introduces the Feature Category Mapping (FCM) and the widely-used selective search method to solve the WSOL task. Our FCM replaces layers after the specific layer in the state-of-the-art CNNs with a set of kernels and learns the weighted pooling for previous feature maps. It is trained with only image-level labels and then map the feature maps to their corresponding categories in the test phase. Together with selective search method, the location of each object is finally obtained. Extensive experimental evaluation on ILSVRC2012 and PASCAL VOC2007 benchmarks shows that SFCM is simple but very effective, and it is able to achieve outstanding classification performance and outperform the state-of-the-art methods in the WSOL task.


title: Learning semantic relationships for better action retrieval in images

abstract: Human actions capture a wide variety of interactions between people and objects. As a result, the set of possible actions is extremely large and it is difficult to obtain sufficient training examples for all actions. However, we could compensate for this sparsity in supervision by leveraging the rich semantic relationship between different actions. A single action is often composed of other smaller actions and is exclusive of certain others. We need a method which can reason about such relationships and extrapolate unobserved actions from known actions. Hence, we propose a novel neural network framework which jointly extracts the relationship between actions and uses them for training better action retrieval models. Our model incorporates linguistic, visual and logical consistency based cues to effectively identify these relationships. We train and test our model on a largescale image dataset of human actions. We show a significant improvement in mean AP compared to different baseline methods including the HEX-graph approach from Deng et al. [8].


title: L1–L2 interactions of vowel systems in young bilingual Mandarin-English children

abstract: Abstract This study examined the influence of L1 (Mandarin)–L2 (English) interactions on the organization of vowel systems and fine-grained spectral features of vowel productions in young bilingual Mandarin-English children. The participants included 39 children (15 bilinguals, 15 Mandarin monolinguals, and 9 English monolinguals) at 5–6 years of age. The bilingual children were divided into Bi-low (at the early stage of English learning with low proficiency in English) and Bi-high (highly proficient in English) groups. Each participant was recorded producing one set of Mandarin words containing /a, i, u, y, ɤ/ and/or one set of English words containing /i, ɪ, e, ɛ, ae, u, ʊ, o, ɑ, ʌ/. Formant frequencies at five temporal locations were measured. Both static (midpoint formant values) and dynamic (formant movement pattern, trajectory length) acoustic properties were examined. Bi-low children showed a strong effect of L1 on L2. The L1 features were maintained and transferred to the new phonetic system. Bi-high children produced L2 vowels in a near-native manner. Meanwhile, they tended to transfer some L2 features to their L1 and moved the L1 vowels closer to L2 vowels, which suggested an assimilatory process. Both static and dynamic spectral features were affected by L1–L2 interactions.


title: Core Structure of gp41 from the HIV Envelope Glycoprotein

abstract: The envelope glycoprotein of human immunodeficiency virus type 1 (HIV-1) consists of a complex of gp120 and gp41. gp120 determines viral tropism by binding to target-cell receptors, while gp41 mediates fusion between viral and cellular membranes. Previous studies identified an alpha-helical domain within gp41 composed of a trimer of two interacting peptides. The crystal structure of this complex, composed of the peptides N36 and C34, is a six-helical bundle. Three N36 helices form an interior, parallel coiled-coil trimer, while three C34 helices pack in an oblique, antiparallel manner into highly conserved, hydrophobic grooves on the surface of this trimer. This structure shows striking similarity to the low-pH-induced conformation of influenza hemagglutinin and likely represents the core of fusion-active gp41. Avenues for the design/discovery of small-molecule inhibitors of HIV infection are directly suggested by this structure.


title: LORE: exploiting sequential influence for location recommendations

abstract: Providing location recommendations becomes an important feature for location-based social networks (LBSNs), since it helps users explore new places and makes LBSNs more prevalent to users. In LBSNs, geographical influence and social influence have been intensively used in location recommendations based on the facts that geographical proximity of locations significantly affects users' check-in behaviors and social friends often have common interests. Although human movement exhibits sequential patterns, most current studies on location recommendations do not consider any sequential influence of locations on users' check-in behaviors. In this paper, we propose a new approach called LORE to exploit sequential influence on location recommendations. First, LORE incrementally mines sequential patterns from location sequences and represents the sequential patterns as a dynamic Location-Location Transition Graph (L2TG). LORE then predicts the probability of a user visiting a location by Additive Markov Chain (AMC) with L2TG. Finally, LORE fuses sequential influence with geographical influence and social influence into a unified recommendation framework; in particular the geographical influence is modeled as two-dimensional check-in probability distributions rather than one-dimensional distance probability distributions in existing works. We conduct a comprehensive performance evaluation for LORE using two large-scale real data sets collected from Foursquare and Gowalla. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art recommendation techniques.


title: Performance analysis of two open source intrusion detection systems

abstract: Several studies have been conducted where authors compared the performance of open source Intrusion detection systems, namely Snort and Suricata. However, most studies were limited to either security indicators or performance measurements under the same operating system. The objective of this study is to give a comprehensive analysis of both products in terms of several security related and performance related indicators. In addition, we tested the products under two different operating systems. Several experiments were run to evaluate the effects of open source intrusion detection and prevention systems Snort and Suricata, operating systems Windows, Linux and various attack types on system resource usage, dropped packets rate and ability to detect intrusions. The results show that Suricata has a higher CPU and RAM utilization than Snort in all cases on both operating systems, but lower percentage of dropped packets when evaluated during five of six simulated attacks. Both products had the same number of correctly identified intrusions. The results show that Linux-based solutions consume more system resources, but Windows-based systems had a higher rate of dropped packets. This indicates that these two intrusion detection and prevention systems should be run on Linux. However, both systems are inappropriate for high volumes of traffic in single-server setting.


title: Strategic Decision making Practice in Croatia-Which Decision making Style is More Effective

abstract: Decision making is a vital part of the business world and any other field of human endeavor. Which way a business organization will take, and where that way will lead it, depends on broad range of decisions made by managers in the managerial structure. Strategic decisions are of the greatest importance for organizational success. Although much empirical research has been done trying to describe and explain its nature and effectiveness, knowledge about strategic decision making is still incomplete. This paper explores the nature of strategic decision making in particular setting - in Croatian companies. The main focus of this research is on the style that decision makers on strategic management level are following when making decisions of life importance for their companies. Two main decision making style that explain the way decision maker collects and processes available information and performs all the activities in strategic decision making process were empirical tested: rational and intuitive one. Besides analyzing their existence on strategic management level in Croatian companies, their effectiveness is analyzed as well. Results showed that decision makers at strategic management level are following both styles somewhat equally in order to function effectively, and that intuitive style is more effective when considering decisions outcomes.


title: Deep learning algorithm for arrhythmia detection

abstract: Most of cardiovascular disorders or diseases can be prevented, but death continues to rise due to improper treatment because of misdiagnose. One of cardiovascular diseases is Arrhythmia. It is sometimes difficult to observe electrocardiogram (ECG) recording for Arrhythmia detection. Therefore, it needs a good learning method to be applied in the computer as a way to help the detection of Arrhythmia. There is a powerful approach in Machine Learning, named Deep Learning. It starts to be widely used for Speech Recognition, Bioinformatics, Computer Vision, and many others. This research used the Deep Learning to classify the Arrhythmia data. We compared the result to other popular machine learning algorithm, such as Naive Bayes, K-Nearest Neighbor, Artificial Neural Network, and Support Vector Machine. Our experiment showed that Deep Learning algorithm achieved the best accuracy, which was 76,51%.


title: Touch communicates distinct emotions

abstract: The study of emotional signaling has focused almost exclusively on the face and voice. In 2 studies, the authors investigated whether people can identify emotions from the experience of being touched by a stranger on the arm (without seeing the touch). In the 3rd study, they investigated whether observers can identify emotions from watching someone being touched on the arm. Two kinds of evidence suggest that humans can communicate numerous emotions with touch. First, participants in the United States (Study 1) and Spain (Study 2) could decode anger, fear, disgust, love, gratitude, and sympathy via touch at much-better-than-chance levels. Second, fine-grained coding documented specific touch behaviors associated with different emotions. In Study 3, the authors provide evidence that participants can accurately decode distinct emotions by merely watching others communicate via touch. The findings are discussed in terms of their contributions to affective science and the evolution of altruism and cooperation.


title: The power of gifts: organizing social relationships in open source communities

abstract: In writings on the open source software development model, it is often argued that it is successful as a result of the gift economy that embraces activ- ities in online communities. However, the theoretical foundations for this argument are seldom discussed and empirically tested. Starting with the 'classic' theories of gift giving, we discuss how they need to be developed in order to explain gift- giving practices in digital domains. In this paper, we argue that the gift economy is important, not only because it creates openness, but also because it organizes relationships between people in a certain way. Open source software development relies on gift giving as a way of getting new ideas and prototypes out into circu- lation. This also implies that the giver gets power from giving away. This power is used as a way of guaranteeing the quality of the code. We relate this practice to how gifts, in the form of new scientific knowledge, are given to the research com- munity, and how this is done through peer review processes.


title: Social trust and social reciprocity based cooperative D2D communications

abstract: Thanks to the convergence of pervasive mobile communications and fast-growing online social networking, mobile social networking is penetrating into our everyday life. Aiming to develop a systematic understanding of the interplay between social structure and mobile communications, in this paper we exploit social ties in human social networks to enhance cooperative device-to-device communications. Specifically, as hand-held devices are carried by human beings, we leverage two key social phenomena, namely social trust and social reciprocity, to promote efficient cooperation among devices. With this insight, we develop a coalitional game theoretic framework to devise social-tie based cooperation strategies for device-to-device communications. We also develop a network assisted relay selection mechanism to implement the coalitional game solution, and show that the mechanism is immune to group deviations, individually rational, and truthful. We evaluate the performance of the mechanism by using real social data traces. Numerical results show that the proposed mechanism can achieve up-to 122% performance gain over the case without D2D cooperation.


title: Learning Programming at the Computational Thinking Level via Digital Game-Play

abstract: This paper outlines an innovative game model for learning computational thinking (CT) skills through digital game-play. We have designed a game framework where students can practice and develop their skills in CT with little or no programming knowledge. We analyze how this game supports various CT concepts and how these concepts can be mapped to programming constructs to facilitate learning introductory computer programming. Moreover, we discuss the potential benefits of our approach as a support tool to foster student motivation and abilities in problem solving. As initial evaluation, we provide some analysis of feedback from a survey response group of 25 students who have played our game as a voluntary exercise. Structured empirical evaluation will follow, and the plan for that is briefly described.


title: Sorting networks on FPGAs

abstract: Computer architectures are quickly changing toward heterogeneous many-core systems. Such a trend opens up interesting opportunities but also raises immense challenges since the efficient use of heterogeneous many-core systems is not a trivial problem. Software-configurable microprocessors and FPGAs add further diversity but also increase complexity. In this paper, we explore the use of sorting networks on field-programmable gate arrays (FPGAs). FPGAs are very versatile in terms of how they can be used and can also be added as additional processing units in standard CPU sockets. Our results indicate that efficient usage of FPGAs involves non-trivial aspects such as having the right computation model (a sorting network in this case); a careful implementation that balances all the design constraints in an FPGA; and the proper integration strategy to link the FPGA to the rest of the system. Once these issues are properly addressed, our experiments show that FPGAs exhibit performance figures competitive with those of modern general-purpose CPUs while offering significant advantages in terms of power consumption and parallel stream evaluation.


title: A Stroke-Based Textual Password Authentication Scheme

abstract: Textual-based password authentication scheme tend to more vulnerable to attacks such as shoulder-surfing and hidden camera. To overcome the vulnerabilities of traditional methods, visual or graphical password schemes have been developed as possible alternative solutions to text-based scheme. Because simply adopting graphical password authentication also has some drawbacks, some hybrid schemes based on graphic and text were developed. In this paper, we proposed a stroke-based textual password authentication scheme. It uses shapes of strokes on the grid as the origin passwords and allows users to login with text passwords via traditional input devices. The method provides strong resistant to hidden-camera and shoulder-surfing. Moreover, the scheme has flexible enhancements to secure the authentication process. The analysis of the security of this approach is also discussed.


Solving the Nurse Rostering Problem Knowledge Representation and Reasoning Seminar


title: A Study of feature extraction algorithms for optical flow tracking

abstract: Sparse optical flow algorithms, such as the Lucas-Kanade approach, provide more robustness to noise than dense optical flow algorithms and are the preferred approach in many scenarios. ::: Sparse optical flow algorithms estimate the displacement for a selected number of pixels in the image. These pixels can be chosen randomly. However, pixels in regions with more variance between the neighbours will produce more reliable displacement estimates. The selected pixel locations should therefore be chosen wisely. In this study, the suitability of Harris corners, Shi-Tomasi's “Good features to track", SIFT and SURF interest point extractors, Canny edges, and random pixel selection for the purpose of frame-by-frame tracking using a pyramidical Lucas-Kanade algorithm is investigated. The evaluation considers the important factors of processing time, feature count, and feature trackability in indoor and outdoor scenarios using ground vehicles and unmanned aerial vehicles, and for the purpose of visual odometry estimation.


title: Recent advances in sensing plant diseases for precision crop protection

abstract: Near-range and remote sensing techniques have demonstrated a high potential in detecting diseases and in monitoring crop stands for sub-areas with infected plants. The occurrence of plant diseases depends on specific environmental and epidemiological factors; diseases, therefore, often have a patchy distribution in the field. This review outlines recent insights in the use of non-invasive optical sensors for the detection, identification and quantification of plant diseases on different scales. Most promising sensor types are thermography, chlorophyll fluorescence and hyperspectral sensors. For the detection and monitoring of plant disease, imaging systems are preferable to non-imaging systems. Differences and key benefits of these techniques are outlined. To utilise the full potential of these highly sophisticated, innovative technologies and high dimensional, complex data for precision crop protection, a multi-disciplinary approach—including plant pathology, engineering, and informatics—is required. Besides precision crop protection, plant phenotyping for resistance breeding or fungicide screening can be optimized by these innovative technologies.


title: Arabic Text Categorization Algorithm using Vector Evaluation Method

abstract: Text categorization is the process of grouping documents into categories based on their contents. This process is important to make information retrieval easier, and it became more important due to the huge textual information available online. The main problem in text categorization is how to improve the classification accuracy. Although Arabic text categorization is a new promising field, there are a few researches in this field. This paper proposes a new method for Arabic text categorization using vector evaluation. The proposed method uses a categorized Arabic documents corpus, and then the weights of the tested document's words are calculated to determine the document keywords which will be compared with the keywords of the corpus categorizes to determine the tested document's best category.


title: Android system security evaluation

abstract: This demonstration presents an application for a system security score evaluation. The security score represents overall security level of the Android OS based device. The proposed application is based on the library that uses standard Android OS API and Google SafetyNet library. We followed up modular approach that allows to use our library independently from our application in any stock Android OS version 5.0 and above. Application evaluates overall security level, gives an explanation of the each parameter, gives advices how to improve overall security of the device, and allows to simulate the evaluation that shows how each parameter affects the security level.


title: A pilot study of mining association between psychiatric stressors and symptoms in tweets

abstract: Suicide is a significant public health issue, causing huge impacts on individuals as well as their families. Psychiatric stressors are major suicide risk factors and can profoundly impact a person in many aspects. In order to facilitate the understanding of psychiatric stressors and the associated symptoms, we extracted stressors and symptoms terms from major online knowledge repositories and psychiatric clinical notes. The current vocabulary collection contains 1,292 psychiatric symptoms and 715 psychiatric stressors, which was leveraged to study the associations between stressors and symptoms in a corpus of suicide related tweets. Using Chi-Square test with Bonferroni correction, 3,500 symptom–stressor pairs were identified with significant association (p-value<0.01).


title: Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

abstract: Recent region-based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks. In this paper, we analyze failure cases of state-of-the-art detectors and observe that most hard false positives result from classification instead of localization. We conjecture that: (1) Shared feature representation is not optimal due to the mismatched goals of feature learning for classification and localization; (2) multi-task learning helps, yet optimization of the multi-task loss may result in sub-optimal for individual tasks; (3) large receptive field for different scales leads to redundant context information for small objects. We demonstrate the potential of detector classification power by a simple, effective, and widely-applicable Decoupled Classification Refinement (DCR) network. DCR samples hard false positives from the base classifier in Faster RCNN and trains a RCNN-styled strong classifier. Experiments show new state-of-the-art results on PASCAL VOC and COCO without any bells and whistles.


title: Design and Analysis of Swapped Port Coupler and Its Application in a Miniaturized Butler Matrix

abstract: This paper presents a novel size reduction technique for designing a Butler matrix using a new type of coupler. This coupler is half the length of a conventional coupler and has a swapped port characteristic wherein the locations of the isolation and coupled ports are switched. These features make the new coupler, called the "swapped port coupler," effective for reducing the size of the Butler matrix. The behavior of the swapped port coupler was mathematically analyzed, and its design equation was derived. Furthermore, the proposed coupler was employed to design a miniaturized Butler matrix. The area of this Butler matrix was 70 × 70 mm2, and its operating frequency was 1 GHz. When the area was normalized to the frequency, the area of the proposed Butler matrix was 10% of a conventional Butler matrix.


title: OneChip: an FPGA processor with reconfigurable logic

abstract: This paper describes a processor architecture called OneChip, which combines a fixed-logic processor core with reconfigurable logic resources. Using the programmable components of this the performance of speed-critical can be improved by customizing OneChip's execution units, or flexibility can be added to the glue logic interfaces of embedded controller applications. OneChip eliminates the shortcomings of other custom compute machines by tightly integrating its reconfigurable resources into a MIPS-like processor. Speedups of close to 50 over strict software implementations on a MIPS R4400 are achievable for computing the DCT.


title: Accelerometer's position free human activity recognition using a hierarchical recognition model

abstract: Monitoring of physical activities is a growing field with potential applications such as lifecare and healthcare. Accelerometry shows promise in providing an inexpensive but effective means of long-term activity monitoring of elderly patients. However, even for the same physical activity the output of any body-worn Triaxial Accelerometer (TA) varies at different positions of a subject's body, resulting in a high within-class variance. Thus almost all existing TA-based human activity recognition systems require firm attachment of TA to a specific body part, making them impractical for long-term activity monitoring during unsupervised free living. Therefore, we present a novel hierarchical recognition model that can recognize human activities independent of TA's position along a human body. The proposed model minimizes the high within-class variance significantly and allows subjects to carry TA freely in any pocket without attaching it firmly to a body-part. We validated our model using six daily physical activities: resting (sit/stand), walking, walk-upstairs, walk-downstairs, running, and cycling. Activity data is collected from four most probable body positions of TA: chest pocket, front trousers pocket, rear trousers pocket, and inner jacket pocket. The average accuracy of about 95% illustrates the effectiveness of the proposed method.


title: A novel human-machine interface for guiding: The NeoASAS smart walker

abstract: In an aging society it is extremely important to develop devices, which can support and aid the elderly in their daily life. This demands tools that extend independent living and promote improved health. In this work it is proposed a new interface approach integrated into a walker. This interface is based on a force sensor and it is intended to extract the user's movement intentions. The interface is designed to be user-friendly, simple and intuitive, efficient and economic, meeting usability aspects and focused on a commercial implementation, but not being demanding at the user cognitive level. Preliminary sets of experiments were performed which showed the sensibility of the force sensor extract navigation commands from the user. These signals presented a higher frequency component that was attenuated by a Benedict-Bordner g-h filter. The presented methodology offers an effective cancelation of the undesired components from force sensor data, allowing the system to extract in real-time voluntary user's navigation commands. Based on this real-time identification of voluntary user's commands, an approach to the control architecture of the robotic walker is being developed, in order to obtain stable and safe user assisted locomotion.


title: Structural Model for the Effects of Perceived Indoor Work Environment on Sick Building Syndrome and Stress

abstract: Sick Building syndrome (SBS) and stress have a prevalent influence on organizational productivity and competitiveness. Unhealthy employees not only tend to have high medical leaves but also low productivity due to ailments and discomforts. Studies that investigate the effects of indoor work environment on Sick Building Syndrome (SBS) have yielded mixed results while their effect on stress has not been empirically established. Furthermore, studies that simultaneously investigate both SBS and stress are almost non-existent. Thus, this study aimed to study the effects of perceived indoor work environment on SBS and stress and the link between SBS and stress. A cross-sectional survey participated by 598 employees from various industries was conducted from September to October 2015. Data were analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM) to assess both the measurement model and the path structure. The results suggest that indoor work environment has significant yet the weak effect on SBS while it has no effect on stress. However, SBS has a strong significant relationship with stress. The implication of this study on the importance of conducive indoor work environment is discussed with suggestions for future studies.


title: Business Process Variability Modeling: A Survey

abstract: It is common for organizations to maintain multiple variants of a given business process, such as multiple sales processes for different products or multiple bookkeeping processes for different countries. Conventional business process modeling languages do not explicitly support the representation of such families of process variants. This gap triggered significant research efforts over the past decade, leading to an array of approaches to business process variability modeling. In general, each of these approaches extends a conventional process modeling language with constructs to capture customizable process models. A customizable process model represents a family of process variants in a way that a model of each variant can be derived by adding or deleting fragments according to customization options or according to a domain model. This survey draws up a systematic inventory of approaches to customizable process modeling and provides a comparative evaluation with the aim of identifying common and differentiating modeling features, providing criteria for selecting among multiple approaches, and identifying gaps in the state of the art. The survey puts into evidence an abundance of customizable process-modeling languages, which contrasts with a relative scarcity of available tool support and empirical comparative evaluations.


title: Fuzzy min-max neural network based decision trees

abstract: This paper presents a new decision tree learning algorithm, fuzzy min-max decision tree (FMMDT) based on fuzzy min-max neural networks. In contrast with traditional decision trees in which a single attribute is selected as the splitting test, the internal nodes of the proposed algorithm contain a fuzzy min-max neural network. In the proposed learning algorithm, the flexibility inherent in the fuzzy logic and the computational efficiency of the min-max neural networks are combined in the decision tree learning framework. FMMDT splits the feature space non-linearly based on multiple attributes which provides not only conceptually more insightful splits but also decision trees with smaller size and depth. The decision trees resulted from the FMMDT learning algorithm have a non-traditional architecture, which enables determining the class label of the instances as early as possible. Moreover, FMMDT creates decision trees which are interpretable by the domain expert. It is shown experimentally that the decision trees resulted from the proposed FMMDT learning algorithm achieve the highest accuracy and the lowest size and depth in comparison with C4.5, BFTree, SimpleCart and NBTree on the most commonly used UCI data sets. Moreover, the experiments reveal that FMMDT creates decision trees with stable structure.


title: WristFlex: low-power gesture input with wrist-worn pressure sensors

abstract: In this paper we present WristFlex, an always-available on-body gestural interface. Using an array of force sensitive resistors (FSRs) worn around the wrist, the interface can distinguish subtle finger pinch gestures with high accuracy (>80 %) and speed. The system is trained to classify gestures from subtle tendon movements on the wrist. We demonstrate that WristFlex is a complete system that works wirelessly in real-time. The system is simple and light-weight in terms of power consumption and computational overhead. WristFlex's sensor power consumption is 60.7 uW, allowing the prototype to potentially last more then a week on a small lithium polymer battery. Also, WristFlex is small and non-obtrusive, and can be integrated into a wristwatch or a bracelet. We perform user studies to evaluate the accuracy, speed, and repeatability. We demonstrate that the number of gestures can be extended with orientation data from an accelerometer. We conclude by showing example applications.


title: A safe and easy methodology for design and sizing of a stand-alone hybrid PV-wind system

abstract: The study presented in this paper is focusing in an on sea oil wellhead located in vicinity of Zarzis region in Tunisia called ElBibane Field. The wellheads user is the Ecumed Petroleum which is oil and natural gas Company that is the Tunisian branch of Candax Energy Ltd. This work aims to perform the design of a wind-solar hybrid system for an on sea oil wellhead electrification. In this work a method was conducted for optimal sizing of PV and wind turbine generators with a battery storage bank. For a given load and energy based on the daily observation of the solar radiation data and the annual wind speed, the power output for each generator is investigated, and so the required number of generating units of wind turbine and photovoltaic modules and the associated storage capacity is computed. The data of solar radiation were estimated using web calculator ‘PVGIS’ and the annual average wind speed was given by web calculator ‘Windfinder’. To ensure a complete study of the hybrid system, the batteries charge controllers and the cross sections of the installation electrical cables between the system components were calculated.


title: Factors Affecting Adolescents' Involvement in Cyberbullying: What Divides the 20% from the 80%?

abstract: Abstract Despite some variations among cyberbullying studies, about 20% of the youth population surveyed appears to be involved in cyberbullying. Coincidentally, the current study found that exactly 20% of the students surveyed were involved in cyberbullying as bullies (7.8%), victims (7.5%), and bully/victims (4.7%). What divides those 20% from the 80% of noninvolved students? This study aimed to produce a parsimonious and accurate model that can predict the occurrence of involvement in cyberbullying among youth. Data were collected from a questionnaire survey administered to 1,036 students enrolled in secondary schools in South Korea. Stepwise logistic regression (SLR) was carried out to predict the dichotomous dependent variable (involved/noninvolved) with 10 independent variables grouped into three categories: (a) demographic, (b) media-related, and (c) school and psychology factors. The result of the SLR analysis yielded a four-step model including the variables of cyber-confidence, weekday game time...


title: Relational recurrent neural networks

abstract: Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. It is unclear, however, whether they also have an ability to perform complex relational reasoning with the information they remember. Here, we first confirm our intuitions that standard memory architectures may struggle at tasks that heavily involve an understanding of the ways in which entities are connected -- i.e., tasks involving relational reasoning. We then improve upon these deficits by using a new memory module -- a \textit{Relational Memory Core} (RMC) -- which employs multi-head dot product attention to allow memories to interact. Finally, we test the RMC on a suite of tasks that may profit from more capable relational reasoning across sequential information, and show large gains in RL domains (e.g. Mini PacMan), program evaluation, and language modeling, achieving state-of-the-art results on the WikiText-103, Project Gutenberg, and GigaWord datasets.


title: A New Framework for Machine Intelligence: Concepts and Prototype

abstract: Machine learning (ML) and artificial intelligence (AI) have become hot topics in many information processing areas, from chatbots to scientific data analysis. At the same time, there is uncertainty about the possibility of extending predominant ML technologies to become general solutions with continuous learning capabilities. Here, a simple, yet comprehensive, theoretical framework for intelligent systems is presented. A combination of Mirror Compositional Representations (MCR) and a Solution-Critic Loop (SCL) is proposed as a generic approach for different types of problems. A prototype implementation is presented for document comparison using English Wikipedia corpus.


title: Chunk-Based Bi-Scale Decoder for Neural Machine Translation

abstract: In typical neural machine translation~(NMT), the decoder generates a sentence word by word, packing all linguistic granularities in the same time-scale of RNN. In this paper, we propose a new type of decoder for NMT, which splits the decode state into two parts and updates them in two different time-scales. Specifically, we first predict a chunk time-scale state for phrasal modeling, on top of which multiple word time-scale states are generated. In this way, the target sentence is translated hierarchically from chunks to words, with information in different granularities being leveraged. Experiments show that our proposed model significantly improves the translation performance over the state-of-the-art NMT model.


title: Cooperative heterogeneous computing for parallel processing on CPU/GPU hybrids

abstract: This paper presents a cooperative heterogeneous computing framework which enables the efficient utilization of available computing resources of host CPU cores for CUDA kernels, which are designed to run only on GPU. The proposed system exploits at runtime the coarse-grain thread-level parallelism across CPU and GPU, without any source recompilation. To this end, three features including a work distribution module, a transparent memory space, and a global scheduling queue are described in this paper. With a completely automatic runtime workload distribution, the proposed framework achieves speedups as high as 3.08 compared to the baseline GPU-only processing.


title: Machine learning anomaly detection in large systems

abstract: We have a need for methods to efficiently determine the health of a system. Diagnostics and prognostics determine system heath through analysis of data from sensors. Anomalies in the data can help us determine if there is a failure or a pending failure. There are common statistical methods to detect anomalies in individual measurements. For systems with many measurements, the anomalies may occur as specific combinations of values. Large systems have various associated states and modes which define the valid measurements. The amount of data to analyze grows very quickly as the system becomes more complex. In recent years techniques have been developed to address large data analysis. Machine Learning encompasses a broad selection of tools to optimize a statistical model of the data. These tools include supervised learning techniques, such as linear regression and logistic regression, in which training data exists to tune the model. Unsupervised learning, such as clustering, is used to explore data which does not have a defined output label associated with inputs data. Standard approaches to training supervised learning systems require a large sample of positive and negative outcome data. Some uses of machine learning involve data where there are very few cases of negative outcomes. There are machine learning algorithms defined as Anomaly Detection which are designed to deal with this type of data. Simple algorithms include Gaussian Distribution Analysis, which assumes independence in distributions of data. Large Systems with anomalies defined in the dependent combinations of data require either a manual creation of combinations of independent variables, or Multivariate Gaussian Distribution Analysis, which does not scale well for large systems. A further complication is the mixture of linear and discrete data. Neural Networks are a type of learning system which has been applied to each of the individual needs addressed above. This paper describes an approach to anomaly detection using neural networks for the specific problems in large systems to efficiently determine system health.


title: A Fire-Alarming Method Based on Video Processing

abstract: This paper presents a fire-alarming method based on video processing. We propose a system that uses color and motion information extracted from video sequences to detect fire. Flame can be recognized according to its color which is a primary element of fire images. Thus choosing a suitable color model is the key to detect flames from fire images. An effective fire detection criterion based on color model is proposed in this paper by intensive experiments and trainings. The detection criterion is used to make a raw localization of fire regions first. However, color alone is not enough for fire detection. To identify a real burning fire, in addition to chromatic features, dynamic features are usually adopted to distinguish other fire aliases. In this paper, both the growth of fire region and the invariability of flame are utilized to further detect the fire regions as a complement of the detection criterion. The effectiveness of the proposed fire-alarming method is demonstrated by the experiments implemented on a large number of scenes.


title: A Four-Channel 94-GHz SiGe-Based Digital Beamforming FMCW Radar

abstract: This paper presents a multi-channel frequency-modulated continuous-wave (FMCW) radar sensor operating in the frequency range from 91 to 97 GHz. The millimeter-wave radar sensor utilizes an SiGe chipset comprising a single signal-generation chip and multiple monostatic transceiver (TRX) chips, which are based on a 200-GHz fT HBT technology. The front end is built on an RF soft substrate in chip-on-board technology and employs a nonuniformly distributed antenna array to improve the angular resolution. The synthesis of ten virtual antennas achieved by a multiple-input multiple-output technique allows the virtual array aperture to be maximized. The fundamental-wave voltage-controlled oscillator achieves a single-sideband phase noise of -88 dBc/Hz at 1-MHz offset frequency. The TX provides a saturated output power of 6.5 dBm, and the mixer within the TRX achieves a gain and a double sideband noise figure of 11.5 and 12 dB, respectively. Possible applications include radar sensing for range and angle detection, material characterization, and imaging.


title: Fault diagnosis for centrifugal pumps using deep learning and softmax regression

abstract: Fault diagnosis of centrifugal pumps is critical to lower its operating and maintenance costs. Due to the non-stationary and non-linear characteristics of vibration signals of centrifugal pumps, a large number of approaches for feature extraction and fault classification have been developed. However, these traditional methods spend too much time extracting features, reducing feature dimension and fusing different features. To resolve the issue, this paper presents an effective unsupervised self-learning method to achieve the fault diagnosis of centrifugal pumps, which uses deep learning method to adaptively extract fault features from non-stationary vibration signals and softmax regression model is used to identify possible failure modes automatically. In particular, the stacked denoising autoencoder (SDA) of deep learning models is selected to learn effective feature representations and we improved fault pattern classification robustness by corrupting the input data. The effectiveness and feasibility of the proposed method are validated by experiments in this paper.


title: Real-time eye-gaze estimation using a low-resolution webcam

abstract: Eye detection and gaze estimation play an important role in many applications, e.g., the eye-controlled mouse in the assisting system for disabled or elderly persons, eye fixation and saccade in psychological analysis, or iris recognition in the security system. Traditional research usually achieves eye tracking by employing intrusive infrared-based techniques or expensive eye trackers. Nowadays, there are more and more needs to analyze user behaviors from tracking eye attention in general applications, in which users usually use a consumer-grade computer or even laptop with an inexpensive webcam. To satisfy the requirements of rapid developments of such applications and reduce the cost, it is no more practical to apply intrusive techniques or use expensive/specific equipment. In this paper, we propose a real-time eye-gaze estimation system by using a general low-resolution webcam, which can estimate eye-gaze accurately without expensive or specific equipment, and also without an intrusive detection process. An illuminance filtering approach is designed to remove the influence from light changes so that the eyes can be detected correctly from the low-resolution webcam video frames. A hybrid model combining the position criterion and an angle-based eye detection strategy are also derived to locate the eyes accurately and efficiently. In the eye-gaze estimation stage, we employ the Fourier Descriptor to describe the appearance-based features of eyes compactly. The determination of eye-gaze position is then carried out by the Support Vector Machine. The proposed algorithms have high performances with low computational complexity. The experiment results also show the feasibility of the proposed methodology.


title: Cynefin : uncertainty, small worlds and scenarios

abstract: Uncertainty, its modelling and analysis have been discussed across many literatures including statistics and operational research, knowledge management and philosophy: (i) adherents to Bayesian approaches have usually argued that uncertainty should either be modelled by probabilities or resolved by discussion that clarifies meaning; (ii) some have followed Knight in distinguishing between contexts of risk and of uncertainty: the former admitting modelling and analysis through probability; the latter not; (iii) there are also host of approaches in the literatures stemming from Zadeh’s concept of a fuzzy set; (iv) theories of sense-making in the philosophy and management literatures see knowledge and uncertainty as opposite extremes of human understanding and discuss the resolution of uncertainty accordingly. Here I provide a personal perspective, taking a Bayesian stance. However, I adopt a softer position than conventional and recognise the concerns in other approaches. In particular, I use the Cynefin framework of decision contexts to reflect on processes of modelling and analysis in statistical, risk and decision analysis. The approach builds on several recent strands of discussion that argue for a convergence of qualitative scenario planning ideas and more quantitative approaches to analysis. I discuss how these suggestions and discussions relate to some earlier thinking on the methodology of modelling and, in particular, the concept of a ‘small world’ articulated by Savage.


title: An IEEE 802.11p-Based Multichannel MAC Scheme With Channel Coordination for Vehicular Ad Hoc Networks

abstract: In recent years, governments, standardization bodies, automobile manufacturers, and academia are working together to develop vehicular ad hoc network (VANET)-based communication technologies. VANETs apply multiple channels, i.e., control channel (CCH) and service channels (SCHs), to provide open public road safety services and the improve comfort and efficiency of driving. Based on the latest standard draft IEEE 802.11p and IEEE 1609.4, this paper proposes a variable CCH interval (VCI) multichannel medium access control (MAC) scheme, which can dynamically adjust the length ratio between CCH and SCHs. The scheme also introduces a multichannel coordination mechanism to provide contention-free access of SCHs. Markov modeling is conducted to optimize the intervals based on the traffic condition. Theoretical analysis and simulation results show that the proposed scheme is able to help IEEE 1609.4 MAC significantly enhance the saturated throughput of SCHs and reduce the transmission delay of service packets while maintaining the prioritized transmission of critical safety information on CCH.


title: Big Data 2.0 Processing Systems: Taxonomy and Open Challenges

abstract: Data is key resource in the modern world. Big data has become a popular term which is used to describe the exponential growth and availability of data. In practice, the growing demand for large-scale data processing and data analysis applications spurred the development of novel solutions from both the industry and academia. For a decade, the MapReduce framework, and its open source realization, Hadoop, has emerged as a highly successful framework that has created a lot of momentum in both the research and industrial communities such that it has become the defacto standard of big data processing platforms. However, in recent years, academia and industry have started to recognize the limitations of the Hadoop framework in several application domains and big data processing scenarios such as large scale processing of structured data, graph data and streaming data. Thus, we have witnessed an unprecedented interest to tackle these challenges with new solutions which constituted a new wave of mostly domain-specific, optimized big data processing platforms. In this article, we refer to this new wave of systems as Big Data 2.0 processing systems. To better understand the latest ongoing developments in the world of big data processing systems, we provide a taxonomy and detailed analysis of the state-of-the-art in this domain. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.


title: The Dreaming Variational Autoencoder for Reinforcement Learning Environments

abstract: Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.


title: A Study on Application Scenario of Video Summarization

abstract: We are living in the era where the video contents are largely available on the internet and hard disk drives. Since the duration of videos ranges from few minutes to many hours, the progression of a system for decisive content classification, browsing, indexing, retrieval and storage become essential. Also, with the advancement of the digital video technologies, huge chunks of video data are producing every day. For processing these data, video summarization plays an important role, which is a process of generating a synopsis of the video. In this paper, we present application scenarios of video summarization and some of the techniques which fit in a particular application. With fast evolution of video technology, nowadays many new multimedia applications are available. We categorize different applications of video summarization and methods for generating the summary of a video. In this paper, we introduce different types of videos and need of summarization for each type of video.


title: Improved Winding Proposal for Wound Rotor Resolver Using Genetic Algorithm and Winding Function Approach

abstract: Among position sensors, resolvers are superior from reliability point of view. However, obtaining lower output voltage harmonics and simple manufacturing process is a challenge in the design and optimization of resolvers. In this paper, a metaheuristic optimization algorithm is used to minimize total harmonic distortion of the output signals, and consequently the estimated position error in concentrated coil wound field resolvers. Meanwhile, to minimize total coil numbers, manufacturing costs, and complexity of the winding process, modified objective function and constraints are proposed. In this way, a modified winding function method is employed for performance analysis of the axial flux resolver. Then, the merits of the optimized winding configuration with respect to fractional slot concentrated windings are shown. The results of the proposed configurations are verified with a three-dimensional time-stepping finite-element method. Finally, the prototype of the studied axial flux resolver is constructed and tested. Good agreement is obtained between simulation and experimental results, confirming the effectiveness of the optimization process.


title: Remittances and Financial Inclusion in Development

abstract: In this paper we focus on the relationship between remittance inflows and financial inclusion in developing countries. We present single equation estimates on remittances and financial inclusion, and system estimates in which economic growth is explained by e.g., financial inclusion, and financial inclusion by, e.g., remittances inflows. These regressions clearly confirm our main hypothesis that remittances have a development impact through their effect on financial inclusion. Overall, our paper indicates the importance of studying the effects of remittances in developing countries. Remittances, in terms of size, are not only one of the main capital inflows in developing countries, often even more substantial than ODA, but they also appear to have a robust positive effect on economic growth.


title: A 3-D Luneburg Lens Antenna Fabricated by Polymer Jetting Rapid Prototyping

abstract: In this work, we designed, built, and tested a low-gain 20 dBi Luneburg Lens antenna using a rapid prototyping machine as a proof of concept demonstrator. The required continuously varying relative permittivity profile was implemented by changing the size of plastic blocks centered on the junctions of a plastic rod space frame. A 12-cm ( 4λ0 at 10 GHz) diameter lens is designed to work at X-band. The effective permittivity of the unit cell is calculated by effective medium theory and simulated by full-wave finite-element simulations. The fabrication is implemented by a polymer jetting rapid prototyping method. In the measurement, the lens antenna is fed by an X-band waveguide. The measured gain of the antenna at X-band is from 17.3 to 20.3 dB. The measured half-power beam width is from 19° to 12.7° while the side lobes are about 25 dB below the main peak. Good agreement between simulation and experimental results is obtained.


title: Aeromechanics of passive rotation

abstract: Flying insects and robots that mimic them flap and rotate (or 'pitch') their wings with large angular amplitudes. The reciprocating nature of flapping requires rotation of the wing at the end of each stroke. Insects or flapping-wing robots could achieve this by directly exerting moments about the axis of rotation using auxiliary muscles or actuators. However, completely passive rotational dynamics might be preferred for efficiency purposes, or, in the case of a robot, decreased mechanical complexity and reduced system mass. Herein, the detailed equations of motion are derived for wing rotational dynamics, and a blade-element model is used to supply aerodynamic force and moment estimates. Passive-rotation flapping experiments with insect-scale mechanically driven artificial wings are conducted to simultaneously measure aerodynamic forces and three-degree-of-freedom kinematics (flapping, rotation and out-of-plane deviation), allowing a detailed evaluation of the blade-element model and the derived equations of motion. Variations in flapping kinematics, wing-beat frequency, stroke amplitude and torsional compliance are made to test the generality of the model. All experiments showed strong agreement with predicted forces and kinematics, without variation or fitting of model parameters.


title: NetPilot: automating datacenter network failure mitigation

abstract: Driven by the soaring demands for always-on and fast-response online services, modern datacenter networks have recently undergone tremendous growth. These networks often rely on commodity hardware to reach immense scale while keeping capital expenses under check. The downside is that commodity devices are prone to failures, raising a formidable challenge for network operators to promptly handle these failures with minimal disruptions to the hosted services. Recent research efforts have focused on automatic failure localization. Yet, resolving failures still requires significant human interventions, resulting in prolonged failure recovery time. Unlike previous work, NetPilot aims to quickly mitigate rather than resolve failures. NetPilot mitigates failures in much the same way operators do -- by deactivating or restarting suspected offending components. NetPilot circumvents the need for knowing the exact root cause of a failure by taking an intelligent trial-and-error approach. The core of NetPilot is comprised of an Impact Estimator that helps guard against overly disruptive mitigation actions and a failure-specific mitigation planner that minimizes the number of trials. We demonstrate that NetPilot can effectively mitigate several types of critical failures commonly encountered in production datacenter networks.


title: A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks

abstract: In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance


title: What Universities Can Learn from Businesses: A Critical Research Study of Adapting Corporate Organizational Development Approach in University Environment

abstract: Universities are facing dramatic changes in their environment, affecting their strategic competitive positions and organizational effectiveness. High education is a big business, but the business approach to strategic planning and organizational development in a university environment is still rarely used. Change management and organizational interventions are among best corporate practices in addressing external and internal strategic and organizational needs. Is it possible effectively using adapted corporate approaches for strategy-driven organizational development in universities? What are factual and perceived key problems and what are key success factors? What are result's logic, time-frame, and expected benefits? This critical research study is based on 128 structured interviews, followed by open question interviews and employeesâ€™ anonymous evaluations in five comprehensive organizational development projects in one university. The projects are focused on clusters of centralized business and financial services at a statesupported mid-size northwest university. This study is highlighting a successfully adapted model and a structured implementation framework for organizational development in a specific university environment. The purpose of the study is to help understand and sharing challenges and practitionersâ€™ experience answering the overarching question: How to make organizational development project in university work?


title: Towards Linux Kernel Memory Safety

abstract: The security of billions of devices worldwide depends on the security and robustness of the mainline Linux kernel. However, the increasing number of kernel-specific vulnerabilities, especially memory safety vulnerabilities, shows that the kernel is a popular and practically exploitable target. Two major causes of memory safety vulnerabilities are reference counter overflows (temporal memory errors) and lack of pointer bounds checking (spatial memory errors). ::: To succeed in practice, security mechanisms for critical systems like the Linux kernel must also consider performance and deployability as critical design objectives. We present and systematically analyze two such mechanisms for improving memory safety in the Linux kernel: (a) an overflow-resistant reference counter data structure designed to accommodate typical reference counter usage in kernel source code, and (b) runtime pointer bounds checking using Intel MPX in the kernel.


title: The neural correlates of trustworthiness evaluations of faces and brands: Implications for behavioral and consumer neuroscience

abstract: When we buy a product of a brand, we trust the brand to provide good quality and reliability. Therefore, trust plays a major role in consumer behavior. It is unclear, however, how trust in brands is processed in the brain and whether it is processed differently from interpersonal trust. In this study, we used fMRI to investigate the neural correlates of interpersonal and brand trust by comparing the brain activation patterns during explicit trustworthiness judgments of faces and brands. Our results showed that while there were several brain areas known to be linked to trustworthiness evaluations, such as the amygdalae, more active in trustworthiness judgments when compared to a control task (familiarity judgment) for faces, no such difference was found for brands. Complementary ROI analysis revealed that the activation of both amygdalae was strongest for faces in the trustworthiness judgments. The direct comparison of the brain activation patterns during the trustworthiness evaluations between faces and brands in this analysis showed that trustworthiness judgments of faces activated the orbitofrontal cortex, another region that was previously linked to interpersonal trust, more strongly than trustworthiness judgments of brands. Further, trustworthiness ratings of faces, but not brands, correlated with activation in the orbitofrontal cortex. Our results indicate that the amygdalae, as well as the orbitofrontal cortex, play a prominent role in interpersonal trust (faces), but not in trust for brands. It is possible that this difference is due to brands being processed as cultural objects rather than as having human-like personality characteristics.


1 Integration of Data Mining Clustering Approach with 2 the Personalized E-Learning System 3


abstract: ShiViz is a new distributed system debugging visualization tool.


title: Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis

abstract: We propose a novel method for counting sentiment orientation that outperforms supervised learning approaches in time and memory complexity and is not statistically significantly different from them in accuracy. Our method consists of a novel approach to generating unigram, bigram and trigram lexicons. The proposed method, called frequentiment, is based on calculating the frequency of features (words) in the document and averaging their impact on the sentiment score as opposed to documents that do not contain these features. Afterwards, we use ensemble classification to improve the overall accuracy of the method. What is important is that the frequentiment-based lexicons with sentiment threshold selection outperform other popular lexicons and some supervised learners, while being 3–5 times faster than the supervised approach. We compare 37 methods (lexicons, ensembles with lexicon’s predictions as input and supervised learners) applied to 10 Amazon review data sets and provide the first statistical comparison of the sentiment annotation methods that include ensemble approaches. It is one of the most comprehensive comparisons of domain sentiment analysis in the literature.


title: Automatic partitioning of full-motion video

abstract: Partitioning a video source into meaningful segments is an important step for video indexing. We present a comprehensive study of a partitioning system that detects segment boundaries. The system is based on a set of difference metrics and it measures the content changes between video frames. A twin-comparison approach has been developed to solve the problem of detecting transitions implemented by special effects. To eliminate the false interpretation of camera movements as transitions, a motion analysis algorithm is applied to determine whether an actual transition has occurred. A technique for determining the threshold for a difference metric and a multi-pass approach to improve the computation speed and accuracy have also been developed.


title: Trainable ISTA for Sparse Signal Recovery

abstract: In this paper, we propose a novel sparse signal recovery algorithm called Trainable ISTA (TISTA). The proposed algorithm consists of two estimation units such as a linear estimation unit and a minimum mean squared error (MMSE) estimator-based shrinkage unit. The estimated error variance required in the MMSE shrinkage unit is precisely estimated from a tentative estimate of the original signal. The remarkable feature of the proposed scheme is that TISTA includes adjustable variables controlling a step size and the error variance for the MMSE shrinkage. The variables are adjusted by standard deep learning techniques. The number of trainable variables of TISTA is equal to the number of iteration rounds and it is much smaller than those of known learnable sparse signal recovery algorithms. This feature leads to highly stable and fast training processes of TISTA. Computer experiments show that TISTA is applicable to various classes of sensing matrices such as Gaussian matrices, binary matrices and matrices with large condition numbers. Numerical results also demonstrate that TISTA shows significantly faster convergence than those of AMP and LISTA in many cases.


title: Scene Recognition by Manifold Regularized Deep Learning Architecture

abstract: Scene recognition is an important problem in the field of computer vision, because it helps to narrow the gap between the computer and the human beings on scene understanding. Semantic modeling is a popular technique used to fill the semantic gap in scene recognition. However, most of the semantic modeling approaches learn shallow, one-layer representations for scene recognition, while ignoring the structural information related between images, often resulting in poor performance. Modeled after our own human visual system, as it is intended to inherit humanlike judgment, a manifold regularized deep architecture is proposed for scene recognition. The proposed deep architecture exploits the structural information of the data, making for a mapping between visible layer and hidden layer. By the proposed approach, a deep architecture could be designed to learn the high-level features for scene recognition in an unsupervised fashion. Experiments on standard data sets show that our method outperforms the state-of-the-art used for scene recognition.


title: Novel monopole antenna on a single AMC cell for low SAR

abstract: The design, simulations and optimized results for a novel low Specific Absorption Rate (SAR) monopole antenna on a single Artificial Magnetic Conductor (AMC) cell are described in this paper. Simulated results show a reduction close to 70 % in the 1 g psSAR for the developed monopole antenna with the AMC in comparison to the monopole antenna without AMC. This allows higher radiation efficiency, battery drain reduction as well as mobile terminal user health risks reduction.


title: A motion planner for nonholonomic mobile robots

abstract: This paper considers the problem of motion planning for a car-like robot (i.e., a mobile robot with a nonholonomic constraint whose turning radius is lower-bounded). We present a fast and exact planner for our mobile robot model, based upon recursive subdivision of a collision-free path generated by a lower-level geometric planner that ignores the motion constraints. The resultant trajectory is optimized to give a path that is of near-minimal length in its homotopy class. Our claims of high speed are supported by experimental results for implementations that assume a robot moving amid polygonal obstacles. The completeness and the complexity of the algorithm are proven using an appropriate metric in the configuration space R/sup 2//spl times/S/sup 1/ of the robot. This metric is defined by using the length of the shortest paths in the absence of obstacles as the distance between two configurations. We prove that the new induced topology and the classical one are the same. Although we concentrate upon the car-like robot, the generalization of these techniques leads to new theoretical issues involving sub-Riemannian geometry and to practical results for nonholonomic motion planning. >


Roskilde University Political Agency at the Digital Crossroads ?


Synthesis of composite hydrogels incorporating D,L-cyclic peptide nanotubes as a platform for materials engineering


Cloudstone: Multi-platform, multi-language benchmark and measurement tools for web 2.0


title: A benchmark for 3D mesh segmentation

abstract: This paper describes a benchmark for evaluation of 3D mesh segmentation salgorithms. The benchmark comprises a data set with 4,300 manually generated segmentations for 380 surface meshes of 19 different object categories, and it includes software for analyzing 11 geometric properties of segmentations and producing 4 quantitative metrics for comparison of segmentations. The paper investigates the design decisions made in building the benchmark, analyzes properties of human-generated and computer-generated segmentations, and provides quantitative comparisons of 7 recently published mesh segmentation algorithms. Our results suggest that people are remarkably consistent in the way that they segment most 3D surface meshes, that no one automatic segmentation algorithm is better than the others for all types of objects, and that algorithms based on non-local shape features seem to produce segmentations that most closely resemble ones made by humans.


title: Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach

abstract: We have proposed an end-to-end learning approach that trained a deep convolutional neural network (CNN) for ::: automatic CT image segmentation, which accomplished a voxel-wised multiple classification to directly map each voxel ::: on 3D CT images to an anatomical label automatically. The novelties of our proposed method were (1) transforming the ::: anatomical structures segmentation on 3D CT images into a majority voting of the results of 2D semantic image ::: segmentation on a number of 2D-slices from different image orientations, and (2) using “convolution” and “deconvolution” ::: networks to achieve the conventional “coarse recognition” and “fine extraction” functions which were ::: integrated into a compact all-in-one deep CNN for CT image segmentation. The advantage comparing to previous works ::: was its capability to accomplish real-time image segmentations on 2D slices of arbitrary CT-scan-range (e.g. body, chest, ::: abdomen) and produced correspondingly-sized output. In this paper, we propose an improvement of our proposed ::: approach by adding an organ localization module to limit CT image range for training and testing deep CNNs. A ::: database consisting of 240 3D CT scans and a human annotated ground truth was used for training (228 cases) and ::: testing (the remaining 12 cases). We applied the improved method to segment pancreas and left kidney regions, ::: respectively. The preliminary results showed that the accuracies of the segmentation results were improved significantly ::: (pancreas was 34% and kidney was 8% increased in Jaccard index from our previous results). The effectiveness and ::: usefulness of proposed improvement for CT image segmentations were confirmed.


title: Mitigating the Risk of Cloud Services Downtime Using Live Migration and High Availability-Aware Placement

abstract: The growing dependency of users on social media, telecommunication services, mobile applications, banking amenities, and other cloud services requires a plan that mitigates inevitable failures and ensures the always-on access to these services. This emanates high availability (HA) concerns regarding the adoption of cloud. To maintain HA, the cloud provider and/or user should design a system that is immune to both application and infrastructure failures. This paper proposes live migration approach to maintain service delivery upon a sudden failure, a virtual machine (VM)/infrastructure overload, or maintenance. It develops a mixed integer linear programming model that minimizes the migration downtime based on the VM memory pages and the optimal HA-aware placement of the VM. It also provides different design considerations to achieve HA-aware applications placement. The proposed placement is used in the migration approach to find new hosts for the VMs. It considers VMs/applications deployments in geographically distributed data centers and satisfies redundancy, applications interdependency, and other HA and performance requirements. Then the deployments are assessed using a formal Petri Net model to improve them in terms of HA. The HA-aware placement and migration approaches are evaluated on 3-tier web applications.


title: Prediction of Time-varying Musical Mood Distributions from Audio

abstract: The appeal of music lies in its ability to express emotions, and it is natural for us to organize music in terms of emotional associations. But the ambiguities of emotions make the determination of a single, unequivocal response label for the mood of a piece of music unrealistic. We address this lack of specificity by modeling human response labels to music in the arousal-valence (A-V) representation of affect as a stochastic distribution. Based upon our collected data, we present and evaluate methods using multiple sets of acoustic features to estimate these mood distributions parametrically using multivariate regression. Furthermore, since the emotional content of music often varies within a song, we explore the estimation of these A-V distributions in a time-varying context, demonstrating the ability of our system to track changes on a short-time basis.


title: Blind deconvolution using a normalized sparsity measure

abstract: Blind image deconvolution is an ill-posed problem that requires regularization to solve. However, many common forms of image prior used in this setting have a major drawback in that the minimum of the resulting cost function does not correspond to the true sharp solution. Accordingly, a range of additional methods are needed to yield good results (Bayesian methods, adaptive cost functions, alpha-matte extraction and edge localization). In this paper we introduce a new type of image regularization which gives lowest cost for the true sharp image. This allows a very simple cost formulation to be used for the blind deconvolution model, obviating the need for additional methods. Due to its simplicity the algorithm is fast and very robust. We demonstrate our method on real images with both spatially invariant and spatially varying blur.


title: Graph mining for next generation sequencing: leveraging the assembly graph for biological insights

abstract: BackgroundThe assembly of Next Generation Sequencing (NGS) reads remains a challenging task. This is especially true for the assembly of metagenomics data that originate from environmental samples potentially containing hundreds to thousands of unique species. The principle objective of current assembly tools is to assemble NGS reads into contiguous stretches of sequence called contigs while maximizing for both accuracy and contig length. The end goal of this process is to produce longer contigs with the major focus being on assembly only. Sequence read assembly is an aggregative process, during which read overlap relationship information is lost as reads are merged into longer sequences or contigs. The assembly graph is information rich and capable of capturing the genomic architecture of an input read data set. We have developed a novel hybrid graph in which nodes represent sequence regions at different levels of granularity. This model, utilized in the assembly and analysis pipeline Focus, presents a concise yet feature rich view of a given input data set, allowing for the extraction of biologically relevant graph structures for graph mining purposes.ResultsFocus was used to create hybrid graphs to model metagenomics data sets obtained from the gut microbiomes of five individuals with Crohn’s disease and eight healthy individuals. Repetitive and mobile genetic elements are found to be associated with hybrid graph structure. Using graph mining techniques, a comparative study of the Crohn’s disease and healthy data sets was conducted with focus on antibiotics resistance genes associated with transposase genes. Results demonstrated significant differences in the phylogenetic distribution of categories of antibiotics resistance genes in the healthy and diseased patients. Focus was also evaluated as a pure assembly tool and produced excellent results when compared against the Meta-velvet, Omega, and UD-IDBA assemblers.ConclusionsMining the hybrid graph can reveal biological phenomena captured by its structure. We demonstrate the advantages of considering assembly graphs as data-mining support in addition to their role as frameworks for assembly.


title: Big data as a promoter of industry 4.0: Lessons of the semiconductor industry

abstract: The catchphrase “Industry 4.0” is widely regarded as a methodology for succeeding in modern manufacturing. This paper provides an overview of the history, technologies and concepts of Industry 4.0. One of the biggest challenges to implementing the Industry 4.0 paradigms in manufacturing are the heterogeneity of system landscapes and integrating data from various sources, such as different suppliers and different data formats. These issues have been addressed in the semiconductor industry since the early 1980s and some solutions have become well-established standards. Hence, the semiconductor industry can provide guidelines for a transition towards Industry 4.0 in other manufacturing domains. In this work, the methodologies of Industry 4.0, cyber-physical systems and Big data processes are discussed. Based on a thorough literature review and experiences from the semiconductor industry, we offer implementation recommendations for Industry 4.0 using the manufacturing process of an electronics manufacturer as an example.


title: Routing Protocols for Wireless Sensor Networks: Classifications and Challenges

abstract: Sensor is an object used to gather information about a physical process, including the occurrence of events. Hence Wireless Sensor Network (WSN) consists of individual sensors or nodes that collaborate to fulfill certain task; and these nodes use wireless communication to enable their collaboration. Routing protocols are in charge of discovering and maintaining the routes in the network. However, the appropriateness of a particular routing protocol mainly depends on the capabilities of the nodes and on the application requirements. In this paper, we classify the routing protocols proposed for WSN. The classification is based on five main key factors namely: energy efficiency, operational model, routing objectives, network architecture, and route selection. Some of these criteria are further detailed classified. We also highlighted some overlapping characteristics of some protocols. The design challenges of WSN are discussed with its application area.