title: Curriculum Learning for Depth Estimation with Deep Convolutional Neural Networks

abstract: Curriculum learning is a machine learning technique adapted from the way humans acquire knowledge and skills, initially mastering simple tasks and progressing to more complex tasks. The work explores curriculum training by creating multiple levels of dataset with increasing complexity on which the trainings are performed. The experiments demonstrated that there is an average of 12% improvement test loss when compared to a non-curriculum approach. The experiment also demonstrates the advantage of creating synthetic dataset and how it aids in the overall improvement of accuracy. An improvement of 26% is attained on the test error loss when curriculum trained model was compared to training on a limited real world dataset. The work also goes onto propose a novel learning approach, the Self Paced Learning approach with Error-Diversity (SPL-ED) An overall reduction of 32% in the test loss is observed when compared to the non-curriculum training limited to real-world dataset.


Fully Automatic Segmentation of Sphenoid Sinus in CT Images with 3 D Convolutional Neural Networks


title: THE DIGITAL FUTURE: REFLECTING ON THE “DARK SIDE” OF INFORMATION TECHNOLOGY USE

abstract: The objective of this panel is to advance knowledge in areas related to the “dark” side of IT use in organizations. Emerging and as yet nascent academic research is beginning to focus on negative impacts of IT use. The panel seeks to (1) introduce a new area of exploration and (2) disseminate new points of view. The panelists will do so by drawing upon their research and practice related insights in five areas that characterize the dark side of IT use, namely - IT usage related stress, addiction, misuse, work overload, and interruptions. These are clearly important areas to examine, given that the ubiquitous and functionally pervasive nature of IT use in organizations is expected to expose users to ever greater levels of conditions that are potent for experiencing this dark side. The proposed panel is thus of relevance and interest to the IS research and practice communities.


title: Brooks' versus Linus' law: an empirical test of open source projects

abstract: In this paper, we investigate collective action in open source software development, where both volunteers and paid professionals essentially produce a public good. By using a large dataset, our logistic regression shows that adding more programming staff to the software projects increases their probability of success. This finding contradicts some dominant theories on software development as well as on the provision of public goods. As governments worldwide increasingly rely on open source software, our analysis has concrete implications for the public sector in contributing to the success of these projects.


title: Scalable trigger processing

abstract: Current database trigger systems have extremely limited scalability. This paper proposes a way to develop a truly scalable trigger system. Scalability to large numbers of triggers is achieved with a trigger cache to use the main memory effectively, and a memory-conserving selection predicate index based on the use of unique expression formats called expression signatures. A key observation is that if a very large number of triggers are created, many will have the same structure, except for the appearance of different constant values. When a trigger is created, tuples are added to special relations created for expression signatures to hold the trigger's constants. These tables can be augmented with a database index or main-memory index structure to serve as a predicate index. The design presented also uses a number of types of concurrency to achieve scalability, including token (tuple)-level, condition-level, rule action-level and data-level concurrency.


title: Aircraft Type Recognition Based on Segmentation With Deep Convolutional Neural Networks

abstract: Aircraft type recognition in remote sensing images is a meaningful task. It remains challenging due to the difficulty of obtaining appropriate representation of aircrafts for recognition. To solve this problem, we propose a novel aircraft type recognition framework based on deep convolutional neural networks. First, an aircraft segmentation network is designed to obtain refined aircraft segmentation results which provide significant details to distinguish different aircrafts. Then, a keypoints’ detection network is proposed to acquire aircrafts’ directions and bounding boxes, which are used to align the segmentation results. A new multirotation refinement method is carefully designed to further improve the keypoints’ precision. At last, we apply a template matching method to identify aircrafts, and the intersection over union is adopted to evaluate the similarity between segmentation results and templates. The proposed framework takes advantage of both shape and scale information of aircrafts for recognition. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 95.6% accuracy on the challenging data set.


title: Data mining and machine learning in computational creativity

abstract: Creative machines are an old idea, but only recently computational creativity has established itself as a research field with its own identity and research agenda. The goal of computational creativity research is to model, simulate, or enhance creativity using computational methods. Data mining and machine learning can be used in a number of ways to help computers learn how to be creative, such as learning to generate new artifacts or to evaluate various qualities of newly generated artifacts. In this review paper, we give an overview of research in computational creativity with a focus on the roles that data mining and machine learning have had and could have in creative systems. WIREs Data Mining Knowl Discov 2015, 5:265-275. doi: 10.1002/widm.1170


title: Closed-Loop and Activity-Guided Optogenetic Control

abstract: Advances in optical manipulation and observation of neural activity have set the stage for widespread implementation of closed-loop and activity-guided optical control of neural circuit dynamics. Closing the loop optogenetically (i.e., basing optogenetic stimulation on simultaneously observed dynamics in a principled way) is a powerful strategy for causal investigation of neural circuitry. In particular, observing and feeding back the effects of circuit interventions on physiologically relevant timescales is valuable for directly testing whether inferred models of dynamics, connectivity, and causation are accurate in vivo. Here we highlight technical and theoretical foundations as well as recent advances and opportunities in this area, and we review in detail the known caveats and limitations of optogenetic experimentation in the context of addressing these challenges with closed-loop optogenetic control in behaving animals.


title: A Robust Fuzzy Local Information C-Means Clustering Algorithm

abstract: This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ¿(g), ¿(s), etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.


title: Hierarchical Attention Networks for Knowledge Base Completion via Joint Adversarial Training

abstract: Knowledge Base (KB) completion, which aims to determine missing relation between entities, has raised increasing attention in recent years. Most existing methods either focus on the positional relationship between entity pair and single relation (1-hop path) in semantic space or concentrate on the joint probability of Random Walks on multi-hop paths among entities. However, they do not fully consider the intrinsic relationships of all the links among entities. By observing that the single relation and multi-hop paths between the same entity pair generally contain shared/similar semantic information, this paper proposes a novel method to capture the shared features between them as the basis for inferring missing relations. To capture the shared features jointly, we develop Hierarchical Attention Networks (HANs) to automatically encode the inputs into low-dimensional vectors, and exploit two partial parameter-shared components, one for feature source discrimination and the other for determining missing relations. By joint Adversarial Training (AT) the entire model, our method minimizes the classification error of missing relations, and ensures the source of shared features are difficult to discriminate in the meantime. The AT mechanism encourages our model to extract features that are both discriminative for missing relation prediction and shareable between single relation and multi-hop paths. We extensively evaluate our method on several large-scale KBs for relation completion. Experimental results show that our method consistently outperforms the baseline approaches. In addition, the hierarchical attention mechanism and the feature extractor in our model can be well interpreted and utilized in the related downstream tasks.


title: Tackling heart failure in the twenty-first century

abstract: Heart failure, or congestive heart failure, is a condition in which the heart cannot supply the body's tissues with enough blood. The result is a cascade of changes that lead to severe fatigue, breathlessness and, ultimately, death. In the past quarter century, much progress has been made in understanding the molecular and cellular processes that contribute to heart failure, leading to the development of effective therapies. Despite this, chronic heart failure remains a major cause of illness and death. And because the condition becomes more common with increasing age, the number of affected individuals is rising with the rapidly ageing global population. New treatments that target disease mechanisms at the cellular and whole-organ level are needed to halt and reverse the devastating consequences of this disease.


title: Compact pseudo-highpass filters formed by cavity and iris resonators

abstract: A new class of broadband waveguide filters for pseudo-highpass applications is presented. The cavities are initially designed for TM110-mode operation with TE10-mode bypass coupling to create transmission zeros below the passband. The broadband characteristic is achieved by utilizing the coupling irises as additional resonances, thus achieving a pseudo-highpass operation with a minimum of physical cavities. Several designs for passbands starting at around 10 and 12 GHz are presented, and bandwidths of up to 41 percent are achieved. The design process allows for implementation of either vertical or horizontal (inline) I/O ports. Both types are verified by measurements.


title: Downlink User Capacity of Massive MIMO Under Pilot Contamination

abstract: Pilot contamination has been regarded as a main limiting factor of time division duplexing (TDD) massive multiple-input-multiple-output (Massive MIMO) systems, as it will make the signal-to-interference-plus-noise ratio (SINR) saturated. However, how pilot contamination will limit the user capacity of downlink Massive MIMO, i.e., the maximum number of users whose SINR targets can be achieved, has not been addressed. This paper provides an explicit expression of the Massive MIMO user capacity in the pilot-contaminated regime where the number of users is larger than the pilot sequence length. This capacity expression characterizes a region within which a set of SINR requirements can be jointly satisfied. The size of this region is fundamentally limited by the pilot sequence length. Furthermore, the scheme for achieving the user capacity, i.e., the uplink pilot training sequences and downlink power allocation, has been identified. Specifically, the generalized Welch bound equality sequences are exploited and it is shown that the power allocated to each user should be proportional to its SINR target. With this capacity-achieving scheme, the SINR requirement of each user can be satisfied and energy-efficient transmission is achieved in the large-antenna-size (LAS) regime. The comparison with two non-capacity-achieving schemes highlights the superiority of our proposed scheme in terms of achieving higher user capacity. Furthermore, for the practical scenario with a finite number of antennas, the actual antenna size required to achieve a significant percentage of the asymptotic performance has been analytically quantified.


title: No Please, After You: Detecting Fraud in Affiliate Marketing Networks

abstract: Cookie stuffing is an activity which allows unscrupulous actors online to defraud affiliate marketing programs by causing themselves to receive credit for purchases made by web users, even if the affiliate marketer did not actively perform any marketing for the affiliate program. Using two months of HTTP request logs from a large public university, we present an empirical study of fraud in affiliate marketing programs. First, we develop an efficient, decision-tree based technique for detecting cookie-stuffing in HTTP request logs. Our technique replicates domain-informed human labeling of the same data with 93.3% accuracy. Second, we find that over one-third of publishers in affiliate marketing programs use fraudulent cookie-stuffing techniques in an attempt to claim credit from online retailers for illicit referrals. However, most realized conversions are credited to honest publishers. Finally, we present a stake holder analysis of affiliate marketing fraud and find that the costs and rewards of affiliate marketing program are spread across all parties involved in affiliate marketing programs.


title: Computational microscopic imaging for malaria parasite detection: a systematic review.

abstract: Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image-based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error-prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer-aided classification for malaria parasite detection.


title: Balls-into-bins with nearly optimal load distribution

abstract: We consider sequential balls-into-bins processes that randomly allocate m balls into n bins. We analyze two allocation schemes that achieve a close to optimal maximum load of ⌈m/n⌉ + 1 and require only O(m) (expected) allocation time. These parameters should be compared with the classic d-choice-process which achieves a maximum load of m/n + log log n/d + O(1) and requires m • d allocation time.


title: FluidRAN: Optimized vRAN/MEC Orchestration

abstract: Virtualized Radio Access Network (vRAN) architectures constitute a promising solution for the densification needs of 5G networks, as they decouple Base Stations (BUs) functions from Radio Units (RUs) allowing the processing power to be pooled at cost-efficient Central Units (CUs). vRAN facilitates the flexible function relocation (split selection), and therefore enables splits with less stringent network requirements compared to state-of-the-art fully Centralized (C-RAN) systems. In this paper, we study the important and challenging vRAN design problem. We propose a novel modeling approach and a rigorous analytical framework, FluidRAN, that minimizes RAN costs by jointly selecting the splits and the RUs-CUs routing paths. We also consider the increasingly relevant scenario where the RAN needs to support multi-access edge computing (MEC) services, that naturally favor distributed RAN (D-RAN) architectures. Our framework provides a joint vRAN/MEC solution that minimizes operational costs while satisfying the MEC needs. We follow a data-driven evaluation method, using topologies of 3 operational networks. Our results reveal that (i) pure C-RAN is rarely a feasible upgrade solution for existing infrastructure, (ii) FluidRAN achieves significant cost savings compared to D-RAN systems, and (iii) MEC can increase substantially the operator's cost as it pushes vRAN function placement back to RUs.


title: Process-Driven Data Quality Management -- An Application of the Combined Conceptual Life Cycle Model

abstract: Process-driven data quality management, which allows sustaining data quality improvements within and beyond the IS domain, is increasingly important. The emphasis on and the integration of data quality into process models allows for a detailed, context-specific definition as well as understanding of data quality (dimensions) and, thus, supports communication across stakeholders. Extant process modeling approaches lack an explicit reference from data quality dimensions to context-specific information product (IP) production. Therefore, we provide a process-driven application of the combined conceptual life cycle (CCLC) model for process exploration and data quality improvement. The paper presents an interpretive, in-depth case study in a medium-sized company, which launched a process optimization initiative to improve data quality. The results show benefits and limitations of the approach, allowing practitioners to tailor the approach to their needs. Based on our insights, suggestions for further improvements of the CCLC model for a process-driven IP production approach are provided.


title: Targeting von Willebrand factor and platelet glycoprotein Ib receptor.

abstract: Atherothrombotic events, such as acute coronary syndrome or stroke, are the result of platelet activation. Von Willebrand factor (vWF), a multimeric glycoprotein, plays a key role in aggregation of platelets, especially under high-shear conditions. Acting as bridging element or ligand between damaged endothelial sites and the glycoprotein Ib (GPIb) receptor on platelets, vWF is responsible for platelet adhesion and aggregation. This vWF activation and further platelet aggregation mainly occurs under high shear stress present in small arterioles or during deficiency of the vWF-cleaving protease ADAMTS13. There are several substances targeting vWF itself or its binding receptor GPIb on platelets. Two antibodies are directed against vWF: AJW200, an IgG4 humanized monoclonal antibody, and 82D6A3, a monoclonal antibody of the collagen-binding A-3 domain of vWF. ALX-0081 and ALX-0681 are bivalent humanized nanobodies targeting the GPIb binding site of vWF. Aptamers are oligonucleotides with drug-like properties that share some of the attributes of monoclonal antibodies. ARC1779 is a second-generation, nuclease-resistant aptamer, binding to the activated vWF A1 domain and ARC15105 is a chemically advanced follower with an assumed higher affinity to vWF. Antibodies targeting GPIbα are h6B4-Fab, a murine monoclonal antibody; GPG-290, a recombinant, chimeric protein containing the amino-terminal 290 amino acids of GPIbα linked to human IgG1 Fc; and the monoclonal antibody SZ2. There are a number of promising preclinical results and development of some agents (AJW 200, ARC1779 and ALX-0081) has already reached Phase II trials.


title: Proofs of retrievability with public verifiability and constant communication cost in cloud

abstract: For data storage outsourcing services, it is important to allow data owners to efficiently and securely verify that the storage server stores their data correctly. To address this issue, several proof-of-retrievability (POR) schemes have been proposed wherein a storage server must prove to a verifier that all of a client's data are stored correctly. While existing POR schemes offer decent solutions addressing various practical issues, they either have a non-trivial (linear or quadratic) communication complexity, or only support private verification, i.e., only the data owner can verify the remotely stored data. It remains open to design a POR scheme that achieves both public verifiability and constant communication cost simultaneously. In this paper, we solve this open problem and propose the first POR scheme with public verifiability and constant communication cost: in our proposed scheme, the message exchanged between the prover and verifier is composed of a constant number of group elements; different from existing private POR constructions, our scheme allows public verification and releases the data owners from the burden of staying online. We achieved these by tailoring and uniquely combining techniques such as constant size polynomial commitment and homomorphic linear authenticators. Thorough analysis shows that our proposed scheme is efficient and practical. We prove the security of our scheme based on the Computational Diffie-Hellman Problem, the Strong Diffie-Hellman assumption and the Bilinear Strong Diffie-Hellman assumption.


title: Human Behavior A self-awareness approach to computer-mediated communication

abstract: Many explanations of both pro- and anti-social behaviors in computer-mediated communication (CMC) appear to hinge on changes in individual self-awareness. In spite of this, little research has been devoted to understanding the effects of self-awareness in CMC. To fill this void, this study examined the effects of individuals' public and private self-awareness in anonymous, time-restricted, and synchronous CMC. Two experiments were conducted. A pilot experiment tested and confirmed the effectiveness of using a Web camera combined with an alleged online audience to enhance users' public self-awareness. In the main study users' private and public self-awareness were manipulated in a crossed 2x2 factorial design. Pairs of participants completed a Desert Survival Problem via a synchronous, text-only chat program. After the task, they evaluated each other on intimacy, task/social orientation, formality, politeness, attraction, and group identification. The results suggest that a lack of private and public self-awareness does not automatically lead to impersonal tendencies in CMC as deindividuation perspectives of CMC would argue. Moreover, participants in this study were able to form favorable impressions in a completely anonymous environment based on brief interaction, which lends strong support to the idealization proposed by hyperpersonal theory. Findings are used to modify and extend current theoretical perspectives on CMC.


title: A Graph Analytical Approach for Topic Detection

abstract: Topic detection with large and noisy data collections such as social media must address both scalability and accuracy challenges. KeyGraph is an efficient method that improves on current solutions by considering keyword cooccurrence. We show that KeyGraph has similar accuracy when compared to state-of-the-art approaches on small, well-annotated collections, and it can successfully filter irrelevant documents and identify events in large and noisy social media collections. An extensive evaluation using Amazon’s Mechanical Turk demonstrated the increased accuracy and high precision of KeyGraph, as well as superior runtime performance compared to other solutions.


title: Are mutation scores correlated with real fault detection?: a large scale empirical study on the relationship between mutants and real faults

abstract: Empirical validation of software testing studies is increasingly relying on mutants. This practice is motivated by the strong correlation between mutant scores and real fault detection that is reported in the literature. In contrast, our study shows that correlations are the results of the confounding effects of the test suite size. In particular, we investigate the relation between two independent variables, mutation score and test suite size, with one dependent variable the detection of (real) faults. We use two data sets, CoreBench and De-fects4J, with large C and Java programs and real faults and provide evidence that all correlations between mutation scores and real fault detection are weak when controlling for test suite size. We also found that both independent variables significantly influence the dependent one, with significantly better fits, but overall with relative low prediction power. By measuring the fault detection capability of the top ranked, according to mutation score, test suites (opposed to randomly selected test suites of the same size), we found that achieving higher mutation scores improves significantly the fault detection. Taken together, our data suggest that mutants provide good guidance for improving the fault detection of test suites, but their correlation with fault detection are weak.


title: Using Social Media for Continuous Monitoring and Mining of Consumer Behaviour

abstract: Social communication and microblogging services have known unprecedented popularity in recent years. This new digital landscape, combined with the ubiquitous online access potential of modern devices, provides novel capabilities to online users and allows them to express their opinions and attitudes about everything in almost real time. In this paper, we present an investigation on the use of social media for the continuous monitoring and mining of consumer behaviour. We analyse hundreds of thousands of microblogging messages containing comments, sentiments and opinions about food and brand products. We present initial results, which confirm previous studies about the potential of using social media monitoring for branding purposes. The results provide strong indications that given the use of such services by millions of users, they can play a key role in supporting and enhancing important business processes. Examples of such processes include company-to-customer relationship management, brand image building and Word-of-Mouth (WoM) branding.


title: 30 GHz CMOS Low Noise Amplifier

abstract: 30 GHz low noise amplifier was designed and fabricated in a 90 nm digital CMOS process. The mm-wave amplifier has a peak gain of 20 dB at 28.5 GHz and a 3 dB bandwidth of 2.6 GHz with the input and output matching better than 12 dB and 17 dB over the entire band respectively. The NF is 2.9 dB at 28 GHz and less than 4.2 dB across the band and it can deliver 2 dBm of power to a matched load at its 1 dB compression point. The amplifier has a measured linearity of IIIP3=-7.5 dBm. It consumes 16.25 mW of power using a low supply voltage of 1 V and occupies an area (excluding the pads) of 1600 mum x 420 mum.


title: Traffic monitoring and DDoS detection using stateful SDN

abstract: We propose to showcase the benefits of stateful SDN in the context of DDoS detection and mitigation. By delegating some local tasks to the switch rather than relying always on the controller, it is possible to monitor data-plane traffic efficiently and to detect malicious network behaviours with high accuracy. Stateful SDN concepts are employed both to improve reactivity and to offload the controller and the control channel by delegating local treatments down to the switches. The demo illustrates how to protect end-hosts from Distributed Denial of Service (DDoS) attacks. Our approach, named StateSec, is built on advanced in-switch processing capabilities to detect and mitigate threats swiftly. StateSec relies on a detection loop to: 1) match and count a configurable set of traffic features (e.g., IP source and destination, port source and destination) without resorting to the controller; 2) use an entropy-based detection algorithm with such monitored features, 3) detect several threats such as (D)DoS and port scans with high accuracy, and 4) take countermeasures by installing OpenFlow rules at the switch.


title: Temporal Data Mining : an overview

abstract: One of the main unresolved problems that arise during the data mining process is treating data that contains temporal information. In this case, a complete understanding of the entire phenomenon requires that the data should be viewed as a sequence of events. Temporal sequences appear in a vast range of domains, from engineering, to medicine and finance, and the ability to model and extract information from them is crucial for the advance of the information society. This paper provides a survey on the most significant techniques developed in the past ten years to deal with temporal sequences.


title: Two-stage pooling of deep convolutional features for image retrieval

abstract: Convolutional Neural Network (CNN) based image representations have achieved high performance in image retrieval tasks. However, traditional CNN based global representations either provide high-dimensional features, which incurs large memory consumption and computing cost, or inadequately capture discriminative information in images, which degenerates the functionality of CNN features. To address those issues, we propose a two-stage partial mean pooling (PMP) approach to construct compact and discriminative global feature representations. The proposed PMP is meant to tackle the limits of traditional max pooling and mean (or average) pooling. By injecting the PMP pooling strategy into the CNN based patch-level mid-level feature extraction and representation, we have significantly improved the state-of-the-art retrieval performance over several common benchmark datasets.


title: Detection and Tracking in Thermal Infrared Imagery

abstract: Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing price and size during recent years have, howeve ...


title: An all-analog multiphase delay-locked loop using a replica delay line for wide-range operation and low-jitter performance

abstract: This paper describes an all-analog multiphase delay-locked loop (DLL) architecture that achieves both wide-range operation and low-jitter performance. A replica delay line is attached to a conventional DLL to fully utilize the frequency range of the voltage-controlled delay line. The proposed DLL keeps the same benefits of conventional DLLs such as good jitter performance and multiphase clock generation. The DLL incorporates dynamic phase detectors and triply controlled delay cells with cell-level duty-cycle correction capability to generate equally spaced eight-phase clocks. The chip has been fabricated using a 0.35-/spl mu/m CMOS process. The peak-to peak jitter is less than 30 ps over the operating frequency range of 62.5-250 MHz, At 250 MHz, its jitter supply sensitivity is 0.11 ps/mV. It occupies smaller area (0.2 mm/sup 2/) and dissipates less power (42 mW) than other wide-range DLL's [2]-[7].


title: Scatternet hybrid deep learning (SHDL) network for object classification

abstract: The paper proposes the ScatterNet Hybrid Deep Learning (SHDL) network that extracts invariant and discriminative image representations for object recognition. SHDL framework is constructed with a multi-layer ScatterNet front-end, an unsupervised learning middle, and a supervised learning back-end module. Each layer of the SHDL network is automatically designed as an explicit optimization problem leading to an optimal deep learning architecture with improved computational performance as compared to the more usual deep network architectures. SHDL network produces the state-of-the-art classification performance against unsupervised and semi-supervised learning (GANs) on two image datasets. Advantages of the SHDL network over supervised methods (NIN, VGG) are also demonstrated with experiments performed on training datasets of reduced size.


Learning Cost-Effective and Interpretable Treatment Regimes


title: A Recursive Recurrent Neural Network for Statistical Machine Translation

abstract: In this paper, we propose a novel recursive recurrent neural network (R 2 NN) to model the end-to-end decoding process for statistical machine translation. R 2 NN is a combination of recursive neural network and recurrent neural network, and in turn integrates their respective capabilities: (1) new information can be used to generate the next hidden state, like recurrent neural networks, so that language model and translation model can be integrated naturally; (2) a tree structure can be built, as recursive neural networks, so as to generate the translation candidates in a bottom up manner. A semi-supervised training approach is proposed to train the parameters, and the phrase pair embedding is explored to model translation confidence directly. Experiments on a Chinese to English translation task show that our proposed R 2 NN can outperform the stateof-the-art baseline by about 1.5 points in BLEU.


title: Network Anomaly Detection with Stochastically Improved Autoencoder Based Models

abstract: Intrusion detection systems do not perform well when it comes to detecting zero-day attacks, therefore improving their performance in that regard is an active research topic. In this study, to detect zero-day attacks with high accuracy, we proposed two deep learning based anomaly detection models using autoencoder and denoising autoencoder respectively. The key factor that directly affects the accuracy of the proposed models is the threshold value which was determined using a stochastic approach rather than the approaches available in the current literature. The proposed models were tested using the KDDTest+ dataset contained in NSL-KDD, and we achieved an accuracy of 88.28% and 88.65% respectively. The obtained results show that, as a singular model, our proposed anomaly detection models outperform any other singular anomaly detection methods and they perform almost the same as the newly suggested hybrid anomaly detection models.


title: 3-D Shape Display of Intelligent Colonoscope Based on FBG Sensor Array and Binocular Vision

abstract: The display of the colonoscope shape inside human body is a new technique under development which helps the doctor to judge visually if the patient colon is in normal situation or tends to twist the colonoscope into an unusual and unsafe shape. This paper presents a real-time shape reconstruction method based on biber Bragg grating (FBG) sensor array and a positioning technique of colonoscope's handle part by means of binocular vision, while the colonoscope inserted into human colon. Detailed analysis include the detecting principles, spatial curve reconstruction algorithm from the information of FBG sensors, and estimation of pose of the handle part of the colonoscope relative to the world frame. Experimental results demonstrate the effectiveness of the proposed techniques. With an accuracy of shape reconstruction to be improved to 4.5 mm compared with earlier research. By adding the pose detecting subsystem for the colonoscope's handle part, the 3D shape display subsystem will work more effectively and become more practical.


title: A Music Recommendation System Based on Music and User Grouping

abstract: In this paper, we present a music recommendation system, which provides a personalized service of music recommendation. The polyphonic music objects of MIDI format are first analyzed for deriving information for music grouping. For this purpose, the representative track of each polyphonic music object is first determined, and then six features are extracted from this track for proper music grouping. Moreover, the user access histories are analyzed to derive the profiles of user interests and behaviors for user grouping. The content-based, collaborative, and statistics-based recommendation methods are proposed based on the favorite degrees of the users to the music groups, and the user groups they belong to. A series of experiments are carried out to show that our approach performs well.


title: Web Document Classification by Keywords Using Random Forests

abstract: Web directory hierarchy is critical to serve user’s search request. Creating and maintaining such directories without human experts involvement requires good classification of web documents. In this paper, we explore web page classification using keywords from documents as attributes and using the random forest learning methods. Our initially results are promising that the random forests learning method performed better than several other well known learning methods. When the number of topics increased from five to seven, random forests still performed better than other methods even though absolute classification rates decreased.


title: Boosting Question Answering by Deep Entity Recognition

abstract: In this paper an open-domain factoid question answering system for Polish, RAFAEL, is presented. The system goes beyond finding an answering sentence; it also extracts a single string, corresponding to the required entity. Herein the focus is placed on different approaches to entity recognition, essential for retrieving information matching question constraints. Apart from traditional approach, including named entity recognition (NER) solutions, a novel technique, called Deep Entity Recognition (DeepER), is introduced and implemented. It allows a comprehensive search of all forms of entity references matching a given WordNet synset (e.g. an impressionist), based on a previously assembled entity library. It has been created by analysing the first sentences of encyclopaedia entries and disambiguation and redirect pages. DeepER also provides automatic evaluation, which makes possible numerous experiments, including over a thousand questions from a quiz TV show answered on the grounds of Polish Wikipedia. The final results of a manual evaluation on a separate question set show that the strength of DeepER approach lies in its ability to answer questions that demand answers beyond the traditional categories of named entities.


title: Genetic Reinforcement Learning for Neurocontrol Problems

abstract: Empirical tests indicate that at least one class of genetic algorithms yields good performance for neural network weight optimization in terms of learning rates and scalability. The successful application of these genetic algorithms to supervised learning problems sets the stage for the use of genetic algorithms in reinforcement learning problems. On a simulated inverted-pendulum control problem, “genetic reinforcement learning” produces competitive results with AHC, another well-known reinforcement learning paradigm for neural networks that employs the temporal difference method. These algorithms are compared in terms of learning rates, performance-based generalization, and control behavior over time.


title: Deep learning from temporal coherence in video

abstract: This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks.


title: Learning to Generalize: Meta-Learning for Domain Generalization

abstract: Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. {Domain Generalization} (DG) techniques attempt to alleviate this issue by producing models which by design generalize well to novel testing domains. We propose a novel {meta-learning} method for domain generalization. Rather than designing a specific model that is robust to domain shift as in most previous DG work, we propose a model agnostic training procedure for DG. Our algorithm simulates train/test domain shift during training by synthesizing virtual testing domains within each mini-batch. The meta-optimization objective requires that steps to improve training domain performance should also improve testing domain performance. This meta-learning procedure trains models with good generalization ability to novel domains. We evaluate our method and achieve state of the art results on a recent cross-domain image classification benchmark, as well demonstrating its potential on two classic reinforcement learning tasks.


title: Fuzzy logic based sentiment analysis of product review documents

abstract: Sentiment Analysis is the process of determining subjectivity, polarity and polarity strength of a piece of text. Survey shows that 81% of Internet users have done on-line research on a product at least once. Manual analysis of this reviews is difficult. To mine the overall sentiment or opinion polarity, sentiment analysis can be used to mine the overall sentiment or opinion polarity of the review. The objective of this paper is to extract features from the product reviews and classify reviews into positive, negative and neutral. Feature based sentiment classification approach is used to extract sentiments. It involves preprocessing to remove noise, extraction of features and corresponding descriptors and tagging their polarity. The proposed technique extends the feature based classification approach to incorporate the effect of various linguistic hedges. This approach uses fuzzy functions to emulate the effect of modifiers, concentrators and dilators. The system was evaluated with SFU corpus and the results suggest that sentiment analysis using fuzzy logic performs remarkably well.


title: ACPP: A Web Server for Prediction and Design of Anti-cancer Peptides

abstract: Cancer is one of the most common diseases, which causes more mortality worldwide. Despite the presence of several therapies against cancer, peptides as therapeutic agents are gaining importance. Experimental studies report that peptides containing apoptotic domain exhibit anticancer activity. Hence in this study, we propose a computational method using support vector machine and protein relatedness measure feature vector, in which provision was made to assess the query protein for the presence of any apoptotic domains or not and then to scan/predict the anti-cancer peptides in the protein. Different datasets, including newly developed positive and negative dataset, AntiCP dataset, and balanced randomly generated peptides were used to validate the proposed method. The validation results on independent dataset suggested (sensitivity = 0.95; specificity = 0.97; MCC = 0.92; and Accuracy = 0.96) that the proposed method outperformed the existing method in predicting anti-cancer peptides. The user friendly webserver includes three different modes (i) Protein scan with apoptotic domain prediction; (ii) Multiple peptide mode; and (iii) Peptide mutation mode for prediction and design of anti-cancer peptides. The server was developed using PERL CGI and freely accessible at http://acpp.bicpu.edu.in/predict.php. The established tool will be useful in investigating and designing potent anti-cancer peptides from the query protein effectively.


AT PUBLIC UNIVERSITIES : EVIDENCE FROM A SIX-CAMPUS RANDOMIZED TRIAL


title: The microbiome in allergic disease: Current understanding and future opportunities—2017 PRACTALL document of the American Academy of Allergy, Asthma & Immunology and the European Academy of Allergy and Clinical Immunology

abstract: PRACTALL is a joint initiative of the American Academy of Allergy, Asthma & Immunology and the European Academy of Allergy and Clinical Immunology to provide shared evidence-based recommendations on cutting-edge topics in the field of allergy and immunology. PRACTALL 2017 is focused on what has been established regarding the role of the microbiome in patients with asthma, atopic dermatitis, and food allergy. This is complemented by outlining important knowledge gaps regarding its role in allergic disease and delineating strategies necessary to fill these gaps. In addition, a review of progress in approaches used to manipulate the microbiome will be addressed, identifying what has and has not worked to serve as a baseline for future directions to intervene in allergic disease development, progression, or both.


title: A 76 GHz GaN-on-silicon power amplifier for automotive radar systems

abstract: This paper describes the first demonstration of a 76 GHz gallium nitride (GaN) power amplifier (PA) on a silicon substrate. The PA microwave monolithic IC (MMIC) was fabricated by using AlGaN/GaN FET with a maximum oscillation frequency of 160GHz and a breakdown voltage of over 50 V. For reducing transmission loss, we used a CPW line on the silicon substrate with low transmission loss of 0.5 dB/mm at 76 GHz. For precise design of the PA, a large signal model of the FET was developed. The developed CPW 3-stage PA exhibited an output of over 12 dBm with over 5 dB gain at 75–81 GHz.


Improve the Quality of Important Sentences for Automatic Text Summarization


title: Predicting scientific success based on coauthorship networks

abstract: We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100,000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a Machine Learning classifier, based only on coauthorship network centrality metrics measured at the time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing – challenging the perception of citations as an objective, socially unbiased measure of scientific success.


title: Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps

abstract: Purpose of Review ::: As rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been proposed, are in development, or are already available today. This review seeks to assess their clinical evidence and help the reader understand the current state of the field.


title: Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction

abstract: We study the classic problem of choosing a prior distribution for a location parameter β = (β1, . . . , βp) as p grows large. First, we study the standard “global-local shrinkage” approach, based on scale mixtures of normals. Two theorems are presented which characterize certain desirable properties of shrinkage priors for sparse problems. Next, we review some recent results showing how Levy processes can be used to generate infinite-dimensional versions of standard normal scale-mixture priors, along with new priors that have yet to be seriously studied in the literature. This approach provides an intuitive framework both for generating new regularization penalties and shrinkage rules, and for performing asymptotic analysis on existing models.


title: Attentive Recurrent Comparators

abstract: Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a \textit{dynamic representation space} and use it for one-shot learning. In the task of one-shot classification on the Omniglot dataset, we achieve the state of the art performance with an error rate of 1.5\%. This represents the first super-human result achieved for this task with a generic model that uses only pixel information.


title: A novel method for salient image enhancement

abstract: Visual Salient Object Detection and enhancement plays a vital role in many computer vision applications. In the traditional image processing approaches which are used to extract salient maps have various assumptions such as color, contrast, context, gradient and histogram. The existed models have achieved good progress to extract saliency map on selected features, still considerable features are unclear. The detection sensitivity of the previous models to object interior texture, background textures, object boundaries and blurred contours is same. The proposed model is based on multi-scale transforms and it is developed by contrast based enhancement operator. This approach includes properties of multi-scales and entropy measure of image. By using this we can achieve more complete and uniformly enhanced salient object regions. This model can enhance the salient object interior textures, background texture while simultaneously reducing blur on object boundaries. It achieves better enhancement measure of entropy, frequency measure and mean absolute errors which are the performance measures of salient object enhancement while compared to the state of art methods.


title: Attachment Style and Subjective Motivations for Sex

abstract: The relation of attachment style to subjective motivations for sex was investigated in an Internet survey of 1999 respondents. The relations of attachment anxiety and avoidance to overall sexual motivation and to the specific motives for emotional closeness, reassurance, self-esteem enhancement, stress reduction, partner manipulation, protection from partner’s negative affect and behavior, power exertion, physical pleasure, nurturing one’s partner, and procreation were explored. As predicted, attachment anxiety was positively related to overall sexual motivation and to all specific motives for sex, with the exception of physical pleasure. Avoidance was negatively related to emotional closeness and reassurance as goals of sex and positively related to manipulative use of sex but minimally related to most other motives. Sexual passion was positively related to attachment anxiety and negatively related to avoidance, and anxiety was related to the maintenance of passion over time, whereas avoidance was relate...


title: Substrate integrated waveguide cavity backed bowtie slot antenna for 60GHz applications

abstract: Substrate integrated waveguide is good resolution for implementing for 60GHz applications. In this paper, we represented SIW cavity backed bowtie antenna which is intended by using Rogers RT/Duriod 5880 with dielectric constant 2.2. The simulation results shows that reflection coefficient, VSWR, Gain, Radiation pattern and finally comparing reflection coefficient for different sizes of Bow tie. Computer simulation technology studio suite is used for simulation.


title: Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques

abstract: The IoT is a network of interconnected everyday objects called things that have been augmented with a small measure of computing capabilities. Lately, the IoT has been affected by a variety of different botnet activities. As botnets have been the cause of serious security risks and financial damage over the years, existing Network forensic techniques cannot identify and track current sophisticated methods of botnets. This is because commercial tools mainly depend on signature-based approaches that cannot discover new forms of botnet. In literature, several studies have conducted the use of Machine Learning ML techniques in order to train and validate a model for defining such attacks, but they still produce high false alarm rates with the challenge of investigating the tracks of botnets. This paper investigates the role of ML techniques for developing a Network forensic mechanism based on network flow identifiers that can track suspicious activities of botnets. The experimental results using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets attacks and their tracks.


title: What makes dietary restraint problematic? Development and validation of the Inflexible Eating Questionnaire

abstract: Abstract This study presents the Inflexible Eating Questionnaire (IEQ), which measures the inflexible adherence to subjective eating rules. The scale's structure and psychometric properties were examined in distinct samples from the general population comprising both men and women. IEQ presented an 11-item one-dimensional structure, revealed high internal consistency, construct and temporal stability, and discriminated eating psychopathology cases from non-cases. The IEQ presented significant associations with dietary restraint, eating psychopathology, body image inflexibility, general psychopathology symptoms, and decreased intuitive eating. IEQ was a significant moderator on the association between dietary restraint and eating psychopathology symptoms. Findings suggested that the IEQ is a valid and useful instrument with potential implications for research on psychological inflexibility in disordered eating.


title: Scalable Multidimensional Hierarchical Bayesian modeling on Spark

abstract: We consider the problem of estimating occurrence rates of rare events for extremely sparse data using pre-existing hierarchies and selected features to perform inference along multiple dimensions. In particular, we focus on the problem of estimating click rates for {Advertiser, Publisher, User} tuples where both the Advertisers and the Publishers are organized as hierarchies that capture broad contextual information at different levels of granularities. Typically, the click rates are low and the coverage of the hierarchies and dimensions is sparse. To overcome these difficulties, we decompose the joint prior of the three-dimensional Click-Through-Rate (CTR) using tensor decomposition and propose a Multidimensional Hierarchical Bayesian framework (abbreviated as MadHab). We set up a specific framework of each dimension to model dimension-specific characteristics. More specifically, we consider the hierarchical beta process prior for the Advertiser dimension and for the Publisher dimension respectively and a feature-dependent mixture model for the User dimension. Besides the centralized implementation, we propose a distributed algorithm through Spark for inference which make the model highly scalable and suited for large scale data mining applications. We demonstrate that on a real world ads campaign platform our framework can effectively discriminate extremely rare events in terms of their click propensity.


title: Philosophy of Mind and the Problem of Free Will in the Light of Quantum Mechanics.

abstract: Arguments pertaining to the mind-brain connection and to the physical effectiveness of our conscious choices have been presented in two recent books, one by John Searle, the other by Jaegwon Kim. These arguments are examined, and it is argued that the difficulties encountered arise from a defective understanding and application of a pertinent part of contemporary science, namely quantum mechanics.


title: Design Optimization of a Permanent Magnet Claw Pole Motor With Soft Magnetic Composite Cores

abstract: In our previous study, the claw pole motor (CPM) shows promising magnetic concentrating performance for permanent magnet motors with soft magnetic composite cores. Meanwhile, its relatively complex structure also increases the core loss and cogging torque. To conduct the high dimensional optimization of the CPM with comprehensive analysis models, this paper presents an improved multilevel strategy of high efficiency. Since the holistic performances, including output torque, efficiency, cogging torque, and back electromotive force, are considered, an orthogonal design is utilized for the surrogate model building for increasing the optimization efficiency. A full factor sample method for the surrogate model is also conducted for comparison. The similar optimization results with the two kinds of models prove the effectiveness of the proposed method and optimal design.


title: A System for Algorithm Animation

abstract: An algorithm or a program can be animated by a movie that graphically represents its dynamic execution. A sorting algorithm, for instance, might be animated by a sequence of frames that shows a set of vertical lines of various heights being per- muted into order of increasing height. Such anima- tions are useful for developing new programs, for debugging, and for explaining how programs work. This paper describes ANIM, a basic system for algo- rithm animation. The output is crude, but ANIM is easy to use; a novice user can animate a program in an hour or two. ANIM cunently produces movies with the X window system, among others; it also renders movies into "stills" that can be included in TROFF or TEX documents.


title: Intuitionistic fuzzy sets: past, present and future

abstract: A composition for preventing the adhesion of an epoxy resin which comprises a polymer of (A) at least one C4 to C21 perfluoroalkyl group-containing polymerizable compound with or without at least one compound copolymerizable therewith, the content of the component (A) being not less than 40% by weight on the weight of the polymer.


title: A Projection-Based Approach for Discovering High Average-Utility Itemsets

abstract: Utility mining has recently been an important issue due to its wide applications. An itemset in traditional utility mining considers individual profits and quantities of items in transactions regardless of its length. The average-utility measure, which is the total utility of an itemset divided by its number of items within it, was then proposed to reveal a better utility effect than the original utility measure. A mining algorithm was also proposed to find high average-utility itemsets from a transaction database. However, the previous mining approach was based on the principle of level-wise processing to find high average-utility itemsets from a database. In this paper, we thus propose an efficient average-utility mining approach which adopts a projection technique and an indexing mechanism to speed up the execution and reduce the memory requirement in the mining process. The proposed approach can project relevant sub-databases for mining, thus avoiding some unnecessary checking. In addition, a pruning strategy is also designed to reduce the number of unpromising itemsets in mining. Finally, the experimental results on synthetic datasets and two real datasets show the superior performance of the proposed approach.


LogKV: Exploiting Key-Value Stores for Log Processing


title: A Generalized Delayed Signal Cancellation Method for Detecting Fundamental-Frequency Positive-Sequence Three-Phase Signals

abstract: A novel scheme for obtaining the fundamental-frequency positive-sequence grid voltage vector based on a generalization of the delayed signal cancellation method is proposed in this paper. The technique is implemented by sampling and storing the instantaneous αβ voltage vector. A mathematical transformation is then proposed through which the current and delayed voltage vectors are combined. It is shown that the proposed transformation has unity gain for the fundamental-frequency positive-sequence voltage vector, while its gain is equal to zero for some chosen components. Cascaded transformations can then be used for eliminating the fundamental-frequency negative-sequence vector, as well as chosen positive- and negative-sequence harmonic vector components and, thus, for accurately obtaining the fundamental-frequency positive-sequence voltage vector. The output of the last transformation block is input to a synchronous reference frame phase-locked loop for detecting frequency and position of the positive-sequence vector. A proposal for making the scheme frequency adaptive is also presented. The good performance of the proposed method is verified with simulations and experiments by using distorted and unbalanced signals, containing fundamental-frequency as well as positive- and negative-sequence harmonic components. The proposed method frequency adaptation capability is also verified.


title: Mechatronic systems—Innovative products with embedded control

abstract: Many technical processes and products in the area of mechanical and electrical engineering are showing an increasing integration of mechanics with digital electronics and information processing. This integration is between the components (hardware) and the information-driven functions (software), resulting in integrated systems called mechatronic systems. Their development involves finding an optimal balance between the basic mechanical structure, sensor and actuator implementation, automatic information processing and overall control. Simultaneous design of mechanics and electronics, hardware and software and embedded control functions resulting in an integrated component or system are all of major importance. This technical progress has a very large influence on a multitude of products in the area of mechanical, electrical and electronic engineering and changes the design, for example, of conventional electromechanical components, machines, vehicles and precision mechanical devices with increasing intensity. This contribution summarizes ongoing developments for mechatronic systems, shows design approaches and examples of mechatronic products and considers various embedded control functions and system's integrity. One field of ongoing developments, automotive mechatronics, where especially large influences can be seen, is described in more detail by discussing mechatronic suspensions, mechatronic brakes, active steering and roll stabilization systems.


title: ROI perspective transform based road marking detection and recognition

abstract: Road marking detection and recognition play an important role in driving assistance system and intelligent transportation system. We proposed a method for road marking recognition which can be applied to highway and city road based on ROI perspective transform. If we can detect the lane lines on the road, we determine the trapezoid ROI where road marking would show up with the guide of detected lane lines. Then the ROI is transformed to square by inverse perspective transform for accurate road marking detection and recognition. If there are no legible lane lines that we can take use of, we estimate the ROI according to experience and adopt the method of connected domain analysis to extract target. Template matching is used for recognition of markings. Overall, the metrics of the method are as follows. First, the detection of lane lines is based on the ATN model which is fast and robust. Second, the detected lines are used as guide for the search of markings, and thus, there is no need to search the makings in the whole image. Third, even there is no lane lines, for example, the lines is worn out or blocked by the vehicle ahead on the city road, the connected domain analysis makes the method suitable for these situations.


title: A Foreground Inference Network for Video Surveillance Using Multi-View Receptive Field

abstract: Foreground (FG) pixel labelling plays a vital role in video surveillance. Recent engineering solutions have attempted to exploit the efficacy of deep learning (DL) models initially targeted for image classification to deal with FG pixel labelling. One major drawback of such strategy is the lacking delineation of visual objects when training samples are limited. To grapple with this issue, we introduce a multi-view receptive field fully convolutional neural network (MV-FCN) that harness recent seminal ideas, such as, fully convolutional structure, inception modules, and residual networking. Therefrom, we implement a system in an encoder-decoder fashion that subsumes a core and two complementary feature flow paths. The model exploits inception modules at early and late stages with three different sizes of receptive fields to capture invariance at various scales. The features learned in the encoding phase are fused with appropriate feature maps in the decoding phase through residual connections for achieving enhanced spatial representation. These multi-view receptive fields and residual feature connections are expected to yield highly generalized features for an accurate pixel-wise FG region identification. It is, then, trained with database specific exemplary segmentations to predict desired FG objects. The comparative experimental results on eleven benchmark datasets validate that the proposed model achieves very competitive performance with the prior- and state-of-the-art algorithms. We also report that how well a transfer learning approach can be useful to enhance the performance of our proposed MV-FCN.


title: LazyCut: content-aware template-based video authoring

abstract: Though there are many commercial video authoring tools available today, video authoring remains as a tedious and extremely time consuming task that often requires trained professional skills. To tackle this issue, this demonstration presents a novel end-to-end system, called LazyCut, which enables fast, flexible and personalized video authoring and sharing. LazyCut provides a semi-automatic video authoring and sharing system that significantly reduces users' efforts in video editing while preserving sufficient flexibility and personalization.


title: Determinants of financial performance: a meta-analysis

abstract: A meta-analysis of results from 320 published studies relates environmental, strategic and organizational factors to financial performance. Some factors e.g., concentration and growth have been studied widely and have a relatively consistent positive impact on performance. Other widely-studied factors e.g., size have few consistent effects. Many factors particularly organizational variables are understudied. We suggest implications for research and management practice.


title: Modeling and Evaluation of Energy Policies in Green Clouds

abstract: Following the as-a-service philosophy, a cloud service provider offers computing utilities in the form of virtual resources instantiated on top of a physical infrastructure. In order to meet business requirements still providing high-quality services, performance evaluation needs to be carefully carried out with the aim of optimizing data center utilization and increasing user satisfaction. In this context, power efficiency plays a critical role pushing service providers towards the application of innovative green strategies. In this paper, we present an analytical framework, based on stochastic reward nets, that allows to evaluate different resource allocation policies in a green cloud. A use case is shown in order to illustrate the approach, modeling scattering and saturation allocation policies and comparing them to a purely physical data center scenario. A validation of the proposed model against the CloudSim framework is presented and several numerical results are provided, demonstrating the effectiveness of the approach as a powerful tool for a cloud service provider to perform well-informed decisions about the resource allocation policies to be enforced.


title: Automatic Shoeprint Retrieval Algorithm for Real Crime Scenes

abstract: This study is to propose a fully automatic crime scene shoeprint retrieval algorithm that can be used to link scenes of crime or determine the brand of a shoe. A shoeprint contour model is proposed to roughly correct the geometry distortions. To simulate the character of the forensic experts, a region priority match and similarity estimation strategy is also proposed. The shoeprint is divided into two semantic regions, and their confidence values are computed based on the priority in the forensic practice and the quantity of reliable information. Similarities of each region are computed respectively, and the matching score between the reference image and an image in the database is the weighted sum. For regions with higher confidence value, the similarities are computed based on the proposed coarse-to-fine global invariant descriptors, which are based on Wavelet-Fourier transform and are invariant under slight geometry distortions and interference such as breaks and small holes, etc. For regions with lower confidence value, similarities are estimated based on computed similarities of regions with higher confidence value. Parameters of the proposed algorithm have learned from huge quantity of crime scene shoeprints and standard shoeprints which can cover most practical cases, and the algorithm can have better performance with minimum user intervention. The proposed algorithm has been tested on the crime scene shoeprint database composed of 210,000 shoeprints provided by the third party, and the cumulative matching score of the top 2 percent is 90.87.


title: Human centric spatial affordances for improving human activity recognition

abstract: Spatial affordance can be defined as the functionality a space, or place, lends to human activity. Different places afford different activity possibilities - sleeping is mostly done in the bedroom, and cooking is mostly done in the kitchen. Semantic place labels like kitchen and bedroom, therefore, provide context with which a robot can better infer human activity. Real rooms, however, often defy simple place labels, as they can be multi-purpose, supporting many different types of human activity. The solution is to identify the spatial affordances associated with the current nexus of human activity - a microlevel place labeling. In this paper, we will demonstrate how to estimate these local spatial affordances by integrating a deep learning based place estimator with human pose estimation. The resulting affordances are then used to improve activity recognition using Bayesian belief network.


title: Dynamic functional characterization and phylogenetic changes due to Long Chain Fatty Acids pulses in biogas reactors

abstract: The process stability of biogas plants is often deteriorated by the accumulation of Long Chain Fatty Acids (LCFA). The microbial community shifts due to LCFA disturbances have been poorly understood as the molecular techniques used were not able to identify the genome characteristics of uncultured microorganisms, and additionally, the presence of limited number of reference genomes in public databases prevented the comprehension of specific functional roles characterizing these microorganisms. The present study is the first research which deciphers by means of high throughput shotgun sequencing the dynamics of the microbial community during an inhibitory shock load induced by single pulses of unsaturated LCFA at two different concentrations (i.e. 2 g/L-reactor and 3 g/L-reactor). The metagenomic analysis showed that only the microbes associated with LCFA degradation could encode proteins related to "chemotaxis" and "flagellar assembly", which promoted the ability to move towards the LCFA sources so as to degrade them. Moreover, the syntrophic interactions found between Syntrophomonas sp. together with Methanosarcina sp. were possibly assigned to the menaquinone-electron transfer. Finally, it was proven that a previously exposed to LCFA inoculum is more efficient in the degradation process of LCFA due to the specialization of the microbial consortium.


title: Benefits of social media in business-to-business customer interface in innovation

abstract: The aim of this research is to explore social media and its benefits especially from business-to-business innovation and related customer interface perspective, and to create a more comprehensive picture of the possibilities of social media for the business-to-business sector. Business-to-business context was chosen because it is in many ways a very different environment for social media than business-to-consumer context, and is currently very little academically studied. A systematic literature review on B2B use of social media and achieved benefits in the inn ovation con text was performed to answer the questions above and achieve the research goals. The study clearly demonstrates that not merely B2C's, as commonly believed, but also B2B's can benefit from the use of social media in a variety of ways. Concerning the broader classes of innovation --related benefits, the reported benefits of social media use referred to increased customer focus and understanding, increased level of customer service, and decreased time-to-market. The study contributes to the existing social media --related literature, because there were no found earlier comprehensive academic studies on the use of social media in the innovation process in the context of B2B customer interface.


title: Structural Diversity and Biological Activities of Indole Diketopiperazine Alkaloids from Fungi.

abstract: Indole diketopiperazine alkaloids are secondary metabolites of microorganisms that are widely distributed in filamentous fungi, especially in the genera Aspergillus and Penicillium of the phylum Ascomycota or sac fungi. These alkaloids represent a group of natural products characterized by diversity in both chemical structures and biological activities. This review aims to summarize 166 indole diketopiperazine alkaloids from fungi published from 1944 to mid-2015. The emphasis is on diverse chemical structures within these alkaloids and their relevant biological activities. The aim is to assess which of these compounds merit further study for purposes of drug development.


title: A Self-Calibrating Radar Sensor System for Measuring Vital Signs

abstract: Vital signs (i.e., heartbeat and respiration) are crucial physiological signals that are useful in numerous medical applications. The process of measuring these signals should be simple, reliable, and comfortable for patients. In this paper, a noncontact self-calibrating vital signs monitoring system based on the Doppler radar is presented. The system hardware and software were designed with a four-tiered layer structure. To enable accurate vital signs measurement, baseband signals in the radar sensor were modeled and a framework for signal demodulation was proposed. Specifically, a signal model identification method was formulated into a quadratically constrained $\ell_{1}$ minimization problem and solved using the upper bound and linear matrix inequality (LMI) relaxations. The performance of the proposed system was comprehensively evaluated using three experimental sets, and the results indicated that this system can be used to effectively measure human vital signs.


title: An articulated multi-vehicle robot for inspection and testing of pipeline interiors

abstract: The authors describe the construction of an articulated multi-vehicle robot for inspection and testing of pipeline interiors. The robot is capable of traveling inside pipes of radius 520-800 mm diameter over a distance 150 m. Output pressure versus extension characteristics of the two-stage air cylinder, reduction of the wave reflection in a wheel-type ultrasonic probe, an umbilical cable assembly housing optical fibers, air tubes and electric cables are key elements of the robot. The articulated robot was tested in the laboratory to evaluate and the scanning performance. The results of the non-destructive test of a weld bead to the longitudinal direction show that the robot can pick up surrounding color images and transmit them to the ground station's monitor with high precision.


title: Automatic identification of cited text spans: a multi-classifier approach over imbalanced dataset

abstract: Recently, a new form of structured summary on scientific papers is explored by grouping cited text spans from the reference paper. Its primary goal is to generate summaries based on the cited paper itself. Previously, traditional scientific summarization focused on citation-based methods by aggregating all citances that cite one unique paper without doing content-based citation analysis, while sometimes citations might differ between researchers or time slots. By investigating original text spans where scholars cited, the new method can reflect exact contributions of reference papers more. Therefore, how to identify cited text spans accurately becomes the first important problem to solve. Generally, it can be converted into finding the sentences in reference paper that is more similar with citation sentences. Taking it as a classification task, we investigate the potential of four actions to improve identification performance. Firstly, feature selections are conducted carefully according to multi-classifiers. Secondly, we apply sampling-based algorithms to preprocess class-imbalanced datasets. Since we integrated results via a weighted voting system, the third action is tuning parameters like, voting weights for multi-classifiers integration or running settings to see if we can improve performance further. Evaluation results show effectiveness of each action and demonstrate that researchers can take these actions for more accurate cited text spans identification when doing scientific summarization.


title: Flood forecasting using Internet of things and artificial neural networks

abstract: Floods are the most common natural disasters, and cause significant damage to life, agriculture and economy. Research has moved on from mathematical modeling or physical parameter based flood forecasting schemes, to methodologies focused around algorithmic approaches. The Internet of Things (IoT) is a field of applied electronics and computer science where a system of devices collects data in real time and transfers it through a Wireless Sensor Network (WSN) to the computing device for analysis. IoT generally combines embedded system hardware techniques along with data science or machine learning models. In this work, an IoT and machine learning based embedded system is proposed to predict the probability of floods in a river basin. The model uses a modified mesh network connection over ZigBee for the WSN to collect data, and a GPRS module to send the data over the internet. The data sets are evaluated using an artificial neural network model. The results of the analysis which are also appended show a considerable improvement over the currently existing methods.


title: Sentiment Analysis: A Survey of Current Researchand Techniques

abstract: Opinions and reviews on products and services are expressed in the Web through blogs, feedback forms; it is essential to develop methods to automatically classify and gauge them to identify the underlying sentiment about the product. Analyzing the polarity of sentiment expressed in data is Opinion Mining (OM). It is a system that identifies and classifies opinion/sentiment as represented in electronic text. Economic and marketing researches depend heavily on accurate method to predict sentiments of opinions extracted from internet and predict online customer’s preferences. OM has many steps, and techniques for each step. This study ensures an overall survey about OM related to product reviews, and classification algorithms used for sentiment classification.


title: Fraud detection in communication networks using neural and probabilistic methods

abstract: Fraud detection refers to the attempt to detect illegitimate usage of a communication network. Three methods to detect fraud are presented. Firstly, a feed-forward neural network based on supervised learning is used to learn a discriminative function to classify subscribers using summary statistics. Secondly, a Gaussian mixture model is used to model the probability density of subscribers' past behavior so that the probability of current behavior can be calculated to detect any abnormalities from the past behavior. Lastly, Bayesian networks are used to describe the statistics of a particular user and the statistics of different fraud scenarios. The Bayesian networks can be used to infer the probability of fraud given the subscribers' behavior. The data features are derived from toll tickets. The experiments show that the methods detect over 85% of the fraudsters in our testing set without causing false alarms.


title: The cognitive systems paradigm

abstract: In this essay, I review the motivations behind the cognitive systems movement and attempt to characterize the paradigm. I propose six features that distinguish research in this framework from other approaches to artificial intelligence, after which I present some positive and negative examples in an effort to clarify the field’s boundaries. In closing, I also discuss some avenues for encouraging research in this critical area.


title: Trust Management for Vehicular Networks: An Adversary-Oriented Overview

abstract: Cooperative Intelligent Transportation Systems, mainly represented by vehicular ad hoc networks (VANETs), are among the key components contributing to the Smart City and Smart World paradigms. Based on the continuous exchange of both periodic and event triggered messages, smart vehicles can enhance road safety, while also providing support for comfort applications. In addition to the different communication protocols, securing such communications and establishing a certain trustiness among vehicles are among the main challenges to address, since the presence of dishonest peers can lead to unwanted situations. To this end, existing security solutions are typically divided into two main categories, cryptography and trust, where trust appeared as a complement to cryptography on some specific adversary models and environments where the latter was not enough to mitigate all possible attacks. In this paper, we provide an adversary-oriented survey of the existing trust models for VANETs. We also show when trust is preferable to cryptography, and the opposite. In addition, we show how trust models are usually evaluated in VANET contexts, and finally, we point out some critical scenarios that existing trust models cannot handle, together with some possible solutions.


title: Vector graphics complexes

abstract: Basic topological modeling, such as the ability to have several faces share a common edge, has been largely absent from vector graphics. We introduce the vector graphics complex (VGC) as a simple data structure to support fundamental topological modeling operations for vector graphics illustrations. The VGC can represent any arbitrary non-manifold topology as an immersion in the plane, unlike planar maps which can only represent embeddings. This allows for the direct representation of incidence relationships between objects and can therefore more faithfully capture the intended semantics of many illustrations, while at the same time keeping the geometric flexibility of stacking-based systems. We describe and implement a set of topological editing operations for the VGC, including glue, unglue, cut, and uncut. Our system maintains a global stacking order for all faces, edges, and vertices without requiring that components of an object reside together on a single layer. This allows for the coordinated editing of shared vertices and edges even for objects that have components distributed across multiple layers. We introduce VGC-specific methods that are tailored towards quickly achieving desired stacking orders for faces, edges, and vertices.


title: Understanding interactive systems

abstract: This article examines differing approaches to the definition, classification and modelling of interactive music systems, drawing together both historical and contemporary practice. Concepts of shared control, collaboration and conversation metaphors, mapping, gestural control, system responsiveness and separation of interface from sound generator are discussed. The article explores the potential of interactive systems to facilitate the creation of dynamic compositional sonic architectures through performance and improvisation.


title: Iris segmentation methodology for non-cooperative recognition

abstract: An overview of the iris image segmentation methodologies for biometric purposes is presented. The main focus is on the analysis of the ability of segmentation algorithms to process images with heterogeneous characteristics, simulating the dynamics of a non-cooperative environment. The accuracy of the four selected methodologies on the UBIRIS database is tested and, having concluded about their weak robustness when dealing with non-optimal images regarding focus, reflections, brightness or eyelid obstruction, the authors introduce a new and more robust iris image segmentation methodology. This new methodology could contribute to the aim of non-cooperative biometric iris recognition, where the ability to process this type of image is required.


1 CYANIDE IN MINING : SOME OBSERVATIONS ON THE CHEMISTRY , TOXICITY AND ANALYSIS OF MINING-RELATED WATERS


title: Learning diverse rankings with multi-armed bandits

abstract: Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two online learning algorithms that directly learn a diverse ranking of documents based on users' clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Moreover, one of our algorithms asymptotically achieves optimal worst-case performance even if users' interests change.


title: Process quality and collaboration quality on B2B e-commerce

abstract: Purpose – The purpose of this study is to propose two additions to the existing IS success model to influence usefulness and satisfaction, and ultimately customer loyalty. This study posits that process quality and collaboration quality will significantly influence perceptions on satisfaction, since both considerations are expected to improve interactions between the organization and its customers.Design/methodology/approach – The developed research model was tested in the context of B2B e‐commerce users, employing structural equation modelling‐partial least square to analyze and test the research hypotheses.Findings – Results show that adding process and collaboration quality to the model affects the way system, information, and service quality influences usefulness and satisfaction, suggesting a shift in perspectives and priorities when it comes to B2B e‐commerce transactions. Furthermore, results show that process and collaboration quality have significant effects on usefulness and satisfaction, reinfo...


title: Neural Associative Memory for Dual-Sequence Modeling

abstract: Many important NLP problems can be posed as dual-sequence or sequence-to-sequence modeling tasks. Recent advances in building end-to-end neural architectures have been highly successful in solving such tasks. In this work we propose a new architecture for dual-sequence modeling that is based on associative memory. We derive AM-RNNs, a recurrent associative memory (AM) which augments generic recurrent neural networks (RNN). This architecture is extended to the Dual AM-RNN which operates on two AMs at once. Our models achieve very competitive results on textual entailment. A qualitative analysis demonstrates that long range dependencies between source and target-sequence can be bridged effectively using Dual AM-RNNs. However, an initial experiment on auto-encoding reveals that these benefits are not exploited by the system when learning to solve sequence-to-sequence tasks which indicates that additional supervision or regularization is needed.


title: Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets

abstract: This paper proposes a new route for applying the generative adversarial nets (GANs) to NLP tasks (taking the neural machine translation as an instance) and the widespread perspective that GANs can't work well in the NLP area turns out to be unreasonable. In this work, we build a conditional sequence generative adversarial net which comprises of two adversarial sub models, a generative model (generator) which translates the source sentence into the target sentence as the traditional NMT models do and a discriminative model (discriminator) which discriminates the machine-translated target sentence from the human-translated sentence. From the perspective of Turing test, the proposed model is to generate the translation which is indistinguishable from the human-translated one. Experiments show that the proposed model achieves significant improvements than the traditional NMT model. In Chinese-English translation tasks, we obtain up to +2.0 BLEU points improvement. To the best of our knowledge, this is the first time that the quantitative results about the application of GANs in the traditional NLP task is reported. Meanwhile, we present detailed strategies for GAN training. In addition, We find that the discriminator of the proposed model shows great capability in data cleaning.


Hyper-network based Change Point Detection in Dynamic Networks


title: Defining the price of hospitality: networked hospitality exchange via Airbnb

abstract: This study examines how money mediates and structures social exchange in a hospitality exchange service, and how social and economic factors guiding exchange get intertwined in this context. We present a qualitative study on the experiences of people who offer to rent out their homes, or parts of them, via the online peer-to-peer renting service Airbnb. Our study suggests that the frame monetary transactions set to exchange relationships contributes to the hosts' sense of control and ease in the exchange. We identified two behavioral patterns that highlight the importance of reputation and trust: (1) hosts divert their accumulated reputational capital into the rental price and (2) they may price their property below "the market price", so that they can choose their exchange partners form a wider pool of candidates.


The Myria Big Data Management and Analytics System and Cloud Services


title: Musical instrument identification using multiscale Mel-frequency cepstral coefficients

abstract: We investigate the benefits of evaluating Mel-frequency cepstral coefficients (MFCCs) over several time scales in the context of automatic musical instrument identification for signals that are monophonic but derived from real musical settings. We define several sets of features derived from MFCCs computed using multiple time resolutions, and compare their performance against other features that are computed using a single time resolution, such as MFCCs, and derivatives of MFCCs. We find that in each task — pair-wise discrimination, and one vs. all classification — the features involving multiscale decompositions perform significantly better than features computed using a single timeresolution.


title: Metacognitive control and strategy selection: Deciding to practice retrieval during learning.

abstract: Retrieval practice is a potent technique for enhancing learning, but how often do students practice retrieval when they regulate their own learning? In 4 experiments the subjects learned foreign-language items across multiple study and test periods. When items were assigned to be repeatedly tested, repeatedly studied, or removed after they were recalled, repeated retrieval produced powerful effects on learning and retention. However, when subjects were given control over their own learning and could choose to test, study, or remove items, many subjects chose to remove items rather than practice retrieval, leading to poor retention. In addition, when tests were inserted in the learning phase, attempting retrieval improved learning by enhancing subsequent encoding during study. But when students were given control over their learning they did not attempt retrieval as early or as often as they should to promote the best learning. The experiments identify a compelling metacognitive illusion that occurs during self-regulated learning: Once students can recall an item they tend to believe they have “learned” it. This leads students to terminate practice rather than practice retrieval, a strategy choice that ultimately results in poor retention.


title: Calming Effects of Deep Touch Pressure in Patients with Autistic Disorder, College Students, and Animals

abstract: ABSTRACT Many people with autistic disorder have problems with oversensitivity to both touch and sound. The author (an autistic person) developed a device that delivers deep touch pressure to help her learn to tolerate touching and to reduce anxiety and nervousness. The "squeeze machine" applies lateral, inwardly directed pressure to both lateral aspects of a person's entire body, by compressing the user between two foam-padded panels. Clinical observations and several studies suggest that deep touch pressure is therapeutically beneficial for both children with autistic disorder and probably children with attention-deficit hyperactivity disorder. Only minor and occasional adverse effects have been noted. Data are reported that show a similar calming effect in nonreferred college students. A review of the animal literature reveals that animals have similar calming reactions, and also suggests possible additional physiological effects of deep touch pressure. At present, there are increasing anecdotal report...


title: An Efficient Parallel and Distributed Algorithm for Counting Frequent Sets

abstract: Due to the huge increase in the number and dimension of available databases, efficient solutions for counting frequent sets are nowadays very important within the Data Mining community. Several sequential and parallel algorithms were proposed, which in many cases exhibit excellent scalability. In this paper we present ParDCI, a distributed and multithreaded algorithm for counting the occurrences of frequent sets within transactional databases. ParDCI is a parallel version of DCI (Direct Count & Intersect), a multi-strategy algorithm which is able to adapt its behavior not only to the features of the specific computing platform (e.g. available memory), but also to the features of the dataset being processed (e.g. sparse or dense datasets). ParDCl enhances previous proposals by exploiting the highly optimized counting and intersection techniques of DCI, and by relying on a multi-level parallelization approach which explicitly targets clusters of SMPs, an emerging computing platform. We focused our work on the efficient exploitation of the underlying architecture. Intra-Node multithreading effectively exploits the memory hierarchies of each SMP node, while Inter-Node parallelism exploits smart partitioning techniques aimed at reducing communication overheads. In depth experimental evaluations demonstrate that ParDCI reaches nearly optimal performances under a variety of conditions.


title: Detection, prediction, and avoidance of dynamic obstacles in urban environments

abstract: We present an approach for robust detection, prediction, and avoidance of dynamic obstacles in urban environments. After detecting a dynamic obstacle, our approach exploits structure in the environment where possible to generate a set of likely hypotheses for the future behavior of the obstacle and efficiently incorporates these hypotheses into the planning process to produce safe actions. The techniques presented are very general and can be used with a wide range of sensors and planning algorithms. We present results from an implementation on an autonomous passenger vehicle that has traveled thousands of miles in populated urban environments and won first place in the DARPA Urban Challenge.


title: Link prediction in citation networks

abstract: In this article, we build models to predict the existence of citations among papers by formulating link prediction for 5 large-scale datasets of citation networks. The supervised machine-learning model is applied with 11 features. As a result, our learner performs very well, with the F1 values of between 0.74 and 0.82. Three features in particular, link-based Jaccard coefficient, difference in betweenness centrality, and cosine similarity of term frequency-inverse document frequency vectors, largely affect the predictions of citations. The results also indicate that different models are required for different types of research areas--research fields with a single issue or research fields with multiple issues. In the case of research fields with multiple issues, there are barriers among research fields because our results indicate that papers tend to be cited in each research field locally. Therefore, one must consider the typology of targeted research areas when building models for link prediction in citation networks.


title: Technology Transfer to China: The Issues of Knowledge and Learning

abstract: The aspect of effectiveness of technology transfer to developing countries still raises important questions from researchers and practitioners alike. Many of these questions pertain to the nature of knowledge and of the learning process. The first question for this paper is what forms of knowledge are transferred abroad by companies. The second question is what are the characteristics of the learning process in recipient firms during technology transfer. Twenty-eight Canadian manufacturing firms were interviewed. Seven case studies were also conducted in China.


title: OLiA – Ontologies of Linguistic Annotation

abstract: This paper describes the Ontologies of Linguistic Annotation (OLiA) as one of the data sets currently available as part of Linguistic Linked Open Data (LLOD) cloud. Within the LLOD cloud, the OLiA ontologies serve as a reference hub for annotation terminology for linguistic phenomena on a great band-width of languages, they have been used to facilitate interoperability and information integration of linguistic annotations in corpora, NLP pipelines, and lexical-semantic resources and mediate their linking with multiple community-maintained terminology repositories.