Applying the Clique Percolation Method to analyzing cross-market branch banking network structure: the case of Illinois

This study applies the Clique Percolation Method (CPM) to an investigation of the changing spatial organization of the Illinois cross-market branch banking network. Nonoverlapping community detection algorithms assign nodes into exclusive communities and, when results are mapped, these techniques may generate spatially disjointed geographical regions, an undesirable characteristic for geographical study. Alternative overlapping community detection algorithms allow overlapping membership where a node can be a member of different communities. Such a structure simultaneously accommodates spatial proximity and spatial separation which occur with respect to a node in relation to other nodes in the system. Applying such a structure in geographical analysis helps preserve well-established principles regarding spatial relationships within the geography discipline. The result can also be mapped for display and correct interpretation. The CPM is chosen in this study due to the complete connection within cliques which simulates the practice by banking institutions of forming highly connected networks through multi-location operations in order to diversify their business and hedge against risks. Applying the CPM helps reveal the spatial pattern of branch banking connections which would otherwise be difficult to see. However, the CPM has been shown to not be among the best performing overlapping community detection algorithms. Future research should explore other possible algorithms for detecting overlapping communities. Detecting communities in a network only reveals certain characteristics of the spatial organization of the network, rather than providing explanation of the spatial-network patterns revealed. Full interpretation of the pattern must rely on the attribute data and additional information. This may illustrate the value of an integrated approach in geographical analysis using both social network analysis and spatial analysis techniques.


Multi-platform molecular profiling of a large cohort of glioblastomas reveals potential therapeutic strategies

Glioblastomas (GBM) are the most aggressive and prevalent form of gliomas with abysmal prognosis and limited treatment options. We analyzed clinically relevant molecular aberrations suggestive of response to therapies in 1035 GBM tumors. Our analysis revealed mutations in 39 genes of 48 tested. IHC revealed expression of PD-L1 in 19% and PD-1 in 46%. MGMT-methylation was seen in 43%, EGFRvIII in 19% and 1p19q co-deletion in 2%. TP53 mutation was associated with concurrent mutations, while IDH1 mutation was associated with MGMT-methylation and TP53 mutation and was mutually exclusive of EGFRvIII mutation. Distinct biomarker profiles were seen in GBM compared with WHO grade III astrocytoma, suggesting different biology and potentially different treatment approaches. Analysis of 17 metachronous paired tumors showed frequent biomarker changes, including MGMT-methylation and EGFR aberrations, indicating the need for a re-biopsy for tumor profiling to direct subsequent therapy. MGMT-methylation, PR and TOPO1 appeared as significant prognostic markers in sub-cohorts of GBM defined by age. The current study represents the largest biomarker study on clinical GBM tumors using multiple technologies to detect gene mutation, amplification, protein expression and promoter methylation. These data will inform planning for future personalized biomarker-based clinical trials and identifying effective treatments based on tumor biomarkers.


Soft Pneumatic Actuators for Rehabilitation

Pneumatic artificial muscles are pneumatic devices with practical and various applications as common actuators. They, as human muscles, work in agonistic-antagonistic way, giving a traction force only when supplied by compressed air. The state of the art of soft pneumatic actuators is here analyzed: different models of pneumatic muscles are considered and evolution lines are presented. Then, the use of Pneumatic Muscles (PAM) in rehabilitation apparatus is described and the general characteristics required in different applications are considered, analyzing the use of proper soft actuators with various technical properties. Therefore, research activity carried out in the Department of Mechanical and Aerospace Engineering in the field of soft and textile actuators is presented here. In particular, pneumatic textile muscles useful for active suits design are described. These components are made of a tubular structure, with an inner layer of latex coated with a deformable outer fabric sewn along the edge. In order to increase pneumatic muscles forces and contractions Braided Pneumatic Muscles are studied. In this paper, new prototypes are presented, based on a fabric construction and various kinds of geometry. Pressure-force-deformation tests results are carried out and analyzed. These actuators are useful for rehabilitation applications. In order to reproduce the whole upper limb movements, new kind of soft actuators are studied, based on the same principle of planar membranes deformation. As an example, the bellows muscle model and worm muscle model are developed and described. In both cases, wide deformations are expected. Another issue for soft actuators is the pressure therapy. Some textile sleeve prototypes developed for massage therapy on patients suffering of lymph edema are analyzed. Different types of fabric and assembly techniques have been tested. In general, these OPEN ACCESS Actuators 2014, 3 85 Pressure Soft Actuators are useful for upper/lower limbs treatments, according to medical requirements. In particular devices useful for arms massage treatments are considered. Finally some applications are considered.


Scalable Content-Aware Collaborative Filtering for Location Recommendation

Location recommendation plays an essential role in helping people find attractive places. Though recent research has studied how to recommend locations with social and geographical information, few of them addressed the cold-start problem of new users. Because mobility records are often shared on social networks, semantic information can be leveraged to tackle this challenge. A typical method is to feed them into explicit-feedback-based content-aware collaborative filtering, but they require drawing negative samples for better learning performance, as users’ negative preference is not observable in human mobility. However, prior studies have empirically shown sampling-based methods do not perform well. To this end, we propose a scalable Implicit-feedback-based Content-aware Collaborative Filtering (ICCF) framework to incorporate semantic content and to steer clear of negative sampling. We then develop an efficient optimization algorithm, scaling linearly with data size and feature size, and quadratically with the dimension of latent space. We further establish its relationship with graph Laplacian regularized matrix factorization. Finally, we evaluate ICCF with a large-scale LBSN dataset in which users have profiles and textual content. The results show that ICCF outperforms several competing baselines, and that user information is not only effective for improving recommendations but also coping with cold-start scenarios.


E-commerce web page classification based on automatic content extraction

Currently, There are many E-commerce websites around the internet world. These E-commerce websites can be categorized into many types which one of them is C2C (Customer to Customer) websites such as eBay and Amazon. The main objective of C2C websites is an online market place that everyone can buy or sell anything at any time. Since, there are a lot of products in the E-commerce websites and each product are classified into its category by human. It is very hard to define their categories in automatic manner when the data is very large. In this paper, we propose the method for classifying E-commerce web pages based on their product types. Firstly, we apply the proposed automatic content extraction to extract the contents of E-commerce web pages. Then, we apply the automatic key word extraction to select words from these extracted contents for generating the feature vectors that represent the E-commerce web pages. Finally, we apply the machine learning technique for classifying the E-commerce web pages based on their product types. The experimental results signify that our proposed method can classify the E-commerce web pages in automatic fashion.


MIMO for DVB-NGH, the next generation mobile TV broadcasting [Accepted From Open Call]

DVB-NGH (Digital Video Broadcasting - Next Generation Handheld) is the next generation technology for mobile TV broadcasting, which has been developed by the DVB project with the most advanced transmission technologies. DVB-NGH is the first broadcasting standard to incorporate multiple-input multiple-output (MIMO) as the key technology to overcome the Shannon limit of single antenna communications. MIMO techniques can be used to improve the robustness of the transmitted signal by exploiting the spatial diversity of the MIMO channel, but also to achieve increased data rates through spatial multiplexing. This article describes the benefits of MIMO that motivated its incorporation in DVB-NGH, reviews the MIMO schemes adopted, and discusses some aspects related to the deployment of MIMO networks in DVB-NGH. The article also provides a feature comparison with the multi-antenna techniques for 3GGP's LTE/LTE-Advanced for cellular networks. Finally, physical layer simulation results calibrated within the DVB-NGH standardization process are provided to illustrate the gain of MIMO for the next generation of mobile TV broadcasting.


Early sensitivity to arguments: how preschoolers weight circular arguments.

Observational studies suggest that children as young as 2 years can evaluate some of the arguments people offer them. However, experimental studies of sensitivity to different arguments have not yet targeted children younger than 5 years. The current study aimed at bridging this gap by testing the ability of preschoolers (3-, 4-, and 5-year-olds) to weight arguments. To do so, it focused on a common type of fallacy-circularity-to which 5-year-olds are sensitive. The current experiment asked children-and, as a group control, adults-to choose between the contradictory opinions of two speakers. In the first task, participants of all age groups favored an opinion supported by a strong argument over an opinion supported by a circular argument. In the second task, 4- and 5-year-olds, but not 3-year-olds or adults, favored the opinion supported by a circular argument over an unsupported opinion. We suggest that the results of these tasks in 3- to 5-year-olds are best interpreted as resulting from the combination of two mechanisms: (a) basic skills of argument evaluations that process the content of arguments, allowing children as young as 3 years to favor non-circular arguments over circular arguments, and (b) a heuristic that leads older children (4- and 5-year-olds) to give some weight to circular arguments, possibly by interpreting these arguments as a cue to speaker dominance.


StackGuard: Simple Stack Smash Protection for GCC

Since 1998, StackGuard patches to GCC have been used to protect entire distributions from stack smashing buffer overflows. Performance overhead and software compatibility issues have been minimal. In its history, the parts of GCC that StackGuard has operated in have twice changed enough to require complete overhauls of the StackGuard patch. Since StackGuard is a mature technology, even seeing re-implementations in other compilers, we propose that GCC adopt StackGuard as a standard feature. This paper describes our recent work to bring StackGuard fully up to date with current GCC, introduce architecture independence, and extend the protection of stack data structures, while keeping the StackGuard patch as small, simple, and modular as possible.


Barriers to Physicians’ Adoption of Healthcare Information Technology: An Empirical Study on Multiple Hospitals

Prior research on technology usage had largely overlooked the issue of user resistance or barriers to technology acceptance. Prior research on the Electronic Medical Records had largely focused on technical issues but rarely on managerial issues. Such oversight prevented a better understanding of users’ resistance to new technologies and the antecedents of technology rejection. Incorporating the enablers and the inhibitors of technology usage intention, this study explores physicians’ reactions towards the electronic medical record. The main focus is on the barriers, perceived threat and perceived inequity. 115 physicians from 6 hospitals participated in the questionnaire survey. Structural Equation Modeling was employed to verify the measurement scale and research hypotheses. According to the results, perceived threat shows a direct and negative effect on perceived usefulness and behavioral intentions, as well as an indirect effect on behavioral intentions via perceived usefulness. Perceived inequity reveals a direct and positive effect on perceived threat, and it also shows a direct and negative effect on perceived usefulness. Besides, perceived inequity reveals an indirect effect on behavioral intentions via perceived usefulness with perceived threat as the inhibitor. The research finding presents a better insight into physicians’ rejection and the antecedents of such outcome. For the healthcare industry understanding the factors contributing to physicians’ technology acceptance is important as to ensure a smooth implementation of any new technology. The results of this study can also provide change managers reference to a smooth IT introduction into an organization. In addition, our proposed measurement scale can be applied as a diagnostic tool for them to better understand the status quo within their organizations and users’ reactions to technology acceptance. By doing so, barriers to physicians’ acceptance can be identified earlier and more effectively before leading to technology rejection.


Learning Low-Dimensional Representations of Medical Concepts

We show how to learn low-dimensional representations (embeddings) of a wide range of concepts in medicine, including diseases (e.g., ICD9 codes), medications, procedures, and laboratory tests. We expect that these embeddings will be useful across medical informatics for tasks such as cohort selection and patient summarization. These embeddings are learned using a technique called neural language modeling from the natural language processing community. However, rather than learning the embeddings solely from text, we show how to learn the embeddings from claims data, which is widely available both to providers and to payers. We also show that with a simple algorithmic adjustment, it is possible to learn medical concept embeddings in a privacy preserving manner from co-occurrence counts derived from clinical narratives. Finally, we establish a methodological framework, arising from standard medical ontologies such as UMLS, NDF-RT, and CCS, to further investigate the embeddings and precisely characterize their quantitative properties.


Nanowire dye-sensitized solar cells.

Excitonic solar cells-including organic, hybrid organic-inorganic and dye-sensitized cells (DSCs)-are promising devices for inexpensive, large-scale solar energy conversion. The DSC is currently the most efficient and stable excitonic photocell. Central to this device is a thick nanoparticle film that provides a large surface area for the adsorption of light-harvesting molecules. However, nanoparticle DSCs rely on trap-limited diffusion for electron transport, a slow mechanism that can limit device efficiency, especially at longer wavelengths. Here we introduce a version of the dye-sensitized cell in which the traditional nanoparticle film is replaced by a dense array of oriented, crystalline ZnO nanowires. The nanowire anode is synthesized by mild aqueous chemistry and features a surface area up to one-fifth as large as a nanoparticle cell. The direct electrical pathways provided by the nanowires ensure the rapid collection of carriers generated throughout the device, and a full Sun efficiency of 1.5% is demonstrated, limited primarily by the surface area of the nanowire array.


SECURE COMPUTER SYSTEMS : MATHEMATICAL FOUNDATIONS


Daily and compulsive internet use and well-being in adolescence: a diathesis-stress model based on big five personality traits.

This study examined the associations between adolescents' daily Internet use and low well-being (i.e., loneliness, low self-esteem, and depressive moods). We hypothesized that (a) linkages between high levels of daily Internet use and low well-being would be mediated by compulsive Internet use (CIU), and (b) that adolescents with low levels of agreeableness and emotional stability, and high levels of introversion would be more likely to develop CIU and lower well-being. Data were used from a sample of 7888 Dutch adolescents (11-21 years). Results from structural equation modeling analyses showed that daily Internet use was indirectly related to low well-being through CIU. In addition, daily Internet use was found to be more strongly related to CIU in introverted, low-agreeable, and emotionally less-stable adolescents. In turn, again, CIU was more strongly linked to loneliness in introverted, emotionally less-stable, and less agreeable adolescents.


Word Alignment Modeling with Context Dependent Deep Neural Network

In this paper, we explore a novel bilingual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in various machine learning tasks (Collobert et al., 2011). We describe in detail how we adapt and extend the CD-DNNHMM (Dahl et al., 2012) method introduced in speech recognition to the HMMbased word alignment model, in which bilingual word embedding is discriminatively learnt to capture lexical translation information, and surrounding words are leveraged to model context information in bilingual sentences. While being capable to model the rich bilingual correspondence, our method generates a very compact model with much fewer parameters. Experiments on a large scale EnglishChinese word alignment task show that the proposed method outperforms the HMM and IBM model 4 baselines by 2 points in F-score.


Contrastive Estimation: Training Log-Linear Models on Unlabeled Data

Conditional random fields (Lafferty et al., 2001) are quite effective at sequence labeling tasks like shallow parsing (Sha and Pereira, 2003) and namedentity extraction (McCallum and Li, 2003). CRFs are log-linear, allowing the incorporation of arbitrary features into the model. To train on u labeled data, we requireunsupervisedestimation methods for log-linear models; few exist. We describe a novel approach,contrastive estimation . We show that the new technique can be intuitively understood as exploiting implicit negative evidence and is computationally efficient. Applied to a sequence labeling problem—POS tagging given a tagging dictionary and unlabeled text—contrastive estimation outperforms EM (with the same feature set), is more robust to degradations of the dictionary, and can largely recover by modeling additional features.


Statistical Phrase-Based Translation

We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models. Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy wordlevel alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems.


HMM-Based Word Alignment in Statistical Translation

In this paper, we describe a new model for word alignment in statistical translation and present experimental results. The idea of the model is to make the alignment probabilities dependent on the differences in the alignment positions rather than on the absolute positions. To achieve this goal, the approach uses a first-order Hidden Markov model (HMM) for the word alignment problem as they are used successfully in speech recognition for the time alignment problem. The difference to the time alignment HMM is that there is no monotony constraint for the possible word orderings. We describe the details of the model and test the model on several bilingual corpora.


Background Removal in Image Indexing and Retrieval

This paper presents our research in image content based indexing and retrieval, a key technology in digital image libraries. In most of the existing image content-based techniques, image features used for indexing and retrieval are global, features are computed over the entire image. The major problem with the global image feature based retrieval methods is that background features can be easily mistakenly taken as object features. When a user attempts to retrieve images using color features, he/she usually means the color feature of an object or objects of interests contained in the image. The approach we describe in this paper utilizes color clusters for image background analysis. Once the background regions are identified, they are removed from the image indexing procedure; therefore, no longer interfering with the meaningful image content during the retrieval process. The algorithm consists of three major steps of computation, fuzzy clustering, color image segmentation, and background analysis.


Induction of fuzzy rules and membership functions from training examples

Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into linguistic variable terms and to make fuzzy reasoning work. In this paper, we propose a general learning method as a framework for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples to rapidly build a prototype fuzzy expert system. Based on the membership functions and the fuzzy rules derived, a corresponding fuzzy inference procedure to process inputs is also developed.


Routing Attacks and Countermeasures in the RPL-Based Internet of Things

The Routing Protocol for Low-Power and Lossy Networks (RPL) is a novel routing protocol standardized for constrained environments such as 6LoWPAN networks. Providing security in IPv6/RPL connected 6LoWPANs is challenging because the devices are connected to the untrusted Internet and are resource constrained, the communication links are lossy, and the devices use a set of novel IoT technologies such as RPL, 6LoWPAN, and CoAP/CoAPs. In this paper we provide a comprehensive analysis of IoT technologies and their new security capabilities that can be exploited by attackers or IDSs. One of the major contributions in this paper is our implementation and demonstration of well-known routing attacks against 6LoWPAN networks running RPL as a routing protocol. We implement these attacks in the RPL implementation in the Contiki operating system and demonstrate these attacks in the Cooja simulator. Furthermore, we highlight novel security features in the IPv6 protocol and exemplify the use of these features for intrusion detection in the IoT by implementing a lightweight heartbeat protocol.


Speech emotion classification using tree-structured sparse logistic regression

The extraction and selection of acoustic features are crucial steps in the development of a system for classifying emotions in speech. Most works in the field use some kind of prosodic features, often in combination with spectral and glottal features, and select appropriate features in classifying emotions. In the methods, feature choices are mostly made regardless of existing relationships and structures between features. However, considering them can be beneficial, potentially both for interpretability and to improve classification performance. To this end, a structured sparse logistic regression model incorporated with the hierarchical structure of features derived from prosody, spectral envelope, and glottal information is proposed in this paper. The proposed model simultaneously addresses tree-structured sparse feature selection and emotion classification. Evaluation of the proposed model on Berlin emotional database showed substantial improvement over the conventional sparse logistic regression model.


Quantum clustering algorithms

By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover. After having introduced this novel approach to unsupervised learning, we illustrate it with a quantized version of three standard algorithms: divisive clustering, k-medians and an algorithm for the construction of a neighbourhood graph. We obtain a significant speedup compared to the classical approach.


A Comparative Study of Polar Code Constructions for the AWGN Channel

We present a comparative study of the performance of various polar code constructions in an additive white Gaussian noise (AWGN) channel. A polar code construction is any algorithm that selects K best among N possible polar bit-channels at the design signal-to-noise-ratio (design-SNR) in terms of bit error rate (BER). Optimal polar code construction is hard and therefore many suboptimal polar code constructions have been proposed at different computational complexities. Polar codes are also non-universal meaning the code changes significantly with the design-SNR. However, it is not known which construction algorithm at what design-SNR constructs the best polar codes. We first present a comprehensive survey of all the well-known polar code constructions along with their full implementations. We then propose a heuristic algorithm to find the best design-SNR for constructing best possible polar codes from a given construction algorithm. The proposed algorithm involves a search among several possible design-SNRs. We finally use our algorithm to perform a comparison of different construction algorithms using extensive simulations. We find that all polar code construction algorithms generate equally good polar codes in an AWGN channel, if the design-SNR is optimized. Keywords—Bhattacharyya bounds, bit-channels, Gaussian approximation, polar codes


False Data Injection Attacks in Control Systems

This paper analyzes the effects of false data injection attacks on Control System. We assume that the system, equipped with a Kalman filter and LQG controller, is used to monitor and control a discrete linear time invariant Gaussian system. We further assume that the system is equipped with a failure detector. An attacker wishes to destabilize the system by compromising a subset of sensors and sending corrupted readings to the state estimator. In order to inject fake sensor measurements without being detected the attacker needs to carefully design its inputs to fool the failure detector, since abnormal sensor measurements usually trigger an alarm from the failure detector. We will provide a necessary and sufficient condition under which the attacker could destabilize the system while successfully bypassing the failure detector. A design method for the defender to improve the resilience of the CPS against such kind of false data injection attacks is also provided.


Automatic Text Summarization based on Betweenness Centrality

Automatic text summary plays an important role in information retrieval. With a large volume of information, presenting the user only a summary greatly facilitates the search work of the most relevant. Therefore, this task can provide a solution to the problem of information overload. Automatic text summary is a process of automatically creating a compressed version of a certain text that provides useful information for users. This article presents an unsupervised extractive approach based on graphs. The method constructs an indirected weighted graph from the original text by adding a vertex for each sentence, and calculates a weighted edge between each pair of sentences that is based on a similarity/dissimilarity criterion. The main contribution of the work is that we do a study of the impact of a known algorithm for the social network analysis, which allows to analyze large graphs efficiently. As a measure to select the most relevant sentences, we use betweenness centrality. The method was evaluated in an open reference data set of DUC2002 with Rouge scores.


The Abandoned Side of the Internet: Hijacking Internet Resources When Domain Names Expire

The vulnerability of the Internet has been demonstrated by prominent IP prefix hijacking events. Major outages such as the China Telecom incident in 2010 stimulate speculations about malicious intentions behind such anomalies. Surprisingly, almost all discussions in the current literature assume that hijacking incidents are enabled by the lack of security mechanisms in the inter-domain routing protocol BGP. In this paper, we discuss an attacker model that accounts for the hijacking of network ownership information stored in Regional Internet Registry (RIR) databases. We show that such threats emerge from abandoned Internet resources (e.g., IP address blocks, AS numbers). When DNS names expire, attackers gain the opportunity to take resource ownership by re-registering domain names that are referenced by corresponding RIR database objects. We argue that this kind of attack is more attractive than conventional hijacking, since the attacker can act in full anonymity on behalf of a victim. Despite corresponding incidents have been observed in the past, current detection techniques are not qualified to deal with these attacks. We show that they are feasible with very little effort, and analyze the risk potential of abandoned Internet resources for the European service region: our findings reveal that currently 73 /24 IP prefixes and 7 ASes are vulnerable to be attacked. We discuss countermeasures and outline research directions towards preventive solutions.


A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring

The impact of air quality on health and on life comfort is well established. In many societies, vulnerable elderly and young populations spend most of their time indoors. Therefore, indoor air quality monitoring (IAQM) is of great importance to human health. Engineers and researchers are increasingly focusing their efforts on the design of real-time IAQM systems using wireless sensor networks. This paper presents an end-to-end IAQM system enabling measurement of CO₂, CO, SO₂, NO₂, O₃, Cl₂, ambient temperature, and relative humidity. In IAQM systems, remote users usually use a local gateway to connect wireless sensor nodes in a given monitoring site to the external world for ubiquitous access of data. In this work, the role of the gateway in processing collected air quality data and its reliable dissemination to end-users through a web-server is emphasized. A mechanism for the backup and the restoration of the collected data in the case of Internet outage is presented. The system is adapted to an open-source Internet-of-Things (IoT) web-server platform, called Emoncms, for live monitoring and long-term storage of the collected IAQM data. A modular IAQM architecture is adopted, which results in a smart scalable system that allows seamless integration of various sensing technologies, wireless sensor networks (WSNs) and smart mobile standards. The paper gives full hardware and software details of the proposed solution. Sample IAQM results collected in various locations are also presented to demonstrate the abilities of the system.


A Delegation Framework for Task-Role Based Access Control in WFMS

Access control is important for protecting information integrity in workflow management system (WfMS). Compared to conventional access control technology such as discretionary, mandatory, and role-based access control models, task-role-based access control (TRBAC) model, an access control model based on both tasks and roles, meets more requirements for modern enterprise environments. However, few discussions on delegation mechanisms for TRBAC are made. In this paper, a framework considering temporal constraints to improve delegation and help automatic delegation in TRBAC is presented. In the framework, the methodology for delegations requested from both users and WfMS is discussed. The constraints for delegatee selection such as delegation loop and separation of duty (SOD) are addressed. With the framework, a sequence of algorithms for delegation and revocation of tasks are constructed gradually. Finally, a comparison is made between our approach and the representative related works.


Application of AHP and Taguchi loss functions in supply chain

Purpose – The purpose of this paper is to develop a decision model to help decision makers with selection of the appropriate supplier. Design/methodology/approach – Supplier selection is a multi-criteria decision-making process encompassing various tangible and intangible factors. Both risks and benefits of using a vendor in supply chain are identified for inclusion in the evaluation process. Since these factors can be objective and subjective, a hybrid approach that applies to both quantitative and qualitative factors is used in the development of the model. Taguchi loss functions are used to measure performance of each supplier candidate with respect to the risks and benefits. Analytical hierarchy process (AHP) is used to determine the relative importance of these factors to the decision maker. The weighted loss scores are then calculated for each supplier by using the relative importance as the weights. The composite weighted loss scores are used for ranking of the suppliers. The supplier with the smallest loss score is recommended for selection. Findings – Inclusion of both risk and benefit categories in the evaluation process provides a comprehensive decision tool. Practical implications – The proposed model provides guidelines for supply chain managers to make an informed decision regarding supplier selection. Originality/value – Combining Taguchi loss function and AHP provides a novel approach for ranking of potential suppliers for outsourcing purposes.


Oscillator-based assistance of cyclical movements: model-based and model-free approaches

In this article, we propose a new method for providing assistance during cyclical movements. This method is trajectory-free, in the sense that it provides user assistance irrespective of the performed movement, and requires no other sensing than the assisting robot’s own encoders. The approach is based on adaptive oscillators, i.e., mathematical tools that are capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Here we present two experiments that we recently conducted to validate our approach: a simple sinusoidal movement of the elbow, that we designed as a proof-of-concept, and a walking experiment. In both cases, we collected evidence illustrating that our approach indeed assisted healthy subjects during movement execution. Owing to the intrinsic periodicity of daily life movements involving the lower-limbs, we postulate that our approach holds promise for the design of innovative rehabilitation and assistance protocols for the lower-limb, requiring little to no user-specific calibration.


Wechsler Intelligence Scale for Children-IV Conceptual and Interpretive Guide

The Wechsler intelligence scales were developed by Dr. David Wechsler, a clinical psychologist with Bellevue Hospital. His initial test, the Wechsler-Bellevue Intelligence Scale, was published in 1939 and was designed to measure intellectual performance by adults. Wechsler constructed the WBIS based on his observation that, at the time, existing intelligence tests for adults were merely adaptations of tests for children and had little face validity for older age groups.


Percutaneous left atrial appendage closure vs warfarin for atrial fibrillation: a randomized clinical trial.

IMPORTANCE
While effective in preventing stroke in patients with atrial fibrillation (AF), warfarin is limited by a narrow therapeutic profile, a need for lifelong coagulation monitoring, and multiple drug and diet interactions.

OBJECTIVE
To determine whether a local strategy of mechanical left atrial appendage (LAA) closure was noninferior to warfarin.

DESIGN, SETTING, AND PARTICIPANTS
PROTECT AF was a multicenter, randomized (2:1), unblinded, Bayesian-designed study conducted at 59 hospitals of 707 patients with nonvalvular AF and at least 1 additional stroke risk factor (CHADS2 score ≥1). Enrollment occurred between February 2005 and June 2008 and included 4-year follow-up through October 2012. Noninferiority required a posterior probability greater than 97.5% and superiority a probability of 95% or greater; the noninferiority margin was a rate ratio of 2.0 comparing event rates between treatment groups.

INTERVENTIONS
Left atrial appendage closure with the device (n = 463) or warfarin (n = 244; target international normalized ratio, 2-3).

MAIN OUTCOMES AND MEASURES
A composite efficacy end point including stroke, systemic embolism, and cardiovascular/unexplained death, analyzed by intention-to-treat.

RESULTS
At a mean (SD) follow-up of 3.8 (1.7) years (2621 patient-years), there were 39 events among 463 patients (8.4%) in the device group for a primary event rate of 2.3 events per 100 patient-years, compared with 34 events among 244 patients (13.9%) for a primary event rate of 3.8 events per 100 patient-years with warfarin (rate ratio, 0.60; 95% credible interval, 0.41-1.05), meeting prespecified criteria for both noninferiority (posterior probability, >99.9%) and superiority (posterior probability, 96.0%). Patients in the device group demonstrated lower rates of both cardiovascular mortality (1.0 events per 100 patient-years for the device group [17/463 patients, 3.7%] vs 2.4 events per 100 patient-years with warfarin [22/244 patients, 9.0%]; hazard ratio [HR], 0.40; 95% CI, 0.21-0.75; P = .005) and all-cause mortality (3.2 events per 100 patient-years for the device group [57/466 patients, 12.3%] vs 4.8 events per 100 patient-years with warfarin [44/244 patients, 18.0%]; HR, 0.66; 95% CI, 0.45-0.98; P = .04).

CONCLUSIONS AND RELEVANCE
After 3.8 years of follow-up among patients with nonvalvular AF at elevated risk for stroke, percutaneous LAA closure met criteria for both noninferiority and superiority, compared with warfarin, for preventing the combined outcome of stroke, systemic embolism, and cardiovascular death, as well as superiority for cardiovascular and all-cause mortality.

TRIAL REGISTRATION
clinicaltrials.gov Identifier: NCT00129545.


Gunshot detection and localization using sensor networks

Acoustic sensors or sensor networks can be efficiently used to spot and observe targets of interest and have the ability to conclude the exact location of an object or event within the sensor network's coverage range. For the detection and spot estimation of shooter, the schemes of gunshot localization require one or more sensing technologies. In current situation the importance of counter sniper systems cannot be denied. An efficient counter sniper system that can provide immediate shooter location will provide many benefits to our security personnel. In this paper, the Time Difference Of Arrival (TDOA) of Shock Wave and Muzzle Blast is used to estimate the shooter location. The proposed design and algorithm was validated and shooter origin was resolute that was very close to theoretical values. The result shows that the scheme worked very fine and is capable of locating sniper fires very efficiently, competently and effectively. The designed algorithm worked very well and results were found to be very accurately.


Fast Priority Queues for Cached Memory

The cache hierarchy prevalent in todays high performance processors has to be taken into account in order to design algorithms that perform well in practice. This paper advocates the adaption of external memory algorithms to this purpose. This idea and the practical issues involved are exemplified by engineering a fast priority queue suited to external memory and cached memory that is based on <i>k</i>-way merging. It improves previous external memory algorithms by constant factors crucial for transferring it to cached memory. Running in the cache hierarchy of a workstation the algorithm is at least two times faster than an optimized implementation of binary heaps and 4-ary heaps for large inputs.


Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection

Outlier detection in high-dimensional data presents various challenges resulting from the “curse of dimensionality.” A prevailing view is that distance concentration, i.e., the tendency of distances in high-dimensional data to become indiscernible, hinders the detection of outliers by making distance-based methods label all points as almost equally good outliers. In this paper, we provide evidence supporting the opinion that such a view is too simple, by demonstrating that distance-based methods can produce more contrasting outlier scores in high-dimensional settings. Furthermore, we show that high dimensionality can have a different impact, by reexamining the notion of reverse nearest neighbors in the unsupervised outlier-detection context. Namely, it was recently observed that the distribution of points' reverse-neighbor counts becomes skewed in high dimensions, resulting in the phenomenon known as hubness. We provide insight into how some points (antihubs) appear very infrequently in k-NN lists of other points, and explain the connection between antihubs, outliers, and existing unsupervised outlier-detection methods. By evaluating the classic k-NN method, the angle-based technique designed for high-dimensional data, the density-based local outlier factor and influenced outlierness methods, and antihub-based methods on various synthetic and real-world data sets, we offer novel insight into the usefulness of reverse neighbor counts in unsupervised outlier detection.


CONCEPTUAL REQUIREMENTS FOR THE AUTOMATIC RECONSTRUCTION OF BUILDING INFORMATION MODELS FROM UNINTERPRETED 3 D MODELS

A multitude of new applications is quickly emerging in the field of Building Information Models (BIM). BIM models describe buildings with respect to their spatial and especially semantic and thematic characteristics. Since BIM models are manually created during the planning and construction phase, they are only available for newly planned or recently constructed buildings. In order to apply the new applications to already existing buildings, methods for the acquisition of BIM models for built-up sites are required. Primary data source are 3D geometry models obtained from surveying, CAD, or computer graphics. Automation of this process is highly desirable, but faces a range of specific problems setting the bar very high for a reconstruction process. This paper discusses these problems and identifies consequential requirements on reconstruction methods. Above, a two-step strategy for BIM model reconstruction is proposed which incorporates CityGML as an intermediate layer between 3D graphics models and IFC/BIM models.


Based Emotion Recognition : A Survey

Human Computer interaction is a very powerful and most current area of research because the human world is getting more digitize. This needs the digital systems to imitate the human behaviour correctly. Emotion is one aspect of human behaviour which plays an important role in human computer interaction, the computer interfaces need to recognize the emotion of the users in order to exhibit a truly intelligent behaviour. Human express the emotion in the form facial expression, speech, and writing text. Every day, massive amount of textual data is gathered into internet such as blogs, social media etc. This comprise a challenging style as it is formed with both plaint text and short messaging language. This paper is mainly focused on an overview of emotion detection from text and describes the emotion detection methods. These methods are divided into the following four main categories: keyword-based, Lexical Affinity method, learning based, and hybrid based approach. Limitations of these emotion recognition methods are presented in this paper and also, addresses the text normalization using different handling techniques for both plaint text and short messaging language.


Efficient Planning in Non-Gaussian Belief Spaces and Its Application to Robot Grasping

The limited nature of robot sensors make many important robo tics problems partially observable. These problems may require the s yst m to perform complex information-gathering operations. One approach to so lving these problems is to create plans inbelief-space , the space of probability distributions over the underlying state of the system. The belief-space plan encodes a st rategy for performing a task while gaining information as necessary. Most approach es to belief-space planning rely upon representing belief state in a particular way (typically as a Gaussian). Unfortunately, this can lead to large errors between the assumed density representation of belief state and the true belief state. Th is paper proposes a new sample-based approach to belief-space planning that has fix ed computational complexity while allowing arbitrary implementations of Bayes filtering to be used to track belief state. The approach is illustrated in the conte xt of a simple example and compared to a prior approach. Then, we propose an applicatio n of the technique to an instance of the grasp synthesis problem where a robot mu st simultaneously localize and grasp an object given initially uncertain obje ct parameters by planning information-gathering behavior. Experimental results ar e presented that demonstrate the approach to be capable of actively localizing and graspi ng boxes that are presented to the robot in uncertain and hard-to-localize config urations.


A model transformation from NL to SBVR

In Requirement Engineering, requirements are usually written in sentences of natural language and natural languages are ambiguous and inconsistent, so the requirements written in natural languages also tend to be ambiguous. To avoid this problem of ambiguity we present an approach of model transformation to generate requirements based on SBVR (Semantics of Business Vocabulary and Business Rules). The information provided in source metamodel (NL) is automatically transformed into target metamodel (SBVR). SBVR metamodel can not only be processed by machine but also provides precise and reliable model for software design. The standard SBVR metamodel is already available but for natural language we proposed our own metamodel because there is no standard metamodel available for natural languages.


NLTK: The Natural Language Toolkit

NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing components, learn structured programming by example, and manipulate sophisticated models from the outset.


Convolutional Neural Networks for Sentence Classification

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.


Parameter estimation for text analysis

Presents parameter estimation methods common with discrete probability distributions, which is of particular interest in text modeling. Starting with maximum likelihood, a posteriori and Bayesian estimation, central concepts like conjugate distributions and Bayesian networks are reviewed. As an application, the model of latent Dirichlet allocation (LDA) is explained in detail with a full derivation of an approximate inference algorithm based on Gibbs sampling, including a discussion of Dirichlet hyperparameter estimation. History: version 1: May 2005, version 2.4: August 2008.


A COEFFICIENT OF AGREEMENT FOR NOMINAL SCALES 1


Least Squares Support Vector Machine Classifiers

In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM's. The approach is illustrated on a two-spiral benchmark classification problem.


Vehicle Routing with Drones

We introduce a package service model where trucks as well as drones can deliver packages. Drones can travel on trucks or fly; but while flying, drones can only carry one package at a time and have to return to a truck to charge after each delivery. We present a heuristic algorithm to solve the problem of finding a good schedule for all drones and trucks. The algorithm is based on two nested local searches, thus the definition of suitable neighbourhoods of solutions is crucial for the algorithm. Empirical tests show that our algorithm performs significantly better than a natural Greedy algorithm. Moreover, the savings compared to solutions without drones turn out to be substantial, suggesting that delivery systems might considerably benefit from using drones in addition to trucks.


2 REINVENTING GROUNDED THEORY : SOME QUESTIONS ABOUT THEORY , GROUND AND DISCOVERY

Grounded theory‖s popularity persists after three decades of broad-ranging critique. We discuss here three problematic notions – “theory,” “ground” and “discovery” – which linger in the continuing use and development of grounded theory procedures. We argue that far from providing the epistemic security promised by grounded theory, these notions – embodied in continuing reinventions of grounded theory – constrain and distort qualitative inquiry. We argue that what is contrived is not in fact theory in any meaningful sense, that “ground” is a misnomer when talking about interpretation and that what ultimately materializes following grounded theory procedures is less like discovery and more akin to invention. The procedures admittedly provide signposts for qualitative inquirers, but educational researchers should be wary, for the significance of interpretation, narrative and reflection can be undermined in the procedures of grounded theory.


Ways of Asking and Replying in Duplicate Question Detection

This paper presents the results of systematic experimentation on the impact in duplicate question detection of different types of questions across both a number of established approaches and a novel, superior one used to address this language processing task. This study permits to gain a novel insight on the different levels of robustness of the diverse detection methods with respect to different conditions of their application, including the ones that approximate real usage scenarios.


Exploitation of Dual-Polarization Diversity for 5G Millimeter-Wave MIMO Beamforming Systems

For the first time, to the best of our knowledge, this paper reports a complete millimeter wave (mmWave) wireless system, which coherently utilizes polarization diversity, multi-input multioutput (MIMO), and beamforming technologies applicable for upcoming fifth generation wireless mobile terminals. Using an interdisciplinary approach across the entire layers constituting of antennas, RF architecture, and digital modem, the polarization of the presented mmWave beamforming antenna system dynamically adapts to the channel environments and MIMO transmission modes. Empirical investigations, which are taken under real-life environments, ascertain that implementing switchable dual-polarized array antennas can alleviate the complexity of the beam searching procedure and MIMO channelization. In addition, experimental results acquired from intensive system level tests indicate more than 22 dB averaged cross-polarization discrimination under polarized MIMO channel conditions at the 60 GHz band. The beam selection algorithm that can be easily implemented based on power metric is confirmed to feature a 99.5% accuracy in comparison with the optimal selection algorithm derived from the theoretical maximal capacity.


The Overview of Research on Microgrid Protection Development

In general, the microgrid can operate in both grid-connection mode and islanded mode that challenges the traditional over-current protection scheme in the distribution network. The novel research and development of protection, including both the microgrid and distribution network are comprehensively analyzed in this paper, which mainly focuses on two aspects. The first one analyses the impact on the network protection from the fault current feature while microgrid-connected. Secondly, several innovative protection schemes for network and microgrid are discussed. It is essential to improve traditional protection schemes by using communication techniques. In order to protect the microgrid in islanded mode, two innovative voltage protection schemes are discussed. However, the differential current protection scheme is possibly still the most effective solution for both network and microgrid.


An improved anomaly detection and diagnosis framework for mobile network operators

The ever increasing complexity of commercial mobile networks drives the need for methods capable of reducing human workload required for network troubleshooting. In order to address this issue, several attempts have been made to develop automatic anomaly detection and diagnosis frameworks for mobile network operators. In this paper, the latest improvements introduced to one of those frameworks are discussed, including more sophisticated profiling and detection capabilities. The new algorithms further reduce the need for human intervention related to the proper configuration of the profiling and anomaly detection apparatus. The main concepts of the new approach are described and illustrated with an explanatory showcase featuring performance data from a live 3 G network.


DFuzzy: a deep learning-based fuzzy clustering model for large graphs

Graph clustering is successfully applied in various applications for finding similar patterns. Recently, deep learning- based autoencoder has been used efficiently for detecting disjoint clusters. However, in real-world graphs, vertices may belong to multiple clusters. Thus, it is obligatory to analyze the membership of vertices toward clusters. Furthermore, existing approaches are centralized and are inefficient in handling large graphs. In this paper, a deep learning-based model ‘DFuzzy’ is proposed for finding fuzzy clusters from large graphs in distributed environment. It performs clustering in three phases. In first phase, pre-training is performed by initializing the candidate cluster centers. Then, fine tuning is performed to learn the latent representations by mining the local information and capturing the structure using PageRank. Further, modularity is used to redefine clusters. In last phase, reconstruction error is minimized and final cluster centers are updated. Experiments are performed over real-life graph data, and the performance of DFuzzy is compared with four state-of-the-art clustering algorithms. Results show that DFuzzy scales up linearly to handle large graphs and produces better quality of clusters when compared to state-of-the-art clustering algorithms. It is also observed that deep structures can help in getting better graph representations and provide improved clustering performance.


A recommender system based on historical usage data for web service discovery

The tremendous growth in the amount of available web services impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, these text-based recommendation approaches depend mainly on user’s perspective, languages, and notations, which easily decrease recommendation’s efficiency. In this paper, we present an approach in which we take into account historical usage data instead of the text-based analysis. We apply collaborative filtering technique on user’s interactions. We propose and implement four algorithms to validate our approach. We also provide evaluation methods based on the precision and recall in order to assert the efficiency of our algorithms.


Detection of leukemia in human blood sample based on microscopic images: A study

At the moment, identification of blood disorders is through visual inspection of microscopic images of blood cells. From the identification of blood disor de s, it can lead to classification of certain dise ases related to blood. This paper describes a preliminar y study of developing a detection of leukemia types using microscopic blood sample images. Analyzing through images is very important as from images, diseases can be detected and diagnosed at earlier stage. Fro m there, further actions like controlling, monitori ng and prevention of diseases can be done. Images are used as they are cheap and do not require expensive tes ting and lab equipments. The system will focus on white blood cells disease, leukemia. The system will use features in microscopic images and examine changes on texture, geometry, color and statistical analysi s. Changes in these features will be used as a classif ier input. A literature review has been done and Reinforcement Learning is proposed to classify type s of leukemia. A little discussion about issues inv olved by researchers also has been prepared.


A Surgical Palpation Probe With 6-Axis Force/Torque Sensing Capability for Minimally Invasive Surgery

A novel surgical palpation probe installing a miniature 6-axis force/torque (F/T) sensor is presented for robot-assisted minimally invasive surgery. The 6-axis F/T sensor is developed by using the capacitive transduction principle and a novel capacitance sensing method. The sensor consists of only three parts, namely a sensing printed circuit board, a deformable part, and a base part. The simple configuration leads to simpler manufacturing and assembly processes in conjunction with high durability and low weight. In this study, a surgical instrument installed with a surgical palpation probe is implemented. The 6-axis F/T sensing capability of the probe has been experimentally validated by comparing it with a reference 6-axis F/T sensor. Finally, a vivo tissue palpation task is performed in a simulated surgical environment with an animal organ and a relatively hard simulated cancer buried under the surface of the organ.


A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0

This paper is a formal overview of standards and patents for Internet of Things (IoT) as a key enabler for the next generation advanced manufacturing, referred as Industry 4.0 (I 4.0). IoT at the fundamental level is a means of connecting physical objects to the Internet as a ubiquitous network that enables objects to collect and exchange information. The manufacturing industry is seeking versatile manufacturing service provisions to overcome shortened product life cycles, increased labor costs, and fluctuating customer needs for competitive marketplaces. This paper depicts a systematic approach to review IoT technology standards and patents. The thorough analysis and overview include the essential standard landscape and the patent landscape based on the governing standards organizations for America, Europe and China where most global manufacturing bases are located. The literature of emerging IoT standards from the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the Guobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study. 2016 Elsevier Ltd. All rights reserved.


TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry

TanDEM-X (TerraSAR-X add-on for digital elevation measurements) is an innovative spaceborne radar interferometer that is based on two TerraSAR-X radar satellites flying in close formation. The primary objective of the TanDEM-X mission is the generation of a consistent global digital elevation model (DEM) with an unprecedented accuracy, which is equaling or surpassing the HRTI-3 specification. Beyond that, TanDEM-X provides a highly reconfigurable platform for the demonstration of new radar imaging techniques and applications. This paper gives a detailed overview of the TanDEM-X mission concept which is based on the systematic combination of several innovative technologies. The key elements are the bistatic data acquisition employing an innovative phase synchronization link, a novel satellite formation flying concept allowing for the collection of bistatic data with short along-track baselines, as well as the use of new interferometric modes for system verification and DEM calibration. The interferometric performance is analyzed in detail, taking into account the peculiarities of the bistatic operation. Based on this analysis, an optimized DEM data acquisition plan is derived which employs the combination of multiple data takes with different baselines. Finally, a collection of instructive examples illustrates the capabilities of TanDEM-X for the development and demonstration of new remote sensing applications.


Human-Computer Interaction for Complex Pattern Recognition Problems

We review some applications of human-computer interaction that alleviate the complexity of visual recognition by partitioning it into human and machine tasks to exploit the differences between human and machine capabilities. Human involvement offers advantages, both in the design of automated pattern classification systems, and at the operational level of some image retrieval and classification tasks. Recent development of interactive systems has benefited from the convergence of computer vision and psychophysics in formulating visual tasks as computational processes. Computer-aided classifier design and exploratory data analysis are already well established in pattern recognition and machine learning, but interfaces and functionality are improving. On the operational side, earlier recognition systems made use of human talent only in preprocessing and in coping with rejects. Most current content-based image retrieval systems make use of relevance feedback without direct image interaction. In contrast, some visual object classification systems can exploit such interaction. They require, however, a domain-specific visible model that makes sense to both human and computer.


Toward an integrated knowledge discovery and data mining process model

Enterprise decision making is continuously transforming in the wake of ever increasing amounts of data. Organizations are collecting massive amounts of data in their quest for knowledge nuggets in form of novel, interesting, understandable patterns that underlie these data. The search for knowledge is a multi-step process comprising of various phases including development of domain (business) understanding, data understanding, data preparation, modeling, evaluation and ultimately, the deployment of the discovered knowledge. These phases are represented in form of Knowledge Discovery and Data Mining (KDDM) Process Models that are meant to provide explicit support towards execution of the complex and iterative knowledge discovery process. Review of existing KDDM process models reveals that they have certain limitations (fragmented design, only a checklist-type description of tasks, lack of support towards execution of tasks, especially those of the business understanding phase etc) which are likely to affect the efficiency and effectiveness with which KDDM projects are currently carried out. This dissertation addresses the various identified limitations of existing KDDM process models through an improved model (named the Integrated Knowledge Discovery and Data Mining Process Model) which presents an integrated view of the KDDM process and provides explicit support towards execution of each one of the tasks outlined in the model. We also evaluate the effectiveness and efficiency offered by the IKDDM model against CRISP-DM, a leading KDDM process model, in aiding data mining users to execute various tasks of the KDDM process. Results of statistical tests


CryptoDL : Towards Deep Learning over Encrypted Data

With increasing growth of cloud services, machine learning services can be run on cloud providers’ infrastructure where training and deploying machine learning models are performed on cloud servers. However, machine learning solutions require access to the raw data, which can create potential security and privacy risks. In this work, we take the first steps towards developing theoretical foundation for implementing deep learning algorithms in encrypted domain and propose a method to adopt deep neural networks (NN) within the practical limitations of current homomorphic encryption schemes. We first design two methods for approximation of activation functions with low degree polynomials. Then, we train NNs with the generated polynomials and analyze the performance of the trained models. Finally, we run the low degree polynomials over encrypted values to estimate the computation costs and time.


Exploiting Topic based Twitter Sentiment for Stock Prediction

*This work is supported by a grant from National Science Foundation (IIS-1111092) and a strategic research grant from City University of Hong Kong (7002770) *The work was done when the first author was visiting University of Illinois at Chicago. Abstract • The Web has seen a tremendous rise in social media. • People’s experiences and opinions expressed in Social Media (e.g., Twitter, Facebook) contain rich social signals (e.g., about consumer sentiment, stock market, economic conditions, etc.). • The goal of this paper is to exploit public sentiment for stock market prediction. • Topic based sentiment is an important factor in stock prediction because it reflects public opinions about current issues/topics. • We employ a Bayesian non-parametric topic-based sentiment time series approach learned from streaming tweets to predict the S&P100 Stock Index.


Intelligent Maze Solving Robot Based on Image Processing and Graph Theory Algorithms

The most important task for maze solving robots is the fast and reliable finding of its shortest path from its initial point to its final destination point. This paper proposes an intelligent maze solving robot that can determine its shortest path on a line maze based on image processing and artificial intelligence algorithms. The image of the line maze is captured by a camera and sent to the computer to be analyzed and processed by a program developed using Visual C++ and OpenCV libraries and based on graph theory algorithms. The developed program solves the captured maze by examining all possible paths exist in the maze that could convey the robot to the required destination point. After that, the best shortest path is determined and then the instructions that guide the car-like robot to reach its desired destination point are sent to the robot through Bluetooth. The robot follows the received guide path to reach its destination. The proposed approach works faster than the traditional methods which push the robot to move through the maze cell by cell in order to find its destination point. Moreover, the proposed method allows the maze solving robot to avoid trapping and falling in infinity loops. Applications of maze solving systems include intelligent traffic control that helps ambulances, fire fighters, or rescuing robots to find their shortest path to their destination.


Monte-Carlo Tree Search for the Game of "7 Wonders"

Monte-Carlo Tree Search algorithm, and in particular with the Upper Confidence Bounds formula, provided huge improvements for AI in numerous games, particularly in Go, Hex, Havannah, Amazons and Breakthrough. In this work we study this algorithm on a more complex game, the game of “7 Wonders”. This card game gathers together several known difficult features, such as hidden information, N-player and stochasticity. It also includes an inter-player trading system that induces a combinatorial search to decide which decisions are legal. Moreover it is difficult to hand-craft an efficient evaluation function since the card values are heavily dependent upon the stage of the game and upon the other player decisions. We show that, in spite of the fact that “7 Wonders” is apparently not so related to abstract games, a lot of known results still hold.


The Open University's repository of research publications and other research outputs Collaboration, creativity and the co-construction of oral and written texts Journal Article Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. (To be included in a special issue of Thinking Skills and Creativity, on " Collaborative Abstract In this paper we explore how primary school children 'learn to collaborate' and 'collaborate to learn' on creative writing projects by using diverse cultural artefacts-including oracy, literacy and ICT. We begin by reviewing some key socio-cultural concepts which serve as a theoretical framework for the research reported. Secondly, we describe the context in which the children talked and worked together to create their projects. This context is a 'learning community' developed as part of an innovative educational programme with the aim of promoting the social construction of knowledge among all participants. We then present microgenetic analyses of the quality of the interaction and dialogues taking place as peers worked together on their projects, and how these collaborative processes and uses of the mediational artefacts were taken up by the children. In order to exemplify these processes, our analyses centre on a selection of examples of dialogues, texts and multimedia products of stories created by groups of 4th grade (9-10 year-old) children. Overall, the work reveals the dynamic functioning in educational settings of some central socio-cultural concepts. These include: co-construction; intertextuality and intercontextuality amongst oracy, literacy and uses of ICT; collaborative creativity; development of dialogical and text production strategies and appropriation of diverse cultural artefacts for knowledge construction.


A comparative analysis of common YouTube comment spam filtering techniques

Ever since its development in 2005, YouTube has been providing a vital social media platform for video sharing. Unfortunately, YouTube users may have malicious intentions, such as disseminating malware and profanity. One way to do so is using the comment field for this purpose. Although YouTube provides a built-in tool for spam control, yet it is insufficient for combating malicious and spam contents within the comments. In this paper, a comparative study of the common filtering techniques used for YouTube comment spam is conducted. The study deploys datasets extracted from YouTube using its Data API. According to the obtained results, high filtering accuracy (more than 98%) can be achieved with low-complexity algorithms, implying the possibility of developing a suitable browser extension to alleviate comment spam on YouTube in future.


A Novel Method for Comparative Analysis of DNA Sequences by Ramanujan-Fourier Transform

Alignment-free sequence analysis approaches provide important alternatives over multiple sequence alignment (MSA) in biological sequence analysis because alignment-free approaches have low computation complexity and are not dependent on high level of sequence identity. However, most of the existing alignment-free methods do not employ true full information content of sequences and thus can not accurately reveal similarities and differences among DNA sequences. We present a novel alignment-free computational method for sequence analysis based on Ramanujan-Fourier transform (RFT), in which complete information of DNA sequences is retained. We represent DNA sequences as four binary indicator sequences and apply RFT on the indicator sequences to convert them into frequency domain. The Euclidean distance of the complete RFT coefficients of DNA sequences are used as similarity measures. To address the different lengths of RFT coefficients in Euclidean space, we pad zeros to short DNA binary sequences so that the binary sequences equal the longest length in the comparison sequence data. Thus, the DNA sequences are compared in the same dimensional frequency space without information loss. We demonstrate the usefulness of the proposed method by presenting experimental results on hierarchical clustering of genes and genomes. The proposed method opens a new channel to biological sequence analysis, classification, and structural module identification.


Safe Exploration of State and Action Spaces in Reinforcement Learning

In this paper, we consider the important problem of safe exploration in reinforcement learning. While reinforcement learning is well-suited to domains with complex transition dynamics and high-dimensional state-action spaces, an additional challenge is posed by the need for safe and efficient exploration. Traditional exploration techniques are not particularly useful for solving dangerous tasks, where the trial and error process may lead to the selection of actions whose execution in some states may result in damage to the learning system (or any other system). Consequently, when an agent begins an interaction with a dangerous and high-dimensional state-action space, an important question arises; namely, that of how to avoid (or at least minimize) damage caused by the exploration of the state-action space. We introduce the PI-SRL algorithm which safely improves suboptimal albeit robust behaviors for continuous state and action control tasks and which efficiently learns from the experience gained from the environment. We evaluate the proposed method in four complex tasks: automatic car parking, pole-balancing, helicopter hovering, and business management.


Optical see-through head up displays' effect on depth judgments of real world objects

Recent research indicates that users consistently underestimate depth judgments to Augmented Reality (AR) graphics when viewed through optical see-through displays. However, to our knowledge, little work has examined how AR graphics may affect depth judgments of real world objects that have been overlaid or annotated with AR graphics. This study begins a preliminary analysis whether AR graphics have directional effects on users' depth perception of real-world objects, as might be experienced in vehicle driving scenarios (e.g., as viewed via an optical see-through head-up display or HUD). Twenty-four participants were asked to judge the depth of a physical pedestrian proxy figure moving towards them at a constant rate of 1 meter/second. Participants were shown an initial target location that varied in distance from 11 to 20 m and were then asked to press a button to indicate when the moving target was perceived to be at the previously specified target location. Each participant experienced three different display conditions: no AR visual display (control), a conformal AR graphic overlaid on the pedestrian via a HUD, and the same graphic presented on a tablet physically located on the pedestrian. Participants completed 10 trials (one for each target distance between 11 and 20 inclusive) per display condition for a total of 30 trials per participant. The judged distance from the correct location was recorded, and after each trial, participants' confidence in determining the correct distance was captured. Across all conditions, participants underestimated the distance of the physical object consistent with existing literature. Greater variability was observed in the accuracy of distance judgments under the AR HUD condition relative to the other two display conditions. In addition, participant confidence levels were considerably lower in the AR HUD condition.


Stochastic Activation Pruning for Robust Adversarial Defense

Neural networks have been found vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep learning systems in the wild. To guard against adversarial examples, we take inspiration from game theory. We cast the problem as a minimax zero-sum game between the adversary and the model. For such games, it is in general optimal for players to use a stochastic policy, also known as a mixed strategy. In light of this, we propose a mixed strategy for the model: stochastic activation pruning. SAP prunes a random subset of activations (preferentially pruning activations with smaller magnitude) and scales up the survivors to compensate. SAP can be applied to pre-trained neural networks—even adversarially trained models—without fine-tuning, providing robustness against adversarial examples. Experiments demonstrate that SAP confers robustness in the adversarial setting, increasing accuracy whilst preserving calibration.


Restructuring of deep neural network acoustic models with singular value decomposition

Recently proposed deep neural network (DNN) obtains significant accuracy improvements in many large vocabulary continuous speech recognition (LVCSR) tasks. However, DNN requires much more parameters than traditional systems, which brings huge cost during online evaluation, and also limits the application of DNN in a lot of scenarios. In this paper we present our new effort on DNN aiming at reducing the model size while keeping the accuracy improvements. We apply singular value decomposition (SVD) on the weight matrices in DNN, and then restructure the model based on the inherent sparseness of the original matrices. After restructuring we can reduce the DNN model size significantly with negligible accuracy loss. We also fine-tune the restructured model using the regular back-propagation method to get the accuracy back when reducing the DNN model size heavily. The proposed method has been evaluated on two LVCSR tasks, with context-dependent DNN hidden Markov model (CD-DNN-HMM). Experimental results show that the proposed approach dramatically reduces the DNN model size by more than 80% without losing any accuracy.


Deep neural networks for small footprint text-dependent speaker verification

In this paper we investigate the use of deep neural networks (DNNs) for a small footprint text-dependent speaker verification task. At development stage, a DNN is trained to classify speakers at the frame-level. During speaker enrollment, the trained DNN is used to extract speaker specific features from the last hidden layer. The average of these speaker features, or d-vector, is taken as the speaker model. At evaluation stage, a d-vector is extracted for each utterance and compared to the enrolled speaker model to make a verification decision. Experimental results show the DNN based speaker verification system achieves good performance compared to a popular i-vector system on a small footprint text-dependent speaker verification task. In addition, the DNN based system is more robust to additive noise and outperforms the i-vector system at low False Rejection operating points. Finally the combined system outperforms the i-vector system by 14% and 25% relative in equal error rate (EER) for clean and noisy conditions respectively.


An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices

Detecting and reacting to user behavior and ambient context are core elements of many emerging mobile sensing and Internet-of-Things (IoT) applications. However, extracting accurate inferences from raw sensor data is challenging within the noisy and complex environments where these systems are deployed. Deep Learning -- is one of the most promising approaches for overcoming this challenge, and achieving more robust and reliable inference. Techniques developed within this rapidly evolving area of machine learning are now state-of-the-art for many inference tasks (such as, audio sensing and computer vision) commonly needed by IoT and wearable applications. But currently deep learning algorithms are seldom used in mobile/IoT class hardware because they often impose debilitating levels of system overhead (e.g., memory, computation and energy). Efforts to address this barrier to deep learning adoption are slowed by our lack of a systematic understanding of how these algorithms behave at inference time on resource constrained hardware. In this paper, we present the first -- albeit preliminary -- measurement study of common deep learning models (such as Convolutional Neural Networks and Deep Neural Networks) on representative mobile and embedded platforms. The aim of this investigation is to begin to build knowledge of the performance characteristics, resource requirements and the execution bottlenecks for deep learning models when being used to recognize categories of behavior and context. The results and insights of this study, lay an empirical foundation for the development of optimization methods and execution environments that enable deep learning to be more readily integrated into next-generation IoT, smartphones and wearable systems.


EmotionSense: a mobile phones based adaptive platform for experimental social psychology research

Today's mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way.
 de the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale experiments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies using traditional methods. In particular, we show how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.


Multimodal Deep Learning for Cervical Dysplasia Diagnosis

To improve the diagnostic accuracy of cervical dysplasia, it is important to fuse multimodal information collected during a patient’s screening visit. However, current multimodal frameworks suffer from low sensitivity at high specificity levels, due to their limitations in learning correlations among highly heterogeneous modalities. In this paper, we design a deep learning framework for cervical dysplasia diagnosis by leveraging multimodal information. We first employ the convolutional neural network (CNN) to convert the low-level image data into a feature vector fusible with other non-image modalities. We then jointly learn the non-linear correlations among all modalities in a deep neural network. Our multimodal framework is an end-to-end deep network which can learn better complementary features from the image and non-image modalities. It automatically gives the final diagnosis for cervical dysplasia with 87.83% sensitivity at 90% specificity on a large dataset, which significantly outperforms methods using any single source of information alone and previous multimodal frameworks.


1 An End-to-End Framework for Evaluating Surface Reconstruction

We present a benchmark for the evaluation and comparison of algorithms which reconstruct a surface from point cloud data. Although a substantial amount of effort has been dedicated to the problem of surface reconstruction, a comprehensive means of evaluating this class of algorithms is noticeably absent. We propose a simple pipeline for measuring surface reconstruction algorithms, consisting of three main phases: surface modeling, sampling, and evaluation.We employ implicit surfaces for modeling shapes which are expressive enough to contain details of varying size, in addition to preserving sharp features. From these implicit surfaces, we produce point clouds by synthetically generating range scans which resemble realistic scan data. We validate our synthetic sampling scheme by comparing against scan data produced via a commercial optical laser scanner, wherein we scan a 3D-printed version of the original implicit surface. Last, we perform evaluation by comparing the output reconstructed surface to a dense uniformly-distributed sampling of the implicit surface. We decompose our benchmark into two distinct sets of experiments. The first set of experiments measures reconstruction against point clouds of complex shapes sampled under a wide variety of conditions. Although these experiments are quite useful for the comparison of surface reconstruction algorithms, they lack a fine-grain analysis. Hence to complement this, the second set of experiments are designed to measure specific properties of surface reconstruction, both from a sampling and surface modeling viewpoint. Together, these experiments depict a detailed examination of the state of surface reconstruction algorithms. An End-to-End Framework for Evaluating Surface Reconstruction


Air-Traffic Complexity Resolution in Multi-Sector Planning

Using constraint programming, we effectively model and efficiently solve the problem of balancing and minimising the traffic complexities of an airspace of adjacent sectors. The traffic complexity of a sector is here defined in terms of the numbers of flights within it, near its border, and on non-level segments within it. The allowed forms of complexity resolution are the changing of the take-off times of not yet airborne flights, the changing of the remaining approach times into the chosen airspace of already airborne flights by slowing down and speeding up within the two layers of feeder sectors around that airspace, as well as the changing of the levels of passage over way-points in that airspace. Experiments with actual European flight profiles obtained from the Central Flow Management Unit (CFMU) show that these forms of complexity resolution can lead to significant complexity reductions and rebalancing.


Underwater image enhancement via dark channel prior and luminance adjustment

Underwater images are degraded mainly by light scattering and color distortion. Light scattering occurs because the light is reflected and deflected multiple times by the suspended particles in the water before reaching the camera. Color distortion is caused due to the absorption degrees vary according to the light wavelength. At last, the underwater images are dominated by a bluish tone. In this paper, we propose a novel underwater image enhancement approach based on dark channel prior and luminance adjustment. The dehazed underwater image obtained by dark channel prior characterizes with low brightness. Then, we utilize a novel luminance adjustment method to enhance image contrast. The enhanced image shows improved global contrast and better preserved image details and edges. Experimental results demonstrate the effectiveness of our method compared to the state-of-the-art methods.


Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor

In this paper, we present a real-time algorithm for automatic recognition of not only physical activities, but also, in some cases, their intensities, using five triaxial wireless accelerometers and a wireless heart rate monitor. The algorithm has been evaluated using datasets consisting of 30 physical gymnasium activities collected from a total of 21 people at two different labs. On these activities, we have obtained a recognition accuracy performance of 94.6% using subject-dependent training and 56.3% using subject-independent training. The addition of heart rate data improves subject-dependent recognition accuracy only by 1.2% and subject-independent recognition only by 2.1%. When recognizing activity type without differentiating intensity levels, we obtain a subject-independent performance of 80.6%. We discuss why heart rate data has such little discriminatory power.


Brief Announcement: Dynamic Determinacy Race Detection for Task Parallelism with Futures

Existing dynamic determinacy race detectors for task-parallel programs are limited to programs with strict computation graphs, where a task can only wait for its descendant tasks to complete. In this paper, we present the first known determinacy race detector for non-strict computation graphs with futures. The space and time complexity of our algorithm are similar to those of the classical SP-bags algorithm, when using only structured parallel constructs such as spawn-sync and async-finish. In the presence of point-to-point synchronization using futures, the complexity of the algorithm increases by a factor determined by the number of future operations, which includes future task creation and future get operations. The experimental results show that the slowdown factor observed for our algorithm relative to the sequential version is in the range of 1.00x to 9.92x, which is very much in line with slowdowns experienced for fully strict computation graphs.


Generalized stacking of layerwise-trained Deep Convolutional Neural Networks for document image classification

This article presents our recent study of a lightweight Deep Convolutional Neural Network (DCNN) architecture for document image classification. Here, we concentrated on training of a committee of generalized, compact and powerful base DCNNs. A support vector machine (SVM) is used to combine the outputs of individual DCNNs. The main novelty of the present study is introduction of supervised layerwise training of DCNN architecture in document classification tasks for better initialization of weights of individual DCNNs. Each DCNN of the committee is trained for a specific part or the whole document. Also, here we used the principle of generalized stacking for combining the normalized outputs of all the members of the DCNN committee. The proposed document classification strategy has been tested on the well-known Tobacco3482 document image dataset. Results of our experimentations show that the proposed strategy involving a considerably smaller network architecture can produce comparable document classification accuracies in competition with the state-of-the-art architectures making it more suitable for use in comparatively low configuration mobile devices.


Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks

This study introduces a novel conditional recycle generative adversarial network for facial attribute transformation, which can transform high-level semantic face attributes without changing the identity. In our approach, we input a source facial image to the conditional generator with target attribute condition to generate a face with the target attribute. Then we recycle the generated face back to the same conditional generator with source attribute condition. A face which should be similar to that of the source face in personal identity and facial attributes is generated. Hence, we introduce a recycle reconstruction loss to enforce the final generated facial image and the source facial image to be identical. Evaluations on the CelebA dataset demonstrate the effectiveness of our approach. Qualitative results show that our approach can learn and generate high-quality identity-preserving facial images with specified attributes.


Less is more ? How demographic sample weights can improve public opinion estimates based on Twitter data

Twitter data is widely acknowledged to hold great promise for the study of political behavior and public opinion. However, a key limitation in previous studies is the lack of information about the sociodemographic characteristics of individual users, which raises concerns about the validity of inferences based on this source of data. This paper addresses this challenge by employing supervised machine learning methods to estimate the age, gender, race, party affiliation, propensity to vote, and income of any Twitter user in the U.S. The training dataset for these classifiers was obtained by matching a large dataset of 1 billion geolocated Twitter messages with voting registration records and estimates of home values across 15 different states, resulting in a sample of nearly 250,000 Twitter users whose sociodemographic traits are known. To illustrate the value of this approach, I offer three applications that use information about the predicted demographic composition of a random sample of 500,000 U.S. Twitter users. First, I explore how attention to politics varies across demographics groups. Then, I apply multilevel regression and postratification methods to recover valid estimate of presidential and candidate approval that can serve as early indicators of public opinion changes and thus complement traditional surveys. Finally, I demonstrate the value of Twitter data to study questions that may suffer from social desirability bias. Working paper. This version: September 21, 2016. I would like to thank John Ahlquist, Drew Dimmery, Pat Egan, Martin Elff, Mik Laver, Jeff Lewis, Thomas Leeper, Jonathan Nagler, Molly Roberts, Arthur Spirling, David Sontag for helpful comments and discussions. I also gratefully acknowledges financial support from the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation.


Effectiveness of Blog Advertising: Impact of Communicator Expertise, Advertising Intent, and Product Involvement

Blog advertising, which refers to the paid sponsorship of bloggers to review, promote, or sell products in their blog writing, is becoming prevalent. This paper investigates the impact of three critical factors on blog advertising effectiveness: communicator expertise, advertising intent, and product involvement. An experiment with a 2×2×2 factorial design was used to test their interaction effects on advertising effectiveness. The results indicate that, for low-involvement products, there is better advertising effectiveness when low-expertise communicators are explicit about the advertising intent or when high-expertise communicators are implicit about the advertising intent. But for high-involvement products, the results show that when low-expertise communicators are explicit about the advertising intent, the outcome is lesser advertising effectiveness. For such products, advertising effectiveness does not differ when high-expertise communicators are implicit or explicit about the advertising intent. Based on these results, some implications for further research and practice are given.


Feature Analysis for Fake Review Detection through Supervised Classification

Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.


Miniature Continuous Coverage Antenna Array for GNSS Receivers

This letter presents a miniature conformal array that provides continuous coverage and good axial ratio from 1100-1600 MHz. Concurrently, it maintains greater than 1.5 dBic RHCP gain and return loss less than -10 dB. The four element array is comprised of two-arm slot spirals with lightweight polymer substrate dielectric loading and a novel termination resistor topology. Multiple elements provide the capability to suppress interfering signals common in GNSS applications. The array, including feeding network, is 3.5" times 3.5" times 0.8" in size and fits into the FRPA-3 footprint and radome.